Reviewer #2 (Public Review):

Summary:<br /> The manuscript by Salazar-Lázaro et al. systematically dissects out the different functional properties of the SNARE-domains of syntaxin-1 and syntaxin-2. By systematically substituting the SNARE-domain (or its C- or N-terminal half) into the non-cognate counterpart, the authors find that the C-terminal half of the SNARE-complex is especially important for maintaining RRP size and clamping spontaneous release. They also mutate single residues, to further nail down the effect. Overall, this is an interesting manuscript, which sheds light on the functionality of different co-expressed SNARES.

Strengths:<br /> The strength of the manuscript is the systematic dissection, using substitution of either SNARE-domain into the other syntaxin, together with the state-of-the art methods. The authors follow up with a substitution of single and paired residues. This is a large undertaking, which has been very well carried out.

Weaknesses:<br /> No major weaknesses. The large number of experiments paint a somewhat complicated picture because the process under study is complicated.

Reviewer #3 (Public Review):

Summary:<br /> In this manuscript, Salazar-Lázaro et al. presented interesting data that C-terminal half of the Syx1 SNARE domain is responsible for clamping of spontaneous release, stabilizing RRP, and also Ca2+-evoked release. The authors routinely utilized the chimeric approach to replace the SNARE domain of Syx1 with its paralogue Syx2 and analyzed the neuronal activity through electrophysiology. The data are straightforward and fruitful. The conclusions are reasonable.

Strengths:<br /> The electrophysiology data that illustrate the important functions of Syx1 in clamping of spontaneous release, stabilizing RRP, and also Ca2+-evoked release were clear and convincing.

Weaknesses:<br /> One weakness is that the authors did not go deep into the underlying molecular mechanisms experimentally, either because of a variety of complicated possibilities or limited space of the manuscript.

Reviewer 1 Public Review:

Summary:

The authors set out to clarify the molecular mechanism of endocytosis (re-uptake) of synaptic vesicle (SV) membrane in the presynaptic terminal following release. They have examined the role of presynaptic actin, and of the actin regulatory proteins diaphanous-related formins ( mDia1/3), and Rho and Rac GTPases in controlling the endocytosis. They successfully show that presynaptic membrane-associated actin is required for normal SV endocytosis in the presynaptic terminal, and that the rate of endocytosis is increased by activation of mDia1/3. They show that RhoA activity and Rac1 activity act in a partially redundant and synergistic fashion together with mDia1/3 to regulate the rate of SV endocytosis. The work adds substantially to our understanding of the molecular mechanisms of SV endocytosis in the presynaptic terminal.

Strengths:

The authors use state-of-the-art optical recording of presynaptic endocytosis in primary hippocampal neurons, combined with well-executed genetic and pharmacological perturbations to document effects of alteration of actin polymerization on the rate of SV endocytosis. They show that removal of the short amino-terminal portion of mDia1 that associates with the membrane interrupts the association of mDia1 with membrane actin in the presynaptic terminal. They then use a wide variety of controlled perturbations, including genetic modification of the amount of mDia1/3 by knock-down and knockout, combined with inhibition of activity of RhoA and Rac1 by pharmacological agents, to document the quantitative importance of each agent, and their synergistic relationship in regulation of endocytosis.

The analysis is augmented by ultrastructural analyses that demonstrate the quantitative changes in numbers of synaptic vesicles and in uncoated membrane invaginations that are predicted by the optical recordings.<br /> The manuscript is well-written and the data are clearly explained. Statistical analysis of the data is strengthened by the very large number of data points analyzed for each experiment.

Weaknesses:

There are no major weaknesses.

Reviewer 2 Public Review:

Summary:

This manuscript expands previous work from the Haucke group which demonstrated the role of formins in synaptic vesicle endocytosis. The techniques used to address the research question are state-of-the-art. As stated above there is a significant advance in knowledge, with particular respect to Rho/Rac signalling.

Strengths:

The major strength of the work was to reveal new information regarding the control of both presynaptic actin dynamics and synaptic vesicle endocytosis via Rho/Rac cascades. In addition, there was further mechanistic insight regarding the specific function of mDia1/3. The methods used were state-of-the-art.

Weaknesses:

There are no major weaknesses.

Reviewer #1 (Public Review):

Summary:<br /> Visual Perceptual Learning (VPL) results in varying degrees of generalization to tasks or stimuli not seen during training. The question of which stimulus or task features predict whether learning will transfer to a different perceptual task has long been central in the field of perceptual learning, with numerous theories proposed to address it. This paper introduces a novel framework for understanding generalization in VPL, focusing on the form invariants of the training stimulus. Contrary to a previously proposed theory that task difficulty predicts the extent of generalization - suggesting that more challenging tasks yield less transfer to other tasks or stimuli - this paper offers an alternative perspective. It introduces the concept of task invariants and investigates how the structural stability of these invariants affects VPL and its generalization. The study finds that tasks with high-stability invariants are learned more quickly. However, training with low-stability invariants leads to greater generalization to tasks with higher stability, but not the reverse. This indicates that, at least based on the experiments in this paper, an easier training task results in less generalization, challenging previous theories that focus on task difficulty (or precision). Instead, this paper posits that the structural stability of stimulus or task invariants is the key factor in explaining VPL generalization across different tasks

Strengths:<br /> - The paper effectively demonstrates that the difficulty of a perceptual task does not necessarily correlate with its learning generalization to other tasks, challenging previous theories in the field of Visual Perceptual Learning. Instead, it proposes a significant and novel approach, suggesting that the form invariants of training stimuli are more reliable predictors of learning generalization. The results consistently bolster this theory, underlining the role of invariant stability in forecasting the extent of VPL generalization across different tasks.

- The experiments conducted in the study are thoughtfully designed and provide robust support for the central claim about the significance of form invariants in VPL generalization.

Weaknesses:<br /> - The paper assumes a considerable familiarity with the Erlangen program and the definitions of invariants and their structural stability, potentially alienating readers who are not versed in these concepts. This assumption may hinder the understanding of the paper's theoretical rationale and the selection of stimuli for the experiments, particularly for those unfamiliar with the Erlangen program's application in psychophysics. A brief introduction to these key concepts would greatly enhance the paper's accessibility. The justification for the chosen stimuli and the design of the three experiments could be more thoroughly articulated.

- The paper does not clearly articulate how its proposed theory can be integrated with existing observations in the field of VPL. While it acknowledges previous theories on VPL generalization, the paper falls short in explaining how its framework might apply to classical tasks and stimuli that have been widely used in the VPL literature, such as orientation or motion discrimination with Gabors, vernier acuity, etc. It also does not provide insight into the application of this framework to more naturalistic tasks or stimuli. If the stability of invariants is a key factor in predicting a task's generalization potential, the paper should elucidate how to define the stability of new stimuli or tasks. This issue ties back to the earlier mentioned weakness: namely, the absence of a clear explanation of the Erlangen program and its relevant concepts.

- The paper does not convincingly establish the necessity of its introduced concept of invariant stability for interpreting the presented data. For instance, consider an alternative explanation: performing in the collinearity task requires orientation invariance. Therefore, it's straightforward that learning the collinearity task doesn't aid in performing the other two tasks (parallelism and orientation), which do require orientation estimation. Interestingly, orientation invariance is more characteristic of higher visual areas, which, consistent with the Reverse Hierarchy Theory, are engaged more rapidly in learning compared to lower visual areas. This simpler explanation, grounded in established concepts of VPL and the tuning properties of neurons across the visual cortex, can account for the observed effects, at least in one scenario. This approach has previously been used/proposed to explain VPL generalization, as seen in (Chowdhury and DeAngelis, Neuron, 2008), (Liu and Pack, Neuron, 2017), and (Bakhtiari et al., JoV, 2020). The question then is: how does the concept of invariant stability provide additional insights beyond this simpler explanation?

