Author response:

We would like to thank the three Reviewers for their thoughtful comments and detailed feedback. We are pleased to hear that the Reviewers found our paper to be “providing more direct evidence for the role of signals in different frequency bands related to predictability and surprise” (R1), “well-suited to test evidence for predictive coding versus alternative hypotheses” (R2), and “timely and interesting” (R3).

We perceive that the reviewers have an overall positive impression of the experiments and analyses, but find the text somewhat dense and would like to see additional statistical rigor, as well as in some cases additional analyses to be included in supplementary material. We therefore here provide a provisional letter addressing revisions we have already performed and outlining the revision we are planning point-by-point. We begin each enumerated point with the Reviewer’s quoted text and our responses to each point are made below.

Reviewer 1:

(1) Introduction:

The authors write in their introduction: "H1 further suggests a role for θ oscillations in prediction error processing as well." Without being fleshed out further, it is unclear what role this would be, or why. Could the authors expand this statement?”

We have edited the text to indicate that theta-band activity has been related to prediction error processing as an empirical observation, and must regrettably leave drawing inferences about its functional role to future work, with experiments designed specifically to draw out theta-band activity.

(2) Limited propagation of gamma band signals:

Some recent work (e.g. https://www.cell.com/cell-reports/fulltext/S2211-1247(23)00503-X) suggests that gamma-band signals reflect mainly entrainment of the fast-spiking interneurons, and don't propagate from V1 to downstream areas. Could the authors connect their findings to these emerging findings, suggesting no role in gamma-band activity in communication outside of the cortical column?”

We have not specifically claimed that gamma propagates between columns/areas in our recordings, only that it synchronizes synaptic current flows between laminar layers within a column/area. We nonetheless suggest that gamma can locally synchronize a column, and potentially local columns within an area via entrainment of local recurrent spiking, to update an internal prediction/representation upon onset of a prediction error. We also point the Reviewer to our Discussion section, where we state that our results fit with a model “whereby θ oscillations synchronize distant areas, enabling them to exchange relevant signals during cognitive processing.” In our present work, we therefore remain agnostic about whether theta or gamma or both (or alternative mechanisms) are at play in terms of how prediction error signals are transmitted between areas.

(3) Paradigm:

While I agree that the paradigm tests whether a specific type of temporal prediction can be formed, it is not a type of prediction that one would easily observe in mice, or even humans. The regularity that must be learned, in order to be able to see a reflection of predictability, integrates over 4 stimuli, each shown for 500 ms with a 500 ms blank in between (and a 1000 ms interval separating the 4th stimulus from the 1st stimulus of the next sequence). In other words, the mouse must keep in working memory three stimuli, which partly occurred more than a second ago, in order to correctly predict the fourth stimulus (and signal a 1000 ms interval as evidence for starting a new sequence).

A problem with this paradigm is that positive findings are easier to interpret than negative findings. If mice do not show a modulation to the global oddball, is it because "predictive coding" is the wrong hypothesis, or simply because the authors generated a design that operates outside of the boundary conditions of the theory? I think the latter is more plausible. Even in more complex animals, (eg monkeys or humans), I suspect that participants would have trouble picking up this regularity and sequence, unless it is directly task-relevant (which it is not, in the current setting). Previous experiments often used simple pairs (where transitional probability was varied, eg, Meyer and Olson, PNAS 2012) of stimuli that were presented within an intervening blank period. Clearly, these regularities would be a lot simpler to learn than the highly complex and temporally spread-out regularity used here, facilitating the interpretation of negative findings (especially in early cortical areas, which are known to have relatively small temporal receptive fields).

I am, of course, not asking the authors to redesign their study. I would like to ask them to discuss this caveat more clearly, in the Introduction and Discussion, and situate their design in the broader literature. For example, Jeff Gavornik has used much more rapid stimulus designs and observed clear modulations of spiking activity in early visual regions. I realize that this caveat may be more relevant for the spiking paper (which does not show any spiking activity modulation in V1 by global predictability) than for the current paper, but I still think it is an important general caveat to point out.”

We appreciate the Reviewer’s concern about working memory limitations in mice. Our paradigm and training followed on from previous paradigms such as Gavornik and Bear (2014), in which predictive effects were observed in mouse V1 with presentation times of 150ms and interstimulus intervals of 1500ms. In addition, we note that Jamali et al. (2024) recently utilized a similar global/local paradigm in the auditory domain with inter-sequence intervals as long as 28-30 seconds, and still observed effects of a predicted sequence (https://elifesciences.org/articles/102702). For the revised manuscript, we plan to expand on this in the Discussion section.

That being said, as the Reviewer also pointed out, this would be a greater concern had we not found any positive findings in our study. However, even with the rather long sequence periods we used, we did find positive evidence for predictive effects, supporting the use of our current paradigm. We agree with the reviewer that these positive effects are easier to interpret than negative effects, and plan to expand upon this in the Discussion when we resubmit.

(4) Reporting of results:

I did not see any quantification of the strength of evidence of any of the results, beyond a general statement that all reported results pass significance at an alpha=0.01 threshold. It would be informative to know, for all reported results, what exactly the p-value of the significant cluster is; as well as for which performed tests there was no significant difference.”

For the revised manuscript, we can include the p-values after cluster-based testing for each significant cluster, as well as show data that passes a more stringent threshold of p<0.001 (1/1000) or p<0.005 (1/200) rather than our present p<0.01 (1/100).

(5) Cluster test:

The authors use a three-dimensional cluster test, clustering across time, frequency, and location/channel. I am wondering how meaningful this analytical approach is. For example, there could be clusters that show an early difference at some location in low frequencies, and then a later difference in a different frequency band at another (adjacent) location. It seems a priori illogical to me to want to cluster across all these dimensions together, given that this kind of clustering does not appear neurophysiologically implausible/not meaningful. Can the authors motivate their choice of three-dimensional clustering, or better, facilitating interpretability, cluster eg at space and time within specific frequency bands (2d clustering)?”

We are happy to include a 3D plot of a time-channel-frequency cluster in the revised manuscript to clarify our statistical approach for the reviewer. We consider our current three-dimensional cluster-testing an “unsupervised” way of uncovering significant contrasts with no theory-driven assumptions about which bounded frequency bands or layers do what.

Reviewer 2:

Sennesh and colleagues analyzed LFP data from 6 regions of rodents while they were habituated to a stimulus sequence containing a local oddball (xxxy) and later exposed to either the same (xxxY) or a deviant global oddball (xxxX). Subsequently, they were exposed to a controlled random sequence (XXXY) or a controlled deterministic sequence (xxxx or yyyy). From these, the authors looked for differences in spectral properties (both oscillatory and aperiodic) between three contrasts (only for the last stimulus of the sequence).

(1) Deviance detection: unpredictable random (XXXY) versus predictable habituation (xxxy)

(2) Global oddball: unpredictable global oddball (xxxX) versus predictable deterministic (xxxx), and

(3) "Stimulus-specific adaptation:" locally unpredictable oddball (xxxY) versus predictable deterministic (yyyy).

They found evidence for an increase in gamma (and theta in some cases) for unpredictable versus predictable stimuli, and a reduction in alpha/beta, which they consider evidence towards the "predictive routing" scheme.

While the dataset and analyses are well-suited to test evidence for predictive coding versus alternative hypotheses, I felt that the formulation was ambiguous, and the results were not very clear. My major concerns are as follows:”

We appreciate the reviewer’s concerns and outline how we will address them below:

(1) The authors set up three competing hypotheses, in which H1 and H2 make directly opposite predictions. However, it must be noted that H2 is proposed for spatial prediction, where the predictability is computed from the part of the image outside the RF. This is different from the temporal prediction that is tested here. Evidence in favor of H2 is readily observed when large gratings are presented, for which there is substantially more gamma than in small images. Actually, there are multiple features in the spectral domain that should not be conflated, namely (i) the transient broadband response, which includes all frequencies, (ii) contribution from the evoked response (ERP), which is often in frequencies below 30 Hz, (iii) narrow-band gamma oscillations which are produced by large and continuous stimuli (which happen to be highly predictive), and (iv) sustained low-frequency rhythms in theta and alpha/beta bands which are prominent before stimulus onset and reduce after ~200 ms of stimulus onset. The authors should be careful to incorporate these in their formulation of PC, and in particular should not conflate narrow-band and broadband gamma.”

We have clarified in the manuscript that while the gamma-as-prediction hypothesis (our H2) was originally proposed in a spatial prediction domain, further work (specifically Singer (2021)) has extended the hypothesis to cover temporal-domain predictions as well.

To address the reviewer’s point about multiple features in the spectral domain: Our analysis has specifically separated aperiodic components using FOOOF analysis (Supp. Fig. 1) and explicitly fit and tested aperiodic vs. periodic components (Supp. Figs 1&2). We did not find strong effects in the aperiodic components but did in the periodic components (Supp. Fig. 2), allowing us to be more confident in our conclusions in terms of genuine narrow-band oscillations. In the revised manuscript, we will include analysis of the pre-stimulus time window to address the reviewer’s point (iv) on sustained low frequency oscillations.

(2) My understanding is that any aspect of predictive coding must be present before the onset of stimulus (expected or unexpected). So, I was surprised to see that the authors have shown the results only after stimulus onset. For all figures, the authors should show results from -500 ms to 500 ms instead of zero to 500 ms.

In our revised manuscript we will include a pre-stimulus analysis and supplementary figures with time ranges from -500ms to 500ms. We have only refrained from doing so in the initial manuscript because our paradigm’s short interstimulus interval makes it difficult to interpret whether activity in the ISI reflects post-stimulus dynamics or pre-stimulus prediction. Nonetheless, we can easily show that in our paradigm, alpha/beta-band activity is elevated in the interstimulus activity after the offset of the previous stimulus, assuming that we baseline to the pre-trial period.

(3) In many cases, some change is observed in the initial ~100 ms of stimulus onset, especially for the alpha/beta and theta ranges. However, the evoked response contributes substantially in the transient period in these frequencies, and this evoked response could be different for different conditions. The authors should show the evoked responses to confirm the same, and if the claim really is that predictions are carried by genuine "oscillatory" activity, show the results after removing the ERP (as they had done for the CSD analysis).

We have included an extra sentence in our Materials and Methods section clarifying that the evoked potential/ERP was removed in our existing analyses, prior to performing the spectral decomposition of the LFP signal. We also note that the FOOOF analysis we applied separates aperiodic components of the spectral signal from the strictly oscillatory ones.

In our revised manuscript we will include an analysis of the evoked responses as suggested by the reviewer.

(4) I was surprised by the statistics used in the plots. Anything that is even slightly positive or negative is turning out to be significant. Perhaps the authors could use a more stringent criterion for multiple comparisons?

As noted above to Reviewer 1 (point 4), we are happy to include supplemental figures in our resubmission showing the effects on our results of setting the statistical significance threshold with considerably greater stringency.

(5) Since the design is blocked, there might be changes in global arousal levels. This is particularly important because the more predictive stimuli in the controlled deterministic stimuli were presented towards the end of the session, when the animal is likely less motivated. One idea to check for this is to do the analysis on the 3rd stimulus instead of the 4th? Any general effect of arousal/attention will be reflected in this stimulus.

In order to check for the brain-wide effects of arousal, we plan to perform similar analyses to our existing ones on the 3rd stimulus in each block, rather than just the 4th “oddball” stimulus. Clusters that appear significantly contrasting in both the 3rd and 4th stimuli may be attributable to arousal.  We will also analyze pupil size as an index of arousal to check for arousal differences between conditions in our contrasts, possibly stratifying our data before performing comparisons to equalize pupil size within contrasts. We plan to include these analyses in our resubmission.

(6) The authors should also acknowledge/discuss that typical stimulus presentation/attention modulation involves both (i) an increase in broadband power early on and (ii) a reduction in low-frequency alpha/beta power. This could be just a sensory response, without having a role in sending prediction signals per se. So the predictive routing hypothesis should involve testing for signatures of prediction while ruling out other confounds related to stimulus/cognition. It is, of course, very difficult to do so, but at the same time, simply showing a reduction in low-frequency power coupled with an increase in high-frequency power is not sufficient to prove PR.

Since many different predictive coding and predictive processing hypotheses make very different hypotheses about how predictions might encoded in neurophysiological recordings, we have focused on prediction error encoding in this paper.

For the hypothesis space we have considered (H1-H3), each hypothesis makes clearly distinguishable predictions about the spectral response during the time period in the task when prediction errors should be present. As noted by the reviewer, a transient increase in broadband frequencies would be a signature of H3. Changes to oscillatory power in the gamma band in distinct directions (e.g., increasing or decreasing with prediction error) would support either H1 and H2, depending on the direction of change. We believe our data, especially our use of FOOOF analysis and separation of periodic from aperiodic components, coupled to the three experimental contrasts, speaks clearly in favor of the Predictive Routing model, but we do not claim we have “proved” it. This study provides just one datapoint, and we will acknowledge this in our revised Discussion in our resubmission.

(7) The CSD results need to be explained better - you should explain on what basis they are being called feedforward/feedback. Was LFP taken from Layer 4 LFP (as was done by van Kerkoerle et al, 2014)? The nice ">" and "<" CSD patterns (Figure 3B and 3F of their paper) in that paper are barely observed in this case, especially for the alpha/beta range.

We consider a feedforward pattern as flowing from L4 outwards to L2/3 and L5/6, and a feedback pattern as flowing in the opposite direction, from L1 and L6 to the middle layers. We will clarify this in the revised manuscript.

Since gamma-band oscillations are strongest in L2/3, we re-epoched LFPs to the oscillation troughs in L2/3 in the initial manuscript. We can include in the revised manuscript equivalent plots after finding oscillation troughs in L4 instead, as well as calculating the difference in trough times within-band between layers to quantify the transmission delay and add additional rigor to our feedforward vs. feedback interpretation of the CSD data.

(8) Figure 4a-c, I don't see a reduction in the broadband signal in a compared to b in the initial segment. Maybe change the clim to make this clearer?

We are looking into the clim/colorbar and plot-generation code to figure out the visibility issue that the Reviewer has kindly pointed out to us.

(9) Figure 5 - please show the same for all three frequency ranges, show all bars (including the non-significant ones), and indicate the significance (p-values or by *, **, ***, etc) as done usually for bar plots.

We will add the requested bar-plots for all frequency ranges, though we note that the bars given here are the results of adding up the spectral power in the channel-time-frequency clusters that already passed significance tests and that adding secondary significance tests here may not prove informative.

(10) Their claim of alpha/beta oscillations being suppressed for unpredictable conditions is not as evident. A figure akin to Figure 5 would be helpful to see if this assertion holds.

As noted above, we will include the requested bar plot, as well as examining alpha/beta in the pre-stimulus time-series rather than after the onset of the oddball stimulus.

(11) To investigate the prediction and violation or confirmation of expectation, it would help to look at both the baseline and stimulus periods in the analyses.

We will include for the Reviewer’s edification a supplementary figure showing the spectrograms for the baseline and full-trial periods to look at the difference between baseline and prestimulus expectation.

Reviewer 3:

Summary:

In their manuscript entitled "Ubiquitous predictive processing in the spectral domain of sensory cortex", Sennesh and colleagues perform spectral analysis across multiple layers and areas in the visual system of mice. Their results are timely and interesting as they provide a complement to a study from the same lab focussed on firing rates, instead of oscillations. Together, the present study argues for a hypothesis called predictive routing, which argues that non-predictable stimuli are gated by Gamma oscillations, while alpha/beta oscillations are related to predictions.

Strengths:

(1) The study contains a clear introduction, which provides a clear contrast between a number of relevant theories in the field, including their hypotheses in relation to the present data set.

(2) The study provides a systematic analysis across multiple areas and layers of the visual cortex.”

We thank the Reviewer for their kind comments.

Weaknesses:

(1) It is claimed in the abstract that the present study supports predictive routing over predictive coding; however, this claim is nowhere in the manuscript directly substantiated. Not even the differences are clearly laid out, much less tested explicitly. While this might be obvious to the authors, it remains completely opaque to the reader, e.g., as it is also not part of the different hypotheses addressed. I guess this result is meant in contrast to reference 17, by some of the same authors, which argues against predictive coding, while the present work finds differences in the results, which they relate to spectral vs firing rate analysis (although without direct comparison).

We agree that in this manuscript we should restrict ourselves to the hypotheses that were directly tested. We have revised our abstract accordingly,  and softened our claim to note only that our LFP results are compatible with predictive routing.

(2) Most of the claims about a direction of propagation of certain frequency-related activities (made in the context of Figures 2-4) are - to the eyes of the reviewer - not supported by actual analysis but glimpsed from the pictures, sometimes, with very little evidence/very small time differences to go on. To keep these claims, proper statistical testing should be performed.

In our revised manuscript, we will either substantiate (with quantification of CSD delays between layers) or soften the claims about feedforward/feedback direction of flow within the cortical column.

(3) Results from different areas are barely presented. While I can see that presenting them in the same format as Figures 2-4 would be quite lengthy, it might be a good idea to contrast the right columns (difference plots) across areas, rather than just the overall averages.

In our revised manuscript we will gladly include a supplementary figure showing the right-column difference plots across areas, in order to make sure to include aspects of our dataset that span up and down the cortical hierarchy.

(4) Statistical testing is treated very generally, which can help to improve the readability of the text; however, in the present case, this is a bit extreme, with even obvious tests not reported or not even performed (in particular in Figure 5).

We appreciate the Reviewer’s concern for statistical rigor, and as noted to the other reviewers, we can add different levels of statistical description and describe the p-values associated with specific clusters. Regarding Figure 5, we must protest as the bar heights were computed came from clusters already subjected to statistical testing and found significant.  We could add a supplementary figure which considers untested narrowband activity and tests it only in the “bar height” domain, if the Reviewer would like.

(5) The description of the analysis in the methods is rather short and, to my eye, was missing one of the key descriptions, i.e., how the CSD plots were baselined (which was hinted at in the results, but, as far as I know, not clearly described in the analysis methods). Maybe the authors could section the methods more to point out where this is discussed.

We have added some elaboration to our Materials and Methods section, especially to specify that CSD, having physical rather than arbitrary units, does not require baselining.

(6) While I appreciate the efforts of the authors to formulate their hypotheses and test them clearly, the text is quite dense at times. Partly this is due to the compared conditions in this paradigm; however, it would help a lot to show a visualization of what is being compared in Figures 2-4, rather than just showing the results.

In the revised manuscript we will add a visual aid for the three contrasts we consider.

We are happy to inform the editors that we have implemented, for the Reviewed Preprint, the direct textual Recommendations for the Authors given by Reviewers 2 and 3. We will implement the suggested Figure changes in our revised manuscript. We thank them for their feedback in strengthening our manuscript.

cita literal

add ISSP como estudio central para este tema en comparación internacional, trackear preguntas cumulative, y mencionar que esto ha sido un elemento fundamental de la agenda de market justice

& moral economy

imply

• A new study from Cambridge scientists identifies five distinct ages or structural eras of the human brain throughout the average lifespan.
• Four major brain development turning points occur around the ages of 9, 32, 66, and 83 years.
• These eras represent different phases of neural network organization, integration, and segregation, correlating with key cognitive, behavioral, and mental health outcomes.
• The first stage, from birth to about 9 years, involves decreasing global integration and increasing local segregation in the brain's networks.
• The second stage, spanning ages 9 to 32, labeled "adolescence," shows increasing network integration and efficiency and is when "adult mode" of brain wiring begins.
• From 32 to 66 years ("adulthood"), there is a transition to decreased integration but increased segregation.
• The study sheds light on why adolescence may last until the early 30s and how brain efficiency and topology change across life.
• Understanding these phases may inform about critical periods for cognitive development and age-related cognitive decline.

