10,000 Matching Annotations
  1. Sep 2025
  2. 52.53.155.43 52.53.155.43
    41597_2025_4724_MOESM1_ESM-html.html
    95
    Visit annotations in context

    Tags

    Annotators

    URL

    52.53.155.43/7b393a549381fe5efe830cf2a501315a6b5b7cc3/41597_2025_4724_MOESM1_ESM-html.html
  3. www.sciencedirect.com www.sciencedirect.com
    Integrative multi-omics analysis decodes HOXC9-driven malignant transformation and metastasis in OSCC
    1
    Visit annotations in context

    Tags

    Annotators

    URL

    sciencedirect.com/science/article/pii/S2589004225011800
  4. www.sciencedirect.com www.sciencedirect.com
    Cyclin-dependent kinase inhibitor 1A mediates mouse line- and fate-dependent cellular responses in Cx3cr1-Cre genetic tools
    1
    Visit annotations in context

    Tags

    Annotators

    URL

    sciencedirect.com/science/article/pii/S2211124725010381
  5. www.sciencedirect.com www.sciencedirect.com
    Phosphatidylserine-positive extracellular vesicles for detecting multiple operable cancers
    1
    Visit annotations in context

    Tags

    Annotators

    URL

    sciencedirect.com/science/article/pii/S2211124725010307
  6. www.biorxiv.org www.biorxiv.org
    Allosteric effects of the coupling cation in melibiose transporter MelB
    3
    1. Public_Reviews 11 Sep 2025

      Reviewer #2 (Public review):

      This manuscript from Hariharan, Shi, Viner, and Guan presents x-ray crystallographic structures of membrane protein MelB and HDX-MS analysis of ligand-induced dynamics. This work improves on the resolution of previously published structures, introduces further sugar-bound structures, and utilises HDX to explore in further depth the previously observed positive cooperatively to cotransported cation Na+. The work presented here builds on years of previous study and adds substantial new details into how Na+ binding facilitates melibiose binding and deepens the fundamental understanding of the molecular basis underlying the symport mechanism of cation-coupled transporters. However, the presentation of the data lacks clarity, and in particular, the HDX-MS data interpretation requires further explanation in both methodology and discussion.

      Comments on Crystallography and biochemical work:

      (1) It is not clear what Figure 2 is comparing. The text suggests this figure is a comparison of the lower resolution structure to the structure presented in this work; however, the figure legend does not mention which is which, and both images include a modelled water molecule that was not assigned due to poor resolution previously, as stated by the authors, in the previously generated structure. This figure should be more clearly explained.

      (2) It is slightly unclear what the ITC measurements add to this current manuscript. The authors comment that raffinose exhibiting poor binding affinity despite having more sugar units is surprising, but it is not surprising to me. No additional interactions can be mapped to these units on their structure, and while it fits into the substrate binding cavity, the extra bulk of additional sugar units is likely to reduce affinity. In fact, from their listed ITC measurements, this appears to be the trend. Additionally, the D59C mutant utilised here in structural determination is deficient in sodium/cation binding. The reported allostery of sodium-sugar binding will likely influence the sugar binding motif as represented by these structures. This is clearly represented by the authors' own ITC work. The ITC included in this work was carried out on the WT protein in the presence of Na+. The authors could benefit from clarifying how this work fits with the structural work or carrying out ITC with the D59C mutant, or additionally, in the absence of sodium.

      Comments on HDX-MS work:

      While the use of HDX-MS to deepen the understanding of ligand allostery is an elegant use of the technique, this reviewer advises the authors to refer to the Masson et al. (2019) recommendations for the HDX-MS article (https://doi.org/10.1038/s41592-019-0459-y) on how to best present this data. For example:

      (1) The Methodology includes a lipid removal step. Based on other included methods, I assumed that the HDX-MS was being carried out in detergent-solubilised protein samples. I therefore do not see the need for a lipid removal step that is usually included for bilayer reconstituted samples. I note that this methodology is the same as previously used for MelB. It should be clarified why this step was included, if it was in fact used, aka, further details on the sample preparation should be included.

      (2) A summary of HDX conditions and results should be given as recommended, including the mean peptide length and average redundancy per state alongside other included information such as reaction temperature, sequence coverage, etc., as prepared for previous publications from the authors, i.e., Hariharan et al., 2024.

      (3) Uptake plots per peptide for the HDX-MS data should be included as supporting information outside of the few examples given in Figure 6.

      (4) A reference should be given to the hybrid significance testing method utilised. Additionally, as stated by Hageman and Weis (2019) (doi:10.1021/acs.analchem.9b01325), the use of P < 0.05 greatly increases the likelihood of false positive ΔD identifications. While the authors include multiple levels of significance, what they refer to as high and lower significant results, this reviewer understands that working with dynamic transporters can lead to increased data variation; a statement of why certain statistical criteria were chosen should be included, and possibly accompanied by volcano plots. The legend of Figure 6 should include what P value is meant by * and ** rather than statistically significant and highly statistically significant.

      (5) Line 316 states a significant difference in seen in dynamics, how is significance measured here? There is no S.D. given in Table S4. Can the authors further comment on the potential involvement in solvent accessibility and buried helices that might influence the overall dynamics outside of their role in sugar vs sodium binding? An expected low rate of exchange suggests that dynamics are likely influenced by solvent accessibility or peptide hydrophobicity? The increased dynamics at peptides covering the Na binding site on overall more dynamic helices suggests that there is no difference between the dynamics of each site.

      (6) Previously stated HDX-MS results of MelB (Hariharan et al., 2024) state that the transmembrane helices are less dynamic than polypeptide termini and loops with similar distributions across all transmembrane bundles. The previous data was obtained in the presence of sodium. Does this remove the difference in dynamics in the sugar-binding helices and the cation-binding helices? Including this comparison would support the statement that the sodium-bound MelB is more stable than the Apo state, along with the lack of deprotection observed in the differential analysis.

      (7) Have the authors considered carrying out an HDX-MS comparison between the WT and the D59C mutant? This may provide some further information on the WT structure (particularly a comparison with sugar-bound). This could be tied into a nice discussion of their structural data.

      (8) Have the authors considered utilising Li+ to infer how cation selectivity impacts the allostery? Do they expect similar stabilisation of a higher-affinity sugar binding state with all cations?

      (9) MD of MelB suggests all transmembrane helices are reorientated during substrate translocation, yet substrate and cotransporter ligand binding only significantly impacts a small number of helices. Can the authors comment on the ensemble of states expected from each HDX experiment? The data presented here instead shows overall stabilisation of the transporter. This data can be compared to that of HDX on MFS sugar cation symporter XylE, where substrate binding induces a transition to OF state. There is no discussion of how this HDX data compares to previous MFS sugar transporter HDX. The manuscript could benefit from this comparison rather than a comparison to LacY. It is unlikely that there are universal mechanisms that can be inferred even from these model proteins. Highlighting differences instead between these transport systems provides broader insights into this protein class. Doi: 10.1021/jacs.2c06148 and 10.1038/s41467-018-06704-1.

      (10) Additionally, the recent publication of SMFS data (by the authors: doi:10.1016/j.str.2022.11.011) states the following: "In the presence of either melibiose or a coupling Na+-cation, however, MelB increasingly populates the mechanically less stable state which shows a destabilized middle-loop C3." And "In the presence of both substrate and co-substrate, this mechanically less stable state of MelB is predominant.". It would benefit the authors to comment on these data in contrast to the HDX obtained here. Additionally, is the C3 loop covered, and does it show the destabilization suggested by these studies? HDX can provide a plethora of results that are missing from the current analysis on ligand allostery. The authors instead chose to reference CD and thermal denaturation methods as comparisons.

      Review 2
    2. Public_Reviews 11 Sep 2025

      Reviewer #3 (Public review):

      Summary:

      The melibiose permease from Salmonella enterica serovar Typhimurium (MelBSt) is a member of the Major Facilitator Superfamily (MFS). It catalyzes the symport of a galactopyranoside with Na⁺, H⁺, or Li⁺, and serves as a prototype model system for investigating cation-coupled transport mechanisms. In cation-coupled symporters, a coupling cation typically moves down its electrochemical gradient to drive the uphill transport of a primary substrate; however, the precise role and molecular contribution of the cation in substrate binding and translocation remain unclear. In a prior study, the authors showed that the binding affinity for melibiose is increased in the presence of Na+ by about 8-fold, but the molecular basis for the cooperative mechanism remains unclear. The objective of this study was to better understand the allosteric coupling between the Na+ and melibiose binding sites. To verify the sugar-recognition specific determinants, the authors solved the outward-facing crystal structures of a uniport mutant D59C with four sugar ligands containing different numbers of monosaccharide units (α-NPG, melibiose, raffinose, or α-MG). The structure with α-NPG bound has improved resolution (2.7 Å) compared to a previously published structure and to those with other sugars. These structures show that the specificity is clearly directed toward the galactosyl moiety. However, the increased affinity for α-NPG involves its hydrophobic phenyl group, positioned at 4 Å-distance from the phenyl group of Tyr26 forms a strong stacking interaction. Moreover, a water molecule bound to OH-4 in the structure with α-NPG was proposed to contribute to the sugar recognition and appears on the pathway between the two specificity-determining pockets. Next, the authors analyzed by hydrogen-to-deuterium exchange coupled to mass spectrometry (HDX-MS) the changes in structural dynamics of the transporter induced by melibiose, Na+, or both. The data support the conclusion that the binding of the coupling cation at a remote location stabilizes the sugar-binding residues to switch to a higher-affinity state. Therefore, the coupling cation in this symporter was proposed to be an allosteric activator.

      Strengths:

      (1) The manuscript is generally well written.

      (2) This study builds on the authors' accumulated knowledge of the melibiose permease and integrates structural and HDX-MS analyses to better understand the communication between the sodium ion and sugar binding sites. A high sequence coverage was obtained for the HDX-MS data (86-87%), which is high for a membrane protein.

      Weaknesses:

      (1) I am not sure that the resolution of the structure (2.7 Å) is sufficiently high to unambiguously establish the presence of a water molecule bound to OH-4 of the α-NPG sugar. In Figure 2, the density for water 1 is not obvious to me, although it is indeed plausible that water mediates the interaction between OH4/OH6 and the residues Q372 and T373.

      (2) Site-directed mutagenesis could help strengthen the conclusions of the authors. Would the mutation(s) of Q372 and/or T373 support the water hypothesis by decreasing the affinity for sugars? Mutations of Thr 121, Arg 295, combined with functional and/or HDX-MS analyses, may also help support some of the claims of the authors regarding the allosteric communication between the two substrate-binding sites.

      (3) The main conclusion of the authors is that the binding of the coupling cation stabilizes those dynamic sidechains in the sugar-binding pocket, leading to a high-affinity state. This is visible when comparing panels c and a from Figure S5. However, there is both increased protection (blue, near the sugar) and decreased protection in other areas (red). The latter was less commented, could the increased flexibility in these red regions facilitate the transition between inward- and outward-facing conformations? The HDX changes induced by the different ligands were compared to the apo form (see Figure S5). It might be worth it for data presentation to also analyze the deuterium uptake difference by comparing the conditions sodium ion+melibiose vs melibiose alone. It would make the effect of Na+ on the structural dynamics of the melibiose-bound transporter more visible. Similarly, the deuterium uptake difference between sodium ion+melibiose vs sodium ion alone could be analyzed too, in order to plot the effect of melibiose on the Na+-bound transporter.

      (4) For non-specialists, it would be beneficial to better introduce and explain the choice of using D59C for the structural analyses.

      (5) In Figure 5a, deuterium changes are plotted as a function of peptide ID number. It is hardly informative without making it clearer which regions it corresponds to. Only one peptide is indicated (213-226), I would recommend indicating more of them in areas where deuterium changes are substantial.

      (6) From prior work of the authors, melibiose binding also substantially increases the affinity of the sodium ion. Can the authors interpret this observation based on the HDX data?

      Review 3
    3. Public_Reviews 11 Sep 2025

      Author response:

      Reviewer #1:

      While the structure of the melibiose permease in both outward and inward-facing forms has been solved previously, there remain unanswered questions regarding its mechanism. Hariharan et al set out to address this with further crystallographic studies complemented with ITC and hydrogen-deuterium exchange (HDX) mass spectrometry.

      They first report 4 different crystal structures of galactose derivatives to explore molecular recognition, showing that the galactose moiety itself is the main source of specificity. Interestingly, they observe a water-mediated hydrogen bonding interaction with the protein and suggest that this water molecule may be important in binding.

      We appreciate the understanding of our work presented in this manuscript by this reviewer.

      The results from the crystallography appear sensible, though the resolution of the data is low, with only the structure with NPG better than 3Å. However, it is a bit difficult to understand what novel information is being brought out here and what is known about the ligands. For instance, are these molecules transported by the protein or do they just bind? They measure the affinity by ITC, but draw very few conclusions about how the affinity correlates with the binding modes. Can the protein transport the trisaccharide raffinose?

      The four structures with a bound sugar of different sizes aimed to identify the binding motif on both the primary substrate (sugar) and the transporter (MelB<sub>St</sub>). Although the resolutions of the structures complexed with melibiose, raffinose, or a-MG are relatively low, the size and shape of the densities at each structure are consistent with the corresponding sugar molecules, which provide valuable data for determining the pose of the bound sugar. Additionally, there is another a-NPG-bound structure at a higher resolution of 2.7 Å. Therefore, our new data support the published binding site with the galactosyl moiety as the main interacting group. The identified water-1 in this study further confirms the orientation of C4-OH. Notably, this transporter does not recognize or transport glucosides where the orientation of C4-OH at the glucopyranosyl ring is opposite. We will provide stronger data to support the water-1.

      Regarding the raffinose question, we should have clearly introduced the historical background. Bacterial disaccharide transporters have broad specificity, allowing them to work on a group of sugars with shared structural elements; for example, one sugar molecule can be transported by several transporters. As reported in the literature, the galactosides melibiose, lactose, and raffinose can be transported by both LacY and MelB of E. coli. We did not test whether MelB<sub>St</sub> can transport the a-NPG and raffinose. To address this issue and strengthen our conclusions, we plan to conduct additional experiments to gather evidence of the translocation of these sugars by MelB<sub>St</sub>.

      The HDX also appears to be well done; however, in the manuscript as written, it is difficult to understand how this relates to the overall mechanism of the protein and the conformational changes that the protein undergoes.

      Previously, we used HDX-MS to examine the conformational transition between inward- and outward-facing conformations using a conformation-specific nanobody to trap MelB<sub>St</sub> in an inward-facing state, as structurally resolved by cryoEM single-particle analysis and published in eLife 2024. That study identified dynamic regions that may be involved in the conformational transitions; however, there was no sugar present. We also solved and published the crystal structure of the apo D59C MelB<sub>St</sub>. The sugar-bound and apo states are virtually identical. To address the positive cooperativity of binding between the sugar and co-transport cations observed in biophysical analysis, in this study, we utilize HDX-MS to analyze the structural dynamics induced by melibiose, Na<sup>+</sup>, or both, focusing on the binding residues at the sugar-binding and cation-binding pockets. The results suggest that the coupling cation stabilizes sugar-binding residues at helices I and V, contributing to affinity but not specificity.

      Since MelB<sub>St</sub> favors the outward-facing conformation, and simulations on the free-energy landscape suggest that the highest affinity of the sugar-bound state is also at an outward-facing state, MelB<sub>St</sub> in both the apo and bound states tend to remain in the outward-facing conformation. We will include a section comparing these differences. Thank you to this reviewer for the critical insight.

      Reviewer #2:

      This manuscript from Hariharan, Shi, Viner, and Guan present x-ray crystallographic structures of membrane protein MelB and HDX-MS analysis of ligand-induced dynamics. This work improves on the resolution of previously published structures, introduces further sugar-bound structures, and utilises HDX to explore in further depth the previously observed positive cooperatively to cotransported cation Na<sup>+</sup>. The work presented here builds on years of previous study and adds substantial new details into how Na<sup>+</sup> binding facilitates melibiose binding and deepens the fundamental understanding of the molecular basis underlying the symport mechanism of cation-coupled transporters. However, the presentation of the data lacks clarity, and in particular, the HDX-MS data interpretation requires further explanation in both methodology and discussion.

      We thank this reviewer for taking the time to read our previous articles related to this manuscript.

      Comments on Crystallography and biochemical work:

      (1) It is not clear what Figure 2 is comparing. The text suggests this figure is a comparison of the lower resolution structure to the structure presented in this work; however, the figure legend does not mention which is which, and both images include a modelled water molecule that was not assigned due to poor resolution previously, as stated by the authors, in the previously generated structure. This figure should be more clearly explained.

      This figure shows a stereo view of a density map created in cross-eye style to demonstrate its quality. We will update this figure with a higher-resolution map, and the density for Wat-1 is clearly visible. This also addresses Reviewer-3’s comment regarding the map resolution.

      (2) It is slightly unclear what the ITC measurements add to this current manuscript. The authors comment that raffinose exhibiting poor binding affinity despite having more sugar units is surprising, but it is not surprising to me. No additional interactions can be mapped to these units on their structure, and while it fits into the substrate binding cavity, the extra bulk of additional sugar units is likely to reduce affinity. In fact, from their listed ITC measurements, this appears to be the trend. Additionally, the D59C mutant utilised here in structural determination is deficient in sodium/cation binding. The reported allostery of sodium-sugar binding will likely influence the sugar binding motif as represented by these structures. This is clearly represented by the authors' own ITC work. The ITC included in this work was carried out on the WT protein in the presence of Na<sup>+</sup>. The authors could benefit from clarifying how this work fits with the structural work or carrying out ITC with the D59C mutant, or additionally, in the absence of sodium.

      While raffinose and a-MG have been reported as substrates of MelB in E. coli, binding data are unavailable; additionally, for MelB<sub>St</sub>, we lack data on the binding of two of the four sugars or sugar analogs. We performed a label-free binding assay using ITC to address this concern with the WT MelB<sub>St</sub>. We will also perform the binding assay with the D59C MelB<sub>St</sub>, since sugar binding has been structurally analyzed with this mutant, as pointed out by this reviewer. Along with other new functional results, we will prepare a new Figure 1 on functional analysis, which will also address the comment regarding extra bulk at the non-galactosyl moiety with poor affinity.

      This D59C uniport mutant exhibits increased thermostability, making it a valuable tool for crystal structure determination, especially since the wild type (WT) is difficult to crystallize at high quality. Asp59 is the only site that responds to the binding of all coupling cations: Na<sup>+</sup>, Li<sup>+</sup>, or H<sup>+</sup>. Notably, this mutant selectively abolishes cation binding and cotransport. However, it still maintains intact sugar binding with slightly higher affinity and preserves the conformational transition, as demonstrated by an electroneutral transport reaction, the melibiose exchange, and fermentation assays with intact cells. Therefore, the structural data derived from this mutant are significant and offer important mechanistic insights into sugar transport. We will provide additional details during the revision.

      Comments on HDX-MS work:

      While the use of HDX-MS to deepen the understanding of ligand allostery is an elegant use of the technique, this reviewer advises the authors to refer to the Masson et al. (2019) recommendations for the HDX-MS article (https://doi.org/10.1038/s41592-019-0459-y) on how to best present this data. For example:

      All authors appreciate this reviewer’s comments and suggestions, which will be incorporated into the revision.

      (1) The Methodology includes a lipid removal step. Based on other included methods, I assumed that the HDX-MS was being carried out in detergent-solubilised protein samples. I therefore do not see the need for a lipid removal step that is usually included for bilayer reconstituted samples. I note that this methodology is the same as previously used for MelB. It should be clarified why this step was included, if it was in fact used, aka, further details on the sample preparation should be included.

      Yes, a lipid/detergent removal step was applied in this study and in previous studies and this information was clearly described in Methods.

      (2) A summary of HDX conditions and results should be given as recommended, including the mean peptide length and average redundancy per state alongside other included information such as reaction temperature, sequence coverage, etc., as prepared for previous publications from the authors, i.e., Hariharan et al., 2024.

      We will update the Table S2. Thank you.

      (3) Uptake plots per peptide for the HDX-MS data should be included as supporting information outside of the few examples given in Figure 6.

      We will prepare the plots in supplementary information.

      (4) A reference should be given to the hybrid significance testing method utilised. Additionally, as stated by Hageman and Weis (2019) (doi:10.1021/acs.analchem.9b01325), the use of P < 0.05 greatly increases the likelihood of false positive ΔD identifications. While the authors include multiple levels of significance, what they refer to as high and lower significant results, this reviewer understands that working with dynamic transporters can lead to increased data variation; a statement of why certain statistical criteria were chosen should be included, and possibly accompanied by volcano plots. The legend of Figure 6 should include what P value is meant by * and ** rather than statistically significant and highly statistically significant.

      We appreciate this comment and will cite this article on the hybrid significance method. We will include volcano plots for each dataset. We fully acknowledge that using a cutoff of P < 0.05 can increase the likelihood of false-positive identifications. However, given the complexity of the samples analyzed in this study, we believe that some important changes may have been excluded due to higher variability within the dataset. By applying multiple levels of statistical testing, we determined that P < 0.05 represents a suitable threshold for this study. The threshold values were marked in the residual plots and explained in the text. For Figure 6, we have revised it by showing the P value directly.

      (5) Line 316 states a significant difference in seen in dynamics, how is significance measured here? There is no S.D. given in Table S4. Can the authors further comment on the potential involvement in solvent accessibility and buried helices that might influence the overall dynamics outside of their role in sugar vs sodium binding? An expected low rate of exchange suggests that dynamics are likely influenced by solvent accessibility or peptide hydrophobicity? The increased dynamics at peptides covering the Na binding site on overall more dynamic helices suggests that there is no difference between the dynamics of each site.

      Table S4 was created to provide an overall view of the dynamic regions. If we understand correctly, this reviewer asked us to comment on the effect of solvent accessibility or hydrophobic regions on the overall dynamics outside the binding residues of the peptides that carry binding residues. Since the HDX rate is influenced by two linked factors: solvent accessibility and hydrogen-bonding interactions that reflect structural dynamics, poor solvent accessibility in buried regions results in low deuterium uptakes. The peptides in our dataset that include the Na<sup>+</sup>-binding site showed low HDX, likely due to poor solvent accessibility and structural stability. It is unclear what this reviewer meant by "increased dynamics at peptides covering the Na binding site on overall more dynamic helices." We do not observe increased dynamics in peptides covering Na<sup>+</sup>-binding sites.

      (6) Previously stated HDX-MS results of MelB (Hariharan et al., 2024) state that the transmembrane helices are less dynamic than polypeptide termini and loops with similar distributions across all transmembrane bundles. The previous data was obtained in the presence of sodium. Does this remove the difference in dynamics in the sugar-binding helices and the cation-binding helices? Including this comparison would support the statement that the sodium-bound MelB is more stable than the Apo state, along with the lack of deprotection observed in the differential analysis.

      Thanks for this suggestion. The previous datasets were collected in the presence of Na<sup>+</sup>. In the current study, we also have a Na-containing dataset. Both showed similar results: the multiple overlapping peptides covering the sugar-binding residues on helices I and V have higher HDX rates than those covering the Na<sup>+</sup>-binding residues, even when Na<sup>+</sup> is present in both datasets.

      (7) Have the authors considered carrying out an HDX-MS comparison between the WT and the D59C mutant? This may provide some further information on the WT structure (particularly a comparison with sugar-bound). This could be tied into a nice discussion of their structural data.

      Thanks for this suggestion. Conducting the HDX-MS comparison between the WT and the D59C mutant is certainly interesting, especially given the growing amount of structural and biochemical/biophysical data available for this mutant. However, due to limited resources, we might consider doing it later.

      (8) Have the authors considered utilising Li<sup>+</sup> to infer how cation selectivity impacts the allostery? Do they expect similar stabilisation of a higher-affinity sugar binding state with all cations?

      Thanks for this suggestion. We have demonstrated that Li<sup>+</sup> also shows positive cooperativity with melibiose through ITC binding measurements. Li<sup>+</sup> binds to MelB<sub>St</sub> with higher affinity than Na<sup>+</sup> but causes many different effects on MelB. It is worth investigating this thoroughly and individually. To address the second question, H<sup>+</sup> is a poor coupling cation with minimal impact on melibiose binding. Since its pKa is around 6.5, only a small subpopulation of MelB<sub>St</sub> is protonated at pH 7.5. The order of sugar-binding cooperativity is the highest with Na<sup>+</sup>, followed by Li<sup>+</sup> and H<sup>+</sup>.

      (9) MD of MelB suggests all transmembrane helices are reorientated during substrate translocation, yet substrate and cotransporter ligand binding only significantly impacts a small number of helices. Can the authors comment on the ensemble of states expected from each HDX experiment? The data presented here instead shows overall stabilisation of the transporter. This data can be compared to that of HDX on MFS sugar cation symporter XylE, where substrate binding induces a transition to OF state. There is no discussion of how this HDX data compares to previous MFS sugar transporter HDX. The manuscript could benefit from this comparison rather than a comparison to LacY. It is unlikely that there are universal mechanisms that can be inferred even from these model proteins. Highlighting differences instead between these transport systems provides broader insights into this protein class. Doi: 10.1021/jacs.2c06148 and 10.1038/s41467-018-06704-1.

      The sugar translocation free-energy landscape simulations showed that both helix bundles move relative to the membrane plane. That analysis aimed to clarify a hypothesis in the field—that the MFS transporter can use an asymmetric mode to transition between inward- and outward-facing states. In the case of MelB, we clearly demonstrated that both domains move and each helix bundle moves as a unit, so the labeling changes were identified only in some extramembrane loops and a few highly flexible helices. Thanks for the suggestion about comparing with XylE. We will include a discussion on it.

      (10) Additionally, the recent publication of SMFS data (by the authors: doi:10.1016/j.str.2022.11.011) states the following: "In the presence of either melibiose or a coupling Na<sup>+</sup>-cation, however, MelB increasingly populates the mechanically less stable state which shows a destabilized middle-loop C3." And "In the presence of both substrate and co-substrate, this mechanically less stable state of MelB is predominant.". It would benefit the authors to comment on these data in contrast to the HDX obtained here. Additionally, is the C3 loop covered, and does it show the destabilization suggested by these studies? HDX can provide a plethora of results that are missing from the current analysis on ligand allostery. The authors instead chose to reference CD and thermal denaturation methods as comparisons.

      Thank this reviewer for reading the single-molecule force spectroscopy (SMFS) study on MelB<sub>St</sub>. The C3 loop mentioned in this SMFS article is partially covered in the dataset Mel or Mel plus Na<sup>+</sup> vs. Apo, and more coverage is in the Na<sup>+</sup> vs. Apo. In either condition, no deprotection was detected. Two possible reasons the HDX data did not reflect the deprotection are: 1) The changes were too subtle and did not pass the statistical tests and 2) the longest labeling time point was still insufficient to detect the changes; much longer labeling times should be considered in future studies.

      Reviewer #3:

      Summary:

      The melibiose permease from Salmonella enterica serovar Typhimurium (MelB<sub>St</sub>) is a member of the Major Facilitator Superfamily (MFS). It catalyzes the symport of a galactopyranoside with Na⁺, H⁺, or Li⁺, and serves as a prototype model system for investigating cation-coupled transport mechanisms. In cation-coupled symporters, a coupling cation typically moves down its electrochemical gradient to drive the uphill transport of a primary substrate; however, the precise role and molecular contribution of the cation in substrate binding and translocation remain unclear. In a prior study, the authors showed that the binding affinity for melibiose is increased in the presence of Na<sup>+</sup> by about 8-fold, but the molecular basis for the cooperative mechanism remains unclear. The objective of this study was to better understand the allosteric coupling between the Na<sup>+</sup> and melibiose binding sites. To verify the sugar-recognition specific determinants, the authors solved the outward-facing crystal structures of a uniport mutant D59C with four sugar ligands containing different numbers of monosaccharide units (α-NPG, melibiose, raffinose, or α-MG). The structure with α-NPG bound has improved resolution (2.7 Å) compared to a previously published structure and to those with other sugars. These structures show that the specificity is clearly directed toward the galactosyl moiety. However, the increased affinity for α-NPG involves its hydrophobic phenyl group, positioned at 4 Å-distance from the phenyl group of Tyr26 forms a strong stacking interaction. Moreover, a water molecule bound to OH-4 in the structure with α-NPG was proposed to contribute to the sugar recognition and appears on the pathway between the two specificity-determining pockets. Next, the authors analyzed by hydrogen-to-deuterium exchange coupled to mass spectrometry (HDX-MS) the changes in structural dynamics of the transporter induced by melibiose, Na<sup>+</sup>, or both. The data support the conclusion that the binding of the coupling cation at a remote location stabilizes the sugar-binding residues to switch to a higher-affinity state. Therefore, the coupling cation in this symporter was proposed to be an allosteric activator.

      Strengths:

      (1) The manuscript is generally well written.

      (2) This study builds on the authors' accumulated knowledge of the melibiose permease and integrates structural and HDX-MS analyses to better understand the communication between the sodium ion and sugar binding sites. A high sequence coverage was obtained for the HDX-MS data (86-87%), which is high for a membrane protein.

      Thank this reviewer for your positive comments.

      Weaknesses:

      (1) I am not sure that the resolution of the structure (2.7 Å) is sufficiently high to unambiguously establish the presence of a water molecule bound to OH-4 of the α-NPG sugar. In Figure 2, the density for water 1 is not obvious to me, although it is indeed plausible that water mediates the interaction between OH4/OH6 and the residues Q372 and T373.

      Thanks for your comments on the resolution. We will improve the density for the Water 1.

      (2) Site-directed mutagenesis could help strengthen the conclusions of the authors. Would the mutation(s) of Q372 and/or T373 support the water hypothesis by decreasing the affinity for sugars? Mutations of Thr 121, Arg 295, combined with functional and/or HDX-MS analyses, may also help support some of the claims of the authors regarding the allosteric communication between the two substrate-binding sites.

      The authors thank this reviewer for the thoughtful suggestions. MelB<sub>St</sub> has been subjected to Cys-scanning mutagenesis (https://doi.org/10.1016/j.jbc.2021.101090). Placing a Cys residue on the hydrogen bond-donor Q372 significantly decreased the transport initial rate, accumulation, and melibiose fermentation, with little effect on protein expression, as shown in Figure 2 of this JBC paper. Although no binding data are available, the poor initial rate of transport with a similar amount of protein expressed suggested that the binding affinity is apparently decreased, supporting the role of water-1 in the binding pocket for better binding. The T373C mutant retained most activities of the WT. We will discuss the functional characterizations of these two mutants. Thanks.

      (3) The main conclusion of the authors is that the binding of the coupling cation stabilizes those dynamic sidechains in the sugar-binding pocket, leading to a high-affinity state. This is visible when comparing panels c and a from Figure S5. However, there is both increased protection (blue, near the sugar) and decreased protection in other areas (red). The latter was less commented, could the increased flexibility in these red regions facilitate the transition between inward- and outward-facing conformations?

      Thanks for this important question. We will discuss the deprotected data in the conformational transition between inward-facing and outward-facing states. The two regions, loop8-9 and loop1-2, are located in the gate area on both sides of the membrane and showed increased deuterium uptakes upon binding of melibiose plus Na<sup>+</sup>. They are likely involved in this process.

      The HDX changes induced by the different ligands were compared to the apo form (see Figure S5). It might be worth it for data presentation to also analyze the deuterium uptake difference by comparing the conditions sodium ion+melibiose vs melibiose alone. It would make the effect of Na<sup>+</sup> on the structural dynamics of the melibiose-bound transporter more visible. Similarly, the deuterium uptake difference between sodium ion+melibiose vs sodium ion alone could be analyzed too, in order to plot the effect of melibiose on the Na<sup>+</sup>-bound transporter.

      We will analyze the data as suggested by this reviewer.

      (4) For non-specialists, it would be beneficial to better introduce and explain the choice of using D59C for the structural analyses.

      As response to the reviewer #1 at page 3, “Asp59 is the only site that responds to the binding of all coupling cations: Na<sup>+</sup>, Li<sup>+</sup>, or H<sup>+</sup>. Notably, this mutant selectively abolishes cation binding and cotransport. However, it still maintains intact sugar binding with slightly higher affinity and preserves the conformational transition, as demonstrated by an electroneutral transport reaction, the melibiose exchange, and fermentation assays with intact cells. Therefore, the structural data derived from this mutant are significant and offer important mechanistic insights into sugar transport. We will provide additional details during the revision.”.

      (5) In Figure 5a, deuterium changes are plotted as a function of peptide ID number. It is hardly informative without making it clearer which regions it corresponds to. Only one peptide is indicated (213-226), I would recommend indicating more of them in areas where deuterium changes are substantial.

      We appreciate this comment, which will make the plots more meaningful. In the previous article published in eLife (2024), we drew boxed to mark the transmembrane regions; however, it generated much confusion, such as why some helices are very short. The revised figure will label the full length of covered positions.

      (6) From prior work of the authors, melibiose binding also substantially increases the affinity of the sodium ion. Can the authors interpret this observation based on the HDX data?

      This is an intriguing mechanistic question. Based on current data, we believe that the bound melibiose physically prevents the release of Na<sup>+</sup> or Li<sup>+</sup> from the cation-binding pocket. The cation-binding pocket and surrounding regions, including the sugar-binding residue Asp124, show low HDX, supporting this idea. Since we lack a structure with both substrates bound, figuring out the details structurally is challenging. However, we have a hypothesis about the intracellular Na<sup>+</sup> release as proposed in the 2024 JBC paper (https://doi.org/10.1016/j.jbc.2024.107427). After sugar release, the rotamer change of Asp55 will help Na<sup>+</sup> exit the cation pocket to the sugar pocket, and the negative membrane potential will facilitate the further movement from MelB to the cytosol. We will discuss this during the revision.

      AuthorResponse
    Visit annotations in context

    Tags

    Annotators

    URL

    biorxiv.org/content/10.1101/2025.07.10.664195v2
  7. www.redalyc.org www.redalyc.org
    El título en las investigaciones: ¿cortos o largos?
    8
    1. AnaFernandaEstradaCampa 11 Sep 2025

      Aportación 1: Considero que es un tema del que se habla poco y por lo tanto se me hace relevante ya que habla de la calidad del titulo. Generalmente se da mayor énfasis a la extensión, cuando en realidad lo central es la precisión, pues esta permite reflejar de forma fiel el objeto de estudio y orientar al lector sobre la pertinencia del trabajo.

      Aportación 2: El texto se apoya en ejemplos concretos como el de la investigación sobre leucemias agudas o el estudio del impacto de la COVID-19 para demostrar cómo la reducción de palabras en un título puede sacrificar información clave. Estos fortalecen el argumento y evidencian que la correspondencia entre problema, objetivo y título debe prevalecer sobre una regla rígida de extensión.

      Aportación 3:La conclusión del artículo es práctica y aplicable: un título no debe juzgarse por ser corto o largo, sino por su capacidad de expresar con exactitud el objeto de estudio. Este enfoque resulta útil para investigadores, tesistas y editores, ya que orienta hacia una valoración más justa y científica de los títulos.

    2. sophiag20 11 Sep 2025

      1-Se enfatiza que un buen título no puede formularse correctamente si no se ha definido antes el problema y el objetivo de la investigación. El título debe surgir como una consecuencia lógica de estos elementos, asegurando que exista una correspondencia clara entre ellos. 2- El texto sostiene que, al evaluar la calidad de un título en una investigación, la prioridad debe ser la precisión, es decir, qué tan bien refleja el contenido del estudio, y no su brevedad o número de palabras. Un título largo puede ser perfectamente válido si expresa claramente el objeto del estudio. 3-Aunque es deseable que los títulos no sean innecesariamente extensos, no se debe recortar información.

    3. katiaamaya2400 11 Sep 2025
      como resultado de la precisión del problema de investigación y del objetivo, este título expresa también con total precisión el “objeto de estudio” de la investigación a realizar

      se logra apreciar en el texto que un título bien formulado funciona como una resumen claro y directo del objeto de estudio, y esto facilita al lector comprender de inmediato el enfoque y la intención de la investigación. No solo le da identidad a la investigación, sino también coherencia y sustento ante los lectores APORTACIÓN 3

    4. DanaReyes 11 Sep 2025

      APORTACIONES:

      1. Resalta que la forma de ser del título, que se más extenso y exprese lo que realmente quiere dar a entender la investigación

      2. De igual forma expresa la forma en que da a entender un título tentativo ósea no uno original como el anterior, podemos observar que nos relata el ejemplo del articulo con el título menos impreciso a lo que describe el contenido del mismo

      3. Como podemos observan nos dan un contexto más exacto y coherente sobre la estructura del título, puede que no sea largo, pero debe ser claro en lo que expresa con las palabras suficientes, no sirve de nada un título extenso si no da a entender nada

    5. DanaReyes 11 Sep 2025
      en la valoración de la calidad del título de un trabajo de investigación, la prioridad, lo verdaderamente relevante es la precisión y no la extensión. Un buen título debe expresar la esencia del objeto del estudio, en plena correspondencia con el objetivo de la investigación, y ello debe lograrse con el número necesario y suficiente de palabras; ni una más ni una menos.

      APORTACIÓN 3.

      Como podemos observan nos dan un contexto más exacto y coherente sobre la estructura del título, puede que no sea largo, pero debe ser claro en lo que expresa con las palabras suficientes, no sirve de nada un título extenso si no da a entender nada

    6. Samantha_Lira 11 Sep 2025
      1. El documento es claro y puntual a lo que nos quiere dar a entender, realmente por mi parte nunca le había dado la importancia que merece a un "Titulo", es cierto que este es el primer gancho a la vista que los lectores tendrán y por medio de este se decide si continuar la lectura o no.
      2. El hecho de que la lectura me de ejemplos de manera extensa, recalcándome que no siempre más es más me gusto mucho ya que me ayuda a una mejor comprensión para poder aplicarlo en mis futuras investigación y saber como redactar de manera correcta un titulo.
      3. En la ultima parte del texto se menciona que el responder en el titulo a las preguntas ¿Cuándo? y ¿Donde? es algo importante ya que se detalla la investigación y pareciera que tiene mas detalle y no solo es un tema al azar.
    7. miguelrr 10 Sep 2025

      1.- El título no debe ser un punto de partida improvisado, sino el resultado de un proceso reflexivo.

      2.- Un título extenso no es un defecto en sí, si aporta claridad y especificidad. Sin embargo, la extensión debe ser funcional, evitando redundancias o detalles irrelevantes.

      3.- Diferenciación entre objeto de estudio y variables. Esto ayuda a evitar títulos que mencionan solo el fenómeno general sin especificar qué se está midiendo o analizando.

    8. Narvaez_LuisBocanegra 10 Sep 2025

      Comentario #1: Es muy importante tener en cuenta el título de la investigación ya que, si este mismo es claro y conciso, el lector se podría interesar mucho más en la investigación.

      Comentario #2: Aunque el título es muy importante, en mi opinión formular correctamente la problematica es un gran paso para considerar cuál va a ser el título de la investigación y obtener los objetivos.

      Comentario #3: Un buen titulo de investigación debe tener en cuenta la presición de la investigación, de lo contario, puede estar sujeto a ausencia de informacion y esto podría desorientar al lector.

    Visit annotations in context

    Annotators

    URL

    redalyc.org/journal/1800/180069886022/
  8. docdrop.org docdrop.org
    allen_2022_oi_220504_1654660888.03092-mtgr7.pdf
    1
    1. Kennedy.Mills 11 Sep 2025
      Everyday ageism was associated with poor physical and mental health across all 4 outcomesexamined (Table 2 and Figure 3). For each additional point on the Everyday Ageism Scale, odds of fairor poor physical health increased by 1.13-fold (95% CI, 1.01-1.17; P < .001) after adjusting forsociodemographic characteristics. The probability of fair or poor physical health was 0.082 for adultsreporting everyday ageism 1 SD below the mean. This increased to 0.134 (63.4%) for those reportingmean levels of everyday ageism and 0.213 for those 1 SD above the mean (for an increase inprobability of 59.0% vs the mean) (Table 2 and Figure 3A). Everyday ageism was associated withnumber of chronic health conditions

      I thought this paragraph was very interesting because it explains how ageism directly affects older adults health. It stood out to me because it showed that as there is an increase in ageism, there is also an increase in the probability of pore physical health as well as poor mental health. I think part of this has to due with stereotypes of older adults having little independence and people not allowing them to complete tasks on their own.

    Visit annotations in context

    Annotators

    URL

    docdrop.org/download_annotation_doc/allen_2022_oi_220504_1654660888.03092-mtgr7.pdf
  9. carolinadatadesk.github.io carolinadatadesk.github.io
    The New  Precision Journalism - Chapter 1
    1
    1. katbia 11 Sep 2025
      1. 如何查找信息； 2.如何评价和分析； 3. 如何通过一种能够穿透信息过载的喧嚣并传达给需要和想要它的人的方式来进行交流。

      I believe this is the core essentials of reporting. It's more true when it comes to data journalism.

    Visit annotations in context

    Annotators

    URL

    carolinadatadesk.github.io/pmeyer/book/Chapter1.htm
  10. press.rebus.community press.rebus.community
    Procrastination | Blueprint for Success in College and Career
    1
    Visit annotations in context

    Annotators

    URL

    press.rebus.community/blueprint2/chapter/18-procrastination/
  11. eng.libretexts.org eng.libretexts.org
    3.4: Project Management Considerations
    1
    Visit annotations in context

    Annotators

    URL

    eng.libretexts.org/Workbench/De_Anza_-_79_Managing_Technology_Projects/03:_EU_GDPR_Compliance/3.04:_Project_Management_Considerations
  12. docdrop.org docdrop.org
    Untitled
    1
    1. rburton 10 Sep 2025
      6 pue s9quiaydag ut Aepsony, yeuLiou 2 usemjaq Apasexy e pue ydumnyy & usemjoq ‘UoHOUSp & puke uoHOUr -oid & usamjaq souaray Ip oy) UROL UeD 7 se ‘<PYysy sxe} 1. uploys om Ayqisuodsax & $3] “sitay) Joape ued aM Puy “ss900ns Io pue ‘Kayes Ino ‘uoneindar mo ‘gof mo jaye oste ueo y8nory}-Mojjo} pue [reyap 0] uotyUaye s,a[doad s9yI1¢Q “BurzeyTe-oFq AsH's 31 SUTTJIWIOS ‘Yyyeap-10 -ofl] sAemye }OU S.J] “a[qei Suyerado ue oO Jo ‘qeorxe} & OFUT ‘uTeX] ® 1o sueydire ue uo 398 aM AJDAOUOY :JUNOD 0} AUeUI 00} sat ‘sn Jo ISOUI 1OJ gS][FIS UoTeALISqo s.as[2 sUCsWIOS UCdn puadap saaq ano op sown Aueu MOF

      I think this is a really interesting way to express the idea that our actions do, in fact, impact the lives of those around us. Especially through our perceptions and how closely we observe the world and things around us

    Visit annotations in context

    Annotators

    URL

    docdrop.org/ocr/download/Amy-Herman-i3orj_ocr.pdf
  13. www.biorxiv.org www.biorxiv.org
    Multiciliated cells adapt the mechanochemical Piezo1-Erk1/2-Yap1 cell proliferation axis to fine-tune centriole number.
    3
    1. EMBOpress 10 Sep 2025

      Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

      Learn more at Review Commons

      Reply to the reviewers

      Manuscript number: RC-2025-03094

      Corresponding author(s): Saurabh S. Kulkarni

      1. General Statements

      We thank the reviewers for their strong praise of the manuscript, highlighting its rigor, depth, and conceptual importance. They consistently described the study as a beautiful, fascinating, and conceptually strong piece of work that addresses a timely question in multiciliated cells. They also noted the high quality of the data, careful quantification, and the use of multiple genetic and pharmacological approaches, all of which improve the reproducibility and credibility of the findings. Importantly, they emphasized the novelty of discovering a direct mechanistic link between Piezo1-mediated mechanotransduction and Foxj1-driven transcriptional control of multiciliation, representing a significant breakthrough for both the cilia field and mechanobiology more broadly. Collectively, these strengths highlight the manuscript’s wide impact and make it highly suitable for publication in a high-impact journal.

      2. Description of the planned revisions

      Reviewer #1:

      There are two experiments that would significantly strengthen these claims.

      • First if their model is correct then even short term treatment with Yoda1 should induce the pathway and effect centriole numbers. While I appreciate the challenge of long term Yoda1 treatment its not clear to me why it would be needed if short term treatment is setting off the transcriptional cascade. Yoda is used throughout the paper to induce all the pathways but we don't know if it actually induces the phenotype. I think this should be addressed with either short term treatments or a dose response to find a dose that does not lead to skin pealing. It is hard to ignore this obvious deficiency.
      • Second, the model predicts that all of this is to regulate Foxj1 levels to regulate the subtle balance between cell size and centriole number. If this is correct, then the overexpression of Foxj1 should have a profound effect on centriole number in multiciliated cells. This is such an easy experiment that would validate many of the claims. RESPONSE:

      We recognize that the reviewer is asking us to test the sufficiency of the pathway with these comments: “If their model is correct, then they should be able to activate the pathway in one way or another to stimulate centriole number. This is a significant limitation to their overall model.” And “If this is correct, then the overexpression of Foxj1 should have a profound effect on centriole number in multiciliated cells.”