- While the paper discusses the transfer of learning between tasks with varying levels of invariant stability, the mechanism of this transfer within each invariant condition remains unclear. A more detailed analysis would involve keeping the invariant's stability constant while altering a feature of the stimulus in the test condition. For example, in the VPL literature, one of the primary methods for testing generalization is examining transfer to a new stimulus location. The paper does not address the expected outcomes of location transfer in relation to the stability of the invariant. Moreover, in the affine and Euclidean conditions one could maintain consistent orientations for the distractors and targets during training, then switch them in the testing phase to assess transfer within the same level of invariant structural stability.

- In the section detailing the modeling experiment using deep neural networks (DNN), the takeaway was unclear. While it was interesting to observe that the DNN exhibited a generalization pattern across conditions similar to that seen in the human experiments, the claim made in the abstract and introduction that the model provides a 'mechanistic' explanation for the phenomenon seems overstated. The pattern of weight changes across layers, as depicted in Figure 7, does not conclusively explain the observed variability in generalizations. Furthermore, the substantial weight change observed in the first two layers during the orientation discrimination task is somewhat counterintuitive. Given that neurons in early layers typically have smaller receptive fields and narrower tunings, one would expect this to result in less transfer, not more.

Reviewer #2 (Public Review):

The strengths of this paper are clear: The authors are asking a novel question about geometric representation that would be relevant to a broad audience. Their question has a clear grounding in pre-existing mathematical concepts, that, to my knowledge, have been only minimally explored in cognitive science. Moreover, the data themselves are quite striking, such that my only concern would be that the data seem almost *too* clean. It is hard to know what to make of that, however. From one perspective, this is even more reason the results should be publicly available. Yet I am of the (perhaps unorthodox) opinion that reviewers should voice these gut reactions, even if it does not influence the evaluation otherwise. Below I offer some more concrete comments:

(1) The justification for the designs is not well explained. The authors simply tell the audience in a single sentence that they test projective, affine, and Euclidean geometry. But despite my familiarity with these terms -- familiarity that many readers may not have -- I still had to pause for a very long time to make sense of how these considerations led to the stimuli that were created. I think the authors must, for a point that is so central to the paper, thoroughly explain exactly why the stimuli were designed the way that they were and how these designs map onto the theoretical constructs being tested.

(2) I wondered if the design in Experiment 1 was flawed in one small but critical way. The goal of the parallelism stimuli, I gathered, was to have a set of items that is not parallel to the other set of items. But in doing that, isn't the manipulation effectively the same as the manipulation in the orientation stimuli? Both functionally involve just rotating one set by a fixed amount. (Note: This does not seem to be a problem in Experiment 2, in which the conditions are more clearly delineated.)

(3) I wondered if the results would hold up for stimuli that were more diverse. It seems that a determined experimenter could easily design an "adversarial" version of these experiments for which the results would be unlikely to replicate. For instance: In the orientation group in Experiment 1, what if the odd-one-out was rotated 90 degrees instead of 180 degrees? Intuitively, it seems like this trial type would now be much easier, and the pattern observed here would not hold up. If it did hold up, that would provide stronger support for the authors' theory.

It is not enough, in my opinion, to simply have some confirmatory evidence of this theory. One would have to have thoroughly tested many possible ways that theory could fail. I'm unsure that enough has been done here to convince me that these ideas would hold up across a more diverse set of stimuli.

Reviewer #1 (Public Review):

Summary:<br /> This paper presents valuable findings that gustation and feeding state influence the preferred environmental temperature preference in flies. Interestingly, the authors showed that by refeeding starved animals with the non-nutritive sugar sucralose, they are able to tune their preference towards a higher temperature in addition to nutrient-dependent warm preference. The authors show that temperature-sensing and sweet-sensing gustatory neurons (SGNs) are involved in the former but not the latter. In addition, their data indicate that peptidergic signals involved in internal state and clock genes are required for taste-dependent warm preference behavior.

The authors made an analogy of their results to the cephalic phase response (CPR) in mammals where the thought, sight, and taste of food prepare the animal for the consumption of food and nutrients. They further linked this behavior to core regulatory genes and peptides controlling hunger and sleep in flies having homologues in mammals. These valuable behavioral results can be further investigated in flies with the advantage of being able to dissect the neural circuitry underlying CPR and nutrient homeostasis.

Strengths:<br /> (1) The authors convincingly showed that tasting is sufficient to drive warm temperature preference behavior in starved flies and that it is independent of nutrient-driven warm preference.

(2) By using the genetic manipulation of key internal sensors and genes controlling internal feeding and sleep states such as DH44 neurons and the per genes for example, the authors linked gustation and temperature preference behavior control to the internal state of the animal.

Weaknesses:<br /> (1) The title is somewhat misleading, as the term homeostatic temperature control linked to gustation only applies to starved flies.

(2) The authors used a temperature preference assay and refeeding for 5 minutes, 10 minutes, and 1 hour. Experimentally, it makes a difference if the flies are tested immediately after 10 minutes or at the same time point as flies allowed to feed for 1 hour. Is 10 minutes enough to change the internal state in a nutrition-dependent manner? Some of the authors' data hint at it (e.g. refeeding with fly food for 10 minutes), but it might be relevant to feed for 5/10 minutes and wait for 55/50min to do the assays at comparable time points.

(3) A figure depicting the temperature preference assay in Figure 1 would help illustrate the experimental approach. It is also not clear why Figure 1E is shown instead of full statistics on the individual panels shown above (the data is the same).

(4) The authors state that feeding rate and amount were not changed with sucralose and glucose. However, the FLIC assay they employed does not measure consumption, so this statement is not correct, and it is unclear if the intake of sucralose and glucose is indeed comparable. This limits some of the conclusions.

(5) The authors make a distinction between taste-induced and nutrient-induced warm preference. Yet the statistics in most figures only show the significance between the starved and refed flies, not the fed controls. As the recovery is in many cases incomplete and used as a distinction of nutritive vs non-nutritive signals (see Figure 1E) it will be important to also show these additional statistics to allow conclusions about how complete the recovery is.

(6) The starvation period used is ranging from 1 to 3 days, as in some cases no effect was seen upon 1 day of starvation (e.g. with clock genes or temperature sensing neurons). While the authors do provide a comparison between 18-21 and 26-29 hours old flies in Figure S1, a comparison for 42-49 and 66-69 hours of starvation is missing. This also limits the conclusion as the "state" of the animal is likely quite different after 1 day vs. 3 days of starvation and, as stated by the authors, many flies die under these conditions.

(7) In Figure 2, glucose-induced refeeding was not tested in Gr mutants or silenced animals, which would hint at post-ingestive recovery mechanisms related to nutritional intake. This is only shown later (in Figure S3) but I think it would be more fitting to address this point here. The data presented in Figure S3 regarding the taste-evoked vs nutrient-dependent warm preference is quite important while in some parts preliminary. It would nonetheless be justified to put this data in the main figures. However, some of the conclusions here are not fully supported, in part due to different and low n numbers, which due to the inherent variability of the behavior do not allow statistically sound conclusions. The authors claim that sweet GRNs are only involved in taste-induced warm preference, however, glucose is also nutritive but, in several cases, does not rescue warm preference at all upon removal of GRN function (see Figures S3A-C). This indicates that the Gal4 lines and also the involved GRs are potentially expressed in tissues/neurons required for internal nutrient sensing.