Hacker News Discussion

• The discussion briefly mentions socioeconomic impacts, noting that median income almost doubles between ages 23 and 35, which aligns with the brain development "adult mode" onset around early 30s.
• Other comments are limited and fragmented, mostly consisting of quick reactions and some contextual mentions without deep analysis of the study.
• There is a general acknowledgment of the relevance of the study's findings for understanding cognitive and life milestone transitions, but no extended debate or critique.

eLife Assessment

Mark and colleagues developed and validated a valuable method for examining subspace generalization in fMRI data and applied it to understand whether the entorhinal cortex uses abstract representations that generalize across different environments with the same structure. The manuscript presents convincing evidence for the conclusion that abstract entorhinal representations of hexagonal associative structures generalize across different stimulus sets.

Reviewer #1 (Public review):

Summary:

This study develops and validates a neural subspace similarity analysis for testing whether neural representations of graph structures generalize across graph size and stimulus sets. The authors show the method works in rat grid and place cell data, finding that grid but not place cells generalize across different environments, as expected. The authors then perform additional analyses and simulations to show that this method should also work on fMRI data. Finally, the authors test their method on fMRI responses from entorhinal cortex (EC) in a task that involves graphs that vary in size (and stimulus set) and statistical structure (hexagonal and community). They find neural representations of stimulus sets in lateral occipital complex (LOC) generalize across statistical structure and that EC activity generalizes across stimulus sets/graph size, but only for the hexagonal structures.

Strengths:

(1) The overall topic is very interesting and timely and the manuscript is well written.

(2) The method is clever and powerful. It could be important for future research testing whether neural representations are aligned across problems with different state manifestations.

(3) The findings provide new insights into generalizable neural representations of abstract task states in entorhinal cortex.

Weaknesses:

(1) There are two design confounds that are not sufficiently discussed.

(1.1) First, hexagonal and community structures are confounded by training order. All subjects learned the hexagonal graph always before the community graph. As such, any differences between the two graphs could be explained (in theory) by order effects (although this is unlikely). However, because community and hexagonal structures shared the same stimuli, it is possible that subjects had to find ways to represent the community structures separately from the hexagonal structures. This could potentially explain why there was no generalization across graph size for community structures.

(1.2) Second, subjects had more experience with the hexagonal and community structures before and during fMRI scanning. This is another possible reason why there was no generalization for the community structure.

(2) The authors include the results from a searchlight analysis to show specificity of the effects for EC. A more convincing way (in my opinion) to show specificity would be to test for (and report the results) of a double dissociation between the visual and structural contrast in two independently defined regions (e.g., anatomical ROIs of LOC and EC). This would substantiate the point that EC activity generalizes across structural similarity while sensory regions like LOC generalize across visual similarity.

Reviewer #2 (Public review):

Summary:

Mark and colleagues test the hypothesis that entorhinal cortical representations may contain abstract structural information that facilitates generalization across structurally similar contexts. To do so, they use a method called "subspace generalization" designed to measure abstraction of representations across different settings. The authors validate the method using hippocampal place cells and entorhinal grid cells recorded in a spatial task, then show perform simulations that support that it might be useful in aggregated responses such as those measured with fMRI. Then the method is applied to an fMRI data that required participants to learn relationships between images in one of two structural motifs (hexagonal grids versus community structure). They show that the BOLD signal within an entorhinal ROI shows increased measures of subspace generalization across different tasks with the same hexagonal structure (as compared to tasks with different structures) but that there was not evidence for the complementary result (ie. increased generalization across tasks that share community structure, as compared to those with different structures). Taken together, this manuscript describes and validates a method for identifying fMRI representations that generalize across conditions and applies it to reveal that entorhinal representations that emerge across specific shared structural conditions.

Strengths:

I found this paper interesting both in terms of its methods and its motivating questions. The question asked is novel and the methods employed are new - and I believe this is the first time that they have been applied to fMRI data. I also found the iterative validation of the methodology to be interesting and important - showing persuasively that the method could detect a target representation - even in the face of random combination of tuning and with the addition of noise, both being major hurdles to investigating representations using fMRI.

Weaknesses:

The primary weakness of the paper in terms of empirical results is that the representations identified in EC had no clear relationship to behavior, raising questions about their functional importance.

The method developed is a clearly valuable tool that can serve as part of a larger battery of analysis techniques, but a small weakness on the methodological side is that for a given dataset, it might be hard to determine whether the method developed here would be better or worse than alternative methods.

Reviewer #3 (Public review):

Summary:

The article explores the brain's ability to generalize information, with a specific focus on the entorhinal cortex (EC) and its role in learning and representing structural regularities that define relationships between entities in networks. The research provides empirical support for the longstanding theoretical and computational neuroscience hypothesis that the EC is crucial for structure generalization. It demonstrates that EC codes can generalize across non-spatial tasks that share common structural regularities, regardless of the similarity of sensory stimuli and network size.

Strengths:

At first glance, a potential limitation of this study appears to be its application of analytical methods originally developed for high-resolution animal electrophysiology (Samborska et al., 2022) to the relatively coarse and noisy signals of human fMRI. Rather than sidestepping this issue, however, the authors embrace it as a methodological challenge. They provide compelling empirical evidence and biologically grounded simulations to show that key generalization properties of entorhinal cortex representations can still be robustly detected. This not only validates their approach but also demonstrates how far non-invasive human neuroimaging can be pushed. The use of multiple independent datasets and carefully controlled permutation tests further underscores the reliability of their findings, making a strong case that structural generalization across diverse task environments can be meaningfully studied even in abstract, non-spatial domains that are otherwise difficult to investigate in animal models.

Weaknesses:

While this study provides compelling evidence for structural generalization in the entorhinal cortex (EC), several limitations remain that pave the way for promising future research. One issue is that the generalization effect was statistically robust in only one task condition, with weaker effects observed in the "community" condition. This raises the question of whether the null result genuinely reflects a lack of EC involvement, or whether it might be attributable to other factors such as task complexity, training order, or insufficient exposure possibilities that the authors acknowledge as open questions. Moreover, although the study leverages fMRI to examine EC representations in humans, it does not clarify which specific components of EC coding-such as grid cells versus other spatially tuned but non-grid codes-underlie the observed generalization. While electrophysiological data in animals have begun to address this, the human experiments do not disentangle the contributions of these different coding types. This leaves unresolved the important question of what makes EC representations uniquely suited for generalization, particularly given that similar effects were not observed in other regions known to contain grid cells, such as the medial prefrontal cortex (mPFC) or posterior cingulate cortex (PCC). These limitations point to important future directions for better characterizing the computational role of the EC and its distinctiveness within the broader network supporting learning and decision making based on cognitive maps.

Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public review):

Summary:

This study develops and validates a neural subspace similarity analysis for testing whether neural representations of graph structures generalize across graph size and stimulus sets. The authors show the method works in rat grid and place cell data, finding that grid but not place cells generalize across different environments, as expected. The authors then perform additional analyses and simulations to show that this method should also work on fMRI data. Finally, the authors test their method on fMRI responses from the entorhinal cortex (EC) in a task that involves graphs that vary in size (and stimulus set) and statistical structure (hexagonal and community). They find neural representations of stimulus sets in lateral occipital complex (LOC) generalize across statistical structure and that EC activity generalizes across stimulus sets/graph size, but only for the hexagonal structures.

Strengths:

(1) The overall topic is very interesting and timely and the manuscript is well-written.

(2) The method is clever and powerful. It could be important for future research testing whether neural representations are aligned across problems with different state manifestations.

(3) The findings provide new insights into generalizable neural representations of abstract task states in the entorhinal cortex.

We thank the reviewer for their kind comments and clear summary of the paper and its strengths.

Weaknesses:

(1) The manuscript would benefit from improving the figures. Moreover, the clarity could be strengthened by including conceptual/schematic figures illustrating the logic and steps of the method early in the paper. This could be combined with an illustration of the remapping properties of grid and place cells and how the method captures these properties.

We agree with the reviewer and have added a schematic figure of the method (figure 1a).

(2) Hexagonal and community structures appear to be confounded by training order. All subjects learned the hexagonal graph always before the community graph. As such, any differences between the two graphs could thus be explained (in theory) by order effects (although this is practically unlikely). However, given community and hexagonal structures shared the same stimuli, it is possible that subjects had to find ways to represent the community structures separately from the hexagonal structures. This could potentially explain why the authors did not find generalizations across graph sizes for community structures.

We thank the reviewer for their comments. We agree that the null result regarding the community structures does not mean that EC doesn’t generalise over these structures, and that the training order could in theory contribute to the lack of an effect. The decision to keep the asymmetry of the training order was deliberate: we chose this order based on our previous study (Mark et al. 2020), where we show that learning a community structure first changes the learning strategy of subsequent graphs. We could have perhaps overcome this by increasing the training periods, but 1) the training period is already very long; 2) there will still be asymmetry because the group that first learn community structure will struggle in learning the hexagonal graph more than vice versa, as shown in Mark et al. 2020.

We have added the following sentences on this decision to the Methods section:

“We chose to first teach hexagonal graphs for all participants and not randomize the order because of previous results showing that first learning community structure changes participants’ learning strategy (mark et al. 2020).”

(3) The authors include the results from a searchlight analysis to show the specificity of the effects of EC. A better way to show specificity would be to test for a double dissociation between the visual and structural contrast in two independently defined regions (e.g., anatomical ROIs of LOC and EC).

Thanks for this suggestion. We indeed tried to run the analysis in a whole-ROI approach, but this did not result in a significant effect in EC. Importantly, we disagree with the reviewer that this is a “better way to show specificity” than the searchlight approach. In our view, the two analyses differ with respect to the spatial extent of the representation they test for. The searchlight approach is testing for a highly localised representation on the scale of small spheres with only 100 voxels. The signal of such a localised representation is likely to be drowned in the noise in an analysis that includes thousands of voxels which mostly don’t show the effect - as would be the case in the whole-ROI approach.

(4) Subjects had more experience with the hexagonal and community structures before and during fMRI scanning. This is another confound, and possible reason why there was no generalization across stimulus sets for the community structure.

See our response to comment (2).

Reviewer #2 (Public review):

Summary:

Mark and colleagues test the hypothesis that entorhinal cortical representations may contain abstract structural information that facilitates generalization across structurally similar contexts. To do so, they use a method called "subspace generalization" designed to measure abstraction of representations across different settings. The authors validate the method using hippocampal place cells and entorhinal grid cells recorded in a spatial task, then perform simulations that support that it might be useful in aggregated responses such as those measured with fMRI. Then the method is applied to fMRI data that required participants to learn relationships between images in one of two structural motifs (hexagonal grids versus community structure). They show that the BOLD signal within an entorhinal ROI shows increased measures of subspace generalization across different tasks with the same hexagonal structure (as compared to tasks with different structures) but that there was no evidence for the complementary result (ie. increased generalization across tasks that share community structure, as compared to those with different structures). Taken together, this manuscript describes and validates a method for identifying fMRI representations that generalize across conditions and applies it to reveal entorhinal representations that emerge across specific shared structural conditions.

Strengths:

I found this paper interesting both in terms of its methods and its motivating questions. The question asked is novel and the methods employed are new - and I believe this is the first time that they have been applied to fMRI data. I also found the iterative validation of the methodology to be interesting and important - showing persuasively that the method could detect a target representation - even in the face of a random combination of tuning and with the addition of noise, both being major hurdles to investigating representations using fMRI.

We thank the reviewer for their kind comments and the clear summary of our paper.

Weaknesses:

In part because of the thorough validation procedures, the paper came across to me as a bit of a hybrid between a methods paper and an empirical one. However, I have some concerns, both on the methods development/validation side, and on the empirical application side, which I believe limit what one can take away from the studies performed.

We thank the reviewer for the comment. We agree that the paper comes across as a bit of a methods-empirical hybrid. We chose to do this because we believe (as the reviewer also points out) that there is value in both aspects of the paper.

Regarding the methods side, while I can appreciate that the authors show how the subspace generalization method "could" identify representations of theoretical interest, I felt like there was a noticeable lack of characterization of the specificity of the method. Based on the main equation in the results section of the paper, it seems like the primary measure used here would be sensitive to overall firing rates/voxel activations, variance within specific neurons/voxels, and overall levels of correlation among neurons/voxels. While I believe that reasonable pre-processing strategies could deal with the first two potential issues, the third seems a bit more problematic - as obligate correlations among neurons/voxels surely exist in the brain and persist across context boundaries that are not achieving any sort of generalization (for example neurons that receive common input, or voxels that share spatial noise). The comparative approach (ie. computing difference in the measure across different comparison conditions) helps to mitigate this concern to some degree - but not completely - since if one of the conditions pushes activity into strongly spatially correlated dimensions, as would be expected if univariate activations were responsive to the conditions, then you'd expect generalization (driven by shared univariate activation of many voxels) to be specific to that set of conditions.

We thank the reviewer for their comments. We would like to point out that we demean each voxel within all states/piles (3-pictures sequences) in a given graph/task (what the reviewer is calling “a condition”). Hence there is no shared univariate activation of many voxels in response to a graph going into the computation, and no sensitivity to the overall firing rate/voxel activation.  Our calculation captures the variance across states conditions within a task (here a graph), over and above the univariate effect of graph activity. In addition, we spatially pre-whiten the data within each searchlight, meaning that noisy voxels with high noise variance will be downweighted and noise correlations between voxels are removed prior to applying our method.

A second issue in terms of the method is that there is no comparison to simpler available methods. For example, given the aims of the paper, and the introduction of the method, I would have expected the authors to take the Neuron-by-Neuron correlation matrices for two conditions of interest, and examine how similar they are to one another, for example by correlating their lower triangle elements. Presumably, this method would pick up on most of the same things - although it would notably avoid interpreting high overall correlations as "generalization" - and perhaps paint a clearer picture of exactly what aspects of correlation structure are shared. Would this method pick up on the same things shown here? Is there a reason to use one method over the other?

We thank the reviewer for this important and interesting point. We agree that calculating correlation between the upper triangular elements of the covariance or correlation matrices picks up similar, but not identical aspects of the data (see below the mathematical explanation that was added to the supplementary). When we repeated the searchlight analysis and calculated the correlation between the upper triangular entries of the Pearson correlation matrices we obtained an effect in the EC, though weaker than with our subspace generalization method (t=3.9, the effect did not survive multiple comparisons). Similar results were obtained with the correlation between the upper triangular elements of the covariance matrices(t=3.8, the effect did not survive multiple comparisons).

The difference between the two methods is twofold: 1) Our method is based on the covariance matrix and not the correlation matrix - i.e. a difference in normalisation. We realised that in the main text of the original paper we mistakenly wrote “correlation matrix” rather than “covariance matrix” (though our equations did correctly show the covariance matrix). We have corrected this mistake in the revised manuscript. 2) The weighting of the variance explained in the direction of each eigenvector is different between the methods, with some benefits of our method for identifying low-dimensional representations and for robustness to strong spatial correlations.  We have added a section “Subspace Generalisation vs correlating the Neuron-by-Neuron correlation matrices” to the supplementary information with a mathematical explanation of these differences.

Regarding the fMRI empirical results, I have several concerns, some of which relate to concerns with the method itself described above. First, the spatial correlation patterns in fMRI data tend to be broad and will differ across conditions depending on variability in univariate responses (ie. if a condition contains some trials that evoke large univariate activations and others that evoke small univariate activations in the region). Are the eigenvectors that are shared across conditions capturing spatial patterns in voxel activations? Or, related to another concern with the method, are they capturing changing correlations across the entire set of voxels going into the analysis? As you might expect if the dynamic range of activations in the region is larger in one condition than the other?

This is a searchlight analysis, therefore it captures the activity patterns within nearby voxels. Indeed, as we show in our simulation, areas with high activity and therefore high signal to noise will have better signal in our method as well. Note that this is true of most measures.

My second concern is, beyond the specificity of the results, they provide only modest evidence for the key claims in the paper. The authors show a statistically significant result in the Entorhinal Cortex in one out of two conditions that they hypothesized they would see it. However, the effect is not particularly large. There is currently no examination of what the actual eigenvectors that transfer are doing/look like/are representing, nor how the degree of subspace generalization in EC may relate to individual differences in behavior, making it hard to assess the functional role of the relationship. So, at the end of the day, while the methods developed are interesting and potentially useful, I found the contributions to our understanding of EC representations to be somewhat limited.

We agree with this point, yet believe that the results still shed light on EC functionality. Unfortunately, we could not find correlation between behavioral measures and the fMRI effect.

Reviewer #3 (Public review):

Summary:

The article explores the brain's ability to generalize information, with a specific focus on the entorhinal cortex (EC) and its role in learning and representing structural regularities that define relationships between entities in networks. The research provides empirical support for the longstanding theoretical and computational neuroscience hypothesis that the EC is crucial for structure generalization. It demonstrates that EC codes can generalize across non-spatial tasks that share common structural regularities, regardless of the similarity of sensory stimuli and network size.

Strengths:

(1) Empirical Support: The study provides strong empirical evidence for the theoretical and computational neuroscience argument about the EC's role in structure generalization.

(2) Novel Approach: The research uses an innovative methodology and applies the same methods to three independent data sets, enhancing the robustness and reliability of the findings.

(3) Controlled Analysis: The results are robust against well-controlled data and/or permutations.

(4) Generalizability: By integrating data from different sources, the study offers a comprehensive understanding of the EC's role, strengthening the overall evidence supporting structural generalization across different task environments.

Weaknesses:

A potential criticism might arise from the fact that the authors applied innovative methods originally used in animal electrophysiology data (Samborska et al., 2022) to noisy fMRI signals. While this is a valid point, it is noteworthy that the authors provide robust simulations suggesting that the generalization properties in EC representations can be detected even in low-resolution, noisy data under biologically plausible assumptions. I believe this is actually an advantage of the study, as it demonstrates the extent to which we can explore how the brain generalizes structural knowledge across different task environments in humans using fMRI. This is crucial for addressing the brain's ability in non-spatial abstract tasks, which are difficult to test in animal models.

While focusing on the role of the EC, this study does not extensively address whether other brain areas known to contain grid cells, such as the mPFC and PCC, also exhibit generalizable properties. Additionally, it remains unclear whether the EC encodes unique properties that differ from those of other systems. As the authors noted in the discussion, I believe this is an important question for future research.

We thank the reviewer for their comments. We agree with the reviewer that this is a very interesting question. We tried to look for effects in the mPFC, but we did not obtain results that were strong enough to report in the main manuscript, but we do report a small effect in the supplementary.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

(1) I wonder how important the PCA on B1(voxel-by-state matrix from environment 1) and the computation of the AUC (from the projection on B2 [voxel-by-state matrix from environment 1]) is for the analysis to work. Would you not get the same result if you correlated the voxel-by-voxel correlation matrix based on B1 (C1) with the voxel-by-voxel correlation matrix based on B2 (C2)? I understand that you would not have the subspace-by-subspace resolution that comes from the individual eigenvectors, but would the AUC not strongly correlate with the correlation between C1 and C2?

We agree with the reviewer comments - see our response to reviewer 2 second issue above. 

