Reviewer #2 (Public review):

Summary:

In this manuscript, Jiang et al. developed a robust workflow for identifying proline hydroxylation sites in proteins. They identified proline hydroxylation sites in HEK293 and RCC4 cells, respectively. The authors found that the more hydrophilic HILIC fractions were enriched in peptides containing hydroxylated proline residues. These peptides showed differences in charge and mass distribution compared to unmodified or oxidized peptides. The intensity of the diagnostic hydroxyproline iminium ion depended on parameters including MS collision energy, parent peptide concentration, and the sequence of amino acids adjacent to the modified proline residue. Additionally, they demonstrate that a combination of retention time in LC and optimized MS parameter settings reliably identifies proline hydroxylation sites in peptides, even when multiple proline residues are present

Strengths:

Overall, the manuscript presents an advanced, standardized protocol for identifying proline hydroxylation. The experiments were well designed, and the developed protocol is straightforward, which may help resolve confusion in the field.

Weaknesses:

(1) The authors should provide a summary of the standard protocol for identifying proline hydroxylation sites in proteins that can easily be followed by others.

(2) Cockman et al. proposed that HIF-α is the only physiologically relevant target for PHDs. Their approach is considered the gold standard for identifying PHD targets. Therefore, the authors should discuss the major progress they made in this manuscript that challenges Cockman's conclusion.

Reviewer #3 (Public review):

Summary:

The authors present a new method for detecting and identifying proline hydroxylation sites within the proteome. This tool utilizes traditional LC-MS technology with optimized parameters, combined with HILIC-based separation techniques. The authors show that they pick up known hydroxy-proline sites and also validate a new site discovered through their pipeline.

Strengths:

The manuscript utilizes state-of-the-art mass spectrometric techniques with optimized collision parameters to ensure proper detection of the immonium ions, which is an advance compared to other similar approaches before. The use of synthetic control peptides on the HILIC separation step clearly demonstrates the ability of the method to reliably distinguish hydroxy-proline from oxidized methionine - containing peptides. Using this method, they identify a site on CDCA2, which they go on to validate in vitro and also study its role in regulation of mitotic progression in an associated manuscript.

Weaknesses:

Despite the authors' claim about the specificity of this method in picking up the intended peptides, there is a good amount of potential false positives that also happen to get picked (owing to the limitations of MS-based readout), and the authors' criteria for downstream filtering of such peptides require further clarification. In the same vein, greater and more diverse cell-based validation approach will be helpful to substantiate the claims regarding enrichment of peptides in the described pathway analyses.

Author response:

Reviewer #1 (Recommendations for the authors):

We appreciate the reviewer recognising that our study has been carefully performed and provides a valuable resource for the community. The characterization of Repo-man proline hydroxylation is also recognised as a novel finding.

With respect to Concerns raised by reviewer 1:

(1) The study applied HILIC-based chromatographic separation with a goal of enriching and separating hydroxyproline-containing peptides. However, as the authors mentioned, such an approach is not specific to proline hydroxylation. In addition, many other chromatography techniques can achieve deep proteome fractionation such as high pH reverse phase fractionation, strong-cation exchange etc. There was no data in this study to demonstrate that the strategy offered improved coverage of proline hydroxylation proteins, as the identifications of the HyPro sites could be achieved through deep fractionation and a highly sensitive LCMS setup. The data of Figure 2A and S1A were somewhat confusing without a clear explanation of the heat map representations.

We do not agree that the apparent concern raised here, i.e., that the method we present is not 100% specific for enriching only hydroxylated peptides, is a serious issue. We show specifically that our method indeed enriches samples for hydroxylated peptides, thereby increasing the chances of identifying proline hydroxylated peptides in a cell extract. We never claimed that it was mono-specific for enrichment of hydroxylated peptides. Further, we note that almost no chromatographic method we know of, including those commonly used to enrich for different types of post translationally-modified peptides (including phospho-peptides) is completely mono-specific for a single type of modified peptide. The reviewer comments that it could have been possible to use alternative methods to identify proline-hydroxylated peptides. This may be true, but we know of no published examples, or previous studies, where this has been demonstrated experimentally on a scale comparable to that we show here. Of course there is always more than one way to approach technical challenges and it may be that future methods will be demonstrated that achieve equivalent, or even superior, results with respect to the detection of proline hydroxylated peptides. To the best of our knowledge, however, our current study provides a robust methodology that goes well beyond any previously published analysis of proline hydroxylation.

(2) The study reported that the HyPro immonium ion is a diagnostic ion for HyPro identification. However, the data showed that only around 5% of the identifications had such a diagnostic ion. In comparison, acetyllysine immonium ion was previously reported to be a useful marker for acetyllysine peptides (PMID: 18338905), and the strategy offered a sensitivity of 70% with a specificity of 98%. In this study, the sensitivity of HyPro immonium ion was quite low. The authors also clearly demonstrated that the presence of immonium ion varied significantly due to MS settings, peptide sequence, and abundance. With further complications from L/I immonium ions, it became very challenging to implement this strategy in a global LC-MS analysis to either validate or invalidate HyPro identifications.

We feel that the reviewer’s initial comment is potentially misleading - it implies that we were proposing here that the 'HyPro immonium ion is a diagnostic ion for HyPro identification’. In contrast, this concept was already widely held in the field before we started this project. Indeed, the fact that the diagnostic HyPro immonium ion is often difficult to detect, has been used as one of the arguments by other researchers to support the view that HIF-α is the only physiologically relevant target for PHD enzymes, a controversy referenced explicitly by Reviewer 2 below. What we actually show here are novel data that help to explain why the diagnostic HyPro immonium ion is often difficult to detect, when standard approaches and technical parameters for MS analysis are used. We beleive that this observation, along with other data we present, is a useful contribution to the field that can help to resolve the previous controversies concerning the true prevalence and biological roles of PHD-catalysed proline hydroxylation on protein targets.

(3) The study aimed to apply the HILIC-based proteomics workflow to identify HyPro proteins regulated by the PHD enzyme. However, the quantification strategy was not rigorous. The study just considered the HyPro proteins not identified by FG-4592 treatment as potential PHD targeted proteins. There are a few issues. First, such an analysis was not quantitative without reproducibility or statistical analysis. Second, it did not take into consideration that data-dependent LC-MS analysis was not comprehensive and some peptide ions may not be identified due to background interferences. Lastly, FG-4592 treatment for 24 hrs could lead to wide changes in gene expressions and protein abundances. Therefore, it is not informative to draw conclusions based on the data for bioinformatic analysis.

We agree that this study is not quantifying or addressing the stoichiometry of proline hydroxylation across the very large number of new PHD target sites we identify. That was not claimed and was not the objective of our study. Nonetheless, we feel the comments of the referee do not adequately take into account the SILAC data we included (cf Figure 8) or the full range of experimental data presented in this study. We would further refer the reviewer also to the data presented in the companion paper by Druker et al., which we cross-referenced extensively in our study and have also made available previously on biorxiv.

(4) The authors performed an in vitro PHD1 enzyme assay to validate that Repo-man can be hydroxylated by PHD1. However, Figure 9 did not show quantitatively PHD1-induced increase in Repo-man HyPro abundance and it is difficult to assess its reaction efficiency to compare with HIF1a HyPro.

Here again we refer to the recent controversy referenced explicitly by Reviewer 2 below, concerning the view expressed by some researchers that only HIF-α is a physiological substrate for PHD enzymes in cells. We were challenged to show that any of the novel protein targets of PHDs we identified were indeed hydroxylated by PHD enzymes in vitro and that is what we demonstrated in Figure 9. This was not an experiment performed to quantify stoichiometry and indeed, it is not possible to draw any firm conclusions about efficiency or stiochiometry in vitro when using catalytic PHD subunits alone, given that we do not yet know whether PHDs may show different properties in cells, dependent on interactions with other factors and/or modifications.

Reviewer #2 (Recommendations for the authors):

We appreciate the reviewer’s comments that our manuscript presents an advanced, standardized protocol for identifying proline hydroxylation, with well designed experiments, which may help resolve confusion in the field.

With respect to Concerns raised by reviewer 2:

(1) The authors should provide a summary of the standard protocol for identifying proline hydroxylation sites in proteins that can easily be followed by others.

We agree and plan to provide a clearly described, step by step guide to assist other researchers who wish to employ our methods for proline hydroxylation analysis in their own studies.

(2) Cockman et al. proposed that HIF-α is the only physiologically relevant target for PHDs. Their approach is considered the gold standard for identifying PHD targets. Therefore, the authors should discuss the major progress they made in this manuscript that challenges Cockman's conclusion.

We agree that our study provides valuable information germane to the recent controversy in the field and the views published by Cockman et al., to the effect that HIF-α is the only physiologically relevant target for PHDs. We will carefully review our statements when preparing a suitably revised version of record with the aim of providing a balanced and objective discussion of this issue.

Reviewer #3 (Recommendations for the authors):

We appreciate the reviewer’s comments that our study employs state-of-the-art mass spectrometric techniques with optimized collision parameters to ensure proper detection of the immonium ions, along with their recognition that our study is, 'an advance compared to other similar approaches before.’ We also appreciate their reference to our companion study by Druker et al, in which we characterise the mechanism and biological role in regulation of mitotic progression of the hydroxylation of P604 in the target protein RepoMan (CDCA2), that is identified in this study.

With respect to the Concern raised by reviewer 3:

Despite the authors' claim about the specificity of this method in picking up the intended peptides, there is a good amount of potential false positives that also happen to get picked (owing to the limitations of MS-based readout), and the authors' criteria for downstream filtering of such peptides require further clarification. In the same vein, greater and more diverse cell-based validation approach will be helpful to substantiate the claims regarding enrichment of peptides in the described pathway analyses..

We agree that this study, which has a focus on methodology and technical approaches for detecting sites of PHD- catalysed proline hydroxylation, cannot exhaustively validate the biological significance of all of the putative sites and targets identified. As the reviewer notes, we have performed a detailed functional characterisation of one such novel PHD-catalyed proline hydroxylation site, i.e. P604 in the protein RepoMan (CDCA2). This functional analysis is presented in the companion paper by Druker et al., which has also been reviewed by eLife and placed on biorxiv (doi: https://doi.org/10.1101/2025.05.06.652400). We hope that publication of our identification of many new putative PHD target sites will encourage other researchers to pursue characterisation of their functional reoles in different biological mechanisms and have tried here to provide some degree of guidance to focus attention on the identification of those sites for which we currently have highest confidence.

eLife Assessment

This valuable study advances our understanding of how bactofilin cytoskeletal proteins associate with cell membranes by identifying and characterizing a conserved membrane-targeting sequence. The evidence is solid, with a well-integrated combination of mutagenesis, biophysical analysis, molecular simulations, and bioinformatics supporting the mechanistic model. The work will be of particular interest to microbiologists and structural biologists studying bacterial cytoskeletons and membrane-protein interactions.

Reviewer #2 (Public review):

Summary:

The authors of this study investigated the membrane-binding properties of bactofilin A from Caulobacter crescentus, a classic model organism for bacterial cell biology. BacA was the progenitor of a family of cytoskeletal proteins that have been identified as ubiquitous structural components in bacteria, performing a range of cell biological functions. Association with the cell membrane is a frequent property of the bactofilins studied and is thought to be important for functionality. However, almost all bactofilins lack a transmembrane domain. While membrane association has been attributed to the unstructured N-terminus, experimental evidence had yet to be provided. As a result, the mode of membrane association and the underlying molecular mechanics remained elusive.

Liu at al. analyze the membrane binding properties of BacA in detail and scrutinize molecular interactions using in-vivo, in-vitro and in-silico techniques. They show that few N-terminal amino acids are important for membrane association or proper localization and suggest that membrane association promotes polymerization. Bioinformatic analyses revealed conserved lineage-specific N-terminal motifs indicating a conserved role in protein localization. Using HDX analysis they also identify a potential interaction site with PbpC, a morphogenic cell wall synthase implicated in Caulobacter stalk synthesis. Complementary, they pinpoint the bactofilin-interacting region within the PbpC C-terminus, known to interact with bactofilin. They further show that BacA localization is independent of PbpC.

Although the phenotypic effects of an abolished BacA-PbpC interaction are mild, these data significantly advance our understanding of bactofilin membrane binding, polymerization, and function at the molecular level. The major strength of the comprehensive study is the combination of complementary in vivo, in vitro and bioinformatic/simulation approaches, the results of which are consistent.

Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public review):

Summary:

The investigators undertook detailed characterization of a previously proposed membrane targeting sequence (MTS), a short N-terminal peptide, of the bactofilin BacA in Caulobacter crescentus. Using light microscopy, single molecule tracking, liposome binding assays, and molecular dynamics simulations, they provide data to suggest that this sequence indeed does function in membrane targeting and further conclude that membrane targeting is required for polymerization. While the membrane association data are reasonably convincing, there are no direct assays to assess polymerization and some assays used lack proper controls as detailed below. Since the MTS isn't required for bactofilin polymerization in other bacterial homologues, showing that membrane binding facilitates polymerization would be a significant advance for the field.

We agree that additional experiments were required to consolidate our results and conclusions. Please see below for a description of the new data included in the revised version of the manuscript.

Major concerns

(1) This work claims that the N-termina MTS domain of BacA is required for polymerization, but they do not provide sufficient evidence that the ∆2-8 mutant or any of the other MTS variants actually do not polymerize (or form higher order structures). Bactofilins are known to form filaments, bundles of filaments, and lattice sheets in vitro and bundles of filaments have been observed in cells. Whether puncta or diffuse labeling represents different polymerized states or filaments vs. monomers has not been established. Microscopy shows mis-localization away from the stalk, but resolution is limited. Further experiments using higher resolution microscopy and TEM of purified protein would prove that the MTS is required for polymerization.

We do not propose that the MTS is directly involved in the polymerization process and state this more clearly now in the Results and Discussion sections of the revised manuscript. To address this point, we performed transmission electron microscopy studies comparing the polymerization behavior of wild-type and mutant BacA variants. The results clearly show that the MTS-free BacA variant (∆2-8) forms polymers that are indistinguishable from those formed by the wild-type protein, when purified from an E. coli overproduction strain (new Figure 1–figure supplement 1). This finding is consistent with structural work showing that bactofilin polymerization is exclusively mediated by the conserved bactofilin domain (Deng et al, Nat Microbiol, 2019). However, at native expression levels, BacA only accumulates to ~200 molecules per cell (Kühn et al, EMBO J, 2006). Under these conditions, the MTS-mediated increase in the local concentration of BacA at the membrane surface and, potentially, steric constraints imposed by membrane curvature, may facilitate the polymerization process. This hypothesis has now been stated more clearly in the Results and Discussion sections.

For polymer-forming proteins, defined localized signals are typically interpreted as slow-moving or stationary polymeric complexes. A diffuse localization, by contrast, suggests that a protein exists in a monomeric or, at most, (small) oligomeric state in which it diffuses rapidly within the cell and is thus no longer detected as distinct foci by widefield microscopy. Our single-molecule data show that BacA variants that are no longer able to interact with the membrane (as verified by cell fractionation studies and in vitro liposome binding assays) have a high diffusion rate, similar to that measured for the non-polymerizing and non-membrane-bound F130R variant. These results demonstrate that a defect in membrane binding strongly reduces the ability of BacA to form polymeric assemblies. To support this hypothesis, we have now repeated all single-particle tracking experiments and included mVenus as a freely diffusible reference protein. Our data confirm that the mobilities of the ∆2-8 and F130R variants are similar and approach those of free mVenus, supporting the idea that the deficiency to interact with the membrane prevents the formation of extended polymeric structures (which should show much lower mobilities). To underscore the relevance of membrane binding for BacA assembly, we have now included a new experiment, in which we used the PbpC membrane anchor (PbpC_1-132-mcherry) to restore the recruitment of the ∆2-8 variant to the membrane (Figure 9 and Figure 9–figure supplement 1). The results obtained show that the ∆2-8 variant transitions from a diffuse localization to polar foci upon overproduction of PbpC_1-132-mcherry. The polymerization-impaired F130R variant, by contrast, remains evenly distributed throughout the cytoplasm under all conditions. These findings further support the idea that polymerization and membrane-association are mutually interdependent processes.

(2) Liposome binding data would be strengthened with TEM images to show BacA binding to liposomes. From this experiment, gross polymerization structures of MTS variants could also be characterized.

We do not have the possibility to perform cryo-electron microscopy studies of liposomes bound to BacA. However, the results of the cell fractionation and liposome sedimentation assays clearly support a critical role of the MTS in membrane binding.

(3) The use of the BacA F130R mutant throughout the study to probe the effect of polymerization on membrane binding is concerning as there is no evidence showing that this variant cannot polymerize. Looking through the papers the authors referenced, there was no evidence of an identical mutation in BacA that was shown to be depolymerized or any discussion in this study of how the F130R mutation might to analogous to polymerization-deficient variants in other bactofilins mentioned in these references.

Residue F130 in the C-terminal polymerization interface of BacA is conserved among bactofilin homologs, although its absolute position in the protein sequence may vary, depending on the length of the N-terminal unstructured tail. The papers cited in our manuscript show that an exchange of this conserved phenylalanine residue abolishes polymer formation. Nevertheless, we agree that it is important to verify the polymerization defect of the F130R variant in the system under study. We have now included size-exclusion chromatography data showing that BacA-F130R forms a low-molecular-weight complex, whereas the wild-type protein largely elutes in the exclusion volume, indicating the formation of large, polymeric species (new Figure 1–figure supplement 1). In addition, we performed transmission electron microscopy analyses of BacA-F130R, which verified the absence of larger oligomers (new Figure 1–figure supplement 2).

(4) Microscopy shows that a BacA variant lacking the native MTS regains the ability to form puncta, albeit mis-localized, in the cell when fused to a heterologous MTS from MreB. While this swap suggests a link between puncta formation and membrane binding the relationship between puncta and polymerization has not been established (see comment 1).

We show that a BacA variant lacking the MTS (∆2-8) regains the ability to form membrane-associated foci when fused to the MTS of MreB. By contrast, a similar variant that additionally carries the F130R exchange (preventing its polymerization) shows a diffuse cytoplasmic localization. In addition, we show that the F130R exchange leads to a loss of membrane binding and to a considerable increase in the mobility of the variants carrying the MTS of E. coli MreB. As described above, we now provide additional data demonstrating that elevated levels of the PbpC membrane anchor can reinstate polar localization for the ∆2-8 variant, whereas it fails to do so for the polymerization-deficient F130R variant (Figure 9 and Figure 9–figure supplement 1). Together, these results support the hypothesis that membrane association and polymerization act synergistically to establish localized bactofilin assemblies at the stalked cell pole.

(5) The authors provide no primary data for single molecule tracking. There is no tracking mapped onto microscopy images to show membrane localization or lack of localization in MTS deletion/ variants. A known soluble protein (e.g. unfused mVenus) and a known membrane bound protein would serve as valuable controls to interpret the data presented. It also is unclear why the authors chose to report molecular dynamics as mean squared displacement rather than mean squared displacement per unit time, and the number of localizations is not indicated. Extrapolating from the graph in figure 4 D for example, it looks like WT BacA-mVenus would have a mobility of 0.5 (0.02/0.04) micrometers squared per second which is approaching diffusive behavior. Further justification/details of their analysis method is needed. It's also not clear how one should interpret the finding that several of the double point mutants show higher displacement than deleting the entire MTS. These experiments as they stand don't account for any other cause of molecular behavior change and assume that a decrease in movement is synonymous with membrane binding.

We now provide additional information on the single-particle analysis. A new supplemental figure now shows a mapping of single-particle tracks onto the cells in which they were recorded for all proteins analyzed (Figure 2–figure supplement 1). Due to the small size of C. crescentus, it is difficult to clearly differentiate between membrane-associated and cytoplasmic protein species. However, overall, slow-diffusing particles tend to be localized to the cell periphery, supporting the idea that membrane-associated particles form larger assemblies (apart from diffusing more slowly due to their membrane association). In addition, we have included a movie that shows the single-particle diffusion dynamics of all proteins in representative cells (Figure 2-video 1). Finally, we have included a table that gives an overview of the number of cells and tracks analyzed for all proteins investigated (Supplementary file 1). Figure 2A and 4D show the mean squared displacement as a function of time, which makes it possible to assess whether the particles observed move by normal, Brownian diffusion (which is the case here). We repeated the entire single-particle tracking analysis to verify the data obtained previously and obtained very similar results. Among the different mutant proteins, only the K4E-K7E variant consistently shows a higher mobility than the MTS-free ∆2-8 variant, with MSD values similar to that of free mVenus. The underlying reason remains unclear. However, we believe that an in-depth analysis of this phenomenon is beyond the scope of this paper. We re-confirmed the integrity of the construct encoding the K4E/K7E variant by DNA sequencing and once again verified the size and stability of the fusion protein by Western blot analysis, excluding artifacts due to errors during cloning and strain construction.

We agree that the single-molecule tracking data alone are certainly not sufficient to draw firm conclusions on the relationship between membrane binding and protein mobility. However, they are consistent with the results of our other in vivo and in vitro analyses, which together indicate a clear correlation between the mobility of BacA and its ability to interact with the membrane and polymerize (processes that promote each other synergistically).

(6) The experiments that map the interaction surface between the N-terminal unstructured region of PbpC and a specific part of the BacA bactofilin domain seem distinct from the main focus of the paper and the data somewhat preliminary. While the PbpC side has been probed by orthogonal approaches (mutation with localization in cells and affinity in vitro), the BacA region side has only been suggested by the deuterium exchange experiment and needs some kind of validation.

The results of the HDX analysis per se are not preliminary and clearly show a change in the solvent accessibility of backbone amides in the C-terminal region in the bactofilin domain in the presence of the PbpC_1-13 peptide. However, we agree that additional experiments would be required to verify the binding site suggested by these data. We agree that further research is required to precisely map and verify the PbpC binding site. However, as this is not the main focus of the paper, we would like to proceed without conducting further experiments in this area.

We now provide additional data showing that elevated levels of the PbpC membrane anchor are able to recruit the MTS-free BacA variant (∆2-8) to the cytoplasmic membrane and stimulate its assembly at the stalked pole (Figure 9). These results now integrate Figure 8 more effectively into the overall theme of the paper.

Reviewer #2 (Public review):

Summary:

The authors of this study investigated the membrane-binding properties of bactofilin A from Caulobacter crescentus, a classic model organism for bacterial cell biology. BacA was the progenitor of a family of cytoskeletal proteins that have been identified as ubiquitous structural components in bacteria, performing a range of cell biological functions. Association with the cell membrane is a common property of the bactofilins studied and is thought to be important for functionality. However, almost all bactofilins lack a transmembrane domain. While membrane association has been attributed to the unstructured N-terminus, experimental evidence had yet to be provided. As a result, the mode of membrane association and the underlying molecular mechanics remained elusive.

Liu at al. analyze the membrane binding properties of BacA in detail and scrutinize molecular interactions using in-vivo, in-vitro and in-silico techniques. They show that few N-terminal amino acids are important for membrane association or proper localization and suggest that membrane association promotes polymerization. Bioinformatic analyses revealed conserved lineage-specific N-terminal motifs indicating a conserved role in protein localization. Using HDX analysis they also identify a potential interaction site with PbpC, a morphogenic cell wall synthase implicated in Caulobacter stalk synthesis. Complementary, they pinpoint the bactofilin-interacting region within the PbpC C-terminus, known to interact with bactofilin. They further show that BacA localization is independent of PbpC.

Strengths:

These data significantly advance the understanding of the membrane binding determinants of bactofilins and thus their function at the molecular level. The major strength of the comprehensive study is the combination of complementary in vivo, in vitro and bioinformatic/simulation approaches, the results of which are consistent.

Thank you for this positive feedback.

Weaknesses:

The results are limited to protein localization and interaction, as there is no data on phenotypic effects. Therefore, the cell biological significance remains somewhat underrepresented.

We agree that it is interesting to investigate the phenotypic effects caused by the reduced membrane binding activity of BacA variants with defects in the MTS. We have now included phenotypic analyses that shed light on the role of region C1 in the localization of PbpC and its function in stalk elongation under phosphate-limiting conditions (see below).

Recommendations for the authors:

Reviewer #2 (Recommendations for the authors):

To address the missing estimation of biological relevance, some additional experiments may be carried out.

For example, given that BacA localizes PbpC by direct interaction, one might expect an effect on stalk formation if BacA is unable to bind the membrane or to polymerize. The same applies to PbpC variants lacking the C1 region. As the mutant strains are available, these data are not difficult to obtain but would help to compare the effect of the deletions with previous data (e.g. Kühn et al.) even if the differences are small.

We have now analyzed the effect of the removal of region C1 on the ability of mVenus-PbpC to promote stalk elongation in C. crescentus under phosphate starvation. Interestingly, our results show that the lack of the BacA-interaction motif impairs the recruitment of the fusion protein to the stalked pole, but it does not interfere with its stimulatory effect on stalk biogenesis. Thus, the polar localization of PbpC does not appear to be critical for its function in localized peptidoglycan synthesis at the stalk base. These results are now shown in Figure 8–Figure supplement 4. The results obtained may be explained by residual transient interactions of mVenus-PbpC with proteins other than BacA at the stalked pole. Notably, PbpC has also been implicated in the attachment of the stalk-specific protein StpX to components of the outer membrane at the stalk base. The polar localization of PbpC may therefore be primarily required to ensure proper StpX localization, consistent with previous work by Hughes et al. (Mol Microbiol, 2013) showing that StpX is partially mislocalized in a strain producing an N-terminally truncated PbpC variant that no longer localizes to the stalk base.

We have also attempted to investigate the ability of the Δ2-8 and F130R variants of BacA-mVenus to promote stalk elongation under phosphate starvation. However, the levels of the WT, Δ2-8 and F130R proteins and their stabilities were dramatically different after prolonged incubation of the cells in phosphate-limited medium, so that it was not possible to draw any firm conclusions from the results obtained (not shown).

In addition, the M23-like endopeptidase LdpA is proposed to be a client protein of BacA (in C. crescentus, Billini et al. 2018, and H. neptunium or R. rubrum, Pöhl et al. 2024). In H. neptunium, it is suggested that the interaction is mediated by a cytoplasmic peptide of LmdC reminiscent of PbpC. This should at least be commented on. It would be interesting to see, if LpdA in C. crescentus is also delocalized and if so, this could identify another client protein of BacA.

We agree that it would be interesting to study the role of BacA in LdpA function. However, we have not yet succeeded in generating a stable fluorescent protein fusion to LdpA, which currently makes it impossible to study the interplay between these two proteins in vivo. The focus of the present paper is on the mode of interaction between bactofilins and the cytoplasmic membrane and on the mutual interdependence of membrane binding and bactofilin polymerization. Given that PbpC is so far the only verified interaction partner of BacA in C. crescentus, we would like to limit our analysis to this client protein.

Further comments:

L105: analyze --> analyzed

Done.

L169: Is there any reason why the MTS of E. coli MreB was doubled?

Previous work has shown that two tandem copies of the N-terminal amphiphilic helix of E. coli MreB were required to partially target a heterologous fusion partner protein (GFP) to the cytoplasmic membrane of E. coli cells (Salje et al, 2011).

Fig. S3:

a) Please decide which tag was used (mNG or mVenus) and adapt the figure or legend accordingly.<br /> b) In the legend for panel (C), please describe how the relative amounts were calculated, as the fractions arithmetically cannot add to > 100%. I guess each band was densiometrically rated and independently normalized to the whole-cell signal?

The fluorescent tag used was mNeonGreen, as indicated in the figure. We have now corrected the legend accordingly. Thank you for making us aware of the wrong labeling of the y-axis. We have now corrected the figure and describe the method used to calculate the plotted values in the legend.

Legend of Fig 1b: It is not clear to me, to which part of panel B the somewhat cryptic LY... strain names belong. I suggest putting them either next to the images, to delete them, or at least to unify the layout (compare, e.g. to Fig S7). (I would delete the LY numbers and stay with the genes/mutations throughout. This is just a suggestion).

These names indicate the strains analyzed in panel B, and we have now clarified this in the legend. It is more straightforward to label the images according to the mutations carried by the different strains. Nevertheless, we would like to keep the strain names in the legend, so that the material used for the analysis can be clearly identified.

Fig. 2a: As some of the colors are difficult to distinguish, I suggest sorting the names in the legend within the graph according to the slope of the curves (e.g. K4E K7E (?) on top and WT being at the bottom).

Thank you for this suggestion. We have now rearranged the labels as proposed.

In the legend (L924), correct typo "panel C" to "panel B".

Done.

Fig. 3: In the legend, I suggest deleting the abbreviations "S" and "P" as they do not show up in the image. In line 929, I suggest adding: average "relative" amount... or even more precisely: "average relative signal intensities obtained..."

We have removed the abbreviations and now state that the bars indicate the “average relative signal intensities” obtained for the different fractions.

Fig 4d: same suggestion as for Fig. 2a.

Done.

Fig 8: In the legend (L978), delete 1x "the"

Done.

L258 and Fig. S5: The expression "To account for biases in the coverage of bacterial species" seems somewhat unclear. I suggest rephrasing and adding information from the M+M section here (e.g. from L593, if this is meant).

We now state that this step in the analysis pipeline was performed “To avoid biases arising from the over-representation of certain bacterial species in UniProt”.

I appreciate the outline of the workflow in panel (a) of Fig. S5. It would be even more useful when some more details about the applied criteria for filtering would be provided (e.g. concerning what is meant with "detailed taxonomic information" or "filter out closely related sequences". Does the latter mean that only one bactofilin sequence per species was used? (As quite many bacteria have more than one but similar bactofilins.)

We removed sequences from species with unclear phylogeny (e.g. candidate species whose precise taxonomic position has not yet been determined). For many pathogenic species, numerous strains have been sequenced. To account for this bias, only one sequence from clusters of highly similar bactofilin sequences (>90% identity) was retained per species. This information has now been included in the diagram. It is true that many bacteria have more than one bactofilin homolog. However, the sequences of these proteins are typically quite different. For instance, the BacA and BacB from C. crescentus only share 52% identity. Therefore, our analysis does not systematically eliminate bactofilin paralogs that coexist in the same species.

L281: Although likely, I am not sure if membrane binding has ever been shown for a bactofilin from these phyla. (See also L 380.) Is there an example? Otherwise, membrane binding may not be a property of these bactofilins.

To our knowledge, the ability of bactofilins from these clades to interact with membranes has not been investigated to date. We agree that the absence of an MTS-like motif may indicate that they lack membrane binding activity, and we have now stated this possibility in the Results and Discussion.

L285: See comment above concerning the M23-like peptidase LpdA. Although not yet directly shown for C. crescentus, it seems likely that BacACc does also localize this peptidase in addition to PbpC. I suggest rephrasing, e.g. "known" --> "shown"

We now use the word “reported”.

L295 and Fig S8: PbpC is ubiquitous. Which criteria/filters have been applied to select the shown sequences?

C. crescentus PbpC is different from E. coli Pbp1C. It is characterized by distinctive, conserved N- and C-terminal tails and only found in C. crescentus and close relatives. The C. crescentus homolog of E. coli PbpC is called PbpZ (Yakhnina et al, J Bacteriol, 2013; Strobel et al, J Bacterol, 2014), whereas C. crescentus PbpC is related to E. coli PBP1A. We have now added this information to the text to avoid confusion.

L311: may replace "assembly" by "polymerization"

Done.

L320: bactofilin --> bactofilin domain?

Yes, this was supposed to read “bactofilin domain”. Thank you for spotting this issue.

L324: The HDX analysis of BacA suggests that the exchange is slowed down in the presence of the PbpC peptide, which is indicative of a physical interaction between these two molecules. To corroborate the claim that BacA polymerization is critical for interaction with the peptide (resp. PbpC), this experiment should be carried out with the polymerization defective BacA version F130R.

(Or tone this statement down, e.g. show --> suggest.)

“suggest”

L386: undergoes --> undergo

Done.

L391-400: This idea is tempting but the suggested mechanism then would be restricted to bactofilins of C. crescentus and close relatives. The bactofilin of Rhodomicrobium, for example, was shown to localize dynamically and not to stick to a positively curved membrane.

In the vast majority of species investigated so far, bactofilins were found to associate with specifically curved membrane regions and to contribute to the establishment of membrane curvature. Unfortu­nately, the sequences of the three co-polymerizing bactofilin paralogs of R. vannielii DSM 166 studied by Richter et al (2023) have not been reported and the genome sequence of this strain is not publicly available. However, in related species with three bactofilin paralogs, only one paralog shows an MTS-like N-terminal peptide and another paralog typically contains an unusual cadherin-like domain of unknown function, as also reported for R. vannielii DSM 166. Therefore, the mechanism controlling the localization dynamics of bactofilins may be complex in the Rhodomicrobium lineage. Nevertheless, at native expression levels, the major bactofilin (BacA) of R. vannielii DSM 166 was shown to localize predominantly to the hyphal tips and the (incipient) bud necks, suggesting that regions of distinct membrane curvature could also play a role in its recruitment. We do not claim that all bactofilins recognize positive membrane curvature, which is clearly not the case. It rather appears as though the curvature preference of bactofilins varies depending on their specific function.

L405-406: I agree that localization of BacA has been shown to be independent of PbpC. However, this does not generally preclude an effect on BacA localization by other "client" or interacting proteins. (See also comment above about the putative BacA interactor LpdA). I suggest either to corroborate or to change this statement from "client binding" to "PbpC binding".

Thank you for pointing out the imprecision of this statement. We now conclude that “PbpC binding” is not critical for BacA assembly and positioning.

Suppl. Fig. S11: In the legend, please correct the copy-paste mismatch (...VirB...).

Done.

L482: delete 1x "at"

Done.

L484: may be better "soluble and insoluble fractions"?

We now describe the two fractions as “soluble and membrane-containing insoluble fractions” to make clear to all readers that membrane vesicles are found in the pellet after ultracentrifugation.

L489-490: check spelling immunoglobulin – immuneglobulin

Done.

L500 and 504: º_C --> ºC

Done.

Suppl. file X (HDX data): please check the table headline, table should be included in Suppl. file 1

We have now included a headline in this file (now Supplementary file 3).

eLife Assessment

This manuscript offers valuable structural and mechanistic insights into the structure and assembly of the Type II internal ribosome entry site (IRES) from encephalomyocarditis virus (EMCV) and the translation initiation complex, revealing a direct interaction between the IRES and the 40S ribosomal subunit. While a solid cryo-EM method was used, enhancing the overall resolution or adding complementary biochemical data would further improve the clarity and impact of this study. This manuscript will attract researchers in cap-independent translation, host-pathogen interactions, and virology.

Reviewer #1 (Public review):

Summary:

The authors have studied how a virus (EMCV) uses its RNA (Type 2 IRES) to hijack the host's protein-making machinery. They use cryo-EM to extract structural information about the recruitment of viral Type 2 IRES to ribosomal pre-IC. The authors propose a novel interaction mechanism in which the EMCV Type 2 IRES mimics 28S rRNA and interacts with ribosomal proteins and initiator tRNA (tRNAi).

Strengths:

(1) Getting structural insights about the Type 2 IRES-based initiation is novel.

(2) The study allows a good comparison of other IRES-based initiation systems.

(3) The manuscript is well-written and clearly explains the background, methods, and results.

Weaknesses:

(1) The main weakness of the work is the low resolution of the structure. This limits the possibility of data interpretation at the molecular level.

However, despite the moderate resolution of the cryo-EM reconstructions, the model fits well into the density. The analysis of the EMCV IRES-48S PIC structure is thorough and includes meaningful comparisons to previously published structures (e.g., PDB IDs - 7QP6 and 7QP7). These comparisons showed that Map B1 represents a closed conformation, in contrast to Map A in the open state (Figure 2). Additionally, the proposed 28S rRNA mimicry strategy supported by structural superposition with the 80S ribosome and sequence similarity between the I domain of the IRES and the h38 region of 28S rRNA (Fig. 4) is well-justified.

(2) The lack of experimental validation of the functional importance of regions like the GNRA and RAAA loops is another limitation of this study.

(3) Minor modifications related to data processing and biochemical studies will further validate and strengthen the findings.

a) In the cryo-EM data section, the authors should include an image showing rejected particles during 2D classification. This would help readers understand why, despite having over 22k micrographs with sufficient particle distribution and good contrast, only a smaller number of particles were used in the final reconstruction. Additionally, employing map-sharpening tools such as Ewald sphere correction, Bayesian polishing, or reference-based motion correction might further improve the quality of the maps. Targeting high-resolution structures would be particularly informative.

b) The strategic modelling of different IRES domains into the density, particularly the domain into the region above the 40S head, is appreciable. However, providing the full RNA tertiary structure (RNAfold) of the EMCV IRES (nucleotides 280-905) would better explain the logic behind the model building and its molecular interpretation.

c) Although the authors compare their findings with other types of IRESs (Types 1, 3, and 4), there is no experimental validation of the functional importance of regions like the GNRA and RAAA loops. Including luciferase-based assays or mutational studies of these regions for validation of structural interpretations is strongly recommended.

Reviewer #2 (Public review):

Summary:

The field of protein translation has long sought the structure of a Type 2 Internal Ribosome Entry Site (IRES). In this work, Das and Hussain pair cryo-EM with algorithmic RNA structure prediction to present a structure of the Type 2 IRES found in Encephalomyocarditis virus (EMCV). Using medium to low resolution cryo-EM maps, they resolve the overall shape of a critical domain of this Type 2 IRES. They use algorithmic RNA prediction to model this domain onto their maps and attempt to explain previous results using this model.

Strengths:

(1) This study reveals a previously unknown/unseen binding modality used by IRESes: a direct interaction of the IRES with the initiator tRNA.

(2) Use of an IRES-associated factor to assemble and pull down an IRES bound to the small subunit of the ribosome from cellular extracts is innovative.

(3) Algorithmic modeling of RNA structure to complement medium to low resolution cryo-EM maps, as employed here, can be implemented for other RNA structures.

Weaknesses:

(1) Maps at the resolution presented prevent unambiguous modelling of the EMCV-IRES. This, combined with the lack of any biochemical data, calls into question any inferences made at the level of individual nucleotides, such as the GNRA loop and CAAA loop (Figure 4).

(2) The EMCV IRES contains an upstream AUG at position 826, where the PIC can assemble (Pestova et al 1996; PMID 8943341). It is unclear if this start codon was mutated in this study. If it were not mutated, placement of AUG-834 over AUG-826 in the P-site is unexplained.

(3) The claims the authors make about (i) the general overall shape and binding site of the IRES, (ii) its gross interaction with the two ribosomal proteins, (iii) the P-in state of the 48S, (iv) the rearrangement of the ternary complex are all warranted. Their claims about individual nucleotides or smaller stretches of the IRES-without any supporting biochemical data-is not warranted by the data.

Reviewer #3 (Public review):

Summary:

Type II IRES, such as those from encephalomyocarditis virus (EMCV) and foot-and-mouth disease virus (FMDV), mediate cap-independent translation initiation by using the full complement of eukaryotic initiation factors (eIFs), except the cap-binding protein eIF4E. The molecular details of how IRES type II interacts with the ribosome and initiation factors to promote recruitment have remained unclear. Das and Hussain used cryo-electron microscopy to determine the structure of a translation initiation complex assembled on the EMCV IRES. The structure reveals a direct interaction between the IRES and the 40S ribosomal subunit, offering mechanistic insight into how type II IRES elements recruit the ribosome.