(8) In Figure 4, fly food and glucose refeeding do not fully recover temperature preference after refeeding. With the statistical comparison to the fed control missing, this result is not consistent with the statement made in line 252. I feel this is an important point to distinguish between state-dependent and taste/nutrition-dependent changes.

(9) The conclusion that clock genes are required for taste-evoked warm preference is limited by the observation that they ingest less sucralose. In addition, the FLIC assay does not allow conclusions about the feeding amount, only the number of food interactions. Therefore, I think these results do not allow clear-cut conclusions about the impact of clock genes in this assay.

(10) CPR is known to be influenced by taste, thought, smell, and sight of food. As the discussion focused extensively on the CPR link to flies it would be interesting to find out whether the smell and sight of food also influence temperature preference behavior in animals with different feeding states.

(11) In the discussion in line 410ff the authors claim that "internal state is more likely to be associated with taste-evoked warm preference than nutrient-induced warm preference." This statement is not clear to me, as neuropeptides are involved in mediating internal state signals, both in the brain itself as well as from gut to brain. Thus, neuropeptidergic signals are also involved in nutrient-dependent state changes, the authors might just not have identified the peptides involved here. The global and developmental removal of these signals also limits the conclusions that can be drawn from the experiments, as many of these signals affect different states, circuits, and developmental progression.

Reviewer #2 (Public Review):

Animals constantly adjust their behavior and physiology based on internal states. Hungry animals, desperate for food, exhibit physiological changes immediately upon sensing, smelling, or chewing food, known as the cephalic phase response (CPR), involving processes like increased saliva and gastrointestinal secretions. While starvation lowers body temperature, the mechanisms underlying how the sensation of food without nutrients induces behavioral responses remain unclear. Hunger stress induces changes in both behavior and physiological responses, which in flies (or at least in Drosophila melanogaster) leads to a preference for lower temperatures, analogous to the hunger-driven lower body temperature observed in mammals. In this manuscript, the authors have used Drosophila melanogaster to investigate the issue of whether taste cues can robustly trigger behavioral recovery of temperature preference in starving animals. The authors find that food detection triggers a warm preference in flies. Starved flies recover their temperature preference after food intake, with a distinction between partial and full recovery based on the duration of refeeding. Sucralose, an artificial sweetener, induces a warm preference, suggesting the importance of food-sensing cues. The paper compares the effects of sucralose and glucose refeeding, indicating that both taste cues and nutrients contribute to temperature preference recovery. The authors show that sweet gustatory receptors (Grs) and sweet GRNs (Gustatory Receptor Neurons) play a crucial role in taste-evoked warm preference. Optogenetic experiments with CsChrimson support the idea that the excitation of sweet GRNs leads to a warm preference. The authors then examine the internal state's influence on taste-evoked warm preference, focusing on neuropeptide F (NPF) and small neuropeptide F (sNPF), analogous to mammalian neuropeptide Y. Mutations in NPF and sNPF result in a failure to exhibit taste-evoked warm preference, emphasizing their role in this process. However, these neuropeptides appear not to be critical for nutrient-induced warm preference, as indicated by increased temperature preference during glucose and fly food refeeding in mutant flies. The authors also explore the role of hunger-related factors in regulating taste-evoked warm preference. Hunger signals, including diuretic hormone (DH44) and adipokinetic hormone (AKH) neurons, are found to be essential for taste-evoked warm preference but not for nutrient-induced warm preference. Additionally, insulin-like peptides 6 (Ilp6) and Unpaired3 (Upd3), related to nutritional stress, are identified as crucial for taste-evoked warm preference. The investigation then extends into circadian rhythms, revealing that taste-evoked warm preference does not align with the feeding rhythm. While flies exhibit a rhythmic feeding pattern, taste-evoked warm preference occurs consistently, suggesting a lack of parallel coordination. Clock genes, crucial for circadian rhythms, are found to be necessary for taste-evoked warm preference but not for nutrient-induced warm preference.

Strengths:<br /> A well-written and interesting study, investigating an intriguing issue. The claims, none of which to the best of my knowledge controversial, are backed by a substantial number of experiments.

Weakness:<br /> The experimental setup used and the procedures for assessing the temperature preferences of flies are rather sparingly described. Additional details and data presentation would enhance the clarity and replicability of the study. I kindly request the authors to consider the following points: i) A schematic drawing or diagram illustrating the experimental setup for the temperature preference assay would greatly aid readers in understanding the spatial arrangement of the apparatus, temperature points, and the positioning of flies during the assay. The drawing should also be accompanied by specific details about the setup (dimensions, material, etc). ii) It would be beneficial to include a visual representation of the distribution of flies within the temperature gradient on the apparatus. A graphical representation, such as a heatmaps or histograms, showing the percentage of flies within each one-degree temperature bin, would offer insights into the preferences and behaviors of the flies during the assay. In addition to the detailed description of the assay and data analysis, the inclusion of actual data plots, especially for key findings or representative trials, would provide readers with a more direct visualization of the experimental outcomes. These additions will not only enhance the clarity of the presented information but also provide the reader with a more comprehensive understanding of the experimental setup and results. I appreciate the authors' attention to these points and look forward to the potential inclusion of these elements in the revised manuscript.

Reviewer #1 (Public Review):

Summary:<br /> Duan et al analyzed brain imaging data in UKBK and found a pattern in brain structure changes by aging. They identified two patterns and found links that can be differentiated by the categorization.

Strengths:<br /> This discovery harbors a substantial impact on aging and brain structure and function.

Weaknesses:<br /> Therefore, the study requires more validation efforts. Most importantly, data underlying the stratification of the two groups are not obvious and lack further details. Can they also stratified by different methods? i.e. PCA?

Are there any external data that can be used for validation?

Other previous discoveries or claims supporting the results of the study should be explored to support the conclusion.

Sex was merely used as a covariate. Were there sex differences during brain aging? What was the sex ratio difference in groups 1 and 2?

Although statistically significant, Figure 3 shows minimal differences. LTL and phenoAge are displayed in adjusted values but what are the actual values that differ between patterns 1 and 2?

It is not intuitive to link gene expression results shown in Figure 8 and brain structure and functional differences between patterns 1 and 2. Any overlap of genes identified from analyses shown in Figure 6 (GWAS) and 8 (gene expression)?

Reviewer #2 (Public Review):

Summary:<br /> The authors aimed to understand the heterogeneity of brain aging by analyzing brain imaging data. Based on the concept of structural brain aging, they divided participants into two groups based on the volume and rate of decrease of gray matter volume (GMV). The group with rapid brain aging showed accelerated biological aging and cognitive decline and was found to be vulnerable to certain neuropsychiatric disorders. Furthermore, the authors claimed the existence of a "last in, first out" mirroring pattern between brain aging and brain development, which they argued is more pronounced in the group with rapid brain aging. Lastly, the authors identified genetic differences between the two groups and speculated that the cause of rapid brain aging may lie in genetic differences.

Strengths:<br /> The authors supported their claims by analyzing a large amount of data using various statistical techniques. There seems to be no doubt about the quality and quantity of the data. Additionally, they demonstrated their strength in integrating diverse data through various analysis techniques to conclude.

Weaknesses:<br /> There appears to be a lack of connection between the analysis results and their claims. Readers lacking sufficient background knowledge of the brain may find it difficult to understand the paper. It would be beneficial to modify the figures and writing to make the authors' claims clearer to readers. Furthermore, the paper gives an overall impression of being less polished in terms of abbreviations, figure numbering, etc. These aspects should be revised to make the paper easier for readers to understand.

