Author response:

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

This study presents a valuable finding on sperm flagellum and HTCA stabilization. The evidence supporting the authors' claims is incomplete. The work will be of broad interest to cell and reproductive biologists working on cilium and sperm biology.

We thank the Editor and the two reviewers for their time and thorough evaluation of our manuscript. We greatly appreciate their valuable guidance on improving our study. In the revised manuscript, we have conducted additional experiments and provided quantitative data in response to the reviewers' comments. Furthermore, we have refined the manuscript and added further context to elucidate the significance of our findings for the readers.

Public Reviews:

Reviewer #1 (Public Review):

In this paper, Wu et al. investigated the physiological roles of CCDC113 in sperm flagellum and HTCA stabilization by using CRISPR/Cas knockouts mouse models, co-IP, and single sperm imaging. They find that CCDC113 localizes in the linker region among radial spokes, the nexin-dynein regulatory complex (N-DRC), and doublet microtubules (DMTs) RS, N-DRC, and DMTs and interacts with axoneme-associated proteins CFAP57 and CFAP91, acting as an adaptor protein that facilitates the linkage between RS, N-DRC, and DMTs within the sperm axoneme. They show the disruption of CCDC113 produced spermatozoa with disorganized sperm flagella and CFAP91, DRC2 could not colocalize with DMTs in Ccdc113-/- spermatozoa. Interestingly, the data also indicate that CCDC113 could localize on the HTCA region, and interact with HTCA-associated proteins. The knockout of Ccdc113 could also produce acephalic spermatozoa. By using Sun5 and Centlein knockout mouse models, the authors further find SUN5 and CENTLEIN are indispensable for the docking of CCDC113 to the implantation site on the sperm head. Overall, the experiments were designed properly and performed well to support the authors' observation in each part. Furthermore, the study's findings offer valuable insights into the physiological and developmental roles of CCDC113 in the male germ line, which can provide insight into impaired sperm development and male infertility. The conclusions of this paper are mostly well supported by data, but some points need to be clarified and discussed.

We thank Reviewer #1 for his or her critical reading and the positive assessment.

(1) In Figure 1, a sperm flagellum protein, which is far away from CCDC113, should be selected as a negative control to exclude artificial effects in co-IP experiments.

We greatly appreciate Reviewer #1’s insightful suggestion. In response, we selected two sperm outer dense fiber proteins, ODF1 and ODF2, which are located distant from the sperm axoneme, as negative controls in the co-IP experiments. As shown in Figure 1- figure supplement 1A and B, neither ODF1 nor ODF2 bound to CCDC113, indicating the interaction observed in Figure 1 is not an artifact.

(2) Whether the detachment of sperm head and tail in Ccdc113-/- mice is a secondary effect of the sperm flagellum defects? The author should discuss this point.

Good question. Considering that CCDC113 is localized in the sperm neck region and interacts with SUN5 and CENTLEIN, it may play a direct role in connecting the sperm head and tail. Indeed, PAS staining revealed that Ccdc113–/– sperm heads exhibit abnormal orientation in stages V–VIII of the seminiferous epithelia (Figure 6C-D). Furthermore, transmission electron microscopy (TEM) analysis indicated that the absence of CCDC113 caused detachment of the damaged coupling apparatus from the sperm head in step 9–11 spermatids (Figure 6E). These results suggest that the detachment of the sperm head and tail in Ccdc113–/– mice may not be a secondary effect of sperm flagellum defects. We have discussed this point further below:

“CCDC113 can interact with SUN5 and CENTLEIN, but not PMFBP1 (Figure 7A-C), and left on the tip of the decapitated tail in Sun5–/– and Centlein–/– spermatozoa (Figure 7K and L). Furthermore, CCDC113 colocalizes with SUN5 in the HTCA region, and immunofluorescence staining in spermatozoa shows that SUN5 is positioned closer to the sperm nucleus than CCDC113 (Figure 7G and H). Therefore, SUN5 and CENTLEIN may be closer to the sperm nucleus than CCDC113. PAS staining revealed that Ccdc113–/– sperm heads are abnormally oriented in stages V–VIII seminiferous epithelia (Figure6 C and D), and TEM analysis further demonstrated that the disruption of CCDC113 causes the detachment of the destroyed coupling apparatus from the sperm head in step 9–11 spermatids (Figure 6E). All these results suggest that the detachment of sperm head and tail in Ccdc113–/– mice may not be a secondary effect of sperm flagellum defects.”

(3) Given that some cytoplasm materials could be observed in Ccdc113-/- spermatozoa (Fig. 5A), whether CCDC113 is also essential for cytoplasmic removal?

Good question. Unremoved cytoplasm could be detected in spermatozoa by using transmission electron microscopy (TEM) analysis, including disrupted mitochondria, damaged axonemes, and large vacuoles. These observations indicate defects in cytoplasmic removal in Ccdc113–/– mice. We have discussed this point as below:

“Moreover, TEM analysis detected excess residual cytoplasm in spermatozoa, including disrupted mitochondria, damaged axonemes, and large vacuoles, indicating defects in cytoplasmic removal in Ccdc113–/– mice (Figure 5A).”

(4) Although CCDC113 could not bind to PMFBP1, the localization of CCDC113 in Pmfbp1-/- spermatozoa should be also detected to clarify the relationship between CCDC113 and SUN5-CENTLEIN-PMFBP1.

We appreciate Reviewer #1’s suggestion. We have analyzed the localization of CCDC113 in Pmfbp1-/- spermatozoa and found that CCDC113 was located at the tip of the decapitated tail in Pmfbp1-/- spermatozoa (Figure 7K and L). This finding has been incorporated into the revised manuscript as below:

“To further elucidate the functional relationships among CCDC113, SUN5, CENTLEIN, and PMFBP1 at the sperm HTCA, we examined the localization of CCDC113 in Sun5-/-, Centlein–/–, and Pmfbp1–/– spermatozoa. Compared to the control group, CCDC113 was predominantly localized on the decapitated flagellum in Sun5-/-, Centlein–/–, and Pmfnp1–/– spermatozoa (Figure 7K and L), indicating SUN5, CENTLEIN, and PMFBP1 are crucial for the proper docking of CCDC113 to the implantation site on the sperm head. Taken together, these data demonstrate that CCDC113 cooperates with SUN5 and CENTLEIN to stabilize the sperm HTCA and anchor the sperm head to the tail.”

Reviewer #2 (Public Review):

Summary:

In the present study, the authors select the coiled-coil protein CCDC113 and revealed its expression in the stages of spermatogenesis in the testis as well as in the different steps of spermiogenesis with expression also mapped in the different parts of the epididymis. Gene deletion led to male infertility in CRISPR-Cas9 KO mice and PAS staining showed defects mapped in the different stages of the seminiferous cycle and through the different steps of spermiogenesis. EM and IF with several markers of testis germ cells and spermatozoa in the epididymis indicated defects in flagella and head-to-tail coupling for flagella as well as acephaly. The authors' co-IP experiments of expressed CCDC113 in HEK293T cells indicated an association with CFAP91 and DRC2 as well as SUN5 and CENTLEIN.

The authors propose that CCDC113 connects CFAP91 and DRC2 to doublet microtubules of the axoneme and CCDC113's association with SUN5 and CENTLEIN to stabilize the sperm flagellum head-to-tail coupling apparatus. Extensive experiments mapping CCDC13 during postnatal development are reported as well as negative co-IP experiments and studies with SUN5 KO mice as well as CENTLEIN KO mice.

Strengths:

The authors provide compelling observations to indicate the relevance of CCDC113 to flagellum formation with potential protein partners. The data are relevant to sperm flagella formation and its coupling to the sperm head.

We are grateful to Reviewer #2 for his or her recognition of the strength of this study.

Weaknesses:

The authors' observations are consistent with the model proposed but the authors' conclusions for the mechanism may require direct demonstration in sperm flagella. The Walton et al paper shows human CCDC96/113 in cilia of human respiratory epithelia. An application of such methodology to the proteins indicated by Wu et al for the sperm axoneme and head-tail coupling apparatus is eagerly awaited as a follow-up study.

We thank Reviewer 2 for his/her kindly help in improving the manuscript.  We now understand that directly detection of CCDC113 precise localization in sperm axoneme and head-tail coupling apparatus (HTCA) using cryo-electron microscopy (cryo-EM) could powerfully strengthen our model. Recent advances in cryo-EM have indeed advanced our understanding of axonemal structures analysis of axonemal structures and determined the structures of native axonemal DMTs from mouse, bovine, and human sperm (Leung et al., 2023; Zhou et al., 2023). However, high-resolution structures of sperm axoneme and HTCA regions, including those involving CCDC113, have yet to be fully characterized. Thus, we would like to discuss this point and consider it a valuable direction for future research.

“Given that the cryo-EM of sperm axoneme and HTCA could powerfully strengthen the role of CCDC113 in stabilizing sperm axoneme and head-tail coupling apparatus, it a valuable direction for future research.”

References:

Bazan, R., Schröfel, A., Joachimiak, E., Poprzeczko, M., Pigino, G., & Wloga, D. (2021). Ccdc113/Ccdc96 complex, a novel regulator of ciliary beating that connects radial spoke 3 to dynein g and the nexin link. PLoS Genet, 17(3), e1009388.

Ghanaeian, A., Majhi, S., McCafferty, C. L., Nami, B., Black, C. S., Yang, S. K., Legal, T., Papoulas, O., Janowska, M., Valente-Paterno, M., Marcotte, E. M., Wloga, D., & Bui, K. H. (2023). Integrated modeling of the Nexin-dynein regulatory complex reveals its regulatory mechanism. Nat Commun, 14(1), 5741.

Leung, M. R., Zeng, J., Wang, X., Roelofs, M. C., Huang, W., Zenezini Chiozzi, R., Hevler, J. F., Heck, A. J. R., Dutcher, S. K., Brown, A., Zhang, R., & Zeev-Ben-Mordehai, T.  (2023). Structural specializations of the sperm tail. Cell, 186(13), 2880-2896.e2817

Walton, T., Gui, M., Velkova, S., Fassad, M. R., Hirst, R. A., Haarman, E., O'Callaghan, C., Bottier, M., Burgoyne, T., Mitchison, H. M., & Brown, A. (2023). Axonemal structures reveal mechanoregulatory and disease mechanisms. Nature, 618(7965), 625-633.

Zhou, L., Liu, H., Liu, S., Yang, X., Dong, Y., Pan, Y., Xiao, Z., Zheng, B., Sun, Y., Huang, P., Zhang, X., Hu, J., Sun, R., Feng, S., Zhu, Y., Liu, M., Gui, M., & Wu, J. (2023). Structures of sperm flagellar doublet microtubules expand the genetic spectrum of male infertility. Cell, 186(13), 2897-2910.e2819.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

(1) Please provide full gel for the Figure 2C experiment (could be as a supplementary file).

Thanks for your insightful suggestions. We have replaced Figure 2C and provided the full gel in Figure 2-figure supplement 1A.

(2) The authors write on Line 163 "In contrast, the flagellum staining appeared reduced in Ccdc113-/- seminiferous tubules (Fig. 2J, red asterisk)." However, the magnification of the pictures is not sufficient to distinguish anything in the panel mentioned, please provide others.

Many thanks for pointing this out. We have provided the iconic figure to show the flagella defect in seminiferous tubules.

(3) Please add statistical p-values for figures.

Thanks for your valuable advice. We have added statistical p-values to the figures in the revised manuscript.

(4) Line 128: Should "speculate" be "speculated"?

Thank you for pointing out this problem. We have corrected it in the revised manuscript, as shown below:

“Given that CFAP91 has been reported to stabilize RS on the DMTs (Bicka et al., 2022; Dymek et al., 2011; Gui et al., 2021) and cryo-EM analysis shows that CCDC113 is closed to DMTs, we speculated that CCDC113 may connect RS to DMTs by binding to CFAP91 and microtubules.”

(5) In lines 384-385, more "-" is typed.

Thank you for pointing out this problem. We have corrected it in the revised manuscript, as shown below:

“Furthermore, CCDC113 colocalizes with SUN5 in the HTCA region, and immunofluorescence staining in spermatozoa shows that SUN5 is closer to the sperm nucleus than CCDC113 (Figure 7G and H). Therefore, SUN5 and CENTLEIN may be closer to the sperm nucleus than CCDC113.”

(6) In general, the article has many typos and should be professionally proofread.

Many thanks for pointing this out. We have thoroughly revised the manuscript with the assistance professional proofreading.

Reviewer #2 (Recommendations For The Authors):

Can the authors indicate in the Materials and Methods if n=3 biological replicates were done for all co-IP, EM, LM, and IF studies? The statistical analysis section indicates this but quantification is missing for most figures including co-IP, most IF, PAS staining, EM, etc.

We thank Reviewer 2 for the insightful comments and guidance to improve our data quality. All the experiments in this study were repeated at least three times to ensure reproducibility. We have quantified the co-IP experiments in Figures 1C-H and 7A-F, the IF data in Figures 2K, 5C, and 5D, as well as the PAS staining in Figure 6C. Since electron microscopy samples require very little testicular tissue and the sections obtained are very thin, the likelihood of capturing sections specifically at the sperm head-tail junction is considerably low. This challenge makes it difficult to perform quantitative analysis and statistical evaluation in the TEM experiment. To address this limitation, we have quantified the percentage of _Ccdc113-/-_sperm heads with abnormal orientation in stages V–VIII of the seminiferous epithelium to indicate impaired head-to-tail anchorage.

Figure S2 is compelling and might be indicated as a major figure instead of a supplementary figure.

We appreciate the positive comment. We have included it as a major figure in Figure 3F.

Figure 4A may be incomplete. Data sets for RNA expression suggest high expression in the ovary and other organs in males and females including the brain and are not indicated by the authors. Figure 4A may be considered for removal with a more complete study for another paper.

Thank you for pointing out this issue. We reviewed RNA expression data from various tissues using RNA-Seq data from Mouse ENCODE (https://www.ncbi.nlm.nih.gov/gene/244608) and found that CCDC113 is highly expressed in the testis, but not significantly in the ovary and brain (Figure 4- figure supplement 1A). Additionally, we re-evaluated CCDC113 protein levels in the spleen, lung, kidney, testis, intestine, stomach, brain, and ovary, confirming that it is highly expressed in the testes, with negligible expression in the ovary and brain (Figure 4- figure supplement 1B). In line with Reviewer 2's suggestion, we have removed Figure 4A in the revised manuscript.

There are grammatical errors throughout the manuscript and Figure 7 is truncated.

Thank you for pointing out this problem. We have thoroughly revised the manuscript with the assistance professional proofreading.

The Introduction and Discussion parts of the paper may need some clarification for the general reader. The material in the "Additional Context " section of the critique below may be a helpful place to introduce what a stage is, and the steps in germ cell development in the testis with the latter of course where and when the flagellum develops.

We appreciate your valuable suggestions. We have referred to the material in the “Additional Context” section to introduce the stages of spermatogenesis and the steps in germ cell development in the testis in the introduction and results.

“Male fertility relies on the continuous production of spermatozoa through a complex developmental process known as spermatogenesis. Spermatogenesis involves three primary stages: spermatogonia mitosis, spermatocyte meiosis, and spermiogenesis. During spermiogenesis, spermatids undergo complex differentiation processes to develop into spermatozoa, which includes nuclear elongation, chromatin remodeling, acrosome formation, cytoplasm elimination, and flagellum development (Hermo et al., 2010).”

Hermo, L., Pelletier, R. M., Cyr, D. G., & Smith, C. E. (2010). Surfing the wave, cycle, life history, and genes/proteins expressed by testicular germ cells. Part 1: background to spermatogenesis, spermatogonia, and spermatocytes. Microscopy research and technique, 73(4), 241–278. https://doi.org/10.1002/jemt.20783

“Pioneering work in the mid-1950s used the PAS stain in histologic sections of mouse testis to visualize glycoproteins of the acrosome and Golgi in seminiferous tubules (Oakberg, 1956). The pioneers discovered in cross-sectioned seminiferous tubules the association of differentiating germ cells with successive layers to define different stages that in mice are twelve, indicated as Roman numerals (XII). For each stage, different associations of maturing germ cells were always the same with early cells in differentiation at the periphery and more mature cells near the lumen. In this way, progressive differentiation from stem cells to mitotic, meiotic, acrosome-forming, and post-acrosome maturing spermatocytes was mapped to define spermatogenesis with the XII stages in mice representing the seminiferous cycle. The maturation process from acrosome-forming cells to mature spermatocytes is defined as spermiogenesis with 16 different steps that are morphologically distinct spermatids (O'Donnell L, 2015).”

Oakberg, E. F. (1956). A description of spermiogenesis in the mouse and its use in analysis of the cycle of the seminiferous epithelium and germ cell renewal. The American journal of anatomy, 99(3), 391-413. https://doi.org/10.1002/aja.1000990303

O'Donnell L. (2015). Mechanisms of spermiogenesis and spermiation and how they are disturbed. Spermatogenesis, 4(2), e979623. https://doi.org/10.4161/21565562.2014.979623

For the Discussion, the authors indicate that the function of CCDC113 in mammals is unknown yet the authors point to the work of Walton et al on human respiratory epithelia that points to a function for CCDC96/113. The work in the manuscript here does indicate a role in sperm flagella and the head-to-tail coupling apparatus but remains descriptive until the methodology of Walton et al is applied. Hopefully, the authors will consider it for a follow-up study.

Thank you for pointing out this problem. We have revised this part and highlighted the Walton et al’s work in the Discussion.

“CCDC113 is a highly evolutionarily conserved component of motile cilia/flagella. Studies in the model organism, Tetrahymena thermophila, have revealed that CCDC113 connects RS3 to dynein g and the N-DRC, which plays essential role in cilia motility (Bazan et al., 2021; Ghanaeian et al., 2023). Recent studies have also identified the localization of CCDC113 within the 96-nm repeat structure of the human respiratory epithelial axoneme, and localizes to the linker region among RS, N-DRC and DMTs (Walton et al., 2023). In this study, we reveal that CCDC113 is indispensable for male fertility, as Ccdc113 knockout mice produce spermatozoa with flagellar defects and head-tail linkage detachment (Figure 3D).”

“Overall, we identified CCDC113 as a structural component of both the flagellar axoneme and the HTCA, where it performs dual roles in stabilizing the sperm axonemal structure and maintaining the structural integrity of HTCA. Given that the cryo-EM of sperm axoneme and HTCA could powerfully strengthen the role of CCDC113 in stabilizing sperm axoneme and head-tail coupling apparatus, it a valuable direction for future research.”

The Discussion may be focused on the key aspects of CCDC113 related to sperm flagella and the head-to-tail coupling apparatus that represent a genuine advance. The more speculative parts of the Discussion that have not been addressed by experimentation in the Results section may be considered for removal in the Discussion section.

Thank you for pointing out this. We have removed the speculative parts of the Discussion that have not been addressed by experimentation in the Results section.

Additional Context to help readers understand the significance of the work:

Pioneering work in the mid-1950s used the periodic acid Schiff (PAS) stain in histologic sections of rodent testis to visualize glycoproteins of the acrosome and Golgi in seminiferous tubules. The pioneers discovered in cross-sectioned seminiferous tubules the association of differentiating germ cells with successive layers to define different stages that in mice are twelve, indicated as Roman numerals (XII). For each stage, different associations of maturing germ cells were always the same with early cells in differentiation at the periphery and more mature cells near the lumen. In this way, progressive differentiation from stem cells to mitotic, meiotic, acrosome-forming, and post-acrosome maturing spermatocytes was mapped to define spermatogenesis with the XII stages in mice representing the seminiferous cycle. The maturation process from acrosome-forming cells to mature spermatocytes is defined as spermiogenesis with 19 different steps that are morphologically distinct spermatids. It is from steps 8-19 of spermiogenesis that the formation of the flagellum takes place. Final maturation occurs in the epididymis as sperm move through the caput, corpus, and cauda of the organ with motile spermatozoa generated.

Thank you very much!

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The authors aimed to investigate the oscillatory activity of GnRH neurones in freely behaving mice. By utilising GCaMP fiber photometry, they sought to record real-time neuronal activity to understand the patterns and dynamics of GnRH neuron firing and their implications for reproductive physiology.

Strengths:

(1) The use of GCaMP fiber photometry allows for high temporal resolution recordings of neuronal activity, providing real-time data on the dynamics of GnRH neurones.

(2) Recording in freely behaving animals ensures that the findings are physiologically relevant and not artifacts of a controlled laboratory environment.

(3) The authors used statistical methods to characterise the oscillatory patterns, ensuring the reliability of their findings.

Weaknesses:

(1) While the study identifies distinct oscillatory patterns in GnRH neurones' calcium dynamics, it falls short in exploring the functional implications of these patterns for GnRH pulsatility and overall reproductive physiology.

The functional roles of pulsatile and surge patterns of GnRH release are extremely well established. We have found perfect correlations between GnRH neuron dendron GCaMP activity and LH pulses as well as the LH surge clearly indicating the function of these activity patterns. We do not know the functional role of the clustered high-frequency basal activity that we have discovered and, as noted in the Discussion, are unsure of its physiological importance. Although it may be minor, it will require future investigation.

(2) The study lacks a broader discussion to include comparisons with existing studies on GnRH neurone activity and pulsatility and highlight how the findings of this study align with or differ from previous research and what novel contributions are made.

The Reviewer fails to recognise that these are first recordings of GnRH neurons in vivo. There are no prior studies for comparison. We have noted the only other in vivo study (undertaken by ourselves) many years ago in anaesthetized mice. It was never expected that electrophysiological recordings of GnRH neurons in acute brain slices (by ourselves and others) would reflect their activity in vivo. Now that we know this to be the case, it would be churlish to point this out explicitly. We have made some modifications to the Discussion by comparing the present data more thoroughly with other in vivo GnRH secretion and kisspeptin neuron activity studies.

(3) The authors aimed to characterise the oscillatory activity of GnRH neurons and successfully identified distinct oscillatory patterns. The results support the conclusion that GnRH neurons exhibit complex oscillatory behaviours, which are critical for understanding their role in reproductive physiology. However, it has not been made clear what exactly the authors mean by "multi-dimensional oscillatory patterns" and how has this been shown.

The study shows three types of GnRH neuron activity; two of which would be classified as oscillatory in nature and these show different temporal dimensions.

Reviewer #2 (Public Review):

Summary:

In this manuscript, the authors report GCaMP fiber-photometry recordings from the GnRH neuron distal projections in the ventral arcuate nucleus. The recordings are taken from intact, male and female, freely behaving mice. The report three patterns of neuronal activity:

(1) Abrupt increases in the Ca2+ signals that are perfectly correlated with LH pulses.

(2) A gradual, yet fluctuating (with a slow ultradian frequency), increase in activity, which is associated with the onset of the LH surge in female animals.

(3) Clustered (high frequency) baseline activity in both female and male animals.

Strengths:

The GCaMP fiber-photometry recordings reported here are the first direct recordings from GnRH neurones in vivo. These recordings have uncovered a rich repertoire of activity suggesting the integration of distinct "surge" and "pulse" generation signals, and an ultradian rhythm during the onset of the surge.

Weaknesses:

The data analysis method used for the characterisation of the ultradian rhythm observed during the onset of the surge is not detailed enough. Hence, I'm left wondering whether this rhythm is in any way correlated with the clusters of activity observed during the rest of the cycle and which have similar duration.

We have provided further information on the characterisation of the ultradian rhythm observed at the time of the surge. Whether this is related to the clustered basal activity is an interesting point but very difficult to resolve. We note that the “basal” and “surge” ultradian oscillations have very different durations of ~30 and ~80 min suggesting that they may be independent phenomenon. However, the only way to really exclude a similar genesis will be to establish the origin of each type of oscillatory activity. Preliminary data in the lab show that the RP3V kisspeptin neurons exhibit an identical pattern of ultradian oscillation at the time of the surge leading us to suspect that the surge oscillation is driven by this input. As noted in the Discussion it is presently difficult to determine where the high basal activity originates.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

(1) Evidence of Multi-Dimensional Oscillatory Patterns: The manuscript presents data showing the oscillatory activity of GnRH neurones with distinct frequency and amplitude characteristics. The analysis includes statistical tests that illustrate the variability in neuronal firing patterns. However, the multi-dimensional nature of this activity has not been demonstrated. It is not clear what is meant by "dimension" with regard to the calcium recordings (oscillatory activity). If the authors refer to the frequency content of the calcium signal then a proper Fourier or Wavelet analysis should be carried out to characterise the multiple frequencies present in the calcium dynamics in male mice and during various stages of the cycle in female mice

The study shows three types of GnRH neuron activity; two of which would be classified as oscillatory in nature. One occurs for ~10 min every hour or so and the other occurs for ~ 12 hours once every 4-5 days. This does not require any analysis to distinguish between the two or claim that they are different i.e. multidimensional. 

(2) Data Interpretation: Expand the discussion on the physiological relevance of the identified oscillatory patterns. Specifically, explore how these patterns might influence GnRH pulsatility, hormone secretion dynamics, and reproductive cycles.

The functional roles of pulsatile and surge patterns of GnRH release are extremely well established. We have found perfect correlations between GnRH neuron dendron GCaMP activity and LH pulses as well as the LH surge clearly indicating the function of these activity patterns. We do not know the functional role of the clustered high-frequency basal activity that we have discovered and, as noted in the Discussion, are unsure of its physiological importance. Although it may be minor, it will require future investigation.

(3) Literature Contextualisation: Broaden the discussion to include comparisons with existing studies on GnRH neuron activity and pulsatility. Highlight how the findings of this study align with or differ from previous research and what novel contributions are made.

The Reviewer fails to recognise that these are first recordings of GnRH neurons in vivo. There are no prior studies for comparison. We have noted the only other in vivo study (undertaken by ourselves) many years ago in anaesthetized mice. It would be naive to expect that electrophysiological recordings of GnRH neurons in acute brain slices (by ourselves and others) would reflect their activity in vivo. Now that we know this to be the case, it would be churlish to point this out explicitly. We have made some modifications to the Discussion by comparing the present data more thoroughly with other in vivo GnRH secretion and kisspeptin neuron activity studies.

(4) Future Directions: Suggest potential follow-up experiments to explore the regulatory mechanisms underlying the observed oscillatory patterns. This could include investigating the role of neurotransmitters, hormonal feedback mechanisms, and other factors that might influence GnRH neuron activity.

By addressing these recommendations, the authors can further strengthen their manuscript and enhance its impact on the field.

Reviewer #2 (Recommendations For The Authors):

Suggestions:

(1) The authors might want to analyse their inter-peak interval data by fitting them to a simple parametric statistical model (the gamma distribution would be a good choice to capture the skewness of these data). This way they would be able to describe the observed variability, and if the fits are not good back up to their claims "The dSEs occurred on average ... and showed no clear modal distribution pattern (Fig. 2D)".

Thank you for the suggestion. We have carried out Shapiro-Wilk tests for male inter-peak interval distribution and found a W value of 0.87 and P value <0.0001****, providing strong evidence that the data is not normally distributed. Skewness and Kurtosis values are 1.39 and 1.81 respectively, indicating that the distribution is right-skewed with a platykurtic distribution, indicating that the data is less peaked and more spread out than the normal distribution (with a kurtosis of 3). This has now been added to the manuscript.

(2) If I understand correctly, in Figure 3D, inter-peak intervals from all 4 stages of the estrus cycle are pooled together. It would also be interesting if the authors gave the interval histograms for the different stages of the cycle separately.

We have now plotted the inter-peak interval distribution histograms for each individual cycle next to the example traces in Figure 3. The descriptions of the distribution pattern are also updated in the figure legends.

(3) In Figure 3C, one can see the mean interval for different animals (as open circles), is that right? Is the statistical test run on these animals mean, or is the entire dSEs dataset used? In any case, it's not clear to the reader how variable intervals are in individual recordings from each animal. Could the authors add this information (could be easily added in the figure caption)?

The reviewer is correct, that each open circle is the mean interval for each animal. The statistical test was run on the animals mean. Now this information is added to the figure legend.

(4) The authors should explain how they identify the regions (clusters) of high-frequency baseline activity, which they present in Figure 4.

The relevant information is now added to the methods section under the heading ‘GCaMP6 fiber photometry and blood sampling’.

(5) The authors should detail how to identify and characterise the ultradian rhythm they observe at the onset of the surge.

The relevant information is now added to the methods section under the heading ‘GCaMP6 fiber photometry and blood sampling’.

(6) The author could perform some kind of wavelet-type analysis to quantify and analyse how the frequency content of the observed Ca2+ signal changes over the cycle. From their current analysis, I am not sure whether the ultradian oscillations they observe during the surge are related to the low-activity cluster events they observe during the other stages of the cycle.

This is an interesting point but very difficult to resolve. We note that the “basal” and “surge” ultradian oscillations have very different durations of ~30 and ~80 min suggesting that they may be independent phenomenon. However, the only way to really exclude a similar genesis will be to establish the origin of each type of oscillatory activity. Preliminary data in the lab show that the RP3V kisspeptin neurons exhibit an identical pattern of ultradian oscillation at the time of the surge leading us to suspect that the surge oscillation is driven by this input. As noted in the Discussion it is presently difficult to determine where the high basal activity originates.

Author response:

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

Response to Reviewer’s comments

We are most grateful for the opportunity to address the reviewer comments. Point-by-point responses are presented below.

Overall, the paper has several strengths, including leveraging large-scale, multi-modal datasets, using computational reasonable tools, and having an in-depth discussion of the significant results.

We thank the reviewer for the very supportive comments.

Based on the comments and questions, we have grouped the concerns and corresponding responses into three categories.

(1) The scope and data selection

The results are somewhat inconclusive or not validated.

The overall results are carefully designed, but most of the results are descriptive. While the authors are able to find additional evidence either from the literature or explain the results with their existing knowledge, none of the results have been biologically validated. Especially, the last three result sections (signaling pathways, eQTLs, and TF binding) further extended their findings, but the authors did not put the major results into any of the figures in the main text.”

The goal of this manuscript is to provide a list of putative childhood obesity target genes to yield new insights and help drive further experimentation. Moreover, the outputs from signaling pathways, eQTLs, and TF binding, although noteworthy and supportive of our method, were not particularly novel. In our manuscript we placed our focus on the novel findings from the analyses. We did, however, report the part of the eQTLs analysis concerning ADCY3, which brought new insight to the pathology of obesity, in Figure 4C.

The manuscript would benefit from an explanation regarding the rationale behind the selection of the 57 human cell types analyzed. it is essential to clarify whether these cell types have unique functions or relevance to childhood development and obesity.

We elected to comprehensively investigate the GWAS-informed cellular underpinnings of childhood development and obesity. By including a diverse range of cell types from different tissues and organs, we sought to capture the multifaceted nature of cellular contributions to obesity-related mechanisms, and open new avenues for targeted therapeutic interventions.

There are clearly cell types that are already established as being key to the pathogenesis of obesity when dysregulated: adipocytes for energy storage, immune cell types regulating inflammation and metabolic homeostasis, hepatocytes regulating lipid metabolism, pancreatic cell types intricately involved in glucose and lipid metabolism, skeletal muscle for glucose uptake and metabolism, and brain cell types in the regulation of appetite, energy expenditure, and metabolic homeostasis.

While it is practical to focus on cell types already proven to be associated with or relevant to obesity, this approach has its limitations. It confines our understanding to established knowledge and rules out the potential for discovering novel insights from new cellular mechanisms or pathways that could play significant roles in the pathogenesis if obesity. Therefore, it was essential to reflect known biology against the unexplored cell types to expand our overall understanding and potentially identify innovative targets for treatment or prevention.

I wonder whether the used epigenome datasets are all from children. Although the authors use literature to support that body weight and obesity remain stable from infancy to adulthood, it remains uncertain whether epigenomic data from other life stages might overlook significant genetic variants that uniquely contribute to childhood obesity.

The datasets utilized in our study were derived from a combination of sources, both pediatric and adult. We recognize that epigenetic profiles can vary across different life stages but our principal effort was to characterize susceptibility BEFORE disease onset.

Given that the GTEx tissue samples are derived from adult donors, there appears to be a mismatch with the study's focus on childhood obesity. If possible, identifying alternative validation strategies or datasets more closely related to the pediatric population could strengthen the study's findings.

We thank the reviewer for raising this important point. We acknowledge that the GTEx tissue samples are derived from adult donors, which might not perfectly align with the study's focus on childhood obesity. The ideal strategy would be a longitudinal design that follows individuals from childhood into adulthood to bridge the gap between pediatric and adult data, offering systematic insights into how early-life epigenetic markers influencing obesity later in life. In future work, we aim to carry out such efforts, which will represent substantial time and financial commitment.

Along the same lines, the Developmental Genotype-Tissue Expression (dGTEx) Project is a new effort to study development-specific genetic effects on gene expression at 4 developmental windows spanning from infant to post-puberty (0-18 years). Donor recruitment began in August 2023 and remains ongoing. Tissue characterization and data production are underway. We hope that with the establishment of this resource, our future research in the field of pediatric health will be further enhanced.

Figure 1B: in subplots c and d, the results are either from Hi-C or capture-C. Although the authors use different colors to denote them, I cannot help wondering how much difference between Hi-C and capture-C brings in. Did the authors explore the difference between the Hi-C and capture-C?

Thank you for your comment. It is not within the scope of our paper to explore the differences between the Hi-C and Capture-C methods. In the context of our study, both methods serve the same purpose of detecting chromatin loops that bring putative enhancers to sometimes genomically distant gene promoters. Consequently, our focus was on utilizing these methods to identify relevant chromatin interactions rather than comparing their technical differences.

(2) Details on defining different categories of the regions of interest

Some technical details are missing.

While the authors described all of their analysis steps, a lot of the time, they did not mention the motivation. Sometimes, the details were also omitted.”

We have added a section to the revision to address the rationale behind different OCRs categories.

Line 129: should "-1,500/+500bp" be "-500/+500bp"?

A gene promoter was defined as a region 1,500 bases upstream to 500 bases downstream of the TSS. Most transcription factor binding sites are distributes upstream (5’) from TSS, and the assembly of transcription machinery occurs up to 1000 bases 5’ from TSS. Given our interest in SNPs that can potentially disrupt transcription factor binding, this defined promoter length allowed us to capture such SNPs in our analyses.

How did the authors define a contact region?

Chromatin contact regions identified by Hi-C or Capture-C assays are always reported as pairs of chromatin regions. The Supplementary eMethods provide details on the method of processing and interaction calling from the Hi-C and Capture-C data.

The manuscript would benefit from a detailed explanation of the methods used to define cREs, particularly the process of intersecting OCRs with chromatin conformation data. The current description does not fully clarify how the cREs are defined.

In the result section titled "Consistency and diversity of childhood obesity proxy variants mapped to cREs", the authors introduced the different types of cREs in the context of open chromatin regions and chromatin contact regions, and TSS. Figure 2A is helpful in some way, but more explanation is definitely needed. For example, it seems that the authors introduced three chromatin contacts on purpose, but I did not quite get the overall motivation.

We apologize for the confusion. Our definition of cREs is consistent throughout the study. Figure 2A will be the first Figure 1A in the revision in order to aid the reader.

The 3 representative chromatin loops illustrate different ways the chromatin contact regions (pairs of blue regions under blue arcs) can overlap with OCRs (yellow regions under yellow triangles – ATAC peaks) and gene promoters.

(1) The first chromatin loop has one contact region that overlaps with OCRs at one end and with the gene promoter at the other. This satisfies the formation of cREs; thus, the area under the yellow ATAC-peak triangle is green.

(2) The second loop only overlapped with OCR at one end, and there was no gene promoter nearby, so it is unqualified as cREs formation.

(3) The third chromatin loop has OCR and promoter overlapping at one end. We defined this as a special cRE formation; thus, the area under the yellow ATAC-peak triangle is green.

To avoid further confusion for the reader, we have eliminated this variation in the new illustration for the revised manuscript.

Figure 2A: The authors used triangles filled differently to denote different types of cREs but I wonder what the height of the triangles implies. Please specify.

The triangles are illustrations for ATAC-seq peaks, and the yellow chromatin regions under them are OCRs. The different heights of ATAC-seq peaks are usually quantified as intensity values for OCRs. However, in our study, when an ATAC-seq peak passed the significance threshold from the data pipeline, we only considered their locations, regardless of their intensities. To avoid further confusion for the reader, we have eliminated this variation in the new illustration for the revised manuscript.

Figure 1B-c. the title should be "OCRs at putative cREs". Similarly in Figure 1B-d.

cREs are a subset of OCRs.

- In the section "Cell type specific partitioned heritability", the authors used "4 defined sets of input genomic regions". Are you corresponding to the four types of regions in Figure 2A? 

Figure 2A is the first Figure 1A in the revision and is modified to showcase how we define OCRs and cREs.

It seems that the authors described the 771 proxies in "Genetic loci included in variant-to-genes mapping" (ln 154), and then somehow narrowed down from 771 to 94 (according to ln 199) because they are cREs. It would be great if the authors could describe the selection procedure together, rather than isolated, which made it quite difficult to understand.

In the Methods section entitled “Genetic loci included in variant-to-genes mapping," we described the process of LD expansion to include 771 proxies from 19 sentinel obesity-significantly associated signals. Not all of these proxies are located within our defined cREs. Figure 2B, now Figure 2A in the revision, illustrates different proportions of these proxies located within different types of regions, reducing the proxy list to 94 located within our defined cREs.

Figure 2. What's the difference between the 771 and 758 proxies?

13 out of 771 proxies did not fall within any defined regions. The remaining 758 were located within contact regions of at least one cell type regardless of chromatin state.

(3) Typos

In the paragraph "Childhood obesity GWAS summary statistics", the authors may want to describe the case/control numbers in two stages differently. "in stage 1" and "921 cases" together made me think "1,921" is one number.

This has been amended in the revision.

Hi-C technology should be spelled as Hi-C. There are many places, it is miss-spelled as "hi-C". In Figure 1, the author used "hiC" in the legend. Similarly, Capture-C sometime was spelled as "capture-C" in the manuscript.

At the end of the fifth row in the second paragraph of the Introduction section: "exisit" should be "exist".

In Figure 2A: "Within open chromatin contract region" should be "Within open chromatin contact region”

These typos and terminology inconsistencies have been amended in the revision.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

In this manuscript, Zhang et al. report a genetic screen to identify novel transcriptional regulators that could coordinate mitochondrial biogenesis. They performed an RNAi-based modifier screen wherein they systematically knocked down all known transcription factors in the developing Drosophila eye, which was already sensitised and had decreased mitochondrial DNA content. Through this screen, they identify CG1603 as a potential regulator of mitochondrial content. They show that protein levels of mitochondrial proteins like TFAM, SDHA, and other mitochondrial proteins and mtDNA content are downregulated in CG1603 mutants. RNA-Seq and ChIP-Seq further show that CG1603 binds to the promoter regions of several known nuclear-encoded mitochondrial genes and regulates their expression. Finally, they also identified YL-1 as an upstream regulator of CG1603. Overall, it is a very important study as our understanding of the regulation of mitochondrial biogenesis remains limited across metazoans. Most studies have focused on PGC-1α as a master regulator of mitochondrial biogeneis, which seems a context-dependent regulator. Also, PGC-1α mediated regulation could not explain the regulation of 1100 genes that are required for mitochondrial biogenesis. Therefore, identifying a new regulator is crucial for understanding the overall regulation of mitochondrial biogenesis.

Reviewer #2 (Public Review):

Summary:

In this study, the authors aim to identify the nuclear genome-encoded transcription factors that regulate mtDNA maintenance and mitochondrial biogenesis. They started with an RNAi screening in developing Drosophila eyes with reduced mtDNA content and identified a number of putative candidate genes. Subsequently, using ChIP-seq data, they built a potential regulatory network that could govern mitochondrial biogenesis. Next, they focused on a candidate gene, CG1603, for further characterization. Based on the expression of different markers, such as TFAM and SDHA, in the RNAi and OE clones in the midgut cells, they argue that CG1603 promotes mitochondrial biogenesis and the expression of ETC complex genes. Then, they used a mutant of CG1603 and showed that both mtDNA levels and mitochondrial protein levels were reduced. Using clonal analyses, they further show a reduction in mitochondrial biogenesis and membrane potential upon loss of CG1603. They made a reporter line of CG1603, showed that the protein is localized to the mitochondria, and binds to polytene chromosomes in the salivary gland. Based on the RNA-seq results from the mutants and the ChIP data, the authors argue that the nucleus-encoded mitochondrial genes that are downregulated >2 folds in the CG1603 mutants and that are bound by CG1603 are related to ETC biogenesis. Finally, they show that YL-1, another candidate in the network, is an upstream regulator of CG1603.

Strengths:

This is a valuable study, which identifies a potential regulator and a network of nucleus-encoded transcription factors that regulate mitochondrial biogenesis. Through in-vivo and in-vitro experimental evidence, the authors identify the role of CG1603 in this process. The screening strategy was smart, and the follow-up experiments were nicely executed.

Weaknesses:

Some additional experiments showing the effects of CG1603 loss on ETC integrity and functionality would strengthen the work.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

(1) Fig 3F: SDHA levels are severely downregulated in CG1603 RNAi clones. Therefore, estimating mitochondrial volume based on the SDHA reporter might be misleading. I suggest the authors perform this experiment with an independent marker of mitochondria, like mitoTracker Green or other dyes. I also suggest checking for mitochondrial number/quantity/size by electron microscopy.

Even though being downregulated, the SDHA-mNeon signal in EC clones clearly outlined mitochondria and the overall mitochondrial network, allowing us to quantify the total mitochondrial volume. Examining mitochondrial number/quantity/size by electron microscopy would further strengthen this statement, and we will consider it in future studies.

(2) The authors might comment on whether there was any decrease in the volume of CG1603i clone cells. And whether this was taken into account while normalising the mitochondrial volume.

The size/volume of CG1603i clone cells were indeed decreased, which was considered while normalizing the mitochondrial volume. We clarified this point in methods section (page 18, line 511-512 (revised version page 18, line 515-517)).

(3) Line 230-234: Collectively, these results demonstrate that CG1603 promotes the expression of both nuclear and mtDNA-encoded ETC genes and boosts mitochondrial biogenesis. CG1603 RNAi produced very few EC clones, consistent with the notion that mitochondrial respiration is necessary for ISCs differentiation.

(4) Quantifying the number of EC clone cells observed might help support this statement.

This is a great point. We quantified the number of EC clone cells, and the data was included in the revised Figure 3—figure supplement.

(5) Figure 5: The intensity of MTGreen in CH1603 clones seems comparable to that in control cells, at least visually. Since the authors claim a reduction in mitochondrial volume in CG1603 mutants, it is crucial to estimate mitochondrial volume based on MTGreen intensity in mutant and control cells.

There are two types of clones shown in Figure 5:  germ cell clones including all 16 germ cells in the same egg chamber and follicle cell clones. We highlight these two types of clones in the revised Figure 5, to emphasize this point. The total MT Green intensity in both germ cell and follicle cell CG1603PBac clones were reduced, compared to germ cells in adjacent egg chambers and adjacent follicle cells in the same egg chamber, respectively. We included the quantification of MTGreen intensity in the revised Figure 5—figure supplement C. Examining mitochondrial number/quantity/size by electron microscopy would further strengthen this statement, and we will consider it in future studies.

(6) Figure 8: It would be interesting to know what happens to steady-state mtDNA levels during YL-1 knockdown. If decreased, could overexpressing CG1603 in YL-1 knockdown cells rescue the phenotype?

YL-1 knockdown reduced steady-state mtDNA levels in eyes, and overexpressing CG1603 restored mtDNA level in YL-1 knockdown cells. These results are included in the revised Figure 8-figure supplement C.

Minor comments:

(7) The paper is lucidly written, but there are minor typos in several places. The authors might proofread it to remove these errors.

We corrected typos and other minor errors in the manuscript.

(8) Quantification for Figure 8 - Supplementary needs to be included.

We performed the quantification, and the result is shown in Figure 8—figure supplement B.

Reviewer #2 (Recommendations For The Authors):

(1) In lines 275-276 and Figure 6E, the authors mention that more than 800 nuclear-encoded mitochondrial genes were reduced by >2-folds in CG1603 mutants. One gene related to mitochondrial replication and three genes related to mtDNA transcription were among them. Was TFAM one of these candidates? What were the reduction levels of TFAM mRNA in RNA seq results? Can the author confirm it via RT-PCR?

In RNAseq analyses, TFAM was differentially expressed with a log2 Fold-Change of “ -0.74”, corresponding to ~1.6-fold decrease, and hence was not one of these candidates that were down-regulated more than two folds in CG1603 mutant. Per reviewer’s suggestion, we carried out RT-PCR and found TFAM was downregulated about 2-fold in CG1603 mutant. We included this result in the revised Figure 6F and listed all differentially expressed genes in Supplementary file 5a.

(2) In many places, the authors argued about the role of CG1603 in ETC biogenesis. Also, the RNA-seq data shows that 64 genes related to the ETC complex were reduced by > 2-fold in CG1603 mutant. Therefore, it would be critical to expand a little on this aspect. For example, what are these genes and related to which of the ETC complex? Can the authors show the reduced levels of some of the candidate genes from each complex via RT-PCR?

We listed all ETC genes that were down-regulated more than two folds in CG1603 mutant in a separate sheet in Supplementary file 5b. We further validated the reduced expression of ETC genes by RT-PCR on three randomly selected candidate genes from each complex. The result is included in the revised Figure 6F.

(3) To make their argument solid on the role of CG1603 on ETC biogenesis, it is important to show the assembly/integrity of ETC complexes as well as the functionality/activity of the ETC complexes in CG1603 mutants.

We purified mitochondria, and assayed assembly/integrity of three ETC complexes (Complex I, II and IV) and their activities, using blue native PAGE analysis and in gel activity analysis, respectively.  The amount of these three complexes, and accordingly, their activities were all markedly reduced in CG1603 mutant compared to wt.  The result is included as Figure 4—figure supplement A.

(4) CG1603 has already been named as cliff. Why do the authors not use this name, or alternatively propose one?

We thank the reviewer for the note. The CG1603 has not been named as cliff when we were preparing this manuscript.

(5) In lines 230-231, based on the TFAM-GFP and SDHA-mNG levels, the authors claim that "these results demonstrate that CG1603 promotes the expression of both nuclear and mtDNA-encoded ETC genes..." The authors may tone down this statement since it sounds overstating. It would be prudent to claim that a subset of genes are regulated by CG1603.

We appreciate the reviewer’s suggestion. We revised the text to tone down this statement (page 8, line 201; page 9, line 229-230).

Author response:

Reviewer #1:

Weaknesses:

However, given that S1P is upstream NF-κB signaling, it is unclear if it offers conceptual innovations as compared to previous studies from the same team (Palazzo et al. 2020; 2022, 2023)

We find distinct differences between the impacts of S1P- and NFkB-signaling on glial activation, neuronal differentiation of the progeny of MGPCs and neuronal survival in damaged retinas. In the current study we demonstrate that 2 consecutive daily intravitreal injections of S1P selectively activated mTor (pS6) and Jak/Stat3 (pStat3), but not MAPK (pERK1/2) signaling in Müller glia.  Further, inhibition of S1P synthesis (SPHK1 inhibitor) decreased ATF3, mTor (pS6) and pSmad1/5/9 levels in activated Müller glia in damaged retinas. Inhibition of NFkB-signaling in damaged chick retinas did not impact the above-mentioned cell signaling pathways (Palazzo et al., 2020). Thus, S1P-signaling impacts cell signaling pathways in MG that are distinct from NFκB, but we cannot exclude the possibility of cross-talk between NFkB and these pathways. Further, inhibition of NFκB-signaling potently decreases numbers of dying cells and increases numbers of surviving ganglion cells (Palazzo et al 2020). Consistent with these findings, a TNF orthologue, which presumably activates NFκB-signaling, exacerbates cell death in damage retinas (Palazzo et al., 2020). By contrast, 5 different drugs targeting S1P-signaling had no effect on numbers of dying cells and only one S1PR1 inhibitor modestly decreased numbers of dying cells (current study). In addition, inhibition of NFκB does not influence the neurogenic potential of MGPCs in damaged chick retinas (Palazzo et al., 2020), whereas inhibition of S1P receptors (S1PR1 and S1PR3) and inhibition of S1P synthesis (SPHK1) significantly increased the differentiation of amacrine-like neurons in damaged retinas (current study). Collectively, in comparison to the effects of pro-inflammatory cytokines and NFκB-signaling, our current findings indicate that S1P-signaling through S1PR1 and S1PR3 in Müller glia has distinct effects upon cell signaling pathways, neuronal regeneration and cell survival in damaged retinas. We will revise text in the Discussion to better highlight these important distinctions between NFκB- and S1P-signaling.

Reviewer #2:

Weaknesses:

The methodology is not very clean. A number of drugs (inhibitors/ antagonists/agonists signal modulators) are used to modulate S1P expression or signaling in the retina without evidence that these drugs are reaching the target cells. No alternative evaluation if the drugs, in fact, are effective. The drug solubility in the vehicle and in the vitreous is not provided, and how did they decide on using a single dose of each drug to have the optimal expected effect on the S1P pathway?

Müller glia are the predominant retinal cell type that expresses S1P receptors. Consistent with these patterns of expression, we report Müller glia-specific effects of different agonists and antagonists that increase or decrease S1P-signaling. Since we compare cell-level changes within contralateral eyes wherein one retina is exposed to vehicle and the other is exposed to vehicle plus drug, it seems highly probable that the drugs are eliciting effects upon the Müller glia. It is possible, but very unlikely, that the responses we observed could have resulted from drugs acting on extra-retinal tissues, which might secondarily release factors that elicit cellular responses in Müller glia. However, this seems unlikely given the distinct patterns of expression for different S1P receptors in Müller glia, and the outcomes of inhibiting Sphk1 or S1P lyase on retinal levels of S1P.

For example, we provide evidence that S1PR1 and S1PR3 expression is predominant in Müller glia in the chick retina using single cell-RNA sequencing and fluorescence in situ hybridization (FISH). Thus, we expect that S1PR1/3-targeting small molecule inhibitors to directly act on Müller glia, which is consistent with our read-outs of cell signaling with injections of S1P in undamaged retinas. We show that SPHK1 and SGPL1, which encode the enzymes that synthesize or degrade S1P, are expressed by different retinal cell types, including the Müller glia. The efficacy of the drugs that target SPHK1 and SGPL1 was assessed by measuring levels of S1P in the retina. By using liquid chromatography and tandem mass spectroscopy (LC-MS/MS), we provide data that inhibition of S1P synthesis (inhibition of SPHK1) significantly decreased levels of S1P in normal retinas, whereas inhibition of S1P degradation (inhibition of SGPL1) increased levels of S1P in damaged retinas (Fig. 5).  These data suggest that the SPHK1 inhibitor and the SGPL1 inhibitor specifically act at the intended target to influence retinal levels of S1P.  Further, inhibition of SPHK1 (to decrease levels S1P) results in decreased levels of ATF3, pS6 (mTor) and pSMAD1/5/9 in Müller glia, consistent with the notion that reduced levels of S1P in the retina impacts signaling at Müller glia. Finally, we find similar cellular responses to chemically different agonists or antagonists, and we find opposite cellular responses to agonists and antagonists, which are expected to be complimentary if the drugs are specifically acting at the intended targets in the retina. We will revise the Discussion to better address caveats and concerns regarding the actions and specificity of different drugs within the retina following intravitreal delivery.

We will provide the drug solubility specifications and estimates of the initial maximum dose per eye for each drug. For chick eyes between P7 and P14, these estimates will assume a volume of about 100 µl of liquid vitreous, 800 µl gel vitreous and an average eye weight of 0.9 grams. We will revise Table 1 (pharmacological compounds) with ranges of reported in vivo ED50’s (mg/kg) for drugs and we will list the calculated initial maximum dose (mg/kg equivalent per eye). Doses were chosen based on estimates of the initial maximum ocular dose that were within the range of reported ED50’s. However, as is the case for any in vivo model system, it is difficult to predict rates of drug diffusion out of the vitreous, how quickly the drugs are cleared from the entire eye, how much of the compound enters the retina, and how quickly the drug is cleared from the retina. Accordingly, we assessed drug specificity and sites of activation by relying upon readouts of cell signaling pathways, parsed with S1P receptor expression patterns, together with measurements of retinal levels of S1P following exposure to drugs targeting enzymes that catalyze synthesis or degradation of S1P, as described above.

Author response:

Reviewer #1 (Public review):

Summary:

Shrestha et al report an investigation of mechanisms underlying gustatory preference for carboxylic acids in Drosophila. They begin with a screen of selected IR mutants, identifying 5 candidates - 2 IR co-receptors and 3 other IRs - whose loss of function causes defects in feeding preference for one or more of the three tested carboxylic acids. The requirement for IR51b, IR94a, and IR94h in carboxylic acid responses is evaluated in more detail using behavior, electrophysiology (labellar sensilla), and calcium imaging (pharyngeal neurons). The behavioral valence of IR94a and IR94h neurons is assessed using optogenetics. Overall the study uses a variety of approaches to test and validate the requirement of IRs in pharyngeal carboxylic acid taste.

Strengths:

The involvement of the identified IRs in gustatory responses to carboxylic acids is very clear from this study. The authors use mutants and transgenic rescue experiments and evaluate outcomes using electrophysiology, behavior, and imaging. Complementary approaches of loss-of-function and artificial activation support the main conclusion that the identified pharyngeal neurons sense carboxylic acids and convey a positive behavioral valence.

Weaknesses:

Some aspects of expression analysis and calcium imaging need to be clarified to better support the conclusions.

(1) The conclusion of two parallel IR-mediated pathways rests on expression analysis of Ir94a-GAL4 and Ir94h-GAL4 lines and the observation that Ir51b expression driven by either can rescue the Ir51b mutant phenotype. However, the expression analysis is not as rigorous as it needs to be for such a conclusion. Prior work found co-expression of Ir94a and Ir94h in the LSO. Here, the co-expression of the two drivers has not been examined, and Ir94a-GAL4 does not appear to be expressed in the LSO. Given the challenges in validating expression patterns in pharyngeal organs, the possibility that the drivers do not entirely capture endogenous expression cannot be ruled out. Rescue experiments using feeding preference or single-cell imaging don't suffice as validation. Plus, the expression of Ir51b could not be defined.

Based on current literature, Ir94a and Ir94h exhibit distinct expression patterns localized to different sensory regions. Specifically, Ir94a is primarily expressed in the V5 region of the VCSO, where it co-localizes with Ir94c-GAL4 (Chen et al., 2017). Conversely, Ir94h is found in the L7-7 sensilla of the LSO, where it co-expresses with Ir94f, and also within the V2 cells of the VCSO. Notably, the projections of Ir94a and Ir94h into the dorso-anterior subesophageal ganglion suggest divergent expression patterns rather than co-expression in the pharyngeal regions (Koh et al., 2014). Regarding co-expression of Ir94a and Ir94h in the LSO, we did not find any evidence to support this claim. Our data reinforce this view, showing that Ir94a-GAL4 expression is limited to the VCSO, while Ir94h-GAL4 is present in both the LSO and VCSO. Thus, the notion of co-expression of Ir94a and Ir94h in the LSO is not substantiated by current evidence.

As a reviewer suggested, it is possible that the GAL4 drivers utilized may not fully reflect the endogenous expression of these receptors. Despite this limitation, our behavioral, expression, and physiological analyses strongly suggest that Ir94a and Ir94h are located in distinct regions, supporting a model of two parallel IR-mediated pathways operating within the sensory system.

In addition, RT-PCR analysis confirmed the presence of Ir51b. However, due to methodological constraints, we were unable to conduct cell-type-specific expression studies using Ir51b-GAL4. This limitation, which we have acknowledged in the manuscript, does not detract from our core findings but highlights an area for future research. Further studies utilizing cell-specific expression analysis and co-expression studies with additional drivers could offer more definitive insights into IR51b’s functional role and its interactions within broader IR-mediated pathways.

(2) The description of methods and results for the ex vivo calcium imaging is not satisfactory. Details about which cells are being analyzed, and in which organs are not included. No solvent stimulus is tested. The temporal dynamics of the responses are not presented. Movies of the imaging are not included as supplementary information - it would be important to visualize those with what was considered modest movement.

We appreciate this valuable feedback. As discussed above, Ir94h is specifically expressed in the L7-7 sensilla of the LSO, while Ir94a is expressed in the V2 cells of the VCSO. This evidence led us to focus specifically on these cells in our calcium imaging study to ensure accuracy and relevance. In our experiments, Adult hemolymph solution (AHL) (108 mM NaCl, 5 mM KCl, 8.2 mM MgCl2, 2 mM CaCl2, 4 mM NaHCO3, 1 mM NaH2PO4, 5 mM HEPES, pH 7.5) was used as the solvent and employed as a pre-stimulus (as mentioned in the Methods section). During this phase, we observed no changes in fluorescence, indicating that AHL itself did not influence the responses. Fluorescence changes occurred only when the test chemical, dissolved in AHL, was introduced. To further confirm that AHL had no impact on the results, we conducted continuous recordings with AHL alone before beginning our main experiments, and these trials confirmed the absence of fluorescence alterations. We have included the temporal dynamics and supplementary video recordings to provide a more comprehensive understanding of our findings.

(3) The observed differences in phenotypes of Ir25a and Ir76b mutants are intriguing, as are those between the co-receptor mutants and Ir51b, Ir94a, and Ir94h, but have not been sufficiently considered. Prior studies have also found roles for other response modes (OFF response), other IRs and GRs, and other organs (labellum, tarsi) in behavioral responses to carboxylic acids. Overall, the authors' model may be overly simplistic, and the discussion does not do justice to how their model reconciles with the body of work that already exists.

Stanley et al. (2021) reported that the gustatory detection of lactic acid requires both IRs and GRs functioning together. Specifically, they found that IR25a mediates the onset peak response (ON response) to lactic acid, while GRs dampen this response and contribute to a removal peak (OFF response). Interestingly, in Ir25a mutants, a small onset peak still occurred, while Gr64a-f mutants showed an enhanced onset, suggesting that IRs and GRs interact dynamically to modulate taste responses.

In our previous work, we also observed the role of sweet GRs, in addition to Ir25a and Ir76b, in detecting carboxylic acids in the labellum (Shrestha et al., 2021). This raises the possibility of a similar interplay with carboxylic acids in our current study, where different IRs may contribute to distinct aspects of sensory responses in the pharynx, leading to the phenotypic differences we observed. Moreover, Chen et al. (2017) demonstrated that sour-sensing neurons in the tarsi express both IR76b and IR25a and specifically respond to carboxylic and inorganic acids without reacting to sweet or bitter compounds. This finding points to a specialized role for these receptors in sour detection and suggests a coordinated response involving multiple sensory organs—such as the labellum, tarsi, and pharynx.

The phenotypic differences observed in our mutants align with a more integrated model of carboxylic acid detection, in which multiple receptors and sensory organs contribute to the overall behavioral response. This supports the idea that our current model offers a more detailed understanding of how different carboxylic acids are detected and processed by the gustatory system.

Reviewer #2 (Public review):

Shrestha et al investigated the role of IR receptors in the detection of 3 carboxylic acids in adult Drosophila. A low concentration of either of these carboxylic acids added to 2 mM sucrose (1% lactic acid (LA), citric acid (CA), or glycolic acid (GA)) stimulates the consumption of adult flies in choice conditions. The authors use this behavioral test to screen the impact of mutations within 33 receptors belonging to the IR family, a large family of receptors derived from glutamate receptors and expressed both in the olfactory and gustatory sensilla of insects. Within the panel of mutants tested, they observed that 3 receptors (IR25a, IR51b, and IR76b) impaired the detection of LA, CA, and GA, and that 2 others impacted the detection of CA and GA (IR94a and IR94h). Interestingly, impairing IR51b, IR94a, and IR94h did not affect the electrophysiological responses of external gustatory sensilla to LA, CA, and GA. Thanks to the use of GAL4 strains associated with these receptors and thanks to the use of poxn mutants (which do not develop external gustatory sensilla but still have functional internal receptors), they show evidence that IR94a and IR94h are only expressed in two clusters of gustatory neurons of the pharynx, respectively in the VCSO (ventral cibarial sense organ) and in the VCSO + LSO (labral sense organ). As for IR51b, the GAL4 approach was not successful but RT-PCR made on different parts of the insect showed an expression both in the pharyngeal organs and in peripheral receptors. These main findings are then complemented by a host of additional experiments meant to better understand the respective roles of IR94a and IR94h, by using optogenetics and brain calcium imaging using GCamp6. They also report a failed attempt to co-express IR51b, IR94a, and IR94h into external receptors, a co-expression which did not confer the capability of bitter-sensitive cells (expressing GR33a-GAL4) to detect either of the carboxylic acids. These data complete and expand previous observations made on this group and others, and dot to 2 new IR receptors which show an unsuspected specific expression, into organs that still remain difficult to study.

The conclusions of this paper are supported by the data presented, but it remains difficult to make general conclusions as concerns the mechanisms by which carboxylic acids are detected.

(1) All experiments were done with 1% of carboxylic acids. What is the dose dependency of the behavioral responses to these acids, and is it conceivable that other receptors are involved at other concentrations?

In our study, we conducted experiments to examine the dose dependency of behavioral responses to carboxylic acids, with results presented in Supplementary Figure 1. We found that lower concentrations of carboxylic acids are perceived as attractive, while higher concentrations are aversive. This differential response suggests that the receptors identified in our study are primarily tuned to detect low concentrations of these acids. Since higher concentrations elicited aversive responses, it is plausible that additional receptors, beyond the scope of our study, may be involved in sensing these higher concentrations. These receptors could be part of other gustatory receptor neurons that respond specifically to increased acid levels, as fruit flies tend to avoid higher concentrations. We propose that future research could investigate these alternative pathways to gain a complete understanding of the behavioral responses to carboxylic acids. In summary, our findings suggest that specific receptors are involved in detecting low concentrations, while distinct receptor pathways—possibly mediated by other GRNs—may regulate responses to higher concentrations.

(2) One result needs to be better discussed and hypotheses proposed - which is why the mutations of most receptors lead to a loss of detection (mutant flies become incapable of detecting the acid) while mutations in IR94a and IR94h make CA and GA potent deterrents. Does it mean that CA and GA are detected by another set of receptors that, when activated, make flies actively avoid CA and GA? In that case, do the authors think that testing receptors one by one is enough to uncover all the receptors participating in the detection of these substances?

As we mentioned above, it is possible that distinct receptor pathways mediate avoidance of GA and CA. This suggests that CA and GA might activate different sets of receptors that trigger avoidance behavior, pointing to a more complex interplay of receptor activity than we initially considered. Certain acids may indeed be detected by multiple receptors, with each receptor contributing uniquely to the behavioral response. Regarding the sufficiency of testing receptors individually, we recognize the limitations of this approach. Examining receptors one by one may not reveal the full spectrum of receptors involved, especially due to potential interactions or compensatory mechanisms that only emerge when certain receptors are inactive. Therefore, a more holistic approach—such as genetic screens for behavioral responses or using complex genetic models to disrupt multiple receptors simultaneously—could provide deeper insights. Moving forward, incorporating receptor interactions that modulate each other, along with more comprehensive assays, could help explain these discrepancies by uncovering previously overlooked receptor functions.

(3) The paper needs to be updated with a recent paper published by Guillemin et al (2024), indicating that LA is detected externally by a combination of IR94e, IR76b and IR25a. IR25a might help to form a fully functional receptor in GR33a neurons (a former study from Chen et al (2017) indicate that IR25a is expressed in all gustatory neurons of the pharynx).

According to Guillemin et al. (2024), the combination of IR94e, IR76b, and IR25a is required for amino acid detection but not for detecting lactic acid (LA). In their calcium imaging experiments, 100 mM LA elicited a response similar to the vehicle control, suggesting that these receptors do not play a role in LA detection.

(4) Although it was not the main focus of the paper, it would have been most interesting if the cells expressing IR94a and IR94h were identified, and placed on the functional map proposed by the group of Dahanukar (Chen et al 2017 Cell Reports, Chen et al 2019 Cell Reports).

The expression patterns of IR94a and IR94h were previously detailed by Chen et al. (2017), showing that IR94h is expressed in the labial sense organ (LSO, specifically in L7-7) and the ventral cibarial sense organ (VCSO, V2), while IR94a is expressed in the VCSO (V5). Given this established information, we referenced these known expression patterns without replicating the mapping in our study. Our primary focus was to investigate the functional role of these neurons within the pharynx, and we believe we have successfully highlighted their specific contributions. However, we recognize that integrating the functional mapping of these neurons in alignment with the work of Dahanukar’s group would have strengthened our findings and provided a more comprehensive understanding. We acknowledge this as a limitation of our study and appreciate your suggestion, as it points to a valuable direction for future research.

Reviewer #3 (Public review):

Summary:

In this work, the authors investigated the molecular and cellular basis of sour taste perception in Drosophila melanogaster, focusing on identifying receptors that mediate attractive responses to certain carboxylic acids. It builds on previous work from the same group that had identified the IR co-receptors IR25a and IR76b for this sensory process, screening a set of mutants in IRs to identify three, IR51b, IR94a, and IR94h, required for feeding preference responses to some or all of the tested acids.

Strengths:

The work is of interest because it assigns sensory roles to IRs of previously unknown function, in particular IR94a and IR94h, and points to pharyngeal neurons in which these receptors are expressed as the relevant sensory neurons (potentially with different roles for IR94a- and IR94h-expressing neurons). The work combines elegant genetics, simple but effective feeding and taste assays, chemo-/opto-genetic activation, and some calcium imaging. Overall the presented data look solid and well-controlled.

Weaknesses:

The in situ expression analysis relies entirely on transgenic driver lines for IR94a and IR94h (which had been previously described, though not fully cited in this work). Importantly, given that many of the behavioral experiments (genetic rescue, physiology, artificial activation) use the IR94a and IR94h GAL4 driver lines, it would be helpful to validate that these faithfully reflect IR94a and IR94h expression (as far as I can tell, such validation wasn't done in the original papers describing these lines as part of a large collection of IR drivers). For IR51b, pharyngeal expression is concluded indirectly from non-quantitative RT-PCR analysis (genetic reporters did not work). The lack of direct detection of gene/protein expression (for example, through RNA FISH, immunofluorescence, or protein tagging) would have made for a more complete characterization of these receptors (for example, there is no direct evidence that they also express IR25a and IR76b, as one might expect). Finally, the relationship of IR94a and IR94h neurons to other types of pharyngeal neurons remains unclear, as are their projection patterns in the SEZ.

Conceptually, the work is of interest mostly to those in the immediate field; there have been a very large number of studies in the past decade (several from this lab) characterizing the contributions of different IRs to various chemosensory processes. The current work doesn't lend much insight into the nature of the minimal functional unit of gustatory IRs (reconstitution of a functional IR in a heterologous neuron/cell has not been achieved here, but this is a limitation of many other previous studies), nor to how different pharyngeal sensory pathways might collaborate to control behavior. Nevertheless, the findings provide a useful contribution to the literature.

We appreciate your thoughtful feedback. As noted in our response, our primary objective was to investigate the sensory functions of IR94a and IR94h. To this end, we conducted behavioral assays, which we validated with additional approaches including genetic rescue, physiological tests, and artificial activation. Throughout these experiments, we extensively utilized Ir94a- and Ir94h-GAL4 driver lines. To ensure these lines accurately reflect the expression of IR94a and IR94h, we verified their expression patterns using immunohistochemistry across various body parts. Our results align with previous findings that show both receptors are exclusively expressed in the pharynx. Regarding IR51b, we employed RT-PCR due to its high sensitivity and specificity, which supported our hypothesis. Nonetheless, we agree that more direct detection methods would have provided a stronger validation of IR51b expression. Our previous study (Sang et al., 2024) also demonstrated the pharyngeal expression of co-expressed receptors, specifically IR25a and IR76b. However, we recognize that the lack of direct evidence for their co-expression with IR51b remains a significant gap. This limitation primarily stems from the unavailability of specific reagents needed for direct assays targeting IR51b, which restricted our experimental approach.

You also raised the potential relationship between IR94a and IR94h neurons and other pharyngeal neuron types, including their projection patterns in the subesophageal zone. This is indeed an important area for future research that could clarify neural connectivity and further our understanding of sensory mechanisms. However, our study was focused on exploring sensory mechanisms in peripheral regions rather than detailed neural mapping in the SEZ. Investigating these connections would undoubtedly provide valuable insights into the neural circuitry involved and represents an intriguing direction for future research.

Author response:

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

Public Reviews: 

Reviewer #1 (Public Review): 

Syngnathid fishes (seahorses, pipefishes, and seadragons) present very particular and elaborated features among teleosts and a major challenge is to understand the cellular and molecular mechanisms that permitted such innovations and adaptations. The study provides a valuable new resource to investigate the morphogenetic basis of four main traits characterizing syngnathids, including the elongated snout, toothlessness, dermal armor, and male pregnancy. More particularly, the authors have focused on a late stage of pipefish organogenesis to perform single-cell RNA-sequencing (scRNA-seq) completed by in situ hybridization analyses to identify molecular pathways implicated in the formation of the different specific traits. 

The first set of data explores the scRNA-seq atlas composed of 35,785 cells from two samples of gulf pipefish embryos that authors have been able to classify into major cell types characterizing vertebrate organogenesis, including epithelial, connective, neural, and muscle progenitors. To affirm identities and discover potential properties of clusters, authors primarily use KEGG analysis that reveals enriched genetic pathways in each cell types. While the analysis is informative and could be useful for the community, some interpretations appear superficial and data must be completed to confirm identities and properties. Notably, supplementary information should be provided to show quality control data corresponding to the final cell atlas including the UMAP showing the sample source of the cells, violin plots of gene count, UMI count, and mitochondrial fraction for the overall

dataset and by cluster, and expression profiles on UMAP of selected markers characterizing cluster identities. 

We thank the reviewer for these suggestions, and have added several figures and supplemental files in response. We added a supplemental UMAP showing the sample that each cell originated (S1). We also added supplemental violin plots for each sample showing the gene count, unique molecular identifier (UMI) count, mitochondrial fraction, and the doublet scores (S2). We added feature plots of zebrafish marker genes for these major cell types and marker genes identified from our dataset to the supplement (S3:S57). We also provided two supplemental files with marker genes. These changes should clarify the work that went into labeling the clusters. Although some of the cluster labels are general, we decided it would be unwise to label clusters with speculated specific annotations. We only gave specific annotations to clusters with concrete markers and/or in situ hybridization (ISH) results that cemented an annotation.  As shown in the new supplemental figures and files, certain clusters had clear, specific markers while others did not. Therefore, we used caution when we annotated clusters without distinct markers. 

The second set of data aims to correlate the scRNA-seq analysis with in situ hybridizations (ISH) in two different pipefish (gulf and bay) species to identify and characterize markers spatially, and validate cell types and signaling pathways active in them. While the approach is rational, the authors must complete the data and optimize labeling protocols to support their statements. One major concern is the quality of ISH stainings and images; embryos show a high degree of pigmentation that could hide part of the expression profile, and only subparts and hardly detectable tissues/stainings are presented. The authors should provide clear and good-quality images of ISH labeling on whole-mount specimens, highlighting the magnification regions and all other organs/structures (positive controls) expressing the marker of interest along the axis. Moreover, ISH probes have been designed and produced on gulf pipefish genome and cDNA respectively, while ISH labeling has been performed indifferently on bay or gulf pipefish embryos and larvae. The authors should specify stages and species on figure panels and should ensure sequence alignment of the probe-targeted sequences in the two species to validate ISH stainings in the bay pipefish. Moreover, spatiotemporal gene expression being a very dynamic process during embryogenesis, interpretations based on undefined embryonic and larval stages of pipefish development and compared to 3dpf zebrafish are insufficient to hypothesize on developmental specificities of pipefish features, such as on the absence of tooth primordia that could represent a very discrete and transient cell population. The ISH analyses would require a clean and precise spatiotemporal expression comparison of markers at the level of the entire pipefish and zebrafish specimens at well-defined stages, otherwise, the arguments proposed on teleost innovations and adaptations turn out to be very speculative. 

We are appreciative of the reviewer’s feedback. We primarily used the in situ hybridization (ISH) data as supplementary to the scRNAseq library and we are aware that further evidence is necessary to identify origins of syngnathid’s evolutionary novelties. Our goal was to provide clues for the developmental genetic basis of syngnathid derived features.  We hope that our study will inspire future investigations and are excited for the prospect that future research could include this reviewer’s ideas. 

All of the developmental stages and species information for the embryos used were in the figure captions as well as in supplemental file 6. Because we primarily used wild caught embryos, we did not have specific ages of most embryos. Syngnathid species are challenging to culture in the laboratory, and extracting embryos requires euthanizing the father which makes it difficult to obtain enough embryos for ISH. In addition, embryos do not survive long when removed from the brood pouch prematurely. We supplemented our ISH with bay pipefish caught off the Oregon coast because these fish have large broods. Wild caught pregnant male bay pipefish were immediately euthanized, and their broods were fixed. Because we did not have their age, we classified them based on developmental markers such as presence of somites and the extent of craniofacial elongation. Although these classification methods are not ideal, they are consistent with the syngnathid literature (Sommer et al. 2012). Since the embryos used for the ISH were primarily wild caught, we had a few different developmental stages represented in our ISH data. For our tooth primordia search, we used embryos from the same brood (therefore, same stage) for these experiments.

We understand the concern for the degree of pigmentation in the samples. We completed numerous bleach trials before embarking on the in situ hybridization experiments. After completing a bleach trial with a probe created from the gene tnmd for ISH_,_ we noticed that the bleached embryos were missing expression domains found in the unbleached embryos. We were, therefore, concerned that using bleached embryos for our experiments would result incorrect conclusions about the expression domains of these genes. We sparingly used bleaching at older stages, hatched larvae, where it was fundamentally necessary to see staining. As stated above, the primary goal of this manuscript was to generate and annotate the first scRNA-seq atlas in a syngnathid, and the ISHs were utilized to support inferred cluster annotations only through a positive identification of marker gene expression in expected tissues/cells. Therefore, the obscuring of gene expression by pigmentation would have resulted in the absence of evidence for a possible cluster annotation, not an incorrect annotation.

For the ease of viewing the ISHs, we improved annotations and clarity. We increased the brightness and contrast of images. In the original submission, we had to lower the image resolution to make the submission file smaller. We hope that these improvements plus the true image quality improves clarity of ISH results. We also included alignments in our supplementary files of bay pipefish sequences to the Gulf pipefish probes to showcase the high degree of sequence similarity. 

Sommer, S., Whittington, C. M., & Wilson, A. B. (2012). Standardised classification of pre-release development in male-brooding pipefish, seahorses, and seadragons (Family Syngnathidae). BMC Developmental Biology, 12, 12–15. 

To conclude, whereas the scRNA-seq dataset in this unconventional model organism will be useful for the community, the spatiotemporal and comparative expression analyses have to be thoroughly pushed forward to support the claims. Addressing these points is absolutely necessary to validate the data and to give new insights to understand the extraordinary evolution of the Syngnathidae family. 

We really appreciate the reviewer’s enthusiasm for syngnathid research, and hope that the additional files and explanation of the supporting role of the ISHs have adequately addressed their concerns. We share the reviewer’s enthusiasm and are excited for future work that can extend this study. 

Reviewer #2 (Public Review):

Summary: 

The authors present the first single-cell atlas for syngnathid fishes, providing a resource for future evolution & development studies in this group. 

Strengths: 

The concept here is simple and I find the manuscript to be well written. I like the in situ hybridization of marker genes - this is really nice. I also appreciate the gene co-expression analysis to identify modules of expression. There are no explicit hypotheses tested in the manuscript, but the discovery of these cell types should have value in this organism and in the determination of morphological novelties in seahorses and their relatives.  

We are grateful for this reviewer’s appreciation of the huge amount of work that went into this study, and we agree that the in situ hybridizations (ISHs) support the scRNAseq study as we intended. We appreciate that the reviewer thinks that this work will add value to the syngnathid field.

Weaknesses: 

I think there are a few computational analyses that might improve the generality of the results. 

(1) The cell types: The authors use marker gene analysis and KEGG pathways to identify cell types. I'd suggest a tool like SAMap (https://elifesciences.org/articles/66747) which compares single-cell data sets from distinct organisms to identify 'homologous' cell types - I imagine the zebrafish developmental atlases could serve as a reasonable comparative reference. 

We appreciate the reviewer’s request, and in fact we would have loved to integrate our dataset with zebrafish. However, syngnathid’s unique craniofacial development makes it challenging to determine the appropriate stage for comparison. While 3 days post fertilization (dpf) zebrafish data were appropriate for comparisons of certain cell types (e.g. epidermal cells), it would have been problematic for other cell types (e.g. osteoblasts) that are not easily detectable until older zebrafish stages. Therefore, determining equivalent stages between these species is difficult and contains potential for error. Future research should focus on trying to better match stages across syngnathids and zebrafish (and other fish species such as stickleback). Studies of this nature promise to uncover the role of heterochrony in the evo-devo of syngnathid’s unique snouts.

(2) Trajectory analyses: The authors suggest that their analyses might identify progenitor cell states and perhaps related differentiated states. They might explore cytoTRACE and/or pseudotime-based trajectory analyses to more fully delineate these ideas.

We thank the reviewer for this suggestion! We added a trajectory analysis using cytoTRACE to the manuscript. It complemented our KEGG analysis well (L172-175; S73) and has improved the manuscript.

(3) Cell-cell communication: I think it's very difficult to identify 'tooth primordium' cell types, because cell types won't be defined by an organ in this way. For instance, dental glia will cluster with other glia, and dental mesenchyme will likely cluster with other mesenchymal cell types. So the histology and ISH is most convincing in this regard. Having said this, given the known signaling interactions in the developing tooth (and in development generally) the authors might explore cell-cell communication analysis (e.g., CellChat) to identify cell types that may be interacting. 

We agree! It would have been a wonderful addition to the paper to include a cell-cell communication analysis. One limitation of CellChat is that it only includes mouse and human orthologs. Given concerns of reviewer #3 for mouse-syngnathid comparisons, we decided to not pursue CellChat for this study. We are looking forward to future cell communication resources that include teleost fishes.

Reviewer #3 (Public Review): 

Summary: 

This study established a single-cell RNA sequencing atlas of pipefish embryos. The results obtained identified unique gene expression patterns for pipefish-specific characteristics, such as fgf22 in the tip of the palatoquadrate and Meckel's cartilage, broadly informing the genetic mechanisms underlying morphological novelty in teleost fishes. The data obtained are unique and novel, potentially important in understanding fish diversity. Thus, I would enthusiastically support this manuscript if the authors improve it to generate stronger and more convincing conclusions than the current forms. 

Thank you, we appreciate the reviewer’s enthusiasm!

Weaknesses: 

Regarding the expression of sfrp1a and bmp4 dorsal to the elongating ethmoid plate and surrounding the ceratohyal: are their expression patterns spatially extended or broader compared to the pipefish ancestor? Is there a much closer species available to compare gene expression patterns with pipefish? Did the authors consider using other species closely related to pipefish for ISH? Sfrp1a and bmp4 may be expressed in the same regions of much more closely related species without face elongation. I understand that embryos of such species are not always accessible, but it is also hard to argue responsible genes for a specific phenotype by only comparing gene expression patterns between distantly related species (e.g., pipefish vs. zebrafish). Due to the same reason, I would not directly compare/argue gene expression patterns between pipefish and mice, although I should admit that mice gene expression patterns are sometimes helpful to make a hypothesis of fish evolution. Alternatively, can the authors conduct ISH in other species of pipefish? If the expression patterns of sfrp1a and bmp4 are common among fishes with face elongation, the conclusion would become more solid. If these embryos are not available, is it possible to reduce the amount of Wnt and BMP signal using Crispr/Cas, MO, or chemical inhibitor? I do think that there are several ways to test the Wnt and/or BMP hypothesis in face elongation. 

We appreciate the reviewer’s suggestion, and their recognition for challenges within this system. In response to this comment, we completed further in situ hybridization experiments in threespine stickleback, a short snouted fish that is much more closely related to syngnathids than is zebrafish, to make comparisons with pipefish craniofacial expression patterns (S76-S79). We added ISH data for the signaling genes (fgf22, bmp4, and sfrp1a) as well as prdm16. Through adding this additional ISH results, we speculated that craniofacial expression of bmp4, sfrp1a, and prdm16 is conserved across species. However, compared to the specific ceratohyal/ethmoid staining seen in pipefish, stickleback had broad staining throughout the jaws and gills. These data suggest that pipefish have co-opted existing developmental gene networks in the development of their derived snouts. We added this interpretation to the results and discussion of the manuscript (L244-L248; L262-277; L444-470).

Recommendations for the authors:  

Reviewing Editor (Recommendations for the Authors): 

We hope that the eLife assessment, as well as the revisions specified here, prove helpful to you for further revisions of your manuscript. 

Revisions considered essential: 

(1) Marker genes and single-cell dataset analyses. While these analyses have been performed to a good standard in broad terms, there is a majority view here that cell type annotations and trajectory analyses can be improved. In particular, there is question about the choice of marker genes for the current annotation. For one it can depend on the use of single marker genes (see tnnti1 example for clusters 17 and 31). Here, we recommend incorporating results from SAMap and trajectory analysis (e.g., cytoTRACE or standard pseudotime).

Because of the reviewer comments, we became aware that we insufficiently communicated how cell clusters were annotated. We did mention in the manuscript that we did not use single marker genes to annotate clusters, but instead we used multiple marker genes for each cluster for the annotation process. We used both marker genes derived from our dataset and marker genes identified from zebrafish resources for cluster annotation. We chose single marker genes for each cluster for visualization purposes and for in situ hybridizations. However, it is clear from the reviewers’ comments that we needed to make more clear how the annotations were performed. To make this effort more clear in our revision, we included two new supplementary files – one with Seurat derived marker genes and one with marker genes derived from our DotPlot method. We also included extensive supplementary figures highlighting different markers. Using Daniocell, we identified 6 zebrafish markers per major cell type and showed their expression patterns in our atlas with FeaturePlots. We also included feature plots of the top 6 marker genes for each cluster. We hope that the addition of these 40+ plots (S3:S57) to the supplement fully addresses these concerns. 

We appreciated the suggestion of cytotrace from reviewer #2! We ran cytotrace on three major cell lineages (neural, muscle, and connective; S73) which complemented our KEGG analysis in suggesting an undifferentiated fate for clusters 8, 10, and 16. We chose to not run SAMap because it is a scRNA-seq library integration tool. Although we compared our lectin epidermal findings to 3 dpf zebrafish scRNA-seq data, we did not integrate the datasets out of concern that we could draw erroneous conclusions for other cell types.  Future work that explores this technical challenge may uncover the role of heterochrony in syngnathid craniofacial development. We detail these changes more fully in our responses to reviewers.

(2) The claims regarding evolutionary novelty and/or the genes involved are considered speculative. In part, this comes from relying too heavily on comparisons against zebrafish, as opposed to more closely related species. For example, the discussion regarding C-type lectin expression in the epidermis and KEGG enrichment (lines 358 - 364) seems confusing. Another good example here is the discussion on sfrp1a (lines 258 - 261). Here, the text seems to suggest craniofacial sfrp1a expression (or specifically ethmoid expression?) is connected to the development of the elongated snout in pipefish. However, craniofacial expression of sfrp1a is also reported in the arctic charr, which the authors grouped into fishes with derived craniofacial structures. Separately, sfrp2 expression was also reported in stickleback fish, for example. Do these different discussions truly support the notion that sfrp1a expression is all that unique in pipefish, rather than that pipefish and zebrafish are only distantly related and that sfrp1a was a marker gene first, and co-opted gene second? The authors should respond to the comments in the public review related to this aspect, and include more informative comparison and discussion. 

A much more nuanced discussion with appropriate comparisons and caveats would be strongly recommended here.  

We appreciate this insight and used it as a motivator to complete and add select comparative ISH data to this manuscript. We added in situ hybridization experiments from stickleback fish for craniofacial development genes (sfrp_1a, prdm16, bmp4_, and fgf22; S76-S79).  After adding stickleback ISH to the manuscript, we were able to make comparisons between pipefish and stickleback patterns and draw more informed conclusions (L244-L248; L262-277; L444-470). We added additional nuance to the discussion of the head, tooth (L485-489), and male pregnancy (L358-L391) sections to address concerns of study limitations. We describe in more detail these additional data in response to reviewers.

(3) In situ hybridization results: as already included above, there is generally weak labeling of species, developmental stages, and other markings that can provide context. The collective feeling here is that as it is currently presented, the ISH results do not go too far beyond simply illustrative purposes. To take these results further, more detailed comparison may be needed. At a minimum, far better labeling can help avoid making the wrong impression. 

Based on the reviewers’ comments, we made changes to improve ISH clarity and add select comparative ISH findings. ISH was used to further interpretation of the scRNAseq atlas. All the developmental stages and species information for the embryos used were in the figure captions as well as in supplemental file 4. Since we primarily used wild caught embryos, we did not have specific ages of most embryos. The technical challenges of acquiring and staging Syngnathus embryos are detailed above. Because we did not have their age, we classified them based on developmental markers (such as presence of somites and the extent of craniofacial elongation). Although these classification methods are not ideal, they are consistent with the syngnathid literature (Sommer et al. 2012).  

We followed reviewer #1’s recommendations by adding an annotated graphic of a pipefish head, aligning bay and Gulf pipefish sequences for the probe regions, expanding out our supplemental figures for ISH into a figure for each probe, and improving labeling. These changes improved the description of the ISH experiments and have increased the quality of the manuscript.

We would have loved to complete detailed comparative studies as suggested, but doing such a complete analysis was not feasible for this study. Therefore, we completed an additional focused analysis. We followed reviewer #3’s idea and added ISHs from threespine stickleback, a short snouted fish, for 4 genes (sfrp1a, prdm16, fgf22, and bmp4). While more extensive ISHs tracking all marker genes through a variety of developmental stages in pipefish and stickleback would have provided crucial insights, we feel that it is beyond the scope of this study and would require a significant amount of additional work. We, thus, primarily interpreted the ISH results as illustrative data points in our discussion. As we state in the response to reviewer 1, the generation and annotation of the first scRNA-seq atlas in a syngnathid is the primary goal of this manuscript.  The ISHs were utilized primarily to support inferred cluster annotations if a positive identification of marker gene expression in expected tissues/cells occurred. 

Reviewer #1 (Recommendations For The Authors): 

While the scRNA-seq dataset offers a valuable resource for evo-devo analyses in fish and the hypotheses are of interest, critical aspects should be strengthened to support the claims of the study. 

Concerning the scRNA-seq dataset, the major points to be addressed are listed below: 

- Supplementary file 3 reports the single markers used to validate cluster annotations. To confirm cluster identities, more markers specific to each cluster should be highlighted and presented on the UMAP. 