      To address reviewers’ suggestions, we will perform the following experiments.

      1. A brief exposure (15 and 30 mins) to Yoda1 and wait for 3 hours to examine changes in centriole amplification. This will avoid skin peeling from long-term exposure.
      2. A brief exposure to Yoda1 (15 mins) followed by a 30-minute wait period, and the cycle repeats a total of 4 times for a total of 3 hours to examine centriole amplification.
      3. The above two experiments will also be done in a constitutively active-Yap background to increase the probability that synergistic activation can lead to centriole amplification.
      4. Although Foxj1 is essential for multiciliogenesis, it is not sufficient to induce multiciliogenesis, as shown by multiple previous studies. Therefore, we do not expect overexpression of Foxj1 to have a profound effect on centriole number. While we will conduct the experiments because we truly want to address the suggestions and gain insight into the answers ourselves, we respectfully ask the Reviewer to consider the following responses to their concern.

      Yoda1 sufficiency: We agree that testing whether acute Yoda1 treatment can induce centriole amplification is an important question. We will conduct experiments with short-pulse and cyclic Yoda1 exposure, including in a constitutively active-YAP background (listed above), to address this possibility. However, several challenges complicate interpretation: (i) PIEZO1 adapts and desensitizes upon activation, (ii) transient signaling may be sufficient to cause secondary signaling but insufficient to drive stable transcriptional programs required for amplification, and (iii) centriole number is inherently variable, making modest effects difficult to resolve. However, we must recognize that failure to observe sufficiency under these conditions would not invalidate the model for two reasons: 1) absence of evidence is not evidence of absence, and thus, we may not have found the right experimental design. 2) PIEZO1–YAP is a necessary input but not sufficient on its own, as elaborated below. For both reasons, we are very careful about the interpretation of results in the manuscript, which shows that this pathway is necessary for centriole amplification using loss-of-function approaches.

      Foxj1 overexpression: Foxj1 is a well-established regulator essential for motile and multiciliogenesis across species (Xenopus, zebrafish, mouse). Loss of Foxj1 reduces cilia number in MCCs, but its activation alone does not have a profound effect on ciliogenesis/cilia number in MCCs. This is because Foxj1 is a part of a larger network essential for multiciliogenesis. This parallels the behavior of other transcriptional regulators, such as Myb, where loss of function impairs centriole amplification, but overexpression does not drive the formation of supernumerary centrioles. Both studies are seminal discoveries in the field of ciliogenesis, but they did not demonstrate the sufficiency of these molecules/pathways. Thus, our results, demonstrating that Foxj1 is necessary to induce tension-dependent centriole amplification, are significant, as the reviewer mentioned. The lack of Foxj1 sufficiency to induce centriole amplification is not a deficiency of the study, but rather evidence that Foxj1 is a part of a larger network essential for tension-dependent centriole amplification.

      Necessity versus sufficiency: We respectfully emphasize that sufficiency is not a prerequisite for demonstrating the significance of a pathway. Mechanochemical signaling is inherently complex, involving many mechanosensitive proteins and pathways. In our case, mechanical stretch increases centriole amplification, with PIEZO1–YAP signaling identified as a key mediator. However, we do not claim that PIEZO1–YAP alone is sufficient. Other pathways, including cadherin-mediated junctions, F-actin–myosin contractility, integrin–focal adhesion signaling, and nuclear mechanotransduction, likely contribute and may regulate unique downstream effectors that collectively promote centriole amplification. Therefore, PIEZO1–YAP should be regarded as one essential component within a larger network.

      __TIMELINE: __We will perform these additional proposed experiments. Since the first author, a postdoctoral researcher on this manuscript, has started a new job and will be coming in on weekends to complete the experiments, we estimate it will take approximately 2-3 months to finish them.

      Reviewer #2:

      1. Considering the Yap-piezo mechanism of action, the authors' logic for the selection of myb, foxj, plk4 and ccno as transcriptional targets is clear, but the HCR-derived signal and the differences seen in the yap morphants are not very strong, notwithstanding the statistical significance. There appear to be distinct subgroups within the treated populations (in Figure S6B, although these data seem quite different in Fig. 7H, so a comment on the technical differences might be helpful), so that the extent to which Yap1 regulates (Myb-)Foxj1 expression in MCCs is not clearly demonstrated by this experiment. Related to this point, it is unclear why 20-25% of the yap1/ piezo1 MO-treated embryos do not show a decline in FOXj1 in Fig. 6, given the qualitative nature of the scoring. Assuming the KD penetrance would vary on a cell-to-cell basis, rather than an embryo-to-embryo basis, this may suggest that there are additional relevant targets (some of which are discussed by the authors). Single-cell analysis might be a way to address this; however, this is not a trivial experiment, it might be sufficient to include a caveat in the text. Furthermore, the conclusion that Foxj1 regulates centriole amplification in a tension-dependent manner is well-supported by the data.

      RESPONSE: We appreciate the reviewer’s thoughtful observation. Differences in the expression of Foxj1 from experiment to experiment are possible due to a combination of factors, including heterogeneity in MCC development across embryos, slightly different embryonic stages, differences in embryo quality between fertilizations, and variability in morpholino delivery and knockdown penetrance, which can occur both across embryos and on a cell-to-cell basis within an embryo. We also note that technical aspects of HCR RNA-FISH, such as proteinase K treatment and washing steps, can affect signal intensity, potentially contributing to the appearance of distinct subgroups within treated populations.

      We agree that single-cell analysis would be a powerful way to dissect these differences, but as the reviewer notes, this is not a trivial experiment and is beyond the scope of the present study. We have therefore added clarifications in the text and discussion to acknowledge these sources of variability and to highlight the possibility of parallel pathways regulating foxj1 expression.

      ********************************************

      Controls for the knockdowns by the various MOs should be provided.

      RESPONSE: We appreciate the reviewer’s comment. The piezo1 MO has been previously established in Kulkarni et al. (2021). Additionally, the current manuscript includes MO control experiments for both erk2 and yap1, through KD at the 1-cell stage using the MO oligonucleotide, followed by mosaic-rescue with the respective WT RNA constructs (mCherry-ERK2 and yap1-GFP) and a nuclear tracer molecule such as H2B-RFP (Fig. 5, E-H, Fig. S5, C&D, Fig. 3, D-F). The mosaic-rescue is a robust experiment that provides an internal control within the same embryo, thereby avoiding differences that may arise due to embryo-to-embryo variability, embryo quality, or differences in fertilization batches. This approach also serves as a valuable tool for detecting cell-autonomous effects, providing a clear readout against uninjected neighboring cells, as the injected cells are labeled with a tracer. We will perform a similar mosaic-rescue experiment for the foxj1 MO.

      TIMELINE: We will conduct mosaic-rescue experiments for the foxj1 MO. We will need 1 month to complete the experiment.

      ********************************************

      __Minor comments:

      __

      Autocorrection of ERK1/2 or MEK1/2 pathways to 1/2 should be avoided. – We are unclear on this comment. Can reviewer please clarify what they mean.

      Reviewer # 3

      Major concerns

      1- The presented data do not yet establish a specific, direct pathway linking mechanotransduction to centriole number, because the molecular players tested (PIEZO1, Ca²⁺, PKC, ERK, YAP, Foxj1) are highly pleiotropic. As such, the observed centriole number phenotypes, and some of the major conclusions, could be indirect. It is therefore critical to test the specificity and causality of the proposed pathway. This could be done with the authors' own strategies and/or with the following potential approaches:

      • Genetic dependency and sufficiency tests: It could be shown that Yoda1 has no effect in PIEZO1 loss-of-function MCCs, and that wild-type PIEZO1, but not conductance-ad PIEZO1 pore mutants restores Yoda1 responsiveness across centriole number, pERK, and YAP readouts. For example, PIEZO1 C terminus was shown to govern Ca²⁺ influx and ERK1/2 activation. Comparing full length PIEZO1 with a C terminal deletion in MCC restricted rescue; loss of rescue of centriole amplification and ERK/YAP activation with the C terminal deletion can provide a genetics anchored specificity test beyond broad inhibitors.

      RESPONSE:

      • To address the reviewer’s concern, we will test whether Yoda1 affects ERK and Yap activation when Piezo1 is depleted. We appreciate the reviewer’s thoughtful suggestion to employ genetic rescue experiments with Piezo1 mutants. Unfortunately, these are not technically feasible in Xenopus, as the Piezo1 coding sequence is exceptionally large (~7.5 kb)____, and repeated attempts by our group to generate and express stable, translatable transcripts have been unsuccessful. To address genetic dependency and specificity despite these technical barriers, we have employed a combination of orthogonal strategies that together provide strong genetic and mechanistic evidence:

      • Mosaic loss-of-function experiments (Fig. 1) demonstrate that Piezo1 regulates centriole number in a cell-autonomous manner, ruling out global epithelial or indirect tissue-wide effects.

      • Pharmacological activation/inhibition with Piezo1-specific agonist (Yoda1) and inhibitors (GSMTx4, gadolinium) produced consistent phenotypes, including activation of downstream ERK and YAP readouts. Notably, Yoda1 is a Piezo-specific agonist, not a broad pharmacological agent.
      • Downstream pathway dissection (calcium chelation, PKC inhibition, ERK2 depletion, and YAP1 knockdown/rescue) consistently converges on the same phenotypes, reduced centriole amplification and altered Foxj1 expression, providing multiple independent lines of evidence that the Piezo1–Ca²⁺–PKC–ERK–YAP axis specifically controls centriole number.

      • Positive feedback regulation of Piezo1 expression by YAP/Foxj1 (Fig. 7) further strengthens the argument for a pathway-specific role rather than pleiotropic, indirect effects. Taken together, while full-length Piezo1 rescue experiments are technically not possible in Xenopus due to gene size constraints, our data employ state-of-the-art genetic, pharmacological, and orthogonal functional assays to rigorously test pathway specificity. These complementary approaches provide compelling evidence for the causal role of Piezo1-mediated mechanotransduction in centriole number control in MCCs.

      • Downstream bypass/rescue experiments: In PIEZO1 loss-of-function or BAPTA conditions, can enforcing MEK/ERK activation or YAP rescue centriole number defect? Conversely, can MEK inhibitors block Yoda1-induced effects.

      RESPONSE: We appreciate the reviewer’s insightful questions.

      • We will express CA Yap in the Piezo1 KD background to assess if we can rescue centriole number. We also note that the converse experiment has already been performed in our study: 1) PKC inhibition abolishes Yoda1-induced ERK phosphorylation and nuclear localization (Fig. 2), 2) both MEK inhibition and ERK2 depletion block Yoda1-induced Yap activation and nuclear entry (Figs. 4, S2). Thus, we have directly demonstrated that MEK inhibition prevents Yoda1-induced effects, satisfying this aspect of the reviewer’s concern.

      ********************************************

      2- Image quantification and analysis must be described in greater detail in the Methods section, as they are central to the major conclusions of the manuscript. For example, the authors should explain how nuclear, cytoplasmic, and centriole segmentation were performed, and how relative protein levels in the nucleus versus the cytoplasm (e.g., YAP, volume- or area-based) were quantified. Specifically, the thresholds and segmentation criteria applied to different cellular structures under various conditions, as well as the use of Imaris and other software, should be clearly detailed.

      RESPONSE: We will describe the methods in greater detail.

      ********************************************

      3- PIEZO1 mRNA was shown to incrase in a Foxj1 linked feedback loop. Does this increase translate into an increase in total protein levels?

      RESPONSE: If the reviewer is referring to Figure 7B, that is the Piezo1 antibody, so yes, the Piezo1 protein levels have increased.

      If the reviewer is referring to Figure 7C and D, we show that loss of Foxj1 leads to a reduction in Piezo1 mRNA expression.

      ********************************************

      4- Is the proposed signaling cascade active in mammalian multiciliated cells (e.g., airway epithelium). If possible, testing this by using one of the major players of the pathway as a readout such as as ERK phosphorylation, YAP nuclear localization in mammalian MCCs will reveal whether regulation of centriole number through this pathway is conserved and would strengthen the generality.

      RESPONSE: We agree with the reviewer that testing conservation of this pathway in mammalian MCCs is of great interest. Indeed, another group is currently investigating the role of Yap in the mammalian airway epithelium; in their temporally controlled Yap knockout model (the global Yap KO being embryonic lethal), they observed that Yap loss led to a reduction in centriole number. To avoid overlap and direct competition with this ongoing work, we chose to focus our efforts on Xenopus.

      Importantly, Xenopus has become a widely recognized and powerful system for MCC biology, enabling mechanistic dissection of centriole amplification and ciliogenesis. Several key discoveries in the field, including the identification of MCIDAS as a master regulator of MCC fate, were first made in Xenopus before being validated in mammals. Similarly, our study provides a mechanistic framework in Xenopus that can inform and guide ongoing studies in the mammalian airway.

      ********************************************

      5- Throughout the results section, there are multiple times where authors raised specific hypothesis about their data (e.g. foxj1 regulation of number control, apical actin/YAP). However, they have not tested them. These hypothesis are very exciting and if possible, testing experimentally, would strengthen the conclusions associated with them.

      RESPONSE: We are not sure what the reviewer means here by “authors raised specific hypothesis about their data (e.g., foxj1 regulation of number control, apical actin/YAP). However, they have not tested them”,

      BECAUSE:

      • Foxj1 regulation of centriole number: We demonstrate a clear reduction in centriole number upon Foxj1 depletion, and importantly, we extend this finding by showing that the reduction is tension-dependent (Fig. 6). We will perform a rescue assay to demonstrate the specificity.
      • Foxj1 and YAP: We never claimed that Foxj1 regulates YAP expression, and this is not part of our proposed model. Instead, our data show that Piezo1–ERK–YAP signaling regulates Foxj1
      • Foxj1 and apical actin: Foxj1 regulation of apical F-actin has already been established in prior work, and in our study, we clearly observe reduced apical actin intensity in Foxj1-depleted MCCs (Fig. 6). To further strengthen this conclusion, we will provide a quantitative analysis of apical actin intensity in Foxj1 morphants. ********************************************

      __TIMELINE: __We will perform these additional proposed experiments. Since the first author, a postdoc on this manuscript, has started a new job and will be coming in on weekends to finish the experiments, we estimate it will take approximately 2-3 months to complete them.

      Minor comments

      MCC vs non MCC identification (Fig. 1): Clarify how non MCCs were distinguished from MCCs (e.g. markers/criteria). – Can the reviewer please clarify which panel or panels? Or provide more specific text that needs to be changed.

      Add the Kintner group reference linking motile cilia number and centriole number in Xenopus MCCs.– Can the reviewer clarify where and which reference? Thank you.

      3. Description of the revisions that have already been incorporated in the transferred manuscript

      Please insert a point-by-point reply describing the revisions that were already carried out and included in the transferred manuscript. If no revisions have been carried out yet, please leave this section empty.

      Reviewer 2

      Major comments:

      1. It should be clarified whether the immunoblots and the related quantitations in Figs. 2 and S2 are all from separate blots/ exposures. If so, they are not useful as controls, and these blots should be repeated with the relevant samples analyzed in parallel. Size markers and labels should be included (2B, 2G; S2B and S2G). An increase in total ERK would alter the interpretation of the increase in nuclear pERK in the IF experiments. RESPONSE: We thank the reviewer for raising this important point regarding clarification of the immunoblots. All experimental groups were analyzed in parallel with their corresponding controls. Because the primary antibodies for pERK and ERK were both raised in rabbit, we optimized our workflow to prevent protein loss during stripping and to ensure accurate visualization. Specifically, lysates from each experimental group were loaded in duplicate on the same gel, separated by a molecular weight ladder that served as a reference point. After transfer, the blot was cut along the ladder, and the two halves were processed in parallel: one probed with anti-pERK and the other with anti-ERK. This strategy ensured that all samples from a single experiment (e.g., Control and Yoda1-treated groups) were analyzed under identical conditions, with staining and imaging performed together at the same exposure. To enhance clarity, we have provided this data as __uncut, full-length __as Supplemental Figure 7 (Figure S7) in the revised revision.

      ********************************************

      Minor comments:

      1. Reference list should be checked for completeness; some citations lack journal/ volume/ page/ year details. – We have corrected the references.
      2. An 'overexposed' version of the image selected for centrioles in Figure 5F might be included with the Chibby-BFP at the same level as in the other figures. At present, the Yap KD cell in the image appears to have normal centrioles; this is potentially confusing, even though the authors clearly explain the matter in the text. – __We have added a new panel to Fig. 5F to avoid confusion.

      __ 3. It might be clearer to present injected/ uninjected in the same orientation in Fig. 6A and B. – __Unfortunately, that is not possible because the injected and uninjected sides are left and right, and they cannot be in the same orientation.

      __ 4. Figure 7B lacks the schematic described in the figure legend. – We have removed the Schematic sentence from the figure legend. That was an error on our side. Thank you for catching it.

      Reviewer 3

      1. Abstract: "how MCCs regulate centriole/cilia numbers remains a major knowledge gap" overstates the field; please soften to reflect recent advances (mechanics/apical area scaling; PIEZO1 implication). – We changed the text to “incompletely understood”.
      2. GsMTx4 rationale: State that GsMTx4 is a spider venom peptide that inhibits cationic mechanosensitive channels (including PIEZO1) and justify its use alongside Yoda1.– GsMTx4 was used in the previous manuscript, and its use was justified there. Here, we are only comparing the results. However, we have added a sentence describing what GSMTx4 is. We have also included a sentence explaining the use of Yoda1. “GsMTx4, a spider venom peptide used in our previous study, inhibits cationic mechanosensitive channels, including Piezo1.”

      “For this experiment, we used the Piezo1 channel-specific chemical agonist, Yoda1, to increase the sensitivity of Piezo1 and upregulate calcium entry into cells”

      Timeline statement: "Centriole amplification to migration and apical docking takes ~4-5 h (personal observation)" is not appropriate; either cite time lapse literature or include your own time lapse data.– We have added a reference that showed imaging for 2 hours, but it was not enough to capture the entire process from intercalation to maturation, so we also kept “personal observation” still in the manuscript. We are unaware of any study that has done time-lapse imaging for 4 hours to capture the entire process of centriole amplification.

      Redundancy: The description of Yoda1 as a channel specific agonist is repeated; keep only once.- Removed

      "WT yap1 GFP construct previously used by Dr. Lance Davidson ..." should move construct description to Methods and keep only the citation in Results.– We moved it to Methods.

      "(Unpublished data; Dr. Mahjoub)" should be removed unless data are shown.- Removed

      Replace "as shown previously in our eLife paper" with "as we previously showed or shown previously (Kulkarni et al., 2021)".– We have made the change.

      The two hypotheses for how Foxj1 could regulate number under tension (actin remodeling vs. transcriptional control of amplification genes) belong in the Discussion unless tested. Moreover, the part on the discussion on yap sequestration by apical actin and the two possibilities presented also should go do discussion. – We have moved both to the discussion section.

      4. Description of analyses that authors prefer not to carry out

      Please include a point-by-point response explaining why some of the requested data or additional analyses might not be necessary or cannot be provided within the scope of a revision. This can be due to time or resource limitations or in case of disagreement about the necessity of such additional data given the scope of the study. Please leave empty if not applicable.

      Reviewer 3

      1- The hypothesis about the centriole pool of Piezo as the mechnosensor for centriole number regulation is very exciting and novel. Can localization controlled variants be used to test whether a centriole associated pool directly senses tension for number control (for example, centrosome targeted PIEZO1 via a PACT tag). Alternatively, broad cellular Ca sensors (GcaMP) or centrosome proximal Ca sensors (e.g., PACT GCaMP) can be used detect local calcium microdomains during tethering or Yoda1 treatment.

      RESPONSE: We appreciate the reviewer's curiosity and excitement; however, these experiments will not alter the conclusion of this paper and will be part of the next study, which aims to delve deeper into how different pools of Piezo1 at centrioles versus cell junctions function in MCCs. To that point, we had thought about these experiments. As mentioned earlier, the Piezo1 coding sequence is exceptionally large (~7.5 kb)____, and repeated attempts by our group to generate and express stable, translatable transcripts have been unsuccessful. Thus, the idea of centrosome-targeted PIEZO1 via a PACT is very exciting; however, it is not technically feasible. Beyond size, PIEZO1 is a trimeric, large plasma-membrane mechanosensitive channel that requires proper ER processing and bilayer incorporation. PACT localizes cargo to the centriole/pericentriolar material, not a membrane compartment; thus, a PACT-anchored PIEZO1 would be membrane-mismatched and almost certainly nonfunctional even if expressed/

      Second, Centrosome-proximal GCaMP (PACT-GCaMP) would show correlation, not causation. This experiment does not address the question “centriole pool of Piezo as the mechanosensor for centriole number regulation”. It will only show if the Ca2+ influx is happening at the basal bodies, but not whether and how that Ca2+ is essential for centriole amplification. For this purpose, we will need to find a way to block Ca2+ influx specifically at basal bodies, rather than junctions, which will require extensive controls.

      We do not claim that any specific Piezo1 or Ca2+ pool is critical for controlling centriole number and thus the suggested experiment would not alter the manuscript's conclusions. We therefore view the above as exciting future directions rather than prerequisites.

      ********************************************

      2- Because the proposed pathway is tension-sensing and YAP pathway is tightly linked to the actin cytoskeleton, the role of actin cysoskeleton in the proposed pathway should be tested directly. The authors mention different hypothesis around actin but has not tested them in the manuscript. For example, actin-depedent sequestration of Yap at the apical surface is intriguing. Does actin polymerization induced by drugs release Yap from the apical surface?

      RESPONSE: We would like to thank the reviewer for their suggestion. As per the reviewers' suggestion, we have moved this section to discussion, stating that “In the future, we plan to address this question by examining how Yap is sequestered by apical actin.”.

      However, we appreciate the reviewer’s enthusiasm and would like to share some experiments we are thinking/planning of to test the hypothesis.

      We plan to examine if the actin polymerization or contractility is responsible for Yap sequestration/release from the apical surface with the following experiments: 1) if the Yap is displaced by Jasplakinolide treatment, which stabilizes filamentous actin, 2) use of ROCK inhibitor to decrease contractility in the absence or presence of Yoda1, 3) Use genetic constructs such as Shroom3 to increase ROCK-mediated contractility to observe changes in Yap localization and dynamics.

      Although these experiments are interesting, they do not alter the conclusion of the current manuscript, and they represent future directions for our research.

      PeerReviewed
    2. EMBOpress 10 Sep 2025

      Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

      Learn more at Review Commons

      Referee #3

      Evidence, reproducibility and clarity

      This manuscript investigates how mechanical tension is transduced into centriole amplification in Xenopus multiciliated cells (MCCs). Building on prior work that centriole number scales with MCC apical area and that this scaling depends on PIEZO1, the study proposes that MCCs repurpose a canonical mechanochemical axis-PIEZO1 → Ca²⁺/PKC → ERK1/2 → YAP → Foxj1-to regulate centriole number rather than mitosis. The authors use tethered vs. untetheredanimal cap explants to modulate tissue tension, combine pharmacologic perturbations with genetic loss of function and rescue, quantititative image analysis and present a model in which tension gated PIEZO1 activates ERK/YAP, influences Foxj1, and tunes centriole number in MCCs.

      The manuscript tackles an important and timely problem with clear disease relevance. It major advance is their presented model that posits that post mitotic MCCs repurpose a canonical mechanotransduction module to regulate organelle number rather than proliferation. It is a conceptually strong study addressing an important problem with a clean mechanical paradigm. However, to support the central claim that centriole number control is a specific, direct consequence of the PIEZO1-Ca²⁺-ERK/YAP pathway within MCCs, the revision should establish specificity and causality and provide experimental data for some of the major conclusions as detailed below. Addressing these points are critical to support the mechanistic conclusions and impact.

      Major concerns:

      1) The presented data do not yet establish a specific, direct pathway linking mechanotransduction to centriole number, because the molecular players tested (PIEZO1, Ca²⁺, PKC, ERK, YAP, Foxj1) are highly pleiotropic. As such, the observed centriole number phenotypes, and some of the major conclusions, could be indirect. It is therefore critical to test the specificity and causality of the proposed pathway. This could be done with the authors' own strategies and/or with the following potential approaches:

      • Genetic dependency and sufficiency tests: It could be shown that Yoda1 has no effect in PIEZO1 loss-of-function MCCs, and that wild-type PIEZO1, but not conductance-dead PIEZO1 pore mutants restores Yoda1 responsiveness across centriole number, pERK, and YAP readouts. For example, PIEZO1 C terminus was shown to govern Ca²⁺ influx and ERK1/2 activation. Comparing full length PIEZO1 with a C terminal deletion in MCC restricted rescue; loss of rescue of centriole amplification and ERK/YAP activation with the C terminal deletion can provide a genetics anchored specificity test beyond broad inhibitors.

      • Downstream bypass/rescue experiments: In PIEZO1 loss-of-function or BAPTA conditions, can enforcing MEK/ERK activation or YAP rescue centriole number defect? Conversely, can MEK inhibitors block Yoda1-induced effects.

      2) The hypothesis about the centriole pool of Piezo as the mechnosensor for centriole number regulation is very exciting and novel. Can localization controlled variants be used to test whether a centriole associated pool directly senses tension for number control (for example, centrosome targeted PIEZO1 via a PACT tag). Alternatively, broad cellular Ca sensors (GcaMP) or centrosome proximal Ca sensors (e.g., PACT GCaMP) can be used detect local calcium microdomains during tethering or Yoda1 treatment.

      3) Because the proposed pathway is tension-sensing and YAP pathway is tightly linked to the actin cytoskeleton, the role of actin cysoskeleton in the proposed pathway should be tested directly. The authors mention different hypothesis around actin but has not tested them in the manuscript. For example, actin-depedent sequestration of Yap at the apical surface is intriguing. Does actin polymerization induced by drugs release Yap from the apical surface?

      4) Image quantification and analysis must be described in greater detail in the Methods section, as they are central to the major conclusions of the manuscript. For example, the authors should explain how nuclear, cytoplasmic, and centriole segmentation were performed, and how relative protein levels in the nucleus versus the cytoplasm (e.g., YAP, volume- or area-based) were quantified. Specifically, the thresholds and segmentation criteria applied to different cellular structures under various conditions, as well as the use of Imaris and other software, should be clearly detailed.

      5) PIEZO1 mRNA was shown to incrase in a Foxj1 linked feedback loop. Does this increase translate into an increase in total protein levels?

      6) Is the proposed signaling cascade active in mammalian multiciliated cells (e.g., airway epithelium). If possible, testing this by using one of the major players of the pathway as a readout such as as ERK phosphorylation, YAP nuclear localization in mammalian MCCs will reveal whether regulation of centriole number through this pathway is conserved and would strengthen the generality.

      7) Throughout the results section, there are multiple times where authors raised specific hypothesis about their data (e.g. foxj1 regulation of number control, apical actin/YAP). However, they have not tested them. These hypothesis are very exciting and if possible, testing experimentally, would strengthen the conclusions associated with them.

      Minor concerns:

      1) Abstract: "how MCCs regulate centriole/cilia numbers remains a major knowledge gap" overstates the field; please soften to reflect recent advances (mechanics/apical area scaling; PIEZO1 implication).

      2) MCC vs non MCC identification (Fig. 1): Clarify how non MCCs were distinguished from MCCs (e.g. markers/criteria).

      3) GsMTx4 rationale: State that GsMTx4 is a spider venom peptide that inhibits cationic mechanosensitive channels (including PIEZO1) and justify its use alongside Yoda1.

      4) Timeline statement: "Centriole amplification to migration and apical docking takes ~4-5 h (personal observation)" is not appropriate; either cite time lapse literature or include your own time lapse data.

      5) Redundancy: The description of Yoda1 as a channel specific agonist is repeated; keep only once.

      6) "WT yap1 GFP construct previously used by Dr. Lance Davidson ..." should move construct description to Methods and keep only the citation in Results.

      7) "(Unpublished data; Dr. Mahjoub)" should be removed unless data are shown.

      8) Add the Kintner group reference linking motile cilia number and centriole number in Xenopus MCCs.

      9) Replace "as shown previously in our eLife paper" with "as we previously showed or shown previously (Kulkarni et al., 2021)".

      10) The two hypotheses for how Foxj1 could regulate number under tension (actin remodeling vs. transcriptional control of amplification genes) belong in the Discussion unless tested. Moreover, the part on the discussion on yap sequestration by apical actin and the two possibilities presented also should go do discussion.

      Significance

      This manuscirpt dissects Piezo1-mediated mechanotransduction to regulation of centriole number in Xenopus multiciliated cells (MCCs) via Ca²⁺, ERK/YAP, and Foxj1. While Piezo1 and its downstream effectors have been implicated broadly in mechanosensation, cellular tension responses, and transcriptional regulation, their specific role in centriole nubmer control in MCCs has been unknown By integrating pharmacological manipulation, genetic perturbation, and functional readouts, the authors demonstrate that this pathway directly influences centriole number.

      The findings extend published knowledge in two main ways:

      (1) they connect a mechanosensitive ion channel to the transcriptional program governing Foxj1 expression and multiciliation, a mechanistic link not previously defined, and

      (2) they highlight the pleiotropic yet coordinated nature of Piezo1 signaling in organelle biogenesis. This work will be of broad interest to cell and developmental biologists studying ciliogenesis, epithelial differentiation, and mechanotransduction, as well as to biomedical researchers interested in multicilaited cells and ciliopathies. By situating a well-studied mechanosensor within the context of MCC biology, the study opens new directions for understanding how tissue-level forces shape organelle number control and function.

      At the same time, the impact of the study is weakened by concerns regarding the causability and specificity of the pathway, since the signaling components examined are highly pleiotropic and it remains challenging to separate direct effects on centriole number from broader cellular consequences. The causal relationships among Piezo1 activity, downstream signaling, and Foxj1 expression require stronger substantiation, and the extent to which this pathway operates in mammalian multiciliated cells remains an open question. Addressing these limitations would strengthen the robustness, generality, and translational relevance of the conclusions.

      PeerReviewed
    3. EMBOpress 10 Sep 2025

      Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

      Learn more at Review Commons

      Referee #2

      Evidence, reproducibility and clarity

      Narayanan, Kulkami and colleagues here examine how the Piezo-Erk-Yap pathway is involved in centriole numerical control in multiciliated cells (MCCs). Using reverse genetic and pharmacological methods in Xenopus embryos, they show that Piezo-mediated ERK signalling through to Yap regulates tension-sensitive centriole number, through a mechanism that involves Foxj1, very likely acting as a transcription factor. The data are carefully controlled, robustly analysed and well presented. Statistical analyses are notably thorough.

      Main points:

      1. It should be clarified whether the immunoblots and the related quantitations in Figs. 2 and S2 are all from separate blots/ exposures. If so, they are not useful as controls, and these blots should be repeated with the relevant samples analysed in parallel. Size markers and labels should be included (2B, 2G; S2B and S2G). An increase in total ERK would alter the interpretation of the increase in nuclear pERK in the IF experiments.

      2. Considering the Yap-piezo mechanism of action, the authors' logic for the selection of myb, foxj, plk4 and ccno as transcriptional targets is clear, but the HCR-derived signal and the differences seen in the yap morphants are not very strong, notwithstanding the statistical significance. There appear to be distinct subgroups within the treated populations (in Figure S6B, although these data seem quite different in Fig. 7H, so a comment on the technical differences might be helpful), so that the extent to which Yap1 regulates (Myb-)Foxj1 expression in MCCs is not clearly demonstrated by this experiment. Related to this point, it is unclear why 20-25% of the yap1/ piezo1 MO -treated embryos do not show a decline in FOXj1 in Fig. 6, given the qualitative nature of the scoring. Assuming the KD penetrance would vary on a cell-to-cell basis, rather than an embryo-to-embryo basis, this may suggest that there are additional relevant targets (some of which are discussed by the authors). Single-cell analysis might be a way to address this; however, this is not a trivial experiment, it might be sufficient to include a caveat in the text. Furthermore, the conclusion that Foxj1 regulates centriole amplification in a tension-dependent manner is well-supported by the data.

      3. Controls for the knockdowns by the various MOs should be provided.

      Minor points:

      1. Autocorrection of ERK1/2 or MEK1/2 pathways to 1/2 should be avoided.

      2. Reference list should be checked for completeness; some citations lack journal/ volume/ page/ year details.

      3. An 'overexposed' version of the image selected for centrioles in Figure 5F might be included with the Chibby-BFP at the same level as in the other figures. At present, the Yap KD cell in the image appears to have the normal centrioles; this is potentially confusing, even though the authors clearly explain matters in the text.

      4. It might be clearer to present injected/ uninjected in the same orientation in Fig. 6A and B.

      5. Figure 7B lacks the schematic described in the figure legend.

      Significance

      This study presents novel insight into the developmentally important process of ciliogenesis in multiciliated cells that will be of specific interest to the fields of cilium biology and mechanobiology, with additional general interest in calcium signalling and cell biology.

      PeerReviewed
    Visit annotations in context

    Tags

    Annotators

    URL

    biorxiv.org/lookup/doi/10.1101/2025.01.21.634139
  14. www.medrxiv.org www.medrxiv.org
    Quantum-enhanced nanodiamond rapid test advances early SARS-CoV-2 antigen detection in clinical diagnostics
    1
    1. rrid 10 Sep 2025

      Reviewer #1: Evidentiary Rating: Potentially Informative

      Written Review: The authors claim that a spin-enhanced fluorescent nanodiamond (FND) lateral flow test for SARS-CoV-2 antigen detection achieves up to 1,100-fold greater sensitivity than conventional gold nanoparticle LFTs using identical antibodies. In a large, blinded clinical evaluation, the assay demonstrated 95.1% sensitivity (Ct ≤ 30) and 100% specificity, enabling SARS-CoV-2 detection on average two days earlier than conventional LFTs and within 0.6 days of RT-qPCR. They assert that this quantum-enhanced diagnostic platform could be adapted to other infectious and non-infectious diseases. 1. The study represents the first large-scale blinded clinical evaluation of spin-enhanced nanodiamond LFTs, moving beyond proof-of-concept to a performance assessment with real clinical samples. 2. The antibody screening process using biolayer interferometry is well executed, but screening on a single recombinant antigen source introduces potential epitope bias; inclusion of diverse antigen sources could further validate pair robustness. 3. The direct head-to-head comparison with in-house AuNP LFTs using identical reagents is a strong methodological choice, eliminating confounding variables common in cross-platform sensitivity claims. 4. While the assay achieves sub-pg/mL LoDs, residual non-specific binding limits ultimate sensitivity exploitation; more work on membrane chemistry or blocking strategies could push performance closer to the theoretical limit. 5. The sample size is adequate for preliminary evaluation, but a larger, more demographically and geographically diverse cohort is necessary to confirm real-world performance, especially in asymptomatic and early infection cases. 6. The assay’s 95.1% sensitivity at Ct ≤ 30 exceeds WHO “desirable” criteria, but the drop in sensitivity at lower viral loads (<10⁴ copies/mL) should be discussed in terms of balancing infectiousness detection with overdiagnosis risk. 7. The ROC, Bayesian regression, and infection dynamics modelling are sophisticated and well described, but providing raw Ct distribution histograms for positive samples would help readers assess viral load representativeness.

    Visit annotations in context

    Annotators

    URL

    medrxiv.org/content/10.1101/2025.06.19.25329920v1
  15. openurl.ebsco.com openurl.ebsco.com
    The Effect of Self-Regulation Based Strategic Reading Education on Comprehension, Motivation, and Self-Regulation Skills.
    4
    1. Jennaanderson1620 10 Sep 2025
      dentifying goals, strategic planning, selecting and using strategies, self-monitoring and self-assessment skills, learning and remembering information and academic skills

      self regulated learning is a process and requires more work and energy, however is very efficient andreally helps you actually learn and grasp concepts

    2. Jennaanderson1620 10 Sep 2025
      self-regulation is to identify goals and using strategies to achieve these goals, and monitoringthe outcomes closely.

      setting up your own strategy that most efficiently helps you chieve your goal, and reflecting how these strategies worked

    3. Jennaanderson1620 10 Sep 2025
      Self-regulation is the skill of regulating cognition, behaviors, actions and motivationsstrategically and autonomously in academic skills and learning to reach the identified goals.

      Self regulation = using reading & cognitive skills that work for each iniviual, an being aware of these strategies.

    4. Jennaanderson1620 10 Sep 2025
      An individual’s success in academic life and afterwards is possiblewith having an advanced level of reading skills. When successful readers’ characteristics areevaluated, it is seen that they use cognitive and metacognitive reading strategies effectively, they havereading motivation, and regulate their own learning by using self-regulating strategies.

      self-regulating strategies = academic success = real world success

    Visit annotations in context

    Annotators

    URL

    openurl.ebsco.com/contentitem/doi:10.29329/ijpe.2019.203.3
  16. angelbravo.cloud angelbravo.cloud
    (CSI)3210 User Experience (UX) Design (UX) - Fall, 2025
    1
    Visit annotations in context

    Annotators

    URL

    angelbravo.cloud/ou/csi3210/f/25/syllabus/syllabus.html
  17. www.biorxiv.org www.biorxiv.org
    Challenges in Replay Detection by TDLM in Post-Encoding Resting State
    2
    1. Public_Reviews 10 Sep 2025

      Reviewer #1 (Public review):

      Summary:

      Participants learned a graph-based representation, but, contrary to the hypotheses, failed to show neural replay shortly after. This prompted a critical inquiry into temporally delayed linear modeling (TDLM)--the algorithm used to find replay. First, it was found that TDLM detects replay only at implausible numbers of replay events per second. Second, it detects replay-to-cognition correlations only at implausible densities. Third, there are concerning baseline shifts in sequenceness across participants. Fourth, spurious sequences arise in control conditions without a ground truth signal. Fifth, when reframing simulations previously published, similar evidence is apparent.

      Strengths:

      (1) This work is meticulous and meets a high standard of transparency and open science, with preregistration, code and data sharing, external resources such as a GUI with the task and material for the public.

      (2) The writing is clear, balanced, and matter-of-fact.

      (3) By injecting visually evoked empirical data into the simulation, many surface-level problems are avoided, such as biological plausibility and questions of signal-to-noise ratio.

      (4) The investigation of sequenceness-to-cognition correlations is an especially useful add-on because much of the previous work uses this to make key claims about replay as a mechanism.

      Weaknesses:

      Many of the weaknesses are not so much flaws in the analyses, but shortcomings when it comes to interpretation and a lack of making these findings as useful as they could be.

      (1) I found the bigger picture analysis to be lacking. Let us take stock: in other work, during active cognition, including at least one study from the Authors, TDLM shows significance sequenceness. But the evidence provided here suggests that even very strong localizer patterns injected into the data cannot be detected as replay except at implausible speeds. How can both of these things be true? Assuming these analyses are cogent, do these findings not imply something more destructive about all studies that found positive results with TDLM?

      (2) All things considered, TDLM seems like a fairly 'vanilla' and low-assumption algorithm for finding event sequences. It is hard to see intuitively what the breaking factor might be; why do the authors think ground truth patterns cannot be detected by this GLM-based framework at reasonable densities?

      (3) Can the authors sketch any directions for alternative methods? It seems we need an algorithm that outperforms TDLM, but not many clues or speculations are given as to what that might look like. Relatedly, no technical or "internal" critique is provided. What is it about TDLM that causes it to be so weak?

      Addressing these points would make this manuscript more useful, workable, and constructive, even if they would not necessarily increase its scientific breadth or strength of evidence.

      Review 1
    2. Public_Reviews 10 Sep 2025

      Reviewer #3 (Public review):

      Summary:

      Kern et al. critically assess the sensitivity of temporally delayed linear modelling (TDLM), a relatively new method used to detect memory replay in humans via MEG. While TDLM has recently gained traction and been used to report many exciting links between replay and behavior in humans, Kern et al. were unable to detect replay during a post-learning rest period. To determine whether this null result reflected an actual absence of replay or sensitivity of the method, the authors ran a simulation: synthetic replay events were inserted into a control dataset, and TDLM was used to decode them, varying both replay density and its correlation with behavior. The results revealed that TDLM could only reliably detect replay at unrealistically (not-physiological) high replay densities, and the authors were unable to induce strong behavior correlations. These findings highlight important limitations of TDLM, particularly for detecting replay over extended, minutes-long time periods.

      Strengths:

      Overall, I think this is an extremely important paper, given the growing use of TDLM to report exciting relationships between replay and behavior in humans. I found the text clear, the results compelling, and the critique of TDLM quite fair: it is not that this method can never be applied, but just that it has limits in its sensitivity to detect replay during minutes-long periods. Further, I greatly appreciated the authors' efforts to describe ways to improve TDLM: developing better decoders and applying them to smaller time windows.

      The power of this paper comes from the simulation, whereby the authors inserted replay events and attempted to detect them using TDLM. Regarding their first study, there are many alternative explanations or possible analysis strategies that the authors do not discuss; however, none of these are relevant if, under conditions where it is synthetically inserted, replay cannot be detected.

      Additionally, the authors are relatively clear about which parameters they chose, why they chose them, and how well they match previous literature (they seem well matched).

      Finally, I found the application of TDLM to a baseline period particularly important, as it demonstrated that there are fluctuations in sequenceness in control conditions (where no replay would be expected); it is important to contrast/calculate the difference between control (pre-resting state) and target (post-resting state) sequenceness values.

      Weaknesses:

      While I found this paper compelling, I was left with a series of questions.

      (1) I am still left wondering why other studies were able to detect replay using this method. My takeaway from this paper is that large time windows lead to high significance thresholds/required replay density, making it extremely challenging to detect replay at physiological levels during resting periods. While it is true that some previous studies applying TDLM used smaller time windows (e.g., Kern's previous paper detected replay in 1500ms windows), others, including Liu et al. (2019), successfully detected replay during a 5-minute resting period. Why do the authors believe others have nevertheless been able to detect replay during multi-minute time windows?

      For example, some studies using TDLM report evidence of sequenceness as a contrast between evidence of forwards (f) versus backwards (b) sequenceness; sequenceness was defined as ZfΔt - ZbΔt (where Z refers to the sequence alignment coefficient for a transition matrix at a specific time lag). This use case is not discussed in the present paper, despite its prevalence in the literature. If the same logic were applied to the data in this study, would significant sequenceness have been uncovered? Whether it would or not, I believe this point is important for understanding methodological differences between this paper and others.

      (2) Relatedly, while the authors note that smaller time windows are necessary for TDLM to succeed, a more precise description of the appropriate window size would greatly improve the utility of this paper. As it stands, the discussion feels incomplete without this information, as providing explicit guidance on optimal window sizes would help future researchers apply TDLM effectively. Under what window size range can physiological levels of replay actually be detected using TDLM? Or, is there some scaling factor that should be considered, in terms of window size and significance threshold/replay density? If the authors are unable to provide a concrete recommendation, they could add information about time windows used in previous studies (perhaps, is 1500ms as used in their previous paper a good recommendation?).

      (3) In their simulation, the authors define a replay event as a single transition from one item to another (example: A to B). However, in rodents, replay often traverses more than a single transition (example: A to B to C, even to D and E). Observing multistep sequences increases confidence that true replay is present. How does sequence length impact the authors' conclusions? Similarly, can the authors comment on how the length of the inserted events impacts TDLM sensitivity, if at all?

      For example, regarding sequence length, is it possible that TDLM would detect multiple parts of a longer sequence independently, meaning that the high density needed to detect replay is actually not quite so dense? (example: if 20 four-step sequences (A to B to C to D to E) were sampled by TDLM such that it recorded each transition separately, that would lead to a density of 80 events/min).