(2) There is a subtle difference between how the method is described for the neural recording and fMRI data. Line 695 states that principal components of the neuron x neuron intercorrelation matrix are computed, whereas line 888 implies that principal components of the data matrix B are computed. Of note, B is a voxel x pile rather than a pile x voxel matrix. Wouldn't this result in U being pile x pile rather than voxel x voxel?

The PCs are calculated on the neuron x neuron (or voxel x voxel) covariance matrix of the activation matrix. We’ve added the following clarification to the relevant part of the Methods:

“We calculated noise normalized GLM betas within each searchlight using the RSA toolbox. For each searchlight and each graph, we had a nVoxels (100) by nPiles (10) activation matrix (B) that describes the activation of a voxel as a result of a particular pile (three pictures’ sequence). We exploited the (voxel x voxel) covariance matrix of this matrix to quantify the manifold alignment within each searchlight.”

(3) It would be very helpful to the field if the authors would make the code and data publicly available. Please consider depositing the code for data analysis and simulations, as well as the preprocessed/extracted data for the key results (rat data/fMRI ROI data) into a publicly accessible repository.

The code is publicly available in git (https://github.com/ShirleyMgit/subspace_generalization_paper_code/tree/main).

(4) Line 219: "Kolmogorov Simonov test" should be "Kolmogorov Smirnov test".

thanks!

(5) Please put plots in Figure 3F on the same y-axis.

(6) Were large and small graphs of a given statistical structure learned on the same days, and if so, sequentially or simultaneously? This could be clarified.

The graphs are learned on the same day.  We clarified this in the Methods section.

Reviewer #2 (Recommendations for the authors):

Perhaps the advantage of the method described here is that you could narrow things down to the specific eigenvector that is doing the heavy lifting in terms of generalization... and then you could look at that eigenvector to see what aspect of the covariance structure persists across conditions of interest. For example, is it just the highest eigenvalue eigenvector that is likely picking up on correlations across the entire neural population? Or is there something more specific going on? One could start to get at this by looking at Figures 1A and 1C - for example, the primary difference for within/between condition generalization in 1C seems to emerge with the first component, and not much changes after that, perhaps suggesting that in this case, the analysis may be picking up on something like the overall level of correlations within different conditions, rather than a more specific pattern of correlations.

The nature of the analysis means the eigenvectors are organized by their contribution to the variance, therefore the first eigenvector is responsible for more variance than the other, we did not check rigorously whether the variance is then splitted equally by the remaining eigenvectors but it does not seems to be the case.

Why is variance explained above zero for fraction EVs = 0 for figure 1C (but not 1A) ? Is there some plotting convention that I'm missing here?

There was a small bug in this plot and it was corrected - thank you very much!

The authors say:

"Interestingly, the difference in AUCs was also 190 significantly smaller than chance for place cells (Figure 1a, compare dotted and solid green 191 lines, p<0.05 using permutation tests, see statistics and further examples in supplementary 192 material Figure S2), consistent with recent models predicting hippocampal remapping that is 193 not fully random (Whittington et al. 2020)."

But my read of the Whittington model is that it would predict slight positive relationships here, rather than the observed negative ones, akin to what one would expect if hippocampal neurons reflect a nonlinear summation of a broad swath of entorhinal inputs.

Smaller differences than chance imply that the remapping of place cells is not completely random.

Figure 2:

I didn't see any description of where noise amplitude values came from - or any justification at all in that section. Clearly, the amount of noise will be critical for putting limits on what can and cannot be detected with the method - I think this is worthy of characterization and explanation. In general, more information about the simulations is necessary to understand what was done in the pseudovoxel simulations. I get the gist of what was done, but these methods should clear enough that someone could repeat them, and they currently are not.

Thanks, we added noise amplitude to the figure legend and Methods.

What does flexible mean in the title? The analysis only worked for the hexagonal grid - doesn't that suggest that whatever representations are uncovered here are not flexible in the sense of being able to encode different things?

Flexible here means, flexible over stimulus’ characteristics that are not related to the structural form such as stimuli, the size of the graph etc.

Reviewer #3 (Recommendations for the authors):

I have noticed that the authors have updated the previous preprint version to include extensive simulations. I believe this addition helps address potential criticisms regarding the signal-to-noise ratio. If the authors could share the code for the fMRI data and the simulations in an open repository, it would enhance the study's impact by reaching a broader readership across various research fields. Except for that, I have nothing to ask for revision.

Thanks, the code will be publicly available: (https://github.com/ShirleyMgit/subspace_generalization_paper_code/tree/main).

• 80% of Generation Z (Gen Z) admit they could not financially sustain themselves for a month after losing their main income source; 27% have no emergency savings.
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a los preliminares

typo

a los preliminares ... definir un espacio vectorial cerrado para el caso de dimensión finita

cual sistema? el problema completo ? el inventario ?

hay que definir el periodo de planificación con alguna letra ... ya hay alguna definida en capítulos siguientes??

es el mismo espacio X de arriba? hay que hablar de él por su nombre ... o será otro que no es necesario especificar?

llama almacén, no instalación. Hay que refiriese a lo largo del trabajo a los objetos de la misma forma y no ponerles nombres sinónimos, porque se pierde precisión. Más aún en esta tesis que tiene muchas cosas similares

explicar más a fondo a qué se hace referencia con niveles en el problema: son clasificaciones de los tipos de decisión que define el problema? hay que aclarar

cual espacio? el espacio al que pertenecen las variables de decisión? el espacio factible donde están las soluciones del problema ?

que problema es este? ya fué definido? poner referencia. pero hasta loq ue he leido no veo que a un problema se le hayan colocado estas 3 características. o si ya está debe ser claro y preciso

sobra ... muy IA

que es optimización estructurada y no estructurada??

será es decir ... en particular reduce una propiedad ... las configuraciones las cumple cualquier conjunto discreto

explicar porqué hay complejidad combinatoria y cómo vá a afectar el problema

quitar ... IA

en la definición vá estas hipótesis adicionales. Qué significa que dos conjuntos son disjuntos en función?? eso está raro. es otro tipo de operación entre conjuntos?

lo mismo ... ya esta definido antes. Se deinie con claridad y completamente una vez y luego se hace referencia cruzada de la definición

ya está definido este espacio ... unifica

almacenes?

qué es una arquitectura? es lo mismo que toplogía, es lo mismo que las componentes del problema? hay que evitar estas ambigüedades

rescribir ... que es una topología aqui? que es una semántica? cualquier concepto nuevo debe ser presentado antes porque entonces deja más preguntas que claridad

cita .... y esto es un teorema ...hay que buscarlo ... la próxima vemos si debe ser incluido, pero si debe ser aclarado

que es análisis suave y no suave?

dominios de la funciones objetivo? que es dominio aquí?

cita

que es un diseño? será un problema? una vez definido el anterior que es el caso híbrido?

hay que definir este concepto ... o decirlo en palabras simples ... no hay que escribir conceptos sueltos que generan las interrogantes que calidad

esto debe definirse antes: esquema maestro (no será problema maestro?) y que es un subproblema

cita

cual? será la propiedad demostrada en la proposición anterior?

cambia esto por su el significado de función afín

problema de progrmación lineal, porque suema muy raro asi

citar ... a que se hace referencia aquí con complejidad global?

preliminares y enlazar cuando ya se expliquen las condiciones KKT

cita

cambia esa linea en el conjunto ... parece una matriz ... divide en varias líneas ... eso no aparece en literatura matemática

quitar ... IA

quitar ... muy IA

cita bibliográfica

a la lista de preliminares

a la lista de preliminares

para la lista de conceptos de los preliminares

lo mismo

por sus siglas en inglés? ... debe mencionarse la primera vez que aparezca y dar su significado

por sus siglas en inglés??

generar lista de conceptos para los preliminares, es decir , esta nomenclatura obliga a presentar las v..a. absolutamente continuas, la normal y su nomenclatura ... deberá ser corto, claro y preciso

lo mismo

lo mismo

lo mismo

conseguir el artículo para incluir la cita en la bibliografía (con doi)

quitar

cambiar por coma, se lee muy de IA

Collective invention is an economic and innovation concept where firms or individuals freely share their technological discoveries, improvements, and know-how with each other—even when they are competitors.

Today, probably spelt purdahnashin.

Database Configuration

For research communities negotiating transformative agreements, they are not only a mechanism to understand and redirect spending on publication outputs and APCs—they are, perhaps more importantly, a way of bringing libraries directly into the mechanics of scholarly publishing. Negotiations expose the true costs of the system, revealing that the subscription fees institutions have been paying for years are often higher than the publishing costs that are now under scrutiny. This shift in visibility is transformative in itself. It enables libraries, funders, and scholars to make informed decisions, to question entrenched assumptions, and to draw on models such as SciELO’s that align investments with open access objectives. The more we understand the real economics of publishing, the more agency we gain in reshaping it.

J'ai mis les deux liens mais sur la solution il est en une seule. C'est quoi la méthode pour les regrouper ?

demote this to after the built up Problem page

Build out the problem specifically. parents busy - struggle with huge amounts of info from all sources and work. conflicting info, guilt increases, confusion, kids miss events, parents miss school events, parents by year 1 are paranoid about missing stuff. it juys takes one missed pyjama day event to feel the pain. School sign-ups and fundraisers lower subscriber rates, key differentiators for schools around what they offer - important to the Heads. Guilty, crying child, or not letting you work because you forgot to book childcare on time, missed deadlines, learning opportunities for kids.

Adjusted for Lean startup mode

agregar abajo: Director de tesis: Yofre Hernán García Gómez, con mi correo

Cerita ini sangat seru dan menyenangkan. Karena menceritakan tentang krisis identitas yang mungkin saja banyak sekali dialami oleh mahasiswa-mahasiswa pada umumnya yang merantau dan jauh dari daerah asalnya. Dia merasa bingung akan dirinya sendiri, entah itu asal, nama, bahkan logat biacaranya. Di cerita ini kita juga dapat belajar bahwa menjadi orang Indonesia sudah cukup untuk menunjukkan siapa diri kita sebenarnya.Teks ini juga menggambarkan betapa kaya dan beragamnya identitas kita sebagai orang Indonesia.

for - definition - Intelligence Theory - a physics of how information is processed to create value and persistence - definition - Intelligent Economics - the first application of Intelligence Theory

for - book - The Last Economy - about how to build human symbiosis - an engineering manual for building Future Three: Human Symbiosis

for - definition - Human Symbiosis - This is a future in which AI works for every individual human being. - This is not the default and we have to fight for it if we want it.

for - definition - digital feudalism - question - similar to techno-feudalism? - This is an AI future in which a small group of elites control the AI of humanity. - There is a Universal Basic Income but it is just enough to survive, not enough to flourish. - This is the default future. Everything is headed this way right now.

for - definition - The Great Fragmentation - An AI future in which nation states compete with each other for AI resources and create AI silos. - This is already happening.

for - futures - AI - human intelligence - digital feudalism - the great fragmentation - human symbiosis

for - book - In Praise of Scribes - Author Johannes Trithemius - history - progress - technology - printing press

Version 3 of this preprint has been peer-reviewed and recommended by Peer Community in Infections.<br /> See the peer reviews and the recommendation.

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

Manuscript number: RC-2025-03160

Corresponding author(s) Padinjat, Raghu

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1. General Statements [optional]

We thank all three reviewers for appreciating the novelty of our analysis of CERT function in a physiological context in vivo. While many studies have been published on the biochemistry and function of CERT in cultured cells, there are limited studies, if any, relating the impact of CRT function at the biochemical level to its function on a physiological process, in our case the electrical response to light.

We also that all reviewers for commenting on the importance of our rescue of dcert mutants with hCERT and the scientific insights raised by this experiment. All reviewers have also noted the importance of strengthening our observation that hCERT, in these cells, is localized at ER-PM MCS rather that the more widely reported localization at the Golgi. We highlight that many excellent studies which have localized CERT at the Golgi are performed in cultured, immortalized, mammalian cells. There are limited studies on the localization of this protein in primary cells, neurons or in polarized cells. With the additional experiments we have proposed in the revision for this aspect of the manuscript, we believe the findings will be of great novelty and widespread interest.

We believe we can address almost all points raised by reviewers thereby strengthening this exciting manuscript.

2. Description of the planned revisions

Insert here a point-by-point reply that explains what revisions, additional experimentations and analyses are planned to address the points raised by the referees.

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

This manuscript dissects the physiological function of ceramide transfer protein (CERT) by studying the phenotype of CERT null Drosophila.

dCERT null animals have a reduced electrical response to light in their photoreceptors, reduced baseline PIP2 accumulation in the cells and delayed re-synthesis of PIP2 and its precursor, PI4P after light stimulation. There are also reduced ER:PM contact sites at the rhabdomere and a corresponding reduction in the localization of PI/PA exchange protein, RDGB at this site. Therefore, the animals seem to have an impaired ability for sustaining phototransduction, which is nonetheless milder than that seen after loss of RDGB, for example. In terms of biochemical function, there is no overall change in ceramides, with some minor increases in specific short chain pools. There is however a large decrease in PE-ceramide species, again selective for a few molecular species. Curiously, decreasing ceramides with a mutant in ceramide synthesis is able to partially rescue both the electrical response and RDGB localization in dCERT flies, implying the increased ceramide species contribute to the phenotype. In addition, a mutation in PE-ceramide synthase largely phenocopies the dCERT null, exhiniting both increases ceramides and decreased PE-ceramide.

In addition, dCERT flies were shown to have reduced localization of some plasma membrane proteins to detergent-resistant membrane fractions, as well as up regulation of the IRE1 and PERK stress-response pathways. Finally, dCERT nulls could be rescued with the human CERT protein, demonstrating conservation of core physiological function between these animals. Surprisingly, CERT is reported to localize to the ER:PM junctions at rhabdomeres, as opposed to the expected ER:Golgi contact sites. Specific areas where the manuscript could be strengthened include:

Figure 2 studies the phototransduction system. Although clear changes in PI4P and PIP2 are seen, it would be interesting to see if changed PA accumulation occur in the dCERT animals, since RDGB localization is disrupted: this is expected to cause PM PA accumulation along with reduced PIP2 synthesis.

It is an important question raised by the reviewer to check PA levels. In the present study we have noticed that localization of RDGB at the base of the rhabdomere in dcert1 is reduced but not completely removed. Consequently, one may consider the situation on dcert1 as a partial loss of function of RDGB and consistent with this, the delay in PI4P and PI(4,5)P2 resynthesis is not as severe as in rdgB9 which is a strong hypomorph (PMID: 26203165).

rdgB9 mutants also show an elevation in PA levels and the reviewer is right that one might expect changes in PA levels too as RDGB is a PI/PA transfer protein. We expect that if measured, there will be a modest elevation in PA levels. However, previous work has shown that elevation of PA levels at the or close to the rhabdomere lead to retinal degeneration Specifically, elevated PA levels by dPLD overexpression disrupts rhabdomere biogenesis and leads to retinal degeneration (PMID: 19349583). Similarly, loss of the lipid transfer protein RDGB leads to photoreceptor degeneration (PMID: 26203165). In this study, we report that retinal degeneration is not a phenotype of dcert1. Thus measurements of PA levels though interesting may not be that informative in the context of the present study. However, if necessary, we can measure PA levels in dcert1.

Lines 228-230 state: "These findings suggest an important contribution for reduced PE - Cer levels in the eye phenotypes of dcert". Does it not also suggest a contribution of the elevated ceramide species, since these are also observed in the CPES animals?

We agree with the reviewer that not only reduced PE-Ceramide but also elevated ceramide levels in GMR>CPESi could contribute to the eye phenotype. This statement will be revised to reflect this conclusion.

Figure 6D is a key finding that human CERT localized to the rhabdomere at ER:PM contact sites, though the reviewer was not convinced by these images. Is the protein truly localized to the contact sites, or simply have a pool of over-expressed protein localized to the surrounding cytoplasm? It also does not rule out localization (and therefore function) at ER:PM contact sites.

Since hCERT completely rescued eye phenotype of dcert1 the localization we observe for hCERT must be at least partly relevant. We will perform additional IHC experiments to

• Co-localize hCERT with an ER-PM MCS marker, e.g RDGB in wild type flies
• Co-localize hCERT with VAP-A that is enriched at the ER-PM MCS. This should help to determine if there are MCS and non-MCS pools of hCERT in these cells. marker, e.g RDGB in wild type flies
• Test if there is a pool of hCERT, in these cells that also localizes (or not) with the Golgi marker Golgin 84. These will be included in the revision to strengthen this important point.

Statistics: There are a large number of t-tests employed that do not correct for multiple comparisons, for example in figures 3B, 3D, 3H, 4C, 6C, S2A, S2B, S3B and S3C.

We will performed multiple comparisons with mentioned data and incorporate in the revised manuscript.

There are two Western blotting sections in the methods.

The first Western blotting methods is for general blots in the paper. The second western blotting section is related to the samples from detergent resistant membrane (DRM) fractions. We will clearly explain this information in the methods section of the manuscript.

Reviewer #1 (Significance (Required)):

Overall, the manuscript is clearly and succinctly written, with the data well presented and mostly convincing. The paper demonstrates clear phenotypes associated with loss of dCERT function, with surprising consequences for the function of a signaling system localized to ER:PM contact sites. To this reviewer, there seem to be three cogent observations of the paper: (i) loss of dCERT leads to accumulation of ceramides and loss of PE-ceramide, which together drive the phenotype. (ii) this ceramide alteration disrupts ER:PM contact sites and thus impairs phototransduction and (iii) rescue by human CERT and its apparent localization to ER:PM contact sites implies a potential novel site of action. Although surprising and novel, the significance of these observations are a little unclear: there is no obvious mechanism by which the elevated ceramide species and decreased PE-ceramide causes the specific failure in phototrasnduction, and the evidence for a novel site of action of CERT at the ER:PM contact sites is not compelling. Therefore, although an interesting and novel set of observations, the manuscript does not reveal a clear mechanistic basis for CERT physiological function.

We thank reviewer for appreciating the quality of our manuscript while also highlighting points through which its impact can be enhanced. To our knowledge this is one of the first studies to tackle the challenging problem of a role for CERT in physiological function. We would like to highlight two points raised:

• We do understand that the localisation of hCERT at ER-PM MCS is unusual compared to the traditional reported localization to ER-Golgi sites. This is important for the overall interpretation of the results in the paper on how dCERT regulates phototransduction. As indicated in response to an earlier comment by the reviewer we will perform additional experiments to strengthen our conclusion of the localization of hCERT.
• With regard to how loss of dCERT affects phototransduction, we feel to likely mechanisms contribute. If the localization of hCERT to ER-PM MCS is verified through additional experiments (see proposal above) then it is important to note that ER-PM MCS in these cells includes the SMC (smooth endoplasmic reticulum) the major site of lipid synthesis. It is possible that loss of dCERT leads to ceramide accumulation in the smooth ER and disruption of ER-PM contacts. That may explain why reducing the levels of ceramide at this site partially rescues the eye phenotype.

The multi-protein INAD-TRP-NORPA complex, central to phototransduction have previously been shown to localise to DRMs in photoreceptors. PE-Ceramides are important contributors to the formation of plasma membrane DRMs and we have presented biochemical evidence that the formation of these DRMs are reduced in the dcert1. This may be a mechanism contributing to reduced phototransduction. This latter mechanism has been proposed as a physiological function of DRMs but we think our data may be the first to show it in a physiological model.