We recognize the reviewer’s concern and had in reality used numerous markers to annotate the clusters. Based upon the reviewer’s comment we decided to make this clear by creating feature plots for every cluster with the top 6 marker genes. These plots showcase gene specificity in UMAP space. We also added feature plots for zebrafish marker genes for key cell types. Through these changes and the addition of 54 supplementary figures (S3:S57), we hope that it is clear that numerous markers validated cluster identity.

For example, as clusters 17 and 37 share the same tnnti1 marker, which other markers permit to differentiate their respective identity. 

This is a fair point. Cluster 17 and 37 both are marked by a tnni1 ortholog.

Different paralogous co-orthologs mark each cluster (cluster 17: LOC125989146; cluster 37: LOC125970863). In our revision to the above comment, additional (6) markers per cluster were highlighted which should remedy this concern. 

- L146: the low number of identified cartilaginous cells (only 2% of total connective tissue cells) appears aberrant compared to bone cell number, while Figure 1 presents a welldeveloped cartilaginous skeleton with poor or no signs of ossification. Please discuss this point. 

We also found this to be interesting and added a brief discussion on this subject to the results section (L147-L149). Single cell dissociations can have variable success for certain cell types. It is possible that the cartilaginous cells were more difficult to dissociate than the osteoblast cells.

- L162: pax3a/b are not specific to muscle progenitors as the genes are also expressed in the neural tube and neural crest derivatives during organogenesis. Please confirm cluster 10 identity.  

Thank you for the reminder, we added numerous feature plots that explored zebrafish (from Daniocell) and pipefish markers (identified in our dataset). Examining zebrafish satellite muscle markers (myog, pabpc4, and jam2a) shows a strong correspondence with cluster #10.

- L198: please specify in the text the pigment cell cluster number. 

We completed this change.

- L199: it is not clear why considering module 38 correlated to cluster 20 while modules 2/24 appear more correlated according to the p-value color code. 

We thank the reviewer for pointing this confusing element out! Although the t-statistic value for module 38 (3.75) is lower than the t-statistics for modules 2 and 24 (5.6 and 5.2, respectively), we chose to highlight module 38 for its ‘connectivity dependence’ score. In our connectivity test, we examined whether removing cells from a specific cell cluster reduced the connectivity of a gene network. We found that removing cluster 20 led to a decrease in module 38’s connectivity (-.13, p=0) while it led to an increase in modules 2 and 24’s connectivity (.145, p=1; .145, p=9.14; our original supplemental files 9-10). Therefore, the connectivity analysis showed that module 38’s structure was more dependent on cluster 20 than in comparison with modules 2 and 24. Although you highlighted an interesting quandary, we decided that this is tangential to the paper and did not add this discussion to the manuscript. 

- Please describe in the text Figure 4A. 

Completed, we thank the reviewer for catching this! 

Concerning embryo stainings, the major points to be addressed are listed below: 

- Figure 1: please enhance the light/contrast of figures to highlight or show the absence of alcian/alizarin staining. Mineralized structures are hardly detectable in the head and slight differences can be seen between the two samples. The developmental stage should be added. Please homogenize the scale bar format (remove the unit on panels E and, G as the information is already in the text legend). It would be useful to illustrate the data with a schematic view of the structures presented in panels B, and E, and please annotate structures in the other panels.  

We thank the reviewer for these suggestions to improve our figure. We increased the brightness and contrast for all our images. We also added an illustration of the head with labels of elements. As discussed, we used wild caught pregnant males and, therefore, do not know the exact age of the specimens. However, we described the developmental stage based on morphological observations. Slight differences in morphology between samples is expected. We and others have noticed that

developmental rate varies, even within the same brood pouch, for syngnathid embryos. We observed several mineralization zones including in the embryos including the upper and lower jaws, the mes(ethmoid), and the pectoral fin. We recognize the cartilage staining is more apparent than the bone staining, though increasing image brightness and contrast did improve the visibility of the mineralization front.

- All ISH stainings and images presented in Figures 4-6/ Figures S2-3 should be revised according to comments provided in the public review. 

We thank the reviewer for providing thorough comments, we provided an in-depth response to the public review. We made several improvements to the manuscript to address their concerns. 

- Figure 4: Figure 4B should be described before 4C in the text or inverse panels / L222 the Meckel's cartilage is not shown on Figure 4C. The schematic views in H should be annotated and the color code described / the ISH data must be completed to correlate spatially clusters to head structures. 

We thank the reviewer for pointing this out, we fixed the issues with this figure and added annotations to the head schematics.

- Figure 5: typo on panels 'alician' = alcian. 

We completed this change. 

- Figures S2-3: data must be better presented, polished / typo in captions 'relavant'= relevant. 

Thank you for this critique, we created new supplementary figures to enhance interpretation of the data (S59-S71). In these new figures, we included a feature plot for each gene and respective ISHs.

- Figure S3: soat2 = no evidence of muscle marker neither by ISH presented nor in the literature. 

We realized this staining was not clear with the previous S2/S3 figures. Our new changes in these supplementary figures based on the reviewer’s ideas made these ISH results clearer. We observed soat2 staining in the sternohyoideus muscle (panel B in S71).

Other points: 

- The cartilage/bone developmental state (Alcian/alizarin staining) and/or ISH for classical markers of muscle development (such as pax3/myf5) could be used to clarify the This could permit the completion of a comparative analysis between the two species and the interpretation of novel and adaptative characters.  

We appreciate this idea! We thought deeply about a well characterized comparative analysis between pipefish and zebrafish for this study. We discussed our concerns in our public response to reviewer 2. We found that it was challenging to stage match all cell types, and were concerned that we could make erroneous conclusions. For example, our pipefish samples were still inside the male brood pouch and possessed yolk sacs. However, we found osteoblast cells in our scRNAseq atlas, and in alizarin staining. Although zebrafish literature notes that the first zebrafish bone appears at 3 dpf (Kimmel et al. 1995), osteoblasts were not recognized until 5 dpf in two scRNAseq datasets (Fabian et al. 2022; Lange et al. 2023). A 5dpf zebrafish is considered larval and has begun hunting. Therefore, we chose to not integrate our data out of concern that osteoblast development may occur at different timelines between the fishes. 

Fabian, P., Tseng, K.-C., Thiruppathy, M., Arata, C., Chen, H.-J., Smeeton, J., Nelson, N., & Crump, J. G. (2022). Lifelong single-cell profiling of cranial neural crest diversification in zebrafish. Nature Communications 2022 13:1, 13(1), 1–13. 

Lange, M., Granados, A., VijayKumar, S., Bragantini, J., Ancheta, S., Santhosh, S., Borja, M., Kobayashi, H., McGeever, E., Solak, A. C., Yang, B., Zhao, X., Liu, Y., Detweiler, A. M., Paul,

S., Mekonen, H., Lao, T., Banks, R., Kim, Y.-J., … Royer, L. A. (2023). Zebrahub – Multimodal Zebrafish Developmental Atlas Reveals the State-Transition Dynamics of Late-Vertebrate Pluripotent Axial Progenitors. BioRxiv, 2023.03.06.531398. 

Kimmel, C., Ballard, S., Kimmel, S., Ullmann, B., Schilling, T. (1995). Stages of Embryonic Development of the Zebrafish. Developmental Dynamics 203:253:-310.

'in situs' in the text should be replaced by 'in situ experiments'.  

We made this change (L395, L663, L666, L762).

- Lines 562-565: information on samples should be added at the start of the result section to better apprehend the following scRNA-seq data.

We thank the reviewer for pointing out this issue. Although we had a few sentences on the samples in the first paragraph of the result section, we understand that it was missing some critical pieces of information. Therefore, we added these additional details to the beginning of the results section (L126-L132). 

- Lines 629-665: PCR with primers designed on gulf pipefish genome could be performed in parallel on bay and gulf cDNA libraries, and amplification products could be sequenced to analyze alignment and validate the use of gulf pipefish ISH probes in bay pipefish embryos. Probe production could also be performed using gulf primers on bay pipefish cDNA pools. 

After the submission of this manuscript, a bay pipefish genome was prepared by our laboratory. We used this genome to align our probes, these alignments demonstrate strong sequence conservation between the species. We included these alignments in our supplemental files.

- L663: the bleaching step must be optimized on pipefish embryos. 

We understand this concern and had completed several bleach optimization experiments prior to publication. Although we found that bleaching improved visibility of staining, we noticed with the probe tnmd that bleached embryos did not have complete staining of tendons and ligaments. The unbleached embryos had more extensive staining than the bleached embryos. We were concerned that bleaching would lead to failures to detect expression domains (false negatives) important for our analysis. Therefore, we did not use bleaching with our in situs experiments (except with hatched fish with a high degree of pigmentation). 

- Indicate the number of specimens analyzed for each labeling condition.  

We thank the reviewer for noticing this issue. We added this information to the methods (L766-767).

- Describe the fixation and pre-treatment methods previous to ISH and skeleton stainings

We thank the reviewer for pointing out this issue, we added these descriptions (L765-766; L772-774). 

Reviewer #3 (Recommendations For The Authors): 

(1) If sfrp1a expression is observed also in other fish species with derived craniofacial structures, it's important to discuss this more in the Discussion. This could be a common mechanism to modify craniofacial structures, although functional tests are ultimately required (but not in this paper, for sure). Can lines 421-428 involve the statement "a prolonged period of chondrocyte differentiation" underlies craniofacial diversity?

This is a great idea, and we added a sentence that captures this ethos (L451-452).

(2) Lines 334-346 need to be rephrased. It's hard to understand which genes are expressed or not in pipefish and zebrafish. Did "23 endocytosis genes" show significant enrichment in zebrafish epidermis, or are they expressed in zebrafish epidermis? 

We thank the reviewer for this comment, we re-phrased this section for clarity (L365-368).

(3) Figure 4 is missing the "D" panel and two "E" panels. 

We thank the reviewer for noticing this, we fixed this figure.

(4) Line 302: "whole-mount" or "whole mount"

We thank the reviewer for the catch!

Author response:

Reviewer #1 (Public review):

Comment 1: In the Results section, the rationale behind selecting the beta band for the central (C3, CP3, Cz, CP4, C4) regions and the theta band for the fronto-central (Fz, FCz, Cz) regions is not clearly explained in the main text. This information is only mentioned in the figure captions. Additionally, why was the beta band chosen for the S-ROI central region and the theta band for the S-ROI fronto-central region? Was this choice influenced by the MVPA results?

We thank the reviewer for the question regarding the rationale for the S-ROI selection in our study. The beta band was chosen for the central region due to its established relevance in motor control (Engel & Fries, 2010), movement planning (Little et al., 2019) and motor inhibition (Duque et al., 2017). The fronto-central theta band (or frontal midline theta) was a widely recognized indicator in cognitive control research (Cavanagh & Frank, 2014), associated with conflict detection and resolution processes. Moreover, recent empirical evidence suggested that the fronto-central theta reflected the coordination and integration between stimuli and responses (Senoussi et al., 2022). Although we have described the cognitive processes linked to these different frequencies in the introduction and discussion sections, along with the potential patterns of results observed in Stroop-related studies, we did not specify the involved cortical areas. Therefore, we have specified these areas in the introduction to enhance the clarity of the revised version (in the fourth paragraph of the Introduction section).

Regarding whether the selection of S-ROIs was influenced by the MVPA results, we would like to clarify here that we selected the S-ROIs based on prior research and then conducted the decoding analysis. Specifically, we first extracted the data representing different frequency indicators (three F-ROIs and three S-ROIs) as features, followed by decoding to obtain the MVPA results. Subsequently, the time-frequency analysis, combined with the specific time windows during which each frequency was decoded, provided detailed interaction patterns among the variables for each indicator. The specifics of feature selection are described in the revised version (in the first paragraph of the Multivariate Pattern Analysis section).

Comment 2: In the Data Analysis section, line 424 states: “Only trials that were correct in both the memory task and the Stroop task were included in all subsequent analyses. In addition, trials in which response times (RTs) deviated by more than three standard deviations from the condition mean were excluded from behavioral analyses.” The percentage of excluded trials should be reported. Also, for the EEG-related analyses, were the same trials excluded, or were different criteria applied?

We thank the reviewer for this suggestion. Beyond the behavioral exclusion criteria, trials with EEG artifacts were also excluded from the data for the EEG-related analyses. We have now reported the percentage of excluded trials for both behavioral and EEG data analyses in the revised version (in the second paragraph of the EEG Recording and Preprocessing section and the first paragraph of the Behavioral Analysis section).

Comment 3: In the Methods section, line 493 mentions: “A 400-200 ms pre-stimulus time window was selected as the baseline time window.” What is the justification in the literature for choosing the 400-200 ms pre-stimulus window as the baseline? Why was the 200-0 ms pre-stimulus period not considered?

We thank the reviewer for this question and would like to provide the following justification. First, although a baseline ending at 0 ms is common in ERP analyses, it may not be suitable for time-frequency analysis. Due to the inherent temporal smoothing characteristic of wavelet convolution in time-frequency decomposition, task-related early activities can leak into the pre-stimulus period (before 0 ms) (Cohen, 2014). This means that extending the baseline to 0 ms will include some post-stimulus activity in the baseline window, thereby increasing baseline power and compromising the accuracy of the results. Second, an ideal baseline duration is recommended to be around 10-20% of the entire trial of interest (Morales & Bowers, 2022). In our study, the epoch duration was 2000 ms, making 200-400 ms an appropriate baseline length. Third, given that the minimum duration of the fixation point before the stimulus in our experiment was 400 ms, we chose the 400 ms before the stimulus as the baseline point to ensure its purity. In summary, considering edge effects, duration requirements, and the need to exclude other influences, we selected a baseline correction window of -400 to -200 ms. To enhance the clarity of the revised version, we have provided the rationale for the selected time windows along with relevant references (in the first paragraph of the Time-frequency analysis section).

Comment 4: Is the primary innovation of this study limited to the methodology, such as employing MVPA and RSA to establish the relationship between late theta activity and behavior?

We thank the reviewer for this insightful question and would like to clarify that our research extends beyond mere methodological innovation; rather, it utilized new methods to explore novel theoretical perspectives. Specifically, our research presents three levels of innovation: methodological, empirical, and theoretical. First, methodologically, MVPA overcame the drawbacks of traditional EEG analyses based on specific averaged voltage intensities, providing new perspectives on how the brain dynamically encoded particular neural representations over time. Furthermore, RSA aimed to identify which indicators among the decoded were directly related to behavioral representation patterns. Second, in terms of empirical results, using these two methods, we have identified for the first time three EEG markers that modulate the Stroop effect under verbal working memory load: SP, late theta, and beta, with late theta being directly linked to the elimination of the behavioral Stroop effect. Lastly, from a theoretical perspective, we proposed the novel idea that working memory played a crucial role in the late stages of conflict processing, specifically in the stimulus-response mapping stage (the specific theoretical contributions are detailed in the second-to-last paragraph of the Discussion section).

Comment 5: On page 14, lines 280-287, the authors discuss a specific pattern observed in the alpha band. However, the manuscript does not provide the corresponding results to substantiate this discussion. It is recommended to include these results as supplementary material.

We thank the reviewer for this suggestion. We added a new figure along with the corresponding statistical results that displayed the specific result patterns for the alpha band (Supplementary Figure 1).

Comment 6: On page 16, lines 323-328, the authors provide a generalized explanation of the findings. According to load theory, stimuli compete for resources only when represented in the same form. Since the pre-memorized Chinese characters are represented semantically in working memory, this explanation lacks a critical premise: that semantic-response mapping is also represented semantically during processing.

We thank the reviewer for this insightful suggestion. We fully agree with the reviewer’s perspective. As stated in our revised version, load theory suggests that cognitive resources are limited and dependent on a specific type (in the second paragraph of the Discussion section). The previously memorized Chinese characters are stored in working memory in the form of semantic representations; meanwhile the stimulus-response mapping should also be represented semantically, leading to resource occupancy. We have included this logical premise in the revised version (in the third-to-last paragraph of the Discussion section).

Comment 7: The classic Stroop task includes both a manual and a vocal version. Since stimulus-response mapping in the vocal version is more automatic than in the manual version, it is unclear whether the findings of this study would generalize to the impact of working memory load on the Stroop effect in the vocal version.

We fully agree with the reviewer’s point that the verbal version of the Stroop task differs from the manual version in terms of the degree of automation in the stimulus-response mapping. Specifically, the verbal version relies on mappings that are established through daily language use, while the manual version involves arbitrary mappings created in the laboratory. Therefore, the stimulus-response mapping in the verbal response version is more automated and less likely to be suppressed. However, our previous research indicated that the degree of automation in the stimulus-response mapping was influenced by practice (Chen et al., 2013). After approximately 128 practice trials, semantic conflict almost disappears, suggesting that the level of automation in stimulus-response mapping for the verbal Stroop task is comparable to that of the manual version (Chen et al., 2010). Given that participants in our study completed 144 practice trials (in the Procedure section), we believe these findings can be generalized to the verbal version.

Comment 8: While the discussion section provides a comprehensive analysis of the study’s results, the authors could further elaborate on the theoretical and practical contributions of this work.

We thank the reviewer for the constructive suggestions. We recognize that the theoretical and practical contributions of the study were not thoroughly elaborated in the original manuscript. Therefore, we have now provided a more detailed discussion. Specifically, the theoretical contributions focus on advancing load theory and highlighting the critical role of working memory in conflict processing. The practical contributions emphasize the application of load theory and the development of intervention strategies for enhancing inhibitory control. A more detailed discussion can be found in the revised version (in the second-to-last paragraph of the Discussion section).

Reviewer #2 (Public review):

Comment 1: As the researchers mentioned, a previous study reported a diminished Stroop effect with concurrent working memory tasks to memorize meaningless visual shapes rather than memorize Chinese characters as in the study. My main concern is that lower-level graphic processing when memorizing visual shapes also influences the Stroop effect. The stage of Stroop conflict processing affected by the working memory load may depend on the specific content of the concurrent working memory task. If that’s the case, I sense that the generalization of this finding may be limited.

We thank the reviewer for this insightful concern. As mentioned in the manuscript, this may be attributed to the inherent characteristics of Chinese characters. In contrast to English words, the processing of Chinese characters relies more on graphemic encoding and memory (Chen, 1993). Therefore, the processing of line patterns essentially occupies some of the resources needed for character processing, which aligns with our study’s hypothesis based on dimensional overlap. Additionally, regarding the results, even though the previous study presents lower-level line patterns, the results still showed that the working memory load modulated the later theta band. We hypothesize that, regardless of the specific content of the pre-presented working memory load, once the stimulus disappears from view, these loads are maintained as representations in the working memory platform. Therefore, they do not influence early perceptual processing, and resource competition only occurs once the distractors reach the working memory platform. Lastly, previous study has shown that spatial loads, which do not overlap with either the target or distractor dimensions, do not influence conflict effect (Zhao et al., 2010). Taken together, we believe that regardless of the specific content of the concurrent working memory tasks, as long as they occupy resources related to irrelevant stimulus dimensions, they can influence the late-stage processing of conflict effect. Perhaps our original manuscript did not convey this clearly, so we have rephrased it in a more straightforward manner (in the second paragraph of the Discussion section).

Comment 2: The P1 and N450 components are sensitive to congruency in previous studies as mentioned by the researchers, but the results in the present study did not replicate them. This raised concerns about data quality and needs to be explained.

We thank the reviewer for this insightful concern. For P1, we aimed to convey that the early perceptual processing represented by P1 is part of the conflict processing process. Therefore, we included it in our analysis. Additionally, as mentioned in the discussion, most studies find P1 to be insensitive to congruency. However, we inappropriately cited a study in the introduction that suggested P1 shows differences in congruency, which is among the few studies that hold this perspective. To prevent confusion for readers, we have removed this citation from the introduction.

As for N450, most studies have indeed found it to be influenced by congruency. In our manuscript, we did not observe a congruency effect at our chosen electrodes and time window. However, significant congruency effects were detected at other central-parietal electrodes (CP3, CP4, P5, P6) during the 350-500 ms interval. The interaction between task type and consistency remained non-significant, consistent with previous results. Furthermore, with respect to the location of the electrodes chosen, existing studies on N450 vary widely, including central-parietal electrodes and frontal-central electrodes (for a review, see Heidlmayr et al., 2020). We speculate that this phenomenon may be related to the extent of practice. With fewer total trials, the task may involve more stimulus conflicts, engaging more frontal brain areas. On the other hand, with more total trials, the task may involve more response conflicts, engaging more central-parietal brain areas (Chen et al., 2013; van Veen & Carter, 2005). Due to the extensive practice required in our study, we identified a congruency N450 effect in the central-parietal region. We apologize for not thoroughly exploring other potential electrodes in the previous manuscript, and we have revised the results and interpretations regarding N450 accordingly in the revised version (in the N450 section of the ERP results and the third paragraph of the Discussion section).

Reference

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Chen, M. J. (1993). A Comparison of Chinese and English Language Processing. In Advances in Psychology (Vol. 103, pp. 97–117). North-Holland. https://doi.org/10.1016/S0166-4115(08)61659-3

Chen, X. F., Jiang, J., Zhao, X., & Chen, A. (2010). Effects of practice on semantic conflict and response conflict in the Stroop task. Psychol. Sci., 33, 869–871.

Chen, Z., Lei, X., Ding, C., Li, H., & Chen, A. (2013). The neural mechanisms of semantic and response conflicts: An fMRI study of practice-related effects in the Stroop task. NeuroImage, 66, 577–584. https://doi.org/10.1016/j.neuroimage.2012.10.028

Cohen, M. X. (2014). Analyzing Neural Time Series Data: Theory and Practice. The MIT Press. https://doi.org/10.7551/mitpress/9609.001.0001

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Duque, J., Greenhouse, I., Labruna, L., & Ivry, R. B. (2017). Physiological Markers of Motor Inhibition during Human Behavior. Trends in Neurosciences, 40(4), 219–236. https://doi.org/10.1016/j.tins.2017.02.006

Engel, A. K., & Fries, P. (2010). Beta-band oscillations—Signalling the status quo? Current Opinion in Neurobiology, 20(2), 156–165. https://doi.org/10.1016/j.conb.2010.02.015

Heidlmayr, K., Kihlstedt, M., & Isel, F. (2020). A review on the electroencephalography markers of Stroop executive control processes. Brain and Cognition, 146, 105637. https://doi.org/10.1016/j.bandc.2020.105637

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Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

In the work: "Endosomal sorting protein SNX4 limits synaptic vesicle docking and release" Josse Poppinga and collaborators addressed the synaptic function of Sortin-Nexin 4 (SNX4). Employing a newly developed in vitro KO model, with live imaging experiments, electrophysiological recordings, and ultrastructural analysis, the authors evaluate modifications in synaptic morphology and function upon loss of SNX4. The data demonstrate increased neurotransmitter release and alteration in synapse ultrastructure with a higher number of docked vesicles and shorter AZ. The evaluation of the presynaptic function of SNX4 is of relevance and tackles an open and yet unresolved question in the field of presynaptic physiology.

Strengths:

The sequential characterization of the cellular model is nicely conducted and the different techniques employed are appropriate for the morpho-functional analysis of the synaptic phenotype and the derived conclusions on SNX4 function at presynaptic site. The authors succeeded in presenting a novel in vitro model that resulted in chronical deletion of SNX4 in neurons. A convincing sequence of experimental techniques is applied to the model to unravel the role of SNX4, whose functions in neuronal cells and at synapses are largely unknown. The understanding of the role of endosomal sorting at the presynaptic site is relevant and of high interest in the field of synaptic physiology and in the pathophysiology of the many described synaptopathies that broadly result in loss of synaptic fidelity and quality control at release sites.

We thank the reviewer for their positive evaluation of our manuscript.

Weaknesses:

The flow of the data presentation is mostly descriptive with several consistent morphological and functional modifications upon SNX loss. The paper would benefit from a wider characterization that would allow us to address the physiological roles of SNX4 at the synaptic site and speculate on the underlying molecular mechanisms. In addition, due to the described role of SNX4 in autophagy and the high interest in the regulation of synaptic autophagy in the field of synaptic physiology, an initial evaluation of the autophagy phenotype in the neuronal SNX4KO model is important, and not to be only restricted to the discussion section.

We thank the reviewer for their suggestions and agree that broader characterization would help us speculate on the underlying mechanism. To address this, we have conducted additional independent experiments investigating the role of SNX4 in neuronal autophagy, as suggested by this reviewer. These experiments are now included in the main figures and are no longer limited to the discussion section. Please see the detailed responses to this reviewer's recommendations below.

Reviewer #2 (Public Review):

Summary:

SNX4 is thought to mediate recycling from endosomes back to the plasma membrane in cells. In this study, the authors demonstrate the increases in the amounts of transmitter release and the number of docked vesicles by combining genetics, electrophysiology, and EM. They failed to find evidence for its role in synaptic vesicle cycling and endocytosis, which may be intuitively closer to the endosome function.

Strengths:

The electrophysiological data and EM data are in principle, convincing, though there are several issues in the study.

We thank the reviewer for their positive evaluation of our manuscript.

Weaknesses:

It is unclear why the increase in the amounts of transmitter release and docked vesicles happened in the SNX4 KO mice. In other words, it is unclear how the endosomal sorting proteins in the end regulate or are connected to presynaptic, particularly the active zone function.

We thank the reviewer for their suggestions and agree that further characterization would help to understand how endosomal sorting proteins regulate presynaptic neurotransmission. We have now added extra data on electrophysiological recordings clarifying SNX4’s role in the synapse. Please see the detailed responses to this reviewer's recommendations below.

Reviewer #3 (Public Review):

Summary:

The study aims to determine whether the endosomal protein SNX4 performs a role in neurotransmitter release and synaptic vesicle recycling. The authors exploited a newly generated conditional knockout mouse to allow them to interrogate the SNX4 function. A series of basic parameters were assessed, with an observed impact on neurotransmitter release and active zone morphology. The work is interesting, however as things currently stand, the work is descriptive with little mechanistic insight. There are a number of places where the data appear to be a little preliminary, and some of the conclusions require further validation.

Strengths:

The strengths of the work are the state-of-the-art methods to monitor presynaptic function.

We thank the reviewers for their positive evaluation of our manuscript.

Weaknesses:

The weaknesses are the fact that the work is largely descriptive, with no mechanistic insight into the role of SNX4. Further weaknesses are the absence of controls in some experiments and the design of specific experiments.

We thank the reviewer for their suggestions and agree that addition of extra control groups and experiments would strengthen interpretation of the observed phenotype. To address this, we have now performed experiments to investigate the miniature excitatory postsynaptic currents and added extra control groups such as overexpression of SNX4 on control background. In addition, we assessed SNX4-mediated neuronal autophagy as a potential molecular mechanism by which SNX4 affects synaptic output. Please see the detailed responses to this reviewers’ recommendations below.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

(1) The characterization of the neurite outgrowth presented in Figure 1 is a necessary starting point for the characterization of the model and the interpretation of the following data. Being the analysis conducted at 21 DIV, a significant portion of the neurite tree is out of the analyzed field. Adding sholl analysis will better indicate the complexity of the that appears to be influenced by SNX4 loss in the representative images shown in Figure 1f.

We fully agree and have now performed a Sholl analysis of dendrite branches to investigate dendritic complexity. (Figure 1(i), page 2-3, line 86-88). SNX4 depletion does not affect dendrite length or dendrite branching.

(2) Analogously, the characterization of synapse number is of relevance for the interpretation of the data. For a better flow of the data, Figure 4 might be presented as Figure 2 (without the repetition of panel h in Figure 1). An explanation of how VAMP2 puncta are processed is necessary in the method section. A double labelling with a postsynaptic marker would allow trafficking organelles to be distinguished from mature synaptic contacts. Indeed, the analysis of VAMP2 intensity along neurite in mature 21DIV neurons should reveal peaks in the intensity profile that represent synaptic contacts. For unexplained reasons, the profile is rather flat in the two experimental groups. Focusing on axonal branches will surely result in a peaked profile for VAMP2 labelling.

We fully agree that the characterization of synapses is relevant for the interpretation of the data. We have now added a section in our Material and Methods how the VAMP2 puncta are processed (p14 line 517-520). Instead of labeling mature synapses using double labeling of VAMP2 and PSD95, we analyzed the number of active synapses in live neurons using SypHy (Fig. 3g). The reviewer is correct that the VAMP2 data presented in Fig 1I and Fig 4 is part of the same dataset and we have clarified this in the figure legend. In Fig 1I only the total number of VAMP2 puncta is plotted as a marker for synapse number, while in Fig 4 we assess VAMP2 as potential SNX4 sorting cargo (Ma et al., 2017). Because of these different aims, we prefer to keep the figures separate. The analysis of VAMP2 intensity along the distance of the soma is a Sholl analysis (Fig. 4d), represents the average VAMP2 intensity over distance from the soma of 35-41 neurons per group. In contrast to a line scan of a single neurite, this average profile lacks the peaks of individual synapses.

(3) Miniature excitatory postsynaptic currents recordings would strengthen the synaptic characterization and complement the electrophysiological recordings shown in Figure 2. Analyzing frequency and amplitude parameters would complement the data on the number of synaptic connections defined by the pre and postsynaptic colocalization puncta as suggested above and may support the data shown in Figure 3 g that suggests a decreased number of active synapses in SNX4-KO cells.

We fully agree that the characterization of miniature excitatory postsynaptic currents would strengthen the synaptic characterization and complement the other electrophysiological data. Therefore, we have now added additional experiments showing the mEPSCs (Fig. 2k-m, page 4) in SNX4 cKO neurons versus control. This data shows that the amplitude and frequency of spontaneous miniature EPSCs (mEPSCs) were not affected upon SNX4 depletion, consistent with a normal first evoked EPSC and RRP estimate. Furthermore, these data suggest that it is unlikely that the observed increase in neurotransmission is due to post-synaptic effects.

(4) Recordings on the first evoked response shown in Figure 2 b and quantified in Figures c and d suggest that SNX4 overexpression per se exerts some effect on the Amplitude and the Charge of the first evoked response. This is also evident in the supplementary Figure 2 with lower frequency trains. An additional experimental group, namely control+SNX4 is needed for the correct interpretation of the observed phenotype. The possibility that SNX4 per se exerts an effect on evoked transmission could be discussed in terms of putative mechanisms and interactions.

We thank the reviewer for their suggestion and agree that an additional experimental group (control + SNX4) would strengthen interpretation of the observed phenotype. We have now added a new experimental condition with overexpression of SNX4 on a control background (Supplementary Fig. 3, page 20). This data shows that the amplitude and charge of the first evoked response were not affected in control + SNX4 neurons compared to control, and no differences were detected in the response to the 40 Hz stimulation train (Supplementary Fig. 3a-e).  Together, these data suggest that SNX4 overexpression in itself does not affect the neurotransmission protocols studied in SNX4 cKO experiments.

(5) To correctly interpret the SyPhy experiments and exclude an effect of SNX silencing on SV recycling, it is suggested to repeat the experiments shown in Figure 3 in the absence and in the presence of bafilomycin. Indeed, the quantifications shown in Figure 3 d and f do not represent "release fraction" as stated (lines 139/140) but they rather refer to an average difference between release fraction and recovered fraction. With the use of bafilomycin, the comparison of the deltaFmax/deltaFNH4Cl with and without bafilomycin would enable the release fraction to be correctly evaluated and compared.

We appreciate the reviewer’s suggestion and agree on the importance of considering the impact of SV recycling when evaluating the released fraction. We agree that the presence of bafilomycin is critical to isolate the released component during stimulation. We have now rephrased this conclusion. To assess synaptic recycling in these assays, bafilomycin in not critically required and we show by multiple independent experiments, including SypHy and FM64 dye assays, that SV recycling is either not affected or the effect is too small to be detected by these methods.

(6) In the ultrastructural analysis, additional quantifications are needed to exclude the accumulation of endosome-like structures. It is not clear if, in the evaluation of total SV number (Figure 5e), the authors counted all vesicles or vesicles < 50nm. This has to be explained and additional quantification of # of SV < 50nm and # SV > 50nm is informative, taking into account the endosomal nature of SNX4. Indeed, although the average size of SV is not changed (fig. 5 d), the density of "bigger vesicle" may result from endosomal-like structure accumulation. An additional suggested quantification is on vesicle # SV > 80nm as previously reported in the cited references dealing with endosomal proteins and presynaptic morphology.

We fully agree that the characterization of vesicle size is important and that it was not clearly stated which vesicles were included in the total number of SV (Fig. 5e). We have now added this to the figure description. We have also added a histogram that contains the vesicle numbers of different bin sizes for SNX4 cKO synapses and control synapses (Supplementary Fig. 4, page 21) including # SVs > 80nm. (Whilst it seems that there are more “bigger” vesicles in the KO, further analysis revealed that this is mostly driven by one experiment and this effect is not consistent.)

(7) Due to the high scientific interest in presynaptic autophagy for SV recycling and degradation, and the paucity of experimental work assessing the proteins involved, an initial evaluation of the neuronal autophagy process (by western blot analysis and immunocytochemistry) for the characterization of the model will better support the paragraph in the discussion (lines 314-322) and contribute to future work in the field. Although very rare, autophagosomes quantification at presynaptic sites can also be performed from the already acquired images. A double membrane structure with the material inside is evident in the representative control image presented!

We appreciate the reviewer’s suggestion and agree that presynaptic autophagy is an interesting potential mechanism that would elaborate our current working model. To address the reviewers’ suggestion, we added multiple independent experiments to investigate basal autophagy markers such as ATG5 using western blot analysis, characterization of p62 levels using immunohistochemistry and performed additional morphometric analysis on the electron microscopy data (Supplementary Fig. 5). In SNX4 cKO neurons, there was no significant difference in P62 puncta numbers or P62 somatic intensity under basal conditions or after blocking autophagic P62 degradation by bafilomycin treatment, suggesting that autophagic flux remains normal. Also, no changes in total ATG5 protein levels were observed and ultrastructural analysis revealed no differences in the total number of autophagosomes. Collectively, these data indicate that SNX4 depletion does not impact the basal autophagic flux, ATG5 protein levels, or the number of autophagosomes.

Minor points:

(1) Dorrbaun et al. 2018 is missing from the reference list. In the legend to figure 1 there is an incorrect reference to Figure 6, rather than Figure 4.

We have now adjusted the figure legend and added the reference (page 16, line 604).

(2) Information on the construct employed for the rescue is missing. Is it a fluorescent tag construct? Representative images of the three autaptic neurons (control, KO, KO+SNX4) would nicely complement data presentation in Figure 2. 

We have now elaborated on this in material and methods section (p12, line 418-421). Unfortunately, we did not obtain pictures of autaptic neurons used for electrophysiology experiments.

Reviewer #2 (Recommendations For The Authors):

(1) Figure 2d and f are somewhat inconsistent. Total charges for the 1st EPSCs differ almost 2-fold in the same condition.

We appreciate the reviewer’s concern. The average EPSCs charge of the first evoked was 89, 122 and 57 pC for control, KO and rescued neurons respectfully. The average charge of the first pulse of 40Hz train was 41,58 and 32 pC for control, KO and rescued neurons respectfully, which is roughly 50% of the naïve response of the same cells. These trains were recorded after 2 or 3 other stimulation paradigms, which can have affected the total charge released in the 40Hz train. That said, the proportional difference between groups is high comparable, with a 37% increased average charge released in SNX4 cKO compared to control in the naïve response and 41% increased response in the first response of the 40 Hz train, and rescued cells show a 53% reduction in average released charge compared to control in the naïve response compared to a 44% reduction in the first response of the 40 Hz train. Although the absolute values differ between these readouts, we conclude that the biological comparison between groups is consistent.

(2) Figure 2h. This type of analysis has a drawback. See Neher (2015) for the problems associated with this analysis.

We fully agree with the reviewer’s comment. As noted in our discussion (page 9 line 285), while this analysis has its limitations, it can still provide an indication of the ready releasable pool.   

(3) The EPSC phenotype may be due to postsynaptic effects. This should be excluded by additional experiments (mEPSC analysis) or further clarification.

We fully agree that the characterization of miniature excitatory postsynaptic currents recording would strengthen the synaptic characterization and complement the electrophysiological recordings. Therefore, we have now added additional experiments showing the mEPSCs (Fig. 2k-m) in SNX4 cKO neurons versus control. This data shows that the amplitude and frequency of spontaneous miniature EPSCs (mEPSCs) were not affected upon SNX4 depletion, suggesting that it is unlikely that the observed increase in neurotransmission is due to post-synaptic effects.

(4) The increased number of docked vesicles observed in EM and the increased slope (vesicle recruitment, Figure 2h) are not consistent with each other. Maybe the definition of docked vesicles is unclear in this version of the manuscript.

As noted in our material & methods (page 15, line 547-548), SVs were defined as docked if there was no distance visible between the SV membrane and the active zone membrane. We have added the pixel size for clarification. Indeed, we do not observe an increase in release probability or first evoked response, which would correspond with an increased docked pool. However, we think that the increase in docked vesicles might contribute to an enhanced SV recruitment (see discussion).

(5) Figure 3: Vesicle cycling was monitored in only a limited condition. It is known that there are multiple pathways of vesicle cycling. Ideally, these pathways should be dissected. At least, the authors mention the possibility that they have missed some "positive" conditions.

We fully agree with the reviewer’s comment that vesicle recycling is complex with several parallel pathways involved. While we did not study individual endocytosis pathways, we used different assays covering various recycling pathways. The SypHy assay (Fig. 3c & f) combined with the 100 AP stimulation paradigm at room temperature predominantly addresses clathrin-mediated endocytosis. Additionally, the FM-64 dye assay at 37 degrees Celsius covers ultrafast endocytosis pathways as well as bulk endocytosis routes. Since neither assay showed major effects, we decided not to pursue further experiments focusing on different endocytosis pathways.

Reviewer #3 (Recommendations For The Authors):

Major points:

(1) Since all of the work here is culture-focussed, the in vivo phenotype is not as relevant, however the in vitro properties are. The incomplete Cre-dependent removal of SNX4 is concerning (especially axonal SNX4 levels identified via immunofluorescence), however, the main concern is that there was no profiling of the other molecular changes within these cultures. This is important, since there may be considerable alterations in the expression of a number of presynaptic proteins which may explain the observed phenotypes. Ideally, these cultures could have been profiled in an unbiased manner via mass spectrometry to identify potential changes in the presynaptic proteome, or at the very least the levels of key fusion molecules would have been assessed via Western blotting.

We thank the reviewer for their suggestion and agree that mass spectrometry would strengthen the interpretation of the observed phenotype. However, due to contractual constraints, we are unable to pursue a mass spectrometry follow-up experiment. We agree that characterizing key fusion molecules is of potential interest. Therefore, based on literature, we selected a likely candidate, VAMP2, which did not show any alterations in expression levels when knocking out SNX4. Given the previously described role of SNX4 in the degradation pathway, one would expect increased degradation of key fusion molecules if they are recycled by SNX4. Other literature indicates that reduced levels of key fusion molecules, such as synaptotagmin or SNAP-25 (Broadie et al., 1994; Washbourne et al., 2001) , do not mimic our phenotype.

(2) The experiments reported in Figure 2, in particular those in 2c and 2d, suggest that overexpression of SNX4 has a dominant-negative effect on neurotransmitter release. This is strongly supported by the supplementary data during a stimulus train (particularly the start point of the 5 Hz train in Supplementary Figure 2). Therefore, the perceived rescue of EPSC charge in Figure 2f, 2g may be a result of SNX4 inhibiting neurotransmitter release. A determination of the impact of SNX4 overexpression (and level of overexpression) in WT neurons is essential to show that this is a bonefide rescue, rather than a direct inhibition by SNX4 overexpression.

We thank the reviewer for their suggestion and agree that an additional experimental group (control + SNX4) would strengthen interpretation of the observed phenotype. We have now added a new experiment with an extra experimental condition with overexpression of SNX4 on a control background (Supplementary Fig. 3 page 21). This data shows that the amplitude and charge of the first evoked response were not affected in control + SNX4 neurons compared to control, and no differences were detected in the response to the 40 Hz stimulation train (Supplementary Fig. 3a-e).  Together, these data suggest that SNX4 overexpression in itself does not affect the neurotransmission protocols studied in SNX4 cKO experiments.

(3) The experiments in Figure 3 clearly reveal a lack of effect of SNX4 depletion on synaptic vesicle endocytosis. However, the assumption that synaptic vesicle recycling is unaffected is a little premature. The fact that the second evoked SypHy peak is significantly larger than the first (Figures 3c-e) suggests that more vesicles may be recycling in KO neurons. Furthermore, the FM dye experiments do not aid interpretation, since there may be insufficient time (10 min) for new vesicles to be generated from endosomal intermediates experiments. Therefore, to confirm an absence of effect on recycling, the authors could either 1) perform the same experiment as 3c, but with 4 stimulation trains (to drive the system harder to reveal any phenotype) or 2) repeat the FM dye experiment but increase the time between loading and unloading to 30 min.

We fully agree with the reviewers' comment that vesicle recycling is an important component to consider and is complex with several parallel pathways involved. We conducted multiple independent experiments covering the most significant recycling pathways. The SypHy assay (Fig. 3c & f) combined with the 100 AP stimulation paradigm at room temperature predominantly addresses clathrin-mediated endocytosis. Additionally, the FM-64 dye assay at 37 degrees Celsius covers ultrafast endocytosis pathways as well as bulk endocytosis routes. To further challenge the system and reveal recycling phenotypes, we included a second 100 AP stimulation in our SypHy assay. While only the increase of the second SypHy peak is significant, the absolute numbers do not differ much from the first peak (0,17 for control and 0,21 for KO second peak and 0,19 for control and 0,22 for KO first peak, Supplementary table1). We nevertheless do not see any effects on recycling after the second peak (mean decay time is 27 for control and 26 for KO Supplementary Table 1). A single 100 AP 40 Hz train depletes all the synchronous release (not shown) and most of the evoked charge (see Fig 2f), hence two of these trains with one minute recovery is already a very demanding protocol. Although increasing the time between loading and unloading to 30 minutes might uncover other recycling components, it has been shown that ultrafast endocytosis occurs within 30 seconds (Watanabe et al., 2013), suggesting that 10 minutes should provide enough time for synaptic vesicle recycling. This is also evident from the fact that we can significantly destain synapses loaded with FM dye by electrical stimulation (Fig 3j), indicating that synaptic vesicle recycling took place. Since neither assay showed major effects, we concluded that under these circumstances, synaptic recycling is not significantly affected. However, we cannot exclude the possibility that recycling deficits in SNX4 cKO neurons could be detected in other paradigms,

(4) There is no obvious effect on VAMP2 levels or location in SNX4 KO neurons (Figure 4). However, when one considers that SNX4 is proposed to have a role in VAMP2 trafficking, it is surprising that an experiment examining the live trafficking of VAMP2-SypHy was not performed. This would have revealed activity-dependent alterations that would have been missed by simply measuring VAMP2 expression and localization, and potentially provided a molecular explanation for the enhanced neurotransmitter release during a stimulus train.

We appreciate the reviewer’s suggestion and agree that it could be a valuable experiment However, overexpressing a VAMP2-pHluorin construct might obscure potential phenotypes related to VAMP2 trafficking. SNX4 is expected to be involved in VAMP2 recycling, even with activity-dependent changes. Mis-sorted VAMP2 would accumulate in acidic vesicles, which could be masked by the VAMP2-pHluorin construct. Similarly, mis-sorting of other SNX4 cargo, such as the transferrin receptor, has been identified through lysosomal degradation, as shown by Western blot analysis of expression levels of the endogenous protein. We did not detect any differences in endogenous levels of VAMP2 within 21 days of SNX4 deletion (Fig 4), indicating that SNX4-dependent endosome sorting is not essential for VAMP2 recycling.

(5) The morphological data in Figure 5 report a series of small changes in docked vesicles and active zone length. In many cases, significance is obtained due to synapses being used as the experimental n, and thus inflating the statistical power. When one considers that no significant effect was observed on evoked release (apart from during a stimulus train), it suggests that the number of docked vesicles does not alter release probability in this system (which the authors point out). Instead, they suggest that an increased supply of vesicles is responsible, via increased recruitment to RRP/releasable pool (but not via increased recycling). If this is the case, it should have been reflected as an increase in the evoked SypHy response in Fig 2c,d (which is borderline significant). What may help is to determine the morphological landscape immediately after a stimulus strain, since this is the only condition where enhanced release is observed, and thus provide a morphological correlate to the physiological data.

We fully agree with the reviewer’s suggestion that an ultrastructural characterization immediately after a stimulus train would be informative. Unfortunately, contract constraints prevent us from performing this experiment. For our ultrastructural morphological data, we treated synapses as individual experimental n since it is not possible to determine whether synapses in a micronetwork on one sapphire originate from the same neuron. We used 18 independent sapphires from 3 independent pups to ensure the technical and biological replication of our data and measuring independent neurons. We fully agree with the reviewers comment to be careful with ‘inflating the statistical power’ due to potential nesting effects when using synapses as experimental n. To mitigate the potential nesting effect of analyzing multiple synapses per neuron, the intracluster correlation (ICC) is calculated per variable and per nesting effect. If ICC was close to 0.1, indicating that a considerable portion of the total variance can be attributed to e.g. synapse or sapphire, multilevel analysis was performed to accommodate nested data (Aarts et al., 2014).

Minor points

(1) When a new mouse model is generated, it is usually accompanied by a thorough characterization of its properties. However, in this case, there was no information provided about the conditional SNX4 knockout mouse. This is surprising and at a minimum, the following should be provided a) the background strain, b) method of generation, c) the number of animals used to establish the colony, d) breeding strategy, e) backcrossing strategy, f) genotyping protocol.

We apologize that a thorough characterization of our novel mouse model was lacking and therefore added this to our material & methods section (page 11, line 377-391).

(2) There is a noticeable difference between WT and KO neurons during train stimulation in Figure 2f, however, this appears to be due to the fact that there is a far higher EPSC charge to begin with in KO neurons. Why is there such a disparity when there is no difference in response to single pulses (Figures 2b-d) or presynaptic plasticity (Figure 2e)?

We understand the reviewer’s concern. We excluded an outlier (3x SD) in the KO dataset that drove the initial far higher EPSC charge in the graph (was already excluded for the statistics, Supplementary table 1). The average charge of the first pulse of 40Hz train is 41 pC and for KO neurons 58 pC, which did not differ significantly.  These trains of Fig. 2f were recorded after 2 or 3 other stimulation paradigms, which can have affected the total charge released in the 40Hz train. That said, the proportional difference between groups is high comparable between Fig 2b-d and 2f, with a 37% increased average charge released in SNX4 cKO compared to control in the naïve response (Fig. 2d) and 41% increased response in the first response of the 40 Hz train (Fig. 2f), and rescued cells show a 53% reduction in average released charge compared to control in the naïve response compared to a 44% reduction in the first response of the 40 Hz train. Although the absolute values differ between these readouts, we conclude that the biological comparison between groups is consistent.

(3) Line 343-344 - "(Supplementary Figure 1a)" should be "(Figure 1a)".

We thank the reviewer for this comment and adjusted this in the manuscript.

Author response:

Reviewer 2:

(1) It appears that the purified γ-secretase complex generates the same amount of Aβ40 and Aβ42, which is quite different in cellular and biochemical studies. Is there any explanation for this?

Roughly equal production of Aβ40 and Aβ42 is a phenomenon seen with purified enzyme assays, and the reason for this has not been identified. However, we suggest that what is meaningful in our studies is the relative difference between the effects of FAD-mutant vs. WT PSEN1 on each proteolytic processing step. All FAD mutations are deficient in multiple cleavage steps in γ-secretase processing of APP substrate, and these deficiencies correlate with stabilization of E-S complexes.

(2) It has been reported the Aβ production lines from Aβ49 and Aβ48 can be crossed with various combinations (PMID: 23291095 and PMID: 38843321). How does the production line crossing impact the interpretation of this work?

In the cited reports, such crossover was observed when using synthetic Aβ intermediates as substrate. In PMID 2391095 (Okochi M et al, Cell Rep, 2013), Aβ43 is primarily converted to Aβ40, but also to some extent to Aβ38. In PMID: 38843321 (Guo X et al, Science, 2024), Aβ48 is ultimately converted to Aβ42, but also to a minor degree to Aβ40. We have likewise reported such product line “crossover” with synthetic Aβ intermediates (PMID: 25239621; Fernandez MA et al, JBC, 2014). However, when using APP C99-based substrate, we did not detect any noncanonical tri- and tetrapeptide co-products of Aβ trimming events in the LC-MS/MS analyses (PMID: 33450230; Devkota S et al, JBC, 2021). In the original report on identification of the small peptide coproducts for C99 processing by γ-secretase using LC-MS/MS (PMID: 19828817; Takami M et al, J Neurosci, 2009), only very low levels of noncanonical peptides were observed. In the present study, we did not search for such noncanonical trimming coproducts, so we cannot rule out some degree of product line crossover.

(3) In Figure 5, did the authors look at the protein levels of PS1 mutations and C99-720, as well as secreted Aβ species? Do the different amounts of PS1 full-length and PS1-NTF/CTF influence FILM results?

This is a good question. Our preliminary investigation by Western Blot shows no correlation between C99 and PSEN1 expressions and FLIM results, but we will fully address the concern in our point-by-point responses submitted with a revised manuscript. 

(4) It is interesting that both Aβ40 and Aβ42 Elisa kits detect Aβ43. Have the authors tested other kits in the market? It might change the interpretation of some published work.

We have not tested other ELISA kits. In light of our findings, it would be a good idea for other investigators to test whatever ELISAs they use for specificity vis-à-vis Aβ43.

Author response:

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

Reviewer 1:

Weaknesses:

The match between fractal and classical cycles is not one-to-one. For example, the fractal method identifies a correlation between age and cycle duration in adults that is not apparent with the classical method. This raises the question as to whether differences are due to one method being more reliable than another or whether they are also identifying different underlying biological differences. It is not clear for example whether the agreement between the two methods is better or worse than between two human scorers, which generally serve as a gold standard to validate novel methods. The authors provide some insight into differences between the methods that could account for differences in results. However, given that the fractal method is automatic it would be important to clearly identify criteria for recordings in which it will produce similar results to the classical method.

We thank the reviewer for the insightful suggestions. In the revised Manuscript, we have added a number of additional analyses that provide a quantitative comparison between the classical and fractal cycle approaches aiming to identify the source of the discrepancies between classical and fractal cycle durations. Likewise, we assessed the intra-fractal and intra-classical method reliability.

Reviewer 2:

One weakness of the study, from my perspective, was that the IRASA fits to the data (e.g. the PSD, such as in Figure 1B), were not illustrated. One cannot get a sense of whether or not the algorithm is based entirely on the fractal component or whether the oscillatory component of the PSD also influences the slope calculations. This should be better illustrated, but I assume the fits are quite good.

Thank you for this suggestion. In the revised Manuscript, we have added a new figure (Fig.S1 E, Supplementary Material 2), illustrating the goodness of fit of the data as assessed by the IRASA method.

The cycles detected using IRASA are called fractal cycles. I appreciate the use of a simple term for this, but I am also concerned whether it could be potentially misleading? The term suggests there is something fractal about the cycle, whereas it's really just that the fractal component of the PSD is used to detect the cycle. A more appropriate term could be "fractal-detected cycles" or "fractal-based cycle" perhaps?

We agree that these cycles are not fractal per se. In the Introduction, when we mention them for the first time, we name them “fractal activity-based cycles of sleep” and immediately after that add “or fractal cycles for short”. In the revised version, we renewed this abbreviation with each new major section and in Abstract. Nevertheless, given that the term “fractal cycles” is used 88 times, after those “reminders”, we used the short name again to facilitate readability. We hope that this will highlight that the cycles are not fractal per se and thus reduce the possible confusion while keeping the manuscript short.

The study performs various comparisons of the durations of sleep cycles evaluated by the IRASA-based algorithm vs. conventional sleep scoring. One concern I had was that it appears cycles were simply identified by their order (first, second, etc.) but were not otherwise matched. This is problematic because, as evident from examples such as Figure 3B, sometimes one cycle conventionally scored is matched onto two fractal-based cycles. In the case of the Figure 3B example, it would be more appropriate to compare the duration of conventional cycle 5 vs. fractal cycle 7, rather than 5 vs. 5, as it appears is currently being performed.

In cases where the number of fractal cycles differed from the number of classical cycles (from 34 to 55% in different datasets as in the case of Fig.3B), we did not perform one-to-one matching of cycles. Instead, we averaged the duration of the fractal and classical cycles over each participant and only then correlated between them (Fig.2C). For a subset of the participants (45 – 66% of the participants in different datasets) with a one-to-one match between the fractal and classical cycles, we performed an additional correlation without averaging, i.e., we correlated the durations of individual fractal and classical cycles (Fig.4S of Supplementary Material 2). This is stated in the Methods, section Statistical analysis, paragraph 2.

There are a few statements in the discussion that I felt were either not well-supported. L629: about the "little biological foundation" of categorical definitions, e.g. for REM sleep or wake? I cannot agree with this statement as written. Also about "the gradual nature of typical biological processes". Surely the action potential is not gradual and there are many other examples of all-or-none biological events.

In the revised Manuscript, we have removed these statements from both Introduction and Discussion.

The authors appear to acknowledge a key point, which is that their methods do not discriminate between awake and REM periods. Thus their algorithm essentially detected cycles of slow-wave sleep alternating with wake/REM. Judging by the examples provided this appears to account for both the correspondence between fractal-based and conventional cycles, as well as their disagreements during the early part of the sleep cycle. While this point is acknowledged in the discussion section around L686. I am surprised that the authors then argue against this correspondence on L695. I did not find the "not-a-number" controls to be convincing. No examples were provided of such cycles, and it's hard to understand how positive z-values of the slopes are possible without the presence of some wake unless N1 stages are sufficient to provide a detected cycle (in which case, then the argument still holds except that its alterations between slow-wave sleep and N1 that could be what drives the detection).

In the revised Manuscript, we have removed the “NaN analysis” from both Results and Discussion. We have replaced it with the correlation between the difference between the durations of the classical and fractal cycles and proportion of wake after sleep onset. The finding is as follows:

“A larger difference between the durations of the classical and fractal cycles was associated with a higher proportion of wake after sleep onset in 3/5 datasets as well as in the merged dataset (Supplementary Material 2, Table S10).” Results, section “Fractal cycles and wake after sleep onset”, last two sentences. This is also discussed in Discussion, section “Fractal cycles and age”, paragraph 1, last sentence. 

To me, it seems important to make clear whether the paper is proposing a different definition of cycles that could be easily detected without considering fractals or spectral slopes, but simply adjusting what one calls the onset/offset of a cycle, or whether there is something fundamentally important about measuring the PSD slope. The paper seems to be suggesting the latter but my sense from the results is that it's rather the former.

Thank you for this important comment. Overall, our paper suggests that the fractal approach might reflect the cycling nature of sleep in a more precise and sensitive way than classical hypnograms. Importantly, neither fractal nor classical methods can shed light on the mechanism underlying sleep cycle generation due to their correlational approach. Despite this, the advantages of fractal over classical methods mentioned in our Manuscript are as follows:

(1) Fractal cycles are based on a real-valued metric with known neurophysiological functional significance, which introduces a biological foundation and a more gradual impression of nocturnal changes compared to the abrupt changes that are inherent to hypnograms that use a rather arbitrary assigned categorical value (e.g., wake=0, REM=-1, N1=-2, N2=-3 and SWS=-4, Fig.2 A).

(2) Fractal cycle computation is automatic and thus objective, whereas classical sleep cycle detection is usually based on the visual inspection of hypnograms, which is time-consuming, subjective and error-prone. Few automatic algorithms are available for sleep cycle detection, which only moderately correlated with classical cycles detected by human raters (r’s = 0.3 – 0.7 in different datasets here).

(3) Defining the precise end of a classical sleep cycle with skipped REM sleep that is common in children, adolescents and young adults using a hypnogram is often difficult and arbitrary.   The fractal cycle algorithm could detect such cycles in 93% of cases while the hypnogram-based agreement on the presence/absence of skipped cycles between two independent human raters was 61% only; thus, 32% lower.

(4) The fractal analysis showed a stronger effect size, higher F-value and R-squared than the classical analysis for the cycle duration comparison in children and adolescents vs young adults. The first and second fractal cycles were significantly shorter in the pediatric compared to the adult group, whereas the classical approach could not detect this difference.

(5) Fractal – but not classical – cycle durations correlated with the age of adult participants.

These bullets are now summarized in Table 5 that has been added to the Discussion of the revised manuscript.

Reviewer #1 (Recommendations for the authors):

The authors have added a lot of quantifications to provide a more complete comparison of classical and fractal cycles that address the points I raised.

Regarding, the question of skipped REM cycles: I am not sure the comparison of skipped cycle accuracies between fractal and manual methods makes sense. To make a fair comparison fractal and 2nd scorer classifications should be compared to the same baseline dataset which doesn't seem to be the case since the number of skipped cycles is not the same. Moreover, it's not indicated whether the fractal method identifies any false positive skipped cycles.

Thank you for this comment. In the revised Manuscript, we have reported the number of false positive skipped cycles identified by the fractal algorithm. Likewise, we have added the comparison between the fractal algorithm and the second scorer detection of cycles with skipped REM sleep (Results, the section “Skipped cycles”, last paragraph). The text has been revised as follows:

“Visual inspection of the hypnograms from Datasets 1 – 6 was performed by two independent researchers. Scorer 1 and Scorer 2 detected that out of 226 first sleep cycles 58 (26%) and 64 (28%), respectively, lacked REM episodes. The agreement on the presence of skipped cycles between two human raters equaled 91% (58 cycles detected by both raters out of 64 cycles detected by either one or two scorers). The fractal cycle algorithm detected skipped cycles in 57 out of 58 (98%) cases detected by Scorer 1 with one false positive (which, however, was tagged as a skipped cycle by Scorer2), and in 58 out of 64 (91%) cases detected by Scorer 2 with no false positives.”

Minor points

I suggest reporting the values of inter-method / inter-scorer correlations with the classical method in the main text since otherwise interpreting the value for fractal vs classical is impossible.

Thank you for this comment. In the revised Manuscript, we have moved this section to the main text (Table 3).

Table 5 + text of discussion: cycle identification based on hypnograms is claimed to be. "based on arbitrary assigned categorical values" the categories are not arbitrary since they correspond to well-validate sleep states, only the number associated it and this does not seem to be very important since it's only for visualization purposes.

Thank you for this comment. In the revised Manuscript, we have removed the phrase “arbitrary assigned“.

Author response:

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

Reviewer #1 (Public review):

Comment 1: In the Results section, the rationale behind selecting the beta band for the central (C3, CP3, Cz, CP4, C4) regions and the theta band for the fronto-central (Fz, FCz, Cz) regions is not clearly explained in the main text. This information is only mentioned in the figure captions. Additionally, why was the beta band chosen for the S-ROI central region and the theta band for the S-ROI fronto-central region? Was this choice influenced by the MVPA results?

We thank the reviewer for the question regarding the rationale for the S-ROI selection in our study. The beta band was chosen for the central region due to its established relevance in motor control (Engel & Fries, 2010), movement planning (Little et al., 2019) and motor inhibition (Duque et al., 2017). The fronto-central theta band (or frontal midline theta) was a widely recognized indicator in cognitive control research (Cavanagh & Frank, 2014), associated with conflict detection and resolution processes. Moreover, recent empirical evidence suggested that the fronto-central theta reflected the coordination and integration between stimuli and responses (Senoussi et al., 2022). Although we have described the cognitive processes linked to these different frequencies in the introduction and discussion sections, along with the potential patterns of results observed in Stroop-related studies, we did not specify the involved cortical areas. Therefore, we have specified these areas in the introduction to enhance the clarity of the revised version (in the fourth paragraph of the Introduction section).

Regarding whether the selection of S-ROIs was influenced by the MVPA results, we would like to clarify here that we selected the S-ROIs based on prior research and then conducted the decoding analysis. Specifically, we first extracted the data representing different frequency indicators (three F-ROIs and three S-ROIs) as features, followed by decoding to obtain the MVPA results. Subsequently, the time-frequency analysis, combined with the specific time windows during which each frequency was decoded, provided detailed interaction patterns among the variables for each indicator. The specifics of feature selection are described in the revised version (in the first paragraph of the Multivariate Pattern Analysis section).

Comment 2: In the Data Analysis section, line 424 states: “Only trials that were correct in both the memory task and the Stroop task were included in all subsequent analyses. In addition, trials in which response times (RTs) deviated by more than three standard deviations from the condition mean were excluded from behavioral analyses.” The percentage of excluded trials should be reported. Also, for the EEG-related analyses, were the same trials excluded, or were different criteria applied?

We thank the reviewer for this suggestion. Beyond the behavioral exclusion criteria, trials with EEG artifacts were also excluded from the data for the EEG-related analyses. We have now reported the percentage of excluded trials for both behavioral and EEG data analyses in the revised version (in the second paragraph of the EEG Recording and Preprocessing section and the first paragraph of the Behavioral Analysis section).

Comment 3: In the Methods section, line 493 mentions: “A 400-200 ms pre-stimulus time window was selected as the baseline time window.” What is the justification in the literature for choosing the 400-200 ms pre-stimulus window as the baseline? Why was the 200-0 ms pre-stimulus period not considered?

We thank the reviewer for this question and would like to provide the following justification. First, although a baseline ending at 0 ms is common in ERP analyses, it may not be suitable for time-frequency analysis. Due to the inherent temporal smoothing characteristic of wavelet convolution in time-frequency decomposition, task-related early activities can leak into the pre-stimulus period (before 0 ms) (Cohen, 2014). This means that extending the baseline to 0 ms will include some post-stimulus activity in the baseline window, thereby increasing baseline power and compromising the accuracy of the results. Second, an ideal baseline duration is recommended to be around 10-20% of the entire trial of interest (Morales & Bowers, 2022). In our study, the epoch duration was 2000 ms, making 200-400 ms an appropriate baseline length. Third, given that the minimum duration of the fixation point before the stimulus in our experiment was 400 ms, we chose the 400 ms before the stimulus as the baseline point to ensure its purity. In summary, considering edge effects, duration requirements, and the need to exclude other influences, we selected a baseline correction window of -400 to -200 ms. To enhance the clarity of the revised version, we have provided the rationale for the selected time windows along with relevant references (in the first paragraph of the Time-frequency analysis section).

Comment 4: Is the primary innovation of this study limited to the methodology, such as employing MVPA and RSA to establish the relationship between late theta activity and behavior?

We thank the reviewer for this insightful question and would like to clarify that our research extends beyond mere methodological innovation; rather, it utilized new methods to explore novel theoretical perspectives. Specifically, our research presents three levels of innovation: methodological, empirical, and theoretical. First, methodologically, MVPA overcame the drawbacks of traditional EEG analyses based on specific averaged voltage intensities, providing new perspectives on how the brain dynamically encoded particular neural representations over time. Furthermore, RSA aimed to identify which indicators among the decoded were directly related to behavioral representation patterns. Second, in terms of empirical results, using these two methods, we have identified for the first time three EEG markers that modulate the Stroop effect under verbal working memory load: SP, late theta, and beta, with late theta being directly linked to the elimination of the behavioral Stroop effect. Lastly, from a theoretical perspective, we proposed the novel idea that working memory played a crucial role in the late stages of conflict processing, specifically in the stimulus-response mapping stage (the specific theoretical contributions are detailed in the second-to-last paragraph of the Discussion section).

Comment 5: On page 14, lines 280-287, the authors discuss a specific pattern observed in the alpha band. However, the manuscript does not provide the corresponding results to substantiate this discussion. It is recommended to include these results as supplementary material.

We thank the reviewer for this suggestion. We added a new figure along with the corresponding statistical results that displayed the specific result patterns for the alpha band (Supplementary Figure 1).

Comment 6: On page 16, lines 323-328, the authors provide a generalized explanation of the findings. According to load theory, stimuli compete for resources only when represented in the same form. Since the pre-memorized Chinese characters are represented semantically in working memory, this explanation lacks a critical premise: that semantic-response mapping is also represented semantically during processing.

We thank the reviewer for this insightful suggestion. We fully agree with the reviewer’s perspective. As stated in our revised version, load theory suggests that cognitive resources are limited and dependent on a specific type (in the second paragraph of the Discussion section). The previously memorized Chinese characters are stored in working memory in the form of semantic representations; meanwhile the stimulus-response mapping should also be represented semantically, leading to resource occupancy. We have included this logical premise in the revised version (in the third-to-last paragraph of the Discussion section).

Comment 7: The classic Stroop task includes both a manual and a vocal version. Since stimulus-response mapping in the vocal version is more automatic than in the manual version, it is unclear whether the findings of this study would generalize to the impact of working memory load on the Stroop effect in the vocal version.

We fully agree with the reviewer’s point that the verbal version of the Stroop task differs from the manual version in terms of the degree of automation in the stimulus-response mapping. Specifically, the verbal version relies on mappings that are established through daily language use, while the manual version involves arbitrary mappings created in the laboratory. Therefore, the stimulus-response mapping in the verbal response version is more automated and less likely to be suppressed. However, our previous research indicated that the degree of automation in the stimulus-response mapping was influenced by practice (Chen et al., 2013). After approximately 128 practice trials, semantic conflict almost disappears, suggesting that the level of automation in stimulus-response mapping for the verbal Stroop task is comparable to that of the manual version (Chen et al., 2010). Given that participants in our study completed 144 practice trials (in the Procedure section), we believe these findings can be generalized to the verbal version.

Comment 8: While the discussion section provides a comprehensive analysis of the study’s results, the authors could further elaborate on the theoretical and practical contributions of this work.

We thank the reviewer for the constructive suggestions. We recognize that the theoretical and practical contributions of the study were not thoroughly elaborated in the original manuscript. Therefore, we have now provided a more detailed discussion. Specifically, the theoretical contributions focus on advancing load theory and highlighting the critical role of working memory in conflict processing. The practical contributions emphasize the application of load theory and the development of intervention strategies for enhancing inhibitory control. A more detailed discussion can be found in the revised version (in the second-to-last paragraph of the Discussion section).

Reviewer #2 (Public review):

Comment 1: As the researchers mentioned, a previous study reported a diminished Stroop effect with concurrent working memory tasks to memorize meaningless visual shapes rather than memorize Chinese characters as in the study. My main concern is that lower-level graphic processing when memorizing visual shapes also influences the Stroop effect. The stage of Stroop conflict processing affected by the working memory load may depend on the specific content of the concurrent working memory task. If that’s the case, I sense that the generalization of this finding may be limited.

We thank the reviewer for this insightful concern. As mentioned in the manuscript, this may be attributed to the inherent characteristics of Chinese characters. In contrast to English words, the processing of Chinese characters relies more on graphemic encoding and memory (Chen, 1993). Therefore, the processing of line patterns essentially occupies some of the resources needed for character processing, which aligns with our study’s hypothesis based on dimensional overlap. Additionally, regarding the results, even though the previous study presents lower-level line patterns, the results still showed that the working memory load modulated the later theta band. We hypothesize that, regardless of the specific content of the pre-presented working memory load, once the stimulus disappears from view, these loads are maintained as representations in the working memory platform. Therefore, they do not influence early perceptual processing, and resource competition only occurs once the distractors reach the working memory platform. Lastly, previous study has shown that spatial loads, which do not overlap with either the target or distractor dimensions, do not influence conflict effect (Zhao et al., 2010). Taken together, we believe that regardless of the specific content of the concurrent working memory tasks, as long as they occupy resources related to irrelevant stimulus dimensions, they can influence the late-stage processing of conflict effect. Perhaps our original manuscript did not convey this clearly, so we have rephrased it in a more straightforward manner (in the second paragraph of the Discussion section).

Comment 2: The P1 and N450 components are sensitive to congruency in previous studies as mentioned by the researchers, but the results in the present study did not replicate them. This raised concerns about data quality and needs to be explained.

We thank the reviewer for this insightful concern. For P1, we aimed to convey that the early perceptual processing represented by P1 is part of the conflict processing process. Therefore, we included it in our analysis. Additionally, as mentioned in the discussion, most studies find P1 to be insensitive to congruency. However, we inappropriately cited a study in the introduction that suggested P1 shows differences in congruency, which is among the few studies that hold this perspective. To prevent confusion for readers, we have removed this citation from the introduction.

As for N450, most studies have indeed found it to be influenced by congruency. In our manuscript, we did not observe a congruency effect at our chosen electrodes and time window. However, significant congruency effects were detected at other central-parietal electrodes (CP3, CP4, P5, P6) during the 350-500 ms interval. The interaction between task type and consistency remained non-significant, consistent with previous results. Furthermore, with respect to the location of the electrodes chosen, existing studies on N450 vary widely, including central-parietal electrodes and frontal-central electrodes (for a review, see Heidlmayr et al., 2020). We speculate that this phenomenon may be related to the extent of practice. With fewer total trials, the task may involve more stimulus conflicts, engaging more frontal brain areas. On the other hand, with more total trials, the task may involve more response conflicts, engaging more central-parietal brain areas (Chen et al., 2013; van Veen & Carter, 2005). Due to the extensive practice required in our study, we identified a congruency N450 effect in the central-parietal region. We apologize for not thoroughly exploring other potential electrodes in the previous manuscript, and we have revised the results and interpretations regarding N450 accordingly in the revised version (in the N450 section of the ERP results and the third paragraph of the Discussion section).

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

Comment 1: In the Introduction, line 108 states: “Second, alpha oscillations (8-13 Hz) can serve as a neural inverse index of mental activity or alertness, while a decrease in alpha power reflects increased alertness or enhanced attentional inhibition of distractors (Arakaki et al., 2022; Tafuro et al., 2019; Zhou et al., 2023; Zhu et al., 2023).” Please clarify which specific psychological process related to conflict processing is reflected by alpha oscillations.

We appreciate your suggestion and we have clearly highlighted the role of alpha oscillations in attentional engagement during conflict processing in the revised version (in the third-to-last paragraph of the introduction).

Comment 2: In Figures 3C and 3E, a space is needed between “amplitude” and the preceding parenthesis. Similar adjustments are required in Figures 4A, 4B, 4C, 5C, and 6C. Additionally, in Figures 3B and 3D, a space should be added between the numbers and “ms.” This issue also appears in Figure 8. Please review all figures for these formatting inconsistencies.

We apologize for the inconsistency in formatting and have corrected them throughout the revised version.

Comment 3: There are some clerical errors in the manuscript that need correction. For instance, on page 19, line 403: “Participants were asked to answer by pressing one of two response buttons (“S with the left ring finger and “L” with the left ring finger).” This should be corrected to: “L” with the right ring finger. I recommend that the authors carefully proofread the manuscript to identify and correct such errors.

We sincerely apologize for the errors present in the manuscript and have now carefully proofread it (in the Procedure section).

Comment 4: On page 13, line 254, the elimination of the Stroop effect should not be interpreted as an improvement in processing.

We greatly appreciate your suggestion. We agree that the elimination of the Stroop effect should not be confused with improvements in processing. We have corrected this in the revised version (the second paragraph of the Discussion section).

Reviewer #3 (Recommendations for the authors):

Comment 1: In the introduction section, the N450 was introduced as “a frontal-central negative deflection”, but in the methods part the N450 was computed using central-parietal electrodes. This inconsistency is confusing and needs to be clarified.

We apologize for this confusion. We have provided a detailed explanation regarding the differences in electrodes and the rationale behind choosing central-parietal electrodes in our response to Reviewer 2’s second comment. To clarify, we have updated the introduction to consistently label them as central-parietal deflections (in the third paragraph of the Introduction section).

Comment 2: I speculate the “beta” was mistakenly written as “theta” in line 212.

We sincerely apologize for this mistake. We have corrected this error (in the RSA results section).

Comment 3: The speculation that “changes in beta bands may be influenced by theta bands, thereby indirectly influencing the behavioral Stroop effect” needs to be rationalized.

We appreciate your suggestion. What we intended to convey is that we found an interaction effect in the beta bands; however, the RSA results did not show a correlation with the behavioral interaction effect. We speculate that beta activity might be influenced by the theta bands. On the one hand, we realize that the idea of beta bands indirectly influencing the behavioral Stroop effect was inappropriate, and we have removed this point in the revised version. On the other hand, we have provided rational evidence for the idea that beta bands may be influenced by theta bands. This is based on the biological properties of theta oscillations, which support communication between different cortical neural signals, and their functional role in integrating and transmitting task-relevant information to response execution (in the third-to-last paragraph of the Discussion section).

Comment 4: Typo in line 479: [10,10].

We sincerely apologize for this mistake. We have corrected this error: [-10,10] (in the Multivariate pattern analysis section).

Reference

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Author response:

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

We are grateful to the editors and reviewers for their careful reading and constructive comments. We have now done our best to respond to them fully through additional analyses and text revisions. In the sections below, the original reviewer comments are in black, and our responses are in red.

To summarize, the major changes in this round of review are as follows:

(1) We have included a new introductory figure (Figure 1) to explain the distinction between feature-based tasks and property-based tasks.

(2) We have included a section on “key predictions” and a section on “overview of this study” in the Introduction to clearly delineate our key predictions and provide a overview of our study.

(3) We have included additional analyses to address the reviewers’ concerns about circularity in Experiments 1 & 2. We show that distance-to-center or visual homogeneity computations performed on object representations obtained from deep networks (instead of the perceptual dissimilarities from Experiment 1) also yields comparable predictions of target-present and target-absent responses in Experiment 2. 

(4) We have extensively reworked the manuscript wherever possible to address the specific concerns raised by the reviewers.

We hope that the revised manuscript adequately addresses the concerns raised in this round of review, and we look forward to a positive assessment.

eLife Assessment

This study uses carefully designed experiments to generate a useful behavioural and neuroimaging dataset on visual cognition. The results provide solid evidence for the involvement of higher-order visual cortex in processing visual oddballs and asymmetry. However, the evidence provided for the very strong claims of homogeneity as a novel concept in vision science, separable from existing concepts such as target saliency, is inadequate.

Thank you for your positive assessment. We agree that visual homogeneity is similar to existing concepts such as target saliency, memorability etc. We have proposed it as a separate concept because visual homogeneity has an independent empirical measure (the reciprocal of target-absent search time in oddball search, or the reciprocal of same response time in a same-different task, etc) that may or may not be the same as other empirical measures such as saliency and memorability. Investigating these possibilities is beyond the scope of our study but would be interesting for future work. We have now clarified this in the revised manuscript (Discussion, p. 42).

However, we’d like to emphasize that the question of whether visual homogeneity is novel or related to existing concepts misses entirely the key contribution of our study.

Our key contribution is a quantitative, falsifiable model for how the brain could be solving property-based tasks like same-different, oddball or symmetry. Most theories of decision making consider feature-based tasks where there is a well-defined feature space and decision variable. Property-based tasks pose a significant challenge to standard theories since it is not clear how these tasks could be solved. In fact, oddball search, same-different and symmetry tasks have been considered so different that they are rarely even mentioned in the same study. Our study represents a unifying framework showing that all three tasks can be understood as solving the same underlying fundamental problem, and presents evidence in favor of this solution.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The authors define a new metric for visual displays, derived from psychophysical response times, called visual homogeneity (VH). They attempt to show that VH is explanatory of response times across multiple visual tasks. They use fMRI to find visual cortex regions with VH-correlated activity. On this basis, they declare a new visual region in human brain, area VH, whose purpose is to represent VH for the purpose of visual search and symmetry tasks.

Thank you for your accurate and positive assessment.

Strengths:

The authors present carefully designed experiments, combining multiple types of visual judgments and multiple types of visual stimuli with concurrent fMRI measurements. This is a rich dataset with many possibilities for analysis and interpretation.

Thank you for your accurate and positive assessment.

Weaknesses:

The datasets presented here should provide a rich basis for analysis. However, in this version of the manuscript, I believe that there are major problems with the logic underlying the authors' new theory of visual homogeneity (VH), with the specific methods they used to calculate VH, and with their interpretation of psychophysical results using these methods. These problems with the coherency of VH as a theoretical construct and metric value make it hard to interpret the fMRI results based on searchlight analysis of neural activity correlated with VH.

We respectfully disagree with your concerns, and have done our best to respond to them fully below.

In addition, the large regions of VH correlations identified in Experiments 1 and 2 vs. Experiments 3 and 4 are barely overlapping. This undermines the claim that VH is a universal quantity, represented in a newly discovered area of visual cortex, that underlies a wide variety of visual tasks and functions.

We respectfully disagree with your assertion. First of all, there is partial overlap between the VH regions, for which there are several other obvious explanations that must be considered first before dismissing VH outright as a flawed construct. We acknowledge these alternatives in the Results (p. 27), and the relevant text is reproduced below.

“We note that it is not straightforward to interpret the overlap between the VH regions identified in Experiments 2 & 4. The lack of overlap could be due to stimulus differences (natural images in Experiment 2 vs silhouettes in Experiment 4), visual field differences (items in the periphery in Experiment 2 vs items at the fovea in Experiment 4) and even due to different participants in the two experiments. There is evidence supporting all these possibilities: stimulus differences (Yue et al., 2014), visual field differences (Kravitz et al., 2013) as well as individual differences can all change the locus of neural activations in object-selective cortex (Weiner and Grill-Spector, 2012a; Glezer and Riesenhuber, 2013). We speculate that testing the same participants on search and symmetry tasks using similar stimuli and display properties would reveal even larger overlap in the VH regions that drive behavior.”

Maybe I have missed something, or there is some flaw in my logic. But, absent that, I think the authors should radically reconsider their theory, analyses, and interpretations, in light of detailed comments below, in order to make the best use of their extensive and valuable datasets combining behavior and fMRI. I think doing so could lead to a much more coherent and convincing paper, albeit possibly supporting less novel conclusions.

We respectfully disagree with your assessment, and we hope that our detailed responses below will convince you of the merit of our claims.

THEORY AND ANALYSIS OF VH

(1) VH is an unnecessary, complex proxy for response time and target-distractor similarity.<br /> VH is defined as a novel visual quality, calculable for both arrays of objects (as studied in Experiments 1-3) and individual objects (as studied in Experiment 4). It is derived from a center-to-distance calculation in a perceptual space. That space in turn is derived from multi-dimensional scaling of response times for target-distractor pairs in an oddball detection task (Experiments 1 and 2) or in a same different task (Experiments 3 and 4).  Proximity of objects in the space is inversely proportional to response times for arrays in which they were paired. These response times are higher for more similar objects. Hence, proximity is proportional to similarity. This is visible in Fig. 2B as the close clustering of complex, confusable animal shapes.

VH, i.e. distance-to-center, for target-present arrays is calculated as shown in Fig. 1C, based on a point on the line connecting target and distractors. The authors justify this idea with previous findings that responses to multiple stimuli are an average of responses to the constituent individual stimuli. The distance of the connecting line to the center is inversely proportional to the distance between the two stimuli in the pair, as shown in Fig. 2D. As a result, VH is inversely proportional to distance between the stimuli and thus to stimulus similarity and response times. But this just makes VH a highly derived, unnecessarily complex proxy for target-distractor similarity and response time. The original response times on which the perceptual space is based are far more simple and direct measures of similarity for predicting response times.

Thank you for carefully thinking through our logic. We agree that a distance-to-centre calculation is entirely unnecessary as an explanation for target-present visual search. The difficulty of target-present search is already known to be directly proportional to the similarity between target and distractor, so there is nothing new to explain here.

However, this is a narrow and selective interpretation of our findings because you are focusing only on our results on target-present searches, which are only half of all our data. The other half is the target-absent responses which previously have had no clear explanation. You are also missing the fact that we are explaining same-different and symmetry tasks as well using the same visual homogeneity computation.

We urge you to think more deeply about the problem of how to decide whether an oddball is present or not in the first place. How do we actually solve this task? There must be some underlying representation and decision process. Our study shows that a distance-to-centre computation can actually serve as a decision variable to solve disparate property-based visual tasks. These tasks pose a major challenge to standard models of decision making, because the underlying representation and decision variable have been unclear. Our study resolves this challenge by proposing a novel computation that can be used by the brain to solve all these disparate tasks, and bring these tasks into the ambit of standard theories of decision making.  

Our results also explain several interesting puzzles in the literature. If oddball search was driven only by target-distractor similarity, the time taken to respond when a target is absent should not vary at all, and should actually take longer than all target-present searches. But in fact, systematic variations in target-absent times have been observed always in the literature, but have never been explained using any theoretical models. Our results explain why target-absent times vary systematically – it is due to visual homogeneity.

Similarly, in same-different tasks, participants are known to take longer to make a “different” response when the two items differ only slightly. By this logic, they should take the longest to make a “same” response, but in fact, paradoxically, participants are actually faster to make “same” responses. This fast-same effect has been noted several times, but never explained using any models. Our results provide an explanation of why “same” responses to an image vary systematically – it is due to visual homogeneity. 

Finally, in symmetry tasks, symmetric objects evoke fast responses, and this has always been taken as evidence for special symmetry computations in the brain. But we show that the same distance-to-center computation can explain both responses to symmetric and asymmetric objects. Thus there is no need for a special symmetry computation in the brain.

(2) The use of VH derived from Experiment 1 to predict response times in Experiment 2 is circular and does not validate the VH theory.<br /> The use of VH, a response time proxy, to predict response times in other, similar tasks, using the same stimuli, is circular. In effect, response times are being used to predict response times across two similar experiments using the same stimuli. Experiment 1 and the target present condition of Experiment 2 involve the same essential task of oddball detection. The results of Experiment 1 are converted into VH values as described above, and these are used to predict response times in experiment 2 (Fig. 2F). Since VH is a derived proxy for response values in Experiment 1, this prediction is circular, and the observed correlation shows only consistency between two oddball detection tasks in two experiments using the same stimuli.

You are indeed correct in noting that both Experiment 1 & 2 involve oddball search, and so at the superficial level, it looks circular that the oddball search data of Experiment 1 is being used to explain the oddball search data of Experiment 2.

However a deeper scrutiny reveals more fundamental differences: Experiment 1 consisted of only oddball search with the target appearing on the left or right, whereas Experiment 2 consisted of oddball search with the target either present or completely absent. In fact, we were merely using the search dissimilarities from Experiment 1 to reconstruct the underlying object representation, because it is well known that neural dissimilarities are predicted well by search dissimilarities (Sripati & Olson, 2009; Zhivago et al, 2014).

To thoroughly refute any lingering concern about circularity, we reasoned that the model predictions for Experiment 2 could have been obtained by a distance-to-center computation on any brain like object representation. To this end, we used object representations from deep neural networks pretrained on object categorization, whose representations are known to match well with the brain, and asked if a distance-to-centre computation on these representations could predict the search data in Experiment 2. This was indeed the case, and these results are now included an additional section in Supplementary Material (Section S1).

(3) The negative correlation of target-absent response times with VH as it is defined for target-absent arrays, based on distance of a single stimulus from center, is uninterpretable without understanding the effects of center-fitting. Most likely, center-fitting and the different VH metric for target-absent trials produce an inverse correlation of VH with target-distractor similarity.

Unfortunately, as we have mentioned above, target-distractor similarity cannot explain how target-absent searches behave, since there is no distractor in such searches.

We do understand your broader concern about the center-fitting algorithm itself. We performed a number of additional analyses to confirm the generality of our results and reject alternate explanations – these are summarized in a new section titled “Confirming the generality of visual homogeneity” (p. 12), and the section is reproduced below for your convenience.   

“Confirming the generality of visual homogeneity

We performed several additional analyses to confirm the generality of our results, and to reject alternate explanations.

First, it could be argued that our results are circular because they involve taking oddball search times from Experiment 1 and using them to explain search response times in Experiment 2. This is a superficial concern since we are using the search dissimilarities from Experiment 1 only as a proxy for the underlying neural representation, based on previous reports that neural dissimilarities closely match oddball search dissimilarities (Sripati and Olson, 2010; Zhivago and Arun, 2014). Nonetheless, to thoroughly refute this possibility, we reasoned that we would get similar predictions of the target present/absent responses in Experiment using any other brain-like object representation. To confirm this, we replaced the object representations derived from Experiment 1 with object representations derived from deep neural networks pretrained for object categorization, and asked if distance-to-center computations could predict the target present/absent responses in Experiment 2. This was indeed the case (Section S1). 

Second, we wondered whether the nonlinear optimization process of finding the best-fitting center could be yielding disparate optimal centres each time. To investigate this, we repeated the optimization procedure with many randomly initialized starting points, and obtained the same best-fitting center each time (see Methods).

Third, to confirm that the above model fits are not due to overfitting, we performed a leave-one-out cross validation analysis. We left out all target-present and target-absent searches involving a particular image, and then predicted these searches by calculating visual homogeneity estimated from all other images. This too yielded similar positive and negative correlations (r = 0.63, p < 0.0001 for target-present, r = -0.63, p < 0.001  for target-absent).

Fourth, if heterogeneous displays indeed elicit similar neural responses due to mixing, then their average distance to other objects must be related to their visual homogeneity. We confirmed that this was indeed the case, suggesting that the average distance of an object from all other objects in visual search can predict visual homogeneity (Section S1).

Fifth, the above results are based on taking the neural response to oddball arrays to be the average of the target and distractor responses. To confirm that averaging was indeed the optimal choice, we repeated the above analysis by assuming a range of relative weights between the target and distractor. The best correlation was obtained for almost equal weights in the lateral occipital (LO) region, consistent with averaging and its role in the underlying perceptual representation (Section S1).

Finally, we performed several additional experiments on a larger set of natural objects as well as on silhouette shapes. In all cases, present/absent responses were explained using visual homogeneity (Section S2).”

The construction of the VH perceptual space also involves fitting a "center" point such that distances to center predict response times as closely as possible. The effect of this fitting process on distance-to-center values for individual objects or clusters of objects is unknowable from what is presented here. These effects would depend on the residual errors after fitting response times with the connecting line distances. The center point location and its effects on distance-to-center of single objects and object clusters are not discussed or reported here.

While it is true that the optimal center needs to be found by fitting to the data, there no particular mystery to the algorithm: we are simply performing a standard gradient-descent to maximize the fit to the data. We have described the algorithm clearly and are making our codes public. We find the algorithm to yield stable optimal centers despite many randomly initialized starting points. We find the optimal center to be able to predict responses to entirely novel images that were excluded during model training. We are making no assumption about the location of centre with respect to individual points. Therefore, we see no cause for concern regarding the center-finding algorithm. 

Yet, this uninterpretable distance-to-center of single objects is chosen as the metric for VH of target-absent displays (VHabsent). This is justified by the idea that arrays of a single stimulus will produce an average response equal to one stimulus of the same kind. But it is not logically clear why response strength to a stimulus should be a metric for homogeneity of arrays constructed from that stimulus, or even what homogeneity could mean for a single stimulus from this set. And it is not clear how this VHabsent metric based on single stimuli can be equated to the connecting line VH metric for stimulus pairs, i.e. VHpresent, or how both could be plotted on a single continuum.

Most visual tasks, such as finding an animal, are thought to involve building a decision boundary on some underlying neural representation. Even visual search has been portrayed as a signal-detection problem where a particular target is to be discriminated from a distractor. However none of these formulations work in the case of property-based visual tasks, where there is no unique feature to look for.