      Review 3
    Visit annotations in context

    Tags

    Annotators

    URL

    biorxiv.org/content/10.1101/2025.06.11.658835v1
  18. gsr25.dididigital.de gsr25.dididigital.de
    GSR 2025 | Snapshot: Thailand
    1
    1. jadbaba 10 Sep 2025
      M. Purton, ‘4 Ways to Make the Cement Industry More Sustainable', World Economic Forum, 13 September 2024, https://www.weforum.org/stories/2024/09/cement-production-sustainable-concrete-co2-emissions/. TCMA and TCA, ‘Thailand 2050 Net Zero Cement & Concrete Roadmap – Published in October 2024', accessed 20 May 2025, https://www.thaicma.or.th/th/ebook_detail/3/197.; Rondo Energy, ‘SCG Cleanergy & Rondo Energy to Deploy World's First Heat Battery to Power Cement Production – Rondo Energy', accessed 20 May 2025, https://www.rondo.com/news-press/scg-cleanergy-amp-rondo-energy-to-deploy-worlds-first-heat-battery-to-power-cement-production.; Joachim Harder, ‘Outlook on ASEAN's Cement Industry – Cement Lime Gypsum', accessed 20 May 2025, https://www.zkg.de/en/artikel/zkg_Outlook_on_ASEAN_s_cement_industry-3403537.html.; Rondo Energy, ‘Product Launch: The Rondo Heat Battery (RHB), Providing the World's Lowest-Cost, Zero-Carbon Industrial Heat – Rondo Energy', accessed 20 May 2025, https://www.rondo.com/news-press/product-launch-the-rondo-heat-battery-rhb-providing-the-worlds-lowest-cost-zero-carbon-industrial-heat.; Rondo Energy, ‘Rondo Energy and Siam Cement Group Plan 90GWh Battery Factory, World's Largest – Rondo Energy', accessed 20 May 2025, https://www.rondo.com/news-press/rondo-energy-and-siam-cement-group-plan-90gwh-battery-factory-worlds-largest.; Rondo Energy, ‘Rondo Energy Announces €75M Project Funding with Breakthrough Energy Catalyst and the European Investment Bank - Rondo Energy', accessed 20 May 2025, https://www.rondo.com/news-press/rondo-energy-announces-75m-project-funding-with-breakthrough-energy-catalyst-and-the-european-investment-bank.; Rondo Energy, “‘Aramco and Rondo Energy Agree on GW-Scale Thermal Storage Deployment, Hydrogen and Carbon Capture. – Rondo Energy', accessed 20 May 2025, https://www.rondo.com/news-press/aramco-and-rondo-energy-agree-on-gw-scale-thermal-storage-deployment-hydrogen-and-carbon-capture; ‘H&M Group Invests in Rondo Energy to Further Strengthen Their Climate Strategy – Rondo Energy', accessed 20 May 2025, https://www.rondo.com/news-press/hm-group-invests-in-rondo-energy-to-further-strengthen-their-climate-strategy. i Clinker is the high-temperature intermediate product in cement production, formed by calcining limestone and other materials. This process is the primary source of GHG emissions in the cement industry

      can all of this be hidden and only appear on the mouse hover over "sources" in "Snapshot Thailand from the following sources"

    Visit annotations in context

    Annotators

    URL

    gsr25.dididigital.de/snapshots/th/
  19. www.biorxiv.org www.biorxiv.org
    Starvation of the bacteria Vibrio atlanticus promotes lightning group-attacks on the dinoflagellate Alexandrium pacificum
    1
    1. Public_Reviews 10 Sep 2025

      Reviewer #2 (Public review):

      Goal summary:

      The authors sought to (i) demonstrate correlations between the dynamics of the dinoflagellate Alexandrium pacificum and the bacterim Vibrio atlanticus in natural populations, ii) demonstrate the occurrence of predation in laboratory experiments, iii) claim coordinated action by the predators in the predation process, iv) demonstrate that predation is induced by predator starvation, and v) test for effects of quorum sensing and iron-uptake genes on the predation process.

      Strengths include:

      (1) Data indicating correlated dynamics in a natural environment that increase the motivation for the study of in vitro interactions.

      (2) Experimental design allowing clear inference of predation based on population counts of both prey and predators in addition to microscopy-based evidence.

      (3) Supplementation of population-level data with molecular approaches to test hypotheses regarding possible involvement of quorum sensing and iron uptake in predation.

      Weaknesses include:

      (1) A lack of early, clear definitions for several important terms used in the paper, including 'predation', 'coordination' and 'coordinated action', 'group attack', and 'wolf-pack hunting', along with a corresponding lack of criteria for what evidence would warrant use of some of these labels. (For example, does mere simultaneity of attacks of an A. pacificum cell by many V. atlanticus cells constitute "coordination"? Or, as it seems to us, does coordination require some form of signalling between predator cells?)

      (2) Absence of controls for cell density in the test for starvation effects on predatory behavior; unclear how the length of incubation affects the density of V. atlanticus cells.

      (3) Lack of clarity in some of the methodological descriptions

      Appraisal:

      The authors convincingly achieve their aim of demonstrating that V. atlanticus can prey on A. pacificum, provide strongly suggestive evidence that such predation is induced by starvation, and clearly demonstrate that both iron availability and, correspondingly, the presence of genes involved in iron uptake, strongly influence the efficacy of predation. However, the evidence for starvation-induction of predation can be strengthened with cell-density controls; evidence for a social component to predation - positive interactions between attacking predators - is lacking.

      Discussion of impact:

      This paper will interest those interested in how microbial behaviour responds to environmental fluctuations, in particular predatory behaviour, but will do so more strongly if the evidence of starvation-induction of predation is strengthened. It will also interest those investigating bacteria-algae interactions and potential ecological controls of algal blooms. It has the potential to interest researchers of microbial cooperation, should the authors be able to provide any evidence of coordination between predator cells.

      Review 2
    Visit annotations in context

    Tags

    Annotators

    URL

    biorxiv.org/content/10.1101/2024.12.18.629110v1
  20. www.polaroid.com www.polaroid.com
    Polaroid Now Generation 3 Memories Set | Polaroid US
    4
    4. marianna.vasquez 10 Sep 2025
      Optimized exposures for sharper picturesBulletpointShoots original full-size Polaroid filmBulletpointIconic Polaroid designBulletpointTwo-lens autofocus = twice the clarit

      small body text, no function visible to increase size.

    Visit annotations in context

    Annotators

    URL

    polaroid.com/en_ca/products/polaroid-now-generation-3-memories-set
  21. www.apple.com www.apple.com
    Apple (Canada)
    1
    1. joe.ole 10 Sep 2025
      AirPods Pro 3

      Apple's use of large graphics, text, and its 'learn more' function allows for greater accessibility for those with learning disabilities or limited literacy. In more detail, these features allow for easy comprehension and navigation as it clearly depicts what the website is selling/advertising, as well as provides users with step-by-step paths to additional information.

    Visit annotations in context

    Annotators

    URL

    apple.com/ca/
  22. socialsci.libretexts.org socialsci.libretexts.org
    3.3: Demand, Supply, and Equilibrium
    2
    1. SammieL05 10 Sep 2025
      Remember that the reduction in quantity supplied is a movement along the supply curve—the curve itself does not shift in response to a reduction in price. Similarly, the increase in quantity demanded is a movement along the demand curve—the demand curve does not shift in response to a reduction in price

      this things move along the curve because they are already on the X/Y axis, other factors are not

    2. SammieL05 10 Sep 2025
      More generally, a surplus is the amount by which the quantity supplied exceeds the quantity demanded at the current price.

      company overproducing, the price exceeds the equilibrium price (the price they should be asking for) ex. they produce 35 million and $8, but buyers consume 15 million at that price surplus = 20 million lbs

    Visit annotations in context

    Annotators

    URL

    socialsci.libretexts.org/Bookshelves/Economics/Introductory_Comprehensive_Economics/Principles_of_Economics_(LibreTexts)/03:_Demand_and_Supply/3.3:_Demand_Supply_and_Equilibrium
  23. minusx.ai minusx.ai
    What makes Claude Code so damn good (and how to recreate that magic in your agent)!?
    1
    1. Tempary 10 Sep 2025
      claude-3-5-haiku. It is used to read large files, parse web pages, process git history and summarize long conversations.

      Хайку ~20B

      достоверных чисел нет; встречаются неофициальные оценки ( Хайку ~20B, Соннет ~70B, Опус ~2T), но это блоги/сабстэки без подтверждения от вендоров

    Visit annotations in context

    Annotators

    URL

    minusx.ai/blog/decoding-claude-code/
  24. www.biorxiv.org www.biorxiv.org
    Neural crest induction requires SALL4-mediated BAF recruitment to lineage specific enhancers
    3
    1. EMBOpress 10 Sep 2025

      Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

      Learn more at Review Commons

      Reply to the reviewers

      Reviewer #1

      Summary: The authors have previously published Mass-spectrometry data that demonstrates a physical interaction between Sall4 and the BAF chromatin complex in iPSC derived neurectodermal cells that are a precursor cell state to neural crest cells. The authors sought to understand the basis of this interaction and investigate the role of Sall4 and the BAF chromatin remodelling complex during neural crest cell specification. The authors first validate this interaction with a co-IP between ARID1B subunit and Sall4 confirming the mass spec data. The authors then utilise in silico modelling to identify the specific interaction between the BAF complex and Sall4, suggesting that this contact is mediated through the BAF complex member DPF2. To functionally validate the role of Sall4 during neural crest specification, the authors utilsie CRISPR-Cas9 to introduce a premature stop codon on one allele of Sall4 to generate iPSCs that are haploinsufficient for Sall4. Due to the reports of Sall4's role in pluripotency, the authors confirm that this model doesn't disrupt pluripotent stem cells and is viable to model the role of Sall4 during neural crest induction. The authors expand this assessment of Sall4 function further during their differentiation model to cranial neural crest cells, assessing Sall4 binding with Cut+Run sequencing, revealing that Sall4 binds to motifs that correspond to key genes in neural crest differentiation. Moreover, reduction in Sall4 expression also reduces the binding of the BAF complex, through Cut and Run for BRG1. Overall, the authors then propose a model by which Sall4 and BRG1 bind to and open enhancer regions in neurectodermal cells that enable complete differentiation to cranial neural crest cells.

      Overall, the data is clear and reproducible and offers a unique insight into the role of chromatin remodellers during cell fate specification.

      We thank the Reviewer for the nice words of appreciation of our manuscript.

      However, I have some minor comments.

      1- Using AlphaFold in silico modelling, he authors propose the interaction between the BAF complex with Sall4 is mediated by DPF2, but don't test it. Does a knockout, or knockdown of DPF2 prevent the interaction?

      We agree with the Reviewer that we are not functionally validating our computational prediction that DPF2 is the specific BAF subunit directly linking SALL4 with BAF. We chose not to perform the validation experiment for two main reasons:

      1) This would be outside of the scope of the paper. In fact, from a mechanistic point of view, we have confirmed via both Mass-spectrometry and co-IP with ARID1B that SALL4 and BAF interact in our system. Moreover, mechanistically we also extensively demonstrate that the interaction with SALL4 is required to recruit BAF at the neural crest induction enhancers and we further demonstrate that depletion of SALL4 impairs this. In our view, this was the focus of the manuscript. On the other hand, detecting with certainty which BAF subunit mediates the interaction with SALL4 would be outside the scope of the paper.

      2) Moreover, after careful consideration, we don’t think that even a knock-out of DPF2 would provide a definite answer to which exact BAF subunit mediates the interaction with SALL4. In fact, knock out of DPF2 could potentially disrupt BAF assembly or stability, and this could result in a disruption of the interaction with SALL4 even if DPF2 is not the very subunit mediating it (in other words the experiment could provide a false positive result). In our opinion, the only effective experiment would be mutating the DPF2 residues that we computationally predicted as responsible for the interaction with SALL4, but again this would be very laborious and out of the scope.

      That being said, we agree with the Reviewer that while the SALL4-BAF interaction was experimentally validated with robust approaches, the role of DPF2 in the interaction was only computationally predicted, which comes as a limitation of the study. We have now added a dedicated paragraph in the discussion to acknowledge such limitation.

      2- OPTIONAL: Does knockout of DPF2 phenocopy the Sall4 ko? This would be very interesting to include in the manuscript, but it would perhaps be a larger body of work.

      See point-1.

      3- Figure 1, the day of IP is not clearly described until later in the test. please outline during in the figure.

      We thank the Reviewer for pointing this out. This has been fixed.

      3- What is the expression of Sall1 (and other Sall paralogs) during differentiation. The same with the protein levels of Sall4, does this remain at the below 50%, or is this just during pluripotency?

      As Recommend by the Reviewer, we have performed time-course WB of SALL1 and SALL4. These experiments revealed that SALL1 remains very lowly expressed in wild-type conditions across time points and all the way through differentiation until CNCC (See updated supplementary Fig. S9). This is consistent with previous studies that demonstrated that SALL4, but not SALL1, is required for early mammalian development (see for example Miller et al. 2016, Development, and Koulle et al. 2025, Biorxiv). We performed the same time-course WB for SALL4 which revealed that SALL4 expression progressively decreases after day-5 (as expected) and it’s very low at CNCC stage (day-14), therefore we would expect the KO to remain at even lower level at this stage.

      4- The authors hypothesise that Sall4 binds to enhancers- with the criteria for an enhancer being that these peaks > 1KB from the TSS are enhancers. Can this be reinforced by overlaying with other ChIP tracks that would give more confidence in this? There are several datasets from Joanna Wysocka's lab that also utilise this protocol which can give you more evidence to reinforce the claim and provide further detail as to the role of Sall4.

      We thank the Reviewer for this great suggestion. As recommended, we have used publicly available ChIP-seq data generated by the Wysocka lab (H3K4me1, H3K4m3) and also generated new H3K27ac CHIP-seq data as well. These experiments and analyses confirmed that these regions are putative CNCC enhancers (and a minority of them putative promoters), decorated with H3K4me1 and with progressive increase in H3K27ac after CNCC induction (day-5). See new Supplementary Figure S6.

      5- The authors state that cells fail to become cranial neural crest cells, however they do not propose what the cells do instead. do they become neural? Or they stay at pluriopotent, which is one option given the higher expression of Nanog, OCT4 and OTX2 that are all expressed in pluripotent stem cells.

      We think that it is likely a mix of both. There is a mixed bag of expression of pluripotency markers, but also high expression of neuroectodermal markers. This suggests that most cells safely reach the neuroectodermal stage but fail to go beyond that, while some of the cells simply do not differentiate or regress back to pluripotency. We would rather refrain on overinterpreting what the KO-cells become, as it is likely an aberrant cell type, but following the Reviewer’s indication we have added a paragraph in the discussion to speculate on this.

      6- In general, I would like to see the gating strategy and controls for the flow cytometry in a supplemental figure.

      As Recommended by the Reviewer, we have added the gating strategy in the Supplementary Fig. S4.

      7- For supplementary figure 1- please include the gene names in the main image panels rather than just the germ layer.

      Done. The figure is now Supplementary Figure S3 since two supplementary figures were added before.

      Reviewer #2

      Summary In this manuscript, the authors build on their previous work (Pagliaroli et al., 2021) where they identified an interaction between the transcription factor SALL4 and the BAF chromatin remodeling complex at Day-5 of an iPSC to CNCC differentiation protocol. In their current work, the authors begin by exploring this interaction further, leveraging AlphaFold to predict interaction surfaces between SALL4 and BAF complex members, considering both SALL4 splice isoforms: a longer SALL4A (associated with developmental processes) and a shorter SALL4B (associated with pluripotency). They propose that SALL4A may interact with DPF2, a BAF complex member, in an isoform-dependent manner. The authors next explore the role of SALL4 in craniofacial development, motivated by patient heterozygous loss of function mutations, leveraging iPSC cells with an engineered SALL4 frameshift mutation (SALL4-het-KO). Using this model, the authors first demonstrate that a reduced expression of SALL4 does not impact the iPSC identity, perhaps due to compensation via upregulation of SALL1. Upon differentiation to neuroectoderm, SALL4 haploinsufficiency causes a reduction in newly accessible sites which are associated with a reduction in SALL4 binding and therefore a loss of BAF complex recruitment. Interestingly, however, there were few transcriptional changes at this stage. Later in the CNCC differentiation at Day-14 when the wildtype cells have switched expression of CNCC markers, the SALL4-het-KO cells fail to switch cadherin expression associated with a transition from epithelial to mesenchymal state, and fail to induce CNCC specification and post-migratory markers. Together the authors propose that SALL4 recruits BAF to CNCC enhancers as early as the neuroectodermal stage, and failure of BAF recruitment in SALL4-het-KO lines results in a loss of open chromatin at regulatory regions required later for induction of the CNCC programme. The failure of the later differentiation is compelling in the light of the early stages of the differentiation progressing normally, and the authors outline an interesting proposed mechanism whereby SALL4 recruits BAF to remodel chromatin ahead of CNCC enhancer activation, a model that can be tested further in future work. The link between SALL4 DNA binding and BAF recruitment is nicely argued, and very interesting as altered chromatin accessibility at Day 5 in the neuroectodermal stage is associated with only few changes in gene expression, while gene expression is greatly impacted later in the CNCC stage at Day 14. The in silico predictions of SALL4-BAF interaction interfaces are perhaps less convincing, requiring experimental follow-up outside the scope of this paper. Some of the associated figures could perhaps be moved to the supplement to enhance the focus on the later functional genomics experiments.

      We thank the Reviewer for the nice words of appreciation of our manuscript.

      Major comments

      1. A lot of emphasis is placed on the AlphaFold predictions in Figure 1, however the predictions in Figure 1B appear to be mostly low or very low confidence scores (coloured yellow and orange). It is unclear how much weight can be placed on these predictions without functional follow-up, e.g. mutating certain residues and showing impact on the interaction by co-IP. The latter parts of the manuscript are much better supported experimentally, and therefore perhaps some of the Figure 1 could move to a Supplemental Figure (e.g. the right-hand part of 1B, and the lower part of Figure 1C showing SALL4B predicted interactions). The limitations of AlphaFold predictions should be acknowledged and the authors should discuss how these predicted interactions could be experimentally explored further in the future.

      As recommended by the Reviewer, we have moved part of the AlphaFold predictions to Supplementary Figure S1, and we added a paragraph in the discussion to acknowledge the limitations of AlphaFold.

      The authors only show data for one heterozygous knockout clone for SALL4. It is usual to have more than one clone to mitigate potential clonal effects. The authors should comment why they only have one clone and include any data for a second clone for key experiments if they already have this. Alternatively, the authors could provide any quality control information generated during production of this line, for example if any additional genotyping was performed.

      We apologize for the confusion and for our lack of clarify on this. We have used two clones (one generated with a 11 bp deletion, one with a 19 bp deletion, both in exon-1, see also the point 6 of your minor points). The two clones were used as biological replicates, so for example the two ATAC-seq replicates performed in each time point were performed with the two different clones, and the three RNA-seq replicates were performed with two technical replicates of the clone with the 11bp deletion and one replicate with the clone with 19 bp deletion. We have clarified this in the methods section of the manuscript and added a Supplementary Figure (S2) showing the editing strategy for the two clones. Thank you for catching it.

      The authors show all genomics data (ATAC-seq, CUT&RUN and ChIP-seq) as heatmaps and average profiles. It would be valuable to see some representative loci for the ATAC seq (perhaps along with SALL4 and BRG1 recruitment) at some representative and interesting loci.

      As recommended by the Reviewer, we have added Genome Browser screenshots of representative loci in Fig. 6.

      Figure 4A. The schematic could be improved by including brightfield or immunofluorescent images at the three stages of the differentiation. Are the iPS cells seeded as single cells, or passaged as colonies before starting the differentiation. Further details are required in the methods to clarify how the differentiation is performed, for example at what Day are the differentiating cells passaged, this is not shown on the schematic in Figure 4A.

      As recommended, we added IF images in the Fig. 4A schematic, and added more details in the methods.

      There is likely some heterogeneity of cell types in the differentiation at Day 5 and Day 14. Can the authors comment on this from previous publications or perhaps conduct some IF for markers to demonstrate what proportions of cells are neuroectoderm at Day 5 and CNCCs at Day 14.

      The differentiation starts with single cells that aggregate to form neuroectodermal clusters, as per original protocol. The CNCCs that we obtain with this protocol homogeneously express CNCC markers, as shown by IF of SOX9 in Fig. 4A. For the day-5, as recommended we have added IF for PAX6 also showing homogeneous expression (Fig. 4A).

      For the motif analysis for Day 5-specific SALL4 binding sites (Figure 4E), was de novo motif calling performed? Were any binding sites reminiscent of a SALL4 binding site observed (e.g. an AT-rich motif)? Could the authors comment on this in the text - if there is no SALL4 binding motif, does this suggest SALL4 is recruited indirectly to these sites via interaction with another transcription factor for example?

      Similar to SALL4, SALL1 also recognizes AT-rich motifs. However, while we found AT-rich motifs as enriched in our day-5 motif analysis (in the regions that gain SALL4 binding upon differentiation), the enrichment is not particularly strong, and several other motifs are significantly more enriched, suggesting that, like the Reviewer mentioned, SALL4 might be recruited indirectly at these sites by other factors. We have added a paragraph on this in the discussion.

      Does SALL1 remain upregulated at Day-5 and Day-14 of the differentiation for the SALL4-het-KO line? Are binding sites known for this TF and were they detected in the motif analysis performed? Further discussion of the impact of the overexpression of SALL1 on the phenotypes observed is warranted - e.g. for Figure 5F, could the sites associated with a gain of BRG1 peaks upon loss of SALL4 be associated with SALL1 being upregulated and 'hijacking' BAF recruitment to distinct sites associated with nervous system development? Is SALL1 still upregulated at Day 5?

      As mentioned above, SALL1 also recognizes AT-rich motifs but similar to SALL4 also binds unspecifically, likely in cooperation with other TFs. Like the Reviewer suggested, it is certainly possible that some of the sites associated with a gain of BRG1 peaks upon loss of SALL4 could be associated with SALL1 being upregulated and 'hijacking' BAF recruitment to distinct sites. While this is speculative, we have added a paragraph on this in the discussion.

      Related to the point above, SALL4A is proposed to have an isoform-specific interaction with the BAF complex. It would be valuable to plot SALL4A and SALL4B expression from the available RNA-seq data at Day 0, 5 and 14 to explore whether stage-specific isoform expression matches with the proposed role of SALL4A to interact with BAF at Day 5. It could be valuable to also look at expression of SALL1, 2 and 3 across the time course to see whether additional compensation mechanisms are at play during the differentiation.

      Thanks for suggesting this. We performed a time course analysis of isoform specific gene expression, which showed that SALL4B expression remains low throughout differentiation, while SALLA4A expression increases upon differentiation cues and it remains at high levels until the end. We have added this to supplementary Fig. S9. Moreover, we have performed an additional experiment, using pomalidomide, which is a thalidomide derivative that selectively degrades SALL4A but not SALL4B. Notably, SALL4A degradation recapitulated the main findings obtained with the CRISPR-KO of SALL4, further supporting that SALL4A is the isoform involved in CNCC induction (see new Fig. 8).

      At line 264, The authors state "SALL4 recruits the BAF complex at CNCC developmental enhancers to increase chromatin accessibility". Given that this analysis is performed at Day 5 of the differentiation, which is labelled as neuroectoderm what evidence do the authors have that these are specifically CNCC enhancers? Statements relating to enhancers should generally be re-phrased to putative enhancers (as no functional evidence is provided for enhancer activity), and further evidence could be provided to support that these are CNCC-specific regulatory elements, e.g. showing representative gene loci from CNCC-specific genes. Discussion of the RNA-seq presented in Supplementary Figure 2B may also be appropriate to introduce here given that large numbers of accessible chromatin sites are detected while the expression of very few genes is impacted, suggesting these sites may become active enhancers at a later developmental stage.

      As also recommended by the other Reviewer, to further characterize these sites, we have used publicly available histone modification CHIP-seq data (H3K4me1, H3K4me3) generated by the Wysocka lab (H3K4me1, H3K4m3) and also generated new H3K27ac CHIP-seq data as well. These experiments and analyses confirmed that these regions are putative CNCC enhancers (and a minority of them putative promoters), all decorated with H3K4me1, and all showing progressive increase in H3K27ac after CNCC induction (day-5). See new Supplementary Figure S6.

      1. Do any of the putative CNCC enhancers detected at Day 5 as being sensitive to SALL4 downregulation and loss of BAF recruitment overlap with previously tested VISTA enhancers (https://enhancer.lbl.gov/vista/)?

      Yes, we have found examples of overlap and have included two of them in the updated Figure 6 as Genome Browser screenshots.

      Minor comments

      1. The authors are missing references in the introduction "a subpopulation of neural crest cells that migrate dorsolaterally to give rise to the cartilage and bones of the face and anterior skull, as well as cranial neurons and glia".

      Fixed, thank you.

      The discussion of congenital malformations associated with SALL4 haploinsufficiency is brief in the introduction. From OMIM, SALL4 heterozygous mutations are implicated with the condition Duane-radial ray syndrome (DRRS) with "upper limb anomalies, ocular anomalies, and, in some cases, renal anomalies... The ocular anomalies usually include Duane anomaly". That Duane anomaly is one phenotype among a number for patients with SALL4 haploinsufficiency could be clarified in the introduction. Of note, this is stated more clearly in the discussion but needs re-wording in the introduction.

      Done, thank you.

      The statements "show that the SALL4A isoform directly interacts with the BAF complex subunit DPF2 through its zinc-finger-3 domain" and "this interaction occurs between the zinc-finger-cluster-3 (ZFC3) domain of SALL4A and the plant homeodomains (PHDs) of DPF2" in the introduction appear overstated and should be toned down. To show this the authors would need to mutate or delete the proposed important zinc-finger domains from SALL4A, which is outside the scope of this work. Notably, this is less strongly-stated elsewhere in the manuscript, e.g "predict that this interaction is mediated by the BAF subunit DPF2", Line 162.

      Done, thank you.

      Could the authors clarify why 3 Alphafold output models are shown for SALL4B in Figure 1C, and only one output model for SALL4A?

      AlphaFold3 produces five separate predicted models per protein combination (e.g., Model_1 … Model_4), each derived from slightly different network parameters or initializations. The final output prioritizes the model with the highest confidence score. This multi-model strategy enables the identification of the most robust conformation while providing a measure of structural uncertainty (as per GitHub documentation for AlphaFold3). wE have conducted the same analysis for SALL4A as we did for SALL4B. Specifically, SALL4A interacts with the AT-rich DNA in models 0, 1, and 2, therefore models 3 and 4 were excluded. When analysing models 1 and 2, we found a higher number of residues involved in the interaction (>800 instead of 396). Similarly to model 0, only the interactions between residues belonging to an annotated functional domain (ZFs and PHDs) were considered.

      In Model 1: SALL4A and DPF2 interact mainly through ZF6 and 7, and not 5 as Model 0.

      In Model 2: SALL4A and DPF2 interact mainly through ZF5 and 6, and not 7 as Models 0. In contrast, this model shows an interaction with ZF1 not shown in the other two models, but with a higher PAE (31 average compared to 25 to 27 average of the other two ZFs.

      Therefore, we considered Model 0 as it is the model with higher confidence and representative of all significant models (includes ZF5, 6, and 7).

      Line 121. The authors state "DPF2, a broadly expressed BAF subunit,", but don't show expression during their CNCC differentiation. It would be good to include expression of DPF2 in Figure 1E.

      Done, thank you.

      The text states "a 11 bp deletion within the 3'-terminus of exon 1 of SALL4", while the figure legend states, "Sanger sequencing confirming the 19 bp deletion in one allele of SALL4 is displayed". The authors should clarify this disparity and experimentally confirm the deletion, e.g. by TA-cloning the two alleles and sequencing these separately to show that one allele is wildtype and the other has a frameshift deletion.

      We apologize for the confusion. As stated above (point-2 of the major comments), we have used two clones (one generated with a 11 bp deletion, one with a 19 bp deletion, both in exon-1, see also the point 6 of your minor points). The two clones were used as biological replicates (see response above for details). The deletion for both clones was experimentally confirmed by Sanger sequencing by the company that generated the lines for us (Synthego). The strategy for the two clones is now shown also in Supplementary Fig. S2.

      The authors generate an 11-bp (or 19-bp?) deletion in exon-1 - it would be valuable to include a discussion whether patients have been identified with deletions and frame-shift mutations in this region of SALL4 exon-1. And also clarify, if not clearly stated in the text, that both SALL4A and SALL4B will be impacted by this mutation. Are there examples of patient mutations which only impact SALL4A?

      As requested, we have added a discussion paragraph to discuss this. And, yes, both SALL4A and SALL4B are impacted by both deletions in both clones (11 bp and 19 bp deletion).

      Regarding patient variants on exon-1 and patient variants that only impact SALL4A. We could only find one published pathogenic 170bp deletion in exon 1 (VCV000642045.7). The majority of the pathogenic or likely pathogenic variances are located on exon2. In particular, of the 63 reported pathogenic (or likely pathogenic) clinical variants, 42 were located on exon 2. Among these, 28 are located in the portion shared by both SALL4A and SALL4B, while the remaining 14 were SALL4A specific.

      For the SALL4 blots in Figure 2B, is the antibody expected to detect both isoforms (SALL4A and SALL4B), and which isoform is shown? If two isoforms are detected, they should both be presented in the figure.

      Yes, the antibody detects both isoforms, and we now present both in the figure 2, as recommended.

      SALL4 expression should be shown for Figure 2C to see whether the >50% down-regulation of SALL4 at the protein level may be partially driven by transcriptional changes.

      Done, thank you. As expected, we observed the SALL4 mRNA expression in the KO line is comparable to wild-type conditions, but still this results in a significant decrease of the SALL4 protein level likely because of autoregulatory mechanisms coupled with non-sense mediated decay of the mutated allele. Also, we note that SALL4 usually makes homodimers, therefore lack of sufficient amount of protein could also lead to degradation of the monomers.

      The number of experimental replicates should be indicated in all figure legends where relevant. Raw data points should be plotted visibly over the violin plots (e.g. Figure 2C).

      Done, thank you.

      For Figure 3A, the images of the DAPI and NANOG/OCT4 staining should be shown separately in addition to the overlay.

      Done, thank you.

      The metric 'Corrected Total Cell Fluorescence (CTCF)' should be described in the methods. The number of images used for the quantification in Figure 3A should be

      Done, thank you.

      Figure 3C - what are the 114 differentially expressed genes? Some interesting genes could be labelled on the plot and the data used to generate this plot should be included as a Supplementary Table. Supplementary Tables should similarly be provided for Figure 6C, Day 14 and Supplementary Figure 2B, Day 5.

      As recommended, we have highlighted some interesting genes in the volcano plot and also included all the expression data for all genes in Supplementary Table S3.

      Figure 4B. The shared peaks are not shown. For completeness, it would be ideal to show these sites also.

      Done, thank you.

      Figure 4C is difficult to interpret. Why is the plot asymmetric to the left versus right? What does the axis represent - % of binding sites?

      The asymmetry is due to the fact that there is a larger number of peaks that are downstream of the TSS than peaks that are upstream of TSS. This is consistent with the fact that many SALL4 peaks are in introns, likely representing intronic enhancers.

      Line 224-225. What do n= 3,729 and n= 6,860 refer to? There appear to be many more binding sites indicated in Figure 4B, therefore these numbers cannot represent 86% and 97% of sites?

      Thank you for pointing this out, we should have specified in the text. Those numbers refer to the genes whose TSS is closest to each SALL4 peak. Notably, multiple peaks can share the same closest TSS, hence the discrepancy between # of peaks and # of nearest genes.

      Raw numbers:

      • Day-0 RAW = 6,104 (peaks = 6,114);
      • Day-5 RAW = 17,131 (peaks = 17,137). Now raw data reported in Supplementary Table 4.

      Figure 4E. Several TFs mentioned in the text (Line 243) are not shown in the figure, it would be good to show all TFs motifs mentioned in the text in this figure. Again, there is no mention of whether a sequence-specific motif is detected for SALL4 (e.g. an AT-rich sequence) from this motif analysis.

      Done, thank you. An AT-rich sequence, resembling the SALL4 motif, was detected in a small minority of sites (this is now shown in Supplementary Figure S5), suggesting that SALL4 engages chromatin in a broad manner, going beyond its preferred motif, possibly in cooperation with other TFs. This is consistent with many studies that in mESCs have shown that SALL4 binds at OCT4/NANOG/SOX2 target motifs. This is now discussed in a dedicated paragraph in the discussion.

      Figure 4G. How was the ATAC-seq data normalized for the WT and SALL4-het-KO lines for this comparison? The background levels of accessibility seem quite different in Replicate 1.

      The bigwigs used to make the heatmaps are normalized by sequencing depth using the Deeptools Suite (normalization by RPKM).

      Figures 5B-C could be exchanged to flow better with the text. A Venn diagram could be included to show the overlap between the sites losing BRG1 in SALL4-het-KO (13,505 sites) and the Day5-specific SALL4 sites (17,137 sites).

      Done, thank you.

      At Day 5, the authors suggest a shift towards neural differentiation. It could be interesting for the authors to perform qRT-PCR at Day 5 for some neural markers or look in the Day 14 data for markers of neural differentiation at the expense of CNCC markers.

      See updated Supplementary Fig. S8, where we show timecourse expression of several genes, including neural markers.

      Is the data used to plot Figure 5D the same as Figure 4G. If so, why is only one replicate shown in Figure 5D?

      Only one replicate was shown in the main figure purely for lack of space, but the experiment was replicated twice (with the two different clones), and the results were exactly the same. See plots below for your convenience:

      Figure 6A. How many replicates are shown? If n=2, boxplots are not an appropriate to represent the distribution of the data. Please include n= X in the figure legend and plot the raw data points also.

      Done, thank you, and as suggested we are no longer using boxplots for this panel.

      Figure 6B. What is the significance of CD99 for CNCC differentiation?

      Figure 6F. No error bars are shown, how many replicates were performed for this time couse? The linear regression line does not appear to add much value and could be removed.

      As suggested, we have removed these plots and replaced them with individual genes plots, which include error bars. See updated Supplementary Figure S8.

      At line 304, the authors state "while SALL4-het-KO showed a significant downregulation of these genes". Perhaps 'failed to induce these genes' may be more accurate unless they were expressed at Day 5 and downregulated at Day 14.

      Done, thank you.

      Lines 332-335. The genes selected for pluripotency, neural plate border, CNCC specification could be plotted separately in the Supplement to show individual gene expression dynamics.

      Done, thank you, see point 24.

      PeerReviewed
    2. EMBOpress 10 Sep 2025

      Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

      Learn more at Review Commons

      Referee #2

      Evidence, reproducibility and clarity

      Summary

      In this manuscript, the authors build on their previous work (Pagliaroli et al., 2021) where they identified an interaction between the transcription factor SALL4 and the BAF chromatin remodeling complex at Day-5 of an iPSC to CNCC differentiation protocol. In their current work, the authors begin by exploring this interaction further, leveraging AlphaFold to predict interaction surfaces between SALL4 and BAF complex members, considering both SALL4 splice isoforms: a longer SALL4A (associated with developmental processes) and a shorter SALL4B (associated with pluripotency). They propose that SALL4A may interact with DPF2, a BAF complex member, in an isoform-dependent manner. The authors next explore the role of SALL4 in craniofacial development, motivated by patient heterozygous loss of function mutations, leveraging iPSC cells with an engineered SALL4 frameshift mutation (SALL4-het-KO). Using this model, the authors first demonstrate that a reduced expression of SALL4 does not impact the iPSC identity, perhaps due to compensation via upregulation of SALL1. Upon differentiation to neuroectoderm, SALL4 haploinsufficiency causes a reduction in newly accessible sites which are associated with a reduction in SALL4 binding and therefore a loss of BAF complex recruitment. Interestingly, however, there were few transcriptional changes at this stage. Later in the CNCC differentiation at Day-14 when the wildtype cells have switched expression of CNCC markers, the SALL4-het-KO cells fail to switch cadherin expression associated with a transition from epithelial to mesenchymal state, and fail to induce CNCC specification and post-migratory markers. Together the authors propose that SALL4 recruits BAF to CNCC enhancers as early as the neuroectodermal stage, and failure of BAF recruitment in SALL4-het-KO lines results in a loss of open chromatin at regulatory regions required later for induction of the CNCC programme. The failure of the later differentiation is compelling in the light of the early stages of the differentiation progressing normally, and the authors outline an interesting proposed mechanism whereby SALL4 recruits BAF to remodel chromatin ahead of CNCC enhancer activation, a model that can be tested further in future work.

      Major comments

      The link between SALL4 DNA binding and BAF recruitment is nicely argued, and very interesting as altered chromatin accessibility at Day 5 in the neuroectodermal stage is associated with only few changes in gene expression, while gene expression is greatly impacted later in the CNCC stage at Day 14. The in silico predictions of SALL4-BAF interaction interfaces are perhaps less convincing, requiring experimental follow-up outside the scope of this paper. Some of the associated figures could perhaps be moved to the supplement to enhance the focus on the later functional genomics experiments.

      1. A lot of emphasis is placed on the AlphaFold predictions in Figure 1, however the predictions in Figure 1B appear to be mostly low or very low confidence scores (coloured yellow and orange). It is unclear how much weight can be placed on these predictions without functional follow-up, e.g. mutating certain residues and showing impact on the interaction by co-IP. The latter parts of the manuscript are much better supported experimentally, and therefore perhaps some of the Figure 1 could move to a Supplemental Figure (e.g. the right-hand part of 1B, and the lower part of Figure 1C showing SALL4B predicted interactions). The limitations of AlphaFold predictions should be acknowledged and the authors should discuss how these predicted interactions could be experimentally explored further in the future.
      2. The authors only show data for one heterozygous knockout clone for SALL4. It is usual to have more than one clone to mitigate potential clonal effects. The authors should comment why they only have one clone and include any data for a second clone for key experiments if they already have this. Alternatively, the authors could provide any quality control information generated during production of this line, for example if any additional genotyping was performed.
      3. The authors show all genomics data (ATAC-seq, CUT&RUN and ChIP-seq) as heatmaps and average profiles. It would be valuable to see some representative loci for the ATAC seq (perhaps along with SALL4 and BRG1 recruitment) at some representative and interesting loci.
      4. Figure 4A. The schematic could be improved by including brightfield or immunofluorescent images at the three stages of the differentiation. Are the iPS cells seeded as single cells, or passaged as colonies before starting the differentiation. Further details are required in the methods to clarify how the differentiation is performed, for example at what Day are the differentiating cells passaged, this is not shown on the schematic in Figure 4A.
      5. There is likely some heterogeneity of cell types in the differentiation at Day 5 and Day 14. Can the authors comment on this from previous publications or perhaps conduct some IF for markers to demonstrate what proportions of cells are neuroectoderm at Day 5 and CNCCs at Day 14.
      6. For the motif analysis for Day 5-specific SALL4 binding sites (Figure 4E), was de novo motif calling performed? Were any binding sites reminiscent of a SALL4 binding site observed (e.g. an AT-rich motif)? Could the authors comment on this in the text - if there is no SALL4 binding motif, does this suggest SALL4 is recruited indirectly to these sites via interaction with another transcription factor for example?
      7. Does SALL1 remain upregulated at Day-5 and Day-14 of the differentiation for the SALL4-het-KO line? Are binding sites known for this TF and were they detected in the motif analysis performed? Further discussion of the impact of the overexpression of SALL1 on the phenotypes observed is warranted - e.g. for Figure 5F, could the sites associated with a gain of BRG1 peaks upon loss of SALL4 be associated with SALL1 being upregulated and 'hijacking' BAF recruitment to distinct sites associated with nervous system development? Is SALL1 still upregulated at Day 5?
      8. Related to the point above, SALL4A is proposed to have an isoform-specific interaction with the BAF complex. It would be valuable to plot SALL4A and SALL4B expression from the available RNA-seq data at Day 0, 5 and 14 to explore whether stage-specific isoform expression matches with the proposed role of SALL4A to interact with BAF at Day 5. It could be valuable to also look at expression of SALL1, 2 and 3 across the time course to see whether additional compensation mechanisms are at play during the differentiation.
      9. At line 264, The authors state "SALL4 recruits the BAF complex at CNCC developmental enhancers to increase chromatin accessibility". Given that this analysis is performed at Day 5 of the differentiation, which is labelled as neuroectoderm what evidence do the authors have that these are specifically CNCC enhancers? Statements relating to enhancers should generally be re-phrased to putative enhancers (as no functional evidence is provided for enhancer activity), and further evidence could be provided to support that these are CNCC-specific regulatory elements, e.g. showing representative gene loci from CNCC-specific genes. Discussion of the RNA-seq presented in Supplementary Figure 2B may also be appropriate to introduce here given that large numbers of accessible chromatin sites are detected while the expression of very few genes is impacted, suggesting these sites may become active enhancers at a later developmental stage.
      10. Do any of the putative CNCC enhancers detected at Day 5 as being sensitive to SALL4 downregulation and loss of BAF recruitment overlap with previously tested VISTA enhancers (https://enhancer.lbl.gov/vista/)?

      Minor comments

      1. The authors are missing references in the introduction "a subpopulation of neural crest cells that migrate dorsolaterally to give rise to the cartilage and bones of the face and anterior skull, as well as cranial neurons and glia".
      2. The discussion of congenital malformations associated with SALL4 haploinsufficiency is brief in the introduction. From OMIM, SALL4 heterozygous mutations are implicated with the condition Duane-radial ray syndrome (DRRS) with "upper limb anomalies, ocular anomalies, and, in some cases, renal anomalies... The ocular anomalies usually include Duane anomaly". That Duane anomaly is one phenotype among a number for patients with SALL4 haploinsufficiency could be clarified in the introduction. Of note, this is stated more clearly in the discussion but needs re-wording in the introduction.
      3. The statements "show that the SALL4A isoform directly interacts with the BAF complex subunit DPF2 through its zinc-finger-3 domain" and "this interaction occurs between the zinc-finger-cluster-3 (ZFC3) domain of SALL4A and the plant homeodomains (PHDs) of DPF2" in the introduction appear overstated and should be toned down. To show this the authors would need to mutate or delete the proposed important zinc-finger domains from SALL4A, which is outside the scope of this work. Notably, this is less strongly-stated elsewhere in the manuscript, e.g "predict that this interaction is mediated by the BAF subunit DPF2", Line 162.
      4. Could the authors clarify why 3 Alphafold output models are shown for SALL4B in Figure 1C, and only one output model for SALL4A?
      5. Line 121. The authors state "DPF2, a broadly expressed BAF subunit,", but don't show expression during their CNCC differentiation. It would be good to include expression of DPF2 in Figure 1E.
      6. The text states "a 11 bp deletion within the 3'-terminus of exon 1 of SALL4", while the figure legend states, "Sanger sequencing confirming the 19 bp deletion in one allele of SALL4 is displayed". The authors should clarify this disparity and experimentally confirm the deletion, e.g. by TA-cloning the two alleles and sequencing these separately to show that one allele is wildtype and the other has a frameshift deletion.
      7. The authors generate an 11-bp (or 19-bp?) deletion in exon-1 - it would be valuable to include a discussion whether patients have been identified with deletions and frame-shift mutations in this region of SALL4 exon-1. And also clarify, if not clearly stated in the text, that both SALL4A and SALL4B will be impacted by this mutation. Are there examples of patient mutations which only impact SALL4A?
      8. For the SALL4 blots in Figure 2B, is the antibody expected to detect both isoforms (SALL4A and SALL4B), and which isoform is shown? If two isoforms are detected, they should both be presented in the figure.
      9. SALL4 expression should be shown for Figure 2C to see whether the >50% down-regulation of SALL4 at the protein level may be partially driven by transcriptional changes.
      10. The number of experimental replicates should be indicated in all figure legends where relevant. Raw data points should be plotted visibly over the violin plots (e.g. Figure 2C).
      11. For Figure 3A, the images of the DAPI and NANOG/OCT4 staining should be shown separately in addition to the overlay.
      12. The metric 'Corrected Total Cell Fluorescence (CTCF)' should be described in the methods. The number of images used for the quantification in Figure 3A should be indicated in the legend, and error bars included if multiple images were quantified.
      13. Figure 3C - what are the 114 differentially expressed genes? Some interesting genes could be labelled on the plot and the data used to generate this plot should be included as a Supplementary Table. Supplementary Tables should similarly be provided for Figure 6C, Day 14 and Supplementary Figure 2B, Day 5.
      14. Figure 4B. The shared peaks are not shown. For completeness, it would be ideal to show these sites also.
      15. Figure 4C is difficult to interpret. Why is the plot asymmetric to the left versus right? What does the axis represent - % of binding sites?
      16. Line 224-225. What do n= 3,729 and n= 6,860 refer to? There appear to be many more binding sites indicated in Figure 4B, therefore these numbers cannot represent 86% and 97% of sites?
      17. Figure 4E. Several TFs mentioned in the text (Line 243) are not shown in the figure, it would be good to show all TFs motifs mentioned in the text in this figure. Again, there is no mention of whether a sequence-specific motif is detected for SALL4 (e.g. an AT-rich sequence) from this motif analysis.
      18. Figure 4G. How was the ATAC-seq data normalized for the WT and SALL4-het-KO lines for this comparison? The background levels of accessibility seem quite different in Replicate 1.
      19. Figures 5B-C could be exchanged to flow better with the text. A Venn diagram could be included to show the overlap between the sites losing BRG1 in SALL4-het-KO (13,505 sites) and the Day5-specific SALL4 sites (17,137 sites).
      20. At Day 5, the authors suggest a shift towards neural differentiation. It could be interesting for the authors to perform qRT-PCR at Day 5 for some neural markers or look in the Day 14 data for markers of neural differentiation at the expense of CNCC markers.
      21. Is the data used to plot Figure 5D the same as Figure 4G. If so, why is only one replicate shown in Figure 5D?
      22. Figure 6A. How many replicates are shown? If n=2, boxplots are not an appropriate to represent the distribution of the data. Please include n= X in the figure legend and plot the raw data points also.
      23. Figure 6B. What is the significance of CD99 for CNCC differentiation?
      24. Figure 6F. No error bars are shown, how many replicates were performed for this time couse? The linear regression line does not appear to add much value and could be removed.
      25. At line 304, the authors state "while SALL4-het-KO showed a significant downregulation of these genes". Perhaps 'failed to induce these genes' may be more accurate unless they were expressed at Day 5 and downregulated at Day 14.
      26. Lines 332-335. The genes selected for pluripotency, neural plate border, CNCC specification could be plotted separately in the Supplement to show individual gene expression dynamics.