Strengths:

The structure reveals a direct interaction between the IRES and the 40S ribosomal subunit, offering mechanistic insight into how type II IRES elements recruit the ribosome.

Weaknesses:

While this reviewer acknowledges the technical challenges inherent in determining the structure of such a highly flexible complex, the overall resolution remains insufficient to fully support the authors' conclusions, particularly given that cryo-EM is the sole experimental approach presented in the manuscript.

The study is biologically significant; however, the authors should improve the resolution or include complementary biochemical validation.

Author response:

Reviewer #1 (Public review):

Summary:

The authors have studied how a virus (EMCV) uses its RNA (Type 2 IRES) to hijack the host's protein-making machinery. They use cryo-EM to extract structural information about the recruitment of viral Type 2 IRES to ribosomal pre-IC. The authors propose a novel interaction mechanism in which the EMCV Type 2 IRES mimics 28S rRNA and interacts with ribosomal proteins and initiator tRNA (tRNAi).

Strengths:

(1) Getting structural insights about the Type 2 IRES-based initiation is novel.

(2) The study allows a good comparison of other IRES-based initiation systems.

(3) The manuscript is well-written and clearly explains the background, methods, and results.

We thank Reviewer 1 for appreciating our efforts and finding structural insights about the type 2 IRES-based initiation presented in this study as novel.

Weaknesses:

(1) The main weakness of the work is the low resolution of the structure. This limits the possibility of data interpretation at the molecular level.

However, despite the moderate resolution of the cryo-EM reconstructions, the model fits well into the density. The analysis of the EMCV IRES-48S PIC structure is thorough and includes meaningful comparisons to previously published structures (e.g., PDB IDs - 7QP6 and 7QP7). These comparisons showed that Map B1 represents a closed conformation, in contrast to Map A in the open state (Figure 2). Additionally, the proposed 28S rRNA mimicry strategy supported by structural superposition with the 80S ribosome and sequence similarity between the I domain of the IRES and the h38 region of 28S rRNA (Fig. 4) is welljustified.

We agree that the low resolution of the map has compromised the data interpretation at the molecular level, and we thank the reviewer for appreciating our findings at this resolution. Due to the compromise in resolution, we have reported findings related to stretches or regions such as loops and stems, rather than individual nucleotides and interactions.  

(2) The lack of experimental validation of the functional importance of regions like the GNRA and RAAA loops is another limitation of this study.

We agree with the lack of any additional experiments other than Cryo-EM for probing the importance of regions such as GNRA and RAAA loops in this study. However, we have cited earlier reports that demonstrate the importance of these regions for overall IRES activity. The essentiality of RAAA loop for type 2 IRES was demonstrated in earlier report López de Quinto and Martínez-Salas, 1997 (Cited in manuscript). Further, the conservation of this loop across the type 2 IRES family adds to the importance of this loop (Manuscript Figure 6B). This loop and its flanking G-C stem are similar to h38 of 28S rRNA, and it appears that RAAA loop adopts a mimicry mechanism to interact with the 40S ribosomal protein- uS19, thus highlighting its importance for interaction with 40S. Experiments destabilising the G-C stem also compromise IRES activity, as shown in the case of FMDV IRES (Fernández et al 2011). Previous studies related to the mutation of the GNRA or GCGA loop in EMCV IRES have shown a deficiency in IRES activity (Roberts and Belsham, 1997; Robertson et al 1999), suggesting the importance of these regions in the viral IRES biology, and these reports are cited in the manuscript. Not only EMCV IRES, but mutation in the GUAA (representative of GNRA) loop of FMDV IRES also showed significant reduction in IRES activity (López de Quinto and Martínez-Salas, 1997). In our study, we observe that GCGA loop interacts with tRNA_i in EMCV IRES-48S PIC, thus implicating the importance of this loop. Moreover, incubation of FMDV IRES with 40S ribosomes has shown a decrease in SHAPE reactivity in domain 3 apex (position 170- 200 nucleotides) (Lozano et al 2018), which corresponds to EMCV IRES domain I apex. Further, we will attempt to address the concern of lack of experimental validation of GNRA and RAAA loops by performing biochemical assays.

(3) Minor modifications related to data processing and biochemical studies will further validate and strengthen the findings.

a) In the cryo-EM data section, the authors should include an image showing rejected particles during 2D classification. This would help readers understand why, despite having over 22k micrographs with sufficient particle distribution and good contrast, only a smaller number of particles were used in the final reconstruction. Additionally, employing mapsharpening tools such as Ewald sphere correction, Bayesian polishing, or reference-based motion correction might further improve the quality of the maps. Targeting high-resolution structures would be particularly informative.

We thank the reviewer for the suggestions, and we would employ suggested processes that may help improve the quality of the maps further. We will include image for rejected 2D classes in the revised manuscript. We agree with the Reviewer’s query related to the substantial number of micrographs and smaller number of particles for the final reconstruction. The total number of micrographs is the summation of multiple datasets, prepared and collected at various times. Among these, around 8000 micrographs have extremely poor particle number and distribution. As a result, the number of particles per micrograph is heterogeneous in the compiled dataset. We obtained only 237054 ‘good particles’ after multiple rounds of 2D & 3D classifications, and the final reconstruction has 28439 particles (~12%). This class was obtained after masked classification for IRES and ternary complex density. Hence, only the particles that show the best density for both IRES and ternary complex are used for reconstructing this map. Another set of particles that have only a portion of IRES and tRNA but NO density for eIF2 forms another map (26792 particles, 11.3%). Thus, we obtained a total of 55231 particles (23.3%) with IRES density.  

b) The strategic modelling of different IRES domains into the density, particularly the domain into the region above the 40S head, is appreciable. However, providing the full RNA tertiary structure (RNAfold) of the EMCV IRES (nucleotides 280-905) would better explain the logic behind the model building and its molecular interpretation.

We thank the reviewer for appreciating the modelling of the domain I apex in the cryo-EM density. We tried to predict the full tertiary structure of the IRES, however, inclusion of the full-length sequence from 280-905 gave models of extremely low confidence, and few domains do not abide by the secondary structure of EMCV IRES as reported in Duke et al 1992. Hence, we used individual domains of EMCV IRES and predicted the tertiary structure independent of other IRES domains. Furthermore, 3D models of FMDV IRES domains 2, 3, and 4 (corresponding to EMCV IRES domains- H, I, and J-K) were predicted from SHAPE reactivity values and RNAComposer server (Figure 3 in Lozano et al 2018). The predicted architecture of domain 3 apex (FMDV IRES) coincides with our I domain apex model (EMCV IRES).

c)  Although the authors compare their findings with other types of IRESs (Types 1, 3, and 4), there is no experimental validation of the functional importance of regions like the GNRA and RAAA loops. Including luciferase-based assays or mutational studies of these regions for validation of structural interpretations is strongly recommended.

We have discussed the possibility of how the other IRESs, such as type 1 and type 5 (Aichi virus), might use similar strategies as EMCV IRES to assemble the 48S PIC, given the similarity in the motif sequence and position across the viral IRESs. Like EMCV IRES, the type 1 IRES (e.g. Poliovirus, Coxsackie virus) also harbours the GNRA loop, preceded by a C-rich loop at its longest domain, known for long-range RNA-RNA interactions. The segment harbouring GNRA loop is highly conserved across the type 1 family of IRESs (Kim et al 2015).The Aichi viral IRES (type 5) harbours a GNRA loop in its longest domain, which is domain J. Deletion of the GNRA loop has compromised the IRES activity; however, substitution mutations in this region either elevated the IRES activity or it remained unaltered (Yu et al 2011). We have hypothesized that these IRESs (type 1 and type 5) might use the GNRA motifs in their longest domain (domain IV in type 1, and domain J in type 5) similar to that of EMCV IRES, where GNRA is present in the longest domain (I) and preceded by a C-rich loop. Thus, GNRA can potentially mediate long-range interactions with tRNA_i as all these IRESs require eIF2-ternary complex for the formation of 48S PIC. Parallelly, like EMCV IRES, type 1 and type 5 IRESs also have similar placement of GNRA motif-containing domain before the eIF4G-binding domain (domain J-K in EMCV IRES, domain V in poliovirus, domain K in Aichi virus). Hence, we suggest the possibility of a similar strategy by these IRESs to interact with tRNA_i during the formation of 48S PIC.  

Reviewer #2 (Public review):

Summary:

The field of protein translation has long sought the structure of a Type 2 Internal Ribosome Entry Site (IRES). In this work, Das and Hussain pair cryo-EM with algorithmic RNA structure prediction to present a structure of the Type 2 IRES found in Encephalomyocarditis virus (EMCV). Using medium to low resolution cryo-EM maps, they resolve the overall shape of a critical domain of this Type 2 IRES. They use algorithmic RNA prediction to model this domain onto their maps and attempt to explain previous results using this model.

Strengths:

(1) This study reveals a previously unknown/unseen binding modality used by IRESes: a direct interaction of the IRES with the initiator tRNA.

(2) Use of an IRES-associated factor to assemble and pull down an IRES bound to the small subunit of the ribosome from cellular extracts is innovative.

(3) Algorithmic modeling of RNA structure to complement medium to low resolution cryoEM maps, as employed here, can be implemented for other RNA structures.

We thank Reviewer 2 for positive and encouraging comments on our work, appreciating our ‘innovative’ approach of using IRES-associated factor to assemble and pull down IRES-bound ribosomal complex.  

Weaknesses:

(1) Maps at the resolution presented prevent unambiguous modelling of the EMCV-IRES. This, combined with the lack of any biochemical data, calls into question any inferences made at the level of individual nucleotides, such as the GNRA loop and CAAA loop (Figure 4).

We understand the concerns raised by the reviewer related to the resolution of the EMCV IRES-48S PIC map. However, we would like to mention that we refrained from commenting on individual nucleotides or molecular interactions in the manuscript. Instead, we discuss about loops, RNA stretches or motifs that could be inferred with more confidence as shown in Manuscript Figure 4. The EMCV IRES can directly interact with the 40S ribosome using its domain H and I (Chamond et al 2014), however, the details this interaction was unknown. We observe that the CAAA loop of domain I apex interacts with 40S ribosome based on the placement of portion of domain I in the cryo-EM map. This is also reflected in the earlier reported SHAPE data (Supplementary figures 2, and 8 in Chamond et al 2014), where a decrease in reactivity is evident in the presence of 40S ribosome. In addition, incubation of EMCV IRES with rabbit reticulocyte lysate (RRL) offered protection to domain I apex regions, which included the CAAA loop (Figure 4b in Maloney and Joseph, 2024).

Furthermore, this decrease in SHAPE reactivity pattern is also evident for FMDV IRES domain 3 apex (like domain I in EMCV IRES) in the presence of 40S ribosome (Lozano et al 2018).

Thus, these studies are consistent with the placement of IRES model in the cryo-EM map.

We aim to improve the resolution of the maps for better clarity and add biochemical experiments to justify the possible interactions.

(2) The EMCV IRES contains an upstream AUG at position 826, where the PIC can assemble (Pestova et al 1996; PMID 8943341). It is unclear if this start codon was mutated in this study. If it were not mutated, placement of AUG-834 over AUG-826 in the P-site is unexplained.

We thank the reviewer for bringing up this point, as we missed mentioning this in the manuscript. The EMCV IRES does not require scanning and directly positions the AUG-834 at the P site (Pestova et al 1996). In Pestova et al 1996, the intensity of the toeprint at AUG-834 is much more intense than that of AUG-826. Further, AUG-834 lies in the Kozak context, whereas AUG-826 has a poor Kozak context. Furthermore, the synthesis of the polypeptide requires placement of AUG-834 at the P site. In our cryo-EM map, we observed that the tRNA_i is in a P_IN state, which indicates the recognition of the start codon, and we reasoned that it is more likely that AUG-834 is placed at the P site than AUG-826. We will mention this in the revised manuscript, as we had NOT mutated AUG-826.

(3) The claims the authors make about (i) the general overall shape and binding site of the IRES, (ii) its gross interaction with the two ribosomal proteins, (iii) the P-in state of the 48S, (iv) the rearrangement of the ternary complex are all warranted. Their claims about individual nucleotides or smaller stretches of the IRES-without any supporting biochemical data-is not warranted by the data.

We thank the reviewer for warranting major claims, and we wish to make further improvements to support our assessment of small stretches and individual nucleotides.

Reviewer #3 (Public review):

Summary:

Type II IRES, such as those from encephalomyocarditis virus (EMCV) and foot-and-mouth disease virus (FMDV), mediate cap-independent translation initiation by using the full complement of eukaryotic initiation factors (eIFs), except the cap-binding protein eIF4E. The molecular details of how IRES type II interacts with the ribosome and initiation factors to promote recruitment have remained unclear. Das and Hussain used cryo-electron microscopy to determine the structure of a translation initiation complex assembled on the EMCV IRES. The structure reveals a direct interaction between the IRES and the 40S ribosomal subunit, offering mechanistic insight into how type II IRES elements recruit the ribosome.

Strengths:

The structure reveals a direct interaction between the IRES and the 40S ribosomal subunit, offering mechanistic insight into how type II IRES elements recruit the ribosome.

Weaknesses:

While this reviewer acknowledges the technical challenges inherent in determining the structure of such a highly flexible complex, the overall resolution remains insufficient to fully support the authors' conclusions, particularly given that cryo-EM is the sole experimental approach presented in the manuscript.

The study is biologically significant; however, the authors should improve the resolution or include complementary biochemical validation.

We thank Reviewer 3 for acknowledging the technical challenges in this study and finding our study biologically significant. We understand the concerns related to low resolution and the requirement of complementary biochemical validation for our reported observations and interpretations in the manuscript. We are attempting to improve the resolution and complement the interpretations with biochemical experiments.

eLife Assessment

This valuable investigation provides new and solid evidence for a specific cognitive deficit in cerebellar degeneration patients. The authors use three tasks that modulate complexity and violations of cognitive expectations. They show specific slowing of reaction times in the presence of violations but not with task complexity. While some alternative interpretations of the results are possible and are discussed, the work provides a new, invaluable data point in describing the cognitive contribution of cerebellar processing.

Reviewer #1 (Public review):

Summary:

The authors test the hypothesis that the contribution of the cerebellum to cognitive tasks is similar to motor tasks, and is related to the processing of prediction errors (here: violation of expectations, VE). In three experiments, they find that cerebellar patients show differences compared to controls in measures of VE, but not task complexity. The findings show that cerebellar disease results in deficits in VE processing in cognitive tasks, and makes a valuable contribution of the field. The authors were able to test a large number of patients with cerebellar disease which is known to primarily affect the cerebellum (i.e. SCA6).

Strengths:

A strength of the study is that it is hypothesis-driven and that the three experiments are very well thought out. Furthermore, a comparatively large group of patients with spinocerebellar ataxia type 6 (SCA6) was tested, a disease which affects primarily the cerebellum.

Weaknesses:

- Acquisition of brain MRI scans would have been useful to perform lesion-behaviour-mapping. But this does not limit the significance of the behavioural findings.<br /> - Exp. 1 and 2: The lack of difference in accuracy was that an unexpected finding? How meaningful are the used paradigms when accuracy was the same in cerebellar patients and controls?<br /> - Exp. 1 and 2: Cerebellar patients have motor dysfunction which impacts reaction time. Can the authors exclude that this contributed at least in part to their findings? Any correlations to SARA score (upper limb function) or oculomotor dysfunction (e.g. presence of nystagmus)?<br /> - Data on the attention probes which have been done would be of interest. Were there any differences in attention between patients and controls, any correlations with the findings?

Comments on revisions:

I am not sure if I can follow the interpretation of the authors that the cerebellum contributes to prediction errors, but not predictions; These two are tightly connected? It may rather be that in patients with slowly progressive chronic disease there is a lot of compensation? It is not so rare that in cognitive tasks cerebellar patients do not perform differently from controls, even though one would expect a difference (e.g. based on fMRI data in controls)? Another factor which likely adds is age, Patients and controls are often middle-aged and elderly, adding to variability, decreasing the chance to see group differences?

Author response:

The following is the authors’ response to the original reviews

Joint Public Review:

Summary:

In this study, Daniel et al. used three cognitive tasks to investigate behavioral signatures of cerebellar degeneration. In the first two tasks, the authors found that if an equation was incorrect, reaction times slowed significantly more for cerebellar patients than for healthy controls. In comparison, the slowing in the reaction times when the task required more operations was comparable to normal controls. In the third task, the authors show increased errors in cerebellar patients when they had to judge whether a letter string corresponded to an artificial grammar.

Strengths:

Overall, the work is methodologically sound and the manuscript well written. The data do show some evidence for specific cognitive deficits in cerebellar degeneration patients.

Thank you for the thoughtful summary and constructive feedback. We are pleased that the methodological rigor and clarity of the manuscript were appreciated, and that the data were recognized as providing meaningful evidence regarding cognitive deficits in cerebellar degeneration.

Weaknesses:

The current version has some weaknesses in the visual presentation of results. Overall, the study lacks a more precise discussion on how the patterns of deficits relate to the hypothesized cerebellar function. The reviewers and the editor agreed that the data are interesting and point to a specific cognitive deficit in cerebellar patients. However, in the discussion, we were somewhat confused about the interpretation of the result: If the cerebellum (as proposed in the introduction) is involved in forming expectations in a cognitive task, should they not show problems both in the expected (1+3 =4) and unexpected (1+3=2) conditions? Without having formed the correct expectation, how can you correctly say "yes" in the expected condition? No increase in error rate is observed - just slowing in the unexpected condition. But this increase in error rate was not observed. If the patients make up for the lack of prediction by using some other strategy, why are they only slowing in the unexpected case? If the cerebellum is NOT involved in making the prediction, but only involved in detecting the mismatch between predicted and real outcome, why would the patients not show specifically more errors in the unexpected condition?

Thank you for asking these important questions and initiating an interesting discussion. While decision errors and processing efficiency are not fully orthogonal and are likely related, they are not necessarily the same internal construct. The data from Experiments 1 and 2 suggest impaired processing efficiency rather than increased decision error. Reaction time slowing without increased error rates suggests that the CA group can form expectations but respond more slowly, possibly due to reduced processing efficiency. Thus, this analysis of our data suggests that the cerebellum is not essential for forming expectations, but it plays a critical role in processing their violations.

Relatedly, a few important questions remain open in the literature concerning the cerebellum’s role in expectation-related processes. The first is whether the cerebellum contributes to the formation of expectations or the processing of their violations. In Experiments 1 and 2, the CA group did not show impairments in the complexity manipulation. Solving these problems requires the formation of expectations during the reasoning process. Given the intact performance of the CA group, these results suggest that they are not impaired in forming expectations. However, in both Experiments 1 and 2, patients exhibited selective impairments in solving incorrect problems compared to correct problems. Since expectation formation is required in both conditions, but only incorrect problems involve a VE, we hypothesize that the cerebellum is involved in VE processes. We suggest that the CA group can form expectations in familiar tasks, but are impaired in processing unexpected compared to expected outcomes. This supports the notion that the cerebellum contributes to VE, rather than to forming expectations.

In Experiment 3, during training, the participant is learning a novel rule (grammar), forming new expectations on how strings of letters should be. Afterwards, during testing, the participant is requested to identify if a novel string is following the rule or not. We examined sensitivity to distinguish between grammatical and non‐grammatical strings of letters, thus taking into account a baseline ability to identify expected strings. Additionally, both in the low‐similarity and highsimilarity conditions, there are expectations regarding whether the strings are following the rule or not. However, in the high‐similarity condition, there is more uncertainty regarding which strings are following the grammatical rule, as demonstrated in a lower sensitivity (d prime). Given the group differences only in the low similarity condition, these results suggest the CA group is impaired only when the rules are more certain. Given these results, we suggest that forming cognitive expectations is not necessarily dependent on the cerebellum. Rather, we propose that the cerebellum is critical for processing rule-based VE (detection or processing of detected errors) under conditions of more certainty. One remaining question for future studies is whether the cerebellum contributes to detection of a mismatch between the expectation and sensory evidence, or the processing of a detected VE. 

We suggest that these key questions are relevant to both motor and non-motor domains and were not fully addressed even in the previous, well-studied motor domain. Importantly, while previous experimental manipulations17,19,40,94–96 have provided important insights regarding the cerebellar role in these processes, some may have confounded these internal constructs due to task design limitations (e.g., lack of baseline conditions). Notably, some of these previous studies did not include control conditions, such as correct trials, where there was no VE. In addition, other studies did not include a control measure (e.g., complexity effect), which limits their ability to infer the specific cerebellar role in expectation manipulation. 

Thus, the current experimental design used in three different experiments provides a valuable novel experimental perspective, allowing us to distinguish between some, but not all, of the processes involved in the formation of expectations and their violations. For instance, to our knowledge, this is the first study to demonstrate a selective impairment in rule-based VE processing in cerebellar patients across both numerical reasoning and artificial grammar tasks. If feasible, we propose that future studies should disentangle different forms of VE by operationalizing them in experimental tasks in an orthogonal manner. This will allow us to achieve a more detailed and well-defined cerebellar motor and non-motor mechanistic account.

Recommendations for the authors:

Editors comments:

The Figures are somewhat sub-standard and should be improved before the paper is made the VOR. Ensure consistent ordering of the group factor (CA, NT) and experimental factor across Figure 3,4, and 6 (panels A). Having the patient group as columns in Figure 4a and in rows in Figure 6a is very confusing.

We have standardized the layout across Figures 2, 4, and 6 so that the group factor (CA, NT) and experimental conditions are consistently ordered. In all panels, the group factor now appears as a column.

Subpanels should be numbered A,B,C... not A, B1, B2.

Subpanel labels have been updated to follow the standard A, B, C format across all figures.

Fonts should have a 100% aspect ratio - they should not be stretched (Figure 6B).

We have corrected the font aspect ratios in all figures (e.g., Figure 6B) to ensure proper proportions and readability. 

Colors should be more suitable to print - use a CYMK color scheme (i.e. avoid neon colors such as the neon green for the CA).

The color scheme across all figures has been revised to be print-friendly using CMYKcompatible, colorblind-accessible palettes. Neon green for the CA group was replaced with a more muted, distinguishable color.

Abstract: "The CA group exhibited a disproportionate cost when comparing expected problems compared to unexpected problems" - I recommend switching unexpected and expected, as the disproportional cost in on the former.

We have changed the wording of the sentence accordingly. 

Upon re-reading the details for the AGL task were not clear to us. Please do not rely on the reference (78) for the details - your paper should contain enough information to have the reader understand the experimental details. For you to appreciate the depth of our not-understanding, here a simple question: The test strings either followed the grammar in Fig 5 or they did not. If they did not, how exactly was similarity to the grammar measured? If they did, what was the difference between the “Grammatical-high” and “Grammatical-low” trials? If the string was grammatical, there should not be a notion of similarity, no? Or where these trials arbitrary split in half? 

We have clarified that 50% of the test strings followed the grammar of the training strings. We also elaborated on the calculation of chunk strength as a measure of similarity between the training and testing strings, similar to the previous papers. The differences between low and high similarity are explained in the paper. Specifically, for each test string, we calculated chunk strength by summing the frequencies of all relevant substrings (e.g., bigrams and trigrams) that appeared in the training set. The test strings whose chunk‐strength values fell above the median for grammatical items were classified as “high similarity,” while those falling below the median were classified as “low similarity.” Also, grammatical strings can be of both low and high similarity; this is precisely the beautiful aspect of this experimental manipulation, showing the importance of uncertainty. We have utilized a 2 × 2 fully orthogonal design (grammaticality × similarity).

Experimental details of the task should be added to the Method section. In the results you should only mention the experimental details that are necessary for understanding the experiments, but details such as the number of trials, etc, can be moved to the methods. 

We have now moved the experimental task details to the Method sections.

Reviewer #1 (Recommendations for the author):

Studies have been done online and not in the lab. Could that have affected the results?

We addressed this in the Methods section, referring to established protocols for online neuropsychological testing[9–12]. Our results align with similar in-lab findings in both the subtraction and AGL tasks, supporting the online approach's robustness. 

Figure 2, B1; Figure 4, B1; Figure 6B: How many patients performed worse than the (worst-performing) controls? There appears to be quite some overlap between patients and controls. In the patients who performed worse, was there any difference from the other patients (e.g. disease severity as assessed by SARA score, repeat length, data of attention probes)?

We appreciate the reviewer’s thoughtful comment. We considered conducting individual-level comparisons to identify patients who performed worse than the lowest-performing controls. However, defining "worse" based on the performance of the lowest control is only one possible criterion. Other definitions—such as a specific number (1/2/3?) of standard deviations below the control mean—are also commonly used in literature, and each may yield different conclusions. This variability highlights the lack of a standardized threshold for what constitutes “worse” or "impaired" performance at the individual level. Given this ambiguity, and in line with prior studies that focus on average group differences rather than “impairment” prevalence, we chose not to include these individual-level comparisons. We believe this approach better aligns with the goals and design of the current study. That said, we agree that examining individual variability is important and may be more appropriate in future studies with larger samples so that percentage is a more robust measure. However, given the rarity of the disease, this would also be a challenge for future studies.  

SARA ataxia scale does not include oculomotor function. In SCA6 oculomotor deficits are frequent, eg, downbeat nystagmus. Please include information on oculomotor dysfunction.

We thank the reviewer for this important observation. While it is true that the SARA scale does not explicitly assess oculomotor function, our experimental design – in all three experiments – has control conditions that help account for general processing differences, including those that could arise from oculomotor deficits. These conditions, such as the correct trials and the complexity effects, allow us to isolate effects specifically related to the violation of expectation while minimizing the influence of broader performance factors, such as eye movement abnormalities. We also note that, while some patients can experience oculomotor symptoms such as downbeat nystagmus, none of our tasks required precise visual tracking or gaze shifts. In our experimental tasks, stimuli were centrally presented, and no visual tracking or saccadic responses were required. Moreover, the response time windows and stimulus durations (>2–5 s) were sufficient to mitigate the effects of delayed visual processing due to oculomotor impairment.

Why was MoCA used and not the CCAS-Schmahmann scale to assess cognitive function?

We selected the MoCA due to its broad clinical utility, time efficiency, and ability to detect mild cognitive impairment specifically in CA[101,102].  

Were there any signs of depression in the patient group that could have affected the results?

None of the patients had a clinical diagnosis of depression or were undergoing psychiatric treatment.  

Additionally, the interaction between group and expectancy was insignificant when RT was the depended vaibale .." = variable

This has been corrected to "variable" in the revised manuscript.

Reviewer #2 (Recommendations for the authors):

The terms 'unexpected' and 'expected' conditions are confusing. [...] Terming this 'violation of expectation' seems unnecessarily complicated to me. 

We thank the reviewer for raising this important concern. We recognize that the terms "expected" and "unexpected" can be ambiguous without clarification, and that "violation of expectation" (VE) may initially appear unnecessary. Our choice to use VE terminology is grounded in an established theoretical framework that distinguishes between mere stimulus correctness and prediction mechanisms. Specifically, VE captures the internal processing of mismatches between anticipated and observed outcomes, which we believe is central to the cerebellar function under investigation. While simpler, technical alternatives (e.g., "correct" vs. "incorrect") could describe the stimuli, we find that VE more accurately reflects the mental constructs under study and is consistent with previous literature in both motor and cognitive domains. 

Both tasks provide an error (or violation of expectation) that is non-informative and therefore unlikely to be used to update a forward model. The authors draw on motor literature to formulate a cognitive task where the presence of an error would engage the cerebellum and lead to longer reaction times in cerebellar patients. But in the motor domain, mismatch of sensory feedback and expectations would lead to an updating of the internal forward model. It seems unlikely to me in the arithmetic and alphabetic addition tasks that patients would update their internal model of addition according to an error presented at the end of each trial. If the error processed in these tasks will not lead to the updating of the internal forward model, can the authors discuss to what extent the cerebellum will be engaged similarly in these tasks, and what exactly connects cerebellar processing in these motor and cognitive tasks.

We thank the reviewer for this thoughtful and important comment. We fully agree that the current tasks do not directly probe learning-related updating of internal models. As stated in the paper, the goal of the present study was not to support or refute a specific claim regarding the cerebellum’s role in learning processes. Rather, our focus was on examining cerebellar involvement in the processing of VE. While we were inspired by models from the motor domain, our design was not intended to induce learning or adaptation per se, but to isolate the processing of unexpected outcomes. We agree that the tasks in their current form are unlikely to engage forward model updating in the same way as in sensorimotor adaptation paradigms. That said, we believe the current findings can serve as a basis for future research exploring the relationship between cerebellar prediction error processing and learning over time. As we also noted in the paper, this is a direction we propose, and actively pursuing, in ongoing research work.

The colour scheme is difficult for anyone with colour blindness or red-green visual impairment. Please adjust.

All figures have been revised to use CMYK-compatible, colorblind-safe palettes, and neon colors have been removed.

The introduction is a bit difficult to understand, because the authors draw on a number of different theories about cerebellar functioning, without clearly delineating how these relate to each other. For example: a) In the paragraph beginning with 'notably': If the cerebellum is required for sequential operations, why does it show the impairment with the rotation of the letters?

We understand the concern that if the cerebellum is involved in sequential operations, its involvement in mental letter rotation, which can be assumed as “continuous transformation,” may appear contradictory. We note that the boundary between continuous and stepwise, procedural operations is not always clear-cut and may vary depending on the participant's strategy or previous knowledge, which is not fully known to the researchers. Furthermore, to our knowledge, prior work on mental rotation has not directly investigated the impact of VE during this task. However, these are two debatable considerations. 

More importantly, a careful reading of our paper suggests that our experiments were designed to examine VE within tasks that involve sequential processing. Notably, we are not claiming that the cerebellum is involved in sequential or procedural processing per se. Rather, our findings point to a more specific role for the cerebellum in processing VE that arises during the construction of multistep procedural tasks. In fact, the results indicate that while the cerebellum may not be directly involved in the procedural process itself, it is critical when expectations are violated within such a context. This distinction is made possible in our study by the inclusion of a control condition (the complexity effect), which allows for a unique dissociation in our experimental design—one that, to our knowledge, has not been sufficiently addressed in previous studies.

Additionally, in the case of arithmetic problem solving—such as the tasks used in prior studies cited in our manuscript21—there is substantial evidence that these problems are typically solved through stepwise, procedural operations. Arithmetic reasoning, used in Experiments 1 and 2, has been robustly associated with procedural, multi-step strategies, which may be more clearly aligned with traditional views of cerebellar involvement in sequential operations. Thus, we propose that the role of the cerebellum in continuous transformations should be further examined. 

We suggest a more parsimonious theory that the cerebellum contributes to VE,  a field that was highly examined before. Yet, to reconcile ours and previous findings, we propose that the cerebellum’s contribution may not be limited to either continuous or stepwise operations per se, but rather to a domain-general process: the processing of VE. This theoretical framework can explain performance patterns across both mental rotation tasks and stepwise, procedural arithmetic.   

The authors mention generation prediction as a function of the cerebellum, processing of prediction errors (or violations of expectations), sequentially, and continuous transformations - but it is unclear whether the authors are trying to dissociate these from each other or whether ALL of these functions have informed task design.

We propose that the cerebellum’s contribution may not be limited to either continuous transformations or stepwise, procedural operations per se, but rather to a domain-general process: the processing of VE. We would like to clarify that we do not claim the cerebellum contributes to continuous transformations only, as suggested in some earlier work[21]. Rather, it could be that the cerebellum may contribute to continuous transformations, but we propose that it also supports multi-step, procedural processes. Given that framework, in the current study, across three separate experiments, we demonstrated that the cerebellum can also contribute to procedural, multi-step reasoning tasks.  

Minor Comments

Typo under paragraph beginning with 'notably' - cerebellum role should be cerebellar role.

Corrected as suggested.

When mentioning sequences as a recruiting feature for the cerebellum in the introduction, Van Overwalle's extensive work in the social domain should be referenced for completeness.

Thank you for the suggestion. We have now cited Van Overwalle’s work on cerebellar involvement in sequence processing within the social domain in the revised Introduction.

eLife Assessment

This study provides fundamental insights into eukaryotic phosphate homeostasis by demonstrating how yeast vacuoles dynamically regulate cytosolic phosphate levels. The conclusions are convincing, supported by an elegant combination of in vitro assays and in vivo measurements. This study will be of interest to cell biologists, particularly for those who are working in the field of phosphate metabolism.

Reviewer #1 (Public review):

The manuscript by Bru et al. focuses on the role of vacuoles as a phosphate buffering system for yeast cells. The authors describe here the crosstalk between the vacuole and the cytosol using a combination of in vitro analyses of vacuoles and in vivo assays. They show that the luminal polyphosphatases of the vacuole can hydrolyze polyphosphates to generate inorganic phosphate, yet they are inhibited by high concentrations. This balances the synthesis of polyphosphates against the inorganic phosphate pool. Their data further show that the Pho91 transporter provides a valve for the cytosol as it gets activated by a decline in inositol pyrophosphate levels. The authors thus demonstrate how the vacuole functions as a phosphate buffering system to maintain a constant cytosolic inorganic phosphate pool.

This is a very consistent and well-written manuscript with a number of convincing experiments, where the authors use isolated vacuoles and cellular read-out systems to demonstrate the interplay of polyphosphate synthesis, hydrolysis, and release. The beauty of this system the authors present is the clear correlation between product inhibition and the role of Pho91 as a valve to release Pi to the cytosol to replenish the cytosolic pool. I find the paper overall an excellent fit and only have a few issues, including :

(1) Figure 3: The authors use in their assays 1 mM ZnCl2 or 1mM MgCl2. Is this concentration in the range of the vacuolar luminal ion concentration? Did they also test the effect of Ca2+, as this ion is also highly concentrated in the lumen?

(2) Regarding the concentration of 30 mM K-PI, did the authors also use higher and lower concentrations? I agree that there is inhibition by 30 mM, but they cannot derive conclusions on the luminal concentration if they use just one in their assay. A titration is necessary here.

(3) What are the consequences on vacuole morphology if the cells lack Pho91?

(4) Discussion: The authors do not refer to the effect of calcium, even though I would expect that the levels of the counterion should affect the phosphate metabolism. I would appreciate it if they would extend their discussion accordingly.

(5) I would appreciate a brief discussion on how phosphate sensing and control are done in human cells. Do they use a similar lysosomal buffer system?

Reviewer #2 (Public review):

Summary:

This manuscript presents a well-conceived and concise study that significantly advances our understanding of polyphosphate (polyP) metabolism and its role in cytosolic phosphate (Pi) homeostasis in a model unicellular eukaryote. The authors provide evidence that yeast vacuoles function as dynamic regulatory buffers for Pi homeostasis, integrating polyP synthesis, storage, and hydrolysis in response to cellular metabolic demands. The work is methodologically sound and offers valuable insights into the conserved mechanisms of phosphate regulation across eukaryotes.

Strengths:

The results demonstrate that the vacuolar transporter chaperone (VTC) complex, in conjunction with luminal polyphosphatases (Ppn1/Ppn2) and the Pi exporter Pho91, establishes a finely tuned feedback system that balances cytosolic Pi levels. Under Pi-replete conditions, inositol pyrophosphates (InsPPs) promote polyP synthesis and storage while inhibiting polyP hydrolysis, leading to vacuolar Pi accumulation.

Conversely, Pi scarcity triggers InsPP depletion, activating Pho91-mediated Pi export and polyP mobilization to sustain cytosolic phosphate levels. This regulatory circuit ensures metabolic flexibility, particularly during critical processes such as glycolysis, nucleotide synthesis, and cell cycle progression, where phosphate demand fluctuates dramatically.

From my viewpoint, one of the most important findings is the demonstration that vacuoles act as a rapidly accessible Pi reservoir, capable of switching between storage (as polyP) and release (as free Pi) in response to metabolic cues. The energetic cost of polyP synthesis-driven by ATP and the vacuolar proton gradient-highlights the evolutionary importance of this buffering system. The study also draws parallels between yeast vacuoles and acidocalcisomes in other eukaryotes, such as Trypanosoma and Chlamydomonas, suggesting a conserved role for these organelles in phosphate homeostasis.

Weaknesses:

While the manuscript is highly insightful, referring to yeast vacuoles as "acidocalcisome-like" may warrant further discussion. Canonical acidocalcisomes are structurally and chemically distinct (e.g., electron-dense, in most cases spherical, and not routinely subjected to morphological changes, and enriched with specific ions), whereas yeast vacuoles have well-established roles beyond phosphate storage. A comment on this terminology could strengthen the comparative analysis and avoid potential confusion in the field.

Reviewer #3 (Public review):

Bru et al. investigated how inorganic phosphate (Pi) is buffered in cells using S. cerevisiae as a model. Pi is stored in cells in the form of polyphosphates in acidocalcisomes. In S. cerevisiae, the vacuole, which is the yeast lysosome, also fulfills the function of Pi storage organelle. Therefore, yeast is an ideal system to study Pi storage and mobilization.

They can recapitulate in their previously established system, using isolated yeast vacuoles, findings from their own and other groups. They integrate the available data and propose a working model of feedback loops to control the level of Pi on the cellular level.

This is a solid study, in which the biological significance of their findings is not entirely clear. The data analysis and statistical significance need to be improved and included, respectively. The manuscript would have benefited from rigorously testing the model, which would also have increased the impact of the study.

Author response:

Reviewer #1 (Public review): 

The manuscript by Bru et al. focuses on the role of vacuoles as a phosphate buffering system for yeast cells. The authors describe here the crosstalk between the vacuole and the cytosol using a combination of in vitro analyses of vacuoles and in vivo assays. They show that the luminal polyphosphatases of the vacuole can hydrolyse polyphosphates to generate inorganic phosphate, yet they are inhibited by high concentrations. This balances the synthesis of polyphosphates against the inorganic phosphate pool. Their data further show that the Pho91 transporter provides a valve for the cytosol as it gets activated by a decline in inositol pyrophosphate levels. The authors thus demonstrate how the vacuole functions as a phosphate buffering system to maintain a constant cytosolic inorganic phosphate pool. 

This is a very consistent and well-written manuscript with a number of convincing experiments, where the authors use isolated vacuoles and cellular read-out systems to demonstrate the interplay of polyphosphate synthesis, hydrolysis, and release. The beauty of this system the authors present is the clear correlation between product inhibition and the role of Pho91 as a valve to release Pi to the cytosol to replenish the cytosolic pool. I find the paper overall an excellent fit and only have a few issues, including: 

(1) Figure 3: The authors use in their assays 1 mM ZnCl2 or 1mM MgCl2. Is this concentration in the range of the vacuolar luminal ion concentration? Did they also test the effect of Ca2+, as this ion is also highly concentrated in the lumen? 

The concentrations inside vacuoles can reach those values. However, given that polyP is a potent chelator of divalent metal ions, what would matter are the concentrations of free Zn²⁺ or Mg²⁺ inside the organelle. These are not known. This is not critical since we use those two conditions only as a convenient tool to differentiate Ppn1 and Ppn2 activity in vitro. In our initial characterisation of Ppn2 (10.1242/jcs.201061), we had also tested Mn, Co, Ca, Ni, Cu. Only Zn and Co supported activity. Ca did not. Andreeva et al. (10.1016/j.biochi.2019.06.001) reached similar conclusions and extended our results.

(2) Regarding the concentration of 30 mM K-PI, did the authors also use higher and lower concentrations? I agree that there is inhibition by 30 mM, but they cannot derive conclusions on the luminal concentration if they use just one in their assay. A titration is necessary here. 