We are proposing that, when we view a search array, the neural response to the search array can be deduced from the neural responses to the individual elements using well known rules, and that decisions about an oddball target being present or absent can be made by computing the distance of this neural response from some canonical mean firing rate of a population of neurons. This distance to center computation is what we denote as visual homogeneity. We have revised our manuscript throughout to make this clearer and we hope that this helps you understand the logic better. 

It is clear, however, what *should* be correlated with difficulty and response time in the target-absent trials, and that is the complexity of the stimuli and the numerosity of similar distractors in the overall stimulus set. Complexity of the target, similarity with potential distractors, and number of such similar distractors all make ruling out distractor presence more difficult. The correlation seen in Fig. 2G must reflect these kinds of effects, with higher response times for complex animal shapes with lots of similar distractors and lower response times for simpler round shapes with fewer similar distractors.

You are absolutely correct that the stimulus complexity should matter, but there are no good empirically derived measures for stimulus complexity, other than subjective ratings which are complex on their own and could be based on any number of other cognitive and semantic factors. But considering what factors are correlated with target-absent response times is entirely different from asking what decision variable or template is being used by participants to solve the task.

The example points in Fig. 2G seem to bear this out, with higher response times for the deer stimulus (complex, many close distractors in the Fig. 2B perceptual space) and lower response times for the coffee cup (simple, few close distractors in the perceptual space). While the meaning of the VH scale in Fig. 2G, and its relationship to the scale in Fig. 2F, are unknown, it seems like the Fig. 2G scale has an inverse relationship to stimulus complexity, in contrast to the expected positive relationship for Fig. 2F. This is presumably what creates the observed negative correlation in Fig. 2G.

Taken together, points 1-3 suggest that VHpresent and VHabsent are complex, unnecessary, and disconnected metrics for understanding target detection response times. The standard, simple explanation should stand. Task difficulty and response time in target detection tasks, in both present and absent trials, are positively correlated with target-distractor similarity.

We strongly disagree. Your assessment seems to be based on only considering target-present searches, which are of course driven by target-distractor similarity. Your  argument is flawed because systematic variations in target-absent trials cannot be linked to any target-distractor similarity since there are no targets in the first place in such trials.

We have shown that target-absent response times are in fact, independent of experimental context, which means that they index an image property that is independent of any reference target (Results, p. 15; Section S4). This property is what we define as visual homogeneity.

I think my interpretations apply to Experiments 3 and 4 as well, although I find the analysis in Fig. 4 especially hard to understand. The VH space in this case is based on Experiment 3 oddball detection in a stimulus set that included both symmetric and asymmetric objects. But the response times for a very different task in Experiment 4, a symmetric/asymmetric judgment, are plotted against the axes derived from Experiment 3 (Fig. 4F and 4G). It is not clear to me why a measure based on oddball detection that requires no use of symmetry information should be predictive of within-stimulus symmetry detection response times. If it is, that requires a theoretical explanation not provided here.

We were simply using an oddball detection task to construct the underlying object representation, on the basis of observations that search dissimilarities are strongly correlated with neural   dissimilarities. In Section S1, we show that similar results could have been obtained using other object representations such as deep networks, as long as the representation is brain-like.

(4) Contrary to the VH theory, same/different tasks are unlikely to depend on a decision boundary in the middle of a similarity or homogeneity continuum.

We have provided empirical proof for our claims, by showing that target-present response times in a visual search task are correlated with “different” responses in the same-different task, and that target-absent response times in the visual search task are correlated with “same” responses in the same-different task (Section S4).

The authors interpret the inverse relationship of response times with VHpresent and VHabsent, described above, as evidence for their theory. They hypothesize, in Fig. 1G, that VHpresent and VHabsent occupy a single scale, with maximum VHpresent falling at the same point as minimum VHabsent. This is not borne out by their analysis, since the VHpresent and VHabsent value scales are mainly overlapping, not only in Experiments 1 and 2 but also in Experiments 3 and 4. The authors dismiss this problem by saying that their analyses are a first pass that will require future refinement. Instead, the failure to conform to this basic part of the theory should be a red flag calling for revision of the theory.

Again, the opposite correlations between target present/absent search times with VH are the crucial empirical validation of our claims that a distance-to-center calculation explain how we perform these property-based tasks. The VH predictions do not fully explain the data. We have explicitly acknowledged this shortcoming, so we are hardly dismissing it as a problem. 

The reason for this single scale is that the authors think of target detection as a boundary decision task, along a single scale, with a decision boundary somewhere in the middle, separating present and absent. This model makes sense for decision dimensions or spaces where there are two categories (right/left motion; cats vs. dogs), separated by an inherent boundary (equal left/right motion; training-defined cat/dog boundary). In these cases, there is less information near the boundary, leading to reduced speed/accuracy and producing a pattern like that shown in Fig. 1G.

Finding an oddball, deciding if two items are same or different and symmetry tasks are disparate visual tasks that do not fit neatly into standard models of decision making. The key conceptual advance of our study is that we propose a plausible neural representation and decision variable that allow all three property-based visual tasks to be reconciled with standard models of decision making.

This logic does not hold for target detection tasks. There is no inherent middle point boundary between target present and target absent. Instead, in both types of trial, maximum information is present when target and distractors are most dissimilar, and minimum information is present when target and distractors are most similar. The point of greatest similarity occurs at then limit of any metric for similarity. Correspondingly, there is no middle point dip in information that would produce greater difficulty and higher response times. Instead, task difficulty and response times increase monotonically with similarity between targets and distractors, for both target present and target absent decisions. Thus, in Figs. 2F and 2G, response times appear to be highest for animals, which share the largest numbers of closely similar distractors.        

Your alternative explanation rests on vague factors like “maximum information” which cannot be quantified. By contrast we are proposing a concrete, falsifiable model for three property-based tasks – same/different, oddball present/absent and object symmetry. Any argument based solely on item similarity to explain visual search or symmetry responses cannot explain systematic variations observed for target-absent arrays and for symmetric objects, for the reasons explained earlier.

DEFINITION OF AREA VH USING fMRI

(1) The area VH boundaries from different experiments are nearly completely non-overlapping.

In line with their theory that VH is a single continuum with a decision boundary somewhere in the middle, the authors use fMRI searchlight to find an area whose responses positively correlate with homogeneity, as calculated across all of their target present and target absent arrays. They report VH-correlated activity in regions anterior to LO. However, the VH defined by symmetry Experiments 3 and 4 (VHsymmetry) is substantially anterior to LO, while the VH defined by target detection Experiments 1 and 2 (VHdetection) is almost immediately adjacent to LO. Fig. S13 shows that VHsymmetry and VHdetection are nearly non-overlapping. This is a fundamental problem with the claim of discovering a new area that represents a new quantity that explains response times across multiple visual tasks. In addition, it is hard to understand why VHsymmetry does not show up in a straightforward subtraction between symmetric and asymmetric objects, which should show a clear difference in homogeneity.

We respectfully disagree. The partial overlap between the VH regions identified in Experiments 1 & 2 can hardly be taken as evidence against the quantity VH itself, because there are several other obvious alternate explanations for this partial overlap, as summarized earlier as well. The VH region does show up in a straightforward subtraction  between symmetric and asymmetric objects (Section S7), so we are not sure what the Reviewer is referring to here.

(2) It is hard to understand how neural responses can be correlated with both VHpresent and VHabsent.

The main paper results for VHdetection are based on both target-present and target-absent trials, considered together. It is hard to interpret the observed correlations, since the VHpresent and VHabsent metrics are calculated in such different ways and have opposite correlations with target similarity, task difficulty, and response times (see above). It may be that one or the other dominates the observed correlations. It would be clarifying to analyze correlations for target-present and target-absent trials separately, to see if they are both positive and correlated with each other.

Thanks for raising this point. We have now confirmed that the positive correlation between VH and neural response holds even when we do the analysis separately for target-present and -absent searches (correlation between neural response in VH region and visual homogeneity (n = 32, r = 0.66, p < 0.0005 for target-present searches & n = 32, r = 0.56, p < 0.005 for target-absent searches).

(3) Definition of the boundaries and purpose of a new visual area in the brain requires circumspection, abundant and convergent evidence, and careful controls.

Even if the VH metric, as defined and calculated by the authors here, is a meaningful quantity, it is a bold claim that a large cortical area just anterior to LO is devoted to calculating this metric as its major task. Vision involves much more than target detection and symmetry detection. Cortex anterior to LO is bound to perform a much wider range of visual functionalities. If the reported correlations can be clarified and supported, it would be more circumspect to treat them as one byproduct of unknown visual processing in cortex anterior to LO, rather than treating them as the defining purpose for a large area of visual cortex.

We totally agree with you that reporting a new brain region would require careful interpretation and abundant and converging evidence. However, this requires many studies worth of work, and historically category-selective regions like the FFA have achieved consensus only after they were replicated and confirmed across many studies. We believe our proposal for the computation of a quantity like visual homogeneity is conceptually novel, and our study represents a first step that provides some converging evidence (through replicable results across different experiments) for such a region. We have reworked our manuscript to make this point clearer (Discussion, p 32).

Reviewer #3 (Public Review):

Summary:

This study proposes visual homogeneity as a novel visual property that enables observers perform to several seemingly disparate visual tasks, such as finding an odd item, deciding if two items are same, or judging if an object is symmetric. In Exp 1, the reaction times on several objects were measured in human subjects. In Exp 2, visual homogeneity of each object was calculated based on the reaction time data. The visual homogeneity scores predicted reaction times. This value was also correlated with the BOLD signals in a specific region anterior to LO. Similar methods were used to analyze reaction time and fMRI data in a symmetry detection task. It is concluded that visual homogeneity is an important feature that enables observers to solve these two tasks.

Thank you for your accurate and positive assessment.

Strengths:

(1) The writing is very clear. The presentation of the study is informative.

(2) This study includes several behavioral and fMRI experiments. I appreciate the scientific rigor of the authors.

We are grateful to you for your balanced assessment and constructive comments.

Weaknesses:

(1) My main concern with this paper is the way visual homogeneity is computed. On page 10, lines 188-192, it says: "we then asked if there is any point in this multidimensional representation such that distances from this point to the target-present and target-absent response vectors can accurately predict the target-present and target-absent response times with a positive and negative correlation respectively (see Methods)". This is also true for the symmetry detection task. If I understand correctly, the reference point in this perceptual space was found by deliberating satisfying the negative and positive correlations in response times. And then on page 10, lines 200-205, it shows that the positive and negative correlations actually exist. This logic is confusing. The positive and negative correlations emerge only because this method is optimized to do so. It seems more reasonable to identify the reference point of this perceptual space independently, without using the reaction time data. Otherwise, the inference process sounds circular. A simple way is to just use the mean point of all objects in Exp 1, without any optimization towards reaction time data.

We disagree with you since the same logic applies to any curve-fitting procedure. When we fit data to a straight line, we are finding the slope and intercept that minimizes the error between the data and the straight line, but we would hardly consider the process circular when a good fit is achieved – in fact we take it as a confirmation that the data can be fit linearly. In the same vein, we would not have observed a good fit to the data, if there did not exist any good reference point relative to which the distances of the target-present and target-absent search arrays predicted these response times.

In Section S2, we show that the visual homogeneity estimates for each object is strongly correlated with the average distance of each object to all other objects (r = 0.84, p<0.0005, Figure S1).

We have performed several additional analyses to confirm the generality of our results and to reject alternate explanations (see Results, p. 12, Section titled “Confirming the generality of visual homogeneity”). In particular, to confirm that the results we obtained are not due to overfitting, we performed a cross-validation analysis, where we removed all searches involving a particular image and predicted these response times using visual homogeneity. This too revealed a significant model correlation confirming that our results are not due to overfitting.

(2) Visual homogeneity (at least given the current from) is an unnecessary term. It is similar to distractor heterogeneity/distractor variability/distractor statics in literature. However, the authors attempt to claim it as a novel concept. The title is "visual homogeneity computations in the brain enable solving generic visual tasks". The last sentence of the abstract is "a NOVEL IMAGE PROPERTY, visual homogeneity, is encoded in a localized brain region, to solve generic visual tasks". In the significance, it is mentioned that "we show that these tasks can be solved using a simple property WE DEFINE as visual homogeneity". If the authors agree that visual homogeneity is not new, I suggest a complete rewrite of the title, abstract, significance, and introduction.

We respectfully disagree that visual homogeneity is an unnecessary term. Please see our comments to Reviewer 1 above. Just like saliency and memorability can be measured empirically, we propose that visual homogeneity can be empirically measured as the reciprocal of the target-absent search time in a search task, or as the reciprocal of the “same” response time in a same-different task. Understanding how these three quantities interact will require measuring them empirically for an identical set of images, which is beyond the scope of this study but an interesting possibility for future work.

(3) Also, "solving generic tasks" is another overstatement. The oddball search tasks, same-different tasks, and symmetric tasks are only a small subset of many visual tasks. Can this "quantitative model" solve motion direction judgment tasks, visual working memory tasks? Perhaps so, but at least this manuscript provides no such evidence. On line 291, it says "we have proposed that visual homogeneity can be used to solve any task that requires discriminating between homogeneous and heterogeneous displays". I think this is a good statement. A title that says "XXXX enable solving discrimination tasks with multi-component displays" is more acceptable. The phrase "generic tasks" is certainly an exaggeration.

Thank you for your suggestion. We have now replaced the term “generic tasks” with the term property-based tasks, which we feel is more appropriate and reflect the fact that oddball search, same-different and symmetry tasks all involve looking for a specific image property.

(4) If I understand it correctly, one of the key findings of this paper is "the response times for target-present searches were positively correlated with visual homogeneity. By contrast, the response times for target-absent searches were negatively correlated with visual homogeneity" (lines 204-207). I think the authors have already acknowledged that the positive correlation is not surprising at all because it reflects the classic target-distractor similarity effect. But the authors claim that the negative correlations in target-absent searches is the true novel finding.

(5) I would like to make it clear that this negative correlation is not new either. The seminal paper by Duncan and Humphreys (1989) has clearly stated that "difficulty increases with increased similarity of targets to nontargets and decreased similarity between nontargets" (the sentence in their abstract). Here, "similarity between nontargets" is the same as the visual homogeneity defined here. Similar effects have been shown in Duncan (1989) and Nagy, Neriani, and Young (2005). See also the inconsistent results in Nagy & Thomas, 2003, Vicent, Baddeley, Troscianko & Gilchrist, 2009. More recently, Wei Ji Ma has systematically investigated the effects of heterogeneous distractors in visual search. I think the introduction part of Wei Ji Ma's paper (2020) provides a nice summary of this line of research. I am surprised that these references are not mentioned at all in this manuscript (except Duncan and Humphreys, 1989).

You are right in noting that Duncan and Humphreys (1989) propose that searches are more difficult when nontargets are dissimilar. However, since our searches have identical distractors, the similarity between nontargets is always constant across target-absent searches, and therefore this cannot predict any systematic variation in target-absent search that is observed in our data. By contrast, our results explain both target-absent searches and target-present searches.

Thank you for pointing us to previous work. These studies show that it is not just the average distractor similarity but the statistics of the distractor similarity that drive visual search. However these studies do not explain why target-absent searches should vary systematically. 

(6) If the key contribution is the quantitative model, the study should be organized in a different way. Although the findings of positive and negative correlations are not novel, it is still good to propose new models to explain classic phenomena. I would like to mention the three studies by Wei Ji Ma (see below). In these studies, Bayesian observer models were established to account for trial-by-trial behavioral responses. These computational models can also account for the set-size effect, behavior in both localization and detection tasks. I see much more scientific rigor in their studies. Going back to the quantitative model in this paper, I am wondering whether the model can provide any qualitative prediction beyond the positive and negative correlations? Can the model make qualitative predictions that differ from those of Wei Ji's model? If not, can the authors show that the model can quantitatively better account for the data than existing Bayesian models? We should evaluate a model either qualitatively or quantitatively.

Thank you for pointing us to prior work by Wei Ji Ma. These studies systematically examined visual search for a target among heterogeneous distractors using simple parametric stimuli and a Bayesian modeling framework. By contrast, our experiments involve searching for single oddball targets among multiple identical distractors, so it is not clear to us that the Wei Ji Ma models can be easily used to generate predictions about these searches used in our study. 

We are not sure what you mean by offering quantitative predictions beyond positive and negative correlations. We have tried to explain systematic variation in target-present and target-absent response times using a model of how these decisions are being made. Our model explains a lot of systematic variation in the data for both types of decisions.

(7) In my opinion, one of the advantages of this study is the fMRI dataset, which is valuable because previous studies did not collect fMRI data. The key contribution may be the novel brain region associated with display heterogeneity. If this is the case, I would suggest using a more parametric way to measure this region. For example, one can use Gabor stimuli and systematically manipulate the variations of multiple Gabor stimuli, the same logic also applies to motion direction. If this study uses static Gabor, random dot motion, object images that span from low-level to high-level visual stimuli, and consistently shows that the stimulus heterogeneity is encoded in one brain region, I would say this finding is valuable. But this sounds like another experiment. In other words, it is insufficient to claim a new brain region given the current form of the manuscript.

We agree that parametric stimulus manipulations are important for studying early visual areas where stimulus dimensions are known (e.g. orientation, spatial frequency). Using parametric stimulus manipulations for more complex stimuli is fraught with issues because the underlying representation may not be encoding the dimensions being manipulated. This is the reason why we attempted to recover the underlying neural representation using dissimilarities measured using visual search, and then asked whether a decision making process operating on this underlying representation can explain how decisions are made. Therefore we disagree that parametric stimulus manipulations are the only way to obtain insight into such tasks.

We have proposed a quantitative model that explains how decisions about target present and absent can be made through distance-to-center computations on an underlying object representation. We feel that the behavioural and the brain imaging results strongly point to a novel computation that is being performed in a localized region in the brain. These results represent an important first step in understanding how complex, property-based tasks are performed by the brain. We have revised our manuscript to make this point clearer.

REFERENCES

- Duncan, J., & Humphreys, G. W. (1989). Visual search and stimulus similarity. Psychological Review, 96(3), 433-458. doi: 10.1037/0033-295x.96.3.433

- Duncan, J. (1989). Boundary conditions on parallel processing in human vision. Perception, 18(4), 457-469. doi: 10.1068/p180457

- Nagy, A. L., Neriani, K. E., & Young, T. L. (2005). Effects of target and distractor heterogeneity on search for a color target. Vision Research, 45(14), 1885-1899. doi: 10.1016/j.visres.2005.01.007

- Nagy, A. L., & Thomas, G. (2003). Distractor heterogeneity, attention, and color in visual search. Vision Research, 43(14), 1541-1552. doi: 10.1016/s0042-6989(03)00234-7

- Vincent, B., Baddeley, R., Troscianko, T., & Gilchrist, I. (2009). Optimal feature integration in visual search. Journal of Vision, 9(5), 15-15. doi: 10.1167/9.5.15

- Singh, A., Mihali, A., Chou, W. C., & Ma, W. J. (2023). A Computational Approach to Search in Visual Working Memory.

- Mihali, A., & Ma, W. J. (2020). The psychophysics of visual search with heterogeneous distractors. BioRxiv, 2020-08.

- Calder-Travis, J., & Ma, W. J. (2020). Explaining the effects of distractor statistics in visual search. Journal of Vision, 20(13), 11-11.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

The authors have not made substantive changes to address my major concerns. Instead, they have responded with arguments about why their original manuscript was good as written. I did not find these arguments persuasive. Given that, I've left my public review the same, since it still represents my opinions about the paper. Readers can judge which viewpoints are more persuasive.

We respectfully disagree: we have tried our best to address your concerns with additional analysis wherever feasible, and by acknowledging any limitations.

Reviewer #3 (Recommendations For The Authors):

(1) As I mentioned above, please consider rewriting title, abstract, introduction, and significance. Please remove the word "visual homogeneity" and instead use distractor heterogeneity/distractor variability/distractor statistics as often used in literature.

To clarify, visual homogeneity is NOT the same as distractor homogeneity. Visual homogeneity refers to a distance-to-center computation and represents an image-computable property that can vary systematically even when all distractors are identical. By contrast distractor heterogeneity varies only when distractors are different from each other.

(2) Better to remove the phrase "generic tasks".

Thanks for your suggestions. We now refer to these tasks as property-based tasks. 

(3) Better to explicitly specify the predictions made by the quantitative model beyond positive and negative correlations.

The predictions of the quantitative model are to explain systematic variation in the response times. We are not sure what else is there to predict in the response times.

(4) If the quantitative model is the key contribution, better to highlight the details and algorithmic contribution of the model, and show the advantage of this model either qualitatively and quantitatively.

Please see our responses above. Our quantitative model explains behavior and brain imaging data on three disparate tasks – the same/different, oddball visual search and symmetry tasks. 

(5) If the new brain region is the key contribution, better to downplay the quantitative model.

Please see our responses above. Our quantitative model explains behavior and brain imaging data on three disparate tasks – the same/different, oddball visual search and symmetry tasks.

Author response:

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

Public Reviews:

Reviewer 1 (Public Review):

The authors explain that an action potential that reaches an axon terminal emits a small electrical field as it ”annihilates”. This happens even though there is no gap junction, at chemical synapses. The generated electrical field is simulated to show that it can affect a nearby, disconnected target membrane by tens of microvolts for tenths of a microsecond. Longer effects are simulated for target locations a few microns away.

To simulate action potentials (APs), the paper does not use the standard Hodgkin-Huxley formalism because it fails to explain AP collision. Instead, it uses the Tasaki and Matsumoto (TM) model which is simplified to only model APs with three parameters and as a membrane transition between two states of resting versus excited. The authors expand the strictly binary, discrete TM method to a Relaxing Tasaki Model (RTM) that models the relaxation of the membrane potential after an AP. They find that the membrane leak can be neglected in determining AP propagation and that the capacitive currents dominate the process.

The strength of the work is that the authors identified an important interaction between neurons that is neglected by the standard models. A weakness of the proposed approach is the assumptions that it makes. For instance, the external medium is modeled as a homogeneous conductive medium, which may be further explored to properly account for biological processes.

The authors provide convincing evidence by performing experiments to record action potential propagation and collision properties and then developing a theoretical framework to simulate the effect of their annihilation on nearby membranes. They provide both experimental evidence and rigorous mathematical and computer simulation findings to support their claims. The work has the potential of explaining significant electrical interaction between nerve centers that are connected via a large number of parallel fibers.

We thank the reviewer for the distinct analysis of our work and the assessment that we ’identified an important interaction between neurons that is neglected by standard models’.

Indeed, we modeled the external (extracellular) medium as homogeneous conductive medium and, compared to real biological systems, this is a simplification. Our intention is to keep our formal model as general as possible, however, it can be extended to account for specific properties. Accessory structures at axon terminals (such as the pinceau at Purkinje cells) most likely evolved to shape ephaptic coupling. In addition, the extracellular medium is neither homogeneous nor isotropic, and to fully mimic a particular neural connection this has to be implemented in a model as well. We agree and look forward to see how specific modification of the external medium in biological systems will affect ephaptic coupling. We hope to facilitate progress on this question by providing our source code for further exploration. Using the tools that have been developed by the BRIAN community one can generate or import arbitrary complex cell morphologies (e.g. NeuroML files). Our source code adds the TM- and RTM model, which allows exploring the direct impact of extracellular properties on target neurons.

Reviewer 2 (Public Review):

In this study, the authors measured extracellular electrical features of colliding APs travelling in different directions down an isolated earthworm axon. They then used these features to build a model of the potential ephaptic effects of AP annihilation, i.e. the electrical signals produced by colliding/annihilating APs that may influence neighbouring tissue. The model was then applied to some different hypothetical scenarios involving synaptic connections. The conclusion was that an annihilating AP at a presynaptic terminal can ephaptically influence the voltage of a postsynaptic cell (this is, presumably, the ’electrical coupling between neurons’ of the title), and that the nature of this influence depends on the physical configuration of the synapse.

As an experimental neuroscientist who has never used computational approaches, I am unable to comment on the rigour of the analytical approaches that form the bulk of this paper. The experimental approaches appear very well carried out, and here I just have one query - an important assumption made is that the conduction velocity of anti- and orthodromically propagating APs is identical in every preparation, but this is never empirically/statistically demonstrated.

My major concern is with the conclusions drawn from the synaptic modelling, which, disappointingly, is never benchmarked against any synaptic data. The authors state in their Introduction that a ’quantitative physical description’ of ephaptic coupling is ’missing’, however, they do not provide such a description in this manuscript. Instead, modelled predictions are presented of possible ephaptic interactions at different types of synapses, and these are then partially and qualitatively compared to previous published results in the Discussion. To support the authors’ assertion that AP annihilation induces electrical coupling between neurons, I think they need to show that their model of ephaptic effects can quantitatively explain key features of experimental data pertaining to synaptic function. Without this, the paper contains some useful high-precision quantitative measurements of axonal AP collisions, some (I assume) high-quality modelling of these collisions, and some interesting theoretical predictions pertaining to synaptic interactions, but it does not support the highly significant implications suggested for synaptic function.

We thank the reviewer for highlighting the potential and the limitation of our model. We demonstrated with empirical data that measured conduction velocities of anti- and orthodromic propagating APs are indeed very similar and values are provided in Appendix 3 – table 1.

In order to address how our model ’of ephaptic effects can quantitatively explain key features of experimental data’, we used the measured modulation of AP rates in Purkinje fibers by Blot and Babour (2014) and our results are now included in the manuscript. In our model, we implemented the ephaptic coupling of the Basket cell (with an annihilating AP) and predicted the modulation of AP rate in the Purkinje cell. Our model predictions are compared to the measured modulation of AP-rates in Purkinje cells and is added as Fig. 5 to the main manuscript (line 264 to 284 ). With this example, we show that ephaptic coupling as described with our RTM model can quantitatively describe key features of experimental data. Both, the rapid inhibition and the rebound activity is described by our model with implementation of non-excitable parts at the pinceau of the Basket cell. Future, experimental research can use the provided formalism to investigate in more detail the ephaptic coupling in systems like the Mauthner cell and the Purkinje cell by exploring how accessory structures and concomitant physical parameters, e.g. the extracellular properties impact ephaptic coupling.

Reviewer 3 (Public Review):

This manuscript aims to exploit experimental measurements of the extracellular voltages produced by colliding action potentials to adjust a simplified model of action potential propagation that is then used to predict the extracellular fields at axon terminals. The overall rationale is that when solving the cable equation (which forms the substrate for models of action potential propagation in axons), the solution for a cable with a closed end can be obtained by a technique of superposition: a spatially reflected solution is added to that for an infinite cable and this ensures by symmetry that no axial current flows at the closed boundary. By this method, the authors calculate the expected extracellular fields for axon terminals in different situations. These fields are of potential interest because, according to the authors, their magnitude can be larger than that of a propagating action potential and may be involved in ephaptic signalling. The authors perform direct measurements of colliding action potentials, in the earthworm giant axon, to parameterise and test their model.

Although simplified models can be useful and the trick of exploiting the collision condition is interesting, I believe there are several significant problems with the rationale, presentation, and application, such that the validity and potential utility of the approach is not established.

Simplified model vs. Hogdkin and Huxley

The authors employ a simplified model that incorporates a two-state membrane (in essence resting and excited states) and adds a recovery mechanism. This generates a propagating wave of excitation and key observables such as propagation speed and action potential width (in space) can be adjusted using a small number of parameters. However, even if a Hodgkin-Huxley model does contain a much larger number of parameters that may be less easy to adjust directly, the basic formalism is known to be accurate and typical modifications of the kinetic parameters are very well understood, even if no direct characterisations already exist or cannot be obtained. I am therefore unconvinced by the utility of abandoning the HodgkinHuxley version.

In several places in the manuscript, the simplified model fits the data well whereas the Hodgkin-Huxley model deviates strongly (e.g. Fig. 3CD). This is unsatisfying because it seems unlikely that the phenomenon could not be modelled accurately using the HH formulation. If the authors really wish to assert that it is ”not suitable to predict the effects caused by AP [collision]” (p9) they need to provide a good deal more analysis to establish the mechanism of failure.

We are not as convinced as the reviewer that, at the current state of parameter estimation, the HH model is suited for predicting ephaptic coupling after ’adjusting’ parameters. There are strong arguments against such an approach. A major function of a model is to make testable predictions rather than to just mimic a biological phenomenon. The predictive power of a model heavily depends on how reasonable model parameters can be estimated or measured. As the reviewer correctly points out in the specific comments (”... the parameters adjusted to fit the model are the membrane capacitance and intracellular resistance. These have a physical reality and could easily be measured or estimated quite accurately...”), our model contains only parameters that can be assessed experimentally, thus it has a better predictive power compared to the HH model with a multitude of parameters for which ”no direct characterisations already exist or cannot be obtained” (citing reviewer from above).

Already the founders of the HH model were well aware of the limitations, as stated by Hodgkin and Huxley in 1952 (J Physiol 117:500–544):

An equally satisfactory description of the voltage clamp data could no doubt have been achieved with equations of very different form ... The success of the equations is no evidence in favour of the mechanism of permeability change that we tentatively had in mind when formulating them.

A catchy but sloppy description for the problem of overfitting with too many parameters is given by the quote of John von Neumann: With four parameters I can fit an elephant, and with five I can make him wiggle his trunk.

We do not rule out the possibility that the HH model eventually can be used to predict ephaptic coupling. However, at the moment, parameter estimation for the HH model prevents its usability for predicting ephaptic coupling.

(In)applicability of the superposition principle

The reflecting boundary at the terminal is implemented using the symmetry of the collision of action potentials. However, at a closed cable there is no reflecting boundary in the extracellular space and this implied assumption is particularly inappropriate where the extracellular field is one objective of the modelling, as here. I believe this assumption is not problematic for the calculation of the intracellular voltage, because extracellular voltage gradients can usually be neglected1, but the authors need to explain how the issue was dealt with for the calculation of the extracellular fields of terminals. I assume they were calculated from the membrane currents of one-half of the collision solution, but this does not seem to be explained. It might be worth showing a spatial profile of the calculated field.

We disagree with the reviewer’s statement ’...at a closed cable there is no reflecting boundary in the extracellular space and this implied assumption is particularly inappropriate...’. We do not imply this assumption in our model! We do not assume any symmetry or boundary condition in the extracellular space. Instead, the extracellular field is calculated for an infinite homogeneous volume conductor (Eq.

6).

We conduct separate calculations for (1) source membrane current, (2) resulting extracellular field, and (3) impact upon a target neuron. The boundary condition used for our calculations only refers to the axial current being zero at the axon terminal. Consequently all the internal current that enters the last compartment must leave the last compartment as membrane current and contributes to the extracellular current and field.

The extracellular field around the axon terminal is not symmetric, as can be seen by it’s impact upon a target in Figure 4—figure supplement 1 which is also not symmetric. The symmetry of the extracellular field when APs are colliding (Cf. symmetry in Fig 1C) is merly the result of the symmetric stimulation and counterpropagation of two APs. We now are describing more specifically the bounday condition for colliding and terminating APs already in the introduction: ’A suitable boundary condition (intracellular, axial current equals zero) can be generated experimentally by a collision of two counter-propagating APs ... Within any cable model, the very same boundary condition also exists within the axon at the synaptic terminal due to the broken translation symmetry for the current loops ...’ Later, at the result section (Discharge of colliding APs), we continue with ’AP propagation is blocked when the axial current is shut down at a boundary condition, e.g. by reaching the axon terminal or by AP collision....’ and implement this condition in our calculations for the axon terminals.

Missing demonstrations

Central analytical results are stated rather brusquely, notably equations (3) and (4) and the relation between them. These merit an expanded explanation at the least. A better explanation of the need for the collision measurements in parameterising the models should also be provided.

We thank the reviewer for pointing out the insufficient explanation of the equations 3 and 4. We rephrased the paragraph ’Discharge of colliding APs’ in order to clarify the origin and the function of the two equations (eq. 3: how much charge is expelled and eq. 4: the resulting extracellular potential that is used for model validation).

Later, in the Discussion, we rephrased the paragraph where we describe the annihilation process and explain further that one term of eq. 4 sometimes is refered to ’activating function’ when using microelectrodes for stimulation.

With respect to the ’explanation of the need for the collision measurement’, we think that the explanations we give at several locations in the manuscript are sufficient as is. We explain and elaborate in the introduction: ’We explore the behaviour of APs at boundaries ... In this study, we first focus on collisions of APs. Our experimental observation of colliding APs provides unique access to the spatial profile of the extracellular potential around APs that are blocked by collisions and thus annihilate..... Recording propagating APs allows to determine both the propagation velocity and the amplitude of the extracellular electric potentials. The collision experiment provides additional information ... In the results we recall: ’The width of the collision is a measure of the characteristic length λ⋆ of the AP and is uniquely revealed by a collision sweep experiment.’

Adjusted parameters

I am uncomfortable that the parameters adjusted to fit the model are the membrane capacitance and intracellular resistance. These have a physical reality and could easily be measured or estimated quite accurately. With a variation of more than 20-fold reported between the different models in Appendix 2 we can be sure that some of the models are based upon quite unrealistic physical assumptions, which in turn undermines confidence in their generality.

The fact that the parameters of our model have physical realities is clearly in favor of our models. We rephrased the legend of the table, now explaining the procedure for the model fitting and the rational behind. Although the values of g⋆ can differ by a factor of 15 and the resulting amplitude is very different, the relationship ri cm \= vpλ⋆ is very similar, independently of the model used and this confirms our analytical framework.

p8 - the values of both the extracellular (100 Ohm m) and intracellular resistivity (1 Ohm m) appear to be in error, especially the former.

We have the following justification for the resistivity values we used. For the intracellular resistivity, literature values range from 0.4 - 1.5 Ohm m, and therefore we selected 1 Ohm m. See: Carpenter et al (1975) doi: 10.1085/jgp.66.2.139; Cole et al (1975) doi: 10.1085/jgp.66.2.133; Bekkers (2014) doi: 10.1007/978-1-46147320-6 35-2.

Estimating extracellular resistivity is less straight forward, since it depends crucially on the structure around the synapse which consists of conducting saline and insulating fatty tissue. Ranges from 3 to 600 Ohm m are reported (Linden et al (2011) doi: 10.1016/j.neuron.2011.11.006) and Bakiri et al (2011) doi: 10.1113/jphysiol.2010.201376). Weiss et al (2008; doi: 10.1073/pnas.0806145105) report extracellular resistivities in the Mauthner Cap between 50-600 Ohm m in SI. Since the pinceau is structurally similar to the Mauthner cells axon cap, we argue that a value of 100 Ohm m is a reasonable choice for our calculations. Additionally, we derived a value from Blot and Barbour (doi:c10.1038/nn.3624), rephrased the paragraph in the main text and added our calculation to the supplementary material (Appendix 1).

(In)applicability to axon terminals

The rationale of the application of the collision formalism to axon terminals is somewhat undermined by the fact that they tend not to be excitable. There is experimental evidence for this in the Calyx of Held and the cerebellar pinceau.

The solution found via collision is therefore not directly applicable in these cases.

We do not agree with the reviewer’s statement that ’the solution found via collision is (therefore) not directly applicable...’. Our model is well suited for application on axon terminals that are not excitable, e.g. the pinceau of the basket cell, as the reviewer points out. We have included a calculation for this case and present the results in the new Fig. 5 (main text line 264 to 284 ).

Comparison with experimental data

More effort should be made to compare the modelling with the extracellular terminal fields that have been reported in the literature.

As outlined above (see: Reponse to reviewer 2), we now compare directly the predictions of our models with measured modulation of AP rates in Purkinje fibers (Blot and Babour 2014) and our results are included in the manuscript (Fig. 5 and main text line 264 to 284). See also our response to reviewer 2 in which we address how our model ’of ephaptic effects can quantitatively explain key features of experimental data’.

Choice of term ”annihilation”

The term annihilation does not seem wholly appropriate to me. The dictionary definitions are something along the lines of complete destruction by an external force or mutual destruction, for example of an electron and a positron. I don’t think either applies exactly here. I suggest retaining the notion of collision which is well understood in this context.

Experimentally, we generated a collision of APs and showed that colliding APs dissapear and do not pass each other. For this process the term annihilation is used in our and in other studies (see e.g. Berg et al (2017) doi: 10.1103/PhysRevX.7.028001; Johnson et al (2018) doi: 10.3389/fphys.2018.00779; Follmann (2015) doi: 10.1103/PhysRevE.92.032707; Shrivastava et al (2018) doi: 10.1098/rsif.2017.0803). The physical processes involved in the termination of an AP at a closed end are essentially identical to those of two colliding APs. This we think justifies using the term annihilation for those processes.

Recommendations for the authors:

We believe the work is of high quality and should motivate future experimental work. We are including the review comments here for your information. The main piece of feedback we are offering is that the broad claims need to be adjusted to the strength of evidence provided: as is, the manuscript provides compelling predictions but the claim that these predictions are in full agreement with data remains to be substantiated. A technical concern raised by the reviewers is that the reflecting boundary condition may need further justification. The authors may wish to respond to this issue in a rebuttal and/or adjust the manuscript as necessary.

We substantiated our claim that our predictions are in full agreement with experimental data. We added to the manuscript a section in which we compare our models’ predictions to published, experimental data. To this aim, we extracted date from the publication of Blot and Babour (2014), we elaborated on the parameters used and run our model accordingly. We added to the Results/Model of ephaptic coupling a paragraph on ’The modulation of activity in Purkinje cells...’ (line 264), where we describe our results and we also included another figure to the main text for illustration (Fig. 5).

We clarified the term ’boundary condition’ by rephrasing parts of the introduction and we explain the rational behind in ’Discharge of colliding APs (...AP propagation is blocked when axial current is shut down...) and in ’Model of ephaptic coupling (Within any cable model, the same boundary...). See also our response to the general comments of reviewer 3 above.

Reviewer 1 (Recommendations For The Authors):

Major:

Accessing data and code requires signing in, which should not be required. The link provided also seems to be not accessible yet - could be pending review.

The repository is now publicly availible. We did provide an access code within the letter to the editor, this code is no longer required.

Line 74: how about morphology? Authors should clarify and emphasize in the introduction that the TM model is a spatially continuous model with partial differential equations as opposed to discrete morphological models to simulate HH equations.

The reviewer is correct that the TM model is continous. However, so is the HH model. The difference between HH and TM is only that the TM model can be solved analytically, which yields a spatially homogeneous analytical solution. It should be noted that this analytical solution can only be valid for a homogeneous (therefore infinite) nerve. Every numerical computation, be it HH or TM, requires a finite number of discrete compartments. In our calculations, we used identical compartment models for HH, TM and RTM model. In each compartment, the differential equations are solved numerically. Since there is no fundamental difference between these models, we obstain from changing the text.

Minor:

Major typo: ventral nerve cord, not ”chord”. Repeated in several places.

Thank you for indicating this typo to us.

Line 25: inhibition, excitation, and modulation?

We changed the line to: ... leads to modulation, e.g. excitation or inhibition

Line 70: better term for ”length” of AP would be ”duration”. Also, the sentence could be simplified to use either ”its” or ”of the AP”

Space and time are not interchangable. Thus, the term lenght can not be replaced by duration. We simplified the structure of the sentence as suggested.

Fig 1A/B: it’s strange that panel B precedes panel A.

Exchanged

Fig 1C: don’t see the ”horizontal line”; also regarding ”The recording was at a medial position”, the caption is not clear until one reads the main text.

We changed the legend to: ... The collision is captured in the recording line at y-position 0 mm, while orthodromic propagation is at the top and antidromic propagation is at the bottom. (D) The peak amplitude as a function of the distance to the collision. Examples of four sweeps at three positions along the nerve cord....

Line 127: the per distance measures could be named as ”specific” conductivity, etc.

We explicitly provide the units thereby defining the quantities unambigously.

Line 176: typo ”ad-hoc”.

Thank you.

Fig 4B: should clarify that the circle in the schematic is not the soma but a synaptic bouton.

We rephrased to ’...(B,C) when the AP is annihilating at a bouton of a neuron terminal (upper neuron in end-to-shaft geometry, similar to the Basket cell–Purkinje cell synapse)...’, and we added a label to Fig 4B.

Reviewer 2 (Recommendations For The Authors):

Can the authors’ model be quantitatively compared with experimental data of ephaptic interactions at synapses (e.g. the Blot & Barbour study described in the Discussion)?

We did so as outlined in our response to the reviewer above.

Can statistical evidence be provided that the velocities of anti- and orthodromic APs are indeed identical in the earthworm nerve recordings?

These data and statistics are available in Appendix 2, now 3 – table 1

Why not reorder ABCD in Fig1 so the subpanels run from left to right?

We adjusted the labels accordingly.

Author response:

Reviewer #1 (Public review):

Summary:

This paper contains what could be described as a "classic" approach towards evaluating a novel taste stimuli in an animal model, including standard behavioral tests (some with nerve transections), taste nerve physiology, and immunocytochemistry of the tongue. The stimulus being tested is ornithine, from a class of stimuli called "kokumi", which are stimuli that enhance other canonical tastes, increasing essentially the hedonic attributes of these other stimuli; the mechanism for ornithine detection is thought to be GPRC6A receptors expressed in taste cells. The authors showed evidence for this in an earlier paper with mice; this paper evaluates ornithine taste in a rat model.

Strengths:

The data show the effects of ornithine on taste: in two-bottle and briefer intake tests, adding ornithine results in a higher intake of most, but not all, stimuli tests. Bilateral nerve cuts or the addition of GPRC6A antagonists decrease this effect. Small effects of ornithine are shown in whole-nerve recordings.

Weaknesses:

The conclusion seems to be that the authors have found evidence for ornithine acting as a taste modifier through the GPRC6A receptor expressed on the anterior tongue. It is hard to separate their conclusions from the possibility that any effects are additive rather than modulatory. Animals did prefer ornithine to water when presented by itself. Additionally, the authors refer to evidence that ornithine is activating the T1R1-T1R3 amino acid taste receptor, possibly at higher concentrations than they use for most of the study, although this seems speculative. It is striking that the largest effects on taste are found with the other amino acid (umami) stimuli, leading to the possibility that these are largely synergistic effects taking place at the tas1r receptor heterodimer.

We would like to thank Reviewer #1 for the valuable comments. Our basis for considering ornithine as a taste modifier stems from our observation that a low concentration of ornithine (1 mM), which does not elicit a preference on its own, enhances the preference for umami substances, sucrose, and soybean oil through the activation of the GPRC6A receptor. Notably, this receptor is not typically considered a taste receptor. The reviewer suggested that the enhancement of umami taste might be due to potentiation occurring at the TAS1R receptor heterodimer. However, we propose that a different mechanism may be at play, as an antagonist of GPRC6A almost completely abolished this enhancement. In the revised manuscript, we will endeavor to provide additional information on the role of ornithine as a taste modifier acting through the GPRC6A receptor.

Reviewer #2 (Public review):

Summary:

The authors used rats to determine the receptor for a food-related perception (kokumi) that has been characterized in humans. They employ a combination of behavioral, electrophysiological, and immunohistochemical results to support their conclusion that ornithine-mediated kokumi effects are mediated by the GPRC6A receptor. They complemented the rat data with some human psychophysical data. I find the results intriguing, but believe that the authors overinterpret their data.

Strengths:

The authors examined a new and exciting taste enhancer (ornithine). They used a variety of experimental approaches in rats to document the impact of ornithine on taste preference and peripheral taste nerve recordings. Further, they provided evidence pointing to a potential receptor for ornithine.

Weaknesses:

The authors have not established that the rat is an appropriate model system for studying kokumi. Their measurements do not provide insight into any of the established effects of kokumi on human flavor perception. The small study on humans is difficult to compare to the rat study because the authors made completely different types of measurements. Thus, I think that the authors need to substantially scale back the scope of their interpretations. These weaknesses diminish the likely impact of the work on the field of flavor perception.

We would like to thank Reviewer #2 for the valuable comments and suggestions. Regarding the question of whether the rat is an appropriate model system for studying kokumi, we have chosen this species for several reasons: it is readily available as a conventional experimental model for gustatory research; the calcium-sensing receptor (CaSR), known as the kokumi receptor, is expressed in taste bud cells; and prior research has demonstrated the use of rats in kokumi studies involving gamma Glu-Val-Gly (Yamamoto and Mizuta, Chem. Senses, 2022). We acknowledge that fundamentally different types of measurements were conducted in the human psychophysical study and the rat study. Kokumi can indeed be assessed and expressed in humans; however, we do not currently have the means to confirm that animals experience kokumi in the same way that humans do. Therefore, human studies are necessary to evaluate kokumi, a conceptual term denoting enhanced flavor, while animal studies are needed to explore the potential underlying mechanisms of kokumi. We believe that a combination of both human and animal studies is essential, as is the case with research on sugars. While sugars are known to elicit sweetness, it is unclear whether animals perceive sweetness identically to humans, even though they exhibit a strong preference for sugars. In the revised manuscript, we will incorporate additional information to address the comments raised by the reviewer. We will also carefully review and revise our previous statements to ensure accuracy and clarity.

Reviewer #3 (Public review):

Summary:

In this study, the authors set out to investigate whether GPRC6A mediates kokumi taste initiated by the amino acid L-ornithine. They used Wistar rats, a standard laboratory strain, as the primary model and also performed an informative taste test in humans, in which miso soup was supplemented with various concentrations of L-ornithine. The findings are valuable and overall the evidence is solid. L-Ornithine should be considered to be a useful test substance in future studies of kokumi taste and the class C G protein-coupled receptor known as GPRC6A (C6A) along with its homolog, the calcium-sensing receptor (CaSR) should be considered candidate mediators of kokumi taste.

Strengths:

The overall experimental design is solid based on two bottle preference tests in rats. After determining the optimal concentration for L-Ornithine (1 mM) in the presence of MSG, it was added to various tastants, including inosine 5'-monophosphate; monosodium glutamate (MSG); mono-potassium glutamate (MPG); intralipos (a soybean oil emulsion); sucrose; sodium chloride (NaCl); citric acid and quinine hydrochloride. Robust effects of ornithine were observed in the cases of IMP, MSG, MPG, and sucrose, and little or no effects were observed in the cases of sodium chloride, citric acid, and quinine HCl. The researchers then focused on the preference for Ornithine-containing MSG solutions. The inclusion of the C6A inhibitors Calindol (0.3 mM but not 0.06 mM) or the gallate derivative EGCG (0.1 mM but not 0.03 mM) eliminated the preference for solutions that contained Ornithine in addition to MSG. The researchers next performed transections of the chord tympani nerves (with sham operation controls) in anesthetized rats to identify the role of the chorda tympani branches of the facial nerves (cranial nerve VII) in the preference for Ornithine-containing MSG solutions. This finding implicates the anterior half-two thirds of the tongue in ornithine-induced kokumi taste. They then used electrical recordings from intact chorda tympani nerves in anesthetized rats to demonstrate that ornithine enhanced MSG-induced responses following the application of tastants to the anterior surface of the tongue. They went on to show that this enhanced response was insensitive to amiloride, selected to inhibit 'salt tastant' responses mediated by the epithelial Na+ channel, but eliminated by Calindol. Finally, they performed immunohistochemistry on sections of rat tongue demonstrating C6A positive spindle-shaped cells in fungiform papillae that partially overlapped in its distribution with the IP3 type-3 receptor, used as a marker of Type-II cells, but not with (i) gustducin, the G protein partner of Tas1 receptors (T1Rs), used as a marker of a subset of type-II cells; or (ii) 5-HT (serotonin) and Synaptosome-associated protein 25 kDa (SNAP-25) used as markers of Type-III cells.

Weaknesses:

The researchers undertook what turned out to be largely confirmatory studies in rats with respect to their previously published work on Ornithine and C6A in mice (Mizuta et al Nutrients 2021).

The authors point out that animal models pose some difficulties of interpretation in studies of taste and raise the possibility in the Discussion that umami substances may enhance the taste response to ornithine (Line 271, Page 9). They miss an opportunity to outline the experimental results from the study that favor their preferred interpretation that ornithine is a taste enhancer rather than a tastant.

At least two other receptors in addition to C6A might mediate taste responses to ornithine: (i) the CaSR, which binds and responds to multiple L-amino acids (Conigrave et al, PNAS 2000), and which has been previously reported to mediate kokumi taste (Ohsu et al., JBC 2010) as well as responses to Ornithine (Shin et al., Cell Signaling 2020); and (ii) T1R1/T1R3 heterodimers which also respond to L-amino acids and exhibit enhanced responses to IMP (Nelson et al., Nature 2001). While the experimental results as a whole favor the authors' interpretation that C6A mediates the Ornithine responses, they do not make clear either the nature of the 'receptor identification problem' in the Introduction or the way in which they approached that problem in the Results and Discussion sections. It would be helpful to show that a specific inhibitor of the CaSR failed to block the ornithine response. In addition, while they showed that C6A-positive cells were clearly distinct from gustducin-positive, and thus T1R-positive cells, they missed an opportunity to clearly differentiate C6A-expressing taste cells and CaSR-expressing taste cells in the rat tongue sections.

It would have been helpful to include a positive control kokumi substance in the two-bottle preference experiment (e.g., one of the known gamma-glutamyl peptides such as gamma-glu-Val-Gly or glutathione), to compare the relative potencies of the control kokumi compound and Ornithine, and to compare the sensitivities of the two responses to C6A and CaSR inhibitors.

The results demonstrate that enhancement of the chorda tympani nerve response to MSG occurs at substantially greater Ornithine concentrations (10 and 30 mM) than were required to observe differences in the two bottle preference experiments (1.0 mM; Figure 2). The discrepancy requires careful discussion and if necessary further experiments using the two-bottle preference format.

We would like to thank Reviewer #3 for the valuable comments and helpful suggestions. We propose that ornithine has two stimulatory actions: one acting on GPRC6A, particularly at lower concentrations, and another on amino acid receptors such as T1R1/T1R3 at higher concentrations. Consequently, ornithine is not preferable at lower concentrations but becomes preferable at higher concentrations. For our study on kokumi, we used a low concentration (1 mM) of ornithine. The possibility mentioned in the Discussion that 'the umami substances may enhance the taste response to ornithine' is entirely speculative. We will reconsider including this description in the revised version. As the reviewer suggested, in addition to GPRC6A, ornithine may bind to CaSR and/or T1R1/T1R3 heterodimers. However, we believe that ornithine mainly binds to GPRC6A, as a specific inhibitor of this receptor almost completely abolished the enhanced response to umami substances, and our immunohistochemical study indicated that GPRC6A-expressing taste cells are distinct from CaSR-expressing taste cells (see Supplemental Fig. 3). We conducted essentially the same experiments using gamma-Glu-Val-Gly in Wistar rats (Yamamoto and Mizuta, Chem. Senses, 2022) and compared the results in the Discussion. The reviewer may have misunderstood the chorda tympani results: we added the same concentration (1 mM) used in the two-bottle preference test to MSG (Fig. 5-B). Fig. 5-A shows nerve responses to five concentrations of plain ornithine. In the revised manuscript, we will strive to provide more precise information reflecting the reviewer’s comments.

Author response:

We thank both reviewers for their considerate reviews. In this provisional response we would like to make a few key points.

Given that we introduced a bespoke likelihood model for the second dataset, Reviewer 1 asks whether "every unique dataset requires a tailored prior or likelihood to produce the best results". Our intention is to advocate for the horseshoe prior model as a 'standard' first analysis for any cell count dataset. If extra knowledge about the data is available, or if any data artefacts are detected, more elaborate likelihoods could be introduced as needed in a follow-up analysis. Our introduction of the zero-inflated Poisson likelihood for the second dataset was one such example, but many alternatives could exist. This iterative approach to model building, sometimes referred to as a `Bayesian workflow' is seen as good practise in Bayesian data analysis literature. In the revised version of the paper, we will try to explain the recommendations and modelling philosophy behind this method while emphasising that tailoring or bespoke modelling is not required for our `standard analysis', what we would regard as the Bayesian replacement for a t-test on counts.

Reviewer 1 notes that "the differences between the results produced by the two Bayesian models in case study 2 are not discussed". We agree that this discrepancy, arising from the specific assumptions of each model is an interesting issue which we should better explore in the paper. In Figure 6 we plotted the actual data values alongside posterior and confidence intervals to explain how the results from the ZIP likelihood and Horseshoe prior compare with those from a t-test. However, our example regions did not highlight cases where differences could be noted between the the two Bayesian models. In the revised version of the paper, we will extend Figure 6 to include further brain regions, such as those mentioned by the referee, and will use that as an opportunity to discuss the broader issue of what to do when the Bayesian models give conflicting results.

We agree with reviewer 2's point that the model description terminology could be made clearer for the target eLife audience. We tried to strike a balance between introducing the reader to the conventional technical terminology used in the Bayesian data analysis necessary for understanding the model while avoiding exhaustive statistical terminology. We erred too much on the side of the latter instead of providing clear links between the model construction and experimental data. In the revised version of the paper, we will augment any technical terms with more biological language and provide a Glossary for reader reference.

Author response:

Reviewer #1:

We agree with Reviewer 1 that the flexibility of SPRAWL also makes it difficult to interpret its outputs. We consider SPRAWL to be a hypothesis-generation tool to answer simple questions of subcellular localization in a statistically robust manner. In this paper we include examples of how it can be incorporated with other tools and wetlab experimentation to build biological intuition. Our hope is that the SPRAWL software, or even the underlying simple statistical ideas are of use to others in the field.

Reviewer #2:

We agree with Reviewer #2 that this manuscript does not demonstrate biological significance of the observed results of applying SPRAWL to massively multiplexed FISH datasets. We agree it would require additional wetlab experiments such as cell-type specific and isoform-resolved fluorescence in-situ hybridization, which we consider beyond the scope of this paper. We believe that the observed correlations of subcellular localization detected by SPRAWL and the differential 3’ UTR usage detected by ReadZS are compelling, although not conclusive, as are the Timp3 experimental studies.

Our understanding is that Baysor is primarily a cell-segmentation algorithm, which is not what SPRAWL attempts to achieve. Baysor states that it identifies “cells of a distinct type will give rise to small molecular neighborhoods with stereotypical transcriptional composition, making it possible to interpret such neighborhoods without performing explicit cell segmentation” which we understand to mean that Baysor identifies spatial groupings of cells with “stereotypical transcriptional composition” rather than subcellular RNA localization. We do not think that SPRAWL and Baysor are comparable, but instead Baysor could be used as an upstream step to SPRAWL to potentially improve cell segmentation.

Reviewer #3:

We thank Reviewer #3 for identifying discrepancies in the paper which we addressed to the best of our abilities.

Author response:

Reviewer 1:

Many thanks for your positive review and clear overview of our paper. We also agree with your interpretation of our results that ‘the information that is decodable and the information that is task-relevant may relate in very different ways’ and we could have emphasised this point more in the paper.

With regards to the qualitative similarities between our models and our data, we agree that due to the fact that one can achieve any desired level of activity, decoding accuracy, performance, etc in a model, we focussed on changes over learning of key metrics that are commonly used in the field. Although this can appear qualitative at times because the raw values can differ between the data and our models, our main results are ultimately strongly quantitative (e.g., Fig. 3c,d, and Fig. 5f). We note that we could have fine tuned the models to have similar activity levels, decoding accuracies etc to our data, and on the face of it this may have made the results appear more convincing, but we felt that such trivial fine tuning does not change any of our key results in any fundamental way and is not the aim of computational modelling. The model one chooses to analyse will always be abstracted from biology in some way, by definition.

Reviewer 2:

Thank you very much for your kind comments and clear overview of our paper. We also hope that our paper ‘provides a valuable analysis of the effect of two parameters on representations of irrelevant stimuli in trained RNNs.’

With regards to our suggested mechanism of suppressing dynamically irrelevant stimuli, we are sorry that we did not provide a sufficient enough explanation of suppressing color representations when they are irrelevant. We hopefully provide a longer explanation here. Our mechanism of suppression of dynamically irrelevant stimuli does not suggest that it becomes un-suppressed later, only the behaviourally relevant variable should be decodable when it is needed (i.e., XOR). Although color decodability did increase slightly in the data and some of the models from the color period to the shape period, it was typically not significant and was therefore not a result that we emphasise in the paper (although this could be analysed further to see if additional mechanisms might explain it). We emphasise throughout that color decoding is typically similar between color and shape periods (either high or low) and either decreases or increases over time in both periods. We also focus on whether color decodability increases or decreases over learning during the color period when it is irrelevant (which we call ‘early color decoding’). Importantly, decoding of color or shape is not needed to perform the task, only decoding of XOR is needed to perform the task. For example, in our two-neuron networks, we observe perfect XOR decoding and only 75% decoding of color and shape, and decoding during the shape period is the same as the network at initialisation before any training. The mechanism we suggest of suppressing dynamically irrelevant stimuli does not predict that that stimulus should be un-suppressed later, only the behaviourally relevant variable should be decodable (i.e., XOR). Instead, what we try to explain is that color inputs can generate 0 firing rate during the color period, when that input does not need to be used and is therefore irrelevant (and color decoding decreases during the color period over learning), but these inputs can be combined with shape inputs later to create a perfectly decodable XOR response.

With regards to interpretation of our results based on metabolic cost constraints, we feel that this is an unnecessarily strong criticism to say that it ‘is not backed up by the presented data/analyses.’ All of our models were trained with only a metabolic cost constraint, a noise strength, and a task performance term. Therefore, the results of the models are directly attributable to the strength of metabolic cost that we use. Additionally, although one could in principle pick any of infinitely many different parameters to change and measure the response in an optimized network, varying metabolic cost and noise are two of the most fundamental phenomena that neural circuits must contend with, and many studies have analysed the impact they have on neural circuit dynamics. Furthermore, in line with previous studies (Yang et al., 2019, Whittington et al., 2022, Sussillo et al., 2015, Orhan et al., 2019, Kao et al., 2021, Cueva et al., 2020, Driscoll et al., 2022, Song et al., 2016, Masse et al., 2019, Schimel et al., 2023), we operationalized metabolic cost in our models through L2 firing rate regularization. This cost penalizes high overall firing rates. (Such an operationalization of metabolic cost also makes sense for our models because network performance is based on firing rates rather than subthreshold activities.) There are however alternative conceivable ways to operationalize a metabolic cost; for example L1 firing rate regularization has been used previously when optimizing neural networks and promotes more sparse neural firing. Interestingly, although our L2 is generally conceived to be weaker than L1 regularization, we still found that it encouraged the network to use purely sub-threshold activity in our task. The regularization of synaptic weights may also be biologically relevant because synaptic transmission uses the most energy in the brain compared to other processes (Faria-Pereira et al., 2022, Harris et al., 2012). Additionally, even subthreshold activity could be regularized as it also consumes energy (although orders of magnitude less than spiking (Zhu et al., 2019)). Therefore, future work will be needed to examine how different metabolic costs affect the dynamics of task-optimized networks.

With regards to color representations in PFC only qualitatively matching those in our models, in line with the comment from Reviewer 1, we agree that due to the fact that one can achieve any desired level of activity, decoding accuracy, performance, etc in a model, we focussed on changes over learning of key metrics that are commonly used in the field. Although this can appear qualitative at times because the raw values can differ between the data and our models, our main results are ultimately strongly quantitative (e.g., Fig. 3c,d, and Fig. 5f). We note that we could have fine tuned the models to have similar activity levels, decoding accuracies etc to our data, and on the face of it this may have made the results appear more convincing, but we felt that such trivial fine tuning does not change any of our key results in any fundamental way and is not the aim of computational modelling. The model one chooses to analyse will always be abstracted from biology in some way, by definition. Finally, of course we note that changes in color decoding could result from other causes, but we focussed on two key phenomena that neural circuits must contend with: noise and metabolic costs. Therefore, it is likely that these two variables play a strong role in stimulus representations in neural circuits

Reviewer 3:

Thank you very much for your thorough and clear overview of our paper and we agree that it is important to investigate phenomena and manipulations in computational models that are almost impossible to do in vivo and we are pleased you found our mathematical analyses rigorous and nicely documented.

Although we agree that it can be useful to study the responses of individual neurons, we focussed on population analyses of all available neurons without omitting or specifically selecting neurons based on their dynamics. We are also not suggesting that the activities of individual ‘neurons’ in the models and data should be similar since our models are highly abstract firing rate models. But rather, the overall computational strategy, which one can access through population decoding and cross-generalised decoding, was what we were interested in comparing between the models and the data and is arguably the correct level of analysis of such models (an data) given our key questions (Vyas et al., 2020, Churchland et al., 2012, Mante et al., 2013, Ebitz et al., 2021).

We also certainly agree and are more than open to the fact that suppression of irrelevant stimuli may already be happening on the inputs arriving in PFC. Indeed, we actually suggest this as the mechanism in Fig. 5 (together with recurrent circuit dynamics that make use of these inputs).

With regards to the dynamics of the two-neuron networks not being ‘informative of what happens in brain networks’, we agree that these models are very simplified and may only contain very fundamental similarities with biological neurons. However, we only used them to illustrate the fundamental mechanism of generating 0 firing rate during the color epoch so that it is more easily understandable for readers as they can see the entire 2-dimensional state space and the entire computational strategy can be seen (Fig. 5a-d). We also note that we did this for both rectified linear and tanh networks, thus showing that such a mechanism is preserved across fundamentally different firing rate nonlinearities. Additionally, after illustrating this fundamental mechanism of networks receiving color information but generating 0 firing rate, we show that the exact same mechanism is at play in the large networks we use throughout the paper (Fig. 5e). We also only compare the large networks to our neural recordings. We do agree though that it would be interesting to further compare fundamental similarities and differences between our models and our neural recordings (always at the right level of analysis that makes sense for our chosen models) to show that the mechanisms we uncover in our models are also strongly relevant for our data.

Author response:

Public Reviews:

Reviewer #1 (Public review):

Summary:

The authors have used full-length single-cell sequencing on a sorted population of human fetal retina to delineate expression patterns associated with the progression of progenitors to rod and cone photoreceptors. They find that rod and cone precursors contain a mix of rod/cone determinants, with a bias in both amounts and isoform balance likely deciding the ultimate cell fate. Markers of early rod/cone hybrids are clarified, and a gradient of lncRNAs is uncovered in maturing cones. Comparison of early rods and cones exposes an enriched MYCN regulon, as well as expression of SYK, which may contribute to tumor initiation in RB1 deficient cone precursors.

Strengths:

(1) The insight into how cone and rod transcripts are mixed together at first is important and clarifies a long-standing notion in the field.

(2) The discovery of distinct active vs inactive mRNA isoforms for rod and cone determinants is crucial to understanding how cells make the decision to form one or the other cell type. This is only really possible with full-length scRNAseq analysis.

(3) New markers of subpopulations are also uncovered, such as CHRNA1 in rod/cone hybrids that seem to give rise to either rods or cones.

(4) Regulon analyses provide insight into key transcription factor programs linked to rod or cone fates.

(5) The gradient of lncRNAs in maturing cones is novel, and while the functional significance is unclear, it opens up a new line of questioning around photoreceptor maturation.

(6) The finding that SYK mRNA is naturally expressed in cone precursors is novel, as previously it was assumed that SYK expression required epigenetic rewiring in tumors.

Weaknesses:

(1) The writing is very difficult to follow. The nomenclature is confusing and there are contradictory statements that need to be clarified.

(2) The drug data is not enough to conclude that SYK inhibition is sufficient to prevent the division of RB1 null cone precursors. Drugs are never completely specific so validation is critical to make the conclusion drawn in the paper.

We thank the reviewer for describing the study’s strengths and weaknesses.  In the upcoming revision, we will:

(1) improve the writing and clarify the nomenclature and contradictory statements, particularly those noted in the Reviewer’s Recommendations for Authors; and

(2) scale back the claims related to the role of SYK in the cone precursor response to RB1 loss; we agree that genetic perturbation of SYK is required to prove it’s role and will perform such analyses in a separate study.

Reviewer #2 (Public review):

Summary:

The authors used deep full-length single-cell sequencing to study human photoreceptor development, with a particular emphasis on the characteristics of photoreceptors that may contribute to retinoblastoma.

Strengths:

This single-cell study captures gene regulation in photoreceptors across different developmental stages, defining post-mitotic cone and rod populations by highlighting their unique gene expression profiles through analyses such as RNA velocity and SCENIC. By leveraging full-length sequencing data, the study identifies differentially expressed isoforms of NRL and THRB in L/M cone and rod precursors, illustrating the dynamic gene regulation involved in photoreceptor fate commitment. Additionally, the authors performed high-resolution clustering to explore markers defining developing photoreceptors across the fovea and peripheral retina, particularly characterizing SYK's role in the proliferative response of cones in the RB loss background. The study provides an in-depth analysis of developing human photoreceptors, with the authors conducting thorough analyses using full-length single-cell RNA sequencing. The strength of the study lies in its design, which integrates single-cell full-length RNA-seq, long-read RNA-seq, and follow-up histological and functional experiments to provide compelling evidence supporting their conclusions. The model of cell type-dependent splicing for NRL and THRB is particularly intriguing. Moreover, the potential involvement of the SYK and MYC pathways with RB in cone progenitor cells aligns with previous literature, offering additional insights into RB development.

Weaknesses:

The manuscript feels somewhat unfocused, with a lack of a strong connection between the analysis of developing photoreceptors, which constitutes the bulk of the manuscript, and the discussion on retinoblastoma. Additionally, given the recent publication of several single-cell studies on the developing human retina, it is important for the authors to cross-validate their findings and adjust their statements where appropriate.

We thank the reviewer for summarizing the main findings and for noting the compelling support for the conclusions, the intriguing cell type-dependent splicing of rod and cone lineage factors, and the insights into retinoblastoma development. 

We concur that some studies of developing photoreceptors were not well connected to retinoblastoma, which diminished the focus.  However, we suggest that it was valuable to highlight how deep, long read sequencing provided new insights into retinoblastoma. For example, our demonstration of similar rod- and cone-related gene expression in early cones and RB cells addressed concerns with the proposed cone cell-of-origin, adding disease relevance.

We will address the Reviewer’s request to cross-validate our findings with those of other single-cell studies of developing human retina and to adjust the related statements in our upcoming revision.

Reviewer #3 (Public review):

Summary:

The authors use high-depth, full-length scRNA-Seq analysis of fetal human retina to identify novel regulators of photoreceptor specification and retinoblastoma progression.

Strengths:

The use of high-depth, full-length scRNA-Seq to identify functionally important alternatively spliced variants of transcription factors controlling photoreceptor subtype specification, and identification of SYK as a potential mediator of RB1-dependent cell cycle reentry in immature cone photoreceptors.

Human developing fetal retinal tissue samples were collected between 13-19 gestational weeks and this provides a substantially higher depth of sequencing coverage, thereby identifying both rare transcripts and alternative splice forms, and thereby representing an important advance over previous droplet-based scRNA-Seq studies of human retinal development.

Weaknesses:

The weaknesses identified are relatively minor. This is a technically strong and thorough study, that is broadly useful to investigators studying retinal development and retinoblastoma.

We thank the reviewer for describing the strengths of the study. Our upcoming revision will address the minor concerns that were raised separately in the Reviewer’s Recommendations for Authors.

Author response:

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

Minor Concern (Original Comment 1):

“We think that this is sufficient to address our concern. Some citations may be in order to underpin the new text.”

We appreciate the reviewer’s assessment that the revised text clarifies the complexity of the upstream circuitry beyond the retina, including inputs from the thalamus. As recommended, we have now included additional citations in the revised manuscript to support these points.

Major Concern (Original Comment 5):

“We do not feel that this important concern has been addressed. The stats are definitively negative. There is no statistical evidence from these data that multisensory integration is occurring in this assay. The anesthesia, paralysis, and low n may provide explanations for this negative result, but it is still a negative result (p=0.5269). To show two examples of multisensory integration for subthreshold stimuli fits the narrative, but this result is not supported. Examples where individual stimuli caused APs (and combined stimuli did not) also occurred, presumably, and at a rate that is statistically indistinguishable to the examples shown in Figure 5. As such, if results from this assay are going to be in the manuscript, acoustic-only and tectum-only examples should be shown as well, although they would not fit the narrative. To be meaningful, this experiment would have to show that multisensory integration is happening in this circuit. Frustrating though it must be, the experiment has given a negative result to that question.”

We understand the reviewer’s concern regarding Figure 5C and the firing of action potentials (APs) in response to multisensory stimuli. We acknowledge that our assay is not suited to answer this question definitively and that our results do not provide statistical support for this hypothesis. In response, we have removed the examples previously shown in Figure 5C, along with the related description in the Results section (lines 420–426), to avoid implying unsupported integration in suprathreshold conditions.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

In this study, the authors describe the construction of an extremely large-scale anatomical model of juvenile rat somatosensory cortex (excluding the barrel region), which extends earlier iterations of these models by expanding across multiple interconnected cortical areas. The models are constructed in such a way as to maintain biological detail from a granular scale - for example, individual cell morphologies are maintained, and synaptic connectivity is founded on anatomical contacts. The authors use this model to investigate a variety of properties, from cell-type specific targeting (where the model results are compared to findings from recent large-scale electron microscopy studies) to network metrics. The model is also intended to serve as a platform and resource for the community by being a foundation for simulations of neuronal circuit activity and for additional anatomical studies that rely on the detailed knowledge of cellular identity and connectivity.

Strengths:

As the authors point out, the combination of scale and granularity of their model is what makes this study valuable and unique. The comparisons with recent electron microscopy findings are some of the most compelling results presented in the study, showing that certain connectivity patterns can arise directly from the anatomical configuration, while other discrepancies highlight where more selective targeting rules (perhaps based on molecular cues) are likely employed. They also describe intriguing effects of cortical thickness and curvature on circuit connectivity and characterize the magnitude of those effects on different cortical layers.

The detailed construction of the model is drawn on a wide range of data sources (cellular and synaptic density measures, neuronal morphologies, cellular composition measures, brain geometry, etc.) that are integrated together; other data sources are used for comparison and validation. This consolidation and comparison also represent a valuable contribution to the overall understanding of the modeled system.

We thank the reviewer for the kind comments.

Weaknesses:

The scale of the model, which is a primary strength, also can carry some drawbacks. In order to integrate all the diverse data sources together, many specific decisions must be made about, for example, translating findings from different species or regions to the modeled system, or deciding which aspects of the system can be assumed to be the same and which should vary. All these decisions will have effects on the predicted results from the model, which could limit the types of conclusions that can be made (both by the others and by others in the community who may wish to use the model for their own work).

We agree that this is a downside of the principle of biophysically detailed modeling that is best addressed by continuous refinement in collaboration with the community. We would like to once again invite any interested party to participate in this process.

As an example, while it is interesting that broad brain geometry has effects on network structure (Figure 7), it is not clear how those effects are actually manifested. I am not sure if some of the effects could be due to the way the model is constructed - perhaps there may be limited sets of morphologies that fit into columns of particular thicknesses, and those morphologies may have certain idiosyncrasies that could produce different statistics of connectivities where they are heavily used. That may be true to biology, but it may also be somewhat artifactual if, for example, the only neurons in the library that fit into that particular part of the cortex differ from the typical neurons that are actually found in that region (but may not have been part of the morphological sampling).

We agree that the limited pool of morphological reconstructions can lead to artifactual results in the way the reviewer pointed out. To investigate that hypothesis, we added a supplementary figure (S14) where we characterize (1): to what degree the morphological composition of a columnar subvolume reflects the overall composition of the model; and (2): The level of morphological diversity in each columnar subvolume. We discuss the results at the end of section 2.6. Briefly, while we cannot fully rule out the possibility of an artificial result, we found a high and virtually uniform level of morphological diversity in all columns and layers. This makes it unlikely that individual idiosyncratic morphologies strongly affect the local connectivity. However, we acknowledge that the minimum level of morphological diversity required is unknown. We believe that at this stage all we can do is characterize this and leave final interpretation to the reader.

I also wonder how much the assumption that the layers have the same relative thicknesses everywhere in the cortex affects these findings, since layer thicknesses do in fact vary across the cortex.

We agree that layer thickness variation would affect circuit properties. Variability of layer thickness can be split into two components: variability stemming from differences in total thickness, which our model covers, and variability of relative, i.e., normalized layer thickness, which we miss. In this region of cortex, though, data on the relative thickness of cortical layers is sparse. The Waxholm Atlas does not distinguish somatosensory cortical layers in its labels [Kleven et al, 2023]. Yusufoğulları (2015) compares layer thicknesses of rat hindlimb and barrel field regions. After normalization against total thickness, the relative difference increased towards the superficial layers from 0 in L6 to 33% in L1. Variability of normalized thicknesses within developed rat barrel cortex, based on layer boundaries reported in Narayanan et al. (2017) vary by 2% to 5% over approximately 2 mm. One major effect of such variability would be to scale the number of neurons in a given layer locally by the corresponding factors. For comparison, the resulting variability in neuron counts due to differences in conicality (Fig. 7D1) was around +-25%. A further effect of variable relative layer thickness would be its impact on the selection of suitable morphologies to be placed in the volume.

In summary, adjustment of layer thickness is a refinement which should be done in future versions of the model, once more data is available. The discussion section has been updated to acknowledge this limitation. However, as outlined at the beginning of this point-by-point reply, we will not conduct such updates to the model in the context of this manuscript, as it describes the version of the model used for a number of follow-up studies.

In addition, the complexity of the model means that some complicated analyses and decisions are only presented in this manuscript with perhaps a single panel and not much textual explanation. I find, for example, that the panels of Figure S2 seem to abstract or simplify many details to the point where I am not clear about what they are actually illustrating - how does Figure S2D represent the results of "the process illustrated in B"? Why are there abrupt changes in connectivity at region borders (shown as discontinuous colors), when dendrites and axons span those borders and so would imply interconnectivity across the borders? What do the histograms in E1 and E2 portray, and how are they related to each other?

We apologize for the confusion. We have updated the figure caption of Figure S2 to better explain its contents.

Overall, the model presented in this study represents an enormous amount of work and stands as a unique resource for the community, but also is made somewhat unwieldy for the community to employ due to the weight of its manifold specific construction decisions, size, and complexity.

Reviewer #2 (Public Review):

Summary:

The authors build a colossal anatomical model of juvenile rat non-barrel primary somatosensory cortex, including inputs from the thalamus. This enhances past models by incorporating information on the shape of the cortex and estimated densities of various types of excitatory and inhibitory neurons across layers. This is intended to enable an analysis of the micro- and mesoscopic organisation of cortical connectivity and to be a base anatomical model for large-scale simulations of physiology.

Strengths:

• The authors incorporate many diverse data sources on morphology and connectivity.

• This paper takes on the challenging task of linking micro- and mesoscale connectivity.

• By building in the shape of the cortex, the authors were able to link cortical geometry to connectivity. In particular, they make an unexpected prediction that cortical conicality affects the modularity of local connectivity, which should be testable.

• The author's analysis of the model led to the interesting prediction that layer 5 neurons connect local modules, which may be testable in the future, and provide a basis to link from detailed anatomy to functional computations.

• The visualisation of the anatomy in various forms is excellent.

• A subnetwork of the model is openly shared (but see question below).

We thank the reviewer for their kind comments.

Weaknesses:

• Why was non-barrel S1 of the juvenile rat cortex selected as the target for this huge modelling effort? This is not explained.

We have added an explanation of this decision to the third paragraph of the introduction.

• There is no effort to determine how specific or generalisable the findings here are to other parts of the cortex. Although there is a link to physiological modelling in another paper, there is no clear pathway to go from this type of model to understand how the specific function of the modelled areas may emerge here (and not in other cortical areas).

With respect to generality against specific findings, our philosophy is as follows: Despite the fact that most of our source data comes from juvenile rat somatosensory cortex, we also had to generalize many data sources across organisms, ages or regions. Hence, in this iteration we focused on investigating the general features of the (multi-region) mammalian cortex, e.g., high-order motifs, connected by L5 neurons across subregions or the effect of curvature on the connectivity. In the future, more specific data sources can be used to build diverging versions of the model, e.g. one for adult vs. juvenile rat. They can then be used to contrast the ages and focus on more specific findings. We already defined a number of structural metrics that can be used to contrast more specific versions of the model quantitatively.

We now clarify this pathway to understanding more specific function in the last paragraph of the discussion.

• In a few places the manuscript could be improved by being more specific in the language, for example:

- "our anatomy-based approach has been shown to be powerful", I would prefer instead to read about specific contributions of past papers to the field, and how this builds on them.

- similarly: "ensuring that the total number of synapses in a region-to-region pathway matches biology." Biology here is a loose term and implies too much confidence in the matching to some ground truth. Please instead describe the source of the data, including the type of experiment.

We have removed or rewritten the mentioned parts. We now clarify that we work based on biological estimates from experiments and cite the experiment sources. We also provide brief descriptions of the types of data and how they were derived.

• Some of the decisions seem a little ad-hoc, and the means to assess those decisions are not always available to the reader e.g.

- pg. 10. "Based on these results, we decided that the local connectome sufficed to model connectivity within a region.". What is the basis for this decision? Can it be formalised?

- "In the remaining layers the results of the objective classification were used to validate the class assignments of individual pyramidal cells. We found the objective classification to match the expert classification closely (i.e., for 80-90% of the morphologies). Consequently, we considered the expert classification to be sufficiently accurate to build the model." The description of the validation is a little informal. How many experts were there? What are their initials? Was inter-rater or intra-rater reliability assessed? What are these numbers? The match with Kanari's classification accuracy should be reported exactly. There are clearly experts among the author list, but we are all fallible without good controls in place, and they should be more explicit about those controls here, in my opinion.

- "Morphology selection was then performed as previously (Markram et al., 2015), that is, a morphology was selected randomly from the top 10% scorers for a given position." A lot of the decisions seem a little ad-hoc, without justification other than this group had previously done the same thing. For example, why 10% here? Shouldn't this be based on selecting from all of the reasonable morphologies?

We have clarified that the density of local connectivity is verified against the validation datasets by comparing the diagonals in Figure 4B, in addition to the quantification of Figure 4C.

For the classification, we have now published a detailed preprint describing the objective confirmation of expert classification by a variety of methods (see Kanari et al. 2024 https://www.biorxiv.org/content/10.1101/2024.09.13.612635v1). We cannot include the full methodology in the current paper, due to its large extent. For the benefit of the reader, we have included the appropriate citation and extended the short description of the methodology. As described in this paper, the classification accuracy varies per layer, cell type, etc. We have now described in more details these results, that can be accessed in details in out preprint.

• I would like to know if one of the key results relating to modularity and cortical geometry can be further explored. In particular, there seem to be sharp changes in the data at the end of the modelled cortical regions, which need to be explored or explained further.

We now explore these results further in supplementary figure S15, which we discuss in the results Section 2.6.

• The shape of the juvenile cortex - a key novelty of this work - was based on merely a scalar reduction of the adult cortex. This is very surprising, and surely an oversimplification. Huge efforts have gone into modelling the complex nonlinear development of the cortex, by teams including the developing Human Connectome Project. For such a fundamental aspect of this work, why isn't it possible to reconstruct the shape of this relatively small part of the juvenile rat cortex?

We agree that a more complex approach should be used in the future. However, as outlined at the beginning of this point-by-point reply, we will not conduct such updates to the model in the context of this manuscript, as it describes the version of the model used for a number of follow-up studies.

• The same relative laminar depths are used for all subregions. This will have a large impact on the model. However, relative laminar depths can change drastically across the cortex (see e.g. many papers by Palomero-Gallagher, Zilles, and colleagues). The authors should incorporate the real laminar depths, or, failing that, show evidence to show that the laminar depth differences across the subregions included in the model are negligible.

This point has also been raised by reviewer #1 above. For convenience, we repeat our reply below.

We agree that layer thickness variation would affect circuit properties. Variability of layer thickness can be split into two components: variability stemming from differences in total thickness, which our model covers, and variability of relative, i.e., normalized layer thickness, which we miss. In this region of cortex, though, data on the relative thickness of cortical layers is sparse. The Waxholm Atlas does not distinguish somatosensory cortical layers in its labels [Kleven et al, 2023]. Yusufoğulları (2015) compares layer thicknesses of rat hindlimb and barrel field regions. After normalization against total thickness, the relative difference increased towards the superficial layers from 0 in L6 to 33% in L1. Variability of normalized thicknesses within developed rat barrel cortex, based on layer boundaries reported in Narayanan et al. (2017) vary by 2% to 5% over approximately 2 mm. One major effect of such variability would be to scale the number of neurons in a given layer locally by the corresponding factors. For comparison, the resulting variability in neuron counts due to differences in conicality (Fig. 7D1) was around +-25%. A further effect of variable relative layer thickness would be its impact on the selection of suitable morphologies to be placed in the volume.

In summary, adjustment of layer thickness is a refinement which should be done in future versions of the model, once more data is available. The discussion section has been updated to acknowledge this limitation. However, as outlined at the beginning of this point-by-point reply, we will not conduct such updates to the model in the context of this manuscript, as it describes the version of the model used for a number of follow-up studies.

• The authors perform an affine mapping between mouse and rat cortex. This is again surprising. In human imaging, affine mappings are insufficient to map between two individual brains of the same species and nonlinear transformations are instead used. That an affine transformation should be considered sufficient to map between two different species is then very surprising. For some models, this may be fine, but there is a supposed emphasis here on biological precision in terms of anatomical location.

We agree that this is a weakness that we will address in future revisions of the model.

• One of the most interesting conclusions, that the connectivity pattern observed is in part due to cooperative synapse formation, is based on analyses that are unfortunately not shown.

We originally decided not to show this part as we underestimated the interest in this particular result. We have now included the result in supplementary figure S10 and discuss the figure in the results.

• Open code:

- Why is only a subvolume available to the community?

We have now made the entire model available under doi.org/10.7910/DVN/HISHXN. The Data and Code availability section has been updated to clarify this.

- Live nature of the model. This is such a colossal model, and effort, that I worry that it may be quite difficult to update in light of new data. For example, how much person and computer time would it take to update the model to account for different layer sizes across subregions? Or to more precisely account for the shape of the juvenile rat cortex?

To provide more information to people interested in participating in model refinements, we have added a new Figure 9. We discuss potential opportunities for refinement at the end of the discussion section.

Reviewer #3 (Public Review):

This manuscript reports a detailed model of the rat non-barrel somatosensory cortex, consisting of 4.2 million morphologically and biophysically detailed neuron models, arranged in space and connected according to highly sophisticated rules informed by diverse experimental data. Due to its breadth and sophistication, the model will undoubtedly be of interest to the community, and the reporting of anatomical details of modeling in this paper is important for understanding all the assumptions and procedures involved in constructing the model. While a useful contribution to this field, the model and the manuscript could be improved by employing data more directly and comparing simple features of the model's connectivity - in particular, connection probabilities - with relevant experimental data.

The manuscript is well-written overall but contains a substantial number of confusing or unclear statements, and some important information is not provided.

Below, major concerns are listed, followed by more specific but still important issues.

Major issues

(1) Cortical connectivity.

Section 2.3, "Local, mid-range and extrinsic connectivity modeled separately", and Figure 4: I am confused about what is done here and why. The authors have target data for connectivity (Figure 4B1). But then they use an apposition-based algorithm that results in connectivity that is quite different from the data (Figure 4B2, C). They then use a correction based on the data (Figure 4E) to arrive at a more realistic connectivity. Why not set the connectivity based on the data right away then? That would seem like a more straightforward approach.

We have completely re-written our description and discussion of connectivity in the model. We now more explicitly motivate our connectivity modeling choices in the first paragraph of section 2.3 of the results and in the second paragraph of the discussion.

The same comment applies to Section 2.4., "Specificity of axonal targeting": the distributions of synapses on different types of target cell compartments were not well captured by the original model based on axon-dendrite overlap and pruning, so the authors introduced further pruning to match data specificity. While details of this process and what worked and what didn't may be interesting to some, overall it is not surprising, as it has been well known that cell types exhibit connectivity that is much more specific than "Peters rule" or its simple variations. The question is, since one has the data, why not use the data in the first place to set up the connectivity, instead of using the convoluted process of employing axon-dendrite overlap followed by multiple corrections?

We would like to point out that we are not employing “Peters rule”, we now make this explicit in the revision in the first paragraph of section 2.3 of the results. Furthermore, we would argue that the match to the Motta et al. data indicates that our approach is more than just a “simple variation”. Finally, we believe that there is important insight in: 1. The specific ways in which the algorithm had to be changed to match the Schneider-Mizell data, e.g. that the connectivity of SST positive neurons did not have to be adapted at all. 2. That the specificity of the other two types could still be matched by a selection of a subset of axonal appositions (i.e., of potential synapses).

Most importantly, what is missing from the whole paper is the characterization of connection probabilities, at least for the local circuit within one area. Such connection probabilities can be obtained from the data that the authors already use here, such as the MICRONS dataset. Another good source of such data is Campagnola et al., Science, 2022. Both datasets are for mouse V1, but they provide a comprehensive characterization across all cortical layers, thus offering a good benchmark for comparison of the model with the data. It would be important for the authors to show how connection probabilities realized in their model for different cell types compared to these data.

We now report connection probabilities in the reworked figure 4 and compare them to reported connection probabilities from many different sources and labs in supplementary figure S8. We prefer a comparison to a wide range of sources to relying on a single report.

(2) Section 2.5, "Structure of thalamic inputs" and Figure 6.

The text in section 2.5 should provide more details on what was done - namely, that the thalamic axons were generated based on the axon density profiles and then synapses were established based on their overall with cortical dendrites. Figure S10 where the target axon densities from data and the model axon densities are compared is not even mentioned here. Now, Figure S10 only shows that the axon densities were generated in a way that matches the data reasonably well. However, how can we know that it results in connectivity that agrees with data? Are there data sources that can be used for that purpose? For example, the authors show that in their model "the peaks of the mean number of thalamic inputs per neuron occur at lower depths than the peaks of the synaptic density". Is this prediction of the model consistent with any available data?

Most importantly, the authors should show how the different cell types in their model are targeted by the thalamic inputs in each layer. Experimental studies have been done suggesting specificity in targeting of interneuron types by thalamic axons, such as PV cells being targeted strongly whereas SST and VIP cells being targeted less.

We have updated the Results section to provide context for the thalamic axon placement, and referred the reader to the methods for more detail. A reference to Figure S10 has now been added to this section as well.

As for validations of the structure of the thalamo-cortical inputs: We found that the existing literature on the topic, such as Cruikshank et al., 2007, 2010 and more recently Sermet et al., 2019, is predominately on the physiological strengths of the pathways. We acknowledge that the authors provide compelling arguments that their findings are likely partially due to differences in the anatomical innervation strengths. On the other hand, Sporns, 2013 cautioned against mixing up structural and functional connectivity. Overall, we believe that it is simply cleaner to perform this validation in the accompanying manuscript (“Part II: Physiology and Experimentation”), using the full physiological model. Note that we have actually performed that validation in the manuscript (see preprint under the following doi: 10.1101/2023.05.17.541168, Figure 3H1).

Note that a higher physiological strength onto PV+ neurons is observed.

(3) "We have therefore made not only the model but also most of our tool chain openly available to the public (Figure 1; step 7)."