Reviewer #1 (Public Review):

Summary:<br /> The authors are trying to develop a microscopy system that generates data output exceeding the previous systems based on huge objectives.

Strengths:<br /> They have accomplished building such a system, with a field of view of 1.5x1.0 cm2 and a resolution of up to 1.2 um. They have also demonstrated their system performance on samples such as organoids, brain sections, and embryos.

Weaknesses:<br /> To be used as a volumetric imaging technique, the authors only showcase the implementation of multi-focal confocal sectioning. On the other hand, most of the real biological samples were acquired under wide-field illumination, and processed with so-called computational sectioning. Despite the claim that it improves the contrast, sometimes I felt that the images were oversharpened and the quantitative nature of these fluorescence images may be perturbed.

Reviewer #2 (Public Review):

Summary:<br /> This manuscript introduced a volumetric trans-scale imaging system with an ultra-large field-of-view (FOV) that enables simultaneous observation of millions of cellular dynamics in centimeter-wide 3D tissues and embryos. In terms of technique, this paper is just a minor improvement of the authors' previous work, which is a fluorescence imaging system working at visible wavelength region (https://www.nature.com/articles/s41598-021-95930-7).

Strengths:<br /> In this study, the authors enhanced the system's resolution and sensitivity by increasing the numerical aperture (NA) of the lens. Furthermore, they achieved volumetric imaging by integrating optical sectioning and computational sectioning. This study encompasses a broad range of biological applications, including imaging and analysis of organoids, mouse brains, and quail embryos, respectively. Overall, this method is useful and versatile.

Weaknesses:<br /> The unique application that only can be done by this high-throughput system remains vague. Meanwhile, there are also several outstanding issues in this paper, such as the lack of technical advances, unclear method details, and non-standardized figures.

Reviewer #1 (Public Review):

Summary:

In their study, Petersen et al. investigated the relationship between parameters of metabolic syndrome (MetS) and cortical thickness using partial least-squares correlation analysis (PLS) and performed subsequently a group comparison (sensitivity analysis). To do this, they utilized data from two large-scale population-based cohorts: the UK BioBank (UKB) and the Hamburg City Health Study (HCHS). They identified a latent variable that explained 77% of the shared variance, driven by several measures related to MetS, with obesity-related measures having the strongest contribution. Their results highlighted that higher cortical thickness in the orbitofrontal, lateral prefrontal, insular, anterior cingulate, and temporal areas is associated with lower MetS severity. Conversely, the opposite pattern was observed in the superior frontal, parietal, and occipital regions. A similar pattern was then observed in the sensitivity analysis when comparing two groups (MetS vs. matched controls) separately.

Interestingly, after including HbA1c (a blood glycemic marker, which reflects insulin resistance much better than non-fasting glucose) in their revision, the authors identified a second latent variable accounting for 22% of shared variance mostly driven by HbA1c and blood glucose. The authors conclude that the distinct covariance profile of this variable likely indicates a separate pathological mechanistic connection between MetS components and brain morphology.

They then mapped local cellular and network topological attributes to the observed cortical changes associated with MetS. This was achieved using cell-type-specific gene expressions from the Allen Human Brain Atlas and the group consensus functional and structural connectomes of the Human Connectome Project (HCP), respectively. This contextualization analysis allowed them to identify potential cellular contributions in these structures driven by endothelial cells, microglial cells, and excitatory neurons. It also indicated functional and structural interconnectedness of areas experiencing similar MetS effects.

Strengths:

The effects of metabolic syndrome on the brain are still incompletely understood, and such multi-scale analyses are important for the field. Despite the study's sole 'correlation-based' nature, it yields valuable results, including several scales of brain parameters (cortical thickness, cellular, and network-based). The results are robust and benefit from two 'large-scale' datasets, resulting in highly powered statistics

Weaknesses:

The weakness of this study lies mostly on the non-causative approach used here. Nevertheless, the authors are aware of the limitations of the study and carefully frame their language accordingly.

Reviewer #2 (Public Review):

Summary:

In this manuscript, Petersen et al. aimed for a comprehensive assessment of the relationship between cardiometabolic risk factors and cortical thickness. They found that a latent variable reflecting higher obesity, hypertension, LDL cholesterol, triglyerides, non-fasting glucose, HbA1c and lower HDL cholesterol was associated with lower cortical thickness in orbitofrontal, lateral prefrontal, insular, anterior cingulate and temporal areas as well as lower subcortical volumes. In sensitivity analyses they showed that this pattern replicated across cohorts and was also consistent with a clinical definition of the metabolic syndrome.

Further, when including cognition into the multivariate analysis, the pattern remained unchanged and mediation analyses showed that the relationship between the first latent variable and worse cognitive performance across several tests was mediated by the brain morphological differences.

The authors investigated the cell types implicated in the regions associated with cardiometabolic risk using the Allen Brain Atlas and found that the density of excitatory neurons type 8, endothelial cells and microglia reliably co-located with the pattern of cortical thickness. Furthermore, they showed that cortial regions more strongly associated with MetS were more closely structurally & functionally connected than others.

Strengths:

This study performed a comprehensive assessment of the combined association of cardiometabolic risk factors and brain structure and investigated micro-and macroscopic underpinnings. A major strength of the study is the methodological approach of partial least squares which allows one to not single out risk factors but to take them into account simultaneously. The large sample size from two cohorts allowed for different sensitivity analyses and convincing evidence for the stability of the first latent variable. The authors demonstrated that the component was also reliably related to cognitive performance and that the association of the individual cardiometabolic risk on cognition was mediated by brain morphological differences, replicating multiple previous studies which evidenced associations of different components of the MetS with worse cognitive performance.

The novel contribution of the study lies in the virtual histology and brain topology investigation of the cortical pattern related to MetS. The virtual histology provided convincing evidence of the co-localization of endothelial, glial and excitatory neuronal cells with the regions of MetS-associated cortical thinning while the brain topology analysis highlighted the disproportionate structural and functional connectivity between associated regions. This analysis provides insights into the role of inflammatory processes and the intricate link between gray matter morphology and microvasculature, both locally and in relation to long-range connectivity. This information is valuable to inform future mechanistic studies.

Weaknesses:

The study is exclusively cross-sectional which does not allow disentangling potential causes from consequences. While studies indicate that most of the differences seen in middle age are probably consequences of the MetS on the vasculature, blood-brain barrier or inflammatory processes, differences in cortical morphology might also represent a risk factor for weight gain.<br /> The study is exploratory in nature and for the contextualization analyses it is difficult to judge whether those were selected from a larger pool of analyses.

Reviewer #1 (Public Review):

This is a well-designed study that explores the BEF relationships in fragmented landscapes. Although there are massive studies on BEF relationships, most of them were conducted at local scales, few considered the impacts of landscape variables. This study used a large dataset to specifically address this question and found that habitat loss weakened the BEF relationships. Overall, this manuscript is clearly written and has important implications for BEF studies as well as for ecosystem restoration.

I have read the response letter provided by the authors and think they have done a great job in addressing my concerns.