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

Summary Non-vesicular lipid transfer by lipid transfer proteins regulates organelle lipid compositions and functions. CERT transfers ceramide from the ER to Golgi to produce sphingomyelin, although CERT function in animal development and physiology is less clear. Using dcert1 (a protein-null allele), this paper shows a disruption of the sole Drosophila CERT gene causes reduced ERG amplitude in photoreceptors. While the level and localization of phototransduction machinery appears unaffected, the level of PIP2 and the localization of RDGB are perturbed. Collectively, these observations establish a novel link between CERT and phospholipase signaling in phototransduction. To understand the molecular mechanism further, the authors performed lipid chromatography and mass spec to characterize ceramide species in dcert1. This analysis reveals that whereas the total ceramide remains unaffected, most PE-ceramide species are reduced. The authors use lace mutant (serine palmitoyl transferase) and CPES (ceramide phosphoethanolamine synthase) RNAi to distinguish whether it is the accumulation of ceramide in the ER or the reduction of sphingolipid derivates in the Golgi that is the cause for the reduced ERG amplitude. Mutating one copy of lace reduces ceramide level by 50% and partially rescues the ERG defect, suggesting that the accumulation of ceramide in the ER is a cause. CPES RNAi phenocopies the reduced ERG amplitude, suggesting the production of certain sphingolipid is also relevant.

Major comments: 1. By showing the reduced PIP2 level, the decreased SMC sites at the base of rhabdomeres, and the diffused RDGB localization in dcert1, the authors favor the model, in which the disruption of ceramide metabolism affects PIP transport. However, it is unclear if the reduced PIP2 level (i.e., reduced PH-PLCd::GFP staining) is specific to the rhabdomeres. It should be possible to compare PH-PLCd::GFP signals in different plasma membranes between wildtype and dcert1. If PH-PLCd::GFP signal is specifically reduced at the rhabdomeres, this conclusion will be greatly strengthened. In addition, the photoreceptor apical plasma membrane includes rhabdomere and stalk membrane. Is the PH-PLCd::GFP signal at the stalk membrane also affected?

Due to the physical organization of optics in the fly eye, the pseudopupil imaging method used in this study collects the signal for the PIP2 probe (PH-PLCd::GFP) mainly from the apical rhabdomere membrane of photoreceptors in live imaging experimental mode. Therefore, the PIP2 signal from these experiments cannot be used to interpret the level of PIP2 either at the stalk membrane or indeed the basolateral membrane.

The point raised by the reviewer, i.e whether CERT selectively controls PIP2 levels at the rhabdomere membrane or not, is an interesting one. To do this, we will need to fix fly photoreceptors and determine the PH-PLCd::GFP signal using single slice confocal imaging. When combined with a stalk marker such as CRUMBS, it should be possible to address the question of which are the membrane domains at which dCERT controls PIP2 levels. If the sole mechanism of action of dCERT is via disruption of ER-PM MCS then only the apical rhabdomere membrane PIP2 should be affected leaving the stalk membrane and basolateral membrane unaffected.

Thank you very much for raising this specific point.

The analysis of RDGB localization should be done in mosaic dcert1 retinas, which will be more convincing with internal control for each comparison. In addition, the phalloidin staining in Figure 2J shows distinct patterns of adherens junctions, indicating that the wildtype and dcert1 were imaged at different focal planes.

We understand that having mosaics is an alternative an elegant way to perform a a side by side analysis of control and mutant. However this would require significant investment of time and effort, perhaps beyond the scope of this study. If we were to perform a mosaic analysis, this would compromise our ERG analysis since ERG is an extracellular recording We feel that this is beyond the scope of this study and perhaps may not be necessary as such (see below).

In the revision we will present equivalent sections of control and dcert1 taken from the nuclear plane of the photoreceptor. This should resolve the reviewer’s concerns.

The significance of ceramide species levels in dcert1 and GMR>CPESRNAi needs to be explained better. Do certain alterations represent accumulation of ceramides in the ER?

Species level analysis of changes in ceramides reveal that elevations in dcert1 are seen mainly in the short chain ceramides (14 and 16 carbon chains). These most likely represent the short chain ceramides synthesised in the ER and accumulating due to the block in further metabolism to PE-Cer due to depletion in CPES.

Species level analysis of changes in ceramides reveal that in dcert1 there is a ceramide transport related defect leading to elevation, primarily, in the short chain ceramides (14 and 16 carbon chains), and this selective supply defect leads to a reduction in PE-Cer levels, with a maximum change in the ratio of short-chain Cer:PE Cer (Figure 3A-D). Though there is no apparent change in the total ceramide level the species specific elevation in the ceramides disturb the fine -balance between the short-chain ceramides and the long and very-long chain ceramides. As the function of long and very-long chain ceramides are implicated in dendrite development and neuronal morphology (doi: 10.1371/journal.pgen.1011880), therefore this alteration in the fine balance between different ceramide species probably impacts the integrity and fluidity of the membrane environment. On the other hand it leads to a possibility of a defined function of the short-chain ceramides in electrical responses to light signalling in the eye, especially with respect to the PE-ceramides that are reduced by around 50%.

In contrast the GMR>CPESRNAi leads to more of a substrate accumulation showing ceramide increase (14, 16, 18, 20 carbon chains) and decrease in PE-Cer levels (Figure 4D, E). In this case Cer accumulation is due to the block in further metabolism to PE-Cer arising from depletion in CPES.

We will include this in the discussion of a revised version.

The suppression by lace is interpreted as evidence that the reduced ERG amplitude in dcert1 is caused by ceramide accumulation in the ER. This interpretation seems preliminary as lace may interact with dcert genetically by other mechanisms.

The dcert1 mutant exhibits increased levels of short-chain ceramides (Fig 3B), whereas the lace heterozygous mutant (laceK05305/+) displays reduced short-chain ceramide levels (Supp Fig 2B). In the laceK05305/+; dcert1 double mutant, ceramide levels are lower than those observed in the dcert1 mutant alone (Supp Fig 2B), indicating a partial genetic rescue of the elevated ceramide phenotype.

Furthermore, through multiple independent genetic manipulations that modulate ceramide metabolism (alterations of dcert, cpes and lace), we consistently observe that increased ceramide levels correlate with a reduction in ERG amplitude, suggesting that ceramide accumulation negatively impacts photoreceptor function. Taken together, these observations indicate that the reduction in ceramide levels in the laceK05305/+; dcert1 double mutant likely contributes to the suppression of the ERG defect observed in the dcert1 mutant.

The authors show that ERG amplitude is reduced in GMR>CPESRNAi. While this phenocopying is consistent with the reduced ERG amplitude in dcert1 being caused by reduced production of PE-ceramide, GMR>CPESRNAi also shows an increase in total ceramide level. Could this support the hypothesis that reduced ERG amplitude is caused by an accumulation of ceramide elsewhere? In addition, is the ERG amplitude reduction in GMR>CPESRNAi sensitive to lace?

We agree that in addition to reduced PE-Ceramide, the elevated ceramide levels in GMR>CPESi could contribute to the eye phenotype. We will introduce lace heterozygous mutant in the GMR>CPESi background to test the contribution of elevated ceramide levels in the *GMR>CPESi * background and incorporate the data in the revision. Thank you for this suggestion.

Along the same line, while the total ceramide level is significantly reduced in lace heterozygotes, is the PE-ceramide level also reduced? If yes, wouldn't this be contradictory to PE-ceramide production being important for ERG amplitude?

Mass spec measurements show that levels of PE-Cer were not reduced in lacek05305/+ compared to wild type. This data will be included in the revised manuscript. However, the ERG amplitude of these flies and also in those with lace depletion using two independent RNAi lines were not reduced.

What is the explanation and significance for the age-dependent deterioration of ERG amplitude in dcert1? Likewise, the significance of no retinal degeneration is not clearly presented.

There could be multiple reasons for the age dependent deterioration of the ERG amplitude, in the absence of retinal degeneration. Drosophila phototransduction cascade depends heavily on ATP production. The age dependent reduction in ATP synthesis could lead to deterioration in the ERG amplitude. These may include instability of the DRMs due to reduced PE-Cer, lower ATP levels due to mitochondrial dysfunction, an perhaps others. A previous study has shown that ATP production is highly reduced along with oxidative stress and metabolic dysfunction in dcert1 flies aged to 10 days and beyond (PMID: 17592126). The same study has also found no neuronal degeneration in dcert1 that phenocopies absence of photoreceptor degeneration in the present study. We will attempt a few experiments to rule in or rule out the these and revise the discussion accordingly.

The rescue of dcert1 phenotype by the expression of human CERT is a nice result. In addition to demonstrating a functional conservation, it allows a determination of CERT protein localization. However, the quality of images in Figure 6D should be improved. The phalloidin staining was rather poor, and the CNX99A in the lower panel was over-exposed, generating bleed-through signals at the rhabdomeres. In addition, the localization of hCERT should be explored further. For instance, does hCERT colocalize with RDGB? Is the hCERT localization altered in lace or GMR>CPESRNAi background?

As indicated in response to reviewer 1:

We will perform additional IHC experiments to

• Co-localize hCERT with an ER-PM MCS marker, e.g RDGB in wild type flies
• Co-localize hCERT with VAP-A that is enriched at the ER-PM MCS. This should help to determine if there are MCS and non-MCS pools of hCERT in these cells. marker, e.g RDGB in wild type flies
• Test if there is a pool of hCERT, in these cells that also localizes (or not) with the Golgi marker Golgin 84. These will be included in the revision to strengthen this important point.

We will also attempt to perform hCERT localization in lace or GMR>CPESRNAi background

Minor comments: 1. In Line 128, Df(732) should be Df(3L)BSC732.

Changes will be incorporated in the main manuscript.

GMR-SMSrRNAi shows an increase in ERG peak amplitude. Is there an explanation for this?

GMR-SMSrRNAi did show slight increase in ERG peak amplitude but was not statistically significant.

Reviewer #2 (Significance (Required)):

Significance As CERT mutations are implicated in human learning disability, a better understanding of CERT function in neuronal cells is certainly of interest. While the link between ceramide transport and phospholipase signaling is novel and interesting, this paper does not clearly explain the mechanism. In addition, as the ERG were measured long after the retinal cells were deficient in CERT or CPES, it is difficult to assess whether the observed phenotype is a primary defect. Furthermore, the quality of some images needs to be improved. Thus, I feel the manuscript in its current form is too preliminary.

We thank reviewer for highlighting the importance and significance of our work in the light of recent studies of CERT function in ID. As with all genetic studies it is difficult to completely disentangle the role of a gene during development from a role only in the adult. However, we will attempt to perhaps use the GAL80ts system to uncouple these two potential components of CERT function in photoreceptors. The goal will be to determine if CERT has a specific role only in adult photoreceptors or if this is coupled to a developmental role. Since ID is as a neurodevelopmental disorder, a developmental role for CERT would be equally interesting.

As previously indicated images will be improved bearing in mind the reviewer comments.

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

Summary: Lipid transfer proteins (LTPs) shuttle lipids between organelle membranes at membrane contact sites (MCSs). While extensive biochemical and cell culture studies have elucidated many aspects of LTP function, their in vivo physiological roles are only beginning to be understood. In this manuscript, the authors investigate the physiological role of the ceramide transfer protein (CERT) in Drosophila adult photoreceptors-a model previously employed by this group to study LTP function at ER-PM contact sites under physiological conditions. Using a combination of genetic, biochemical, and physiological approaches, they analyze a protein-null mutant of dcert. They show that loss of dcert causes a reduction in electrical response to light with progressive decrease in electroretinogram (ERG) amplitude with age but no retinal degeneration. Lipidomic analysis shows that while the total levels of ceramides are not changed in dcert mutants, they do observe significant change in certain species of ceramides and depletion of downstream metabolite phosphoethanolamine ceramide (PE-Cer). Using fluorescent biosensors, the authors demonstrate reduced PIP2 levels at the plasma membrane, unchanged basal PI4P levels and slower resynthesis kinetics of both lipids following depletion. Electron microscopy and immunolabeling further reveal a reduced density of ER-PM MCSs and mislocalization of the MCS-resident lipid transfer protein RDGB. Genetic interaction studies with lace and RNAi-mediated knockdown of CPES support the conclusion that both ER ceramide accumulation and PM PE-Cer depletion contribute to the observed defects in dcert mutants. In addition, detergent-resistant membrane fractionation indicates altered plasma membrane organization in the absence of dcert. The study also reports upregulation of unfolded protein response transcripts, including IRE1 and PERK, suggesting increased ER stress. Finally, expression of human CERT rescues the reduced electrical response, demonstrating functional conservation across species. Overall the manuscript is well written that builds on established work and experiments are technically rigorous. The results are clearly presented and provide valuable insights into the physiological role of CERT.

Major comments: 1.The reduced ERG amplitude appears to be the central phenotype associated with the loss of dcert, and most of the experiments in this manuscript effectively build a mechanistic framework to explain this observation. However, the experiments addressing detergent-resistant membrane domains (DRMs) and the unfolded protein response (UPR) seem somewhat disconnected from the main focus of the study. The DRM and UPR data feel peripheral and could benefit from few experiments for functional linkage to the ERG defect or should be moved to supplementary.

We agree with the reviewer that further experiments are needed to link the DRM data to the ERG defects. That would need specific biochemical alteration at the PM to modulate PE-Cer species and their effect on scaffolding proteins required for phototransduction (that is beyond the scope of the present study). We will consider moving these to the supplementary section as suggested by the reviewer.

2.The changes in ceramide species and reduction in PE-Cer are key findings of the study. These results should be further validated by performing a genetic rescue using the BAC or hCERT fly line to confirm that the lipidomic changes are specifically due to loss of CERT function.

Thank you for this comment. We will include this in the revised manuscript.

3.Figure 2B-C and 2E-F: Representative images corresponding to the quantified data should be included to illustrate the changes in PIP2 and PI4P reporters. Given that the fluorescence intensity of the PIP2 reporter at the PM is reduced in the dcert mutant relative to control, the authors should also verify that the reporter is expressed at comparable levels across genotypes.

• As mentioned by the reviewer we will include representative images alongside our quantified data both of the basal ones and that of the kinetic study.

• Western blot of reporters (PH-PLCd::GFP and P4M::GFP) across genotypes will be added to the revised manuscript. 4.Figure 2J-K: The partial mislocalization of RDGB represents an important observation that could mechanistically explain the reduced resynthesis of PI4P and PIP2 and consequently, the decreased ERG amplitude in dcert mutants. However, this result requires further validation. First, the authors should confirm whether this mislocalization is specific to RDGB by performing co-staining with another ER-PM MCS marker, such as VAP-A, to assess whether overall MCS organization is disrupted. Second, the quantification of RDGB enrichment at ER-PM MCSs should be refined. From the representative images, RDGB appears redistributed toward the photoreceptor cell body, but the presented quantification does not clearly reflect this shift. The authors should therefore include an analysis comparing RDGB levels in the cell body versus the submicrovillar region across genotypes. This analysis should be repeated for similar experiments across the study. Additionally, the total RDGB protein level should be quantified and reported. Finally, since RDGB mislocalization could directly contribute to the decreased ERG amplitude, it would be valuable to test whether overexpression of RDGB in dcert mutants can rescue the ERG phenotype.

• In our ultrastructural studies (Fig. 2H, 2I and Sup. Fig. 1A, 1B) we did see reduction in PM-SMC MCS that was corroborated with RDGB staining.

• Comparative ratio analysis of RDGB localisation at ER-PM MCS vs cell body will be included in the manuscript for all RDGB staining.
• We have done western analysis for total RDGB protein level in ROR and dcert1. This data will be included in the revised manuscript.
• This is a very interesting suggestion and we will test if RDGB overexpression can rescue ERG phenotype in dcert1.

5.Figure 3F and I-J: Inclusion of appropriate WT and laceK05205/+ controls is necessary to allow proper interpretation of the results. These controls would strengthen the conclusions regarding the functional relationship between dcert and lace.

Changes will be incorporated as per the suggestion.

6.Figure 5C: The representative images shown here appear to contradict the findings described in Figure 2A. In Figure 5C, Rhodopsin 1 levels seem markedly reduced in the dcert mutants, whereas the text states that Rh1 levels are comparable between control and mutant photoreceptors. The authors should replace or reverify the representative images to ensure that they accurately reflect the conclusions presented in the text.

We will reverify the representative images and changes will be accordingly incorporated.

7.Figure 6D: The reported localization of hCERT to ER-PM MCSs is a key and potentially insightful observation, as it suggests the subcellular site of dcert activity in photoreceptors. However, the representative images provided are not sufficiently conclusive to support this claim. The authors should validate hCERT localization by co-staining with established markers like RDGB for ER-PM CNX99A for the ER and a Golgi marker since mammalian CERT is classically localized to ER-Golgi interfaces. Optionally, the authors could also quantify the relative distribution of hCERT among these compartments to provide a clearer assessment of its subcellular localization.

As indicated in response to reviewer 1:

We will perform additional IHC experiments to

• Co-localize hCERT with an ER-PM MCS marker, e.g RDGB in wild type flies
• Co-localize hCERT with VAP-A that is enriched at the ER-PM MCS. This should help to determine if there are MCS and non-MCS pools of hCERT in these cells. marker, e.g RDGB in wild type flies
• Test if there is a pool of hCERT, in these cells that also localizes (or not) with the Golgi marker Golgin 84. These will be included in the revision to strengthen this important point.

Minor comments: 1.In the first paragraph of introduction, authors should consider citing few of the key MCS literature.

Additional literature will be included as per the suggestion.

2.Line 132: data not shown is not acceptable. Authors should consider presenting the findings in the supplemental figure.

Data will be added in supplement as per the suggestion.

3.The authors should include a comprehensive table or Excel sheet summarizing all statistical analyses. This should include the sample size, type of statistical test used and exact p-values. Providing this information will improve the transparency, reproducibility and overall rigor of the study.

We will provide all the statistical analyses in mentioned format as per the suggestion.

4.The materials and methods section can be reorganized to include citation for flystocks which do not have stock number or RRIDs if the stocks were previously described but are not available from public repositories. They should expand on the details of various quantification methods used in the study. Finally including a section of Statistical analyses would further enhance transparency and reproducibility

• Stock details will be added wherever missing as per the suggestion.
• Statistical analyses section will be included in the material and methods. **Referee cross-commenting**

1.I concur with Reviewer 1 regarding the need for more detailed reporting of statistical analyses.

We will perform multiple comparisons with mentioned data and incorporate in the revised manuscript.

2.I also agree with Reviewer 3 that the discussion should be expanded to address the age-dependent deterioration of ERG amplitude observed in the dcert mutants. This progressive decline could provide valuable insight into the long-term requirement of CERT function and signaling capacity at the photoreceptor membrane.

Expanded discussion on the age dependent ERG amplitude decline will be incorporated in the discussion as per the suggestion.

Reviewer #3 (Significance (Required)):

This study explores the physiological function of CERT, a LTP localized at MCSs in Drosophila photoreceptors and uncovers a novel role in regulating plasma membrane PE-Cer levels and GPCR-mediated signaling. These findings significantly advances our understanding of how CERT-mediated lipid transport regulates G-protein coupled phospholipase C signaling in vivo. This work also highlights Drosophila photoreceptors as a powerful system to analyze the physiological significance of lipid-dependent signaling processes. This work will be of interest to researchers in neuronal cell biology, membrane dynamics and lipid signaling community. This review is based on my expertise in neuronal cell biology.

We thank the reviewer for appreciating the significance of our work from a neuroscience perspective.