In fact it is not the whole model that is made publicly available, but only about 5% of it (211,000 out of 4,200,000 neurons). Also, why is "most" of the tool chain made openly available, and not the whole tool chain?

We have now made the entire model available under doi.org/10.7910/DVN/HISHXN. This has also been added to the Key resource table.

With regard to the tool chain, everything is on our public github (https://github.com/BlueBrain/) except for the algorithm for detecting axonal appositions. For that tool there are currently unresolved potential copyright issues with former collaboration partners. We are working to resolve them.

Other issues

"At each soma location, a reconstruction of the corresponding m-type was chosen based on the size and shape of its dendritic and axonal trees (Figure S6). Additionally, it was rotated to according to the orientation towards the cortical surface at that point."

After this procedure, were cells additionally rotated around the white matter-pia axis? If yes, then how much and randomly or not? If not, then why not? Such rotations would seem important because otherwise additional order potentially not present in the real cortex is introduced in the model affecting connectivity and possibly also in vivo physiology (such as the dynamics of the extracellular electric field).

They are indeed additionally randomly rotated. We have clarified this in the revision.

The term "new in vivo reconstructions" for the 58 neurons used in this paper in addition to "in vitro reconstructions" is a misnomer. It is not straightforward to see where the procedure is described, but then one finds that the part of Methods that describes experimental manipulations is mostly about that (so, a clearer pointer to that part of Methods could be useful). However, the description in Methods makes it clear that it is only labeling that is done in vivo; the microscopy and reconstruction are done subsequently in vitro. I would recommend changing the terminology here, as it is confusing. Also, can the authors show reconstructions of these neurons in the supplementary figures? Is the reconstruction shown in Figure 4A representative?

The term is used because the staining is done in vivo. To the best of our knowledge, the reconstruction process cannot be performed in vivo. However, to avoid any confusion we modified the text to clarify this distinction to in-vivo stained.

With respect to the reconstruction in Figure 4: The intent of the panel is to demonstrate the concept of targeted long-range axons that our morphologies are missing, necessitating the use of a second algorithm for longer-range connectivity. As such, it is not one of the reconstructions we used, but one of Janelia MouseLight. While we mentioned MouseLight in the figure caption, we formulated it in a way that could be misunderstood to mean that we merely used the MouseLight browser to render one of our morphologies. We apologize for the confusion, and we have fixed the figure caption.

In this revision we have added exemplars of representative morphology reconstructions (in slice stained and in vivo stained) in a new supplementary figure, as requested (Figure S5). It is referenced in the last paragraph of section 2.1.

In the Discussion, "This was taken into account during the modeling of the anatomical composition, e.g. by using three-dimensional, layer-specific neuron density profiles that match biological measurements, and by ensuring the biologically correct orientation of model neurons with respect to the orientation towards the cortical surface. As local connectivity was derived from axo-dendritic appositions in the anatomical model, it was strongly affected by these aspects.

However, this approach alone was insufficient at the large spatial scale of the model, as it was limited to connections at distances below 1000μm."

As mentioned above, it is not clear that this approach was sufficient for local connectivity either. It would be great if the authors showed a systematic comparison of local connection probabilities between different cell types in their model with experimental data and commented here in the Discussion about how well the model agrees with the data.

As mentioned in the reply to a previous comment, we now report connection probabilities.

In the Discussion: "The combined connectome therefore captures important correlations at that level, such as slender-tufted layer 5 PCs sending strong non-local cortico-cortical connections, but thick-tufted layer 5 PCs not." (Also the corresponding findings in Results.)

If I understand this statement correctly, it may not agree with biological data. See analysis from MICRONS dataset in Bodor et al., https://www.biorxiv.org/content/10.1101/2023.10.18.562531v1.

Our statement was indeed misleading and formulated too strongly. While thick-tufted pyramidal cells do form long-range intra-cortical connections, the structural strength of these pathways is weaker than for slender-tufted PCs, which are associated with the IT (intra-telencephalic) projection type. We have made this clear in the revision.

Table 2 is confusing. What do pluses and minuses mean? What does it mean that some entries have two pluses? This table is not mentioned anywhere else in the text. If pluses mean some meaningful predictions of the model, then their distribution in the table seems quite liberal and arbitrary. It is not clear to me that the model makes that many predictions, especially for type-specificity and plasticity. Also, why is the hippocampus mentioned in this table? I don't see anything about the hippocampus anywhere else in the paper.

We have clarified the description of the table in its caption and removed references to hippocampus, which were left from an earlier draft of the paper.

In the Discussion, "Thus, we made the tools to improve our model also openly available (see Data and Code availability section)."

As mentioned before, the authors themselves write that they made "most of our tool chain openly available to the public", but not all of it.

With regard to the tool chain, everything is on our public github (https://github.com/BlueBrain/) except for the algorithm for detecting axonal appositions. For that tool there are currently unresolved potential copyright issues with former collaboration partners. We are working to resolve them.

Table S2 has multiple question marks. It is not clear whether the "predictions" listed in that table are truly well-thought-out and/or whether experimental confirmations are real.

Some of the citations in that table were broken due to technical difficulties with the citation manager used. We apologize and have fixed this in the revision.

Introduction: It would be quite appropriate to cite here Einevoll et al., Neuron, 2019 ("The Scientific Case for Brain Simulations").

We now reference this important work.

Recommendations for the authors:

Reviewing Editor's note:

Consultation with the reviewers highlighted three main issues: the integration of connection probability profiles, non-uniform cortical thickness, and the overall organization of the manuscript.

Reviewer #1 (Recommendations For The Authors):

Apart from the points discussed in the public review, my main concern is that the manuscript itself is not as tightly constructed as it should be, to the detriment of the reader's ability to understand the model itself and the conclusions from the presented analyses.

There are places where the text references seemingly incorrect figure panels or refers to panels that don't exist:

- Section 2.2, first paragraph - refers to Figure 2D, E but those panels do not exist in Figure 2.

- Section 2.2, second paragraph - refers to Figure 3D3 - perhaps it should be 3B3?

- Section 2.8, first paragraph - has no figure references but seems like it should be referring to parts of Figure 8 (perhaps Figure 8B1 specifically?)

- Is the reference to Figure S11A on page 16 supposed to be to S12A?

In other places, figure labels and descriptions are not clear, and terminology is not always well-defined or explained.

- Figure 8 and the associated section 2.8 are very difficult to draw conclusions from as presented - several of the terms used are opaque and not clearly defined in the text or legends. I could not easily infer how the normalization works for the "normalized node participation per layer", or what "position in simplex" means for "unique neurons in core", and what their "relative counts" are relative to.

- Are "targets" in Figure S12A the same as "sinks"? If so, it would be better to use a single term consistently throughout.

- Figure S12 - figures in part B do not have enough labels to interpret - what is the y-axis of the "rich-club analysis" graph? Also, the figures in part B bottom are labeled "long-range" rather than "mid-range" connections.

In general, I found the use of both letters and numbers for figure panels (e.g. Figure 7E1) more confusing than helpful - it didn't seem like panels with the same letter were visually grouped consistently, and it sometimes made it more difficult to follow the flow of a figure. I would recommend using only letters in nearly every case here.

We thank the reviewer for directing our attention to these issues. We have fixed them in the revision. However, we have decided to keep our original panel numbering scheme. Panels with the same letter are meant to be conceptually grouped as they address related or similar measures.

Other minor points:

- Section 2.4 - paragraph 2 - sentence 5 "inhbititory" -> "inhibitory".

- Figure 5B figure legend - references Schneider-Mizell et al. 2023 but probably should be Motta et al. 2019?

- Figure 5C - figure key "expcected" -> "expected".

- The lower part of Figure 7C looks like it belongs to panel D2 instead of panel C due to relative spacing.

We once again thank the reviewer, and we have fixed the listed issues.

Reviewer #2 (Recommendations For The Authors):

(1) Abstract:

- Is it really 'integrating whole brain-scale data'? This seems a bit misleading.

- "We delineated the limits of determining connectivity from anatomy" - here I think you mean determining connectivity from morphology, or dendrite/axon appositions. Electron microscopy is still anatomy and presumably would be much closer to function.

We originally used the term “anatomy” as connectivity depends on the correct placement of neurons in addition to their morphology. However, as the reviewer points out, this term is misleading as it would encompass electron microscopy, which can go beyond what we do with the model. We have updated the text to read “morphology and placement”.

(2) Introduction:

"Investigating the multi-scale interactions that shape perception requires a model of multiple cortical subregions with inter-region connectivity, but it also requires the subcellular resolution provided by a morphologically detailed model." - This statement, as written, is not true in my opinion. You can argue for the value of morphologically-detailed neuron models to the study of perception, but they are not required for the investigation of perception.

We have updated the text to be clearer: subcellular resolution is only required for certain aspects that are related to perception.

(3) Results:

- Pg. 9/10. There are three sentences in a row that are of the style: "ensuring that the total number of synapses in a region-to-region pathway matches biology." Biology here is a loose term and implies too much confidence in the matching to some ground truth. Please instead describe the source of the data, including the type of experiment here already. o Pg. 10. On the first read, I found it quite hard to follow what exactly was done in Figure 4.

What are the target values adapted from Reimann et al., 2019, for example?

- Pg. 10. "Based on these results, we decided that the local connectome sufficed to model connectivity within a region.". What is the basis for this decision? Can it be formalised? o Pg. 16, Figure 7 B-C. The apparent effect of geometry on modularity is potentially very interesting. However, are the sharp drop-offs in values for modularity (but also conicality and height) true, or are some artefacts due to columns at the edges of the sampled area?

We have discussed these points above in the general comments and strengths and weaknesses.

- Pg. 18. Simplicial cores define central subnetworks, tied together by mid-range connections. This work, in particular leading to the conclusion of the layer 5 highway hubs, stands out as being a successful attempt to simplify the highly detailed model to a degree that it generates useable new understanding.

We thank the reviewer for the kind comment.

(4) Figures:

Figure 2: The caption doesn't seem to match the Figure (e.g. there are no brain regions depicted in A). o Figure 4f. This is a key panel, but is squished into a small corner of Figure 4, and therefore hard-to-read.

We have fixed this in the revision.

Reviewer #3 (Recommendations For The Authors):

In Major comments, point (1) discusses the issue of connectivity known from data. For all the aspects of connectivity mentioned there, I would recommend the authors re-build their model using the connectivity data directly. It would be interesting to test whether a model constructed in such a way would have any difference in simulated neural activity relative to the model they have constructed.

This is indeed a very interesting avenue of research. However, we believe that it is best conducted in separate manuscripts. First, in Pokorny et al., 2024 (https://doi.org/10.1101/2024.05.24.593860) we conduct this investigation, comparing the emerging activity in the model to the one for simpler connectivity models. Additionally, in Egas-Santander et al., 2024 (https://www.biorxiv.org/content/10.1101/2024.03.15.585196v3) we found that simpler connectomes lead to less reliable spiking activity globally. Finally, in the accompanying manuscript (https://www.biorxiv.org/content/10.1101/2023.05.17.541168v5) we compare activity with and without the targeting specificity of Schneider-Mizell et al.

In Major comments, point (2) discusses thalamic inputs. I would recommend the authors to address the issues mentioned there.

We have replied to those comments above.

In addition, panels F and G of Figure 6 are mentioned in the caption but are not shown in the figure. In panel B, the choice of visualization is strange. It would make sense to show box plots for all the data instead of bars for mean values and points for randomly selected 50 cells. Panels E1 and E2 lack units.

We have removed mentions of panels F and G and changed the style of plot. Units for E1 and E2 are now explained in the figure caption.

In Major comments, point (3) touches upon model and tool sharing. I would recommend making such statements more accurate and reflecting what exactly is provided to the community since not everything is shared.

We have now made the entire model available under doi.org/10.7910/DVN/HISHXN.

With regard to the tool chain, everything is on our public github (https://github.com/BlueBrain/) except for the algorithm for detecting axonal appositions. For that tool there are currently unresolved potential copyright issues with former collaboration partners. We are working to resolve them.

I would recommend the authors address all the other points mentioned in the public review as well. In addition, below are some smaller issues that should be fixed.

Figure 2: the caption appears to be partially wrong and partially misassigned to the figure panels.

We fixed the issue.

Also, note that in L6 the types L6_TPC:A and L6_TPC:C are listed in the figure, but L6_TPC:B is not mentioned.

There is indeed no TPC:B type in layer 6. The distinction between TPC:A and TPC:B is based on early or late bifurcations of the apical dendrite and is only observed in layer 5.

Figure 3, panel B2: the caption refers to colors in panel (C), but the authors probably meant to refer to panel (A).

We fixed the issue.

"The placement of morphological reconstructions matched expectation, showing an appropriately layered structure with only small parts of neurites leaving the modeled volume (Figure 2D, E)."

Figure 2 does not have panels D and E.

"The volume was clearly dominated by dendrites, filling between 23% and 47% of the space, compared to 2% to 11% for axons (Figure 3D3)." There is no panel D or D3 in Figure 3.

"Recently, the MICrONS dataset (MICrONS-Consortium et al., 2021) has been analyzed with respect to the axonal targeting of inhibitory subtypes in a 100 x 100 μm subvolume spanning all layers (Schneider-Mizell et al., 2023)."

100 x 100 μm is an area (and should be 100 x 100 μm^2), not a volume.

Figure S11B requires a legend for the color map.

We fixed the issues.

Table S1: What is the difference between L6_BP and L6_BPC? They both are referred to as L6 bipolar cells.

We have changed the description of L6_BPC to “Layer 6 bitufted pyramidal cell”.

Author response:

Reviewer #1:

We sincerely thank you for your thoughtful review and constructive comments on our work and we appreciate your positive assessment of our study’s innovative design, which allows for improved observation of 3D cell spheroids from an additional lateral view. Your comments underscore the importance of our approach in advancing methods for investigating cell behaviors in tumor organoid studies.

In response to your suggestions, we will first add a detailed image of the ‘First surface mirror’ in Fig. 1 to provide a reference for readers and other researchers, thereby facilitating broader use of this method in similar observations. Regarding the suitable sample sizes for this device, as the spheroid sizes are relatively small compared to the mirror and culture dish, we have been able to image samples up to 5 mm in height, which provides ample capacity for most spheroids under 1 mm. We will include additional experiments and explanations in the manuscript to clarify this further.

Concerning the ring-shaped seeding pattern of spheroids, we have conducted extensive culture experiments to optimize this method. The agarose microwells-based method has proven to be highly tolerant of variations. Within these microwells, cells have a propensity to self-aggregate, leading to the formation of spheroid structures. We will add a discussion in the revised manuscript to address this issue.

Lastly, this device can accommodate the fluorescence imaging of 3D spheroid samples. We will supplement the discussion with a schematic illustrating the principles of fluorescence imaging using this device, providing a foundation for future work in this area. We will also regarding language improvements to enhance the overall quality of the manuscript.

Thank you once again for your valuable insights, which have greatly contributed to the strengthening of our manuscript.

Reviewer #2:

We sincerely thank you for your detailed and supportive review of our manuscript. Your recognition of our system’s capabilities for in situ observation of 3D structures along multiple axes, as well as its potential applications in studying therapeutic effects, is highly encouraging. Your comments on the advantages of this system for analyzing cell migration, morphological changes, and responses to therapeutic agents are especially appreciated.

Thank you again for your thoughtful feedback and for highlighting the contributions of our work. Your insights have been invaluable in refining the focus and clarity of our study, and we hope that our revisions meet your expectations.

Author response:

Public reviews:

Reviewer #1:

Epigenetic regulation complex (PRC2) is essential for neural crest specification, and its misregulation has been shown to cause severe craniofacial defects. This study shows that Eed, a core PRC2 component, is critical for craniofacial osteoblast differentiation and mesenchymal proliferation after neural crest induction. Using mouse genetics and single-cell RNA sequencing, the researcher found that conditional knockout of Eed leads to significant craniofacial hypoplasia, impaired osteogenesis, and reduced proliferation of mesenchymal cells in post-migratory neural crest populations.

Overall, the study is superficial and descriptive. No in-depth mechanism was analyzed and the phenotype analysis is not comprehensive.

We thank the reviewer for sharing their expertise and for taking the time to provide a helpful suggestion to improve our study. We are gratified that the striking phenotypes we report from Eed loss in post-migratory neural crest craniofacial tissues were appreciated. The breadth and depth of our phenotyping techniques, including skeletal staining, micro-CT, echocardiogram, immunofluorescence, histology, and unbiased single-cell gene expression analysis, provide comprehensive data in support our conclusion that PRC2 is required for craniofacial osteoblast differentiation. We hypothesize that epigenetic regulation of chromatin accessibility downstream of PRC2 activity is the molecular mechanism that underlies these phenotypes. To test this hypothesis in our revision, we are using CUT&Tag to profile H3K27me3 epigenetic modifications genome-wide and at the loci encoding the differentially expressed genes revealed by our single-cell transcriptomics in developing craniofacial structures. We anticipate that these experiments will reveal an epigenetic mechanism underlying the phenotypes we report from Eed loss in post-migratory neural crest craniofacial tissues.

Reviewer #2:

Summary:The role of PRC2 in post-neural crest induction was not well understood. This work developed an elegant mouse genetic system to conditionally deplete EED upon SOX10 activation. Substantial developmental defects were identified for craniofacial and bone development. The authors also performed extensive single-cell RNA sequencing to analyze differentiation gene expression changes upon conditional EED disruption.

Strengths:

(1) Elegant genetic system to ablate EED post neural crest induction.

(2) Single-cell RNA-seq analysis is extremely suitable for studying the cell type-specific gene expression changes in developmental systems.

We thank the reviewer for their generous and helpful comments on our study. We are pleased that our mouse genetic and single-cell RNA sequencing approaches were appropriate in pairing the craniofacial phenotypes we report with distinct gene expression changes in post-migratory neural crest tissues upon Eed deletion.

Weaknesses:

(1) Although this study is well designed and contains state-of-the-art single-cell RNA-seq analysis, it lacks the mechanistic depth in the EED/PRC2-mediated epigenetic repression. This is largely because no epigenomic data was shown.

Thank you for this suggestion. As described in response to Reviewer #1, we will include H2K27me3 CUT&Tag data in craniofacial tissue harvested from E12.5 and E16.5 Sox10-Cretg+ Eedfl/fl and Sox10-Cretg+ Eedfl/wt  embryos in our revision. Our analyses will including genome-wide and targeted metaplot visualizations across genotypes and developmental timepoints and assess how H3K27me3 occupancy relates to gene expression changes in our single-cell RNA sequencing data.

(2) The mouse model of conditional loss of EZH2 in neural crest has been previously reported, as the authors pointed out in the discussion. What is novel in this study to disrupt EED? Perhaps a more detailed comparison of the two mouse models would be beneficial.

We acknowledge the study the reviewer has indicated (Schwarz et al. Development 2014). This elegant investigation uses Wnt1-Cre to delete Ezh2 and found a similar phenotype to ours in the form of catastrophic craniofacial hypoplasia. We sought to add depth to the study of PRC2’s vital role in neural crest development by ablating Eed, which has a unique function in the PRC2 complex by binding to H3K27me3 and allosterically activating Ezh2. In this sense, we sought to test if phenotypes arising from deletion of Eed, the PRC2 “reader”, differ from phenotypes arising from deletion of Ezh2, the PRC2 “writer”, in neural crest derived tissues. Due to limitations associated with the Wnt1-Cre transgene (Lewis et al. Developmental Biology 2013), we used the Sox10-Cre allele which targets the migratory neural crest and is completely recombined by E10.5, instead of Wnt1-Cre which targets pre-migratory neural crest cells. A more detailed comparison of these mouse models will be included in the Discussion section of our revised manuscript, and we thank the reviewer for this thoughtful suggestion.

(3) The presentation of the single-cell RNA-seq data may need improvement. The complexity of the many cell types blurs the importance of which cell types are affected the most by EED disruption.

We agree with the reviewer’s critique of the scRNA-seq data presentation. Because Sox10+ cells were not sorted (via FACS, for example) from craniofacial tissues before single-cell RNA sequencing, we identified a breath of cell types in UMAP space unrelated to epigenetic disruption of neural crest derived tissues. We will include subcluster visualization plots in the figures of our revised manuscript to highlight specific changes in clusters, such as osteoblasts and mesenchymal stem cells, that arise from Eed loss in post-migratory neural crest craniofacial tissues.

(4) While it's easy to identify PRC2/EED target genes using published epigenomic data, it would be nice to tease out the direct versus indirect effects in the gene expression changes (e.g Figure 4e).

We agree with the reviewer that our single-cell RNA sequencing data do not provide insight into direct versus indirect changes in gene expression downstream of PRC2. We hope that the aforementioned CUT&Tag experiment will provide the necessary mechanistic insight into H3K27me3 occupancy and direct effects on gene expression resulting from PRC2 inactivation in our mouse model.

Author response:

Public Reviews:

Reviewer #1 (Public review):

One of the roadblocks in PfEMP1 research has been the challenges in manipulating var genes to incorporate markers to allow the transport of this protein to be tracked and to investigate the interactions taking place within the infected erythrocyte. In addition, the ability of Plasmodium falciparum to switch to different PfEMP1 variants during in vitro culture has complicated studies due to parasite populations drifting from the original (manipulated) var gene expression. Cronshagen et al have provided a useful system with which they demonstrate the ability to integrate a selectable drug marker into several different var genes that allows the PfEMP1 variant expression to be 'fixed'. This on its own represents a useful addition to the molecular toolbox and the range of var genes that have been modified suggests that the system will have broad application. As well as incorporating a selectable marker, the authors have also used selective linked integration (SLI) to introduce markers to track the transport of PfEMP1, investigate the route of transport, and probe interactions with PfEMP1 proteins in the infected host cell.

What I particularly like about this paper is that the authors have not only put together what appears to be a largely robust system for further functional studies, but they have used it to produce a range of interesting findings including:

- Co-activation of rif and var genes when in a head-to-head orientation.

- The reduced control of expression of var genes in the 3D7-MEED parasite line.

- More support for the PTEX transport route for PfEMP1.

- Identification of new proteins involved in PfEMP1 interactions in the infected erythrocyte, including some required for cytoadherence.

In most cases the experimental evidence is straightforward, and the data support the conclusions strongly. The authors have been very careful in the depth of their investigation, and where unexpected results have been obtained, they have looked carefully at why these have occurred.

(1) In terms of incorporating a drug marker to drive mono-variant expression, the authors show that they can manipulate a range of var genes in two parasite lines (3D7 and IT4), producing around 90% expression of the targeted PfEMP1. Removal of drug selection produces the expected 'drift' in variant types being expressed. The exceptions to this are the 3D7-MEED line, which looks to be an interesting starting point to understand why this variant appears to have impaired mutually exclusive var gene expression and the EPCR-binding IT4var19 line. This latter finding was unexpected and the modified construct required several rounds of panning to produce parasites expressing the targeted PfEMP1 and bind to EPCR. The authors identified a PTP3 deficiency as the cause of the lack of PfEMP1 expression, which is an interesting finding in itself but potentially worrying for future studies. What was not clear was whether the selected IT4var19 line retained specific PfEMP1 expression once receptor panning was removed.

This is a very interesting point. We do not have systematic long-term data for the Var19 line but medium-term data. After panning the Var19 line, the binding assays were done within 3 months without additional panning. The first binding assay was 2 months after the panning and the last binding assays three weeks later. While there is inherent variation in these assays that precludes detection of smaller changes, the last assay showed the highest level of binding, giving no indication for rapid loss of the binding phenotype. Hence, we can say that the binding phenotype appears to be stable for many weeks without panning the cells again and there was no indication for a rapid loss of binding in these parasites.

Systematic long-term experiments to assess how long the Var19 parasites retain binding would be interesting, but given that the binding-phenotype appears to remain stable over many weeks, this would only make sense if done for a much longer time (6 months or more). Due to the time needed to carry out such an experiment this would not be practical to still include into the present study. But this might be advisable if the Var19 line is used in future experiments that go over extended periods of time. We intend to include a statement in the discussion of the revised manuscript to highlight that if long-term work with this line is planned, monitoring the binding phenotype and potentially re-panning might be advisable.

(2) The transport studies using the mDHFR constructs were quite complicated to understand but were explained very clearly in the text with good logical reasoning.

We are aware of this being a complex issue and are glad this was nevertheless understandable.

(3) By introducing a second SLI system, the authors have been able to alter other genes thought to be involved in PfEMP1 biology, particularly transport. An example of this is the inactivation of PTP1, which causes a loss of binding to CD36 and ICAM-1. It would have been helpful to have more insight into the interpretation of the IFAs as the anti-SBP1 staining in Figure 5D (PTP-TGD) looks similar to that shown in Figure 1C, which has PTP intact. The anti-EXP2 results are clearly different.

We realize the description of the PTP1-TGD IFA data and that of the other TGDs was rather cursory. We intend to amend this in the revision.

(4) It is good to see the validation of PfEMP1 expression includes binding to several relevant receptors. The data presented use CHO-GFP as a negative control, which is relevant, but it would have been good to also see the use of receptor mAbs to indicate specific adhesion patterns. The CHO system if fine for expression validation studies, but due to the high levels of receptor expression on these cells, moving to the use of microvascular endothelial cells would be advisable. This may explain the unexpected ICAM-1 binding seen with the panned IT4var19 line.

We agree with the reviewer that it is desirable to have better binding systems for studying individual binding interactions. As the main purpose of this paper was to introduce the system and show binding, we did not move to more complicated binding systems. However, we would like to point out that the CSA binding was done on receptor alone in addition to the CSA-expressing HBEC-5i cells and was competed successfully with soluble CSA. In addition, apart from the additional ICAM1-binding of the Var19 line, all binding phenotypes were conform with expectations. We therefore hope the tools used for binding studies are acceptable at this stage of introducing the system while future work interested in specific PfEMP1 receptor interactions are advised to use better systems, ideally including also endothelial organoid models, inhibitory antibodies and possibly domain competition. We intend to add a sentence to the discussion highlighting that future work using this system to study individual receptor-interactions could benefit from using optimized binding systems.

(5) The proxiome work is very interesting and has identified new leads for proteins interacting with PfEMP1, as well as suggesting that KAHRP is not one of these. The reduced expression seen with BirA* in position 3 is a little concerning but there appears to be sufficient expression to allow interactions to be identified with this construct. The quantitative impact of reduced expression for proxiome experiments will clearly require further work to define it.

This is a valid point. Clearly there seems to be some impact on binding when BirA* is placed in the extracellular domain (either through reduced presentation or direct reduction of binding efficiency of the modified PfEMP1). The exact impact on the proxiome is indeed difficult to assess. However, we hope that the general coverage of proteins proximal to PfEMP1 with the 3 PfEMP1-BirA* constructs will aid in the identification of proteins involved in PfEMP1 transport and surface display as illustrated with two of the hits targeted here.

(6) The reduced receptor binding results from the TryThrA and EMPIC3 knockouts were very interesting, particularly as both still display PfEMP1 on the surface of the infected erythrocyte. While care needs to be taken in cross-referencing adhesion work in P. berghei and whether the machinery truly is functionally orthologous, it is a fair point to make in the discussion. The suggestion that interacting proteins may influence the "correct presentation of PfEMP1" is intriguing and I look forward to further work on this.

We hope we future work will be able to shed light on this.

Overall, the authors have produced a useful and reasonably robust system to support functional studies on PfEMP1, which may provide a platform for future studies manipulating the domain content in the exon 1 portion of var genes. They have used this system to produce a range of interesting findings and to support its use by the research community.<br /> Finally, a small concern. Being able to select specific var gene switches using drug markers could provide some useful starting points to understand how switching happens in P. falciparum. However, our trypanosome colleagues might remind us that forcing switches may show us some mechanisms but perhaps not all.

Point noted! From non-systematic data with the Var01 line that has been cultured for extended periods of time (several years), it seems other non-targeted vars remain silent in our SLI “activation” lines but how much SLI-based var-expression “fixing” tampers with the integrity of natural switching mechanisms is indeed very difficult to gage at this stage. We intend to add a statement to the manuscript that even if mutually exclusive expression is maintained, it is not certain the mechanisms controlling var expression all remain intact.

Reviewer #2 (Public review):

Summary

Croshagen et al develop a range of tools based on selection-linked integration (SLI) to study PfEMP1 function in P. falciparum. PfEMP1 is encoded by a family of ~60 var genes subject to mutually exclusive expression. Switching expression between different family members can modify the binding properties of the infected erythrocyte while avoiding the adaptive immune response. Although critical to parasite survival and Malaria disease pathology, PfEMP1 proteins are difficult to study owing to their large size and variable expression between parasites within the same population. The SLI approach previously developed by this group for genetic modification of P. falciparum is employed here to selectively and stably activate the expression of target var genes at the population level. Using this strategy, the binding properties of specific PfEMP1 variants were measured for several distinct var genes with a novel semi-automated pipeline to increase throughput and reduce bias. Activation of similar var genes in both the common lab strain 3D7 and the cytoadhesion competent FCR3/IT4 strain revealed higher binding for several PfEMP1 IT4 variants with distinct receptors, indicating this strain provides a superior background for studying PfEMP1 binding. SLI also enables modifications to target var gene products to study PfEMP1 trafficking and identify interacting partners by proximity-labeling proteomics, revealing two novel exported proteins required for cytoadherence. Overall, the data demonstrate a range of SLI-based approaches for studying PfEMP1 that will be broadly useful for understanding the basis for cytoadhesion and parasite virulence.

Comments

(1) While the capability of SLI to actively select var gene expression was initially reported by Omelianczyk et al., the present study greatly expands the utility of this approach. Several distinct var genes are activated in two different P. falciparum strains and shown to modify the binding properties of infected RBCs to distinct endothelial receptors; development of SLI2 enables multiple SLI modifications in the same parasite line; SLI is used to modify target var genes to study PfEMP1 trafficking and determine PfEMP1 interactomes with BioID. Curiously, Omelianczyk et al activated a single var (Pf3D7_0421300) and observed elevated expression of an adjacent var arranged in a head-to-tail manner, possibly resulting from local chromatin modifications enabling expression of the neighboring gene. In contrast, the present study observed activation of neighboring genes with head-to-head but not head-to-tail arrangement, which may be the result of shared promoter regions. The reason for these differing results is unclear although it should be noted that the two studies examined different var loci.

The point that we are looking at different loci is very valid and we realize this is not mentioned in the discussion. In the revision we intend to add this as a possible reason for this discrepancy. As stated in the discussion, the head-to-head scenario was observed before in lines obtained with panning. However, given the rather few examples where this was analyzed, it is well possible that this varies with gene locus and we will make sure that the revised version of the manuscript will be careful to highlight that it is not clear how much this observation in our work can be generalized.

(2) The IT4var19 panned line that became binding-competent showed increased expression of both paralogs of ptp3 (as well as a phista and gbp), suggesting that overexpression of PTP3 may improve PfEMP1 display and binding. Interestingly, IT4 appears to be the only known P. falciparum strain (only available in PlasmoDB) that encodes more than one ptp3 gene (PfIT_140083100 and PfIT_140084700). PfIT_140084700 is almost identical to the 3D7 PTP3 (except for a ~120 residue insertion in 3D7 beginning at residue 400). In contrast, while the C-terminal region of PfIT_140083100 shows near-perfect conservation with 3D7 PTP3 beginning at residue 450, the N-terminal regions between the PEXEL and residue 450 are quite different. This may indicate the generally stronger receptor binding observed in IT4 relative to 3D7 results from increased PTP3 activity due to multiple isoforms or that specialized trafficking machinery exists for some PfEMP1 proteins.

We thank the reviewer for pointing this out, it is an interesting idea that the PTP3 duplication could be a reason for the superior binding of IT4. We intend to add this point to the discussion of the revision.

So far it seems the PTP3 issue occurred only with Var19. The thought of an extra layer of control, particularly for PfEMP1 variants that might be associated with virulence such as Var19, is very attractive. At present, the manuscript alludes to the possibility of an extra layer of control in the discussion. As var-type specificity and existence of such mechanisms in vivo are so far not known we decided not to speculate on this.

Reviewer #3 (Public review):

Summary:

The submission from Cronshagen and colleagues describes the application of a previously described method (selection linked integration) to the systematic study of PfEMP1 trafficking in the human malaria parasite Plasmodium falciparum. PfEMP1 is the primary virulence factor and surface antigen of infected red blood cells and is therefore a major focus of research into malaria pathogenesis. Since the discovery of the var gene family that encodes PfEMP1 in the late 1990s, there have been multiple hypotheses for how the protein is trafficked to the infected cell surface, crossing multiple membranes along the way. One difficulty in studying this process is the large size of the var gene family and the propensity of the parasites to switch which var gene is expressed, thus preventing straightforward gene modification-based strategies for tagging the expressed PfEMP1. Here the authors solve this problem by forcing the expression of a targeted var gene by fusing the PfEMP1 coding region with a drug-selectable marker separated by a skip peptide. This enabled them to generate relatively homogenous populations of parasites all expressing tagged (or otherwise modified) forms of PfEMP1 suitable for study. They then applied this method to study various aspects of PfEMP1 trafficking.

Strengths:

The study is very thorough, and the data are well presented. The authors used SLI to target multiple var genes, thus demonstrating the robustness of their strategy. They then perform experiments to investigate possible trafficking through PTEX, they knock out proteins thought to be involved in PfEMP1 trafficking and observe defects in cytoadherence, and they perform proximity labeling to further identify proteins potentially involved in PfEMP1 export. These are independent and complimentary approaches that together tell a very compelling story.

Weaknesses:

(1) When the authors targeted IT4var19, they were successful in transcriptionally activating the gene, however, they did not initially obtain cytoadherent parasites. To observe binding to ICAM-1 and EPCR, they had to perform selection using panning. This is an interesting observation and potentially provides insights into PfEMP1 surface display, folding, etc. However, it also raises questions about other instances in which cytoadherence was not observed. Would panning of these other lines have been successfully selected for cytoadherent infected cells? Did the authors attempt panning of their 3D7 lines? Given that these parasites do export PfEMP1 to the infected cell surface (Figure 1D), it is possible that panning would similarly rescue binding. Likewise, the authors knocked out PTP1, TryThrA, and EMPIC3 and detected a loss of cytoadhesion, but they did not attempt panning to see if this could rescue binding. To ensure that the lack of cytoadhesion in these cases is not serendipitous (as it was when they activated IT4var19), they should demonstrate that panning cannot rescue binding.

These are very important points. Indeed, we had repeatedly attempted to pan 3D7 when we failed to get the SLI-generated 3D7 PfEMP1 expressor lines to bind, but this had not been successful. After the move to IT4 which readily bound we made no further efforts to understand why 3D7 does not bind but the fact that PfEMP1 is on the surface indicates this is not a PTP3 issue. Also, as the parent 3D7 could not be panned, we assumed it is not easily fixed.

Panning the TGD lines: we see the reasoning for conducting panning experiments with the TGD lines, but on second thought we are unsure this should be attempted. The outcome might not be easily interpretable if panning leads to increased binding and considerable follow up analyses would be needed to define what has happened. The reason for this is that at least two forces will contribute to the selection in panning experiments with TGD lines that lost binding. Firstly, panning would work against the SLI of the TGD, resulting in a tug of war between the TGD-SLI and binding: a very low frequency of parasites can be expected to loop out the TGD plasmid and would normally be eliminated during standard culturing due to the SLI drug used for the TGD. These revertant cells would bind and the panning would enrich them (hence, panning and SLI are opposed in the case of a TGD abolishing binding). It is unclear how strong such an effect can be, but this might lead to mixed populations that complicate interpretations. The second selecting force are possible compensatory changes to restore binding. These can come in two flavors: reversal of potential independent changes that may have occurred in the TGD parasites and that are in reality causing the binding loss (the concern of the reviewer) or new changes to compensate the loss of the TGD target (in case the TGD is the cause of the binding loss). As both of the TGDs in the paper show some residual binding and have VAR01 on the surface to at least some extent, it is possible that new compensatory changes might indeed occur that indirectly increase binding again. In summary, even if more binding after panning of the lines occurs, it is not clear whether this is due to a compensatory change ameliorating the TGD or reversal of an unrelated change. The impact of repeated panning against SLI is also unknown. To determine the cause, the panned TGD lines would need to be subjected to a complex and time-consuming analysis (WGS, RNASeq, possibly Maurer’s clefts IFA phenotype) to find out whether they had an unrelated chance change that was reverted or a new compensatory change that helps binding.

The detection of VAR01 on the surface of these TGDs speaks against a PTP3 effect. While we can’t fully exclude other changes in the TGDs that might affect binding, we conducted WGS which did not show any obvious alterations that could be responsible. To fully exclude loss of ptp3 expression as the reason as seen with Var19 (something we would not have seen in the WGS if it is only due to a transcriptional change), we intend to carry out RNASeq with the two TGD lines. The third TGD mentioned by the reviewer (targeting ptp1) was a positive control of a known PfEMP1 trafficking protein, so we assume this does not need to be further validated.

(2) The authors perform a series of trafficking experiments to help discern whether PfEMP1 is trafficked through PTEX. While the results were not entirely definitive, they make a strong case for PTEX in PfEMP1 export. The authors then used BioID to obtain a proxiome for PfEMP1 and identified proteins they suggest are involved in PfEMP1 trafficking. However, it seemed that components of PTEX were missing from the list of interacting proteins. Is this surprising and does this observation shed any additional light on the possibility of PfEMP1 trafficking through PTEX? This warrants a comment or discussion.

This is an interesting comment and we agree we should have discussed this. A likely reason why PTEX components are not picked up as interactors is that BirA* is expected to become unfolded when it passes through the channel and in that state can’t biotinylate. Labelling likely would only be possible if PfEMP1 lingered at the PTEX translocation step before BirA* became unfolded to go through the channel which we would not expect under physiological conditions. We intend to add a sentence to the discussion why we think PTEX components would not be detected in our BioIDs even if PfEMP1 passes through it but that this might also be an argument against it passing through PTEX.

Author response:

Reviewer #1 (Public review):

The results of this manuscript look at the interplay between pleiotropy, standing genetic variation, and parallelism (i.e. predictability of evolution) in gene expression. Ultimately, their results suggest that (a) pleiotropic genes typically have a smaller range in variation/expression, and (b) adaptation to similar environments tends to favor changes in pleiotropic genes, which leads to parallelism in mechanisms (though not dramatically). However, it is still uncertain how much parallelism is directly due to pleiotropy, instead of a complex interplay between them and ancestral variation.

I have a few things that I was uncertain about. It may be these things are easily answered but require more discussion or clarity in the manuscript.

(1) The variation being talked about in this manuscript is expression levels, and not SNPs within coding regions (or elsewhere). The cause of any specific gene having a change in expression can obviously be varied - transcription factors, repressors, promoter region variation, etc. Is this taken into account within the "network connectivity" measurement? I understand the network connectivity is a proxy for pleiotropy - what I'm asking is, conceptually, what can be said about how/why those highly pleiotropic genes have a change (or not) in expression. This might be a question for another project/paper, but it feels like a next step worth mentioning somewhere.

In current study, we are only able to detect significant and repeatable expression changes but unable to identify the underlying causal variants. An eQTL study in the founder population in combination with genomic resequencing for both evolved and ancestral populations would be required to address this question.

(2) The authors do have a passing statement in line 361 about cis-regulatory regions. Is the assumption that genetic variation in promoter regions is the ultimate "mechanism" driving any change in expression? In the same vein, the authors bring up a potential confounding factor, though they dismiss it based on a specific citation (lines 476-481; citation 65). I'm of the mindset that in order to more confidently disregard this "issue" based on previous evidence, it requires more than one citation. Especially since the one citation is a plant. That specific point jumps out to me as needing a more careful rebuttal.

It was not our intention to claim that the expression changes in our experiment are caused by cis-regulatory variation only. We believe that the observed expression variation has both cis- and trans-genetic components, where as some studies tend to estimate much higher cisvariation for gene expression in Drosophila populations (e.g. [1, 2]). We mentioned the positive correlation between cis-regulatory polymorphism and expression variation to (1) highlight the genetic control of gene expression and (2) make the connection between polygenic adaptation and gene expression evolutionary parallelism.

(3) I feel like there isn't enough exploration of tissue specificity versus network connectivity. Tissue specificity was best explained by a model in which pleiotropy had both direct and indirect effects on parallelism; while network connectivity was best explained (by a small margin) via the model which was mostly pleiotropy having a direct effect on ancestral variation, that then had a direct effect on parallelism. When the strengths of either direct/indirect effects were quantified, tissue specificity showed a stronger direct effect, while network connectivity had none (i.e. not significant). My confusion is with the last point - if network connectivity is explained by a direct effect in the best-supported model, how does this work, since the direct effect isn't significant? Perhaps I am misunderstanding something.

To clarify, for network connectivity, there’s a significant “indirect” effect on parallelism (i.e. network connectivity affect ancestral gene expression and ancestral gene expression affect parallelism). Hence, in table 2, the direct effect of network connectivity on parallelism is weak and not significant while the indirect effect via ancestral variation is significant.

Also, network connectivity might favor the most pleiotropic genes being transcription factor hubs (or master regulators for various homeostasis pathways); while the tissue specificity metric perhaps is a kind of a space/time element. I get that a gene having expression across multiple tissues does fit the definition of pleiotropy in the broad sense, but I'm wondering if some important details are getting lost - I'm just thinking about the relative importance of what tissue specificity measurements say versus the network connectivity measurement.

We examined the statistical relationship between the two measures and found a moderate positive correlation on the basis of which we argued that the two measures may capture different aspects of pleiotropy. We appreciate the reviewer’s suggestions about the biological basis of the two estimates of pleiotropy, but we think that without further experimental insights, an extended discussion of this topic is too premature to provide meaningful insights to the readership.

Reviewer #2 (Public review):

Summary:

Lai and collaborators use a previously published RNAseq dataset derived from an experimental evolution set up to compare the pleiotropic properties of genes whose expression evolved in response to fluctuating temperature for over 100 generations. The authors correlate gene pleiotropy with the degree of parallelisms in the experimental evolution set up to ask: are genes that evolved in multiple replicates more or less pleiotropic?

They find that, maybe counter to expectation, highly pleiotropic genes show more replicated evolution. Such an effect seems to be driven by direct effects (which the authors can only speculate on) and indirect effects through low variance in pleiotropic genes (which the authors indirectly link to genetic variation underlying gene expression variance).

Weaknesses:

The results offer new insights into the evolution of gene expression and into the parameters that constrain such evolution, i.e., pleiotropy. Although the conclusions are supported by the data, I find the interpretation of the results a little bit complicated.

Major comment:

The major point I ask the authors to address is whether the connection between polygenic adaptation and parallelism can indeed be used to interpret gene expression parallelism. If the answer is not, please rephrase the introduction and discussion, if the answer is yes, please make it explicit in the text why it is so.

Our answer is yes, we interpreted gene expression parallelism (high ancestral variance -> less parallelism) using the same framework that links polygenic adaptation and parallelism (high polygenicity = less trait parallelism). We believe that our response covers several of the reviewer’s concerns.

The authors' argument: parallelism in gene expression is the same as parallelism in SNP allele frequency (AFC) (see L389-383 here they don't mention that this explanation is derived from SNP parallelism and not trait parallelism, and see Figure 1 b). In previous publications, the authors have explained the low level of AFC parallelism using a polygenic argument. Polygenic traits can reach a new trait optimum via multiple SNPs and therefore although the trait is parallel across replicates, the SNPs are not necessarily so.

Importantly, our rationale is based on the idea that gene expression is rarely the direct target of selection, but rather an intermediate trait [3]. Recently, we have specifically tested this assumption for gene expression and metabolite concentrations and our analysis showed that both traits were are redundant [4], as previously shown for DNA sequences [5]. The important implication for this manuscript is that gene expression is also redundant, so that adaptation can be achieved by distinct changes in gene expression in replicate populations adapting to the same selection pressure. This implies that we can use the same simulation framework for gene expression as for sequencing data. In our case different SNP frequencies correspond to different expression levels (averaged across individuals from a population), which in turn increases fitness by modifying the selected trait. Importantly, the selected trait in our simulations is not gene expression, but a not defined high level phenotype. A key insight from our simulations is that with increasing polygenicity the expression of a gene is more variable in the ancestral population.

In the current paper, they seem to be exchanging SNP AFC by gene expression, and to me, those are two levels that cannot be interchanged. Gene expression is a trait, not an SNP, and therefore the fact that a gene expression doesn't replicate cannot be explained by a polygenic basis, because again the trait is gene expression itself. And, actually, the results of the simulations show that high polygenicity = less trait parallelism (Figure 4).

As detailed above, because adaptation can be reached by changes in gene expression at different sets of genes, redundancy is also operating on the expression level not just on the level of SNPs. To clarify, the x-axis of Fig. 4 is the expression variation in the ancestral population.

Now, if the authors focus on high parallel genes (present in e.g. 7 or more replicates) and they show that the eQTLs for those genes are many (highly polygenic) and the AFC of those eQTLs are not parallel, then I would agree with the interpretation. But, given that here they just assess gene expression and not eQTL AFC, I do not think they can use the 'highly polygenic = low parallelism' explanation.

The interpretation of the results to me, should be limited to: genes with low variance and high pleiotropy tend to be more parallel, and the explanation might be synergistic pleiotropy.

While we understand the desire to model the full hierarchy from eQTLs to gene expression and adaptive traits, we raise caution that this would be a very challenging task. eQTLs very often underestimate the contribution of trans-acting factors, hence the understanding of gene expression evolution based on eQTLs is very likely incomplete and cannot explain the redundancy of gene expression during adaptation. Hence, we think that the focus on redundant gene expression is conceptually simpler and thus allows us to address the question of pleiotropy without the incorporation of allele frequency changes.  

Reviewer #3 (Public review):

The authors aim to understand how gene pleiotropy affects parallel evolutionary changes among independent replicates of adaptation to a new hot environment of a set of experimental lines of Drosophila simulans using experimental evolution. The flies were RNAsequenced after more than 100 generations of lab adaptation and the changes in average gene expression were obtained relative to ancestral expression levels from reconstructed ancestral lines. Parallelism of gene expression change among lines is evaluated as variance in differential gene expression among lines relative to error variance. Similarly, the authors ask how the standing variation in gene expression estimated from a handful of flies from a reconstructed outbred line affects parallelism. The main findings are that parallelism in gene expression responses is positively associated with pleiotropy and negatively associated with expression variation. Those results are in contradiction with theoretical predictions and empirical findings. To explain those seemingly contradictory results the authors invoke the role of synergistic pleiotropy and correlated selection, although they do not attempt to measure either.

Strengths:

(1) The study uses highly replicated outbred laboratory lines of Drosophila simulans evolved in the lab under a constant hot regime for over 100 generations. This allows for robust comparisons of evolutionary responses among lines.

(2) The manuscript is well written and the hypotheses are clearly delineated at the onset.

(3) The authors have run a causal analysis to understand the causal dependencies between pleiotropy and expression variation on parallelism.

(4) The use of whole-body RNA extraction to study gene expression variation is well justified.

Weaknesses:

(1) It is unclear how well phenotypic variation in gene expression of the evolved lines has been estimated by the sample of 20 males from a reconstructed outbred line not directly linked to the evolved lines under study. I see this as a general weakness of the experimental design.

Our intention was not to measure the phenotypic variance of the evolved lines, but rather to estimate the phenotypic variance at the beginning of the experiment. Hence, we measured and investigated the variation of gene expression in the ancestral population since this was the beginning of the replicated experimental evolution. Furthermore, since the ancestral population represents the natural population in Florida, the gene expression variation reflects the history of selection history acting on it.

(2) There are no estimates of standing genetic variation of expression levels of the genes under study, only phenotypic variation. I wished the authors had been clear about that limitation and had discussed the consequences of the analysis. This also constitutes a weakness of the study.

The reviewer is correct that we do not aim to estimate the standing genetic variation, which is responsible for differences in gene expression. While we agree that it could be an interesting research question to use eQTL mapping to identify the genetic basis of gene expression, we caution that trans-effects are difficult to estimate and therefore an important component of gene expression evolution will be difficult to estimate. Hence, we consider that our focus on variation in gene expression without explicit information about the genetic basis is simpler and sufficient to address the question about the role of pleiotropy.

(3) Moreover, since the phenotype studied is gene expression, its genetic basis extends beyond expressed sequences. The phenotypic variation of a gene's expression may thus likely misrepresent the genetic variation available for its evolution. The genetic variation of gene expression phenotypes could be estimated from a cross or pedigree information but since individuals were pool-sequenced (by batches of 50 males), this type of analysis is not possible in this study.

We agree with the reviewer that gene expression variation may also have a non-genetic basis, we discuss this in depth in the discussion of the manuscript.  

(4) The authors have not attempted to estimate synergistic pleiotropy among genes, nor how selection acts on gene expression modules. It makes any conclusion regarding the role of synergistic pleiotropy highly speculative.

We mentioned synergistic pleiotropy as a possible explanation for our results. A positive correlation between the fitness effect of gene expression variation would predict more replicable evolutionary changes. A similar argument has been made by [6]. 

I don't understand the reason why the analysis would be restricted to significantly differentially expressed genes only. It is then unclear whether pleiotropy, parallelism, and expression variation do play a role in adaptation because the two groups of adaptive and non-adaptive genes have not been compared. I recommend performing those comparisons to help us better understand how "adaptive" genes differentially contribute to adaptation relative to "nonadaptive" genes relative to their difference in population and genetic properties.

We agree with the reviewer that the comparison between the pleiotropy of adaptive and nonadaptive genes is interesting. We performed the analysis but omitted from the current manuscript for simplicity. Similar to the results in [6], non-adaptive genes are more pleiotropic than the adaptive genes. For adaptive genes we find a positive correlation between the level of pleiotropy and evolutionary parallelism. Thus, high pleiotropy limits the evolvability of a gene, but moderate and potentially synergistic pleiotropy increases the repeatability of adaptive evolution. We included this result in the revised manuscript and discuss it.

There is a lack of theoretical groundings on the role of so-called synergistic pleiotropy for parallel genetic evolution. The Discussion does not address this particular prediction. It could be removed from the Introduction.

We modestly disagree with the reviewer, synergistic pleiotropy is covered by theory and empirical results also support the importance of synergistic pleiotropy. 

References

(1) Genissel A, McIntyre LM, Wayne ML, Nuzhdin SV. Cis and trans regulatory effects contribute to natural variation in transcriptome of Drosophila melanogaster. Molecular biology and evolution. 2008;25(1):101-10. Epub 20071112. doi: 10.1093/molbev/msm247. PubMed PMID: 17998255.

(2) Osada N, Miyagi R, Takahashi A. Cis- and Trans-regulatory Effects on Gene Expression in a Natural Population of Drosophila melanogaster. Genetics. 2017;206(4):2139-48. Epub 20170614. doi: 10.1534/genetics.117.201459. PubMed PMID: 28615283; PubMed Central PMCID: PMCPMC5560811.

(3) Barghi N, Hermisson J, Schlötterer C. Polygenic adaptation: a unifying framework to understand positive selection. Nature reviews Genetics. 2020;21(12):769-81. Epub 2020/07/01. doi: 10.1038/s41576-020-0250-z. PubMed PMID: 32601318.

(4) Lai WY, Otte KA, Schlötterer C. Evolution of Metabolome and Transcriptome Supports a Hierarchical Organization of Adaptive Traits. Genome biology and evolution. 2023;15(6). Epub 2023/05/26. doi: 10.1093/gbe/evad098. PubMed PMID: 37232360; PubMed Central PMCID: PMCPMC10246829.

(5) Barghi N, Tobler R, Nolte V, Jaksic AM, Mallard F, Otte KA, et al. Genetic redundancy fuels polygenic adaptation in Drosophila. PLoS biology. 2019;17(2):e3000128. Epub 2019/02/05. doi: 10.1371/journal.pbio.3000128. PubMed PMID: 30716062.

(6) Rennison DJ, Peichel CL. Pleiotropy facilitates parallel adaptation in sticklebacks. Molecular ecology. 2022;31(5):1476-86. Epub 2022/01/09. doi: 10.1111/mec.16335. PubMed PMID: 34997980; PubMed Central PMCID: PMCPMC9306781.

Author response:

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

eLife Assessment 

This valuable study is a detailed investigation of how chromatin structure influences replication origin function in yeast ribosomal DNA, with a focus on the role of the histone deacetylase Sir2 and the chromatin remodeler Fun30. Convincing evidence shows that Sir2 does not affect origin licensing but rather affects local transcription and nucleosome positioning which correlates with increased origin firing. Overall, the evidence is solid and the model plausible. However, the methods employed do not rigorously establish a key aspect of the mechanism where initiation precisely occurs or rigorously exclude alternative models and the effect of Sir2 on transcription is not re-examined in the fun30 context. 

Clarification on Sir2 Effect on Transcription in the fun30 Context

We appreciate the reviewers’ thorough assessment but would like to clarify that the effect of Sir2 on transcription in the fun30 context was addressed in both the original and revised manuscripts. However, we recognize that the presentation of the qPCR results may have been unclear, as we initially plotted absolute transcript levels without normalizing for rDNA array size differences among the genotypes. We have now corrected this.

After normalizing for copy number variations, the qPCR data show that the sir2 fun30 double mutant results in a ~40-fold increase in C-pro transcription relative to WT, compared to a 4-fold and 19-fold increase in fun30 and sir2 single mutants, respectively (Figure 5, figure supplement 6). These results have been discussed in the manuscript result section, where we note that "C-pro RNA levels were approximately twice as high in sir2 fun30 compared to sir2 cells when adjusted for rDNA size differences." This observation is critical for addressing both alternative models of MCM disappearance and for pinpointing transcription initiation sites, as detailed in the following sections.

Public Reviews: 

Reviewer #1 (Public review): 

Summary: 

This paper presents a mechanistic study of rDNA origin regulation in yeast by SIR2. Each of the ~180 tandemly repeated rDNA gene copies contains a potential replication origin. Earlyefficient initiation of these origins is suppressed by Sir2, reducing competition with origins distributed throughout the genome for rate-limiting initiation factors. Previous studies by these authors showed that SIR2 deletion advances replication timing of rDNA origins by a complex mechanism of transcriptional de-repression of a local PolII promoter causing licensed origin proteins (MCMcomplexes) to re-localize (slide along the DNA) to a different (and altered) chromatin environment. In this study, they identify a chromatin remodeler, FUN30, that suppresses the sir2∆ effect, and remarkably, results in a contraction of the rDNA to about onequarter it's normal length/number of repeats, implicating replication defects of the rDNA. Through examination of replication timing, MCM occupancy and nucleosome occupancy on the chromatin in sir2, fun30, and double mutants, they propose a model where nucleosome position relative to the licensed origin (MCM complexes) intrinsically determines origin timing/efficiency. While their interpretations of the data are largely reasonable and can be interpreted to support their model, a key weakness is the connection between Mcm ChEC signal disappearance and origin firing. While the cyclical chromatin association-dissociation of MCM proteins with potential origin sequences may be generally interpreted as licensing followed by firing, dissociation may also result from passive replication and as shown here, displacement by transcription and/or chromatin remodeling. Moreover, linking its disappearance from chromatin in the ChEC method with such precise resolution needs to be validated against an independent method to determine the initiation site(s). Differences in rDNA copy number and relative transcription levels also are not directly accounted for, obscuring a clearer interpretation of the results. Nevertheless, this paper makes a valuable advance with the finding of Fun30 involvement, which substantially reduces rDNA repeat number in sir2∆ background. The model they develop is compelling and I am inclined to agree, but I think the evidence on this specific point is purely correlative and a better method is needed to address the initiation site question. The authors deserve credit for their efforts to elucidate our obscure understanding of the intricacies of chromatin regulation. At a minimum, I suggest their conclusions on these points of concern should be softened and caveats discussed. Statistical analysis is lacking for some claims. 

Strengths are the identification of FUN30 as suppressor, examination of specific mutants of FUN30 to distinguish likely functional involvement. Use of multiple methods to analyze replication and protein occupancies on chromatin. Development of a coherent model. 

Weaknesses are failure to address copy number as a variable; insufficient validation of ChEC method relationship to exact initiation locus; lack of statistical analysis in some cases. 

Review of revised version and response letter: 

In the response, the authors make some improvements by better quantifying 2D gels, adding some missing statistical analyses, analyzing the effect of fun30 on rDNA replication in strains with reduced rDNA copy number, and using ChIP-seq of MCMs to support the ChEC-seq data. However, these additions do not address the main issue that is at the heart of their model: where initiation precisely occurs and whether the location is altered in the mutant(s). Thus, mechanistic insight is limited.

We discuss the issue regarding the initiation site below.

Under the section "Addressing Alternative Explanations", the authors claim that processes like transcription and passive replication cannot affect the displaced complex specifically. Why? They are not on same DNA (as mentioned in the Fig 1 legend). 

Premature origin activation, not transcription, drives the disappearance of repositioned MCM complexes in sir2 mutants in HU.

Indeed, the reviewer is correct in suggesting that C-pro transcription confined to rDNA units with repositioned MCM complexes could selectively displace those complexes, potentially explaining the selective disappearance of displaced MCMs in sir2 cells. However, our analysis of C-pro transcription and MCM occupancy in G1 versus HU across the genotypes allows us to rule out this possibility.

We show that the fraction of repositioned MCMs in G1 cells is proportional to the level of C-pro transcription (WT < fun30 << sir2 < sir2 fun30), consistent with the involvement of transcription in the repositioning process during MCM loading in G1. Accordingly, with approximately twice the transcription in sir2 fun30 compared to sir2, we observe more repositioned MCMs in sir2 fun30 cells than in sir2 cells in G1 (Fig 5C).

However, if the disappearance of repositioned MCMs in HU were solely due to C-pro transcription rather than origin activation, we would expect the repositioned MCMs to disappear more quickly in sir2 fun30 cells. Contrary to this expectation, our data show that repositioned MCM complexes are more stable in sir2 fun30 mutants compared to sir2 mutants, indicating that transcription is not the primary factor in the disappearance of displaced MCM complexes in HU; rather, rDNA origin activation appears to be the key factor.

Replication initiation site in sir2. Using multiple independent approaches, including 2D gels, ChIP-seq, and EdU incorporation, we have demonstrated that rDNA origins fire prematurely in sir2 mutants, a conclusion that the reviewer does not contest. Once an origin fires, the MCM signal disappears from the site of its initial deposition, as expected, and this is confirmed in our MCM ChIP and HU ChEC data, both at rDNA origins and across the genome.

Given that the majority of MCM complexes in sir2 mutants are repositioned, it is expected that these repositioned complexes disappear following premature origin activation. With less than half of the licensed origins (or <30% of total rDNA copies) retaining MCM at non-repositioned sites in sir2 mutants, if only these non-repositioned complexes were firing, and the repositioned MCM complexes were disappearing via mechanisms other than replication initiation (e.g., transcription), rDNA replication in sir2 mutants would be severely compromised rather than accelerated. Given this, and the strong experimental evidence that repositioned MCM complexes fire prematurely, continued focus on alternative explanations for MCM complex disappearance seems unwarranted.

We present this analysis in the results section as follows:

“Finally, although deletion of FUN30 could suppress replication initiation at the rDNA either by inhibiting the firing of the active, repositioned MCM complex or by preventing MCM repositioning to the "active location" in the first place, our results suggest that suppression occurs through the former mechanism. Consistent with previous reports that fun30 mutants are deficient in transcriptional silencing (Neves-Costa et al. 2009), C-pro RNA levels were approximately twice as high in sir2 fun30 cells compared to sir2 cells when adjusted for rDNA size (Figure 5—figure supplement 6).

Moreover, deletion of FUN30 shifts the distribution toward the repositioned MCM location over the non-repositioned one in G1 cells (Figure 5C), aligning with the increased C-pro transcription observed in fun30 mutants. This shift is evident in both sir2 and SIR2 cells. Despite the increased transcription-mediated repositioning in sir2 fun30 cells compared to sir2 cells during G1, repositioned MCM persists longer in sir2 fun30 cells than in sir2 cells after release into HU. Additionally, sir2 fun30 mutants exhibit reduced MCM accumulation at the RFB compared to sir2 mutants after release into HU, supporting the conclusion that MCM disappearance in HU reflects origin activation rather than transcription-mediated displacement.”

The model in Fig 7 implies that initiation sites are different in WT versus the mutants and this determines their timing/efficiency. But they also suggest that the same site might be used with different efficiencies in this response. I agree that both are possibilities and are not resolved. 

Adjustment of the model to account for repositioned MCMs in WT cells In Figure 5—figure supplement 5, we demonstrate that even in WT cells, a small fraction of repositioned MCMs (~5%) can be detected, and that these repositioned MCM complexes disappear prematurely. However, because this represents a very small fraction of MCMs in WT cells, we initially did not include it in our overall model in Figure 7. In light of the reviewer's comment, we have now revised the model to incorporate this detail.

Supporting their model requires better resolution to determine the actual replication initiation site. While this may be challenging, it should be feasible with methods to map nascent strands like DNAscent, or Okazaki fragment mapping.

The initiation site in sir2 mutants has been thoroughly analyzed and supported by extensive experimental data, as discussed above. While high-resolution techniques such as DNAscent or Okazaki fragment mapping could potentially offer another layer of validation, the likelihood of obtaining finer detail that would change the conclusions is minimal. The methods we employed provide sufficient resolution to pinpoint the initiation site, and our results align consistently with established replication models.

Further experimentation would not only be redundant but also unlikely to provide new insights beyond revalidation. Given the strength of our current data, we believe the conclusions regarding replication initiation are robust and well-supported, making additional experiments unnecessary at this stage. Our priority is to focus on advancing other aspects of the research that require deeper exploration.

The 2D gel analysis of strains with reduced rDNA copy numbers adequately addresses the copy number variable with regard to the replication effect. 

Overall, the paper is improved by providing additional data and improved analysis. The paper nicely characterizes the effect of Fun30. The model is reasonable but remains lacking in precise details of mechanism. 

Reviewer #2 (Public review): 

Summary: 

In this manuscript, the authors follow up on their previous work showing that in the absence of the Sir2 deacetylase the MCM replicative helicase at the rDNA spacer region is repositioned to a region of low nucleosome occupancy. Here they show that the repositioned displaced MCMs have increased firing propensity relative to non-displaced MCMs. In addition, they show that activation of the repositioned MCMs and low nucleosome occupancy in the adjacent region depend on the chromatin remodeling activity of Fun30. 

Strengths: 

The paper provides new information on the role of a conserved chromatin remodeling protein in regulation of origin firing and in addition provides evidence that not all loaded MCMs fire and that origin firing is regulated at a step downstream of MCM loading. 

Weaknesses: 

The relationship between the authors results and prior work on the role of Sir2 (and Fob1) in regulation of rDNA recombination and copy number maintenance is not explored, making it difficult to place the results in a broader context. Sir2 has previously been shown to be recruited by Fob1, which is also required for DSB formation and recombination-mediated changes in rDNA copy number. Are the changes that the authors observe specifically in fun30 sir2 cells related to this pathway? Is Fob1 required for the reduced rDNA copy number in fun30 sir2 double mutant cells? 

Reviewer #3 (Public review): 

Summary: 

Heterochromatin is characterized by low transcription activity and late replication timing, both dependent on the NAD-dependent protein deacetylase Sir2, the founding member of the sirtuins. This manuscript addresses the mechanism by which Sir2 delays replication timing at the rDNA in budding yeast. Previous work from the same laboratory (Foss et al. PLoS Genetics 15, e1008138) showed that Sir2 represses transcription-dependent displacement of the Mcm helicase in the rDNA. In this manuscript, the authors show convincingly that the repositioned Mcms fire earlier and that this early firing partly depends on the ATPase activity of the nucleosome remodeler Fun30. Using read-depth analysis of sorted G1/S cells, fun30 was the only chromatin remodeler mutant that somewhat delayed replication timing in sir2 mutants, while nhp10, chd1, isw1, htl1, swr1, isw2, and irc5 had no effect. The conclusion was corroborated with orthogonal assays including two-dimensional gel electrophoresis and analysis of EdU incorporation at early origins. Using an insightful analysis with an Mcm-MNase fusion (Mcm-ChEC), the authors

show that the repositioned Mcms in sir2 mutants fire earlier than the Mcm at the normal position in wild type. This early firing at the repositioned Mcms is partially suppressed by Fun30. In addition, the authors show Fun30 affects nucleosome occupancy at the sites of the repositioned Mcm, providing a plausible mechanism for the effect of Fun30 on Mcm firing at that position. However, the results from the MNAse-seq and ChEC-seq assays are not fully congruent for the fun30 single mutant. Overall, the results support the conclusions providing a much better mechanistic understanding how Sir2 affects replication timing at rDNA, 

Strengths 

(1) The data clearly show that the repositioned Mcm helicase fires earlier than the Mcm in the wild type position. 

(2) The study identifies a specific role for Fun30 in replication timing and an effect on nucleosome occupancy around the newly positioned Mcm helicase in sir2 cells. 

Weaknesses 

(1) It is unclear which strains were used in each experiment. 

(2) The relevance of the fun30 phospho-site mutant (S20AS28A) is unclear. 

(3) For some experiments (Figs. 3, 4, 6) it is unclear whether the data are reproducible and the differences significant. Information about the number of independent experiments and quantitation is lacking. This affects the interpretation, as fun30 seems to affect the +3 nucleosome much more than let on in the description. 

Recommendations for the authors:  

Reviewer #2 (Recommendations for the authors): 

The authors have addressed my concerns by the addition of new experiments and analysis. 

One point remains unclear regarding additional support for the Mcm-ChEC results using ChIP experiments to verify whether MCM redistributes in sir2D cells. In their rebuttal, the authors state that, "New supporting based evidence: ChIP at rDNA Origins. Our ChIP analysis also shows that the disappearance of the MCM signal at rDNA origins in sir2Δ cells released into HU is accompanied by signal accumulation at the replication fork barrier (RFB), indicative of stalled replication forks at this location (Figure 5 figure supplement 3)...." The ChIP data in Figure 5 supplement 3 show accumulation of the Mcm2 ChIP signal to the left of the RFB in sir2D cells but it doesn't look like there is any decrease in the MCM signal in sir2D relative to wild-type cells for the peak C-Pro. There is a new MCM peak suggesting perhaps a new MCM loading event. 

Figure 5 figure supplement 3 shows the relative abundance of the MCM ChIP signal across the ~2 kb rDNA region, spanning from the MCM loading site at the rDNA origin (on the left) to the replication fork barrier (RFB) on the right. The MCM-ChIP data are normalized to the highest signal within this rDNA region rather than across the entire genome, meaning that only the relative abundance of MCM within this region is represented, and not comparisons between different conditions. We have now presented the results with the same axes for both alpha factor and HU.

In wild-type (WT) cells, the MCM signal remains primarily at the initial loading site. However, in sir2 mutants, a significant portion of the MCM signal shifts rightward, consistent with rDNA origin activation and the movement of MCM along with the progressing replication fork. While some replication forks stall at the RFB, others are positioned between the MCM loading site and the RFB. The additional MCM peak observed does not represent a new MCM loading event, as the experiment was conducted during S-phase, when new MCM loading is not possible.

Reviewer #3 (Recommendations for the authors): 

In this revision the authors addressed my concerns and improved the manuscript and the presentation of the data. All my recommendations were implemented.

Author response:

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

Reviewer #2 (Public review):

Summary:

In their manuscript the authors report that fecal transplantation from young mice into old mice alleviates susceptibility to gout. The gut microbiota in young mice is found to inhibit activation of the NLRP3 inflammasome pathway and reduce uric acid levels in the blood in the gout model.

Strengths:

They focused on the butanoate metabolism pathway based on the results of metabolomics analysis after fecal transplantation and identified butyrate as the key factor in mitigating gout susceptibility. In general, this is a well-performed study.

Weaknesses:

The discussion on the current results and previous studies regarding the effect of butyrate on gout symptoms is insufficient. The authors need to provide a more thorough discussion of other possible mechanisms and relevant literature.

Reviewer #2 (Recommendations for the authors):

General comments:

I appreciate the authors' efforts to answer the comments raised in my previous review (as Reviewer#2). However, I still detect some issues that need to be fully addressed, with inadequate or even no answers for several comments.

Thank you for your valuable feedback. Your previous suggestions have been incredibly helpful for our paper. Although we have strived to make the article as comprehensive as possible, there may still be some areas that are not perfectly refined.

The response to comment 1: The author's statement is not very convincing. What are the trends of inflammation factors? The data in Figure 1G-H suggest that butyrate may not be the only factor to explain this phenomenon. Authors should carefully interpret the data in Figure 1G-H.

Sorry for the inadequate clarification on your question. We utilize antibiotics for treatment in order to establish the relationship between gut microbiota, age, and gout. Our research findings indicate that there is a trend for serum uric acid levels to increase with age, and we also observe that the older the age, the more pronounced the stimulation to MSU. We found that after clearing the gut microbiota and then stimulating with MSU, the trend of inflammation factors and serum uric acid level changing with age disappears. Thus, we can preliminarily draw the conclusion that the gut microbiota is closely associated with age, gout, and hyperuricemia.

The response to comment 2: I understand the importance of evaluating a range of indicators, but food thickness is the most crucial clinical marker for diagnosing goats. Please move the data from Supplemental Figure 1A to the main figure.

Thank you for your suggestions. We have included the most significant results in the main figure, and the description of “foot thickness” has already been provided descriptively in the manuscript. Additionally, considering the layout and arrangement of the images, we have placed it in the supplementary figures 1.

The response to comment 3: The immunostaining for ZO-1 and Occludin is unclear. Please provide higher magnification images to confirm the specific staining.

Thank you for your valuable feedback. We have enhanced the clarity of the images. In addition to adding immunohistochemical images in Supplementary Material 4, we have also submitted independent images.

The response to comment 4: The authors still haven't directly addressed my comment.

Please accept our sincere apologies for not providing a clearer response to your question. The indicators related to uric acid-producing enzymes and uric acid transporters have been separately analyzed according to different age groups. The specific results are detailed in section " The expression of uric acid-producing enzymes activity and uric acid transporters at the mRNA level across different age groups" of Supplementary Material 4.

No response was given for comment 5. Please address it.

In a PCoA plot, the distance between samples reflects the similarity in the structure of the microbial communities: the closer the distance, the more similar the composition of the communities; the greater the distance, the more pronounced the differences. We judge based on the relative distances of each group in the plot, observing their degree of proximity.

The response to comment 6: I understand the author's statement, and I suggest incorporating it into the discussion section of the revised manuscript.

Thank you for your suggestions. We have incorporated the relevant content into our discussion.

The response to comment 7: Again, please incorporate this statement into the discussion section of the revised manuscript.

Thank you for your suggestions. We have incorporated the relevant content into our discussion.

Reviewer #3 (Public review):

Summary:

The revised manuscript presents interesting findings on the role of gut microbiota in gout, focusing on the interplay between age-related changes, inflammation, and microbiota-derived metabolites, particularly butyrate. The study provides valuable insights into the therapeutic potential of microbiota interventions and metabolites for managing hyperuricemia and gout. While the authors have addressed many of the previous concerns, a few areas still require clarification and improvements to strengthen the manuscript's clarity and overall impact.

(1) While the authors mention that outliers in the data do not affect the conclusions, there remains a concern about the reliability of some figures (e.g., Figure 2D-G). It is recommended to provide a more detailed explanation of the statistical analysis used to handle outliers. Additionally, the clarity of the Western blot images, particularly IL-1β in Figure 3C, should be improved to ensure clear and supportive evidence for the conclusions.

Thank you for your suggestion. We respond as follows: (1) Outliers can occasionally constitute intrinsic elements of the dataset, reflecting genuine occurrences within the experimental context. The elimination of such outliers has the potential to introduce bias into the results, thereby facilitating misconceptions regarding the underlying phenomenon under investigation. In order to maintain the transparency and integrity of the dataset, we have elected to retain the outliers within our analysis. This decision is based on the recognition that these values may represent genuine experimental observations or unique conditions that are inherently meaningful to the phenomenon under investigation. By preserving these data points, we aim to provide a comprehensive and unbiased representation of the experimental results, allowing for a more nuanced interpretation of the findings. (2) Due to the scarcity of samples, we are unable to fulfill your request in the short term. Furthermore, we have noted that the band for IL-1β in Figure 3C is indeed visible and we consider it suitable for subsequent analysis.

(2) The manuscript raises a key question about why butyrate supplementation and FMT have different effects on uric acid metabolism and excretion. While the authors have addressed this by highlighting the involvement of multiple bacterial genera, it is still recommended to expand on the differences between these interventions in the discussion, providing more mechanistic insights based on available literature.

Thank you for your suggestion. We have enriched the discussion in the manuscript and included additional comparisons

(3) It is noted that IL-6 and TNF-α results in foot tissue were requested and have been added to supplementary material. However, the main text should clearly reference these additions, and the supplementary figures should be thoroughly reviewed for consistency with the main findings. The use of abbreviations (e.g., ns for no significant difference) and labeling should also be carefully checked across all figures.

Thank you for your valuable feedback. We have revised the manuscript in accordance with your suggestions.

(4) The manuscript presents butyrate as a key molecule in gout therapy, yet there are lingering concerns about its central role, especially given that other short-chain fatty acids (e.g., acetic and propionic acids) also follow similar trends. The authors should consider further acknowledging these other SCFAs and discussing their potential contribution to gout management. Additionally, the rationale for focusing primarily on butyrate in subsequent research should be made clearer.

Thank you for your input. We have incorporated additional evidence into the discussion, explaining why we ultimately chose butyrate in subsequent research.

(5) The full-length uncropped Western blot images should be provided as requested, to ensure transparency and reproducibility of the data.

Thank you for your suggestion. We have already included the relevant explanations in the manuscript.

(6) Despite the authors' revisions, several references still lack page numbers. Please ensure that all references are properly formatted, including complete page ranges.

Thank you for your suggestions; we will make more detailed revisions to the references.

The manuscript has improved with the revisions made, particularly regarding clarifications on experimental design and the inclusion of supplementary data. However, some concerns about data quality, mechanistic insights, and clarity in the figures remain. Addressing these points will enhance the overall impact of the work and its potential contribution to the understanding of the gut microbiome in gout and hyperuricemia. A final revision, with careful attention to both major and minor points, is highly recommended before resubmission.

Once again, we are grateful for your suggestions and recognition. Your input has been of immense help to our manuscript and has also provided us with a valuable learning opportunity.

Author response:

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

eLife Assessment

The aim of this valuable study is to identify novel genes involved in sleep regulation and memory consolidation. It combines transcriptomic approaches following memory induction with measurements of sleep and memory to discover molecular pathways underlying these interlinked behaviors. The authors explore transcriptional changes in specific mushroom body neurons and suggest roles for two genes involved in RNA processing, Polr1F and Regnase-1, in the regulation of sleep and memory. Although this work exploits convincing and validated methodology, the strength of the evidence is incomplete to support the main claim that these two genes establish a definitive link between sleep and memory consolidation.

We appreciate the reconsideration of our manuscript and recognize that we should have toned down the claims, especially with respect to the link between sleep and memory consolidation.  We have now changed the title, the abstract and the main text and also Figure 5 to essentially just state our findings.  While there is a little speculation in the Discussion, we point out that future work would be required to draw conclusions. We believe the manuscript still represents a considerable advance in showing the modulation of RNA processing genes during sleep-dependent memory consolidation in the relevant neurons, and also showing how one such gene affects sleep and translation and a second affects sleep and memory. 

Public Reviews:

Reviewer #2 (Public review):

Prior work by the Sehgal group has shown that a small group of neurons in the fly brain (anterior posterior (ap) α'β' mushroom body neurons (MBNs)) promote sleep and sleep-dependent appetitive memory specifically under fed conditions (Chouhan et al., (2021) Nature). Here, Li, Chouhan et al. combine cell-specific transcriptomics with measurements of sleep and memory to identify molecular processes underlying this phenomenon. They define transcriptional changes in ap α'β' MBNs and suggest a role for two genes downregulated following memory induction (Polr1F and Regnase-1) in regulating sleep and memory.

The transcriptional analyses in this manuscript are impressive. The authors have now included additional experiments that define acute and developmental roles for Polr1F and Regnase-1 respectively in regulating sleep. They have also provided additional data to strengthen their conclusion that Polr1F knockdown in α'β' mushroom body neurons enhances sleep.

The resubmitted work represents a convincing investigation of two novel sleep-regulatory proteins that may also play important roles in memory formation.

The authors have comprehensively addressed my comments, which I very much appreciate. I congratulate them on this excellent work.

We very much appreciate the reviewer’s positive feedback. Thank you!

Reviewer #3 (Public review):

Previous work (Chouhan et al., 2022) from the Sehgal group investigated the relationship between sleep and long-term memory formation by dissecting the role of mushroom body intrinsic neurons, extrinsic neurons, and output neurons during sleep-dependent and sleep-independent memory consolidation. In this manuscript, Li et al., profiled transcriptome in the anterior-posterior (ap) α'/β' neurons and identified genes that are differentially expressed after training in fed condition, which supports sleep-dependent memory formation. By knocking down candidate genes systematically, the authors identified Polr1F and Regnase-1 as two important hits that play potential roles in sleep and memory formation. What is the function of sleep and how to create a memory are two long-standing questions in science. The present study used a new approach to identify novel components that may link sleep and memory consolidation in a specific type of neuron. Importantly, these components implicated that RNA processing may play a role in these processes.

While I am enthusiastic about the innovative approach employed to identify RNA processing genes involved in sleep regulation and memory consolidation, I feel that the data presented in the manuscript is insufficient to support the claim that these two genes establish a definitive link between sleep and memory consolidation. First, the developmental role of Regnase-1 in reducing sleep remains unclear because knocking down Regnase-1 using the GeneSwitch system produced neither acute nor chronic sleep loss phenotype. In the revised manuscript, the author used the Gal80ts to restrict the knockdown of Regnase-1 in adult animals and concluded that Regnase-1 RNAi appears to affect sleep through development. Conducting overexpression experiments of Regnase-1 would lend some credibility to the phenotypes, however, this is not pursued in the revised manuscript. Second, while constitutive Regnase-1 knockdown produced robust phenotypes for both sleep-dependent and sleep-independent memory, it also led to a severe short-term memory phenotype. This raises the possibility that flies with constitutive Regnase-1 knockdown are poor learners, thereby having little memory to consolidate. The defect in learning could be simply caused by chronic sleep loss before training. Thus, this set of results does not substantiate a strong link between sleep and long-term memory consolidation. Lastly, the discussion on the sequential function of training, sleep, and RNA processing on memory consolidation appears speculative based on the present data.

We thank the reviewer for the enthusiasm about the approach. As noted above, we have now removed all claims about a link between sleep and memory, and instead just emphasize that we have identified RNA processing genes that affect sleep and memory.  We agree with the reviewer that the basis of the Regnase-1 memory phenotype is unclear as the flies may be poor learners.  Also, the learning/memory defects could be secondary to sleep loss or, as Reviewer 4 below suggests, all the behavioral deficits could be caused by impaired development/function of the relevant ap ɑ′/β′ cells. We have now included this possibility in the discussion of the manuscript.  And we have modified the discussion on training, RNA processing, sleep and memory to emphasize the need for future experiments to address the sequence and relationship of these different processes. 

Reviewer #4 (Public review):

Summary:

Li and Chouhan et al. follow up on a previous publication describing the role of anterior-posterior (ap) and medial (m) ɑ′/β′ Kenyon cells in mediating sleep-dependent and sleep-independent memory consolidation, respectively, based on feeding state in Drosophila melanogaster. The authors sequenced bulk RNA of ap ɑ′/β′ Kenyon cells 1h after flies were either trained-fed, trained-starved or untrained-fed and find a small number of genes (59) differentially expressed (3 upregulated, 56 downregulated) between trained-fed and trained-starved conditions. Many of these genes encode proteins involved in the regulation of gene expression. The authors then screened these differentially expressed genes for sleep phenotypes by expressing RNAi hairpins constitutively in ap ɑ′/β′ Kenyon cells and measuring sleep patterns. Two hits were selected for further analysis: Polr1F, which promoted sleep, and Regnase-1, which reduced sleep. The pan-neuronal expression of Polr1F and Regnase-1 RNAi constructs was then temporally restricted to adult flies using the GeneSwitch system. Polr1F sleep phenotypes were still observed, while Regnase-1 sleep phenotypes were not, indicating developmental defects. Appetitive memory was then assessed in flies with constitutive knockdown of Polr1F and Regnase-1 in ap ɑ′/β′ Kenyon cells. Polr1F knockdown did not affect sleep-dependent or sleep-independent memory, while Regnase-1 knockdown disrupted sleep-dependent memory, sleep-independent memory, as well as learning. Polr1F knockdown increased pre-ribosomal RNA transcripts in the brain, as measured by qPCR, in line with its predicted role as part of the RNA polymerase I complex. A puromycin incorporation assay to fluorescently label newly synthesized proteins also indicated higher levels of bulk translation upon Polr1F knockdown. Regnase-1 knockdown did not lead to observable changes in measurements of bulk translation.

Strengths:

The proposed involvement of RNA processing genes in regulating sleep and memory processes is interesting, and relatively unexplored. The methods are satisfactory.

Weaknesses:

The main weakness of the paper is in the overinterpretation of their results, particularly relating to the proposed link between sleep and memory consolidation, as stated in the title. Constitutive Polr1F knockdown in ap ɑ′/β′ Kenyon cells had no effect on appetitive long-term memory, while constitutive Regnase-1 knockdown affected both learning and memory. Since the effects of constitutive Regnase-1 knockdown on sleep could be attributed to developmental defects, it is quite plausible that these same developmental defects are what drive the observed learning and memory phenotypes. In this case, an alternative explanation of the authors' findings is that constitutive Regnase-1 knockdown disrupts the entire functioning of ap ɑ′/β′ Kenyon cells, and as a consequence behaviors involving these neurons (i.e. learning, memory and sleep) are disrupted. It will be important to provide further evidence of the function of RNA processing genes in memory in order to substantiate the memory link proposed by the authors.

As noted above, we have removed claims of a link between sleep and memory and instead focused the manuscript on our findings of RNA processing genes modulated during sleep-dependent memory. We concur that impaired development of ap ɑ′/β′neurons could account for the sleep and memory phenotype observed and have included this possibility in the manuscript.

Recommendations for the authors:

Reviewer #4 (Recommendations for the authors):

The title of the paper should be reconsidered to reflect the results. The evidence for a link between RNA processing genes and memory is weak.

We have changed the title.

Line 328. The term "central dogma" is misused. The central dogma refers to the unidirectional flow of information from DNA to protein. Instead the authors mean "gene expression".

Changed, thank you.

A couple of minor comments relating to the figures:

Figure 1b. It is not clear what the number 10570 in the bottom right corner refers to.

Fixed.

Figure 3b. RU- and RU+ annotation is missing (as shown in 3d).

Fixed.

Author response:

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

Reviewer #1 (Public Review)

(1) The identification of the proximal to distal degeneration of the tailgut within the human tail is difficult to distinguish with the current images present in Figure 3. A picture within a picture of the area containing the tail gut could be provided to prominently demonstrate the cellular architecture. Additionally, quantification of the localization of apoptosis would strongly support this observation, as well as provide a visualization of the tail's regression overall. For example, a graph plotting the number of apoptotic cells versus the rostral to caudal locations of the transverse sections while accounting for the CS stage of each analyzed embryo could be created; this could even be further broken down by region of tail, for example, tailgut, ventral ectodermal ridge, somite, etc.

To provide more information on apoptosis, we prepared serial sections from an additional 6 human tails, 5 of which were processed for fluorescence anti-caspase 3 immunohistochemistry with DAPI staining (Fig 4) and H&E (Fig 6). This confirmed our previous finding of apoptosis especially in the tailgut and ventral mesoderm. We have not quantified the apoptosis, given the difficulty of deciding whether anti-caspase signals represent single or multiple dying cells. Instead, we performed a tissue area analysis from caudal to rostral along the tail (new section on p 9). This shows a progressive enlargement of the neural tube, no change in the notochord and a striking reduction in tailgut area (Fig 4C,D). The smaller tailgut has fewer nuclei in cross section rostrally compared with more caudally (Fig 4E). Given that apoptosis is present in the tailgut at all rostro-caudal levels, this is consistent with a rostralto-caudal loss of the tailgut, as is also found in mouse and rat embryos.

(2) The identification of the mode of formation of the secondary neural tube is probably the most interesting question to be addressed, however, Figure 7's evidence is not completely satisfying in its current form. While I agree that it is unlikely that multiple polarization foci form within the most caudal part of the tail and coalesce more rostrally, I am equally unsure that a single polarization would form rostrally and then split and re-coalesce as it moves caudally, as is currently depicted by 7B. Multiple groups have recently shown the influence of geometric confinement on neuroectoderm and its ability to polarize and form a singular central lumen (Karzbrun 2021, Knight 2018), or the inverse situation of a lack of confinement resulting in the presence of multiple lumens. The tapering of the diameter of the tail and its shared perimeter and curvature with the polarization bears a striking resemblance to this controlled confinement. An interesting quantification to depict would include the number of lumens versus the transverse section diameter and CS stage to see if there is any correlation between embryo size and the number of multiple polarizations. Anecdotally, the fusion of multiple polarizations/lumens tends to occur often in these human organoid-type platforms, while splitting to multiple lumens as the tissues mature does not. Other supplements to Figure 7 could include 3D renderings of lumens of interest as depicted in Catala 2021, especially if it demonstrates the recoalescence as seen in 7B. The non-pathologic presence of multiple polarizations in human tails compared to the rodent pathogenic counterpart is interesting given that rodents obviously maintain this appendage while it is lost in humans.   

The additional 6 sectioned human embryo tails (as described above) provide further information in support of the original findings of the paper: (i) that the secondary neural tube formation initially involves a single lumen, and (ii) that neural tube duplication occurs in many tails at more rostral levels. Neural tube duplication was observed in 15/25 of our sectioned tails: hence, overall 60% of human tails exhibited neural tube duplication in this study. We have replaced all the cross sectional images in the original Fig 7 (now Fig 6) to better illustrate the findings of neural tube duplication at relatively rostral levels of the human tail. Additionally, the axial position of sections containing duplicated neural tube are indicated by arrows in the graph of neural tube areas (Fig 4C). From this analysis it appears that neural tube duplication is not contingent on an increasing tail diameter, as raised by the reviewer, because some tails show a transition to neural tube duplication, and then return to an single lumen morphology more rostrally. While the 3D renderings of lumens would be interesting, we consider it beyond the scope of the present study.

(3) Of potential interest is the process of junctional neurulation describing the mechanistic joining of the primary and secondary neural tube, which has recently been explored in chick embryos and demonstrated to have relevance to human disease (Dady 2014, Eibach 2017, Kim 2021). While it is clear this paper's goal does not center on the relationship between primary and secondary neurulation, such a mechanism may be relevant to the authors' interpretation of their observations of lumen coalescence. I wonder if the embryos studied provide any evidence to support junctional neurulation.  