The concentration of 30 mM was not arbitrarily chosen. It is the luminal P_i concentration that the vacuoles could reach through when they entered a plateau of luminal Pi. We consider this as an upper limit because polyP kept increasing which luminal P_i did not. Thus, there is in principle no physiological motivation for trying higher values. But we will probably add a titration to the revised version.

(3) What are the consequences on vacuole morphology if the cells lack Pho91? 

We had not observed significant abnormalities during a screen of the genome-wide deletion collection of yeast (10.1371/journal.pone.0054160)

(4) Discussion: The authors do not refer to the effect of calcium, even though I would expect that the levels of the counterion should affect the phosphate metabolism. I would appreciate it if they would extend their discussion accordingly. 

We will pick this up in the discussion. However, the situation is much more complex because major pools of counterions (up to hundreds of mM) are constituted by vacuolar lysine, arginine, polyamines, Mg, Zn etc. Their interplay with polyP is probably complex and worth to be treated in a dedicated project.

(5) I would appreciate a brief discussion on how phosphate sensing and control are done in human cells. Do they use a similar lysosomal buffer system? 

Mammalian cells have their Pi exporter XPR1 mainly on a lysosome-like compartment (10.1016/j.celrep.2024.114316). Whether and how it functions there for Pi export from the cytosol is not entirely clear. We will address this situation in the revision.

Reviewer #2 (Public review): 

Summary: 

This manuscript presents a well-conceived and concise study that significantly advances our understanding of polyphosphate (polyP) metabolism and its role in cytosolic phosphate (Pi) homeostasis in a model unicellular eukaryote. The authors provide evidence that yeast vacuoles function as dynamic regulatory buffers for Pi homeostasis, integrating polyP synthesis, storage, and hydrolysis in response to cellular metabolic demands. The work is methodologically sound and offers valuable insights into the conserved mechanisms of phosphate regulation across eukaryotes. 

Strengths: 

The results demonstrate that the vacuolar transporter chaperone (VTC) complex, in conjunction with luminal polyphosphatases (Ppn1/Ppn2) and the Pi exporter Pho91, establishes a finely tuned feedback system that balances cytosolic Pi levels. Under Pi-replete conditions, inositol pyrophosphates (InsPPs) promote polyP synthesis and storage while inhibiting polyP hydrolysis, leading to vacuolar Pi accumulation. 

Conversely, Pi scarcity triggers InsPP depletion, activating Pho91-mediated Pi export and polyP mobilization to sustain cytosolic phosphate levels. This regulatory circuit ensures metabolic flexibility, particularly during critical processes such as glycolysis, nucleotide synthesis, and cell cycle progression, where phosphate demand fluctuates dramatically. 

From my viewpoint, one of the most important findings is the demonstration that vacuoles act as a rapidly accessible Pi reservoir, capable of switching between storage (as polyP) and release (as free Pi) in response to metabolic cues. The energetic cost of polyP synthesis-driven by ATP and the vacuolar proton gradient-highlights the evolutionary importance of this buffering system. The study also draws parallels between yeast vacuoles and acidocalcisomes in other eukaryotes, such as Trypanosoma and Chlamydomonas, suggesting a conserved role for these organelles in phosphate homeostasis. 

Weaknesses: 

While the manuscript is highly insightful, referring to yeast vacuoles as "acidocalcisome-like" may warrant further discussion. Canonical acidocalcisomes are structurally and chemically distinct (e.g., electron-dense, in most cases spherical, and not routinely subjected to morphological changes, and enriched with specific ions), whereas yeast vacuoles have well-established roles beyond phosphate storage. A comment on this terminology could strengthen the comparative analysis and avoid potential confusion in the field. 

Yeast vacuoles show all major chemical features of acidocalcisomes. They are acidified, contain high concentrations of Ca, polyP (which make them electron-dense, too), other divalent ions, such as Mg, Zn, Mn etc, and high concentrations of basic amino acids. Thus, they clearly have an acidocalcisome-like character. In addition, they have hydrolytic, lysosome-like functions and, depending on the strain background, they can be larger than acidocalcisomes described e.g. in protists. We will elaborate this point, which is obvious to us but probably not to most readers, in the revised version.

Reviewer #3 (Public review): 

Bru et al. investigated how inorganic phosphate (Pi) is buffered in cells using S. cerevisiae as a model. Pi is stored in cells in the form of polyphosphates in acidocalcisomes. In S. cerevisiae, the vacuole, which is the yeast lysosome, also fulfills the function of Pi storage organelle. Therefore, yeast is an ideal system to study Pi storage and mobilization. 

They can recapitulate in their previously established system, using isolated yeast vacuoles, findings from their own and other groups. They integrate the available data and propose a working model of feedback loops to control the level of Pi on the cellular level. 

This is a solid study, in which the biological significance of their findings is not entirely clear. The data analysis and statistical significance need to be improved and included, respectively. The manuscript would have benefited from rigorously testing the model, which would also have increased the impact of the study.

It is not clear to us what the reviewer would see as a more rigorous test of the model.

eLife Assessment

This important study suggests that adolescent mice exhibit less accuracy than adult mice in a sound discrimination task when the sound frequencies are very similar. The evidence supporting this observation is solid and suggests that it arises from cognitive control differences between adolescent and adult mice. The adolescent period is largely understudied, despite its contribution to shaping the adult brain, which makes this study interesting for a broad range of neuroscientists.

Reviewer #1 (Public review):

Summary:

Praegel et al. explore the differences in learning an auditory discrimination task between adolescent and adult mice. Using freely-moving (Educage) and head-fixed paradigms, they compare behavioral performance and neuronal responses over the course of learning. The mice were initially trained for seven days on an easy pure frequency tone Go/No-go task (frequency difference of one octave), followed by seven days of a harder version (frequency difference of 0.25 octave). While adolescents and adults showed similar performance on the easy task, adults performed significantly better on the harder task. Quantifying the lick bias of both groups, the authors then argue that the difference in performance is not due to a difference in perception, but rather to a difference in cognitive control. The authors then used neuropixel recordings across 4 auditory cortical regions to quantify the neuronal activity related to the behavior. At the single cell level, the data shows earlier stimulus-related discrimination for adults compared to adolescents in both the easy and hard tasks. At the neuronal population level, adults displayed a higher decoding accuracy and lower onset latency in the hard task as compared to adolescents. Such differences were not only due to learning, but also to age as concluded from recordings in novice mice. After learning, neuronal tuning properties had changed in adults but not in adolescent. Overall, the differences between adolescent and adult neuronal data correlates with the behavior results in showing that learning a difficult task is more challenging for younger mice.

Strengths:

The behavioral task is well designed, with the comparison of easy and difficult tasks allowing for a refined conclusion regarding learning across age. The experiments with optogenetics and novice mice are completing the research question in a convincing way.

The analysis, including the systematic comparison of task performance across the two age groups, is most interesting and reveals differences in learning (or learning strategies?) that are compelling.

Neuronal recording during both behavioral training and passive sound exposure is particularly powerful, and allows interesting conclusions.

Weaknesses:

The weaknesses listed by this reviewer were addressed by adequate revisions.

Reviewer #2 (Public review):

Summary:

The authors aimed to find out how and how well adult and adolescent mice discriminate tones of different frequencies and whether there are differences in processing at the level of the auditory cortex that might explain differences in behavior between the two groups. Adolescent mice were found to be worse at sound frequency discrimination than adult mice. The performance difference between the groups was most pronounced when the sounds are close in frequency and thus difficult to distinguish and could, at least in part, be attributed to the younger mice' inability to withhold licking in no-go trials. By recording the activity of individual neurons in the auditory cortex when mice performed the task or were passively listening as well as in untrained mice the authors identified differences in the way that the adult and adolescent brains encode sounds and the animals' choice that could potentially contribute to the differences in behavior.

Strengths:

The study combines behavioural testing in freely-moving and head-fixed mice, optogenetic manipulation and high density electrophysiological recordings in behaving mice to address important open questions about age differences in sound-guided behavior and sound representation in the auditory cortex.

Weaknesses:

The weaknesses listed by this reviewer were addressed by adequate revisions.

Author response:

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

Reviewer #1 (Public review):

Summary:

Praegel et al. explore the differences in learning an auditory discrimination task between adolescent and adult mice. Using freely-moving (Educage) and head-fixed paradigms, they compare behavioral performance and neuronal responses over the course of learning. The mice were initially trained for seven days on an easy pure frequency tone Go/No-go task (frequency difference of one octave), followed by seven days of a harder version (frequency difference of 0.25 octave). While adolescents and adults showed similar performance on the easy task, adults performed significantly better on the harder task. Quantifying the lick bias of both groups, the authors then argue that the difference in performance is not due to a difference in perception, but rather to a difference in cognitive control. The authors then used neuropixel recordings across 4 auditory cortical regions to quantify the neuronal activity related to the behavior. At the single cell level, the data shows earlier stimulus-related discrimination for adults compared to adolescents in both the easy and hard tasks. At the neuronal population level, adults displayed a higher decoding accuracy and lower onset latency in the hard task as compared to adolescents. Such differences were not only due to learning, but also to age as concluded from recordings in novice mice. After learning, neuronal tuning properties had changed in adults but not in adolescent. Overall, the differences between adolescent and adult neuronal data correlates with the behavior results in showing that learning a difficult task is more challenging for younger mice.

Strengths:

The behavioral task is well designed, with the comparison of easy and difficult tasks allowing for a refined conclusion regarding learning across age. The experiments with optogenetics and novice mice are completing the research question in a convincing way.

The analysis, including the systematic comparison of task performance across the two age groups, is most interesting, and reveals differences in learning (or learning strategies?) that are compelling.

Neuronal recording during both behavioral training and passive sound exposure is particularly powerful, and allows interesting conclusions.

Weaknesses:

The presentation of the paper must be strengthened. Inconsistencies, missing information or confusing descriptions should be fixed.

We have carefully re-read the manuscript and reviewed it for inconsistencies. We made several corrections in the figures. For example, we removed redundant lines from violin plots and statistics, applied consistent labels, matched y- and x-limits of graphics, and adjusted labels. We also clarified descriptions of some experiment by adding explanations to the text.

The recording electrodes cover regions in the primary and secondary cortices. It is well known that these two regions process sounds quite differently (for example, one has tonotopy, the other not), and separating recordings from both regions is important to conclude anything about sound representations. The authors show that the conclusions are the same across regions for Figure 4, but is it also the case for the subsequent analysis? Comparing to the original manuscript, the authors have now done the analysis for AuDp and AUDv separately, and say that the differences are similar in both regions. The data however shows that this is not the case (Fig S7). And even if it were the case, how would it compatible with the published literature?

To address this and previous concerns about regional differences, the manuscript now includes 4 figures (4-1, 4-3, 6-2, 7-1) and 5 supplemental tables (3,4, 5, 6, 8) that explicitly compare results across brain regions.

Following the reviewer’s request for subsequent analysis, we now added a new supplemental figure (Fig. S6-2) and two new supplementary tables (Tables S5, S6). We show that similar to expert mice (supplementary Table 3, and supplementary Table 4), the firing properties of adolescent and adult novice mice differ across auditory subregions (supplementary Table 5). We also show that the different auditory subregions have different firing properties (supplementary Table 6). With respect to task engagement, we show that (similar to Fig. S4-2) the neuronal discriminability in different auditory subregions is similar in both novice and expert mice (Fig. S6-2).

Following the comment on Fig. S7-1, we made three changes to the revised manuscript. First, we now highlight that the differences firing properties between adolescent and adult neurons in AUDp and AUDv were distinct, but not significantly different within age-group comparisons. Second, we clearly state that the learning related changes in the measured parameters are different between AUDp and AUDv. Note, however, the greater changes in adult neurons after learning remains consistent between AUDp and AUDv. Third, we softened our original claim but still highlighted the stronger learning-induced plasticity in adults.

Regarding the concern that different regions should show different patterns due to their known differences (e.g. tonotopy). Of course we agree that different areas differ functionally (as shown in our own previous work and here as well). However, it is still plausible, and biologically reasonable, that developmental changes may proceed in a similar direction across different areas, even if their baseline coding properties differ.

Reviewer #2 (Public review):

Summary:

The authors aimed to find out how and how well adult and adolescent mice discriminate tones of different frequencies and whether there are differences in processing at the level of the auditory cortex that might explain differences in behavior between the two groups. Adolescent mice were found to be worse at sound frequency discrimination than adult mice. The performance difference between the groups was most pronounced when the sounds are close in frequency and thus difficult to distinguish and could, at least in part, be attributed to the younger mice' inability to withhold licking in no-go trials. By recording the activity of individual neurons in the auditory cortex when mice performed the task or were passively listening as well as in untrained mice the authors identified differences in the way that the adult and adolescent brains encode sounds and the animals' choice that could potentially contribute to the differences in behavior.

Strengths:

The study combines behavioural testing in freely-moving and head-fixed mice, optogenetic manipulation and high density electrophysiological recordings in behaving mice to address important open questions about age differences in sound-guided behavior and sound representation in the auditory cortex.

Weaknesses:

For some of the analyses that the authors conducted it is unclear what the rationale behind them is and, consequently, what conclusion we can draw from them.

We have carefully re-read the manuscript and reviewed it for analyses that lacked a clear rationale or conclusion. To address this, we have made several changes to clarify the reasoning and strengthen the interpretation of the results.

Reviewer #1 (Recommendations for the authors):

It would have helped if the authors had highlighted the changes they made to the manuscript compared to the original version - especially since many replies to the reviewers' comments were as vague as "...we fixed some of the wording so it adheres to the data shown", or "we refined our interpretation", without further details.

The revised version has improved substantially, and the main claims have been discussed in a more objective way. Important new analyses have been added to allow for a refined interpretation of the results. However, the presentation of the data could still be strengthened significantly (in response to comment A from last review).

We apologize for the lack of detail in some of our previous responses. Our intention was to keep the replies concise, assuming that the side-by-side version with tracked changes would make the edits sufficiently clear. However, we understand the need for greater transparency. Thus, below we provide the following five lists describing the major changes: (1) List of specific reviewer recommendations, (2) list of corrections in figures, (3) list of clarity issues, (4) list of fixed mistakes, (5) list of new figures. We hope this breakdown makes the revisions clearer and more accessible.

List of specific reviewer recommendations:

l.108 mentions a significant change in the vertical line of Fig 1F - Could this significance be indicated and quantified in the figure?

We quantified and indicated the significance of the vertical line in Fig. 1f and Fig. 1i.

Fig.1G - the thick and thin lines should be defined, as well as the grey and white dots (same values for adolescents, not for adults).

(a) We removed the thin inner lines from the violin plot. We define the bar (thick line) of the violin plot in an additional sentence in the methods section under data analysis (LL820-823). b) We adjusted the marker outlines in the adult data (Fig. 1G).

the figure axis legends should be consistent (trails in Fig D vs # trails in Fig 1F)

We adjusted the axis legend to # trials in Fig. 1D.

l.110: is d' always calculated based on the 100 last trials of a session, or is it just for Figure 1F? -etc...

d’ is always calculated based on the last 100 trials. To clarify this, we added a description in the methods section (L830).

List of corrections in the figures:

(1) We removed the internal lines from violin plots in throughout Fig. 1-7.

(2) We removed the underline of the statistics throughout Fig. 1-7.

(3) We consistently applied ‘adolescent’ and ‘adult’ figure labels and titles with lowercase letters throughout Fig. 1-7.

(4) We applied consistent labelling of ‘time (ms)’ throughout Fig. 1-7.

(5) We matched the size of dashed lines throughout Fig. 1-6.

(6) We adjusted the x-label of Fig. 1d, Fig. S-1-1 a, Fig. 3c, Fig. 3h-i, Fig, 4d to ‘# trials’.

(7) We removed the x-label of ‘Experimental Group’ from Fig. 1 to enhance consistency with other figures.

(8) We removed misaligned dots from the violin plots in Fig. 1g, Fig. 2f, Fig. 3f,g.

(9) We corrected the plot in Fig. S1-1b.

(10) We adjusted the y-limits of Fig. S1-1c to be consistent with Fig. S1-1d,e.

(11) We adjusted the x-labels and y-labels of Fig, 2, Fig. S3-1, Fig, S3-2 and Fig. 3b to ‘freq. (kHz)’.

(12) We added the age of adolescent and adult mice to the schematic timeline in Fig. 2a.

(13) We added a label of the reinforcement delay to the schematic trial structure in Fig. 3b.

(14) We added within-group statistics to Fig. 3e and the figure legend.

(15) We adjusted the x-label of Fig. 3d to ‘# sessions’.

(16) We adjusted the x-label of Fig. 3d and Fig. S3-1b to ‘# licks’.

(17) We changed the y-label in Fig. S3-1a, and Fig. S3-2d, e to ‘lick ratio’ to avoid confusion with the lick rate (Hz) that was calculated in Fig. 4 and Fig. 6.

(18) We replaced the titles ‘CAMKII’ with ‘dTomato’ in Fig. S3-2 to correctly highlight that both the experimental and control injection were CAMKII injections.

(19) We adjusted the x-labels and y-labels of Fig, 2, Fig. S3-1, Fig, S3-2 and Fig. 3b to ‘freq. (kHz)’.

(20) We adjusted the y-label of Fig. S4-1c to ‘# neurons’.

(21) We matched the x-ticks in Fig. 4e,f.

(22) We matched the x-ticks in Fig. 6d-g.

(23) We changed the x-label in Fig. 4g, S4-2 and S6-2 to ‘duration (ms)’ to match the figure label with the manuscript.

(24) We consistently label ‘Hit’, ‘Miss’, ‘FA’ and ‘CR’ with capital letters in Fig. 4d-e.

(25) We replaced the double figure label ‘C.’ in Fig. S4-2 with ‘D.’.

(26) We adjusted the dot-size in Fig. 5 to be equal for all graphs.

(27) We added ticks to the experimental timeline in Fig. 6a.

(28) We corrected the y-label in Fig.7c. Now it correctly reflects 5 attenuations from 72-32 dB SPL.

(29) We matched the y-label of Fig. 7e-h and Fig. S7-1.

List of clarity issues:

(1) We replaced the term ‘lower response bias’ with ‘higher lick bias’ (L24) to accurately describe the more negative (lower) criterion-bias, which highlights a higher tendency to lick.

(2) We replaced the term ‘response bias’ with ‘lick bias’ to consistently describe the calculated criterion-bias (L24, L149, L164, L455, L456, L468).

(3) We clarify that the age-related differences were ‘more pronounced’ instead of simply ‘higher’ to accurately reflect not simply the increase in adolescent lick-bias, but also the decrease in adult lick-bias (L31).

(4) We clarified that adolescent sound representations are not merely ’distinct’, but ‘not fully mature’ in L83.

(5) We clarified in L180 that the impulsive responses we observed in adolescent mice could be related to being ‘less impacted by punishments’.

(6) We clarified the differences in firing properties of auditory sub-regions analyzed in Supplementary Table 3 (L287-295).

(7) We explained and clarified the reference to Fig. 3j (LL252-253).

(8) We added statistics to Fig.S4-2 to support our claim that there are no differences in the onset-latency, duration of discriminability and maximal discriminability between different sub-regions within age-groups (LL 314-315).

(9) We expanded our explanation of the results in Table 3 (LL370-379).

(10) We separated the reference to Fig. 6b and Fig. 6c to clarify their meaning (LL358-361).

(11) We clarified the differences in basic firing properties during the FRA protocol in Fig. 7 (LL409-418).

(12) We expanded our explanation of the differences of the learning related firing properties in AUDp and AUDv of Fig. S7-1 (LL426-433).

(13) We changed the term ‘plasticity profiles’ to ‘learning related plasticity’ to further clarify our limitation that L5/6 and L2/3 may exhibit distinct learning related changes (L496).

(14) We changed the term ‘sluggish’ (L481) to ‘delayed’ to more precisely explain differences between adolescent and adult tuning properties.

(15) We clarified that the running d’ was calculated in bins of 25 trials, instead of ‘the last 25 trials’ (LL845-846).

List of fixed mistakes:

(1) We corrected and matched the age to more accurately reflect the age mice were recorded (P37-42 and P77-82).

(2) We corrected the attenuation range from 72-42 to 72-32 dB SPL to correctly reflect the 5 attenuations used in the protocol.

(3) We corrected the number of channels shown in the voltage trace from 10 to 11 (Fig. S4-1a)

(4) We corrected the number of neurons recorded in novice adolescent mice in the legend of Fig. 6 from 140 to 130 (Fig. 6b).

(5) We removed redundant, or double brackets, commas, dots, and semi-colons in the figure legends.

(6) We corrected the LME statistics Table 2.

List of new figures and tables:

(1) We added a new supplementary figure to accompany Figure 6. Specifically, Fig. S6-2, shows the interaction of the three measured discriminability properties (onset delay, duration of discriminability, and maximal discriminability) in novice compared to expert mice in the easy and hard task (Go compared to No Go). The figure compares the different auditory sub-regions (similar to Fig. S4-2). We show that the discriminability properties within different groups is not significantly different among the four different sub-regions.

(2) Supplementary Table 5: We compared the firing properties in different auditory subregions in novice mice, and found (similar to expert mice) that the firing properties differ between adult and adolescent mice across the four different sub-regions.

(3) Supplementary Table 6: We compared the firing properties between different subregions, separately for adolescent and adult novice mice. Similar to expert mice, we found that different auditory subregions differ in their auditory firing properties.

Reviewer #2 (Recommendations for the authors):

The authors largely addressed my suggestions.

Comparing hit vs correct rejection trials in the population decoding analysis (L313-314): The authors acknowledge that comparing these two trial types conflates choice and stimulus decoding but I am not convinced that the changes to the manuscript text make this clear enough to the reader.

Thank you for pointing this out. We have made additional revisions to clarify this, and other issues more explicitly, as follows:

(1) We have expanded the explanation of how our population decoding analysis conflates stimulus and choice, and we acknowledge the limitations of this approach in the Abstract (L28), the Results section (L324-326, LL367-370) and the Discussion (LL516-519).

(2) We replaced the analysis of impulsivity on the head-fixed task. Instead of analyzing all it is, we focus only on ITIs following FA trials (Fig. S3-1c,d). This is more consistent with the analysis in the Educage (Fig. S2-1), where we show that adolescents exhibit increased impulsivity after FA trials. We found a similar result for ITIs following FA trials in the head-fixed task.

(3) To provide complementary insight, we now further justify our use of the Fisher separation metric alongside decoding accuracy in Figure 5, with a clearer rationale provided in LL343-345

(4) We also clarified our reasoning for focusing on 62 dB SPL in the FRA-based analysis in LL400-403.

Dalam konteks ini, 'tos' merujuk pada ungkapan penghormatan yang diucapkan saat mengangkat gelas, biasanya selama acara sosial atau pertemuan keluarga. toast /toʊst/

kata benda Sebuah ungkapan penghormatan yang diucapkan ketika mengangkat gelas. Contoh: We all raised our glasses for a toast to the bride and groom. Kami semua mengangkat gelas untuk sebuah tos bagi pengantin baru.

Dalam konteks ini, 'bodoh' digunakan untuk menggambarkan seseorang yang kurang cerdas atau tidak memahami sesuatu, sehingga dianggap tidak layak untuk menjadi murid dalam sekte abadi. dumb /dʌm/

Adjektiva Kondisi atau sifat orang yang tidak memiliki kecerdasan atau tidak mampu berbicara. Contoh: He was too dumb to understand the instructions. Dia terlalu bodoh untuk memahami instruksi tersebut.

Dalam konteks ini, 'nilai' merujuk pada kualitas atau prestasi seseorang yang dapat dianggap sebagai kontribusi positif, meskipun yang bersangkutan tidak memiliki banyak prestasi yang mencolok.

merit /ˈmɛrɪt/

noun Kualitas atau prestasi yang patut dihargai atau dipertimbangkan. Contoh: His merit in the team was recognized by everyone. Nilai dia di tim diakui oleh semua orang.

Dalam konteks kalimat, 'cluelessly' menggambarkan keadaan di mana Tie Zhu tidak memiliki pemahaman atau informasi tentang situasi yang terjadi, dan 'bodoh-bodoh saja' menunjukkan ketidakpahaman atau kepolosan dalam pertanyaan yang ia ajukan. cluelessly /ˈkluː.les.li/

adverb tanpa pemahaman; dengan cara yang menunjukkan ketidakpahaman Contoh: He nodded cluelessly after hearing the instructions. Ia mengangguk bodoh-bodoh saja setelah mendengar instruksi.

Dalam konteks ini, 'memaksa' digunakan untuk menunjukkan bahwa orang tua Tie Zhu merasa didorong pergi oleh pria tua tersebut, menandakan rasa tidak hormat dan tekanan yang dirasakan. forced /fɔːrst/

adjektiva dilakukan dengan paksaan atau tekanan; tidak sukarela. Contoh: He was forced to leave the party. Dia dipaksa untuk meninggalkan pesta. kata kerja (past tense) memaksa seseorang untuk melakukan sesuatu, seringkali dengan cara yang tidak diinginkan. Contoh: They forced him to sign the contract. Mereka memaksanya untuk menandatangani kontrak.

Dalam konteks ini, 'bound' digunakan untuk menunjukkan bahwa ada sesuatu yang ditakdirkan atau pasti terjadi, dalam hal ini, bahwa Tie Zhu akan lebih baik daripada ayahnya. bound /baʊnd/

kata sifat ditakdirkan untuk menjadi Contoh: He is bound to succeed. Dia ditakdirkan untuk berhasil. kata kerja mengikat atau membatasi Contoh: The dog is bound in the yard. Anjing itu terikat di halaman.

Dalam konteks ini, 'kebanyakan' merujuk pada sebagian besar anggota keluarga Wang yang diundang ke pesta. most /moʊst/

adjektiva Sebagian terbesar dari sesuatu. Contoh: Most people enjoy the festival. Kebanyakan orang menikmati festival tersebut. kata ganti Bagian terbesar dari sesuatu. Contoh: Most of them left early. Kebanyakan dari mereka pergi lebih awal.

Dalam konteks ini, 'keberatan' merujuk pada tidak adanya penolakan atau bantahan dari Tie Zhu terhadap pernyataan bahwa dia dibesarkan di bawah pengawasan guru, sehingga menunjukkan penerimaan dan kebenaran pernyataan tersebut. objections /əbˈdʒɛkʃənz/

Kata benda Sesuatu yang menentang atau tidak setuju dengan suatu pernyataan atau situasi. Contoh: She raised her objections to the proposed plan. Dia mengajukan keberatannya terhadap rencana yang diusulkan.

Dalam konteks ini, 'kesedihan' menggambarkan perasaan berat yang dialami oleh ayah Tie Zhu akibat perlakuan buruk dari kerabat di masa lalu dan bagaimana kedatangan mereka sekarang membawa kebahagiaan menggantikan rasa sakit tersebut. sorrow /ˈsɔːroʊ/

kata benda perasaan sedih atau duka akibat kehilangan, kekecewaan, atau penderitaan. Contoh: He felt deep sorrow after the loss of his friend. Dia merasakan kesedihan yang mendalam setelah kehilangan temannya.

ignorant /ɪɡˈnɔːrənt/ tidak tahu\ Dalam konteks ini, 'tidak tahu' menunjukkan bahwa Tie Zhu tidak memiliki pengetahuan atau pemahaman tentang para immortal, meskipun ia menyadari pentingnya situasi tersebut berdasarkan ekspresi orang tuanya.

1. Adjektiva: Tidak memiliki pengetahuan atau informasi tentang sesuatu
2. He was ignorant of the rules of the game.
3. Ia tidak tahu aturan permainan.

sternly /ˈstɜːrnli/ dengan tegas\ Dalam konteks ini, 'dengan tegas' digunakan untuk menunjukkan cara yang serius dan kuat dalam menyampaikan pesan penting kepada Tie Zhu tentang tanggung jawabnya terhadap keluarganya.

1. Adverb: Dengan cara yang serius dan penuh ketegasan.
2. He spoke sternly to the children.
3. Dia berbicara dengan tegas kepada anak-anak.

heavily patted /ˈhɛvɪli ˈpætɪd/ menepuk dengan kuat\ Dalam konteks ini, 'menepuk dengan kuat' menunjukkan tindakan yang penuh emosi dan kekuatan, sebagai ungkapan dukungan atau keprihatinan yang mendalam.

1. verb: Menepuk dengan kuat, seringkali untuk menunjukkan dukungan atau penghiburan.
2. He heavily patted his friend on the back to comfort him.
3. Dia menepuk dengan kuat temannya di punggung untuk menghiburnya.

akan melakukan

Dalam konteks ini, 'about' digunakan untuk menunjukkan bahwa seseorang akan melakukan tindakan tertentu yang berkaitan dengan orang lain, dalam kasus ini adalah membungkuk sebagai tanda penghormatan. about /əˈbaʊt/

Preposisi Menyatakan topik atau subjek yang sedang dibicarakan. Contoh: They talked about the weather. Mereka berbicara tentang cuaca. Preposisi Digunakan untuk menunjukkan arah atau bagian dari suatu tindakan atau peristiwa. Contoh: She was about to leave. Dia akan pergi.

hesitantly /ˈhɛzɪtəntli/ ragu-ragu\ Dalam konteks ini, 'hesitantly' berarti Tie Zhu berbicara dengan ketidakpastian atau keraguan karena dia tidak memahami situasi yang sedang berlangsung.

1. Adverbia: Dengan ragu-ragu; dengan ketidakpastian atau keraguan.
2. She answered the question hesitantly.
3. Dia menjawab pertanyaan itu dengan ragu-ragu.

settled /ˈsɛtld/ diselesaikan\ Penggunaan 'diselesaikan' dalam konteks ini menunjukkan bahwa permasalahan atau situasi telah disepakati atau dijadikan final, memungkinkan untuk melanjutkan ke langkah berikutnya tanpa adanya perselisihan lebih lanjut.

1. adjektiva: telah ditentukan atau disepakati, dalam hal ini merujuk pada penyelesaian masalah.
2. The issue has been settled amicably.
3. Masalah telah diselesaikan dengan baik.

delighted /dɪˈlaɪtɪd/ senang\ Dalam konteks kalimat, 'senang' digunakan untuk menggambarkan perasaan bahagia dan puas yang dirasakan oleh ibu Tie Zhu. Hal ini menunjukkan rasa suka cita yang mendalam terhadap sesuatu yang terjadi.

1. adjective: sangat senang; merasa bahagia karena sesuatu yang menyenangkan
2. She was delighted by the surprise party.
3. Dia sangat senang dengan pesta kejutan itu.

spot /spɒt/ tempat\ Dalam konteks ini, 'tempat' merujuk pada posisi atau kesempatan yang dianggap sangat berharga untuk diisi oleh calon murid di secta immortal.

1. noun: Tempat atau posisi tertentu.
2. She found a nice spot to have a picnic.
3. Dia menemukan tempat yang bagus untuk piknik.
4. verb: Melihat atau menemukan sesuatu.
5. I spotted a deer in the woods.
6. Saya melihat seekor rusa di hutan.

pondered /ˈpɒndərd/ merenungkan\ Dalam konteks ini, 'merenungkan' menggambarkan tindakan berpikir secara mendalam dan serius, mencerminkan pergumulan batin sebelum memberikan pernyataan penting.

1. verb: berpikir dengan mendalam atau merenungkan sesuatu
2. He pondered the meaning of life.
3. Dia merenungkan makna hidup.

berlalu begitu saja

passed = berlalu.

passed by = berlalu begitu saja / terlewat tanpa sadar.

10 years have passed. → 10 tahun telah berlalu. (netral, pernyataan fakta)

10 years have passed by. → 10 tahun sudah lewat begitu saja. (lebih terasa “mengalir / cepat hilang tanpa terasa”)

🔹 pass (tanpa “by”) = melewati / lulus / menyerahkan → banyak arti tergantung konteks.

Contoh:

I passed the exam. → aku lulus ujian.

Pass me the salt, please. → tolong operin garam.

The car passed the bus. → mobil itu menyalip/melewati bus.

🔹 pass by (phrasal verb) = lewat, berlalu (tanpa interaksi, cuma numpang lewat).

Contoh:

The car passed by quickly. → mobil itu melintas/berlalu dengan cepat.

Ten years passed by so fast. → sepuluh tahun berlalu begitu cepat.

🔑 Bedanya gampang: pass = bisa banyak makna (lulus, ngoper, nyalip, melewati).

pass by = khusus: lewat / berlalu / melintas.

noun Bagian atas tubuh manusia atau hewan yang berisi otak, mata, mulut, dan organ sensorik lainnya. Contoh: He nodded his head in agreement. Dia menganggukkan kepalanya sebagai tanda setuju. verb Bergerak atau pergi ke suatu tempat. Contoh: They will head to the meeting after lunch. Mereka akan pergi ke rapat setelah makan siang.

mengandung Dalam konteks ini, 'mengandung' digunakan untuk menunjukkan bahwa Tie Zhu tidak dapat menahan kegembiraannya saat melihat buku-buku. contain /kənˈteɪn/

verb memiliki sesuatu di dalamnya; mengandung Contoh: The box contains old toys. Kotak itu mengandung mainan lama. verb membatasi atau menahan emosi atau perasaan Contoh: She could not contain her laughter. Dia tidak dapat menahan tawanya.

kerutan

De toepassing van intelligente systemen in het hoger onderwijs is een actief onderzoeksgebied, gedreven door de overvloed aan beschikbare data en de urgentie om effectieve, datagestuurde strategieën te definiëren om uitval van studenten tegen te gaan en hun studieprestaties te verbeteren. Dit werk past machine learning-technieken toe om voorspellingsmodellen te ontwikkelen die kunnen bijdragen aan de vroege detectie van studenten die het risico lopen hun studie af te breken of hun diploma niet op tijd af te ronden. Het evalueert ook het beste moment om de voorspelling uit te voeren in het inschrijvingsjaar van de student. De modellen zijn gebaseerd op data van bachelorstudenten van een Polytechnische Universiteit in Portugal, ingeschreven tussen 2009 en 2017, en bevatten academische, sociaal-demografische en macro-economische informatie in drie verschillende fasen van het eerste studiejaar van de studenten. Vijf machine learning-algoritmen worden gebruikt om voorspellingsmodellen in elke fase te trainen, en de meest relevante kenmerken voor de best presterende modellen worden geïdentificeerd. De resultaten tonen aan dat de beste modellen gebruikmaken van Random Forest, waarbij strategieën worden toegepast om om te gaan met de onevenwichtige aard van de data, of waarbij dergelijke strategieën op dataniveau worden toegepast. De beste resultaten worden behaald aan het einde van het eerste semester, wanneer er al enige informatie beschikbaar is over de studieprestaties na inschrijving. De algehele resultaten zijn redelijk vergelijkbaar met die van vergelijkbare studies die zich richten op de vroege voorspelling van uitval of studieprestaties van studenten.

eLife Assessment

This study presents a valuable finding on the representational structure of task encoding in the prefrontal cortex. The evidence supporting the claims of the authors is solid, representing an impressive data collection effort and best-practice fMRI analyses. However, at least including visual regions as a control and controlling for behavioral differences in the task in representation analyses would have strengthened the study. The work will be of interest to cognitive neuroscientists interested in the neural basis of cognitive control.

Reviewer #1 (Public review):

Summary:

Bhandari and colleagues present tour-de-force analyses that compare the representational geometry in the lateral prefrontal cortex and primary auditory cortex between two complex cognitive control tasks, with one having a "flat" structure where subjects are asked to form rote memory of all the stimulus-action mappings in the task and one having a "hierarchical" task structure that allows clustering of task conditions and that renders certain stimulus dimensions irrelevant for choices. They discovered that the lPFC geometry is high-dimensional in nature in that it allows above-chance separation between different dichotomies of task conditions. The separability is significantly higher for task-relevant features than task-irrelevant ones. They also found task features that are represented in an "abstract" format (e.g., audio features), i.e., the neural representation generalizes across specific task conditions that share this variable. The neural patterns in lPFC are highly relevant for behaviors as they are correlated with subjects' reaction times and choices.

Strengths:

Typically, geometry in coding patterns is reflected in single-unit firings; this manuscript demonstrates that such geometry can be recovered using fMRI BOLD signals, which is both surprising and important. The tasks are well designed and powerful in revealing the differences in neural geometry, and analyses are all done in a rigorous way. I am thus very enthusiastic about this paper and identify no major issues.

I am curious about the consequence of dimensionality collapse in lPFC. The authors propose a very interesting idea that separability is critical for cognitive control; indeed, separability is high for task-relevant information. What happens when task-relevant separation is low or task-irrelevant separation is high, and will this lead to behavioral errors? Maybe a difference score between the separability of task-relevant and task-irrelevant features is a signature of the strength of cognitive control?

Weaknesses:

The authors show a difference between flat and hierarchical tasks, but the two tasks are different in accuracy, with the flat task having more errors. Will this difference in task difficulty/errors contribute to the task differences in results reported?

Reviewer #2 (Public review):

Summary:

The authors study the influence of tasks on the representational geometry of the lPFC and auditory cortex (AC). In particular, they use two context-dependent tasks: a task with a hierarchical structure and a task with a flat structure, in which each context/stimulus maps to a specific response. Their primary finding is that the representational geometry in the lPFC, in contrast to AC, aligns with the optimal organization of the task. They conclude that the geometry of representations adapts, or is tailored, to the task in the lPFC, therefore supporting control processes.

Strengths:

(1) Dataset:<br /> The dataset is impressive and well-sampled. Having data from both tasks collected in the same subjects is a great property. If it is publicly available, it will be a significant contribution to the community.

(2) Choice of methods:<br /> The choice of analyses are largely well-suited towards the questions at hand - cross-condition generalization, RSA + regression, in combination with ANOVAs, are well-suited to characterizing task representations.

(3) I found some of their results, in particular, those presented in Figures 4 and 5, to be particularly compelling.

(4) The correlation analysis with behavior is also a nice result.

Weaknesses:

(1) Choice of ROIs:<br /> A strength of fMRI is its spatial coverage of the whole brain. In this study, however, the authors focus on only two ROIs: the lPFC and auditory cortex. Though I understand the justification for choosing lPFC from decades of research, the choice of AC as a control feels somewhat arbitrary - AC is known to have worse SNR in fMRI data, and limiting a 'control' to a single region seems arbitrary. For example, why not also include visual regions, given that the task also involves two visual features?

(2) Construction of ROIs:<br /> The choice and construction of the ROIs feel a bit arbitrary, as the lPFC region was constructed out of 10 parcels from Schaefer, while the AC was constructed from a different methodology (neurosynth). Did both parcels have the same number of voxels/vertices? It would be helpful to include a visualization of these masks as a figure.

(3) Task dimensionality:<br /> In some ways, the main findings - that representation dimensionality is tailored to the task - seem to obviously follow from the choice of two tasks, particularly from a normative modeling perspective. For example, the flat task is effectively a memorization task, and is incompressible in the sense that there are no heuristics to solve it. In contrast, the hierarchical task can have several strategies, an uncompressed (memorized) strategy, and a compressed strategy. This is analogous to other studies evaluating representations during 'rich' vs. 'lazy'/kernel learning in ANNs. However, it seems unlikely (if not impossible) to form a 'rich' representation in the flat task. Posed another way, the flat task will always necessarily have a higher dimensionality than the hierarchical task. Thus, is their hypothesis - that representational geometry is tailored to the task - actually falsifiable? I understand the authors posit alternative hypotheses, e.g., "a fully compressed global axis with no separation among individual stimulus inputs could support responding [in the flat task]" (p. 36). But is this a realistic outcome, for example, in the space of all possible computational models performing this task? I understand that directly addressing this comment is challenging (without additional data collection or modeling work), but perhaps some additional discussion around this would be helpful.