• *

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

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

• *

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

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

• *

We can address all reviewer points in the revision. However, we will not be able to perform a mosaic analysis of the impact of dcert1 mutant in the retina. We feel this is beyond the scope of this revision. In our response, we have highlighted how controls included in the revision offset the need for a mosaic analysis at this stage.

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Referee #3

Evidence, reproducibility and clarity

Summary:

Lipid transfer proteins (LTPs) shuttle lipids between organelle membranes at membrane contact sites (MCSs). While extensive biochemical and cell culture studies have elucidated many aspects of LTP function, their in vivo physiological roles are only beginning to be understood. In this manuscript, the authors investigate the physiological role of the ceramide transfer protein (CERT) in Drosophila adult photoreceptors-a model previously employed by this group to study LTP function at ER-PM contact sites under physiological conditions. Using a combination of genetic, biochemical, and physiological approaches, they analyze a protein-null mutant of dcert. They show that loss of dcert causes a reduction in electrical response to light with progressive decrease in electroretinogram (ERG) amplitude with age but no retinal degeneration. Lipidomic analysis shows that while the total levels of ceramides are not changed in dcert mutants, they do observe significant change in certain species of ceramides and depletion of downstream metabolite phosphoethanolamine ceramide (PE-Cer). Using fluorescent biosensors, the authors demonstrate reduced PIP2 levels at the plasma membrane, unchanged basal PI4P levels and slower resynthesis kinetics of both lipids following depletion. Electron microscopy and immunolabeling further reveal a reduced density of ER-PM MCSs and mislocalization of the MCS-resident lipid transfer protein RDGB. Genetic interaction studies with lace and RNAi-mediated knockdown of CPES support the conclusion that both ER ceramide accumulation and PM PE-Cer depletion contribute to the observed defects in dcert mutants. In addition, detergent-resistant membrane fractionation indicates altered plasma membrane organization in the absence of dcert. The study also reports upregulation of unfolded protein response transcripts, including IRE1 and PERK, suggesting increased ER stress. Finally, expression of human CERT rescues the reduced electrical response, demonstrating functional conservation across species.Overall the manuscript is well written that builds on established work and experiments are technically rigorous. The results are clearly presented and provide valuable insights into the physiological role of CERT.

Major comments:

1.The reduced ERG amplitude appears to be the central phenotype associated with the loss of dcert, and most of the experiments in this manuscript effectively build a mechanistic framework to explain this observation. However, the experiments addressing detergent-resistant membrane domains (DRMs) and the unfolded protein response (UPR) seem somewhat disconnected from the main focus of the study. The DRM and UPR data feel peripheral and could benefit from few experiments for functional linkage to the ERG defect or should be moved to supplementary. 2.The changes in ceramide species and reduction in PE-Cer are key findings of the study. These results should be further validated by performing a genetic rescue using the BAC or hCERT fly line to confirm that the lipidomic changes are specifically due to loss of CERT function. 3.Figure 2B-C and 2E-F: Representative images corresponding to the quantified data should be included to illustrate the changes in PIP2 and PI4P reporters. Given that the fluorescence intensity of the PIP2 reporter at the PM is reduced in the dcert mutant relative to control, the authors should also verify that the reporter is expressed at comparable levels across genotypes. 4.Figure 2J-K: The partial mislocalization of RDGB represents an important observation that could mechanistically explain the reduced resynthesis of PI4P and PIP2 and consequently, the decreased ERG amplitude in dcert mutants. However, this result requires further validation. First, the authors should confirm whether this mislocalization is specific to RDGB by performing co-staining with another ER-PM MCS marker, such as VAP-A, to assess whether overall MCS organization is disrupted. Second, the quantification of RDGB enrichment at ER-PM MCSs should be refined. From the representative images, RDGB appears redistributed toward the photoreceptor cell body, but the presented quantification does not clearly reflect this shift. The authors should therefore include an analysis comparing RDGB levels in the cell body versus the submicrovillar region across genotypes. This analysis should be repeated for similar experiments across the study. Additionally, the total RDGB protein level should be quantified and reported. Finally, since RDGB mislocalization could directly contribute to the decreased ERG amplitude, it would be valuable to test whether overexpression of RDGB in dcert mutants can rescue the ERG phenotype. 5.Figure 3F and I-J: Inclusion of appropriate WT and laceK05205/+ controls is necessary to allow proper interpretation of the results. These controls would strengthen the conclusions regarding the functional relationship between dcert and lace. 6.Figure 5C: The representative images shown here appear to contradict the findings described in Figure 2A. In Figure 5C, Rhodopsin 1 levels seem markedly reduced in the dcert mutants, whereas the text states that Rh1 levels are comparable between control and mutant photoreceptors. The authors should replace or reverify the representative images to ensure that they accurately reflect the conclusions presented in the text. 7.Figure 6D: The reported localization of hCERT to ER-PM MCSs is a key and potentially insightful observation, as it suggests the subcellular site of dcert activity in photoreceptors. However, the representative images provided are not sufficiently conclusive to support this claim. The authors should validate hCERT localization by co-staining with established markers like RDGB for ER-PM CNX99A for the ER and a Golgi marker since mammalian CERT is classically localized to ER-Golgi interfaces. Optionally, the authors could also quantify the relative distribution of hCERT among these compartments to provide a clearer assessment of its subcellular localization.

Minor comments:

1.In the first paragraph of introduction, authors should consider citing few of the key MCS literature. 2.Line 132: data not shown is not acceptable. Authors should consider presenting the findings in the supplemental figure. 3.The authors should include a comprehensive table or Excel sheet summarizing all statistical analyses. This should include the sample size, type of statistical test used and exact p-values. Providing this information will improve the transparency, reproducibility and overall rigor of the study. 4.The materials and methods section can be reorganized to include citation for flystocks which do not have stock number or RRIDs if the stocks were previously described but are not available from public repositories. They should expand on the details of various quantification methods used in the study. Finally including a section of Statistical analyses would further enhance transparency and reproducibility

Referee cross-commenting

1.I concur with Reviewer 1 regarding the need for more detailed reporting of statistical analyses. 2.I also agree with Reviewer 3 that the discussion should be expanded to address the age-dependent deterioration of ERG amplitude observed in the dcert mutants. This progressive decline could provide valuable insight into the long-term requirement of CERT function and signaling capacity at the photoreceptor membrane.

Significance

This study explores the physiological function of CERT, a LTP localized at MCSs in Drosophila photoreceptors and uncovers a novel role in regulating plasma membrane PE-Cer levels and GPCR-mediated signaling. These findings significantly advances our understanding of how CERT-mediated lipid transport regulates G-protein coupled phospholipase C signaling in vivo. This work also highlights Drosophila photoreceptors as a powerful system to analyze the physiological significance of lipid-dependent signaling processes. This work will be of interest to researchers in neuronal cell biology, membrane dynamics and lipid signaling community. This review is based on my expertise in neuronal cell biology.

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

Learn more at Review Commons

Referee #2

Evidence, reproducibility and clarity

Summary

Non-vesicular lipid transfer by lipid transfer proteins regulates organelle lipid compositions and functions. CERT transfers ceramide from the ER to Golgi to produce sphingomyelin, although CERT function in animal development and physiology is less clear. Using dcert1 (a protein-null allele), this paper shows a disruption of the sole Drosophila CERT gene causes reduced ERG amplitude in photoreceptors. While the level and localization of phototransduction machinery appears unaffected, the level of PIP2 and the localization of RDGB are perturbed. Collectively, these observations establish a novel link between CERT and phospholipase signaling in phototransduction. To understand the molecular mechanism further, the authors performed lipid chromatography and mass spec to characterize ceramide species in dcert1. This analysis reveals that whereas the total ceramide remains unaffected, most PE-ceramide species are reduced. The authors use lace mutant (serine palmitoyl transferase) and CPES (ceramide phosphoethanolamine synthase) RNAi to distinguish whether it is the accumulation of ceramide in the ER or the reduction of sphingolipid derivates in the Golgi that is the cause for the reduced ERG amplitude. Mutating one copy of lace reduces ceramide level by 50% and partially rescues the ERG defect, suggesting that the accumulation of ceramide in the ER is a cause. CPES RNAi phenocopies the reduced ERG amplitude, suggesting the production of certain sphingolipid is also relevant.

Major comments:

1. By showing the reduced PIP2 level, the decreased SMC sites at the base of rhabdomeres, and the diffused RDGB localization in dcert1, the authors favor the model, in which the disruption of ceramide metabolism affects PIP transport. However, it is unclear if the reduced PIP2 level (i.e., reduced PH-PLC::GFP staining) is specific to the rhabdomeres. It should be possible to compare PH-PLC::GFP signals in different plasma membranes between wildtype and dcert1. If PH-PLC::GFP signal is specifically reduced at the rhabdomeres, this conclusion will be greatly strengthened. In addition, the photoreceptor apical plasma membrane includes rhabdomere and stalk membrane. Is the PH-PLC::GFP signal at the stalk membrane also affected?
2. The analysis of RDGB localization should be done in mosaic dcert1 retinas, which will be more convincing with internal control for each comparison. In addition, the phalloidin staining in Figure 2J shows distinct patterns of adherens junctions, indicating that the wildtype and dcert1 were imaged at different focal planes.
3. The significance of ceramide species levels in dcert1 and GMR>CPESRNAi needs to be explained better. Do certain alterations represent accumulation of ceramides in the ER?
4. The suppression by lace is interpreted as evidence that the reduced ERG amplitude in dcert1 is caused by ceramide accumulation in the ER. This interpretation seems preliminary as lace may interact with dcert genetically by other mechanisms.
5. The authors show that ERG amplitude is reduced in GMR>CPESRNAi. While this phenocopying is consistent with the reduced ERG amplitude in dcert1 being caused by reduced production of PE-ceramide, GMR>CPESRNAi also shows an increase in total ceramide level. Could this support the hypothesis that reduced ERG amplitude is caused by an accumulation of ceramide elsewhere? In addition, is the ERG amplitude reduction in GMR>CPESRNAi sensitive to lace?
6. Along the same line, while the total ceramide level is significantly reduced in lace heterozygotes, is the PE-ceramide level also reduced? If yes, wouldn't this be contradictory to PE-ceramide production being important for ERG amplitude?
7. What is the explanation and significance for the age-dependent deterioration of ERG amplitude in dcert1? Likewise, the significance of no retinal degeneration is not clearly presented.
8. The rescue of dcert1 phenotype by the expression of human CERT is a nice result. In addition to demonstrating a functional conservation, it allows a determination of CERT protein localization. However, the quality of images in Figure 6D should be improved. The phalloidin staining was rather poor, and the CNX99A in the lower panel was over-exposed, generating bleed-through signals at the rhabdomeres. In addition, the localization of hCERT should be explored further. For instance, does hCERT colocalize with RDGB? Is the hCERT localization altered in lace or GMR>CPESRNAi background?

Minor comments:

1. In Line 128, Df(732) should be Df(3L)BSC732.
2. GMR-SMSrRNAi shows an increase in ERG peak amplitude. Is there an explanation for this?

Significance

As CERT mutations are implicated in human learning disability, a better understanding of CERT function in neuronal cells is certainly of interest. While the link between ceramide transport and phospholipase signaling is novel and interesting, this paper does not clearly explain the mechanism. In addition, as the ERG were measured long after the retinal cells were deficient in CERT or CPES, it is difficult to assess whether the observed phenotype is a primary defect. Furthermore, the quality of some images needs to be improved. Thus, I feel the manuscript in its current form is too preliminary.

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

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Referee #1

Evidence, reproducibility and clarity

This manuscript dissects the physiological function of ceramide transfer protein (CERT) by studying the phenotype of CERT null Drosophila.

dCERT null animals have a reduced electrical response to light in their photoreceptors, reduced baseline PIP2 accumulation in the cells and delayed re-synthesis of PIP2 and its precursor, PI4P after light stimulation. There are also reduced ER:PM contact sites at the rhabdomere and a corresponding reduction in the localization of PI/PA exchange protein, RDGB at this site. Therefore, the animals seem to have an impaired ability for sustaining phototransduction, which is nonetheless milder than that seen after loss of RDGB, for example. In terms of biochemical function, there is no overall change in ceramides, with some minor increases in specific short chain pools. There is however a large decrease in PE-ceramide species, again selective for a few molecular species. Curiously, decreasing ceramides with a mutant in ceramide synthesis is able to partially rescue both the electrical response and RDGB localization in dCERT flies, implying the increased ceramide species contribute to the phenotype. In addition, a mutation in PE-ceramide synthase largely phenocopies the dCERT null, exhiniting both increases ceramides and decreased PE-ceramide.

In addition, dCERT flies were shown to have reduced localization of some plasma membrane proteins to detergent-resistant membrane fractions, as well as up regulation of the IRE1 and PERK stress-response pathways. Finally, dCERT nulls could be rescued with the human CERT protein, demonstrating conservation of core physiological function between these animals. Surprisingly, CERT is reported to localize to the ER:PM junctions at rhabdomeres, as opposed to the expected ER:Golgi contact sites.

Specific areas where the manuscript could be strengthened include:

Figure 2 studies the phototransduction system. Although clear changes in PI4P and PIP2 are seen, it would be interesting to see if changed PA accumulation occur in the dCERT animals, since RDGB localization is disrupted: this is expected to cause PM PA accumulation along with reduced PIP2 synthesis.

Lines 228-230 state: "These findings suggest an important contribution for reduced PE - Cer levels in the eye phenotypes of dcert". Does it not also suggest a contribution of the elevated ceramide species, since these are also observed in the CPES animals?

Figure 6D is a key finding that human CERT localized to the rhabdomere at ER:PM contact sites, though the reviewer was not convinced by these images. Is the protein truly localized to the contact sites, or simply have a pool of over-expressed protein localized to the surrounding cytoplasm? It also does not rule out localization (and therefore function) at ER:PM contact sites.

Statistics: There are a large number of t-tests employed that do not correct for multiple comparisons, for example in figures 3B, 3D, 3H, 4C, 6C, S2A, S2B, S3B and S3C.

There are two Western blotting sections in the methods.

Significance

Overall, the manuscript is clearly and succinctly written, with the data well presented and mostly convincing. The paper demonstrates clear phenotypes associated with loss of dCERT function, with surprising consequences for the function of a signaling system localized to ER:PM contact sites. To this reviewer, there seem to be three cogent observations of the paper: (i) loss of dCERT leads to accumulation of ceramides and loss of PE-ceramide, which together drive the phenotype. (ii) this ceramide alteration disrupts ER:PM contact sites and thus impairs phototransduction and (iii) rescue by human CERT and its apparent localization to ER:PM contact sites implies a potential novel site of action. Although surprising and novel, the significance of these observations are a little unclear: there is no obvious mechanism by which the elevated ceramide species and decreased PE-ceramide causes the specific failure in phototrasnduction, and the evidence for a novel site of action of CERT at the ER:PM contact sites is not compelling. Therefore, although an interesting and novel set of observations, the manuscript does not reveal a clear mechanistic basis for CERT physiological function.

Ouch, absolutely scary.

But we don't stop to think! That is, except in puzzle games.

In a way, they portray the ideal of blank state perfect justice. All speedrunners have the same starting point, all competitive shooters have a fixed "egalitarian" "non-discriminatory" spawn. In a game you are not the nerd, you are the hero, you perform a new person and aren't scrutinised by it, you are given a second chance to start again.

Games are fair. No, they are not. They rely on your setup, on whether you can communicate with teammates, on who in your friend groups plays them, what free offer is given, and what characters which may represent you are found therein. Yet the myth continues, it's pervasive, it's the free will made-up CEO Enterpreneur mindset.

Indeed, so many myths to unpack in one sentence. We are not an exceptional species. We do not have free will. Options are mostly noise, choice is manufactured. Infinite growth is not a thing. Yet, we take this for granted. We assume them. I currently do, in a way, even if I acknowledge their mythology... and in doing it, I am given a burden on myself, an expectation, I am placed in a race, a competition against others with these goals. Survival is obscured, it's about thriving.

As argued in the show The Good Place, life is more complicated now that is has ever been. We have so many options! Choice overload incoming.

Bauman and Castell's globalisation.

I accept the argument, but understand that many other mediums also seek this. Perchance shopping, series, podcasts, etc. might not, but while doing sports competitively, or while being an artisti publicly, or particularly while showing yourself on social media and selling yourself on OnlyFans, as a product... you are the protagonist! So it is not only games, but arguably, games are a significant masculine reduct that has replaced, say, war, factory working, and revolutions (which provided less individuality anyhow, but at least promised social belonging and a sense of working toward a future).

Multiplayer games may dilute it a bit, and story games may have their protagonists, but who's in control of the vessel is us.

That's actually really interesting... if we posit the idea that prefaces relativist rationalisation that we "ought to be unique and maximise our very own specific set of goals" (for identity formation), then having less idiosyncratic or atypical or group-coded goals can make it harder to differentiate (am I a male or a female type of dissociation/confusion), which is arguably a frustrating experience.

business_checking personal_checking business_savings personal_savings

Should we keep partial payment false in the sample API? as most merchants might not need this?

Or thats the way it's for India also

billing address part is missing

business_checking personal_checking business_savings personal_savings

This article tells us the reason why Facebook spent $19B to buy WhatsApp. In some large developing countries like India and Mexico, WhatsApp is so much more popular than Facebook, and WhatsApp users would become more and more so that Facebook would lose mobile social networking competitiveness in foreign countries. So the goal that Facebook dropped $19B on WhatsApp was not making profits, but to keep the linchpin of Facebook in the mobile social networking.

Learn the key differences between classes and structures in Swift. Understand when to use each and how they impact memory management, inheritance, and performance in your iOS and macOS development projects.

1. ADJECTIVE 극심한, 심각한

2. ADJECTIVE (처벌이) 가혹한[혹독한] (=harsh)

3. ADJECTIVE 엄한, 엄격한 (=stern)

1. Verb 방향 감각을 잃게 하다

2. Verb 혼란스럽게[갈피를 못 잡게] 만들다 (→orient)

Noun 불법 거래, 밀거래, 밀매

1. ADJECTIVE 흔히 호감 미묘한, 감지하기 힘든

2. ADJECTIVE 교묘한, 영리한

3. ADJECTIVE 절묘한

1. 회전하다[시키다]

2. 방향을 바꾸다, 뒤돌아보다[보게 하다]

3. 반항하다, 공격[비난]하다, 적대하다((on, upon))

1. Noun 고객 (=customer)

1. Verb (고려할 가치가 없다고) 묵살[일축]하다 (=wave aside)

evade 1. Verb (어떤 일이나 사람을) 피하다[모면하다]

2. Verb (특히 법적·도덕적 의무를) 회피하다

3. Verb (취급·논의를) 피하다[회피하다]

deter Verb 단념시키다, 그만두게 하다 (→deterrent)

I'm from China. Even China is known as a socialist country, capitalism also plays a very important role in its economy. I think China is a socialist country because the government has the fully control over state-owned enterprises, and if the government wants, they can also control private enterprises. However, there are also ways that we can see capitalism in China. There are so many private sectors in China, and private sector is a major contributor to GDP. Normally, business owners have the right to decide what to produce and what's the price.

ACH Direct Debit

Requires approval makes sense. What do we mean by requires integration?

Graham's test annotation

eLife Assessment

This important study advances our understanding of population-level immune responses to influenza in both children and adults. The strength of the evidence supporting the conclusions is compelling, with high-throughput profiling assays and mathematical modeling. The work will be of interest to immunologists, virologists, vaccine developers, and those working on mathematical modeling of infectious diseases.