We agree this is an important point to address in the paper, and a new section has been inserted in the Discussion: ‘Transition from primary to secondary neurulation’ (pp 13-14). In brief, we find no evidence for a specific mode of ‘junctional neurulation’ in the human embryos. In any event, its existence is hypothetical in humans, suggested largely as an ‘embryological explanation’ for the finding of rare interrupted spinal cord defects in neurosurgical patients (Eibach, 2017). In chick neurulation there is longitudinal dorso-ventral overlap between the primary and secondary neural tubes (Dryden, 1980), with the junctional zone derived from ingressing cells at the node-streak border (Dady, 2014), a known source of neuromesodermal progenitors (NMPs). However, this is a very different developmental situation from the human so-called ‘junctional neurulation’ defect (Eibach, 2017), in which the spinal cord is physically and functionally interrupted, with only a rudimentary filament connecting the rostral and caudal parts.

Reviewer #1 (Recommendations For The Authors):

(1) Figures 3, 4, and 7, would be easier to digest quickly with inclusions of labels that mark the rostral and caudal transverse sections. For example, "caudal" over 3G and "rostral" over 3F.  

Figures 3 and 4 have been combined to form revised Figure 3, and the rostral/caudal sections are no longer included, as these are superseded by the new Figure 4. Similarly Figure 7 has been replaced by new images in the revised Figure 6, with clear labelling of axial levels.

(2) The manuscript does a nice job of comparing and contrasting the human findings to mouse, however, there are several instances where it would be nice to continue this trend within the text, such as including the rate of somite formation for rodents in the sections that you state the quantified human and published organoid findings, as well as the total number of somite rodents' exhibit. Additionally, the last sentence of the "Morphology of human PNP closure" section correctly states that human PNP's seem to close via Mode 2 neurulation that is seen in the mouse. However, my read of the literature (published by Dr. Copp) demonstrates that the PNP in mice actually closes via Mode 3 at the most caudal portion. If this is the case, it would be pointed to explicitly state that regionally dependent morphogenetic difference between the two species.  

We agree these are important points to include. The additional somite data (for mouse) has been inserted in the Results section on ‘Somite formation’ (p 8), and the apparent absence of Mode 3 during human spinal neural tube closure is now included in the new Discussion section, ‘Transition from primary to secondary neurulation’ (pp 13-14).

(3) The introduction to secondary neural tube formation with the hypothesis diagrams in Figure 7 is slightly jarring. At the beginning of the Figure, a schematic depicting the morphogenetic differences between primary and secondary would be helpful in introducing the readership to these complex embryologic events. An example of this could be similar to Figure 1 in Dr. Copp's paper:

Nikolopoulou, E., et al. Neural tube closure: cellular, molecular and biomechanical mechanisms. Development 144, 552-566 (2017).  

We feel that a summary diagram of primary and secondary neurulation would simply reproduce diagrams that are already widespread in the literature. As noted by the Reviewer, our article in Development (Nikolopoulou, 2017) contains just such a summary diagram as Figure 1. Therefore, we prefer to explicitly cite this article/figure in our Introduction (see modified first sentence, third paragraph, p 3), so that readers can consult the freely accessible Nikolopoulou review for more detail. The diagram in Figure 7 (now revised Figure 6) has been completely redrawn to make much clearer the hypotheses being examined in the study of human secondary neural tube formation, and neural tube duplication.

(4) Finally, a matter of semantics, the second paragraph of the introduction describes myelomeningocele as a neurodegenerative defect, while it is true amniotic fluid further degrades exposed neural tissue while exposed, to me, the term neurodegenerative defect suggests a lifelong degeneration, which is not the case for human patients. Perhaps shortening to neurological defect is a compromise. Thank you for the important and interesting work.  

We agree that ‘neurodegenerative’ can mean different things to different people. Literally, it refers to degeneration of neural tissue, which of course includes neuroepithelial loss due to amniotic fluid action in the uterus. Nevertheless, to avoid confusion, the word has been removed and the sentence expanded to include a reference to the adverse effects of amniotic fluid on the exposed neuroepithelium (see Introduction, second paragraph, p 3).

Reviewer #2 (Public Review)

It is not clear how the gestational age of the specimens was determined or how that can be known with certainty. There is no information given in the methods on this. With this in mind, bunching the samples at 2-day intervals in Figure 1J will lead to inaccuracies in assessing the rate of somite formation. This is pointed out as a major difference between specimens and organoids in the abstract but a similar result in the results section. The data supporting either of these statements is not convincing.

Human embryos were assigned to Carnegie stages based on standard morphological criteria. This was stated, with references, in the first Results paragraph, and we have now also included this information in the Methods (first paragraph, p 19). We assigned the embryos to 2-day intervals based on the standard literature timing of these Carnegies stages, as described in O’Rahilly and Muller (1987). We have clarified both Carnegie staging and assignment of embryos to 2-day intervals in a new sentence within the Methods, first paragraph, p 19. “Embryos were assigned to Carnegie Stages (CS) using morphological criteria (O'Rahilly and Muller 1987; Bullen and Wilson 1997) and to 2-day post-conception intervals for regression analysis based on timings in Table 0-1 of O’Rahilly and Muller (1987).” This has also been inserted in the legend to Figure 1J.

The regression analysis of somite number against days post-conception (Figure 1J) allowed a conclusion to be drawn on the rate of somite formation in early human embryos. We have added 95% confidence intervals to our finding of a new somite formed every 7.1 h in humans. We consider this to be important for comparison with non-human species and organoid systems. On p 8, second paragraph, we simply state our finding of a 7.1 h somite periodicity in human embryos, compared with 5 h in the organoid system (and 2 h in mouse and rat – as suggested by Reviewer 1). We are careful not to say it is a ‘major difference’ or ‘similar result’ in different parts of the paper, as the Reviewer has drawn attention to.

Whenever possible, give the numbers of specimens that had the described findings. For example, in Figure 2C - how many embryos were examined with the massive rounded end at CS13? Apoptosis in Figures 3 and 4?  

Numbers of embryos analysed in Figures 2 and 3 (the latter now a combined version of the original Figures 3 and 4) are shown in Table 2. We have also created a new Supplementary Figure 1 to show additional examples of human embryonic tails, which illustrate the consistency of morphology through the stages from CS13 to CS18. Numbers of samples that contributed to Figures 4-6 are detailed in the legends.

For Figure 2I-K, it would be informative to superimpose the individual data points on the box plots distinguishing males from females, as in Figure 1I.  

This was attempted but the data points overlie the box plots and look confusing. Instead, we have created Supplementary Table 2 which gives the raw data on which Figure 2I-K are

based. We have also drawn attention to the fact that not all embryos yielded all types of measurement, especially tail lengths.

Is it possible to quantitate apoptosis and proliferation data?  

We have not quantified apoptosis, given the difficulty of deciding whether anti-caspase signals represent single or multiple dying cells. Instead, we performed a new tissue area analysis along the body axis, which has shed light on the possible direction (rostral to caudal) of tailgut loss in the human caudal region (see response to Reviewer 1 above). Since the cell proliferation data were limited in extent, and not a major focus of the paper, we have removed that analysis completely from the revised version.

The Tunel staining in Figure 3 is difficult to make out.  

We have extended our analysis of anti-caspase 3 immunohistochemisty and removed the TUNEL images.

Reviewer #2 (Recommendations For The Authors)

The anatomy of the sections in Figures 3, 4, and 7 is difficult to discern. Is it possible to insert adjacent panels tracing and labeling the structures in each panel? Also, drawings showing the axial level of each section would be helpful.

To clarify the axial levels of sections, we have inserted images of mouse and human embryos as parts A and B of the revised Figure 3. We have tried to clarify the morphology of sections by labelling all relevant structures in the sections themselves.   

High-magnification views of the tailbud in Figure 5 would be more informative. Staining is difficult to see after CS13. The low-magnification views can be shown in an insert. Figures 5 and 6 can be combined.

At the reviewer’s suggestion, we have merged Figures 5 and 6 into a revised Figure 5. Now, the sections provide higher magnification images of the areas of expression as shown in the lower magnification whole mount images. We feel this makes the gene expression findings much clearer than before.

Some of the writing in the abstract, introduction, and results is very descriptive, with a lack of summary and integration of information. For instance, the abstract could be rewritten to include an overall conclusion at the end and a better description of the longstanding questions addressed. Moreover, the abstract suggests multiple lumens are not found in human specimens. Another example is the second paragraph of the introduction lists various NTDs but doesn't provide an integrative conclusion of the information. The discussion is much better but lacks a conclusion at the end.

We agree that more concluding sentences should be used, as the Reviewer suggests. To this end, we have rewritten the Abstract (p 2) to emphasise the long-standing questions that our study addresses, and concluding sentences are now included in other places (e.g. somite results, p 8). A new ‘Conclusions’ section has been added at the end of the Discussion (pp 17-18).

ADDITIONAL CHANGES MADE TO REVISED MANUSCRIPT

Title. This has been amended to: “Spinal neural tube formation and tail development in human embryos” to reflect the greater focus on developmental events, and less on tail regression.

Additional studies have been added to Supplementary Table 1, to include the main transcriptomic studies of human embryos in the primary/secondary neurulation stage range. This takes the number of previous studies to 28 and the total number of embryos to 925. See p 4, top and p 12, first paragraph for corresponding changes to the text.

We added a sentence to the Discussion (p 13, first paragraph) to counter the claim that humans have undergone ‘tail-loss’, as included in Xia et al, 2024, “On the genetic basis of tail-loss evolution in humans and apes”. Nature 626:1042-8. Clearly, the human embryo is tailed, which undermines these authors’ statement.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

In the paper, Yan and her colleagues investigate at which stage of development different categorical signals can be detected with EEG using a steady-state visual evoked potential paradigm. The study reports the development trajectory of selective responses to five categories (i.e., faces, limbs, corridors, characters, and cars) over the first 1.5 years of life. It reveals that while responses to faces show significant early development, responses to other categories (i.e., characters and limbs) develop more gradually and emerge later in infancy. The paper is well-written and enjoyable, and the content is well-motivated and solid.

Strengths:

(1) This study contains a rich dataset with a substantial amount of effort. It covers a large sample of infants across ages (N=45) and asks an interesting question about when visual category representations emerge during the first year of life.

(2) The chosen category stimuli are appropriate and well-controlled. These categories are classic and important for situating the study within a well-established theoretical framework.

(3) The brain measurements are solid. Visual periodicity allows for the dissociation of selective responses to image categories within the same rapid image stream, which appears at different intervals. This is important for the infant field, as it provides a robust measure of ERPs with good interpretability.

Weaknesses:

The study would benefit from a more detailed explanation of analysis choices, limitations, and broader interpretations of the findings. This includes:

a) improving the treatment of bias from specific categories (e.g., faces) towards others;

b) justifying the specific experimental and data analysis choices;

c) expanding the interpretation and discussion of the results.

I believe that giving more attention to these aspects would improve the study and contribute positively to the field.

We thank the reviewer for their clear summary of the work and their constructive feedback. To address the reviewer’s concerns, in the revised manuscript we now provide a detailed explanation of analysis choices, limitations, and broader interpretations, as summarized in the point-by-point responses in the section: Reviewer #1 (Recommendations For The Authors) below, for which we give here an overview in points (a), (b), and (c):

(a) The reviewer is concerned that using face stimuli as one of the comparison categories may hinder the detection of selective responses to other categories like limbs. Unfortunately, because of the frequency tagging design of our study we cannot compare the responses to one category vs. only some of the other categories (e.g. limbs vs objects but not faces). In other words, our experimental design does not enable us to do this analysis suggested by the reviewer. Nonetheless, we underscore that faces compromise only ¼ of contrast stimuli and we are able to detect significant selective responses to limbs, corridors and characters in infants after 6-8 months of age even as faces are included in the contrast and the response to faces continues to increase (see Fig 4). We discuss the reviewer’s point regarding how contrast can contribute to differences in findings in the discussion on pages 12-13, lines 344-351. Full details below in Reviewer 1: Recommendations for Authors - Frequency tagging category responses.

(b) We expanded the justification of specific experimental and data analysis choices, see details below in Reviewer 1: Recommendations for Authors ->Specific choices for experiment and data analysis.

(c) We expand the interpretation and discussion, see details below in Reviewer 1: Recommendations for Authors -> More interpretation and discussion.

Reviewer #2 (Public Review):

Summary:

The current work investigates the neural signature of category representation in infancy. Neural responses during steady-state visually-evoked potentials (ssVEPs) were recorded in four age groups of infants between 3 and 15 months. Stimuli (i.e., faces, limbs, corridors, characters, and cars) were presented at 4.286 Hz with category changes occurring at a frequency of 0.857 Hz. The results of the category frequency analyses showed that reliable responses to faces emerge around 4-6 months, whereas responses to libs, corridors, and characters emerge at around 6-8 months. Additionally, the authors trained a classifier for each category to assess how consistent the responses were across participants (leave-one-out approach). Spatiotemporal responses to faces were more consistent than the responses to the remaining categories and increased with increasing age. Faces showed an advantage over other categories in two additional measures (i.e., representation similarity and distinctiveness). Together, these results suggest a different developmental timing of category representation.

Strengths:

The study design is well organized. The authors described and performed analyses on several measures of neural categorization, including innovative approaches to assess the organization of neural responses. Results are in support of one of the two main hypotheses on the development of category representation described in the introduction. Specifically, the results suggest a different timing in the formation of category representations, with earlier and more robust responses emerging for faces over the remaining categories. Graphic representations and figures are very useful when reading the results.

Weaknesses:

(1) The role of the adult dataset in the goal of the current work is unclear. All results are reported in the supplementary materials and minimally discussed in the main text. The unique contribution of the results of the adult samples is unclear and may be superfluous.

(2) It would be useful to report the electrodes included in the analyses and how they have been selected.

We thank the reviewer for their constructive feedback and for summarizing the strengths and weaknesses of our study. We revised the manuscript to address these two weaknesses.

(1) The reviewer indicates that the role of the adult dataset is unclear. The goal of testing adult participants was to validate the EEG frequency tagging paradigm. We chose to use adults because a large body of fMRI research shows that both clustered and distributed responses to visual categories are found in adults’ high-level visual cortex. Therefore, the goal of the adult data is to determine whether with the same amount of data as we collect on average in infants, we have sufficient power to detect categorical responses using the frequency tagging experimental paradigm as we use in infants. Because this data serves as a methodological validation purpose, we believe it belongs to the supplemental data.

We clarify this in the Results, second paragraph, page 5 where now write: “As the EEG-SSVEP paradigm is novel and we are restricted in the amount of data we can obtain in infants, we first tested if we can use this paradigm and a similar amount of data to detect category-selective responses in adults. Results in adults validate the SSVEP paradigm for measuring category-selectivity: as they show that (i) category-selective responses can be reliably measured using EEG-SSVEP with the same amount of data as in infants (Supplementary Figs S1-S2), and that (ii) category information from distributed spatiotemporal response patterns can be decoded with the same amount of data as in infants (Supplementary Fig S3).”

(2) The reviewer asks us to report the electrodes used in the analysis and their selection. We note that the selection of electrodes included in the analyses has been reported in our original manuscript (Methods, section: Univariate EEG analyses). On pages 18-19, lines 530-538, we write: “Both image update and categorical EEG visual responses are reported in the frequency and time domain over three regions-of-interest (ROIs): two occipito-temporal ROIs (left occipitotemporal (LOT): channels 57, 58, 59, 63, 64, 65 and 68; right occipitotemporal (ROT) channels: 90, 91, 94, 95, 96, 99, and 100) and one occipital ROI (channels 69, 70, 71, 74, 75, 76, 82, 83 and 89). These ROIs were selected a priori based on a previously published study51. We further removed several channels in these ROIs for two reasons: (1) Three outer rim channels (i.e., 73, 81, and 88) were not included in the occipital ROI for further data analysis for both infant and adult participants because they were consistently noisy. (2) Three channels (66, 72, and 84) in the occipital ROI, one channel (50) in the LOT ROI, and one channel (101) in the ROT ROI were removed because they did not show substantial responses in the group-level analyses.”

In the section Reviewer 2, Recommendations for the authors, we also addressed the reviewer’s minor points.

Reviewer #3 (Public Review):

Yan et al. present an EEG study of category-specific visual responses in infancy from 3 to 15 months of age. In their experiment, infants viewed visually controlled images of faces and several non-face categories in a steady state evoked potential paradigm. The authors find visual responses at all ages, but face responses only at 4-6 months and older, and other category-selective responses at later ages. They find that spatiotemporal patterns of response can discriminate faces from other categories at later ages.

Overall, I found the study well-executed and a useful contribution to the literature. The study advances prior work by using well-controlled stimuli, subgroups of different ages, and new analytic approaches.

I have two main reservations about the manuscript: (1) limited statistical evidence for the category by age interaction that is emphasized in the interpretation; and (2) conclusions about the role of learning and experience in age-related change that are not strongly supported by the correlational evidence presented.

We thank the reviewer for their enthusiasm and their constructive feedback.

(1) The overall argument of the paper is that selective responses to various categories develop at different trajectories in infants, with responses to faces developing earlier. Statistically, this would be most clearly demonstrated by a category-by-age interaction effect. However, the statistical evidence for a category by interaction effect presented is relatively weak, and no interaction effect is tested for frequency domain analyses. The clearest evidence for a significant interaction comes from the spatiotemporal decoding analysis (p. 10). In the analysis of peak amplitude and latency, an age x category interaction is only found in one of four tests, and is not significant for latency or left-hemisphere amplitude (Supp Table 8). For the frequency domain effects, no test for category by age interaction is presented. The authors find that the effects of a category are significant in some age ranges and not others, but differences in significance don't imply significant differences. I would recommend adding category by age interaction analysis for the frequency domain results, and ensuring that the interpretation of the results is aligned with the presence or lack of interaction effects.

The reviewer is asking for additional evidence for age x category interaction by repeating the interaction analysis in the frequency domain. The reason we did not run this analysis in the original manuscript is that the categorical responses of interest are reflected in multiple frequency bins: the category frequency (0.857 Hz) and its harmonics, and there are arguments in the field as to how to quantify response amplitudes from multiple frequency bins (Peykarjou, 2022). Because there is no consensus in the field and also because how the different harmonics combine depends not just on their amplitudes but also on their phase, we chose to transform the categorical responses across multiple frequency bins from the frequency domain to the time domain. The transformed signal in the time domain includes both phase and amplitude information across the category frequency and its harmonics. Therefore, subsequent analyses and statistical evaluations were done in the time domain.

However, we agree with the reviewer that adding category by age interaction analysis for the frequency domain results can further solidify the results. Thus, in the revised manuscript we added a new analysis, in which we quantified the root mean square (RMS) amplitude value of the responses at the category frequency (0.857 Hz) and its first harmonic (1.714 Hz) for each category condition and infant. Then we used a LMM to test for an age by category interaction. The LMM was conducted separately for the left and right lateral occipitotemporal ROIs. Results of this analysis find a significant category by age interaction, that is, in both hemispheres, the development of response RMS amplitudes varied across category (left occipitotemporal ROIs: βcategory x age = -0.21, 95% CI: -0.39 – -0.04, t(301) = -2.40, pFDR < .05; right occipitotemporal ROIs: βcategory x age = -0.26, 95% CI: -0.48 – -0.03, t(301) = -2.26, pFDR < .05). We have added this analysis in the manuscript, pages 7-8, lines 186-193: “We next examined the development of the category-selective responses separately for the right and left lateral occipitotemporal ROIs. The response amplitude was quantified by the root mean square (RMS) amplitude value of the responses at the category frequency (0.857 Hz) and its first harmonic (1.714 Hz) for each category condition and infant. With a  LMM analysis, we found significant development of response amplitudes in the both occipitotemporal ROIs which varied by category (left occipitotemporal ROIs: βcategory x age = -0.21, 95% CI: -0.39 – -0.04, t(301) = -2.40, pFDR < .05; right occipitotemporal ROIs: βcategory x age = -0.26, 95% CI – -0.48 – -0.03, t(301) = -2.26, pFDR < .05, LMM as a function of log (age) and category; participant: random effect).” We also added the formula for the LMM analysis in Table 1 in the Methods section, page 21.

(2) The authors argue that their results support the claim that category-selective visual responses require experience or learning to develop. However, the results don't bear strongly on the question of experience. Age-related changes in visual responses could result from experience or experience-independent maturational processes. Finding age-related change with a correlational measure does not favor either of these hypotheses. The results do constrain the question of experience, in that they suggest against the possibility that category-selectivity is present in the first few months of development, which would in turn suggest against a role of experience. However the results are still entirely consistent with the possibility of age effects driven by experience-independent processes. The manner in which the results constrain theories of development could be more clearly articulated in the manuscript, with care taken to avoid overly strong claims that the results demonstrate a role of experience.

Thanks for the comment. We agree with this nuanced point. It is possible that development of category-selective visual responses is a maturational process. In response to this comment, we have revised the manuscript to discuss both perspectives, see revised discussion section – A new insight about cortical development: different category representations emerge at different times during infancy, pages 14-15, lines 403-426, where we now write: “In sum, the key finding from our study is that the development of category selectivity during infancy is non-uniform: face-selective responses and representations of distributed patterns develop before representations to limbs and other categories. We hypothesize that this differential development of visual category representations may be due to differential visual experience with these categories during infancy. This hypothesis is consistent with behavioral research using head-mounted cameras that revealed that the visual input during early infancy is dense with faces, while hands become more prevalent in the visual input later in development and especially when in contact with objects 41,42. Additionally, a large body of research has suggested that young infants preferentially look at faces and face-like stimuli 17,18,33,34, as well as look longer at faces than other objects 41, indicating that not only the prevalence of faces in babies’ environments but also longer looking times may drive the early development of face representations. Further supporting the role of visual experience in the formation of category selectivity is a study that found that infant macaques that are reared without seeing faces do not develop face-selectivity but develop selectivity to other categories in their environment like body parts40. An alternative hypothesis is that differential development of category representations is maturational. For example, we found differences in the temporal dynamics of visual responses among four infant age groups, which suggests that the infant’s visual system is still developing during the first year of life. While the mechanisms underlying the maturation of the visual system in infancy are yet unknown, they may include myelination and cortical tissue maturation 66-71. Future studies can test these alternatives by examining infants’ visual diet, looking behavior, and brain development and examine responses using additional behaviorally relevant categories such as food 72–74. These measurements can test how environmental and individual differences in visual experiences may impact infants’ developmental trajectories. Specifically, a visual experience account predicts that differences in visual experience would translate into differences in development of cortical representations of categories, but a maturational account predicts that visual experience will have no impact on the development of category representations.”

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Major points:

Bias from faces to other categories:

- Frequency tagging category responses:

We see faces from non-face objects and limbs from non-limb objects. Non-limb objects include faces; I suspect that finding the effects of limbs is challenging with faces in the non-limbs category. How would you clarify the choice of categories, and to what extent are the negative (i.e., non-significant) effects on other categories not because of the heavy bias to faces?

The reviewer is concerned that using face stimuli as one of the comparison categories may hinder the ability to detect selective responses to other categories like limbs in our study. Unfortunately, because of the frequency tagging design of our study, we cannot compare the responses to one category to only some of the other categories (e.g. limbs vs objects but not faces), so our experimental design does not enable us to do the analysis suggested by the reviewer. Nonetheless, we underscore that faces compromise only ¼ of contrast stimuli in the category frequency tagging and we are able to detect significant selective responses to limbs, corridors and characters in infants after 6-8 months of age, when faces are included in the contrast and the responses to faces continue to increase more than for other categories (see Fig 4).

We address this point in the discussion where we consider differences between our findings and those of Kosakowski et al. 2022, on pages 12-13, lines 344-351 we write: “We note that, the studies differ in several ways: (i) measurement modalities (fMRI in 27 and EEG here), (ii) the types of stimuli infants viewed: in 27 infants viewed isolated, colored and moving stimuli, but in our study, infants viewed still, gray-level images on phase-scrambled backgrounds, which were controlled for several low level properties, and (iii) contrasts used to detect category-selective responses, whereby in 27 the researchers identified within predefined parcels – the top 5% of voxels that responded to the category of interest vs. objects, here we contrasted the category of interest vs. all other categories the infant viewed. Thus, future research is necessary to determine whether differences between findings are due to differences in measurement modalities, stimulus format, and data analysis choices.”

- Decoding analyses:

Figure 5 Winner-take-all classification. First, the classifier may be biased towards the categories with strong and clean data, similar to the last point, this needs clarification on the negative effect. Second, it could be helpful to see how exactly the below-chance decoded categories were being falsely classified to which categories at the group level. Decoding accuracy here means a 20% chance the selection will go to the target category, but the prediction and the exact correlation coefficient the winner has is not explicit; concerning a value of 0.01 correlation could take the winner among negative or pretty bad correlations with other categories. It would be helpful to report how exactly the category was correlated, as it could be a better way to define the classification bias, for example, correlation differences between hit and miss classification. Also, the noise ceiling of the correlation within each group should be provided. Third, this classifier needs improvement in distinguishing between noise and signals to identify the type of information it extracts. Do you have thoughts about that?

Thanks for the questions, answers below:

In the winner-take-all (WTA) classifier analysis, at each iteration, the LOOCV classifier computed the correlation between each of the five category vectors from the left-out participant (test data, for an unknown stimulus) and each of the mean spatiotemporal vectors across the N-1 participants (training data, labeled data). The winner-take-all (WTA) classifier classifies the test vector to the category that yields the highest correlation with the training vector. For a given test pattern, correct classification yielded a score of 1 and an incorrect classification yielded a score of 0. Then we computed the group mean decoding performance across all N iterations for each category and the group mean decoding accuracies across five categories.

For the classification data in Fig 5, the statistics and differences from chance are provided in 5B, where we report overall classification across all categories from an infant’s brain data. Like the reviewer, we were interested in assessing if successful classification is uniform across categories or is driven by some categories. As is visible in 5C, decoding success is non-uniform across categories, and is higher for faces than other categories. Because this is broken by category we cannot compare to chance, and what is reported in Fig 5c is percentage infants in each age group that a particular category was successfully decoded. Starting from 4 months of age, faces can be decoded from distributed brain data in a majority of infants, but other categories only in 20-40% of infants. 

The reviewer also asks about what levels of correlations drive the classification. The analysis of RSMs in Fig 6a shows the mean correlations of distributed responses to different images within and between categories per age group. As is evident from the RSM, reproducible responses for a category only start to emerge at 4-6 months of age and the highest within category correlations are for faces. To quantify what drives the classification we measure distinctiveness - within category minus between-category correlations of distributed responses; all individual infant data per category are in Fig 6C. Distinctiveness values vary by age and category, see text related to Fig 6 in section: What is the nature of categorical spatiotemporal patterns in individual infants?

Figure 6 Category distinctiveness. An analysis that runs on a "single item level" would ideally warrant a more informative category distinction. Did you try that? Does it work?

Thanks for the question. We agree that doing an analysis at the single item level would be interesting. However, none of the images were repeated, so we do not have sufficient SNR to perform this analysis.

Specific choices for experiment and data analysis:

- Although using the SSVEP paradigm is familiar to the field, the choice could be detailed for understanding or evaluation of the effectiveness of the paradigm. For example, how the specific frequency for entrainment was chosen, and are there any theories or related warrants for studying in infants?

Thanks for the questions. We choose to use the SSVEP paradigm over traditional ERP designs for several reasons, as described which have been listed in our original manuscript (Results part, first paragraph, pages 4-5, lines 90-94): “We used the EEG-SSVEP approach because: (i) it affords a high signal-to-noise ratio with short acquisitions making it effective for infants 23,46, (ii) it has been successfully used to study responses to faces in infants23,46,49, and (iii) it enables measuring both general visual response to images by examining responses at the image presentation frequency (4.286 Hz), as well as category-selective responses by examining responses at the category frequency (0.857 Hz, Fig 1A).”

With regards to our choice of presentation rate, a previous study in 4-6-month-olds by de Heering and Rossion (2015) used SSVEP showing infants faces and objects presented the visual stimuli at 6 Hz (i.e. 167 ms per image) to study infants’ categorical responses to natural faces relative to objects. Here, we chose to use a relatively slower presentation rate, which was 4.286 Hz (i.e. 233 ms per image), so that our infant participants would have more time to process each image yet still unlikely to make eye movements across a stimulus. Both de Heering et (2015) and our study have found significant selective responses to faces relative to other categories in 4-6-month-olds, across these presentation rates. As discussed in a recent review of frequency tagging with infants: The visual oddball paradigm (Peykarjou, 2022), there are many factors to consider when adapting SSVEP paradigms to infants. We agree that an interesting direction for future studies is examination of how SSVEP parameters such as stimulus and oddball presentation rate, and overall duration of acquisition affects the sensitivity of the SSVEP paradigm in infants. We added a discussion point on this on page 12, lines 332-334 where we write: “As using SSVEP to study high-level representations is a nascent field52–54, future work can further examine how SSVEP parameters such as stimulus and target category presentation rate may affect the sensitivity of measurements in infants (see review by54).”

- There is no baseline mentioned in the study. How was the baseline considered in the paradigm and data analysis? The baseline is important for evaluating how robust/ reliable the periodic responses within each group are in the first place. It also helps us to see how different the SNR changes in the fast periodic responses from baseline across age groups. Would the results be stable if the response amplitudes were z-scored by a baseline?

Thanks for the question. Previous studies using a similar frequency tagging paradigm have compared response amplitude at stimulus-related frequencies to that of neighboring frequency bins as their baseline for differentiating signal from noise. We use a more statistically powerful method, the Hotelling’s T2 statistic to test whether response amplitudes were statistically different from 0 amplitude. Importantly, this method takes into consideration both the amplitude and phase information of the response. That is, a significant response is expected to have consistent phase information across participants as well as significant amplitude.

- Statistical inferences: could the variance of data be considered appropriately in your LLM? Why?

As we have explained in our original manuscript (Methods part, section-Statistical Analyses of Developmental Effects, page 21 lines 611-615): “LMMs allow explicit modeling of both within-subject effects (e.g., longitudinal measurements) and between-subject effects (e.g., cross-sectional data) with unequal number of points per participants, as well as examine main and interactive effects of both continuous (age) and categorical (e.g., stimulus category) variables. We used random-intercept models that allow the intercept to vary across participants (term: 1|participant).” This statistical model is widely used in developmental studies that combine both longitudinal and cross-sectional measurements (e.g. Nordt et al. 2022, 2023; Natu et al. 2021; Grotheer et al. 2022).

- The sampling of the age groups. Why are these age groups considered, as 8-12 months are not considered? Or did the study first go with an equal sampling of the ages from 3 to 15 months? Then how was the age group defined? The log scale of age makes sense for giving a simplified view of the effects, but the sampling procedure could be more detailed.

Thanks for the question. Our study recruited infants longitudinally for both anatomical MRI and EEG studies. Some of the infants participated in both studies and some only in one of the studies. Infants were recruited at around newborn, 3 months, 6 months, and 12 months. We did not recruit infants between 8-12 months of age because around 9 months there is little contrast between gray and white matter in anatomical MRI scans that were necessary for the MRI study. For the EEG study we binned the subjects by age group such that there were a similar number of participants across age groups to enable similar statistical power. The division of age groups was decided based on the distribution of the infants included in the analyses.

We have now added the sampling procedure details in the Methods, part, under section: Participants, pages 15-16, lines 440-445: “Sixty-two full-term, typically developing infants were recruited. Twelve participants were part of an ongoing longitudinal study that obtained both anatomical MRI and EEG data in infants. Some of the infants participated in both studies and some only in one of the studies. Infants were recruited at around newborn, 3 months, 6 months, and 12 months. We did not recruit infants between 8-12 months of age because around 9 months there is little contrast between gray and white matter in anatomical MRI scans that were necessary for the MRI study.”

- 30 Hz cutoff is arbitrary, but it makes sense as most EEG effects can be expected in a lower frequency band than higher. However, this specific choice is interesting and informative, when faced with developmental data and this type of paradigm. Would the results stay robust as the cutoff changes? Would the results benefit from going even lower into the frequency cutoff?

In the time domain analyses, we choose the 30 Hz cutoff to be consistent with previous EEG studies including those done with infants. However, as our results from the frequency domain (Fig 3, right panel, and supplementary Fig S6-S9) show that there are barely any selective categorical responses above about 6 Hz. Therefore, we expect that using a lower frequency cutoff, such as 10 Hz, will not lead to different results.

More interpretation and discussion:

- You report the robust visual responses in occipital regions, the responses that differ across age groups, and their characteristics (i.e., peak latency and amplitude) in time curves. This part of the results needs more interpretation to help the data be better situated in the field; I wondered whether this relates to the difference in the signal processing of the information. Could this be the signature of slow recurrence connection development? Or how could this be better interpreted?

Thanks for the question. Changes in speed of processing can arise from several related reasons including (i) myelination of white matter connections that would lead to faster signal transmission (Lebenberg et al. 2019; Grotheer et al. 2022), (ii) maturation of cortical visual circuits affecting temporal integration time, and (iii) development of feedback connections. Our data cannot distinguish among these different mechanisms. Future studies that combine functional high temporal resolution measurements with structural imaging of tissue properties could elucidate changes in cortical dynamics over development.

We added this as a discussion point, on page 15 lines 416-420 we write: “For example, we found differences in the temporal dynamics of visual responses among four infant age groups, which suggests that the infant’s visual system is still developing during the first year of life. While underlying maturational mechanisms are yet unknown, they may include myelination and cortical tissue maturation68–73.”

- The supplementary material includes a detailed introduction to the methods when facing the developing visual acuity, which justifies the choice of the paradigm. I appreciate this thorough explanation. Interestingly, high visual acuity has its potential developmental downside; for instance, low visual acuity would aid in the development of holistic processing associated with face recognition (as discussed by Vogelsang et al., 2018, in PNAS). How do you view this point in relation to the emergence of complex cognitive processes, as here the category-selective responses?

Thanks for linking this to the Vogelsang (2018) study. Just as faces are processed in a hierarchical manner, starting with low-level features (edges, contours) and progressing to high-level features (identity, expression), other complex visual categories like cars, scenes, and body parts follow similar hierarchies. Early holistic processing could provide a foundation for recognizing objects quickly and efficiently, while feature-based processing might allow for more precise recognition and categorization as acuity increases. Therefore, as visual acuity improves, an infant’s brain can integrate finer details into those holistic representations, supporting more refined and complex cognitive processes. The balance between low- and high-level visual acuity highlights the intricate interplay between sensory processing and cognitive development across various domains.

Minor points:

Paradigm:

- Are the colored cartoon images for motivating infants' fixation counterbalanced across categories in the paradigm? Or how exactly were the cartoon images presented in the paradigm?

Response: Yes, the small cartoon images that were presented at the center of the screen during stimuli presentation were used to engage infants’ attention and accommodation to the screen. For each condition, they were randomly drawn from a pool of 70 images (23 flowers, 22 butterflies, 25 birds) from categories unrelated to the ones under test. They were presented in random order with durations uniformly distributed between 1 and 1.5 s.  We have added these details of the paradigm to the Methods section, page 17, lines 479-481: “To motivate infants to fixate and look at the screen, we presented at the center of the screen small (~1°) colored cartoon images such as butterflies, flowers, and ladybugs. They were presented in random order with durations uniformly distributed between 1 and 1.5 s.”

Analysis:

- Are the visual responses over the occipital cortex different across different category conditions in the first place? I guess this should not be different; this probably needs one more supplementary figure.

The visual responses reflect the responses to images that are randomly drawn from the five stimuli categories at a presentation frequency of 4.286 Hz. The only difference between the five conditions is that the stimuli presentation order is different. Therefore, the visual response over the occipital cortex across conditions should not be different within an age group.

In the revised manuscript, we have added Supplementary Figure S5 that shows the frequency spectra distribution and the response topographies of the visual response at 4.286 Hz and its first 3 harmonics separately for each condition and age group and a new Supplementary Materials section: 5. Visual responses over occipital cortex per condition for all age groups. On page 5, lines 116-120, we now write: “Analysis of visual responses in the occipital ROI separately by category condition revealed that visual responses were not significantly across category condition (Supplementary Fig S5, no significant main effect of category (βcategory = 0.08, 95% CI: -0.08 – 0.24, t(301) \= 0.97, p = .33), or category by age interaction (βcategory x age = -0.04, 95% CI: -0.11 – 0.03, t(301) \= -1.09, p = .28, LMM on RMS of response to first three harmonics).”

- The summary of epochs used for each category for each age group needs to be included; this is important while evaluating whether the effects are due to not having enough data for categories or others.

This part of information is provided in the manuscript in the Methods section, page 18 lines 521-524, and supplementary Table S2. Our analysis shows that there was no significant difference in the number of pre-processed epochs across different age groups (F(3,57) = 1.5, p \= .2).

- Numbers of channels of EEG being interpolated should be provided; is that a difference across age groups?

Thanks for the suggestion. We have now added information about the number of channels being interpolated for each age groups in the Methods section (page 18, lines 525-528): “The number of electrodes being interpolated for each age group were 10.0 ± 4.8 for 3-4-month-olds, 9.9 ± 3.7 for 4-6-month-olds, 9.9 ± 3.9 for 6-8-month-olds, and 7.7 ± 4.7 for 12-15-month-olds. There was no significant difference in the number of electrodes being interpolated across infant age-groups (F(3,55) = 0.78, p = .51).”

- I noticed that the removal of EEG artifacts (i.e., muscles and eye-blinks) for data analysis is missing; did the preprocessing pipeline involve any artifacts removing procedures that are typically used in both infants and adults SSVEP data analysis? If so, please provide more information.

In our analysis, artifact rejection was performed in two steps. First, the continuous filtered data were evaluated according to a sample-by-sample thresholding procedure to locate consistently noisy channels. Channels with more than 20% of samples exceeding a 100-150 μV amplitude threshold were replaced by the average of their six nearest spatial neighbors. Once noisy channels were interpolated in this fashion, the EEG was re-referenced from the Cz reference used during the recording to the common average of all sensors and segmented into epochs (1166.7-ms). Finally, EEG epochs that contained more than 15% of time samples exceeding threshold (150-200 microvolts) were excluded on a sensor-by-sensor basis. This method is provided in the manuscript under Methods section, page 18 lines 510-516.

Figure:

- Supplementary Figure 8. The illustration of the WTA classifier was not referred to anywhere in the main text.

Thanks for pointing this out. The supplementary Figure 8 should be noted as supplementary Figure 10 instead. We have now mentioned it in the manuscript, page 10, line 267.

- Figure 5 WTA classifier needed to be clarified. It was correlation-based but used to choose the most correlated response patterns averaged across the N-1 subjects for the leave-one-out subject. The change from correlation coefficients to decoding accuracy could be clearer as I spent some time making sense of it. The correlation coefficient here evaluates how correlated the two vectors are, but the actual decoding accuracy estimated at the end is the percentage of participants who can be assigned to the "ground truth" label, so one step in between is missing. Can this be better illustrated?

Thanks for surfacing that this is not described sufficiently clearly and for your suggestions. The spatiotemporal vector was calculated separately for each category. This is illustrated in Fig 5A. At each iteration, the LOOCV classifier computed the correlation between each of the five category vectors from the left-out participant (test data, for an unknown stimulus) and each of the mean spatiotemporal vectors across the N-1 participants (training data, labeled data). The winner-take-all (WTA) classifier classifies the test vector to the category that yields the highest correlation with the training vector. This is illustrated in Fig 5A, with spatiotemporal patterns and correlation values from an example infant shown.  For a given test pattern, correct classification yields a score of 1 and an incorrect classification yields a score of 0.  We compute the percentage correct across all categories for each left-out-infant, and then mean decoding performance across all participants in an age group (Fig 5B). We have now added these details in the Methods part, section – Decoding analyses, Group-level, page 20 lines 590-597, where we write: “At each iteration, the LOOCV classifier computed the correlation between each of the five category vectors from the left-out participant (test data, for an unknown stimulus) and each of the mean spatiotemporal vectors across the N-1 participants (training data, labeled data). The winner-take-all (WTA) classifier classifies the test vector to the category of the training vector that yields the highest correlation with the training vector (Fig 5A). For a given test pattern, correct classification yields a score of 1 and an incorrect classification yields a score of 0.  For each left-out infant, we computed the percentage correct across all categories, and then the mean decoding performance across all participants in an age group (Fig 5B).”

Reviewer #2 (Recommendations For The Authors):

I only have some minor comments.

Typo on line 90 ("Infants participants in 5 conditions, which [...]").

Thanks for pointing this out. We have now corrected ‘participants’ to ‘participated’.

Typo on lines 330: "[...] in example 4-5-months-olds.".

Thanks for pointing this out. We changed ‘4-5-months-olds’ to ‘4-5-month-olds’.

Figure 2 - bar plots: rotating and spacing out values on the x-axis may improve readability. Ditto for the line plots in Figure 4.

Thanks for the suggestions. In the revised manuscript, we have improved the readability of Figure 2.

Caption of Figure 6: description of the distinctiveness plots may refer to panel C, instead of the bottom panels of section B.

Thanks for pointing this out. We have now corrected this information in the manuscript.

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Summary:

Opioids and related drugs are powerful analgesics that reduce suffering from pain. Unfortunately, their use often leads to addiction and there is an opioid-abuse epidemic that affects people worldwide. This study represents an ongoing effort to develop non-opioid analgesics for pain management. The findings point to an alternative approach to control post-surgical pain in lieu of opioid medications.

Strengths:

(1) The study responds to the urgent need for the development of non-opioid analgesics.

(2) The study demonstrates the efficacy of Clarix Flo (FLO) and HC-HA/PTX3 from the human amniotic membrane (AM) in reducing pain in a mouse model without the adverse effects of opioids.

(3) The study further explored the underlying mechanisms of how HC-HA/PTX3 produces its effects on neurons, suggesting the molecules/pathways involved in pain relief.

(4) The potential use of naturally derived biologics from human birth tissues (AM) is safe and sustainable, compared to synthetic pharmaceuticals.

(5) The study was conducted with scientific rigor, involving purification of active components, comparative analysis with multiple controls, and mechanistic explorations.

Weaknesses:

(1) It should be cautioned that while the preclinical findings are promising, these results still need to be translated into clinical settings that are complex and often unpredictable.

(2) The study shows the efficacy of FLO and HC-HA/PTX3 in one preclinical model of post-surgical pain. The observed effect may be variable in other pain conditions.

We thank the reviewer for these good comments and support! We agree with your suggestions and have provided more information in the discussion (Pages 11-12) and conclusion to address these comments.

Reviewer #2 (Public review):

Summary:

This is an outstanding piece of work on the potential of FLO as a viable analgesic biologic for the treatment of postsurgical pain. The authors purified the HC-HA/PTX3 from FLO and demonstrated its potential as an effective non-opioid therapy for postsurgical pain. They further unraveled the mechanisms of action of the compound at cellular and molecular levels.

Strengths:

Prominent strengths include the incorporation of behavioral assessment, electrophysiological and imaging recordings, the use of knockout and knockdown animals, and the use of antagonist agents to verify biological effects. The integrated use of these techniques, combined with the hypothesis-driven approach and logical reasoning, provides compelling evidence and novel insight into the mechanisms of the significant findings of this work.

Weaknesses:

I did not find any significant weaknesses even with a critical mindset. The only minor suggestion is that the Results section may focus on the results from this study and minimize the discussions of background information.

We thank the reviewer for your support! We revised the result section as suggested and reduced the discussion of background information.   

Reviewer #3 (Public review):

Summary:

Non-opioid analgesics derived from human amniotic membrane (AM) product represents a novel and unique approach to analgesia that may avoid the traditional harms associated with opioids. Here, the study investigators demonstrate that HC-HAPTX3 is the primary bioactive component of the AM product FLO responsible for anti-nociception in mouse-model and in-vitro dorsal root ganglion (DRG) cell culture experiments. The mechanism is demonstrated to be via CD44 with an acute cytoskeleton rearrangement that is induced that inhibits Na+ and Ca++ current through ion channels. Taken together, the studies reported in the manuscript provide supportive evidence clarifying the mechanisms and efficacy of HC-HAPTX3 antinociception and analgesia.

Strengths:

Extensive experiments including murine behavioral paw withdrawal latency and Catwalk test data demonstrating analgesic properties. The breadth and depth of experimental data are clearly supporting mechanisms and antinociceptive properties.

Weaknesses:

A few changes to the text of the manuscript would be recommended but no major weaknesses were identified.

We thank the reviewer for your support! We revised these texts as suggested. 

Recommendations for the authors: Reviewer #1 (Recommendations for the authors):

(1) The study showed an effect on baseline nociception and acute post-surgical pain. Chronic post-surgical pain is a major problem and should be considered.

We thank the reviewer for this comment. To further improve the translational potential, we will extend current findings and employ chronic post-surgical pain models, such as skin/muscle incision and retraction (SMIR) in the thigh of the rodent,(1-3) as well as chronic pain models such as neuropathic pain in the future.  We acknowledged this limitation in the discussion. (Page 12)

(2) Indicate the source of cultures DRGs.

We added “Method 15 Culturing DRG neurons” in the revised manuscript.   

(3) The size of DRG neurons was described in cross-sectional area (Figure 2 caption) and diameter (method). Be consistent.

We thank the reviewer for this comment. Cross-sectional area has often been used for describing the size of DRG neurons for in vivo calcium imaging studies, including our previous work (4, 5). In order to keep consistent and make data comparable between studies, we also used the cross-sectional area in current study in Fig 2 in vivo calcium imaging experiment.  On the other hand, cell-diameter has been routinely/widely used for in vitro experiments such as in vitro electrophysiology recording and immunofluorescence staining of cultured DRG neurons. To be consistent with this tradition, we used cell-diameter in these experiments.  Methods for measuring the area and diameter are explicitly described for each experimental setting, and consistent between the current study and our previous studies (6). In the manuscript, our previously published studies have also been cited in the Methods section. (Method “4 In vivo calcium imaging in mice” and “10.2 Intrinsic excitability studies of DRG neurons”).

(4) Clarify what "% of total" means in Figure 2. For bar graphs in 2B-D, the percent of total activated neurons (small, medium, and large) does not add up to 100.

“% of total” represented the proportion of activated neurons relative to the total number of neurons counted from the same analyzed image. This information was added to the figure legend of Figure 2 (B-C) and Method “4 In vivo calcium imaging in mice”  in the revised manuscript. At the end of each experiment, we can over-exposure the image to unravel all neuronal profiles and count the total number of neurons on that field/image. Only a small portion of neurons in each size category responded to the test stimulation, and hence the total does not add up to 100.

(5) Discuss clinical data or human studies to validate the efficacy and safety of FLO or HC-HA/PTX3 in patients.

Thanks for the great suggestion. We provided a brief discussion (Page 11-12).

Cryopreserved AM/UC has been clinically validated through several hundred peer-reviewed publications since 1995, including 12 studies specifically assessing FLO (Clarix Flo). These studies collectively support the safety and preliminary effectiveness of Clarix Flo in managing some clinical pain conditions such as knee osteoarthritis(7, 8), discogenic pain (9), rotator cuff tears(10), and painful neuropathy of the lower extremities (11). Currently, HC-HA/PTX3 is limited to pre-clinical research, and to our knowledge, there are no available data on its clinical efficacy and safety.

(6) Introduction, last sentence of the second paragraph, delete "also".

Thanks for carefully examining our manuscript. It was revised as suggested.

Reviewer #2 (Recommendations for the authors):

My only recommendation for improving the writing and presentation is to shorten the discussion of background information in Results.

We thank the reviewer for your support and comments!  We previously intended to provide some background information to help readers understand the premise and rationale of the study, before illustrating our findings. Nevertheless, we reduced some background information in the result section as suggested by this reviewer to make it more straightforward. 

Reviewer #3 (Recommendations for the authors):

P4 last sentence - "Our findings highlight the potential of a naturally derived biologic from human birth tissues as an effective non-opioid treatment for post-surgical pain and unravel the underlying mechanisms." - another sentence clause is required before "unravel".

As advised, we revised the sentence to: “Collectively, our findings highlight the potential of naturally derived biologics from human birth tissues as an effective non-opioid treatment for post-surgical pain. Moreover, we unravel the underlying mechanisms of pain inhibition induced by FLO and HC-HA/PTX3.”

P7 second paragraph - please edit the following sentence for clarity: "Since HC-HA/PTX3 mimics FLO in producing pain inhibition, and it has high purity and is more water-soluble than FLO, making it suitable for probing cellular mechanisms.".

As advised, we have revised the sentence. “Since HC-HA/PTX3 mimics FLO in its ability to inhibit pain and has higher purity and greater water solubility compared to FLO, it is well-suited for investigating cellular mechanisms.”

References:

(1) Flatters SJ. Characterization of a model of persistent postoperative pain evoked by skin/muscle incision and retraction (SMIR). Pain. 2008;135(1-2):119-30.

(2) Ying YL, Wei XH, Xu XB, She SZ, Zhou LJ, Lv J, et al. Over-expression of P2X7 receptors in spinal glial cells contributes to the development of chronic postsurgical pain induced by skin/muscle incision and retraction (SMIR) in rats. Experimental neurology. 2014;261:836-43.

(3) Cao S, Bian Z, Zhu X, and Shen SR. Effect of Epac1 on pERK and VEGF Activation in Postoperative Persistent Pain in Rats. Journal of molecular neuroscience : MN. 2016;59(4):554-64.

(4) Chen Z, Huang Q, Song X, Ford NC, Zhang C, Xu Q, et al. Purinergic signaling between neurons and satellite glial cells of mouse dorsal root ganglia modulates neuronal excitability in vivo. Pain. 2022;163(8):1636-47.

(5) Chen Z, Zhang C, Song X, Cui X, Liu J, Ford NC, et al. BzATP Activates Satellite Glial Cells and Increases the Excitability of Dorsal Root Ganglia Neurons In Vivo. Cells. 2022;11(15).

(6) Ford NC, Barpujari A, He SQ, Huang Q, Zhang C, Dong X, et al. Role of primary sensory neurone cannabinoid type-1 receptors in pain and the analgesic effects of the peripherally acting agonist CB-13 in mice. Br J Anaesth. 2022;128(1):159-73.

(7) Castellanos R, and Tighe S. Injectable Amniotic Membrane/Umbilical Cord Particulate for Knee Osteoarthritis: A Prospective, Single-Center Pilot Study. Pain Med. 2019;20(11):2283-91.

(8) Mead OG, and Mead LP. Intra-Articular Injection of Amniotic Membrane and Umbilical Cord Particulate for the Management of Moderate to Severe Knee Osteoarthritis. Orthop Res Rev. 2020;12:161-70.

(9) Buck D. Amniotic Umbilical Cord Particulate for Discogenic Pain. J Am Osteopath Assoc. 2019;119(12):814-9.

(10) Ackley JF, Kolosky M, Gurin D, Hampton R, Masin R, and Krahe D. Cryopreserved amniotic membrane and umbilical cord particulate matrix for partial rotator cuff tears: A case series. Medicine (Baltimore). 2019;98(30):e16569.

(11) Buksh AB. Ultrasound-guided injections of amniotic membrane/umbilical cord particulate for painful neuropathy of the lower extremity. Cogent Medicine. 2020;7(1):1724067.

Author response:

eLife Assessment

“The work presented is important for our understanding of the development of the cardiac conduction system and its regulation by T-box transcription factors. The conclusions are supported by convincing data. Overall, this is an excellent study that advances our understanding of cardiac biology and has implications beyond the immediate field of study.”

We appreciate the positive assessment of this work and the recognition of its importance in advancing our understanding of the cardiac conduction system, its regulation by T-box transcription factors, and contribution beyond the immediate field.

Reviewer #1 (Public review):

Summary:

In a heroic effort, Ozanna Burnicka-Turek et al. have made and investigated conduction system-specific Tbx3-Tbx5 deficient mice and investigated their cardiac phenotype. Perhaps according to expectations, given the body of literature on the function of the two T-box transcription factors in the heart/conduction system, the cardiomyocytes of the ventricular conduction system seemed to convert to "ordinary" ventricular working myocytes. As a consequence, loss of VCS-specific conduction system propagation was observed in the compound KO mice, associated with PR and QRS prolongation and elevated susceptibility to ventricular tachycardia.

Strengths:

Great genetic model. Phenotypic consequences at the organ and organismal levels are well investigated. The requirement of both Tbx3 and Tbx5 for maintaining VCS cell state has been demonstrated.

We thank Reviewer #1 for acknowledging the effort involved in generating and characterizing the Tbx3/Tbx5 double conditional knockout mouse model and for highlighting the significance of this work in elucidating the role of these transcription factors in maintaining the functional and transcriptional identity of the ventricular conduction system.

Weaknesses:

The actual cell state of the Tbx3/Tbx5 deficient conducting cells was not investigated in detail, and therefore, these cells could well only partially convert to working cardiomyocytes, and may, in reality, acquire a unique state.

We agree with Reviewer #1 that the Tbx3/Tbx5 double mutant ventricular conduction myocardial cells may only partially convert to working cardiomyocytes or may acquire a unique state.  The transcriptional state of the double mutant VCS cells was investigated by bulk profiling of key genes associated with specific conduction and non-conduction cardiac regions, including fast conduction, slow conduction, or working myocardium. Neither the bulk transcriptional approaches nor the optical mapping approaches we employed capture single-cell data; in both cases, the data represents aggregated signals from multiple cells (1, 2). Single cell approaches for transcriptional profiling and cellular electrophysiology would clarify this concern and are appropriate for future studies.

(1) O’Shea C, Nashitha Kabri S, Holmes AP, Lei M, Fabritz L, Rajpoot K, Pavlovic D (2020) Cardiac optical mapping – State-of-the-art and future challenges. The International Journal of Biochemistry & Cell Biology 126:105804. doi: 10.1016/j.biocel.2020.105804.

(2) Efimov IR, Nikolski VP, and Salama G (2004) Optical Imaging of the Heart. Circulation Research 95:21-33. doi: 10.1161/01.RES.0000130529.18016.35.

Reviewer #2 (Public review):

Summary:

The goal of this work is to define the functions of T-box transcription factors Tbx3 and Tbx5 in the adult mouse ventricular cardiac conduction system (VCS) using a novel conditional mouse allele in which both genes are targeted in cis. A series of studies over the past 2 decades by this group and others have shown that Tbx3 is a transcriptional repressor that patterns the conduction system by repressing genes associated with working myocardium, while Tbx5 is a potent transcriptional activator of "fast" conduction system genes in the VCS. In a previous work, the authors of the present study further demonstrated that Tbx3 and Tbx5 exhibit an epistatic relationship whereby the relief of Tbx3-mediated repression through VCS conditional haploinsufficiency allows better toleration of Tbx5 VCS haploinsufficiency. Conversely, excess Tbx3-mediated repression through overexpression results in disruption of the fast-conduction gene network despite normal levels of Tbx5. Based on these data the authors proposed a model in which repressive functions of Tbx3 drive the adoption of conduction system fate, followed by segregation into a fast-conducting VCS and slow-conduction AVN through modulation of the Tbx5/Tbx3 ratio in these respective tissue compartments.

The question motivating the present work is: If Tbx5/Tbx3 ratio is important for slow versus fast VCS identity, what happens when both genes are completely deleted from the VCS? Is conduction system identity completely lost without both factors and if so, does the VCS network transform into a working myocardium-like state? To address this question, the authors have generated a novel mouse line in which both Tbx5 and Tbx3 are floxed on the same allele, allowing complete conditional deletion of both factors using the VCS-specific MinK-CreERT2 line, convincingly validated in previous work. The goal is to use these double conditional knockout mice to further explore the model of Tbx3/Tbx5 co-dependent gene networks and VCS patterning. First, the authors demonstrate that the double conditional knockout allele results in the expected loss of Tbx3 and Tbx5 specifically in the VCS when crossed with Mink-CreERT2 and induced with tamoxifen. The double conditional knockout also results in premature mortality. Detailed electrophysiological phenotyping demonstrated prolonged PR and QRS intervals, inducible ventricular tachycardia, and evidence of abnormal impulse propagation along the septal aspect of the right ventricle. In addition, the mutants exhibit downregulation of VCS genes responsible for both fast conduction AND slow conduction phenotypes with upregulation of 2 working myocardial genes including connexin-43. The authors conclude that loss of both Tbx3 and Tbx5 results in "reversion" or "transformation" of the VCS network to a working myocardial phenotype, which they further claim is a prediction of their model and establishes that Tbx3 and Tbx5 "coordinate" transcriptional control of VCS identity.

We appreciate Reviewer #2’s detailed summary of the study’s aims, methodologies, and findings, as well as their thoughtful suggestions for further analysis. We are grateful for their recognition of our genetic model’s novelty and robustness.

Overall Appraisal:

As noted above, the present study does not further explore the Tbx5/Tbx3 ratio concept since both genes are completely knocked out in the VCS. Instead, the main claims are that the absence of both factors results in a transcriptional shift of conduction tissue towards a working myocardial phenotype, and that this shift indicates that Tbx5 and Tbx3 "coordinate" to control VCS identity and function.

We agree with this reviewer’s assessment of the assertions in our manuscript.  The novel combined Tbx5/Tbx3 double mutant model does not further explore the TBX5/TBX3 ratio concept, which we previously examined in detail (1). Instead, as the Reviewer notes, this manuscript focuses on testing a model that the coordinated activity of Tbx3 and Tbx5 defines specialized ventricular conduction identity.

(1) Burnicka-Turek O, Broman MT, Steimle JD, Boukens BJ, Petrenko NB, Ikegami K, Nadadur RD, Qiao Y, Arnolds DE, Yang XH, Patel VV, Nobrega MA, Efimov IR, Moskowitz IP (2020) Transcriptional Patterning of the Ventricular Cardiac Conduction System. Circulation Research 127:e94-e106. doi:10.1161/CIRCRESAHA.118.314460. 

Strengths:

(1) Successful generation of a novel Tbx3-Tbx5 double conditional mouse model.

(2) Successful VCS-specific deletion of Tbx3 and Tbx5 using a VCS-specific inducible Cre driver line.

(3) Well-powered and convincing assessments of mortality and physiological phenotypes.

(4) Isolation of genetically modified VCS cells using flow.

We thank Reviewer #2 for acknowledging the listed strengths of our study.

Weaknesses:

(1) In general, the data is consistent with a long-standing and well-supported model in which Tbx3 represses working myocardial genes and Tbx5 activates the expression of VCS genes, which seem like distinct roles in VCS patterning. However, the authors move between different descriptions of the functional relationship and epistatic relationship between these factors, including terms like "cooperative", "coordinated", and "distinct" at various points. In a similar vein, sometimes terms like "reversion" are used to describe how VCS cells change after Tbx3/Tbx5 conditional knockout, and other times "transcriptional shift" and at other times "reprogramming". But these are all different concepts. The lack of a clear and consistent terminology for describing the phenomena observed makes the overarching claims of the manuscript more difficult to evaluate.

We discriminate prior work on the “long-standing and well-supported model’ supported by investigation of the role of Tbx5 and Tbx3 independently from this work examining the coordinated role of Tbx5 and Tbx3. Prior work demonstrated that Tbx3 represses working myocardial genes and Tbx5 activates expression of VCS genes, consistent with the reviewer’s suggestion of their distinct roles in VCS patterning. However, the current study uniquely evaluates the combined role of Tbx3 and Tbx5 in distinguishing specialized conduction identify from working myocardium, for the first time.

We appreciate Reviewer #2’s feedback regarding the need for consistent terminology when describing the impact of the double Tbx3 and Tbx5 mutant. We will edit the manuscript to replace terms like “reversion” with “transcriptional shift” or “transformation” when describing the observed phenotype, and we will use “coordination” to describe the combined role of Tbx5 and Tbx3 in maintaining VCS-specific identity.

(2) A more direct quantitative comparison of Tbx5 Adult VCS KO with Tbx5/Tbx3 Adult VCS double KO would be helpful to ascertain whether deletion of Tbx3 on top of Tbx5 deletion changes the underlying phenotype in some discernable way beyond mRNA expression of a few genes. Superficially, the phenotypes look quite similar at the EKG and arrhythmia inducibility level and no optical mapping data from a single Tbx5 KO is presented for comparison to the double KO.

We thank Reviewer #2 for the suggestions that a direct comparison between Tbx5 single conditional knockout and Tbx3/Tbx5 double conditional knockout models may help isolate the specific contribution of Tbx3 deletion in addition to Tbx5 deletion.

Previous studies have assessed the effect of single Tbx5 CKO in the VCS of murine hearts (1, 3, 5). Arnolds et al. demonstrated that the removal of Tbx5 from the adult ventricular conduction system results in VCS slowing, including prolonged PR and QRS intervals, prolongation of the His duration and His-ventricular (HV) interval (3). Furthermore, Burnicka-Turek et al. demonstrated that the single conditional knockout of Tbx5 in the adult VCS caused a shift toward a pacemaker cell state, with ectopic beats and inappropriate automaticity (1). Whole-cell patch clamping of VCS-specific Tbx5-deficient cells revealed action potentials characterized by a slower upstroke (phase 0), prolonged plateau (phase 2), delayed repolarization (phase 3), and enhanced phase 4 depolarization - features characteristic of nodal action potentials rather than typical VCS action potentials (3). These observations were interpreted as uncovering nodal potential of the VCS in the absence of Tbx5. Based on the role of Tbx3 in CCS specification (2), we hypothesized that the nodal state of the VCS uncovered in the absence of Tbx5 was enabled by maintained Tbx3 expression. This motivated us to generate the double Tbx5 / Tbx3 knockout model to examine the state of the VCS in the absence of both T-box TFs.

In the current study, we demonstrate that the VCS-specific deletion of Tbx3 and Tbx5 results in the loss of fast electrical impulse propagation in the VCS, similar to that observed in the single Tbx5 mutant. However, unlike the Tbx5 single mutant, the Tbx3/Tbx5 double deletion does not cause a gain of pacemaker cell state in the VCS. Instead, the physiological data suggests a transition toward non-conduction working myocardial physiology. This conclusion is supported by the presence of only a single upstroke in the optical action potential (OAP) recorded from the His bundle region and VCS cells in Tbx3/Tbx5 double conditional knockout mice. The electrical properties of VCS cells in the double knockout are functionally indistinguishable from those of ventricular working myocardial cells. As a result, ventricular impulse propagation is significantly slowed, resembling activation through exogenous pacing rather than the rapid conduction typically associated with the VCS. We will edit the text of the manuscript to more carefully distinguish the observations between these models, as suggested.

(1) Burnicka-Turek O, Broman MT, Steimle JD, Boukens BJ, Petrenko NB, Ikegami K, Nadadur RD, Qiao Y, Arnolds DE, Yang XH, Patel VV, Nobrega MA, Efimov IR, Moskowitz IP (2020) Transcriptional Patterning of the Ventricular Cardiac Conduction System. Circulation Research 127:e94-e106. doi:10.1161/CIRCRESAHA.118.314460. 

(2) Mohan RA, Bosada FM, van Weerd JH, van Duijvenboden K, Wang J, Mommersteeg MTM, Hooijkaas IB, Wakker V, de Gier-de Vries C, Coronel R, Boink GJJ, Bakkers J, Barnett P, Boukens BJ, Christoffels VM (2020) T-box transcription factor 3 governs a transcriptional program for the function of the mouse atrioventricular conduction system. Proc Natl Acad Sci U S A. 117:18617-18626. doi: 10.1073/pnas.1919379117.

(3) Arnolds DE, Liu F, Fahrenbach JP, Kim GH, Schillinger KJ, Smemo S, McNally EM, Nobrega MA, Patel VV, Moskowitz IP (2012) TBX5 drives Scn5a expression to regulate cardiac conduction system function. The Journal of Clinical Investigation 122:2509–2518. doi: 10.1172/JCI62617.

(4) Frank DU, Carter KL, Thomas KR, Burr RM, Bakker ML, Coetzee WA, Tristani-Firouzi M, Bamshad MJ, Christoffels VM, Moon AM (2012) Lethal arrhythmias in Tbx3-deficient mice reveal extreme dosage sensitivity of cardiac conduction system function and homeostasis. Proc Natl Acad Sci U S A. 109:E154-63. doi: 10.1073/pnas.1115165109.

(5) Moskowitz IP, Pizard A, Patel VV, Bruneau BG, Kim JB, Kupershmidt S, Roden D, Berul CI, Seidman CE, Seidman JG (2004) The T-Box transcription factor Tbx5 is required for the patterning and maturation of the murine cardiac conduction system. Development 131:4107-4116. doi: 10.1242/dev.01265. PMID: 15289437.

(3) The authors claim that double knockout VCS cells transform to working myocardial fate, but there is no comparison of gene expression levels between actual working myocardial cells and the Tbx3/Tbx5 DKO VCS cells so it's hard to know if the data reflect an actual cell state change or a more non-specific phenomenon with global dysregulation of gene expression or perhaps dedifferentiation. I understand that the upregulation of Gja1 and Smpx is intended to address this, but it's only two genes and it seems relevant to understand their degree of expression relative to actual working myocardium. In addition, the gene panel is somewhat limited and does not include other key transcriptional regulators in the VCS such as Irx3 and Nkx2-5. RNA-seq in these populations would provide a clearer comparison among the groups.

And

the main claims are that the absence of both factors results in a transcriptional shift of conduction tissue towards a working myocardial phenotype, and that this shift indicates that Tbx5 and Tbx3 "coordinate" to control VCS identity and function. However, only limited data are presented to support the claim of transcriptional reprogramming since the knockout cells are not directly compared to working myocardial cells at the transcriptional level and only a small number of key genes are assessed (versus genome-wide assessment).

We appreciate Reviewer #2’s suggestion to expand the gene expression analysis in Tbx3/Tbx5-deficient VCS cells by including other specific genes and comparisons with “native”/actual working ventricular myocardial cells and broadening the gene panel. In this study, we evaluated core cardiac conduction system markers, revealing a loss of conduction system-specific gene expression in the double mutant VCS. Furthermore, we evaluated key working myocardial markers normally excluded from the conduction system, Gja1 and Smpx, revealing a shift towards a working myocardial state in the double mutant VCS (Figure 4). We agree that a more comprehensive analysis, such as transcriptome-wide approaches, would offer greater clarity on the extent and specificity of the observed shift from conduction to non-conduction identity. These approaches are appropriate directions for future studies.

(4) From the optical mapping data, it is difficult to distinguish between the presence of (a) a focal proximal right bundle branch block due to dysregulation of gene expression in the VCS but overall preservation of the right bundle and its distal ramifications; from (b) actual loss of the VCS with reversion of VCS cells to a working myocardial fate. Related to this, the authors claim that this experiment allows for direct visualization of His bundle activation, but can the authors confirm or provide evidence that the tissue penetration of their imaging modality allows for imaging of a deep structure like the AV bundle as opposed to the right bundle branch which is more superficial? Does the timing of the separation of the sharp deflection from the subsequent local activation suggest visualization of more distal components of the VCS rather than the AV bundle itself? Additional clarification would be helpful.

And

In addition, the optical mapping dataset is incomplete and has alternative interpretations that are not excluded or thoroughly discussed.

We agree with Reviewer #2 that the resolution of the optical mapping experiment may be insufficient to precisely localize the conduction block due to the limited signal strength from the VCS. It is possible that the region defined as the His Bundle also includes portions of the right bundle branch. Our control mice show VCS OAP upstrokes consistent with those reported by Tamaddon et al. (2000) using Di-4-ANEPPS (1). We appreciate the Reviewer’s attention to alternative interpretations, and we will incorporate these caveats into the manuscript text.

(1) Tamaddon HS, Vaidya D, Simon AM, Paul DL, Jalife J, Morley GE (2000) High-resolution optical mapping of the right bundle branch in connexin40 knockout mice reveals slow conduction in the specialized conduction system. Circulation Research 87:929-36. doi: 10.1161/01.res.87.10.929. 

Impact:

The present study contributes a novel and elegantly constructed mouse model to the field. The data presented generally corroborate existing models of transcriptional regulation in the VCS but do not, as presented, constitute a decisive advance.

And

In sum, while this study adds an elegantly constructed genetic model to the field, the data presented fit well within the existing paradigm of established functions of Tbx3 and Tbx5 in the VCS and in that sense do not decisively advance the field. Moreover, the authors' claims about the implications of the data are not always strongly supported by the data presented and do not fully explore alternative possibilities.

We appreciate Reviewer # 2’s acknowledgment of the elegance and novelty of the mouse model we generated. However, we respectfully disagree with their assessment that this work merely corroborates existing models without providing a decisive advance. Previous studies have investigated single Tbx5 or Tbx3 gene knockouts in-depth and established the T-box ratio model for distinguishing fast VCS from slow nodal conduction identity (1) that the reviewer alludes to in earlier comments. In contrast, this study aimed to explore a different model, that the combined effects of Tbx5 and Tbx3 distinguish adult VCS identity from non-conduction working myocardium. The coordinated Tbx3 and Tbx5 role in conduction system identify remained untested due to the lack of a mouse model that allowed their simultaneous removal. The very model the reviewer recognizes as “novel and elegantly constructed” has allowed the examination of the coordinated role of Tbx5 and Tbx3 for the first time. While we acknowledge the opportunity for additional depth of investigation of this model in future studies, the data we present provides consistent experimental support for the coordinated requirement of both Tbx5 and Tbx3 for ventricular cardiac conduction system identity.

(1) Burnicka-Turek O, Broman MT, Steimle JD, Boukens BJ, Petrenko NB, Ikegami K, Nadadur RD, Qiao Y, Arnolds DE, Yang XH, Patel VV, Nobrega MA, Efimov IR, Moskowitz IP (2020) Transcriptional Patterning of the Ventricular Cardiac Conduction System. Circulation Research 127:e94-e106. doi:10.1161/CIRCRESAHA.118.314460. 

Reviewer #3 (Public review):

Summary:

In the study presented by Burnicka-Turek et al., the authors generated for the first time a mouse model to cause the combined conditional deletion of Tbx3 and Tbx5 genes. This has been impossible to achieve to date due to the proximity of these genes in chromosome 5, preventing the generation of loss of function strategies to delete simultaneously both genes. It is known that both Tbx3 and Tbx5 are required for the development of the cardiac conduction system by transcription factor-specific but also overlapping roles as seen in the common and diverse cardiac defects found in patients with mutations for these genes. After validating the deletion efficiency and specificity of the line, the authors characterized the cardiac phenotype associated with the cardiac conduction system (CCS)-specific combined deletion of T_bx5_ and Tbx3 in the adult by inducing the activation of the CCS-specific tamoxifen-inducible Cre recombination (MinK-creERT) at 6 weeks after birth. Their analysis of 8-9-week-old animals did not identify any major morphological cardiac defects. However, the authors found conduction defects including prolonged PR and QTR intervals and ventricular tachycardia causing the death of the double mutants, which do not survive more than 3 months after tamoxifen induction. Molecular and optical mapping analysis of the ventricular conduction system (VCS) of these mutants concluded that, in the absence of Tbx5 and Tbx3 function, the cells forming the ventricular conduction system (VCS) become working myocardium and lose the specific contractile features characterizing VCS cells. Altogether, the study identified the critical combined role of Tbx3 and Tbx5 in the maintenance of the VCS in adulthood.

Strengths:

The study generated a new animal model to study the combined deletion of Tbx5 and Tbx3 in the cardiac conduction system. This unique model has provided the authors with the perfect tool to answer their biological questions. The study includes top-class methodologies to assess the functional defects present in the different mutants analyzed, and gathered very robust functional data on the conduction defects present in these mutants. They also applied optical action potential (OAP) methods to demonstrate the loss of conduction action potential and the acquisition of working myocardium action potentials in the affected cells because of Tbx5/Tbx3 loss of function. The study used simpler molecular and morphological analysis to demonstrate that there are no major morphological defects in these mutants and that indeed, the conduction defects found are due to the acquisition of working myocardium features by the VCS cells. Altogether, this study identified the critical role of these transcription factors in the maintenance of the VCS in the adult heart.

We appreciate the Reviewer’s comments regarding the originality and utility of our model and the strengths of our methodological approach. The Reviewer’s appreciation of the molecular and morphological analyses as well as their constructive feedback is highly valuable.

Weaknesses:

In the opinion of this reviewer, the weakness in the study lies in the morphological and molecular characterization. The morphological analysis simply described the absence of general cardiac defects in the adult heart, however, whether the CCS tissues are present or not was not investigated. Lineage tracing analysis using the reporter lines included in the crosses described in the study will determine if there are changes in CCS tissue composition in the different mutants studied. Similarly, combining this reporter analysis with the molecular markers found to be dysregulated by qPCR and western blot, will demonstrate that indeed the cells that were specified as VCS in the adult heart, become working myocardium in the absence of Tbx3 and Tbx5 function.

We appreciate the reviewer’s concern regarding the morphology of the cardiac conduction system in the Tbx3/Tbx5 double conditional knockout model. We did not observe any structural abnormalities, as the Reviewer notes. We agree with their suggestion for using Genetic Inducible Fate Mapping to mark cardiac conduction cells expressing MinKCre. In fact, we utilized this approach to isolate VCS cells for transcriptional profiling. Specifically, we combined the tamoxifen-inducible MinKCreERT allele with the Cre-dependent R26Eyfp reporter allele to label MinKCre-expressing cells in both control VCS and VCS-specific double Tbx3/Tbx5 knockouts. EYFP-positive cells were isolated for transcriptional studies, ensuring that our analysis exclusively targeted conduction system-lineage marked cells. The ability to isolate MinKCre-marked cells from both controls and Tbx5/Tbx3 double mutants indicates that VCS cells persisted in the double knockout. Nonetheless, the suggestion for in-vivo marking by Genetic Inducible Fate Mapping and morphologic analysis is a valuable recommendation for future studies.

Author response:

Public Reviews:

Reviewer #1 (Public review):

Mutations in CDHR1, the human gene encoding an atypical cadherin-related protein expressed in photoreceptors, are thought to cause cone-rod dystrophy (CRD). However, the pathogenesis leading to this disease is unknown. Previous work has led to the hypothesis that CDHR1 is part of a cadherin-based junction that facilitates the development of new membranous discs at the base of the photoreceptor outer segments, without which photoreceptors malfunction and ultimately degenerate. CDHR1 is hypothesized to bind to a transmembrane partner to accomplish this function, but the putative partner protein has yet to be identified.

The manuscript by Patel et al. makes an important contribution toward improving our understanding of the cellular and molecular basis of CDHR1-associated CRD. Using gene editing, they generate a loss of function mutation in the zebrafish cdhr1a gene, an ortholog of human CDHR1, and show that this novel mutant model has a retinal dystrophy phenotype, specifically related to defective growth and organization of photoreceptor outer segments (OS) and calyceal processes (CP). This phenotype seems to be progressive with age. Importantly, Patel et al, present intriguing evidence that pcdh15b, also known for causing retinal dystrophy in previous Xenopus and zebrafish loss of function studies, is the putative cdhr1a partner protein mediating the function of the junctional complex that regulates photoreceptor OS growth and stability.

This research is significant in that it:

(1) provides evidence for a progressive, dystrophic photoreceptor phenotype in the cdhr1a mutant and, therefore, effectively models human CRD; and

(2) identifies pcdh15b as the putative, and long sought after, binding partner for cdhr1a, further supporting the theory of a cadherin-based junction complex that facilitates OS disc biogenesis.

Nonetheless, the study has several shortcomings in methodology, analysis, and conceptual insight, which limits its overall impact.

Below I outline several issues that the authors should address to strengthen their findings.

Major comments:

(1) Co-localization of cdhr1a and pcdh15b proteins

The model proposed by the authors is that the interaction of cdhr1a and pcdh15b occurs in trans as a heterodimer. In cochlear hair cells, PCDH15 and CDHR23 are proposed to interact first as dimers in cis and then as heteromeric complexes in trans. This was not shown here for cdhr1a and pcdh15b, but it is a plausible configuration, as are single heteromeric dimers or homodimers. Regardless, this model depends on the differential compartmental expression of the cdhr1a and pcdh15b proteins. Data in Figure 1 show convincing evidence that these two proteins can, at least in some cases, be distributed along the length of photoreceptor membranes that are juxtaposed, as would be the case for OS and CP. If pcdh15b is predominantly expressed in CPs, whereas cdhr1a is predominantly expressed in OS, then this should be confirmed with actin double labeling with cdhr1a and pcdh15b since the apicobasal oriented (vertical) CPs would express actin in this same orientation but not in the OS. This would help to clarify whether cdhr1a and pcdh15b can be trafficked to both OS and CP compartments or whether they are mutually exclusive.

First let me thank the reviewer for taking the time to comprehensively evaluate our work and provide constructive criticism which will improve the quality of our final version.

To address this issue, we are undertaking imaging of actin/cdhr1a and actin/pcdh15b using SIM in both transverse and axial sections. Additionally, we have recently established an immuno-gold-TEM protocol and are going to provide data showcasing co-labeling of cdhr1a and pcdh15b at TEM resolution.

Photoreceptor heterogeneity goes beyond the cone versus rod subtypes discussed here and it is known that in zebrafish, CP morphology is distinct in different cone subtypes as well as cone versus rod. It would be important to know which specific photoreceptor subtypes are shown in zebrafish (Figures 1A-C) and the non-fish species depicted in Figures 1E-L. Also, a larger field of view of the staining patterns for Figures 1E-L would be a helpful comparison (could be added as a supplementary figure).

The revised manuscript will include clear labeling of the different cone cell types as well as lower magnification images to be included as supplemental figures.

(2) Cdhr1a function in cell culture

The authors should explain the multiple bands in the anti-FLAG blots. Also, it would be interesting to confirm that the cdhr1a D173 mutant prevents the IP interaction with pcdh15b as well as the additive effects in aggregate assays of Figure 2.

We believe that the D173 mutation results in no cdhr1a polypeptide, based on the lack of in situ signal in our WISH studies (figures showing absence of cdhr1a mRNA will be provided in a new supplemental figure). However, we will clone the D173 mutant and attempt co-IP with pchd15b in our cell culture system as well as the aggregation assay using K562 cells.

Is it possible that the cultured cells undergo proliferation in the aggregation assays shown in Figure 2? Cells might differentially proliferate as clusters form in rotating cultures. A simple assay for cell proliferation under the different transfection conditions showing no differences would address this issue and lend further support to the proposed specific changes to cell adhesion as a readout of this assay.

This is a possibility, however we did not use rotating cultures, this was a monolayer culture. We did not observe any differences in total cell number between the differing transfections. As such, we do not feel proliferation explains the aggregation of K562 cells.

Also, the authors report that the number of clusters was normalized to the field of view, but this was not defined. Were the n values different fields of view from one transfection experiment, or were they different fields of view from separate transfection experiments? More details and clarification are needed.

This will be clarified in the revised manuscript, in short we replicated this experiment 3 times, quantifying 5 different fields of view in each replicate.

(3) Methodological issues in quantification and statistical analyses

Were all the OS and CP lengths counted in the observation region or just a sample within the region? If the latter, what were the sampling criteria? For CPs, it seems that the length was an average estimate based on all CPs observed surrounding one cone or one-rod cell. Is this correct? Again, if sampled, how was this implemented? In Fig 4M', the cdhr1a-/- ROS mostly looks curvilinear. Did the measurements account for this, or were they straight linear dimension measurements from base to tip of the OS as depicted in Fig 5A-E? A clearer explanation of the OS and CP length quantification methodology is required.

The revised manuscript will clearly outline measurement methods. In short, we measured every CP/OS in the imaged regions. We did not average CPs/cell, we simply included all CP measurements in our analysis. All our CP measurements (actin or cdhr1a or pcdh15), were done in the presence of a counter stain, WGA, prph2, gnb1 or PNA to ensure proper measurements (landmark) and association with proper cell type.

All measurements were taken as best as possible to reflect a straight linear dimension for consistency.

How were cone and rod photoreceptor cell counts performed? The legend in Figure 4 states that they again counted cells in the observation region, but no details were provided. For example, were cones and rods counted as an absolute number of cells in the observation region (e.g., number of cones per defined area) or relative to total (DAPI+) cell nuclei in the region? Changes in cell density in the mutant (smaller eye or thinner ONL) might affect this quantification so it would be important to know how cell quantification was normalized.

The revised manuscript will clearly outline measurement methods. In short, rod and cone cell counts were based on the number of outer segments that were observed in the imaging region and previously measured for length. We did not observe any eye size differences in our mutant fish.

In Figure 6I, K, measuring the length of the signal seems problematic. The dimension of staining is not always in the apicobasal (vertical) orientation. It might be more accurate to measure the cdhr1a expression domain relative to the OS (since the length of the OS is already reduced in the mutants). Another possible approach could be to measure the intensity of cdhr1 staining relative to the intensity within a Prph2 expression domain in each group. The authors should provide complementary evidence to support their conclusion.

The revised manuscript will clearly outline measurement methods. In short, all of our CP measurements (actin or cdhr1a or pcdh15), were done in the presence of a counter stain, WGA, prph2, gnb1 or PNA to ensure proper measurements and association with proper cell type.

A better description of the statistical methodology is required. For example, the authors state that "each of the data points has an n of 5+ individuals." This is confusing and could indicate that in Figure 4F alone there were ~5000 individuals assayed (~100 data points per treatment group x n=5 individuals per data point x 10 treatment groups). I don't think that is what the authors intended. It would be clearer if the authors stated how many OS, CP, or cells were counted in their observation region averaged per individual, and then provided the n value of individuals used per treatment group (controls and mutants), on which the statistical analyses should be based.

This will be addressed in the revised manuscript. In short we had an n=5 (individual fish) analyzed for each genotype/time point. We will also include numbers of OS/CP quantified in the observation regions.

There are hundreds of data points in the separate treatment groups shown in several of the graphs. It would not be correct to perform the ANOVA on the separate OS or CP length measurements alone as this will bias the estimates since they are not all independent samples. For example, in Figure 6H, 5dpf pcdh15b+/- have shorter CPs compared to WT but pcdh15b-/- have longer compared to WT. This could be an artifact of the analysis. Moreover, the authors should clarify in the Methods section which ANOVA post hoc tests were used to control for multiple pairwise comparisons.

This will be clarified in the revised manuscript.

(4) Cdhr1a function in photoreceptors

The cdhr1a IHC staining in 5dpf WT larvae in Figure 3E appears different from the cdhr1a IHC staining in 5dpf WT larvae in Figure 1A or Figure 6I. Perhaps this is just the choice of image. Can the authors comment or provide a more representative image?

The image in figure 3E was captured using a previous non antigen retrieval protocol which limits the resolution of the cdhr1a signal along the CP. In the revised manuscript we will include an image that better represents cdhr1a staining in the WT and mutant.

The authors show that pcdh15b localization after 5dpf mirrored the disorganization of the CP observed with actin staining. They also show in Figure 5O that at 180dpf, very little pcdh15b signal remains. They suggest based on this data that total degradation of CPs has occurred in the cdhr1a-/- photoreceptors by this time. However, although reduced in length, COS and cone CPs are still present at 180dpf (Figure 5E, E'). Thus, contrary to the authors' general conclusion, it is possible that the localization, trafficking, and/or turnover of pcdh15b is maintained through a cdhr1a-dependent mechanism, irrespective of the degree to which CPs are maintained. The experiments presented here do not clearly distinguish between a requirement for maintenance of localization versus a secondary loss of localization due to defective CPs.

We agree, this point will be addressed in our revised manuscript.

(5) Conceptual insights

The authors claim that cdhr1a and pcdh15b double mutants have synergistic OS and CP phenotypes. I think this interpretation should be revisited.

First, assuming the model of cdhr1a-pcdh15b interaction in trans is correct, the authors have not adequately explained the logic of why disrupting one side of this interaction in a single mutant would not give the same severity of phenotype as disrupting both sides of this interaction in a double mutant.

Second, and perhaps more critically, at 10dpf the OS and CP lengths in cdhr1a-/- mutants (Figure 7J, T) are significantly increased compared to WT. In contrast, there are no significant differences in these measurements in the pcdh15b-/- mutants. Yet in double homozygous mutants, there is a significant reduction of ~50% in these measurements compared to WT. A synergistic phenotype would imply that each mutant causes a change in the same direction and that the magnitude of this change is beyond additive in the double mutants (but still in the same direction). Instead, I would argue that the data presented in Figure 7 suggest that there might be a functionally antagonistic interaction between cdhr1a and pcdh15b with respect to OS and CP growth at 10dpf.

If these proteins physically interacted in vivo, it would appear that the interaction is complex and that this interaction underlies both OS growth-promoting and growth-restraining (stabilizing) mechanisms working in concert. Perhaps separate homodimers or heterodimers subserve distinct CP-OS functional interactions. This might explain the age-dependent differences in mutant CP and OS length phenotypes if these mechanisms are temporally dynamic or exhibit distinct OS growth versus maintenance phases. Regardless of my speculations, the model presented by the authors appears to be too simplistic to explain the data.

We agree with the reviewer, as such we will address this conclusion in our revised manuscript. To do so we will revise our final model and include more flexibility in the proposed mechanisms.

Reviewer #2 (Public review):

Summary:

The goal of this study was to develop a model for CDHR1-based Con-rod dystrophy and study the role of this cadherin in cone photoreceptors. Using genetic manipulation, a cell binding assay, and high-resolution microscopy the authors find that like rods, cones localize CDHR1 to the lateral edge of outer segment (OS) discs and closely oppose PCDH15b which is known to localize to calyceal processes (CPs). Ectopic expression of CDHR1 and PCDH15b in K652 cells indicates these cadherins promote cell aggregation as heterophilic interactants, but not through homophilic binding. This data suggests a model where CDHR1 and PCDH15b link OS and CPs and potentially stabilize cone photoreceptor structure. Mutation analysis of each cadherin results in cone structural defects at late larval stages. While pcdh15b homozygous mutants are lethal, cdhr1 mutants are viable and subsequently show photoreceptor degeneration by 3-6 months.

Strengths:

A major strength of this research is the development of an animal model to study the cone-specific phenotypes associated with CDHR1-based CRD. The data supporting CDHR1 (OS) and PCDH15 (CP) binding is also a strength, although this interaction could be better characterized in future studies. The quality of the high-resolution imaging (at the light and EM levels) is outstanding. In general, the results support the conclusions of the authors.

Weaknesses:

While the cellular phenotyping is strong, the functional consequences of CDHR1 disruption are not addressed. While this is not the focus of the investigation, such analysis would raise the impact of the study overall. This is particularly important given some of the small changes observed in OS and CP structure. While statistically significant, are the subtle changes biologically significant? Examples include cone OS length (Figures 4F, 6E) as well as other morphometric data (Figure 7I in particular). Related, for quantitative data and analysis throughout the manuscript, more information regarding the number of fish/eyes analyzed as well as cells per sample would provide confidence in the rigor. The authors should also note whether the analysis was done in an automated and/or masked manner.

First let me thank the reviewer for taking the time to comprehensively evaluate our work and provide constructive criticism which will improve the quality of our final version.

The revised manuscript will clearly outline both methods and statistics used for quantitation of our data. (please see comments from reviewer 1). While we do not include direct evidence of the mechanism of CDHR1 function, we do propose that its role is important in anchoring the CP and the OS, particularly in the cones, while in rods it may serve to regulate the release of newly formed disks (as previously proposed in mice). We do plan to test both of these hypothesis directly, however, that will be the basis of our future studies.