Reviewer #2 (Public Review):

Summary:<br /> In this manuscript, Yan et al. assess the effect of two facets of habitat fragmentation (i.e., habitat loss and habitat fragmentation per se) on biodiversity, ecosystem function, and the biodiversity-ecosystem function (BEF) relationship in grasslands of an agro-pastoral ecotone landscape in northern China. The authors use a stratified random sampling to select 130 study sites located within 500 m - radius landscapes varying along gradients of habitat loss and habitat fragmentation per se. In these study sites, the authors measure grassland specialist and generalist plant richness via field surveys, as well as above-ground biomass by harvesting and dry-weighting the grass communities in each 3 x 1m2 plots of the 130 study sites. The authors find that habitat loss and fragmentation per se have different effects on biodiversity, ecosystem function and the BEF relationship: whereas habitat loss was associated with a decrease in plant richness, fragmentation per se was not; and whereas fragmentation per se was associated with a decrease in above-ground biomass, habitat loss was not. Finally, habitat loss, but not fragmentation per se was linked to a decrease in the magnitude of the positive biodiversity-ecosystem functioning relationship, via reducing the percentage of grassland specialists in the community.

Strengths:<br /> This study by Yan et al. is an exceptionally well-designed, well-written, clear and concise study shedding light on a longstanding, important question in landscape ecology and biodiversity-ecosystem functioning research. Via a stratified random sampling approach (cf. also "quasi-experimental design" Butsic et al. 2017), Yan et al. create an ideal set of study sites, where habitat loss and habitat fragmentation per se (usually highly correlated) are decorrelated and hence, separate effects of each of these facets on biodiversity and ecosystem function can be assessed statistically in "real-world" (and not experimental, cf. Duffy et al. 2017) communities. The authors use adequate and well-described methods to investigate their questions. The findings of this study add important empirical evidence from real-world grassland ecosystems that help to advance our theoretical understanding on landscape-moderation of biodiversity effects and provide important guidelines for conservation management likewise. Also, all figures are well-designed and clear.

Weaknesses:<br /> I found only a few minor issues, mostly unclear descriptions that have now been revised for more clarity.

Reviewer #3 (Public Review):

Summary: The authors aim to solve how landscape context impacts the community BEF relationship. They found habitat loss and fragmentation per se have inconsistent effects on biodiversity and ecosystem function. And habitat loss rather than fragmentation per se can weaken the positive BEF relationship by decreasing the degree of habitat specialization of the community.

Strengths: The authors provide a good background, and they have a good grasp of habitat fragmentation and BEF literature.<br /> A major strength of this study is separating the impacts of habitat loss and fragmentation per se using the convincing design selection of landscapes with different combinations of habitat amount and fragmentation per se.<br /> Another strength is considering the role of specialists and generalists in shaping the BEF relationship.

Weaknesses:<br /> No<br /> In the revised manuscript, the authors have provided more detailed information about the ecological significance of these fragmentation indices. and also integrated two environmental factors related to water and temperature (soil water content and land surface temperature) into the data analysis to control their potential impact.

Reviewer #2 (Public Review):

The authors have used microfluidic channels to study the response of budding yeast to variable environments. Namely, they tested the ability of the cells to divide when the medium was repeatedly switched between two different conditions at various frequencies. They first characterized the response to changes in glucose availability or in the presence of hyper-osmotic stress via the addition of sorbitol to the medium. Subsequently, the two stresses were combined by applying the alternatively or simultaneously (in-phase). Interestingly, they observed that the in-phase stress pattern allowed more divisions and low levels of cell mortality compared to the alternating stresses where cells were dividing slowly and many cells died. A number of mutants in the HOG pathway were tested in these conditions to evaluate their responses. Moreover, the activation of the MAPK Hog1 and the transcriptional induction of the hyper-osmotic stress promoter STL1 were quantified by fluorescence microscopy.

Overall, the manuscript is well structured and data are presented in a clear way. The time-lapse experiments were analyzed with high precision. The experiments confirm the importance of performing dynamic analysis of signal transduction pathways. While the experiments reveal some unexpected behavior, I find that the biological insights gained on this system remain relatively modest.

In the discussion section, the authors mention two important behaviors that their data unveil: resource allocation (between glycolysis and HOG-driven adaptation) and regulation of the HOG-pathway based on the presence of glucose. These types of behaviors had been already observed in other reports (Sharifan et al. 2015 or Shen et al. 2023, for instance). The experimental set-up used in this study provides highlights new aspects of the interplay between hyper-osmotic stress response and glucose availability.

The authors have tested various processes that could explain the slow growth observed in the alternating stress regime. Unfortunately, neither glycogen accumulation, cell-cycle arrest via Sic1 or the inhibition of protein production in starved cells could explain the observed behavior. However, one clear evidence that is presented is the link between glycerol accumulation during the sorbitol treatment and the cell death phenotype upon starvation in alternating stress condition.

One question which remains open is to what extent the findings presented here can be extended to other types of perturbations which for instance would combine Nitrogen limitation and hyper-osmotic stress.

Reviewer #1 (Public Review):

In this study, the authors aimed to investigate how cells respond to dynamic combinations of two stresses compared to dynamic inputs of a single stress. They applied the two stresses - carbon stress and hyperosmotic stress - either in or out of phase, adding and removing glucose and sorbitol.

Both a strength and a weakness is that the cells' hyperosmotic response strongly requires glucose. For in-phase stress, cells are exposed to hyperosmotic shock without glucose, limiting their ability to respond with the well-studied HOG pathway; for anti-phase stress, cells do have glucose when hyperosmotically shocked, but experience a hypo-osmotic shock when both glucose and sorbitol are simultaneously removed. Responding with the HOG pathway and so amassing intracellular glycerol amplifies the impact of this hypo-osmotic shock. Counterintuitively then, it is the presence of glucose rather than the stress of its absence that is deleterious for the cells.

The bulk of the paper supports these conclusions with clean, compelling time-lapse microscopy, including extensive analysis of gene deletions in the HOG network and measurements of both division and death rates. The methodology the authors develop is powerful and widely applicable.

The authors' findings demonstrate the tight links that can exist between metabolism and the ability to respond to stress and the novel insights that can be gained using multiple dynamic inputs.

Reviewer #3 (Public Review):

Summary:

Using a combination of approaches, including automated feature selection and hierarchical clustering, the author identified a set of genes persistently associated with extrachromosomal DNA (ecDNA) presence across cancer types. The authors further validated the gene set identified using gene ontology enrichment analysis and identified that upregulated genes in extrachromosomal DNA-containing tumors are enriched in biological processes like DNA damage and cell proliferation, whereas downregulated genes are enriched in immune response processes.

Comments for the previous version:

Major comments:

(1) The authors presented a solid comparative analysis of ecDNA-containing and ecDNA-free tumors. An established automated feature selection approach, Boruta, was used to select differentially expressed genes (DEG) in ecDNA(+) and ecDNA(-) TCGA tumor samples, and the iterative selection process and two-tier multiple hypothesis testing ensured the selection of reliable DEGs. The author showed that the DEG selected using Boruta has stronger predictive power than genes with top log-fold changes.

(2) The author performed a thorough interpretation of the findings with GO enrichment analysis of biological processes enriched in the identified DEG set and presented interesting findings, including the enrichment in DNA damage process among the genes upregulated in ecDNA(+) tumors.

(3) Overall, the authors achieved their aims with solid data mining and analysis approaches applied to public data tumor data sets.

(4) While it may not be the scope of this study, it will be interesting to at least have some justification for choosing Boruta over other feature selection methods, such as Recursive Feature Elimination (RFE) and backward stepwise selection.