(4) Related to the above:<br /> The authors have a section on p. 27: "Local structure of lPFC representational geometry of the flat task shows high separability with no evidence for abstraction" - I understand a generalization analysis can be done in the feature space, but in practice, the fact that the flat task doubles as a memorization task implies that there are no useful abstractions, so it seems to trivially follow that there would be no abstract representations. In fact, the use of task abstractions in the stimulus space would be detrimental to task performance here. I could understand the use of this analysis as a control, but the phrasing of this section seems to indicate that this is a surprising result.

(5) Statistical inferences:<br /> Throughout the manuscript, the authors appear to conflate failure to reject the null with acceptance of the null. For example, p. 24: "However, unlike left lPFC, paired t-tests showed no reliable difference in the separability of the task-relevant features vs the orthogonal, task-irrelevant features... Therefore, the overall separability of pAC representations is not shaped by either task-relevance of task structure."

Reviewer #3 (Public review):

Summary:

In this paper, Bhandari, Keglovits, et al. explore the representational structure of task encoding in the lateral prefrontal cortex. Through an impressive fMRI data-collection effort, they compare and contrast neural representations across tasks with different high-level stimulus-response structures. They find that the lateral prefrontal cortex shows enhanced encoding of task-relevant information, but that most of these representations do not generalize across conditions (i.e., have low abstraction). This appears to be driven in part by the representation of task conditions being clustered by the higher-order task properties ('global' representations), with poor generalization across these clusters ('local' representations). Overall, this paper provides an interesting account of how task representations are encoded in the PFC.

Strengths:

(1) Impressive dataset, which may provide further opportunities for investigating prefrontal representations.

(2) Clever task design, allowing the authors to confound several features within a complex paradigm.

(3) Best-practice analysis for decoding, similarity analyses, and assessments of representational geometry.

(4) Extensive analyses to quantify the structure of PFC task representations.

Weaknesses:

(1) The paper would benefit from improved presentational clarity: more scaffolding of design and analysis decisions, clearer grounding to understand the high-level interpretations of the analyses (e.g., context, cluster, abstraction), and better visualizations of the key findings.

(2) The paper would benefit from stronger theoretical motivation for the experimental design, as well as a refined discussion on the implications of these findings for theories of cognitive control.

Author response:

We thank the reviewers and editors for their careful and constructive assessment of our manuscript. We have provided a provisional response to the eLife assessment and the reviewer’s public comments below, addressing their main concerns and outlining our planned revisions that we believe will substantially strengthen our paper.  

eLife Assessment

This study presents a valuable finding on the representational structure of task encoding in the prefrontal cortex. The evidence supporting the claims of the authors is solid, representing an impressive data collection effort and best-practice fMRI analyses. However, at least including visual regions as a control and controlling for behavioral differences in the task in representation analyses would have strengthened the study. The work will be of interest to cognitive neuroscientists interested in the neural basis of cognitive control.

We plan to address both specific methodological weaknesses mentioned in the assessment in our forthcoming revision. First, the revision will include analyses of an early visual cortex ROI as an additional control region, allowing us to test whether the primary auditory cortex findings generalize to the sensory cortex across input modalities. Preliminary results indicate that the early visual cortex ROI exhibits a similar pattern of results, with evidence for coding both task-relevant and task-irrelevant visual dimensions across both tasks, as well as the context dimension specifically in the hierarchy task. Second, we will include behavioral performance as a covariate for the relevant statistical comparison across tasks to mitigate concerns over performance-related confounds. In addition, we will include a set of control analyses that demonstrate that equating the amount of data for pattern analyses across the two tasks by subsampling from the hierarchy task, while reducing our overall power, does not appreciably alter our results. We note that our analyses of representational geometries relied only on neural data from correct trials and, in the first-level modelling of the fMRI data, already controlled for differences in trial-by-trial response times. Therefore, our analyses of decoding and representation similarity are not directly affected by differences in performance across the two tasks. Finally, we have provided clarifications regarding Reviewer 2’s questions about the size and construction of the regions of interest employed in the study, as well as about the language employed to discuss null results.  

Public Reviews:

Reviewer #1 (Public review):

Summary:

Bhandari and colleagues present tour-de-force analyses that compare the representational geometry in the lateral prefrontal cortex and primary auditory cortex between two complex cognitive control tasks, with one having a "flat" structure where subjects are asked to form rote memory of all the stimulus-action mappings in the task and one having a "hierarchical" task structure that allows clustering of task conditions and that renders certain stimulus dimensions irrelevant for choices. They discovered that the lPFC geometry is high-dimensional in nature in that it allows above-chance separation between different dichotomies of task conditions. The separability is significantly higher for task-relevant features than task-irrelevant ones. They also found task features that are represented in an "abstract" format (e.g., audio features), i.e., the neural representation generalizes across specific task conditions that share this variable. The neural patterns in lPFC are highly relevant for behaviors as they are correlated with subjects' reaction times and choices.

Strengths:

Typically, geometry in coding patterns is reflected in single-unit firings; this manuscript demonstrates that such geometry can be recovered using fMRI BOLD signals, which is both surprising and important. The tasks are well designed and powerful in revealing the differences in neural geometry, and analyses are all done in a rigorous way. I am thus very enthusiastic about this paper and identify no major issues.

I am curious about the consequence of dimensionality collapse in lPFC. The authors propose a very interesting idea that separability is critical for cognitive control; indeed, separability is high for task-relevant information. What happens when task-relevant separation is low or task-irrelevant separation is high, and will this lead to behavioral errors? Maybe a difference score between the separability of task-relevant and taskirrelevant features is a signature of the strength of cognitive control?

We appreciate the reviewers’ positive evaluation of our paper.

Weaknesses:

The authors show a difference between flat and hierarchical tasks, but the two tasks are different in accuracy, with the flat task having more errors. Will this difference in task difficulty/errors contribute to the task differences in results reported?

To address the Reviewer’s concern about the difference in behavioural performance between the two tasks influencing our results, we will take several approaches. First, we will include behavioral performance as a covariate for the relevant statistical comparison across tasks. This should ensure that any differences we observe across tasks are over and above those that can be explained by the difference in behavioral performance. Second, we will include a set of decoding analyses that control for differences in performance across the tasks. We note that all our analyses of representational geometries relied on neural data from correct trials only. In addition, the first-level modelling of the fMRI data already controlled for trial-by-trial variability in response times. Therefore, our decoding and representation similarity analyses should not directly be affected by differences in performance across the two tasks. However, one possible issue with this approach is that the larger number of errors in the flat task means that less data was available for estimating multivoxel patterns in the flat task compared to the hierarchy task, resulting in differential power to detect decoding effects across the two tasks. We note that the on average, this difference was not substantial: on average, 21.7 runs were available per participant for the flat task, while 23.8 runs per participant were available for the hierarchy task. Moreover, rerunning our analyses with the number of runs equated for each participant does not meaningfully alter the pattern of results. These additional analyses will be included in the supplement in the forthcoming revised manuscript.  

Reviewer #2 (Public review):

Summary:

The authors study the influence of tasks on the representational geometry of the lPFC and auditory cortex (AC). In particular, they use two context-dependent tasks: a task with a hierarchical structure and a task with a flat structure, in which each context/stimulus maps to a specific response. Their primary finding is that the representational geometry in the lPFC, in contrast to AC, aligns with the optimal organization of the task. They conclude that the geometry of representations adapts, or is tailored, to the task in the lPFC, therefore supporting control processes.

Strengths:

(1) Dataset:

The dataset is impressive and well-sampled. Having data from both tasks collected in the same subjects is a great property. If it is publicly available, it will be a significant contribution to the community.

(2) Choice of methods:

The choice of analyses are largely well-suited towards the questions at hand - crosscondition generalization, RSA + regression, in combination with ANOVAs, are well-suited to characterizing task representations.

(3) I found some of their results, in particular, those presented in Figures 4 and 5, to be particularly compelling.

(4) The correlation analysis with behavior is also a nice result.

We thank the reviewer for noting the strengths of the paper. We respond to the weaknesses noted below. 

Weaknesses:

(1) Choice of ROIs:

A strength of fMRI is its spatial coverage of the whole brain. In this study, however, the authors focus on only two ROIs: the lPFC and auditory cortex. Though I understand the justification for choosing lPFC from decades of research, the choice of AC as a control feels somewhat arbitrary - AC is known to have worse SNR in fMRI data, and limiting a 'control' to a single region seems arbitrary. For example, why not also include visual regions, given that the task also involves two visual features?

We agree with the reviewer that the whole-brain fMRI data certainly provide ample opportunities to explore the nature of these representations across the brain. Our focus in this paper is squarely on the principles of coding and flexibility in the lPFC. We believe that a whole-brain exploration addresses a separate question that would be out of the scope of this study. To clarify, we are not arguing that the lPFC is the only region in the brain that employs the coding principles that our study brings to light. Our contention is only that lPFC employs these principles, and it differs at least from the primary sensory cortex. The questions of whether these principles generalize beyond lPFC (quite likely) and, if so, how broadly, are distinct from the ones addressed in the manuscript. We intend to follow up with another manuscript that addresses these questions.

Nevertheless, given the focus of this paper, we agree that a second control region, which allows one to test if the primary auditory cortex findings generalize to the sensory cortex more broadly, would strengthen our claims. We will include an early visual cortex ROI in our forthcoming revision. Preliminary results indicate that the early visual cortex ROI shows a similar set of findings – with evidence for coding of task-relevant and taskirrelevant visual dimensions across both tasks, but also specifically the context dimension in the hierarchy task. These results will be detailed in the forthcoming revision

(2) Construction of ROIs:

The choice and construction of the ROIs feel a bit arbitrary, as the lPFC region was constructed out of 10 parcels from Schaefer, while the AC was constructed from a different methodology (neurosynth). Did both parcels have the same number of voxels/vertices? It would be helpful to include a visualization of these masks as a figure.

We defined the lPFC ROIs by selecting Schaefer parcels in the frontal lobe that were previously mapped onto the Control A resting state network identified by Yeo et al. (2011). This network aligns with the multiple-demand network, which has also been identified in the macaque, where it includes the lPFC regions that abut the principal sulcus. Prior results from these regions in the monkey brain provide the scientific premise for our hypotheses. The two lPFC ROIs in each hemisphere were constructed out of 5 Schaefer parcels in each hemisphere. These parcels cluster into the same functional network and tend to behave similarly in univariate analyses. Given that our hypotheses do not distinguish between the different parcels, we elected to improve power by merging them into left and right dlPFC ROIs. 

On the other hand, the same approach could not be used to identify the primary auditory cortex. As Yeo et al. noted in their paper, the 17 resting state networks they identify did not adequately parcellate somatomotor and auditory cortices into distinct networks, likely due to their proximity (see Fig 14 and related text in Yeo et al. (2011)). We therefore relied on a different approach to define the primary auditory cortex, using an association test in Neurosynth to obtain a map of regions associated with the term “primary auditory”. In the revised manuscript, we will also include a primary auditory cortex ROI, defined again using a term-based association test in Neurosynth.

Our lPFC ROIs and pAC ROIs are of similar size. In the left hemisphere, the lPFC ROI (constructed from merging Schaefer parcels 128-thru-132) has, on average, 624.55 voxels. The left pAC ROI (defined with Neurosynth) has, on average, 628 voxels. In the right hemisphere, the lPFC ROI (constructed from merging Schaefer parcels 330-thru334), has 470.8 voxels on average. The right pAC ROI has, on average, 568 voxels. A table reporting the size of our parcels and ROIs was included in the supplement. In our forthcoming revision, we will additionally include a supplementary figure visualizing the ROI masks. 

(3) Task dimensionality:

In some ways, the main findings - that representation dimensionality is tailored to the task - seem to obviously follow from the choice of two tasks, particularly from a normative modeling perspective. For example, the flat task is effectively a memorization task, and is incompressible in the sense that there are no heuristics to solve it. In contrast, the hierarchical task can have several strategies, an uncompressed (memorized) strategy, and a compressed strategy. This is analogous to other studies evaluating representations during 'rich' vs. 'lazy'/kernel learning in ANNs. However, it seems unlikely (if not impossible) to form a 'rich' representation in the flat task. Posed another way, the flat task will always necessarily have a higher dimensionality than the hierarchical task. Thus, is their hypothesis - that representational geometry is tailored to the task - actually falsifiable? I understand the authors posit alternative hypotheses, e.g., "a fully compressed global axis with no separation among individual stimulus inputs could support responding [in the flat task]" (p. 36). But is this a realistic outcome, for example, in the space of all possible computational models performing this task? I understand that directly addressing this comment is challenging (without additional data collection or modeling work), but perhaps some additional discussion around this would be helpful.

We thank the reviewer for this comment, which gives us a chance to clarify our argument.

As noted by the reviewer, whether a network takes advantage of the compressibility of a task depends on its learning regime (i.e. rich vs lazy). One way to frame our question regarding the lPFC’s coding strategy, then, is to ask whether it operates in a rich or a lazy learning regime (which would predict, respectively, task-tailored vs task-agnostic representations). The reviewer’s concern is that the two task structures we employed are differentially compressible, and therefore, it is inevitable that we observe tailored representations and therefore, our hypotheses are not falsifiable.

First, it is important to clarify the theoretical premise behind our design and how it relates logically to our hypotheses. Under a lazy learning regime, a network would encode highdimensional representations of both tasks, regardless of their compressibility. On the other hand, under a rich learning regime, representational dimensionality will likely be shaped by the tasks’ structure. If the two tasks differ in their compressibility, only in the rich learning regime would the network learn representations of different dimensionality. Therefore, observing representations with dimensionality tailored to the task structure rules out the possibility that the lPFC is operating in a lazy regime. Therefore, the hypotheses are certainly testable.

The second point of clarification is that, contrary to the reviewer’s assertion, the flat task is, in fact, compressible – the task can be solved with a categorical representation of the response categories, with no sensitivity to the different specific stimuli within each category. Indeed, it is possible to train a simple, three-layer feedforward artificial neural network to perform the flat task perfectly with only 2 units in the hidden layer, demonstrating this compressibility. While we agree with the reviewer that in the space of all possible architectures one might consider the two tasks may differ in compressibility, particularly at the local levels, as we noted above, this does not imply that our hypotheses are not testable.

Finally, as a third point of clarification, our focus in this paper is on understanding the nature of coding in the lPFC in particular. Arguments based on a normative modelling perspective properly apply to the representations learned by an agent (such as an ANN or a human) as a whole. In a minimal feedforward ANN with a single hidden layer trained in a regime which encourages compression (i.e. a rich learning regime), it would indeed be the case that the representational dimensionality in that hidden layer would be higher for less compressible tasks. However, when applied to humans, such an argument applies to the brain as a whole rather than to an individual region of the brain like the lPFC. As such, it is less straightforward to predict how a single region might represent a task without additional information about the region’s inputs, outputs and broader position in a network. Even for a highly compressible task, a particular brain region may nevertheless be sensitive to all task dimensions. Conversely, even when a task is not compressible, a particular population within the brain may be invariant to some task features. For example, the primary auditory cortex is expected to be invariant to visual task dimensions.

Therefore, how a task is represented in the lPFC in particular (as opposed to the whole brain) depends on its computational function and coding principles, which remain debated. For instance, as some accounts (such as the guided activation theory) posit, if the primary function of the lPFC is to encode ‘context’ and shape downstream processing based on context, we might only expect to see the abstract coding of the auditory context in the hierarchy task (and, perhaps, the response categories across both tasks as they encode the ’context’ for the lower-level response decision), while being invariant to lowerlevel features of the input. In our paper, we specifically contrast two accounts of lPFC coding that have emerged in the literature – one positing that the lPFC learns a representation tailored to the structure of the task, and another that the lPFC encodes a high-dimensional representation that privileges sensitivity to many task features and their non-linear mixture at the cost of generalization. Regardless of the compressibility of the tasks in question, how the lPFC encodes the two tasks is an empirical question.

In our forthcoming revision, we will clarify these points in the discussion. We will also include the results of neural network simulations alluded to above.

(4) Related to the above:

The authors have a section on p. 27: "Local structure of lPFC representational geometry of the flat task shows high separability with no evidence for abstraction" - I understand a generalization analysis can be done in the feature space, but in practice, the fact that the flat task doubles as a memorization task implies that there are no useful abstractions, so it seems to trivially follow that there would be no abstract representations. In fact, the use of task abstractions in the stimulus space would be detrimental to task performance here. I could understand the use of this analysis as a control, but the phrasing of this section seems to indicate that this is a surprising result.

As explained above, there is no need for high local separability in the flat task. The lPFC could have completely abstracted over the individual trial-types that contributed to each response category, encoding only the response categories. Indeed, as also noted above, it is possible to train a simple, three-layer feedforward artificial neural network to perform the flat task perfectly with only 2 units in the hidden layer. The two hidden layer units code for each of the two response categories. 

(5) Statistical inferences:

Throughout the manuscript, the authors appear to conflate failure to reject the null with acceptance of the null. For example, p. 24: "However, unlike left lPFC, paired t-tests showed no reliable difference in the separability of the task-relevant features vs the orthogonal, task-irrelevant features... Therefore, the overall separability of pAC representations is not shaped by either task-relevance of task structure."

We thank the reviewer for pointing these out. These sentences will be corrected in the revision. For instance, the sentence above will be modified to “Therefore, we find no evidence that the overall separability of pAC representations is shaped by either taskrelevance or task structure.”

Reviewer #3 (Public review):

Summary:

In this paper, Bhandari, Keglovits, et al. explore the representational structure of task encoding in the lateral prefrontal cortex. Through an impressive fMRI data-collection effort, they compare and contrast neural representations across tasks with different highlevel stimulus-response structures. They find that the lateral prefrontal cortex shows enhanced encoding of task-relevant information, but that most of these representations do not generalize across conditions (i.e., have low abstraction). This appears to be driven in part by the representation of task conditions being clustered by the higher-order task properties ('global' representations), with poor generalization across these clusters ('local' representations). Overall, this paper provides an interesting account of how task representations are encoded in the PFC.

Strengths:

(1) Impressive dataset, which may provide further opportunities for investigating prefrontal representations.

(2) Clever task design, allowing the authors to confound several features within a complex paradigm.

(3) Best-practice analysis for decoding, similarity analyses, and assessments of representational geometry.

(4) Extensive analyses to quantify the structure of PFC task representations.

Weaknesses:

(1) The paper would benefit from improved presentational clarity: more scaffolding of design and analysis decisions, clearer grounding to understand the high-level interpretations of the analyses (e.g., context, cluster, abstraction), and better visualizations of the key findings.

(2) The paper would benefit from stronger theoretical motivation for the experimental design, as well as a refined discussion on the implications of these findings for theories of cognitive control.

We thank the reviewer for highlighting the strengths of our paper and their feedback on the writing. We have reviewed these helpful suggestions with an eye to which we may implement in our revision to improve clarity. Our forthcoming revision will 1) provide clearer scaffolding to aid the reader in understanding our design, analyses and our interpretation of the results 2) incorporate the MDS-based visualization of the representational geometries, which is currently presented in the Supplement, as a figure panel in the main text, 3) provide a justification for the particular task structures we picked in the introduction and 4) incorporate a new paragraph in the Discussion section to highlight the implications of our findings for cognitive control.

eLife Assessment

The study introduces new tools for measuring the intracellular calcium concentration close to transmitter release sites, which may be relevant for synaptic vesicle fusion and replenishment. This approach yields important new information about the spatial and temporal profile of calcium concentrations near the site of entry at the plasma membrane. This experimental work is complemented by a coherent, open-source, computational model that successfully describes changes in calcium domains. The conclusions are solid and well supported by the data.

Reviewer #1 (Public Review):

This paper describes technically impressive measurements of calcium signals near synaptic ribbons in zebrafish bipolar cells. The data presented provides high spatial and temporal resolution information about calcium concentrations along the ribbon at various distances from the site of entry at the plasma membrane. This is important information. The experiments appear to be well-done and provide strong evidence for the main conclusions reached.

Strengths

The technical aspects of the measurements are impressive. The authors use calcium indicators bound to the ribbon and high-speed line scans to resolve changes with a spatial resolution of ~250 nm and temporal resolution of less than 10 ms. These spatial and temporal scales are much closer to those relevant for vesicle release than previous measurements. Hence the results provide a unique window onto these events.

The use of calcium indicators with very different affinities and of different intracellular calcium buffers helps provide confirmation of key results.

Reviewer #2 (Public review):

Summary:

The study introduces new tools for measuring intracellular Ca2+ concentration gradients around retinal rod bipolar cell (rbc) synaptic ribbons. This is done by comparing the Ca2+ profiles measured with mobile Ca2+ indicator dyes versus ribbon-tethered (immobile) Ca2+ indicator dyes. The Ca2+ imaging results provide a straightforward demonstration of Ca2+ gradients around the ribbon and validate their experimental strategy. This experimental work is complemented by a coherent, open-source, computational model that successfully describes changes in Ca2+ domains as a function of Ca2+ buffering. In addition, the authors try to demonstrate that there is heterogeneity among synaptic ribbons within an individual rbc terminal.

Strengths:

The study introduces a new set of tools for estimating Ca2+ concentration gradients at ribbon AZs, and the experimental results are accompanied by an open-source, computational model that nicely describes Ca2+ buffering at the rbc synaptic ribbon. In addition, the dissociated retinal preparation remains a valuable approach for studying ribbon synapses. Lastly, excellent EM.

Comments on revisions:

Several concerns were raised about the kinetic analyses, and the authors have carefully acknowledged the critiques. The ideal outcome would have been a more complete kinetic readout and analyses (in particular a better readout of risetime would have improved the results). In the absence of a suitable readout of the risetime, the authors scaled back their claims and improved on the description of the falling phase of the signals. The authors have given a reasonable response under the circumstances.

In addition, the authors provided more context to their results.

I have no further concerns.

Reviewer #3 (Public review):

Summary:

In this study, the authors have developed a new Ca indicator conjugated to the peptide, which likely recognizes synaptic ribbons and have measured microdomain Ca near synaptic ribbons at retinal bipolar cells. This interesting approach allows one to measure Ca close to transmitter release sites, which may be relevant for synaptic vesicle fusion and replenishment. Though microdomain Ca at the active zone of ribbon synapses has been measured by Hudspeth and Moser, the new study uses the peptide recognizing synaptic ribbons, potentially measuring the Ca concentration relatively proximal to the release sites.

Strengths:

The study is, in principle, technically well done, and the peptide approach is technically interesting, which allows one to image Ca near the particular protein complexes. The approach is potentially applicable to other types of imaging.

Weaknesses:

Peptides may not be entirely specific, and genetic approach tagging particular active zone proteins with fluorescent Ca indicator proteins may well be more specific. The readers should be aware of this, when interpreting the results.

Author response:

The following is the authors’ response to the previous reviews

Reviewer #1 (Public review): 

This paper describes technically-impressive measurements of calcium signals near synaptic ribbons in goldfish bipolar cells. The data presented provides high spatial and temporal resolution information about calcium concentrations along the ribbon at various distances from the site of entry at the plasma membrane. This is important information. Important gaps in the data presented mean that the evidence for the main conclusions is currently inadequate. 

Strengths 

The technical aspects of the measurements are impressive. The authors use calcium indicators bound to the ribbon and high speed line scans to resolve changes with a spatial resolution of ~250 nm and temporal resolution of less than 10 ms. These spatial and temporal scales are much closer to those relevant for vesicle release than previous measurements. 

The use of calcium indicators with very different affinities and of different intracellular calcium buffers helps provide confirmation of key results. 

Thank you very much for this positive evaluation of our work.

Weaknesses 

Multiple key points of the paper lack a statistical test or summary data from populations of cells. For example, the text states that the proximal and distal calcium kinetics in Figure 2A differ. This is not clear from the inset to Figure 2A - where the traces look like scaled versions of each other. Values for time to half-maximal peak fluorescence are given for one example cell but no statistics or summary are provided. Figure 8 shows examples from one cell with no summary data. This issue comes up in other places as well. 

Thank you for this fair and valuable feedback. Following also the suggestion by the Editor, we have now removed the rise-time kinetic fitting results from the manuscript and only retain the bi-exponential decay time constant values. Further, we explicitly detail the issues with kinetic fitting, and state that the precise quantitative conclusions should not be drawn from the differences in kinetic parameters (pages 7 and 2728). 

We have included the results of paired-t-tests to compare the amplitudes of proximal vs. distal calcium signals shown in Fig. 2A & B, Fig. 3C & D, Fig. 4C & D, Fig. 5A-D, and Fig. 8E&F. Because proximal and distal calcium signals were obtained from the same ribbons within 500-nm distances, as the Reviewer pointed out, “the traces look like scaled versions of each other”. For experiments where we make comparisons across cells or different calcium indicators, as shown in Fig. 3E & F, Fig.5E, and Fig. 8B&C, we have included the results of an unpaired t-test. We have also included the t-test statistics information in the respective figure legends in the revised version.

In Figure 8, we have shown example fluorescence traces from two different cells at the bottom of the A panel, and example traces from different ribbons of RBC a in the D, and the summary data is described in B-C and E-F, with statistics provided in the figure legends.

The rise time measurements in Figure 2 are very different for low and high affinity indicators, but no explanation is given for this difference. Similarly, the measurements of peak calcium concentration in Figure 4 are very different with the two indicators. That might suggest that the high affinity indicator is strongly saturated, which raises concerns about whether that is impacting the kinetic measurements. 

Yes, we do believe that the high-affinity indicator is partially saturated, and therefore, the measurement with the low-affinity indicator dye is a more accurate reflection of the measured Ca²⁺ signal. We now state this more explicitly in the text. Further, we note that the rise time values are no longer listed due to lack of statistical significance for such comparisons, as noted above.

Reviewer #2 (Public review): 

Summary: 

The study introduces new tools for measuring intracellular Ca2+ concentration gradients around retinal rod bipolar cell (rbc) synaptic ribbons. This is done by comparing the Ca2+ profiles measured with mobile Ca2+ indicator dyes versus ribbon-tethered (immobile) Ca2+ indicator dyes. The Ca2+ imaging results provide a straightforward demonstration of Ca2+ gradients around the ribbon and validate their experimental strategy. This experimental work is complemented by a coherent, open-source, computational model that successfully describes changes in Ca2+ domains as a function of Ca2+ buffering. In addition, the authors try to demonstrate that there is heterogeneity among synaptic ribbons within an individual rbc terminal. 

Strengths: 

The study introduces a new set of tools for estimating Ca2+ concentration gradients at ribbon AZs, and the experimental results are accompanied by an open-source, computational model that nicely describes Ca2+ buffering at the rbc synaptic ribbon. In addition, the dissociated retinal preparation remains a valuable approach for studying ribbon synapses. Lastly, excellent EM. 

Thank you very much for this positive evaluation of our work.

Comments on revisions: 

Specific minor comments: 

(1) Rewrite the final sentence of the Abstract. It is difficult to understand. 

Thank you for pointing that out. We have updated the final sentence of the Abstract.

(2) Add a definition in the Introduction (and revisit in the Discussion) that delineates between micro- and nano-domain. A practical approach would be to round up and round down. If you round up from 0.6 um, then it is microdomain which means ~ 1 um or higher. Likewise, round down from 0.3 um to nanodomain? If you are using confocal, or even STED, the resolution for Ca imaging will be in the 100 to 300 nm range. The point of your study is that your new immobile Ca2-ribbon indicator may actually be operating on a tens of nm scale: nanophysiology. The Results are clearly written in a way that acknowledges this point but maybe make such a "definition" comment in the intro/discussion in order to: 1) demonstrate the power of the new Ca2+ indicator to resolve signals at the base of the ribbon (effectively nano), and 2) (Discussion) to acknowledge that some are achieving nanoscopic resolution (50 to 100nm?) with light microscopy (as you ref'd Neef et al., 2018 Nat Comm).  

Thank you for the valuable comments. We have now provided this information in the introduction and discussion.  

(3) Suggested reference: Grabner et al. 2022 (Sci Adv, Supp video 13, and Fig S5). Here rod Cav channels are shown to be expressed on both sides the ribbon, at its base, and they are within nanometers from other AZ proteins. This agrees with the conclusions from your imaging work.  

Thank you for the valuable suggestion. We have now provided this information in the introduction and discussion.

(4) In the Discussion, add a little more context to what is known about synaptic transmission in the outer and inner retina.. First, state that the postsynaptic receptors (for example: mGluR6-OnBCs vs KARs-OffBCs, vs. AMPAR-HCs), and possibly the synaptic cleft (ground squirrel), are known to have a significant impact on signaling in the outer retina. In the inner retina, there are many more unknowns. For example, when I think of the pioneering Palmer JPhysio study, which you sight, I think of NMDAR vs AMPAR, and uncertainty in what type postsynaptic cell was patched (GC or AC....). Once you have informed the reader that the postsynapse is known to have a significant impact on signaling, then promote your experimental work that addresses presynaptic processes: "...the new tool and results allow us to explore release heterogeneity, ribbon by ribbon in dissociated preps, which we eventually plan to use at ribbon synapses within slices......to better understand how the presynapse shapes signaling......". 

Thank you for the valuable comments. We have now provided this information in the introduction and discussion.

Reviewer #3 (Public review): 

Summary: 

In this study, the authors have developed a new Ca indicator conjugated to the peptide, which likely recognizes synaptic ribbons and have measured microdomain Ca near synaptic ribbons at retinal bipolar cells. This interesting approach allows one to measure Ca close to transmitter release sites, which may be relevant for synaptic vesicle fusion and replenishment. Though microdomain Ca at the active zone of ribbon synapses has been measured by Hudspeth and Moser, the new study uses the peptide recognizing synaptic ribbons, potentially measuring the Ca concentration relatively proximal to the release sites. 

Strengths: 

The study is, in principle, technically well done, and the peptide approach is technically interesting, which allows one to image Ca near the particular protein complexes. The approach is potentially applicable to other types of imaging. 

Thank you very much for this appreciation.

Weaknesses: 

Peptides may not be entirely specific, and genetic approach tagging particular active zone proteins with fluorescent Ca indicator proteins may well be more specific. Although the authors are aware of this and the peptide approach is generally used for ribbon synapses, the authors should be aware of this, when interpreting the results. 

We acknowledge the reviewer’s point and believe the peptides and genetic approaches to measure local calcium signals have their merits, each with separate advantages and disadvantages.  

Reviewer #1 (Recommendations for the authors): 

The revisions helped with some concerns about the original paper, but some issues were not adequately addressed. I have left two primary concerns in my public review. To summarize those: 

The difference in kinetics of proximal and distal locations is emphasized and quantified in the paper, but the quantification consists of a fit to the average responses. This does not give an idea of whether the difference observed is significant or not. Without an estimate of the error across measurements the difference in kinetic quoted is not interpretable. 

Thank you for this feedback. Since the kinetics information is a minor part of the manuscript, we have followed the Editor’s advice to significantly tone down the comparison of kinetic fit parameters (completely removing the rise-time comparisons), in order to put more focus on the better-documented conclusions. We also note that we did establish statistical significance of the differences in fluorescence signal amplitudes. 

Somewhat relatedly, the difference in amplitude and kinetics of the calcium signals measured with low and high affinity indicators is quite concerning. The authors added one sentence stating that the high affinity indicator might be saturated. This is not adequate. Should we distrust the measurements using the high affinity indicator? The differences between the results using the low and high affinity indicators is in some cases large - e.g. larger than the differences cited as a key result between distal and proximal locations. This issue needs to be dealt with directly in the paper. 

Thank you for this feedback. Yes, the measurements from high-affinity indicators cannot report the Ca2+ as accurately as low-affinity indicators. However, the value of HA indicators is in their ability to detect lowamplitude signals that lower-affinity indicators may miss due to lower signal-to-noise resolution.  We added a sentence on page 12 to further stress this point.

Related to the point about statistics, it is not clear how to related the horizontal lines in Figure 8 to the actual measurements. It is critical for the evaluation of the conclusions from that figure to understand what is plotted and what the error bars are on the plotted data. 

We apologize for the earlier ambiguity in Fig. 8. In this figure, we first compare proximal (panel B) and distal (panel C) calcium signals across several RBCs, labeled RBC-a through RBC-d. Each RBC contains multiple ribbons, and for each cell, we present the average calcium signals from multiple ribbons using box plots in panels B and C. In these box plots, the horizontal lines represent the average calcium signal for each cell, while the size of the error bars reflects the variability in proximal and distal calcium signals among the ribbons within that RBC.

For example, RBC-a had five identifiable ribbons. In panels D–F, we use RBC-a to illustrate the variability in calcium signals across individual ribbons. Specifically, we distinguished proximal and distal calcium signals from five ribbons (ribbons 1–5) within RBC-a. When feasible, we acquired multiple x–t line scans at a single ribbon, shown now as individual data points, to assess variability in calcium signals recorded from the same ribbon.

The box plots in panels E and F display the average calcium signal (horizontal lines) for each ribbon, based on multiple recordings. These plots demonstrate considerable variability between ribbons of RBC-a. Importantly, the lack of or minimal error bars for repeated measurements at the same ribbon indicates that the proximal and distal calcium signals are consistent within a ribbon. These findings emphasize that the observed variability among ribbons and among cells reflects true biological heterogeneity in local calcium domains, rather than experimental noise.

Hogescholen en universiteiten registreren een aanzienlijke hoeveelheid gegevens over hun studenten, wat een aanzienlijk potentieel biedt voor het genereren van informatie, kennis en monitoring. Zowel schooluitval als schooluitval in het hoger onderwijs vormen een obstakel voor economische groei, werkgelegenheid, concurrentievermogen en productiviteit, en hebben een directe impact op het leven van studenten en hun families, hogescholen en de samenleving als geheel. De hier beschreven dataset is het resultaat van de aggregatie van informatie uit verschillende onsamenhangende databronnen en bevat demografische, sociaaleconomische, macro-economische en academische gegevens over inschrijving en academische prestaties aan het einde van het eerste en tweede semester. De dataset wordt gebruikt om machine learning-modellen te bouwen voor het voorspellen van academische prestaties en uitval. Deze maakt deel uit van een Learning Analytic-tool die is ontwikkeld aan het Polytechnic Institute of Portalegre en die informatie verstrekt aan het tutoringteam met een schatting van het risico op uitval en falen. De dataset is nuttig voor onderzoekers die vergelijkende studies willen uitvoeren naar de academische prestaties van studenten en ook voor training op het gebied van machine learning.

I believe being sub certified so early in our educational careers will really help us a lot with having experience in the classroom.

This is an awesome thing you do, letters of recommendation can be very helpful and this gives us all an incentive to do our best on every assignment.

I think that this is really important. When in a classroom it is always best to have positive relationships and respect.

I am very curious what all the Â's mean, they are in a very important part of the syllabus.

eLife Assessment

This useful study presents a hierarchical computational model that integrates locomotion, navigation, and learning in Drosophila larvae. The evidence supporting the model is solid, as it qualitatively replicates empirical behavioral data, but the experimental data is incomplete. While some simplifications in neuromechanical representation and sensory-motor integration are limiting factors, the study could be of use to researchers interested in computational modeling of biological movement and adaptive behavior.

Reviewer #1 (Public review):

Summary:

The paper presents a three-layered hierarchical model for simulating Drosophila larva locomotion, navigation, and learning. The model consists of a basic locomotory layer that generates crawling and turning using a coupled oscillator framework, incorporating intermittency in movement through alternating runs and pauses. The intermediate layer enables navigation by allowing larvae to actively sense and respond to odor gradients, facilitating chemotaxis. The adaptive learning layer integrates a spiking neural network model of the Mushroom Body, simulating associative learning where larvae modify their behavior based on past experiences. The model is validated through simulations of free exploration, chemotaxis, and odor preference learning, demonstrating close agreement with empirical behavioral data. This modular framework provides a valuable advance for modeling larva behavior.

Strengths:

Every modeling paper requires certain assumptions and abstractions. The main strength of this paper lies in its modular and hierarchical approach to modeling behavior, making connections to influential theories of motor control in the brain. The authors also provide a convincing discussion of the experimental evidence supporting their layered behavioral architecture. This abstraction is valuable, offering researchers a useful conceptual framework and marking a significant step forward in the field. Connections to empirical larval movement are another major strength.

Weaknesses:

While the model represents a conceptual advance in the field, some of its assumptions and choices fall behind state-of-the-art approaches. One limitation is the paper's simplified representation of larval neuromechanics, in which the body is reduced to a two-segment structure with basic neural control. Another limitation is the absence of an explicit neuromuscular control system, which would better capture the role of segmental central pattern generators (CPGs) and neuronal circuits in regulating peristalsis and turning in Drosophila larvae. Many detailed neuromechanical models, as cited by the authors, have already been published. These abstractions overlook valuable experimental studies that detail segmental dynamics during crawling and the larval connectome.

The strength of the model could also be its weakness. The model follows a subsumption architecture, where low-level behaviors operate autonomously while higher layers modulate them. However, this approach may underestimate the complexity of real neural circuits, which likely exhibit more intricate feedback mechanisms between sensory input and motor execution.

Reviewer #2 (Public review):

Summary:

Sakagiannis et al. propose a hierarchically layer architecture to larval locomotion and foraging. They go from exploration to chemotaxis and odour preference test after associative learning.

Strengths:

A new locomotion model based on two oscillators that also incorporates peristaltic strides.

Weaknesses:

• The model is not always clearly or sufficiently explained (chemotaxis and odour test).

• Data analysis of the model movement is not very thorough.

• Comparisons with locomotion of behaving animals missing in chemotaxis and odour preference test after associative learning.

• Overall it is hard to judge the descriptive and predictive value of the model.

Reviewer #3 (Public review):

Summary:

This paper presents a framework for a multilevel agent-based model of the drosophila larva, using a simplified larval body and locomotor equations coupled to oscillators and sensory input. The model itself is built upon significant existing literature, particularly Wystrach, Lagogiannis, and Webb 2016 and Jürgensen et al. 2024. The aim is to generate an easily configurable, well-documented platform for organism-scale behavioral simulation in specific experiments. The authors demonstrate qualitative similarity between in vivo behavioral experiments to calibrated models.

Strengths:

The goal is excellent - a system to rapidly run computational experiments that align naturally with behavioral experiments would be well-suited to develop intuitions and cut through hypotheses. The authors provide quantitative descriptions that show that the best-fit parameters in their models produce results that agree with several properties of larval locomotion.

The description of model calibration in the appendix is clear and explains several aspects of the model better than the main text.

In addition, the code is well-organized using contemporary Python tooling and the documentation is nicely in progress (although it remains incomplete). However, see notes for difficulties with installation.

Weaknesses:

(1) As presented here the modeling itself is described in an unclear fashion and without a particular scientific question. The majority of the effort appears to be calibrating modest extensions of existing models and applying them to very simple experiments. This could be an effective first part of a paper on the software tool, but the paper needs to point to a scientific question or, if it is a tool paper, a gap in the current state of modeling tools needed to address scientific goals. While the manuscript has a good overview of larval behavioral papers, the discussion of modeling is more of an afterthought. However, the paper is a modeling paper and the contribution is to modeling and particularly with this work's minor adaptions of existing models, it is unclear what the principle contribution is intended to be.