Reviewer #3 (Public review):

Summary:

The manuscript by Patel et al investigates the hypothesis that CDHR1a on photoreceptor outer segments is the binding partner for PCDH15 on the calyceal processes, and the absence of either adhesion molecule results in separation between the two structures, eventually leading to degeneration. PCDH15 mutations cause Usher syndrome, a disease of combined hearing and vision loss. In the ear, PCDH15 binds CDH23 to form tip links between stereocilia. The vision loss is less understood. Previous work suggested PCDH15 is localized to the calyceal processes, but the expression of CDH23 is inconsistent between species. Patel et al suggest that CDHR1a (formerly PCDH21) fulfills the role of CDH23 in the retina.

The experiments are mainly performed using the zebrafish model system. Expression of Pcdh15b and Cdhr1a protein is shown in the photoreceptor layer through standard confocal and structured illumination microscopy. The two proteins co-IP and can induce aggregation in vitro. Loss of either Cdhr1a or Pcdh15, or both, results in degeneration of photoreceptor outer segments over time, with cones affected primarily.

The idea of the study is logical given the photoreceptor diseases caused by mutations in either gene, the comparisons to stereocilia tip links, and the protein localization near the outer segments. The work here demonstrates that the two proteins interact in vitro and are both required for ongoing outer segment maintenance. The major novelty of this paper would be the demonstration that Pcdh15 localized to calyceal processes interacts with Cdhr1a on the outer segment, thereby connecting the two structures. Unfortunately, the data presented are inadequate proof of this model.

Strengths:

The in vitro data to support the ability of Pcdh15b and Cdhr1a to bind is well done. The use of pcdh15b and cdhr1a single and double mutants is also a strength of the study, especially being that this would be the first characterization of a zebrafish cdhr1a mutant.

Weaknesses:

(1) The imaging data in Figure 1 is insufficient to show the specific localization of Pcdh15 to calyceal processes or Cdhr1a to the outer segment membrane. The addition of actin co-labelling with Pcdh15/Cdhr1a would be a good start, as would axial sections. The division into rod and cone-specific imaging panels is confusing because the two cell types are in close physical proximity at 5 dpf, but the cone Cdhr1a expression is somehow missing in the rod images. The SIM data appear to be disrupted by chromatic aberration but also have no context. In the zebrafish image, the lines of Pcdh15/Cdhr1a expression would be 40-50 um in length if the scale bar is correct, which is much longer than the outer segments at this stage and therefore hard to explain.

First let me thank the reviewer for taking the time to comprehensively evaluate our work and provide constructive criticism which will improve the quality of our final version.

To address this issue, we are undertaking imaging of actin/cdhr1a and actin/pcdh15b using SIM in both transverse and axial sections. Additionally, we have recently established an immuno-gold-TEM protocol and are going to provide data showcasing co-labeling of cdhr1a and pcdh15b at TEM resolution. We are also going to include lower magnification images to complement the SIM images presented in figure 1.

(2) Figure 3E staining of Cdhr1a looks very different from the staining in Figure 1. It is unclear what the authors are proposing as to the localization of Cdhr1a. In the lab's previous paper, they describe Cdhr1a as being associated with the connecting cilium and nascent OS discs, and fail to address how that reconciles with the new model of mediating CP-OS interaction. And whether Cdhr1a localizes to discrete domains on the disc edges, where it interacts with Pcdh15 on individual calyceal processes.

The image in figure 3E was captured using a previous non antigen retrieval protocol which limits the resolution of the cdhr1a signal along the CP. In the revised manuscript we will include an image that better represents cdhr1a staining in the WT and mutant.

(3) The authors state "In PRCs, Pcdh15 has been unequivocally shown to be localized in the CPs". However, the immunostaining here does not match the pattern seen in the Miles et al 2021 paper, which used a different antibody. Both showed loss of staining in pcdh15b mutants so unclear how to reconcile the two patterns.

We agree that our staining appears different, but we attribute this to our antigen retrieval protocol which differed from the Miles et al paper. We also point to the fact that pcdh15b localization has been shown to be similar to our images in other species (monkey and frog). As such, we believe our protocol reveals the proper localization pattern which might be lost/hampered in the procedure used in Miles et al 2021.

(4) The explanation for the CRISPR targets for cdhr1a and the diagram in Figure 3 does not fit with crRNA sequences or the mutation as shown. The mutation spans from the latter part of exon 5 to the initial portion of exon 6, removing intron 5-6. It should nevertheless be a frameshift mutation but requires proper documentation.

This was an overlooked error in figure making, we apologize and will address this typo in the revised manuscript.

(5) There are complications with the quantification of data. First, the number of fish analyzed for each experiment is not provided, nor is the justification for performing statistics on individual cell measurements rather than using averages for individual fish. Second, all cone subtypes are lumped together for analysis despite their variable sizes. Third, t-tests are inappropriately used for post-hoc analysis of ANOVA calculations.

As we discussed for reviewer 1 and 2, all methods and quantification/statistics will be clearly described in the revised manuscript.

(6) Unclear how calyceal process length is being measured. The cone measurements are shown as starting at the external limiting membrane, which is not equivalent to the origin of calyceal processes, and it is uncertain what defines the apical limit given the multiple subtypes of cones. In Figure 5, the lines demonstrating the measurements seem inconsistently placed.

As we discussed for reviewer 1 and 2, all methods and quantification/statistics will be clearly described in the revised manuscript.

(7) The number of fish analyzed by TEM and the prevalence of the phenotype across cells are not provided. A lower magnification view would provide context. Also, the authors should explain whether or not overgrowth of basal discs was observed, as seen previously in cdhr1-null frogs (Carr et al., 2021).

The revised manuscript will include the aforementioned stats and lower magnification images. We will also compare our results directly to Carr 2021.

(8) The statement describing the separation between calyceal processes and the outer segment in the mutants is not backed up by the data. TEM or co-labelling of the structures in SIM could be done to provide evidence.

We will work to include more TEM and co-labeling data for the revised manuscript (see comments to reviewer 1)

(9) "Based on work in the murine model and our own observations of rod CPs, we hypothesize that zebrafish rod CPs only extend along the newly forming OS discs and do not provide structural support to the ROS." Unclear how murine work would support that conclusion given the lack of CPs in mice, or what data in the manuscript supports this conclusion.

In the revised manuscript we will improve our discussion of murine CPs, in that we still detect the juxtaposition of cdhr1 and pcdh15, along a potential remanent of the CP as previously described in SEM studies. Our findings do not indicate that mice or rats have CPs, we simply wanted to outline that the behavior of cdhr1 and pcdh15 still remains conserved, despite the absence of long traditional CPs.

(10) The authors state "from the fact that rod CPs are inherently much smaller than cone CPs" without providing a reference. In the manuscript, the measurements do show rod CPs to be shorter, but there are errors in the cone measurements, and it is possible that the RPE pigment is interfering with the rod measurements.

We will include a reference where rod CPs have been found to be shorter (monkey and frog data). We have no doubt that in zebrafish the rod CPs are significantly shorter. All our CP measurements are done with a counter stain for rods and cones to be sure that we are measuring the correct cell type.

(11) The discussion should include a better comparison of the results with ocular phenotypes in previously generated pcdh15 and cdhr1 mutant animals.

In the revised manuscript we will include this in our discussion.

(12) The images in panels B-F of the Supplemental Figure are uncannily similar, possibly even of the same fish at different focal planes.

We assure the reviewer that each of the images in supplemental figure 1 are distinct and represent different in situ experiments.

Author response:

We thank the reviewers for the positive and constructive feedback on our manuscript. We appreciate you highlighting the importance of our work in advancing our understanding of HIV latency and viral reactivation. The reviewers had mostly minor comments that we are in the process of addressing by completing additional experiments that are responsive to reviewer comments as well as some clarification of the text. These include:

(1) The impact of INTS12 knockout on cell viability.

We did not see an effect of the knockout of INTS12 on cell viability in the flow cytometry gating of live/dead cells, nor a gross difference in cell proliferation. However, we will test cell viability and proliferation more quantitatively and include this data in the revision.

(2) The effect of INTS12 knockout on additional LRAs.

There is published data that the Integrator complex inhibits HIV reactivation via additional LRAs that we will better highlight in the revision. In addition, we have data that we did not include in the original submission suggesting that INST12 knockout affects the degree of HIV reactivation with additional LRAs. We will confirm these results and include the data in the revision.

(3) Extend the discussion on how exquisitely sensitive HIV transcription is to pausing and transcriptional elongation and the insights this provides about general HIV transcriptional regulation.

Yes, we agree with this and will extend the discussion in this manner. We will also include additional data that we recently obtained that further emphasizes this point.

(4) Comparison to another CRISPR screen using the same library (Hsieh et al., PLOS Pathogens, 2023).

Indeed, INST12 was one of the hits in the previous paper (Hsieh et al., 2023) but was not specifically described or validated in that paper. We will point that out in the revision. Also, the Hsieh et al paper already described the library in more detail, but we will include additional text in the revision to emphasize that it casts a wide net on processes involved in transcriptional regulation.

(5) We made a mistake on the numbering of the supplemental figures which lead to some misunderstanding. We will correct this as well as add other suggestions of the reviewers for clarifications.

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

D'Oliviera et al. have demonstrated cleavage of human TRMT1 by the SARS-CoV-2 main protease in vitro. Following, they solved the structure of Mpro (Nsp5)-C145A bound to TRMT1 substrate peptide, revealing binding conformation distinct from most viral substrates. Overall, this work enhances our understanding of substrate specificity for a key drug target of CoV2. The paper is well-written and the data is clearly presented. It complements the companion article by demonstrating interaction between Mpro and TRMT1, as well as TRMT1 cleavage under isolated conditions in vitro. They show that cleaved TRMT1 has reduced tRNA binding affinity, linking a functional consequence to TRMT1 cleavage by MPro. Importantly, the revelation for flexible substrate binding of Nsp5 is fundamental for understanding Nsp5 as a drug target. Trmt1 cleavage assays by Mpro revealed similar kinetics for TRMT1 cleavage as compared to nsp8/9 viral polyprotein cleavage site. They purify TRMT1-Q350K, in which there is a mutation in the predicted cleavage consensus sequence, and confirm that it is resistant to cleavage by recombinant Mpro. I am unable to comment critically on the structural analyses as it is outside of my expertise. Overall, I think that these findings are important for confirming TRMT1 as a substrate of Mpro, defining substrate binding and cleavage parameters for an important drug target of SARS-CoV-2, and may be of interest to researchers studying RNA modifications.

We thank the reviewer for their positive assessment and summary of our work in this paper!

Reviewer #2 (Public review):

Summary:

The manuscript 'Recognition and Cleavage of Human tRNA Methyltransferase TRMT1 by the SARS-CoV-2 Main Protease' from Angel D'Oliviera et al., uncovers that TRMT1 can be cleaved by SARS-CoV-2 main protease (Mpro) and defines the structural basis of TRMT1 recognition by Mpro. They use both recombinant TRMT1 and Mpro as well as endogenous TRMT1 from HEK293T cell lysates to convincingly show cleavage of TRMT1 by the SARS-CoV-2 protease. Using in vitro assays, the authors demonstrate that TRMT1 cleavage by Mpro blocks its enzymatic activity leading to hypomodification of RNA. To understand how Mpro recognizes TRMT1, they solved a co-crystal structure of Mpro bound to a peptide derived from the predicted cleavage site of TRMT1. This structure revealed important protein-protein interfaces and highlights the importance of the conserved Q530 for cleavage by Mpro. They then compare their structure with previous X-ray crystal structures of Mpro bound to substrate peptides derived from the viral polyprotein and propose the concept of two distinct binding conformations to Mpro: P3´-out and P3´-in conformations (here P3´ stands for the third residue downstream of the cleavage site). It remains unknown what is the physiological role of these two binding conformations on Mpro function, but the authors established that Mpro has dramatically different cleavage efficiencies for three distinct substrates. In an effort to rationalize this observation, a series of mutations in Mpro's active site and the substrate peptide were tested but unexpectedly had no significant impact on cleavage efficiency. While molecular dynamic simulations further confirmed the propensity of certain substrates to adopt the P3´-out or P3´-in conformation, it did not provide additional insights into the dramatic differences in cleavage efficiencies between substrates. This led the authors to propose that the discrimination of Mpro for preferred substrates might occur at a later stage of catalysis after binding of the peptide. Overall, this work will be of interest to biologists studying proteases and substrate recognition by enzymes and RNA modifications as well as help efforts to target Mpro with peptide-like drugs.

We thank the reviewer for this thorough and accurate summary of our work in this manuscript.

Strengths:

• The authors' statements are well supported by their data, and they used relevant controls when needed. Indeed, they used the Mpro C145A inactive variant to unambiguously show that the TRMT1 cleavage detected in vitro is solely due to Mpro's activity. Moreover, they used two distinct polyclonal antibodies to probe TRMT1 cleavage.

• They demonstrate the impact of TRMT1 cleavage on RNA modification by quantifying both its activity and binding to RNA.

• Their 1.9 Å crystal structure is of high quality and increases the confidence in the reported protein-protein contacts seen between TRMT1-derived peptide and Mpro.

• Their extensive in vitro kinetic assay was performed in ideal conditions although it is sometimes unclear how many replicates were performed.

• They convincingly show how Mpro cleavage is conserved among most but not all mammalian TRMT1 bringing an interesting evolutionary perspective on virus-host interactions.

• The authors test multiple hypotheses to rationalize the preference of Mpro for certain substrates.

• While this reviewer is not able to comment on the rigor of the MD simulations, the interpretations made by the authors seem reasonable and convincing.

• The concept of two binding conformations (P3´-out or P3´-in) for the substrate in the active site of Mpro is significant and can guide drug design.

We thank the reviewer for these positive assessments of manuscript strengths!

Weaknesses:

• The two polyclonal antibodies used by the authors seem to have strong non-specific binding to proteins other than TRMT1 but did not impact the author's conclusions or statements. This is a limitation of the commercially available antibodies for TRMT1.

Yes, there are some levels of non-specific binding for all of the TRMT1 antibodies we have tested (this limitation of commercially available TRMT1 antibodies is also observed and noted by Zhang et al), but we agree that this does not impact the overall conclusions and that by using multiple different antibodies to show the same effects, we can have high confidence in the Western blot analysis and interpretation.

• Despite the reasonable efforts of the authors, it remains unknown why Mpro shows higher cleavage efficiency for the nsp4/5 sequence compared to TRMT1 or nsp8/9 sequences. This is a challenging problem that will take substantially more effort by several labs to decipher mechanistically.

True! To our knowledge and despite significant past efforts of many research groups studying similar coronavirus proteases (e.g. SARS-CoV-1 Mpro) a clear understanding of the detailed mechanistic relationship between cleavage sequence and cleavage kinetics remains mostly undefined. This is a great and important problem for mechanistic and computational groups with deep interests in proteases to tackle in the future! To highlight these and similar open questions, we have added a short paragraph to the Discussion section (second from the last paragraph).

• The peptide cleavage kinetic assay used by the authors relies on a peptide labelled with a fluorophore (MCA) on the N-terminus and a quencher (Dpn) on the C-terminus. This design allows high-throughput measurements compatible with plate readers and is a robust and convenient tool. Nevertheless, the authors did not control for the impact of the labels (MCA and Dpn) on the activity of Mpro. While in most cases the introduced fluorophore/quencher do not impact activity, sometimes it can.

Yes, we agree that it is possible the MCA and Dnp labels could have effects on the measured cleavage rates. These fluorophore/quencher peptide cleavage assays are the standard assays used by many labs in the protease field to study diverse proteases and diverse cleavage targets. When other labs have compared cleavage kinetic parameters measured with fluorophore/quencher-based peptide cleavage assays versus HPLC-based peptide cleavage assays, these are often found to be quite similar (e.g. Lee, J., Worrall, L.J., Vuckovic, M. et al. Crystallographic structure of wild-type SARS-CoV-2 main protease acyl-enzyme intermediate with physiological C-terminal autoprocessing site. Nat Commun 11, 5877 (2020). https://doi.org/10.1038/s41467-020-19662-4), although there are also examples where differences arise. In any case, we agree there could be some effects on the cleavage kinetics introduced by the fluorophore and/or quencher groups. However, our main focus in this paper is to show how a sequence in the human tRNA-modifying enzyme TRMT1 is cleaved by Mpro (and in this revision we have also added new data to show the functional effects of cleavage on TRMT1 activity); it will take significant future work to fully dissect the detailed relationships between peptide sequence, including the quantitative effects of fluorophore/quencher labels, and protease-directed cleavage kinetics. Based on our work in this paper and many past studies of similar proteases, understanding how peptide sequence or conformation relates to cleavage efficiency is a longer-term and very challenging problem that we view as beyond the scope of this work. We have added a brief section elaborating on this in the Discussion.

• An unanswered question not addressed by the authors is if the peptides undergo conformational changes upon Mpro binding or if they are pre-organized to adopt the P3´-out and P3´-in conformations. This might require substantially more work outside the scope of this immediate article.

We agree this is unanswered; we considered additional MD experiments to address this, but ultimately decided that since both of these sequences are cleaved in the context of much larger polypeptides (FL TRMT1 or the viral polypeptide), any simple analysis to assess the possibility of pre-organization and relate this preferred binding conformation to cleavage kinetics would be difficult to interpret in a biologically meaningful way. We think this and similar questions about how pre-organization of peptides or amino acid sequences in the polypeptides might influence protease binding and cleavage activity are interesting and important future questions for protease-focused groups in this field.

Reviewer #3 (Public review):

Summary:

In this manuscript, the authors have used a combination of enzymatic, crystallographic, and in silico approaches to provide compelling evidence for substrate selectivity of SARS-CoV-2 Mpro for human TRMT1.

Strengths:

In my opinion, the authors came close to achieving their intended aim of demonstrating the structural and biochemical basis of Mpro catalysis and cleavage of human TRMT1 protein. The revised version of the manuscript has addressed most of the questions I had posed in my earlier review.

We thank the reviewer for their positive assessment of this work, and we are glad to hear the manuscript revisions were helpful in addressing the first round of reviews and questions.

Weaknesses:

Although several new hypotheses are generated from the Mpro structural data, the manuscript falls a bit short of testing them in functional assays, which would have solidified the conclusions the authors have drawn.

Toward showing some of the functional effects of TRMT1 cleavage, in this revised version of the manuscript we have added new data and a new results section (‘Cleavage of TRMT1 results in complete loss of tRNA m2,2G modification activity and reduced tRNA binding in vitro’) showing that cleavage of TRMT1 results in reduced tRNA binding to TRMT1 (Figure 2D) and the complete loss of TRMT1-mediated tRNA modification activity in vitro (Figure 2C). This complements the in-cell data presented by Zhang et al showing that cleavage of TRMT1 in SARS-CoV-2 infected human cells results in the reduction of m2,2G modification levels. We think these data are a strong addition to this paper that broadens the impacts of our reported results more directly into the RNA modifications field.

In terms of showing the further, downstream biological effects of TRMT1 cleavage and/or the specific impacts of TRMT1 cleavage on SARS-CoV-2 propagation and replication, while we agree further functional assays could absolutely heighten the overall impact, we view the main focus of our paper as showing how TRMT1 is recognized and cleaved by Mpro at the structural level and characterizing the biochemistry of the TRMT1-Mpro interaction and the effects of cleavage on TRMT1 tRNA-modifying activity. Zhang et al present some cellular data suggesting that loss of TRMT1 and/or TRMT1 cleavage during infection is actually detrimental to SARS-CoV-2 replication and infectivity. However, a full understanding of how TRMT1-mediated m2,2G modification of tRNA impacts viral translation, whether TRMT1 plays other roles during the viral life cycle, or whether TRMT1 cleavage (even if not important for viral fitness) contributes to cellular phenotypes during infection, will take a significant amount of future cell biology and virology work to unravel. Indeed, our understanding is that characterizing some of the endogenous cleavage targets for the HIV protease and determining the downstream biological effects and impacts on HIV infection took well over a decade. We hope that the biochemical and structural characterization of the Mpro-TRMT1 interaction presented in our paper will provide the necessary fundamental groundwork and impetus for future virology and cellular biochemistry studies to further investigate the biological roles of TRMT1 cleavage by SARS-CoV-2 Mpro.

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

eLife Assessment:

This manuscript provides important structural insights into the recognition and degradation of the host tRNA methyltransferase by SARS-CoV-2 protease nsp5 (Mpro). The data convincingly support the main conclusions of the paper. These results will be of interest to researchers studying structures and substrate recognition and specificity of viral proteases.

We thank the eLife editors and reviewers for handling this manuscript and the overall positive assessment of our work.

In this revised version of the manuscript we have included significant, new experimental data with recombinant purified, catalytically active TRMT1 that directly shows cleavage of TRMT1 reduces its tRNA binding affinity (by gel shift assays) and results in the complete loss of tRNA modifying activity in vitro (by radiolabel-based methyltransferase assays). Because these added experiments provide new information about how Mpro-mediated cleavage specifically impacts TRMT1 tRNA binding and m2,2G modification activity, and thus new information about the functional effects of loss of the TRMT1 Zn finger domain, we would strongly suggest adding that “this work may be of interest to researchers studying RNA modifications”, or a similar phrase, in the eLife assessment.

Please find below our point-by-point response to each of the reviewer comments, which outlines additional changes to the manuscript.

Public Reviews:

Reviewer #1 (Public Review):

D'Oliviera et al. have demonstrated cleavage of human TRMT1 by the SARS-CoV-2 main protease in vitro. Following this, they solved the structure of Mpro-C145A bound to TRMT1 substrate peptide, revealing binding conformation distinct from most viral substrates. Overall, this work enhances our understanding of substrate specificity for a key drug target of CoV2. The paper is well-written and the data is clearly presented. It complements the companion article by demonstrating the interaction between Mpro and TRMT1 and TRMT1 cleavage under isolated conditions in vitro. Importantly, the revelation of flexible substrate binding of Nsp5 is fundamental for understanding Nsp5 as a drug target. Trmt1 cleavage assays revealed similar kinetics for TRMT1 cleavage as compared to the nsp8/9 viral polyprotein cleavage site, however, it would have been more rigorous for the authors to independently reproduce the kinetics reported for nsp8/9 using their specific experimental conditions. The finding that murine TRMT1 lacks a conserved consensus sequence is interesting, but is not experimentally tested here and is reported elsewhere. I am unable to comment critically on the structural analyses as it is outside of my expertise. Overall, I think that these findings are important for confirming TRMT1 as a substrate of Mpro and defining substrate binding and cleavage parameters for an important drug target of SARS-CoV-2.

We thank the reviewer for their positive assessment and summary of our work in this paper!

We absolutely agree that comparing to nsp8/9 cleavage kinetics measured in our own hands would be more rigorous here, and we have carried out these measurements in triplicate under the same conditions as were used to measure all the other peptide cleavage kinetics in this manuscript. Figures 5A & B (as well as Table S3 and Dataset S2) have been updated with our new nsp8/9 kinetic data (kcat = 0.019 +/- 0.002 s-1 and KM = 40 +/- 7.5 µM). As expected, our newly measured nsp8/9 kinetic parameters are very similar to those that we had previously cited from MacDonald et al (kcat = 0.013 +/- 0.001 s-1, KM = 36 +/- 6.0 µM), and show that Mpro-mediated TRMT1 peptide cleavage has similar proteolysis kinetics to the nsp8/9 viral polypeptide cleavage site.

We have also purified full-length human TRMT1 Q530K, which is the key change in the cleavage consensus sequence that likely makes murine TRMT1 resistant to Mpro-mediated cleavage. In in vitro cleavage assays we find that indeed TRMT1 Q530K is entirely resistant to cleavage by recombinant Mpro and we have added this data to the manuscript in Figure 6D. These findings are consistent with previously cited data from Lu et al, which suggest mouse and hamster TRMT1 are not cleaved in HEK293T cells expressing Mpro.

With the addition of the TRMT1 Q530K mutant data, we decided to move the evolutionary analysis together with this kinetic data to a new section in the Results. We think these additions and changes make the paper stronger and clearer, and thank the reviewer for these suggestions!

Reviewer #2 (Public Review):

Summary:

The manuscript 'Recognition and Cleavage of Human tRNA Methyltransferase TRMT1 by the SARS-CoV-2 Main Protease' from Angel D'Oliviera et al., uncovers that TRMT1 can be cleaved by SARS-CoV-2 main protease (Mpro) and defines the structural basis of TRMT1 recognition by Mpro. They use both recombinant TRMT1 and Mpro as well as endogenous TRMT1 from HEK293T cell lysates to convincingly show cleavage of TRMT1 by the SARS-CoV-2 protease. To understand how Mpro recognizes TRMT1, they solved a co-crystal structure of Mpro bound to a peptide derived from the predicted cleavage site of TRMT1. This structure revealed important protein-protein interfaces and highlights the importance of the conserved Q530 for cleavage by Mpro. They then compared their structure with previous X-ray crystal structures of Mpro bound to substrate peptides derived from the viral polyprotein and proposed the concept of two distinct binding conformations to Mpro: P3´-out and P3´-in conformations (here P3´ stands for the third residue downstream of the cleavage site). It remains unknown what is the physiological role of these two binding conformations on Mpro function, but the authors established that Mpro has dramatically different cleavage efficiencies for three distinct substrates. In an effort to rationalize this observation, a series of mutations in Mpro's active site and the substrate peptide were tested but unexpectedly had no significant impact on cleavage efficiency. While molecular dynamic simulations further confirmed the propensity of certain substrates to adopt the P3´-out or P3´-in conformation, they did not provide additional insights into the dramatic differences in cleavage efficiencies between substrates. This led the authors to propose that the discrimination of Mpro for preferred substrates might occur at a later stage of catalysis after binding of the peptide. Overall, this work will be of interest to biologists studying proteases and substrate recognition by enzymes as well as help efforts to target Mpro with peptide-like drugs.<br />

We thank the reviewer for this thorough and accurate summary of our work in this manuscript.

Strengths:

• The authors' statements are well supported by their data, and they used relevant controls when needed. Indeed, they used the Mpro C145A inactive variant to unambiguously show that the TRMT1 cleavage detected in vitro is solely due to Mpro's activity. Moreover, they used two distinct polyclonal antibodies to probe TRMT1 cleavage.

• Their 1.9 Å crystal structure is of high quality and increases the confidence in the reported protein-protein contacts seen between TRMT1-derived peptide and Mpro.

• Their extensive in vitro kinetic assay was performed in ideal conditions although it is unclear how many replicates were performed.

• The authors test multiple hypotheses to rationalize the preference of Mpro for certain substrates.

• While this reviewer is not able to comment on the rigor of the MD simulations, the interpretations made by the authors seem reasonable and convincing.

• The concept of two binding conformations (P3´-out or P3´-in) for the substrate in the active site of Mpro is significant and can guide drug design.

We thank the reviewer for these positive assessments of manuscript strengths!

Weaknesses:

• While the authors convincingly show that TRMT1 is cleaved by Mpro, the exact cleavage site was never confirmed experimentally. It is most likely that the predicted site is the main cleavage site as proposed by the authors (region 527-534). Nevertheless, in Fig 1C (first lane from the right) there are two bands clearly observed for the cleavage product containing the MT Domain. If the predicted site was the only cleavage site recognized by Mpro, then a single band for the MT domain would be expected. This observation suggests that there might be two cleavage sites for Mpro in TRMT1. Indeed, residues RFQANP (550-555) in TRMT1 might be a secondary weaker cleavage site for Mpro, which would explain the two observed bands in Fig 1C. A mass spectrometry analysis of the cleaved products would clarify this.

We agree with the reviewer that based on the originally presented data it is possible there could be an additional Mpro-targeted cleavage site in TRMT1 beyond the 527-534 region that we validated through peptide cleavage assays of the TRMT1 526-536 peptide. Because it may be difficult to unambiguously identify and differentiate other putative cleavage sites that are nearby to 527-534 (e.g. the suggested possibility of 550-555) by mass spectrometry, we instead carried out additional in vitro cleavage assays with purified FL TRMT1 Q530K. Mutation of the invariant P1 Gln residue in the cleavage sequence is expected to prevent cleavage at this site, and allow us to probe whether there are other sites in TRMT1 that can be cleaved by Mpro (and if so, more straightforwardly identify them by mass spectrometry). We compared cleavage of purified WT FL TRMT1 and FL TRMT1 Q530K with recombinant Mpro in in vitro cleavage assays and found that TRMT1 Q530K is not cleaved by Mpro over the course of a 2h cleavage reaction. In these experiments, we also saw clear cleavage of WT FL TRMT1 over the course of 2h into only a single detectable band. Together, both of these pieces of data strongly suggest that the 527-534 region is the only Mpro-targeted cleavage site in TRMT1 (if there was an additional cleavage site, we should have seen some amount of cleavage in the Q530K mutant, but we do not). Overall, we feel that the updated WT and Q530K experiments clearly demonstrate that there is only one Mpro-mediated cleavage site in human TRMT1, which also is consistent with experiments in Zhang et al showing that Q530N mutations also block TRMT1 cleavage by co-expressed Mpro in human cells.

The updated WT and Q530K cleavage assays have been added to the manuscript in Figure 6D.

• A control is missing in Fig 1D. Since the authors use western blots to show the gradual degradation of endogenous TRMT1, a control with a protein that does not change in abundance over the course of the measurement is important. This is required to show that the differences in intensity of TRMT1 by western blotting are not due to loading differences etc.

Yes, we agree this is an important control and have repeated these experiments and blotted for TRMT1 and GAPDH as a loading control. The updated Western blots are now shown in Figure 2B, and show the same result as the older data.

• The two polyclonal antibodies used by the authors seem to have strong non-specific binding to proteins other than TRMT1 but did not impact the author's conclusions. This is a limitation of the commercially available antibodies for TRMT1, and unless the authors select a new monoclonal antibody specific to TRMT1 (costly and lengthy process), this limitation seems out of their control.

Yes, there are some levels of non-specific binding for all of the TRMT1 antibodies we have tested (this limitation of commercially available TRMT1 antibodies is also observed and noted by Zhang et al), but we agree that this does not impact the overall conclusions and that by using multiple different antibodies to show the same effects, we can have high confidence in the Western blot analysis and interpretation.

• The recombinantly purified TRMT1 seems to have some non-negligible impurities (extra bands in Fig 1C). This does not impact the conclusions of the authors but might be relevant to readers interested in working with TRMT1 for biochemical, structural, or other purposes.

Yes, our initial isolations of recombinant TRMT1 for the first version of this paper produced smaller amounts of TRMT1 with some impurities; we agree that these do not impact the conclusions of the cleavage experiments. However, since our first submission, we have optimized our purification protocols for TRMT1 and are now able to obtain larger quantities of higher purity recombinant human TRMT1 from bacterial cells and we have used this material for the TRMT1 activity and tRNA binding assays added in this revision; we have also included updates to the expression and purification section for recombinant TRMT1. We hope that these improvements will be helpful to readers interested in working on TRMT1.

• Despite the reasonable efforts of the authors, it remains unknown why Mpro shows higher cleavage efficiency for the nsp4/5 sequence compared to TRMT1 or nsp8/9 sequences.

True! To our knowledge and despite significant past efforts of many research groups studying similar coronavirus proteases (e.g. SARS-CoV-1 Mpro) a clear understanding of the detailed mechanistic relationship between cleavage sequence and cleavage kinetics remains mostly undefined. This is a great and important problem for mechanistic and computational groups with deep interests in proteases to tackle in the future! To highlight these and similar open questions, we have added a short paragraph to the Discussion section (second from the last paragraph).

• The peptide cleavage kinetic assay used by the authors relies on a peptide labelled with a fluorophore (MCA) on the N-terminus and a quencher (Dpn) on the C-terminus. This design allows high-throughput measurements compatible with plate readers and is a robust and convenient tool. Nevertheless, the authors did not control for the impact of the labels (MCA and Dpn) on the activity of Mpro. It is possible that the differences in cleavage efficiencies between peptides are due to unexpected conformational changes in the peptide upon labelling. Moreover, the TRMT1 peptide has an E at the N-terminus and an R at the C-terminus (while the nsp4/5 peptide has an S and M, respectively). It is possible that these two terminal residues form a salt bridge in the TRMT1 peptide that might constrain the conformation of the peptide and thus reduce its accessibility and cleavage by Mpro. Enzymatic assays in the absence of labels and MD simulations with the bona fide peptides (including the labels) used in the kinetic measurements are needed to prove that the cleavage efficiencies are not biased by the fluorescence assay.

These fluorophore/quencher peptide cleavage assays are the standard assays used by many labs in the protease field to study diverse proteases and diverse cleavage targets. When other labs have compared cleavage kinetic parameters measured with fluorophore/quencher-based peptide cleavage assays versus HPLC-based peptide cleavage assays, these are often found to be quite similar (e.g. Lee, J., Worrall, L.J., Vuckovic, M. et al. Crystallographic structure of wild-type SARS-CoV-2 main protease acyl-enzyme intermediate with physiological C-terminal autoprocessing site. Nat Commun 11, 5877 (2020). https://doi.org/10.1038/s41467-020-19662-4), although there are also examples where differences arise. In any case, we agree there could be some effects on the cleavage kinetics introduced by the fluorophore and/or quencher groups or sequence-specific conformational preferences of the peptides. However, because our main focus in this paper is to show how a sequence in the human tRNA-modifying enzyme TRMT1 is cleaved by Mpro (and in this revision we have also added new data to show the functional effects of cleavage on TRMT1 activity), and the broad focus of our lab is understanding the mechanisms controlling the function and activity of RNA-modifying enzymes, we will leave it to other labs focused more specifically on protease biochemistry to fully dissect the detailed relationships between peptide sequence and conformation to protease-directed cleavage kinetics. As discussed above, based on our work in this paper and many past studies of similar proteases, understanding how sequence relates to cleavage efficiency is a longer-term and very challenging problem that we view as beyond the scope of this work. As noted above, we have added a brief section explaining this in the Discussion.

• The authors used A431S variant in TRMT1-derived peptide to disrupt the P3´-in conformation. While this reviewer agrees with the rationale behind A431S design, it is important to confirm experimentally that the mutation disrupted the P3´-in conformation in favor of the P3´-out conformer. The authors could use their MD simulations to determine if the TRMT1 A431S variant favors the P3´-out conformation.

Thank you for this suggestion; we agree and have carried out the suggested MD simulations with TRMT1 A531S peptides bound to Mpro. Surprisingly, these simulations suggest that the A531S peptide can still readily adopt the P3’-in conformation by orienting the Ser sidechain in a different way as compared to its positioning in the Mpro-nsp4/5 structure. Since this somewhat changes our interpretation of the results of the A531S kinetic experiments, we have rewritten this section of the manuscript by: (a) removing the ‘TRMT1 mutations predicted to alter peptide binding conformation have little effect on cleavage kinetics’ section in the Results, (b) instead adding several sentences talking about the A531S mutation to the previous section of the results, and including this mutation as another example of how mutations to either Mpro or TRMT1 residues that might be expected to impact cleavage kinetics do not in fact affect cleavage rates, and finally (c) adding the new MD simulation results to the A531S kinetic data in Figure S5 in the Supporting Information. We thank the reviewer for suggesting this important follow-up simulation!

• An unanswered question not addressed by the authors is if the peptides undergo conformational changes upon Mpro binding or if they are pre-organized to adopt the P3´-out and P3´-in conformations.

We agree this is unanswered; we considered additional MD experiments to address this, but ultimately decided that since both of these sequences are cleaved in the context of much larger polypeptides (FL TRMT1 or the viral polypeptide), any simple analysis to assess the possibility of pre-organization and relate this preferred binding conformation to cleavage kinetics would be difficult to interpret in a biologically meaningful way. We think this and similar questions about how pre-organization of peptides or amino acid sequences in the polypeptides might influence protease binding and cleavage activity are interesting and important future questions for protease-focused groups in this field.

• While the authors describe at great length the hydrogen bonds involved in the substrate recognition by Mpro, they occluded to highlight important stacking interactions in this interface. For instance, Phe533 from TRMT1 stacks with Met49 while L529 from TRMT1 packs against His41 of Mpro. Both hydrogen bonding and stacking interactions seem important for TRMT1-derived peptide recognition by Mpro.

Thank you for these suggestions toward additional structural analysis. We have added a short description of L529 packing in the S2 pocket to the main text and Figure S3B. We have also added a short description of F533 packing in the S3’ pocket to the main text and Figure S3C.

Reviewer #3 (Public Review):

Summary:

In this manuscript, the authors have used a combination of enzymatic, crystallographic, and in silico approaches to provide compelling evidence for substrate selectivity of SARS-CoV-2 Mpro for human TRMT1.

Strengths:

In my opinion, the authors came close to achieving their intended aim of demonstrating the structural and biochemical basis of Mpro catalysis and cleavage of human TRMT1 protein. The combination of orthogonal approaches is highly commendable.

We thank the reviewer for their positive assessment of this work!

Weaknesses:

It would have been of high scientific impact if the consequences of TRMT1 cleavage by Mpro on cellular metabolism were provided. Furthermore, assays to investigate the effect of inhibition of this Mpro activity on SARS-CoV-2 propagation and infection would have been extremely useful in providing insights into host- SARS-CoV-2 interactions.

Toward showing some of the consequences of TRMT1 cleavage, in this revised version of the manuscript we have added new data and a new results section (‘Cleavage of TRMT1 results in complete loss of tRNA m2,2G modification activity and reduced tRNA binding in vitro’) showing that cleavage of TRMT1 results in reduced tRNA binding to TRMT1 (Figure 2D) and the complete loss of TRMT1-mediated tRNA modification activity in vitro (Figure 2C). This complements the in-cell data presented by Zhang et al showing that cleavage of TRMT1 in SARS-CoV-2 infected human cells results in the reduction of m2,2G modification levels. We think these data are a strong addition to this paper that broadens the impacts of our reported results more directly into the RNA modifications field.

In terms of showing the further, downstream biological effects of TRMT1 cleavage and/or the specific impacts of TRMT1 cleavage on SARS-CoV-2 propagation and replication, while we agree this would absolutely heighten the overall impact, we view the main focus of our paper as showing how TRMT1 is recognized and cleaved by Mpro at the structural level and characterizing the biochemistry of the TRMT1-Mpro interaction and the effects of cleavage on TRMT1 tRNA-modifying activity. Zhang et al present some cellular data suggesting that loss of TRMT1 and/or TRMT1 cleavage during infection is actually detrimental to SARS-CoV-2 replication and infectivity. However, a full understanding of how TRMT1-mediated m2,2G modification of tRNA impacts viral translation, whether TRMT1 plays other roles during the viral life cycle, or whether TRMT1 cleavage (even if not important for viral fitness) contributes to cellular phenotypes during infection, will take a significant amount of future cell biology and virology work to unravel. Indeed, our understanding is that characterizing some of the endogenous cleavage targets for the HIV protease and determining the downstream biological effects and impacts on HIV infection took well over a decade. We hope that the biochemical and structural characterization of the Mpro-TRMT1 interaction presented in our paper will provide the necessary fundamental groundwork and impetus for future virology and cellular biochemistry studies to further investigate the biological roles of TRMT1 cleavage by SARS-CoV-2 Mpro.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Please list Mpro alias Nsp5 in the Abstract and Introduction, as this is the nomenclature used in the companion article.

OK, we have made these changes.

Reviewer #2 (Recommendations For The Authors):

In addition to the points mentioned in the public review, this reviewer encourages the authors to address the following points:

• Citation 14 is important for this work since the authors used multiple structures from that earlier study for comparison. Citation 14 seems outdated since it refers to a preprint that has been published since then in Nat Comm. The authors should cite the peer-reviewed work https://pubmed.ncbi.nlm.nih.gov/35729165/

Thank you, we have updated this reference.

• The description of the hydrogen bonds is tedious to read. The authors could instead classify them into two groups. Hydrogen bonds between main chain backbones or hydrogen bonds between side chains. For instance, they mention the contact between Mpro Glu166-TRMT1 Arg528. This can lead to confusion that a salt bridge is formed while these two residues interact only via their main chain backbones. Indeed, the side chain of R528 is exposed to the solvent.

OK, we have taken this suggestion and tried to simplify and clarify this portion of the text (along with the accompanying structure Figure 3 showing key hydrogen bonds; see below).

• For Figure 2, please label the residues of the peptide with the TRMT1 numbering. This will help the reader to follow the text while looking at the figure.

OK we have added the TRMT1 numbering to what is now Figure 3A, and labeled key TRMT1 residues in Figures 3B, C, and D.

• Fig 2B is important but crowded. The authors could use two panels to show two different views of this interface.

Thank you for this suggestion, we have split B (now C and D in Figure 3) into two panels, rotated 90 degrees from one another, with each view showing a different subset of TRMT1-Mpro interactions. These updated panels are less crowded, and will hopefully be much clearer to readers.

• For increased clarity, the authors could color P3´-out in orange and P3´-in teal in Fig 3D.

OK, we have made this change.

• Please proofread the method section. There should be a space between values and their units. For example, 20mM HEPES should be 20 mM HEPES.

Thank you, we have corrected these formatting errors in the methods section of the revised version of the manuscript.

• The authors did not identify the mechanism for the higher efficiency of nsp4/5 cleavage despite testing several mutants and MD simulations. Did the author consider changes in the network of water molecules that might be identified in the MD simulations?

We did look at the positioning of waters in nsp4/5 vs nsp8/9 vs TRMT1 MD simulations. In the nsp4/5 simulation we do see a slightly higher density of water molecules positioned at approximately reasonable attack angles for substrate hydrolysis. If we consider water molecules with an attack angle on the scissile amide of 82 – 96 degrees and an attack distance of 4 Å or closer, the probabilities for these conditions in the simulations are: nsp4/5 – 19%, nsp8/9 – 9%, TRMT1 – 6%. More water positioned at reasonable attack positions for nsp4/5 might be consistent with its higher cleavage efficiency, but: (a) these are relatively small differences in water positioning across these 3 Mpro-substrate simulations that would not be enough to clearly explain the large differences in observed kinetics, and (b) hydrolysis happens in the later steps of the catalytic cycle, so to accurately capture this we would likely need to simulate reaction intermediates formed after initial attack of the active site Cys.

We very much appreciate the reviewer’s enthusiasm in pushing us to understand the mechanistic basis for Mpro-directed cleavage efficiencies, and we would have absolutely loved to figure this out! (As it appears to be a long-standing question in the field!) But as discussed above and in the manuscript, we think that it will take a detailed dissection of different steps in the catalytic cycle to understand where and how this selectivity arises. We will leave it to research groups focused more exclusively on the details of protease biochemistry and simulations of reactive intermediates to take up these significant and long-term challenges!

• In the PDB deposition, Y154 from chain B should be fixed.

• In the PDB deposition, some added glycerols seem to conflict. Although this is not important for the biological work discussed in this study, the authors should check if glycerol 403 in chain A and 402, 403 in chain B are properly modeled. Does the density justify placing a glycerol there?

• In the PDB deposition, there are over 51 RSRZ outliers. The authors should double-check if they cannot fix them with additional refinements. While such outliers in poorly defined linkers are understandable, this is unexpected for well-defined regions in the map.

We have made a number of updates to our PDB deposition to address the above three points. (1) We have reexamined and tweaked the loop region at Y154 chain B; this region of the structure has relatively poorly defined electron density, but we now have a model where Y154 is no longer a Ramachandran outlier. The PDB model is now free of any Ramachandran outliers. (2) We have reexamined each of the modeled glycerol molecules and removed one of these (GOL 402), which had a weaker fit to the electron density. The remaining two glycerols appear to be well-modeled (omit maps leaving out each glycerol show strong Fo-Fc density that clearly looks like a glycerol in shape, adding each glycerol back into the model decreases Rwork and Rfree, and the refined 2Fo-Fc map fits well to the modeled glycerols). (3) We agree there are a large number of RSRZ outliers in this structure. We have reexamined many of these, and come to the same conclusion as for our original deposition: that most of these result from residues where there is clear enough density for placing the backbone into the map, but very poor density for the sidechain. Modeling different sidechain positions for the RSRZ outliers we reexamined did not appreciably improve the model fit or change their RSRZ outlier status. For example, Y154 in chains A and B remain some of the worst RSRZ outliers; while the density for these loop regions is generally not very good, it is clear that the backbone atoms of Y154 can be modeled into the structure, but there is very very weak density for the sidechain. We tried modeling alternative and/or multiple sidechain conformations for Y154, but this did not significantly reduce the size of the RSRZ outlier. In short, while we could remove some of these residues or truncate the sidechain where the sidechain density is very poor to lower the total number of RSRZ outliers, we think the best model is one where we leave these residues built into the structure and accept the higher number of RSRZ outliers. Importantly, none of the significant RSRZ outliers are key residues of biological interest that would affect our interpretation of the structure and/or TRMT1-Mpro biochemistry.

We have deposited a new, re-refined PDB model (9DW6) that incorporates these changes and supersedes our old PDB entry (8D35). We have updated the manuscript with the new PDB ID. We thank the reviewer for these suggestions that improved the overall structural model.

Reviewer #3 (Recommendations For The Authors):

The crystal structure entry in the PDB should mention the Cys-to-Ala substitution in Mpro.

Thank you, we have made this change

Fig 2A and 2B: Can the authors highlight the Gln520-Ala531 peptide bind with a different color, please? It gets lost in panel B.

Yes, we have made significant revisions to what is now Figure 3, and have highlighted the scissile peptide bond atoms in orange in each of these panels. Thank you for this suggestion, we agree it helps readers to orient themselves within the structure.

"Importantly, the identified Mpro-targeted residues in human TRMT1 are conserved in the human population (i.e. no missense polymorphisms), showing that human TRMT1 can be recognized and cleaved by SARS-CoV-2 Mpro." Is TRMT1 prone to a high frequency of missense polymorphisms? If so, then this point makes sense. If not, it is not clear if this really informs on any biologically relevant mechanism.

Given (i) that primate TRMT1 was previously identified under positive selection (i.e. rapid evolution) in an evolutionary screen (Cariou et al PNAS 2022) and (ii) that our study is mostly in vitro, we thought it was important to, first, make sure that this sequence of TRMT1 used in functional assays is not specific to a reference sequence that we tested in vitro, but is actually the sequence of TRMT1 in the human population. Further, we were also looking for whether some variations in the Mpro cleavage site of TRMT1 were possibly present in some humans (could these be linked with severe COVID or susceptibility, for example?).

Overall, this statement aims to anchor our in vitro results to the TRMT1 sequences actually present in humans. However, we agree this does not inform “biologically relevant mechanism”. We therefore took out the “Importantly” that was probably misleading.

"TRMT1 engages the Mpro active site in a distinct binding conformation."

This is reported as an observation with little analysis. What is the structural basis of this conformational difference between the bound peptides? Why are the psi angles different? Is there a steric factor that is different between these peptide chains? This section can be substantially improved in detail from its current state.

See our related answer to the next comment below.

"Molecular dynamics simulations suggest kinetic discrimination happens during later steps of Mpro-catalyzed substrate cleavage." This section could have partly addressed my previous comment. It is not clear why there is such a large difference in the psi-angle. With access to several peptide-bound structures, the authors should derive and provide insights into the underlying fundamental principles. After all, this is a major point of discovery in their investigation.

We agree that it is not entirely clear why TRMT1 seems to favor the P3’-in conformation when binding to Mpro. The only other known peptide-bound structure that adopts a similar P2’ psi angle is nsp6/7, but there are not clear sequence, steric, or interaction features that distinguish TRMT1 and nsp6/7 from the other 6 peptide-Mpro structures that favor a P3’-out conformation with larger P2’ psi angle. In particular, the identity of the P1’ and P3’ residues, which would probably be expected to have the largest impact on this conformation, have no clear commonality in TRMT1 and nsp6/7 that give hints about why these adopt this unique conformation. As we describe in the discussion section of the manuscript, and has been observed by many other studies of Mpro, the protease active site is very plastic and able to accommodate a diverse range of sequences surrounding the invariant P1 Gln. Furthermore, while the crystal structures of TRMT1 and other nsp cleavage sequences bound to Mpro show a single peptide conformation in the active site, our MD simulations suggest that both P3’-in and P3’-out type conformations are present in solution for TRMT1, nsp4/5, and nsp8/9, just with different populations. It is very likely that there is a delicate energetic balance between these conformations that may depend subtly on multiple sequence features of the peptide and how they interact with each other and the flexible Mpro active site. As with our replies to questions from Reviewer 2 above about deciphering the underlying principles that connect peptide sequence to cleavage efficiency, we expect that dissecting the detailed links between sequence and binding conformation will be a long-term challenge for mechanistic and biocomputational groups focused on viral protease enzymes; systematic mutation of all residues in the cleavage sequence to multiple different amino acid identities followed by structure determination either experimentally and/or computationally will likely be required to uncover the key sequence or steric properties and interactions that underly and drive favored peptide binding conformations.

To highlight these questions as significant and difficult future challenges toward understanding the fundamental principles underlying SARS-CoV Mpro proteolysis, we have added an additional paragraph (second from the last paragraph) in the discussion section.

This work can be taken to a whole new level if the authors were to provide insights into how TRMT1 degradation by Mpro affects host cell biology and how the inhibition of this activity affects CoV biology.

We certainly agree that showing the biological effects of TRMT1 degradation on host cell biology and/or viral biology could raise the impact of this work. But as discussed in more detail above in our response to the weakness listed in Reviewer 3’s public review, we see the main focus of this work as showing the biochemical and structural basis for TRMT1 recognition and cleavage by SARS-CoV-2 Mpro, and directly showing the immediate effects of this cleavage on the TRMT1-tRNA interaction and modification activity. As was the case with other viral proteases, like the HIV-1 protease, understanding the potentially diverse and nuanced downstream biological effects of host protein cleavage and its impacts on cellular phenotypes or viral fitness could take many years of careful cell biology and virology work. We hope that our paper provides the key first steps to viral biology labs taking on this significant but important challenge for TRMT1!

Author response:

Reviewer #1 (Public review):

Summary:

This work investigated the role of CXXC-finger protein 1 (CXXC1) in regulatory T cells. CXXC1-bound genomic regions largely overlap with Foxp3-bound regions and regions with H3K4me3 histone modifications in Treg cells. CXXC1 and Foxp3 interact with each other, as shown by co-immunoprecipitation. Mice with Treg-specific CXXC1 knockout (KO) succumb to lymphoproliferative diseases between 3 to 4 weeks of age, similar to Foxp3 KO mice. Although the immune suppression function of CXXC1 KO Treg is comparable to WT Treg in an in vitro assay, these KO Tregs failed to suppress autoimmune diseases such as EAE and colitis in Treg transfer models in vivo. This is partly due to the diminished survival of the KO Tregs after transfer. CXXC1 KO Tregs do not have an altered DNA methylation pattern; instead, they display weakened H3K4me3 modifications within the broad H3K4me3 domains, which contain a set of Treg signature genes. These results suggest that CXXC1 and Foxp3 collaborate to regulate Treg homeostasis and function by promoting Treg signature gene expression through maintaining H3K4me3 modification.

Strengths:

Epigenetic regulation of Treg cells has been a constantly evolving area of research. The current study revealed CXXC1 as a previously unidentified epigenetic regulator of Tregs. The strong phenotype of the knockout mouse supports the critical role CXXC1 plays in Treg cells. Mechanistically, the link between CXXC1 and the maintenance of broad H3K4me3 domains is also a novel finding.

Weaknesses:

(1) It is not clear why the authors chose to compare H3K4me3 and H3K27me3 enriched genomic regions. There are other histone modifications associated with transcription activation or repression. Please provide justification.

Thank you for highlighting this important point. We prioritized H3K4me3 and H3K27me3 because they are well-established markers of transcriptional activation and repression, respectively. These modifications provide a robust framework for investigating the dynamic interplay of chromatin states in Treg cells, particularly in regulating the balance between activation and suppression of key genes. While histone acetylation, such as H3K27ac, is linked to enhancer activity and transcriptional elongation, our focus was on promoter-level regulation, where H3K4me3 and H3K27me3 are most relevant. Although other histone modifications could provide additional insights, we chose to focus on these two to maintain clarity and feasibility in our analysis. We are happy to further elaborate on this rationale in the manuscript if necessary.

(2) It is not clear what separates Clusters 1 and 3 in Figure 1C. It seems they share the same features.

We apologize for not clarifying these clusters clearly. Cluster 1 and 3 are both H3K4me3 only group, with H3K4me3 enrichment and gene expression levels being higher in Cluster 1. At first, we divided the promoters into four categories because we wanted to try to classify them into four categories: H3K4me3 only, H3K27me3 only, H3K4me3-H3K27me3 co-occupied, and None. However, in actual classification, we could not distinguish H3K4me3-H3K27me3 co-occupied group. Instead, we had two categories of H3K4me3 only, with cluster 1 having a higher enrichment level for H3K4me3 and gene expression levels.

(3) The claim, "These observations support the hypothesis that FOXP3 primarily functions as an activator by promoting H3K4me3 deposition in Treg cells." (line 344), seems to be a bit of an overstatement. Foxp3 certainly can promote transcription in ways other than promoting H3K3me3 deposition, and it also can repress gene transcription without affecting H3K27me3 deposition. Therefore, it is not justified to claim that promoting H3K4me3 deposition is Foxp3's primary function.

We appreciate the reviewer’s thoughtful observation regarding our claim about FOXP3’s role in promoting H3K4me3 deposition. We acknowledge that FOXP3 is a multifunctional transcription factor with diverse mechanisms of action, including transcriptional activation independent of H3K4me3 deposition and transcriptional repression that does not necessarily involve H3K27me3 deposition.

Our intention was not to imply that promoting H3K4me3 deposition is the exclusive or predominant function of FOXP3 but rather to highlight that this mechanism contributes significantly to its role in regulating Treg cell function. We agree that our wording may have overstated this point, and we will revise the text to provide a more nuanced interpretation. Specifically, we will clarify that our observations suggest FOXP3 can facilitate transcriptional activation, in part, by promoting H3K4me3 deposition, but this does not preclude its other regulatory mechanisms.

(4) For the in vitro suppression assay in Figure S4C, and the Treg transfer EAE and colitis experiments in Figure 4, the Tregs should be isolated from Cxxc1 fl/fl x Foxp3 cre/wt female heterozygous mice instead of Cxxc1 fl/fl x Foxp3 cre/cre (or cre/Y) mice. Tregs from the homozygous KO mice are already activated by the lymphoproliferative environment and could have vastly different gene expression patterns and homeostatic features compared to resting Tregs. Therefore, it's not a fair comparison between these activated KO Tregs and resting WT Tregs.

Thank you for this insightful comment and for pointing out the potential confounding effects associated with using Treg cells from homozygous Foxp3Cre/Cre (or Cre/Y) Cxxc1fl/fl mice. We agree that using Treg cells from _Foxp3_Cre/+ _Cxxc1_fl/fl (referred to as “het-KO”) and their littermate _Foxp3_Cre/+ _Cxxc1_fl/+ (referred to as “het-WT”) female mice would provide a more balanced comparison, as these Treg cells are less likely to be influenced by the activated lymphoproliferative environment present in homozygous KO mice.

To address this concern, we will perform additional experiments using Treg cells isolated from _Foxp3_Cre/+ _Cxxc1_fl/fl (“het-KO”) and their littermate _Foxp3_Cre/+ _Cxxc1_fl/+ (“het-WT”) female mice. We will update the manuscript with these new data to provide a more accurate assessment of the impact of CXXC1 deficiency on Treg cell function.

(5) The manuscript didn't provide a potential mechanism for how CXXC1 strengthens broad H3K4me3-modified genomic regions. The authors should perform Foxp3 ChIP-seq or Cut-n-Taq with WT and Cxxc1 cKO Tregs to determine whether CXXC1 deletion changes Foxp3's binding pattern in Treg cells.

Thank you for your insightful comments and valuable suggestions. We greatly appreciate your recommendation to explore the potential mechanism by which CXXC1 enhances broad H3K4me3-modified genomic regions.

In response, we plan to conduct CUT&Tag experiments for Foxp3 in both WT and Cxxc1 cKO Treg cells.

Reviewer #2 (Public review):

FOXP3 has been known to form diverse complexes with different transcription factors and enzymes responsible for epigenetic modifications, but how extracellular signals timely regulate FOXP3 complex dynamics remains to be fully understood. Histone H3K4 tri-methylation (H3K4me3) and CXXC finger protein 1 (CXXC1), which is required to regulate H3K4me3, also remain to be fully investigated in Treg cells. Here, Meng et al. performed a comprehensive analysis of H3K4me3 CUT&Tag assay on Treg cells and a comparison of the dataset with the FOXP3 ChIP-seq dataset revealed that FOXP3 could facilitate the regulation of target genes by promoting H3K4me3 deposition.

Moreover, CXXC1-FOXP3 interaction is required for this regulation. They found that specific knockdown of Cxxc1 in Treg leads to spontaneous severe multi-organ inflammation in mice and that Cxxc1-deficient Treg exhibits enhanced activation and impaired suppression activity. In addition, they have also found that CXXC1 shares several binding sites with FOXP3 especially on Treg signature gene loci, which are necessary for maintaining homeostasis and identity of Treg cells.

The findings of the current study are pretty intriguing, and it would be great if the authors could fully address the following comments to support these interesting findings.

Major points:

(1) There is insufficient evidence in the first part of the Results to support the conclusion that "FOXP3 functions as an activator by promoting H3K4Me3 deposition in Treg cells". The authors should compare the results for H3K4Me3 in FOXP3-negative conventional T cells to demonstrate that at these promoter loci, FOXP3 promotes H3K4Me3 deposition.

We appreciate the reviewer’s critical observation regarding our claim about FOXP3’s role in promoting H3K4me3 deposition. We acknowledge that FOXP3 is a multifunctional transcription factor with diverse mechanisms of action, including transcriptional activation independent of H3K4me3 deposition and transcriptional repression that does not necessarily involve H3K27me3 deposition.

Our intention was not to imply that promoting H3K4me3 deposition is the exclusive or predominant function of FOXP3 but rather to highlight that this mechanism contributes significantly to its role in regulating Treg cell function. We agree that our wording may have overstated this point, and we will revise the text to provide a more nuanced interpretation. Specifically, we will clarify that our observations suggest FOXP3 can facilitate transcriptional activation, in part, by promoting H3K4me3 deposition, but this does not preclude its other regulatory mechanisms.

We will compare H3K4me3 levels at the promoter loci of interest between FOXP3-negative conventional T cells and FOXP3-positive regulatory T cells. This comparison will help elucidate whether FOXP3 directly promotes H3K4me3 deposition at these loci.

(2) In Figure 3 F&G, the activation status and IFNγ production should be analyzed in Treg cells and Tconv cells separately rather than in total CD4+ T cells. Moreover, are there changes in autoantibodies and IgG and IgE levels in the serum of cKO mice?

We appreciate the reviewer’s constructive feedback on the analyses presented in Figures 3F and 3G and the additional suggestion to investigate autoantibodies and serum immunoglobulin levels.

Regarding Figures 3F and 3G, we agree that separating Treg cells and Tconv cells for analysis of activation status and IFN-γ production would provide a more precise understanding of the cellular dynamics in Cxxc1 cKO mice.

To address this, we will reanalyze the data to examine Treg and Tconv cells independently and include these results in the revised manuscript.

As for the changes in autoantibodies and serum IgG and IgE levels, we acknowledge that these parameters are important indicators of systemic immune dysregulation.

We will now measure serum autoantibodies and immunoglobulin levels in Cxxc1 cKO mice and WT controls.

(3) Why did Cxxc1-deficient Treg cells not show impaired suppression than WT Treg during in vitro suppression assay, despite the reduced expression of Treg cell suppression assay -associated markers at the transcriptional level demonstrated in both scRNA-seq and bulk RNA-seq?

Thank you for your thoughtful question. We appreciate your interest in understanding the apparent discrepancy between the reduced expression of Treg-associated suppression markers at the transcriptional level and the lack of impaired suppression observed in the in vitro suppression assay.

There are several potential explanations for this observation:

(1) Functional Redundancy: Treg cell suppression is a complex, multi-faceted process involving various effector mechanisms such as cytokine production (e.g., IL-10, TGF-β), cell-cell contact, and metabolic regulation. Thus, even though the transcriptional signature of suppression-associated genes is altered, compensatory mechanisms may still allow Cxxc1-deficient Treg cells to retain functional suppression capacity under these specific in vitro conditions.

(2) In Vitro Assay Limitations: The in vitro suppression assay is a simplified model of Treg function that may not capture all the complexities of Treg-mediated suppression in vivo. While we observed altered gene expression in Cxxc1-deficient Treg cells, this might not directly translate to a functional defect under the specific conditions of the assay. In vivo, additional factors such as cytokine milieu, cell-cell interactions, and tissue-specific environments may be required for full suppression, which could be missing in the in vitro assay.

(4) Is there a disease in which Cxxc1 is expressed at low levels or absent in Treg cells? Is the same immunodeficiency phenotype present in patients as in mice?

Thank you for your insightful question regarding the role of CXXC1 in Treg cells and its potential link to human disease. To our knowledge, no specific human disease has been identified where CXXC1 is expressed at low levels or absent specifically in Treg cells. There is currently no direct evidence of an immunodeficiency phenotype in human patients that parallels the one observed in Cxxc1-deficient mice.

Reviewer #3 (Public review):

In the report entitled "CXXC-finger protein 1 associates with FOXP3 to stabilize homeostasis and suppressive functions of regulatory T cells", the authors demonstrated that Cxxc1-deletion in Treg cells leads to the development of severe inflammatory disease with impaired suppressive function. Mechanistically, CXXC1 interacts with Foxp3 and regulates the expression of key Treg signature genes by modulating H3K4me3 deposition. Their findings are interesting and significant. However, there are several concerns regarding their analysis and conclusions.

Major concerns:

(1) Despite cKO mice showing an increase in Treg cells in the lymph nodes and Cxxc1-deficient Treg cells having normal suppressive function, the majority of cKO mice died within a month. What causes cKO mice to die from severe inflammation?

Considering the results of Figures 4 and 5, a decrease in Treg cell population due to their reduced proliferative capacity may be one of the causes. It would be informative to analyze the population of tissue Treg cells.

We thank the reviewer for this insightful comment and acknowledge the importance of understanding the causes of severe inflammation and early mortality in cKO mice. Based on our data and previous studies, we propose the following explanations:

(1) Reduced Treg Proliferative Capacity: As shown in Figure 5I, the decreased proportion of FOXP3+Ki67+ Treg cells in cKO mice likely reflects impaired proliferative capacity, which may limit the expansion of functional Treg cells in response to inflammatory cues, particularly in peripheral tissues where active suppression is required.

(2) Altered Treg Function and Activation: Cxxc1-deficient Treg cells exhibit increased expression of activation markers (Il2ra, Cd69) and pro-inflammatory genes (Ifng, Tbx21). This suggests a functional dysregulation that may impair their ability to suppress inflammation effectively, despite their presence in lymphoid organs.

(3) Tissue Treg Populations: Although our study focuses on lymph node-resident Treg cells, tissue-resident Treg cells play a crucial role in maintaining local immune homeostasis. It is plausible that Cxxc1 deficiency compromises the accumulation or functionality of tissue Treg cells, contributing to uncontrolled inflammation in non-lymphoid organs. Unfortunately, we currently lack data on tissue Treg populations, which limits our ability to directly address this hypothesis.

Regarding the suggestion to analyze tissue Treg populations, we agree that this would be an important next step in understanding the cause of the severe inflammation and early mortality in Cxxc1-deficient mice.

We plan to perform detailed analyses of Treg cell populations in various tissues, including the gut, lung, and liver, to determine if there are specific defects in tissue-resident Treg cells that could contribute to the observed phenotype.

(2) In Figure 5B, scRNA-seq analysis indicated that Mki67+ Treg subset are comparable between WT and Cxxc1-deficient Treg cells. On the other hand, FACS analysis demonstrated that Cxxc1-deficient Treg shows less Ki-67 expression compared to WT in Figure 5I. The authors should explain this discrepancy.

Thank you for pointing out the apparent discrepancy between the scRNA-seq and FACS analyses regarding Ki-67 expression in Cxxc1-deficient Treg cells.

In Figure 5B, the scRNA-seq analysis identified the Mki67+ Treg subset as comparable between WT and Cxxc1-deficient Treg cells. This finding reflects the overall proportion of cells expressing Mki67 transcripts within the Treg population. In contrast, the FACS analysis in Figure 5I specifically measures Ki-67 protein levels, revealing reduced expression in Cxxc1-deficient Treg cells compared to WT.

To address this discrepancy more comprehensively, we will further analyze the scRNA-seq data to directly compare Mki67 mRNA expression levels between WT and Cxxc1-deficient Treg cells.

In addition, the authors concluded on line 441 that CXXC1 plays a crucial role in maintaining Treg cell stability. However, there appears to be no data on Treg stability. Which data represent the Treg stability?

We appreciate the reviewer’s observation and recognize that our wording may have been overly conclusive. Our data primarily highlight the impact of Cxxc1 deficiency on Treg cell homeostasis and transcriptional regulation, rather than providing direct evidence for Treg cell stability. Specifically, the downregulation of Treg-specific suppressive genes (Nt5e, Il10, Pdcd1) and the upregulation of pro-inflammatory markers (Gzmb, Ifng, Tbx21) indicate a shift in functional states. While these findings may suggest an indirect disruption in the maintenance of suppressive phenotypes, they do not constitute a direct measure of Treg cell stability.

To address the reviewer’s concern, we will revise our conclusion to more accurately state that our data support a role for CXXC1 in maintaining Treg cell homeostasis and functional balance, without overextending claims about Treg cell stability. Thank you for bringing this to our attention, as it will help us improve the clarity and precision of our manuscript.

(3) The authors found that Cxxc1-deficient Treg cells exhibit weaker H3K4me3 signals compared to WT in Figure 7. This result suggests that Cxxc1 regulates H3K4me3 modification via H3K4 methyltransferases in Treg cells. The authors should clarify which H3K4 methyltransferases contribute to the modulation of H3K4me3 deposition by Cxxc1 in Treg cells.

Thank you for pointing out the need to clarify the role of H3K4 methyltransferases in the modulation of H3K4me3 deposition by CXXC1 in Treg cells.

In our study, we found that Cxxc1-deficient Treg cells exhibit reduced H3K4me3 levels, as shown in Figure 7. CXXC1 has been previously reported to function as a non-catalytic component of the Set1/COMPASS complex, which contains H3K4 methyltransferases such as SETD1A and SETD1B. These methyltransferases are the primary enzymes responsible for H3K4 trimethylation.

References:

(1) Lee J.H., Skalnik D.G. CpG-binding protein (CXXC finger protein 1) is a component of the mammalian Set1 histone H3-Lys4 methyltransferase complex, the analogue of the yeast Set1/COMPASS complex. J. Biol. Chem. 2005; 280:41725–41731.

(2). J. P. Thomson, P. J. Skene, J. Selfridge, T. Clouaire, J. Guy, S. Webb, A. R. W. Kerr, A. Deaton, R. Andrews, K. D. James, D. J. Turner, R. Illingworth, A. Bird, CpG islands influence chromatin structure via the CpG-binding protein Cfp1. Nature 464, 1082–1086 (2010).

(3) Shilatifard, A. 2012. The COMPASS family of histone H3K4 methylases: mechanisms of regulation in development and disease pathogenesis. Annu. Rev. Biochem. 81:65–95.

(4) Brown D.A., Di Cerbo V., Feldmann A., Ahn J., Ito S., Blackledge N.P., Nakayama M., McClellan M., Dimitrova E., Turberfield A.H. et al. The SET1 complex selects actively transcribed target genes via multivalent interaction with CpG Island chromatin. Cell Rep. 2017; 20:2313–2327.

Furthermore, it would be important to investigate whether Cxxc1-deletion alters Foxp3 binding to target genes.

Thank you for this important suggestion regarding the impact of Cxxc1 deletion on FOXP3 binding to target genes. We agree that understanding whether Cxxc1 deficiency affects FOXP3’s ability to bind to its target genes would provide valuable insight into the regulatory role of CXXC1 in Treg cell function.

To address this, we plan to perform CUT&Tag experiments to assess FOXP3 binding profiles in Cxxc1-deficient versus wild-type Treg cells. These experiments will allow us to determine if Cxxc1 loss disrupts FOXP3’s occupancy at key regulatory sites, which may contribute to the observed functional impairments in Treg cells.

(4) In Figure 7, the authors concluded that CXXC1 promotes Treg cell homeostasis and function by preserving the H3K4me3 modification since Cxxc1-deficient Treg cells show lower H3K4me3 densities at the key Treg signature genes. Are these Cxxc1-deficient Treg cells derived from mosaic mice? If Cxxc1-deficient Treg cells are derived from cKO mice, the gene expression and H3K4me3 modification status are inconsistent because scRNA-seq analysis indicated that expression of these Treg signature genes was increased in Cxxc1-deficient Treg cells compared to WT (Figure 5F and G).

Thank you for the insightful comment. To clarify, the Cxxc1-deficient Treg cells analyzed for H3K4me3 modification in Figure 7 were indeed derived from Cxxc1 conditional knockout (cKO) mice, not mosaic mice.

The scRNA-seq analysis presented in Figures 5F and G revealed an upregulation of Treg signature genes in Cxxc1-deficient Treg cells. This finding suggests that the loss of Cxxc1 drives these cells toward a pro-inflammatory, activated state, underscoring the pivotal role of CXXC1 in maintaining Treg cell homeostasis and suppressive function.

Regarding the apparent discrepancy between the reduced H3K4me3 levels and the increased expression of these genes, it is important to note that H3K4me3 primarily functions as an epigenetic mark that facilitates chromatin accessibility and transcriptional regulation, acting as an upstream modulator of gene expression. However, gene expression levels are also influenced by downstream compensatory mechanisms and complex inflammatory environments. In this context, the reduction in H3K4me3 likely reflects the direct role of CXXC1 in epigenetic regulation, whereas the upregulation of gene expression in Cxxc1-deficient Treg cells may result as a side effect of the inflammatory environment.

To further substantiate our findings, we performed RNA-seq analysis on Treg cells from Foxp3_Cre/+ _Cxxc1_fl/fl (“het-KO”) and their littermate _Foxp3_Cre/+ _Cxxc1_fl/+ (“het-WT”) female mice, as presented in Figure S6C. This analysis revealed a notable reduction in the expression of key Treg signature genes, including _Icos, Ctla4, Tnfrsf18, and Nt5e, in het-KO Treg cells. Importantly, the observed changes in gene expression were consistent with the altered H3K4me3 modification status, further supporting the epigenetic regulatory role of CXXC1. These results further emphasize the critical role of CXXC1 promotes Treg cell homeostasis and function by preserving the H3K4me3 modification.

Author response:

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

Public Reviews: 

Reviewer #1 (Public Review): 

Summary: 

The manuscript by Rowell et al aims to identify differences in TCR recombination and selection between foetal and adult thymus in mice. Authors sequenced the unpaired bulk TCR repertoire in foetal and adult mice thymi and studied both TCRB and TCRa characteristics in the double positive (DP, CD4+CD8+) and single positive (SP4 CD4+CD8CD3+ and SP8 CD4-CD8+CD3+) populations. They identified age-related differences in TCRa and TCRB segment usage, including a preferential bias toward 3'TRAV and 5' TRAJ rearrangements in foetal cells compared to adults who had a larger perveance for 5'TRAV segments. By depleting the thymocyte population in adult thymi using hydrocortisone, the authors demonstrated that the repertoire became more foetal like, they therefore argue that the preferential 5'TRAV rearrangements in adults may be resulting from prolonged/progressive TCRa rearrangements in the adult thymocytes. In line with previous studies, Authors demonstrate that the foetal TCR repertoire was less diverse, less evenly distributed and had fewer non-template insertions while containing more clonal expansions. In addition, the authors claim that changes in V-J usage and CDR1 and CDR2 in the DP vs SP repertoires indicated that positive selection of foetal thymocytes are less dependent on interactions with the MHC. 

Strengths: 

Overall, the manuscript provides an extensive analysis of the foetal and adult TCR repertoire in the thymus, resulting in new insights in T cell development in foetal and adult thymi. 

Weaknesses: 

Three major concerns arise:

(1) the authors have analysed TCR repertoires of only 4 foetal and 4 adult mice, considering the high spread the study may have been underpowered. 

Given the concerns of the reviewer we have sequenced more libraries and added more data to include repertoires from 7 embryos and 6 young adults (biological replicates from different sorts). We believe that including more replicates has indeed strengthened our study. 

Our experimental approach was to sequence TCR transcripts, and in studies using RNA-sequencing of inbred mice, often only 3 individuals (biological replicates) are sequenced.

Our study sequenced from 7 foetal thymuses (generating TCRα and TCRβ repertoires from 4 FACS-sorted cell populations); 6 adult thymuses (generating TCRα and TCRβ repertoires from 4 FACS-sorted cell populations); and 5 adult thymuses from hydrocortisone-treated mice (generating TCRα and TCRβ repertoires from FACS-sorted CD3lo and CD3hi DP populations). We thus analysed 124 distinct repertoires from different populations and libraries, and many tens of thousands of unique sequences.  

(2) Gating strategies are missing and 

We have included gating strategies for cell-sorting as SFig7 and SFig8.

(3) the manuscript is very technical and clearly aimed for a highly specialised audience with expertise in both thymocyte development and TCR analysis. Authors are recommended to provide schematics of the TCR rearrangements/their findings and include a summary conclusions/implications of their findings at the end of each results section rather than waiting till the discussion. This will help the reader to interpret their findings while reading the results. 

We have modified the manuscript to include a more general introductory paragraph (page 3) to introduce the reader to the topic and we have included brief summaries of the findings at the end of each result section (pages 7,9,10,12,13,15).

Reviewer #2 (Public Review): 

Summary: 

The authors comprehensively assess differences in the TCRB and TCRA repertoires in the fetal and adult mouse thymus by deep sequencing of sorted cell populations. For TCRB and

TCRA they observed biased gene segment usage and less diversity in fetal thymocytes. The TCRB repertoire was less evenly distributed and displayed more evidence of clonal expansions and repertoire sharing among individuals in fetal thymocytes. In both fetal and adult thymocytes they show skewing of V segment (CDR1-2) repertoires in CD4 and CD8 as compared to DP thymocytes, which they attribute to MHC-I vs MHC-II restriction during positive selection. However the authors assess these effects to be weaker in fetal thymocytes, suggesting weaker MHC-restriction. They conclude that in multiple respects fetal repertoires are distinct from and more innate-like than adult. 

Strengths: 

The analyses of the F18.5 and adult thymic repertoires are comprehensive with respect to the cell populations analyzed and the diversity of approaches used to characterize the repertoires. Because repertoires were analyzed in pre- and post-selection thymocyte subsets, the data offer the potential to assess repertoire selection at different developmental stages. The analysis of repertoire selection in fetal thymocytes may be unique. 

Weaknesses: 

(1) Problematic experimental design and some lack of familiarity with prior work have resulted in highly problematic interpretations of the data, particularly for TCRA repertoire development. 

The authors note fetal but not adult thymocytes to be biased towards usage of 3' V segments and 5'J segments. It should be noted that these basic observations were made 20 years ago using PCR approaches (Pasqual et al., J.Exp.Med. 196:1163 (2002)), and even earlier by others.

We have cited this manuscript (Introduction, page 5) which used PCR of genomic DNA to investigate some TCRα VJ rearrangements in foetal and adult thymus. In contrast, our study uses next generation sequencing of transcripts to investigate all possible combinations of TCRα and TCRβ VJ combinations in different sorted thymocyte populations ex vivo. The greater sensitivity of this more modern technology has thus enabled us to detect many more TCRαVJ rearrangements than the 2002 study, and to conclude on basis of stringent statistical testing that the foetal repertoire is enriched for 3’V to 5’J combinations (Fig. 4). 

The authors also note that in fetal thymus this bias persists after positive selection, and it can be reproduced in adults during recovery from hydrocortisone treatment. The authors conclude that there are fewer rounds of sequential TCRA rearrangements in the fetal thymus, perhaps due to less time spent in the DP compartment in fetus versus adult. However, the repertoire difference noted by the authors does not require such an explanation. What the authors are analyzing in the fetus is the leading edge of a synchronous wave of TCRA rearrangements, whereas what they are analyzing in adults is the unsynchronized steady state distribution. It is certainly true, as has been shown previously, that the earliest TCRA rearrangements use 3' TRAV and 5'TRAJ segments. But analysis of adult thymocytes has shown that the progression from use of 3' TRAV and 5' TRAJ to use of 5' TRAV and 3' TRAJ takes several days (Carico et al., Cell Rep. 19:2157 (2017)). The same kinetics, imposed on fetal development, would put development of a more complete TCRA repertoire at or shortly after birth. In fact, Pasqual showed exactly this type of progression from F18 through D1 after birth, and could reproduce the progression by placing F16 thymic lobes in FTOC. It is not appropriate to compare a single snapshot of a synchronized process in early fetal thymocytes to the unsynchronized steady state situation in adults. In fact, the authors' own data support this contention, because when they synchronize adult thymocytes by using hydroxycortisone, they can replicate the fetal distribution. Along these lines, the fact that positive selection of fetal thymocytes using 3' TRAV and 5' TRAJ segments occurs within 2 days of thymocyte entry into the DP compartment does not mean that DP development in the fetus is intrinsically rapid and restricted to 2 days. It simply means that thymocytes bearing an early rearranging TCR can be positively selected shortly after TCR expression. The expectation would be that those DP thymocytes that had not undergone early positive selection using a 3' TRAV and a 5' TRAJ would remain longer in the DP compartment and continue the progression of TCRA rearrangements, with the potential for selection several days later using more 5'TRAV and 3'TRAJ. 

We agree with this summary provided by the reviewer which corresponds closely to the points we made ourselves in the manuscript. Indeed, we discuss the synchronization and kinetics of first wave of T-cell development in Results page 13 and Discussion page 17, which was the rationale for the hydrocortisone experiment.  We have also discussed findings from Carico et al 2017 in this context (see pages 13, 16, 17).  

(2) The authors note 3' V and 5'J biases for TCRB in fetal thymocytes. The previously outlined concerns about interpreting TCRA repertoire development do not directly apply here. But it would be appropriate to note that by deep sequencing, Sethna (PNAS 114:2253 (2017)) identified skewed usage of some of the same TRBV gene segments in fetal versus adult.  It should also be noted that Sethna did not detect significantly skewed usage of TRBJ  segments. Regardless, one might question whether the skewed usage of TRBJ segments detected here should be characterized as relating to chromosomal location. There are two logical ways one can think about chromosomal location of TRBJ segments - one being TRBJ1 cluster vs TRBJ2 cluster, the other being 5' to 3' within each cluster. The variation reported here does not obviously fit either pattern. Is there a statistically significant difference in aggregate use of the two clusters? There is certainly no clear pattern of use 5' to 3' across each cluster. 

We have included a statistical comparison of the aggregate TRBJ use between the J1 cluster and the J2 cluster (see SFig5) and Results page 9. 

(3) The authors show that biases in TCRA and TCRB V and J gene usage between fetal and adult thymocytes are mostly conserved between pre- and post-selection thymocytes (Fig 2). In striking contrast, TCRA and TCRB combinatorial repertoires show strong biases preselection that are largely erased in post-selection thymocytes (Fig 3). This apparent discrepancy is not addressed, but interpretation is challenging. 

I think the reviewer is referring to heatmaps for individual gene segment usage shown in Figure 2 in comparison to combinatorial usage shown in Figure 4. There is not a discrepancy in the data, but rather the differences between these two figures lie in the way in which the comparisons are made and visualised.  The heatmaps in Figure 2A-D show mean proportional usage of each individual gene segment for each cell type in the two life stages, clustered by Euclidian distance. This visualisation clearly shows bias in foetal 3’ TRAV usage and 5’TRAJ usage (looking at areas of red, which have higher usage), with less pronounced enrichment for TRBV and TRBJ.  The heatmaps also show differences in intensity between different cell populations in each life-stage. 

In contrast, in Figure 4 the tiles show combinations with statistically significant (P<0.05) differences in mean counts for each VJ combination in each cell type between 7 foetal and 6 adult repertoires by Student’s t-test, after correcting for False discovery rate (FDR) due to multiple combinations.  It is the case, that there are fewer significant differences in proportional combinatorial VxJ use between foetal and adult repertoires after selection. We find this an interesting finding and have expanded our discussion of this aspect of the data (page 10).  More than half of the significant differences persist after repertoire selection, and the reduction in each individual SP population, of course in part reflects the lineage divergence.

(4) The observation that there is a higher proportion of nonproductive TCRB rearrangements in fetal thymus compared to adult is challenging to interpret, given that the results are based upon RNA sequencing so are unlikely to reflect the ratio in genomic DNA due to processes like NMD.

We have added two sentences to explain that transcripts of non-productive rearrangements are eliminated by nonsense-mediated decay (NMD), but some non-productive transcripts are detected in many studies of TCR repertoire sequencing, and we have cited three studies from different groups that document this (see Results, page 10-11). We have not commented on how the increase in non-productive TCR rearrangements in the foetal populations (in comparison to adult) relates to rearrangements in genomic DNA or NMD.   We have likewise not commented on the possible significance or biological role of nonproductive TCR transcripts, but simply reported our findings.

(5) An intriguing and paradoxical finding is that fetal DP, CD4 and CD8 thymocytes all display greater sharing of TCRB CDR3 sequences among individuals than do adults (Fig 5DE), whereas DP and CD8 thymocytes are shown to display greater CDR3 amino acid triplet motif sharing in adults (with a similar trend in CD4). 

As foetal DP, CD4SP and CD8SP TCRbeta repertoires have fewer non-template insertions and lower means CDR3 length, they are expected to share more CDR3 repertoires than their adult counterparts.  However, in the case of CDR3 amino acid triplet motifs (k-mers) what is being analysed is the sharing of each possible individual k-mer. If k-mers are shared more in the adult for some populations, but CDR3 repertoires are shared more in the foetus, we think it means that some k-mers appear in many different CDR3 sequences in the adult, so that they are over-represented in multiple different CDR3s (presumably due to selection processes, although we agree that this is just an assumption).  

The authors attribute high amino acid triplet sharing to the result of selection of recurrent motifs by contact with pMHC during positive selection. But this interpretation seems highly problematic because the difference between fetal and adult thymocytes is dramatic even in unfractionated DP thymocytes, the vast majority of which have not yet undergone positive selection. How then to explain the differences in CDR3 sharing visualized by the different approaches? 

The TCRβ repertoire has been selected in the adult DP population through the process of β-selection, which is believed to involve immune synapse formation and MHC-interactions (Allam et al 2021,10.1083/jcb.201908108). We have now included this reference in the introduction to make this clear (page 4). However, we agree with the reviewer’s comments that it is challenging to explain the k-mer analysis and that we have not been able to actually show that increased k-mer sharing in the adult is a direct consequence of increased positive selection: it was our interpretation of this seemingly paradoxical finding.  For clarity, we have therefore removed the k-mer analyses from the manuscript.

(6) The authors conclude that there is less MHC restriction in fetal thymocytes, based on measures of repertoire divergence from DP to CD4 and CD8 populations (Fig. 6). But the authors point to no evidence of this in analysis of TRBV usage, either by PC or heatmap analyses (A,B,D). The argument seems to rest on PC analysis of TRAV usage (Fig S6), despite the fact that dramatic differences in the SP4 and SP8 repertoires are readily apparent in the fetal thymocyte heatmaps. The data do not appear to be robust enough to provide strong support for the authors' conclusion. 

We have written the text very carefully so as not to make the claim too strong, stating in the abstract: “In foetus we identified less influence of MHC-restriction on α-chain and β-chain combinatorial VxJ usage and CDR1xCDR2 (V region) usage in SP compared to adult, indicating weaker impact of MHC-restriction on the foetal TCR repertoire.” We are not saying that MHC-restriction does not impact VJ gene usage in foetal repertoires, but rather that it has less influence (particularly when compared to life-stage).  Evidence for this comes from:  [1] Heatmaps in Fig2A-D which show that all repertoires cluster first by life-stage ahead of cell type; [2] Fig3A and B: PCA of adult and foetal TCRβ VXJ combinations: All repertoires cluster by life-stage on PC1.  PC2 separates adult repertoires by cell type (adult SP8 are positive on PC2 while adult SP4 are negative on PC2, and DP cells are between them) but for foetal repertoires the SP8 and SP4 are highly dispersed with some SP4 cells falling on positive side of PC2.  Only foetal DP repertoires cluster tightly. [3] Fig6A-C: PCA of β−chain CDR1xCDR2 (corresponding to Vβ gene segment usage) again shows the same pattern.  Adult repertoires separate by cell type on PC2, (SP8 positive on PC2, SP4 negative on PC2, with DP in between), but foetal SP8 repertoires are much more dispersed.  [5] SFig6J-K: PCA of α−chain CDR1xCDR2 (Vα usage) frequency distributions: adult repertoires cluster together and are separated by cell type on PC2 (SP4 positive, SP8 negative), but foetal populations are highly dispersed and fail to cluster by cell type on either axis. [6] We have additionally added new PCA analyses to explore differences in MHC-restriction between foetal and adult SP populations.  This is shown in the new Figure 7. We reasoned that in a PCA that included foetal and adult repertoires together, the foetal repertoires might not segregate by SP cell type (MHC-restriction) because of their overall bias towards particular VJ combinations, which would mean that effectively the PCA would be imposing adult MHC restriction on the foetal repertoires.  We therefore carried out PCA in which we analysed the adult repertoires separately from the foetal repertoires.  As expected for adult repertoires, PCA separated SP4 repertoires from SP8 repertoires on PC1 in each comparison (β-chain VxJ (Fig. 7B), α-chain VxJ (Fig. 7F), β-chain CDR1xCDR2 (V region) (Fig. 7H) and α-chain CDR1xCDR2 (V region) (Fig. 7L)). In contrast, for foetal TCRα repertoires (α-chain VxJ and α-chain CDR1xCDR2 (V region)), PCA failed to separate SP4 from SP8 repertoires on PC1 or PC2, so we did not detect impact of MHC-restriction on foetal TCRβ repertoires (Fig. 7E and K).  For foetal TCRβ repertoires, PCA separated SP4 β-chain VxJ from SP8 on PC2, accounting for only 11.1% of variance (Fig. 7A) (in contrast to the 44.2% of variance accounted for by MHC-restriction in adult β-chain VxJ PCA (Fig. 7B)). Thus, in adult repertoires ~4-fold more of the variance in β-chain VxJ usage can be accounted for by MHC-restriction than in foetal repertoires. PCA of foetal β-chain CDR1xCDR2 (V region) separated SP4 from SP8 on PC1, accounting for 28.8% of variance, whereas in PCA of adult β-chain CDR1xCDR2, MHCrestriction accounted for 56.1% (>2-foldmore than in foetus).  Thus, even when we  considered only V-region usage alone, we detected a stronger influence of MHC-restriction on the TCRβ repertoire in adult compared to foetal thymus.  