      Significance

      This work provides a conceptual advance in understanding the aetiology of human SALL4-mediated craniofacial malformations in a cell-type specific manner. Leveraging an in vitro differentiation system, the authors define development timepoints and cell types impacted by altered SALL4 dosage. Additionally, the authors provide interesting mechanistic insights how the teratogen thalidomide may impact craniofacial development through proteasomal targeting and degradation of SALL4, and subsequent impact on neural crest differentiation progression.

      Several audiences will be interested in this work: stem cell and developmental biologists (especially those interested in neural crest and facial development), and researchers interested in enhancer regulation, chromatin biology or gene regulatory mechanisms. Clinician scientists and geneticists will be interested in the proposed implications for mechanisms of disease.

      Field of expertise: We have expertise in mechanisms of gene regulation and in vitro models of early development. We are not experts in modeling protein interactions in silico.

      PeerReviewed
    3. EMBOpress 10 Sep 2025

      Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

      Learn more at Review Commons

      Referee #1

      Evidence, reproducibility and clarity

      Summary: The authors have previously published Mass-spectrometry data that demonstrates a physical interaction between Sall4 and the BAF chromatin complex in iPSC derived neurectodermal cells that are a precursor cell state to neural crest cells. The authors sought to understand the basis of this interaction and investigate the role of Sall4 and the BAF chromatin remodelling complex during neural crest cell specification. The authors first validate this interaction with a co-IP between ARID1B subunit and Sall4 confirming the mass spec data. The authors then utilise in silico modelling to identify the specific interaction between the BAF complex and Sall4, suggesting that this contact is mediated through the BAF complex member DPF2. To functionally validate the role of Sall4 during neural crest specification, the authors utilsie CRISPR-Cas9 to introduce a premature stop codon on one allele of Sall4 to generate iPSCs that are haploinsufficient for Sall4. Due to the reports of Sall4's role in pluripotency, the authors confirm that this model doesn't disrupt pluripotent stem cells and is viable to model the role of Sall4 during neural crest induction. The authors expand this assessment of Sall4 function further during their differentiation model to cranial neural crest cells, assessing Sall4 binding with Cut+Run sequencing, revealing that Sall4 binds to motifs that correspond to key genes in neural crest differentiation. Moreover, reduction in Sall4 expression also reduces the binding of the BAF complex, through Cut and Run for BRG1. Overall, the authors then propose a model by which Sall4 and BRG1 bind to and open enhancer regions in neurectodermal cells that enable complete differentiation to cranial neural crest cells.

      Overall, the data is clear and reproducible and offers a unique insight into the role of chromatin remodellers during cell fate specification.

      However, I have some minor comments.

      1. Using AlphaFold in silico modelling, he authors propose the interaction between the BAF complex with Sall4 is mediated by DPF2, but don't test it. Does a knockout, or knockdown of DPF2 prevent the interaction?
      2. OPTIONAL: Does knockout of DPF2 phenocopy the Sall4 ko? This would be very interesting to include in the manuscript, but it would perhaps be a larger body of work.
      3. Figure 1, the day of IP is not clearly described until later in the test. please outline during in the figure

      3- What is the expression of Sal1 (and other Sall paralogs) during differentiation. The same with the protein levels of Sall4, does this remain at the below 50%, or is this just during pluripotency? 4. The authors hypothesise that Sall4 binds to enhancers- with the criteria for an enhancer being that these peaks > 1KB from the TSS are enhancers. Can this be reinforced by overlaying with other ChIP tracks that would give more confidence in this? There are several datasets from Joanna Wysocka's lab that also utilise this protocol which can give you more evidence to reinforce the claim and provide further detail as to the role of Sall4 5. The authors state that cells fail to become cranial neural crest cells, however they do not propose what the cells do instead. do they become neural? Or they stay at pluriopotent, which is one option given the higher expression of Nanog, OCT4 and OTX2 that are all expressed in pluripotent stem cells. 6. In general, I would like to see the gating strategy and controls for the flow cytometry in a supplemental figure. 7. For supplementary figure 1- please include the gene names in the main image panels rather than just the germ layer.

      Significance

      The strength of this study lies in its well-designed and clearly presented experiments and datasets. In particular, identifying the specific SALL4 isoform that interacts with the BAF complex-and further exploring the implications of this interaction-is a major highlight. The authors also make effective use of in silico modelling with AlphaFold, offering valuable mechanistic insight into how this interaction is mediated.

      The topic should have appeal to researchers in developmental biology and epigenetics. This study represents a significant step forward in validating the interaction between SALL4 and the BAF complex, and it highlights the requirement of SALL4 for BAF-mediated chromatin remodelling during neural crest specification. These findings are likely to be of interest to those studying the gene regulatory mechanisms underlying craniofacial development.

      However, while the authors outline the roles of SALL4 and the BAF complex in chromatin remodeling during neural crest development, the downstream effects on cell fate specification could be more thoroughly examined. Currently, Gene Ontology analysis is the primary method used to interpret these consequences, and additional functional validation would strengthen the conclusions.

      Intended audience: Basic research, epigenetics in pluripotency and neural crest development.

      PeerReviewed
    Visit annotations in context

    Tags

    Annotators

    URL

    biorxiv.org/lookup/doi/10.1101/2025.06.10.658805
  25. www.biorxiv.org www.biorxiv.org
    Septins function in exocytosis via physical interactions with the exocyst complex in fission yeast cytokinesis
    2
    1. Public_Reviews 10 Sep 2025

      eLife Assessment

      How secretion is regulated during cell division and how membrane trafficking factors cooperate with the cytoskeleton during cell division remain poorly understood. In this work the authors find protein-protein interactions and localization dependencies between the polymeric septin cytoskeleton and the exocyst complex, using fission yeast as a model organism and using alphafold 3 based structural predictions. The work provides a valuable body of new information that will be of great interest to the cell biology community. The evidence is solid and provides the authors and the community a framework to test if the identified interfaces reflect bona fide interaction sites in vivo and in vitro in future.

      Summary
    2. Public_Reviews 10 Sep 2025

      Author response:

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

      Reviewer #1 (Public Review): 

      Summary: 

      In this manuscript, Singh, Wu and colleagues explore functional links between septins and the exocyst complex. The exocyst in a conserved octameric complex that mediates the tethering of secretory vesicles for exocytosis in eukaryotes. In fission yeast cells, the exocyst is necessary for cell division, where it localizes mostly at the rim of the division plane, but septins, which localize in a similar manner, are non-essential. The main findings of the work are that septins are required for the specific localization of the exocyst to the rim of the division plane, and the likely consequent localization of the glucanase Eng1 at this same location, where it is known to promote cell separation. In the absence of septins, the exocyst still localizes to the division plane but is not restricted to the rim. They also show some defects in the localization of secretory vesicles and glucan synthase cargo. They further propose that interactions between septins and exocysts are direct, as shown through Alphafold2 predictions (of unclear strength) and clean coIP experiments. 

      Strengths: 

      The septin, exocyst and Eng1 localization data are well supported, showing that the septin rim recruits the exocyst and (likely consequently) the Eng1 glucanase at this location. One major finding of the manuscript is that of a physical interaction between septins and exocyst subunits. Indeed, many of the coIPs supporting this discovery are very clear. 

      Weaknesses: 

      I am less convinced by the strength of the physical interaction of septins with the exocyst complex. Notably, one important open question is whether septins interact with the intact exocyst complex, as claimed in the text, or whether the interactions occur only with individual subunits. The two-hybrid and coIP data only show weak interactions with individual subunits, and some coIPs (for instance Sec3 and Exo70 with Spn1 and Spn4) are negative, suggesting that the exocyst complex does not remain intact in these experiments.

      Given the known structure of the full exocyst complex and septin filaments (at least in S. cerevisiae), the Alphafold2 predicted structure could be used to probe whether the proposed interaction sites are compatible with full complex formation.  

      We thank the reviewer for these important and insightful comments. We agree that our current data, particularly the data from yeast two-hybrid and co-immunoprecipitation (coIP) assays, primarily reveal interactions between individual septin and exocyst subunits, and do not conclusively demonstrate binding of septins to the fully assembled exocyst complex. We realize this as a key limitation and have revised the manuscript text accordingly to clarify this point.

      We also appreciate the reviewer’s suggestion to use structural prediction to further assess their interaction plausibility. We have now employed the full Saccharomyces cerevisiae exocyst complex (with 4.4 Å resolution) published by the Guo group (Mei et al., 2018) to examine the interfaces of septin and the exocyst interactions, assuming that the S. pombe exocyst has the similar structure. We focused on checking all the interacting residues on the exocyst complex and septins from our AlphaFold modeling to determine whether these predicted interactions are structurally compatible. Our analysis reveals that majority subunit interactions are sterically feasible, while a few would likely require partial disassembly or flexible conformations. These new insights have been added to the revised Results and Discussion sections (Figure Supplement S4, S5 and Videos 4-7).

      While we cannot fully resolve whether septins engage with the whole exocyst complex versus selected subunits, our combined data support a model that septins scaffold or spatially regulate the exocyst localization at the division site, potentially through dynamic and multivalent interactions. We now explicitly state this more cautious interpretation in the revised manuscript.

      Mei, K., Li, Y., Wang, S., Shao, G., Wang, J., Ding, Y., Luo, G., Yue, P., Liu, J.-J., Wang, X. and Dong, M.-Q., Wang, H-W, Guo W. 2018. Cryo-EM structure of the exocyst complex. Nature Struct & Mol. Biol, 25(2), pp.139-146.

      The effect of spn1∆ on Eng1 localization is very clear, but the effect on secretory vesicles (Ypt3, Syb1) and glucan synthase Bgs1 is less convincing. The effect is small, and it is not clear how the cells are matched for the stage of cytokinesis. 

      For localizations and quantifications of Eng1, Ypt3, Syb1, and Bgs1 shown in Figures 6 and 7, cells with a closed septum (at or after the end of contractile-ring constriction) were quantified or highlighted. To quantify their fluorescence intensity at the division site using line scan, the line width used was 3 pixels. For Syb1 (Figure 6D), we quantified cells at the end of ring constriction (when Rlc1-tdTomato constricted to a dot) in the middle focal plane. The exact same lines were drawn in both Rlc1 and Syb1 channels. The center of line scan was defined as the pixel with the brightest Rlc1 value. All data were aligned by the center and plotted. For Bgs1 (Figure 7A), we quantified the cells that Rlc1 signal had disappeared from the division site. The line was drawn in the Bgs1 channel in the middle focal plane. The center of line scan was defined as the pixel with the brightest Bgs1 value.

      All data were aligned by the center and plotted. These details were added to the Materials and Methods.

      Reviewer #2 (Public Review): 

      Summary: 

      This interesting study implicates the direct interaction between two multi-subunit complexes, known as the exocyst and septin complexes, in the function of both complexes during cytokinesis in fission yeast. While previous work from several labs had implicated roles for the exocyst and septin complexes in cytokinesis and cell separation, this study describes the importance of protein:protein interaction between these complexes in mediating the functions of these complexes in cytokinesis. Previous studies in neurons had suggested interactions between septins and exocyst complexes occur but the functional importance of such interactions was not known. Moreover, in baker's yeast where both of these complexes have been extensively studied - no evidence of such an interaction has been uncovered despite numerous studies which should have detected it. Therefore while exocyst:septin interactions appear to be conserved in several systems, it appears likely that budding yeast are the exception--having lost this conserved interaction. 

      Strengths: 

      The strengths of this work include the rigorous analysis of the interaction using multiple methods including Co-IP of tagged but endogenously expressed proteins, 2 hybrid interaction, and Alphafold Multimer. Careful quantitative analysis of the effects of loss of function in each complex and the effects on localization and dynamics of each complex was also a strength. Taken together this work convincingly describes that these two complexes do interact and that this interaction plays an important role in post Golgi vesicle targeting during cytokinesis. 

      Weaknesses: 

      The authors used Alphafold Multimer to predict (largely successfully) which subunits were most likely to be involved in direct interactions between the complexes. It would be very interesting to compare this to a parallel analysis on the budding yeast septin and exocyst complexes where it is quite clear that detectable interactions between the exocyst and septins (using the same methods) do not exist. Presumably the resulting pLDDT scores will be significantly lower. These are in silico experiments and should not be difficult to carry out. 

      We thank the reviewer for this insightful suggestion. To assess the specificity of the predicted interactions between septins and the exocyst complex in S. pombe, we performed a comparative AlphaFold2 analysis using some of the homologous subunits from Saccharomyces cerevisiae. We modeled two interactions between Cdc10-Sec5 and Cdc10-Sec15 (Cdc10 is the Spn2 homolog) using the same pipeline and parameters at the time when we did the modeling for S. pombe. We did not find interactions between them using the criteria we used for the fission yeast proteins in this study. These results support the notion that the predicted septin–exocyst interactions in S. pombe are not generalizable to budding yeast. Unfortunately, we did not test all other combinations at that time and the AlphaFold2 platform is not available to us now (showing system error messages when we tried recently). We thank the reviewer again for this helpful suggestion, which should strengthen the evolutionary interpretation of the septin-exocyst interactions once it is able to be systematically carried out.

      Reviewer #3 (Public Review): 

      Septins in several systems are thought to guide the location of exocytosis, and they have been found to interact with the exocyst vesicle-tethering complex in some cells. However, it is not known whether such interactions are direct or indirect. Moreover, septin-exocyst physical associations were not detected in several other systems, including yeasts, making it unclear whether such interactions reflect a conserved septin-exocytosis link or whether they may missed if they depend on septin polymerization or association into higher-order structures. Singh et. al., set out to define whether and how septins influence the exocyst during S. pombe cytokinesis. Based on three lines of evidence, the authors conclude that septins directly bind to exocyst subunits to regulate localization of the exocyst and vesicle secretion during cytokinesis. The conclusions are consistent with the data presented, but some interpretations need to be clarified and extended: 

      (1) The first line of evidence examines septin and exocyst localization during cytokinesis in wild-type and septin-mutant or exocyst-mutant yeast. Quantitative imaging convincingly shows that the detailed localization of the exocyst at the division site is perturbed in septin mutants, and that this is accompanied by modest accumulation of vesicles and vesicle cargos. Whether that is sufficient to explain the increased thickness of the division septum in septin mutants remains unclear.

      The modest accumulation of vesicles and vesicle cargos at the division site is one of the reasons for the increased thickness of the division septum in septin mutants. It is more likely that the misplaced exocyst can still tether vesicles along the division plane (less likely at the rim) without septins. Due to the lack of the glucanase Eng1 at the rim of the division plane in septin mutants, daughter-cell separation is delayed and then cells continue to thicken the septum. We have added these points to the Discussion.

      (2) The second line of evidence involves a comprehensive Alphafold2 analysis of potential pair-wise interactions between septin and exocyst subunits. This identifies several putative interactions in silico, but it is unclear whether the identified interaction surfaces would be available in the full septin or exocyst complexes.  

      We thank the reviewer for raising this important point. We fully agree that a key limitation of pairwise AlphaFold predictions is that they do not account for the higher-order structural context of multimeric protein complexes, such as septin hetero-oligomers or the assembled exocyst complex. As a result, some of the predicted interfaces could indeed be conformationally restricted in the native state.

      To address this concern, we predicted the S. pombe exocyst and septin structures using AlphaFold3. We mapped predicted contact residues onto the predicted structure. Most predicted interfaces (86% for the exocyst and 86-96% for septins) appear to be located on accessible surfaces in the assembled complexes (Figure supplement S4, S5, videos 4 - video 7), suggesting that these interactions are sterically plausible. We have added this important caveat to the text of the revised manuscript highlighting the interface accessibility within the assembled complexes. We appreciate the reviewer’s insight, which helped us strengthen the interpretation and limitations of the AlphaFold-based analysis.

      (3) The third line of evidence uses co-immunoprecipitation and yeast two hybrid assays to show that several physical interactions predicted by Alphafold2 can be detected, leading the authors to conclude that they have identified direct interactions. However, both methods leave open the possibility that the interactions are indirect and mediated by other proteins in the fission yeast extract (co-IP) or budding yeast cell (two-hybrid). 

      We thank the reviewer for this important clarification. We agree that coimmunoprecipitation (co-IP) and yeast two-hybrid (Y2H) assays cannot conclusively distinguish between direct and indirect interactions. As the reviewer points out, co-IPs may reflect associations mediated by bridging proteins within the fission yeast extract, and Y2H readouts can be influenced by fusion context or endogenous host proteins. In our manuscript, we have now revised the relevant statements in the Results and Discussion sections to clarify that the observed associations are consistent with direct interactions predicted by AlphaFold2, but cannot alone establish direct binding. We have also tempered our terminology—substituting phrases such as “direct interaction” with “physical association consistent with direct binding,” where appropriate.

      (4) Based on prior studies it would be expected that the large majority of both septins and exocyst subunits are present in cells and extracts as stoichiometric complexes. Thus, one would expect any septin-exocyst interaction to yield associations detectable with multiple subunits, yet co-IPs were not detected in some combinations. It is therefore unclear whether the interactions reflect associations between fully-formed functional complexes or perhaps between transient folding intermediates. 

      We thank the reviewer for this thoughtful observation. We agree that both septins and exocyst subunits are generally understood to exist in cells as stable, stoichiometric complexes, and that interactions between fully assembled complexes might be expected to yield co-immunoprecipitation signals involving multiple subunits from each complex. However, it was also found that >50% of septins Spn1 and Spn4 are in the cytoplasm even during cytokinesis when the septin double rings are formed (Table 1 of Wu and Pollard, Science 2005, PMID: 16224022). Thus, it is possible that there are pools of free septin and exocyst subunits in the cytoplasm, which were detected in our Co-IP assays. 

      In our experiments, we observed selective co-IP signals between certain septin and exocyst subunits, while other combinations did not yield detectable interactions. We believe these findings could reflect several other possibilities besides the possible interactions among the free subunits in the cytoplasm:

      (1) Some interactions may only be strong enough between specific subunits at exposed interfaces under the Co-IP conditions, rather than through wholesome complex–complex interactions;

      (2) The detergent and/or salt conditions used in our co-IPs may disrupt labile complex interfaces or partially dissociate multimeric assemblies.

      To address this concern, we now include in the Discussion a paragraph highlighting the possibility that some of the observed interactions may not reflect binding between fully assembled, functional complexes. Notably, most detected interactions pairs are consistent with the AlphaFold predictions, which suggest specific subunit interfaces may be responsible for mediating contact. While we cannot fully resolve whether septins engage with the whole exocyst complex versus selected subunits, our combined data supports a model that septins scaffold or spatially regulate the exocyst localization at the division site, potentially through dynamic and multivalent interactions. We now explicitly state this more cautious interpretation in the revised manuscript. Future biochemical studies using native complex purifications, cross-linking mass spectrometry, or in vitro reconstitution with fully assembled septin and exocyst complexes, or in vivo FRET assays will be essential to clarify whether the interactions we observe occur between intact assemblies or intermediate forms.

      Reviewer #1 (Recommendations for the Authors): 

      A major finding from the manuscript is the description of physical interaction of septin subunits with exocyst subunits. The analysis starts from Alphafold2 predictions, shown in Figures 3 and S3. However, some of the most useful metrics of Alphafold, the PAE plot and the pTM and ipTM values, are not provided. It is thus very difficult to estimate the value of the predicted structures (which are also obscured by all side chains). The power of a predicted structure is that it suggests binding interfaces, which is not explored here. At the very least, it would not be difficult to examine whether the proposed binding interfaces are free in the septin filaments and octameric exocyst complex. 

      Please also see response to reviewer #1 (Public Review).

      We thank the reviewer for these very helpful suggestions. We agree that inclusion of AlphaFold2 model confidence metrics—specifically the Predicted Aligned Error (PAE) plots, as well as pTM and ipTM values—is essential for evaluating the reliability of the predicted septin–exocyst interfaces.

      In the revised manuscript, we have now included the PAE plots (Figure 3 and Supplementary S3) and summarizes the pTM scores for each predicted septin–exocyst subunit pair. We also provide a short description of these metrics in the figure legend to help guide interpretation. The old Alphafold2 version (alphafold2advanced) that we used doesn’t give iPTM score, so are not included. However, according to our methodology, we only counted the interacting residues which have pLDDT scores >50%, predicting the resulting iPTM score should not be very weak.

      In addition, we have updated Figures 3 and S3 to show simplified ribbon diagrams of the interface regions, with side chains hidden by default and selectively displayed only at predicted interaction hotspots. This improves structural clarity and makes the interface regions easier to interpret. We mentioned in the Discussion that the preliminary studies show that the predicted interacting interfaces of Sec15 and Sec5 with septin subunits are accessible for interaction in the whole exocyst complex. The new Figure Supplement S4 and S5 and Videos 4-7 now show the interface residues of both the exocyst and septins that are involved in the interactions.

      Two further points on the interaction: 

      The 2H interaction data is not very convincing. The insets showing beta-gal assays do not look very different from the negative control (compare for instance in panel 4E the Sec15BD alone, last column, with the Sec15-BD in combination with Spn4-AD, third column: roughly same color), which suggests it is mostly driven by autoactivation of Sec15-BD. Providing growth information in addition to beta-gal may be helpful. 

      We appreciate the reviewer’s close evaluation of the yeast two-hybrid (Y2H) assay data, and we agree that the signals observed in the Spn4–Sec15 combination is indeed weak. Unfortunately, we did not perform growth assays. However, we would like to clarify that this is consistent with the nature of the interactions that we are investigating. The interaction between individual septin and exocyst subunits is not strong and/or transient as supported by the weak interactions by Co-IP experiments. Given the exocyst only tethers/docks vesicles on the plasma membrane for tens of seconds before vesicle fusion, the multivalent interactions between septins and the exocyst should be very dynamic and not be too strong. 

      As evidenced by our Co-IP experiments and multivalent interactions predicted by Alphafold2, the interaction between Spn4 and Sec15 is detectable but weak, suggesting that this may be a low-affinity or transient interaction. Given that Y2H assays have known limitations in detecting such low-affinity interactions—especially those that depend on conformational context or are not optimal in the yeast nucleus—it is perhaps not surprising that the X-gal color development is subtle. These limitations of the Y2H system have been well-documented (e.g., Braun et al., 2009; Vidal & Fields, 2014), particularly for interactions with affinities in the micromolar range or those requiring conformational specificity. Therefore, the weak signal observed is in line with expectations for a lowaffinity, transient interaction such as between Spn4 and Sec15.

      Vidal, M. and Fields, S., 2014. The yeast two-hybrid assay: still finding connections after 25 years. Nature methods, 11(12), pp.1203-1206.

      Braun, P., Tasan, M., Dreze, M., Barrios-Rodiles, M., Lemmens, I., Yu, H., Sahalie, J.M., Murray, R.R., Roncari, L., De Smet, A.S. and Venkatesan, K., 2009. An experimentally derived confidence score for binary protein-protein interactions. Nature methods, 6(1), pp.91-97.

      In the coIP experiments, I am confused by the presence of tubulin signal in some of the IPs. For instance, in Fig 4B, but not 4D, where the same Sec15-GFP is immunoprecipitated. There is also a signal in 4C but not 4A. This needs to be clarified. 

      The presence of tubulin in some immunoprecipitates is not unexpected, particularly in experiments involving cytoskeleton-associated proteins such as septins and exocyst subunits. The occasional presence of tubulin in our co-IP samples is consistent with well-documented reports showing tubulin as a frequent non-specific co-purifying protein, particularly under native lysis conditions used to preserve large complexes (Vega and Hsu, 2003; Gavin et al., 2006; Mellacheruvu et al., 2013; Hein et al., 2015). The CRAPome database and quantitative interactomics studies highlight tubulin as one of the most common background proteins in affinity-based workflows. Importantly, tubulin was used as a loading control but not as a marker for interaction in our study, and its variable presence does not reflect a specific interaction with Sec15-GFP or other bait proteins, and we have clarified this point in the revised figure legend.

      Gavin, A.C., Aloy, P., Grandi, P., Krause, R., Boesche, M., Marzioch, M., Rau, C., Jensen, L.J., Bastuck, S., Dümpelfeld, B. and Edelmann, A., 2006. Proteome survey reveals modularity of the yeast cell machinery. Nature, 440(7084), pp.631-636.

      Mellacheruvu, D., Wright, Z., Couzens, A.L., Lambert, J.P., St-Denis, N.A., Li, T., Miteva, Y.V., Hauri, S., Sardiu, M.E., Low, T.Y. and Halim, V.A., 2013. The CRAPome: a contaminant repository for affinity purification–mass spectrometry data. Nature methods, 10(8), pp.730736.

      Hein, M.Y., Hubner, N.C., Poser, I., Cox, J., Nagaraj, N., Toyoda, Y., Gak, I.A., Weisswange, I., Mansfeld, J., Buchholz, F. and Hyman, A.A., 2015. A human interactome in three quantitative dimensions organized by stoichiometries and abundances. Cell, 163(3), pp.712-723.

      Vega, I.E., Hsu, S.C. 2003. The septin protein Nedd5 associates with both the exocyst complex and microtubules and disruption of its GTPase activity promotes aberrant neurite sprouting in PC12 cells. Neuroreport, 14, pp.31-37.

      Regarding the localization of Ypt3 and Syb1 in WT and spn1∆ in Figure 6C-D and Bgs1 in Figure 7A, it would help to add a contractile ring marker to be able to match the timing of cytokinesis between WT and mutants and ensure that cells of same stage are compared (and add some quantification for Ypt3). In fact, in Figure 7A, next to the cells being pointed at, there are very similar localizations of Bgs1 in WT and spn1∆ at the rim of the ingressing septum, which makes me wonder how the quantified cells were chosen. 

      For localizations and quantifications of Eng1, Ypt3, Syb1, and Bgs1 shown in Figures 6 and 7, cells with a closed septum (at or after the end of contractile-ring constriction) were quantified or highlighted. To quantify their fluorescence intensity at the division site using line scan, the line width used was 3 pixels. For Syb1 (Figure 6D), we quantified cells at the end of ring constriction (when Rlc1-tdTomato constricted to a dot) in the middle focal plane. The exact same lines were drawn in both Rlc1 and Syb1 channels. The center of line scan was defined as the pixel with the brightest Rlc1 value. All data were aligned by the center and plotted. For Bgs1 (Figure 7A), we quantified the cells that Rlc1 signal had disappeared from the division site. The line was drawn in the Bgs1 channel in the middle focal plane. The center of line scan was defined as the pixel with the brightest Bgs1 value. All data were aligned by the center and plotted. These details were added to the Materials and Methods.

      Finally, the manuscript would benefit from some figure reorganization/compaction. Unless work on the binding interfaces is added, Figure 3 and S3 could be removed and summarized by providing the pTM and ipTM values of the predicted interactions. Figure 5 could be combined with Figure 2, as it is essentially a repeat with additional exocyst subunits. 

      Because the binding interfaces are added, we keep the original Figures 3 and S3. The experiments in Figure 5 could not be performed before the interaction tests between septins and the exocyst. Thus, to aid the flow of the story, we keep Figures 2 and 5 separated.

      Minor comments: 

      The last sentence of the first paragraph of the results does not make much sense at this point of the paper. After the first paragraph, there is no evidence that colocalization would be required for proper function.  

      We agree that the sentence in question may have overstated the functional implications of colocalization too early in the Results section, before presenting supporting evidence. Our intention was to introduce the hypothesis that spatial proximity between septins and exocyst subunits may be relevant for their coordination during cytokinesis, which we examine in later figures. We have revised the sentence to more accurately reflect the observational nature of the data at this stage in the manuscript as below:

      "These observations suggest the spatial proximity between septins and the exocyst during certain stage of cytokinesis, raising the possibility of their functional coordination, which we would further investigate below."

      What is the indicated n in Figure 6B? Number of cells? 

      Yes, the n in Figure 6B refers to the thin sections of electron microscopy quantified in the analysis. We have now updated the figure legend to explicitly state this for clarity.

      The causal inference made between the alteration of Exocyst localization in septin mutants and the thicker septum is possible, but by no means certain. It should be phrased more cautiously. 

      We agree that our original phrasing may have overstated the causal relationship between altered exocyst localization in septin mutants and septum thickening. Our data supports a correlation between these phenotypes, but additional experiments would be required to establish direct causality.

      To reflect this, we have revised the relevant sentence in the Discussion to read:

      “The modest accumulation of vesicles and vesicle cargos at the division site is one of the reasons for the increased thickness of the division septum in septin mutants. It is more likely that the misplaced exocyst can still tether vesicles along the division plane without septins. Due to the lack of the glucanase Eng1 at the rim of the division plane in septin mutants, daughter-cell separation is delayed and then cells continue to thicken the septum.”

      Reviewer #2 (Recommendations for the Authors): 

      (1) In the display of the AlphaFold Model for the interactions (Figure 3 and Supplemental Figure 3) it is difficult to identify which subunits are where. Residue numbers and subunits should be labeled and only side chains important for the interactions should be present in the model. 

      We appreciate this valuable suggestion. We agree that clearer visual labeling is essential for interpreting the predicted interactions and have revised Figures 3 and S3 accordingly to improve readability and emphasize key structural features.

      Specifically, we have:

      • Labeled each subunit with its name and color-coded consistently across panels.

      •  Annotated key interface residues with residue numbers directly in the figure.

      • Removed non-interacting side chains to declutter the model and highlight only those involved in predicted interactions as well as expanded the figure legend for explanation.

      (2) In Table 1 the column label "Genetic Interaction at 25C" is confusing when synthetic growth defects are shown with a "plus". Rather this column could be labeled "Growth of double mutants at 25C" and then designate the relative growth rate observed at 25C as in Table 2. Designating a negative effect on growth with a plus is confusing. 

      Thanks for the thoughtful suggestions. We have made the suggested changes by deleting the last column so that Tables 1 and 2 are consistent.

      (3) In Figure 4, why is tubulin being co-immunoprecipitated in two of the four anti-GFP IPs? Are the IPs dirty and if so why does it vary between the four experiments? If they are dirty can the non-specific tubulin be removed by additional washes with IP buffer or conversely is it necessary to do minimal washes in order to detect the exocyst-septin interaction by coIP? A comment on this would be helpful. 

      The presence of tubulin in some immunoprecipitates is not unexpected, particularly in experiments involving cytoskeleton-associated proteins such as septins and exocyst subunits. The occasional presence of tubulin in our co-IP samples is consistent with welldocumented reports showing tubulin as a frequent non-specific co-purifying protein, particularly under native lysis conditions used to preserve large complexes (Vega and Hsu, 2003; Gavin et al., 2006; Mellacheruvu et al., 2013; Hein et al., 2015). The CRAPome database and quantitative interactomics studies highlight tubulin as one of the most common background proteins in affinity-based workflows. Importantly, tubulin was used as a loading control but not marker for interaction in our study, and its variable presence does not reflect a specific interaction with Sec15-GFP or other bait proteins, and we have clarified this point in the revised figure legend.

      Gavin, A.C., Aloy, P., Grandi, P., Krause, R., Boesche, M., Marzioch, M., Rau, C., Jensen, L.J., Bastuck, S., Dümpelfeld, B. and Edelmann, A., 2006. Proteome survey reveals modularity of the yeast cell machinery. Nature, 440(7084), pp.631-636.

      Mellacheruvu, D., Wright, Z., Couzens, A.L., Lambert, J.P., St-Denis, N.A., Li, T., Miteva, Y.V., Hauri, S., Sardiu, M.E., Low, T.Y. and Halim, V.A., 2013. The CRAPome: a contaminant repository for affinity purification–mass spectrometry data. Nature methods, 10(8), pp.730736.

      Hein, M.Y., Hubner, N.C., Poser, I., Cox, J., Nagaraj, N., Toyoda, Y., Gak, I.A., Weisswange, I., Mansfeld, J., Buchholz, F. and Hyman, A.A., 2015. A human interactome in three quantitative dimensions organized by stoichiometries and abundances. Cell, 163(3), pp.712-723.

      Vega, I.E., Hsu, S.C. 2003. The septin protein Nedd5 associates with both the exocyst complex and microtubules and disruption of its GTPase activity promotes aberrant neurite sprouting in PC12 cells. Neuroreport, 14, pp.31-37. 

      In response to the second part of reviewer’s comment, we washed the pulldown product for 5 times each time with 1 ml IP buffer at 4ºC. We used this standard protocol for all the Co-IP experiments to detect the interaction between different septin-exocyst subunits. So, we are not sure if and how more washes or more stringent buffer conditions can interfere with detection of the interactions.

      Reviewer #3 (Recommendations for the Authors): 

      In addition to the issues noted in the public review, there were some confusing findings and references to previous literature that merit further consideration or discussion: 

      • The current gold standard for validating Alphafold predictions involves making targeted mutants suggested by the structural predictions. The absence of any such validation weakens the conclusions significantly. 

      We agree that the targeted mutagenesis based on AlphaFold2-predicted interaction interfaces represents a powerful approach to experimentally validate the in silico models. While we did not pursue structure-guided mutagenesis in this study, our goal was to identify putative interactions between septin and exocyst subunits as a foundation for future functional work. Our current conclusions are intentionally limited to proposing putative interfaces, supported by co-immunoprecipitation and genetic interaction data.

      We recognize that direct validation of specific contact residues would significantly strengthen the model. Accordingly, we have revised the Discussion to explicitly state this limitation and to note that structure-based mutagenesis will be an important next step to test the functional relevance of predicted interactions. We have added the following statement:

      “Future studies are needed to refine the residues involved in the interactions because the predicted interacting residues from AlphaFold are too numerous. However, it is encouraging that most of the predicted interacting residues are clustered in several surface patches. Experimental validation through targeted mutagenesis is an important next step.”

      • Much of the writing appears to imply that differences in mutant phenotypes indicate differences in septin (or exocyst) subunit behaviors/functions. However, my reading of the work in budding yeast is that such differences reflect the partial functionality that can be conferred by aberrant partial septin complexes that assemble and may polymerize in mutants lacking different subunits. In this view, which is supported by data showing that essentially all septins are in stoichiometric octameric complexes in cells, the wild-type functions are all mediated by the full complex. Similarly, the separate exocyst subunit localizations based on tagged Sec3 (Finger et al) were not supported by later work from the Brennwald lab with untagged Sec3, and the idea that different exocyst subunits may function separately from the full complex has very limited support in yeast. I would suggest that the text be edited to better reflect the literature, or that different views be better justified. 

      Thanks for the suggestions. We have revised the text accordingly.

      • The comprehensive set of Alphafold2 predictions is a major strength of the paper, but it is unclear to this reader whether the multiple predicted interactions truly reflect multivalent multimode interactions or whether many (most?) predictions would not be consistent with interactions between full complexes and may not indicate physiological interactions. Better discussion of these issues is needed to interpret the findings. 

      We appreciate the reviewer’s suggestion to use structural prediction to further assess interaction plausibility. We have now employed the full Saccharomyces cerevisiae exocyst complex (with 4.4 Å resolution) published by the Guo group to examine the interfaces of septins and the exocyst interactions, assuming that the S. pombe exocyst has the similar structure. We mapped predicted contact residues onto the predicted structure. Most predicted interfaces (86% for the exocyst and 86-96% for septins) appear to be located on accessible surfaces in the assembled complexes (Figure supplement S4, S5, videos 4 - video 7), suggesting that these interactions are sterically plausible. We have added this important caveat to the text of the revised manuscript highlighting the interface accessibility within the assembled complexes. We appreciate the reviewer’s insight, which helped us strengthen the interpretation and limitations of the AlphaFold-based analysis.

      • Some but not all co-IP blots appear to show tubulin (negative control) coming down with the GFP pull-downs. Why is that, and what does it imply for the reliability of the co-IP protocol? 

      The presence of tubulin in some immunoprecipitates is not unexpected, particularly in experiments involving cytoskeleton-associated proteins such as septins and exocyst subunits. The occasional presence of tubulin in our co-IP samples is consistent with welldocumented reports showing tubulin as a frequent non-specific co-purifying protein, particularly under native lysis conditions used to preserve large complexes (Vega and Hsu, 2003; Gavin et al., 2006; Mellacheruvu et al., 2013; Hein et al., 2015). The CRAPome database and quantitative interactomics studies highlight tubulin as one of the most common background proteins in affinity-based workflows. Importantly, tubulin was used as a loading control but not a marker for interaction in our study, and its variable presence does not reflect a specific interaction with Sec15-GFP or other bait proteins, and we have clarified this point in the revised figure legend.

      Gavin, A.C., Aloy, P., Grandi, P., Krause, R., Boesche, M., Marzioch, M., Rau, C., Jensen, L.J., Bastuck, S., Dümpelfeld, B. and Edelmann, A., 2006. Proteome survey reveals modularity of the yeast cell machinery. Nature, 440(7084), pp.631-636.

      Mellacheruvu, D., Wright, Z., Couzens, A.L., Lambert, J.P., St-Denis, N.A., Li, T., Miteva, Y.V., Hauri, S., Sardiu, M.E., Low, T.Y. and Halim, V.A., 2013. The CRAPome: a contaminant repository for affinity purification–mass spectrometry data. Nature methods, 10(8), pp.730736.

      Hein, M.Y., Hubner, N.C., Poser, I., Cox, J., Nagaraj, N., Toyoda, Y., Gak, I.A., Weisswange, I., Mansfeld, J., Buchholz, F. and Hyman, A.A., 2015. A human interactome in three quantitative dimensions organized by stoichiometries and abundances. Cell, 163(3), pp.712-723.

      Vega, I.E., Hsu, S.C. 2003. The septin protein Nedd5 associates with both the exocyst complex and microtubules and disruption of its GTPase activity promotes aberrant neurite sprouting in PC12 cells. Neuroreport, 14, pp.31-37.

      • Why were two different protocols used for different yeast-two-hybrid analyses? 

      The purpose of using two protocols was to test which protocol is more reliable and sensitive.

      • The different genetic interactions between septin and exocyst mutants when combined with TRAPP-II mutants merits further discussion: might the difference reflect relocation of exocyst from rim to center in septin mutants versus inactivation of exocyst in exocyst mutants? 

      We appreciate this insightful comment and agree that this distinction is likely meaningful. The reviewer correctly notes that septin mutants may not abolish exocyst function but rather cause its spatial mislocalization: from the rim to the center of the division site, whereas the exocyst mutants likely result in partial or complete loss of vesicle tethering activity at the plasma membrane.

      To address this important nuance, we have expanded the Discussion as follows:

      “The genetic interactions between mutations in the exocyst and septins when combined with TRAPP-II mutants may reflect fundamentally different consequences for compromising the exocyst function (Tables 1 and 2). In septin mutants, the exocyst complex still localizes to the division site but is mispositioned from the rim to the center of the division plane. This mislocalization allows partial retention of exocyst function, leading to very mild synthetic or additive defects when combined with compromised TRAPP-II trafficking and tethering. In contrast, in exocyst subunit mutants, the exocyst becomes partial or non-functional, resulting in a more severe loss of exocyst activity. These differing consequences could explain the qualitative differences in genetic interactions observed with TRAPP-II mutants (Tables 1 and 2). Thus, septins and the exocyst also work in different genetic pathways for certain functions in fission yeast cytokinesis.”

      • The vesicle accumulation in septin mutants was quite modest. Does that imply that most vesicles are still fusing in the septum? Further discussion would be beneficial to understand what the authors think this means. 

      We thank the reviewer for this important point. We agree that the modest vesicle accumulation observed in septin mutants suggests that a significant proportion of vesicles continue to successfully fuse at the division site, even in the absence of fully functional septin structures.

      We now discuss this in greater detail in the revised manuscript:

      “The relatively modest vesicle accumulation in septin mutants suggests that septins are not absolutely required for vesicle tethering or fusion per se at the division site. Instead, septins primarily function to spatially organize the targeting sites of exocyst-directed vesicles by stabilizing the localization of the exocyst at the rim of the cleavage furrow. In septin mutants, mislocalization of the exocyst reduces the spatial precision of membrane insertion but still permits vesicle tethering and fusion, albeit in a less controlled manner. Thus, septins likely play a modulatory rather than essential role in exocytic vesicle delivery during cytokinesis. This interpretation aligns with our localization and genetic interaction data, which indicates that septins act as scaffolds to optimize secretion geometry, rather than as core components of the fusion machinery.”

      • It was unclear to this reader why relocation of some exocyst complexes from the rim to the center of the septal region would lead to dramatic thickening of the septum. Further discussion would be beneficial to understand what the authors think this means. 

      The modest accumulation of vesicles and vesicle cargos at the division site is one of the reasons for the increased thickness of the division septum in septin mutants. It is more likely that the misplaced exocyst can still tether vesicles along the division plane without septins. Because of the lack of glucanase Eng1 at the rim of the division plane in septin mutants, daughter-cell separation is delayed and then cells continue to thicken the septum. We have added these points to the Discussion.

      AuthorResponse
    Visit annotations in context

    Tags

    Annotators

    URL

    biorxiv.org/content/10.1101/2024.07.09.602728v2
  26. www.biorxiv.org www.biorxiv.org
    Dual transcranial electromagnetic stimulation of the precuneus boosts human long-term memory
    2
    1. Public_Reviews 10 Sep 2025

      Reviewer #3 (Public review):

      Summary:

      Borghi and colleagues present results from 4 experiments aimed at investigating the effects of dual γtACS and iTBS stimulation of the precuneus on behavioral and neural markers of memory formation. In their first experiment (n = 20), they find that a 3-minute offline (i.e., prior to task completion) stimulation that combines both techniques leads to superior memory recall performance in an associative memory task immediately after learning associations between pictures of faces, names, and occupation, as well as after a 15-minute delay, compared to iTBS alone (+ tACS sham) or no stimulation (sham for both iTBS and tACS). Performance in a second task probing short-term memory was unaffected by the stimulation condition. In a second experiment (n = 10), they show that these effects persist over 24 hours and up to a full week after initial stimulation. A third (n = 14) and fourth (n = 16) experiment were conducted to investigate neural effects of the stimulation protocol. The authors report that, once again, only combined iTBS and γtACS increases gamma oscillatory activity and neural excitability (as measured by concurrent TMS-EEG) specific to the stimulated area at the precuneus compared to a control region, as well as precuneus-hippocampus functional connectivity (measured by resting state MRI), which seemed to be associated with structural white matter integrity of the bilateral middle longitudinal fasciculus (measured by DTI).

      Strengths:

      Combining non-invasive brain stimulation techniques is a novel, potentially very powerful method to maximize the effects of these kinds of interventions that are usually well-tolerated and thus accepted by patients and healthy participants. It is also very impressive that the stimulation-induced improvements in memory performance resulted from a short (3 min) intervention protocol. If the effects reported here turn out to be as clinically meaningful and generalizable across populations as implied, this approach could represent a promising avenue for treatment of impaired memory functions in many conditions.

      Methodologically, this study is expertly done! I don't see any serious issues with the technical setup in any of the experiments. It is also very commendable that the authors conceptually replicated the behavioral effects of experiment 1 in experiment 2 and then conducted two additional experiments to probe the neural mechanisms associated with these effects. This certainly increases the value of the study and the confidence in the results considerably.

      The authors used a within-subject approach in their experiments, which increases statistical power and allows for stronger inferences about the tested effects. They also used to individualize stimulation locations and intensities, which should further optimize the signal-to-noise ratio.

      Review 3
    2. Public_Reviews 10 Sep 2025

      Author response:

      The following is the authors’ response to the previous reviews

      Reviewer #1 (Public review):

      Summary:

      The authors make a bold claim that a combination of repetitive transcranial magnetic stimulation (intermittent theta burst-iTBS) and transcranial alternating current stimulation (gamma tACS) causes slight improvements in memory in a face/name/profession task.

      Strengths:

      The idea of stimulating the human brain non-invasively is very attractive because, if it worked, it could lead to a host of interesting applications. The current study aims to evaluate one such exciting application.

      Weaknesses:

      (1) The title refers to the "precuneus-hippocampus" network. A clear definition of what is meant by this terminology is lacking. More importantly, mechanistic evidence that the precuneus and the hippocampus are involved in the potential effects of stimulation remains unconvincing.

      Thank you for the observation. We believe that the evidence collected supports our state relative to the stimulation of the precuneus and the involvement of the hippocampus. In particular, given the existing evidence on TMS methodology and precuneus non-invasive stimulation (see Koch et al., Brain, 2022, Koch et al., Alzheimer's research & therapy, 2025), the computation of the biophysical model with the E-field we produced (see Biophysical modeling and E-field calculation section in the supplementary information), together with the individual identification of the precuneus through the RM (see iTBS+γtACS neuromodulation protocol and MRI data acquisition in the main text), we can reasonably assume that the individually identified PC was stimulated.

      As we acknowledged in the Limitations section, we cannot entirely rule out the possibility that our results might also reflect stimulation of more superficial parietal regions adjacent to the precuneus. Nor do we provide direct evidence of microscopic changes in the precuneus following stimulation. However, the results we provide in terms of changes in precuneus oscillatory activity and precuneus-hippocampi connectivity sustain both our thesis of the precuneus stimulation and of hippocampi involvement in the stimulation effects.