(2) While the models presented do qualitatively agree with experimental data in specific situations, there is no effort to challenge the model assumptions or compare them to alternative models. Simply because the data is consistent in a small number of simple experiments does not mean that the models are correct. Moreover, given the highly empirical nature of the modeling, I wonder what results are largely the model putting out what was put in, particularly with regards to kinematic results like frequency and body length or the effect of learning simply changing the sensory gain constant. It is difficult to imagine how at this level of empirical modeling, it would appear quite difficult to integrate the type of cell-type-specific perturbation or functional observation that is common in larval experiments.

(3) The central framing of a "layered control architecture" does not have a significant impact on the work presented here and the paper would do better with less emphasis on it. Given the limited empirical models, there are only so many parameters where different components can influence one another, and as best as I can tell from the paper there is only chemotaxis and modulation of a chemotactic gain constant that are incorporated so far. However, since these are empirical functions it says little about how the layers are actually controlled by the nervous system - indeed, the larval nervous system appears to have many levels of local and long-range module of circuits at both the sensory and motor layers. It is not clear how this aspect would contribute beyond the well-appreciated concept of a relatively finite set of behavioral primitives in an insect brain, particularly for the fly larva. What would be a contradictory model and how would the authors differentiate between that and the one they currently propose? If focusing only on olfactory learning and chemotaxis, how does the current framing add to the existing understanding?

(4) The paper uses experimental data to calibrate the models, however, the experiments are not described at all in the text.

Author response:

We thank all three anonymous reviewers for their thoughtful evaluations of our manuscript and for recognizing the conceptual advance in combining agent-based behavioral simulations with systems neuroscience models. We are especially encouraged by the acknowledgement of the framework’s potential to support simulation of neural control of individual animal behavior in realistic sensory environments.

Below, we respond to each reviewer’s public comments in turn. Throughout, we have aimed to clarify our rationale for modeling choices, acknowledge limitations, and outline concrete steps for improvement in the revised manuscript.

Furthermore, the call for a better description of the model implementation as voiced by all three reviewers and additional requests from community members has prompted us to formulate a separate technically detailed description of the publicly available larvaworld software package as well as of the readily implemented models in form of a preprint paper (Sakagiannis et al., 2025, bioRxiv, DOI: https://doi.org/10.1101/2025.06.15.659765).

Reviewer #1:

We are happy to read that this reviewer considers the proposed behavioral architecture ‘a significant step forward in the field’, and that she/he recognizes the strengths of our work in the modular and hierarchical approach that provides connections to influential theories of motor control in the brain, in the experimental evidence it is based on, and in the valuable abstractions that we have chosen for the larval behavioral modeling.

The reviewer raises important points about the simplifications we have made, both conceptually and in the specific implementation of larval behaviors. Our main goal in this study is to introduce a conceptual framework that integrates agent-based modeling with systems neuroscience models in a modular fashion. To serve this purpose, we aimed for a minimal yet representative implementation at the motor layer of the architecture, calibrated to larval locomotion kinematics. This choice enables efficient simulation while allowing us to test top-down modulation and adaptive mechanisms in higher layers without the computational overhead of a full neuromechanical model. In addition to chemotaxis, we have recently used this simplified approach to model thermotaxis in larvae (Kafle et al., 2025, iScience, DOI: https://doi.org/10.1016/j.isci.2025.112809).

The reviewer notes the absence of explicit segmental neuromuscular control or central pattern generators (CPGs). We deliberately abstracted from these mechanisms, representing the larval body as two segments with basic kinematic control, to focus on reproducing overall locomotor patterns. This bisegmental simplification, which we illustrate in Supplemental Video “Bisegmental larva-body simplification”, retains the behavioral features relevant to our current aims. However, the modular structure of the framework means that more detailed neuromechanical models—incorporating CPG dynamics or connectome-derived circuit models—can be integrated in future work without altering the architecture as a whole.

We fully agree that real neural circuits are more complex than a strict subsumption architecture implies. In the Drosophila larva, there is clear evidence for ascending sensory feedback from the motor periphery to premotor and higher brain circuits, as well as neuromodulatory influences. These add layers of complexity beyond the predominantly descending control in our present model. At the same time, both larval and adult connectome data show that across-level descending and ascending connections are sparse compared to the dense within-layer connectivity. We see value in casting our model as a hierarchical control system precisely to make the strengths and limitations of such an abstraction explicit. The revised manuscript will include further discussion of these points.

In summary, our design choices reflect a trade-off: by limiting the biological detail in the lower layers, we gain computational efficiency and maintain a clear modular structure that can host models at different levels of abstraction. This ensures that the architecture remains both a tool for immediate behavioral simulation and a scaffold for integrating richer neural and biomechanical models as they become available.

Reviewer #2:

We thank the reviewer for recognizing the novelty of our locomotory model, particularly the implementation of peristaltic strides based on our new analyses of empirical larval tracks, and for providing constructive feedback that will help us improve the manuscript.

The reviewer highlights the need for clearer explanations of the chemotaxis and odor preference modules. We expand these sections in the revised manuscript with more explicit descriptions of model structure, parameterization, and calibration. As mentioned above, we have also prepared a separate preprint dedicated to the larvaworld Python package, which contains detailed implementation notes and hands-on tutorials that allow users to adapt or extend individual modules.

Regarding the comparison to empirical behavior in chemotaxis, our present analysis is indeed primarily qualitative. However, we would like to emphasize that the temporal profile of odor concentration at the larval head in our simulations matches that measured in Gomez-Marin et al. (Nature Comm., 2011, DOI: https://doi.org/10.1038/ncomms1455) using only one additional free parameter, while all parameters of the basic locomotory model had been fitted to a separate exploration dataset before and were kept fixed in the chemotaxis experiments. In addition to the simulation of chemotaxis in the present paper, we recently used larvaworld in a practical model application to estimate a species-specific parameter of thermotaxis from experiments across different drosophilids (Kafle et al., 2025, iScience, DOI: https://doi.org/10.1016/j.isci.2025.112809).

The preference index in our simulations was computed using the same definition as in the established experimental group assay for larval memory retention, enabling a direct quantitative comparison between simulated and empirical results. Variability in the simulated outcomes arose naturally from inter-individual differences in body length and locomotory parameters, derived from real larval measurements, as well as from the random initial orientation of each individual in the arena. These factors contributed to variation in individual tracks and ultimately produced preference index values that closely matched those observed experimentally. In the revised manuscript, we also discuss handedness, as highlighted by the reviewer, as another meaningful expression of inter-individual variability in Drosophila larvae and insects more generally.

Finally, we acknowledge the reviewer’s concern about the scalability and broader applicability of the model. While the present paper focuses on three specific behavioral paradigms (exploration, chemotaxis, odor preference), the modular structure of the architecture is designed for flexibility: modules at any layer can be exchanged for more detailed or alternative implementations, and new sensory modalities or behaviors can be integrated without redesigning the system. The larvaworld package, associated codebase, and documentation are openly available to encourage adoption and adaptation by the larval research community.

Reviewer #3:

This public review provides an excellent account of our central aim to build an easily configurable, well-documented platform for organism-scale behavioral simulation and we are happy to read that the reviewer considers this an excellent goal.

We thank the reviewer for her/his account of our well-organized code using contemporary Python tooling. We are currently further improving code readability and code documentation, and we will release a new version of the larvaworld Python package. We further agree with the reviewer’s assessment that understanding the model calibration currently requires reading of the appendix. For the revised manuscript we thus aim at improving our description of all calibration and modeling steps along the way. We will also make sure to improve the description of the experimental datasets used for calibration.

We recognize that our description of the paper’s scientific contribution could be clearer. In revision, we will sharpen the Introduction and Discussion to highlight our main contributions:

(1) Promoting a shift from isolated neural circuit modeling to integrated agent-based simulations in realistic environments.

(2) Proposing the layered behavioral architecture, adopting the subsumption paradigm for modular integration.

(3) Providing the larvaworld software as a ready-to-use, extensible modeling platform.

(4) Implementing an empirically calibrated locomotory model and demonstrating its integration with navigation and learning modules in replicated behavioral paradigms.

We agree with the reviewer that the next challenge is to integrate the empirically based behavioral simulations presented here with functional brain models capable of reproducing or predicting experimental findings at the level of cellular neurophysiology, including the effects of cell-type-specific manipulations such as gene knock-down or optogenetic activation/inhibition. However, based on our experience with systems-level modeling, we deliberately invested in behavioral simulation because functional models of the nervous system—including our own—often lack translation into simulated agent behavior. In many cases, model output is limited to one or more variables that can at best be interpreted as a behavioral bias, and most often represents an “average animal” that fails to capture inter-individual differences. By linking our spiking mushroom body model to behavioral simulations in a group of individual agents during memory retention tests (Figure 6C,D), we were able to achieve a first successful direct comparison between simulated and experimental behavior metrics—in this case, the behavioral preference index reported in Jürgensen et al. (iScience, 2024, DOI: https://doi.org/10.1016/j.isci.2023.108640).

Finally, we reiterate that the layered behavioral architecture is designed to promote a modular modeling paradigm. Our adoption of a subsumption architecture does not conflict with the concept of behavioral primitives; on the contrary, the notion that such primitives follow (semi-)autonomous motor programs and can be combined into more complex behaviors was the starting point for our implementation of the architecture in the fly larva. In our view, a genuinely contradictory paradigm for neural control of behavior would require a non-modular, strictly non-hierarchical organization of the nervous system and, by extension, of behavioral control.

so this milestone is important. It prevents the execution to go to infinity

so what should be the optimal frequency? one per week seems too sparse. And btw I read somewhere that allocate a "mind wandering" slot in everyday also improves concentration when we're in the main task

eLife Assessment

NeuroSC is an accessible and interactive tool for streamlined observation of neuronal morphology, membrane contact, and synaptic connectivity across developmental stages in the nematode C. elegans. This important tool relies on solid electron microscopy datasets. This resource will be of high interest to C. elegans researchers interested in nervous system wiring and circuit function.

Reviewer #1 (Public review):

The authors have done a terrific job and addressed the questions raised in my previous review. There are only some minor requests that I have and list below.

Reviewer #2 (Public review):

Summary

The past several years has seen publication of both new (Witvliet et al., 2021) and newly analyzed (Cook et al., 2019; Moyle et al., 2021; Brittin et al., 2021) data for the C. elegans connectome. The increase in data availability for a single species allows researchers to examine variability due to both stochastic events and due to changes over development. The quantity of these data are huge. To help the community make these data more accessible, the authors present a new online tool that allows examination of 3D models for C. elegans neurons in the central neuropil across development. In addition to visualizing the overall structure of the neuronal processes and locations of synapses, the NeuroSC tool also allows users to probe into the C-PHATE visualization results, which this group previously pioneered to describe similarities in neuron adjacency (Moyle et al., 2021).

Strengths

The ability to visualize the data from both a connectomics and contactomics perspective across developmental time has significant power. The original C. elegans connectome (White et al., 1986) presented their circuits as line drawings with chemical and electrical synapses indicated through arrows and bars. While these line drawings are incredibly useful, they were necessary simplifications for a 2D publication and lack details of the complex architecture seen within each EM image. Koonce et al takes advantage of their own and others segmented image data of each neuronal process within the nerve ring to create a web interface where users can visualize 3D models for their neuron of choice. The C-PHATE visualization is intended to allow users to explore similarities among different neurons in terms of adjacency and then go directly to the 3D model for these neurons. The 3-D models it generates are beautiful and will likely be showing up in many future presentations and publications. The tool doesn't require any additional downloading and is open source. This revision includes an option where hovering over an individual neurons, synapse, or contact will pull up a statistics panel. The addition of text to the video tutorials in the revision is very useful.

Weaknesses

There are several bugs with this tool, which make it a bit clunky to use and suggest a lack of rigorous testing. There are also issues with data availability. I was disappointed that my "recommendations for the authors", which focused on the user interface, were not addressed in the response to reviewers.

Reviewer #3 (Public review):

Summary:

This work provides graphical tools for reconstructing the detailed anatomy of a nervous system from a series of sections imaged by electron microscopy. Contact between neuronal processes can direct outgrowth and is necessary for connectivity, thus function. A bioinformatic approach is used to group neurons according to shared features (e.g., contact, synapses) in a hierarchy of "relatedness" that can be interrogated at each step. In this work, Koonze et al analyze vEM data sets for the C. elegans nerve ring (NR), a dense fascicle of processes from181 neurons. In a bioinformatic approach, the clustering algorithm Diffusion Condensation (DC) groups neurons according to similar cell biological features in iterations that remove chunks of differences in feature data with each step ultimately merging all NR neurons in one cluster. DC results are displayed with C-Phate a 3D visualization tool to produce a trajectory that can be interrogated for cell identities and other features at each iterative step. In previous work by these authors, this approach was utilized to identify subgroups of neuronal processes or "strata" in the NR that can be grouped by physical contact and connectivity. Here they expand their analysis to include a series of available vEM data sets across C. elegans larval development. This approach suggests that strata initially established during embryonic development are largely preserved in the adult. Importantly, exceptions involving stage specific-specific reorganization of neuronal placement in specific strata were also detected. A case study featured in the paper demonstrates the utility of this approach for visualizing the integration of newly generated neurons into the existing NR anatomy. Visualization tools used in this work are publicly available at NeuroSCAN.

Strengths:

A web-based app, NeuroSCAN, that individual researchers can use to interrogate the structure and organization of the C. elegans nerve ring across development.

Weaknesses:

minor revisions

Comments on Revisions:

The authors have satisfactorily addressed my critiques.

Author response:

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

Reviewer #1 (Public review)

Comment 

Koonce et al. have generated a web-based visualization tool for exploring C. elegans neuronal morphology, contact area between neurons, and synaptic connectivity data. Here, the authors integrate volumetric segmentation of neurons and visualization of contact area patterns of individual neurons generated from Diffusion Condensation and C-PHATE embedding based on previous work from adult volumetric electron microscopy (vEM) data, extended to available vEM data for earlier developmental stages, which effectively summarizes modularity within the collated C. elegans contactomes to date. Overall, NeuroSC's relative ease of use for generating visualizations, its ability to quickly toggle between developmental stages, and its integration of a concise visualization of individual neurons' contact patterns strengthen its utility.

We thank that reviewer for this positive assessment of our work.

Comment

NeuroSC provides an accessible and convenient platform. However, many of the characteristics of NeuroSC overlap with that of an existing tool for visualizing connectomics data, Neuroglancer, which is a widely-used and shared platform with data from other organisms. The authors do not make clear their motivation for generating this new tool rather than building on a system that has already collated previous connectomics data. Although the field will benefit from any tool that collates connectomics data and makes it more accessible and user-friendly, such a tool is only useful if it is kept up-to-date, and if data formatting for submitting electron microscopy data to be added to the tool is made clear. It is unclear from this manuscript whether NeuroSC will be updated with recently published and future C. elegans connectomes, or how additional datasets can be submitted to be added in the future.

We have added new language to more explicitly state the motivations for developing NeuroSC (Introduction, lines 98-111, and discussion lines 375-384). In a new discussion section, we also include comparisons of the features of NeuroSC with other existing tools, like Neuroglancer and Webknossos, (lines 393-417).

Briefly, the functional features of NeuroSC are substantially different (and do not exist) in other web-based tools for navigating EM datasets, including NeuroGlancer. This is because the intended use of NeuroSC is substantially different (and purposefully synergistic) to the intended use, and tools available, in NeuroGlancer. 

NeuroGlancer is a versatile tool designed primarily for web-based visualizations and sharing of large EM datasets. NeuroSC was not designed to enable this type of access to the primary EM data (purposefully done because these features were already available through tools like NeuroGlancer). 

Instead, the explicit goal of NeuroSC is to provide a platform specifically optimized for examining neuronal relationships across connectomic datasets. NeuroSC builds on the segmentations emerging from programs like NeuroGlancer, but the tools are tailored to explore relationships such as contact profiles in the context of neuronal morphologies and synaptic positions, and across datasets that represent different animals or different developmental stages. 

To achieve this, all datasets in NeuroSC were optimized to facilitate comparisons across different connectomes of segmented neuronal features, including: 1) alignment of the neurons that are compared upon the display of the segmentations; 2) synchronization of the 3D windows; 3) implementation of a ‘universal color code’ across datasets for each neuron and relationship for easy visual comparisons; 4) use of the specific neuronal names to label instances of the same cells across all available datasets. The use of precise neuronal names among separate data sets allows integration of these objects with other catalogued datasets, including genomic and neuronal activity profiles.

The formatting and display of the datasets used in NeuroSC was accompanied by the development of new tools including: 1) Rendering of the contact profiles of all neurons in the context of the morphology of the cell and the synapses and 2) C-PHATE diagrams to inspect multidimensional relationship hierarchies based on these contact profiles. In NeuroSC, C-PHATEs can be navigated and compared across multiple stages of development while visualizing neuronal reconstructions, allowing users to compare neuronal relationships across individual datasets.

We agree with the reviewer that these tools are most useful when integrated. With that intention in mind, we designed NeuroSC as a series of modular, open-source tools that could be integrated into other programs, including Neuroglancer. In that sense our intent was not to produce another free-standing tool, but a set of tools that, if useful, could be integrated to other existing web-based connectomic resources to enhance the user experience of navigating complex EM datasets and draw biological meaning from the relationships between the neurons. Additionally, we intentionally designed NeuroSC to enable the ability to integrate new methods of understanding neuron relationships as they arise. We have dedicated a more detailed section to the discussion (lines 369- 417) to better convey this intention and directly address the unique abilities of NeuroSC as a complementary tool to the powerful existing tools, including Neuroglancer.

Comment

The interface for visualizing contacts and synapses would be improved with better user access to the quantitative underlying data. When contact areas or synapses are added to the viewer, adding statistics on the magnitude of the contact area, the number of synapses, and the rank of these values among the neuron's top connections, would make the viewer more useful for hypothesis generation. Furthermore, synapses are currently listed individually, with names that are not very legible to the web user. Grouping them by pre- and postsynaptic neurons and linking these groups across developmental stages would also be an improvement.

[what do they even mean by linking?]

We thank the reviewer for this insightful comment and have implemented several improvements to address these suggestions. Specifically, we have added new features to enhance user access to quantitative data within the NeuroDevSCAN viewer:

Cell, Patch, and Synapse Statistics: Users can now see a statistics panel when clicking on a rendered neuron, contact patch, or a synapse. These panels provide the following information, respectively, and are highlighted in lines 303-315):

Cell Stats: Click on a cell rendering to show cell stats which displays the total volume and surface area of the selected neuron within the defined neuropil area of our datasets (see Methods). 

Contact Stats: Click on a patch rendering to show ‘contact stats’. This pop up displays quantifications of the selected contact relationship. Rank compares the summed surface area of contacts ("patches") between these two neurons relative to all other contact relationships for the primary neuron for the cell and the whole nerve ring. A rank of 1, for example, means this neuron pair shares the largest contact surface area of the examined relationship. “Total surface area” is displayed in nanometers, and is the summed surface area of all patches of this identity. Contact percentages are presented in two ways: (1) as the proportion of the primary cell's total surface area occupied by the contact in question, and (2) as the proportion of the total surface area of the nerve ring occupied by that same contact. (Showcased in figure S5). 

Synapse Stats: A click on a synapse rendering now shows ‘synapse stats’, which displays the number of synapses of the selected identity within the primary neuron, including any polyadic synapse combinations involving the primary neurons. (Showcased in figure S7).

(1) Grouping and Readability Improvements: While individual synapses are still visualized, their display has been improved for legibility. We have condensed the lengthy naming scheme to improve clarity and codified the synapse type by using superscript letters C, E, U to represent chemical, electrical and undefined synapses, respectively. This is explained and shown in figure S7, we added arrows to indicate the directionality of presumed information flow at each synapse. 

(2) Developmental Linkage: We can link objects across datasets via cellular identity, but each synapse in the dataset does not yet have an identity attributed to its spatial coordinates, preventing us from linking specific synapses across development beyond their connectivity (ie, that a given synapses connects cell X to cell Y, for instance), also addressed in R1.11.  

Together, these improvements substantially enhance the utility of the viewer for hypothesis generation by making key quantitative data readily accessible.

Comment

While the DC/C-PHATE visualizations are a useful tool for the user, it is difficult to understand when grouping or splitting of cell contact patterns is biologically significant. DC is a deterministic algorithm applied to a contactome from a single organism, and the authors do not provide quantitative metrics of distances between individual neurons or a number of DC iterations on the C-PHATE plot, nor is the selection process for the threshold for DC described in this manuscript. In the application of DC/C-PHATE to larval stage nerve ring strata organization shown by the authors, qualitative observations of C-PHATE plots colored based on adult data seem to be the only evidence shown for persistent strata during development (Figure 3) or changing architectural motifs across stages (Figure 4). Quantitation of differences in neuron position within the DC hierarchy, or differences in modularity across stages, is needed to support these conclusions. Furthermore, illustrating the quantitative differences in C-PHATE plots used to make these conclusions will provide a more instructive guide for users of NeuroSC in generating future hypotheses.

There are several ways to visualize DC outputs, and one way to quantitatively compare DC clustering events of neurons is via Sankey diagrams. To make the inclusion of these resources more clear, we have highlighted them in lines 175-178 (Supplemental Tables 3-6). ‘DC outputs for each strata across animals can also be inspected using Sankey diagrams (Supplemental Tables 3-6). These spreadsheets detail the neuron members at each iteration of DC, allowing the user to derive quantitative comparisons of clustering events.’

As the reviewer points out, DC is a deterministic algorithm that will iteratively cluster neurons based on the similarity of their contact profiles. To better explain the selection process for the threshold, the number of DC iterations and the quantitative metrics between the neurons, we have added new text in the Diffusion Condensation methods section.  Briefly:

Number of DC iterations: During diffusion Condensation (DC) we track the modularity of the resulting clusters at each iteration and select the iteration with the highest modularity to define the clusters that represent the strata  (Moyle et al., 2021), (Brugnone et al., 2019). Mathematically, modularity is calculated by comparing the actual number of edges within clusters to the expected number of such edges in a randomized network with the same degree distribution (Newman et al., 2006). A higher modularity value implies that nodes within the same cluster are more densely connected to each other than to nodes in other clusters. We now better explain this in lines 562-567.

Threshold for merging points: The threshold (epsilon) used to merge data points in each iteration is set as a small fraction of the spatial extent of the data: for each coordinate dimension (x, y, z), we compute the range (maximum minus minimum), take the maximum of these three values, and divide it by 10,000. This process is performed iteratively for each round of clustering until all data points cluster into a single point. We have updated the manuscript to clarify this threshold selection and included this information in the revised algorithm description and pseudocode. We now better explain this in lines 556-559.

Distances between neurons in DC C-PHATE: In our previous description in Box 1 algorithm 1, we had provided a general algorithm for DC for any high dimensional dataset. We have now revised the algorithm to indicate how we used DC for these EM datasets. 

Distances between neurons are determined by the pixel overlap between their segmented shapes in the EM dataset. We use these distances to build a graph with weighted edges, in which the weight of the edge represents the pixel overlap (the adjacency in the actual EM segmentation). Affinities between neurons, which are a proxy for their distance in the graph, are then computed as now revised in Box 1, Algorithm 1. This process is done iteratively as neurons cluster. To better communicate this, we have changed the text in lines 533-538.  

Comment

R1.5. While the case studies presented by the authors help to highlight the utility of the different visualizations offered by the NeuroSC platform, the authors need to be more careful with the claims they make from these correlative observations. For example, in Figure 4, the authors use C-PHATE clustering patterns to make conclusions about changes in clustering patterns of individual neurons across development based on single animal datasets. In this and many other cases presented in this study with the limited existing datasets, it is difficult to differentiate between developmental changes and individual variability between the neurite positions, contacts, and synapse differences within these data. This caveat needs to be clearly addressed.

We now better explain in the manuscript that the selected case study, of the AVF neuron outgrowth, is not one of just correlation based solely on an EM dataset. Instead, the case study represents the NeuroSC-driven exploration of a biologically significant event supported by several independent datasets, as now explained in lines 257-276.

Briefly, we agree with the reviewer that examining differences across individual EM datasets is insufficient evidence to make conclusions about developmental changes. But the strength of NeuroSC is in its ability to combine and compare multiple datasets, bolstering observations that are not possible by looking at just one dataset, and providing new insights on the way to new hypotheses. We now better explain that we are not looking at single connectomes in isolation and then deriving conclusions, but instead using NeuroSC to compare across 9 EM datasets. We better explain how the tools in NeuroSC, including C-PHATE, enabled comparisons across these multiple connectomes to identify apparent differences in neuronal relationships. We then explain that by using NeuroSC, we could examine these variations in neuronal relationships at the level of individual, cell biological differences of neuronal morphologies between the developmental datasets. This could be due, as pointed by the reviewer, to differences due to development, or just differences between individual animals. In the case of AVF, that features are absent in all early specimens, then arise and persist in all specimens after a certain time point, which lead us to hypothesize they result from a developmental event. Because the segmented objects in NeuroSC are linked to neuronal identities, we are also able to cross reference our observations from the EM datasets with information in other datasets and the literature. In the specific case of postembryonic development of AVF outgrowth, we can now tie the knowledge, from developmental lineage information and molecular profiles, that AVF is a postembryonically born neuron (Sulston et al. 1977, Sun et al 2022, Poole et al 2024, wormatlas.org) to the outgrowth dynamics of its neurites using the postembryonic EM datasets. Our findings using  NeuroSC provide a proof of concept of the utility of the resource and extended our understanding of how the outgrowth of this neuron affects the relationships between the neural circuits in the nerve ring.

Comment

R1.6. Given that recent studies have also quantified contact area between neurons across multiple connectomes (Cook et al., Current Biology, 2023; Yim et al., Nature Communications, 2024), and that the authors use a slightly different approach to quantify contact area, a direct comparison between contact area values obtained in this study with prior studies seems appropriate.

We acknowledge that there are multiple different approaches to calculate adjacencies. In the papers cited above, there are 3 different algorithms used:

(1) Brittin 2019 (python parse Track EM, boundary thresholds), used in Cook et al 2023, Moyle 2021, and this study).

(2) Witvliet 2021 (Matlab 2D masks), used in Cook et al 2023.

(3) Yim 2024 (3D masks), used in Yim et al 2024.

To briefly describe the different approaches, and the methods we chose for this paper:

Algorithm 1 (used in this study) defines adjacency based on distances between boundary points in TrakEM2 segmentations, allowing threshold tuning to accommodate differences in resolution and image quality across datasets—an important feature for consistent cross-dataset comparisons.

Algorithm 2 infers contact via morphological dilation of VAST segmentations, identifying adjacency through overlapping expanded boundaries. 

Algorithm 3 uses voxelwise contact detection with directional surface area measurements and normalization to account for dataset size differences. 

In NeuroSC, we use algorithm 1, mostly because we had tested the rigor of this method in (Moyle et al. 2021), where we have shown that results were robust across a range of thresholds. This flexibility enables tailored application across datasets of varying quality and scale, critical for NeuroSC’s mission of curating data sets across differing methodologies to allow for direct relationship comparisons. We detail the methodology for defining thresholds for each dataset in methods section lines 492-521, defined in Supplementary table 1. Another difference between our analysis and the previously cited work is that for our analysis we also chose to include all individually resolved neurons, including post-embryonic cells, without collapsing them into left/right or dorsal/ventral symmetry classes. In this way our approach retains the full cellular resolution of the nervous system. 

Comment

Neuroglancer is not mentioned at all in the manuscript, despite it being a very similar and widely accepted platform for vEM data visualization across model organisms. An explicit comparison of NeuroSC and Neuroglancer would be appropriate, given the similarity of the tools. Currently, published C. elegans data (Witvliet et al., 2021; Yim et al., 2024) use Neuroglancer-based viewers, and directly comparing NeuroSC and highlighting its strengths relative to Neuroglancer would strengthen the paper.

In the original manuscript we had not mentioned tools like Neuroglancer because we envisioned them as distinct, in intended use and output, from NeuroSC. But, as explained in R1.2 comment, in the revised version we have included a section in the Introduction lines 98-108 and in the Discussion (lines 369- 417) that compares these types of web-based tools and highlights synergies. 

Comment

Assigning shorthand names to strata, such as "shallow reflex circuit" (page 4, line 172), may oversimplify this group of neurons. Either more detailed support for shorthand names of C-PHATE modules should be included, or less speculative names for strata should be used.

We appreciate this comment and understand that the original language used in the manuscript to describe strata categorizations may run the risk of oversimplification. We have now clarified the text to communicate that: 1) Strata are labeled by numbers (Strata 1, Strata 2, Strata 3 and Strata 4), rather than functional features of the neurons forming part of the strata, and that 2) the assignment of ‘strata’ is just one level of classification available via DC/CPHATE (as explained below). 

To be sure, we have observed and published (Moyle et. al. Nature 2021) that within a given stratum, many neurons share the functional identities that we have used as summary descriptors for the strata (eg, shallow reflex circuits for Stratum 1; sensory and integrative circuits in Strata 3 and Strata 4; command interneurons in Strata 2, etc). However, those cell types are not the only members of the strata. We have adjusted the language in lines 197-204 to reflect this more clearly. “Stratum 1, which contains most neurons contributing to shallow reflex circuits that control aversive head movements in response to noxious stimuli, displayed the fewest changes among the developmental connectomes (Figure 3B–F; Supplementary Table 3). In contrast, C. elegans exhibit tractable behaviors that adapt to changing environmental conditions (Flavell et al., 2020). Strata 3 and 4 contain most neurons involved in circuits associated with such learned behaviors, including mechano- and thermo-sensation. This is reflected in Strata 3 and 4 showing the most change in neuronal relationships across postembryonic development.“

Comment

The authors state that NeuroSC can be applied to other model organisms. Since model organisms with greater neuron numbers include more individual neurons per cell class, the authors should support this by quantitatively demonstrating how DC/C-PHATE relationships correlate with shared functional roles among C. elegans neurons.

We now clarify in the manuscript that, like in other organisms, C. elegans neurons are also grouped into functional classes with shared characteristics. In the context of the cylindrical nerve ring of the animal, these neuronal classes are sometimes bilaterally symmetric (forming left-right pairs), four-fold symmetric and six-fold symmetric. We now explain in the discussion that the DC/CPHATE analyses group these neuron classes and their relationships (lines 442-451). In the specific section mentioned by the reviewer, we now also add new text to contextualize this concept and how it might relate to the possible use of these tools in organisms with larger nervous systems: ‘However, our previous work has demonstrated that DC/CPHATE clustering of C. elegans neurons consistently pulls out clusters of shared neuron classes and shared functional roles Moyle et al. (2021). Building on this foundation, we envision applying similar clustering approaches to larger connectomes, aiming to identify classes and functionally related neuronal groups in more complex nervous systems. We suggest that contact profiles, along with neuron morphologies and synaptic partners, can act as ‘fingerprints’ for individual neurons and neuron classes. These ‘fingerprints’ can be aligned across animals of the same species to create identities for neurons. Frameworks for systematic connectomics analysis in tractable model systems such as C. elegans are critical in laying a foundation for future analyses in other organisms with up to a billion-fold increase in neurons (Toga et al., 2012).’

Comment

Lack of surface smoothing in NeuroSC leads to processes sometimes appearing to have gaps, which could be remedied by smoothing with a surface mesh. 

We thank the reviewer for the suggestion, and understand the visibility of gaps in certain neuron processes can be distracting. But this was an intentional choice, with our main goal being to show the most accurate representation of the available data segmentation and avoid any rendering interpretations. In this way, we render the data with the highest fidelity we can and as close as possible to the ground truth of the EM segmentation. We have added language to describe this in the methods, lines 490-491, and in Figure legend 5b.

Comment

Toggling between time points while maintaining the same neurons and contact area in NeuroSC is a really valuable feature. The tool would be improved even more by extending this feature to synapses, specifically by allowing the user to add an entire group of synapses to the viewer at once (e.g. "all synapses between AIM and PVQ"), and to keep this synapse group invariant when toggling between developmental stages.

We thank the reviewer for this suggestion. In response we have now implemented a new feature to ‘clone’ a rendered scene across time while preserving the original elements to ease comparisons. Once the user has rendered a scene, they can use the in-viewer developmental slider to clone the renderings and assigned colors, but display the renderings of the newly selected timepoint. These renderings populate a new window tab which can be dragged to align developmental stage windows side by side. We have added a sentence to account for this in lines 315-317 and to the legend of supplemental Figure S11. 

Reviewer #2 (Public review)

Comment

The ability to visualize the data from both a connectomics and contactomics perspective across developmental time has significant power. The original C. elegans connectome (White et al., 1986) presented their circuits as line drawings with chemical and electrical synapses indicated through arrows and bars. While these line drawings remain incredibly useful, they were also necessary simplifications for a 2D publication and they lack details of the complex architecture seen within each EM image. Koonce et al take advantage of segmented image data of each neuronal process within the nerve ring to create a web interface where users can visualize 3D models for their neuron of choice. The C-PHATE visualization allows users to explore similarities among different neurons in terms of adjacency and then go directly to the 3D model for these neurons. The 3D models it generates are beautiful and will likely be showing up in many future presentations and publications. The tool doesn't require any additional downloading and is open source.

We thank that reviewer for this positive assessment of our work.

Comment

While it's impossible to create one tool that will satisfy all potential users, I found myself wanting to have numbers associated with the data. For example, knowing the number of connections or the total surface area of contacts between individual neurons wasn't possible through the viewer, which limits the utility of taking deep analytical dives. While connectivity data is readily accessible through other interfaces such as Nemanode and WormWiring, a more thorough integration may be helpful to some users.

We thank the reviewer for this feedback and in response have now implemented displays with quantitative information in NeuroSC. Now, upon hovering over a contact patch or synapse, the user will see the quantitative data of the relationship. For contact patches, you will see the total area shared between two neurons in that dataset. On hovering over a synapse, you will see how many synapses there are in total with the same members and throughout the dataset. We agree that this improves user analyses, (see also R1.3 response).

Comment

There were several issues with the user interface that made it a bit clunky to use. For example, as I added additional neurons to the filter search box, the loading time got longer and longer. I ran an experiment uploading all of the amphid neurons, one pair at a time. Each additional neuron pair added an additional 5-10 seconds to the loading. By the time I got to the last pair, it took over a minute to load. Issues like these, some of which may be unavoidable given the size of the data, could be conveyed through better documentation. I did not find the tutorial very helpful and the supplementary movies lacked any voiceover, so it wasn't always clear what they were trying to show.

We appreciate that some of the more complex models can take a while to load. One of our core goals is to keep the high resolution of our models to most accurately represent the EM data, so we had to compromise between resolution and loading times. But to address this concern we have now added a ‘loading’ prompt that reassures the user when there is a wait. We also added, as suggested, text guidance throughout all of the supplemental videos (Supplemental Videos 1-4).

Reviewer #3 (Public review)

Comment

A web-based app, NeuroSC, that individual researchers can use to interrogate the structure and organization of the C. elegans nerve ring across development In the opinion of this reviewer, only minor revisions are required.

We thank that reviewer for this positive assessment of our work.

Comment

Contact is defined by length, why not contact area? How are these normalized for changes in the overall dimensions of neurons during development?

To clarify our methodology: the adjacency algorithm that we use generates a 2D adjacency profile by summing the number of adjacent boundary points per EM section, which are then summed across all EM z slices.

Contact area can be derived by multiplying the adjacency length in each slice by pixel resolution and z-thickness. Prompted by the reviewer we have now also calculated and display contact surface areas, along with their ranks among all contact relationships for a given neuron. These can be inspected directly via the interface by clicking on a rendered cell or contact patch (Figure S5 and lines 308-312). We believe these additional surface area metrics enhance the interpretability and utility of the viewer.

We apply normalization at the level of the adjacency threshold to account for dataset-specific differences such as contrast, boundary definition, and age-related changes in neuropil packing density. This normalization is applied before running the adjacency algorithm. We do not normalize by individual neuron size, as the contact data are intended to reflect relational differences between neurons, rather than absolute morphological scaling. In fact, our addition of a scale-spheroid within each rendered model emphasizes the large increase in spatial scale that the nerve ring experiences during larval growth.  

Comment

Figure 1, C&D, explanation unclear for how the adjacency matrix is correlated with C-Phate schematic in D.

We thank the reviewer for the comment and have clarified this section by adding greater detail to the explanation of how an adjacency matrix is computed (lines 149-155), as well as a description now in the figure legend 1C. Additionally, we revised Figure 1C and D to simplify neuron representations/colors and to simplify the adjacency heat map gradient. We also extended the area of contact between neurons on Figure 1C to better reflect what would be considered a “contact”. Lastly, in the figure, we changed the color and placement for the z plane arrow and label from black to white, to make it more visible, to highlight the method of computing adjacency for each z slice. 

Comment

Figure 4, panels F & G, unclear why AVF is shown in panel G (L3) but not panel F (L1). Explanation (see below) should be provided earlier, i.e., AVF is not generated until the end of the L1.

We have now clarified this important point by adding labels to Figure 4 panels F and G, ‘Pre-AVF outgrowth’ and ‘Post-AVF outgrowth’ respectively. Briefly, the point is that AVF grows into the nerve ring after the L2 stage, and that is why it is absent in panel F (L1 stage, now with the label ‘Pre-AVF outgrowth’).  

Comment

Line 146 What is the justification for the statement: "By end of Larval Stage 1 (L1), neuronal differentiation has concluded...."? This statement is confusing since this sentence also states that "90% of neurons in the neuropil...have entered the nerve ring..." which would suggest that at least 10% additional NR neurons have NOT fully differentiated.

We have fixed this sentence in the text. Now the sentence reads ‘By Larval stage 1 (L1) 90% of the neurons in the neuropil (161 neurons out of the 181 neurons) have grown into the nerve ring and adopted characteristic morphologies and positions. 

Lines 171-175 What is meant by the statement that "degree of these changes mapped onto...plasticity? What are examples of "behavioral plasticity?"

We have added the following new lines of text (lines 200-204) and now additionally cite a review discussing C. elegans behaviors to clarify and give context to behavioral plasticity. ‘C. elegans exhibit tractable behaviors which can adapt due to changing environmental conditions  (Flavell et. al. Genetics 2020). Strata 3 and 4 contain most neurons belonging to circuits associated with such learned behaviors, including chemo, mechano and thermo sensation. This is seemingly reflected by strata 3 and 4 harboring the most readily recognized set of changes in neuronal relationships across postembryonic development.’  

Comment

Lines 189-190 The meaning of this sentence is unclear, "The logic in....merge events."

This sentence has been deleted and we have instead refocused our descriptions of C-PHATES comparisons by neuronal clustering trajectories and cluster members (rather than iterations).

Comment

Lines 193-208 This section reports varying levels of convergence across larval development in C-Phate maps for the interneurons AIML and PVQL. Iterations leading to convergence varied: 16 (L1), 14 (L2), 22 (L3), 20 (l4), 14 (adult). The authors suggest that these differences are biologically significant and reflect the reorganization of AIML and PVQL contact relationships especially between the L4 and adult. Are these differences in iterations significant?

We agree this could be confusing and instead of focusing on comparing the iteration at which each merging event occurs, we now focus on examining the differences in members of clusters, before and after the merge event. Cluster membership is easier to interpret than the differences in the number of DC iterations (lines 224-229).