Reviewer #3 (Public Review): 

Summary:

This study provides a comparison of TCR gene segment usage between foetal and adult thymus.

Strengths:

Interesting computational analyses was performed to find interesting differences in TCR gene usage within unpaired TCRa and TCRb chains between foetal and adult thymus.  

Weaknesses:

This study was significantly lacking insight and interpretation into what the data analysed actually means for the biology. The dataset discussed in the paper is from only two experiments. One comparing foetal and adult thymi from 4 mice per group and another which involved hydrocortisone treatment. The paper uses TCR sequencing methodology that sequences each TCR alpha and beta chains in an unpaired way, meaning that the true identity of the TCR heterodimer is lost. This also has the added problem of overestimating clonality, and underestimating diversity.

We have discussed the limitations and benefits of our approach of sequencing TCRβ and TCRα repertoires separately in the Discussion (page 19).  This approach allows the analysis of thousands of sequences from different cell types and different individuals at relatively low cost. We have made no claims in our manuscript about overall diversity or pairing, and given that each chain’s gene locus rearranges at a different time point in development, we believe it is of interest to consider the repertoires individually within this context.

Limited detail in the methods sections also limits the ability for readers to properly interpret the dataset. What sex of mice were used? Are there any sex differences? What were the animal ethics approvals for the study?

We have included this information in the Methods (page 19).  Both sexes were used and we found no sex differences, although that was not the focus of our study. All animal experimentation in the UK is carried out under UK Home Office Regulations (following ethical review). This is included in the Methods (page 19).  

Recommendations for the authors:  

Reviewer #1 (Recommendations For The Authors): 

Major points: 

- Group sizes are very small (4 foetal and 4 adult mice). Considering the spread in TCR analysis (eg fig 1 B-H, Sup figures 2-4), the study is likely underpowered as it often looks like one mouse prevents or supports a statistical difference. Authors should therefore consider increasing the group size. 

We have sequenced more libraries and included more data, from 7 foetal and 6 young adult animals (biological replicates).  

- The authors should include a gating strategy for their sorted cells. This is essential to verify the quality of their findings. 

We have added this to the Methods and SFig7 and SFig8.

Authors should include a summary sentence at the end of each result section which interprets the main finding. Furthermore, the manuscript would greatly benefit from a schematic figure of their main findings, particularly with regards to the rearrangements and selection differences in foetal and adult thymi. 

We have added a summary sentence to the end of each results section.

- Authors should be more careful with their claim that MHC has less of an effect foetal TCR selection. Authors demonstrated that there is a difference in VJ recombination between the foetal and adult TCR repertoire, skewing the foetal TCR repertoire to certain variable and junctional segments. Since both CDR1 and CDR2 are encoded by the variable gene, this is likely to affect their ability to interact with the MHC during positive selection. Have Authors considered whether the selection process is actually a bystander effect of the differences in the rearrangement process? One way to support the authors claim is to demonstrate that mice with an alternative MHC background, have similar foetal/adult gene rearrangements but a different TCR repertoire in the SP populations. 

Time and resources have prevented us from repeating our experiments in another strain of inbred mice.  However, we note that a previous PCR study that showed 3’TRAV to 5’TRAJ bias in foetal repertoires was carried out in BALB/c mice (Pasqual JEM 2002). We have added this point to the Discussion (page 17). 

- (supplementary) tables have not been provided. 

Supplementary Tables were uploaded with the submission.  STables 1 and 2 show antibodies used for cell sorts and STable 3 primers used.

Moderate points: 

- The loading plots in Figure 3 onward are visually strong. Authors could consider including an V and J (separate) loading plots for Figure 3 E, F and G to demonstrate preferential V and J usage. 

We have included additional loading plots in Figure 7 for the new PCA we have added (see Fig. 7C, D,I and J).

- "the proportion of non-productive rearrangements was higher in the foetal SP8 population than adults (Fig 5A)" Authors should explain how non-productive TCRs end up in SP populations as they need to pass positive and negative selection which both require interactions between the TCR and the MHC. 

As we used RNA sequencing in our study, we did not comment on how the increase in nonproductive TCRbeta rearrangements in the foetal populations (in comparison to adult) relates to rearrangements in genomic DNA or to nonsense-mediated decay (NMD) that is believed to down-regulate transcripts of non-productively rearranged TCR.  We have not commented on the possible significance or biological role of non-productive TCR transcripts, but simply reported our findings. 

- Authors have studied CDR3 sequential amino acid triplets (k-mers). However, CDR3 regions are longer than 3 amino acids in length, hence authors should provide 1) an overview/comparison of the identified k-mers in foetal or adult thymocytes 2) explain how different k-mers relate to each other, eg whether they are expressed in the same TCR. Have authors considered using alternative programs to identify CDR3 motifs that are based on the full CDR3amino acid sequence, eg TCRdist provides motifs and indicated which amino acids are germline encoded or inserted. 

In light of this comment from this reviewer and also comments from Reviewer 2, we have removed the comparison of k-mers from the manuscript.  Please see response to point 5 of Reviewer 2.  

- The term "innate-like" is confusing as it implies that foetal cells are not antigen specific.

However, once in the circulation, foetal cells will respond in an antigen-specific manner.

Hence authors should use another term. 

We have removed the term “innate-like” from the abstract and the first time we used it in the first paragraph of the Discussion. However, the second time we used the term, we are actually taking it from the manuscript we cited (Beaudin et al 2016) and in this case we left it in. We agree that foetal cells are likely to respond in an antigen-specific manner. 

- To support their hypothesis in the discussion "However, as TCRd gene segments are nested.... so that 5' TRAV segments are not favoured" can authors confirm that there are indeed less yd T cells in the foetal repertoire? 

We have removed this section from the discussion, because although it is interesting, it is highly speculative, and the manuscript is already quite complicated to interpret.

Minor points: 

- The authors may find the publication by De Greef 2021 PNAS of interest to identify TRBD segments 

- Authors need to clarify that they mean CDR3-beta in the sentence "The mean predicted CDR3 length.... compared to young adult" 

We have included new data in the manuscript to show that mean CDR3 length is lower in all foetal populations of beta (Fig5C) and alpha (SFig5C) and clarified which we are referring to in the text. 

- Authors should bring the section "During TCRb gene rearrangement, these segments.... Initiating the sequence of rearrangements" forward and include a schematic." Forward to figure 2 and provide the reader with a visual schematic of the foetal vs adult recombination events. 

- Discussion: "The first wave of foetal abT-cells that leave the thymus... tolerant to both self and maternal MHC/antigens". Have Authors considered the alternative hypothesis published by Thomas 2019 in Curr Opin System Biol that the observed bias could potentially provide better protection against childhood pathogens? 

We have indeed considered this, as stated in the first paragraph of the Discussion “The first wave of foetal αβT-cells that leave the thymus must provide early protection against infection in the neonatal animal”. We have now cited the Thomas 2019 study.

- Discussion: Authors should rephrase the sentence "The transition from DP to SP cell in the foetus.... From DN3 to SP cell may be slower" as it is unclear what the authors mean. 

We have rephrased this (see page 17)

- Discussion "TRAV and TRAJ Array" do authors mean "TRAV and TRAJ area"? 

We did indeed mean array (as in series of gene segments) but we have changed the wording for clarity (page 14).

- Methods, Fluorescence activated cell sorting: can authors clarify whether they stained, sorted and sequenced the full thymus and /or specify how many cells were included. Can authors also explain why foetal and adult cells were treated differently (eg the volume of master mix)? 

- Methods Fluorescence activated cell sorting authors should specify what they mean with "mastermix of either 1:50 (foetal thymus) or 1:100 (adult thymus)". Does this mean all antibodies in the foetal mastermix were 1:50 and all antibodies in the adult master mix were 1:100? If so, why were different concentrations used and why were antibodies not individually titrated before use?  

We have clarified the methods and antibodies used are listed with clones in supplementary tables.

Figures: 

- Several figures did not fit on the page and therefore missed the top or side 

- Figure 1A: missing a label on the Y axis

This is visible

- Figure 2A-D: please indicate the 5' and 3' terminus in each graph. The cell type legend should include two separate colours for the two DP populations. 

We have added 5’ and 3’ labels.  The two DP populations are clearly labelled.

- Figure 4: please indicate the 5' and 3' terminus in each graph. 

We have added 5’ and 3’ labels.   

- Figure 5C: y axis should read mean CDR3B length (aa), Figure 5D and E: y axis should read Jaccard Index CDR3B, Figure 5 F and G: y axis should read Jaccard index CDR3B k-mers. Same comment for Sup Fig 5 but then CDR3a. 

We have added these labels for both Figure 5 and Supplementary Figure 6 (was SFig5 previously).

- Figure 6C top label should read CDR1B x CDR2B with highest contribution 

We have added this label.

- Figure 7: please indicate the 5' and 3' terminus in each graph. 

We have added 5’ and 3’ labels.  This is now Figure 8, as we have added new analyses (new Figure 7).

- Supplementary Figure 1-4 are missing a colour legend next to the graphs.

We have added the legends in.  

Reviewer #2 (Recommendations For The Authors): 

(1) The authors need to provide better support for the notion that the fetal thymus produces ab T cells with properties and functions that are distinct from adult T cells. There are several  ways they might provide a more meaningful assessment: (1) They could analyze the fetal repertoire at multiple time points. (2) They could compare instead the steady state distributions in early postnatal and adult thymus samples. (3) They could compare the peripheral T cell repertoires in the first week of life versus adult. This last approach would allow them to draw the most impactful conclusion. 

We appreciate these suggestions.  Sadly, it is beyond our budget for the current manuscript and beyond the scope of our current study that we believe provides interesting new information.

(2) Fig S2D shows TRBJ1-4 in black lettering meant to indicate no significant difference whereas the figure shows use of this gene segment to be elevated in adult. I believe TRBJ1-4 should be in blue lettering.

This is now coloured correctly.

(3) The figure call out on p11 (Fig5I-J) should be H-I.

This is now corrected.

(4) Please indicate in the main text that Jaccard analysis in Fig 5 D-E is for TCRB.

This is now corrected.

(5) The analysis of usage of TCRB CDR1xCDR2 combinations in Fig6D is said to "reflect the bias observed in their TRBV gene usage (Fig 2C)". Isn't it the case that every TRBV gene presents a distinct CDR1xCDR2 combination, meaning that there is no difference between TRBV usage and TRBV CDR1xCDR2 usage? If so, please make this clearer.

Yes, this is the case, we have made this clearer in the text.

Reviewer #3 (Recommendations For The Authors): 

In general, although there is lots of interesting analyses that can be done with these large datasets, I feel as though the authors did not fully interpret the real meaning and significance of many of these results. Whilst there were some speculation on why a foetal repertoire might be different to those of adults in the discussion sections, the rationale for each individual analyses was not clearly explained. I would suggest that the rationale and a thorough explanation of each analyses be added to the results section, including a finishing sentence on what it means. 

We have added short summaries to each results section to make the points we are making clearer.

The authors did not mention how many cells were sorted for from each thymus for sequencing. Was the cell number normalised between each population? As this might have an influence on various downstream measurements of diversity, evenness and clonality, if there is a sampling issue. 

This is explained in the methods.  We used sampling to allow comparisons between repertoires of different sizes, and this is also explained in the methods.

The authors should include the cell sorting profiles and example flow cytometry plots, including gating strategies and the post sort purity of each sorted population. 

We have included sorting strategies in the methods (SFig7 and SFig8).

I think the manuscript could also be improved if there were some basic characterisation of foetal vs. adult thymus development. How many thymocytes are in a foetal vs adult thymus at the timepoints chosen? 

I think there were some interesting findings in this paper. Given that overall, the foetal thymus appeared to be less diverse than that of the adult, one question I thought would be interesting to discuss was the overlap between the two repertoires. Is the foetal thymus simply a sub-fraction of the adult repertoire or is it totally distinct with no overlapping sequences? 

Our analyses indicate that the repertoires are actually different. This is evident in Fig4 and in PCA loading plots shown in Fig, 3C and new Fig. 7C, D, I and J.

I think that some of the interpretation in the results section may be a bit vague. "When we compaired by thymocyte population, each adult population clustered together, with adult SP4 separating from adult SP8 on PC2 and DP cells scoring in between, suggesting that PC2 might correspond to MHC restriction of the adult populations." - whilst I think I know what the authors mean, I do believe that this could be explained in clearer detail and more explicit. SP4 and SP8 are known to be positively selected in the thymus on distinct MHC class I and MHC class II molecules for example. 

We have tried to clarify the text describing that PCA and additionally added a new Figure (new Fig. &) to compare the influence of MHC-restriction on the TCR repertoire in foetal and adult thymus.

In the methods section, the age and sex of mice used were not explained at all. What was used in the experiment? Are there any sex differences? 

Age and sex of mice is given in the methods.  We have not detected sex differences.

This is a huge omission from the manuscript. In general, I don't believe the methods section has described the analysis in sufficient detail for replication. All analysis code and data should be publicly accessible and be in a format that allows for the reader to replicate the figures in the paper upon running the code. Perhaps even allowing them to run their own TCR datasets.  Overall, I think the manuscript needs some rewriting to include additional details and deeper interpretation of each individual analyses. 

Sequencing data files will be made publicly available on UCL Research Data Repository.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The authors report compound heterozygous deleterious variants in the kinase domains of the non-receptor tyrosine kinases (NRTK) TNK2/ACK1 in familial SLE. They suggest that ACK1 and BRK deficiencies are associated with human SLE and impair efferocytosis.

Strengths: 

The identification of similar mutations in non-receptor tyrosine kinases (NRTKs) in two different families with familial SLE is a significant finding in human disease. Furthermore, the paper provides a detailed analysis of the molecular mechanisms behind the impairment of efferocytosis caused by mutations in ACK1 and BRK.

Weaknesses: 

A critical point in this paper is whether the loss of function of ACK1 or BRK contributes to the onset of familial SLE. The authors emphasize that inhibitors of ACK1/BRK worsened IgG deposition in the kidneys in a pristane-induced SLE model, which contributes not to the onset but to the exacerbation of SLE, thus only partially supporting their claim.

The evidence supporting that the loss of function of ACK1 or BRK contributes to the onset of SLE in the patients from the 2 families mostly relies on the genetic analysis. As the reviewer states, the observation that inhibitors of ACK1/BRK worsened IgG deposition in the kidneys in a pristane-induced SLE model supports the genetic evidence.

To further address the possible role of ACK1 or BRK variants in the onset of autoimmunity in vivo, we treated wild-type (WT) BALB/cByJ female mice with inhibitors in the absence of pristane.

The results indicated that mice that had received a weekly injection of ACK1 or BRK inhibitors developed a large array of serum anti-nuclear IgG antibodies, including but not limited to autoantibodies associated with SLE such as anti-histones, anti-chromatin, anti U1-snRNP, anti-SSA, and anti-Ku in comparison to the control group inhibitor treated mice (Revised Fig 3A). However, they did not develop glomerular deposit of IgG after 12 weeks of treatment, in contrast to mice that have received Pristane (Revised Fig. 3B,C, Figure 3-figure supplement 1).

These additional data suggests that inhibition of ACK1 and BRK stimulates the production of serum autoantibodies, which strengthen the claim that ACK1 and BRK kinase deficiency contribute to autoimmunity in BALB/cByJ.

Reviewer #2 (Public Review):

Summary: 

In this manuscript, the authors revealed that genetic deficiencies of ACK1 and BRK are associated with human SLE. First, the authors found that compound heterozygous deleterious variants in the kinase domains of the non-receptor tyrosine kinases (NRTK) TNK2/ACK1 in one multiplex family and PTK6/BRK in another family. Then, by an experimental blockade of ACK1 or BRK in a mouse SLE model, they found an increase in glomerular IgG deposits and circulating autoantibodies. Furthermore, they reported that ACK and BRK variants from the SLE patients impaired the MERTK-mediated anti-inflammatory response to apoptotic cells in human induced pluripotent stem cells (hiPSC)-derived macrophages. This work identified new SLE-associated ACK and BRK variants and a role for the NRTK TNK2/ACK1 and PTK6/BRK in efferocytosis, providing a new molecular and cellular mechanism of SLE pathogenesis.

Strengths: 

This work identified new SLE-associated ACK and BRK variants and a role for the NRTK TNK2/ACK1 and PTK6/BRK in efferocytosis, providing a new molecular and cellular mechanism of SLE pathogenesis.

Weaknesses: 

Although the manuscript is well-organized and clearly stated, there are some points below that should be considered:

In this study, the authors used forward genetic analyses to identify novel gene mutations that may cause SLE, combined with GWAS studies of SLE. To further explore the importance of these variants, haplotype analysis of two candidate genes could be performed, to observe the evolution and selection relationship of candidate genes in the population (UK 1000 biobank, for example). 

To investigate whether ACK1/TNK2 or BRK/PTK6 were subject to selection, we gathered data using different metrics quantifying negative selection in the human genome. We collected the f parameter from SnIPRE1, lofTool2, and evoTol3, as well as intraspecies metrics from RVIS4, LOEUF5, and pLI6 (including pRec). We also used our in-house CoNeS metric7. None of these indicators suggest that the genes are under strong negative selection (Revised Figure 2-figure supplement 2). This is consistent with the deficiency being recessive. We also tested the variants with a MAF greater than 0.005. We found them to be neutral. We therefore did not test whether they were associated with any phenotype in the UK Biobank.

Although the authors focused on SLE and macrophage efferocytosis in their studies, direct evidence of how macrophage efferocytosis significantly affects SLE is lacking. This point should at least be explicitly introduced and discussed by citing appropriate literature.

We provide a more detailed description of the role of macrophage efferocytosis in autoimmunity and SLE in the revised manuscript. Specifically, we state (in the results section, paragraph: ACK1 and BRK kinase domain variants may lose the ability to link MERTK to RAC1, AKT and STAT3 activation for efferocytosis): “NRTKs such as ACK1 8 and PTK2/FAK 9 are also downstream targets of the TAM family receptor MERTK which is expressed on macrophages and controls the anti-inflammatory engulfment of apoptotic cells, a process known as efferocytosis 10-12. Efferocytosis allows for the clearance of apoptotic cells before they undergo necrosis and release intracellular inflammatory molecules, and simultaneously leads to increased production of anti-inflammatory molecules (TGFb, IL-10, and PGE2) and a decreased secretion of proinflammatory cytokines (TNF-alpha, IL-1b, IL-6) 10-14. In line with these findings, mice deficient in molecular components used by macrophages to efficiently perform efferocytosis, such as MFG-E8, MERTK, TIM4, and C1q, develop phenotypes associated with autoimmunity10,11,14-27. Furthermore, defects in efferocytosis are also observed in patients with SLE and glomerulonephritis14,28-31.“

It is still not clear how the target molecules identified in this paper may influence macrophage efferocytosis. More direct evidence should be established. 

Our studies show that wt -but not variants- of ACK1 and BRK are activated by MERTK, a key receptor that mediates the recognition of apoptotic cells. Our studies also show that wt -but not variants- activate RAC1 which is necessary for engulfment and phosphorylate AKT and STAT3 which are involved in the anti-inflammatory response to PtdSer recognition.

The TAM family receptor MERTK mediates recognition of PtdSer on apoptotic cells via GAS6 and Protein S 10,15,32 leading to their engulfment, which involves activation of RAC1 for actin reorganization and the formation of a phagocytic cup 9,33. Using IP kinase assays we show that MERTK and GAS6 can activate the kinase activity of wild-type ACK1 8 or BRK but not of the patient’s ACK1 or BRK variant alleles (Figure 4D). To further support the role of ACK1 and BRK downstream from PtdSer recognition and uptake of apoptotic cells, we show that reference ACK1 and BRK alleles, in contrast to the patient variant alleles, can activate RAC1 to generate RAC-GTP which is necessary for engulfment 9,33 (Figure 4C).

PtdSer recognition also typically stimulates an anti-inflammatory process mediated in part via AKT 34 and STAT3 and their target genes such as SOCS3 35-41 and results in the inhibition of LPS-mediated production of inflammatory mediators such as TNF and IL-1b, and the production of cytokines such as IL-10, TGFb 11,25-27,42. Consistent with this literature and the findings of the paper, we show that reference ACK1 and BRK, unlike the patient’s variant alleles, can phosphorylate AKT and STAT3 (Figure 4A, B). The role of ACK1 and BRK in these signaling pathways is further supported by our transcriptomics data comparing the response of controls, patients, and inhibitor-treated iPSC-derived macrophages to apoptotic thymocytes by RNA-seq. Specifically, we show Transcriptional repressors including the AKT targets ATF3, TGIF1, NFIL3, and KLF4, the STAT3 targets SOCS3 and DUSP5, as well as CEBPD and the inhibitor of E-BOX DNA Binding ID3 were among the top-ten genes which expression is induced by apoptotic cells in WT macrophages (Figure 4F), but this regulation was lost in mutant and inhibitor-treated macrophages (Figure 4F).

For some transcriptional repressors mentioned in their studies, the authors should check whether there is clear experimental evidence. If not, it is recommended to supplement the experimental verifications for clarity.

Transcriptional repressors including the AKT targets ATF3, TGIF1, NFIL3, and KLF4, the STAT3 targets SOCS3 and DUSP5, as well as CEBPD and the inhibitor of E-BOX DNA Binding ID3 were among the top-ten genes which expression is induced by apoptotic cells in WT macrophages (Figure 4F), but this regulation was lost in mutant and inhibitor-treated macrophages (Figure 4F).

In the manuscript we cited published evidence, to the best of our knowledge, for the role of these genes in the regulation of inflammatory responses. Specifically we state: “ATF3, TGIF1, NFIL3, and KLF4 are involved in the negative regulation of inflammation in macrophages 35-38, SOCS3 is an inhibitor of the macrophage inflammatory response and DUSP5 is a negative regulator of ERK activation 39,40,43. These data suggest that the kinase domain of ACK1 and BRK contribute to the macrophage anti-inflammatory gene expression program driven by apoptotic cells.”

In Figures 4C and 4D, it is seen that the usage of inhibitors causes cytoskeletal changes, however this reviewer would not have expected such large change. Did the authors check whether the cells die after heavy treatment by the inhibitors?

We carefully examine the viability of Isogenic WT, BRK and ACK1 mutant macrophages (left panel) and of WT macrophages treated with ACK1 or BRK inhibitors and we did not observed changes in viability (Figure 4-figure supplement 2).

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

A crucial step in the development of SLE is the production of autoantibodies. It is shown in Figure 2F that inhibitors of ACK1/BRK enhanced the production of autoantibodies against histones and SSA in a pristane-induced SLE model, which is a significant result that could support the authors' claim. Strangely, this autoantigen panel does not include double-stranded DNA, RNP, or Sm, which should be presented regarding antibody production.

We thank the reviewer for this comment. In the revised manuscript (Revised Figure 3 – Supplement 1) we added the remainder of the autoantibody panel, which includes double-stranded DNA, RNP, and Sm autoantibody levels. We also added the results for serum IgG autoantibody levels in BALB/cByJ mice treated for three months with DMSO, ACK1, or BRK inhibitors but did not receive a pristane injection (Revised Figure 3A). This data shows that mice which received ACK1 or BRK inhibitors had increased serum IgG autoantibodies in comparison to DMSO treated controls.

Additionally, if there is information that inhibitors of ACK1/BRK promote the differentiation of follicular helper T cells, memory B cells, and plasma cells in a pristane-induced SLE model, it could be considered indirect evidence supporting the authors' claims.

These are not available at present to the best of our knowledge.

Reviewer #2 (Recommendations For The Authors):

Minor points:

* In the literature, unpaired t-tests and ordinary one-way ANOVA (Tukey's multiple comparisons test) were used for statistical analysis, which requires data to be normally distributed. This part of the proposal is reflected in the text, and the non-conforming results need to be statistically analyzed using the non-parametric test of graphpad prism.

We would like to thank the reviewer for pointing out this oversight. In the revised manuscript, for all applicable datasets, we tested whether the data was normally distributed using a Shapiro-Wilk normality test. For datasets that were normally distributed statistical significance was determined by a Student t test or ordinary one-way ANOVA with Tukey’s multiple comparisons test depending on the number of conditions being compared and the experimental setup. In contrast, for datasets that were not normally distributed statistical significance was determined using a Mann-Whitney, Kruskal-Wallis multiple comparisons tests, or Wilcoxon matched-pairs signed rank test depending on the experimental setup. P values below 0.05 were considered significant for all statistical tests.

The authors used different methods to represent the level of significant difference. Therefore, it is suggested that the significance level should be expressed by letters. 

As suggested by the reviewer, in the revised manuscript we have designated the significance level throughout all figures using letters (p, or q values).

For RNA-seq, more information should be provided in the paper. For example, the correlation between sample biological replicates, the total number of differentially expressed genes, and randomly selected genes for qRT-PCR results verification.

We would like to thank the reviewer for pointing out this oversight. In the revised manuscript we provided more information regarding the RNA-seq dataset, including a Principal Component Analysis (PCA) showing correlation between sample replicates (Revised Figure 4-figure supplement 1A), as well as a table indicating the number of upregulated and downregulated genes between relevant datasets (Revised Figure 4-figure supplement 1B).

The results of the RNA-seq analysis indicated that ACK1 and BRK contribute to the macrophage anti-inflammatory gene expression program driven by apoptotic cells. MERTK-dependent anti-inflammatory program elicited by apoptotic cells on macrophages is best evidenced by the reduction of LPS-mediated production of inflammatory mediators such as TNF or IL1b 25-27,34,44. Therefore, to validate the RNA-seq results in a functional manner we tested the decrease of LPS-induced production of TNF and IL1b by apoptotic cells in isogenic WT, ACK1 deficient, and BRK deficient macrophages. Consistent with the RNA-seq data, the functional assays indicated that ACK1 and BRK kinase activities are required for the decrease of TNF and IL1b production induced by LPS in response to apoptotic cells (Revised Figure 4H,I).

The raw data files for the RNA-seq analysis have been deposited in the NCBI Gene Expression Omnibus under accession number GEO: GSE118730.

The authors did not have the formats for some of the citations correct. This should be fixed. 

References were reformatted.

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Author Response:

eLife Assessment

This manuscript makes an important contribution to the understanding of protein-protein interaction (PPI) networks by challenging the widely held assumption that their degree distributions uniformly follow a power law. The authors present convincing evidence that biases in study design, such as data aggregation and selective research focus, may contribute to the appearance of power-law-like distributions. While the power law assumption has already been questioned in network biology, the methodological rigor and correction procedures introduced here are valuable for advancing our understanding of PPI network structure.

Thanks for this assessment which perfectly reflects our study.

Reviewer #1 (Public Review):

This manuscript was previously reviewed and this earlier evaluation resulted in two conflicting assessments. I fully endorse the favourable opinion of former Reviewer 1 and find most negative comments of former Reviewer 2 inappropriate.

This work is absolutely necessary. Even though the authors find it difficult to be fully assertive in the end, their ground work in trying to demonstrate the existence of bias in PPI data is undeniably valuable. Other authors have tried before to show the limitation of unequivocally assigning the degree distribution to a power law but these doubts have had a weak impact. This new study is a great opportunity to discuss further a concern for a simplistic view of PPI network topology. The recent contribution of Broido & Clauset was definitely one to bounce on. The approach of this new manuscript is compelling. Dividing the study in several parts, each reflecting an attempt to bring out commonly used shortcuts in PPI network analyses, makes sense.

Surprisingly, the authors do not refer to the endless controversy of labeling hubs as party or date, which is another manifestation of the interpretative bias of PPI data.

This is a good point. In particular, it may be interesting if hub nodes that emerge from considering only prey interactions differ regarding party and date nodes. We now refer to this distinction in the Discussion:

“[...] Further work will be needed to establish if true hub proteins exist in the PPI network and what their role is. For instance, it was previously claimed (Han et al., 2004) – and controversially discussed (Agarwal et al., 2010) – that the correlation of gene expression values between hub nodes with their interaction partners follows a bimodal distribution, leading to the distinction of party (high correlation) and date (low correlation) hubs. In the future, it would be interesting to study if the ratio of party and date hubs changes when considering prey degree only.”

The only worthy point prompted by former Reviewer 2 is the effect of spoke expansion. In their response, the authors suggest that it would probably extend questioning and even if it is considered as future work, it could be mentioned in the main manuscript.

Thank you for this comment. We agree that considering different expansion methods is an interesting research question regarding its effect on the PL property. We have added the following sentences to the Discussion to highlight the opportunity for future work:

“[...] An additional complexity arising in AP-MS studies is that more than two interaction partners can be detected. These -ary interactions are commonly transformed into binary interactions using either the spoke model, which reports all interactions with the bait protein (as used by IntAct, for example), or the matrix expansion model, which reports all pairwise interactions. Both expansion models can, in principle, introduce false positives and it would be interesting to consider the effect of expansion model choice on the PL property in future work.”

In the end, this submission is an invitation to constructively rethink the analysis of PPI networks and it feeds the discussion on modelling degree distributions that should not be considered as a solved issue.

Reviewer #2 (Public Review):

Many naturally occurring networks are assumed to have a power-law (PL) degree distribution. This assumption has certainly been widely held in the field of protein interactomes (PPIs), although important studies around 2010 have conclusively shown that many of these PL distributions are either the result of data mis-handling or of sloppy statistical procedures (see e.g. Porter and Stumpf in Science around 2014, which I would advise the authors to cite). The value of the present study is to introduce a new mechanism, experiment bias, to explain the appearance of such distributions in the PPI case, and in particular to show how correcting empirically for this mechanism can lead to a reappraisal of which proteins are genuine hubs in these networks. The claims are well supported by empirical evidence and some theoretical analysis. Overall, this is a worthwhile contribution and, while its significance is somewhat dented by the fact that the PL enthusiasm of many had already been tempered by the studies mentioned above,

Thanks a lot for your constructive feedback. We now cite the work by Porter and Stumpf and have addressed your specific recommendations as detailed below.

Reviewer #3 (Public Review):

I would like to congratulate the authors to an impressive piece of work highlighting important real and potential biases, which may lead to power-law distributed node degrees in protein-protein interaction networks. This manuscript is easy to follow and very well written manuscript. I truly enjoyed the concise and convincing scientific presentation. Even if some of the concerns have already been discussed or raised in the past, the manuscript assesses potential biases in PPIs in a rigorous manner.

I deem the following observations highly relevant to be communicated to the community again:

(1) PL-like distributions emerge by aggregation of data sets alone.

(2) Research interest in itself is PL-distributed and drives PL-like properties in PPI networks

(3) Bait usage is a major driver of PL-like behaviour.

(4) Accounting for biases changes the biological interpretation of the networks

(5) Simulation studies further corroborate these findings.

Thank you for this positive assessment of our work.

Author response:

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

Public Reviews:

Reviewing editor:

The biological significance of the results presented in this manuscript is the potential absence of active sequestration mechanisms in certain species, leading to variation in their ability to transport and store specific compounds, such as alkaloids. The concept of passive accumulation is introduced as an evolutionary intermediate between toxin consumption and sequestration.

I agree with the reviewers' comments on the limitations of the current manuscript. Additionally, I'd like to raise a point about combining data from LC/MS and GC/MS as these techniques have different sensitivities. GC-MS excels in annotation, allowing for confident identification of detected compounds. However, it may have limitations in the number of extractable substances. Conversely, LC-MS/MS offers a broader range of detectable substances, but annotation can be more challenging. While methods to bridge this gap exist, the current approach might not fully account for the potential influence of the analysis equipment on the observed differences in alkaloid numbers between the Texas and Panama samples analyzed by LC-MS/MS. To address this, consider including data from both methods (if possible) to gain a more comprehensive understanding of the alkaloid profiles. Alternatively, analyzing the Texas and Panama samples with GC-MS could be considered for a more focused comparison with the other samples.

Thank you for the suggestion. Unfortunately, we do not have GC-MS data for the Texas and Panama samples. While the strength of these two datasets is that they present two independent lines of data corroborating that “undefended” frogs have detectable alkaloid levels, we have more explicitly made clear for readers that the datasets should not be compared directly. We reviewed the text to check that we carefully acknowledge in the manuscript the higher sensitivity of our LC-MS assay, and we added more detail about the differences between the two assay types (section 4d): “The UHPLC-HESI-MSMS pipeline used on the samples from Panama and Texas allows for higher sensitivity to detect a broader array of compounds compared to our GC-MS methods, but has lower retention-time resolution and produces less reliable structural predictions. Furthermore, due to the lack of liquid-chromatography-derived references for poison-frog alkaloids, precise alkaloid annotations from the UHPLC-HESI-MSMS dataset could not be obtained. Therefore, the UHPLC-HESI-MSMS and GC-MS datasets are not directly comparable, and UHPLC-HESI-MSMS data are not included in Fig. 2”. We have also revised the asterisk accompanying the table to further reinforce that alkaloid numbers between the two assay types should not be compared. It now states: “Note that the UHPLC-HESI-MS/MS and GC-MS assays differed in both instrument and analytical pipeline, so “Alkaloid Number” values from the two assay types should not be compared to each other directly”. We further point out differences between the two assay types in section 2b: “Similarly, the analysis of UHPLC-HESI-MS/MS data was untargeted, and thus enables a broader survey of chemistry compared to that from prior GC-MS studies.”

Finally, we point out that the output from the analytical pipeline for UHPLC-HESI-MSMS annotates compounds as “alkaloids,” using broader criteria than the targeted GC-MS component of our study. In an effort to make the datasets more comparable, at least conceptually, we now include an assessment of which alkaloids identified by UHPLC-HESI-MSMS match known molecular formulae and structural classes in frogs (see Table S6 and revised text on lines 335-343 and 410-415.

Reviewer #1 (Public Review):

This is a very relevant study, clearly with the potential of having a high impact on future research on the evolution of chemical defense mechanisms in animals. The authors present a substantial number of new and surprising experimental results, i.e., the presence in low quantities of alkaloids in amphibians previously deemed to lack these toxins. These data are then combined with literature data to weave the importance of passive accumulation mechanisms into a 4-phases scenario of the evolution of chemical defense in alkaloid-containing poison frogs.

In general, the new data presented in the manuscript are of high quality and high scientific interest, the suggested scenario compelling, and the discussion thorough. Also, the manuscript has been carefully prepared with a high quality of illustrations and very few typos in the text. Understanding that the majority of dendrobatid frogs, including species considered undefended, can contain low quantities of alkaloids in their skin provides an entirely new perspective to our understanding of how the amazing specializations of poison frogs evolved. Although only a few non-dendrobatids were included in the GCMS alkaloid screening, some of these also included minor quantities of alkaloids, and the capacity of passive alkaloid accumulation may therefore characterize numerous other frog clades, or even amphibians in general.

Thank you for the kind evaluation.

While the overall quality of the work is exceptional, major changes in the structure of the submitted manuscript are necessary to make it easier for readers to disentangle scope, hypotheses, evidence and newly developed theories.

Based on reviewer comments, we revised the manuscript structure substantially to make the different aspects of the paper more readily identifiable to readers. Specifically we moved the content of Figure 2 into a new section in the introduction. We also added more introductory text to better introduce the main ideas of the new model and to summarize the scope and aim of the paper. We reorganized the result section headings and moved Figure 1 (now Fig. 3) down into section 2c.

Reviewer #2 (Public Review):

Summary:

This was a well-executed and well-written paper. The authors have provided important new datasets that expand on previous investigations substantially. The discovery that changes in diet are not so closely correlated with the presence of alkaloids (based on the expanded sampling of non-defended species) is important, in my opinion.

Strengths:

Provision of several new expanded datasets using cutting edge technology and sampling a wide range of species that had not been sampled previously. A conceptually important paper that provides evidence for the importance of intermediate stages in the evolution of chemical defense and aposematism.

Thank you for kind comments.

Weaknesses:

There were some aspects of the paper that I thought could be revised. One thing I was struck by is the lack of discussion of the potentially negative effects of toxin accumulation, and how this might play out in terms of different levels of toxicity in different species.

Thank you for the suggestion. We now explicitly address the possible negative effects of toxin accumulation and how costs may play out with respect to varying levels of chemical defense among different organisms, including poison frogs. We note early on that, “short-term alkaloid feeding experiments (e.g., Daly et al., 1994; Sanchez et al., 2019) demonstrate that both defended and undefended dendrobatids can survive the immediate effects of alkaloid intake, although the degree of resistance and the alkaloids that different species can resist vary'' (section 2c), and we address the sparse literature suggesting some species-level variation in alkaloid resistance in frogs. Later, we make the point that, “origins of chemical defenses are also shaped by the cost of resisting and accumulating toxins, which can change over evolutionary time as animals adapt to novel relationships with toxins” (section 2d). We broadly discuss costs of target-site resistance, a common mode of molecular resistance in poison frogs and other animals, and compensatory molecular adaptations that offset the costs. We also discuss examples from the literature of negative effects of high levels of resistance and toxin accumulation that are not completely offset. We also note that to the best of our knowledge, potential lifetime fitness costs to alkaloid consumption by dendrobatids have not been evaluated.

Further, are there aspects of ecology or evolutionary history that might make some species less vulnerable to the accumulation of toxins than others? This could be another factor that strongly influences the ultimate trajectory of a species in terms of being well-defended. I think the authors did a good job in terms of describing mechanistic factors that could affect toxicity (e.g. potential molecular mechanisms) but did not make much of an attempt to describe potential ecological factors that could impact trajectories of the evolution of toxicity. This may have been done on purpose (to avoid being too speculative), but I think it would be worth some consideration.

We agree that other factors can influence the trajectory of chemical defense. We incorporated these ideas into the new section 2d, which provides a somewhat brief overview of ecological factors that could influence the origins of chemical defense, the physiological costs of toxin resistance and accumulation, and some of the possible eco-evo factors that shape chemical defense once it evolves.

In the discussion, the authors make the claim that poison frogs don't (seem to) suffer from eating alkaloids. I don't think this claim has been properly tested (the cited references don't adequately address it). To do so would require an experimental approach, ideally obtained data on both lifespan and lifetime reproductive success.

We agree with the reviewer that more data are necessary to make this broad claim, which we have removed. We revised this to state: “regardless, it is clear that all or nearly all dendrobatid poison frogs consume alkaloid-containing arthropods as part of their regular diet” (section 2c). We then expand on this statement with data from short-term experimental work that support the notion that at least some dendrobatids are resistant (i.e., can survive) the immediate effects of alkaloids. We also point out later in the manuscript that, “as far as we are aware, the possible lifetime fitness costs (e.g., in reproductive success) of alkaloid consumption in dendrobatids have not been measured” (section 2d).

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

While in general I am very open to "unorthodox" ways to write a manuscript (i.e., differing from the standard structure intro-methods-results-discussion) I feel there is much room for improvement in this case. When reading the manuscript line by line, I was several times totally uncertain about the scope and content of the original data in the manuscript. It is too often unclear which of the outlined theories are new and why they are presented, which hypotheses were tested and why, which data were newly obtained, which technological improvements led to the novel and surprising results, and why no alternative hypotheses are tested. I feel the authors need to fundamentally reconsider the structure of the manuscript - which does not mean everything needs to be rewritten, but some major reshuffling of paragraphs from one section to the other may already lead to substantial improvement. I will in the following list (not ordered by priority) different issues that I encountered, without always providing a specific suggestion for improvement - please come up with an improved structure that removes these issues in one way or the other!

Thank you for the suggestions. We did our best to improve the structure of the paper. Specifically, we substantially revised the introduction to provide a clearer background of the ideas leading up to the new evolutionary model. We moved most of what was previously figure 2 (now Fig. 1) into an earlier part of the introduction in the main text. We moved what was previously figure 1 (now Fig. 3) to much later in the discussion (section 2c). We attempted to clarify and separate throughout the text the new data from existing data. Please see our responses below for additional details.

Line 42-45: Please provide a reference on this statement on traversing adaptive landscapes.

We added the following reference: Martin, CH and PC Wainwright. 2013. Multiple fitness peaks on the adaptive landscape drive adaptive radiation in the wild. Science 339: 208-211. https://doi.org/10.1126/science.1227710

Line 50: Why are these phases "likely" to occur? - no evidence is presented for this hypothesized high likelihood. Presenting this scenario already in the second paragraph of the intro is very weird. Are these really the only possible phases? Wouldn't it be possible to come up with totally different scenarios? In my opinion, this specific four-phase scenario should be more clearly labelled as a novel theory presented in this paper, and perhaps it should come much later in the introduction.

Thank you for the suggestion. We moved this paragraph down into a new subsection of the introduction. We also revised the language to clarify that the model is a new evolutionary theory based on new and existing ideas.

Line 51: Here you use for the first time the term "elimination". While it is intuitively clear what is meant by it, there still could be different meanings. The alkaloids could simply be passively excreted, or they could be actively biochemically decomposed. Later in the Discussion the authors imply that elimination requires some kind of metabolic process, but this perhaps should be made clearer already in the introduction.

We now spend more time in the introduction describing pharmacokinetics as well as the terms we used (including elimination), which are slightly modified from terms in pharmacokinetics.

Figure 1. I have major concerns about this figure. I found the figure very confusing, and the authors really need to reconsider and modify (simplify) it. The figure caption starts with "Major processes involved..." as if this was established textbook knowledge rather than a totally hypothetical illustration of how different factors (sequestration, elimination....) can lead to defended or undefended phenotypes. Only later on in the caption it becomes clear this is just a suggestion/hypothesis/model: "we hypothesize...".

We revised the figure (now Fig. 3) and its legend. It now starts with the following text: “Hypothesized physiological processes that interact to determine the defense phenotype.” We also simplify the figure by removing two lines and recoding the table (see comment below).

Secondly, the way the graph is drawn suggests some kind of experimental result where specific evolutionary pathways lead to very specific degrees of "defendedness", recognizable by the points on the right axis stacked very precisely one above the other. Do you really want to imply that you want to suggest such a specific model, where particular accumulation/intake/elimination rates lead to exactly these outcomes? Also, wouldn't it be possible to somewhat simplify the categories in the table? Again, why so specific, is there any experimental evidence for it? Why sometimes 1 plus, 2 plus, 3 plus? Wouldn't it be better to just suggest categories such as strong, weak and absent?

We simplified the figure by removing the secondary (dashed) passive accumulation and active sequestration lines. We also changed the + signs to “low,” “med,” or “high” and tried to simplify the text in the figure and in the legend.

Line 101-103: "We propose ..." Here, as the concluding statement of the introduction, the authors suggest a very general hypothesis which seems rather disconnected from the four-phase model and from the experimental results. Here, at the latest, I would have expected to learn (1) what the overall scope of the paper is, (2) which kind of approaches were followed and which novel experimental results will be presented in the following, and (3) how the experimental results will be used to derive a new theory / novel. Again, it is obvious that the scope of the paper is broader than testing just a single and narrow hypothesis, but rather to support and develop a broader theory and evolutionary model, but this should be clear to readers once they arrive at this line.

Thank you for the suggestion. We added a paragraph to the end of the first section of the introduction that outlines the content of the rest of the paper. We also reorganized some of the subheadings to make the flow of ideas and the source of data in each subsection clearer. We split up and moved what was previously in section 2a into parts of the introduction and discussion. We moved the results text about diet and the discussion about resistance to section 2a, to better provide data and discussion of phases 1 and 2.

Figure 2. My opinion on this figure is much less strong than on Fig. 1. However, the authors may want to reconsider whether it really makes sense to here show all the historical trees and theories (which are not really systematically reviewed in the text) or if they maybe wish to go on with panel D only (the most recent tree and scenario which is also used to consistently for further discussion in the manuscript).

We moved the content from Fig. 2A–C to the main text (now section 1b) and narrowed the focus of Fig. 2 (now Fig. 1) to what was previously panel 2D.

Results and Discussion: The whole section on phases 1 to 2 is not based on any new results. This is OK (as I said, I have no problems with "unorthodox" manuscript structure) but it should be clearer to readers why this is presented here and what it represents. A new theory? A recapitulation of textbook knowledge? Something necessary to later understand the experimental results?

We split up and moved what was previously in section 2a into parts of the introduction and discussion. Now, section 2a still focuses on phases 1 and 2 but presents the diet data from our study (phase 1) and a review of known resistance mechanisms (phase 2; previously in the discussion section).

Line 168. Here we have arrived at the "core" of the paper, that is, the actual experimental results. Surprisingly, you find alkaloids in dendrobatids usually considered "undefended". This is great, surprising and of high importance. However, I am missing at least some technical/methodological discussion about this finding, except for the statement that it was based on GCMS. Why have previous studies not detected these alkaloids? Did you use particularly sensitive GCMS instruments? Did you look more in depth than it was done in previous studies? Can you totally exclude these contaminations/artefacts?

We added the following paragraph to section 2b: “The large number of structures that we identified is in part due to the way we reviewed GC-MS data: in addition to searching for alkaloids with known fragmentation patterns, we also searched for anything that could qualify as an alkaloid mass spectrometrically but that may not match a previously known structure in a reference database. Similarly, the analysis of UHPLC-HESI-MS/MS data was untargeted, and thus enables a broader survey of chemistry compared to that from prior GC-MS studies. Structural annotations in our UHPLC-HESI-MS/MS analysis were made using CANOPUS, a deep neural network that is able to classify unknown metabolites based on MS/MS fragmentation patterns, with 99.7% accuracy in cross-validation (Dührkop et al., 2021).” We also moved the paragraph on contamination from the methods section into section 2b.

Line 169. This sentence (and several others in the subsequent paragraphs) do a poor job in explaining the taxon and specimen sampling. The particular sentence in this line is unclear: Did you include 27 species of dendrobatids AND IN ADDITION representatives of the main undefended clades, or did these 27 species INCLUDE representatives of the main undefended clades?

We now present a brief overview of sampling in the last paragraph of the introduction (section 1c). We clarified sampling of the species: “In total we surveyed 104 animals representing 32 species of Neotropical frogs including 28 dendrobatid species, two bufonids, one leptodactylid, and one eleutherodactylid (see Methods). Each of the major undefended clades in Dendrobatidae (Fig. 1, Table 1) is represented in our dataset, with a total of 14 undefended dendrobatid species surveyed.” We also reviewed and clarified similar language in other places in the text (e.g., section 2b).

Line 177. "undefended lineages" - of dendrobatids or of frogs in general? Given that you also include non-dendrobatids.

Dendrobatids. The sentence now reads “Overall, we detected alkaloids in skins from 13 of 14 undefended dendrobatid species included in our study, although often with less diversity and relatively lower quantities than in defended lineages (Fig. 2, Table 1, Table S3, Table S4).”

Line 188: "defe" should probably changed to "defended"?

Corrected.

Table 1. The taxon sampling clearly focuses on dendrobatids, with only a few other taxa. This is fine, however, it does not allow to test the hypothesis that something "special" predisposes dendrobatids to passive accumulation and alkaloid resistance. For this, a wider taxon sampling of other frog families would have been necessary to have a larger number of "control" data. Again, this is fine for the purpose of the study and is discussed later (line 399) but only very briefly. I feel it should be mentioned earlier on.

Thank you for the suggestion. We now address this point earlier in the manuscript so that readers will not have the impression that there are sufficient data to infer that dendrobatids are predisposed to passive accumulation. We propose several phylogenetic alternatives, making it clear that determining the number and timing of origins of passive accumulation is not possible with our data (section 2c), ultimately noting that “discriminating a single origin [of passive accumulation] – no matter the timing – from multiple ones would require better phylogenetic resolution and more extensive alkaloid surveys, as we only assessed four non-dendrobatid species”.

Reviewer #2 (Recommendations For The Authors):

P2L60 - The description of figure 1 is somewhat confusing, as it first focuses on the graph in the bottom panel, then moves to describing aspects of the table (top panel), then back to the graph. I think it might make more sense to describe these two panels separately and in order.

Thank you for the suggestion. We revised the figure (now Fig. 3) and its legend for clarity.

P3L94 - Saying that three transitions makes this group "ideal" for studying complex phenotypic transitions is a bit hyperbolic, in my opinion. I suggest toning down this description.

Thank you for the suggestion. We changed “ideal” to “suitable.”

P3L101 - "We propose that changes in toxin metabolism through selection on mechanisms of toxin resistance likely play a major role in the evolution of acquired chemical defenses." This hypothesis appears to be a combination of earlier ideas, with a somewhat different emphasis. The authors acknowledge this and go through some of the earlier ideas, in the legend of figure 2. I would have preferred to see more discussion of this (particularly with reference to the history of the idea in reference to poison frogs) in the main body of the text.

Thank you for the suggestion. We now more extensively discuss these prior studies in the introduction (section 1b and 1c). We also revised this figure (now Fig. 1) to focus on what was previously figure 2 panel D.

P3L102 - Figure 2 - the phrase "Resistance to consuming some alkaloids" seems inappropriate - perhaps "Resistance to alkaloid poisoning after consumption" (or something similar) would be more accurate?

We changed this to “Low alkaloid resistance”.

P4L153 - "Accumulation of alkaloids in skin glands could help to prevent alkaloids from reaching their targets". This could be true, but why would skin glands be a preferred location of sequestration to avoid toxicity? The authors should explain why such glands would be particularly likely to serve as places of sequestration.

Thank you for pointing out this ambiguity. We decided to remove our discussion of sequestration into skin glands, because it is challenging to discuss this process in toxin resistance without too much speculation.

P4L154 - "Although direct evidence is lacking, some poison frogs may biotransform alkaloids into less toxic forms until they can be eliminated from the body, e.g., using cytochrome p450s". This would seem to contradict the argument of this process being a precursor to accumulating effective toxins.

We agree that these processes seem contradictory. However, a few papers are starting to suggest that metabolic detoxification may be initially useful for lineages that eventually evolve toxin sequestration. This is because detoxification or elimination (clearance) of toxins allows increased intake of toxins. Because there is some delay in the removal of toxins from an animal’s body, increased consumption ultimately leads to higher toxin exposure and possible toxin diffusion into various body cavities, which can increase selective pressure to evolve other kinds of resistance mechanisms. This pattern was shown in an experiment with toxin-resistant fruit flies (Douglas et al., 2022). Many toxin-sequestering species still metabolize some toxins even if they sequester the majority – as we argue, the defense phenotype is the result of a balance among intake, elimination, and accumulation, all of which can interact simultaneously. In poison frogs specifically there is some evidence that p450s are upregulated after toxin consumption (Caty et al. 2019). One possible prediction is that the type of resistance that an animal has changes as toxin sequestration evolves. We talk a bit more about these patterns in section 2e.

P5L186 - Table 1 legend - change "defe" to "defended"

Corrected.

P12L414 - "do not appear to suffer substantially from doing so as it is part of their regular diet". I don't think this claim has been properly tested, as of yet. It would require looking at the effects of a diet with and without toxins over the lifespan of the frogs, and the impact of that difference on both survival and fertility.

Reviewer 1 also made this important observation, which we address above.

P12L432 - "for toxin-resistant organisms, there is little cost to accumulating a toxin, yet there may be benefits in doing so." Yet toxin resistance may itself be a continuous trait, so there may be a cost that depends on the degree of toxin resistance. I don't see why the authors are proposing toxin resistance as a discrete trait when their main point is that toxin accumulation is not.

We agree and removed this statement.

Author response:

ANALYTICAL

(1) Figure 3 shows that the relationship between learning rate and informativeness for our rats was very similar to that shown with pigeons by Gibbon and Balsam (1981). We used multiple criteria to establish the number of trials to learn in our data, with the goal of demonstrating that the correspondence between the data sets was robust. To establish that they are effectively the same does require using an equivalent decision criterion for our data as was used for Gibbon and Balsam’s data. However, the criterion they used—at least one peck at the response key on at least 3 out of 4 consecutive trials—cannot be sensibly applied to our magazine entry data because rats make magazine entries during the inter-trial interval (whereas pigeons do not peck at the response key in the inter-trial interval). Therefore, evidence for conditioning in our paradigm must involve comparison between the response rate during CS and the baseline response rate. There are two ways one could adapt the Gibbon and Balsam criterion to our data. One way is to use a non-parametric signed rank test for evidence that the CS response rate exceeds the pre-CS response rate, and adopting a statistical criterion equivalent to Gibbon and Balsam’s 3-out-of-4 consecutive trials (p<.3125). The second method estimates the nDkl for the criterion used by Gibbon and Balsam. This could be done by assuming there are no responses in the inter-trial interval and a response probability of at least 0.75 during the CS (their criterion). This would correspond to an nDkl of 2.2 (odds ratio 27:1). The obtained nDkl could then be applied to our data to identify when the distribution of CS response rates has diverged by an equivalent amount from the distribution of pre-CS response rates.

(2) A single regression line, as shown in Figure 6, is the simplest possible model of the relationship between response rate and reinforcement rate and it explains approximately 80% of the variance in response rate. Fixing the log-log slope at 1 yields the maximally simple model. (This regression is done in the logarithmic domain to satisfy the homoscedasticity assumption.) When transformed into the linear domain, this model assumes a truly scalar relation (linear, intercept at the origin) and assumes the same scale factor and the same scalar variability in response rates for both sets of data (ITI and CS). Our plot supports such a model. Its simplicity is its own motivation (Occam’s razor).

If regression lines are fitted to the CS and ITI data separately, there is a small increase in explained variance (R2 = 0.82). We leave it to further research to determine whether such a complex model, with 4 parameters, is required. However, we do not think the present data warrant comparing the simplest possible model, with one parameter, to any more complex model for the following reasons:

· When a brain—or any other machine—maps an observed (input) rate to a rate it produces (output rate), there is always an implicit scalar. In the special case where the produced rate equals the observed rate, the implicit scalar has value 1. Thus, there cannot be a simpler model than the one we propose, which is, in and of itself, interesting.

· The present case is an intuitively accessible example of why the MDL (Minimum Description Length) approach to model complexity (Barron, Rissanen, & Yu, 1998; Grünwald, Myung, & Pitt, 2005; Rissanen, 1999) can yield a very different conclusion from the conclusion reached using the Bayesian Information Criterion (BIC) approach. The MDL approach measures the complexity of a model when given N data specified with precision of B bits per datum by computing (or approximating) the sum of the maximum-likelihoods of the model’s fits to all possible sets of N data with B precision per datum. The greater the sum over the maximum likelihoods, the more complex the model, that is, the greater its measured wiggle room, it’s capacity to fit data. Recall that von Neuman remarked to Fermi that with 4 parameters he could fit an elephant. His deeper point was that multi-parameter models bring neither insight nor predictive power; they explain only post-hoc, after one has adjusted their parameters in the light of the data. For realistic data sets like ours, the sums of maximum likelihoods are finite but astronomical. However, just as the Sterling approximation allows one to work with astronomical factorials, it has proved possible to develop readily computable approximations to these sums, which can be used to take model complexity into account when comparing models. Proponents of the MDL approach point out that the BIC is inadequate because models with the same number of parameters can have very different amounts of wiggle room. A standard illustration of this point is the contrast between logarithmic model and power-function model. Log regressions must be concave; whereas power function regressions can be concave, linear, or convex—yet they have the same number of parameters (one or two, depending on whether one counts the scale parameter that is always implicit). The MDL approach captures this difference in complexity because it measures wiggle room; the BIC approach does not, because it only counts parameters.

· In the present case, one is comparing a model with no pivot and no vertical displacement at the boundary between the black dots and the red dots (the 1-parameter unilinear model) to a bilinear model that allows both a change in slope and a vertical displacement for both lines. The 4-parameter model is superior if we use the BIC to take model complexity into account. However, 4-parameter has ludicrously more wiggle room. It will provide excellent fits—high maximum likelihood—to data sets in which the red points have slope > 1, slope 0, or slope < 0 and in which it is also true that the intercept for the red points lies well below or well above the black points (non-overlap in the marginal distribution of the red and black data). The 1-parameter model, on the other hand, will provide terrible fits to all such data (very low maximum likelihoods). Thus, we believe the BIC does not properly capture the immense actual difference in the complexity between the 1-parameter model (unilinear with slope 1) to the 4-parameter model (bilinear with neither the slope nor the intercept fixed in the linear domain).

· In any event, because the pivot (change in slope between black and red data sets), if any, is small and likewise for the displacement (vertical change), it suffices for now to know that the variance captured by the 1-parameter model is only marginally improved by adding three more parameters. Researchers using the properly corrected measured rate of head poking to measure the rate of reinforcement a subject expects can therefore assume that they have an approximately scalar measure of the subject’s expectation. Given our data, they won’t be far wrong even near the extremes of the values commonly used for rates of reinforcement. That is a major advance in current thinking, with strong implications for formal models of associative learning. It implies that the performance function that maps from the neurobiological realization of the subject’s expectation is not an unknown function. On the contrary, it’s the simplest possible function, the scalar function. That is a powerful constraint on brain-behavior linkage hypotheses, such as the many hypothesized relations between mesolimbic dopamine activity and the expectation that drives responding in Pavlovian conditioning (Berridge, 2012; Jeong et al., 2022; Y.  Niv, Daw, Joel, & Dayan, 2007; Y. Niv & Schoenbaum, 2008).

The data in Figure 6 are taken from the last 5 sessions of training. The exact number of sessions was somewhat arbitrary but was chosen to meet two goals: (1) to capture asymptotic responding, which is why we restricted this to the end of the training, and (2) to obtain a sufficiently large sample of data to estimate reliably each rat’s response rate. We have checked what the data look like using the last 10 sessions, and can confirm it makes very little difference to the results.<br /> Finally, as noted by the reviews, the relationship between the contextual rate of reinforcement and ITI responding should also be evident if we had measured context responding prior to introducing the CS. However, there was no period in our experiment when rats were given unsignalled reinforcement (such as is done during “magazine training” in some experiments). Therefore, we could not measure responding based on contextual conditioning prior to the introduction of the CS. This is a question for future experiments that use an extended period of magazine training or “poor positive” protocols in which there are reinforcements during the ITIs as well as during the CSs. The learning rate equation has been shown to predict reinforcements to acquisition in the poor-positive case (Balsam, Fairhurst, & Gallistel, 2006).

(3) One of us (CRG) has earlier suggested that responding appears abruptly when the accumulated evidence that the CS reinforcement rate is greater than the contextual rate exceeds a decision threshold (C.R.  Gallistel, Balsam, & Fairhurst, 2004). The new more extensive data require a more nuanced view. Evidence about the manner in which responding changes over the course of training is to some extent dependent on the analytic method used to track those changes. We presented two different approaches. The approach shown in Figures 7 and 8, extending on that developed by Harris (2022), assumes a monotonic increase in response rate and uses the slope of the cumulative response rate to identify when responding exceeds particular milestones (percentiles of the asymptotic response rate). This analysis suggests a steady rise in responding over trials. Within our theoretical model, this might reflect an increase in the animal’s certainty about the CS reinforcement rate with accumulated evidence from each trial. While this method should be able to distinguish between a gradual change and a single abrupt change in responding (Harris, 2022) it may not distinguish between a gradual change and multiple step-like changes in responding and cannot account for decreases in response rate.<br /> The other analytic method we used relies on the information theoretic measure of divergence, the nDkl (Gallistel & Latham, 2023), to identify each point of change (up or down) in the response record. With that method, we discern three trends. First, the onset tends to be abrupt in that the initial step up is often large (an increase in response rate by 50% or more of the difference between its initial value and its terminal value is common and there are instances where the initial step is to the terminal rate or higher). Second, there is marked within-subject variability in the response rate, characterised by large steps up and down in the parsed response rates following the initial step up, but this variability tends to decrease with further training (there tend to be fewer and smaller steps in both the ITI response rates and the CS response rate as training progresses). Third, the overall trend, seen most clearly when one averages across subjects within groups is to a moderately higher rate of responding later in training than after the initial rise. We think that the first tendency reflects an underlying decision process whose latency is controlled by diminishing uncertainty about the two reinforcement rates and hence about their ratio. We think that decreasing uncertainty about the true values of the estimated rates of reinforcement is also likely to be an important part of the explanation for the second tendency (decreasing within-subject variation in response rates). It is less clear whether diminishing uncertainty can explain the trend toward a somewhat greater difference in the two response rates as conditioning progresses. It is perhaps worth noting that the distribution of the estimates of the informativeness ratio is likely to be heavy tailed and have peculiar properties (as witness, for example, the distribution of the ratio of two gamma distributions with arbitrary shape and scale parameters) but we are unable at this time to propound an explanation of the third trend.

(4) There is an error in the description provided in the text. The pre-CS period used to measure the ITI responding was 10 s rather than 20 s. There was always at least a 5-s gap between the end of the previous trial and the start of the pre-CS period.

(5) Details about model fitting will be added in a revision. The question about fitting a single model or multiple models to the data in Figure 6 is addressed in response 2 above. In Figure 6, each rat provides 2 behavioural data points (ITI response rate and CS response rate) and 2 values for reinforcement rate (1/C and 1/T). There is a weak but significant correlation between the ITI and CS response rates (r = 0.28, p < 0.01; log transformed to correct for heteroscedasticity). By design, there is no correlation between the log reinforcement rates (r = 0.06, p = .404).

CONCEPTUAL

(1) It is important for the field to realize that the RW model cannot be used to explain the results of Rescorla’s (Rescorla, 1966; Rescorla, 1968, 1969) contingency-not-pairing experiments, despite what was claimed by Rescorla and Wagner (Rescorla & Wagner, 1972; Wagner & Rescorla, 1972) and has subsequently been claimed in many modelling papers and in most textbooks and reviews (Dayan & Niv, 2008; Y. Niv & Montague, 2008). Rescorla programmed reinforcements with a Poisson process. The defining property of a Poisson process is its flat hazard function; the reinforcements were equally likely at every moment in time when the process was running. This makes it impossible to say when non-reinforcements occurred and, a fortiori, to count them. The non-reinforcements are causal events in RW algorithm and subsequent versions of it. Their effects on associative strength are essential to the explanations proffered by these models. Non-reinforcements—failures to occur, updates when reinforcement is set to 0, hence also the lambda parameter—can have causal efficacy only when the successes may be predicted to occur at specified times (during “trials”). When reinforcements are programmed by a Poisson process, there are no such times. Attempts to apply the RW formula to reinforcement learning soon foundered on this problem (Gibbon, 1981; Gibbon, Berryman, & Thompson, 1974; Hallam, Grahame, & Miller, 1992; L.J. Hammond, 1980; L. J. Hammond & Paynter, 1983; Scott & Platt, 1985). The enduring popularity of the delta-rule updating equation in reinforcement learning depends on “big-concept” papers that don’t fit models to real data and discretize time into states while claiming to be real-time models (Y. Niv, 2009; Y. Niv, Daw, & Dayan, 2005).

The information-theoretic approach to associative learning, which sometimes historically travels as RET (rate estimation theory), is unabashedly and inescapably representational. It assumes a temporal map and arithmetic machinery capable in principle of implementing any implementable computation. In short, it assumes a Turing-complete brain. It assumes that whatever the material basis of memory may be, it must make sense to ask of it how many bits can be stored in a given volume of material. This question is seldom posed in associative models of learning, nor by neurobiologists committed to the hypothesis that the Hebbian synapse is the material basis of memory. Many—including the new Nobelist, Geoffrey Hinton— would agree that the question makes no sense. When you assume that brains learn by rewiring themselves rather than by acquiring and storing information, it makes no sense.

When a subject learns a rate of reinforcement, it bases its behavior on that expectation, and it alters its behavior when that expectation is disappointed. Subjects also learn probabilities when they are defined. They base some aspects of their behavior on those expectations, making computationally sophisticated use of their representation of the uncertainties (Balci, Freestone, & Gallistel, 2009; Chan & Harris, 2019; J. A. Harris, 2019; J.A. Harris & Andrew, 2017; J. A. Harris & Bouton, 2020; J. A. Harris, Kwok, & Gottlieb, 2019; Kheifets, Freestone, & Gallistel, 2017; Kheifets & Gallistel, 2012; Mallea, Schulhof, Gallistel, & Balsam, 2024 in press).

(2) Rate estimation theory is oblivious to the temporal order in which experience with different predictors occurs. The matrix computation finds the additive solution, if it exists, to the data so far observed, on the assumption that predicted rates have remained the same. This is the stationarity assumption, which is implicit in a rate computation and was made explicit in the formulation of RET (C.R. Gallistel, 1990). When the additive solution does not exist, the RET algorithm treats the compound of two predictors as a third predictor, and computes the additive solution to the 3-predictor problem. Because it is oblivious to the order in which the data have been acquired, it predicts one-trial overshadowing and retroactive blocking and unblocking (C.R. Gallistel, 1990 pp 439 & 452-455).

The RET algorithm is but one component of the information-theoretic model of associative learning (aka, TATAL, The Analytic Theory of Associative Learning Wilkes & Gallistel, 2016)). It solves the assignment-of-credit problem, not the change-detection problem. Because rates of reinforcement do sometimes change, the stationarity assumption, which is essential to the RET algorithm, must be tested when each new reinforcement occurs and when the interval since the last reinforcement has become longer than would be expected or the number of reinforcements has become significantly fewer than would be expected given the current estimate of the probability of reinforcement (C. R. Gallistel, Krishan, Liu, Miller, & Latham, 2014). In the information-theoretic approach to associative learning, detecting non-stationarity is done by an information-theoretic change-detecting algorithm. The algorithm correctly predicts that omitted reinforcements to extinction will be a constant (C.R. Gallistel, 2024 under review; Gibbon, Farrell, Locurto, Duncan, & Terrace, 1980). To put the prediction another way, unreinforced trials to extinction will increase in proportional to the trials/reinforcement during training (C.R. Gallistel, 2012; Wilkes & Gallistel, 2016). In other words, it predicts the best and most systematic data on the partial reinforcement extinction effect (PREE) known to us. The profound challenge to neo-Hullian delta-rule updating models that is posed by the PREE has been recognized for the better part of a century. To the best of our knowledge, no other formalized model of associative learning has overcome this challenge (Dayan & Niv, 2008; Mellgren, 2012). Explaining extinction algorithmically is straightforward when one adopts an information-theoretic perspective, because computing reinforcement-by-reinforcement the Kullback-Leibler divergence in a sequence of earlier rate (or probability!) estimates from the most recent estimate and multiplying the vector of divergences by the vector of effective sample sizes (C. R. Gallistel & Latham, 2022) detects and localized changes in rates and probabilities of reinforcement (C.R. Gallistel, 2024 under review). The computation presupposes the existence of a temporal map, a time-stamped record of past events. This supposition is strongly resisted by neuroscience-oriented reinforcement-learning modelers, who try to substitute the assumption of decaying eligibility traces.

The very interesting Pearce-Ganesan findings (Ganesan & Pearce, 1988) are not predicted by RET, but nor do they run counter its predictions. RET has nothing to say about how subjects categorize appetitive reinforcements; nor, at this time, does the information-theoretic approach to an understanding of associative have anything to say about that.

The same is not true for the Betts, Brandon & Wagner results (Betts, Brandon, & Wagner, 1996). They pretrained a blocking cue that predicted a painful paraorbital shock to one eye of a rabbit. This cue elicited an anticipatory blink in the threatened eye. It also potentiated the startle reflex made to a loud noise in one ear. A new cue that was then introduced, which always occurred in compound with the pretrained blocking cue. In one group, the painful shock continued to be delivered to the same eye as before; in another group, it was delivered to the skin around the other eye. In the group that continued to receive the shock to the same eye, the old cue effectively blocked conditioning of the new cue for both the eyeblink and the potentiated startle response. However, in the group for which the location of the shock changed to the other eye, the old cue did not block conditioning of the eyeblink response to the new cue but did block conditioning of the startle response to the new cue. The information-theoretic analysis of associative learning focusses on the encoding of measurable predictive temporal relationships, rather than on general and, to our mind, vague notions like CS processing and US processing. A painful shock elicits fear in a rabbit no matter where on the body surface it is experienced, because fear is a reaction to a very broad category of dangers, and fear potentiates the startle reflex regardless of the threat that causes fear. Once that prediction of such a threat is encoded; redundant cues will not be encoded that same way because the RET algorithm blocks the encoding of redundant predictions. A painful shock near an eye elicits a blink of the threatened eye as well as the fear that potentiates the startle. An appropriate encoding for the eye blink must specify the location of the threat. RET will attribute prediction of the threat to the new eye to the new cue—and not to the old cue, the pretrained blocker— while continuing to attribute to the old cue the prediction of a fear-causing threat, because the change in location does not alter that prediction. Therefore, the new cue will be encoded as predicting the new location of the threat to the eye, but not as predicting the large category non-specific threats that elicit fear and the potentiation of the startle, because that prediction remains valid. Changing that prediction would violate the stationarity assumption; predictive relations do not change unless the data imply that they must have changed. Unless we have made a slip in our logic, this would seem to explain Betts et al’s (1996) results. It does so with no free parameters, unlike AESOP, which has a notoriously large number of free parameters.

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Author response:

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

eLife Assessment

This important study addresses how 3' splice site choice is modulated by the conserved spliceosome-associated protein Fyv6. The authors provide compelling evidence Fyv6 functions to enable selection of 3' splice sites distal to a branch point and in doing so antagonizes more proximal, suboptimal 3' splice sites. The study would be improved through a more nuanced discussion of alternative possibilities and models, for instance in discussing the phenotypic impact of Fyv6 deletion.

We thank the editors and reviewers for their supportive comments and assessment of this manuscript. We have improved the discussion at several points as suggested by the reviewers to include discussion of alternative possibilities.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

A key challenge at the second chemical step of splicing is the identification of the 3' splice site of an intron. This requires recruitment of factors dedicated to the second chemical step of splicing and exclusion of factors dedicated to the first chemical step of splicing. Through the highest resolution cyroEM structure of the spliceosome to-date, the authors show the binding site for Fyv6, a factor dedicated to the second chemical step of splicing, is mutually exclusive with the binding site for a distinct factor dedicated to the first chemical step of splicing, highlighting that splicing factors bind to the spliceosome at a specific stage not only by recognizing features specific to that stage but also by competing with factors that bind at other stages. The authors further reveal that Fyv6 functions at the second chemical step to promote selection of 3' splice sites distal to a branch point and thereby discriminate against proximal, suboptimal 3' splice site. Lastly, the authors show by cyroEM that Fyv6 physically interacts with the RNA helicase Prp22 and by genetics Fyv6 functionally interacts with this factor, implicating Fyv6 in 3'SS proofreading and mRNA release from the spliceosome. The evidence for this study is robust, with the inclusion of genomics, reporter assays, genetics, and cyroEM. Further, the data overall justify the conclusions, which will be of broad interest.

Strengths:

(1) The resolution of the cryoEM structure of Fyv6-bound spliceosomes at the second chemical step of splicing is exceptional (2.3 Angstroms at the catalytic core; 3.0-3.7 Angstroms at the periphery), providing the best view of this spliceosomal intermediate in particular and the core of the spliceosome in general.

(2) The authors observe by cryoEM three distinct states of this spliceosome, each distinguished from the next by progressive loss of protein factors and/or RNA residues. The authors appropriately refrain from overinterpreting these states as reflecting distinct states in the splicing cycle, as too many cyroEM studies are prone to do, and instead interpret these observations to suggest interdependencies of binding. For example, when Fyv6, Slu7, and Prp18 are not observed, neither are the first and second residues of the intron, which otherwise interact, suggesting an interdependence between 3' splice site docking on the 5' splice site and binding of these second step factors to the spliceosome.

(3) Conclusions are supported from multiple angles.

(4) The interaction between Fyv6 and Syf1, revealed by the cyroEM structure, was shown to account for the temperature-sensitive phenotypes of a fyv6 deletion, through a truncation analysis.

(5) Splicing changes were observed in vivo both by indirect copper reporter assays and directly by RT-PCR.

(6) Changes observed by RNA-seq are validated by RT-PCR.

(7) The authors go beyond simply observing a general shift to proximal 3'SS usage in the fyv6 deletion by RNA-seq by experimentally varying branch point to 3' splice site distance experimentally in a reporter and demonstrating in a controlled system that Fyv6 promotes distal 3' splice sites.

(8) The importance of the Fyv6-Syf1 interaction for 3'SS recognition is demonstrated by truncations of both Fyv6 and of Syf1.

(9) In general, the study was executed thoroughly and presented clearly.

We thank the reviewer for their recognition of the strengths of our multi-faceted approach that led to highly supported conclusions.

Weaknesses:

(1) Despite the authors restraint in interpreting the three states of the spliceosome observed by cyroEM as sequential intermediates along the splicing pathway, it would be helpful to the general reader to explicitly acknowledge the alternative possibility that the difference states simply reflect decomposition from one intermediate during isolation of the complex (i.e., the loss of protein is an in vitro artifact, if an informative one).

We thank the reviewer for noticing our restraint in interpreting these structures, and we agree that the scenario described by the reviewer is a possibility. We have now explicitly mentioned this in the Discussion on lines 755-757.

(2) The authors acknowledge that for prp8 suppressors of the fyv6 deletion, suppression may be indirect, as originally proposed by the Query and Konarska labs - that is, that defects in the second step conformation of the spliceosome can be indirectly suppressed by compensating, destabilizing mutations in the first step spliceosome. Whereas some of the other suppressors of the fyv6 deletion can be interpreted as impacting directly the second step spliceosome (e.g., because the gene product is only present in the second step conformation), it seems that many more suppressors beyond prp8 mutants, especially those corresponding to bulky substitutions, which would more likely destabilize than stabilize, could similarly act indirectly by destabilization of first step conformation. The authors should acknowledge this where appropriate (e.g., for factors like Prp8 that are present in both first and second step conformations).

We agree that this is also a possibility and have now included this on lines 480-486.

Reviewer #2 (Public Review):

In this manuscript, Senn, Lipinski, and colleagues report on the structure and function of the conserved spliceosomal protein Fyv6. Pre-mRNA splicing is a critical gene expression step that occurs in two steps, branching and exon ligation. Fyv6 had been recently identified by the Hoskins' lab as a factor that aids exon ligation (Lipinski et al., 2023), yet the mechanistic basis for Fyv6 function was less clear. Here, the authors combine yeast genetics, transcriptomics, biochemical assays, and structural biology to reveal the function of Fyv6. Specifically, they describe that Fyv6 promotes the usage of distal 3'SSs by stabilizing a network of interactions that include the RNA helicase PRP22 and the spliceosome subunit SYF1. They discuss a generalizible mechanism for splice site proofreading by spliceosomsal RNA helicases that could be modulated by other, regulatory splicing factors.

This is a very high quality study, which expertly combines various approaches to provide new insights into the regulation of 3'SS choice, docking, and undocking. The cryo-EM data is also of excellent quality, which substantially extends on previous yeast P complex structures. This is also supported by the authors use of the latest data analysis tools (Relion-5, AlphaFold2 multimer predictions, Modelangelo). The authors re-evaluate published EM densities of yeast spliceosome complexes (B*, C,C*,P) for the presence or absence of Fyv6, substantiate Fyv6 as a 2nd step specific factor, confirm it as the homolog of the human protein FAM192A, and provide a model for how Fyv6 may fit into the splicing pathway. The biochemical experiments on probing the splicing effects of BP to 3'SS distances after Fyv6 KO, genetic experiments to probe Fyv6 and Syf1 domains, and the suppressor screening add substantially to the study and are well executed. The manuscript is clearly written and we particularly appreciated the nuanced discussions, for example for an alternative model by which Prp22 influences 3'SS undocking. The research findings will be of great interest to the pre-mRNA splicing community.

We thank the reviewer for their positive comments on our manuscript.

We have only few comments to improve an already strong manuscript.

Comments:

(1) Can the authors comment on how they justify K+ ion positions in their models (e.g. the K+ ion bridging G-1 and G+1 nucleotides)? How do they discriminate e.g. in the 'G-1 and G+1' case K+ from water?

The assignment of K+ at this position is justified by both longer coordination distances and relatively high cryo-EM density compared to structured water molecules in the same vicinity. We have added a panel to figure3-figure supplement 4C to show the density for the G-1/G+1 bridging K+ ion and to show the adjacent density for putative water molecules which coordinate the ion. The K+ ion density is larger and has stronger signal than the adjacent water molecules. The coordination distances are also longer than would be expected for a Mg2+. For these reasons and because K+ was present in the purification buffer, we modelled the density as K+.

(2) The authors comment on Yju2 and Fyv6 assignments in all yeast structures except for the ILS. Can the authors comment on if they have also looked into the assignment of Yju2 in the yeast ILS structure in the same manner? While it is possible that Fyv6 could dissociate and Yju2 reassociate at the P to ILS transition, this would merit a closer look given that in the yeast P complex Yju2 had been misassigned previously.

We thank the reviewer for pointing out this very interesting topic! We have used ModelAngelo to analyze the S. cerevisiae ILS structure for support of density assignment as Yju2 (and not Fyv6). This analysis supports the assignment as Yju2 in this structure and we have no evidence to doubt its presence in those particular purified spliceosomes. We have updated Figure 4- figure supplement 1B accordingly.

That being said, we do think that this issue should be studied more carefully in the future. The S. cerevisiae ILS structure (5Y88) was determined by purifying spliceosome complexes with a TAP-tag on Yju2. So the conclusion that Yju2 is part of the ILS spliceosome involves some circular logic: Yju2 is part of ILS spliceosome complexes because it is present in ILS complexes purified with Yju2. We also note that Yju2 was absent in ILS complexes recently determined from metazoans by the Plaschka group.  We have added some additional nuance to the Discussion to raise this important mechanistic point at lines 711-718.

(3) For accessibility to a general reader, figures 1c, d, e, 2a, b, would benefit from additional headings or labels, to immediately convey what is being displayed. It is also not clear to us if Fig 1e might fit better in the supplement and be instead replaced by Supplementary Figure 1a (wt) , b (delta upf1), and a new c (delta fyv6) and new d (delta upf1, delta fyv6). This may allow the reader to better follow the rationale of the authors' use of the Fyv6/Upf1 double deletion.

We thank the reviewer for the suggestion and have updated Figures 1 C-E to include additional information in the headings and labels. We have not changed the labels in Figures 2A, B but have added additional clarifying language to the legend.

In terms of rearranging the figures, we thank the reviewer for the suggestion but have decided that the figures are best left in their current ordering.

(4) The authors carefully interpret the various suppressor mutants, yet to a general reader the authors may wish to focus this section on only the most critical mutants for a better flow of the text.

We thank the reviewer for this suggestion. While this section of the manuscript does contain (to quote Reviewer #3) “extensive new information regarding functional interactions”, it was a bit long. We have reduced this section of the manuscript by ~200 words for a more focused presentation for general readers.

Reviewer #3 (Public Review):

In this manuscript the authors expand their initial identification of Fyv6 as a protein involved in the second step of pre-mRNA splicing to investigate the transcriptome-wide impact of Fyv6 on splicing and gain a deeper understanding of the mechanism of Fyv6 action.

They first use deep sequencing of transcripts in cells depleted of Fyv6 together with Upf1 (to limit loss of mis-spliced transcripts) to identify broad changes in the transcriptome due to loss of Fyv6. This includes both changes in overall gene expression, that are not deeply discussed, as well as alterations in choice of 3' splice sites - which is the focus of the rest of the manuscript

They next provide the highest resolution structure of the post-catalytic spliceosome to date; providing unparalleled insight into details of the active site and peripheral components that haven't been well characterized previously.

Using this structure they identify functionally critical interactions of Fyv6 with Syf1 but not Prp22, Prp8 and Slu7. Finally, a suppressor screen additionally provides extensive new information regarding functional interactions between these second step factors.

Overall this manuscript reports new and essential information regarding molecular interactions within the spliceosome that determine the use of the 3' splice site. It would be helpful, especially to the non-expert, to summarize these in a table, figure or schematic in the discussion.

We thank the reviewer for the positive comments and suggestions. We did include a summary figure in panel 7H. However, it was a bit buried. To highlight the summary figure more clearly, we have moved panel 7H to its own figure (Fig. 8).

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

(1) The resolution of some panels is poor, nearly illegible (e.g., Supp Fig 1A, B).

The resolution of panels in supplemental figure 1 has been increased. However, this may be an artifact of the PDF conversion process. We will pay attention to this during the publication process.

(2) Panel S6B: 6HYU is a structure of DHX8, not DDX8

We have corrected DDX8 to DHX8 in Supplemental Fig. S6D and associated figure legend.

(3) The result that Syf1 truncations can suppress the Fyv6 deletion is impressive. The subsequent discussion seems muddled. A discussion of Fyv6 binding at the first step, instead of Yju2, doesn't seem relevant here (though worthy of consideration in the discussion), given that the starting mutation is the Fyv6 deletion. Further, conjuring rebinding of Yju2 based on the data in the paper seems unnecessarily speculative (assumes that biochemical state III is on pathway), unless I am unaware of some other evidence for such rebinding. Instead, a simpler explanation would seem to be that in the absence of Fyv6, Syf1 inappropriately binds Yju2 instead at the second step and that deletion of the common Fyv6/Yju2 binding site on Syf1 suppresses this defect. In this case, the ts phenotype of the Fyv6 deletion would result from inappropriate binding of Yju2, and the splicing defect would be due to loss of Fyv6 activity. Alternatively, especially considering the work of the labs of Query and Konarska, the authors should consider the possibility that i) the Fyv6 deletion destabilizes the second step conformation, shifting an equilibrium to the first step conformation, and that ii) the Syf1 truncation destabilizes binding of Yju2, thereby restoring the equilibrium. In this case the ts phenotype of the Fyv6 deletion is due to a disturbed equilibrium and the splicing defect is due to the failure of Fyv6 to function at the second step.

We believe the reviewer is specifically referencing the final paragraph of this Results section (the paragraph that comes just before the section “Mutations in many different splicing factors…”). In retrospect, we agree that our discussion was convoluted. In particular, we emphasized rebinding of Yju2 based on its presence in the cryo-EM structure of the yeast ILS complex. However, given some uncertainties about whether or not Yju2 is a bona fide ILS component (as discussed above). We don’t think it is appropriate to over-emphasize rebinding of Yju2 and have decided to incorporate the elegant mechanisms proposed by the reviewer. This paragraph has now been edited accordingly (lines 386-395).

(4) The authors imply they have performed biochemical studies, which I think is misleading. Of course, RT-PCR and primer extension assays for example are performed in vitro, but these are an analysis of RNA events that occurred in vivo. In my view a higher threshold should be used for defining "biochemistry". To me "biochemistry" would imply that the authors have, for example, investigated 3' splice site usage in splicing extracts of the fyv6 deletion or engaged in an analysis of the Syf1-Fyv6 interaction involving the expression of the interacting domains in bacteria followed by a binding analysis in the test tube.

We disagree with the reviewer on this point. Biochemistry is defined as the “branch of sciences concerned with the chemical substances, reactions, and physico chemical processes which occur within living organisms; biological or physical chemistry.” (Oxford English Dictionary). Biochemical studies are not defined by whether or not they take place in vitro, in vivo, or even in silico. Indeed, much of the history of biochemistry (especially in studies of metabolism, for example) involved experiments occurring in vivo that reported on the molecular properties and mechanisms of biological processes. We think many of our experiments fall into this category including our structure/function analysis of splicing factors and the use of the ACT1-CUP1 reporter substrate.

(5) The monovalents are shown; inositol phosphate is shown; is the binding of Prp22 to RNA shown?

We have added a panel to Figure 3-figure supplement 4D showing density for the 3' exon within Prp22.

(6) The authors invoke undocking of the 3'SS in the P complex. Where is the 3'SS in the ILS? The author's model predicts: undocked.

In all ILS structures to date, the 3′ SS is undocked, in agreement with this prediction. We have now noted this observation in line 760.

(7) Would be helpful to show fyv6 deletion in Fig 1b.

We have included growth data for an additional fyv6 deletion strain (in a cup1Δ background) in Figure 1b. The results are quite similar to the upf1_Δ_ background except with slightly worse growth at 23°C.

Reviewer #2 (Recommendations For The Authors):

Minor comments

(1) Fig.3b is the arrow indicating the right rotation?

This typo has been fixed.

(2) Fig.4b, panel H is annotated, which should read 'F'.

This typo has been fixed.

(3) Line 178: "Finally, we analyzed the sequence features of the alternative 3ʹ SS activated by loss of Fyv6." We would suggest 'used after' instead of 'activated by'.

We have replaced ‘activated by’ with ‘with increased use after’.

(4) In Line 544, the authors speculate on a Slu7 requirement for 3'SS docking and on 3'SS docking maintenance. In the results section (Line 265) they however only mention the latter possibility. These statements should be consistent.

We thank the reviewer for pointing this out. We have added a reference to docking maintenance to the results section at line 325.

(5) Line 476: "Unexpectedly, Prp22 I1133R was actually deleterious when Fyv6 was present for this reporter." We suggest removing "actually".

We have removed ‘actually’.

(6) The authors describe the observed changes in splicing events in absolute numbers (e.g. in Fig 1c). To better assess for the reader whether these numbers reflect large or small effects of Fyv6 in defining mRNA isoforms, it would be more useful to state these as percent changes of total events or to provide a reference number for how many introns are spliced in S.c. See for example the statements in Lines 132 and 145.

We have added a percentage at line 138 that indicates ~20% of introns in yeast showed splicing changes.

Reviewer #3 (Recommendations For The Authors):

Do the authors have a proposed explanation for the observed DGE in non-intron containing genes in the Fyv6 depleted cells?

The simplest explanation is that this is an indirect effect due to splicing changes occurring in other genes (such as transcription factors, ribosomal protein genes, etc..). It is possible that this can be further dissected in the future using shorter-term knockdown of Fyv6 using Anchors Away or AID-tagging. However, that is beyond the scope of the current manuscript, and we do not wish to comment on these non-intron containing genes further at present.

Figure 2A - What is going on with the events that show no FAnS value under one condition (i.e. are up against the X or Y axis)? These are of interest as most on the Y- axis are blue.

The events along one of the axes denote alternative splice sites that are only detected under one condition (either when Fyv6 is present or when it is absent). At this stage, we do not wish to interpret these events further since most have a relatively low number of reads overall.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

This study reports single-cell RNA sequencing results of lung adenocarcinoma, comparing 4 treatment-naive and 5 post-neoadjuvant chemotherapy tumor samples.<br /> The authors claim that there are metabolic reprogramming in tumor cells as well as stromal and immune cells after chemotherapy.

The most significant findings are in the macrophages that there are more pro-tumorigenic cells after chemotherapy, i.e. CD45+CD11b+ARG+ cells. In the treatment-naive samples, more anti-tumorigenic CD45+CD11b+CD86+ macrophages are found. They sorted each population and performed functional analyses.

Strengths:

Comparison of the treatment-naive and post-chemotherapy samples of lung adenocarcinoma.

Weaknesses:

(1) Lengthy descriptive clustering analysis, with indistinct direct comparisons between the treatment-naive and the post-chemotherapy samples.

Thank you for your detailed review and valuable feedback. We have simplified the descriptive clustering analysis by removing redundant parts and retaining only the key content relevant to our findings. This should help readers to more easily grasp and focus on the main results.

(2) No statistical analysis was performed for the comparison.