      Despite this consideration, we agree on the fact that a clear definition of what is meant by the terminology “precuneus-hippocampus network” is lacking. Moreover, since our data and previous evidence sustain the notion of PC stimulation, while this study does not produce direct evidence of the hippocampi stimulation - but only of the effect of the neuromodulation protocol on its connection with the precuneus, we soften the claim in the title. We remove the mention of the precuneus-hippocampus network so that the modified title will be as follows: “Dual transcranial electromagnetic stimulation of the precuneus boosts human long-term memory.”

      (2) The question of the extent to which the stimulation approach and the stimulation parameters used in these experiments causes specific and functionally relevant neural effects remains open. Invasive recordings that could address this question remain out of the scope of this non-invasive study. The authors conducted scalp EEG experiments in an attempt to address this question using non-invasive methods. However, the results shown in Fig. 3 are unclear. The results are inconsistently reported in units of microvolts squared in some panels (3A, 3B) and in units of microvolts in other panels (3C). Also, there is insufficient consideration of potential contamination by signal components reflecting eye movements, other muscle artifacts, or another volume-conducted signal reflecting aggregate activity inside the brain.

      As you correctly noted, Figure 3 presents results obtained from the TMS–EEG recordings. However, there is no inconsistency regarding the measurement units, as we are referring to two distinct indices: one in the frequency domain—oscillatory power shown in Figures 3A and 3B, expressed in microvolts squared (μV<sup>²</sup>)—and one in the time domain—the TMS-evoked potential shown in Figure 3C, expressed in microvolts (μV).

      Regarding the concern about artifacts, this is an important issue on which our group has a strong expertise, having published well-established, highly cited procedures on how to record and clean TMS-EEG signals (e.g., Casula et al., Clinical Neurophysiology, 2017; Rocchi et al., Brain Stimulation, 2021). In the current study, we adopted a well-established and rigorous approach for both data acquisition and preprocessing. This ensured that the recorded TMS–EEG signals were not contaminated by physiological or electrical artifacts.

      As regards the recording procedure, all participants were instructed to fixate on a black cross to minimize eye movements. To avoid auditory-related components caused by the TMS click, we adopted an ad-hoc procedure optimized for TMS-EEG recordings (Rocchi et al., Brain Stimulation, 2021). First, participants were given earphones that continuously played an ad-hoc masking noise composed of white noise mixed with specific time-varying frequencies of the TMS click (Rocchi et al., Brain Stimulation, 2021). The masking noise volume was adjusted to ensure that participants could not detect the TMS click, or as much as tolerated (always below 90 dB). To further reduce the impact of the TMS click on the EEG signal, we placed ear defenders (SNR=30) on top of the earphones. Please see TMS–EEG data acquisition section in the main text.

      As regards the offline cleaning process, we applied Independent Component Analysis (INFOMAX-ICA) to the EEG data to identify and remove components associated with muscle activity, eye movements, blinking, and residual TMS-related artifacts, in line with the most recent guidelines on TMS–EEG preprocessing (Hernandez-Pavon et al., Brain Stimulation, 2023). Specifically, for TMS-related muscle artefacts, we strictly followed the criteria based on their scalp topography, spectral content, timing, and amplitude, which we published in a paper focused on this topic (Casula et al., Clinical Neurophysiology, 2017). We add this detail in the TMS–EEG preprocessing and analysis section in the supplementary information (lines 119-120).

      (3) Figure 3 indicates "Precuneus oscillatory activity ...", but evidence that the activity presented reflects precuneus activity is lacking. The maps shown at the bottom of Figure 3C suggest that the EEG signals recorded with scalp EEG reflect activity generated across a wide spatial range, with a peak encompassing at least tens of centimeters. Thus, evidence that effects specifically reflect precuneus activity, as the paper's title and text throughout the manuscript suggest, is lacking.

      We believe there may have been a misunderstanding. As indicated in the figure caption, panels A and B represent oscillatory activity, whereas panel C displays the TMS-evoked potentials (TEPs). Therefore, the topographical maps mentioned (i.e., those in panel C) did not refer to oscillatory activity, but to differences in TEP amplitude. Specifically, the topographies shown in Figure 3C illustrate statistically significant differences in TEP amplitudes between post-stimulation time points (T1—immediately after stimulation, and T2—20 minutes after stimulation) and the pre-stimulation baseline (T0).

      In this figure, we focused our analysis on a cluster of electrodes overlying the individually identified precuneus, capturing EEG responses to single TMS pulses delivered to that target. This approach, widely used in previous literature (e.g., Koch et al., NeuroImage, 2018; Casula et al., Annals of Neurology, 2022; Koch et al., Brain, 2022; Maiella et al., Clinical Neurophysiology, 2024; Koch et al., Alzheimer’s Research & Therapy, 2025), supports the interpretation that the observed responses reflect precuneus-related activity. Furthermore, the wide spatial range change you mention proved to be statistically different only when conducting the TMS-EEG over the precuneus (i.e., administering the TMS single pulse over the precuneus) and not when performing it over the left parietal cortex. We modified the discussion section in the main text to make it more clear (lines 196-199).

      “Moreover, we observed specific cortical changes in the posteromedial parietal areas, as evidenced by the whole-brain analysis conducted on TMS-EEG data when performed over the precuneus and the absence of effect when TMS-EEG was performed on the lateral posterior parietal cortex used as a control condition.”

      That said, we do not state that the effects observed specifically reflect the precuneus activity; indeed, we think the effect of the stimulation is broader, as discussed in the Discussion section. We rather sustain, in line with the literature (Koch et al., Neuroimage 2018; Koch et al., Brain, 2022; Koch et al., Alzheimer's research & therapy, 2025), the idea that the effects observed are a consequence of the precuneus stimulation by the dual stimulation.

      (4) The paper as currently presented (e.g., Figure 3) also lacks rigorous evidence of relevant oscillatory activity. Prior to filtering EEG signals in a particular frequency band, clear evidence of oscillations in the frequency band of interest should be shown (e.g., demonstration of a clear peak that emerges naturally in the frequency range of interest when spectral analysis is applied to "raw" signals). The authors claim that gamma oscillations change because of the stimulation, but a clear peak in the gamma range prior to stimulation is not apparent in the data as currently presented. Thus, the extent to which spectral measurements during stimulation reflect physiological gamma oscillations remains unclear.

      If we understand correctly, your concern relates to the lack of a clear gamma peak before neuromodulation, which may suggest uncertainty about the observed changes in gamma oscillatory activity. Is that correct?

      First, it is important to underline that the natural frequency typically observed in the precuneus falls within the beta range, not the gamma range (see Rosanova et al., Journal of Neuroscience, 2009; Casula et al., Annals of Neurology, 2022). This explains why a prominent gamma peak is not expected at baseline (T0).

      Differently, our neuromodulatory protocol was specifically aimed at boosting gamma oscillatory activity given its well-established role in learning and memory processes (Griffiths & Jensen, Trends in Neurosciences, 2023). Thus, to assess the effect of the neuromodulatory protocol, we compared the oscillatory activity before (T0) and after stimulation (T1 and T2), which showed a clear increase in the gamma band. This effect is visible in the raw oscillatory power plot and is most clearly represented in Figure 3B, where the gamma band emerged as the only frequency range showing significant changes across time points.

      (5) Concerns remain regarding the rigor of statistical analyses in the revised manuscript (see also point 8 below). Figure 3B shows an undefined statistical test with p<0.05. The statistical test that was used is not explained. Also, a description of how corrections for multiple comparisons were made is missing. Figures 3A and 3C are not accompanied by statistics, making the results difficult to interpret. For Figure 4C, a claim was made based on a significant p-value for one statistical test and a non-significant p-value in another test. This is a common statistical mistake (see Figure 1 and accompanying discussion in Makin and Orban de Xivry (2019) Science Forum: Ten common statistical mistakes to watch out for when writing or reviewing a manuscript. eLife 8:e48175).

      All statistical tests are described in the Statistical Analysis section of the main text. Specifically, to assess cortical oscillation changes in Experiment 3, we conducted repeated-measures ANOVAs with stimulation condition (iTBS+γtACS vs. iTBS+sham-tACS) and time (ΔT1 = T1–T0; ΔT2 = T2–T0) as within-subject factors, for each frequency band. To further explore the effects of stimulation at each time point, we performed paired t-tests with Bonferroni correction for multiple comparisons. A one-tailed hypothesis was adopted, based on our a priori prediction of gamma-band increase derived from previous work (Maiella et al., 2022).

      Please note that Figures 3A and 3C are purely descriptive and are therefore not accompanied by statistical tests. Figure 3A shows the full spectral profile across frequencies and conditions, while statistical significance for these data is reported in Figure 3B. Similarly, the upper part of Figure 3C displays the TMS-evoked potential (TEP) in the precuneus, while the statistical comparison of TEP amplitudes across time points is shown in the lower part of Figure 3C.

      Regarding Figure 4C and the article you cited, are you referring to the error described as “Interpreting comparisons between two effects without directly comparing them”? If we understand correctly, this refers to the mistake of inferring an effect by observing that a significant result occurs in one condition or group, while the corresponding result in another condition or group is not significant, without directly testing the difference between them.

      In the case of Experiment 4, which investigates fMRI effects and is illustrated in Figure 4, we employed a general linear model that explicitly modeled both conditions and time points, allowing for a direct statistical comparison. Therefore, the connectivity effect reported does not fall into the category of the error you mentioned.

      Importantly, Figure 4C does not depict the effect of the neuromodulatory protocol itself. Rather, its purpose is to show that, within the real stimulation condition, there is a correlation between the observed effect and the integrity of the bilateral Middle Longitudinal Fasciculus. No conclusions or assumptions were made based on the absence of a significant correlation in the sham condition. However, since it was an exploratory analysis, we decided to soften our claims relative to the neural mechanism in the discussion section of the main text (lines 241-246).

      (6) In the second question posed in the original review, I highlighted that it was unclear how such stimulation would produce memory enhancement. The authors replied that, in the absence of mechanisms, there are many other studies that suffer from the same problem. This raises the question of placebo effects. The paper does not sufficiently address or discuss the possibility that any potential stimulation effects may reflect placebo effects.

      We agree with the reviewer on the potential role of a placebo effect in our study. For this reason, our experimental study had several stimulation conditions, including a placebo condition, which corresponded to the sham iTBS-sham tACS condition, which did not produce any effect.

      (7) The third major concern in the original review was the lack of evidence for a mechanism that is specific to the precuneus. Evidence for specific involvement of the precuneus remains lacking in the revised manuscript. The authors state: "the non-invasive stimulation protocol was applied to an individually identified precuneus for each participant". However, the meaning of this statement is unclear. Specifically, it is unclear how the authors know that they are specifically targeting the precuneus. Without directly recording from the precuneus and directly demonstrating effects, which is outside of the scope of the study, specific involvement of the precuneus seems speculative. Also, it does not seem as though a figure was included in the paper to show how the stimulation protocol specifically targets the precuneus. In their response to the original reviews, the authors state that posterior medial parietal areas are the only regions that show significant differences following the stimulation, but they did not cite a specific figure, or statistics reported in the text, that show this. In any event, posterior medial parietal areas encompass a wide area of the brain, so this would still not provide evidence for an effect specifically involving the precuneus.

      We respectfully disagree with the claim that targeting the precuneus in our study is speculative. The statement that “without directly recording from the precuneus and directly demonstrating effects, which is outside the scope of the study, specific involvement of the precuneus seems speculative” would, by that logic, implicitly call into question a large body of cognitive neuroscience research employing non-invasive techniques such as EEG and fMRI.

      Our methodological approach—combining MRI-guided stimulation, biophysical modeling, and TMS–EEG—is well established and widely used for targeting and studying the role of specific cortical regions, including the precuneus (e.g., Wang et al., Science, 2014; Koch et al., NeuroImage, 2018; Casula et al., Annals of Neurology, 2022, 2023; Koch et al., Brain, 2022; Maiella et al., Clinical Neurophysiology, 2024; Koch et al., Alzheimer’s Research & Therapy, 2025).

      In line with previously published protocols (Santarnecchi et al., Human Brain Mapping, 2018; Özdemir et al., PNAS, 2020; Mantovani et al., Journal of Psychiatric Research, 2021), we identified individual targets (i.e., the precuneus) for each participant based on structural and resting-state functional MRI data (see MRI Data Acquisition and Preprocessing section in the main text). This target was then accurately localized using MRI-guided stereotaxic neuronavigation, ensuring reproducible and anatomically precise stimulation across subjects.

      Finally, concerning the last comment about the lack of figures/statistics showing how the stimulation protocol targets the precuneus and the specificity of the effect observed, we would like to let the focus go over:

      Figure 3 in the main text, where we show the results of the TME-EEG over the posterior medial parietal areas;

      Figure S1 in the supplementary information, which shows with the e-fied simulation how the stimulation protocol targets the brain;

      the Precuneus iTBS+γtACS increases gamma oscillatory activity section in the main text results, where we report the results of the statistical analysis of the TMS-EEG conducted over the precuneus and the left posterior parietal cortex, used as a control condition to test for the specificity of the neuromodulation protocol.

      (8) Regarding chance levels, it is unfortunate that the authors cannot quantify what chance levels are in the immediate and delayed recall conditions. This makes interpretation of the results challenging. In the immediate and delayed conditions, the authors state that the chance level is 33%. It would be useful to mark this in the figures. If I understand correctly, chance is 33% in Fig. 2A. If this is the case and if I am interpreting the figure correctly:

      Gray bars for the sham condition appear to be below chance (~20-25%). Why is this condition associated with an accuracy level that is lower than chance?

      Cyan bars and red bars do not appear to be significantly different from chance (i.e., 33%), with red slightly higher than cyan. What statistic was performed to obtain the level of significance indicated in the figure? The highest average value for the red condition appears to be around 35%. More details are needed to fully explain this figure and to support the claims associated with this figure.

      The immediate and recall conditions you mention correspond to a free recall task. In this case, the notion of a fixed "chance level" is not straightforward as it would be in recognition or forced-choice paradigms, which is why we did not quantify it at first. I will now try to explain this extensively.

      Unlike multiple-choice tasks, where participants select the answer from a limited set of alternatives and the probability of a correct response by chance can be precisely quantified (e.g., 33% in a 3-alternative forced choice), free recall involves the spontaneous retrieval of items from memory without external cues or predefined options. As such, the response range in free recall is essentially unconstrained, encompassing the entire vocabulary of the participant.

      Because of this open-ended nature, the probability of correctly recalling a studied item purely by chance is exceedingly low and could be approximated to zero. Also, in our task, participants had to correctly recollect both name and occupation, doubling the possibility of the answers.

      This assumption is further supported by the fact that random guesses in free recall are unlikely to match any of the studied items, given the vast number of possible alternatives. As a result, performance above zero can be reasonably interpreted as reflecting genuine memory retrieval, rather than random guessing.

      As regards statistics, repeated-measures ANOVAs with stimulation condition as a within-subject factor (i.e., iTBS+γtACS; iTBS+sham-tACS; sham-iTBS+sham-tACS) for each dependent variable (see statistical analysis section in main text).

      (9) In the revised version of the paper, the authors did not address concerns associated with the block design (please see question 4d in the original review).

      We are sorry for the misunderstanding. We did not address your concerns related to block design since it does not apply to our study. As reported in the paper you mentioned in the original review, block design involves data collection performed in response to different stimuli of a given class presented in succession. If this is the case, it does not correspond to our experimental design since both TMS-EEG and fMRI were conducted in the resting state (i.e., without the presentation of stimuli) on different days according to the different randomized stimulation conditions.  

      In sum, this study presents an admirable aspirational goal, the notion that a non-invasive stimulation protocol could modulate activity in specific brain regions to enhance memory. However, the evidence presented at the behavioral level and at the mechanistic level (e.g. the putative involvement of specific brain regions) remains unconvincing.

      We hope our response will be carefully considered, fostering a constructive exchange and leading to a reassessment of your evaluation.

      Reviewer #2 (Public review):

      Summary:

      The manuscript by Borghi and colleagues provides evidence that the combination of intermittent theta burst TMS stimulation and gamma transcranial alternating current stimulation (γtACS) targeting the precuneus increases long-term associative memory in healthy subjects compared to iTBS alone and sham conditions. Using a rich dataset of TMS-EEG and resting-state functional connectivity (rs-FC) maps and structural MRI data, the authors also provide evidence that dual stimulation increased gamma oscillations and functional connectivity between the precuneus and hippocampus. Enhanced memory performance was linked to increased gamma oscillatory activity and connectivity through white matter tracts.

      Strengths:

      The combination of personalized repetitive TMS (iTBS) and gamma tACS is a novel approach to targeting the precuneus, and thereby, connected memory-related regions to enhance long-term associative memory. The authors leverage an existing neural mechanism engaged in memory binding, theta-gamma coupling, by applying TMS at theta burst patterns and tACS at gamma frequencies to enhance gamma oscillations. The authors conducted a thorough study that suggests that simultaneous iTBS and gamma tACS could be a powerful approach for enhancing long-term associative memory. The paper was well-written, clear, and concise.

      Comments on Revision:

      I thank the authors for their thoughtful responses to my first review and their inclusion of more detailed methodological discussion of their rationale for the stimulation protocol conditions and timing. Regarding the apparent difference in connectivity at baseline between conditions, the explanation that this is due to intrinsic dynamics, state, or noise implies the baseline is reflecting transient changes in dynamics rather than a true or stable baseline. Based on this, it looks like iTBS solely is significantly greater than the baseline before the iTBS and γtACS condition but maybe not that much lower than post-stimulation period for iTBS and γtACS. A longer baseline period should be used to ensure transient states are not driving baseline levels such that these endogenous fluctuations would average out. This also raises questions about whether the effect of iTBS and γtACS or iTBS alone are dependent on the intrinsic state at the time when stimulation begins. Their additional clarification of memory scoring is helpful but also reveals that the effect of dual iTBS+γtACS specifically on the association between faces and names is just significant. This modest increase in associative memory should be taken into consideration when interpreting these findings.

      We thank the reviewer for the feedback. We fully agree that considering baseline dynamics is critical when assessing the neurophysiological and connectivity effects of stimulation protocols.

      In Experiments 3 and 4, baseline measurements were specifically included in our design to account for the possibility that intrinsic dynamics, state, or noise could influence the observed effects of neuromodulation. Indeed, if we had compared only post-stimulation connectivity between the real and sham conditions, the effects might have appeared larger. The inclusion of baseline measurements allows us to contextualize and better isolate the neuromodulatory impact by controlling such endogenous fluctuations. Importantly, the fMRI connectivity measurements, which comprise the baseline, are derived from 10-minute BOLD signal acquisitions, which help mitigate the influence of transient fluctuations and provide a quite stable estimate of intrinsic connectivity.

      Moreover, regarding the possibility that stimulation effects may depend on the intrinsic state at stimulation onset, we hypothesize that gamma-frequency entrainment induced by tACS could reduce the variability of intrinsic dynamics, promoting a more stable neural state that is favorable for the induction of long-term plasticity.

      As regards the memory scoring, we would like to clarify that the significant improvement observed in the dual iTBS+γtACS condition does not pertain solely to the face–name association. Rather, it concerns the more demanding task of recalling the association between face, name, and occupation. While we agree that the observed effect could be considered modest, it is worth noting that it follows from only 3 minutes of stimulation.

      Reviewer #3 (Public review):

      Summary:

      Borghi and colleagues present results from 4 experiments aimed at investigating the effects of dual γtACS and iTBS stimulation of the precuneus on behavioral and neural markers of memory formation. In their first experiment (n = 20), they find that a 3-minute offline (i.e., prior to task completion) stimulation that combines both techniques leads to superior memory recall performance in an associative memory task immediately after learning associations between pictures of faces, names, and occupation, as well as after a 15-minute delay, compared to iTBS alone (+ tACS sham) or no stimulation (sham for both iTBS and tACS). Performance in a second task probing short-term memory was unaffected by the stimulation condition. In a second experiment (n = 10), they show that these effects persist over 24 hours and up to a full week after initial stimulation. A third (n = 14) and fourth (n = 16) experiment were conducted to investigate neural effects of the stimulation protocol. The authors report that, once again, only combined iTBS and γtACS increases gamma oscillatory activity and neural excitability (as measured by concurrent TMS-EEG) specific to the stimulated area at the precuneus compared to a control region, as well as precuneus-hippocampus functional connectivity (measured by resting state MRI), which seemed to be associated with structural white matter integrity of the bilateral middle longitudinal fasciculus (measured by DTI).

      Strengths:

      Combining non-invasive brain stimulation techniques is a novel, potentially very powerful method to maximize the effects of these kinds of interventions that are usually well-tolerated and thus accepted by patients and healthy participants. It is also very impressive that the stimulation-induced improvements in memory performance resulted from a short (3 min) intervention protocol. If the effects reported here turn out to be as clinically meaningful and generalizable across populations as implied, this approach could represent a promising avenue for treatment of impaired memory functions in many conditions.

      Methodologically, this study is expertly done! I don't see any serious issues with the technical setup in any of the experiments. It is also very commendable that the authors conceptually replicated the behavioral effects of experiment 1 in experiment 2 and then conducted two additional experiments to probe the neural mechanisms associated with these effects. This certainly increases the value of the study and the confidence in the results considerably.

      The authors used a within-subject approach in their experiments, which increases statistical power and allows for stronger inferences about the tested effects. They also used to individualize stimulation locations and intensities, which should further optimize the signal-to-noise ratio.

      Weaknesses:

      I think one of the major weaknesses of this study is the overall low sample size in all of the experiments (between n = 10 and n = 20). This is, as I mentioned when discussing the strengths of the study, partly mitigated by the within-subject design and individualized stimulation parameters. The authors mention that they performed a power analysis but this analysis seemed to be based on electrophysiological readouts similar to those obtained in experiment 3. It is thus unclear whether the other experiments were sufficiently powered to reliably detect the behavioral effects of interest. In the revised manuscript, the authors provide post-hoc sensitivity analyses that help contextualize the strength of the findings.

      While the authors went to great lengths trying to probe the neural changes likely associated with the memory improvement after stimulation, it is impossible from their data to causally relate the findings from experiments 3 and 4 to the behavioral effects in experiments 1 and 2. This is acknowledged by the authors and there are good methodological reasons for why TMS-EEG and fMRI had to be collected in separate experiments, but readers should keep in mind that this limits inferences about how exactly dual iTBS and γtACS of the precuneus modulate learning and memory.

      We thank the reviewer for the feedback.

      Reviewer #1 (Recommendations for the authors):

      I suggest:

      (1) Removing all mechanistic claims about the precuneus and hippocampus.

      We soften our claims about the precuneus-hippocampus network.

      (2) Repeating and focusing on the behavioral experiments with a much larger number of images and stronger statistical power to try to demonstrate a compelling behavioral correlate of the proposed stimulation protocol.

      We clarified the misunderstanding relative to the chance level of the behavioral experiments raised by the reviewer.

      Reviewer #2 (Recommendations for the authors):

      Use longer baseline to establish stable gamma level for comparisons in Figure 3

      If we understand correctly, you propose to increase the baseline to establish the gamma oscillatory activity as expressed in Figure 3 (showing the results of experiment 3). Is that right? In the figure, you see a baseline of -100; 0ms, which we use for a merely graphical reason, since no activity is usually observable before the TMS pulse. However, to establish the level of gamma, we used a larger baseline correction ranging from -700 ms to -300 ms (i.e., 400ms). We added this important information in the cortical oscillation section of the supplementary information (lines 134-135).

      Reviewer #3 (Recommendations for the authors):

      I think that the authors did a great job responding to the concerns raised by the reviewers. All of my own comments have been satisfactorily addressed. I will update my public review to be more concise, so that it only includes the overall assessment of the manuscript, including the strengths and weaknesses, but without the requests for clarification. Strengths and weaknesses remain largely the same, as the authors did not conduct additional experiments.

      Thank you.

      AuthorResponse
    Visit annotations in context

    Tags

    Annotators

    URL

    biorxiv.org/content/10.1101/2024.10.25.620008v3
  27. www.biorxiv.org www.biorxiv.org
    A genome-wide MAGIC kit for recombinase-independent mosaic analysis in Drosophila
    2
    1. Public_Reviews 10 Sep 2025

      Reviewer #3 (Public review):

      Summary:

      In the manuscript by Shen, Yeung, and colleagues, the authors generate an improved and expanded Mosaic analysis by gRNA-induced crossing-over (MAGIC) toolkit for use in making mosaic clones in Drosophila. This is a clever method by which mitotic clones can be induced in dividing cells by using CRISPR/Cas9 to generate double-strand breaks at specific locations that induce crossing over at those locations. This is conceptually similar to previous mosaic methods in flies that utilized FRT sites that had been inserted near centromeres along with heat-shock inducible FLPase. The advantage of the MAGIC system is that it can be used along with chromosomes lacking FRT sites already introduced, such as those found in many deficiency collections or in EMS mutant lines. It may also be simpler to implement than FRT-based mosaic systems. There are two flavors of the MAGIC system: nMAGIC and pMAGIC. In nMAGIC, the main constituents are a transgene insertion that contains gRNAs that target DNA near the centromere, along with a fluorescent marker. In pMAGIC, the main constituents are a transgenic insertion that contains gRNAs that target DNA near the centromere, along with ubiquitous expression of GAL80. As such, nMAGIC can be used to generate clones that are not labelled, whereas pMAGIC (along with a GAL4 line and UAS-marker) can be used much like MARCM to positively label a clone of cells. This manuscript introduces MAGIC transgenic reagents that allow all 4 chromosomes to be targeted. They demonstrate its use in a variety of tissues, including with mutants not compatible with current FLP/FRT methods, and also show it works well in tissues that prove challenging for FLP/FRT mosaic analyses (such as motor neurons). They further demonstrate that it can be used to generate mosaic clones in non-melanogaster hybrid tissues. Overall, this work represents a valuable improvement to the MAGIC method that should promote even more widespread adoption of this powerful genetic technique.

      Strengths:

      (1) Improves the design of the gRNA-marker by updating the gRNA backbone and also the markers used. GAL80 now includes a DE region that reduces the perdurance of the protein and thus better labeling of pMAGIC clones. The data presented to demonstrate these improvements is rigorous and of high quality.

      (2) Introduces a toolkit that now covers all chromosome arms in Drosophila. In addition, the efficiency of 3 target different sites is characterized for each chromosome arm (e.g., 3 different gRNA-Marker combinations), which demonstrate differences in efficiency. This could be useful to titrate how many clones an experimenter might want (e.g., lower efficiency combinations might prove advantageous).

      (3) The manuscript is well written and easy to follow. The authors achieved their aims of creating and demonstrating MAGIC reagents suitable for mosaic analysis of any Drosophila chromosome arm.

      (4) The MAGIC method is a valuable addition to the Drosophila genetics toolkit, and the new reagents described in this manuscript should allow it to become more widely adopted.

      Weaknesses:

      (1) The MAGIC method might not be well known to most readers, and the manuscript could have benefited from schematics introducing the technique.

      (2) Traditional mosaic analyses using the FLP/FRT system have strongly utilized heat-shock FLPase for inducible temporal control over mitotic clones, as well as a way to titrate how many clones are induced (e.g., shorter heat shocks will induce fewer clones). This has proven highly valuable, especially for developmental studies. A heat-shock Cas9 is available, and it would have been beneficial to determine the efficiency of inducing MAGIC clones using this Cas9 source.

      Review 3
    2. Public_Reviews 10 Sep 2025

      Author response:

      Reviewing Editor Comments:

      The following are some consolidated review remarks after discussions amongst all three reviewers:

      The reviewers feel the evidence level could be raised from 'convincing' to 'compelling' if the following key (and partially shared) suggestions by the reviewers are followed adequately:

      (1) Expand labeling options for nMAGIC, which is currently just a BFP marker. This would increase the utility of the method. A far-red marker would be very helpful. Could the authors just do this for one chromosome arm and make the reagent available for others to generate other chromosome arms?

      This is a great suggestion. We will make an nMAGIC vector containing a far-red fluorescent marker and generate a 40D2 version of this nMAGIC gRNA-maker to demonstrate its utility. This vector will be available for others to make additional nMAGIC gRNA-markers.

      (2) Verify that destabilized GAL80 is potent enough to suppress GAL4. Repeat Figure 1C-E with tub-GAL80-DE-SV40.

      We will use a tub-GAL80-DE-SV40 gRNA-marker to test suppression of pxn-Gal4.

      (3) Concern about the health of the induced mitotic clones. This is an important consideration, but the reviewers were not sure what the necessary experiments would be. To gauge twin-spot clone sizes? Please address.

      We will assess the health of induced mitotic clones in wing imaginal discs. We will do this by generating twin spots with a nMAGIC gRNA-marker in wing discs and compare the sizes of the two cell populations (BFP<sup>+/+</sup> and BFP<sup>-/-</sup>) in twin spots.

      (4) Include a schematic of the MAGIC method as Figure 1 or add it to Figure 1. Many may not be familiar with the method, so to promote its adoption, the authors should clearly introduce the MAGIC method in this paper (and not rely on readers to go to previous publications). For this paper to become a MAGIC reference paper, it should be self-contained.

      We will add a diagram of the MAGIC method in the revised manuscript.

      (5) Determine the utility of using a hs-Cas9 line for temporal induction of MAGIC clones. This is a traditional method for mitotic clone induction (with hsFLP/FRTs), and its use with the MAGIC system (especially pMAGIC) could also make it more attractive, especially to label small populations of neurons born at known times. To this point, the authors could generate pMAGIC clones using hs-Cas9 for commonly used adult target neurons, such as projection neurons, central complex neurons, or mushroom body neurons. The method to label small numbers of these adult neurons is well worked out with known GAL4 lines, and demonstrating that pMAGIC could have similar results would capture the attention of many not familiar with the pMAGIC method.

      We thank the reviewers for this suggestion. We will test hs-Cas9 in inducing pMAGIC clones in one of the neuronal populations in the adult brain, as suggested by the reviewers.

      In addition, we will address all other minor concerns of the reviewers.

      AuthorResponse
    Visit annotations in context

    Tags

    Annotators

    URL

    biorxiv.org/content/10.1101/2025.06.30.662354v1
  28. www.biorxiv.org www.biorxiv.org
    A Drug Repurposing Approach Reveals Targetable Epigenetic Pathways in Plasmodium vivax Hypnozoites
    1
    1. Public_Reviews 10 Sep 2025

      Author response:

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      Plasmodium vivax can persist in the liver of infected individuals in the form of dormant hypnozoites, which cause malaria relapses and are resistant to most current antimalarial drugs. This highlights the need to develop new drugs active against hypnozoites that could be used for radical cure. Here, the authors capitalize on an in vitro culture system based on primary human hepatocytes infected with P. vivax sporozoites to screen libraries of repurposed molecules and compounds acting on epigenetic pathways. They identified a number of hits, including hydrazinophthalazine analogs. They propose that some of these compounds may act on epigenetic pathways potentially involved in parasite quiescence. To provide some support to this hypothesis, they document DNA methylation of parasite DNA based on 5-methylcytosine immunostaining, mass spectrometry, and bisulfite sequencing.

      Strengths:

      -The drug screen itself represents a huge amount of work and, given the complexity of the experimental model, is a tour de force.

      -The screening was performed in two different laboratories, with a third laboratory being involved in the confirmation of some of the hits, providing strong support that the results were reproducible.

      -The screening of repurposing libraries is highly relevant to accelerate the development of new radical cure strategies.

      We thank the reviewer for pointing out the strengths of our report.

      Weaknesses:

      The manuscript is composed of two main parts, the drug screening itself and the description of DNA methylation in Plasmodium pre-erythrocytic stages. Unfortunately, these two parts are loosely connected. First, there is no evidence that the identified hits kill hypnozoites via epigenetic mechanisms. The hit compounds almost all act on schizonts in addition to hypnozoites, therefore it is unlikely that they target quiescence-specific pathways. At least one compound, colforsin, seems to selectively act on hypnozoites, but this observation still requires confirmation. Second, while the description of DNA methylation is per se interesting, its role in quiescence is not directly addressed here. Again, this is clearly not a specific feature of hypnozoites as it is also observed in P. vivax and P. cynomolgi hepatic schizonts and in P. falciparum blood stages. Therefore, the link between DNA methylation and hypnozoite formation is unclear. In addition, DNA methylation in sporozoites may not reflect epigenetic regulation occurring in the subsequent liver stages.

      We agree our report lacks direct evidence that hydrazinophthalazines are interacting with parasite epigenetic mechanisms. We spent significant resources attempting several novel approaches to establish a direct connection, but technological advances are needed to enable such studies, which we mention in the introduction and discussion. We disagree that schizonticidal activity automatically excludes the possibility a hypnozonticidal hit is acting on quiescence-specific pathways because both hypnozoites and schizonts are under epigenetic control and these pathways are likely performing different functions in different stages. Also important is the use of the word ‘specific’ as this term could be used to indicate parasite versus host (a drug that clears a parasite infection with a safety margin), parasite-directed effect versus host-directed effect (a drug acting via an agonistic or antagonistic effect on parasite or host pathway(s), but leading to parasite death in either case), hypnozoite versus schizont, or P. vivax versus other Plasmodium species. We were careful to indicate the usage of ‘specific’ throughout the text. Given the almost-nonexistent hit rate when screening diverse small molecule libraries screening against P. vivax hypnozoites, and remarkable increase in hits when screening epigenetic inhibitors as described in this report, our data suggests epigenetic pathways are important to the regulation of hypnozoite dormancy in addition to regulation of other parasite stages, but those effects are outside the scope of this report.

      -The mode of action of the hit compounds remains unknown. In particular, it is not clear whether the drugs act on the parasite or on the host cell. Merely counting host cell nuclei to evaluate the toxicity of the compounds is probably acceptable for the screen but may not be sufficient to rule out an effect on the host cell. A more thorough characterization of the toxicity of the selected hit compounds is required.

      We agree, and mention in the results and discussion, that the effect could be mediated through host pathways. This is not unlike the 8-aminoquinolones, which are activated by host cytochromes and kill via ROS, which is a nonspecific mechanism (that is, the compound is not directly interacting with a parasite target) leading to a parasite-specific effect (the parasite cannot tolerate the ROS produced, but the host can). During screening, it is generally the case that detecting hits with direct effects on the target organism are more desirable, so hits are counterscreened for general cytotoxicity. In this report, we show an effect on the parasite in direct comparison to the effect on host primary hepatocytes in the P. vivax assay itself, and follow up on hits with general counterscreens using two mammalian cell lines using CellTiter Glo, which does not rely on nuclei counts. Some compounds did show general cytotoxic effects, but with selectivity (more potency) against P. vivax liver stages, while other hits like the hydrazinophthalazines did not show an effect against primary hepatocytes and show only weak toxicity against mammalian cells at the highest dose tested. Further studies are needed to determine if the effect is indeed host- or parasite-directed and, if hydrazinophthalazines are to be developed into marketed antimalarials, extensive safety testing would be part of the development process.

      -There is no convincing explanation for the differences observed between P. vivax and P. cynomolgi. The authors question the relevance of the simian model but the discrepancy could also be due to the P. vivax in vitro platform they used.

      Fully characterizing the chemo-sensitivity of P. vivax and P. cynomolgi liver stages is outside the scope of this report. Rather, we report tool compounds which could be used in future studies to further characterize these sister species. We also make the point that P. cynomolgi is the gold standard for in vivo antirelapse activity, but it is still a model species, not a target species, and so few experimental hypnozonticidal compounds have been reported that the predictive value of P. cynomolgi is not fully understood. We found that several of our hits were species-specific using our in vitro platforms, thus future studies are needed to ensure this predictive value.

      -Many experiments were performed only once, not only during the screen (where most compounds were apparently tested in a single well) but also in other experiments. The quality of the data would be increased with more replication.

      Due to their size, compound library screens are typically performed once, with confirmation in dose-response assays, which were repeated several times. Rhesus PK studies was performed once on three animals, which is typical. All other studies were performed at least twice and most were performed three times or more. We provide a data table showing readers the source material for all replication as well as other source data tables showing the raw data for dose-response and other assays.

      -While the extended assay (12 days versus 8 days) represents an improvement of the screen, the relevance of adding inhibitors of core cytochrome activity is less clear, as under these conditions the culture system deviates from physiological conditions.

      We agree that cytochrome inhibitors render the platform less physiologically relevant, but the goal of screening is to detect hits which could be improved upon using medicinal chemistry, including metabolic stability. Metabolic stability is better assessed using standard assays such as liver microsomes, thus our goal was to characterize the effects of test compounds on the parasite without the confounding effect of hepatic metabolism.

      Reviewer #2 (Public Review):

      Summary:

      In this manuscript, inhibitors of the P. vivax liver stages are identified from the Repurposing, Focused Rescue, and Accelerated Medchem (ReFRAME) library as well as a 773-member collection of epigenetic inhibitors. This study led to the discovery that epigenetics pathway inhibitors are selectively active against P. vivax and P. cynomolgi hypnozoites. Several inhibitors of histone post-translational modifications were found among the hits and genomic DNA methylation mapping revealed the modification on most genes. Experiments were completed to show that the level of methylation upstream of the gene (promoter or first exon) may impact gene expression. With the limited number of small molecules that act against hypnozoites, this work is critically important for future drug leads. Additionally, the authors gleaned biological insights from their molecules to advance the current understanding of essential molecular processes during this elusive parasite stage.

      Strengths:

      -This is a tremendously impactful study that assesses molecules for the ability to inhibit Plasmodium hypnozoites. The comparison of various species is especially relevant for probing biological processes and advancing drug leads.

      -The SI is wonderfully organized and includes relevant data/details. These results will inspire numerous studies beyond the current work.

      We thank the reviewer for pointing out the strengths of our report.

      Reviewer #3 (Public Review):

      Although this work represents a massive screening effort to find new drugs targeting P. vivax hypnozoites, the authors should balance their statement that they identified targetable epigenetic pathways in hypnozoites.

      -They should emphasize the potential role of the host cell in the presentation of the results and the discussion, as it is known that other pathogens modify the epigenome of the host cell (i.e. toxoplasma, HIV) to prevent cell division. Also, hydrazinophtalazines target multiple pathways (notably modulation of calcium flux) and have been shown to inhibit DNA-methyl transferase 1 which is lacking in Plasmodium.

      -In a drug repurposing approach, the parasite target might also be different than the human target.

      -The authors state that host-cell apoptotic pathways are downregulated in P. vivax infected cells (p. 5 line 162). Maybe the HDAC inhibitors and DNA-methyltransferase inhibitors are reactivating these pathways, leading to parasite death, rather than targeting parasites directly.

      We agree caution must be taken as we did not directly confirm the mechanism of our hits. Many follow up studies will be needed to do so. We do point out in the discussion that the mechanism of hits could be host-directed. We agree with the notion that some of these hits could be affecting parasitized host cell pathways, which lead to death of the parasitized cell, with the parasite being collateral damage, yet such a mechanism could lead to a safe and effective novel antimalarial.

      It would make the interpretation of the results easier if the authors used EC50 in µM rather than pEC50 in tables and main text. It is easy to calculate when it is a single-digit number but more complicated with multiple digits.

      We apologize for the atypical presentation of potency data. However, there is growing concern in drug discovery when Standard Deviation is applied to Potency data because Standard Deviation is a linear calculation and Potency is a log effect, making the math incompatible. We understand thousands of papers are reported every year using this mathematically incorrect method, making our presentation of these data less familiar. However, we define pEC50 in its use in the text and table legends and hope to increase its use in the broader scientific community.

      Authors mention hypnozoite-specific effects but in most cases, compounds are as potent on hypnozoite and schizonts. They should rather use "liver stage specific" to refer to increased activity against hypnozoites and schizonts compared to the host cell. The same comment applies to line 351 when referring to MMV019721. Following the same idea, it is a bit far-fetched to call MMV019721 "specific" when the highest concentration tested for cytotoxicity is less than twice the EC50 obtained against hypnozoites and schizonts.

      We have reviewed and revised statements in the manuscript to ensure the effect we are describing is accurate in terms of parasite versus parasite form.

      Page 5 lines 187-189, the authors state "...hydrazinophtalazines were inactive when tested against P. berghei liver schizonts and P. falciparum asexual blood stages, suggesting that hypnozoite quiescence may be biologically distinct from developing schizonts". The data provided in Figure 1B show that these hydrazinophtalazines are as potent in P. vivax schizonts than in P. vivax hypnozoites, so the distinct activity seems to be Plasmodium species specific and/or host-cell specific (primary human hepatocytes rather than cell lines for P. berghei) rather than hypnozoite vs schizont specific.

      We agree the effect of hydrazinophtalazine could be more species specific than stage specific, but the context of our comment has to do with current methods in antimalarial discovery and development. Given the biological uniqueness of the various Plasmodium species and stages, any hypnozonticidal hit may or may not have pan-species or pan-stage activity; our goal was to characterize this. Regardless of the mechanism, we found it interesting that the hydrazinophtalazines kill P. vivax hypnozoites, but not P. cynomolgi hypnozoites nor other species and stages used in antimalarial drug development. This result makes the point that hypnozoite-focused assays may be required to detect and develop hypnozonticidal hits, regardless of what other species or stages they may or may not act on.

      Why choose to focus on cadralazine if abandoned due to side effects? Also, why test the pharmacokinetics in monkeys? As it was a marketed drug, were no data available in humans?

      Cadralazine was found more potent than hydralazine and PK data was available from humans, thus dose prediction calculations showed an efficacious dose was more achievable with cadralazine than hydralazine. Side effects are often dependent on dose and regimen, which are very likely to be much different for treating malaria versus hypertension. Thus, the potential side effects of cadralazine if it was to be used as an antimalarial are simply unknown and are not disqualifying at this step. The PK study was done in Rhesus macaques so we could calculate the dose needed to achieve coverage of EC90 during a planned follow up in a Rhesus-P. cynomolgi relapse model. However, this planned in vivo efficacy study was not justified once we concurrently discovered cadralazine was inactive on P. cynomolgi in vitro.

      In the counterscreen mentioned on page 6, the authors should mention that the activity of poziotinib in P. berghei and P. cynomolgi is equivalent to cell toxicity, so likely not due to parasite specificity.

      Poziotinib shows activity against mammalian cell lines but not against the primary hepatocyte cultures supporting dose-response assays against P. vivax liver forms, which do not replicate. Thus, poziotinib appears selective in the liver stage assay but also may have a much more potent effect in continuously replicating cell lines.

      To improve the clarity and flow of the manuscript, could the authors make a recapitulative table/figure for all the data obtained for poziotinib and hydrazinophtalazines in the different assays (8-days vs 12-days) and laboratory settings rather than separate tables in main and supplementary figures. Maybe also reorder the results section notably moving the 12-day assay before the DNA methylation part.

      We apologize for the large amount of data presented but believe we are presenting it in the clearest way possible. All raw data is available if readers wish to re-analyze or re-organize our findings.

      The isobologram plot shows an additive effect rather than a synergistic effect between cadralazine and 5-azacytidine, please modify the paragraph title accordingly. Please put the same axis scale for both fractional EC50 in the isobologram graph (Figure 2A).

      The isobologram shows the effect approaching synergy at some combinations. The isobologram was rendered using standard methods. The raw data is available if readers wish to re-analyze it.

      Concerning the immunofluorescence detection of 5mC and 5hmC, the authors should be careful with their conclusions. The Hoechst signal of the parasites is indistinguishable because of the high signal given by the hepatocyte nuclei. The signal obtained with the anti-5hmC in hepatocyte nuclei is higher than with the anti-5mC, thus if a low signal is obtained in hypnozoites and schizonts, it might be difficult to dissociate from the background. In blood stages (Figure S18), the best to obtain a good signal is to lyse the red blood cell using saponin, before fixation and HCl treatment.

      We spent many hours using high resolution imaging of hundreds of parasites trying to detect clear 5hmC signal in both hypnozoites and schizonts but never saw a clearly positive signal. Indeed, the host signal can be confounding, thus we felt the most clear and unbiased way to quantify and present these data was using HCI. We appreciate the suggestion to lyse cells first for detecting in the blood stage.

      To conclude that 5mC marks are the predominate DNA methylation mark in both P. falciparum and P. vivax, authors should also mention that they compare different stages of the life cycle, that might have different methylation levels.

      We do mention at the start of this section our reasoning that quantifying marks in sporozoites was technically achievable, but not in a mixed culture of parasites and hepatocytes. We agree they could have different marks at these different stages.

      Also, the authors conclude that "[...] 5mC is present at low level in P. vivax and P. cynomolgi sporozoites and could control liver stage development and hypnozoite quiescence". Based on the data shown here, nothing, except presence the of 5mC marks, supports that DNA methylation could be implicated in liver stage development or hypnozoite quiescence.

      We clearly show sporozoite and liver stage DNA is methylated, which implicates this fundamental cell function exists in P. vivax liver stages, and that compounds with characterized activity against DNMT are active on liver stages. We acknowledge we were unable to show a direct effect and use the qualifier ‘could’ for this very reason.

      How many DNA-methyltransferase inhibitors were present in the epigenetic library? Out of those, none were identified as hits, maybe the hydrazinophtalazines effect is not linked to DNMT inhibition but another target pathway of these molecules like calcium transport?