(5) The authors showed that DESEQ-selected DEGs with top log-fold changes have less strong predictive power and speculated that this may be due to the fact that genes with top log-fold changes (LFC) are confined only to a small subset of samples. It will be interesting to select DEGs with top log-fold changes after first partitioning the tumor samples. For example, randomly partition the tumor samples, identify the DEGs with top LFC, combine the DEGs identified from each partition, then evaluate the predictive power of these DEGs against the Boruta-selected DEGs.

(6) While the authors showed that the presence of mutations was not able to classify ecDNA(+) and (-) tumor samples, it will be interesting to see if variant allele frequencies of the genes containing these mutations have predictive power.

Comments for the revised version:

The authors addressed the comments and recommendations with solid analysis and explanations in the revision. The added analysis using GLM is especially appreciated and provides convincing evidence for the predicting power of the Boruta-selected genes. The only comment is at this point is that it is recommended that the author provide some justification for choosing Boruta over other feature selection methods. It is not necessary to provide benchmarking results - justification based on the review of previous literature is sufficient, as it is not well explained in the paper why Boruta was chosen in the first place. Is it state-of-the-art? Has it demonstrated better performance in other settings? A few sentences answering these questions should suffice.

Reviewer #1 (Public Review):

Recently discovered extrachromosomal DNA (ecDNA) provides an alternative non-chromosomal means for oncogene amplification and a potent substrate for selective evolution of tumors. The current work aims to identify key genes whose expression distinguishes ecDNA+ and ecDNA- tumors and the associated processes to shed light on the biological mechanisms underlying ecDNA genesis and their oncogenic effects. This is clearly an important question and through detailed analysis this work points to specific GO processes associated (up and down) with ecDNA+ tumors, namely, specific DNA damage repair processes and specific oncogenic processes.

In the initial submission I had commented on lack of clarity of method, potential biases, and in some cases inappropriate interpretation. In the revised version, the authors have addressed all my comments satisfactorily and I think this is an important work furthering our understanding of mechanisms underlying ecDNA+ tumors.

Reviewer #2 (Public Review):

In their manuscript Lin et al. describe an important study on the transcriptional programs associated with the presence of extrachromosomal DNA in a cohort of 870 cancers of different origins. The authors find that compared to cancers lacking such amplifications, ecDNA+ cancers express higher levels of DNA damage repair-associated genes, but lower levels of immune-related gene programs.

This work is very timely and its findings have the potential to be very impactful, as the transcriptional context differences between ecDNA+ and ecDNA- cancers are currently largely unknown. The observation that immune programs are downregulated in ecDNA+ cancers may initiate new preclinical and translational studies that impact the way ecDNA+ cancers are treated in the future. Thus, this study has important theoretical implications that have the potential to substantially advance our understanding of ecDNA+ cancers.

Strengths:

The authors provide compelling evidence for their conclusions based on large patient datasets. The methods they used and analyses are rigorous.

Weaknesses:

The biological interpretation of the data remains observational. The direct implication of these genes in ecDNA(+) tumors is not tested experimentally.

Reviewer #1 (Public Review):

This paper is of importance to scientists interested in molecular mechanisms by which actin point mutations affect its function to ultimately lead to disease states. This work thoroughly characterizes the effect of the E334Q mutation in cytoplasmic gamma-actin on two binding partners: cofilin and myosin (non-muscle myosin 2 and myosin 5). Overall, the data showing effects on cofilin function and myosin binding are convincing and the experiments performed expertly using state-of-the art approaches. Additional binding partners of actin that were not examined here may also have altered function when interacting with the mutant actin.

Comments on revised version:

The authors seem to have done a pretty thorough job with the rebuttal.

Reviewer #1 (Public Review):

The authors deploy a combination of their own previously developed computational methods and databases (SIGNOR and CellNOptR) to model the FLT3 signaling landscape in AML and identify synergistic drug combinations that may overcome the resistance AML cells harboring ITD mutations in the TKI domain of FLT3 to FLT3 inhibitors. I did not closely evaluate the details of these computational models since they are outside of my area of expertise and have been previously published. The manuscript has significant issues with data interpretation and clarity, as detailed below, which, in my view, call into question the main conclusions of the paper.

The authors train the model by including perturbation data where TKI-resistant and TKI-sensitive cells are treated with various inhibitors and the activity (i.e. phosphorylation levels) of the key downstream nodes are evaluated. Specifically, in the Results section (p. 6) they state "TKIs sensitive and resistant cells were subjected to 16 experimental conditions, including TNFa and IGF1 stimulation, the presence or absence of the FLT3 inhibitor, midostaurin, and in combination with six small-molecule inhibitors targeting crucial kinases in our PKN (p38, JNK, PI3K, mTOR, MEK1/2 and GSK3)". I would appreciate more details on which specific inhibitors and concentrations were used for this experiment. More importantly, I was very puzzled by the fact that this training dataset appears to contain, among other conditions, the combination of midostaurin with JNK inhibition, i.e. the very combination of drugs that the authors later present as being predicted by their model to have a synergistic effect. Unless my interpretation of this is incorrect, it appears to be a "self-fulfilling prophecy", i.e. an inappropriate use of the same data in training and verification/test datasets.

My most significant criticism is that the proof-of-principle experiment evaluating the combination effects of midostaurin and SP600125 in FLT3-ITD-TKD cell line model does not appear to show any synergism, in my view. The authors' interpretation of the data is that the addition of SP600125 to midostaurin rescues midostaurin resistance and results in increased apoptosis and decreased viability of the midostaurin-resistant cells. Indeed, they write on p.9: "Strikingly, the combined treatment of JNK inhibitor (SP600125) and midostaurin (PKC412) significantly increased the percentage of FLT3ITD-TKD cells in apoptosis (Fig. 4D). Consistently, in these experimental conditions, we observed a significant reduction of proliferating FLT3ITD- TKD cells versus cells treated with midostaurin alone (Fig. 4E)." However, looking at Figs 4D and 4E, it appears that the effects of the midostaurin/SP600125 combination are virtually identical to SP600125 alone, and midostaurin provides no additional benefit. No p-values are provided to compare midostaurin+SP600125 to SP600125 alone but there seems to be no appreciable difference between the two by eye. In addition, the evaluation of synergism (versus additive effects) requires the use of specialized mathematical models (see for example Duarte and Vale, 2022). That said, I do not appreciate even an additive effect of midostaurin combined with SP600125 in the data presented.

In my view, there are significant issues with clarity and detail throughout the manuscript. For example, additional details and improved clarity are needed, in my view, with respect to the design and readouts of the signaling perturbation experiments (Methods, p. 15 and Fig 2B legend). For example, the Fig 2B legend states: "Schematic representation of the experimental design: FLT3 ITD-JMD and FLT3 ITD-JMD cells were cultured in starvation medium (w/o FBS) overnight and treated with selected kinase inhibitors for 90 minutes and IGF1 and TNFa for 10 minutes. Control cells are starved and treated with PKC412 for 90 minutes, while "untreated" cells are treated with IGF1 100ng/ml and TNFa 10ng/ml with PKC412 for 90 minutes.", which does not make sense to me. The "untreated" cells appear to be treated with more agents than the control cells. The logic behind cytokine stimulation is not adequately explained and it is not entirely clear to me whether the cytokines were used alone or in combination. Fig 2B is quite confusing overall, and it is not clear to me what the horizontal axis (i.e. columns of "experimental conditions", as opposed to "treatments") represents. The Method section states "Key cell signaling players were analyzed through the X-Map Luminex technology: we measured the analytes included in the MILLIPLEX assays" but the identities of the evaluated proteins are not given in the Methods. At the same time, the Results section states "TKIs sensitive and resistant cells were subjected to 16 experimental conditions" but these conditions do not appear to be listed (except in Supplementary data; and Fig 2B lists 9 conditions, not 16). In my subjective view, the manuscript would benefit from a clearer explanation and depiction of the experimental details and inhibitors used in the main text of the paper, as opposed to various Supplemental files/figures. The lack of clarity on what exactly were the experimental conditions makes the interpretation of Fig 2 very challenging. In the same vein, in the PCA analysis (Fig 2C) there seems to be no reference to the cytokine stimulation status while the authors claim that PC2 stratifies cells according to IGF1 vs TNFalpha. There are numerous other examples of incomplete or confusing legends and descriptions which, in my view, need to be addressed to make the paper more accessible.