We appreciate your constructive feedback and are committed to improving our research methodology and reporting to enhance the scientific rigor of our studies.

(3) Difficult to match data to the text.

Thank you for your feedback. We understand that there were difficulties in matching the data to the text. We have reviewed the manuscript carefully to ensure that all data points are clearly linked to the corresponding sections in the text.

(4) ARG1 is a cytosolic enzyme that can be detected by intracellular staining after fixation. It is unclear how the staining and sorting was performed to measure function of sorted cells.

We apologize for the error caused by miscommunication within our research team. We are currently using both ARG1 and CD206 antibodies in our studies. Due to a communication error, the technician mistakenly assumed ARG1 was another name for CD206 (MRC1), resulting in the incorrect labeling of CD206 as ARG1 in our experimental records. In reality, we used the CD206 antibody, which is consistent with the same surface marker shown in figure 6e. We have made corrections in the manuscript and experimental figures. Thank you for pointing this out, and we regret any misunderstanding this may have caused.

Reviewer #2 (Public Review):

In this study, Huang et al. performed a scRNA-seq analysis of lung adenocarcinoma (LUAD) specimens from 9 human patients, including 5 who received neoadjuvant chemotherapy (NCT), and 4 without treatment (control). The new data was produced using 10 × Genomics technology and comprises 83622 cells, of which 50055 and 33567 cells were derived from the NCT and control groups, respectively. Data was processed via R Seurat package, and various downstream analyses were conducted, including CNV, GSVA, functional enrichment, cell-cell interaction, and pseudotime trajectory analyses. Additionally, the authors performed several experiments for in vitro and in vivo validation of their findings, such as immunohistochemistry, immunofluorescence, flow cytometry, and animal experiments.

The study extensively discusses the heterogeneity of cell populations in LUAD, comparing the samples with and without chemotherapy. However, there are several shortcomings that diminish the quality of this paper:

• The number of cells included in the dataset is limited, and the number of patients from different groups is low, which may reduce the attractiveness of the dataset for other researchers to reuse. Additionally, there is no metadata on patients' clinical characteristics, such as age, sex, history of smoking, etc., which would be valuable for future studies.

Thank you for your insightful feedback. We recognize that the limited number of cells and the small number of patients from different groups in our dataset may affect its appeal for reuse by other researchers. Additionally, we acknowledge the absence of metadata on patients' clinical characteristics, such as age, sex, and smoking history, which would indeed be valuable for future studies. We have compiled statistics on the patient's metadata and other information in the Supplementary Table 2.

We appreciate your suggestions and will consider incorporating these aspects in future research to enhance the dataset's utility and attractiveness.

• Several crucial details about the data analysis are missing: How many PCs were used for reduction? Which versions of Seurat/inferCNV/other packages were used? Why monocle2 was used and not monocle3 or other packages? Also, the authors use R version 3.6.1, and the current version is 4.3.2.

Thank you for your detailed review and valuable suggestions. Below are our responses to the points you raised:

Principal Components (PCs) Used for Reduction: We used the first 20 principal components (PCs) for dimensionality reduction. This choice was based on preliminary tests showing that 20 PCs captured the major variation in our data effectively.

Versions of Packages: The versions of the packages used are as follows:

Seurat: Version 4.0.1

inferCNV: Version 1.18.1

monocle2: Version 2.14.0

Choice of monocle2 over monocle3 or Other Packages: We chose monocle2 because it performed better on our specific dataset, and its algorithms suited our research needs. Additionally, we are more familiar with the functionalities and outputs of monocle2, which allowed us to better interpret and apply the results.

R Version: We used R version 3.6.1 at the beginning of our study to ensure consistency and reproducibility throughout the analysis. Although the current version of R is 4.3.2, we maintained the same version throughout our research. We will consider upgrading to the latest version of R and re-testing for compatibility and performance in future studies.

We appreciate your attention to these details and will include this information in the revised manuscript.

• It seems that the authors may lack a fundamental understanding of scRNA-seq data processing and the functions of Seurat. For instance, they state, 'Next, we classified cell types through dimensional reduction and unsupervised clustering via the Seurat package.' However, dimensional reduction and unsupervised clustering are not methods for cell classification. Typically, cell types are classified using marker genes or other established methods.

Thank you for your insightful comments. We appreciate your guidance on the proper understanding and application of scRNA-seq data processing and the functions of Seurat.

You are correct in noting that dimensional reduction and unsupervised clustering are not methods for cell classification. We apologize for the confusion in our original statement. What we intended to convey was that we performed dimensional reduction and unsupervised clustering using the Seurat package as preliminary steps in our analysis. Following these steps, we classified cell types based on established marker genes.

"Therefore, to identify subclusters within each of these nine major cell types, we performed principal component analysis" (Line 127). Principal component analysis is a method for dimensionality reduction, not cell clustering.

The authors did not mention the normalization or scaling of the data, which are crucial steps in scRNA-seq data preprocessing.

Thank you for your insightful comments. We apologize for any confusion caused by our description in the manuscript. You are correct that principal component analysis (PCA) is primarily a method for dimensionality reduction rather than cell clustering. To clarify, we used PCA to reduce the dimensionality of our single-cell RNA-seq (scRNA-seq) data, which is a preliminary step before clustering the cells.

In the revised manuscript, we have provided a more detailed description of our data preprocessing pipeline, including the normalization and scaling steps that are indeed crucial for scRNA-seq data analysis. Specifically, we performed the following steps:

Normalization: We normalized the gene expression data to account for differences in sequencing depth and other technical variations.

Scaling: We scaled the normalized data to ensure that each gene contributes equally to the PCA, which mitigates the effect of highly variable genes dominating the analysis.

Following these preprocessing steps, we conducted PCA to reduce the dimensionality of the data, which facilitated the subsequent clustering of cells into subclusters.

We hope this addresses your concerns, and we appreciate your valuable feedback that helped us improve the clarity and accuracy of our manuscript.

• Numerous style and grammar mistakes are present in the main text. For instance, certain sections of the methods are written in the present tense, suggesting that parts of a protocol were copied without text editing. Furthermore, some sections of the introduction are written in the past tense when the present tense would be more suitable. Clusters are inconsistently referred to by numbers or cell types, leading to confusion. Additionally, the authors frequently use the term "evolution" when describing trajectory analysis, which may not be appropriate. Overall, significant revisions to the main text are required.

Thank you for your detailed review and valuable feedback on our manuscript. We highly appreciate your suggestions and have made the following revisions to address the issues you pointed out:

Tense Consistency: We have thoroughly reviewed and corrected the use of tenses throughout the manuscript. The Methods section now consistently uses the past tense, while the Introduction section uses the present tense where appropriate, ensuring coherence and consistency.

Cluster Naming Consistency: We have standardized the naming conventions for clusters, consistently using either numbers or cell types to avoid any confusion.

Appropriate Terminology: We have reviewed our use of the term "evolution" in the context of trajectory analysis. Where necessary, we have replaced it with more accurate terms such as "trajectory progression" or "developmental pathway" to better convey the intended meaning.

• Some figures are not mentioned in order or are not referenced in the text at all, such as Figure 5l (where it is also unclear how the authors selected the root cells). Additionally, many figures have text that is too small to be read without zooming in. Overall, the quality of the figures is inconsistent and sometimes very poor.

Thank you for your detailed review and valuable feedback on our manuscript. We have addressed the issues you raised as follows:

Unreferenced Figures in the Text:

We acknowledge the oversight regarding Figure 5l not being mentioned in the text. In the revised version, we will ensure that all figures are properly referenced and discussed within the relevant sections of the manuscript.

Text Size in Figures:

We understand the difficulty in reading small text within the figures. We will redesign all figures to ensure that text and annotations are legible at normal viewing sizes. This will involve increasing the resolution and text size in all figures to enhance readability.

Inconsistent Quality of Figures:

To address the inconsistency in figure quality, we will standardize the formatting of all figures and ensure they meet a high standard of clarity and presentation. This will improve the overall visual quality and professionalism of the manuscript.

The results section lacks clarity on several points:<br /> • The authors state that "myofibroblasts exclusively originated from the control group". However, pathways up-regulated in myofibroblasts (such as glycolysis) were enhanced after chemotherapy, as indicated by GSVA score. Similarly, why are some clusters of TAMs from the control group associated with pathways enriched in chemotherapy group?

Thank you for your insightful comments and questions regarding our manuscript. We appreciate the opportunity to clarify these points.

Regarding the statement that "myofibroblasts exclusively originated from the control group," we acknowledge the confusion and would like to provide a more detailed explanation. While the initial identification indicated that myofibroblasts were predominantly found in the control group, subsequent analyses, including the Gene Set Variation Analysis (GSVA), revealed that certain pathways up-regulated in myofibroblasts, such as glycolysis, were indeed enhanced following chemotherapy. This suggests that chemotherapy may induce or enhance specific functional states in these cells that are not initially apparent from their origin alone.

Similarly, the observation that some clusters of Tumor-Associated Macrophages (TAMs) from the control group are associated with pathways enriched in the chemotherapy group can be explained by the dynamic nature of cellular responses to treatment. TAMs, like other immune cells, can exhibit plasticity and adapt to the tumor microenvironment altered by chemotherapy. This plasticity may result in the activation of pathways typically associated with a chemotherapy response, even in cells originating from the control group.

We will revise the manuscript to better articulate these findings and include additional data to support our explanations. This will help clarify the observed discrepancies and provide a more comprehensive understanding of the cellular dynamics in response to chemotherapy.

• Further explanation is necessary regarding the distinctions between malignant and non-malignant cells, as well as regarding the upregulation of metabolism-related pathways in fibroblasts from the NCT group. Additionally, clarification is needed regarding why certain TAMs from the control group are associated with pathways enriched in the chemotherapy group.

Thank you for your detailed review and for highlighting the areas that require further clarification. We appreciate the opportunity to provide additional explanations and improve our manuscript.

We recognize the need to more clearly differentiate between malignant and non-malignant cells in our manuscript. We will include additional details on the criteria and markers used to distinguish these cell types. Specifically, we will elaborate on the molecular and phenotypic characteristics that were used to identify malignant cells, such as specific genetic mutations, aberrant signaling pathways, and distinct cell surface markers, as opposed to those used for identifying non-malignant cells.

As mentioned above, the association of certain TAMs from the control group with pathways enriched in the chemotherapy group can be attributed to the inherent plasticity and adaptability of TAMs. We will provide a more detailed explanation of how TAMs can exhibit different functional states based on microenvironmental cues. This will include a discussion on the potential pre-existing heterogeneity within TAM populations and how even in the absence of direct chemotherapy exposure, some TAMs may display pathway activities similar to those seen in the chemotherapy group due to microenvironmental influences or intrinsic properties.

• In the section titled 'Chemo-driven Pro-mac and Anti-mac Metabolic Reprogramming Exerted Diametrically Opposite Effects on Tumor Cells': The markers selected to characterize the anti- and pro-macrophages are commonly employed for describing M1 or M2 polarization. It is uncertain whether this new classification into anti- and pro-macrophages is necessary. Additionally, it should be noted that pro-macrophages are anti-inflammatory, while anti-macrophages are pro-inflammatory, which could lead to confusion. M2 macrophages are already recognized for their role in stimulating tumor relapse after chemotherapy.

Thank you for your feedback. We appreciate the opportunity to clarify the rationale behind our terminology and the focus on functional phenotypic changes in macrophages before and after chemotherapy.

Our intention in introducing the terms "pro-macrophages" and "anti-macrophages" was to highlight the distinct functional phenotypic changes in macrophages observed before and after chemotherapy. These terms were chosen to emphasize the functional roles these macrophages play in the tumor microenvironment in response to chemotherapy, rather than strictly adhering to the conventional M1/M2 polarization paradigm.

We acknowledge that M2 macrophages are well-documented in stimulating tumor relapse after chemotherapy. Our use of "pro-macrophages" is intended to build on this established knowledge by providing a more nuanced understanding of their role in the post-chemotherapy tumor microenvironment. Similarly, "anti-macrophages" highlight the macrophages' role in mounting an anti-tumor response.

• The authors suggest that there is "reprogramming of CD8+ cytotoxic cells" following chemotherapy (Line 409). It remains unclear whether they imply the reprogramming of other CD8+ T cells into cytotoxic cells. While it is indicated that cytotoxic cells from the control group differ from those in the NCT group and that NCT cytotoxic T cells exhibit higher cytotoxicity, the authors did not assess the expression of NK and NK-like T cell markers (aside from NKG7), which may possess greater cytotoxic potential than CD8+ cytotoxic cells. This could also elucidate why cytotoxic cells from the NCT and control groups are positioned on separate branches in trajectory analysis. Overall, with 22.5k T cells in the dataset, only 3 subtypes were identified, suggesting a need for improved cell annotations by the authors.

Thank you for your valuable feedback regarding the classification and characterization of CD8+ cytotoxic cells following chemotherapy, and the need for improved cell annotations.

We appreciate your point on the potential ambiguity around the "reprogramming of CD8+ cytotoxic cells" post-chemotherapy. In our study, we observed that CD8+ T cells from the control and NCT groups differ significantly in their cytotoxic profiles, with the NCT group's cytotoxic T cells displaying enhanced cytotoxicity. However, we did not imply the reprogramming of other CD8+ T cells into cytotoxic cells. Instead, our findings suggest a shift in the functional state of existing CD8+ cytotoxic cells, driven by chemotherapy, which aligns with the upregulation of genes associated with cytotoxic functions.

We acknowledge that the expression of NK and NK-like T cell markers (apart from NKG7) was not comprehensively assessed. We agree that these markers may possess greater cytotoxic potential and could elucidate the separation observed in the trajectory analysis between cytotoxic cells from the NCT and control groups. This distinction may be attributed to differential cytotoxic potentials and functional states induced by chemotherapy.

Furthermore, with 22,530 T cells in the dataset, only three subtypes were initially identified. We recognize the need for more refined cell annotations to capture the full spectrum of T cell diversity. This could involve a deeper analysis of additional markers to distinguish between various cytotoxic populations, including NK and NK-like T cells, and their respective roles in the tumor microenvironment post-chemotherapy.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

I would recommend simplifying the manuscript and focusing on the differences between the treatment-naive and post-chemotherapy samples.

Thank you for your valuable feedback on our manuscript. We greatly appreciate your suggestions and have carefully considered the proposed modifications.

Upon re-evaluating our manuscript, we believe that the current structure and content most effectively convey our research findings. Our study aims to not only compare the treatment-naive and post-chemotherapy samples but also to highlight several important secondary findings that are integral to the overall research.

Nevertheless, we understand your recommendation to simplify the manuscript. To address this, we have made some subtle adjustments to improve the readability and conciseness of the text. Additionally, we have included a section in the discussion that more explicitly highlights the differences between the treatment-naive and post-chemotherapy samples.

IRB number for the human sample collection as well as animal experiments need to be provided.

Thank you for your thorough review and for highlighting the need for the inclusion of the IRB number for the human sample collection and animal experiments.

We apologize for this oversight and appreciate your attention to this important detail. The Institutional Review Board (IRB) approval number for the human sample collection is [B2019-436].

This number has been added to the Methods section of our revised manuscript to ensure compliance with ethical standards and to provide transparency for our research.

I put a question on the macrophage sorting experiment in the public review. Please clarify how the ARG1 staining was achieved with the preservation of cell viability.

We apologize for the error caused by miscommunication within our research team. We are currently using both ARG1 and CD206 antibodies in our studies. Due to a communication error, the technician mistakenly assumed ARG1 was another name for CD206 (MRC1), resulting in the incorrect labeling of CD206 as ARG1 in our 0experimental records. In reality, we used the CD206 antibody, which is consistent with the same surface marker shown in figure 6e. We have made corrections in the manuscript and experimental figures. Thank you for pointing this out, and we regret any misunderstanding this may have caused.

Reviewer #2 (Recommendations For The Authors):

Minor comments:

• Line 65- "Chemotherapy drugs, however, are very toxic and are prone to invalid". Line 75-77: "This heterogeneity in the TME includes the differences between tumor cells and tumor cells and the differences between various stromal cells and immune cells. Actively exploring the changes of multiple cells in the TME of LUAD after chemotherapy may finally find an excellent way to overcome chemotherapy resistance for LUAD." Please rewrite these parts.

Thank you for your valuable comment. We have revised the manuscript according to your suggestion:

Original (Line 65): "Chemotherapy drugs, however, are very toxic and are prone to invalid." Revised: "However, chemotherapy drugs are highly toxic and can often become ineffective."

Original (Line 75-77): "This heterogeneity in the TME includes the differences between tumor cells and tumor cells and the differences between various stromal cells and immune cells. Actively exploring the changes of multiple cells in the TME of LUAD after chemotherapy may finally find an excellent way to overcome chemotherapy resistance for LUAD."

Revised: "The heterogeneity within the tumor microenvironment (TME) encompasses not only the variations between different tumor cells but also among various stromal and immune cell types. Investigating the dynamic changes in multiple cell populations within the TME of LUAD following chemotherapy may provide crucial insights into overcoming chemotherapy resistance in LUAD."

• Line 87: "The internal processes of the cells respectively drive immune cells and cancer cells to obtain glucose and glutamine preferentially."-> The internal metabolic changes in the cells drive...

Thank you for your valuable comment. We have revised the manuscript according to your suggestion:

Original (Line 87): "The internal processes of the cells respectively drive immune cells and cancer cells to obtain glucose and glutamine preferentially."

Revised: "The internal metabolic changes in the cells drive immune cells and cancer cells to preferentially obtain glucose and glutamine."

• Line 93: "an essential feature that affects the effect of chemotherapy"-> an essential feature that affects chemotherapy.

Thank you for your valuable comment. We have revised the manuscript according to your suggestion:

Original (Line 93): "Metabolic reprogramming in various cell types in the tumor microenvironment after undergoing chemotherapy may be an essential feature that affects the effect of chemotherapy."

Revised: "Metabolic reprogramming in various cell types in the tumor microenvironment after undergoing chemotherapy may be an essential feature that affects chemotherapy."

• Line 84: What do the immune cells depend on glucose for?

Thank you for your valuable comment. We have revised the manuscript according to your suggestion:

Original (Line 84): "However, recent studies have shown that tumor-infiltrating immune cells depend on glucose and immune cells especially macrophages consume more glucose than malignant cells."

Revised: "However, recent studies have shown that tumor-infiltrating immune cells rely on glucose for their energy needs and functionality, with immune cells, particularly macrophages, consuming more glucose than malignant cells."

• Line 223: "According to previous research, myofibroblast has been described"-> myofibroblasts have been described.

Thank you for your valuable comment. We have revised the manuscript according to your suggestion:

Original (Line 223): "According to previous research, myofibroblast has been described as a cancer-associated fibroblast that participated in extensive tissue remodeling, angiogenesis, and tumor progression."

Revised: "According to previous research, myofibroblasts have been described as cancer-associated fibroblasts that participate in extensive tissue remodeling, angiogenesis, and tumor progression."

• Line 239: "Considering the essential fibroblasts"-> Considering the essential role of fibroblasts.

Thank you for your valuable comment. We have revised the manuscript according to your suggestion:

Original (Line 239): "Considering the essential fibroblasts and their complicated function in shaping the tumor microenvironment..."

Revised: "Considering the essential role of fibroblasts and their complicated function in shaping the tumor microenvironment..."

• Line 251: "Further in vitro studies were required to elucidate these notable fibroblasts' potential function..." -> are required.

Thank you for your valuable comments. We have revised the manuscript according to your suggestions:

Original (Line 251): "Further in vitro studies were required to elucidate these notable fibroblasts' potential function..."

Revised: "Further in vitro studies are required to elucidate these notable fibroblasts' potential function..."

• Line 309: "Interestingly, we found that two subtypes, Anti-mac and Mix, can be converted to Pro-mac through pseudotime time analysis." -> via trajectory analysis we found that two subtypes...

Thank you for your valuable comments. We have revised the manuscript according to your suggestions:

Original (Line 309): "Interestingly, we found that two subtypes, Anti-mac and Mix, can be converted to Pro-mac through pseudotime time analysis."

Revised: "Interestingly, via trajectory analysis we found that two subtypes, Anti-mac and Mix, can be converted to Pro-mac."

• Line 458: "the interactions between malignant and macrophages"-> the interactions between malignant cells and macrophages.

Thank you for your valuable comments. We have revised the manuscript according to your suggestions:

Original (Line 458): "the interactions between malignant and macrophages"

Revised: "the interactions between malignant cells and macrophages."

• Line 486: "The 5-year survival rate is still gloomy" -> The 5-year survival rate is still low.

Thank you for your valuable comments. We have revised the manuscript according to your suggestions:

Original (Line 486): "The 5-year survival rate is still gloomy."

Revised: "The 5-year survival rate is still low."

• Line 491: "More and more efforts are devoted to targeted metabolism to overcome chemoresistance" -> More efforts are devoted to target cell metabolism...

Thank you for your valuable comments. We have revised the manuscript according to your suggestions:

Original (Line 491): "More and more efforts are devoted to targeted metabolism to overcome chemoresistance."

Revised: "More efforts are devoted to targeting cell metabolism to overcome chemoresistance."

• Line 594: "Repeat the above steps twice" -> This procedure was repeated twice.

Thank you for your valuable comments. We have revised the manuscript according to your suggestions:

Original (Line 594): "Repeat the above steps twice."

Revised: "This procedure was repeated twice."

• Line 620: How were the new potential markers verified? List the exact genes and experiments or a reference to a Figure.

Thank you for your valuable comments. We have provided detailed information on how the new potential markers were verified, including the exact genes involved and the specific experiments conducted. A reference to the relevant Figure has also been added to the manuscript.

• Line 637: Which immune cells were used as a background in CNV analysis? All immune cells or just T cells?

Thank you for your valuable comments. In this study, all immune cells were used as background control cells.

• Line 658: in a single cell

Thank you for your valuable comments. We have revised the manuscript according to your suggestions.

• Line 672: "a variety of environmental factors potentially affect" -> potentially affects/ may potentially affect.

Thank you for your valuable comments. We have revised the manuscript according to your suggestions:

Original (Line 672): "a variety of environmental factors potentially affect"

Revised: "A variety of environmental factors may potentially affect"

• Line 683: Which metabolites were tested?

The metabolites tested included those related to glycolysis and oxidative phosphorylation (OXPHOS), such as glucose and various metabolites indicative of mitochondrial activity. The contents of these metabolites were analyzed to verify consistency with gene expression levels as mentioned in the analysis of metabolic pathways section.

• Line 718: Required or acquired?

The correct term should be "acquired" in the context of discussing drug resistance in tumor cells. The sentence likely refers to the "acquired drug resistance" of tumor cells, which is a common challenge in chemotherapy.

• Line 726: What are the A549 cells?

A549 cells are a human lung adenocarcinoma cell line commonly used in cancer research, particularly for studying lung cancer. In this study, A549 cells were used in animal experiments, mixed with tumor-associated macrophages (TAMs), and implanted into nude mice to study tumor formation and progression.

• Line 631: "we set the following cut-off thresholds to reveal the marker genes of each cluster: adjusted P-value <0.01 and multiple changes >0.5." What metric is "multiple changes"? Commonly used measures are adjuster P-value and average Log2FC.

Thank you for your valuable comment. We have revised the manuscript according to your suggestion. The term "multiple changes" was indeed a misstatement. The correct metric should be "log2 fold change (Log2FC)," which is a commonly used measure in gene expression studies. We have updated the manuscript to reflect this, using "adjusted P-value <0.01 and average Log2FC > 0.5" instead of "multiple changes > 0.5."

• Figure 1f: "Samplied" -> Samples. What do the numbers on the left side of each column mean?

Thank you for your valuable comment. The term "Samplied" was indeed a typographical error and has been corrected to "Samples". The numbers on the left side of each column likely represent cluster IDs or sample identifiers corresponding to the different patient samples or clusters analyzed in the study. We have clearly labeled these numbers in the figure to avoid any confusion.

• Figure 2b: Please add a scale.

Thank you for your valuable comment. We agree that adding a scale bar is crucial for accurately interpreting the size of the cells or structures shown in the figure. We have now included an appropriate scale bar during the figure preparation stage to provide this reference.

• Figure 3d/4c: What is the matrix_27/3 metric? Is it average expression?

Thank you for your valuable comment. The term "matrix_27/3" refers to a specific metric used in our analysis. This metric indeed represents the average expression levels of genes within a particular subset of the dataset. We will clarify this in the figure legend and the methods section to ensure that readers have a clear understanding of what the metric represents. Additionally, we will make sure that all such metrics are consistently and accurately described throughout the manuscript.

• Figure 6e: Why CD206 staining is shown instead of ARG if ARG was chosen as the main gene for classification of Pro-macrophages?

We apologize for the confusion regarding the use of CD206 staining in Figure 6e. This issue arose due to a miscommunication within our research team. While ARG1 was initially intended as the primary marker for Pro-macrophages, the technician mistakenly assumed ARG1 was another name for CD206 (MRC1), leading to the incorrect labeling of CD206 as ARG1 in our experimental records. In actuality, CD206 was used for the staining, which is consistent with the surface marker shown in Figure 6e. We have corrected this error in the manuscript and updated the experimental figures accordingly. We sincerely apologize for any misunderstanding this may have caused and appreciate the reviewer for bringing this to our attention.

• Figures 6h and k: Please explain why do NCT Anti-macrophages show higher glucose and lactate uptake than the Anti-macrophages from the control group, while the size of tumors is the lowest in NCT Anti-macrophages in vivo?

Thank you for your insightful comment. The observation that NCT Anti-macrophages exhibit higher glucose and lactate uptake while the tumor size is lowest could be attributed to the metabolic reprogramming induced by chemotherapy. It is possible that the enhanced metabolic activity in Anti-macrophages, characterized by increased glucose and lactate uptake, is linked to a more aggressive anti-tumor response in the NCT group. This heightened metabolic activity could reflect an increased energy demand necessary for sustaining enhanced immune functions, ultimately contributing to the reduction in tumor size. We will expand upon this explanation in the revised manuscript to provide a clearer interpretation of these findings.

• The supplementary Table 1 needs a better legend/more explanation.

Thank you for your valuable feedback. We have revised the legend for Supplementary Table 1 to provide a more detailed explanation of its contents.

• No tSNE plot showing epithelial cells colored by patient, which may be important for observation of cell heterogeneity, especially in the epithelial cell population.

Thank you for pointing this out. We agree that a tSNE plot showing epithelial cells colored by patient would be valuable for observing cell heterogeneity within the epithelial population.

• Several acronyms not explained in the text (for example GSVA, NMF).

Thank you for bringing this to our attention. We have ensured that all acronyms, including GSVA (Gene Set Variation Analysis) and NMF (Non-negative Matrix Factorization), are clearly defined in the text at their first mention.

• Availability of data and material section: Please describe "other experimental data" in more detail.

Thank you for your suggestion. We have expanded the "Availability of Data and Material" section to provide a more detailed description of the "other experimental data" referenced. This will include specific types of data generated, their formats, and 10how they can be accessed by other researchers. This clarification will enhance transparency and facilitate the reuse of our data by the research community.

Author response:

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

Public Reviews:

Reviewer #1:

(1) Given that this is one of the first studies to report the mapping of longitudinal intactness of proviral genomes in the globally dominant subtype C, the manuscript would benefit from placing these findings in the context of what has been reported in other populations, for example, how decay rates of intact and defective genomes compare with that of other subtypes where known.  

Most published studies are from men living with HIV-1 subtype B and the studies are not from the hyperacute infection phase and therefore a direct head-to-head comparison with the FRESH study is difficult.  However, we can cite/highlight and contrast our study with a few a few examples from acute infection studies as follows.

a. Peluso et. al., JCI, 2020, showed that in Caucasian men (SCOPE study), with subtype B infection, initiating ART during chronic infection virus intact genomes decayed at a rate of 15.7% per year, while defective genomes decayed at a rate of 4% per year.  In our study we showed that in chronic treated participants genomes decreased at a rate of 25% (intact) and 3% (defective) per month for the first 6 months of treatment.

b. White et. al., PNAS, 2021, demonstrated that in a cohort of African, white and mixed-race American men treated during acute infection, the rate of decay of intact viral genomes in the first phase of decay was <0.3 logs copies in the first 2-3 weeks following ART initiation. In the FRESH cohort our data from acute treated participants shows a comparable decay rate of 0.31 log copies per month for virus intact genomes.

c. A study in Thailand (Leyre et. al., 2020, Science Translational Medicine), of predominantly HIV-1 CRF01-AE subtype compared HIV-reservoir levels in participants starting ART at the earliest stages of acute HIV infection (in the RV254/SEARCH 010 cohort) and participants initiating ART during chronic infection (in SEARCH 011 and RV304/SEARCH 013 cohorts). In keeping with our study, they showed that the frequency of infected cells with integrated HIV DNA remained stable in participants who initiated ART during chronic infection, while there was a sharp decay in these infected cells in all acutely treated individuals during the first 12 weeks of therapy.  Rates of decay were not provided and therefore a direct comparison with our data from the FRESH cohort is not possible.

d. A study by Bruner et. al., Nat. Med. 2016, described the composition of proviral populations in acute treated (within 100 days) and chronic treated (>180 days), predominantly male subtype B cohort. In comparison to the FRESH chronic treated group, they showed that in chronic treated infection 98% (87% in FRESH) of viral genomes were defective, 80% (60% in FRESH) had large internal deletions and 14% (31% in FRESH) were hypermutated.  In acute treated 93% (48% in FRESH) were defective and 35% (7% in FRESH) were hypermutated.  The differences frequency of hypermutations could be explained by the differences in timing of infection specifically in the acute treated groups where FRESH participants initiate ART at a median of 1 day after infection.  It is also possible that sex- or race-based differences in immunological factors that impact the reservoir may play a role.  

This study also showed that large deletions are non-random and occur at hotspots in the HIV-1 genome. The design of the subtype B IPDA assay (Bruner et. al., Nature, 2019) is based on optimal discrimination between intact and deleted sequences - obtained with a 5′ amplicon in the Ψ region and a 3′ amplicon in Envelope. This suggest that Envelope is a hotspot for large while deletions in Ψ is the site of frequent small deletions and is included in larger 5′ deletions. In the FRESH cohort of HIV-1 subtype C, genome deletions were most frequently observed between Integrase and Envelope relative to Gag (p<0.0001–0.001).

e. In 2017, Heiner et. al., in Cell Rep, also described genetic characteristics of the latent HIV-1 reservoir in 3 acute treated and 3 chronic treated male study participants with subtype B HIV.  Their data was similar to Bruner et. al. above showing proportions of intact proviruses in participants who initiated therapy during acute/early infection at 6% (94% defective) and chronic infection at 3% (97% defective). In contrast the frequencies in FRESH in acute treated were 52% intact and 48% defective and in chronic infection were 13% intact and 87% defective.  These differences could be attributed to the timing of treatment initiation where in the aforementioned study early treatment ranged from 0.6-3.4 months after infection.

(2) Indeed, in the abstract, the authors indicate that treatment was initiated before the peak. The use of the term 'peak' viremia in the hyperacute-treated group could perhaps be replaced with 'highest recorded viral load'. The statistical comparison of this measure in the two groups is perhaps more relevant with regards to viral burden over time or area under the curve viral load as these are previously reported as correlates of reservoir size.

We have edited the manuscript text to describe the term peak viraemia in hyperacute treated participants more clearly (lines 443-444). We have now performed an analysis of area under the curve to compare viral burden in the two study groups and found associations with proviral DNA levels after one year. This has been added to the results section (lines 162-163).

Reviewer #2:

(1) Other factors also deserve consideration and include age, and environment (e.g. other comorbidities and coinfections.)

We agree that these factors could play a role however participants in this study were of similar age (18-23), and information on co-morbidities and coinfections are not known.

Reviewer #3:

(1) The word reservoir should not be used to describe proviral DNA soon after ART initiation. It is generally agreed upon that there is still HIV DNA from actively infected cells (phase 1 & 2 decay of RNA) during the first 6-12 months of ART. Only after a full year of uninterrupted ART is it really safe to label intact proviral HIV DNA as an approximation of the reservoir. This should be amended throughout.

We agree and where appropriate have amended the use of the word reservoir to only refer to the proviral load after full viral suppression, i.e., undetectable viral load.

(2) All raw, individualized data should be made available for modelers and statisticians. It would be very nice to see the RNA and DNA data presented in a supplementary figure by an individual to get a better grasp of intra-host kinetics.

We will make all relevant data available and accessible to interested parties on request. We have now added a section on data availability (lines 489-491).

(3) The legend of Supplementary Figure 2 should list when samples were taken.

The data in this figure represents an overall analysis of all sequences available for each participant at all time points.  This has now been explained more clearly in the figure legend.

Recommendations for The Authors:

Reviewer #1:

(1) It is recommended that the introduction includes information to set the scene regarding what is currently reported on the composition of the reservoir for those not in the immediate field of study i.e., the reported percentage of defective genomes and in which settings/populations genome intactness has been mapped, as this remains an area of limited information.

We have now included summary of other reported findings in the field in the introduction (lines 89-92, 9498) and discussion (lines 345-350).  A more detailed overview has been provided in the response to public reviews.

(2) It may be beneficial to state in the main text of the paper what the purpose of the Raltegravir was and that it was only administered post-suppression. Looking at Table 1, only the hyperacute treatment group received Raltegravir and this could be seen as a confounder as it is an integrase inhibitor. Therefore, this should be explained.

Once Raltegravir became available in South Africa, all new acute infections in the study cohort had an intensified 4-drug regimen that included Raltegravir.  A more detailed explanation has now been included in the methods section (lines 435-437).

(3) Can the authors explain why the viral measures at 6 months post-ART are not shown for chronictreated individuals in Figure 1 or reported on in the text?

The 6 months post-ART time point has been added to Figure 1.

(4) Can the authors indicate in the discussion, how the breakdown of proviral composition compares to subtype B as reported in the literature, for example, are the common sites of deletion similar, or is the frequency of hypermutation similar?

Added to discussion (lines 345-350).

(5) Do the numbers above the bars in Figure 3 represent the number of sampled genomes? If so, this should be stated.

Yes, the numbers above the bars represent the number of sampled genomes. This has been added to the Figure 3 legend.

(6) In the section starting on line 141, the introduction implies a comparison with immunological features, yet what is being compared are markers of clinical disease progression rather than immune responses. This should be clarified/corrected.

This has been corrected (line 153).

(7) Line 170 uses the term 'immediately' following infection, however, was this not 1 -3 days after?

We have changed the word “immediately” to “1-3 days post-detection” (line 181).

(8) Can the sampling time-points for the two groups be given for the longitudinal sequencing analysis?

The sequencing time points for each group is depicted in Figure 2.

(9) Line 183 indicates that intact genomes contributed 65% of the total sequence pool, yet it's given as 35% in the paragraph above. Should this be defective genomes?

Yes, this was a typographical error.  Now corrected to read “defective genomes” (line 193).

(10) The section on decay kinetics of intact and defective genomes seems to overlap with the section above and would flow better if merged.

Well noted, however we choose to keep these sections separate.

(11) Some references in the text are given in writing instead of numbering.

This has been corrected.

(12) In the clonal expansion results section, can it be indicated between which two time-points expansion was measured?

This analysis was performed with all sequences available for each participant at all time points.  We have added this explanation to the respective Figure legend.

Reviewer #2:

(1) The statement on line 384 "Our data showed that early ART...preserves innate immune factors" - what innate immune factors are being referred to?

We have removed this statement.

(2) HLA genotyping methods are not included in the Methods section

Now included and referenced (lines 481-483).

(3) Are CD4:CD8 ratios available for the cohorts? This could be another informative clinical parameter to analyse in relation to HIV-1 proviral load after 1 year of ART – as done for the other variables (peak VL, and the CD4 measures).

Yes, CD4:CD8 ratios are available. We performed the recommended analysis but found no associations with HIV-1 proviral load after 1 year of ART. We have added this to the results section (lines 163-164).

(4) Reference formatting: Paragraph starting at line 247 (Contribution of clonal expansion...) - the two references in this paragraph are not cited according to the numbering system as for the rest of the manuscript. The Lui et al, 2020 reference is missing from the reference list - so will change all the numbering throughout.

This has been corrected.

Reviewer #3:

(1) To allow comparison to past work. I suggest changing decay using % to half-life. I would also mention the multiple studies looking at total and intact HIV DNA decay rates in the intro.

We do not have enough data points to get a good estimate of the half-life and therefor report decay as percentage per month for the first 6 months. 

(2) Line 73: variability is the wrong word as inter-individual variability is remarkably low. I think the authors mean "difference" between intact and total.

We have changed the word variability to difference as suggested.

(3) Line 297: I am personally not convinced that there is data that definitively shows total HIV DNA impacting the pathophysiology of infection. All of this work is deeply confounded by the impact of past viremia. The authors should talk about this in more detail or eliminate this sentence.

We have reworded the statement to read “Total HIV-1 DNA is an important biomarker of clinical outcomes.” (Lines 308-309).

(4) Line 317; There is no target cell limitation for reservoir cells. The vast majority of CD4+ T cells during suppressive ART are uninfected. The mechanism listing the number of reservoir cells is necessarily not target cell limitation.

We agree. The statement this refers to has been reworded as follows: “Considering, that the majority of CD4 T cells remain uninfected it is likely that this does not represent a higher number of target cells, and this warrants further investigation.” (lines 325-326).

(5) Line 322: Some people in the field bristle at the concept of total HIV DNA being part of the reservoir as defective viruses do not contribute to viremia. Please consider rephrasing. 

We acknowledge that there are deferring opinions regarding total HIV DNA being part of the reservoir as defective viruses do not contribute to viremia, however defective HIV proviruses may contribute to persistent immune dysfunction and T cell exhaustion that are associated comorbidities and adverse clinical outcomes in people living with HIV.  We have explained in the text that total HIV-DNA does not distinguish between replication-competent and -defective viruses that contribute to the viral reservoir.

(6) Line 339: The under-sampling statement is an understatement. The degree of under-sampling is massive and biases estimates of clonality and sensitivity for intact HIV. Please see and consider citing work by Dan Reeves on this subject.

We agree and have cited work by Dan Reeves (line 358).

(7) Line 351: This is not a head-to-head comparison of biphasic decay as the Siliciano group's work (and others) does not start to consider HIV decay until one year after ART. I think it is important to not consider what happens during the first year of ART to be reservoir decay necessarily.

Well noted.

(8) Line 366-371: This section is underwritten. In nearly all PWH studies to date, observed reservoirs are highly clonal.

We agree that observed reservoirs are highly clonal but have not added anything further to this section.

(9) It would be nice to have some background in the intro & discussion about whether there is any a priori reason that clade C reservoirs, or reservoirs in South African women, might differ (or not) from clade B reservoirs observed in different study participants.

We have now added this to the introduction (lines 94-103).

(10) Line 248: This sentence is likely not accurate. It is probable that most of the reservoir is sustained by the proliferation of infected CD4+ T cells. 50% is a low estimate due to under-sampling leading to false singleton samples. Moreover, singletons can also be part of former clones that have contracted, which is a natural outcome for CD4+ T cells responding to antigens &/or exhibiting homeostasis. The data as reported is fine but more complex ecologic methods are needed to truly probe the clonal structure of the reservoir given severe under sampling.

Well noted.

Author response:

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

We thank the reviewers for their time and thoughtful comments on our manuscript. 

We realised a preliminary version of Figure 2 was initially submitted, which we are replacing now with a novel version. Differences between the two figures are : 1) The schematic in Figure 2a was replaced with a new one in line with that of Figure 3a; 2) in Figure 2c details about the statistical analysis were removed from the legend and one datapoint that was erroneously removed at day 5 for the ΔMYR1-Luc condition was included. Regardless, these changes do not affect the results and the conclusions initially drawn.

Public Reviews:

Reviewer #1 (Public review): 

Previous studies have highlighted some of these paracrine activities of Toxoplasma - and Rasogi et al (mBio, 2020) used a single cell sequencing approach of cells infected in vitro with the WT or MYR KO parasites - and one of their conclusions was that MYR-1 dependent paracrine activities counteract ROP-dependent processes.

Similarly, Chen et al (JEM 2020) highlighted that a particular rhoptry protein (ROP16) could be injected into uninfected macrophages and move them to an anti-inflammatory state that might benefit the parasite. 

We are aware of both these studies, where the injection of rhoptry proteins into cells that the parasite does not invade alters the host transcriptional profile establishing a permissive environment. However, here we propose a different paracrine effect that goes beyond the injected/uninfected cell. Specifically, we propose that one or more MYR1-dependent effectors alter the cytokine secretion profile of infected cells, which leads to overall changes in the immune response such as cell types recruited to the site of infection, or the activation state. 

There are caveats around immunity and as yet no insight into how this works. In Figure 2 there is a marked defect in the ability of the parasites to expand at day 2 and day 5. Together, these data sets suggest that this paracrine effect mediated by MYR-1 works early - well before the development of adaptive responses. 

Yes, we also hypothesise an early effect based on the data. Growth continues until day 5 at least, and then plateaus towards day 7, which makes us believe that the effect takes place within the first 5 days. We agree with the reviewer that the MYR1-mediated rescue acts before the involvement of the adaptive immune response, which is supported by our results obtained in Rag2-/- mice shown in Figure 3e. 

Reviewer #2 (Public review): 

Summary: 

In this manuscript by Torelli et al., the authors propose that the major function of MYR1 and MYR1-dependent secreted proteins is to contribute to parasite survival in a paracrine manner rather than to protect parasites from cell-autonomous immune response. The authors conclude that these paracrine effects rescue ∆MYR1 or knockouts of MYR1-dependent effectors within pooled in vivo CRISPR screens. 

Strengths: 

The authors raised a more general concern that pooled CRISPR screens (not only in Toxoplasma but also other microbes or cancers) would miss important genes by "paracrine masking effect". Although there is no doubt that pooled CRISPR screens (especially in vivo CRISPR screens) are powerful techniques, I think this topic could be of interest to those fields and researchers. 

Weaknesses: 

In this version, the reviewer is not entirely convinced of the 'paracrine masking effect' because the in vivo experiments should include appropriate controls (see major point 2). 

(1) It is convincing that co-infection of WT and ∆MYR1 parasites could rescue the growth of ∆MYR1 in mice shown by in vivo luciferase imaging. Also, this is consistent with ∆MYR1 parasites showing no in vivo fitness defect in the in vivo CRISPR screens conducted by several groups. Meanwhile, it has been reported previously and shown in this manuscript that ∆MYR1 parasites have an in vitro growth defect; however, ∆MYR1 parasites show no in vitro fitness defect the in vitro pooled CRISPR screen. The authors show that the competition defect of ∆MYR1 parasites cannot be rescued by co-infection with WT parasites in Figure 1c, which might indicate that no paracrine rescue occurred in an in vitro environment. The authors seem not to mention these discrepancies between in vitro CRISPR screens and in vitro competition assays. Why do ∆MYR1 parasites possess neutral in vitro fitness scores in in vitro CRISPR screens? Could the authors describe a reasonable hypothesis? 

The reviewer raises a very interesting point, which at this stage, we cannot fully explain. A technical explanation could be that the relatively small growth defect detected for clean KOs, is not well represented in the CRISPR screens due to the variability of guides, where smaller differences in growth are not reliably captured and hidden within the noise of the assays. Another technical explanation may be median-centering: if the majority of KOs in the pool have a small growth defect, median centering would push these towards a zero. We have observed and reported this phenomenon in Young et al., 2019 for libraries containing a larger fraction of genes with a negative fitness score. In the library used here focusing on secreted proteins, we have not observed a strong trend to negative fitness scores, but cannot exclude smaller shifts. Because we have no solid base to favour any of the above mentioned explanations, we have decided to not speculate too much on this in the manuscript. However, we wanted to show all the data as the difference between these results may not be technical, but biological, which could inform future studies or results by us and others.  

(2) The authors developed a mixed infection assay with an inoculum containing a 20:80 ratio of ΔMYR1-Luc parasites with either WT parasites or ΔMYR1 mutants not expressing luciferase, showing that the in vivo growth defect of ∆MYR1 parasites is rescued by the presence of WT parasites. Since this experiment lacks appropriate controls, interpretation could be difficult. Is this phenomenon specific to MYR1? If a co-inoculum of ∆GRA12-Luc with either WT parasites or GRA12 parasites not expressing luciferase is included, the data could be appropriately interpreted. 

We are not quite sure what appropriate controls the reviewer refers to. We show here in Figures 3c and 3f that increasing parasite load by co-infecting mice with ∆MYR1 parasites is not sufficient to rescue ∆MYR1-Luc parasite growth. Co-infection with WT parasites, however, does result in increased ∆MYR1-Luc parasitaemia at day 7 p.i., indicating that MYR1 competence is required for the in vivo trans-rescue we describe. As ∆GRA12 parasites have a very strong cell-autonomous restriction in vitro and severe growth defect in vivo (Torelli et al., BioRxiv), these parasites would be rapidly depleted, which is also observed in all CRISPR screens from various laboratories. Therefore we do not think that co-infection with GRA12-deficient parasites would be an informative experiment here. We do speculate that mutant parasites for other proteins required for export (i.e. MYR 2, 3, 4, ROP17) could also be trans-rescued in addition to mutants for other MYR-dependent proteins such as GRA24 and GRA28, which remodel cytokine secretion and could individually, or synergistically, affect host cell immunity. Dissecting which Toxoplasma factor/s and host cytokine signalling pathways drive this trans-rescue effect is highly interesting, but beyond the scope of this manuscript. Here, we focused on the basic concept that an individual mutant can be rescued in trans in vivo, which we think is of importance beyond the field of Toxoplasma research. 

(3) In the Discussion part, the authors argue that the rescue phenotype of mixed infection is not due to co-infection of host cells (lines 307-310). This data is important to support the authors' paracrine hypothesis and should be shown in the main figure.

We understand the reviewer’s concern for rescue by co-infection of the same cell, but we largely exclude this hypothesis as Toxoplasma cell-autonomous effectors, such as GRA12 and ROP18, would also be rescued if that were to happen on a larger scale. We previously performed an in vivo experiment to assess co-infection rates of peritoneal exudate cells (PECs) by imaging using infection doses comparable to those used in the trans-rescue experiments. The total infection rate of PECs was 2.3%, so the overall number of infected cells per image was low, and not suitable for publication purposes. We tried to capture more cells using FACS analysis, however, PECs are highly autofluorescent in the yellow/green channels, which prevented us from drawing adequate conclusions using our GFP and mCherry strains. Because we see no rescue of GRA12 or ROP18 in CRISPR screens, and the overall in vivo co-infection rates were very low as observed by imaging, we did not think that generating strains expressing different fluorochromes compatible with standard FACS analysis, and then performing more in vivo experiments was best use of resources at the time. 

(4) In the Discussion part, the authors assume that the rescue phenotype is the result of multiple MYR1-dependent effectors. I admit that this hypothesis could be possible since a recently published paper described the concerted action of numerous MYR1-dependent or independent effectors contributing to the hypermigration of infected cells (Ten Hoeve et al., mBio, 2024). I think this paragraph would be kind of overstated since the authors did not test any of the candidate effectors. Since the authors possess ∆IST parasites, they can test whether IST is involved in the "paracrine masking effect" or not to support their claim. 

MYR1 deletion impairs the export of multiple Toxoplasma effectors into the host cell, including GRA16, GRA24, GRA28, HCE1/TEEGR etc, many of which can influence cytokine levels. As such, we speculate that it is a combination of multiple effector proteins that are responsible for the trans-rescue. As stated above, which parasite effectors, host cell types and cytokines are involved in the phenotype we describe are part of ongoing and future studies. Here, we wanted to focus on the key message, that in in vivo CRISPR screens, paracrine rescue of individual mutants can occur. While we will test IST mutants, it is probably not the top candidate as it only prevents upregulation of ISGs after exposure to IFN-γ, but has probably no role in already stimulated cells. As we still observe strong rescue past day 3, when IFN-γ levels are already elevated (Nishiyama 2020 Parasitol Int), IST probably plays no dominant role. 

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors): 

(1) Figure 1 - it's not obvious what concentration of IFN-gamma is being used in these assays (sorry if this is stated somewhere else). 

All in vitro experiments were performed with 100 U/ml IFN-γ as stated in the Material & Methods section, however added this information in the figure legend of Figure 1.

(2) Figure 3 This reviewer wonders if earlier differences are buried in the data sets. In Figure 3b it looks like there are early differences but this is lost in the collated data analysis in 3c. An early difference is quite apparent in Figure 2. 

We agree with the reviewer that a difference is visible at day 3 and 5 in Figure 3b, however differences between experimental groups became statistically significant only at day 7 in Figure 3c (N = 4 biological replicates). We cannot compare results between Figure 3c and Figure 2c as the latter reports 100% WT or ΔMYR1 infections and not 20:80 mixes.

(3) The authors conclude from their in vitro studies that MYR-1 is not required for in vitro growth in IFN-g activated macrophages. Given that the WT parasites still rescue MYR KO parasites in RAG mice it does imply that this paracrine effect would impact early innate responses. Since RAG mice do have a strong ILC/NK cell response that leads to the local production of IFN-g it would seem like a reasonable candidate. Do the authors know if the MYR KO have improved growth in the absence of IFN-g in vivo? This could be done using KO mice or with IFN-g neutralization. 

MYR1 displayed a neutral score in CRISPR screens in IFN-γ KO mice (Tachibana et al Cell Reports 2023), suggesting that lack of IFN-γ does not specifically improve MYR1 mutant growth compared to other mutants in a pool. We believe that the rescue is rather driven by other cytokines that have been shown to be altered in a MYR1 dependent manner (i.e CCL2, IL-6, IL-12). But as laid out before, this is subject of future studies.  

This is a submission that might benefit from a graphical model of how the authors view this system working. 

We agree with the reviewer and we added a graphical model to the manuscript. 

Reviewer #2 (Recommendations for the authors): 

The authors previously published a study that combines CRISPR screens in Toxoplasma and host transcriptome by scRNA-seq (Butterworth et al., Cell Host Microbe 2023). I think the authors possess transcriptome of ∆MYR1-infected HFFs. Although I understand this screen is conducted in in-vitro culture and human fibroblasts, are there any differentially expressed genes or pathways that could explain the paracrine rescue phenomenon described in this manuscript?

We thank the reviewer for this insightful comment, which is however hard to address.  Thousands of host cell genes within multiple pathways are affected by MYR1 deletion (Naor et al. mBio 2018; Butterworth et al. Cell Host Microbe 2023). Therefore the PerturbSeq dataset is not helpful to pinpoint specific immune mechanisms of rescue, and is speculative without any experimentation to back it up. However, we added a sentence in line 350 of the discussion to highlight known MYR1-related effects on immune-related pathways. “Individual MYR-related effectors that may be responsible for the paracrine rescue have not been investigated here and we hypothesise that the phenotype is likely the concerted result of multiple effectors that affect cytokine secretion. For example, previous studies showed that both GRA18 and GRA28 can induce release of CCL22 from infected cells (He 2018 eLife; Rudzki 2021 mBio), while GRA16 and HCE1/TEEGR impair NF-kB signalling and the potential release of pro-inflammatory cytokines such as IL-6, IL-1β and TNF (Seo 2020 Int J Mol Sci; Braun 2019 Nat Microbiol). Regardless of the effector(s), our results highlight an important novel function of MYR1-dependent effectors by establishing a supportive environment in trans for Toxoplasma growth within the peritoneum.”

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Strengths and weaknesses:

Although the revised manuscript has significantly improved in the quality of pictures, there seems to be still a discrepancy in Figure 2A: quantification result suggested that NIC (1um) treatment increased the number of colonies from 300 to around 450 (1.5 folds), whereas representative picture shown that the difference was 3 to 12 living organoids (4 folds).

As reviewer points out, the selected picture was not representative image of “control” group in Figure2A. We replaced it by the new representative image in this revised version.

Recommendations for the authors:

Reviewer #2 (Recommendations for the authors):

A minor point to be corrected:

Please consider removing "In consistent with this notion", which is repetitive with "Similarly".

" NIC is supposed to activate Wnt signaling via Hippo-YAP/TAZ and Notch signaling. In consistent with this notion. Similarly, the expression of target proteins (Sox9, TCF4 and, C-myc)..."

We corrected it according to the reviewer’s suggestion.

Author response:

Public Reviews:

Reviewer #1 (Public review):

Summary:

In their manuscript, Gomez-Frittelli and colleagues characterize the expression of cadherin6 (and -8) in colonic IPANs of mice. Moreover, they found that these cdh6-expressing IPANs are capable of initiating colonic motor complexes in the distal colon, but not proximal and midcolon. They support their claim by morphological, electrophysiological, optogenetic, and pharmacological experiments.

Strengths:

The work is very impressive and involves several genetic models and state-of-the-art physiological setups including respective controls. It is a very well-written manuscript that truly contributes to our understanding of GI-motility and its anatomical and physiological basis. The authors were able to convincingly answer their research questions with a wide range of methods without overselling their results.

We greatly appreciate the reviewer’s time, careful reading and support of our study.

Weaknesses:

The authors put quite some emphasis on stating that cdh6 is a synaptic protein (in the title and throughout the text), which interacts in a homophilic fashion. They deduct that cdh6 might be involved in IPAN-IPAN synapses (line 247ff.). However, Cdh6 does not only interact in synapses and is expressed by non-neuronal cells as well (see e.g., expression in the proximal tubuli of the kidney). Moreover, cdh6 does not only build homodimers, but also heterodimers with Chd9 as well as Cdh7, -10, and -14 (see e.g., Shimoyama et al. 2000, DOI: 10.1042/0264-6021:3490159). It would therefore be interesting to assess the expression pattern of cdh6-proteins using immunostainings in combination with synaptic markers to substantiate the authors' claim or at least add the possibility of cell-cell-interactions other than synapses to the discussion. Additionally, an immunostaining of cdh6 would confirm if the expression of tdTomato in smooth muscle cells of the cdh6-creERT model is valid or a leaky expression (false positive).

We agree with the reviewer that Cdh6 could be mediating some other cell-cell interaction besides synapses between IPANs, and will include more on this in the discussion. Cdh6 primarily forms homodimers but, as the reviewer points out, has been known to also form heterodimers with some other cadherins. We performed RNAscope in the colonic myenteric plexus with Cdh7 and found no expression (data not shown). Cdh10 is suggested to have very low expression (Drokhlyansky et al., 2020), possibly in putative secretomotor vasodilator neurons, and Cdh14 has not been assayed in any RNAseq screens. We attempted to visualize Cdh6 protein via antibody staining (Duan et al., 2018) but our efforts did not result in sufficient signal or resolution to identify synapses in the ENS, which remain broadly challenging to assay. Similarly, immunostaining with Cdh6 antibody was unable to confirm Cdh6 protein in tdT-expressing muscle cells, or by RNAscope. We will address these caveats in the discussion section.

(1) E. Drokhlyansky, C. S. Smillie, N. V. Wittenberghe, M. Ericsson, G. K. Griffin, G. Eraslan, D. Dionne, M. S. Cuoco, M. N. Goder-Reiser, T. Sharova, O. Kuksenko, A. J. Aguirre, G. M. Boland, D. Graham, O. Rozenblatt-Rosen, R. J. Xavier, A. Regev, The Human and Mouse Enteric Nervous System at Single-Cell Resolution. Cell 182, 1606-1622.e23 (2020).

(2) X. Duan, A. Krishnaswamy, M. A. Laboulaye, J. Liu, Y.-R. Peng, M. Yamagata, K. Toma, J. R. Sanes, Cadherin Combinations Recruit Dendrites of Distinct Retinal Neurons to a Shared Interneuronal Scaffold. Neuron 99, 1145-1154.e6 (2018).

Reviewer #2 (Public review):

Summary:

Intrinsic primary afferent neurons are an interesting population of enteric neurons that transduce stimuli from the mucosa, initiate reflexive neurocircuitry involved in motor and secretory functions, and modulate gut immune responses. The morphology, neurochemical coding, and electrophysiological properties of these cells have been relatively well described in a long literature dating back to the late 1800's but questions remain regarding their roles in enteric neurocircuitry, potential subsets with unique functions, and contributions to disease. Here, the authors provide RNAscope, immunolabeling, electrophysiological, and organ function data characterizing IPANs in mice and suggest that Cdh6 is an additional marker of these cells.

Strengths:

This paper would likely be of interest to a focused enteric neuroscience audience and increase information regarding the properties of IPANs in mice. These data are useful and suggest that prior data from studies of IPANs in other species are likely translatable to mice.

We appreciate the reviewer’s support of our study and insightful critiques for its improvement.

Weaknesses:

The advance presented here beyond what is already known is minimal. Some of the core conclusions are overstated and there are multiple other major issues that limit enthusiasm. Key control experiments are lacking and data do not specifically address the properties of the proposed Cdh6+ population.

Major weaknesses:

(1) The novelty of this study is relatively low. The main point of novelty suggests an additional marker of IPANs (Cdh6) that would add to the known list of markers for these cells. How useful this would be is unclear. Other main findings basically confirm that IPANs in mice display the same classical characteristics that have been known for many years from studies in guinea pigs, rats, mice and humans.

We appreciate the already existing markers for IPANs in the ENS and the existing literature characterizing these neurons. The primary intent of this study was to use these well established characteristics of IPANs in both mice and other species to characterize Cdh6-expressing neurons in the mouse myenteric plexus and confirm their classification as IPANs.

(2) Some of the main conclusions of this study are overstated and claims of priority are made that are not true. For example, the authors state in lines 27-28 of the abstract that their findings provide the "first demonstration of selective activation of a single neurochemical and functional class of enteric neurons". This is certainly not true since Gould et al (AJP-GIL 2019) expressed ChR2 in nitrergic enteric neurons and showed that activating those cells disrupted CMC activity. In fact, prior work by the authors themselves (Hibberd et al., Gastro 2018) showed that activating calretinin neurons with ChR2 evoked motor responses. Work by other groups has used chemogenetics and optogenetics to show the effects of activating multiple other classes of neurons in the gut.

We believe our phrasing in this sentence was misleading. Whilst single neurochemical classes of enteric neurons have been manipulated to alter gut functions, all such instances to date do not represent manipulation of a single functional class of enteric neurons. In the given examples, NOS and calretinin are each expressed to varying degrees across putative motor neurons, interneurons and IPANs. In contrast, Chd6 is restricted to IPANs and therefore this study is the first optogenetic investigation of enteric neurons from a single putative functional class. We will alter this segment in the revised manuscript to emphasize this point and differentiate this study from those previous.

(3) Critical controls are needed to support the optogenetic experiments. Control experiments are needed to show that ChR2 expression a) does not change the baseline properties of the neurons, b) that stimulation with the chosen intensity of light elicits physiologically relevant responses in those neurons, and c) that stimulation via ChR2 elicits comparable responses in IPANs in the different gut regions focused on here.

We completely agree controls are essential. However, our paper is not the first to express ChR2 in enteric neurons. Authors of our paper have shown in Hibberd et al. 2018 that expression of ChR2 in a heterogeneous population of myenteric neurons did not change network properties of the myenteric plexus. This was demonstrated in the lack of change in control CMC characteristics in mice expressing ChR2 under basal conditions (without blue light exposure). Regarding question (b), that it should be shown that stimulation with the chosen intensity of light elicits physiologically relevant responses in those neurons. We show the restricted expression of ChR2 in IPANs and that motor responses (to blue light) are blocked by selective nerve conduction blockade.

Regarding question (c), that our study should demonstrate that stimulation via ChR2 elicits comparable responses in IPANs in the different gut regions. We would not expect each region of the gut to behave comparably. This is because the different gut regions (i.e. proximal, mid, distal) are very different anatomically, as is anatomy of the myenteric plexus and myenteric ganglia between each region, including the density of IPANs within each ganglia, in addition to the presence of different patterns of electrical and mechanical activity [Spencer et al., 2020]. Hence, it is difficult to expect that between regions stimulation of ChR2 should induce similar physiological responses. The motor output we record in our study (CMCs) is a unified motor program that involves the temporal coordination of hundreds of thousands of enteric neurons and a complex neural circuit that we have previously characterized [Spencer et al., 2018]. But, never has any study until now been able to selectively stimulate a single functional class of enteric neurons (with light) to avoid indiscriminate activation of other classes of neurons.

(1) T. J. Hibberd, J. Feng, J. Luo, P. Yang, V. K. Samineni, R. W. Gereau, N. Kelley, H. Hu, N. J. Spencer, Optogenetic Induction of Colonic Motility in Mice. Gastroenterology 155, 514-528.e6 (2018).

(2) N. J. Spencer, L. Travis, L. Wiklendt, T. J. Hibberd, M. Costa, P. Dinning, H. Hu, Diversity of neurogenic smooth muscle electrical rhythmicity in mouse proximal colon. American Journal of Physiology-Gastrointestinal and Liver Physiology 318, G244–G253 (2020).

(3) N. J. Spencer, T. J. Hibberd, L. Travis, L. Wiklendt, M. Costa, H. Hu, S. J. Brookes, D. A. Wattchow, P. G. Dinning, D. J. Keating, J. Sorensen, Identification of a Rhythmic Firing Pattern in the Enteric Nervous System That Generates Rhythmic Electrical Activity in Smooth Muscle. J. Neurosci. 38, 5507–5522 (2018).

(4) The electrophysiological characterization of mouse IPANs is useful but this is a basic characterization of any IPAN and really says nothing specifically about Cdh6+ neurons. The electrophysiological characterization was also only done in a small fraction of colonic IPANs, and it is not clear if these represent cell properties in the distal colon or proximal colon, and whether these properties might be extrapolated to IPANs in the different regions. Similarly, blocking IH with ZD7288 affects all IPANs and does not add specific information regarding the role of the proposed Cdh6+ subtype.

Our electrophysiological characterization was guided to be within a subset of Cdh6+ neurons by Hb9:GFP expression. As in the prior comment (1) above, we used these experiments to confirm classification of Cdh6+ (Hb9:GFP+) neurons in the distal colon as IPANs. We will clarify that these experiments were performed in the distal colon and agree that we cannot extrapolate that these properties are also representative of IPANs in the proximal colon. We apologize that this was confusing. Finally, we agree with the reviewer that ZD7288 affects all IPANs in the ENS and will clarify this in the text.

(5) Why SMP IPANs were not included in the analysis of Cdh6 expression is a little puzzling. IPANs are present in the SMP of the small intestine and colon, and it would be useful to know if this proposed marker is also present in these cells.

We agree with the reviewer. In addition to characterizing Cdh6 in the myenteric plexus, it would be interesting to query if sensory neurons located within the SMP also express Cdh6. Our preliminary data (n=2) show ~6-12% tdT/Hu neurons in Cdh6-tdT ileum and colon (data not shown). We will add a sentence to the discussion.

(6) The emphasis on IH being a rhythmicity indicator seems a bit premature. There is no evidence to suggest that IH and IT are rhythm-generating currents in the ENS.

Regarding the statement there is no evidence to suggest that IH and IT are rhythm-generating currents in the ENS. We agree with the reviewer that evidence of rhythm generation by IH and IT in the ENS has not been explicitly confirmed. We are confident the reviewer agrees that an absence of evidence is not evidence of absence, although the presence of IH has been well described in enteric neurons. We will modify the text in the results to indicate more clearly that IH and IT are known to participate in rhythm generation in thalamocortical circuits, though their roles in the ENS remain unknown. Our discussion of the potential role of IH or IT in rhythm generation or oscillatory firing of the ENS is constrained to speculation in the discussion section of the text.

(7) As the authors point out in the introduction and discuss later on, Type II Cadherins such as Cdh6 bind homophillically to the same cadherin at both pre- and post-synapse. The apparent enrichment of Cdh6 in IPANs would suggest extensive expression in synaptic terminals that would also suggest extensive IPAN-IPAN connections unless other subtypes of neurons express this protein. Such synaptic connections are not typical of IPANs and raise the question of whether or not IPANs actually express the functional protein and if so, what might be its role. Not having this information limits the usefulness of this as a proposed marker.

We agree with the reviewer that the proposed IPAN-IPAN connection is novel although it has been proposed before (Kunze et al., 1993). As detailed in our response to Reviewer #1, we attempted to confirm Cdh6 protein expression, but were unsuccessful, due to insufficient signal and resolution. We therefore discuss potential IPAN interconnectivity in the discussion, in the context of contrasting literature.

(1) W. A. A. Kunze, J. B. Furness, J. C. Bornstein, Simultaneous intracellular recordings from enteric neurons reveal that myenteric ah neurons transmit via slow excitatory postsynaptic potentials. Neuroscience 55, 685–694 (1993).

(8) Experiments shown in Figures 6J and K use a tethered pellet to drive motor responses. By definition, these are not CMCs as stated by the authors.

The reviewer makes a valid criticism as to the terminology, since tethered pellet experiments do not record propagation. We believe the periodic bouts of propulsive force on the pellet is triggered by the same activity underlying the CMC. In our experience, these activities have similar periodicity, force and identical pharmacological properties. Consistent with this, we also tested full colons (n = 2) set up for typical CMC recordings by multiple force transducers, finding that CMCs were abolished by ZD7288, similar to fixed pellet recordings (data not shown).

(9) The data from the optogenetic experiments are difficult to understand. How would stimulating IPANs in the distal colon generate retrograde CMCs and stimulating IPANs in the proximal colon do nothing? Additional characterization of the Cdh6+ population of cells is needed to understand the mechanisms underlying these effects.

We agree that the different optogenetic responses in the proximal and distal colon are challenging to interpret, but perhaps not surprising in the wider context. It is not only possible that the different optogenetic responses in this study reflect regional differences in the Chd6+ neuronal populations, but also differences in neural circuits within these gut regions. A study some time ago by the authors showed that electrical stimulation of the proximal mouse colon was unable to evoke a retrograde (aborally) propagating CMC (Spencer, Bywater, 2002), but stimulation of the distal colon was readily able to. We concluded that at the oral lesion site there is a preferential bias of descending inhibitory nerve projections, since the ascending excitatory pathways have been cut off. In contrast, stimulation of the distal colon was readily able to activate an ascending excitatory neural pathway, and hence induce the complex CMC circuits required to generate an orally propagating CMC. Indeed, other recent studies have added to a growing body of evidence for significant differences in the behaviors and neural circuits of the two regions (Li et al., 2019, Costa et al., 2021a, Costa et al., 2021b, Nestor-Kalinoski et al., 2022). We will expand this discussion.

(1) N. J. Spencer, R. A. Bywater, Enteric nerve stimulation evokes a premature colonic migrating motor complex in mouse. Neurogastroenterology & Motility 14, 657–665 (2002).

(2) Li Z, Hao MM, Van den Haute C, Baekelandt V, Boesmans W, Vanden Berghe P (2019) Regional complexity in enteric neuron wiring reflects diversity of motility patterns in the mouse large intestine. Elife 8.

(3). Costa M, Keightley LJ, Hibberd TJ, Wiklendt L, Dinning PG, Brookes SJ, Spencer NJ (2021a) Motor patterns in the proximal and distal mouse colon which underlie formation and propulsion of feces. Neurogastroenterol Motil e14098.

(4) Costa M, Keightley LJ, Hibberd TJ, Wiklendt L, Smolilo DJ, Dinning PG, Brookes SJ, Spencer NJ (2021b) Characterization of alternating neurogenic motor patterns in mouse colon. Neurogastroenterol Motil 33:e14047.

(5) Nestor-Kalinoski A, Smith-Edwards KM, Meerschaert K, Margiotta JF, Rajwa B, Davis BM, Howard MJ (2022) Unique Neural Circuit Connectivity of Mouse Proximal, Middle, and Distal Colon Defines Regional Colonic Motor Patterns. Cell Mol Gastroenterol Hepatol 13:309-337.e303.

Author response:

We are very pleased to see these positive reviews of our preprint.

Reviewers 1 and 3 raise issues around PIP-PP1 interactions.

(1) Role of the “RVxF-ΦΦ-R-W string”

Most PIPs interact with the globular PP1 catalytic core through short linear interaction motifs (SLiMs) and Choy et al (PNAS 2014) previously showed that many PIPs interact with PP1 through conserved trio of SLiMs, RVxF-ΦΦ-R, which is also present in the Phactrs.

Previous structural analysis showed the trajectory of the PPP1R15A/B, Neurabin/Spinphilin (PPP1R9A/B), and PNUTS (PPP1R10) PIPs across the PP1 surface encompasses not only the RVxF-ΦΦ-R trio, but also additional sequences C-terminal to it (Chen et al, eLife, 2015). This extended trajectory is maintained in the Phactr1-PP1 complex (Fedoryshchak et al, eLife (2020). Based on structural alignment we proposed the existence of an additional hydrophobic “W” SLiM that interacts with the PP1 residues I133 and Y134.

The extended “RVxF-ΦΦ-R-W” interaction brings sequences C-terminal to the “W” SLiM into the vicinity of the hydrophobic groove that adjoins the PP1 catalytic centre. In the Phactr1/PP1 complex, these sequences remodel the groove, generating a novel pocket that facilitates sequence-specific substrate recognition.

This raises the possibility that sequences C-terminal to the extended “RVxF-ΦΦ-R-W string” in the other complexes also confer sequence-specific substrate recognition, and our study aims to test this hypothesis. Indeed, the hydrophobic groove structures of the Neurabin/Spinophilin/PP1 and Phactr1/PP1 complexes differ significantly (Ragusa et al, 2010; see Fedoryshchak et al 2020, Fig2 FigSupp1).

(2) Orientation of the W side chain

Reviewer 1 points out that in the substrate-bound PP1/PPP1R15A/Actin/eIF2 pre-dephosphorylation complex the W sidechain is inverted with respect to its orientation in  PP1-PPP1R15B complex (Yan et al, NSMB 2021). The authors proposed that this may reflect the role of actin in assembly of the quaternary complex. This does not necessarily invalidate the notion that sequences C-terminal to the “W” motif might play a role in actin-independent substrate recognition, and we therefore consider our inclusion of the R15A/B fusions in our analysis to be reasonable.

(3) Conservation of W

The motif ‘W’ does not mandate tryptophan - Phactrs and PPP1R15A/B indeed have W at this position but Neurabin/spinophilin contain VDP, which makes similar interactions. Similarly the _“_RVxF” motifs in Phactr1, Neurabin/Spinophilin, PPP1R15A/B and PNUTS are LIRF, KIKF, KV(R/T)F and TVTW respectively.

In our revision, we will present comparisons of the differentially remodelled/modified PP1 hydrophobic groove in the various complexes, discuss the different orientations of the tryptophan in the previously published PPP1R15A/PP1 and PPP1R15B/PP1 structures. We will also address the other issues raised by the referees.

Author response:

We thank the reviewers for their constructive reviews, we are working on a response and revised manuscript which we will submit when complete.

Author response:

Public Reviews:

Reviewer #1 (Public review):

Summary:

Ma, Yang et al. report a new investigation aimed at elucidating one of the key nutrients S. Typhimurium (STM) utilizes with the nutrient-poor intracellular niche within the macrophage, focusing on the amino acid beta-alanine. From these data, the authors report that beta-alanine plays an important role in mediating STM infection and virulence. The authors employ a multidisciplinary approach that includes some mouse studies and ultimately propose a mechanism by which panD, involved in B-Ala synthesis, mediates the regulation of zinc homeostasis in Salmonella. The impact of this work is questionable. There are already many studies reporting Salmonella-effector interactions, and while this adds to that knowledge it is not a significant advance over previous studies. While the authors are investigating an interesting question, the work has two important weaknesses; if addressed, the conclusions of this work and broader relevance to bacterial pathogenesis would be enhanced.

Strengths:

This reviewer appreciates the multidisciplinary nature of the work. The overall presentation of the figure graphics are clear and organized.

Weaknesses:

First, this study is very light on mechanistic investigations, even though a mechanism is proposed. Zinc homeostasis in cells, and roles in bacteria infections, are complex processes with many players. The authors have not thoroughly investigated the mechanisms underlying the roles of B-Ala and panD in impacting STM infection such that other factors cannot be ruled out. Defining the cellular content of Zn2+ STM in vivo would be one such route. With further mechanistic studies, the possibility cannot be ruled out that the authors have simply deleted two important genes and seen an infection defect - this may not relate directly to Zn2+ acquisition.

Thank you for your patient and thoughtful reading as well as the constructive comments and advice about our manuscript. We will revise the manuscript based on your comments and suggestions.

You are right that this work have not thoroughly investigated the mechanisms underlying the roles of β-Ala, panD and zinc in impacting Salmonella infection. We will perform additional experiments to detect the content of zinc during Salmonella infection in vivo and in vitro, according to your suggestions.

We agree that other unknown mechanism(s) are also involved in the virulence regulation by β-Ala in Salmonella, as our results showed that the double mutant Δ_panD_Δ_znuA_ (cannot synthesis of β-Ala and uptake of zinc) is more attenuated than the single mutant Δ_znuA_ (Figure 5D), suggesting that the contribution of β-Ala to the virulence of Salmonella is partially dependent on zinc acquisition_._ We will reword the related description throughout the manuscript for clarity.

Second, the authors hint at their newly described mechanism/pathway being important for disease and possibly a target for therapeutics. This claim is not justified given that they have employed a single STM strain, which was isolated from chickens and is not even a clinical isolate. The authors could enhance the impact of their findings and relevance to human disease by demonstrating it occurs in human clinical isolates and possibly other serovars. Further, the use of mouse macrophage as a model, and mice, have limited translatability to human STM infections.

We thank your comments and advice regarding our manuscript and are delighted to accept them.

You are right that our current findings are relatively limited and not sufficient for disease therapeutics. We will reword the related description throughout the manuscript. Based on this comment, we will also use Salmonella Typhi and human macrophages to perform additional experiments to extend our findings. Salmonella Typhi is a human-limited Salmonella serovar and the cause of typhoid fever, a severe lethal systemic disease. Salmonella Typhimurium (STM) cause systemic disease in mice, which is similar to the symptoms of typhoid fever in human and has been widely used to explore the pathogenesis of Salmonella.

Reviewer #2 (Public review):

Summary:

Salmonella exploits host- and bacteria-derived β-alanine to efficiently replicate in host macrophages and cause systemic disease. β-alanine executes this by increasing the expression of zinc transporter genes and therefore the uptake of zinc by intracellular Salmonella

Strengths:

The experiments designed are thorough and the claims made are directly related to the outcome of the experiments. No overreaching claims were made.

Weaknesses:

A little deeper insight was expected, particularly towards the mechanistic aspects. For example, zinc transport was found to be the cause of the b-alanine-mediated effect on Salmonella intracellular replication. It would have been very interesting to see which are the governing factors that may get activated or inhibited due to Zn accumulation that supports such intracellular replication.

We appreciate your review and advice. We will design and perform additional experiments to further investigate the mechanisms by which β-Ala, panD and zinc influence Salmonella infection, according to your suggestions. For example, we will detect the content of zinc during Salmonella infection in vivo and in vitro.

Reviewer #3 (Public review):

Summary:

Salmonella is interesting due to its life within a compact compartment, which we call SCV or Salmonella containing vacuole in the field of Salmonella. SCV is a tight-fitting vacuole where the acquisition of nutrients is a key factor by Salmonella. The authors among many nutrients, focussed on beta-alanine. It is also known from many other studies that Salmonella requires beta-alanine. The authors have done in vitro RAW macrophage infection assays and In vivo mouse infection assays to see the life of Salmonella in the presence of beta-alanine. They concluded by comprehending that beta-alanine modulates the expression of many genes including zinc transporters which are required for pathogenesis.

Strengths:

This study made a couple of knockouts in Salmonella and did a transcriptomic investigation to understand the global gene expression pattern.

Weaknesses:

The following questions are unanswered:

(1) It is not clear how the exogenous beta-alanine is taken up by macrophages.

We thank the reviewer for the question. It is reported that β-alanine is delivered to eukaryotic cells through TauT (SLC6A6) and PAT1 (SLC36A1) transporters (Am J Physiol Cell Physiol. 2020 Apr 1;318(4):C777-C786; Br J Pharmacol 161: 589 –600, 2010; Biochim Biophys Acta 1194: 44 –52, 1994). We will add this information in the revised manuscript.

(2) It is not clear how the Beta-alanine from the cytosol of the macrophage enters the SCV.

Thank you for pointing it out. You are right that the above question is not clear. We will do our best to achieve this issue, via reviewing literature, designing and performing additional experiments.

(3) It is not clear how the beta-alanine from SCV enters the bacterial cytosol.

Thank you for the question. We have attempted to find the transporter of β-alanine in Salmonella, but we found that the CycA transporter transports β-alanine  in Escherichia coli but not in Salmonella, despite Salmonella is the closely related species of E. coli.

According to your suggestion, we will perform additional experiments to verify whether BasC is involved in the transport of β-alanine into Salmonella cytosol.

(4) There is no clarity on the utilization of exogenous beta-alanine of the host and the de novo synthesis of beta-alanine by panD of Salmonella.

Thank you for the question. Our results showed that β-alanine concentrations were downregulated in the Salmonella-infected RAW264.7 cells, and the replication of Salmonella in RAW264.7 cells was significantly increased with the addition of β-alanine to the culture medium (RPMI) of RAW264.7 cells, implying that intracellular Salmonella use host-derived β-alanine for growth. Unfortunately, we have not found the transporter of exogenous β-alanine into Salmonella cytosol. We will perform additional experiments to verify whether BasC is involved in the transport of β-alanine into Salmonella cytosol, or search for other transporters that are responsible for the uptake of β-alanine into Salmonella.

Upon confirming the β-alanine transporter in Salmonella, we will compare the intracellular replication and virulence between WT and the transporter mutant strain, via cell and mice infection assays. If the replication ability and virulence of the mutant strain decreases relative to WT, suggesting that Salmonella uptakes the exogenous beta-alanine of the host to enhance intracellular replication and its virulence in mice.

We have found that the replication of Salmonella panD mutant in macrophages and the virulence in mice were significantly decreased relative to WT, suggesting that the de novo synthesis of β-alanine is important for Salmonella intracellular replication and virulence_. To further confirm that both uptake of host-derived β-alanine and de novo synthesis of β-alanine are critical for the full virulence of _Salmonella, we will generate the double mutant of panD and β-alanine transporter gene. If the replication ability and virulence of the double mutant decreases compared with each of the single mutant, suggesting that Salmonella both utilizes the exogenous beta-alanine of the host and de novo synthesis of β-alanine for full virulence.

Author response:

Reviewer #1 (Public review):

Summary:

The paper by Tolossa et al. presents classification studies that aim to predict the anatomical location of a neuron from the statistics of its in-vivo firing pattern. They study two types of statistics (ISI distribution, PSTH) and try to predict the location at different resolutions (region, subregion, cortical layer).

Strengths:

This paper provides a systematic quantification of the single-neuron firing vs location relationship.

The quality of the classification setup seems high.

The paper uncovers that, at the single neuron level, the firing pattern of a neuron carries some information on the neuron's anatomical location, although the predictive accuracy is not high enough to rely on this relationship in most cases.

Thank you for your thoughtful feedback. The level of predictive accuracy offered by our current approach, while far above chance, is insufficient for electrode localization in most cases. Although, we speculate that our results represent a lower limit on possible performance—future improvements are almost certain as larger datasets are generated, more diverse features of neural activity are employed, and more advanced ML tools are implemented. We note that the current performance indicates a far more reliable embedding of anatomy in spiking than precedented by the modest statistical significance previously described in the literature. It would have been impossible to achieve this without the tremendous resources provided by the Allen Institute. In our revision, we will clarify that major performance improvements are both possible and probable.

Weaknesses:

As the authors mention in the Discussion, it is not clear whether the observed differences in firing are epiphenomenal. If the anatomical location information is useful to the neuron, to what extent can this be inferred from the vicinity of the synaptic site, based on the neurotransmitter and neuromodulator identities? Why would the neuron need to dynamically update its prediction of the anatomical location of its pre-synaptic partner based on activity when that location is static, and if that information is genetically encoded in synaptic proteins, etc (e.g., the type of the synaptic site)? Note that the neuron does not need to classify all possible locations to guess the location of its pre-synaptic partner because it may only receive input from a subset of locations.  If an argument on activity-based estimation being more advantageous to the neuron than synaptic site-based estimation cannot be made, I believe limiting the scope of the paper (e.g., in the Introduction) to an epiphenomenal observation and its quantification will improve the scientific quality.

Summarily, in response to the two reviewers, we will minimize our discussion of this question in the revision. However, given that our results are either epiphenomenal or functional, we feel that it is important to indicate these possibilities, even if this indication is succinct and conservative.

In pursuit of a more concise revision, we will not expand our discussion to accommodate this interesting conversation with the reviewer, but we are excited to briefly offer our perspective here.

Regarding the epiphenomenal nature of our observations: this is a complex question that would be challenging but not impossible to validate experimentally. It has been previously established that neurons, especially those that integrate inputs from a variety of regions and are involved in diverse functions, could benefit from mechanisms for dynamically parsing inputs (Gutig, Sompolinsky 2006). Neurotransmitter and neuromodulator identities may indeed convey some information about presynaptic neuron location (e.g., NE may originate from the locus coeruleus). However, hypothetically, the binding of a neurotransmitter only bears on the postsynaptic neuron via ionic current, or second messenger activity. Postsynaptic neurons do not consume or otherwise endocytose the neurotransmitter, thus the ability of a neuron to “know” the presynaptic identity is a function of induced postsynaptic activity. Certainly, there are multiple streams of information that can provide insight into anatomical location all taking the ultimate form of neural activity and membrane dynamics. This would be broadly consistent with (for example) reward prediction error which is evident in dopamine release, firing rates, spiking patterns, and oscillatory rhythms.

We could imagine a possible role for the embedding of location in spiking patterns. It is important to note that many neurons in neighboring areas share common neurotransmitters (e.g., glutamate, GABA). Neurons receiving input from multiple regions with similar neurotransmitter profiles could benefit from additional information in the spiking patterns for distinguishing input sources, especially for multimodal integration. For instance, an inferior parietal lobule neuron or microcircuit could be downstream from both auditory cortex (listening) and Broca’s area (speaking). Imagine an individual is in a crowded coffee shop waiting for their drink order to be called while speaking to their friend. In this scenario, it may be important to recognize region-specific activity and thus selectively attend to it. Thus, it is unlikely that neurons actively update a “location prediction,” but rather that location-related information is passively embedded in spike patterning and this might be dynamically leveraged in computation. We emphasize that this is a simplified conceptual example and not a hypothesis that we test in the paper. This conversation, however, is a wonderful example of the thought experiments that we hope will grow from this type of work.

Reviewer #2 (Public review):

Summary:

In this manuscript, Tolossa et al. analyze Inter-spike intervals from various freely available datasets from the Allen Institute and from a dataset from Steinmetz et al. They show that they can modestly decode between gross brain regions (Visual vs. Hippocampus vs. Thalamus), and modestly separate sub-areas within brain regions (DG vs. CA1 or various visual brain areas).

Strengths:

The paper is reasonably well written, and the definitions are quite well done. For example, the authors clearly explained transductive vs. inductive inference in their decoders. E.g., transductive learning allows the decoder to learn features from each animal, whereas inductive inference focuses on withheld animals and prioritizes the learning of generalizable features.

Thank you!

Weaknesses:

However, even with some of these positive aspects, I still found the manuscript to be a laundry list of results, where some results are overly explained and not particularly compelling or interesting, whereas interesting results are not strongly described or emphasized. The overall problem is that the study is not cohesive, and the authors need to either come up with a tool or demonstrate a scientific finding. The current version attempts to split the middle and thus is not as impactful as it could be

In our revision, we will endeavor to present our results in line with your suggestions. Thank you for the careful and thorough feedback that will improve the readability of our manuscript. We strove to be complete in establishing the logic leading to our ultimate finding—that a robust code for anatomical location can be extracted from single neuron spike trains, but not from more traditional descriptions of neural activity. Our detection of this code, albeit not perfect in performance, is, in most cases, both far above chance levels and is robust to animal identity and laboratory of origin. Our presentation of these results is cohesive in as much as we sequentially establish a series of results that build towards a concluding set of experiments. We start by establishing a baseline via standard measurements and then explore more challenging problems through more complex models that build toward our final test.  Based on your feedback, we will contract and expand elements of this sequence.

While our findings raise the possibility of developing a computational tool for electrode localization, pending additional features and/or datasets, our current focus is on establishing the neurobiological principle of anatomical embedding in spike trains. The purpose of briefly mentioning a possible application is that we hope to encourage those engaged in machine-learning on multi-modal neural data that this problem is tractable, yet still open. Based on your feedback, we will clarify that the focus of our current work is not an introduction of a new tool.

Author response:

Public Reviews: 

Reviewer #1 (Public review): 

Summary: 

Mitotic kinesins carry out crucial roles in intracellular motility and mitotic spindle organization. Although many mitotic kinesins have been extensively studied, a few conserved mitotic motors remain poorly explored, including chromosome-associated kinesins. Here, Furusaki et al reconstitute recombinant chromosome-associated kinesin or chromokinesin (Kid) and reveal processive plus-end motility along microtubules. The authors purify multiple versions of Kid, revealing dimeric organization and their processive microtubule plus-ended motility which depends on their conserved motor domains, neck linkers, and coiled-coil regions. The study reveals for the first time that KID can recruit and transport duplex DNA along microtubules using its conserved C-terminal DNA binding domain. The work provides crucial revised thinking about the mechanisms of Chromokinesins mitosis as physical processive motors that mobilize chromosomes towards the microtubule plus ends in early metaphase. 

Strengths: 

The authors reconstitute multiple chromosome-associated kinesin (KID) orthologs from Xenopus and humans with microtubules and determine their oligomerization. The study shows how coiled-coil and neck linker regions of KID are essential for its function as its deletion leads to non-processive motility. CHimeras placing the KID coiled-coil and neck linker on the KIF1A motor domain led to the production of a processive recombinant motor supporting the compatibility of their motility mechanisms. The KID c-terminal tail binds and transports only double-stranded DNA and its deletion or single-stranded DNA leads to defects in this activity.

Thank you very much.

Weaknesses: 

A minor weakness in the studies is that they do not resolve the mechanisms of KID in binding large duplex DNA molecules or condensed chromatin. The authors suggest a model in which KID forms multimers along large chromosomes that lead to their transport, but this model was not directly tested. 

Thank you very much for your suggestion.

We will attempt to observe the movement of longer dsDNA and/or DNA-bead complexes and compare their motility with that of a single KID motor to elucidate the cooperativity of the motor protein.

Reviewer #2 (Public review): 

Summary: 

Previous work in the field highlighted the role of the kinesin-10 motor protein Kid (KIF22) in the polar ejection force during prometaphase. However, the biochemical and biophysical properties of Kid that enabled it to serve in this role were unclear. The authors demonstrate that human and xenopus Kid proteins are processive kinesins that function as homodimeric molecules. The data are solid and support the findings although the text could use some editing to improve clarity. 

Strengths: 

A highlight of the work is the reconstitution of DNA transport in vitro. 

A second highlight is the demonstration that the monomer vs dimer state is dependent on protein concentration.