      We supply the complete list of inhibitors in the epigenetic library as a supplemental file, the library contained 773 compounds. Hydrazinophtalazines were not included in the library, but several other DNA methyltransferase inhibitors were inactive. It is possible that hydrazinophtalazine activity is linked to other mechanisms but the inactivity of other DNMT inhibitors does not preclude the possibility hydrazinophtalazines are acting through DNMT.

      The authors state (line 344): "These results corroborate our hypothesis that epigenetic pathways regulate hypnozoites". This conclusion should be changed to "[...] that epigenetic pathways are involved in P. vivax liver stage survival" because:

      -The epigenetic inhibitors described here are as active on hypnozoite than liver schizonts.

      -Again, we cannot rule out that the host cell plays a role in this effect and that the compound may not act directly on the parasite.

      The same comment applies to the quote in lines 394 to 396. There is no proof in the results presented here that DNA methylation plays any role in the effect of hydrazinophtalazines in the anti-plasmodial activity obtained in the assay.

      We maintain that we use words throughout the text that express uncertainty about the mechanisms involved. It is important to point out that, prior to this paper, the number of hypnozonticidal hits was incredibly low and this field is just emerging. The fundamental role of epigenetic mechanisms is regulation of gene expression. Finding several hypnozonticial hits when screening epigenetic libraries implies epigenetic pathways are important for hypnozoite survival. We intentionally do not specify exact mechanisms or if they are host or parasite pathways. Host-parasite interactions in the liver stage are incredibly difficult to resolve and are outside the scope of this report. Furthermore, this statement is not exclusive to schizonts, but since screens of diversity sets against schizonts result in a much higher hit rate, the focus of this comment is unearthing rare hypnozonticidal hits.

      AuthorResponse
    Visit annotations in context

    Tags

    Annotators

    URL

    biorxiv.org/content/10.1101/2023.01.31.526483v3
  29. www.biorxiv.org www.biorxiv.org
    A cell atlas of the developing human outflow tract of the heart and its adult derivatives
    3
    1. Public_Reviews 10 Sep 2025

      Reviewer #1 (Public review):

      Summary:

      The study by Bobola et al reports single-nucleus expression analysis with some supporting spatial expression data of human embryonic and fetal cardiac outflow tracts compared to adult aortic valves. The transcription factor GATA6 is identified as a top regulator of one of the mesenchymal subpopulations, and potential interacting factors and downstream target genes are identified bioinformatically. Additional bioinformatic tools are used to describe cell lineage relationships and trajectories for developmental and adult cardiac cell types.

      Strengths:

      The studies of human tissue and extensive gene expression data will be valuable to the field.

      Weaknesses:

      (1) The expression data are largely confirmatory of previous studies in humans and mice. Thus, it is not clear what novel biological insights are being reported. While there is some novelty and impact in using human tissue, there are extensive existing publications and data sets in this area.

      (2) Major conclusions regarding spatial localization, differential gene expression, or cell lineage relationships based on bioinformatic data are not validated in the context of intact tissues.

      (3) The conclusions regarding lineage relationships are based on common gene expression in the current study and may not reflect cellular origins or lineage relationships that have previously been reported in genetic mouse models.

      (4) An additional limitation is the exclusive examination of adult aortic valve leaflets that represent only a subset of outflow tract derivatives in the mature heart. The conclusion, as stated in the title regarding adult derivatives of the outflow tract, is not accurate based on the limited adult tissue evaluated, exclusive bioinformatic approach, and lack of experimental lineage analysis of cell origins.

      Review 1
    2. Public_Reviews 10 Sep 2025

      Reviewer #2 (Public review):

      Summary:

      The manuscript by Leshem et al. presents a transcriptomic analysis of the developing human outflow tract (OFT) at embryonic and fetal stages using snRNAseq and spatial transcriptomics. Additionally, the authors analyze transcriptomic data from the adult aortic valve to compare embryonic and adult cell populations, aiming to identify persistent embryonic transcriptional signatures in adult cells. A total of 15 clusters were identified from the embryonic and fetal OFT samples, including three mesenchymal and four endothelial clusters. Using SCENIC analysis on the embryonic snRNAseq data, the authors identified GATA6 as a key regulator of valve precursor cells. Spatial transcriptomic analysis of four fetal OFT sections further revealed the spatial distribution of mesenchymal nuclei, smooth muscle cells, and valvular interstitial cells. Trajectory analysis identified two distinct developmental origins of fetal mesenchymal cells: the neural crest and the second heart field. Finally, the authors used snRNAseq data from the adult aortic valve to propose that embryonic transcriptional signatures persist in a subset of adult cells.

      Strengths:

      (1) The study offers a rich and detailed dataset, combining snRNA-seq and spatial transcriptomics in human embryonic and fetal OFT, which are challenging to obtain.

      (2) The use of SCENIC and trajectory analysis adds mechanistic insight into cell lineage and regulatory programs during valve development.

      (3) This study confirms GATA6 as a key regulator of valve precursor cells.

      (4) Comparison between embryonic/fetal and adult datasets represents a novel attempt to trace persistence of developmental transcriptional programs.

      Weaknesses:

      (1) A major limitation is the lack of experimental validation to support key conclusions, particularly the claim of persistent embryonic transcriptional signatures in adult cells.

      (2) The manuscript would benefit from a clearer discussion of how these results advance beyond previous studies in human heart and valve development.

      (3) The comparison between embryonic and adult data is interesting, but would be more convincing with additional evidence supporting the proposed persistence of embryonic transcriptional signatures in adult cells.

      Review 2
    3. Public_Reviews 10 Sep 2025

      Reviewer #3 (Public review):

      Leshem et al have generated a transcriptional cell atlas of the human outflow tract at two developmental timepoints and its adult valvular derivatives. This carefully performed study provides a useful resource for the study of known genes implicated in outflow tract defects and potentially also for discovering new disease genes. The authors reveal neural crest and mesodermal contributions to different outflow tract components and show that GATA6, known to play a role in arterial valve development, controls a set of genes expressed in endocardium-derived cells during valve development. Interestingly, the results suggest lineage persistence of expression of certain genes through to the adult timepoint, a main new finding of this study.

      The following points should be addressed to reinforce the conclusions and emphasize the novel features of this study.

      (1) It would be helpful to clarify how these new findings confirm or diverge from what is known from analysis of neural crest and mesodermal lineage contributions to different cell populations in the mouse heart. Did the authors identify any human-specific populations of cells, such as the LGR5 population reported by Sahara et al?

      (2) The authors should clarify in the introduction and results that they consider the endocardium to be on the SHF trajectory as indicated in Figure S4C. Please add a reference for this point.

      (3) The GATA6 results are interesting and support this experimental approach. The paper would be reinforced if the authors could provide any functional validation (in addition to their GATA6 genomic occupancy data) that the designated target genes are regulated by GATA6. This might involve looking at mutant mouse embryos or cultured cells. Do the authors consider that GATA6 may regulate the endocardial to mesenchymal transition during the early stages of valve development? Or the valve interstitial cell versus fibroblast fate choice?

      (4) Do the new findings reveal whether human valves have a direct SHF to VIC trajectory (ie, without transiting through endocardium) as has been recently shown in the murine non-coronary valve leaflet? Relevant to this point, Figure 5E appears to show contributions to a single adult aortic valve leaflet - this should be explained, or corrected.

      Review 3
    Visit annotations in context

    Tags

    Annotators

    URL

    biorxiv.org/content/10.1101/2023.04.05.535627v3
  30. www.biorxiv.org www.biorxiv.org
    Dissecting organoid-bacteria interaction highlights decreased contractile force as a key factor for heart infection
    2
    1. Public_Reviews 10 Sep 2025

      Reviewer #1 (Public review):

      Summary:

      In the study by Wang et al. entitled "Dissecting organoid-bacteria interaction highlights decreased contractile force as a key factor for heart infection", a simple cardiac organoid (CO) model was established, by combining a heterologous mixture of patient-specific human induced pluripotent stem cells (hiPSC)-derived cardiomyocytes (CMs) in combination with primary HUVECs (Human Umbilical Vein Endothelial Cells) and human mesenchymal stem cells (MSCs, representing stromal cells). This model was applied for investigating the interplay of COs' bacterial infections in vitro, aiming at revealing pathological mechanisms of bacterial infections of the heart in vivo, which may induce myocarditis and consequently heart failure in affected patients.

      Strengths:

      The paper is systematic, well written, and easy to follow.

      Based on their results, the authors state that: "In this study, by developing quantitative tools for analyzing bacterial-cardiac organoid interactions in a 3D, dynamic, clinically relevant setting, we discovered the significant role of cardiac contractility in preventing bacterial infection."

      In principle, the idea of establishing a simple yet functionally and physiologically relevant in vitro model and relevant analytical tools for enabling the study of complex pathological mechanisms of cardiovascular diseases is intriguing.

      Weaknesses:

      However, despite the combination of numerous analytical tools established and applied in the study, the work has substantial experimental limitations, indicating that the bold conclusions may represent a misinterpretation or overinterpretation of the findings.

      Key limitations and questions:

      (1) It seems that iPSCs from only one patient ("dilated cardiomyopathy (DCM) cells were derived from a 47-year-old Asian male with an LMNA gene mutation") were used in the study. Moreover, it seems that only one iPSC-line/clone from that DCM patient was used and compared to a single control iPSC line from a "healthy donor". Therefore, despite the different assays and experimental controls used in the study, there is a high risk that the observed phenomena reflect iPSC-line-/ clone-dependent effects, rather than revealing general pathophysiologic mechanisms. Thus, key experiments must be shown by cardiomyocytes/ cardiac organoids derived from additional independent iPSC-lines representing different patients and other non-diseased control lines as well. Moreover, it is established good experimental practice in the iPS cell field to generate and include isogenic iPSC controls i.e. iPSC lines of the same genetic background but with corrections of the hypothesised gene mutation underlying the respective e.g., cardiovascular disease.

      (2) In Figure 1 (A) immunohistochemical staining for cardiomyocytes for the cardiac marker Troponin is shown, apparently indicating successful cardiomyogenic differentiation of the applied hiPSC lines. In supplemental Figure S1, a flow cytometry analysis specific to cTnT is shown to reveal the CMs content resulting from the monolayer differentiation of respective iPSC lines. Already, the exemplified plots indicate that the CMs' content/ purity for DCM-CMs was notably lower compared to healthy cardiomyocytes (CM; control). This is an important issue, since the non-CMs ("contaminating bystander cells") may have a substantial effect on the functional (including contractile) properties of the COs.

      Interestingly, based on the method description, it seems that COs were generated from cryopreserved iPSC-CMs and iPSC-DCMs, including intermediate seeding and culture on Matrigel before COs formation. However, it remains unclear whether the CMs FACS analysis, which is apparently: "Representative FACS plots for analysis of the cell types in DCM monolayer culture after 33 days of differentiation" shows a CMs purity relevant to CO formation, or something different.

      The lineage phenotype of non-CMs in respective differentiations should also be clarified. Moreover, it should be noted in the results that the CMs content in COs is lower than the 6:2:2 (CM:ECs:MSC) ratio indicated by the authors, since the CMs purity is not 100%, and is particularly reduced in the iPSC-DCMs.

      Finally, to investigate the important latter questions of the "real CMs content" in COs, systematic technologies should be applied to quantify the lineage composition in COs (e.g. by IF staining for the 3 lineages plus DAPI, followed by COs clearance, confocal microscopy "3D stags" and automated, ImageJ-based quantitative cell counts for total cell number definition (see e.g. doi: 10.1038/s41596-024-00976-2) per CO, and quantification of respective lineage content as well.

      These questions are of key importance since the presence of non-CMs and their phenotype has profound consequences on the cardiac organoid model, its contractile/ biophysical properties, and, in general, on models' sensitivity to bacterial infections as well.

      (3) Figure 2: (F) Why is this figure (Confocal Observations) showing only healthy cardiac organoids (HCOs) but not DCM-COs?

      The overall quality of these pictures is poor and not informative regarding the structural identity and tissue composition of the COs, which actually is an important topic in the frame of the paper, as the 3D structure and tissue composition - and differences between HCOs and DCM-COs - are of key importance to their contractile properties.

      Moreover, the expective overlay of the cardiac markers alpha-actinin and MHC is not obvious from Figure 2F (see also comments on Figure 7, below).

      In Figure 2E: COs at later stages/days should be shown, in particular at that stage, which was used for the functional assays i.e., bacteria infections and contraction pattern monitoring.

      (4) Figure 7 (A) (B) - In the IF sections, it seems that there is no overlay between the expression of the cardiac marker MHC (seems to be expressed in the centre of COs only) and the cardiac markers alpha-actinin (which seems to be unexpectedly expressed in all cells on the sections) and Troponin (which seems to be vocally expressed on the outside, excluding the area of MHC expression).

      (F) Quantification of the mean area of gene expression, e.g., for MHC indicates a larger area after MHC expression; this seems to entirely contradict the IF pictures (in Figures 7 A-D) of MHC expression before and after infection. This contraction is deemed very critical to this reviewer as it may indicate that the IF staining, data analysis, and/or data interpretation in this part of the manuscript is poor, misleading, or simply wrong.

      (5) Overall, from the perspective of this reviewer, the CO-derived results do not reflect in a meaningful way the contractile and hydrodynamic conditions in the mouse heart or the human heart. Thus, it seems that the conclusions may rather represent a hypothesised outcome bias.

      Review 1
    2. Public_Reviews 10 Sep 2025

      Reviewer #2 (Public review):

      Summary:

      The authors tried deconvoluting, for the first time, the effect of various components of heart contraction on initial bacterial adhesion, which increases the risk of infective endocarditis. The proposed organoid platform might be used to develop and test novel therapeutic agents for infective endocarditis.

      Strengths:

      (1) Use of a broad range of methods: finite element methods, -omics, particle tracking, animal experiments to investigate the connections between contractility and infective endocarditis.

      (2) Detailed procedure and supportive information, which will allow other groups to replicate the results and extend the application of the proposed organoid platform.

      (3) Despite the complexity of the work reported, the manuscript is rather readable and understandable by non-specialists.

      Weaknesses:

      There is a minor issue with some of the vocabulary (e.g., magnificent amount of bacteria).

      Review 2
    Visit annotations in context

    Tags

    Annotators

    URL

    biorxiv.org/content/10.1101/2025.04.08.647814v1
  31. www.biorxiv.org www.biorxiv.org
    Diverse calcium dynamics underlie place field formation in hippocampal CA1 pyramidal cells
    2
    1. Public_Reviews 10 Sep 2025

      Reviewer #3 (Public review):

      Summary:

      In this manuscript, Sumegi et al. use calcium imaging in head-fixed mice to test whether new place fields tend to emerge due to events that resemble behavioral time scale plasticity (BTSP) or other mechanisms. An impressive dataset was amassed (163 sessions from 45 mice with 500-1000 neurons per sample) to study spontaneous emergence of new place fields in area CA1 that had the signature of BTSP. The authors observed that place fields could emerge due to BTSP and non-BTSP-like mechanisms. Interestingly, when non-BTSP mechanisms seemed to generate a place field, this tended to occur on a trial with a spontaneous reset in neural coding (a remapping event). Novelty seemed to upregulate non-BTSP events relative to BTSP events. Finally, large calcium transients (presumed plateau potentials) were not sufficient to generate a place field.

      Strengths:

      I found this manuscript to be exceptionally well written, well powered, and timely given the outstanding debate and confusion surrounding whether all place fields must arise from BTSP event. Working at the same institute, Albert Lee (e.g. Epszstein et al., 2011 - which should be cited) and Jeff Magee (e.g. Bittner et al., 2017) showed contradictory results for how place fields arise. These accounts have not fully been put toe-to-toe and reconciled in the literature. This manuscript addresses this gap and shows that both accounts are correct - place fields can emerge due to a pre-existing map and due to BTSP.

      Weaknesses:

      I find only three significant areas for improvement in the present study:

      First, can it be concluded that non-BTSP events occur exclusively due to a global remapping event, as stated in the manuscript "these PFF surges included a high fraction of both non-BTSP- and BTSP-like PFF events, and were associated with global remapping of the CA1 representation"? Global remapping has a precise definition that involves quantifying the stability of all place fields recorded. Without a color scale bar in Figure 3D (which should be added), we cannot know whether the overall representations were independent before and after the spontaneous reset. It would be good to know if some neurons are able to maintain place coding (more often than expected by chance), suggestive of a partial-remapping phenomenon.

      Second, BTSP has a flip side that involves weakening of existing place fields when a novel field emerges. Was this observed in the present study? Presumably place fields can disappear due to this bidirectional-BTSP or due to global remapping. For a full comparison of the two phenomena, the disappearance of place fields must also be assessed.

      Finally, it would be good to know if place fields differ according to how they are born. For example, are there differences in reliability, width, peak rate, out of field firing, etc for those that arise due BTSP vs non-BTSP.

      Comments on revisions:

      The authors have mostly addressed my feedback. Compelling evidence for a fundamental observation.

      Review 3
    2. Public_Reviews 10 Sep 2025

      Author response:

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

      Reviewer #1 (Public review):

      Summary:

      The authors aimed to investigate the cellular mechanisms underlying place field formation (PFF) in hippocampal CA1 pyramidal cells by performing in vivo two-photon calcium imaging in head-restrained mice navigating a virtual environment. Specifically, they sought to determine whether BTSP-like (behavioral time scale synaptic plasticity) events, characterized by large calcium transients, are the primary mechanism driving PFFs or if other mechanisms also play a significant role. Through their extensive imaging dataset, the authors found that while BTSP-like events are prevalent, a substantial fraction of new place fields are formed via non-BTSP-like mechanisms. They further observed that large calcium transients, often associated with BTSP-like events, are not sufficient to induce new place fields, indicating the presence of additional regulatory factors (possibly local dendritic spikes).

      Strengths

      The study makes use of a robust and extensive dataset collected from 163 imaging sessions across 45 mice, providing a comprehensive examination of CA1 place-cell activity during navigation in both familiar and novel virtual environments. The use of two-photon calcium imaging allows the authors to observe the detailed dynamics of neuronal activity and calcium transients, offering insights into the differences between BTSP-like and non-BTSP-like PFF events. The study's ability to distinguish between these two mechanisms and analyze their prevalence under different conditions is a key strength, as it provides a nuanced understanding of how place fields are formed and maintained. The paper supports the idea that BTSP is not the only driving force behind PFF, and other mechanisms are likely sufficient to drive PFF, and BTSP events may also be insufficient to drive PFF in some cases. The longer-than-usual virtual track used in the experiment allowed place cells to express multiple place fields, adding a valuable dimension to the dataset that is typically lacking in similar studies. Additionally, the authors took a conservative approach in classifying PFF events, ensuring that their findings were not confounded by noise or ambiguous activity.

      Weaknesses

      Despite the impressive dataset, there are several methodological and interpretational concerns that limit the impact of the findings. Firstly, the virtual environment appears to be poorly enriched, relying mainly on wall patterns for visual cues, which raises questions about the generalizability of the results to more enriched environments. Prior studies have shown that environmental enrichment can significantly influence spatial coding, and it would be important to determine how a more immersive VR environment might alter the observed PFF dynamics. Secondly, the study relies on deconvolution methods in some cases to infer spiking activity from calcium signals without in vivo ground truth validation. This introduces potential inaccuracies, as deconvolution is an estimate rather than a direct measure of spiking, and any conclusions drawn from these inferred signals should be interpreted with caution. Thirdly, the figures would benefit from clearer statistical annotations and visual enhancements. For example, several plots lack indicators of statistical significance, making it difficult for readers to assess the robustness of the findings. Furthermore, the use of bar plots without displaying underlying data distributions obscures variability, which could be better visualized with violin plots or individual data points. The manuscript would also benefit from a more explicit breakdown of the proportion of place fields categorized as BTSP-like versus non-BTSP-like, along with clearer references to figures throughout the results section. Lastly, the authors' interpretation of their data, particularly regarding the sufficiency of large calcium transients for PFF induction, needs to be more cautious. Without direct confirmation that these transients correspond to actual BTSP events (including associated complex spikes and calcium plateau potentials), concluding that BTSP is not necessary or sufficient for PFF formation is speculative.

      Reviewer #2 (Public review):

      Summary:

      The authors of this manuscript aim to investigate the formation of place fields (PFs) in hippocampal CA1 pyramidal cells. They focus on the role of behavioral time scale synaptic plasticity (BTSP), a mechanism proposed to be crucial for the formation of new PFs. Using in vivo two-photon calcium imaging in head-restrained mice navigating virtual environments, employing a classification method based on calcium activity to categorize the formation of place cells' place fields into BTSP, non-BTSP-like, and investigated their properties.

      Strengths:

      A new method to use calcium imaging to separate BTSP and non-BTSP place field formation. This work offers new methods and factual evidence for other researchers in the field.

      The method enabled the authors to reveal that while many PFs are formed by BTSP-like events, a significant number of PFs emerge with calcium dynamics that do not match BTSP characteristics, suggesting a diversity of mechanisms underlying PF formation. The characteristics of place fields under the first two categories are comprehensively described, including aspects such as formation timing, quantity, and width.

      Weaknesses:

      There are some issues about data and statistics that need to be addressed before these research findings can be considered as rigorous conclusions.

      While the authors mentioned 3 features of PF generated by BTSP during calcium imaging in the Introduction, the classification method used features 1 and 2. The confirmation by feature 3 in its current form is important but not strong enough.

      Some key data is missing such as the excluded PFs, the BTSP/non-BTSP of each animal, etc

      Impact:

      This work is likely to provide a new method to classify BTSP and non-BTSP place field formation using calsium image to the field.

      Reviewer #3 (Public review):

      Summary:

      In this manuscript, Sumegi et al. use calcium imaging in head-fixed mice to test whether new place fields tend to emerge due to events that resemble behavioral time scale plasticity (BTSP) or other mechanisms. An impressive dataset was amassed (163 sessions from 45 mice with 500-1000 neurons per sample) to study the spontaneous emergence of new place fields in area CA1 that had the signature of BTSP. The authors observed that place fields could emerge due to BTSP and non-BTSP-like mechanisms. Interestingly, when non-BTSP mechanisms seemed to generate a place field, this tended to occur on a trial with a spontaneous reset in neural coding (a remapping event). Novelty seemed to upregulate non-BTSP events relative to BTSP events. Finally, large calcium transients (presumed plateau potentials) were not sufficient to generate a place field.

      Strengths:

      I found this manuscript to be exceptionally well-written, well-powered, and timely given the outstanding debate and confusion surrounding whether all place fields must arise from BTSP event. Working at the same institute, Albert Lee (e.g. Epszstein et al., 2011 - which should be cited) and Jeff Magee (e.g. Bittner et al., 2017) showed contradictory results for how place fields arise. These accounts have not fully been put toe-to-toe and reconciled in the literature. This manuscript addresses this gap and shows that both accounts are correct - place fields can emerge due to a pre-existing map and due to BTSP.

      We thank the Reviewer for his/her appreciation of the importance of our study. We have included the additional reference.

      Weaknesses:

      I find only three significant areas for improvement in the present study:

      First, can it be concluded that non-BTSP events occur exclusively due to a global remapping event, as stated in the manuscript "these PFF surges included a high fraction of both non-BTSP- and BTSP-like PFF events, and were associated with global remapping of the CA1 representation"? Global remapping has a precise definition that involves quantifying the stability of all place fields recorded. Without a color scale bar in Figure 3D (which should be added), we cannot know whether the overall representations were independent before and after the spontaneous reset. It would be good to know if some neurons are able to maintain place coding (more often than expected by chance), suggestive of a partial-remapping phenomenon.

      We have performed the analysis suggested by the Reviewer and determined what fraction of CA1PCs retained its original tuning property after the representation switch. We found that the remapping was essentially global, as only a small fraction (5.4%) of CA1PCs retained their pre-switch tuning curve after the switch. This is now described in the Results.

      We now state in the figure legend for the former Figure 3D (now Figure 3F) that the color scale applies to all subpanels.

      We would like to note that we do not conclude that non-BTSP events occur exclusively during global remapping – we have found a sizable fraction of PFF by non-BTSP mechanism also in the familiar environment with no signs of change in the population representation. We agree nonetheless that PFF is dominated by BTSP under these conditions, whereas the contribution of non-BTSP is larger during global remapping events.

      Second, BTSP has a flip side that involves the weakening of existing place fields when a novel field emerges. Was this observed in the present study? Presumably place fields can disappear due to this bidirectional BTSP or due to global remapping. For a full comparison of the two phenomena, the disappearance of place fields must also be assessed.

      In this study we focused on the birth of new PFs – yet, PFs not only form but also disappear constantly. The factors driving PF weakening are even less explored and understood than those driving PF birth. In fact, we observed (as illustrated by several examples in our MS) that many PFs weaken, or disappear completely during the course of an imaging session. These effects are sometimes accompanied by a new PFF event elsewhere (e.g. Figure 2 – figure supplement 2E bottom), whereas in other cases they are not (e.g. Figure 5A, middle). Similarly, some BTSP events seem to coincide with disappearance of another PF, but others are not (e.g. Figure 2A bottom, first PF along the track; Figure 3 – figure supplement 1A left, first PF). The picture is further complicated in the case of global remapping events (i.e. representation switches, Figure 3 – figure supplement 2B) that, by definition, include both new PFF and PF disappearance. We feel that exploration of the complex mechanisms at play in PF disappearance is outside the scope of the current study, but could be the subject of an interesting future investigation.

      Finally, it would be good to know if place fields differ according to how they are born. For example, are there differences in reliability, width, peak rate, out-of-field firing, etc for those that arise due to BTSP vs non-BTSP.

      We have analyzed several properties of the PFs and found no significant difference in either their width (BTSP: 46.4 ± 24.4 cm; non-BTSP: 50.4 ± 32.5 cm, p = 0.28) or peak rates (BTSP: 19.0 ± 14.7 a.u./s; non-BTSP: 21.4 ± 16.8 a.u./s, p = 0.27) or the out-of-field firing rates (BTSP: 0.64 ± 0.68 a.u./s; non-BTSP: 0.83 ± 1.25 a.u./s, p = 0.09, all unpaired t-test). We have included these data into the Results section.

      Reviewer #1 (Recommendations for the authors):

      Consider adding additional visual cues or environmental elements to the virtual reality (VR) setup to create a more enriched and immersive environment. Collect data from a couple of mice in the enriched environment and compare the PFF dynamics to the original environment. This would help determine whether the findings on PFF dynamics hold in a setting where spatial coding may be more robust. Including floor cues, distal visual markers, or varying textures might provide a more comprehensive understanding of the factors influencing BTSP-like and non-BTSP-like events.

      We thank the Reviewer for her/his suggestion of analyzing data obtained from a more enriched VR environment compared to the one we used in our study. We have now included data obtained in a profoundly different VR environment, which did not have sparse dominant visual landmarks, but the entire wall was covered with a rich pattern with different shapes of different colors. Our data from 11 imaging sessions from 4 mice revealed BTSP- and non-BTSP-like PFF events with approximately the same ratio to that found in our regular maze. These results are described in the Results section and are presented in a new supplementary figure (Figure 2 – figure supplement 2). 

      Wherever deconvolved spikes were used for analysis, provide a comparison of results obtained directly from the GCaMP ΔF/F signals versus those derived from the deconvolved spiking data. This could illustrate any differences and help readers understand the limitations and reliability of the inference method.

      We have adopted a currently widely accepted method in the field to infer spikes from fluorescent traces using the Suite2p software package. All of our analyses were then performed on the inferred spikes. To address the concerns of the Reviewer, we analyzed the relationship between the peak [Ca<sup>2+</sup>] transients and inferred spike activity (new Figure 3 – figure supplement 1C-E). Our results clearly demonstrate a robust, highly significant correlation between these measures at the level of individual cells (new Figure 3 – figure supplement 1D) and the Spearman correlation coefficients show a distribution that is very different from random distributions (new Figure 3 – figure supplement 1E). From these, we conclude that using directly the fluorescent data would have resulted in largely similar PF detection and identification.

      Improve the visual clarity of figures by enlarging key elements such as arrows that indicate BTSP-like events. Consider using colors that stand out more clearly to guide readers' attention. Include annotations of statistical significance directly on the figures (e.g., adding NS or * indicators) to make it clear which comparisons are statistically significant. This will help readers quickly interpret the data without needing to refer back to the text.

      Based on the suggestion of the Reviewer, we have enlarged the arrows. We have also indicated statistical results on the figures. Because some of the results of factorial ANOVA tests are difficult to be comprehensively indicated on our plots, we kept the description of the statistical results in the legends as well. We hope that these alterations will make data interpretation easier.

      Replace or supplement bar plots with violin plots or scatter plots that show the distribution of individual data points. This change would offer a clearer picture of data variability and underlying trends, aiding readers in assessing the robustness of the results.

      We have changed the plots and now present all data points.

      Add more detailed quantification in the results section, specifying the total number of newly formed place fields, the proportion that are categorized as BTSP-like versus non-BTSP-like, and how many events did not fit these categories. Explicitly state what fraction of the total recorded place field formations are represented by the 59 non-BTSP-like events mentioned, as this is currently difficult to discern.

      The number of BTSP- and non-BTSP-like PFF events are given in the MS. As described in the Methods, after identifying BTSP- and non-BTSP-like PFF events using the shift and gain criteria, we have manually checked each of these ROIs and the spatial footprint of every new PFF events for these cells and excluded ROIs with non-soma-like shapes and activities with spurious footprints suggesting contamination, creating a ‘cleaned’ dataset. We did not perform such visual inspection and manual curation of every ROI’s spatial footprints that belong to the two additional categories (no gain with shift, gain without shift, 872 events). Since these classes are also overestimated without curation, we cannot provide a precise fraction of the BTSP- and non-BTSP-like PFF events from the total recorded PFF population. However, - assuming that factors leading to exclusion affect all groups equally - we can provide their fractions by comparing the numbers of newly born PFs in all categories before the visual inspections. In the normal maze, we found 806 candidate BTSP-like (52%),164 non-BTSP-like (10%) PFFs and an additional 593 PFs (38%) could not be included in these two groups [40 PFs (3%) with formation lap gain and backward shift but significant backward drift; 238 PFs (15%) with formation lap gain but without backward shift; 315 PFs (20%) with no formation lap gain but with backward shift]. These data have been included in the Methods.

      Ensure that all statements describing specific findings are consistently linked to the appropriate figures and panels. There are instances in the text where results are discussed without clear references, which can make it challenging for readers to verify the data. For example, the section on population remapping in a novel environment should point directly to the relevant figure panels to guide readers.

      We regret that our text was not linked properly to the appropriate figures. We corrected this during the revision.

      Given that BTSP-like events are inferred rather than directly confirmed, it would be prudent to frame conclusions about their sufficiency in more tentative terms, acknowledging the limitations of the current data. Consider adding a discussion of potential future experiments that could confirm whether these large transients truly represent BTSP events, including evidence for complex spikes or calcium plateau potentials.

      The Reviewer is correct that we do not have direct evidence that all large somatic Ca<sup>2+</sup> events represent dendritic plateau potentials. Now we discuss this and other limitations in the MS (Discussion section).

      Reviewer #2 (Recommendations for the authors):

      Although the author has outlined three characteristics of place fields (PFs) generated by behavioral time scale synaptic plasticity (BTSP) during calcium imaging in the Introduction section, as follows: ' First, the prolonged CSB results in large [Ca<sup>2+</sup>] transient during the initial PFF event, typically followed by weaker Ca2+ signals on consecutive traversals through the PF. Second, due to the long and asymmetric temporal kernel of the plasticity (favoring potentiation of inputs active 1-2 seconds before the CSB) a substantial backward shift in the spatial position of the PF center can be observed on linear tracks after the formation lap. Third, the width of the new PF is generally proportional to the running speed of the animal during the PFF event.' Figure 3B, which displays the third feature of classified BTSP and non-BTSP data, serves as an important confirmation of the classification results using the first two features. Even though the Spearman correlation indicated a significant difference, the raw data distributions of BTSP and non-BTSP appear similar, suggesting that a distribution of bootstrap and more stringent confirmation should be conducted to be convincing.

      As described in the MS, because of the difference in the number of events in the two groups, we randomly subsampled the BTSP-like events to the sample size of the non-BTSP-like PFF events 10000 times and performed regression analysis. This bootstrapping revealed that both the r and p values of the fit to the non-BTSP data fell outside the 95% confidence interval of the bootstrapped BTSP values, indicating that the difference between the groups was robust.

      In further analysis during the revision, we found that the PF width variance explained by distance from landmarks is substantially larger than the variance explained by the running speed during the formation lap. We performed a cross-validated analysis by these two factors (Figure 3D), which highlights that speed explains some of the PF width variance of BTSP-like PFFs, but none of the non-BTSP PFFs.

      The proportions of the three types should be provided. page 6: ' Using a conservative approach, we categorized a new PF to be formed by a BTSP-like mechanism if it had both positive gain and negative shift values (Figure 2A; n = 310 new PFs), whereas new PFs exhibiting neither positive gain nor negative shift were considered as non-BTSP-like events (Figure 2B; n = 59). All other newly formed PFs (no-gain with backward shift and gain without backward shift) were excluded from further analysis.' The number of excluded newly formed PFs should be disclosed, as well as the distribution ratio of these three types in each animal.

      The number of BTSP- and non-BTSP-like PFF events are given in the MS. As described in the Methods, after identifying BTSP- and non-BTSP-like PFF events using the shift and gain criteria, we have manually checked each of these ROIs and the spatial footprint of every new PFF events for these cells and excluded ROIs with non-soma-like shapes or spurious activities, creating a ‘cleaned’ dataset. We did not perform such visual inspection and manual curation of every ROI’s spatial footprints that belonged to the two additional categories (no gain with shift, gain without shift, 872 events). Since these classes are also overestimated without curation, we cannot provide a precise fraction of the BTSP- and non-BTSP-like PFF events from the total recorded PFF population. However, - assuming that factors leading to exclusion affect all groups equally - we can provide their fractions by comparing the numbers of newly born PFs in all categories before the visual inspections. In the normal maze, we found 806 candidate BTSP-like (52%),164 non-BTSP-like (10%) PFFs and an additional 593 PFs (38%) could not be included in these two groups [40 PFs (3%) with formation lap gain and backward shift but significant backward drift; 238 PFs (15%) with formation lap gain but without backward shift; 315 PFs (20%) with no formation lap gain but with backward shift]. These data have been included in the Methods.

      Figure 2C, while showing an overall decrease in amplitude from the formation lap to the next lap, could benefit from a pairwise analysis of the corresponding formation lap and the following lap of each session to provide more convincing and detailed results.

      We now present all data with connected lines across consecutive laps to illustrate the changes in each ROI. Our statistical analysis included the pairwise comparison of amplitudes.

      The experiment's time range is broad (11-99 days); it is worth investigating whether different training intervals might influence the results.

      Based on the suggestion of the Reviewer, we have analyzed the elapsed time and the number of sessions from the first training to the recording, and we demonstrate that there is no correlation of these parameters with the number of new PFFs. These data are now presented in Figure 2 – figure supplement 1C.

      It is unclear whether the formation of place fields also generates characteristic features of dendritic properties.

      It is not clear to us which ‘characteristic dendritic features of dendritic properties’ generated by PFF the Reviewer refers to. Since we did not image dendrites of individual CA1PCs, we have no information about dendritic properties of the neurons.

      It may be necessary to add a clearer figure to illustrate the correlation between width and speed following the downsampling of non-BTSP-like events (refer to Figure 3B).

      We have performed extensive additional analysis on the relationship of PF width with various behavioral factors, including the speed of the animal in the formation lap. Inspection of the PF width distributions along the track revealed a close association of PF width with the distance of the animal from the nearest visual landmark in the corridor, so that PFs close to landmarks were narrower than PFs between landmarks. We found that the PF width variance explained by distance from landmarks is substantially larger than the variance explained by the running speed during the formation lap. Nevertheless, there is a clear difference between BTSP-like and non-BTSP-like PFFs: running speed explains some variance in the case of BTSP-like PFFs, but none for non-BTSP-like PFFs.

      We have included these findings into the Results section and created two new panels in Figure 3 (C, D) and Figure 3 – figure supplement 1 (A, B).

      It is recommended that statistical results be labeled in the figures with n.s. or stars for better readability.

      Based on the suggestion of the Reviewer, we have indicated statistical results on the figures. Because some of the results of factorial ANOVA tests are difficult to be comprehensively indicated on our plots, we kept the description of the statistical results in the legends as well. We hope that these alterations will make data interpretation easier. We hope that these alterations will make data interpretation easier.

      AuthorResponse
    Visit annotations in context

    Tags

    Annotators

    URL

    biorxiv.org/content/10.1101/2024.10.01.616005v2
  32. www.biorxiv.org www.biorxiv.org
    Male-Biased Cyp17a2 Governs Antiviral Sexual Dimorphism in Fish via STING Stabilization and Viral Protein Degradation
    2
    1. Public_Reviews 10 Sep 2025

      Reviewer #1 (Public review):

      Summary:

      In this manuscript, Lu & Cui et al. observe that adult male zebrafish are more resistant to infection and disease following exposure to Spring Viremia of Carp Virus (SVCV) than female fish. The authors then attempt to identify some of the molecular underpinnings of this apparent sexual dimorphism and focus their investigations on a gene called cytochrome P450, family 17, subfamily A, polypeptide 2 (cyp17a2) because it was among the genes that they found to be more highly expressed in kidney tissue from males than in females. Their investigations lead them to propose a direct connection between cyp17a2 and modulation of interferon signaling as the key underlying driver of the difference between male and female susceptibility to SVCV.

      Strengths:

      Strengths of this study include the interesting observation of a substantial difference between adult male and female zebrafish in their susceptibility to SVCV, and also the breadth of experiments that were performed linking cyp17a2 to infection phenotypes and molecularly to the stability of host and virus proteins in cell lines. The authors place the infection phenotype in an interesting and complex context of many other sexual dimorphisms in infection phenotypes in vertebrates. This study succeeds in highlighting an unexpected factor involved in antiviral immunity that will be an important subject for future investigations of infection, metabolism, and other contexts.

      Weaknesses:

      Weaknesses of this study include an indirect connection between the majority of experiments and the proposed mechanism underlying the sexual dimorphism phenotype, widespread reliance on over-expression when investigating protein-protein interaction and localization, and an insufficient amount of description of the data presented in the figures. Specific examples of areas for clarification or improvement include:

      (1) Figure 10 outlines a mechanistic link between cyp17a2 and the sexual dimorphism the authors report for SVCV infection outcomes. The data presented on increased susceptibility of cyp17a2-/- mutant male zebrafish support this diagram, but this conclusion is fairly weak without additional experimentation in both males and females. The authors justify their decision to focus on males by stating that they wanted to avoid potential androgen-mediated phenotypes in the cpy17a2 mutant background (lines 152-156), but this appears to be speculation. It also doesn't preclude the possibility of testing the effects of increased cyp17a2 expression on viral infection in both males and females. This is of critical importance if the authors intend to focus the study on sexual dimorphism, which is how the introduction and discussion are currently structured.

      (2) The authors present data indicating an unexpected link between cyp17a2 and ubiquitination pathways. It is unclear how a CYP450 family member would carry out such activities, and this warrants much more attention. One brief paragraph in the discussion (starting at line 448) mentions previous implications of CYP450 proteins in antiviral immunity, but given that most of the data presented in the paper attempt to characterize cyp17a2 as a direct interactor of ubiquitination factors, more discussion in the text should be devoted to this topic. For example, are there any known domains in this protein that make sense in this context? Discussion of this interface is more relevant to the study than the general overview of sexual dimorphism that is currently highlighted in the discussion and throughout the text.

      (3) Figures 2-9 contain information that could be streamlined to highlight the main points the authors hope to make through a combination of editing, removal, and movement to supplemental materials. There is a consistent lack of clarity in these figures that could be improved by supplementing them with more text to accompany the supplemental figures. Using Figure 2 and an example, panel (A) could be removed as unnecessary, panel (B) could be exchanged for a volcano plot with examples highlighting why cyp17a2 was selected for further study and also the full dataset could be shared in a supplemental table, panel (C) could be modified to indicate why that particular subset was chosen for plotting along with an explanation of the scaling, panel (D) could be moved to supplemental because the point is redundant with panels (A) and (C), panel (E) could be presented as a heatmap, in panels (G) and (H) data from EPC cells could be moved to supplemental because it is not central to the phenotype under investigation, panels (J) to (L) and (N) to (P) could be moved to supplemental because they are redundant with the main points made in panels (M) and (Q). Similar considerations could be made with Figures 3-9

      (4) The data in Figure 3 (A)-(C) do not seem to match the description in the text. That is, the authors state that cyp17a2 overexpression increases interferon signaling activity in cells, but the figure shows higher increases in vector controls. Additionally, the data in panel (H) are not described. What genes were selected and why, and where are the data on the rest of the genes from this analysis? This should be shared in a supplemental table.

      (5) Some of the reagents described in the methods do not have cited support for the applications used in the study. For example, the antibody for TRIM11 (line 624, data in Figures 6 & 7) was generated for targeting the human protein. Validation for use of this reagent in zebrafish should be presented or cited. Furthermore, the accepted zebrafish nomenclature for this gene would be preferred throughout the text, which is bloodthirsty-related gene family, member 32.

      Review 1
    2. Public_Reviews 10 Sep 2025

      Reviewer #2 (Public review):

      The manuscript identified Cyp17a2 as a master regulator of male-biased antiviral immunity in a sex chromosome-free model (zebrafish) challenging established immunological paradigms.

      Strengths:

      (1) The bifunctional role of Cyp17a2 (host-directed STING stabilization and virus-directed P degradation) represents a significant conceptual advance.

      (2) First demonstration of K33 chains as a critical regulatory switch for both host defense proteins and viral substrates.

      (3) Comprehensive validation across biological scales: organismal (survival, histopathology), cellular (transcriptomics, Co-IPs), and molecular (ubiquitination assays, site-directed mutagenesis).

      (4) Functional conservation in cyprinids (zebrafish and gibel carp) strengthens biological significance.

      Weaknesses:

      (1) Colocalization analyses (Figures 4G, 6I, 9D) require quantitative metrics (e.g., Pearson's coefficients) rather than representative images alone.

      (2) Figure 1 survival curves need annotated statistical tests (e.g., "Log-rank test, p=X.XX")

      (3) Figure 2P GSEA should report exact FDR-adjusted *p*-values (not just "*p*<0.05").

      (4) Section 2 overextends on teleost sex-determination diversity, condensing to emphasize relevance to immune dimorphism would strengthen narrative cohesion.

      (5) Limited discussion on whether this mechanism extends beyond Cyprinidae and its implications for teleost adaptation.

      Review 2
    Visit annotations in context

    Tags

    Annotators

    URL

    biorxiv.org/content/10.1101/2025.06.23.661124v1
  33. www.biorxiv.org www.biorxiv.org
    Genome reorganization and its functional impact during breast cancer progression
    3
    1. Public_Reviews 10 Sep 2025

      Reviewer #1 (Public review):

      Summary:

      In their manuscript, Metz Reed and colleagues present an exceptionally thorough analysis of three-dimensional genome reorganization during breast cancer progression using the well-characterized MCF10 model system. The integration of high-resolution Micro-C contact maps with multi-omics profiling provides compelling insights into stage-specific dynamics of chromatin compartments, TAD boundaries, and looping events. The discovery that stable chromatin loops enable epigenetic reprogramming of cancer genes, while structural changes selectively drive metastasis-associated pathways, represents a significant conceptual advance. This work substantially deepens our understanding of genome topology in malignancy. To further enhance this impactful study, we offer the following constructive suggestions.

      Strengths:

      This work sets a benchmark for integrative 3D genomics in oncology. Its methodological sophistication and conceptual advances establish a new paradigm for studying nuclear architecture in disease.

      Weaknesses:

      Major Issues

      (1) Functional tests would strengthen the observed links between structure and gene changes. For example, the COL12A1 gene loop formation correlates with its increased expression. Disrupting this loop using CRISPR-dCas9 at chr6 position 75280 kb could prove whether the loop causes COL12A1 activation. Such experiments would turn strong correlations into clear mechanisms.

      (2) The H3K27ac looping idea needs deeper validation. Data suggests H3K27ac loss weakens loops without affecting CTCF. Testing how cohesin proteins interact with H3K27ac-modified sites would clarify this process. Degron systems could rapidly remove H3K27ac to observe real-time effects. Also, the AP-1 motifs found at dynamic loop sites deserve functional tests. Knocking down AP-1 factors might show if they control loop formation.

      (3) Connecting findings to patient data would boost clinical relevance. The MCF10 model is excellent for controlled studies. Checking if TAD boundary weakening occurs in actual patient metastases would show real-world importance. Comparing primary and metastatic tumor samples from the same patients could reveal new structural biomarkers. If tissue is scarce, testing cancer cells with added stroma cells might mimic tumor environment effects.

      Minor Issues

      Adding a clear definition for static loops would help readers. For example, state that static loops show less than 10 percent contact change across replicates. In the ABC model analysis, removing promoter regions from the enhancer list would focus results on true long-range interactions. Briefly noting why this study sees TAD weakening while other cancer types show different patterns would provide useful context.