I am not sure that I see significant value in the patient-specific logic models because they are not supported by empirical evidence. Treating primary cells from AML patients with relevant drug combinations would be a feasible and convincing way to validate the computational models and evaluate their potential benefit in the clinical setting.

Reviewer #2 (Public Review):

Summary:

This manuscript by Latini et al describes a methodology to develop Boolean-based predictive logic models that can be applied to uncover altered protein/signalling networks in cancer cells and discover potential new therapeutic targets. As a proof-of-concept, they have implemented their strategy on a hematopoietic cell line engineered to express one of two types of FLT3 internal tandem mutations (FLT3-ITD) found in patients, FLT3-ITD-TKD (which are less sensitive to tyrosine kinase inhibitors/TKIs) and FLT3-ITD-JMD (which are more sensitive to TKIs).

Strengths:

This useful work could potentially represent a step forward towards personalised targeted therapy, by describing a methodology using Boolean-based predictive logic models to uncover altered protein/signalling networks within cancer cells.

Authors have validated their approach by analysing independent, real-world data

Weaknesses:

No weaknesses were observed by this reviewer for the revised version.

Reviewer #3 (Public Review):

Summary: The paper "Unveiling the signaling network of FLT3-ITD AML improves drug sensitivity prediction" reports the combination of prior knowledge signaling networks, multiparametric cell-based data on the activation status of 14 crucial proteins emblematic of the cell state downstream of FLT3 obtained under a variety of perturbation conditions and Boolean logic modeling, to gain mechanistic insight into drug resistance in acute myeloid leukemia patients carrying the internal tandem duplication in the FLT3 receptor tyrosine kinase and predict drug combinations that may reverse pharmacoresistant phenotypes. Interestingly, the utility of the approach was validated in vitro and using real-world data.

Strengths:

The model predictions have been validated in vitro and using external data.

This is a complex study, but readability is enhanced by the inclusion of a section that summarizes the study design, plus relevant figures. The availability of data as supplementary material and the availability of code in GitHub are also high points.

Weaknesses:

There are some apparent discrepancies between predicted and observed data that have been seemingly overlooked.

Reviewer #2 (Public Review):

The authors examine the use of metformin in the treatment of hepatic ischemia/reperfusion injury (HIRI) and suggest the mechanism of action is mediated in part by the gut microbiota and changes in hepatic ferroptosis. The concept is intriguing and their results have potential to better understand the pleiotropic functions of metformin. The histological and imaging studies were considered a strength and reveal a significant impact of metformin post-HIRI. The connections with GABA producing bacteria adds to our understanding of the chemical signals exchanged between the host and microbiota. While the authors have characterized these connections in mice, how/if these observations translate to humans remains to be determined.

Reviewer #1 (Public Review):

Many drugs have off-target effects on the gut microbiota but the downstream consequences for drug efficacy and side effect profiles remain unclear. Herein, Wang et al. use a mouse model of liver injury coupled to antibiotic and microbiota transplantation experiments. Their results suggest that metformin-induced shifts in gut microbial community structure and metabolite levels may contribute to drug efficacy. This study provides valuable mechanistic insights that could be dissected further in future studies, including efforts to identify which specific bacterial species, genes, and metabolites play a causal role in drug response. Importantly, although some pilot data from human subjects is shown, the clinical relevance of these findings for liver disease remain to be determined.

Comments on revised version:

The authors have now addressed my original concerns.

Reviewer #3 (Public Review):

In this manuscript, the authors define the developmental trajectory resulting in a diverse mTEC compartment. Using a variety of approaches, including a novel CCL21-fate mapping model, data is presented to argue that embryonic CCL21-expressing thymocyte attracting mTECs naturally convert to into self-antigen displaying mTEC subsets, including Aire+ mTECs and thymic tuft cells. Perhaps somewhat surprisingly, a large fraction of cTECs were also marked for having expressed CCL21, suggesting that there exists some conversion of mTEC (progenitors) into cTEC, a developmentally interesting observation that could be followed up later. Overall, the experimental setup, writing, and conclusions, are all outstanding. The one question I have, which may be more of a curiosity of this reviewer than a requirement for the manuscript, is whether thymocytes themselves are required for the conversion/maturation of attracting TECs to mTECs? For example, in CD3e-/- (or Rag-/-) mice, are mTECs arrested at the thymocyte attracting stage, or is the conversion process 'pre-programed'? In the same vein, do cTECs (or the immature cTECs) maintain CCL21 expression in the absence of mature thymocytes? These are not critical studies but are fairly straightforward (effort- and time-wise) that would aid in placing this process in the overall scope of thymus development.

Reviewer #1 (Public Review):

The work by Ohigashi and colleagues addresses the developmental and lineage relationship of a newly characterized thymus epithelial cell (TEC) progenitor subset. The authors take advantage of an elegant and powerful set of experimental approaches to demonstrate that CCL21-expressing TECs appear early in thymus organogenesis and that these cells, which are centrally located, go on to give rise to medullary (m)TECs. What makes the findings intriguing is that these CCL21-expressing mTECs are a distinct subset, which do not express RANK or AIRE, and transcriptomic and lineage tracing approaches point to these cells as potential mTEC progenitor-like cells. Of note, using in vitro and in vivo precursor-product cell transfer experiments, the authors show that this subset has a developmental potential to give rise to AIRE+ self-antigen-displaying mTECs, revealing that CCL21-expressing mTECs can give rise to distinct mTEC subsets. This functional duality provides an attractive rationale for the necessary function of mTECs, which is to attract CCR7+ thymocytes that have just undergone positive selection in the thymus cortex to enter the medulla to undergo tolerance-induction against self-antigen-displaying mTECs. Overall, the work is well supported and offers new insights into the diverse functions of the medullary compartment, and how two distinct subsets of mTECs can achieve it.

Reviewer #2 (Public Review):

The authors set out to discover a developmental pathway leading to functionally diverse mTEC subsets. They show that Ccl21 is expressed early during thymus ontogeny in the medullary area. Fate-mapping gives evidence for the Ccl21 positive history of Aire positive mTECs as well as of thymic tuft cells and postnatally of a certain percentage of cTECs. Therefore, the differentiation potential of Ccl21+ TECs is tested in reaggregate thymus experiments - using embryonic or postnatal Ccl21+ TECs. From these experiments, the authors conclude that at least embryonic mTECs in large part pass through a Ccl21 positive stage prior to differentiation towards an Aire expressing or tuft cell stage.