Lines 240-241 States that AVF neurons "terminally differentiate in the embryo" which is not correct. AVF neurons are generated from neuronal precursors (P0 and P1) at the end of the L1 stage which accounts for their outgrowth into the NR during the L2 stage. 

We thank the reviewer for the correction and have edited the text to read: ‘AVF neurons are generated from neuronal precursors (P0 and P1) at the end of the L1 stage (Sulston et al. (1983); Sun and Hobert (2023); Poole et al. (2024); Hall and Altun (2008); Sulston and Horvitz (1977). AVF neurons do not grow into the nerve ring until the L2 stage, and continue to grow until the Adult stage (lines 261-266).’

Comment

Lines 289-315. A detailed and highly technical description of website architecture would seem more appropriate for the Methods section.

We agree and have moved this section to the methods as suggested (lines 663-690).

Comment

Line 307 "source data is" should be "source data are"

Thank you- we have fixed this grammatical error.

Comment

Line 324 "circuits identities" should be "circuit identity".

Thank you- we have fixed this grammatical error.

Comment

Trademark/copyright conflict with these sites? https://compumedicsneuroscan.com/about/ https://www.neuroscanai.com/

We thank the reviewer for drawing our attention to this. To avoid potential conflicts, we have proactively altered the name to NeuroSC throughout the paper.

Plumbing pipes Guide: Understanding which pipe is better & is PPR pipe the one to consider.

Learning to understand!

DOI: 10.24272/j.issn.2095-8137.2024.397

Resource: (Jianzhen Li lab The Chinese University of Hong Kong Cat# lhb, RRID:AB_2651067)

Curator: @scibot

SciCrunch record: RRID:AB_2651067

What is this?

DOI: 10.24272/j.issn.2095-8137.2024.397

Resource: (Jianzhen Li lab The Chinese University of Hong Kong Cat# fshb, RRID:AB_2651068)

Curator: @scibot

SciCrunch record: RRID:AB_2651068

What is this?

DOI: 10.1080/2162402X.2025.2543620

Resource: None

Curator: @dhovakimyan1

SciCrunch record: RRID:IMSR_JAX:000664

What is this?

I think I saw an experiment like this somewhere on Instagram. I noticed how easy it was to be influenced by words of another. I wonder what would have happened if some of the students saw another experiment similar prior to theirs, would their results be different?

Once for a key-club community service event, we were teaching a group 3rd graders how to make some type of craft and art. After the students were done they had all of their art presented, but then I had ignorantly said "I really like this one" which is a big no no. I was then later told that I cannot say things like that because it might put another student down and keep them from trying other things in the future.

I was introduced to the concept of Growth vs Fixed mindset very early on in my education. Even though I was taught this I would say I am fairly fixed on a lot of things and that I just cannot do something. As of now I would say I have a growth mindset, so I wonder if it can be as easy to change how you think with the flick of a switch?

This reminds me of what Dr Alberto Gutierrez mentioned, in the story with one of his cousins. How his cousin was hyped up in highs-school, but when something challenging occurred he sort of crumbled and hid from the problem. How much is too much affirmation?

Mindsets and Achievement

• "The students with a fixed mindset believed that if you worked hard it meant that you didn't have the ability, and that things would just come naturally to you if you did."
• This is the exact mindset I remember having coming out of high school during the peak of covid. The discomfort of forcing the students to study at home and not live the actual college lifestyle.

How Do Students Learn These Mindsets ?

• "Instead of giving them confidence, it made them fragile, so much so that a brush with difficulty erased their confidence, their enjoyment, and their good performance, and made them ashamed of their work."
• While we are in school, teachers tend to sweeten things up, so that we feel better about ourselves. When in reality, all it is doing is deteriorating our brains and starting bad habits. We should be told how things really are to prepare us for the real world.

** Brainology**

• "Their studies and ours also found that negatively stereotyped students (such as girls in math, or African-American and Hispanic students in math and verbal areas) showed substantial benefits from being in a growth mindset workshop."
• Being stereotyped at a young age can be damaging to the brain, almost limiting the mindset because of your ethnicity sounds insane.

What Do We Value?

• "Most of all, teach them that by taking on challenges, making mistakes, and putting forth effort, they are making themselves smarter."
• We often think taking the easy route is the best, but in reality, when we work on more difficult situations it helps develop our brain and allows us to think thoroughly.

Having a fixed mindset is deciding to limit your brain to endless ideas. I believe all students should grow out of a fixed mindset and find their purpose. Being able to grow out of the fixed mindset might even show you how strong your brain really is or how easy it maybe to rewire it.

I felt this annotation was significant to those that have inspirational idols because they don't realize that those idols constantly worked hard for people to notice their achievements. When viewing our idols through their hard work and effort, it helps us to learn how to use our talents in developing our success.

I believe this annotation was important because school is a place where you can grow and learn so it's great that the session workshop was able to redirect a group of student's minds in realizing that they are part of a learning community.

I found this information important because most parents don't understand that there's a difference between giving your kid confidence and helping your kid gain the confidence in their learning ability. I liked the way Dweck, Mueller, and Kamins studied this test to improve their theory on the fixed and growth mindsets as well.

I believe this annotation is important because many fixed mindset students tend to compare themselves to others such as, Einstein, but when students have a growth mindset, they believe that they can be just as smart as Einstein through hard work and dedication.

I found this annotation important because there are many students that give up when things get hard and don't strive to their excellence or is afraid of taking risks and challenges, so it is good that we learn the different mindsets, so that students don't get any demotivation.

I can't find the Appendix?

A lesson learnt from Haskell

Debating between professional and DIY appliance repair in Sydney? Learn the key differences, risks, and benefits to help you make the best decision for your home appliances.

I see full sentences copied verbatim from this reference into the current article.

Is these also being written by AI?

• The virtues highlighted—self-control, generosity and other higher principles of life.
• Hospitality emphasized through the Persian understanding of giving gifts—communicating the ideas of returning honor.
• Patriarchal ideas of male lineage formulate cultural alliances while women are seen as diplomatic instruments.

• the ethical virtues of pietas are displayed in Siawosh’s devotion. Ritual respect understood from the perfumed letters to show adherence to the cultural order.
• Yet again, loyalty and honesty are highlighted, along with diplomacy
• “Tender green of spring”—refers to a metaphor for the joyand prospering of righteousness. This is a shift into the emotionaspects of the character.

• There is divine disfavor here—kingship is seen with Iran’s moral victory. Zoroastrian ideals are embedded in the religious perspective—truth and what it means to be righteous over ideas of evil. Courage and discipline are placed over Garsivaz’s loyalty to Afrasiyab because it supports the wrongdoing.
• There are geographical and symbolic Persians rhymthmic prose utilized in words like— balkh and Zabolestan.
• In regards to the patriarchal status, kings are the center of all power. There are emphasis on authority and succession.

• This reinforces the central tragic theme of the entire play—It is a cosmic casualty. All disaster is referred to the divine hand of the gods bringing about judgement upon the pride of the people. The belief are purely Greek in their expressions, especially in how they understood pride to be dealt with the most shameful judgement.

• The scale of the slaughter conveyed. This is seen as an act of divine judgement for the Persians.

• The hyperbolic opening to set the scene of the destress and mood.

• Grendel is seen as the divine wrath upon the people, a curse thing. The Christian ideas of punishment and the permission of human trials by God. Grendel’s evils are condemned for killing innocent warriors. This is also emphasized by the Gummere rendition (https://studylib.net/doc/9435792/file?utm_source=chatgpt.com). Both capture the theological imagery and how the medieval scribes would have understood or edited the story to fit their culture.

• “undarkly” an old English diction meaning—clear. This provides and archaic hue by the author.

Here he is basically saying that the lower class (famers and laborers) are not deserving of wealth because they did not “work for it”

Donggyun used to mention with me about this. It's possible that the improvement in performance is just due to noise/artifact (e.g randomness in seed), but might not be real improvement.

this is gold

Yes different methods break in different ways but they might share some failure modes (e.g sample inefficiency). We want to find a big bit to flip, i.e the one that is shared by as much existing methods as possible.

need elaboration

With the learning in this class, how are student's that has a harder time to grasp and understand the class's lecture, assignments, the modules, and training? Especially if English is their 2nd language or have a harder time understanding and if they were in ESL/ ELL classes in their high school years and was not able to/ were never able to pass/ get out of the Special Education Class?

yes and it is also more than that isn't it, or it can be. you can see yourself, the way you act and behave and speak in an extraordinary level of detail, whilst at the same time knowing what is in the mind of the main character i.e. you.

Stephen Grosz psychoanalyst and author of The Examined Life...

a bit cheesy (it's the times uk so) but still

the third annotation

I never knew what the term "open pedagogy" was. Discovering what it fulfills is very interesting because it makes education more flexible, showcasing that students can not only learn from information that is given but also make other students learn and be welcomed to creative thoughts and ideas.

Television news is today as political as it was back in the 1950s. One could say that Fox aligns more with Republican views and that NBC favors more Democratic views.

It is worth noting the difference in technology here. TV has come a long way from the days of being offered via UHF (Ultra-high Definition) to now being offered in HD (high definition), and what about the many types of TVs that exist today? LCD (Liquid Crystal Display) and OLED (Organic Light-Emitting) are just a couple. Back in the old days, at most we could only choose from black and white or colored TV.

Before talking pictures, the big thing was radio. In 1930, talking pictures were still relatively new. It would be interesting to see how much business radio stations lost due to the creation of talking pictures. One movie that illustrates this change is "Singing in the Rain"

The only current broadcasting that comes to mind is PBS, which is even losing funding. While most streaming services and cable TV providers are privately owned, these companies still need to obtain federal licensing. However, it seems like the standards to get a license are much lower today.

Something that has not changed from the old days and now is that television entertainment is all about making money.

I find it so fascinating to read in detail about the different processes of communication. In my own life, it feels like such an ingrained ability and people communicate without seeming to have a second thought about it. I do not usually comprehend the scale with which communication has grown and changed over the centuries. Thinking back on the history of communication, shifting from the "talking era" to the "manuscript era" and comparing it to where we are now, with social media and global mass communication is a staggering thought.

Good references 👍

this is fact, gifts can only take someone so far in life, but to work hard and put effort into something can take someone even farther and give them more opportunities.

Giving a sterotype to certain groups and telling them that they either "got it or they don't" can be harmful in growing their brains. They can assume that others will excel at a task because they "have it" while they will sya that they themselves "Don't have it" because they aren't part of that group. It can also be harmful to the group as they can feel like they also can't "have it" because they are part of a group of people.

Effort is the key to success, not only talent. And "talent is nothing without hard work" - Cristiano Ronaldo. Anyone has the potential to do anything, the only thing holding someone back is their mindset.

The students given appraisal for intelligence suffered the consequences of having a big ego and when faced with difficulty they would've rather avoided it and thus learned a "fixed mindset", however the student praised for their efforts were able to maintain their confidence and were able to improve as they ddint back away from challenging problems and faced them to learn more giving them a "growth mindset"

This is important because it proves that learning about growth mindset can actually improve grades. And it also proves that there's no such thing as being "math person." Putting effort will allow anyone to be anything they want if they work hard enough for it.

These students had a growth mindset, they realized that through effort they could expand their brains and become smarter through effort. Even if they don't believe they can be as smart as Einstein they believe tat people can still grow and improve te abilities that they have.

This shows how praising someone for their efforts rather than their "intelligence" then they'd choose to actually go through the efforts of failing to get better. Further increasing their confidence and motivation to push forward and grow.

The idea that even just thinking that intelligence is something that can grow or is fixed actually can affect the ability to grow the mind is quite an astonishing fact, It affects the " motivation, learning, and school achievement" of the person.

This shows that mindset of people can hold them back as usually a person with a growth mindset tends to be on the correct side. They are usually also straightforward and believe if you work hard for something you will accomplish it. While on the other hand someone with a fixed mindset believes that no matter how hard you work for something then it means you didn't have the ability to do it, and that it should come naturally.

Intrapersonal communication (the way you talk to yourself in your mind) is a form of communication that I have always thought to be incredibly important. Ever sense I was a child, I have always had a "think before I do" mentality and it has carried through to adulthood. It has helped me in many situation to reason through problems in my head and avoid conflict and confusion with others. it also plays a huge role in how we see ourselves and the world around us. Learning to communicate with yourself kindly and productively, plays a key role in how we communicate to others.

Mass communication, especially on the scale that we are witnessing it today, is such a double edged sword. The ability to project information into society rapidly and in easily accessible ways can be a great benefit, as is the case with amber alerts, which help to increase public vigilance on a large scale. A major drawback with mass communication as I see it, comes with people that have little regard for its ethical use. The ability to spread highly palatable and sometimes subtle information that can sway the way in which we think, act, and feel, perhaps without even being aware, means that society as a whole must be hyper aware of the media they are consuming.

My dad has connections with the local newspaper from Star, and they print magazines now instead of only newspapers. Currently they continue to grow, which is interesting in this mostly digital world today. I find it interesting especially because of how you only really find newspapers in small town areas such as Star, and usually at the cafes. Newspapers have their niche places that you find them

Essentially, Intrapersonal is thoughts withing your head/personal self Interpersonal communication is within friends/family/ to one person, Group communication is generally with multiple others than yourself, sometimes towards a shared goal Public communication is the X post, facebook post, or instagram pic you posted last week, Mass communication is used in situations like newspapers, PSAs, and school rallies

Goal-oriented communication could also happen intrapersonally, could it not? it is a form of group communication, but could be considered a form of intrapersonal communication due to the way that most people plan within their brains. For instance, I have a goal, so I plan, and work on that goal by myself, but use intrapersonal goal-oriented communication.

I'm curious if people who have been diagnosed have more intrapersonal communication than neurotypical people. Is it as significant as people who are neurotypical? Are there as many internal discussions? What if people who have ADHD have more significant questions?

This reminds me of how my father and one of his employees communicate. There is a language barrier between them, so most of the time when they work together he's using sound affects and grand gestures. It's quite funny to watch, so if he needs to talk about blending something, he generally uses the "SZHHZZZZ" (idk blender sounds) and twirling a finger to simulate the blender spinning. She does the same with him.

hello! you're on hypothes.is... it has such potential. if you find something interesting on the internet please @everythingness!... @noone

What measures were taken during the Riots? How many cities were under Republican control?

In this excerpt, "I'm whacked" is an informal way of saying that the speaker is extremely tired or exhausted. The surrounding conversation reflects on activities and feelings of fatigue related to playing golf and other casual interactions. The phrase suggests a sense of drain, either physical or emotional, after engaging in a lengthy or challenging experience.

中文解释： 在这一摘录中，“我累坏了”是一种非正式的表达方式，意思是说说话者感到非常疲惫或精疲力尽。周围的对话回顾了与打高尔夫有关的活动和疲惫感。这句话暗示了在经历了一段漫长或具有挑战性的经历后所感受到的身体或情感上的疲惫。

English Explanation:

In the excerpt, "feisty" describes an energetic and spirited attitude, particularly regarding a female professional golfer who makes the sport seem enjoyable and engaging. The conversation highlights how her feistiness brings excitement to golf, indicating that her lively nature enhances the experience. The context also includes humorous banter about golf, suggesting that the enjoyment of the game can depend on the people you are playing with.

Chinese Explanation:

在这个摘录中，“feisty”形容一种充满活力和精神的态度，特别是指一位女性职业高尔夫球手，她让这项运动看起来既有趣又吸引人。对话强调她的活泼使高尔夫变得更加刺激，表明她的生动个性提升了这项运动的体验。上下文还包含了关于高尔夫的幽默对话，暗示打球的乐趣可能取决于与你一起打球的人。

whacking

English Explanation: In this excerpt, the term "whacking" refers to the act of hitting a golf ball with force, often used informally to describe swinging a golf club. The conversation involves two individuals discussing golf, and one mentions following a female professional golfer online who is skilled and energetic. This suggests that "whacking" can also imply enjoying the game and making it look exciting. The mention of playing with a partner, Martin Gibson, indicates that one's experience can vary based on the playing companion.

Chinese Explanation: 在这段摘录中，“whacking”一词指的是用力击打高尔夫球，通常以非正式的方式形容挥动高尔夫球杆。对话中，两个人在讨论高尔夫，其中一人提到在线关注一位女性职业高尔夫球手，她技艺高超且充满活力。这表明“whacking”也意味着享受这项运动并使其看起来充满乐趣。提到与合作伙伴马丁·吉布森一起打球，说明与打球伙伴的配合会影响个人的游戏体验。

compare traditional value of rhetoric?

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

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Reply to the reviewers

We are very grateful for the positive feedback from all three reviewers. Below, we address each point in detail and outline proposed experiments and revision plans, with changes indicated by an underscore.

__Reviewer #1 (Evidence, reproducibility and clarity (Required)):

In this paper "Magnesium depletion unleashes two unusual modes of colistin resistance with different fitness costs," the authors examine how Pseudomonas aeruginosa evolves resistance to colistin, a last-resort antibiotic for multidrug-resistant Gram-negative infections. Although colistin resistance is a major clinical challenge, its underlying mechanisms, particularly under nutrient-limited conditions typical of infections, are not fully understood. The study shows that under low magnesium (Mg²_⁺_) conditions-mimicking infection or biofilm stress-P. aeruginosa can develop colistin resistance via two distinct genetic pathways, each with unique fitness costs. The first involves mutations in genes such as htrB2 and lpxO2, granting strong resistance but compromising the outer membrane and increasing susceptibility to other antibiotics. The second involves regulatory mutations (e.g., in the oprH/phoP/phoQ promoter) that confer resistance with minimal membrane defects and generally lower fitness costs. These resistance strategies lead to different trade-offs: membrane-compromising mutations reduce bacterial fitness without colistin, while regulatory mutations typically avoid these penalties, with context-dependent effects. The study underscores clinical relevance, noting that in infections-such as in cystic fibrosis-other microbes like Candida albicans may deplete magnesium, indirectly promoting resistance evolution. Overall, this work offers important insights into antibiotic resistance in nutrient-stressed, polymicrobial environments, highlighting how magnesium availability shapes resistance evolution and fitness costs. The findings suggest new avenues for therapeutic intervention and call for a reevaluation of antibiotic strategies in nutrient-competitive infection settings.

Work is timely and important. Colistin resistance represents an urgent threat as colistin is a last-resort antibiotic used against multidrug-resistant Gram-negative pathogens. Insights into mechanisms evolving under nutrient limitation are highly relevant given the prevalence of such environmental conditions during infection and microbial biofilm growth. The study reveals two previously uncharacterized pathways to colistin resistance in P. aeruginosa triggered by magnesium (Mg²_⁺_) depletion, each with distinct genetic signatures and trade-offs. This finding directly impacts the understanding of polymicrobial infection dynamics, especially where magnesium sequestration by fungi/ or other microbes may occur. The identification of fitness costs and pleiotropic effects associated with specific resistance mutations provides crucial guidance for clinicians considering antibiotic stewardship and combination therapy strategies.

__

We thank the reviewer for their summary of our study and its potential impact.

__Drawbacks

• Experimental scope: While the study is comprehensive for P. aeruginosa, the broader applicability to other Gram-negative pathogens is not directly tested.__

In our revision, we now explicitly point out that the magnesium limitation we have observed broadly applies to Gram-negative bacteria, as we demonstrated in our previous PLOS Biology paper. Therefore, we expect the same themes (and even genes, which are broadly conserved) to apply to Gram-negative bacteria in general. However, a full-fledged experimental study of other Gram-negative pathogens is outside the scope of our current study, which required a 90-day experimental evolution.

__Strengths

• Experimental evolution: This work uses laboratory evolution under controlled Mg²_⁺_-limited conditions to simulate selection pressures relevant to infection microenvironments. • Genetics: Systematic identification and functional validation of key mutations-particularly in htrB2, lpxO2, and the oprH/phoP/phoQ promoter-give mechanistic depth to the findings. • Two distinct resistance modes: Evidence for (i) one pathway leading to colistin resistance via htrB2 mutations, resulting in high resistance but significant membrane integrity loss and increased susceptibility to other antibiotics. (ii) a second pathway providing resistance without compromising membrane integrity, highlighting evolutionary flexibility and ecological implications. • Fitness assessments: measurement of the costs associated with each resistance strategy, both in terms of membrane integrity and susceptibility to other agents. • Relevance: Connection to natural scenarios, such as magnesium sequestration by fungi (e.g. Candida albicans) in polymicrobial environments, underscores the ecological and clinical significance. • This manuscript is well written with clearly logical hypothesis testing__

We thank the reviewer for their appraisal, especially for recognizing the rigor and broader biological implications of our study.

__Drawbacks

• Experimental scope: While the study is comprehensive for P. aeruginosa, the broader applicability to other Gram-negative pathogens is not directly tested.__

We agree with the reviewer's point about broader applicability in other Gram-negative bacteria, as many of the lipid A biosynthesis genes are conserved among diverse bacterial lineages. We will include this point in our revised Discussion to suggest relevance to other Gram-negative bacteria:

"We previously showed that magnesium sequestration by fungi applies not only to P. aeruginosa but to other Gram-negative bacteria as well (ref). Our current study lays a foundation for developing evolution-guided strategies to combat multidrug-resistant P. aeruginosa and other Gram-negative bacteria that can also acquire colistin resistance. Since many other antibiotic mechanisms are similarly dependent on metal ions (refs), our work suggests that nutritional competition for metal ions may alter initial antibiotic resistance in Gram-negative bacteria and potentiate new evolutionary pathways of antibiotic resistance."

• __ Mechanistic depth: Some inferred mechanisms (e.g., the precise molecular impact of late-occurring adaptive mutations) merit deeper biochemical analysis.__ We will emphasize in our Revision that the MS data of endpoint clones and triple mutants reveal that their lipid A structures are identical. This suggests that the role of other late-occurring mutations in enhancing resistance is likely through lipid A-independent pathways.

• __ Results Lines 414- 423: While correlation is most what makes sense for some drugs, causality is implied (membrane defects increase susceptibility), but could be strengthened by directly measuring antibiotic uptake (e.g., fluorescence) or membrane permeability for these 3 antibiotics.__ We thank the reviewer for highlighting the issue of causality. For the three antibiotics tested, the most direct way to measure their effect is by measuring their impact on bacterial growth directly, which is what we have done. Our membrane permeability assay using NpN uptake operates under the same conditions suggested by the reviewer and directly measures molecular uptake. Moreover, only fluorescently labeled vancomycin is commercially available among the three antibiotics tested. Since it binds to the cell wall, its utility to measure membrane defects is more limited than the NpN assay we have already used. However, in response to this comment, we will make clear in our revision that we infer that increased susceptibility to other antibiotics is due to their increased membrane permeability.

__ o Effect is mild and mostly not significant. It is also not clear whether authors only tested a handful of mutants shown in Fig. 7B-D or whether other clones were also tested. The sample of endpoints (P2, P5, P8) covers well-characterized lineages, but additional evolved clones or a broader panel could boost generality about other antibiotics. The authors note "significantly lower MICs" statistical treatment is implied; explicit statistical values and replicate numbers should be given in the text or figures.__

We slightly disagree with the reviewer that the results are not significant. Even two-to-three-fold differences in MICs translate to large differences in microbial competition. These three endpoint clones are representative of all eight evolved strains after 90-day evolution experiments. Moreover, we will emphasize in the Revision that we have tested all the mutations found in the endpoint clones; we know what these are from whole genome sequencing of multiple endpoint clones. In addition, we will explicitly state the p-value in the legend of Figure 7.

• __ The structural or physiological nature of "mild" vs. "severe" membrane defects could be better defined/quantified.__ Although we agree with the reviewer's suggestion, the variability of the SEM assay makes the classification of membrane defects based on cell morphology hard to quantify. We therefore only use the SEM images as representative of the various defects observed. For a more quantitative assay of the membrane defects, we instead rely on the standard NpN uptake assay to quantify membrane permeability as a quantifiable readout for membrane defects.

• __ Quantitative limits: Authors should add in the discussion that statistical robustness could be strengthened-for example, by including longer-term evolutionary predictions.__ We are not sure what the reviewer means and so cannot address this point completely. We ask the reviewer to rephrase this point, and we will address it to the best of our abilities.

• __ in vivo relevance: While the ecological context is discussed, direct in vivo confirmation (e.g., in animal infection models) of the observed resistance trajectories would increase translational impact and relevance.__ We agree with the reviewer's point. However, it is not trivial to directly perform evolution experiments of microbes in animal models. There are only a handful of labs worldwide that have working CF-relevant animal models. However, the colistin resistance mutations we identified provide a tool to look deeper into how colistin-resistant P. aeruginosa can evolve in vivo.

• __ Some sections are repetitive or overly detailed; condense where possible (especially on mutation lists and background for each claim).__

We will condense our manuscript as the reviewer suggested in our revision. Adding a graphical summary as suggested will also allow us to be more succinct in our description.

__Other comments

• Authors should provide clarification on how the Mg²_⁺_ concentrations used in vitro compare to those found in clinically relevant infection settings. This would be helpful to enhance significance.__

We thank the reviewer for raising this good point. Based on our previous work, we know the Mg2+ levels in our model (0.3-0.45mM) are within the physiological range of Mg2+ in infection settings (0.1-0.8mM). We will highlight this point in the introduction.

• __ Authors should explicitly report statistical methods (e.g., types of tests, adjustments for multiple comparisons) in figure legends for reproducibility.__

We will include the details of our statistical tests in each panel of figures both in the main text and the supplement.

• __ Nomenclature for key mutations and their position within the genetic context (e.g., htrB2 mutation specifics) could be more detailed in figures or supplemental materials.__

We will name each of the particular mutations tested to be specific about the nature of all the evolved mutations in our figure legends.

• __ The manuscript could benefit from a graphical summary illustrating the two distinct evolutionary pathways and their respective fitness landscapes.__ We thank the reviewer for this suggestion to enhance the clarity of our work. We will make a new graphical summary highlighting two different evolutionary pathways as a new figure.

• __ A brief discussion of therapeutic implications-such as combining colistin with agents that target membrane integrity-would help bridge the gap from mechanism to clinical management.__ In our discussion, we have suggested that collateral sensitivity (line 446-453) and PhoPQ kinase inhibitors (line 512-515) could be exploited to combat colistin resistance. To make this point more clearly, we will slightly expand our Discussion to include the therapeutic implications of our study.

• __ Additional discussion on whether the fitness costs are reversible or can be compensated by further adaptation would be valuable for long-term dynamics.__ We thank the reviewer for raising this interesting point. The evolution trajectory of P8 suggests that fitness costs can be compensated by later-occurring mutations during evolution. We will further discuss this point to highlight the importance of understanding the mutational dynamics of antibiotic resistance evolution.

• __ It would be valuable for the authors to comment on, or further analyze, whether there is a direct association between specific fitness costs and sensitivity to other antibiotics. Such information could inform on evolutionary constraints and possible trade-offs relevant to clinical settings.__

We will include a supplemental figure showing the correlation between fitness costs and antibiotic susceptibility for P2, P5, and P8.

__ Main figures and support for claims

The main and supplementary figures comprehensively illustrate the evolutionary trajectories, genetic bases, and phenotypic outcomes associated with colistin resistance under magnesium depletion in P. aeruginosa. The figures effectively detail: • Genetic pathways involved including the experimental evolution design (colistin selection under Mg²_⁺_ depletion), whole-genome sequencing results, and timelines of observed mutations (e.g., in htrB2, lpxO2, oprH/phoP/phoQ promoter, PA4824). • Phenotypes and biochemical analyses such as lipid A structure (via mass spectrometry), minimum inhibitory concentration (MIC) assays, and epistasis analyses between mutations are depicted. • Fitness trade-offs are demonstrated using bacterial survival, membrane integrity (e.g., scanning electron microscopy images), membrane permeability assays (NPN uptake), and competitive fitness assays. • Mechanistic claims about the necessity of early mutations, the requirement of the PhoPQ pathway at different evolutionary stages, and the fitness cost imposed by certain resistance mutations. To further enhance the rigor and clarity of the manuscript, the authors should implement the following improvements: • Labelling consistency: In some instances, figure legends could provide more granular detail about specific mutations (e.g., positions of amino acid changes). • Graphical summary: A schematic summary figure that visually integrates the three main evolutionary resistance trajectories, the mutational order, corresponding lipid A changes, and fitness costs, would enhance readability. • Replicates: Plots should more thoroughly indicate the number of replicates and show individual data points (not just means {plus minus} SD), add number of replicates in each experiment. • Supplementary: figures referenced in the text (e.g., lipid A structures or mutation reversion outcomes) should be made more prominent or better cross-referenced from the main results section. Authors should highlight when supplementary data provide critical functional confirmation (e.g., confirming mutation function or fitness reversal).__

We thank the reviewer for their appreciation of our work and constructive feedback.

__Statistics

The authors have appropriately incorporated statistical analyses throughout the figures. To enhance the robustness and credibility of their findings, authors should also cross-check • Tests in legends: Every figure and supplementary figure should clearly state the type of statistical test used, how many biological replicates, and any corrections for multiple comparisons.__

As mentioned above, we will provide more details about the statistical tests of each panel.

• __ Effect sizes: Where appropriate, reporting effect sizes-rather than just p values-would contextualize the biological impact.__ We agree with the reviewer; we will mention the magnitude of MIC changes in the corresponding figure legends.

• __ Raw data accessibility: For full transparency, consider sharing underlying raw data and analysis scripts.

__ We will provide the raw data of each panel.

__Overall, the main and supplementary figures effectively illustrate and substantiate the key claims-particularly the alternative molecular pathways, phenotypic trade-offs, and the role of environmental magnesium in mediating colistin resistance. Statistical analysis is generally robust and appropriately presented throughout, though improvements could include more explicit reporting, additional controls, and accessible raw data. The visual and quantitative data in the figures provide support for the authors' conclusions about the evolution of antibiotic resistance under nutrient limitation in microbial environments. Understanding these alternative pathways is important for designing better treatment strategies and for predicting how resistance might evolve under varying clinical and environmental conditions.

__

We thank the reviewer for their positive assessment.

__ Reviewer #1 (Significance (Required)):

Overall, this work offers important insights into antibiotic resistance in nutrient-stressed, polymicrobial environments, highlighting how magnesium availability shapes resistance evolution and fitness costs. The findings suggest new avenues for therapeutic intervention and call for a reevaluation of antibiotic strategies in nutrient-competitive infection settings.__

We sincerely thank the reviewer for constructive and thoughtful feedback and the acknowledgement of our figure presentation and experimental design. We feel very encouraged by the reviewer's perspective that our study provides unique insights into resistance evolution in polymicrobial environments and may inform therapeutic strategies.

__My expertise: Gut microbiome, gut microbiota resilience, ecology, and evolution in microbial communities, antimicrobial resistance, high-throughput drug-bacteria interactions

Reviewer #2 (Evidence, reproducibility and clarity (Required)):

Summary: The paper by Hsieh and colleagues unravels the molecular basis of colistin resistance in Pseudomonas aeruginosa under low magnesium (Mg2+) conditions. Colistin is a last resort antibiotic that compromises bacterial cell wall integrity. Bacteria can respond (phenotypically and genotypically) to colistin by modifying membrane-anchored lipopolysaccharides. Mg2+ depletion can trigger similar responses. In their study, Hsieh et al. find that Mg2+ depletion (induced by a co-infecting fungal pathogen, Candida albicans) leads to evolutionary trajectories and resistance mechanisms that differ from those observed under Mg-rich conditions. The authors conducted a series of detailed genetic, chemical and fitness-based experiments to elucidate the molecular, physiological and evolutionary basis of these new resistance mechanisms.__

We thank the reviewer for their summary of our study.__

Major comments: __ 1. The authors reconstituted key mutations observed during experimental evolution in the ancestral background. Moreover, they took clones from the final stage of the evolution experiment and restored the ancestral state of the mutated genes. This dual approach is extremely strong and allows to decipher the causal effects of colistin resistance. I like to applaud the authors for this rigorous approach.

We thank the reviewer's appreciation about the rigor and comprehensive analyses of our study.

2. I understand that this work focusses on evolved mutants isolated from a previous experiment. The focus is on Mg2+ limitation. However, it would still have been nice to include a characterised colistin resistent strain featuring more standard resistance mechanisms. How different would such a strain be in the analyses shown in Fig. 3? Would morphological changes (Fig. 5A), fitness trade-offs (Fig. 6) and collateral sensitivity (Fig. 7) also occur in such a mutant. I do not regard it as imperative to include data from such a strain. But putting the new data into context (at least in the discussion) would clearly increase the overall impact of this work.

We thank the reviewer for raising this fascinating and vital point. We will address the point in our Revision using the monoculture (high Mg2+) evolved strains, which acquired many known mutations for colistin resistance, as our reference. We will provide a supplemental figure about the membrane permeability, fitness costs, and collateral sensitivity of monoculture evolved strains. We will also contrast their difference from co-culture evolved strains in the revised Discussion.__

1. I recommend to discuss the findings in the context of the work conducted by Jochumsen et al. 2016 Nature Communications https://doi.org/10.1038/ncomms13002. To me, this is one of the most insightful papers on the genetic basis and epistasis of colistin resistance.__

We thank the reviewer for pointing out this important reference. We will include this reference and its findings in the Discussion.

__Minor comments:

1. First section of results and Fig. 1. It is unclear what parts are repetition from the ref. 37 and what is new. Please clarify.__

We thank the reviewer for this suggestion. Figures 1A and 1B summarize the previous paper; all other panels are new data. We will make this clear in the revised text and figure legend.

5. MIC-data (e.g. Fig. 2) come in discrete categories (based on the underlying dilution series). This comes with some challenges for statistical analysis. First, linear models like ANOVAs are based on normally distributed residuals. This is violated with discrete data distributions. Second, there is often no within-treatment variation (e.g., Fig. 2B), which makes statistical analyses obsolete. These points need to be addressed. Moreover, how is it possible to have subtle variations in MIC (e.g., Fig. 2A, P2 endpoint clone) with classic dilution series (as indicated on the y-axis, 128, 256, 512)? Please explain.

We agree with the reviewer that statistical analysis of MIC data is not straightforward. ANOVAs are not well-suited for this type of discrete data, and the lack of variation within replicates reduces the power of non-parametric tests such as the Mann-Whitney U test. To improve the statistical reporting of MIC data, we will apply non-parametric tests and include effect size measurements, as recommended by Reviewer 1.

Moreover, the design of dilution series may underestimate the true nature of antibiotic susceptibility. To address these issues, we have also performed survival assays to assess colistin resistance in both the endpoint and reversion strains; we will also include statistics to assess the significance of their different survival frequencies.

We thank the reviewer for highlighting the point about subtle variations in a classical dilution series. Our endpoint strains grew robustly in media containing 192 μg/mL colistin-the highest concentration used in our evolution experiment. To more accurately determine and compare their maximum MICs, we expanded the colistin concentration range using finer fold increases (1.5×, 2×, 2.5×, 3×, 3.5×, and 4×) from 192 to 768 μg/mL. We will update these details in the Materials & Methods.

__ Lines 264-269. This analysis focusses on enzyme impairment. However, mutations could also change enzyme activity. Could any of these mutations have such an effect?__

The answer is "yes". As evolved strains with lpxA mutation still have lipid A, we suspect this mutation does not altogether abolish lipid A synthesis. However, this mutation could affect the amount of lipid A or change enzyme specificity. These are interesting ideas for further investigation, but they fall beyond the scope of our current study. We will, however, include the requested detail in the discussion.

__ Figure 5A. Some arrows seem to be out of place and point at void spaces. Please check.__

We thank the reviewer for pointing out this error, which we will correct.

8. The use of polymyxin B is not well justified (Fig. 5 and Fig. S13). Did the authors aim to test whether there is cross-resistance to other antimicrobial peptides?

We will more clearly justify our choice of using polymyxin B for directly assaying binding of polymyxin antibiotics to bacterial cells using fluorescence-labeled polymyxins, since no such reagents exist for colistin and since previous studies (including ours) have shown similarity of susceptibility to colistin and polymyxin B:

"Although P2 and P5 endpoint clones have more permeable membranes, they exhibited greater resistance to polymyxin antibiotics, including colistin (polymyxin E) (Fig. 5D), and polymyxin B (Fig. S13A) than WT cells. To investigate how membrane-compromised cells gain increased resistance to antibiotics that target the outer membrane, we used dansyl-labeled polymyxin B [51] to quantify the binding of polymyxins to P. aeruginosa; dansyl-labeled polymyxin fluoresces upon binding the hydrophobic portion of bacterial membranes. We used polymyxin B binding as a surrogate for how bacterial cells bind to all polymyxin antibiotics, including colistin."

__ Line 564. Please indicate the dilution factor used.__

Thank you for pointing out this inadvertent omission. We will update our Materials & Methods accordingly, as in response to the Reviewer 2's comment 5.

__Reviewer #2 (Significance (Required)):

This is a very strong and well designed study. It provides novel and relevant insights into the resistance mechanisms against an important last resort antibiotic.__

We sincerely thank the reviewer for their thoughtful summary and generous evaluation of our work.

__Reviewer #3 (Evidence, reproducibility and clarity (Required)):

This manuscript reports on biologically interesting and clinically-relevant findings, that upon passaging in the presence of spent media from C. albicans, P. aeruginosa develops resistance to colistin through lipid A modifications. The authors thoroughly characterize novel lipid A structures seen in their resistant mutants, and test a variety of genetically constructed mutants to determine the contributions of specific mutant alleles to resistance.__

We thank the reviewer for the appreciation of our experimental design and comprehensive genetic and biochemical analyses of our evolved strains.

However, additional experiments are needed to demonstrate the specific role and necessity of the lipid modifications for colistin resistance.

We are also grateful for the reviewer's feedback and constructive criticisms to improve the clarity and impact of our manuscript. We have listed detailed responses to the reviewer below.

1. __ Evidence that the lipid A mutations are causal for colistin resistance is sparse:
2. Both the htrB2 mutations (in P2 and P5) are posited to be loss-of-function alleles. However, the phenotypes of the individual alleles are different (shown in Fig 2A and 2B). While the mutation in P2 shows a ~2x increase in resistance, the mutation in P5 does not. Thus it is not clear that the specific lipid A modifications seen in the htrB2 mutants are sufficient to confer colistin resistance. Can the authors test a clean deletion mutant of htrB2? Further, reversion of the htrB2 mutation in P2 has only a mild effect on colistin resistance, while reversion in P5 leads to a ~3-4x reduction in colistin resistance (Fig. S3), once again making it hard to parse out the exact effect of the lipid A modifications seen in the htrB2 mutants.
3. Similarly, a single lpxO2 mutation does not have any effect on colistin resistance (in P5), indicating that the modifications seen in this mutant are not sufficient to lead to resistance.__ We thank the reviewer for making this suggestion. The reviewer is correct that a clean deletion will directly assess the effects of htrB2 mutations. We will make htrB2 deletion in WT and the triple mutants and endpoint clones of P2 and P5 to check the effect of htrB2 deletion on colistin resistance.

Additionally, as Reviewer 2 pointed out, both mutation reconstruction and reversion experiments are required for understanding the roles of each mutation and interactions among different mutations in contributing to resistance. Combining all the results of htrB2 and lpxO2 mutations in these two orthogonal genetic experiments, it is the synergistic interactions among these mutations that lead to enhanced resistance after evolution. This explains why we saw genetic background effects of htrB2 mutation (P2 vs P5) and why each single mutation is required for resistance but doesn't contribute to resistance significantly by itself.