      Review 1
    2. Public_Reviews 10 Sep 2025

      Reviewer #2 (Public review):

      Employing the MCF10 breast-cancer progression series, the authors integrate high-resolution Micro-C chromatin-conformation capture with RNA-seq and ChIP-seq to delineate the sequential reorganization of compartments, topologically associated domains (TADs), and long-range loops across benign, pre-neoplastic, and metastatic states, and couple these 3D alterations to gene expression and enhancer activity. Four principal findings emerge: (i) largely static chromatin frameworks still gate differential gene output, with up-regulated loci most affected; (ii) enhancer-promoter contact strength covaries with transcriptional amplitude; (iii) 127 genes gain expression concomitant with increased chromatin contacts; and (iv) progression-associated genes acquire altered histone marks at distal enhancers that remain tethered by stable loops. While the conclusions are broadly supported, methodological and analytical refinements are required.

      (1) Model representativeness.<br /> The long-term culture-adapted MCF10 genome harbours extensive aneuploidies and translocations. Validation of key COL12A1/WNT5A loop dynamics in an independent breast-cancer line (e.g., MDA-MB-231, T47D) or in patient-derived organoids/PDX models would strengthen generalizability.

      (2) The study remains purely correlative; no perturbation experiments are conducted to demonstrate causal roles of chromatin loops on gene expression. CRISPR interference (CRISPR-Cas9-KRAB/HDAC) or enhancer deletion/inversion should be applied to 3-5 pivotal loops (e.g., COL12A1, WNT5A) to test their impact on target-gene expression and cellular phenotypes (e.g., proliferation, migration).

      (3) The manuscript lacks integration with clinical datasets. Integrate TCGA-BRCA data to assess whether elevated COL12A1/WNT5A expression associates with overall survival (OS) or distant metastasis-free survival (DMFS).

      Review 2
    3. Public_Reviews 10 Sep 2025

      Reviewer #3 (Public review):

      Summary:

      The authors tackle an important problem: defining the topological changes that occur during tumorigenesis. To study this, they use an established stepwise cell model of breast cancer. A strength of their study is a careful, robust differential analysis of topological features across each cell state, which is presented clearly and rigorously. They define changes in compartmentalization, TAD structure, and chromatin looping. Intriguingly, when the authors integrate differential gene expression with chromatin looping, they see that most differentially regulated genes are not involved in loop changes, suggesting that changes in promoter or enhancer chromatin marks may play a bigger role in regulating transcription than differential loops. The differential topology analysis and its integration with transcription is very well done- one of the best versions of this I have read in the 3D genome field! However, the paper is framed largely as a cancer biology study, and it teaches us much less about this. I am worried that some of the trends for each topologic feature are not going to be consistent across the pre-malignant-malignant-metastatic spectrum and would like the authors to soften some of their claims a bit regarding how this clarifies our understanding of cancer evolution.

      Weaknesses:

      Major Concerns:

      (1) The integration of gene expression and chromatin loops is intriguing. The authors' differential analysis, however, omits consideration of genes that are on and simply further upregulated versus genes that transition on/off or off/on. It would be nice to see the authors break out looping patterns for these two different patterns of regulation, as it may be instructive regarding the rules for how EP loops govern transcription.

      (2) Given the paucity of differential loops at the majority of genes whose expression changes, the authors should examine chromatin subcompartments, as these may associate more with differential transcription.

      (3) The authors could push their TAD analysis further by integrating it with transcription. Can they look at genes and their enhancers that span these altered boundaries to see if these shifts impact transcription?

      (4) The progression of cancer critically goes from a benign -> pre-malignant -> malignant -> metastatic series of steps. The AT1 line is described as 'premalignant' and thus the authors' series omits a malignant line. While I think adding such a sample is an unreasonable request at this point (as it would have had to have been studied in 'batch' with these other samples), the authors should acknowledge that they omit this step and spend some time discussing the genetic, morphologic, and phenotypic features for their 3 conditions. The images in Figure 1S aren't particularly useful- they don't tell the reader that these cells are malignant/benign. The karyotypic data are intriguing but not fully analyzed, so it is hard to know what true phenotype these cells represent. For example, malignant means DCIS/invasive carcinoma - so then what does this pre-malignant cell model represent? The described alteration in the AT1 line is a Ras oncogene, so in some sense, the transition to this line really is just +/- Ras. The authors could spend some time thinking about the effects of Ras specifically on the 3D genome.

      Review 3
    Visit annotations in context

    Tags

    Annotators

    URL

    biorxiv.org/content/10.1101/2025.05.14.654144v2
  34. www.mdpi.com www.mdpi.com
    Deciphering the Heterogeneity of Pancreatic Cancer: DNA Methylation-Based Cell Type Deconvolution Unveils Distinct Subgroups and Immune Landscapes
    13
    Visit annotations in context

    Tags

    Annotators

    URL

    mdpi.com/2075-4655/9/3/34
  35. onlinelibrary.wiley.com onlinelibrary.wiley.com
    Juvenile Rats Exposed to Antibiotics Early in Life Display Sexual Dimorphisms on Social Behaviour and Sex Hormone Deconjugating Activity
    1
    Visit annotations in context

    Tags

    Annotators

    URL

    onlinelibrary.wiley.com/doi/10.1111/jnc.70224
  36. www.biorxiv.org www.biorxiv.org
    Untitled document
    1
    1. Public_Reviews 10 Sep 2025

      Author response:

      The following is the authors’ response to the original reviews

      Reviewer #1 (Public review):

      Summary:

      The question of how central nervous system (CNS) lamination defects affect functional integrity is an interesting topic, though it remains a subject of debate. The authors focused on the retina, which is a relatively simple yet well-laminated tissue, to investigate the impact of afadin - a key component of adherens junctions on retinal structure and function. Their findings show that the loss of afadin leads to significant disruptions in outer retinal lamination, affecting the morphology and localization of photoreceptors and their synapses, as illustrated by high-quality images. Despite these severe changes, the study found that some functions of the retinal circuits, such as the ability to process light stimuli, could still be partially preserved. This research offers new insights into the relationship between retinal lamination and neural circuit function, suggesting that altered retinal morphology does not completely eliminate the capacity for visual information processing.

      Strengths:

      The retina serves as an excellent model for investigating lamination defects and functional integrity due to its relatively simple yet well-organized structure, along with the ease of analyzing visual function. The images depicting outer retinal lamination, as well as the morphology and localization of photoreceptors and their synapses, are clear and well-described. The paper is logically organized, progressing from structural defects to functional analysis. Additionally, the manuscript includes a comprehensive discussion of the findings and their implications.

      Weaknesses:

      While this work presents a wealth of descriptive data, it lacks quantification, which would help readers fully understand the findings and compare results with those from other studies. Furthermore, the molecular mechanisms underlying the defects caused by afadin deletion were not explored, leaving the role of afadin and its intracellular signaling pathways in retinal cells unclear. Finally, the study relied solely on electrophysiological recordings to demonstrate RGC function, which may not be robust enough to support the conclusions. Incorporating additional experiments, such as visual behavior tests, would strengthen the overall conclusions. 

      We would like to thank the reviewer for the thoughtful and valuable comments that helped us to further improve the manuscript. We have revised the manuscript to address the following three points in response to the reviewer's comments.

      While this work presents a wealth of descriptive data, it lacks quantification, which would help readers fully understand the findings and compare results with those from other studies.

      In response, we quantified the position of each retinal cell type and measured retinal thickness in the cHet and cKO mice at 1M, as presented in Figures 2F–M. To reflect these additions, we have included explanatory text in the revised manuscript (see lines 507–533).

      Furthermore, the molecular mechanisms underlying the defects caused by afadin deletion were not explored, leaving the role of afadin and its intracellular signaling pathways in retinal cells unclear.

      As AJ components, such as catenin and cadherin, are known to be associated with several signaling pathways, including Notch and Wnt signals (PMID: 37255594), we speculated that these pathways might be disrupted in the afadin cKO retina. Since these pathways are involved in cell proliferation, we examined the number of progenitor cells in the afadin cKO retina at developmental stages P1, P3, and P5 (new Figure S6C, see lines 868-870). No significant differences were observed at any of these stages. We also quantified the number of each retinal cell type at P14 when differentiation is complete. In the cKO retina, the number of BCs significantly increased, whereas the number of photoreceptors significantly reduced (new Figure S4C, see lines 620-622). To our knowledge, activation or inactivation of any AJ-associated signaling pathway does not reproduce the cell fate alterations observed in the afadin cKO retina. These findings suggest that the above pathways related to AJ may be unchanged in the cKO retina. However, we cannot exclude the possibility that multiple signaling pathways may be affected simultaneously or other pathways affected in the cKO retina.

      Finally, the study relied solely on electrophysiological recordings to demonstrate RGC function, which may not be robust enough to support the conclusions. Incorporating additional experiments, such as visual behavior tests, would strengthen the overall conclusions.

      We appreciate the reviewer’s insightful suggestion. To more robustly evaluate visual function in the cKO mice, we performed optomotor response (OMR) and visual cliff tests using cHet, cKO, and optic nerve crush (ONC) mice with Aki Hashio, Yuki Emori, and Mao Hiratsuka. We added their name as co-authors to the new manuscript. In the OMR test, cKO mice exhibited fewer responses to visual stimuli than cHet mice but significantly more than ONC mice. Furthermore, although no significant difference was detected between cKO and ONC mice in the visual cliff test, some cKO mice displayed cautious behavior suggestive of depth perception. These results indicate that cKO mice retain partial visual function, which is consistent with the MEA analysis. We have included these data as the new Figure 8 and incorporated the findings into the revised manuscript in the Introduction (lines 130-131 and 133-134), Methods (lines 378-406), Results (lines 775-816), and Discussion sections (lines 1026-1035).

      Reviewer #2 (Public review):

      Summary:

      Ueno et al. described substantial changes in the afadin knockout retina. These changes include decreased numbers of rods and cones, an increased number of bipolar cells, and disrupted somatic and synaptic organization of the outer limiting membrane, outer nuclear layer, and outer plexiform layer. In contrast, the number and organization of amacrine cells and retinal ganglion cells remain relatively intact. They also observed changes in ERG responses and RGC receptive fields and functions using MEA recordings.<br /> Strengths:

      The morphological characterization of retinal cell types and laminations is detailed and relatively comprehensive.

      Weaknesses:

      (1) The major weakness of this study, perhaps, is that its findings are predominantly descriptive and lack any mechanistic explanation. As afadin is key component of adherent junctions, its role in mediating retinal lamination has been reported previously (see PMCID: PMC6284407). Thus, a more detailed dissection of afadin's role in processes, such as progenitor generation, cell migration, or the formation of retinal lamination would provide greater insight into the defects caused by knocking out afadin.

      Thank you for valuable comments. We agree with the reviewer's point that findings are predominantly descriptive and lack any mechanistic explanation. However, we would like to clarify that the study cited in the comment (PMCID: PMC6284407) analyzed the role of afadin in dendritic stratification of direction-selective RGCs within the IPL, where “lamination” refers to the layering of RGC dendrites in the IPL. Here, we analyzed the function of afadin in the laminar construction of the overall retina.

      In response to the reviewer’s comment, we have added new analyses addressing retinal lamination, as well as the number and spatial distribution of progenitor cells, during development in the cKO retina. These new results are shown in Figures 4E, 9C–F, S5A–C, and S6C of the revised manuscript, and corresponding explanations added in the revised text (lines 643–662 and 855–870).

      (2) The authors observed striking changes in the numbers of rods, cones, and BCs, but not in ACs or RGCs. The causes of these distinct changes in specific cell classes remain unclear. Detailed characterizations, such as the expression of afadin in early developing retina, tracing cell numbers across various early developmental time points, and staining of apoptotic markers in developing retinal cells, could help to distinguish between defects in cell generation and survival, providing a better understand of the underlying causes of these phenotypes.

      Thank you for the insightful comment. Following the reviewer’s suggestion, we quantified the number of retinal cell types at P14 when cell differentiation is complete (new Figure S4C). At P14, the numbers of photoreceptors and BCs were significantly reduced in the cKO retina, while Müller glia, which was significantly reduced at 1M, showed no difference. We further examined the number of rods and BCs at P1, P3, and P5 (new Figures S4E, F). No significant differences were detected at P1 or P3, however, at P5, rod marker expression was significantly decreased, while the number of BCs was significantly increased. These results suggest that the defects in cell fate determination of BCs and rods begin to emerge between P3 and P5, a period for which rods and BCs actively differentiate. We speculate that cells originally destined to become rods may instead differentiate into BCs in the cKO retina. In addition, we found a significant increase in apoptotic cells at P1, P3, P5, and P14 (new Figure S6B). Furthermore, Müller glia and rod photoreceptors showed significantly greater reduction at 1M compared to P14, suggesting that the reduction in Müller glia observed at 1M may be due to post-differentiation cell death. These are presented in Figures S4C, S4E–F, and S6B, and described in the revised manuscript (lines 620-635 and 827-838).

      (3) Although the total number of ACs or RGCs remains unchanged, their localizations are somewhat altered (Figures 2E and 4E). Again, the cause of the altered somatic localization in ACs and RGCs is unclear.

      Thank you for the valuable question. In response to the reviewer’s comment, we analyzed the position of RGCs and ACs in the developing cKO retina. In the cKO retina at P1, retinal cells were organized into distinct multicellular compartments with clear boundaries, and acellular regions extending to the outer retinal surface were observed at these boundaries. These acellular regions contained dendritic processes of RGCs and ACs, which are components of the IPL, indicating that elements of the IPL extended vertically across the retina. As development progressed, the compartment boundaries gradually shifted toward the inner retina. At P14, the IPL was mainly located on the inner retina, as in the normal retina. However, some IPL structures remained in the outer retina and may correspond to the acellular patches. We have included the above data in the revised manuscript as Figures S5A and S5B and revised the manuscript to include this point (lines 643-660).

      (4) One conclusion that the authors emphasise is that the function of RGCs remains detectable despite a major disrupted outer plexiform layer. However, the organization of the inner plexiform layer remains largely intact, and the axonal innervation of BCs remains unchanged. This could explain the function integrity of RGCs. In addition, the resolution of detecting RGCs by MEA is low, as they only detected 5 clusters in heterozygous animals. This represents an incomplete clustering of RGC functional types and does not provide a full picture of how functional RGC types are altered in the afadin knockout.

      We appreciate the reviewer’s insightful comments. Although our clustering of RGC subtypes in afadin cHet retinas resulted in only five clusters, the key finding of our study is the preservation of RGC receptive fields in afadin cKO retinas, despite severe photoreceptor loss (reduced to about one-third of normal) and disruption of photoreceptor-bipolar cell synapses in the OPL. This suggests that even with crucial damage to the OPL, the primary photoreceptor-bipolar-RGC pathway can still function as long as the IPL remains intact. Moreover, the presence of rod-driven responses in RGCs indicates that the AII amacrine cell-mediated rod pathway may also continue to function. We agree that our functional clustering in afadin cHet retinas was incomplete. However, we guess that the absence of RGCs with fast temporal responses in afadin cKO retinas may not simply be due to the loss of specific RGC subtypes but due to disrupted synaptic connections between photoreceptors and fast-responding BCs. Furthermore, the structural abnormalities in retinal lamination in afadin cKO retinas may alter RGC response properties, making strict functional classification less meaningful. We would like to emphasize the finding that disruption of the retinal lamination in afadin cKO retinas leads to the absence of RGCs with fast temporal response properties, rather than focusing solely on the classification of RGC subtypes.

      Minor Comments:

      (1) Line 56-67: "Overall, these findings provide the first evidence that retinal circuit function can be partially preserved even when there are significant disruptions in retinal lamination and photoreceptor synapses" There is existing evidence showing substantial adaption in retinal function when retinal lamination or photoreceptor synapses are disrupted, such as PMCID: PMC10133175.

      Thank you for your comment. We agree that the original sentence was ambiguous in its wording, and we have revised it to clarify our intended meaning (lines 48-50):

      "Overall, these findings provide the first evidence that retinal circuit function can be partially preserved even when there are significant disruptions in both retinal lamination and photoreceptor synapses."

      In response, we have cited this study and added the following sentence to the Discussion section of the revised manuscript. The paper you mentioned is crucial for discussing and considering the results of our study. We have cited this study and added the following sentence to the Discussion section of the revised manuscript (lines 910-915):

      “Furthermore, RFs of RGCs are also detected in several mouse models of retinitis pigmentosa, in which rod photoreceptors are degenerated and surviving cone photoreceptors lose their OS discs and pedicles, instead forming abnormal processes resembling synaptic dendrites (Barhoum et al., 2008; Ellis et al., 2023; Scalabrino et al., 2022).”

      (2) Line 114-115: "we focused on afadin, which is a scaffolding protein for nectin and has no ortholog in mice." The term "Ortholog" is misused here, as the mouse has an afadin gene. Should the intended meaning be that afadin has no other isoforms in mouse?

      Thank you for pointing it out. As we misused "Ortholog" as "Paralog", we revised the sentence (line 108).

      Recommendations for the authors:

      (1) The introduction to afadin is insufficient. Please provide more background information about this protein.

      Following the reviewer’s recommendations, we expanded the Introduction in the revised manuscript to provide a more detailed background on afadin, as follows (lines 108-119):

      “Afadin regulates the localization of nectin, which initiates cell–cell adhesion and promotes AJ formation by recruiting the cadherin–catenin complex. (Ohama et al., 2018; Takai and Nakanishi, 2003). In addition, afadin interacts with various cell adhesion and signaling molecules, as well as the actin cytoskeleton, and contributes to the accumulation of β-catenin, αE-catenin, and E-cadherin at AJs (Sakakibara et al., 2018; Sato et al., 2006). Afadin KO mice exhibit severe disruption of AJs in the ectoderm, along with other developmental defects, leading to embryonic lethality (Ikeda et al., 1999; Zhadanov et al., 1999). Conditional deletion of afadin in RGCs leads to disruption of dendrites in ON-OFF direction-selective RGCs (Duan et al., 2018). However, the effect of afadin loss on retinal lamination, circuit formation, and function is poorly understood.”

      (2) In Figure 1A (Bottom), regarding the peptide+ image, what does the green signal represent?

      The green signal observed in the peptide+ image represents the background and non-specific staining. We have added the sentence to the legend of Figure 1A in the revised manuscript (lines 1067-1068).

      (3) In the RESULTS section on page 17, the statement "Nectin-1, unlike nectin-2 and nectin-3, was partially co-localized with afadin at the OPL and IPL, in addition to the OLM" suggests that nectin-2 is also expressed at the IPL, as shown in Figure S1A. Providing high-power images, similar to those in Figure S1B, could help readers clearly recognize the staining signals.

      Following your suggestion, we added higher-magnification images of Nectin-2 signals in the IPL to Figure S1A and included the following clarification in the Figure legend (lines 1356-1358):

      “Nectin-2 and nectin-3 were localized in the OLM. The Nectin-2 signal in the IPL was insufficient for reliable assessment of its localization and colocalization.”

      (4) Figure S2A requires an uncropped scan of the membrane after Western blotting to demonstrate that there are no non-specific bands when using this afadin antibody, which was also utilized for IHC.

      We revised the new Figure S2C to include the uncropped membrane scan. Faint non-specific bands were observed in the Western blot, consistent with detecting non-specific signals in immunostaining using the anti-afadin antibody pre-absorbed with its antigen peptide.

      (5) IHC staining is necessary to demonstrate the knockout of afadin in retinal cells, as the paper does not show Cre expression in the retinal cells of the Dkk3-Cre mouse line. This would also help verify the specificity of the afadin antibody.

      In the cKO retina, the laminar structure was disrupted, and the background signal was generally high, making it difficult to reliably assess whether afadin expression was lost using immunostaining with the anti-afadin antibody. Therefore, in addition to the Western blot analysis already presented, we evaluated Cre activity in the Dkk3-Cre mouse line by crossing it with the R26-H2B-EGFP reporter line. Cre-mediated recombination was observed in all retinal cells at P0 and 1M. We have added these results to a revised Figure S2A and B and included explanatory text in the revised manuscript (lines 455–458).

      (6) Why is the outer nuclear layer (ONL) severely impaired in the cKO mice when afadin is not expressed in this layer? Additionally, given that afadin is highly expressed in the inner plexiform layer (IPL), why does the cKO not affect its structure?

      We speculate that the AJ defect in the outer retina during development may cause severe disruption of the ONL in afadin cKO mice. As shown in new Figure 9, ectopic AJs and aberrant position of mitotic cells were observed in the P0 cKO retina. These defects caused abnormal cell migration and position, resulting in the ONL disruption. On the other hand, in the IPL, afadin and other cell adhesion molecules may function redundantly, and thus, the IPL structure would be kept intact in the afadin cKO retina. We have added this interpretation to the Discussion section of the revised manuscript (lines 998–1005).

      (7) In the RESULTS section on page 20, the authors state, "We further investigated adherens junctions (AJs) in the cKO retina by immunostaining with OLM adherens junction markers β-catenin, N-cadherin, and nectin-1. We found that these signals were dispersed in the cKO retina (Figure S2C)." It appears that β-catenin, N-cadherin, and nectin-1 can still be detected in the cKO retina.

      We agree with the reviewer that β-catenin, N-cadherin, and nectin-1 can still be detected in the cKO retina. We used the term 'dispersed' to indicate that the signal was “scattered” rather than “disappeared”. To avoid confusion, we have revised the wording in the revised manuscript (line 499).\

      (8) In Figure 3, please indicate where the zoomed-in images were captured from the low-power images. Additionally, point out the locations of zoomed-in images in other figures as well.

      Following the reviewer’s suggestion, we updated Figures 2D, 3A-C, 4A, S2D, S3A, S3D, S3E, and S5D. The related Figure legends have also been revised.

      (9) The authors should include individual data points in all statistical graphics to provide a clearer presentation of the data.

      As suggested by the reviewers, we have revised all statistical graphs to display individual data points. Furthermore, the statistical analysis of synapse counts in Figures 3E, 3F, and S3C has been changed to linear mixed models (LMM) or generalized LMM to account for the variability in the number of synapses within individual mice.

      (10) In the RESULTS section on page 23, the statement "These data indicate that the rosette-like structure in the cKO may be an ectopic IPL, termed 'acellular patches'". What is the mechanism that may cause the rosette-like structure to translocate from the IPL to the outer region of the retina?

      Thank you for raising a valuable question. To clarify the mechanism of acellular patch formation in the cKO mice, we analyzed the position of RGCs and ACs in the developing cKO retina. In the cKO retina at P1, retinal cells were organized into distinct multicellular compartments with clear boundaries, and acellular regions extending to the outer retinal surface were observed at these boundaries. These acellular regions contained dendritic processes of RGCs and ACs, which are components of the IPL, indicating that elements of the IPL extended vertically across the retina. As development progressed, the compartment boundaries gradually shifted toward the inner retina. At P14, the IPL was mainly located on the inner retina, as in the normal retina. However, some IPL structures remained in the outer retina and may correspond to the acellular patches. We have included these findings in the revised manuscript as Figures S5A and S5B and added the corresponding description to the text (lines 643–665).

      (11) Is the blood vessel structure normal in the cKO retina? Could this impact the survival of retinal cells?

      Thank you for your valuable comment. We performed immunostaining with an anti-CD31 antibody, a marker for blood vessels, as shown in the new Figure S2G. No apparent differences were observed in the cKO retina. We have added the following description to the revised manuscript (lines 539–543):

      “It has been reported that defects in the distal processes of Müller glia are associated with abnormal retinal vasculature (Shen et al., 2012). Thus, we immunostained the cKO retina with anti-CD31, a blood vessel marker, but no apparent vascular abnormalities were detected (Figure S2G).”

      (12) In the RESULTS section on pages 26-29, there is a lot of statistical information included in parentheses. It would be more concise to place this information in the figure legends, if possible.

      Following the reviewer's suggestion, we have moved the statistical information from the main text (pages 26–29) to the corresponding Figure legends.

      (13) In the RESULTS section on page 28, the authors state, "On the other hand, the inner retina was apparently normal, and both the inner nuclear layer (INL) and IPL could be recognized." However, in Fig 7A, it appears that the INL is mixed with the ONL and cannot be clearly identified.

      We agree with the reviewer that the INL is mixed with the ONL and cannot be clearly identified. Accordingly, we have revised the description in the text (lines 740–742) as follows:

      “On the other hand, the inner retina was apparently normal, and both the IPL and the proximal part of the INL could be recognized.”.

      (14) It is mentioned in the manuscript that "The receptive field (RF) area in the cKO retinas was significantly smaller than that in the cHet retinas." Is there an impairment in the dendritic fields of RGCs in the cKO retina that could lead to a smaller RF?

      Thank you for asking an interesting question. The dendritic field reflects the region where presynaptic cells can form synaptic contacts, whereas the receptive field is dynamically shaped by spatiotemporal excitatory and inhibitory inputs, gap junctions, and membrane properties of the dendrites. Consequently, the size of the dendritic field does not necessarily correspond to that of the receptive field. Moreover, the disruption of the retinal lamination in the afadin cKO retina may alter the morphology of RGC dendritic fields—even when RNA expression levels are identical—which makes it difficult to exactly compare the morphology of the same RGC subtype between afadin cHet and afadin cKO retinas. Additionally, due to the presence of over 40 RGC subtypes and the rosette-like structures in the afadin cKO retina, it is challenging to trace the complete dendritic arborization of individual RGCs. For these reasons, we rather hesitate to compare the dendritic field size and the receptive field size.

      (15) Figure 7H was not cited in the corresponding section of the main text.

      Thank you for pointing it out. We have added a citation of Figure 7H in the revised manuscript (line 759).

      (16) In Figure 8C, is there a difference in the number of pHH3+ mitotic cells between the cHet and cKO mice?

      We quantified the number of pHH3-positive cells in the cKO retina at P0, as shown in the new Figure 9B. The number of mitotic cells was significantly increased in the cKO retina (see lines 853-855). In contrast, the number of BrdU-labeled progenitor cells at P1, P3, and P5 was not significantly different between cHet and cKO retinas, as presented in the new Figure S6C. These results suggest that although the total number of progenitor cells remain unchanged in cKO retinas, the M phase may be prolonged.

      (17) The results related to Figure 8 should be moved to a location before Figure 5, as Figure 8 is also related to the lamination defects.

      In the original manuscript, Figures 2–7 presented the phenotypes observed in the cKO retina, while Figure 8 addressed the possible cause of the lamination defects. Since the revised Figure 8 presents behavioral tests evaluating visual function, the phenotypic analyses are presented in the revised Figures 2–8. In response to the reviewers’ comments, we further analyzed the distribution of mitotic and progenitor cells during development and included these results as revised Figure 9.

      (18) In the DISCUSSION section on page 32, the authors state, "A few photoreceptor-bipolar cell-retinal ganglion cell (BC-RGC) pathways (vertical pathways of the retina) are inferred to be maintained in the cKO retina." The authors could verify this using retrograde transsynaptic tracing with a pseudorabies virus injected into the superior colliculus.

      Thank you for your interesting suggestion. This is an important point, and the recommended experiment idea sounds excellent. We attempted this analysis; however, the virus injected into the superior colliculus successfully labeled RGCs but failed to reach BCs and photoreceptors in normal mice. We guess that light stimulation evoked RGC firings evidently show that the photoreceptor-bipolar cell-retinal ganglion cell (BC-RGC) pathways function.

      AuthorResponse
    Visit annotations in context

    Tags

    Annotators

    URL

    biorxiv.org/content/10.1101/2024.12.24.630271v4
  37. edtech.dk edtech.dk
    The language of mathematics and prompting
    1
    1. niels 10 Sep 2025
      expressed

      You may also view the table form as in the abstract definition of a function

      $$ f = {(1, \sqrt{2}), (2, \pi), (3, -1)} \subseteq {1, 2, 3}\times \mathbb{R}. $$

    Visit annotations in context

    Annotators

    URL

    edtech.dk/IMO25/intro.html
  38. nmoer.pressbooks.pub nmoer.pressbooks.pub
    Chapter 4: Common Writing Assignments
    1
    1. hgallegos1 10 Sep 2025
      Making use of a writing process ensures that you stay organized and focused while allowing you to break up a larger assignment into several distinct tasks.

      Breaking big assignments into smaller steps makes them much easier to manage and to stay organized.

    Visit annotations in context

    Annotators

    URL

    nmoer.pressbooks.pub/english1101/chapter/chapter-3-common-writing-assignments-mytext-cnm/
  39. zhuanlan.zhihu.com zhuanlan.zhihu.com
    classify-图像预处理
    2
    1. William_1 10 Sep 2025
      img_remove_1dim = np.squeeze(img_add_dim)

      在不使用axis指定位置的情况下np.squeeze(img_add_dim)表示删除目标维度中,所以长度为1的维度.

      例如:原数组 (1, 3, 1, 5) → squeeze 后 (3, 5)

      添加aixs参数后,只删除指定位置的长度为1的维度,如果指定的位置维度不为1,则会报错.

      例如:arr_squeezed = np.squeeze(arr, axis=0) # 只去掉第 0 维

    2. William_1 10 Sep 2025
      img_add_dim = img[np.newaxis]

      np.newaxis 是 NumPy 提供的一个特殊对象（本质上就是 None 的别名），专门用来在切片时 增加一个新维度。

      例子: 假设原图像数组是： img.shape # (H, W, C) = (512, 148, 3)

      img_add_dim = img[np.newaxis] print(img_add_dim.shape) # (1, 512, 148, 3)

      这里 np.newaxis 在最前面加了一维，结果变成 四维数组。

    Visit annotations in context

    Annotators

    URL

    zhuanlan.zhihu.com/p/696147115
  40. observatoire.francophonie.org observatoire.francophonie.org
    Qui parle français dans le monde – Organisation internationale de la Francophonie – Langue française et diversité linguistique
    1
    Visit annotations in context

    Annotators

    URL

    observatoire.francophonie.org/qui-parle-francais-dans-le-monde/
  41. bmjopen.bmj.com bmjopen.bmj.com
    Association between influenza vaccination, all-cause mortality and cardiovascular mortality: a protocol for a living systematic review and prospective meta-analysis
    1
    Visit annotations in context

    Tags

    Annotators

    URL

    bmjopen.bmj.com/content/12/3/e054171
  42. systematicreviewsjournal.biomedcentral.com systematicreviewsjournal.biomedcentral.com
    Research priority setting for implementation science and practice: a living systematic review protocol
    1
    Visit annotations in context

    Tags

    Annotators

    URL

    systematicreviewsjournal.biomedcentral.com/articles/10.1186/s13643-025-02786-3
  43. Local file Local file
    brainrules_paperback_020209.indd
    1

    Annotators

  44. www.biorxiv.org www.biorxiv.org
    Misclassification in memory modification in AppNL-G-F knock-in mouse model of Alzheimer’s disease
    2
    1. Public_Reviews 09 Sep 2025

      Reviewer #2 (Public review):

      Summary:

      This manuscript proposes that the use of a latent cause model for assessment of memory-based tasks may provide improved early detection in Alzheimer's Disease as well as more differentiated mapping of behavior to underlying causes. To test the validity of this model, the authors use a previously described knock-in mouse model of AD and subject the mice to several behaviors to determine whether the latent cause model may provide informative predictions regarding changes in the observed behaviors. They include a well-established fear learning paradigm in which distinct memories are believed to compete for control of behavior. More specifically, it's been observed that animals undergoing fear learning and subsequent fear extinction develop two separate memories for the acquisition phase and the extinction phase, such that the extinction does not simply 'erase' the previously acquired memory. Many models of learning require the addition of a separate context or state to be added during the extinction phase and are typically modeled by assuming the existence of a new state at the time of extinction. The Niv research group, Gershman et al. 2017, have shown that the use of a latent cause model applied to this behavior can elegantly predict the formation of latent states based on a Bayesian approach, and that these latent states can facilitate the persistence of the acquisition and extinction memory independently. The authors of this manuscript leverage this approach to test whether deficits in production of the internal states, or the inference and learning of those states, may be disrupted in knock-in mice that show both a build-up of amyloid-beta plaques and a deterioration in memory as the mice age.

      Strengths:

      I think the authors' proposal to leverage the latent cause model and test whether it can lead to improved assessments in an animal model of AD is a promising approach for bridging the gap between clinical and basic research. The authors use a promising mouse model and apply this to a paradigm in which the behavior and neurobiology are relatively well understood - an ideal situation for assessing how a disease state may impact both the neurobiology and behavior. The latent cause model has the potential to better connect observed behavior to underlying causes and may pave a road for improved mapping of changes in behavior to neurobiological mechanisms in diseases such as AD.<br /> The authors also compare the latent cause model to the Rescorla-Wagner model and a latent state model allowing for better assessment of the latent cause model as a strong model for assessing reinstatement.

      Weaknesses:

      I have several substantial concerns which I've detailed below. These include important details on how the behavior was analyzed, how the model was used to assess the behavior, and the interpretations that have been made based on the model.<br /> (1) There is substantial data to suggest that during fear learning in mice separate memories develop for the acquisition and extinction phases, with the acquisition memory becoming more strongly retrieved during spontaneous recovery and reinstatement. The Gershman paper, cited by the authors, shows how the latent causal model can predict this shift in latent causes by allowing for the priors to decay over time, thereby increasing the posterior of the acquisition memory at the time of spontaneous recovery. In this manuscript, the authors suggest a similar mechanism of action for reinstatement, yet the model does not appear to return to the acquisition memory after reinstatement, at least based on the simulation and examples shown in figures 1 and 3. More specifically, in figure 1, the authors indicate that the posterior probability of the latent cause, z<sub>A</sub> (the putative acquisition memory), increases, partially leading to reinstatement. This does not appear to be the case as test 3 (day 36) appears to have similar posterior probabilities for z<sub>A</sub> as well as similar weights for the CS as compared to the last days of extinction. Rather, the model appears to mainly modify the weights in the most recent latent cause, z<sub>B</sub> - the putative the 'extinction state', during reinstatement. The authors suggest that previous experimental data have indicated that spontaneous recovery or reinstatement effects are due to an interaction of the acquisition and extinction memory. These studies have shown that conditioned responding at a later time point after extinction is likely due to a balance between the acquisition memory and the extinction memory, and that this balance can shift towards the acquisition memory naturally during spontaneous recovery, or through artificial activation of the acquisition memory or inhibition of the extinction memory (see Lacagnina et al. for example). Here the authors show that the same latent cause learned during extinction, z<sub>B</sub>, appears to dominate during the learning phase of reinstatement, with rapid learning to the context - the weight for the context goes up substantially on day 35 - in z<sub>B</sub>. This latent cause, z<sub>B</sub>, dominates at the reinstatement test, and due to the increased associative strength between the context and shock, there is a strong CR. For the simulation shown in figure 1, it's not clear why a latent cause model is necessary for this behavior. This leads to the next point.

      (2) The authors compared the latent cause model to the Rescorla-Wagner model. This is very commendable, particularly since the latent cause model builds upon the RW model, so it can serve as an ideal test for whether a more simplified model can adequately predict the behavior. The authors show that the RW model cannot successfully predict the increased CR during reinstatement (Appendix figure 1). Yet there are some issues with the way the authors have implemented this comparison:<br /> (2A) The RW model is a simplified version of the latent cause model and so should be treated as a nested model when testing, or at a minimum, the number of parameters should be taken into account when comparing the models using a method such as the Bayesian Information Criterion, BIC.<br /> (2B) The RW model provides the associative strength between stimuli and does not necessarily require a linear relationship between V and the CR. This is the case in the original RW model as well as in the LCM. To allow for better comparison between the models, the authors should be modeling the CR in the same manner (using the same probit function) in both models. In fact, there are many instances in which a sigmoid has been applied to RW associative strengths to predict CRs. I would recommend modeling CRs in the RW as if there is just one latent cause. Or perhaps run the analysis for the LCM with just one latent cause - this would effectively reduce the LCM to RW and keep any other assumptions identical across the models.<br /> (2C) In the paper, the model fits for the alphas in the RW model are the same across the groups. Were the alphas for the two models kept as free variables? This is an important question as it gets back to the first point raised. Because the modeling of the reinstatement behavior with the LCM appears to be mainly driven by latent cause z<sub>B</sub>, the extinction memory, it may be possible to replicate the pattern of results without requiring a latent cause model. For example, the 12-month-old App NL-G-F mice behavior may have a deficit in learning about the context. Within the RW model, if the alpha for context is set to zero for those mice, but kept higher for the other groups, say alpha_context = 0.8, the authors could potentially observe the same pattern of discrimination indices in figure 2G and 2H at test. Because the authors don't explicitly state which parameters might be driving the change in the DI, the authors should show in some way that their results cannot simply be due to poor contextual learning in the 12 month old App NL-G-F mice, as this can presumably be predicted by the RW model. The authors' model fits using RW don't show this, but this is because they don't consider this possibility that the alpha for context might be disrupted in the 12-month-old App NL-G-F mice. Of course, using the RW model with these alphas won't lead to as nice of fits of the behavior across acquisition, extinction, and reinstatement as the authors' LCM, the number of parameters are substantially reduced in the RW model. Yet the important pattern of the DI would be replicated with the RW model (if I'm not mistaken), which is the important test for assessment of reinstatement.

      (3) As stated by the authors in the introduction, the advantage of the fear learning approach is that the memory is modified across the acquisition-extinction-reinstatement phases. Although perhaps not explicitly stated by the authors, the post-reinstatement test (test 3) is the crucial test for whether there is reactivation of a previously stored memory, with the general argument being that the reinvigorated response to the CS can't simply be explained by relearning the CS-US pairing, because re-exposure the US alone leads to increase response to the CS at test. Of course there are several explanations for why this may occur, particularly when also considering the context as a stimulus. This is what I understood to be the justification for the use of a model, such as the latent cause model, that may better capture and compare these possibilities within a single framework. As such, it is critical to look at the level of responding to both the context alone and to the CS. It appears that the authors only look at the percent freezing during the CS, and it is not clear whether this is due to the contextual-US learning during the US re-exposure or to increased responding to the CS - presumably caused by reactivation of the acquisition memory. The authors do perform a comparison between the preCS and CS period, but it is not clear whether this is taken into account in the LCM. For example, the instance of the model shown in figure 1 indicates that the 'extinction cause', or cause z6, develops a strong weight for the context during the reinstatement phase of presenting the shock alone. This state then leads to increased freezing during the final CS probe test as shown in the figure. If they haven't already, I think the authors must somehow incorporate these different phases (CS vs ITI) into their model, particularly since this type of memory retrieval that depends on assessing latent states is specifically why the authors justified using the latent causal model. In more precise terms, it's not clear whether the authors incorporate a preCS/ITI period each day the cue is presented as a vector of just the context in addition to the CS period in which the vector contains both the context and the CS. Based on the description, it seemed to me that they only model the CRs during the CS period on days when the CS is presented, and thereby the context is only ever modeled on its own (as just the context by itself in the vector) on extinction days when the CS is not presented. If they are modeling both timepoints each day that the CS I presented, then I would recommend explicitly stating this in the methods section.

      (4) The authors fit the model using all data points across acquisition and learning. As one of the other reviewers has highlighted, it appears that there is a high chance for overfitting the data with the LCM. Of course, this would result in much better fits than models with substantially fewer free parameters, such as the RW model. As mentioned above, the authors should use a method that takes into account the number of parameters, such as the BIC.

      (5) The authors have stated that they do not think the Barnes maze task can be modeled with the LCM. Whether or not this is the case, if the authors do not model this data with the LCM, the Barnes maze data doesn't appear valuable to the main hypothesis. The authors suggest that more sophisticated models such as the LCM may be beneficial for early detection of diseases such as Alzheimer's, so the Barnes maze data is not valuable for providing evidence of this hypothesis. Rather, the authors make an argument that the memory deficits in the Barnes maze mimic the reinstatement effects providing support that memory is disrupted similarly in these mice. Although, the authors state that the deficits in memory retrieval are similar across the two tasks, the authors are not explicit as to the precise deficits in memory retrieval in the reinstatement task - it's a combination of overgeneralizing latent causes during acquisition, poor learning rate, over differentiation of the stimuli.

      Review 2
    2. Public_Reviews 09 Sep 2025

      Reviewer #3 (Public review):

      Summary:

      This paper seeks to identify underlying mechanisms contributing to memory deficits observed in Alzheimer's disease (AD) mouse models. By understanding these mechanisms, they hope to uncover insights into subtle cognitive changes early in AD to inform interventions for early-stage decline.

      Strengths:

      The paper provides a comprehensive exploration of memory deficits in an AD mouse model, covering early and late stages of the disease. The experimental design was robust, confirming age-dependent increases in Aβ plaque accumulation in the AD model mice and using multiple behavior tasks that collectively highlighted difficulties in maintaining multiple competing memory cues, with deficits most pronounced in older mice.

      In the fear acquisition, extinction, and reinstatement task, AD model mice exhibited a significantly higher fear response after acquisition compared to controls, as well as a greater drop in fear response during reinstatement. These findings suggest that AD mice struggle to retain the fear memory associated with the conditioned stimulus, with the group differences being more pronounced in the older mice.

      In the reversal Barnes maze task, the AD model mice displayed a tendency to explore the maze perimeter rather than the two potential target holes, indicating a failure to integrate multiple memory cues into their strategy. This contrasted with the control mice, which used the more confirmatory strategy of focusing on the two target holes. Despite this, the AD mice were quicker to reach the target hole, suggesting that their impairments were specific to memory retrieval rather than basic task performance.

      The authors strengthened their findings by analyzing their data with a leading computational model, which describes how animals balance competing memories. They found that AD mice showed somewhat of a contradiction: a tendency to both treat trials as more alike than they are (lower α) and similar stimuli as more distinct than they are (lower σx) compared to controls.

      Weaknesses:

      While conceptually solid, the model struggles to fit the data and to support the key hypothesis about AD mice's inability to retain competing memories. These issues are evident in Figure 3:

      (1) The model misses trends in the data, including the gradual learning of fear in all groups during acquisition, the absence of a fear response at the start of the experiment, and the faster return of fear during reinstatement compared to the gradual learning of fear during acquisition. It also underestimates the increase in fear at the start of day 2 of extinction, particularly in controls.

      (2) The model explains the higher fear response in controls during reinstatement largely through a stronger association to the context formed during the unsignaled shock phase, rather than to any memory of the conditioned stimulus from acquisition (as seen in Figure 3C). In the experiment, however, this memory does seem to be important for explaining the higher fear response in controls during reinstatement (as seen in Author Response Figure 3). The model does show a necessary condition for memory retrieval, which is that controls rely more on the latent causes from acquisition. But this alone is not sufficient, since the associations within that cause may have been overwritten during extinction. The Rescorla-Wagner model illustrates this point: it too uses the latent cause from acquisition (as it only ever uses a single cause across phases) but does not retain the original stimulus-shock memory, updating and overwriting it continuously. Similarly, the latent cause model may reuse a cause from acquisition without preserving its original stimulus-shock association.

      These issues lead to potential overinterpretation of the model parameters. The differences in α and σx are being used to make claims about cognitive processes (e.g., overgeneralization vs. over differentiation), but the model itself does not appear to capture these processes accurately.

      The authors could benefit from a model that better matches the data and captures the retention and retrieval of fear memories across phases. While they explored alternatives, including the Rescorla-Wagner model and a latent state model, these showed no meaningful improvement in fit. This highlights a broader issue: these models are well-motivated but may not fully capture observed behavior.

      Conclusion:

      Overall, the data support the authors' hypothesis that AD model mice struggle to retain competing memories, with the effect becoming more pronounced with age. While I believe the right computational model could highlight these differences, the current models fall short in doing so.

      Review 3
    Visit annotations in context

    Tags

    Annotators

    URL

    biorxiv.org/content/10.1101/2025.01.24.634705v3
  45. socialsci.libretexts.org socialsci.libretexts.org
    1.1: Who Are You, Really?
    6
    Visit annotations in context

    Annotators

    URL

    socialsci.libretexts.org/Bookshelves/Counseling_and_Guidance/College_Success/01:_You_and_Your_College_Experience/1.01:_Who_Are_You_Really
  46. eng.libretexts.org eng.libretexts.org
    2.1: Information Systems
    1
    1. zbah1967 09 Sep 2025
      The Eras of Business Computing Era Hardware Operating System Applications Mainframe (1960s) Terminals connected to mainframe computer. Time-sharing (TSO) on MVS Custom-written MRP software PC (mid-1980s) IBM PC or compatible. Sometimes connected to mainframe computer via expansion card. MS-DOS WordPerfect, Lotus 1-2-3 Client-Server (late 80s to early 90s) IBM PC “clone” on a Novell Network. Windows for Workgroups Microsoft Word, Microsoft Excel World Wide Web (mid-90s to early 2000s) IBM PC “clone” connected to company intranet. Windows XP Microsoft Office, Internet Explorer Web 2.0 (mid-2000s to present) Laptop connected to company Wi-Fi. Windows 7 Microsoft Office, Firefox Post-PC (today and beyond) Apple iPad iOS Mobile-friendly websites, mobile apps

      I want to replicate this for my flash cards.