Reviewer #1 (Public review):

The authors present exciting new experimental data on the antigenic recognition of 78 H3N2 strains (from the beginning of the 2023 Northern Hemisphere season) against a set of 150 serum samples. The authors compare protection profiles of individual sera and find that the antigenic effect of amino acid substitutions at specific sites depends on the immune class of the sera, differentiating between children and adults. Person-to-person heterogeneity in the measured titers is strong, specifically in the group of children's sera. The authors find that the fraction of sera with low titers correlates with the inferred growth rate using maximum likelihood regression (MLR), a correlation that does not hold for pooled sera. The authors then measure the protection profile of the sera against historical vaccine strains and find that it can be explained by birth cohort for children. Finally, the authors present data comparing pre- and post- vaccination protection profiles for 39 (USA) and 8 (Australia) adults. The data shows a cohort-specific vaccination effect as measured by the average titer increase, and also a virus-specific vaccination effect for the historical vaccine strains. The generated data is shared by the authors and they also note that these methods can be applied to inform the bi-annual vaccine composition meetings, which could be highly valuable.

Thanks to the authors for the revised version of the manuscript. A few concerns remain after the revision:

(1) We appreciate the additional computational analysis the authors have performed on normalizing the titers with the geometric mean titer for each individual, as shown in the new Supplemental Figure 6. We agree with the authors statement that, after averaging again within specific age groups, "there are no obvious age group-specific patterns." A discussion of this should be added to the revised manuscript, for example in the section "Pooled sera fail to capture the heterogeneity of individual sera," referring to the new Supplemental Figure 6.

However, we also suggested that after this normalization, patterns might emerge that are not necessarily defined by birth cohort. This possibility remains unexplored and could provide an interesting addition to support potential effects of substitutions at sites 145 and 275/276 in individuals with specific titer profiles, which as stated above do not necessarily follow birth cohort patterns.

(2) Thank you for elaborating further on the method used to estimate growth rates in your reply to the reviewers. To clarify: the reason that we infer from Fig. 5a that A/Massachusetts has a higher fitness than A/Sydney is not because it reaches a higher maximum frequency, but because it seems to have a higher slope. The discrepancy between this plot and the MLR inferred fitness could be clarified by plotting the frequency trajectories on a log-scale.

For the MLR, we understand that the initial frequency matters in assessing a variant's growth. However, when starting points of two clades differ in time (i.e., in different contexts of competing clades), this affects comparability, particularly between A/Massachusetts and A/Ontario, as well as for other strains. We still think that mentioning these time-dependent effects, which are not captured by the MLR analysis, would be appropriate. To support this, it could be helpful to include the MLR fits as an appendix figure, showing the different starting and/or time points used.

(3) Regarding my previous suggestion to test an older vaccine strain than A/Texas/50/2012 to assess whether the observed peak in titer measurements is virus-specific: We understand that the authors want to focus the scope of this paper on the relative fitness of contemporary strains, and that this additional experimental effort would go beyond the main objectives outlined in this manuscript. However, the authors explicitly note that "Adults across age groups also have their highest titers to the oldest vaccine strain tested, consistent with the fact that these adults were first imprinted by exposure to an older strain." This statement gives the impression that imprinting effects increase titers for older strains, whereas this does not seem to be true from their results, but only true for A/Texas. It should be modified accordingly.

Reviewer #2 (Public review):

This is an excellent paper. The ability to measure the immune response to multiple viruses in parallel is a major advancement for the field, that will be relevant across pathogens (assuming the assay can be appropriately adapted). I only had a few comments, focused on maximising the information provided by the sera. These concerns were all addressed in the revised paper.

Reviewer #3 (Public review):

The authors use high throughput neutralisation data to explore how different summary statistics for population immune responses relate to strain success, as measured by growth rate during the 2023 season. The question of how serological measurements relate to epidemic growth is an important one, and I thought the authors present a thoughtful analysis tackling this question, with some clear figures. In particular, they found that stratifying the population based on the magnitude of their antibody titres correlates more with strain growth than using measurements derived from pooled serum data. The updated manuscript has a stronger motivation, and there is substantial potential to build on this work in future research.

Comments on revisions:

I have no additional recommendations. There are several areas where the work could be further developed, which were not addressed in detail in the responses, but given this is a strong manuscript as it stands, it is fine that these aspects are for consideration only at this point.

Author response:

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

Reviewer #1 (Public review):

The authors present exciting new experimental data on the antigenic recognition of 78 H3N2 strains (from the beginning of the 2023 Northern Hemisphere season) against a set of 150 serum samples. The authors compare protection profiles of individual sera and find that the antigenic effect of amino acid substitutions at specific sites depends on the immune class of the sera, differentiating between children and adults. Person-to-person heterogeneity in the measured titers is strong, specifically in the group of children's sera. The authors find that the fraction of sera with low titers correlates with the inferred growth rate using maximum likelihood regression (MLR), a correlation that does not hold for pooled sera. The authors then measure the protection profile of the sera against historical vaccine strains and find that it can be explained by birth cohort for children. Finally, the authors present data comparing pre- and post- vaccination protection profiles for 39 (USA) and 8 (Australia) adults. The data shows a cohort-specific vaccination effect as measured by the average titer increase, and also a virus-specific vaccination effect for the historical vaccine strains. The generated data is shared by the authors and they also note that these methods can be applied to inform the bi-annual vaccine composition meetings, which could be highly valuable.

Thanks for this nice summary of our paper.

The following points could be addressed in a revision:

(1) The authors conclude that much of the person-to-person and strain-to-strain variation seems idiosyncratic to individual sera rather than age groups. This point is not yet fully convincing. While the mean titer of an individual may be idiosyncratic to the individual sera, the strain-to-strain variation still reveals some patterns that are consistent across individuals (the authors note the effects of substitutions at sites 145 and 275/276). A more detailed analysis, removing the individual-specific mean titer, could still show shared patterns in groups of individuals that are not necessarily defined by the birth cohort.

As the reviewer suggests, we normalized the titers for all sera to the geometric mean titer for each individual in the US-based pre-vaccination adults and children. This is only for the 2023-circulating viral strains. We then faceted these normalized titers by the same age groups we used in Figure 6, and the resulting plot is shown. Although there are differences among virus strains (some are better neutralized than others), there are not obvious age group-specific patterns (eg, the trends in the two facets are similar). This observation suggests that at least for these relatively closely related recent H3N2 strains, the strain-to-strain variation does not obviously segregate by age group. Obviously, it is possible (we think likely) that there would be more obvious age-group specific trends if we looked at a larger swath of viral strains covering a longer time range (eg, over decades of influenza evolution). We have added the new plots shown as a Supplemental Figure 6 in the revised manuscript.

(2) The authors show that the fraction of sera with a titer 138 correlates strongly with the inferred growth rate using MLR. However, the authors also note that there exists a strong correlation between the MLR growth rate and the number of HA1 mutations. This analysis does not yet show that the titers provide substantially more information about the evolutionary success. The actual relation between the measured titers and fitness is certainly more subtle than suggested by the correlation plot in Figure 5. For example, the clades A/Massachusetts and A/Sydney both have a positive fitness at the beginning of 2023, but A/Massachusetts has substantially higher relative fitness than A/Sydney. The growth inference in Figure 5b does not appear to map that difference, and the antigenic data would give the opposite ranking. Similarly, the clades A/Massachusetts and A/Ontario have both positive relative fitness, as correctly identified by the antigenic ranking, but at quite different times (i.e., in different contexts of competing clades). Other clades, like A/St. Petersburg are assigned high growth and high escape but remain at low frequency throughout. Some mention of these effects not mapped by the analysis may be appropriate.

Thanks for the nice summary of our findings in Figure 5. However, the reviewer is misreading the growth charts when they say that A/Massachusetts/18/2022 has a substantially higher fitness than A/Sydney/332/2023. Figure 5a (reprinted at left panel) shows the frequency trajectory of different variants over time. While A/Massachusetts/18/2022 reaches a higher frequency than A/Sydney/332/2023, the trajectory is similar and the reason that A/Massachusetts/18/2022 reached a higher max frequency is that it started at a higher frequency at the beginning of 2023. The MLR growth rate estimates differ from the maximum absolute frequency reached: instead, they reflect how rapidly each strain grows relative to others. In fact, A/Massachusetts/18/2022 and A/Sydney/332/2023 have similar growth rates, as shown in Supplemental Figure 6b (reprinted at right). Similarly, A/Saint-Petersburg/RII-166/2023 starts at a low initial frequency but then grows even as A/Massachusetts/18/2022 and A/Sydney/332/2023 are declining, and so has a higher growth rate than both of those. 

In the revised manuscript, we have clarified how viral growth rates are estimated from frequency trajectories, and how growth rate differs from max frequency in the text below:

“To estimate the evolutionary success of different human H3N2 influenza strains during 2023, we used multinomial logistic regression, which analyzes strain frequencies over time to calculate strain-specific relative growth rates [51–53]. There were sufficient sequencing counts to reliably estimate growth rates in 2023 for 12 of the HAs for which we measured titers using our sequencing-based neutralization assay libraries (Figure 5a,b and Supplemental Figure 9a,b). Note that these growth rates estimate how rapidly each strain grows relative to the other strains, rather than the absolute highest frequency reached by each strain “.  

(3) For the protection profile against the vaccine strains, the authors find for the adult cohort that the highest titer is always against the oldest vaccine strain tested, which is A/Texas/50/2012. However, the adult sera do not show an increase in titer towards older strains, but only a peak at A/Texas. Therefore, it could be that this is a virus-specific effect, rather than a property of the protection profile. Could the authors test with one older vaccine virus (A/Perth/16/2009?) whether this really can be a general property?

We are interested in studying immune imprinting more thoroughly using sequencing-based neutralization assays, but we note that the adults in the cohorts we studied would have been imprinted with much older strains than included in this library. As this paper focuses on the relative fitness of contemporary strains with minor secondary points regarding imprinting, these experiments are beyond the scope of this study. We’re excited for future work (from our group or others) to explore these points by making a new virus library with strains from multiple decades of influenza evolution. 

Reviewer #2 (Public review):

This is an excellent paper. The ability to measure the immune response to multiple viruses in parallel is a major advancement for the field, which will be relevant across pathogens (assuming the assay can be appropriately adapted). I only have a few comments, focused on maximising the information provided by the sera.

Thanks very much!

Firstly, one of the major findings is that there is wide heterogeneity in responses across individuals. However, we could expect that individuals' responses should be at least correlated across the viruses considered, especially when individuals are of a similar age. It would be interesting to quantify the correlation in responses as a function of the difference in ages between pairs of individuals. I am also left wondering what the potential drivers of the differences in responses are, with age being presumably key. It would be interesting to explore individual factors associated with responses to specific viruses (beyond simply comparing adults versus children).

We thank the reviewer for this interesting idea. We performed this analysis (and the related analyses described) and added this as a new Supplemental Figure 7, which is pasted after the response to the next related comment by the reviewer. 

For 2023-circulating strains, we observed basically no correlation between the strength of correlation between pairs of sera and the difference in age between those pairs of sera (Supplemental Figure 7), which was unsurprising given the high degree of heterogeneity between individual sera (Figure 3, Supplemental Figure 6, and Supplemental Figure 8). For vaccine strains, there is a moderate negative correlation only in the children, but not in the adults or the combined group of adults and children. This could be because the children are younger with limited and potentially more similar vaccine and exposure histories than the adults. It could also be because the children are overall closer in age than the adults.

Relatedly, is the phylogenetic distance between pairs of viruses associated with similarity in responses?

For 2023-circulating strains, across sera cohorts we observed a weak-to-moderate correlation between the strength of correlation between the neutralizing titers across all sera to pairs of viruses and the Hamming distances between virus pairs. For the same comparison with vaccine strains, we observed moderate correlations, but this must be caveated with the slightly larger range of Hamming distances between vaccine strains. Notably, many of the points on the negative correlation slope are a mix of egg- and cell-produced vaccine strains from similar years, but there are some strain comparisons where the same year’s egg- and cell-produced vaccine strains correlate poorly.

Figure 5C is also a really interesting result. To be able to predict growth rates based on titers in the sera is fascinating. As touched upon in the discussion, I suspect it is really dependent on the representativeness of the sera of the population (so, e.g., if only elderly individuals provided sera, it would be a different result than if only children provided samples). It may be interesting to compare different hypotheses - so e.g., see if a population-weighted titer is even better correlated with fitness - so the contribution from each individual's titer is linked to a number of individuals of that age in the population. Alternatively, maybe only the titers in younger individuals are most relevant to fitness, etc.

We’re very interested in these analyses, but suggest they may be better explored in subsequent works that could sample more children, teenagers and adults across age groups. Our sera set, as the reviewer suggests, may be under-powered to perform the proposed analysis on subsetted age groups of our larger age cohorts. 

In Figure 6, the authors lump together individuals within 10-year age categories - however, this is potentially throwing away the nuances of what is happening at individual ages, especially for the children, where the measured viruses cross different groups. I realise the numbers are small and the viruses only come from a small numbers of years, however, it may be preferable to order all the individuals by age (y-axis) and the viral responses in ascending order (x-axis) and plot the response as a heatmap. As currently plotted, it is difficult to compare across panels

This is a good suggestion. In the revised manuscript we have included a heatmap of the children and pre-vaccination adults, ordered by the year of birth of each individual, as Supplemental figure 8. That new figure is also pasted in this response.

Reviewer #3 (Public review):

The authors use high-throughput neutralisation data to explore how different summary statistics for population immune responses relate to strain success, as measured by growth rate during the 2023 season. The question of how serological measurements relate to epidemic growth is an important one, and I thought the authors present a thoughtful analysis tackling this question, with some clear figures. In particular, they found that stratifying the population based on the magnitude of their antibody titres correlates more with strain growth than using measurements derived from pooled serum data. However, there are some areas where I thought the work could be more strongly motivated and linked together. In particular, how the vaccine responses in US and Australia in Figures 6-7 relate to the earlier analysis around growth rates, and what we would expect the relationship between growth rate and population immunity to be based on epidemic theory.

Thank you for this nice summary. This reviewer also notes that the text related to figures 6 and 7 are more secondary to the main story presented in figures 3-5. The main motivation for including figures 6 and 7 were to demonstrate the wide-ranging applications of sequencing-based neutralization data. We have tried to clarify this with the following minor text revisions, which do not add new content but we hope smooth the transition between results sections. 

While the preceding analyses demonstrated the utility of sequencing-based neutralization assays for measuring titers of currently circulating strains, our library also included viruses with HAs from each of the H3N2 influenza Northern Hemisphere vaccine strains from the last decade (2014 to 2024, see Supplemental Table 1). These historical vaccine strains cover a much wider span of evolutionary diversity that the 2023-circulating strains analyzed in the preceding sections (Figure 2a,b and Supplemental Figure 2b-e). For this analysis, we focused on the cell-passaged strains for each vaccine, as these are more antigenically similar to their contemporary circulating strains than the egg-passaged vaccine strains since they lack the mutations that arise during growth of viruses in eggs [55–57] (Supplemental Table 1). 

Our sequencing-based assay could also be used to assess the impact of vaccination on neutralization titers against the full set of strains in our H3N2 library. To do this, we analyzed matched 28-day post-vaccination samples for each of the above-described 39 pre-vaccination samples from the cohort of adults based in the USA (Table 1). We also analyzed a smaller set of matched pre- and post-vaccination sera samples from a cohort of eight adults based in Australia (Table 1). Note that there are several differences between these cohorts: the USA-based cohort received the 2023-2024 Northern Hemisphere egg-grown vaccine whereas the Australia-based cohort received the 2024 Southern Hemisphere cell-grown vaccine, and most individuals in the USA-based cohort had also been vaccinated in the prior season whereas most individuals in the Australia-based cohort had not. Therefore, multiple factors could contribute to observed differences in vaccine response between the cohorts.

Reviewer #3 (Recommendations for the authors):

Main comments:

(1) The authors compare titres of the pooled sera with the median titres across individual sera, finding a weak correlation (Figure 4). I was therefore interested in the finding that geometric mean titre and median across a study population are well correlated with growth rate (Supplemental Figure 6c). It would be useful to have some more discussion on why estimates from a pool are so much worse than pooled estimates.

We thank this reviewer for this point. We would clarify that pooling sera is the equivalent of taking the arithmetic mean of the individual sera, rather than the geometric mean or median, which tends to bias the measurements of the pool to the outliers within the pool. To address this reviewer’s point, we’ve added the following text to the manuscript:

“To confirm that sera pools are not reflective of the full heterogeneity of their constituent sera, we created equal volume pools of the children and adult sera and measured the titers of these pools using the sequencing-based neutralization assay. As expected, neutralization titers of the pooled sera were always higher than the median across the individual constituent sera, and the pool titers against different viral strains were only modestly correlated with the median titers across individual sera (Figure 4). The differences in titers across strains were also compressed in the serum pools relative to the median across individual sera (Figure 4). The failure of the serum pools to capture the median titers of all the individual sera is especially dramatic for the children sera (Figure 4) because these sera are so heterogeneous in their individual titers (Figure 3b). Taken together, these results show that serum pools do not fully represent individual-level heterogeneity, and are similar to taking the arithmetic mean of the titers for a pool of individuals, which tends to be biased by the highest titer sera”.

(2) Perhaps I missed it, but are growth rates weekly growth rates? (I assume so?)

The growth rates are relative exponential growth rates calculated assuming a serial interval of 3.6 days. We also added clarifying language and a citation for the serial growth interval to the methods section:

The analysis performing H3 HA strain growth rate estimates using the evofr[51] package is at https://github.com/jbloomlab/flu_H3_2023_seqneut_vs_growth. Briefly, we sought to make growth rate estimates for the strains in 2023 since this was the same timeframe when the sera were collected. To achieve this, we downloaded all publicly-available H3N2 sequences from the GISAID[88] EpiFlu database, filtering to only those sequences that closely matched a library HA1 sequence (within one HA1 amino-acid mutation) and were collected between January 2023 and December 2023. If a sequence was within one HA1 amino-acid mutation of multiple library HA1 proteins then it was assigned to the closest one; if there were multiple equally close matches then it was assigned fractionally to each match. We only made growth rate estimates for library strains with at least 80 sequencing counts (Supplemental Figure 9a), and ignored counts for sequences that did not match a library strain (equivalent results were obtained if we instead fit a growth rate for these sequences as an “other” category). We then fit multinomial logistic regression models using the evofr[51] package assuming a serial interval of 3.6 days[101]  to the strain counts. For the plot in Figure 5a the frequencies are averaged over a 14-day sliding window for visual clarity, but the fits were to the raw sequencing counts. For most of the analyses in this paper we used models based on requiring 80 sequencing counts to make an estimate for strain growth rates, and counting a sequence as a match if it was within one amino-acid mutation; see https://jbloomlab.github.io/flu_H3_2023_seqneut_vs_growth/ for comparable analyses using different reasonable sequence count cutoffs (e.g., 60, 50, 40 and 30, as depicted in Supplemental Figure 9).  Across sequence cutoffs, we found that the fraction of individuals with low neutralization titers and number of HA1 mutations correlated strongly with these MLR-estimated strain growth rates.

(3)  I found Figure 3 useful in that it presents phylogenetic structure alongside titres, to make it clearer why certain clusters of strains have a lower response. In contrast, I found it harder to meaningfully interpret Figure 7a beyond the conclusion that vaccines lead to a fairly uniform rise in titre. Do the 275 or 276 mutations that seem important for adults in Figure 3 have any impact?