- In P8, the effect of a single lpxA mutation is not tested. Further, the resistance of a P-oprH + lpxA mutant is the same as that of just the P-oprH mutant, indicating that the lpxA mutation likely does not directly alter colistin resistance. It is possible that mutations in lpxA were selected to compensate for fitness defects resulting from the other mutations, or for adaptation to some other component of the media conditions.

This is an excellent suggestion. We will assess the MIC and fitness of reconstructed strains with the lpxA mutation to update the role of this mutation.

- While reversion of the htrB2 and lpxO2 mutations do lead to ~3-4x reduced resistance in P5 indicating some contribution of these mutations, it is specific to this population, and thus not clear whether it is due to the specific lipid A modifications (some of which are seen in the other populations too). A specific combination of lipid A modifications may confer colistin resistance, but this needs to be demonstrated by generating just those clean deletion mutants and showing an effect on resistance.

In response to this comment and comment 1, we will make lpxO2 deletions in WT, the triple mutant and the endpoint clone of P5 to test colistin resistance. However, our results of reverting single htrB2 or lpxO2 mutation to WT are robust and use two independent assays, including the standard MIC test and colistin survival assay. So, we are confident that each mutation is necessary for enhancing colistin resistance.

__ Overall, given the high levels of colistin resistance still exhibited by single mutant revertants (Fig. S3) and the absence of double or triple revertants, it is hard to come to any conclusions regarding causality. This is especially the case for P8 but also true of P2 and P5. What are the other mutations in these populations, and what role do they play in colistin resistance?__

We respectfully disagree with the reviewer on this point. One point that we have made and will re-emphasize in our Revision is that we have assayed all the mutations in these populations; this is one of the advantages of our experimental evolution and genome sequencing strategy. All the mutations that could play a role in colistin resistance have therefore been tested. Furthermore, due to genetic epistasis of mutations in different evolutionary lineages, we do not necessarily expect that a single revertant would altogether abolish colistin resistance, as has been demonstrated in several previous studies. As Reviewer 2 pointed out, combining mutation reconstruction and reversion is the best way to establish causality, and we have done so. Therefore, it is not correct to say that we cannot come to 'any conclusions regarding causality'.

__ Figure 4 is titled "The PhoPQ pathway synergizes with early-arising mutations to confer colistin resistance.", but instead what this figure shows is that the mutation upstream of oprH increases PhoP activity. I'm not sure what the synergy here is. The same is true for the section starting on line 276. Further, the first sentence of that section states "We next investigated why the mutations conferring robust colistin resistance in low Mg2+ conditions are not observed in Mg2+ replete conditions.". However, there are no experiments there testing whether the mutations conferred resistance in Mg2+ conditions, instead the authors just test whether the mutations they are studying increase PhoP activity, and require PhoPQ to confer resistance.__

We thank the reviewer for raising this point. We apologize for the unclear writing. We will use this opportunity to improve the clarity of this section by rewriting it to focus on two points: 1. Evolved resistance is PhoPQ-dependent, instead of PmrAB-dependent. 2. Two lineages evolved enhanced resistance by boosting PhoPQ activity in both high and low Mg2+ conditions. We will also remove the statement highlighted by the reviewer from this section that obfuscates the motivation of this section. We feel this approach will more clearly show how lipid A-related mutations contribute to resistance in low Mg2+.

__ The authors claim that the identified mutations did not appear in the high magnesium conditions because they had a fitness cost under those conditions, but figure 6A shows that the evolved strains have fitness costs in low magnesium conditions as well. Further, the authors suggest that because the studied mutations act via increased PhoPQ activity, they do not lead to resistance under high magnesium conditions (lines 376-379). However, the increased PhoPQ activity is mediated by the P-oprH mutation in the isolates which likely increases PhoPQ activity even in high magnesium conditions. Overall, it is not clear why the mutations in the low magnesium condition were not selected for under high magnesium conditions.__

The reviewer is correct about the fitness cost in high Mg2+ and low Mg2+ conditions. These fitness experiments were carried out in the absence of colistin, which explains the finding that there are fitness defects in both conditions. As is well known, evolution for antibiotic resistance will ultimately select for resistant mutants, despite their fitness costs. In contrast, colistin MIC of these endpoint strains in high Mg2+ conditions was still much lower than the colistin concentration we applied during evolution (Fig. S15), indicating it is much less likely for these mutations to be selected for in high Mg2+. We will clarify this point in our revised Results and Discussion.

We agree with the reviewer about the P-oprH mutations (PhoPQ expression) and will note that, unlike the other mutations, it is not clear why these emerge only in the low Mg2+ condition.

__ The authors used C. albicans spent BHI media as their low magnesium condition, but this condition has a lot of other C. albicans metabolites that may be affecting the results. It is possible that what the authors are observing is not related to magnesium at all, and the authors should test the phenotypes in normal BHI medium depleted for magnesium or some defined medium where magnesium levels can be controlled.__

We thank the reviewer for mentioning this important point. In our prior PLOS Biology paper (https://doi.org/10.1371/journal.pbio.3002694.g005), we demonstrated that supplementing Mg2+ in evolved co-culture populations reduces colistin resistance, suggesting this evolved resistance is Mg2+ dependent. We also know that the MIC of our endpoint strains in C. albicans-spent BHI with supplemented Mg2+ (MIC of all three endpoint clones is less than 48 mg/mL colistin) is much lower than in C. albicans-spent BHI. We will mention this detail in the paper and include the data in our revision if the reviewer and editor require it.

Other comments: - The authors use MIC assays as well as % survival to measure resistance against colistin, and sometimes use both in the same figure (e.g. Figure 2). This makes direct comparisons difficult. It would be better to consistently use one assay, preferably the MIC, at least in all the main figures. If the survival data needs to be included, it could go in the supplementary figures.

We thank the reviewer for this suggestion. We will move the MIC data of mutation-reversion strains to the main Fig. 2D-F.

- While the mutations seen in the low and high magnesium conditions were shown in the previous manuscript, given the extensive dissection here, it would be useful for readers if the authors gave some details about the serial passaging and evolution experiment, identification of mutations, and some mention of what mutations were seen in high Mg populations.

We will add these details in the introduction.

- Given that oprH is present in an operon, it would be more accurate to call that mutation as being in the promoter of the oprH-phoP-phoQ operon rather than it being an oprH mutation (at least in the text, e.g. lines 127-129).

We agree. We will change this as the reviewer requested.

- Unlike what is stated on lines 287-290, deletion of oprH in P2 leads to a greater than 2x reduction in colistin MIC, suggesting that OprH is playing a role (albeit a smaller role than phoP) - Line 50 has a typo, remove "160". - Line 122: Specify which Pa and Ca strain backgrounds were used. - Line 132: Were representative isolates derived from terminal passages? This should be defined.

We will change these points according to the reviewer's suggestions; we thank them for these suggestions.

- Line 215-219: It is interesting that Pa WT grown in spent medium additionally results in lipid A that is hexa-acylated. Is this sufficient to alter colistin resistance on its own?

We find that WT PAO1 in low Mg2+ conditions has PagP-mediated acylation, which can slightly increase colistin resistance, but not to the extent of resistance as our evolved strains.

- It would be useful to see a PCA plot for the samples shown in figures S6 and S7.

We will include such a plot in Figures S6 and S7

- Fig. S11: What are the colistin MICs of pmrA and phoP deletions in the WT background?

MIC of pmrA and phoP deletions in WT is 1.5ug/mL. We will include these data in the Revision.

- Instead of qualitative data, can the authors quantify cell length and perhaps some measure of cell shape (instead of just showing images in Fig. 5A and S12).

We thank the reviewer for raising this point. A similar comment was raised by Reviewer 1. As it's challenging to quantify membrane changes from the morphological data obtained through SEM (a point which we will now clarify in our Revision), we used a quantifiable NpN uptake assay to quantify membrane defects of our evolved strains.

- What is the WT MIC in high magnesium conditions? Please show that in Fig. S15.

We will include this detail in Fig. S15

- I am not an expert in lipid modifications and structures, but in figure S5, P2 and P4 show high peaks with lower m/z that seem specific to low magnesium conditions, but they are not labeled or discussed. What are these peaks?

We thank the reviewer for bringing up this concern. The unlabeled lipids in these spectra are cardiolipin, not lipid A. These peaks are present in all the samples, and the reason they appear larger in the P1 and P4 low magnesium conditions is that both spectra are scaled to the relative intensity of one another. It is important to note that MALDI-TOF MS is not a quantitative technique, and the relative intensity of the peak heights between two samples should not be used to compare the amounts of lipids in one sample versus another. Therefore, we cannot say that these lipids are present in greater quantities in low magnesium conditions versus high magnesium conditions.

- Lines 357-358 state that "mutant cells minimally bind polymyxin B (Fig. S13B)", but the figure shows increased binding compared to the WT. The legend of the figure also says something similar. Are the phoP pmrA mutants expected to bind more polymyxin B because they can't modify lipid A?

We thank the reviewer for pointing out this substantial error. We will change 'minimally bind' to 'demonstrate increased binding'.

- Given the fitness defects in just regular medium, is the data shown in Figure 7 specific collateral sensitivity to the antibiotics tested? Are there other conditions where P2 and P5 do not show increased sensitivity?

These are all the antibiotics we have tested. It is conceivable that P2 and P5 might not show increased sensitivity to other antibiotics that use the same mode of action as colistin or polymyxin B.

__Reviewer #3 (Significance (Required)):

This study aims to dissect novel mechanisms of colistin resistance in P. aeruginosa that arise upon passaging in C. albicans spent media. While the authors identify novel lipid A modifications associated with the evolved strains, the significance of the modifications for resistance, and the mechanisms for why these evolutionary trajectories were not selected for in high magnesium are not clear from the data presented.__

We thank the reviewer for recognizing the integrity of our work and for the constructive feedback on improving the clarity of our writing. We understand that some concerns may stem from a lack of clarity in our original submission, but that additional genetic experiments are necessary. We have already identified all mutations that arose independently across different lineages and characterized their contributions to resistance, which we believe supports a robust inference of causality. To strengthen our conclusions, we will incorporate additional experiments, including htrB2 deletion, lpxO2 deletion, and lpxA mutation, to better dissect the roles of these genes and mutations in colistin resistance. We hope this revision plan will ameliorate the reviewer's concerns.

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Referee #3

Evidence, reproducibility and clarity

This manuscript reports on biologically interesting and clinically-relevant findings, that upon passaging in the presence of spent media from C. albicans, P. aeruginosa develops resistance to colistin through lipid A modifications. The authors thoroughly characterize novel lipid A structures seen in their resistant mutants, and test a variety of genetically constructed mutants to determine the contributions of specific mutant alleles to resistance. However, additional experiments are needed to demonstrate the specific role and necessity of the lipid modifications for colistin resistance.

1. Evidence that the lipid A mutations are causal for colistin resistance is sparse:
   • Both the htrB2 mutations (in P2 and P5) are posited to be loss-of-function alleles. However, the phenotypes of the individual alleles are different (shown in Fig 2A and 2B). While the mutation in P2 shows a ~2x increase in resistance, the mutation in P5 does not. Thus it is not clear that the specific lipid A modifications seen in the htrB2 mutants are sufficient to confer colistin resistance. Can the authors test a clean deletion mutant of htrB2? Further, reversion of the htrB2 mutation in P2 has only a mild effect on colistin resistance, while reversion in P5 leads to a ~3-4x reduction in colistin resistance (Fig. S3), once again making it hard to parse out the exact effect of the lipid A modifications seen in the htrB2 mutants.
   • Similarly, a single lpxO2 mutation does not have any effect on colistin resistance (in P5), indicating that the modifications seen in this mutant are not sufficient to lead to resistance.
   • In P8, the effect of a single lpxA mutation is not tested. Further, the resistance of a P-oprH + lpxA mutant is the same as that of just the P-oprH mutant, indicating that the lpxA mutation likely does not directly alter colistin resistance. It is possible that mutations in lpxA were selected to compensate for fitness defects resulting from the other mutations, or for adaptation to some other component of the media conditions.
   • While reversion of the htrB2 and lpxO2 mutations do lead to ~3-4x reduced resistance in P5 indicating some contribution of these mutations, it is specific to this population, and thus not clear whether it is due to the specific lipid A modifications (some of which are seen in the other populations too). It is possible that a specific combination of lipid A modifications confers colistin resistance, but this needs to be demonstrated by generating just those clean deletion mutants and showing an effect on resistance.
2. Overall, given the high levels of colistin resistance still exhibited by single mutant revertants (Fig. S3) and the absence of double or triple revertants, it is hard to come to any conclusions regarding causality. This is especially the case for P8 but also true of P2 and P5. What are the other mutations in these populations, and what role do they play in colistin resistance?
3. Figure 4 is titled "The PhoPQ pathway synergizes with early-arising mutations to confer colistin resistance.", but instead what this figure shows is that the mutation upstream of oprH increases PhoP activity. I'm not sure what the synergy here is. The same is true for the section starting on line 276. Further, the first sentence of that section states "We next investigated why the mutations conferring robust colistin resistance in low Mg2+ conditions are not observed in Mg2+ replete conditions.". However, there are no experiments there testing whether the mutations conferred resistance in Mg2+ conditions, instead the authors just test whether the mutations they are studying increase PhoP activity, and require PhoPQ to confer resistance.
4. The authors claim that the identified mutations did not appear in the high magnesium conditions because they had a fitness cost under those conditions, but figure 6A shows that the evolved strains have fitness costs in low magnesium conditions as well. Further, the authors suggest that because the studied mutations act via increased PhoPQ activity, they do not lead to resistance under high magnesium conditions (lines 376-379). However, the increased PhoPQ activity is mediated by the P-oprH mutation in the isolates which likely increases PhoPQ activity even in high magnesium conditions. Overall, it is not clear why the mutations in the low magnesium condition were not selected for under high magnesium conditions.
5. The authors used C. albicans spent BHI media as their low magnesium condition, but this condition has a lot of other C. albicans metabolites that may be affecting the results. It is possible that what the authors are observing is not related to magnesium at all, and the authors should test the phenotypes in normal BHI medium depleted for magnesium or some defined medium where magnesium levels can be controlled.

Other comments:

• The authors use MIC assays as well as % survival to measure resistance against colistin, and sometimes use both in the same figure (e.g. Figure 2). This makes direct comparisons difficult. It would be better to consistently use one assay, preferably the MIC, at least in all the main figures. If the survival data needs to be included, it could go in the supplementary figures.
• While the mutations seen in the low and high magnesium conditions were shown in the previous manuscript, given the extensive dissection here, it would be useful for readers if the authors gave some details about the serial passaging and evolution experiment, identification of mutations, and some mention of what mutations were seen in high Mg populations.
• Given that oprH is present in an operon, it would be more accurate to call that mutation as being in the promoter of the oprH-phoP-phoQ operon rather than it being an oprH mutation (at least in the text, e.g. lines 127-129).
• Unlike what is stated on lines 287-290, deletion of oprH in P2 leads to a greater than 2x reduction in colistin MIC, suggesting that OprH is playing a role (albeit a smaller role than phoP)
• Line 50 has a typo, remove "160".
• Line 122: Specify which Pa and Ca strain backgrounds were used.
• Line 132: Were representative isolates derived from terminal passages? This should be defined.
• Line 215-219: It is interesting that Pa WT grown in spent medium additionally results in lipid A that is hexa-acylated. Is this sufficient to alter colistin resistance on its own?
• It would be useful to see a PCA plot for the samples shown in figures S6 and S7.
• Fig. S11: What are the colistin MICs of pmrA and phoP deletions in the WT background?
• Instead of qualitative data, can the authors quantify cell length and perhaps some measure of cell shape (instead of just showing images in Fig. 5A and S12).
• What is the WT MIC in high magnesium conditions? Please show that in Fig. S15.
• I am not an expert in lipid modifications and structures, but in figure S5, P2 and P4 show high peaks with lower m/z that seem specific to low magnesium conditions, but they are not labeled or discussed. What are these peaks?
• Lines 357-358 state that "mutant cells minimally bind polymyxin B (Fig. S13B)", but the figure shows increased binding compared to the WT. The legend of the figure also says something similar. Are the phoP pmrA mutants expected to bind more polymyxin B because they can't modify lipid A?
• Given the fitness defects in just regular medium, is the data shown in Figure 7 specific collateral sensitivity to the antibiotics tested? Are there other conditions where P2 and P5 do not show increased sensitivity?

Significance

This study aims to dissect novel mechanisms of colistin resistance in P. aeruginosa that arise upon passaging in C. albicans spent media. While the authors identify novel lipid A modifications associated with the evolved strains, the significance of the modifications for resistance, and the mechanisms for why these evolutionary trajectories were not selected for in high magnesium are not clear from the data presented.

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Referee #2

Evidence, reproducibility and clarity

Summary: The paper by Hsieh and colleagues unravels the molecular basis of colistin resistance in Pseudomonas aeruginosa under low magnesium (Mg2+) conditions. Colistin is a last resort antibiotic that compromises bacterial cell wall integrity. Bacteria can respond (phenotypically and genotypically) to colistin by modifying membrane-anchored lipopolysaccharides. Mg2+ depletion can trigger similar responses. In their study, Hsieh et al. find that Mg2+ depletion (induced by a co-infecting fungal pathogen, Candida albicans) leads to evolutionary trajectories and resistance mechanisms that differ from those observed under Mg-rich conditions. The authors conducted a series of detailed genetic, chemical and fitness-based experiments to elucidate the molecular, physiological and evolutionary basis of these new resistance mechanisms.

Major comments:

1. The authors reconstituted key mutations observed during experimental evolution in the ancestral background. Moreover, they took clones from the final stage of the evolution experiment and restored the ancestral state of the mutated genes. This dual approach is extremely strong and allows to decipher the causal effects of colistin resistance. I like to applaud the authors for this rigorous approach.
2. I understand that this work focusses on evolved mutants isolated from a previous experiment. The focus is on Mg2+ limitation. However, it would still have been nice to include a characterised colistin resistent strain featuring more standard resistance mechanisms. How different would such a strain be in the analyses shown in Fig. 3? Would morphological changes (Fig. 5A), fitness trade-offs (Fig. 6) and collateral sensitivity (Fig. 7) also occur in such a mutant. I do not regard it as imperative to include data from such a strain. But putting the new data into context (at least in the discussion) would clearly increase the overall impact of this work.
3. I recommend to discuss the findings in the context of the work conducted by Jochumsen et al. 2016 Nature Communications https://doi.org/10.1038/ncomms13002. To me, this is one of the most insightful papers on the genetic basis and epistasis of colistin resistance.

Minor comments:

1. First section of results and Fig. 1. It is unclear what parts are repetition from the ref. 37 and what is new. Please clarify.
2. MIC-data (e.g. Fig. 2) come in discrete categories (based on the underlying dilution series). This comes with some challenges for statistical analysis. First, linear models like ANOVAs are based on normally distributed residuals. This is violated with discrete data distributions. Second, there is often no within-treatment variation (e.g., Fig. 2B), which makes statistical analyses obsolete. These points need to be addressed. Moreover, how is it possible to have subtle variations in MIC (e.g., Fig. 2A, P2 endpoint clone) with classic dilution series (as indicated on the y-axis, 128, 256, 512)? Please explain.
3. Lines 264-269. This analysis focusses on enzyme impairment. However, mutations could also change enzyme activity. Could any of these mutations have such an effect?
4. Figure 5A. Some arrows seem to be out of place and point at void spaces. Please check.
5. The use of polymyxin B is not well justified (Fig. 5 and Fig. S13). Did the authors aim to test whether there is cross-resistance to other antimicrobial peptides?
6. Line 564. Please indicate the dilution factor used.

Significance

This is a very strong and well designed study. It provides novel and relevant insights into the resistance mechanisms against an important last resort antibiotic.

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Referee #1

Evidence, reproducibility and clarity

In this paper "Magnesium depletion unleashes two unusual modes of colistin resistance with different fitness costs," the authors examine how Pseudomonas aeruginosa evolves resistance to colistin, a last-resort antibiotic for multidrug-resistant Gram-negative infections. Although colistin resistance is a major clinical challenge, its underlying mechanisms, particularly under nutrient-limited conditions typical of infections, are not fully understood.

The study shows that under low magnesium (Mg²⁺) conditions-mimicking infection or biofilm stress-P. aeruginosa can develop colistin resistance via two distinct genetic pathways, each with unique fitness costs. The first involves mutations in genes such as htrB2 and lpxO2, granting strong resistance but compromising the outer membrane and increasing susceptibility to other antibiotics. The second involves regulatory mutations (e.g., in the oprH/phoP/phoQ promoter) that confer resistance with minimal membrane defects and generally lower fitness costs. These resistance strategies lead to different trade-offs: membrane-compromising mutations reduce bacterial fitness without colistin, while regulatory mutations typically avoid these penalties, with context-dependent effects. The study underscores clinical relevance, noting that in infections-such as in cystic fibrosis-other microbes like Candida albicans may deplete magnesium, indirectly promoting resistance evolution. Overall, this work offers important insights into antibiotic resistance in nutrient-stressed, polymicrobial environments, highlighting how magnesium availability shapes resistance evolution and fitness costs. The findings suggest new avenues for therapeutic intervention and call for a reevaluation of antibiotic strategies in nutrient-competitive infection settings.

Work is timely and important. Colistin resistance represents an urgent threat as colistin is a last-resort antibiotic used against multidrug-resistant Gram-negative pathogens. Insights into mechanisms evolving under nutrient limitation are highly relevant given the prevalence of such environmental conditions during infection and microbial biofilm growth. The study reveals two previously uncharacterized pathways to colistin resistance in P. aeruginosa triggered by magnesium (Mg²⁺) depletion, each with distinct genetic signatures and trade-offs. This finding directly impacts the understanding of polymicrobial infection dynamics, especially where magnesium sequestration by fungi/ or other microbes may occur. The identification of fitness costs and pleiotropic effects associated with specific resistance mutations provides crucial guidance for clinicians considering antibiotic stewardship and combination therapy strategies.

Strengths

• Experimental evolution: This work uses laboratory evolution under controlled Mg²⁺-limited conditions to simulate selection pressures relevant to infection microenvironments.
• Genetics: Systematic identification and functional validation of key mutations-particularly in htrB2, lpxO2, and the oprH/phoP/phoQ promoter-give mechanistic depth to the findings.
• Two distinct resistance modes: Evidence for (i) one pathway leading to colistin resistance via htrB2 mutations, resulting in high resistance but significant membrane integrity loss and increased susceptibility to other antibiotics. (ii) a second pathway providing resistance without compromising membrane integrity, highlighting evolutionary flexibility and ecological implications.
• Fitness assessments: measurement of the costs associated with each resistance strategy, both in terms of membrane integrity and susceptibility to other agents.
• Relevance: Connection to natural scenarios, such as magnesium sequestration by fungi (e.g. Candida albicans) in polymicrobial environments, underscores the ecological and clinical significance.
• This manuscript is well written with clearly logical hypothesis testing

Drawbacks

• Experimental scope: While the study is comprehensive for P. aeruginosa, the broader applicability to other Gram-negative pathogens is not directly tested.
• Mechanistic depth: Some inferred mechanisms (e.g., the precise molecular impact of late-occurring adaptive mutations) merit deeper biochemical analysis.
• Results Lines 414- 423: While correlation is most what makes sense for some drugs, causality is implied (membrane defects increase susceptibility), but could be strengthened by directly measuring antibiotic uptake (e.g., fluorescence) or membrane permeability for these 3 antibiotics.
  • Effect is mild and mostly not significant. It is also not clear whether authors only tested a handful of mutants shown in Fig. 7B-D or whether other clones were also tested. The sample of endpoints (P2, P5, P8) covers well-characterized lineages, but additional evolved clones or a broader panel could boost generality about other antibiotics. The authors note "significantly lower MICs" statistical treatment is implied; explicit statistical values and replicate numbers should be given in the text or figures.
• The structural or physiological nature of "mild" vs. "severe" membrane defects could be better defined/quantified.
• Quantitative limits: Authors should add in the discussion that statistical robustness could be strengthened-for example, by including longer-term evolutionary predictions.
• in vivo relevance: While the ecological context is discussed, direct in vivo confirmation (e.g., in animal infection models) of the observed resistance trajectories would increase translational impact and relevance.
• Some sections are repetitive or overly detailed; condense where possible (especially on mutation lists and background for each claim).

Other comments

• Authors should provide clarification on how the Mg²⁺ concentrations used in vitro compare to those found in clinically relevant infection settings. This would be helpfu to enhance significance.
• Authors should explicitly report statistical methods (e.g., types of tests, adjustments for multiple comparisons) in figure legends for reproducibility.
• Nomenclature for key mutations and their position within the genetic context (e.g., htrB2 mutation specifics) could be more detailed in figures or supplemental materials.
• The manuscript could benefit from a graphical summary illustrating the two distinct evolutionary pathways and their respective fitness landscapes.
• A brief discussion of therapeutic implications-such as combining colistin with agents that target membrane integrity-would help bridge the gap from mechanism to clinical management.
• Additional discussion on whether the fitness costs are reversible or can be compensated by further adaptation would be valuable for long-term dynamics.
• It would be valuable for the authors to comment on, or further analyze, whether there is a direct association between specific fitness costs and sensitivity to other antibiotics. Such information could inform on evolutionary constraints and possible trade-offs relevant to clinical settings.

Main figures and support for claims

The main and supplementary figures comprehensively illustrate the evolutionary trajectories, genetic bases, and phenotypic outcomes associated with colistin resistance under magnesium depletion in P. aeruginosa. The figures effectively detail: - Genetic pathways involved including the experimental evolution design (colistin selection under Mg²⁺ depletion), whole-genome sequencing results, and timelines of observed mutations (e.g., in htrB2, lpxO2, oprH/phoP/phoQ promoter, PA4824). - Phenotypes and biochemical analyses such as lipid A structure (via mass spectrometry), minimum inhibitory concentration (MIC) assays, and epistasis analyses between mutations are depicted. - Fitness trade-offs are demonstrated using bacterial survival, membrane integrity (e.g., scanning electron microscopy images), membrane permeability assays (NPN uptake), and competitive fitness assays. - Mechanistic claims about the necessity of early mutations, the requirement of the PhoPQ pathway at different evolutionary stages, and the fitness cost imposed by certain resistance mutations. To further enhance the rigor and clarity of the manuscript, the authors should implement the following improvements: - Labelling consistency: In some instances, figure legends could provide more granular detail about specific mutations (e.g., positions of amino acid changes). - Graphical summary: A schematic summary figure that visually integrates the three main evolutionary resistance trajectories, the mutational order, corresponding lipid A changes, and fitness costs, would enhance readability. - Replicates: Plots should more thoroughly indicate the number of replicates and show individual data points (not just means {plus minus} SD), add number of replicates in each experiment. - Supplementary: figures referenced in the text (e.g., lipid A structures or mutation reversion outcomes) should be made more prominent or better cross-referenced from the main results section. Authors should highlight when supplementary data provide critical functional confirmation (e.g., confirming mutation function or fitness reversal).

Statistics

The authors have appropriately incorporated statistical analyses throughout the figures. To enhance the robustness and credibility of their findings, authors should also cross-check - Tests in legends: Every figure and supplementary figure should clearly state the type of statistical test used, how many biological replicates, and any corrections for multiple comparisons. - Effect sizes: Where appropriate, reporting effect sizes-rather than just p values-would contextualize the biological impact. - Raw data accessibility: For full transparency, consider sharing underlying raw data and analysis scripts.

Overall, the main and supplementary figures effectively illustrate and substantiate the key claims-particularly the alternative molecular pathways, phenotypic trade-offs, and the role of environmental magnesium in mediating colistin resistance. Statistical analysis is generally robust and appropriately presented throughout, though improvements could include more explicit reporting, additional controls, and accessible raw data. The visual and quantitative data in the figures provide support for the authors' conclusions about the evolution of antibiotic resistance under nutrient limitation in microbial environments. Understanding these alternative pathways is important for designing better treatment strategies and for predicting how resistance might evolve under varying clinical and environmental conditions.

Significance

Overall, this work offers important insights into antibiotic resistance in nutrient-stressed, polymicrobial environments, highlighting how magnesium availability shapes resistance evolution and fitness costs. The findings suggest new avenues for therapeutic intervention and call for a reevaluation of antibiotic strategies in nutrient-competitive infection settings.

My expertise:

Gut microbiome, gut microbiota resilience, ecology, and evolution in microbial communities, antimicrobial resistance, high-throughput drug-bacteria interactions

This is very important because often times we expect very different things from what the college expects from us, so being able to find the right blend between what we expect and what the college expects is very helpful in our success as students.

Even if they did see it they had their own worries and duties to do.

This is important because this is another reason why it was weird that no one cared about the Boy falling from the sky.

This is what the Icarus is telling us, that life must go on, and people have things to do, places to go. That while some big event happened, it might not affect most people, and therefore, they ignore.

Nobody really cares about how a boy falls out of the sky.

Who are these "old masters"?

this is the test

As explained below, System.out.println is the subroutine. This means a command is a subroutine with an inputted data (in this case "Hello World!").

What the poet said here is sarcasm, because uncleanliness is portrayed as the opposite of beautiful, and he says that a girl is beautiful even though she had made stains in the book.

How he knew it was a beautiful girl stood out to me cus all she wrote was "pardon the egg salad stains"

What? Maybe he meant that the ride into the future on a vessel more lasting then themselves being the present day??

to me this is saying that when we read poetry we write in the margins to almost prove to ourselves that we are really thinking deep and trying to better understand the poem we read rather than appearing to have just skimmed the poem giving it no thought

The notes are just simply writing in the margins of the book.

The students would be more subtle about how they would write their "marks on the book".

The slave trade started in the 1400s and ended in the 1800s. Why didn’t African people take effective preventive measures to avoid being taken by those human traffickers?

“Those people were barbaric. Why did they take even the children?”

According to history, by 1839 the slave trade should have already been eradicated. Why were people still being shipped as slaves?”

Alan!

All our lives, we are taught that the economy was this big complicated system that is hard for just anyone to understand. We are taught to trust the economists because they know best and truly understand the economy better than anyone. While this may be true, Stanford poses real world questions that make the economy easier to understand. This way we can make an economic profile of my community and therefore understand how the economy works around us.

In this paragraph, the Stanford critiques the field of economics by pointing out how deeply embedded it is in systems of power and wealth. Stanford argues that many economists, both in academia and in the professional world, are influenced—consciously or subconsciously—by the interests of the institutions and systems that fund and support them. These institutions, such as banks, corporations, schools and governments, benefit from maintaining the current economic system. Because of this, economists tend to view inequality, competition, and wealth accumulation as natural and inevitable or even good for society.

yes

Testing.

eLife Assessment

This valuable study reports convincing evidence about associations between 35 polygenic indices (PGIs) for social, behavioral, and psychological traits, along with some non-fatal health conditions (e.g., BMI) and all-cause mortality in data from Finnish population-based surveys and a twin cohort linked with administrative registers. PGIs for education, depression, alcohol use, smoking, BMI, and self-rated health showed the strongest associations with all-cause mortality, on the order of ~10% increment in risk per PGI standard deviation. Effect sizes from twin-difference analyses tended to be slightly larger than the effect sizes from population cohorts, opposite the pattern generally observed when testing PGI associations with their target phenotypes and supporting robustness of findings to confounding by population stratification.

Reviewer #1 (Public review):

Lahtinen et al. evaluated the association between polygenic scores and mortality. This question has been intensely studied (Sakaue 2020 Nature Medicine, Jukarainen 2022 Nature Medicine, Argentieri 2025 Nature Medicine), where most studies use PRS as an instrument to attribute death to different causes. The presented study focuses on polygenic scores of non-fatal outcomes and separates the cause of death into "external" and "internal". The majority of the results are descriptive, and the data doesn't have the power to distinguish effect sizes of the interesting comparisons: (1) differences between external vs. internal (2) differences between PGI effect and measured phenotype. I have two main comments:

(1) The authors should clarify whether the p-value reported in the text will remain significant after multiple testing adjustment. Some of the large effects might be significant; for example, Figure 2C (note that the small prediction accuracy of PGI in older age groups has been extensively studied, see Jiang, Holmes, and McVean, 2021, PLoS Genetics).

(2) The authors might check if PGI+Phenotype has improved performance over Phenotype only. This is similar to Model 2 in Table 1, but slightly different.

Reviewer #2 (Public review):

Summary:

This study provides a comprehensive evaluation of the association between polygenic indices (PGIs) for 35 lifestyle and behavioral traits and all-cause mortality, using data from Finnish population- and family-based cohorts. The analysis was stratified by sex, cause of death (natural vs. external), age at death, and participants' educational attainment. Additional analyses focused on the six most predictive PGIs, examining their independent associations after mutual adjustment and adjustment for corresponding directly measured baseline risk factors.

Strengths:

Large sample size with long-term follow-up.

Use of both population- and family-based analytical approaches to evaluate associations.

Weaknesses:

It is unclear whether the PGIs used for each trait represent the most current or optimal versions based on the latest GWAS data.

If the Finnish data used in this study also contributed to the development of some of the PGIs, there is a risk of overestimating their associations with mortality due to overfitting or "double-dipping." Similar inflation of effect sizes has been observed in studies using the UK Biobank, which is widely used for PGI construction.

for - youtube - BBC - AI2027 - Futures - AI - progress trap - AI - to AI2027 website - https://hyp.is/0VHJqH3cEfCm9JM_EB3ypQ/ai-2027.com/

summary - This dystopian futures scenario is the brainchild of former OpenAI researcher Daniel Kokotajlo, - It is premised on human behavior in modernity including - confirmation bias of AI researchers - entrenched competing political ideologies that motivate an AI arms race - entrenched capitalist market behavior that motivates an AI arms race - AI becoming embodied, resulting in Artificially Embodied Artificial Intelligence (AEAI), posing the danger to humanity because it's no longer just talk, but action - Can it happen? The probability is not zero.We don't really understand the behavior of the AI LLM's we design, they are nonpredictable, and as we give them even greater power, that is a slippery slope - AI can become humanity's ultimate progress trap, which is ironic, because the technology that promises to be the most efficient of all, can become so efficient, it no longer need human beings - Remember Jerry Kaplan's book "Humans need not apply"? - https://hyp.is/o0lBFH3fEfC1QLfnLSs5Bg/www.youtube.com/watch?v=JiiP5ROnzw8 - This dystopian futures scenario goes further and explores the idea that "humans need not exist"!

question - What about emulating climate change gamification of "Bend the Curve" of emissions? - Use the AI 2027 trajectory as a template and see how much real-life follows this trajectory - Just as we have the countdown to the https://climateclock.world/ ( 3 years and change remaining as of today) - perhaps we can have an AI 2027 clock? - What can we do to "bend the dystopian AI 2027 curve" AWAY from the dystopian future?

eLife Assessment

In this valuable study, the authors developed long-term imaging tools to simultaneously monitor the temporal and spatial dynamics of excitatory and inhibitory synapses and reported that excitatory and inhibitory synapses need to develop synergistically during synaptogenesis to maintain balance. While the analysis and quantification of the imaging data are incomplete, there is convincing evidence that the developed tools are feasible. If these tools can function stably in vivo, their applications will be much broader.

Reviewer #1 (Public review):

Summary:

By imaging the dynamics of synaptic proteins in cultured neurons, this study presents significant findings regarding the dynamics of excitatory and inhibitory synaptic proteins during development. The evidence shows that the ratios of excitatory and inhibitory synaptic proteins are stable during synapse development. This discovery advances our understanding of the complex mechanisms governing synapse formation. The strength of the evidence is robust, as it is supported by a combination of biological assays and endogenous labeling.

Strengths:

This research sheds light on the dynamics of the excitatory and inhibitory synapses during development. It is crucial to understand that while excitatory synapses and inhibitory synapses are developed independently, the ratio of their number is relatively stable during development, maintaining a stable excitatory/inhibitory ratio.

Important findings and implications in the research include:

(1) Persistent Synapse Dynamics: Excitatory and inhibitory synapses remain highly dynamic even in mature neurons (DIV12-14), challenging the dogma that synaptic structures are stable after the synaptogenesis stage.

(2) Maintained E/I Balance: Despite ongoing synapse turnover (formation/elimination) and presynaptic terminal reduction, the overall density and ratio of excitatory-to-inhibitory synapses remain relatively stable during circuit maturation (Figure 7).

(3) Developmental Shifts: While presynaptic compartments decrease over time, postsynaptic sites increase, suggesting independent regulation of pre- and postsynaptic elements within a stable E/I framework.

Weaknesses:

This study focuses on specific synaptic proteins within synapses, which may not fully represent the dynamics of other synaptic machinery; also, whether similar observations exist in vivo is still unknown. Further research is needed to explore the implications of these findings in more complex neuronal environments.

Reviewer #2 (Public review):

Summary:

The Garbett et al. identified a critical need to begin to understand the interplay between the assembly, maturation, and elimination of excitatory and inhibitory synapses. They also detail the lack of reliable tools to address this gap in knowledge. Here, the authors developed synaptic reporters expressed by lentiviruses (mClover3-Homer1c, HaloTag-Syb2, and tdTomato-Gephyrin). They combined these reporters with resonance scanning confocal imaging to measure synapses over a 15-hour period during neuron development and in mature neurons in primary hippocampal cultures. Using these reporters in the same neuron, the authors compared the ratios of postsynaptic excitatory and inhibitory specializations that co-localize with presynaptic terminals during development and in mature neurons and found that they are stable across time points. Finally, the authors developed CRISPR/Cas9 tools (TKIT) to knock-in endogenous fluorescent tags (GFP/tdTomato-Gephyrin) or epitope tags (HA-Bassoon and HA-Homer1) to begin to study synapse dynamics using endogenous proteins. I believe this paper highlights an important gap in knowledge and begins to offer methodologies to determine the dynamic coordination between excitatory and inhibitory synapses.

Strengths:

(1) The experiments are well-designed and carefully controlled.

(2) The authors carefully validated the reporter and TKIT constructs.

(3) The authors provide strong proof-of-principle for the use of the reporter constructs to track synapse formation, maintenance, and elimination over a 15-hour period.

(4) Ingenious use of technologies (reporters, TKIT, and resonance scanning confocal microscopy) to develop a platform for future studies of synapse dynamics.

(5) Strong evidence supporting that the ratio of excitatory and inhibitory synapses (those that oppose syb2) stays constant through development.

Weaknesses:

Overall, this is a well-executed study that develops tools to simultaneously image excitatory and inhibitory synapse dynamics and represents an important first step to address the fundamental question regarding the coordination between these two types of synapses.

Minor weaknesses of the manuscript include:

(1) The lack of a characterization of endogenous Homer1-positive excitatory synapses using TKIT.

(2) Discussion about other approaches to study excitatory and inhibitory synapses using endogenous proteins (e.g., intrabodies - FingR or nanobodies) should be included.

(3) The activity state of a neuron and/or a synapse might alter the dynamic properties (formation, maintenance, and/or elimination). A discussion on whether the overexpression of Homer1 and/or gephyrin might alter synapse/neuron activity would provide greater interpretability of the results. A discussion of the potential limitations and benefits of the reporter and TKIT approaches would be beneficial.

(4) A description and interpretation of the computational approach to calculate particle tracking would be helpful. I found that particle tracking figures, while elegant, are difficult to interpret.