    Visit annotations in context

    Annotators

    URL

    eng.libretexts.org/Courses/Prince_Georges_Community_College/INT_1010:_Concepts_in_Computing/02:_Introduction_to_Information_Systems/2.01:_Information_Systems
  47. www.nordangliaeducation.com www.nordangliaeducation.com
    Why is Homework Important?
    1
    1. jrdiede 09 Sep 2025
      advises that spending time doing homework brings several benefits, more so for the students who put in two to three hours a night.

      I agree that it serves many benefits but that 2-3 hours can be a lot, especially for younger kids!!

    Visit annotations in context

    Annotators

    URL

    nordangliaeducation.com/news/2021/03/31/why-is-homework-important
  48. inst-fs-iad-prod.inscloudgate.net inst-fs-iad-prod.inscloudgate.net
    Writing Spaces 5
    1
    1. Hannahlichtenberger 09 Sep 2025
      Easing open the cover of J.D. Salinger’s Nine Stories that I annotatedtwo decades ago, I can see in the rounded letters of my adolescenthandwriting are notes about Salinger’s life and the underlined head-ing “3 Themes” followed by the bulleted points “communication, inno-cence of children, perversion of adults.”

      I like the idea of writing in a book you are reading verses on another piece of paper. Somehow it makes me feel more engaged with the text.

    Visit annotations in context

    Annotators

    URL

    inst-fs-iad-prod.inscloudgate.net/files/71efe96a-0886-43ba-b48e-3716c16eafeb/Sprouse - Reading In Conversation.pdf
  49. www.biorxiv.org www.biorxiv.org
    High-content high-resolution microscopy and deep learning assisted analysis reveals host and bacterial heterogeneity during Shigella infection
    2
    1. Public_Reviews 09 Sep 2025

      Reviewer #4 (Public review):

      Summary

      In this study, López-Jiménez and colleagues demonstrate the utility of using high-content microscopy in dissecting host and bacterial determinants that play a role in the establishment of infection using Shigella flexneri as a model. The manuscript nicely identifies that infection with Shigella results in a block to DNA replication and protein synthesis. At the same time, the host responds, in part, via the entrapment of Shigella in septin cages.

      Strengths:

      The main strength of this manuscript is its technical aspects. They nicely demonstrate how an automated microscopy pipeline coupled with artificial intelligence can be used to gain new insights regarding elements of bacterial pathogenesis, using Shigella flexneri as a model system. Using this pipeline enabled the investigators to enhance the field's general understanding regarding the role of septin cages in responding to invading Shigella. This platform should be of interest to those who study a variety of intracellular microbial pathogens.

      Another strength of the manuscript is the demonstration - using cell biology-based approaches- that infection with Shigella blocks DNA replication and protein synthesis. These observations nicely dovetail with the prior findings of other groups. Nevertheless, their clever click-chemistry-based approaches provide visual evidence of these phenomena and should interest many.

      Weaknesses:

      There are two main weaknesses of this work. First, the studies are limited to findings obtained using a single immortalized cell line. It is appreciated that HeLa cells serve as an excellent model for studying aspects of Shigella pathogenesis and host responses. However, it would be nice to see that similar observations are observed with an epithelial cell line of intestinal, preferably colonic origin, and eventually, with a non-immortalized cell line, although it is appreciated that the latter studies are beyond the scope of this work.

      The other weakness is that the studies are minimally mechanistic. For example, the investigators have data to suggest that infection with Shigella leads to an arrest in DNA replication and protein synthesis; however, no follow-up studies have been conducted to determine how these host cell processes are disabled. Interestingly, Zhang and colleagues recently identified that the Shigella OspC effectors target eukaryotic translation initiation factor 3 to block host cell translation (PMID: 38368608).

      Review 2
    2. Public_Reviews 09 Sep 2025

      Author response:

      The following is the authors’ response to the original reviews

      Reviewer #1 (Public Review):

      Summary:

      In this study, López-Jiménez and colleagues demonstrated the utility of using high-content microscopy in dissecting host and bacterial determinants that play a role in the establishment of infection using Shigella flexneri as a model. The manuscript nicely identifies that infection with Shigella results in a block to DNA replication and protein synthesis. At the same time, the host responds, in part, via the entrapment of Shigella in septin cages.

      Strengths:

      The main strength of this manuscript is its technical aspects. They nicely demonstrate how an automated microscopy pipeline coupled with artificial intelligence can be used to gain new insights regarding elements of bacterial pathogenesis, using Shigella flexneri as a model system. Using this pipeline enabled the investigators to enhance the field's general understanding regarding the role of septin cages in responding to invading Shigella. This platform should be of interest to those who study a variety of intracellular microbial pathogens.

      Another strength of the manuscript is the demonstration - using cell biology-based approaches- that infection with Shigella blocks DNA replication and protein synthesis. These observations nicely dovetail with the prior findings of other groups. Nevertheless, their clever click-chemistry-based approaches provide visual evidence of these phenomena and should interest many.

      We thank the Reviewer for their enthusiasm on technical aspects of this paper, regarding both the automated microscopy pipeline coupled with artificial intelligence and the click-chemistry based approaches to dissect DNA replication and protein synthesis by microscopy.

      Weaknesses:

      There are two main weaknesses of this work. First, the studies are limited to findings obtained using a single immortalized cell line. It is appreciated that HeLa cells serve as an excellent model for studying aspects of Shigella pathogenesis and host responses. However, it would be nice to see that similar observations are observed with an epithelial cell line of intestinal, preferably colonic origin, and eventually, with a non-immortalized cell line, although it is appreciated that the latter studies are beyond the scope of this work.

      The immortalized cell line HeLa is widely regarded as a paradigm to study infection by Shigella and other intracellular pathogens. However, we agree that future studies beyond the scope of this work should include other cell lines (eg. epithelial cells of colonic origin, macrophages, primary cells). 

      The other weakness is that the studies are minimally mechanistic. For example, the investigators have data to suggest that infection with Shigella leads to an arrest in DNA replication and protein synthesis; however, no follow-up studies have been conducted to determine how these host cell processes are disabled. Interestingly, Zhang and colleagues recently identified that the Shigella OspC effectors target eukaryotic translation initiation factor 3 to block host cell translation (PMID: 38368608). This paper should be discussed and cited in the discussion.

      We appreciate the Reviewer’s concern about the lack of follow up work on observations of host DNA and protein synthesis arrest upon Shigella infection, which will be the focus of future studies. We acknowledge the recent work of Zhang et al. (Cell Reports, 2024) considering their similar results on protein translation arrest, and this reference has been more fully discussed in the revised version of the manuscript.

      Reviewer #2 (Public Review):

      Summary:

      Septin caging has emerged as one of the innate immune responses of eukaryotic cells to infections by intracellular bacteria. This fascinating assembly of eukaryotic proteins into complex structures restricts bacteria motility within the cytoplasm of host cells, thereby facilitating recognition by cytosolic sensors and components of the autophagy machinery. Given the different types of septin caging that have been described thus far, a single-cell, unbiased approach to quantify and characterise septin recruitment at bacteria is important to fully grasp the role and function of caging. Thus, the authors have developed an automated image analysis pipeline allowing bacterial segmentation and classification of septin cages that will be very useful in the future, applied to study the role of host and bacterial factors, compare different bacterial strains, or even compare infections by clinical isolates.

      Strengths:

      The authors developed a solid pipeline that has been thoroughly validated. When tested on infected cells, automated analysis corroborated previous observations and allowed the unbiased quantification of the different types of septin cages as well as the correlation between caging and bacterial metabolic activity. This approach will prove an essential asset in the further characterisation of septin cages for future studies.

      We thank the Reviewer for their positive comments, and for highlighting the strength of our imaging and analysis pipeline to analyse Shigella-septin interactions.

      Weaknesses:

      As the main aim of the manuscript is to describe the newly developed analysis pipeline, the results illustrated in the manuscript are essentially descriptive. The developed pipeline seems exceptionally efficient in recognising septin cages in infected cells but its application for a broader purpose or field of study remains limited.

      The main objective of this manuscript is the development of imaging and analysis tools to study Shigella infection, and in particular, Shigella interactions with the septin cytoskeleton. In future work we will provide more mechanistic insight with novel experiments and broader applicability, using different cell lines (in agreement with Reviewer 1), mutants or clinical isolates of Shigella and different bacteria species (eg. Listeria, Salmonella, mycobacteria).

      Reviewer #3 (Public Review):

      Summary:

      The manuscript uses high-content imaging and advanced image-analysis tools to monitor the infection of epithelial cells by Shigella. They perform some analysis on the state of the cells (through measurements of DNA and protein synthesis), and then they focus on differential recruitment of Sept7 to the bacteria. They link this recruitment with the activity of the bacterial T3SS, which is a very interesting discovery. Overall, I found numerous exciting elements in this manuscript, and I have a couple of reservations. Please see below for more details on my reservations. Nevertheless, I think that these issues can be addressed by the authors, and doing so will help to make it a convincing and interesting piece for the community working on intracellular pathogens. The authors should also carefully re-edit their manuscript to avoid overselling their data (see below for issues I see there). I would consider taking out the first figure and starting with Figure 3 (Figure 2 could be re-organized in the later parts)- that could help to make the flow of the manuscript better.

      Strengths:

      The high-content analysis including the innovative analytical workflows are very promising and could be used by a large number of scientists working on intracellular bacteria. The finding that Septins (through SEPT7) are differentially regulated through actively secreting bacteria is very exciting and can steer novel research directions.

      We thank the Reviewer for their constructive feedback and excitement for our results, including our findings on T3SS activity and Shigella-septin interactions. In accordance with the Reviewer’s comments, we avoid overselling our data in the revised version of the manuscript.

      Weaknesses:

      The manuscript makes a connection between two research lines (1: Shigella infection and DNA/protein synthesis, 2: regulation of septins around invading Shigella) that are not fully developed - this makes it sometimes difficult to understand the take-home messages of the authors.

      We agree that the manuscript is mostly technical and therefore some of our experimental observations would benefit from follow up mechanistic studies in the future. We highlight our vision for broader applicability in response to weaknesses raised by Reviewer 2.

      It is not clear whether the analysis that was done on projected images actually reflects the phenotypes of the original 3D data. This issue needs to be carefully addressed.

      We agree with the Reviewer that characterizing 3D data using 2D projected images has limitations.

      We observe an increase in cell and nuclear surface that does not strictly imply a change in volume. This is why we measure Hoechst intensity in the nucleus using SUM-projection (as it can be used as a proxy of DNA content of the cell). However, we agree that future use of other markers (such as fluorescently labelled histones) would make our conclusions more robust.

      Regarding the different orientation of intracellular bacteria, we agree that investigation of septin recruitment is more challenging when bacteria are placed perpendicular to the acquisition plane. In a first step, we trained a Convolutional Neural Network (CNN) using 2D data, as it is easier/faster to train and requires fewer annotated images. In doing so, we already managed to correctly identify 80% of Shigella interacting with septins, which enabled us to observe higher T3SS activity in this population. In future studies, we will maximize the 3D potential of our data and retrain a CNN that will allow more precise identification of Shigella-septin interactions and in depth characterization of volumetric parameters.

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      (1) To conclude that cell volume is indeed increased, the investigators should consider staining the cells with markers that demarcate cell boundaries and/or are confined to the cytosol, i.e., a cell tracker dye.

      Staining using our SEPT7 antibody enables us to define cell boundaries for cellular area measurements (Novel Figure 1 - figure supplement 1A). However, we agree with the Reviewer that staining cells with additional markers (such as a cell tracker dye) would be required to conclude that cell volume is increased. We therefore adjust our claims in the main text (lines 107-115 and 235-246).

      (2) Line 27: I understand what is meant by "recruited to actively pathogenic bacteria with increased T3SS activation." However, one could argue that there are many different roles of the intracytosolic bacteria in pathogenesis in terms of pathogenesis, not just actively secreting effectors.

      T3SS secretion by cytosolic bacteria is tightly regulated and both T3SS states (active, inactive) likely contribute to the pathogenic lifestyle of S. flexneri. In agreement with this, we removed this statement from the manuscript (lines 27, 225 and 274).

      (3) Line 88: Please clarify in the text that HeLa cells are being studied.

      We explicitly mention that the epithelial cell line we study is HeLa in the main text (line 93), in addition to the Materials and methods (line 328).

      (4) Line 97: is it possible to quantify the average distance of the nuclei from the cell perimeter? This would help provide some context as to what it means to be a certain distance from the nucleus, i.e., is there another way to point out that distance from nuclei correlates with movement inward post-invasion at the periphery?

      To provide more context to the inward movement of bacteria to the cell centre, we provide calculations based on measurements in Figure 1G, I. If we approximate geometric shape of both cells and nucleus to a circle, the median radius of a HeLa cell is 31.1 µm<sup>2</sup> (uninfected cell) and 36.3 µm<sup>2</sup> (infected cell). Similarly, the median radius of the nucleus is 22.2 µm<sup>2</sup> (uninfected cell) and 24.57 µm<sup>2</sup> (infected cell).

      However, we note that Figure 1F shows distance of bacteria to the centroid of the cell, which is the geometric centre of the cell, and which does not necessarily coincide with the geometric centre of the nucleus. We also note that nuclear area increases with infection (in a bacterial dose dependent manner). Finally, we note that these measurements are performed on max projections of 3D Z-stacks. In this case we cannot fully appreciate distance to the nucleus for bacteria located above it.

      (5) Lines 212-213 - there is no Figure 9A, B - I think this should be Figure 7A, B.

      Text has been updated (lines 216-217).

      Reviewer #2 (Recommendations For The Authors):

      Testing the analysis pipeline as a proof-of-concept question such as the comparison of caging around the laboratory strain as compared to one or a few clinical isolates or mutants of interest would help stress the relevance of this new, remarkable tool.

      We thank the Reviewer for their enthusiasm.

      Future research in the Mostowy lab will capitalise on the high-content tools generated here to explore the frequency and heterogeneity of septin cage entrapment for a wide variety of S. flexneri mutants and Shigella clinical isolates.

      The sentence in line 215 ends with "in agreement with" followed by a reference.

      Text has been updated (line 219).

      The sentence in line 217 on the correlation between caging and T3SS is not very clear.

      Text has been clarified (lines 221-223).

      There is a typo in line 219 : "protrusSions"

      Text has been updated (line 223).

      Reviewer #3 (Recommendations For The Authors):

      Major points

      The quantitative analysis approach in Figure 1 has multiple issues. Some examples:<br /> (1) How was the cell area estimated? Normally, a marker for the whole cell (CellMask or similar) or cells expressing GFP would be good indicators. Here it is not clear to me what was done.

      The cell area was estimated using SEPT7 antibody staining which is enriched under the cell cortex. CellProfiler was used to segment cells based on SEPT7 staining, using a propagation method from the identified nucleus based on Otsu thresholding. To provide more clarity on how this was performed, we now include a new figure (Figure 1- figure supplement 1A) showing a representative image of HeLa cells stained with SEPT7 and the corresponding cell segmentation performed with CellProfiler software, together with an updated figure legend explaining the procedure (lines 784–787).

      (2) The authors use Hoechst and integrated z-projections (Figure 1 S1) as a proxy to estimate nuclear volume. Hoechst staining depends on the organization of the DNA within the nucleus and I find that the authors need to do better controls to estimate nuclear size - this would be possible with cells expressing fluorescently labeled histones, or even better with a fluorescently tagged nuclear pore/envelope marker. The current quantification approach is misleading.

      We understand Reviewer #3’s concerns about using Hoechst staining as a proxy of nuclear volume, due to potential differences in DNA organisation within the nucleus.

      Following the recommendation of Reviewer #3 in the following point 3, text has been updated (lines 107–115 and 235-246).

      (3) Was cell density assessed for the measurements? If cells are confluent, bacteria could spread between cells within 3 hrs, if cells are less dense, this does not occur. When epithelial cells are infected for some hours, they have the tendency to round up a bit (and to appear thicker in z), but a bit smaller in xy. My suggestion to the authors (as they use these findings to follow up with experiments on the underlying processes) would be to tone down their statements - eg, Hoechst staining could be simply indicated as altered, but not put in a context of size (this would require substantial control experiments).

      Local cell density was not directly measured, but the experiment was set up to infect at roughly 80% confluency (cells were seeded at 10<sup>4</sup> cells/well 2 days prior to infection in a 96-well microplate, as described in the Materials and methods section) and to ensure bacterial spread between cells.

      In agreement with Reviewer #3 we tone down statements in the main text (see response to point 2 above).

      In addition, I found Figure 1 (and parts of Figure 2) disconnected from the rest of the manuscript, and it may even be an idea to take it out of the manuscript (that could also help to deal with my feedback relating to Figure 1). I would suggest starting the manuscript with the current Figure 3 and building the biological story with a stronger focus on SEPT7 (and its links with T3 secretion and actively pathogenic bacteria) from there on. As it stands, the two parts of the manuscript are not well connected.

      We carefully considered this comment but following revisions we have not reorganised the manuscript. We believe that high-content characterisation of S. flexneri infection in Figure 1 and 2 provides insightful information about changes in host cells in response to infection. Following this, we move onto characterising intracellular bacteria (and in particular those entrapped in septin cages) in the second part of the manuscript (Figure 3-7). Similar methods were used to analyse both host and bacterial cells and results obtained offer complementary views on host-pathogen interactions.

      My major reservation with the experimental work of the current version of the manuscript relates to Figure 5: The analysis of the septin phenotypes in Figure 5 seems to be problematic - to me, it appears that analysis and training were done on projected image stacks. As bacteria are rod-shaped their orientation in space has an enormous impact on how the septin signal appears in a projection - this can lead to wrong interpretation of the phenotypes. The authors need to do some quantitative controls analyzing their data in 3D. To be more clear: the example "tight" (second row) shows a bacterium that appears short. It may be that it's actually longer if one looks in 3D, and the septin signal could possibly fall in the category "rings" or even "two poles".

      The deep learning training and subsequent analysis of septin-cage entrapment is done on projected Z-stacks, which presents limitations. Future work in the Mostowy lab will exploit this first study and dive deeper into 3D aspects of the data.

      To address Reviewer #3’s concern, we include a sentence explaining that this analysis was performed using 2D max projections (lines 708 and 724), as well as acknowledging its limitations in the main text (lines 259-262).

      Minor points

      The scale bar in Fig 1 is very thin.

      We corrected the scale bar in Fig. 1 to make it more visible.

      Could it be that Figure 1F is swapped with Figure1E in the description?

      Descriptions for Figure 1E and F are correct.

      Line 27: what does "actively pathogenic bacteria" mean? I propose to change the term.

      We agree with Reviewer #3 that “actively pathogenic bacteria” should be removed from the text. This update is also in agreement with Reviewer #1 (see Reviewer #1 point 2).

      Line 28: "dynamics" can be confusing as it relates to dynamic events imaged by time-lapse.

      Although we are making a snapshot of the infection process at 3 hpi, we capture asynchronous processes in both host and bacterial cells (eg. host cells infected with different bacterial loads, bacterial cells undergoing actin polymerisation or septin cage entrapment). We agree that we are not following dynamics of full events over time. However, our high content approach enables us to capture different stages of dynamic processes. To avoid confusion, we replace “dynamics” by “diverse interactions” (line 28), and we discuss the importance of follow-up studies studying microscopy timelapses (line 274).

      Paragraph 59 following: the concept of heterogeneity was investigated in some detail for viral infection by the Pelkmans group (PMID: 19710653) using advanced image analysis tools. Advanced machine-learning-based analysis was then performed on Salmonella invasion by Voznica and colleagues (PMID: 29084895). It would be great to include these somewhat "old" works here as they really paved the way for high-content imaging, and the way analyses were performed then should be also discussed in light of how analyses can be performed now with the approaches developed by the authors.

      We agree. These landmark studies have now been included in the main text (lines 71-74).

      Line 181: I do not know what "morphological conformations" means, perhaps the authors can change the wording or clarify.

      We substituted the phrase “morphological conformations” by “morphological patterns” to improve clarity in the main text (lines 185).

      The authors claim (eg in the abstract) that they are measuring the dynamic infection process. To me, it appears that they look at one time-point, so no dynamic information can be extracted. I suggest that the authors tone down their claims.

      Please note our response above (Minor points, Line 28) which also refers to this question.

      AuthorResponse
    Visit annotations in context

    Tags

    Annotators

    URL

    biorxiv.org/content/10.1101/2024.03.06.583762v2
  50. inst-fs-iad-prod.inscloudgate.net inst-fs-iad-prod.inscloudgate.net
    ARTHS-191-2996 Koblik, K 2025FA
    1
    1. jmendoza996 09 Sep 2025
      On peer review days, you will come to class with 3 hard copies of your essay, in as close to final draft formas you can make it.

      Be sure to be prepared for peer review days to receive points for the day

    Visit annotations in context

    Annotators

    URL

    inst-fs-iad-prod.inscloudgate.net/files/32370bba-2eb4-4bd7-98e9-ccaa216ba49d/ARTHS-191-2996 Koblik, K 2025FA.pdf
  51. www.biorxiv.org www.biorxiv.org
    In silico design and validation of high-affinity RNA aptamers for SARS-CoV-2 comparable to neutralizing antibodies
    1
    1. Public_Reviews 09 Sep 2025

      Reviewer #2 (Public review):

      Summary:

      This manuscript proposes a workflow for discovering and optimizing RNA aptamers, with application in the optimization of a SARS-CoV-2 RBD. The authors took a previously identified RNA aptamer, computationally docked it into one specific RBD structure, and searched for variants with higher predicted affinity. The variants were subsequently tested for RBD binding using gel retardation assays and competition with antibodies, and one was found to be a stronger binder by about three-fold than the founding aptamer.

      Overall, this would be an interesting study if it were performed with truly high-affinity aptamers, and specificity was shown for RBD or several RBD variants.

      Strengths:

      The computational workflow appears to mostly correctly find stronger binders, though not de novo binders.

      Weaknesses:

      (1) Antibody competition assays are reported with RBD at 40 µM, aptamer at 5 µM, and a titration of antibody between 0 and 1.2 µg. This approach does not make sense. The antibody concentration should be reported in µM. An estimation of the concentration is 0-8 pmol (from 0-1.2 µg), but that's not a concentration, so it is unknown whether enough antibody molecules were present to saturate all RBD molecules, let alone whether they could have displaced all aptamers.

      (2) These are not by any means high-affinity aptamers. The starting sequence has an estimated (not measured, since the titration is incomplete) KD of 110 µM. That's really the same as non-specific binding for an interaction between an RNA and a protein. This makes the title of the manuscript misleading. No high-affinity aptamer is presented in this study. If the docking truly presented a bound conformation of an aptamer to a protein, a sub-micromolar Kd would be expected, based on the number of interactions that they make.

      (3) The binding energies estimated from calculations and those obtained from the gel-shift experiments are vastly different, as calculated from the Kd measurements, making them useless for comparison, except for estimating relative affinities.

      Review 2
    Visit annotations in context

    Tags

    Annotators

    URL

    biorxiv.org/content/10.1101/2025.06.04.657802v2
  52. www.medrxiv.org www.medrxiv.org
    Associations Between Meteorological Factors and Influenza A/B Incidence in Subtropical China: A Six-Year Surveillance Study with Deep Learning Modelling for Influenza Early Warning
    3
    1. Public_Reviews 09 Sep 2025

      Reviewer #1 (Public review):

      This is my first review of this manuscript. The authors included previous reviews for a different journal with a length of 90 and 39 pages; I did not review this reply in my assessment of the paper itself. Influenza prediction is not my area of expertise.

      A major concern is that the model is trained in the midst of the COVID-19 pandemic and its associated restrictions and validated on 2023 data. The situation before, during, and after COVID is fluid, and one may not be representative of the other. The situation in 2023 may also not have been normal and reflective of 2024 onward, both in terms of the amount of testing (and positives) and measures taken to prevent the spread of these types of infections. A further worry is that the retrospective prospective split occurred in October 2020, right in the first year of COVID, so it will be impossible to compare both cohorts to assess whether grouping them is sensible.

      The outcome of interest is the number of confirmed influenza cases. This is not only a function of weather, but also of the amount of testing. The amount of testing is also a function of historical patterns. This poses the real risk that the model confirms historical opinions through increased testing in those higher-risk periods. Of course, the models could also be run to see how meteorological factors affect testing and the percentage of positive tests. The results only deal with the number of positive (only the overall number of tests is noted briefly), which means there is no way to assess how reasonable and/or variable these other measures are. This is especially concerning as there was massive testing for respiratory viruses during COVID in many places, possibly including China.

      (1) Although the authors note a correlation between influenza and the weather factors. The authors do not discuss some of the high correlations between weather factors (e.g., solar radiation and UV index). Because of the many weather factors, those plots are hard to parse.

      (2) The authors do not actually compare the results of both methods and what the LSTM adds.

      Minor comments:

      (3) The methods are long and meandering. They could be cleaned up and shortened. E.g., there is no need for 30 lines on PCR testing; the study area should come before the study design. The authors discuss similar elements in multiple places; this whole section can be shortened considerably without affecting the content.

      (4) How reliable is the "Our Word in Data" website for subnational coverage of restrictions? Some of the authors are from Putian and should be able to confirm the accuracy for both studied areas.

      (5) Figure 2A is hard to parse; it would make more sense to plot these as line plots (y=count, x=month).

      Review 1
    2. Public_Reviews 09 Sep 2025

      Reviewer #2 (Public review):

      Summary:

      The study aimed to assess the associations between meteorological drivers and influenza is important although not new. The authors used only 6 years of surveillance data and deep learning models, combining distributed lag non-linear models (DLNM) with Bayesian-optimized LSTM neural networks for predictive modeling. The key interest in this area is to explore the subtropical locations, where influenza is less common and circulates year-round. The authors further claimed that such an association could be able to provide an early warning in the community. In this direction, the current manuscript has several scopes of improvements and clarification of the claims, as I list here.

      Strengths:

      Study design based on a prospective cohort to analyse the data for retrospective outcomes.

      Weaknesses:

      (1) The rationale of the study is not clearly stated.

      (2) Several issues with methodological and data integration should be clarified.

      (3) Validation of the models is not presented clearly.

      (4) The claim for providing tools for 'early warning' was not validated by analysis and results.

      Review 2
    3. Public_Reviews 09 Sep 2025

      Author response:

      Reviewer # 1 (Public review):

      A major concern is that the model is trained in the midst of the COVID-19 pandemic and its associated restrictions and validated on 2023 data. The situation before, during, and after COVID is fluid, and one may not be representative of the other. The situation in 2023 may also not have been normal and reflective of 2024 onward, both in terms of the amount of testing (and positives) and measures taken to prevent the spread of these types of infections. A further worry is that the retrospective prospective split occurred in October 2020, right in the first year of COVID, so it will be impossible to compare both cohorts to assess whether grouping them is sensible.

      We fully concur with the reviewer that the COVID-19 pandemic represents a profound confounding factor that fundamentally impacts the interpretation and generalizability of our model. This is a critical point that deserves a more thorough treatment. In the revised manuscript, we will add a dedicated subsection in the Discussion to explicitly analyze the pandemic’s impact. We will reframe our model’s contribution not as a universally generalizable tool for a hypothetical “normal” future, but as a robust framework demonstrated to capture complex epidemiological dynamics under the extreme, non-stationary conditions of a real-world public health crisis. We will argue that its strong performance on the 2023 validation data, a unique post-NPI “rebound” year, specifically showcases its utility in modeling volatile periods.

      The outcome of interest is the number of confirmed influenza cases. This is not only a function of weather, but also of the amount of testing. The amount of testing is also a function of historical patterns. This poses the real risk that the model confirms historical opinions through increased testing in those higher-risk periods. Of course, the models could also be run to see how meteorological factors affect testing and the percentage of positive tests. The results only deal with the number of positive (only the overall number of tests is noted briefly), which means there is no way to assess how reasonable and/or variable these other measures are. This is especially concerning as there was massive testing for respiratory viruses during COVID in many places, possibly including China.

      The reviewer raises a crucial point regarding surveillance bias, which is inherent in studies using reported case data. We acknowledge this limitation and will address it more transparently.

      (1) Clarification of Available Data: Our manuscript states that over the six-year period, a total of 20,488 ILI samples were tested, yielding 3,155 positive cases (line 471; Figure 1). We will make this denominator more prominent in the Methods section. However, the reviewer is correct that our models for Putian and the external validation for Sanming utilize the daily positive case counts as the outcome. The reality of our surveillance data source is that while we have the aggregate total of tests over six years, obtaining a reliable daily denominator of all respiratory virus tests conducted (not just for ILI patients as per the surveillance protocol) is not feasible. This is a common constraint in real-world public health surveillance systems.

      (2) Justification and Discussion: We will add a detailed paragraph to the Limitations section to address this. We will justify our use of case counts as it is the most direct metric for assessing public health burden and planning resource allocation (e.g., hospital beds, antivirals). We will also explain that modeling the positivity rate presents its own challenges, as the ILI denominator is also subject to biases (e.g., shifts in healthcare-seeking behavior, co-circulation of other pathogens causing similar symptoms). We will thus frame our work as forecasting the direct surveillance signal that public health officials monitor daily.

      Although the authors note a correlation between influenza and the weather factors. The authors do not discuss some of the high correlations between weather factors (e.g., solar radiation and UV index). Because of the many weather factors, those plots are hard to parse.

      This is an excellent point. Our preliminary analysis (Supplementary Figure S2) indeed confirms a strong positive correlation between solar radiation and the UV index. Perhaps the reviewer overlooked the contents of the supplementary information document. We have included the figure for their review. Our original discussion did explicitly address this multicollinearity, summarized as follows: We acknowledge the high correlation between certain meteorological variables. We then explain that our two-stage modeling approach is designed to mitigate this issue. In the first stage, the DLNM models assess the impact of each variable individually, thus isolating their non-linear and lagged effects without being confounded by interactions. In the second stage, the LSTM network, by its nature, is a powerful non-linear function approximator that is robust to multicollinearity and can learn the complex, interactive relationships between all input features, including correlated ones.

      Figure S2. Scatterplot matrix illustrating correlations between Influenza cases and meteorological factors. This comprehensive scatterplot matrix visualizes the relationships between influenza-like illness (ILI) cases, influenza A and B cases, and multiple meteorological variables, including average temperature, humidity, precipitation, wind speed, wind direction, solar radiation, and ultraviolet (UV) index. The figure is composed of three distinct sections that collectively provide an in-depth analysis of these relationships:

      (1) Upper-right triangle: This section presents a Pearson correlation coefficient matrix, with color intensity reflecting the strength of correlations between the variables. Red cells represent positive correlations, while green cells represent negative correlations. The closer the coefficient is to 1 or -1, the darker the cell and the stronger the correlation, with statistically significant correlations marked by asterisks. This matrix allows for a rapid identification of notable relationships between influenza cases and meteorological factors.

      (2) Lower-left triangle: This section contains scatterplots of pairwise comparisons between variables. These scatterplots facilitate the visual identification of potential linear or non-linear relationships, as well as any outliers or anomalies. This visualization is essential for evaluating the nature of interactions between meteorological factors and influenza cases.

      (3) Diagonal: The diagonal displays the density distribution curves for each individual variable. These curves provide an overview of the distribution characteristics of each variable, revealing central tendencies, variance, and any skewness present in the data.

      The authors do not actually compare the results of both methods and what the LSTM adds.

      We thank the reviewer for this comment and realize we may not have signposted the comparison clearly enough. Our manuscript does present a direct comparison between the LSTM and ARIMA models in the Results section (lines 737-745) and Table 2, where performance metrics (MAE, RMSE, MAPE, SMAPE) for both models on the 2023 validation set are detailed, showing LSTM’s superior performance, particularly for Influenza A. Furthermore, Figure 6 (panels A and B) visualizes the LSTM’s predictions against observed values, and Supplementary Figure S3 does the same for the ARIMA model, allowing for a visual comparison of their fit.

      To address the reviewer’s concern, in the revised manuscript, we will:

      (1) Add a more explicit comparative statement in the Results section, directly contrasting the key metrics and highlighting the LSTM’s advantages in capturing peak activities.

      (2) Consider combining the visualizations from Figure 6 and Supplementary Figure S3 into a single, more powerful comparative figure that shows the observed data, the LSTM predictions, and the ARIMA predictions on the same plot.

      Meandering methods; reliability of “Our Word in Data”; Figure 2A is hard to parse.

      We will address these points comprehensively.

      (3) Methods: We will significantly streamline and restructure the Methods section. We also wish to provide context that the manuscript’s current structure reflects an effort to incorporate feedback from multiple rounds of peer review across different journals, which may have led to some repetition. We will perform a thorough edit to improve its conciseness and logical flow.

      (4) Data Reliability: The reviewer raises a crucial and highly insightful question regarding the validity of using a national-level index to represent local public health interventions. This is a critical aspect of our model’s construction, and we are grateful for the opportunity to provide a more thorough justification.

      We acknowledge that the ideal variable would be a daily, quantitative, city-level index of non-pharmaceutical interventions (NPIs). However, the practical reality of the data landscape in China is that such granular, publicly accessible databases for subnational regions do not exist. Given this constraint, our choice of the Our World in Data (OWID) national stringency index was the result of a careful consideration process, and we believe it serves as the best available proxy for our study context.

      In the revised manuscript, we will significantly expand the Methods section to articulate our rationale, which is threefold:

      National Policy Coherence: During the COVID-19 pandemic in mainland China, core NPIs, particularly mandatory face-covering policies in shared public spaces, were implemented with a high degree of national uniformity. While local governments had some autonomy, they operated within a centrally defined framework, ensuring a baseline level of policy consistency across the country.

      Local Context Alignment: A key factor supporting the use of this national proxy is the specific epidemiological context of Putian during the study period. For the vast majority of the pandemic, Putian was classified as a low-risk area with only sporadic COVID-19 cases. Consequently, the city’s public health measures consistently aligned with the standard national guidelines. It did not experience prolonged or exceptionally strict local lockdowns that would cause a significant deviation from the national-level policy trends captured by the OWID index.

      Validation by Local Public Health Experts: Most critically, and to directly address your suggestion, our co-authors from the Putian Center for Disease Control and Prevention have meticulously reviewed the OWID stringency index against their on-the-ground, institutional knowledge of the mandates that were in effect. They have confirmed that the categorical levels (0-4) and the temporal trends of the OWID index provide a faithful representation of the public health restrictions concerning face coverings as experienced by the population of Putian.

      Therefore, we will revise our manuscript to make it clear that the use of the OWID index was not a choice of convenience, but a necessary and well-vetted decision. Given the unavailability of official local data, the OWID index, cross-validated by our local experts, represents the most rigorous and appropriate variable available to account for the profound impact of NPIs on influenza transmission in our model.

      (5) Figure 2A: We agree completely and will replace the heatmap with a multi-line plot or a stacked area chart to better visualize the temporal dynamics of influenza subtypes.

      We have preliminarily completed the redrawing of Figure 3A. The new and old versions are presented for your review to determine which figure is more suitable for this manuscript in terms of scientific accuracy and visual impact.

      Reviewer #2 (Public review):

      Weakness (1):

      The rationale of the study is not clearly stated.

      We appreciate the reviewer’s critique and acknowledge that the unique contribution of our study needs to be articulated more forcefully. Our introduction (lines 105-140) attempted to outline the limitations of existing studies, but we will revise it to be much sharper. The revised introduction will state unequivocally that our study’s rationale is to address a confluence of specific, unresolved gaps in the literature: 1) The persistent challenge of forecasting influenza in subtropical regions with their erratic seasonality; 2) The lack of studies that build subtype-specific models for Influenza A and B, which we show have distinct meteorological drivers; 3) The methodological gap in integrating the explanatory power of DLNM with the predictive power of a rigorously, Bayesian-optimized LSTM network; and 4) The unique opportunity to develop and test a model on data that encompasses the unprecedented disruption of the COVID-19 pandemic, a critical test of model robustness.

      Weakness (2):

      Several issues with methodological and data integration should be clarified.

      We interpret this as a general statement, with the specific issues detailed in the reviewer’s subsequent points and the “Recommendations for the authors” section. We will meticulously address each of these specific points in our revision. For instance, as a demonstration of our commitment to clarification, we will provide a much more detailed justification for our choice of benchmark model (ARIMA), as detailed in our response to Recommendation #11.

      Reviewer #2 (Recommendation  for the authors):

      The authors should justify why the baseline model selection was made by comparing the LSTM model only with ARIMA? How the outcomes could be sensitive to other commonly used machine learning methods, such as Random Forest or XGBoost, etc, as a benchmark for their performance.

      The reviewer raises a highly pertinent question regarding the selection of our benchmark model. A robust comparison is indeed essential for contextualizing the performance of our proposed LSTM network. Our choice to benchmark against the ARIMA model was a deliberate and principled decision, grounded in the specific literature of influenza forecasting at the intersection of climatology and epidemiology.

      In the revised manuscript, we will expand our justification within the Methods section and reinforce it in the Discussion. Our rationale is as follows:

      (1) ARIMA as the Established Standard: As we briefly noted in our original introduction (lines 110-113), the ARIMA model is arguably the most widely established and frequently cited statistical method for time-series forecasting of influenza incidence, including studies investigating meteorological drivers. It serves as the conventional benchmark against which novel methods in this specific domain are often evaluated. Therefore, demonstrating superiority over ARIMA is the most direct and scientifically relevant way to validate the incremental value of our deep learning approach.

      (2) A Focused Scientific Hypothesis: Our primary hypothesis was that the LSTM network, with its inherent ability to capture complex non-linearities and long-term dependencies, could overcome the documented limitations of linear autoregressive models like ARIMA in the context of climate-influenza dynamics. Our study was designed specifically to test this hypothesis.

      (3) Avoiding a “Bake-off” without a Clear Rationale: While other machine learning models like Random Forest or XGBoost are powerful, they are not established as the standard baseline in this particular niche of literature. Including them would shift the focus from a targeted comparison against the conventional standard to a broader, less focused “bake-off” of various algorithms. Such an exercise, while potentially interesting, would risk diluting the core message of our paper and would be undertaken without a clear, literature-driven hypothesis for why one of these specific tree-based models should be the next logical benchmark.

      Therefore, we will argue in the revised manuscript that our focused comparison with ARIMA provides the clearest and most meaningful assessment of our model’s contribution to the existing body of work on climate-informed influenza forecasting. We will, however, explicitly acknowledge in the Discussion that future work could indeed benefit from a broader comparative analysis as the field continues to evolve and adopt a wider array of machine learning techniques.

      Similarly, for some of the reviewer’s recommendations that do not require significant time and effort to implement, such as recommendation 7, we have also redrawn Figure 3 based on your feedback. It is provided for your review.

      Figure 3 presents the time series of the cases. I wonder whether the data for these factors and outcomes are daily or aggregated by week/month? I suggest representing it in 9x1 format with a single x-axis to compare, instead of 3x3 format. Authors can refer similar plot in https://doi.org/ 10.1371/journal.pcbi.1012311 in Figure 1.

      We are deeply grateful for the reviewer’s valuable suggestion and thoughtful provision of reference illustrations. Based on their input, we have redrawn Figure 3 and have included it for their review.

      Weakness (3):

      Validation of the models is not presented clearly.

      We were concerned by this comment and conducted a thorough self-assessment of our manuscript. We believe we have performed a multi-faceted validation, but we have evidently failed to present it with sufficient clarity and structure. Our validation strategy, detailed across the Methods and Results sections, includes:

      • Internal Out-of-Time Validation: Using 2023 data as a hold-out set to test the model trained on 2018-2022 data (lines 695-696, 705-710; Figure 6A, B).

      • External Validation: Testing the trained model on an independent dataset from a different city, Sanming (lines 730-736; Figure 6I, J).

      • Benchmark Model Comparison: Quantitatively comparing the LSTM’s performance against the standard ARIMA model using multiple error metrics (lines 737-745; Table 2).

      • Interpretability Validation (Sanity Check): Using SHAP analysis to ensure the model’s predictions are driven by epidemiologically plausible factors (lines 746-755; Figure 6E-H).

      To address the reviewer’s valid critique of our presentation, we will significantly restructure the relevant parts of the Results section. We will create explicit subheadings such as “Internal Validation,” “External Validation,” and “Comparative Performance against ARIMA Benchmark” to make our comprehensive validation process unambiguous and easy to follow.

      Weakness (4):

      The claim for providing tools for 'early warning' was not validated by analysis and results.

      We agree with this assessment entirely. This aligns with the eLife Assessment and comments from Reviewer #1. Our primary revision will be to systematically recalibrate the manuscript's language. We will replace all instances of “early warning tool” with more accurate and modest phrasing, such as “high-performance forecasting framework” or “a foundational model for future warning systems.” We will ensure that our revised title, abstract, and conclusions precisely reflect what our study has delivered: a robust predictive model, not a field-ready public health intervention tool.

      AuthorResponse
    Visit annotations in context

    Tags

    Annotators

    URL

    medrxiv.org/content/10.1101/2025.07.13.25331478v1
  53. www.biorxiv.org www.biorxiv.org
    Applying 3D correlative structured illumination microscopy and X-ray tomography to characterise herpes simplex virus-1 morphogenesis
    2
    1. Public_Reviews 09 Sep 2025

      Reviewer #1 (Public review):

      Summary:

      Nahas et al. investigated the roles of herpes simplex virus 1 (HSV-1) structural proteins using correlative cryo-light microscopy and soft X-ray tomography. The authors generated nine viral variants with deletions or mutations in genes encoding structural proteins. They employed a chemical fixation-free approach to study native-like events during viral assembly, enabling observation of a wider field of view compared to cryo-ET. The study effectively combined virology, cell biology, and structural biology to investigate the roles of viral proteins in virus assembly and budding.

      Strengths:

      (1) The study presented a novel approach to studying viral assembly in cellulo.

      (2) The authors generated nine mutant viruses to investigate the roles of essential proteins in nuclear egress and cytoplasmic envelopment.

      (3) The use of correlative imaging with cryoSIM and cryoSXT allowed for the study of viral assembly in a near-native state and in 3D.

      (4) The study identified the roles of VP16, pUL16, pUL21, pUL34, and pUS3 in nuclear egress.

      (5) The authors demonstrated that deletion of VP16, pUL11, gE, pUL51, or gK inhibits cytoplasmic envelopment.

      (6) The manuscript is well-written, clearly describing findings, methods, and experimental design.

      (7) The figures and data presentation are of good quality.

      (8) The study effectively correlated light microscopy and X-ray tomography to follow virus assembly, providing a valuable approach for studying other viruses and cellular events.

      (9) The research is a valuable starting point for investigating viral assembly using more sophisticated methods like cryo-ET with FIB-milling.

      (10) The study proposes a detailed assembly mechanism and tracks the contributions of studied proteins to the assembly process.

      (11) The study includes all necessary controls and tests for the influence of fluorescent proteins.

      Weaknesses:

      Overall, the manuscript does not have any major weaknesses, just a few minor comments, which were mostly solved in the revised version of the manuscript.

      Comments on the latest version:

      I reviewed the responses and the updated manuscript, and I am very pleased with how the authors have revised it. The manuscript was already strong, but with the addition of the summary table and the separated images, it is now excellent.

      Review 1
    2. Public_Reviews 09 Sep 2025

      Reviewer #3 (Public review):

      Summary:

      Kamal L. Nahas et al. demonstrated that pUL16, pUL21, pUL34, VP16, and pUS3 are involved in the egress of the capsids from the nucleous, since mutant viruses ΔpUL16, ΔpUL21, ΔUL34, ΔVP16, and ΔUS3 HSV-1 show nuclear egress attenuation determined by measuring the nuclear:cytoplasmic ratio of the capsids, the dfParental, or the mutants. Then, they showed that gM-mCherry+ endomembrane association and capsid clustering were different in pUL11, pUL51, gE, gK, and VP16 mutants. Furthermore, the 3D view of cytoplasmic budding events suggests an envelopment mechanism where capsid budding into spherical/ellipsoidal vesicles drives the envelopment.

      Strengths:

      The authors employed both structured illumination microscopy and cellular ultrastructure analysis to examine the same infected cells, using cryo-soft-X-ray tomography to capture images. This combination, set here for the first time, enabled the authors to obtain holistic data regarding a biological process, as a viral assembly. Using this approach, the researchers studied various stages of HSV-1 assembly. For this, they constructed a dual-fluorescently labelled recombinant virus, consisting of eYFP-tagged capsids and mCherry-tagged envelopes, allowing for the independent identification of both unenveloped and enveloped particles. They then constructed nine mutants, each targeting a single viral protein known to be involved in nuclear egress and envelopment in the cytoplasm, using this dual-fluorescent as the parental one. The experimental setting, both the microscopic and the virological, is robust and well-controlled. The manuscript is well-written, and the data generated is robust and consistent with previous observations made in the field.

      I congratulate the authors. The work is robust, and I personally highlight the way they managed to include others' results merged among their own, providing a complete view of the story.

      Comments on the latest version:

      I reviewed the responses and the updated manuscript, and I agree with the reviewer's #1 words: "The manuscript was already strong, but with the addition of the summary table and the separated images, it is now excellent."

      Review 3
    Visit annotations in context

    Tags

    Annotators

    URL

    biorxiv.org/content/10.1101/2024.03.13.584906v3