We are certainly interested in the questions this reviewer raises, and in trying to understand how well a seasonal vaccine protects against the most successful influenza variants that season. However, these post-vaccination sera were taken when neutralizing titers peak ~30 days after vaccination. Because of this, in the larger cohort of US-based post-vaccination adults, the median titers across sera to most strains appear uniformly high. In the Australian-based post-vaccination adults, there was some strain-to-strain variation in median titers across sera, but of course this must be caveated with the much smaller sample size. It might be more relevant to answer this question with longitudinally sampled sera, when titers begin to wane in the following months.

(4)  It could be useful to define a mechanistic relationship about how you would expect susceptibility (e.g. fraction with titre < X, where X is a good correlate) to relate to growth via the reproduction number: R = R0 x S. For example, under the assumption the generation interval G is the same for all, we have R = exp(r*G), which would make it possible to make a prediction about how much we would expect the growth rate to change between S = 0.45 and 0.6, as in Fig 5c. This sort of brief calculation (or at least some discussion) could add some more theoretical underpinning to the analysis, and help others build on the work in settings with different fractions with low titres. It would also provide some intuition into whether we would expect relationships to be linear.

This is an interesting idea for future work! However, the scope of our current study is to provide these experimental data and show a correlation with growth; we hope this can be used to build more mechanistic models in future.

(5) A key conclusion from the analysis is that the fraction above a threshold of ~140 is particularly informative for growth rate prediction, so would it be worth including this in Figure 6-7 to give a clearer indication of how much vaccination reduces contribution to strain growth among those who are vaccinated? This could also help link these figures more clearly with the main analysis and question.

Although our data do find ~140 to be the threshold that gives max correlation with growth rate, we are not comfortable strongly concluding 140 is a correlate of protection, as titers could influence viral fitness without completely protecting against infection. In addition, inspection of Figure 5d shows that while ~140 does give the maximal correlation, a good correlation is observed for most cutoffs in the range from ~40 to 200, so we are not sure how robustly we can be sure that ~140 is the optimal threshold.

(6)  In Figure 5, the caption doesn't seem to include a description for (e).

Thank you to the reviewer for catching this – this is fixed now.

(7)  The US vs Australia comparison could have benefited from more motivation. The authors conclude ,"Due to the multiple differences between cohorts we are unable to confidently ascribe a cause to these differences in magnitude of vaccine response" - given the small sample sizes, what hypotheses could have been tested with these data? The comparison isn't covered in the Discussion, so it seems a bit tangential currently.

Thank you to the reviewer for this comment, but we should clarify our aim was not to directly compare US and Australian adults. We are interested in regional comparisons between serum cohorts, but did not have the numbers to adequately address those questions here. This section (and the preceding question) were indeed both intended to be tangential to the main finding, and hopefully this will be clarified with our text additions in response to Reviewer #3’s public reviews.

https://ipfs.io/ipns/k51qzi5uqu5div1auuxm59ygav4p7gdg9z4e9iggtu6m43rmc3xw75mczx2b7x

https://ipfs.io/ipns/k51qzi5uqu5div1auuxm59ygav4p7gdg9z4e9iggtu6m43rmc3xw75mczx2b7x/📝/?isPathWritable=true&path=/♖/hyperpost/🌐/🧊/

阿萨德

的方法

测试

resonates

ask good questions

dearly beloved we are gathered here in the presence of technology and

bots

doesn't rot your brain

There's the rub

They are all integral holons

great question

do not just save the links save the very things you have seen

connect it with the body of your work annotate it

so that it will become discoverable

not just by you

but through these 1 can find the Other who shares your Interests

can help everybody else

learn

share learning

mutual learning

the fish

that are sort of coming back

from their time in the ocean are the experienced hands -

estuaries

are where freshwater meets salt II basically where

a River empties into an ocean

or what Bush referred to as the Common Record

Trailblazers

can't remember (recall) what we know what we know it would be important

It might be worth having some open source follow up research and reading for learners to engage with? e.g. https://www.research.ed.ac.uk/en/publications/improving-university-students-mental-health-using-multi-component/

ACH Direct Debits are processed through the Automated Clearing House (ACH) payments system, operated by the National Automated Clearing House Association (Nacha).

Can we add timeline view similar to Stripe or Finix docs?

Is these needed in docs?

with ACH Direct Debit

ACH Direct Debit

Can we keep it all caps?

eLife Assessment

This is a useful study that examines the relationship between neuropeptide signaling and the precision of vocal motor output using the songbird as a model system. The study presents evidence based on differential expression patterns and genetic or pharmacological inhibition of various neuropeptide genes for a causal role in song performance; however, this evidence is incomplete.

Reviewer #1 (Public review):

Summary:

This study provides evidence that neuropeptide signaling, particularly via the CRH-CRHBP pathway, plays a key role in regulating the precision of vocal motor output in songbirds. By integrating gene expression profiling with targeted manipulations in the song vocal motor nucleus RA, the authors demonstrate that altering CRH and CRHBP levels bidirectionally modulate song variability. These findings reveal a previously unrecognized neuropeptidergic mechanism underlying motor performance control, supported by molecular and functional evidence.

Strengths:

Neural circuit mechanisms underlying motor variability have been intensively studied, yet the molecular bases of such variability remain poorly understood. The authors address this important gap using the songbird (Bengalese finch) as a model system for motor learning, providing experimental evidence that neuropeptide signaling contributes to vocal motor variability. They comprehensively characterize the expression patterns of neuropeptide-related genes in brain regions involved in song vocal learning and production, revealing distinct regulatory profiles compared to non-vocal related regions, as well as developmental, revealing distinct regulatory profiles compared to non-vocal regions, as well as developmental and behavioral dependencies, including altered expression following deafening and correlations with singing activity over the two days preceding sampling. Through these multi-level analyses spanning anatomy, development, and behavior, the authors identify the CRH-CRHBP pathway in the vocal motor nucleus RA as a candidate regulator of song variability. Functional manipulations further demonstrate that modulation of this pathway bidirectionally alters song variability.

Overall, this work represents an effective use of songbirds, though a well-established neuroethological framework uncovers how previously uncharacterized molecular pathways shape behavioral output at the individual level.

Weaknesses:

(1) This study uses Bengalese finches (BFs) for all experiments-bulk RNA-seq, in situ hybridization across developmental stages, deafening, gene manipulation, and CRH microinfusion-except for the sc/snRNA-seq analysis. BFs differ from zebra finches (ZFs) in several important ways, including faster song degradation after deafening and greater syllable sequence complexity. This study makes effective use of these unique BF characteristics and should be commended for doing so.

However, the major concern lies in the use of the single-cell/single-nucleus RNA-seq dataset from Colquitt et al. (2021), which combines data from both ZFs and BFs for cell-type classification. Based on our reanalysis of the publicly available dataset used in both Colquitt et al. (2021) and the present study, my lab identified two major issues:

(a) The first concern is that the quality of the single-cell RNA-seq data from BFs is extremely poor, and the number of BF-derived cells is very limited. In other words, most of the gene expression information at the single-cell (or "subcellular type") level in this study likely reflects ZF rather than BF profiles. In our verification of the authors' publicly annotated data, we found that in the song nucleus RA, only about 18 glutamatergic cells (2.3%) of a total of 787 RA_Glut (RA_Glut1+2+3) cells were derived from BFs. Similarly, in HVC, only 53 cells (4.1%) out of 1,278 Glut1+Glut4 cells were BF-derived. This clearly indicates that the cell-subtype-level expression data discussed in this study are predominantly based on ZF, not BF, expression profiles.

Recent studies have begun to report interspecies differences in the expression of many genes in the song control nuclei. It is therefore highly plausible that the expression patterns of CRHBP and other neuropeptide-signaling-related genes differ between ZFs and BFs. Yet, the current study does not appear to take this potential species difference into account. As a result, analyses such as the CellChat results (Fig. 2F and G) and the model proposed in Fig. 6G are based on ZF-derived transcriptomic information, even though the rest of the experimental data are derived from BF, which raises a critical methodological inconsistency.

(b) The second major concern involves the definition of "subcellular types" in the sc/snRNA-seq dataset. Specifically, the RA_Glut1, 2, and 3 and HVC_Glu1 and 4 clusters-classified as glutamatergic projection neuron subtypes-may in fact represent inter-individual variation within the same cell type rather than true subtypes. Following Colquitt et al. (2021), Toji et al. (PNAS, 2024) demonstrated clear individual differences in the gene expression profiles of glutamatergic projection neurons in RA.

In our reanalysis of the same dataset, we also observed multiple clusters representing the same glutamatergic projection neurons in UMAP space. This likely occurs because Seurat integration (anchor-based mutual nearest neighbor integration) was not applied, and because cells were not classified based on individual SNP information using tools such as Souporcell. When classified by individual SNPs, we confirmed that the RA_Glut1-3 and HVC_Glu1 and 4 clusters correspond simply to cells from different individuals rather than distinct subcellular types. (Although images cannot be attached in this review system, we can provide our analysis results if necessary.)

This distinction is crucial, as subsequent analyses and interpretations throughout the manuscript depend on this classification. In particular, Figure 6G presents a model based on this questionable subcellular classification. Similarly, the ligand-receptor relationships shown in Figure 2G - such as the absence of SST-SSTR1 signaling in RA_Glut3 but its presence in RA_Glut1 and 2-are more plausibly explained by inter-individual variation rather than subcellular-type specificity.

Whether these differences are interpreted as individual variation within a single cell type or as differences in projection targets among glutamatergic neurons has major implications for understanding the biological meaning of neuropeptide-related gene expression in this system.

(2) Based on the important finding that "CRHBP expression in the song motor pathway is correlated with singing," it is necessary to provide data showing that the observed changes in CRHBP and other neuropeptide-related gene expression during the song learning period or after deafening are not merely due to differences in singing amount over the two days preceding brain sampling.

Without such data, the following statement cannot be justified: "Regarding CRHBP expression in the song motor pathway increases during song acquisition and decreases following deafening."

(3) In Figure 5B, the authors should clearly distinguish between intact and deafened birds and show the singing amount for each group. In practice, deafening often leads to a reduction in both the number of song bouts and the total singing time. If, in this experiment, deafened birds also exhibited reduced singing compared to intact birds, then the decreased CRHBP expression observed in HVC and RA (Figures 3 and 4) may not reflect song deterioration, but rather a simple reduction in singing activity.

As a similar viewpoint, the authors report that CRHBP expression levels in RA and HVC increase with age during the song learning period. However, this change may not be directly related to age or the decline in vocal plasticity. Instead, it could correlate with the singing amount during the one to two days preceding brain sampling. The authors should provide data on the singing activity of the birds used for in situ hybridization during the two days prior to sampling.

Reviewer #2 (Public review):

Summary:

The results presented here are a useful extension of two of their previous papers (Colquitt et al 2021, Colquitt et al 2023), where they used single-cell transcriptomics to characterize the inhibitory and excitatory cell types and gene expression patterns of the song circuit, comparing them to mammalian and reptilian brains, and characterized the effect of deafening on these gene expression patterns. In this paper, they focus on the differential expression of various neuropeptidergic systems in the songbird brain. They discover a role for the CRHBP gene in song performance and causally show its influence on song variability.

Strengths:

The authors leverage the advantages of the 'nucleated' structure of the songbird neural circuitry and use a robust approach to compare neuropeptidergic gene expression patterns in these circuits. Their analysis of the expression patterns of the CRHBP gene in different cell types supports their conclusion that interneurons are particularly amenable to this modulation. Their use of a knockdown strategy along with pharmacological manipulation provides strong support for a causal role of neuropeptidergic modulation on song behaviour. These results have important implications as they bring into focus neuropeptide modulation of the song-motor circuit and pave the way for future studies focussing on how this signalling pathway regulates plasticity during song learning and maintenance.

Weaknesses:

While the results demonstrating the bidirectional modulation of CRH and CRHBP on song performance shed light on their role in song plasticity, it would be important to show this in juvenile finches during sensorimotor learning. We also don't get a clear picture of the 'causal' role of this signalling pathway on the song pre-motor area, HVC, as the knockdown and pharmacological manipulation studies were done in RA, whereas we see a modulation of CRHBP expression during deafening and song learning in both RA and HVC. Given the role of interneurons in the HVC in song acquisition (e.g., Vallentin et al. 2016, Science), it would have been interesting to see the results of HVC-specific manipulation of this neuropeptidergic pathway and/or how it affects the song learning process. Perhaps a short discussion of this would help to give the readers some perspective. Finally, a more direct demonstration of the neurophysiological effect of the signalling pathway would also strengthen our understanding of precisely how these modulate the song circuit plasticity, which I understand might be beyond the scope of this study.

Technical/minor:

In the Methods section, several clarifications would be beneficial. For instance, the description of the design matrices would benefit from being presented in a more general statistical form (e.g., linear model equations) rather than using R syntax. This would make the modeling approach more accessible to readers unfamiliar with software-specific syntax. In addition, while some variables (e.g., cdr_scale, frac_mito_scale) are briefly defined, others (e.g., tags, cut3,nsongs_last_two_days_cut3) could be more clearly described. This applies to the descriptions of both the gene set enrichment analysis and the neuropeptide-receptor analysis, which rely heavily on package-specific terminology (e.g., fgseaMultilevel, computeCommunProb), making it difficult for readers to understand the conceptual or statistical basis of the analyses. It would improve clarity if the authors provided a complete list of variable definitions, types (categorical or continuous), and any scaling/transformations applied would enhance clarity and reproducibility.

Reviewer #3 (Public review):

Summary:

The stable production of learned vocalizations like human language and birdsong requires auditory feedback. What happens in the brain areas that generate stable vocalizations as performance deteriorates is not well understood. Using a species of songbird, the current study investigates individual cells within the evolutionarily-conserved brain regions that generate learned vocalizations to describe that the complement of neuropeptide (short proteins) signals may be a key feature of behavioral change. Because neuropeptides are important across species, these findings may help explain diminishing stability in learned behaviors even in humans.

Strengths:

The experiments are solid and follow a strong progression from description through manipulation. The songbird model is appropriate and powerful to inform on generalizable biological mechanisms of precisely learned behaviors, including human speech.

Weaknesses:

While it is always possible to perform more experiments, most of the weaknesses are in the presentation of the project, not in the evidence or analysis, which are leading-edge and appropriate. Generally, the ability to follow the findings and to independently assess rigor would be enhanced with increased explicit mention of the statistical thresholds and subjective descriptions. In addition, two prior pieces of relevant work seem to be omitted, including one performing deafening, gene expression measures, and behavioral assessment in zebra finches, and another describing neuropeptide complements in zebra finch singing nuclei based largely on mass spectrometry. The former in particular should be related to the current findings.

Author response:

We thank the reviewers for their time and their constructive comments.

Reviewer 1 makes several incisive comments about the single-cell RNA-sequencing dataset used in this  version of the manuscript, which was previously published in Colquitt, 2021. The Reviewer correctly  notes that this dataset consists primarily of nuclei from zebra finches, with a relatively small proportion of  the data coming from Bengalese finches. However, all other data presented here comes from assays and  experiments in Bengalese finches. This discrepancy could lead to two issues of interpretation. First, there  could be substantive expression differences in the CRH signaling pathway between these two species,  making it difficult to interpret its cellular expression profile. Second, the Reviewer describes that in their  reanalysis of this dataset they determined that what had been described as distinct cell types – namely  HVC-Glut-1 vs. HVC-Glut-4 (corresponding to the HVC  RA  projection neurons) and the three RA-Glut  types – are likely to be single cell types. The Reviewer notes that inter-individual differences in gene  expression, which were not analyzed in the original publication, could have generated this apparent cell  type diversity.

To the first point, we agree that the use of the published dataset that consists primarily of zebra finch  data is not ideal when making claims of cell type-specific expression in Bengalese finches. To rectify this  issue, we have generated additional sets of snRNA-seq from Bengalese finches that encompass multiple  areas of the song system as well as adjacent comparator regions outside of the principal song areas.  Our initial analysis of these datasets indicates that the cellular patterns of expression of the CRH system  is consistent with what has been presented here. In our revision, we will include a reanalysis of  neuropeptide expression using these more extensive datasets.

To the second point, we also agree that some of the instances of glutamatergic neuron diversity could  have been generated either by issues stemming from the integration of two species or through  interindividual differences. In our analysis of our newer snRNA-seq data, we also identify a single HVC  RA  projection neuron type (not two) and that RA projection neuron types fall into one or two classes (not  three), similar to what Reviewer 1 described. We have deconvolved these datasets by genotype, as  suggested by the Reviewer, and do not see substantial interindividual variation across the CRH system.  However, our revision will explicitly address these issues.

Reviewer 1 also brings up several important questions concerning the relationships between CRHBP  and singing and the challenge of interpreting the influences of song acquisition and deafening on CRHBP  expression, given the variation in singing that generally accompanies these changes to song. To address  in part this issue, our regression analysis of deafening-associated gene expression differences includes  a term for the number of songs sung on the day of euthanasia as well as an interaction term between  song destabilization and singing amount. This design controls for the amount that a bird sang in the  period before brain collection. This analysis was included in  (Colquitt et al., 2023) , and will be further  elaborated and discussed in the revised version of this manuscript. Notably, CRHBP expression shows a  significant interaction between song destabilization and singing amount, suggesting that reduction of  CRHBP following deafening is greater than what would be expected from any reductions in singing  alone. This specific analysis will be included in the revised manuscript as well.

However, despite these statistical controls, we cannot fully rule out that singing is playing a fundamental  role in driving the CRHBP expression differences we see across conditions. Indeed, a number of studies  have described an association between the amount a bird sings and the variability of its song  (Chen et  al., 2013; Hayase et al., 2018; Hilliard et al., 2012; Miller et al., 2010; Ohgushi et al., 2015) , with a general trend of higher amounts of singing correlated with a reduction in variability. This relationship is  consistent with what we see for CRHBP expression in RA and HVC: high in unmanipulated adult males  and decreased during states of high variability and plasticity (post-deafening and juveniles). A model that  combines these observations, and that we will include in the Discussion of the revised manuscript, is one  in which singing induces the expression of CRHBP in RA and HVC, limiting CRH binding to its receptors,  thereby limiting this pathway’s proposed effects on the excitability and synaptic plasticity of projection  neurons.

Reviewer 2 suggests multiple interesting avenues to more fully characterize the role of the CRH pathway  in song performance and learning. First, we agree that HVC is a compelling target to investigate CRH’s  role in song, given the similarity of CRHBP expression in HVC and RA across deafening, song  acquisition, and singing. As the Reviewer notes, a number of studies have demonstrated key roles for  interneurons in shaping neuronal dynamics in HVC and regulating song structure. Here, we focused on  RA due to the direct influence of RA projection neurons have on syringeal and respiration motoneurons  controlling song production, and the following expectation that manipulations of CRH signaling in this  region would have particularly measurable effects on song.  However, we agree with the reviewer that it  would be of additional interest to investigate manipulations of CRH signalling in HVC.  We are  considering whether it will be feasible given the usual constraints of time, personnel, and other  competing demands to carry such experiments as an addition to the current manuscript. Depending on  how that goes, we will either add new experimental data to the manuscript, or simply acknowledge the  interest of such experiments in Discussion and defer their pursuit to future study.