Reviewer #3 (Public review):

In the present study, the authors describe the development of new tools and imaging strategies to assess the concomitant development of excitatory and inhibitory synapses in dissociated neuron cultures. To this end, they generate fluorescently tagged constructs of excitatory and inhibitory synapse marker proteins using either conventional overexpression or CRISPR-based strategies. They then image these marker proteins over a timespan of 15 hours to assess synaptic dynamics at different developmental timepoints. Based on their data, they conclude that excitatory and inhibitory synapse development occur in concert to maintain a functional balance despite individual synapse turnover.

Overall, this study addresses an interesting question, i.e., the interplay between the development of excitatory and inhibitory synapses, which has important implications, particularly for neurodevelopmental disorders in which the balance of excitation and inhibition is disrupted. The experiments are technically solid and well-executed, and the individual images are highly compelling.

However, a number of aspects remain to be addressed in order for the study to support the claims made by the authors. First, the novelty aspect of the development of the fluorescently tagged synaptic proteins is unclear, since reporters of this nature are in routine use in many labs. Second, the analysis of the acquired images often seems incomplete, with only example images but no quantification shown, or the distinction between spatial and temporal dynamics appearing unclear. Third, given this incomplete analysis, the interpretations of the authors are not always convincingly supported by the data presented. In conclusion, substantial improvements are required to render the main messages of the study clear and compelling.

eLife Assessment

This paper presents valuable findings on the processing of sound mixtures in the auditory cortex of ferrets, a species widely used for studies of auditory processing. Using the convenient and relatively high-resolution method of functional ultrasound imaging, the authors provide convincing evidence that background noise invariance emerges across the auditory cortical processing hierarchy. They also draw informative comparisons with previously published fMRI data obtained in humans. This work will be of interest to researchers studying the auditory cortex and the neural mechanisms underlying auditory scene analysis and hearing in noise.

Reviewer #1 (Public review):

This is a very interesting paper addressing the hierarchical nature of the mammalian auditory system. The authors use an unconventional technique to assess brain responses -- functional ultrasound imaging (fUSI). This measures blood volume in cortex at a relatively high spatial resolution. They present dynamic and stationary sounds in isolation and together, and show that the effect of the stationary sounds (relative to the dynamic sounds) on blood volume measurements decreases as one ascends the auditory hierarchy. Since the dynamic/stationary nature of sounds is related to their perception as foreground/background sounds, this suggests that neurons in higher levels of the cortex may be increasingly invariant to background sounds.

The study is interesting, well conducted and well written. In the revised manuscript, the authors have addressed all the points I raised in my review.

Reviewer #2 (Public review):

Summary:

Noise invariance is an essential computation in sensory systems for stable perception across a wide range of contexts. In this paper, Landemard et al. perform functional ultrasound imaging across primary, secondary and tertiary auditory cortex in ferrets to uncover the mesoscale organization of background invariance in auditory cortex. Consistent with previous work, they find that background invariance increases throughout the cortical hierarchy. Importantly, they find that background invariance is largely explained by progressive changes in spectro-temporal tuning across cortical stations which are biased towards foreground sound features. To test if these results are broadly relevant, they then re-analyze human fMRI data and find that spectro-temporal tuning fails to explain background invariance in human auditory cortex.

Strengths:

(1) Novelty of approach: Though the authors have published on this technique previously, functional ultrasound imaging offers unprecedented temporal and spatial resolution in a species where large-scale calcium imaging is not possible and electrophysiological mapping would take weeks or months. Combining mesoscale imaging with a clever stimulus paradigm, they address a fundamental question in sensory coding.

(2) Quantification and execution: the results are generally clear and well supported by statistical quantification.

(3) Elegance of modeling: The spectrotemporal model presented here is explained clearly and most importantly, provides a compelling framework for understanding differences in background invariance across cortical areas.

Comments on revised version:

The authors have addressed all of my previous concerns and their publicly shared data is easy to view, this is a nice contribution to the field.

Reviewer #3 (Public review):

This paper investigates invariance to natural background noise in the auditory cortex of ferrets and humans. The authors first replicate, in ferrets, a finding from human neuroimaging showing that invariance to background noise increases along the cortical hierarchy (i.e. from primary to non-primary auditory cortex). Next, the authors ask whether this pattern of invariance could be explained by differences in tuning to low-level acoustic features across primary and non-primary regions. The authors conclude that this tuning can explain the spatial organization of background invariance in ferrets, but not in humans. The conclusions of the paper are well supported by the data.

The paper is very straightforwardly written, with a generally clear presentation including well-designed and visually appealing figures. Not only does this paper provide an important replication in a non-human animal model commonly used in auditory neuroscience, but also it extends the original findings in three ways. First, the authors reveal a more fine-grained gradient of background invariance by showing that background invariance increases across primary, secondary and tertiary cortical regions. Second, the authors address a potential mechanism that might underlie this pattern of invariance by considering whether differences in tuning to frequency and spectrotemporal modulations across regions could account for the observed pattern of invariance. The spectrotemporal modulation encoding model used here is a well-established approach in auditory neuroscience and seems appropriate for exploring potential mechanisms underlying invariance in auditory cortex, particularly in ferrets. Third, the authors provide a more complete picture of invariance by additionally analyzing foreground invariance, a complementary measure not explored in the original study.

Comments on author revisions:

The authors have thoroughly addressed the concerns raised in my initial review.

Author response:

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

Reviewer #1(Public review):

(1) Changes in blood volume due to brain activity are indirectly related to neuronal responses. The exact relationship is not clear, however, we do know two things for certain: (a) each measurable unit of blood volume change depends on the response of hundreds or thousands of neurons, and (b) the time course of the volume changes are slow compared to the potential time course of the underlying neuronal responses. Both of these mean that important variability in neuronal responses will be averaged out when measuring blood changes. For example, if two neighbouring neurons have opposite responses to a given stimulus, this will produce opposite changes in blood volume, which will cancel each other out in the blood volume measurement due to (a). This is important in the present study because blood volume changes are implicitly being used as a measure of coding in the underlying neuronal population. The authors need to acknowledge that this is a coarse measure of neuronal responses and that important aspects of neuronal responses may be missing from the blood volume measure.

The reviewer is correct: we do not measure neuronal firing but use blood volume as a proxy for bulk local neuronal activity, which does not capture the richness of single neuron responses. This is why the paper focuses on large-scale spatial representations as well as cross-species comparison. For this latter purpose, fMRI responses are on par with our fUSI data, with both neuroimaging techniques showing the same weakness. We have now added this point to the discussion: 

“Second, we used blood volume as a proxy for local neuronal activity. Thus, our signal ignores any heterogeneity that might exist at the level of local neuronal populations. However, our main findings are related to the large-scale organization of cortical responses and how they relate to those of humans. For this purpose, the functional spatial resolution of our signal, driven by the spatial resolution of neurovascular coupling, should be adapted. In addition, using hemodynamic signals provides a much better comparison with human fMRI data, where the same limitations are present.”

(2) More importantly for the present study, however, the effect of (b) is that any rapid changes in the response of a single neuron will be cancelled out by temporal averaging. Imagine a neuron whose response is transient, consisting of rapid excitation followed by rapid inhibition. Temporal averaging of these two responses will tend to cancel out both of them. As a result, blood volume measurements will tend to smooth out any fast, dynamic responses in the underlying neuronal population. In the present study, this temporal averaging is likely to be particularly important because the authors are comparing responses to dynamic (nonstationary) stimuli with responses to more constant stimuli. To a first approximation, neuronal responses to dynamic stimuli are themselves dynamic, and responses to constant stimuli are themselves constant. Therefore, the averaging will mean that the responses to dynamic stimuli are suppressed relative to the real responses in the underlying neurons, whereas the responses to constant stimuli are more veridical. On top of this, temporal following rates tend to decrease as one ascends the auditory hierarchy, meaning that the comparison between dynamic and stationary responses will be differently affected in different brain areas. As a result, the dynamic/stationary balance is expected to change as you ascend the hierarchy, and I would expect this to directly affect the results observed in this study.

It is not trivial to extrapolate from what we know about temporal following in the cortex to know exactly what the expected effect would be on the authors' results. As a first-pass control, I would strongly suggest incorporating into the authors' filterbank model a range of realistic temporal following rates (decreasing at higher levels), and spatially and temporally average these responses to get modelled cerebral blood flow measurements. I would want to know whether this model showed similar effects as in Figure 2. From my guess about what this model would show, I think it would not predict the effects shown by the authors in Figure 2. Nevertheless, this is an important issue to address and to provide control for.

We understand the reviewer’s concern about potential differences in response dynamics in stationary vs non-stationary sounds. It seems that the reviewer is concerned that responses to foregrounds may be suppressed in non-primary fields because foregrounds are not stationary, and non-primary regions could struggle to track and respond to these sounds. Nevertheless, we observed the contrary, with non-primary regions overrepresenting non-stationary (dynamic) sounds, over stationary ones. For this reason, we are inclined to think that this explanation cannot falsify our findings. 

We understand the comment that temporal following rates might differ across regions in the auditory hierarchy and agree. In fact, we do show that tuning to temporal rates differs across regions and partly explains the differences in background invariance we observe. In this regard, we think the reviewer’s suggestion is already implemented by our spectrotemporal model, which incorporates the full range of realistic temporal following rates (up to 128 Hz). The temporal averaging is done as we take the output of the model (which varies continuously through time) and average it in the same window as we used for fUSI data. When we fit this model to the ferret data, we find that voxels in non-primary regions, especially VP (tertiary auditory cortex), tend to be more tuned to low temporal rates (Figure 2F, G), and that background invariance is stronger in voxels tuned to low rates. This is, however, not true in humans, suggesting that background invariance in humans relies on different computational mechanisms. We have added a sentence to clarify this: “The model included a range of realistic temporal rates and this axis was the most informative to discriminate foregrounds from backgrounds.”

(3) I do not agree with the equivalence that the authors draw between the statistical stationarity of sounds and their classification as foreground or background sounds. It is true that, in a common foreground/background situation - speech against a background of white noise - the foreground is non-stationary and the background is stationary. However, it is easy to come up with examples where this relationship is reversed. For example, a continuous pure tone is perfectly stationary, but will be perceived as a foreground sound if played loudly. Background music may be very non-stationary but still easily ignored as a background sound when listening to overlaid speech. Ultimately, the foreground/background distinction is a perceptual one that is not exclusively determined by physical characteristics of the sounds, and certainly not by a simple measure of stationarity. I understand that the use of foreground/background in the present study increases the likely reach of the paper, but I don't think it is appropriate to use this subjective/imprecise terminology in the results section of the paper.

We appreciate the reviewer’s comment that the classification of our sounds into foregrounds and backgrounds is not verified by any perceptual experiments. We use those terms to be consistent with the literature (McWalter and McDermott, 2018; McWalter and McDermott, 2019), including the paper we derived this definition from (Kell et al., 2019). These terms are widely used in studies where no perceptual or behavioral experiments are included, and even when animals are anesthetized. We have clarified and justified this choice in the beginning of the Results section:

“We used three types of stimuli: foregrounds, backgrounds, and combinations of those. We use those terms to refer to sounds differing in their stationarity, under the assumption that stationary sounds carry less information than non-stationary sounds, and are thus typically ignored.”

We have also added a paragraph in the discussion to emphasize the limits of this definition:

“First, this study defined foregrounds and backgrounds solely based on their acoustic stationarity, rather than perceptual judgments. This choice allowed us to isolate the contribution of acoustic factors in a simplified setting. Within this controlled framework, we show that acoustic features of foreground and background sounds drive their separation in the brain and the hierarchical extraction of foreground sound features.”

(4) Related to the above, I think further caveats need to be acknowledged in the study. We do not know what sounds are perceived as foreground or background sounds by ferrets, or indeed whether they make this distinction reliably to the degree that humans do. Furthermore, the individual sounds used here have not been tested for their foreground/background-ness. Thus, the analysis relies on two logical jumps - first, that the stationarity of these sounds predicts their foreground/background perception in humans, and second, that this perceptual distinction is similar in ferrets and humans. I don't think it is known to what degree these jumps are justified. These issues do not directly affect the results, but I think it is essential to address these issues in the Discussion, because they are potentially major caveats to our understanding of the work.

We agree with the reviewer that the foreground-background distinction might be different in ferrets. In anticipation of that issue, we had enriched the sound set with more ecologically relevant sounds, such as ferret and other animal vocalizations. Nevertheless, we have emphasized this limitation in addition to the limitation of our definition of foregrounds and backgrounds in the discussion: 

“In addition, most of the sounds included in our study likely have more relevance for humans compared to ferrets (see table \ref{tbl1}). Despite including ferret vocalizations and environmental sounds that are more ecologically relevant for ferrets, it is not clear whether ferrets would behaviorally categorize foregrounds and backgrounds as humans do. Examining how ferrets naturally orient or respond to foreground and background sounds under more ecologically valid conditions, potentially with free exploration or spontaneous listening paradigms, could help address this issue.”

Reviewer #2(Public review);

(1) Interpretation of the cerebral blood volume signal: While the results are compelling, more caution should be exercised by the authors in framing their results, given that they are measuring an indirect measure of neural activity, this is the difference between stating "CBV in area MEG was less background invariant than in higher areas" vs. saying "MEG was less background invariant than other areas". Beyond framing, the basic properties of the CBV signal should be better explored:

a) Cortical vasculature is highly structured (e.g. Kirst et al.( 2020) Cell). One potential explanation for the results is simply differences in vasculature and blood flow between primary and secondary areas of auditory cortex, even if fUS is sensitive to changes in blood flow, changes in capillary beds, etc (Mace et al., 2011) Nat. Methods.. This concern could be addressed by either analyzing spontaneous fluctuations in the CBV signal during silent periods or computing a signal-to-noise ratio of voxels across areas across all sound types. This is especially important given the complex 3D geometry of gyri and sulci in the ferret brain.

We agree with the reviewers that there could be differences in vasculature across subregions of the auditory cortex and note that this point would also be valid for the published human fMRI data. Nevertheless, even if small differences in vasculature were present, it is unlikely that they would affect our analyses and results, which are designed to be independent of local vascular density. First, we normalize the signal in each voxel using the silent periods, so that the absolute strength of the raw signal, or baseline blood volume in each voxel, is factored in our analysis. Second, we only focus on reliably responsive voxels in each region and do see comparable sound-evoked responses in all regions (Figure S2). Third, our analysis mostly relies on voxel-based correlation across sounds, which is independent of the mean and variance of the voxel responses. Differences in noise, measured through test-retest reliability, can affect values of correlation, which is why we used a noise-correction procedure. After this procedure, invariance does not depend on test-retest, and differences across regions are still seen when matching for test-retest (new  Figure S7). Thus, we believe that differences in vascular architecture across regions are unlikely to affect our results. We added this point in the Methods section when discussing the noise-correction:

“After this correction, the differences we observed between brain regions were present regardless of voxels' test-retest reliability, or noise level (Figure S7). Thus, potential differences in vasculature across regions are unlikely to affect our results.”

b) Figure 1 leaves the reader uncertain what exactly is being encoded by the CBV signal, as temporal responses to different stimuli look very similar in the examples shown. One possibility is that the CBV is an acoustic change signal. In that case, sounds that are farther apart in acoustic space from previous sounds would elicit larger responses, which is straightforward to test. Another possibility is that the fUS signal reflects time-varying features in the acoustic signal (e.g. the low-frequency envelope). This could be addressed by cross-correlating the stimulus envelope with fUS waveform. The third possibility, which the authors argue, is that the magnitude of the fUS signal encodes the stimulus ID. A better understanding of the justification for only looking at the fUS magnitude in a short time window (2-4.8 s re: stimulus onset) would increase my confidence in the results.

We thank the reviewer for raising that point as it highlights that the layout of Figure 1 is misleading. While Figure 1B shows an example snippet of our sound streams, Figure 1D shows the average timecourse of CBV time-locked to a change in sound (foreground or background, isolated or in a mixture). This is the average across all voxels and sounds, aiming at illustrating the dynamics for the three broad categories. In Figure 1E however, we show the cross-validated cross-correlation of CBV across sounds (and different time lags). To obtain this, we compute for each voxel the response to each sound at each time lag, thus obtaining two vectors (size: number of sounds) per lag, one per repeat. Then, we correlate all these vectors across the two repeats, obtaining one cross-correlation matrix per voxel. We finally average these matrices across all voxels. The presence of red squares with high correlations demonstrates that the signal encodes sound identity, since CBV is more similar across two repeats of the same sound (e.g., in the foreground only matrix, 0-5 s vs 0-5 s), than two different sounds (0-5 s vs. 7-12 s). We modified the figure layout as well as the legend to improve clarity.

(2) Interpretation of the human data: The authors acknowledge in the discussion that there are several differences between fMRI and fUS. The results would be more compelling if they performed a control analysis where they downsampled the Ferret fUS data spatially and temporally to match the resolution of fMRI and demonstrated that their ferret results hold with lower spatiotemporal resolution.

We agree with the reviewer that the use of different techniques might come in the way of cross-species comparison. We already control for the temporal aspect by using the average of stimulus-evoked activity across time (note that due to scanner noise, sounds are presented cut into small pieces in the fMRI experiments). Regarding the spatial aspect, there are several things to consider. First, both species have brains of very different sizes, a factor that is conveniently compensated for by the higher spatial resolution of fUSI compared to fMRI (0.1 vs 2 mm). Downsampling to fMRI resolution would lead to having one voxel per region per slice, which is not feasible. We also summarize results with one value per region, which is a form of downsampling that is fairer across species. Furthermore, we believe that we already established in a previous study (Landemard et al, 2021 eLife) that fUSI and fMRI data are comparable signals. We indeed could predict human fMRI responses to most sounds from ferret fUSI responses to the same identical sounds. We clarified these points in the discussion:

“In addition, fMRI has a worse spatial resolution than fUSI (here, 2 vs. 0.1 mm voxels). However, this difference in resolution compensates for the difference in brain size between humans and ferrets. In our previous work, we showed that a large fraction of cortical responses to natural sounds could be predicted from one species to the other using these methods (Landemard et al., 2021).”

Reviewer #3 (Public review):

As mentioned above, interpretation of the invariance analyses using predictions from the spectrotemporal modulation encoding model hinges on the model's ability to accurately predict neural responses. Although Figure S5 suggests the encoding model was generally able to predict voxel responses accurately, the authors note in the introduction that, in human auditory cortex, this kind of tuning can explain responses in primary areas but not in non-primary areas (Norman-Haignere & McDermott, PLOS Biol. 2018). Indeed, the prediction accuracy histograms in Figure  S5C suggest a slight difference in the model's ability to predict responses in primary versus non-primary voxels. Additional analyses should be done to a) determine whether the prediction accuracies are meaningfully different across regions and b) examine whether controlling for prediction accuracy across regions (i.e., subselecting voxels across regions with matched prediction accuracy) affects the outcomes of the invariance analyses.

The reviewer is correct: the spectrotemporal model tends to perform less well in human non-primary cortex. We believe this does not contradict our results but goes in the same direction: while there is a gradient in invariance in both ferrets and humans, this gradient is predicted by the spectrotemporal model in ferrets, but not in humans (possibly indeed because predictions are less good in human non-primary auditory cortex). Regardless of the mechanism, this result points to a difference across species. In ferrets, we found a significantly better prediction accuracy in VP (p=0.001, permutation test) and no differences between MEG and dPEG (p=0.89). In humans, prediction accuracy was slightly higher in primary compared to non-primary auditory cortex, but this effect was not significant (p=0.076). In both species, when matching prediction accuracy between regions, the gradients in invariance were preserved. We have added these analyses to the manuscript (Figure S5).

A related concern is the procedure used to train the encoding model. From the methods, it appears that the model may have been fit using responses to both isolated and mixture sounds. If so, this raises questions about the interpretability of the invariance analyses. In particular, fitting the model to all stimuli, including mixtures, may inflate the apparent ability of the model to "explain" invariance, since it is effectively trained on the phenomenon it is later evaluated on. Put another way, if a voxel exhibits invariance, and the model is trained to predict the voxel's responses to all types of stimuli (both isolated sounds and mixtures), then the model must also show invariance to the extent it can accurately predict voxel responses, making the result somewhat circular. A more informative approach would be to train the encoding model only on responses to isolated sounds (or even better, a completely independent set of sounds), as this would help clarify whether any observed invariance is emergent from the model (i.e., truly a result of low-level tuning to spectrotemporal features) or simply reflects what it was trained to reproduce.

We thank the reviewer for this suggestion. We have run an additional prediction using only the sounds presented in isolation, which replicates our main results (new Figure S6). We have added this control to the manuscript:

“Results were similar if the model was fit solely on isolated sounds, excluding mixtures from the training set (Figure S6).”

Finally, the interpretation of the foreground invariance results remains somewhat unclear. In ferrets (Figure 2I), the authors report relatively little foreground invariance, whereas in humans (Figure 5G), most participants appear to show relatively high levels of foreground invariance in primary auditory cortex (around 0.6 or greater). However, the paper does not explicitly address these apparent crossspecies differences. Moreover, the findings in ferrets seem at odds with other recent work in ferrets (Hamersky et al. 2025 J. Neurosci.), which shows that background sounds tend to dominate responses to mixtures, suggesting a prevalence of foreground invariance at the neuronal level. Although this comparison comes with the caveat that the methods differ substantially from those used in the current study, given the contrast with the findings of this paper, further discussion would nonetheless be valuable to help contextualize the current findings and clarify how they relate to prior work.

We thank the reviewer for this point. While we found a trend for higher background invariance than foreground invariance in ferret primary auditory cortex, this difference was not significant and many voxels exhibit similar levels of background and foreground invariance (for example in Figure 2D, G). Thus, we do not think our results are inconsistent with Hamersky et al., 2025, though we agree the bias towards background sounds is not as strong in our data. This might indeed reflect differences in methodology, both in the signal that is measured (blood volume vs spikes), and the sound presentation paradigm. Our timescales are much slower and likely reflect responses post-adaptation, which might not be as true for Hamersky et al. We have added this point to the discussion, as well as a comment on the difference between ferrets and humans in foreground invariance in primary auditory cortex:

“In ferrets, primary auditory cortex has been found to over-represent backgrounds in mixtures compared to foregrounds (Hamersky et al., 2025). In contrast, we found a slight, non-significant bias towards foregrounds in primary regions. This difference could be driven by a difference in timescales, as we looked at slower timescales in which adaptation might be more present, reducing the strength of background encoding. In humans, we found a much smaller gap between background and foreground invariance in primary auditory cortex, which was not predicted by the spectrotemporal model. Additional, more closely controlled experiments would be needed to confirm and understand this species difference.”

Reviewer #1 (Recommendations for the authors):

(1) In the introduction, explain the relationship between background/foreground and stationarity/non-stationarity, and thus why stationary/nonstationary stimuli could be used to probe differences in background/foreground processing.

We have added a sentence at the beginning of the results section to justify our choice (see public review).  

(2) Avoid use of the background/foreground terminology in Results (and probably Methods).

For consistency with previous literature, we decided to keep this terminology, though imperfect. We further justified our choice in the beginning of the Results section (see previous point).

(3) In the Discussion, explain what the implications of the results are for background/foreground processing, and, importantly, highlight any caveats that result from stationarity not being a direct measure of background/foreground.

We added a paragraph in the Discussion to highlight this point choice (see public review).

Reviewer #2 (Recommendations for the authors):

(1) Figure 1: Showing a silent period in the examples would help in understanding the fUS signal.

In Figure 1D, we show the average timecourse of CBV time-locked to a change in sound (foreground or background, isolated or in a mixture). This is the average across all voxels and sounds. Thus, it would not be very informative to show an equivalent plot for a silent period, as it would look flat by definition. However, we updated the layout and legend of Figure 1 to make it clearer and avoid confusion.

(2) "Responses were not homogenous" - would make more sense to say something like "responses were not spatially distributed".

We removed these words which were indeed not necessary: “We found that reliable soundevoked responses were confined to the central part of ventral gyrus of the auditory cortex.”

(3) Figure 2D: The maps shown in Figure 2D are difficult to understand for the noninitiated in fUS. At a minimum, labels should be added to indicate A-P, M-L, D-V. I cannot see the white square in the primary figure. An additional graphic would be helpful here to understand the geometry of the measurement.

We thank the reviewer for pointing out that reading these images is indeed an acquired skill. We added an annotated image of anatomy with indications of main features to guide the reader in Figure 1. We also added missing white squares. 

(4) Figure 2F: Can the authors better justify why the summary statistic is shown for all three areas, but the individual data only compares primary vs. higher order?`

We now show individual data for all three areas.

(5) More methods information is needed to understand how recordings were stitched across days. Was any statistical modeling used to factor out the influence of day on overall response levels?

We simply concatenated voxels recorded across different sessions and days. The slices were sampled randomly to avoid any systematic effect. Because different slices were sampled in different sessions, any spatial structure spanning several slices is unlikely to be artefactual. For instance, the map of average responses in Figure 2A shows a high level of continuity of spatial patterns across slices. This indicates that this pattern reflects a true underlying organization rather than session-specific noise. It also shows that the overall response levels are not affected by the day or recording session. We added a section in the Methods (“Combining different recordings”) to clarify this point:

“The whole dataset consisted of multiple slices, each recorded in a different recording session. Slices to image on a given day were chosen at random to avoid any systematic bias. Responses were consistent across neighboring slices recorded on different sessions, as shown by the maps of average responses (Figure 2A, Figure S2) where any spatial continuity across different slices must reflect a true underlying signal in the absence of common noise.”

Reviewer #3 (Recommendations for the authors):

(1) Figures:

The figures are generally very well done and visually appealing. However, I have a few suggestions and questions.

a)  In Figure 1G, the delta CBV ranges from 0.5 to 1.5, although in subsequent figures (e.g., Figure 2D), the range is much larger (-15 to 45). Is it possible that the first figure is a proportion rather than a percentage, or is there some other explanation for the massive difference in scale? Not being very familiar with this measure, it was confusing.

The same scale is used in both figures, the major difference being that in Figure 1D, we take the average over all voxels and sounds (for each category), which will include many nonresponsive voxels, and for responsive voxels, sounds that they do not respond a lot to. On the other hand, Figure 2D shows the response of a single, responsive voxel. Thus, the values it reaches for its preferred sounds (45%) are an extreme, which weighs only little in Figure 1D. We have changed the legend of Figure 1D to make this more explicit.

b)  Similar to the first point, the strength of the correlations in the matrices of Figure 1E is very small (~ 0.05) compared to the test-retest reliabilities plotted in Figure 2B (~0.5). Again, I was confused by this large difference in scale.

Two main factors explain the difference in values between Figure 1E and Figure 2B. First, in Figure 1B, each correlation is done on the average activity in a window of 0.3 s, opposed to 2.4 s in Figure 2B. More averaging leads to better SNR, which inevitably leads to higher testretest correlations. Second, in Figure 1B, the cross-correlation matrices are averaged across all responsive voxels without any criterion for reliability. On the other hand, Figure 2B show example voxels with good test-retest reliability. 

c)  In Figure 2D, the example voxels are supposed to be shown in white. It appears that this example voxel is only shown for the non-primary voxel. Please be sure to add these voxels throughout the other panels and figures as well. 

We fixed this mistake and added the example voxel in all panels.

d)  Why do the invariance results (e.g., Figure 2F) for individual animals combine across dPEG and VP, while the overall results (across all animals) split things across all three regions? The results in Table 2 do, in fact, provide this data. Upon further examination of the data in Table 2, it seems like there is only a significant difference between background invariance between dPEG and VP for one of the two animals, and that this might be what drives the effect when pooling across all animals. This seems important to both show visually in the figure and to potentially discuss. There is still very clearly a difference between primary and non-primary, but whether there is a real difference between dPEG and VP seems more unclear.

We added the values for single animals in the plot and highlighted this limitation in the text:

“While background invariance was overall highest in VP, the differences within non-primary areas were more variable across animals (see table 2).”

e)  Again, as in Figure 2F, the cross symbols seem like a bad choice as markers since the vertical components of the cross are suggestive of the error of the measurement. However, no error is actually plotted in these figures. I recommend using a different marker and including some measure of error in the invariance plots.

We replaced the crosses with circles to avoid confusion. The measure of error is provided by the representation of values for single animals.

f) The caption for Figure 4C states that each line corresponds to one animal, but does not precisely state what this line represents. Is this the median or something?

Each line indeed represents the median across voxels for one animal. We added this information to the legend.

g)  In Figure 5, the captions for panels D and E are swapped.

This has now been corrected.

(2) Discussion:

(a) In the paragraph on methodological differences, it mentions that the fMRI voxel size is around 2 mm. This may be true in general, but given the comparison to Kell & McDermott 2019, the voxel size should reflect that used in their study (1 mm).

The reviewer might refer to this sentence from the methods of Kell et al., 2019: “T1weighted anatomical images were collected in each participant (1-mm isotropic voxels) for alignment and cortical surface reconstruction.” However, this does not correspond to the resolution of the functional data, which is 2 mm, as mentioned a bit further in the Methods:  “In-plane resolution was 2 × 2 mm (96 × 96 matrix), and slice thickness was 2.8 mm with a 10% gap, yielding an effective voxel size of 2 × 2 × 3.08 mm.”

(b) In the next paragraph on the control of attention, it mentions that attentional differences could play a role. However, in Kell & McDermott 2019, they manipulated attention (attend visual versus attend auditory) and found that it did not substantially affect the observed pattern invariance. I suppose it could potentially affect the degree to which an encoding model could explain the invariance. This seems important, and given that the data was already collected, it could be worth it to analyze that data.

As the reviewer points out, Kell et al. 2019 ran an additional experiment in which they manipulated auditory vs. visual attention. However, the auditory task was just based on loudness and ensured that the participants were awake and paying attention to the stimuli, but not specifically to the foreground or background. This type of attention did not lead to changes in the observed patterns of invariance, which might have been the case for selective attention to backgrounds or foregrounds in the mixture. Given that these manipulations were not done in the ferret experiments, we chose to not include the analysis of this dataset in the scope of this paper. However, future work investigating that topic further would indeed be of interest.

(c) The mention of "a convolutional neural network trained to recognize digits in noise" should make more obvious that this is visual recognition rather than auditory recognition.

We clarified this sentence to make clear that the recognition is visual and not auditory: “For instance, in a convolutional neural network trained to visually recognize digits in different types of noise, when local feedback is implemented, early layers encode noise properties, while later layers represent clean signal.”

(d) Finally, one explanation of the results in the discussion is that "primary auditory areas could be recruited to maintain background representations, enabling downstream cortical regions to use these representations to specifically suppress background information and enhance foreground representations." This "background-related information" being used to "facilitate further extraction of foregrounds" is similar to what is argued in Hicks & McDermott PNAS 2024.

We thank the reviewer for suggesting this relevant reference and added it in this paragraph of the discussion.

(3) Methods:

In the "Cross-correlation matrices" section, it mentions that time-averaged responses from 2.4 to 4.8 s were used. It would be helpful to provide an explanation of why this particular time window was used. Additionally, I wondered whether one could look at adaptation type effects (e.g., that of Khalighinejad et al., 2019) or whether fUSI does not offer this kind of temporal precision?

The effects shown in Khalighinejad et al., 2019, are indeed likely too fast to be observed with our methods. However, there are still dynamics in the fUSI signal and in its invariance (Figure S1). Each individual combination of foreground and background is presented for 4.8 s (Figure 1B). Therefore, we chose the range 2.4-4.8 s as the biggest window we could use (to improve SNR) while minimizing contamination from the previous or next sound (indeed, blood volume typically lags neuronal activity by 1.5-2 s). We added this precision to the methods.

In the "Human analyses" section, it is very unclear which set of data was used from Kell & McDermott 2019. For example, that paper contains 4 different experiments, none of which has 7 subjects. Upon closer reading, it seems that only 7 of the 11 participants from Experiment 1 also heard the background sounds in isolation (thus enabling the foreground invariance analyses). However, they stated that there were only 3 female participants in that experiment, while you state that you used data from 7 females. It would be helpful to double-check this and to more clearly state exactly which participants (i.e., from which experiment) were used and why (e.g., why not use data from Experiment 4 in the visual task/attention condition?).

We added a sentence to clarify which datasets were used: “Specifically, we used data from Experiment 1 which provided the closest match to our experimental conditions, and only considered the last 7 subjects that heard both the foregrounds and the backgrounds in isolation, in addition to the mixtures.” 

It was a mistake to mention that it was all female, as the original dataset has 3 females and 8 males, of which we used 7 without any indication of their sex. Thus, we removed this mention from the text.

In the "Statistical testing" section, why were some tests done with 1000 permutations/shuffles while others were done with 2000?

We homogenized and used 1000 permutations/shuffles for all statistical tests.

(4) Miscellany:

(a) The Hamersky et al. 2023 preprint has recently been published (referenced in the public review), and so you could consider updating the reference.

This reference has now been updated.

(b) There are a few borderline statistical tests that could use a bit more nuance. For example (on page 4), "In primary auditory cortex (MEG), there was no significant difference between values of foreground invariance and background invariance (p = 0.063, obtained by randomly permuting the sounds' background and foreground labels, 1000 times)." This test is quite close to being significant, and this might be acknowledged.

We emphasized the trend to nuance the interpretation of these results: “In primary auditory cortex (MEG), foreground invariance was slightly lower than background invariance, although this difference was not significant (p=0.063, obtained by randomly permuting the sounds' background and foreground labels, 1000 times).”

(5) Potential typos:

(a)   Should the title be "natural sound mixtures" instead of "natural sounds mixtures"?

(b) The caption for Figure 1 says "We imaged the whole auditory through successive slices across several days." I believe this should the "the whole auditory [cortex]." c) In the first paragraph of the discussion, there is a sentence ending in "...are segregated in hemody-namic signal." I believe this should be "hemody-namic signal."

These errors are now all corrected.

for - youtube - Google Talks - Humans need not apply - Jerry Kaplan - 10 years after the book "Humans need not apply - the AI 2027 project - https://hyp.is/kWXQ0n3cEfCIUz_j42HHiA/www.youtube.com/watch?v=1UufaK3pQMg

eLife Assessment

This study presents experiments suggesting intriguing mesoscale reorganization of functional connectivity across distributed cortical and subcortical circuits during learning. The approach is technically impressive and the results are potentially of valuable significance. However, in its current form, the strength of evidence is incomplete. More in-depth analyses and the acquisition of data from additional animals in the primary experiment could bolster these findings.

Reviewer #1 (Public review):

Summary:

This study aims to address an important and timely question: how does the mesoscale architecture of cortical and subcortical circuits reorganize during sensorimotor learning? By using high-density, chronically implanted ultra-flexible electrode arrays, the authors track spiking activity across ten brain regions as mice learn a visual Go/No-Go task. The results indicate that learning leads to more sequential and temporally compressed patterns of activity during correct rejection trials, alongside changes in functional connectivity ranks that reflect shifts in the relative influence of visual, frontal, and motor areas throughout learning. The emergence of a more task-focused subnetwork is accompanied by broader and faster propagation of stimulus information across recorded regions.

Strengths:

A clear strength of this work is its recording approach. The combination of stable, high-throughput multi-region recordings over extended periods represents a significant advance for capturing learning-related network dynamics at the mesoscale. The conceptual framework is well motivated, building on prior evidence that decision-relevant signals are widely distributed across the brain. The analysis approach, combining functional connectivity rankings with information encoding metrics is well motivated but needs refinement. These results provide some valuable evidence of how learning can refine both the temporal precision and the structure of interregional communication, offering new insights into circuit reconfiguration during learning.

Weaknesses:

The technical approach is strong and the conceptual framing is compelling, but several aspects of the evidence remain incomplete. In particular, it is unclear whether the reported changes in connectivity truly capture causal influences, as the rank metrics remain correlational and show discrepancies with the manipulation results. The absolute response onset latencies also appear slow for sensory-guided behavior in mice, and it is not clear whether this reflects the method used to define onset timing or factors such as task structure or internal state. Furthermore, the small number of animals, combined with extensive repeated measures, raises questions about statistical independence and how multiple comparisons were controlled. The optogenetic experiments, while intended to test the functional relevance of rank-increasing regions, leave it unclear how effectively the targeted circuits were silenced. Without direct evidence of reliable local inhibition, the behavioral effects or lack thereof are difficult to interpret. Details on spike sorting are limited.

Reviewer #2 (Public review):

Summary:

Wang et al. measure from 10 cortical and subcortical brain as mice learn a go/no-go visual discrimination task. They found that during learning, there is a reshaping of inter-areal connections, in which a visual-frontal subnetwork emerges as mice gain expertise. Also visual stimuli decoding became more widespread post-learning. They also perform silencing experiments and find that OFC and V2M are important for the learning process. The conclusion is that learning evoked a brain-wide dynamic interplay between different brain areas that together may promote learning.

Strengths:

The manuscript is written well and the logic is rather clear. I found the study interesting and of interest to the field. The recording method is innovative and requires exceptional skills to perform. The outcomes of the study are significant, highlighting that learning evokes a widespread and dynamics modulation between different brain areas, in which specific task-related subnetworks emerge.

Weaknesses:

I had several major concerns:

(1) The number of mice was small for the ephys recordings. Although the authors start with 7 mice in Figure 1, they then reduce to 5 in panel F. And in their main analysis, they minimize their analysis to 6/7 sessions from 3 mice only. I couldn't find a rationale for this reduction, but in the methods they do mention that 2 mice were used for fruitless training, which I found no mention in the results. Moreover, in the early case, all of the analysis is from 118 CR trials taken from 3 mice. In general, this is a rather low number of mice and trial numbers. I think it is quite essential to add more mice.

(2) Movement analysis was not sufficient. Mice learning a go/no-go task establish a movement strategy that is developed throughout learning and is also biased towards Hit trials. There is an analysis of movement in Figure S4, but this is rather superficial. I was not even sure that the 3 mice in Figure S4 are the same 3 mice in the main figure. There should be also an analysis of movement as a function of time to see differences. Also for Hits and FAs. I give some more details below. In general, most of the results can be explained by the fact that as mice gain expertise, they move more (also in CR during specific times) which leads to more activation in frontal cortex and more coordination with visual areas. More needs to be done in terms of analysis, or at least a mention of this in the text.

(3) Most of the figures are over-detailed, and it is hard to understand the take-home message. Although the text is written succinctly and rather short, the figures are mostly overwhelming, especially Figures 4-7. For example, Figure 4 presents 24 brain plots! For rank input and output rank during early and late stim and response periods, for early and expert and their difference. All in the same colormap. No significance shown at all. The Δrank maps for all cases look essentially identical across conditions. The division into early and late time periods is not properly justified. But the main take home message is positive Δrank in OFC, V2M, V1 and negative Δrank in ThalMD and Str. In my opinion, one trio map is enough, and the rest could be bumped to the Supplementary section, if at all. In general, the figure in several cases do not convey the main take home messages. See more details below.

(4) The analysis is sometimes not intuitive enough. For example, the rank analysis of input and output rank seemed a bit over complex. Figure 3 was hard to follow (although a lot of effort was made by the authors to make it clearer). Was there any difference between the output and input analysis? Also, the time period seems redundant sometimes. Also, there are other network analysis that can be done which are a bit more intuitive. The use of rank within the 10 areas was not the most intuitive. Even a dimensionality reduction along with clustering can be used as an alternative. In my opinion, I don't think the authors should completely redo their analysis, but maybe mention the fact that other analyses exist.