Likewise, Reviewer 2 suggests other ways in which an understanding of the role of CRH signalling could  be further enriched with additional experiments, including investigating the influence of CRH signaling on  song acquisition, when song transitions from a variable and plastic state to a precise and stereotyping  state, and pursuing direct evidence that CRH influences the neurophysiology of glutamatergic neurons in  HVC or RA. These are both excellent suggestions for ways in neuropeptide signalling could be further  linked to alterations in behavior; As we proceed with revisions we will consider whether we can address  some of these suggestions within the scope of the current manuscript, versus note them in discussion as  directions for future research.

Chen Q, Heston JB, Burkett ZD, White SA. 2013. Expression analysis of the speech-related genes  FoxP1 and FoxP2 and their relation to singing behavior in two songbird species.  J Exp Biol  216 :3682–3692. doi:10.1242/jeb.085886

Colquitt BM, Li K, Green F, Veline R, Brainard MS. 2023. Neural circuit-wide analysis of changes to gene  expression during deafening-induced birdsong destabilization.  Elife  12 :e85970. doi:10.7554/eLife.85970

Hayase S, Wang H, Ohgushi E, Kobayashi M, Mori C, Horita H, Mineta K, Liu W-C, Wada K. 2018. Vocal  practice regulates singing activity-dependent genes underlying age-independent vocal learning in  songbirds.  PLoS Biol 16 :e2006537. doi:10.1371/journal.pbio.2006537

Hilliard AT, Miller JE, Fraley ER, Horvath S, White SA. 2012. Molecular microcircuitry underlies functional  specification in a basal ganglia circuit dedicated to vocal learning.  Neuron  73 :537–552.  doi:10.1016/j.neuron.2012.01.005

Miller JE, Hilliard AT, White SA. 2010. Song practice promotes acute vocal variability at a key stage of  sensorimotor learning.  PLoS One  5 :e8592. doi:10.1371/journal.pone.0008592

Ohgushi E, Mori C, Wada K. 2015. Diurnal oscillation of vocal development associated with clustered  singing by juvenile songbirds.  J Exp Biol  218 :2260–2268.  doi:10.1242/jeb.115105

eLife Assessment

The authors aim to understand why Kupffer cells (KCs) die in metabolic-associated steatotic liver disease (MASLD). This is a useful study using in vitro studies and an in vivo genetic mouse model, suggesting that increased glycolysis contributes to KC death in MASLD. However, the data presented are incomplete as some inconsistencies in the results presented are identified in the characterisation of KCs. This work will be of interest to researchers in the immunology and metabolism fields.

Reviewer #1 (Public review):

Summary:

The authors aim to investigate the mechanisms underlying Kupffer cell death in metabolic-associated steatotic liver disease (MASLD). The authors propose that KCs undergo massive cell death in MASLD and that glycolysis drives this process. However, there appears to be a discrepancy between the reported high rates of KC death and the apparent maintenance of KC homeostasis and replacement capacity.

Strengths:

This is an in vivo study.

Weaknesses:

There are discrepancies between the authors' observations and previous reports, as well as inconsistencies among their own findings.

Before presenting the percentage of CLEC4F⁺TUNEL⁺ cells, the authors should have first shown the number of CLEC4F⁺ cells per unit area in Figure 1. At 16 weeks of age, the proportion of TUNEL⁺ KCs is extremely high (~60%), yet the flow cytometry data indicate that nearly all F4/80⁺ KCs are TIMD4⁺, suggesting an embryonic origin. If such extensive KC death occurred, the proportion of embryonically derived TIMD4⁺ KCs would be expected to decrease substantially. Surprisingly, the proportion of TIMD4⁺ KCs is comparable between chow-fed and 16-week HFHC-fed animals. Thus, the immunostaining and flow cytometry data are inconsistent, making it difficult to explain how massive KC death does not lead to their replacement by monocyte-derived cells.

These data suggest that despite the reported high rate of cell death among CLEC4F⁺TIMD4⁺ KCs, the population appears to self-maintain, with no evidence of monocyte-derived KC generation in this model, which contradicts several recent studies in the field.

Moreover, there is no evidence that TIMD4⁺CLEC4F⁺ KCs increase their proliferation rate to compensate for such extensive cell death. If approximately 60% of KCs are dying and no monocyte-derived KCs are recruited, one would expect a much greater decrease in total KC numbers than what is reported.

It is also unexpected that the maximal rate of KC death occurs at early time points (8 weeks), when the mice have not yet gained substantial weight (Figure 1B). Previous studies have shown that longer feeding periods are typically required to observe the loss of embryo-derived KCs.

Furthermore, it is surprising that the HFD induces as much KC death as the HFHC and MCD diets. Earlier studies suggested that HFD alone is far less effective than MASH-inducing diets at promoting the replacement of embryonic KCs by monocyte-derived macrophages.

In Figure 2D, TIMD4 staining appears extremely faint, making the results difficult to interpret. In contrast, the TUNEL signal is strikingly intense and encompasses a large proportion of liver cells (approximately 60% of KCs, 15% of hepatocytes, 20% of hepatic stellate cells, 30% of non-KC macrophages, and a proportion of endothelial cells is also likely affected). This pattern closely resembles that typically observed in mouse models of acute liver failure. Given this apparent extent of cell death, it is unexpected that ALT and AST levels remain low in MASH mice, which is highly unusual.

No statistical analysis is provided for Figure 5D, and it is unclear which metabolites show statistically significant changes in Figure 5C.

In addition, there is no evaluation of liver pathology in Clec4f-Cre × Chil1flox/flox mice. It remains possible that the observed effects on KC death result from aggravated liver injury in these animals. There is also no evidence that Chil1 deficiency affects glucose metabolism in KCs in vivo.

Finally, the authors should include a more direct experimental approach to modulate glycolysis in KCs and assess its causal role in KC death in MASH.

Reviewer #2 (Public review):

Summary:

In this manuscript, He et al. set out to investigate the mechanisms behind Kupffer Cell death in MASLD. As has been previously shown, they demonstrate a loss of resident KCs in MASLD in different mouse models. They then go on to show that this correlates with alterations in genes/metabolites associated with glucose metabolism in KCs. To investigate the role of glucose metabolism further, they subject isolated KCs in vitro to different metabolic treatments and assess cleaved caspase 3 staining, demonstrating that KCs show increased Cl. Casp 3 staining upon stimulation of glycolysis. Finally, they use a genetic mouse model (Chil1KO) where they have previously reported that loss of this gene leads to increased glycolysis and validate this finding in BMDMs (KO). They then remove this gene specifically from KCs (Clec4fCre) and show that this leads to increased macrophage death compared with controls.

Strengths:

As we do not yet understand why KCs die in MASLD, this manuscript provides some explanation for this finding. The metabolomics is novel and provides insight into KC biology. It could also lead to further investigation; here, it will be important that the full dataset is made available.

Weaknesses:

Different diets are known to induce different amounts of KC loss, yet here, all models examined appear to result in 60% KC death. One small field of view of liver tissue is shown as representative to make these claims, but this is not sufficient, as anything can be claimed based on one field of view. Rather, a full tissue slice should be included to allow readers to really assess the level of death. Additionally, there is no consistency between the markers used to define KCs and moMFs, with CLEC4F being used in microscopy, TIM4 in flow, while the authors themselves acknowledge that moKCs are CLEC4F+TIM4-. As moKCs are induced in MASLD, this limits interpretation. Additionally, Iba1 is referred to as a moMF marker but is also expressed by KCs, which again prevents an accurate interpretation of the data. Indeed, the authors show 60% of KCs are dying but only 30% of IBA1+ moMFs, as KCs are also IBA1+, this would mean that KCs die much more than moMFs, which would then limit the relevance of the BMDM studies performed if the phenotype is KC specific. Therefore, this needs to be clarified. The claim that periportal KCs die preferentially is not supported, given that the majority of KCs are peri-portal. Rather, these results would need to be normalised to KC numbers in PP vs PC regions to make meaningful conclusions. Additionally, KCs are known to be notoriously difficult to keep alive in vitro, and for these studies, the authors only examine cl. Casp 3 staining. To fully understand that data, a full analysis of the viability of the cells and whether they retain the KC phenotype in all conditions is required. Finally, in the Cre-driven KO model, there does not seem to be any death of KCs in the controls (rather numbers trend towards an increase with time on diet, Figure 6E), contrary to what had been claimed in the rest of the paper, again making it difficult to interpret the overall results. Additionally, there is no validation that the increased death observed in vivo in KCs is due to further promotion of glycolysis.

Reviewer #3 (Public review):

This manuscript provides novel insights into altered glucose metabolism and KC status during early MASLD. The authors propose that hyperactivated glycolysis drives a spatially patterned KC depletion that is more pronounced than the loss of hepatocytes or hepatic stellate cells. This concept significantly enhances our understanding of early MASLD progression and KC metabolic phenotype.

Through a combination of TUNEL staining and MS-based metabolomic analyses of KCs from HFHC-fed mice, the authors show increased KC apoptosis alongside dysregulation of glycolysis and the pentose phosphate pathway. Using in vitro culture systems and KC-specific ablation of Chil1, a regulator of glycolytic flux, they further show that elevated glycolysis can promote KC apoptosis.

However, it remains unclear whether the observed metabolic dysregulation directly causes KC death or whether secondary factors, such as low-grade inflammation or macrophage activation, also contribute significantly. Nonetheless, the results, particularly those derived from the Chil1-ablated model, point to a new potential target for the early prevention of KC death during MASLD progression.

The manuscript is clearly written and thoughtfully addresses key limitations in the field, especially the focus on glycolytic intermediates rather than fatty acid oxidation. The authors acknowledge the missing mechanistic link between increased glycolysis and KC death. Still, several interpretations require moderation to avoid overstatement, and certain experimental details, particularly those concerning flow cytometry and population gating, need further clarification.

Strengths:

(1) The study presents the novel observation of profound metabolic dysregulation in KCs during early MASLD and identifies these cells as undergoing apoptosis. The finding that Chil1 ablation aggravates this phenotype opens new avenues for exploring therapeutic strategies to mitigate or reverse MASLD progression.

(2) The authors provide a comprehensive metabolic profile of KCs following HFHC diet exposure, including quantification of individual metabolites. They further delineate alterations in glycolysis and the pentose phosphate pathway in Chil1-deficient cells, substantiating enhanced glycolytic flux through 13C-glucose tracing experiments.

(3) The data underscore the critical importance of maintaining balanced glucose metabolism in both in vitro and in vivo contexts to prevent KC apoptosis, emphasizing the high metabolic specialization of these cells.

(4) The observed increase in KC death in Chil1-deficient KCs demonstrates their dependence on tightly regulated glycolysis, particularly under pathological conditions such as early MASLD.

Weaknesses:

(1) The novelty is questionable. The presented work has considerable overlap with a study by the same lab, which is currently under review (citation 17), and it should be considered whether the data should not be presented in one paper.

(2) The authors report that 60% of KCs are TUNEL-positive after 16 weeks of HFHC diet and confirm this by cleaved caspase-3 staining. Given that such marker positivity typically indicates imminent cell death within hours, it is unexpected that more extensive KC depletion or monocyte infiltration is not observed. Since Timd4 expression on monocyte-derived macrophages takes roughly one month to establish, the authors should consider whether these TUNEL-positive KCs persist in a pre-apoptotic state longer than anticipated. Alternatively, fate-mapping experiments could clarify the dynamics of KC death and replacement.

(3) The mechanistic link between elevated glycolytic flux and KC death remains unclear.

(4) The study does not address the polarization or ontogeny of KCs during early MASLD. Given that pro-inflammatory macrophages preferentially utilize glycolysis, such data could provide valuable insight into the reason for increased KC death beyond the presented hyperreliance on glycolysis.

(5) The gating strategy for monocyte-derived macrophages (moMFs) appears suboptimal and may include monocytes. A more rigorous characterization of myeloid populations by including additional markers would strengthen the study's conclusions.

(6) While BMDMs from Chil1 knockout mice are used to demonstrate enhanced glycolytic flux, it remains unclear whether Chil1 deficiency affects macrophage differentiation itself.

(7) The authors use the PDK activator PS48 and the ATP synthase inhibitor oligomycin to argue that increased glycolytic flux at the expense of OXPHOS promotes KC death. However, given the high energy demands of KCs and the fact that OXPHOS yields 15-16 times more ATP per glucose molecule than glycolysis, the increased apoptosis observed in Figure 4C-F could primarily reflect energy deprivation rather than a glycolysis-specific mechanism.

(8) In Figure 1C, KC numbers are significantly reduced after 4 and 16 weeks of HFHC diet in WT male mice, yet no comparable reduction is seen in Clec4Cre control mice, which should theoretically exhibit similar behavior under identical conditions.

eLife Assessment

This study examines the role of the fungal pathogen Candida albicans in the progression of colorectal cancer, a relevant and urgent topic given the global incidence of colon cancer. While the findings are useful and provide solid experimental work and insight into how Candida may contribute to tumor progression, the small patient sample size, reliance on in vitro models, and absence of in vivo validation may limit its impact. This work will interest scientists studying cancer progression and the role played by pathogens.

Reviewer #1 (Public review):

Summary:

This study addresses the emerging role of fungal pathogens in colorectal cancer and provides mechanistic insights into how Candida albicans may influence tumor-promoting pathways. While the work is potentially impactful and the experiments are carefully executed, the strength of evidence is limited by reliance on in vitro models, small patient sample size, and the absence of in vivo validation, which reduces the translational significance of the findings.

Strengths:

(1) Comprehensive mechanistic dissection of intracellular signaling pathways.

(2) Broad use of pharmacological inhibitors and cell line models.

(3) Inclusion of patient-derived organoids, which increases relevance to human disease.

(4) Focus on an emerging and underexplored aspect of the tumor microenvironment, namely fungal pathogens.

Weaknesses:

(1) Clinical association data are inconsistent and based on very small sample numbers.

(2) No in vivo validation, which limits the translational significance.

(3) Species- and cell type-specificity claims are not well supported by the presented controls.

(4) Reliance on colorectal cancer cell lines alone makes it difficult to judge whether findings are specific or general epithelial responses.

Reviewer #2 (Public review):

The authors in this manuscript studied the role of Candida albicans in Colorectal cancer progression. The authors have undertaken a thorough investigation and used several methods to investigate the role of Candida albicans in Colorectal cancer progression. The topic is highly relevant, given the increasing burden of colon cancer globally and the urgent need for innovative treatment options.

However, there are some inconsistencies in the figures and some missing details in the figures, including:

(1) The authors should clearly explain in the results section which patient samples are shown in Figure 1B.

(2) What do a, ab, b, b written above the bars in Figure 1F represent? Maybe authors should consider removing them, because they create confusion. Also, there is no explanation for those letters in the figure legend.

(3) The authors should submit all the raw images of Western blot with appropriate labels to indicate the bands of protein of interest along with molecular weight markers.

(4) The authors should do the quantification of data in Figure 2d and include it in the figure.

(5) In Figure 2h, the authors should indicate if the quantification represents VEGF expression after 6h or 12h of C. albicans co-culture with cells.

(6) In Figure 2i, quantification of VEGF should be done and data from three independent experiments should be submitted. The authors should also mention the time point.

test

خب میخوام یه پیوند بزنم با بحث تفکر کند و سریع.

ببین اونجا ما آشنا شدیم که خیلی از تصمیماتمون رو بدون فکر و منطق روشن و صرفا با سیستم سریع میگیریم، خب ممکنه اینا اشتباه باشن. (و با خلوت و تفکر و نوشتن و تحلیل و شکافتن این افکار، دیدیم واقعا خیلی اشتباه میکردیم.)

اما ببین حد و حدود داره، افراط هم نباید کرد. نباید سر لانچ اول پروداکت چندین هفته فقط فکر کنی و با این توجیه خودت رو آروم کنی که "نه من دارم روی فلسفه و اصول فکر میکنم، یا باید کند فکر کنم که دچار خطا نشم."

حالا سوال: راه حل چیه؟ جواب: افکار سیستم سریع رو میاری توی سیستم کند، واضح و شفاف میشه واست، قضاوتش میکنی، اصلاح میکنی و تغییر میدی، حالا که مطمئن شدی درسته، اون رو در عمل به کار میبندی تا internalize بشه. و تو نمیخواد هررر بار از نو بشینی و کند فکر کنی و بعد تصمیم بگیری، بلکه یک بار فکر میکنی و n بار بار عمل میکنی. توئیت مرتبط: https://x.com/sinamoradi2002/status/1932931652746891644?s=20

در نهایت تو باید به این برسی: شهود تربیت شده + good taste

یادت باشه کماندو کسی نیست که فقط مهارتهای پایه‌ای داره و شرایط متنوع رو مدیریت میکنه، بلکه یک ویژگی دیگه هم داره: ضربتی و سریع میتونه مشکلات رو حل کنه.

انگار داره اینو میگه: ارزیابی عملکرد نباید مبتنی بر رتبه سازمانی باشه، باید ارزشی که طرف خلق میکنه (با حل مشکلات واقعی) رو بسنجی.

چند تا معیار نادرست دیگه که اتفاقا خیلی توی مدل کارمندی مورد توجه هستن: 1. ساعت کار 2. زحمت فرد (قابل تقدیره ولی خب) 3. ارتباطات و پارتی و...

دنبال آدمایی باشن که هنوز اول راه هستن، اما پتانسیل بالایی دارن. نرخ پیشرفت خیلی سریعی دارن. (یه بیان دیگه رشد نمایی خواهند داشت.)

نکته: مهم هست که در هر فاز، چه چیزی رو outcome لحاظ کنیم. مثلا در مراحل اولیه استارتاپ، رسیدن به فلسفه و اصول درست، بینش و شخصیت افراد، سرعت iterate کردن مهم هست. در حالی که توی فازهای بعدی، معیارهایی مثل رضایت کاربران، تعداد کاربران، درآمد و... معیار هستن.

اگر شفاف و منطقی فکر کنیم، اصلا تمرکز نکردن روی چند چیز محدود و قربانی نکردن سایر موارد ایراد داره. ببین تو اگه به پروداکتت واقعا اعتقاد داری پس باید قید بقیه چیزا رو به خاطرش بزنی، اینکه این کار رو نمیکنی یعنی خودت هم شک داری بهش.

حالا اینجا 2 تا بحث دیگه هم مطرح میشه: اولی گذران زندگی، دومی risk management، سعی کن این 2 هدف رو در یک مسیر جانبی مشترک ادغام کنی (تا وقت بیشتری رو صرف پروداکت اصلی خودت بکنی.) و دوما یادت باشه تو واسه‌ی چی سراغ مسیر جانبی رفتی یعنی "صرفا برای گذران زندگی و مدیریت ریسک"، پس یهو یادت نره، غرق درش نشی، بیش از حد zoom in نکنی، افراط نکنی، طمع نکنی. که این تو رو از مسیر اصلیت دور میکنه و چه بدتر اگر گیر این تله‌ها بیفتی و به طرق مختلف خودت رو توجیه کنی و درگیر کذب‌ها بشی.

یادت باشه.که: Everything is a product, including communication.

• صادقانه، واضح، دقیق، ساده و هدفمند ارتباط برقرار کن.

سعنی باید درست Design کنی. مایندست platform thinking، flywheels و be good میتونه کمک کنه.

بحث سر چرایی هست. به عبارتی تو why to do رو حل بکن، how to do و what to do مراحل بعدی هستن.

جمله فریدریش نیچه درباره زندگی : هرکس چرایی برای زندگی کردن دارد می‌تواند تقریبا هر چگونه ای را تحمل کند.

https://x.com/MRezaZare_/status/1990353384071713038?s=20

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