The authors are using Ccl21a as a marker for a bipotent progenitor that is detectable in the embryonic thymus and is still present at the adult stage mainly giving rise to mTECs. The choice of this marker gene is very interesting since Ccl21 expression can directly be linked to an important aspect in thymus biology: the expression of Ccl21 by cells in the thymic medulla allows trafficking of T cells into the medulla in order to undergo T cell selection. Making use of the Ccl21 detection, the authors can nicely show that cells actively expressing Ccl21 are localized throughout the medulla at an embryonic stage but also in adult thymus tissue. This suggests, that this progenitor is not accumulating at a specific area inside the medulla. This is a new finding. Moreover, the finding that a Ccl21+ progenitor population plays a functional role in thymocyte trafficking towards the medulla has not been described. Thus, Ccl21 expression may be used to localize a late bipotent progenitor in the thymic lobes. In addition, in Fig.8, the authors provide evidence that these progenitor cells have the potential to self-maintain as well as to differentiate in reaggregate experiments at E17 (not at 4 weeks of age). The first point is of great interest and importance since these cells in theory can be of therapeutic use.

Overall assessment:

The authors highlight a developmental pathway starting from a Ccl21-expressing TEC progenitor that contributes to a functionally diverse mTEC repertoire. This is a welcome addition to current knowledge of TEC differentiation.

Reviewer #1 (Public Review):

This study reports a long-term, multisite study of tropical herbivory on Piper plants. The results are clear that lack of water leads to lower plant survival and altered herbivory. The results varied substantially among sites. The caveats are that ecosystem processes beyond water availability are not investigated although they are brought into play in the title and in the paper, that herbivory beyond leaf damage was not reported (there might be none, reader needs to be shown the evidence for this), that herbivore diversity is defined by leaf damage (authors need to give evidence that this is a valid inference), that the plots were isolated from herbivores beyond their borders, that the effects of extreme climate events were isolated to Peru, that intraspecific variation in the host plants needs to be explained and interpreted in more detail, the results as reported are extremely complicated, the discussion is overly long and diffuse.

Reviewer #2 (Public Review):

This is an important and large experimental study examining the effects of plant species richness, plant genotypic richness, and soil water availability on herbivory patterns on Piper species in tropical forests.

A major strength is the size of the study and the fact that it tackled so many potentially important factors simultaneously. The authors examined both interspecific plant diversity and intraspecific plant diversity. They crossed that with a water availability treatment. And they repeated the experiment across five geographically separated sites.

The authors find that both water availability and plant diversity, intraspecific and interspecific, influence herbivore diversity and herbivory, but that the effects differ in important ways across sites. I found the study to be solid and the results to be very convincing. The results will help the field grapple with the importance of environmental change and biodiversity loss and how they structure communities and alter species interactions.

Reviewer #1 (Public Review):

The authors developed an extension to the pairwise sequentially Markov coalecent model that allows to simultaneously analyze multiple types of polymorphism data. In this paper, they focus on SNPs and DNA methylation data. Since methylation markers mutate at a much faster rate than SNPs, this potentially gives the method better power to infer size history in the recent past. Additionally, they explored a model where there are both local and regional epimutational processes.

Integrating additional types of heritable markers into SMC is a nice idea which I like in principle. However, a major caveat to this approach seems to be a strong dependence on knowing the epimutation rate. In Fig. 6 it is seen that, when the epimutation rate is known, inferences do indeed look better; but this is not necessarily true when the rate is not known. (See also major comment #1 below about the interpretation of these plots.) A roughly similar pattern emerges in Supp. Figs. 4-7; in general, results when the rates have to be estimated don't seem that much better than when focusing on SNPs alone. This carries over to the real data analysis too: the interpretation in Fig. 7 appears to hinge on whether the rates are known or estimated, and the estimated rates differ by a large amount from earlier published ones.

Overall, this is an interesting research direction, and I think the method may hold more promise as we get more and better epigenetic data, and in particular better knowledge of the epigenetic mutational process. At the same time, I would be careful about placing too much emphasis on new findings that emerge solely by switching to SNP+SMP analysis.

Major comments:<br /> - For all of the simulated demographic inference results, only plots are presented. This allows for qualitative but not quantitative comparisons to be made across different methods. It is not easy to tell which result is actually better. For example, in Supp. Fig. 5, eSMC2 seems slightly better in the ancient past, and times the trough more effectively, while SMCm seems a bit better in the very recent past. For a more rigorous approach, it would be useful to have accompanying tables that measure e.g. mean-squared error (along with confidence intervals) for each of the different scenarios, similar to what is already done in Tables 1 and 2 for estimating $r$.

- 434: The discussion downplays the really odd result that inputting the true value of the mutation rate, in some cases, produces much worse estimates than when they are learned from data (SFig. 6)! I can't think of any reason why this should happen other than some sort of mathematical error or software bug. I strongly encourage the authors to pin down the cause of this puzzling behaviour. (Comment addressed in revision. Still, I find the explanation added at 449ff to be somewhat puzzling -- shouldn't the results of the regional HMM scan only improve if the true mutation rate is given?)

- As noted at 580, all of the added power from integrating SMPs/DMRs should come from improved estimation of recent TMRCAs. So, another way to study how much improvement there is would be to look at the true vs. estimated/posterior TMRCAs. Although I agree that demographic inference is ultimately the most relevant task, comparing TMRCA inference would eliminate other sources of differences between the methods (different optimization schemes, algorithmic/numerical quirks, and so forth). This could be a useful addition, and may also give you more insight into why the augmented SMC methods do worse in some cases. (Comment addressed in revision via Supp. Table 7.).

- A general remark on the derivations in Section 2 of the supplement: I checked these formulas as best I could. But a cleaner, less tedious way of calculating these probabilities would be to express the mutation processes as continuous time Markov chains. Then all that is needed is to specify the rate matrices; computing the emission probabilities needed for the SMC methods reduces to manipulating the results of some matrix exponentials. In fact, because the processes are noninteracting, the rate matrix decomposes into a Kronecker sum of the individual rate matrices for each process, which is very easy to code up. And this structure can be exploited when computing the matrix exponential, if speed is an issue.

- Most (all?) of the SNP-only SMC methods allow for binning together consecutive observations to cut down on computation time. I did not see binning mentioned anywhere, did you consider it? If the method really processes every site, how long does it take to run?

- 486: The assumed site and region (de)methylation rates listed here are several OOM different from what your method estimated (Supp. Tables 5-6). Yet, on simulated data your method is usually correct to within an order of magnitude (Supp. Table 4). How are we to interpret this much larger difference between the published estimates and yours? If the published estimates are not reliable, doesn't that call into question your interpretation of the blue line in Fig. 7 at 533? (Comment addressed in revision.)

Reviewer #2 (Public Review):

A limitation in using SNPs to understand recent histories of genomes is their low mutation frequency. Tellier et al. explore the possibility of adding hypermutable markers to SNP based methods for better resolution over short time frames. In particular, they hypothesize that epimutations (CG methylation and demethylation) could provide a useful marker for this purpose. Individual CGs in Arabidopsis tends to be either close to 100% methylated or close to 0%, and are inherited stably enough across generations that they can be treated as genetic markers. Small regions containing multiple CGs can also be treated as genetic markers based on their cumulative methylation level. In this manuscript, Tellier et al develop computational methods to use CG methylation as a hypermutable genetic marker and test them on theoretical and real data sets. They do this both for individual CGs and small regions. My review is limited to the simple question of whether using CG methylation for this purpose makes sense at a conceptual level, not at the level of evaluating specific details of the methods. I have a small concern in that it is not clear that CG methylation measurements are nearly as binary in other plants and other eukaryotes as they are in Arabidopsis. However, I see no reason why the concept of this work is not conceptually sound. Especially in the future as new sequencing technologies provide both base calling and methylating calling capabilities, using CG methylation in addition to SNPs could become a useful and feasible tool for population genetics in situations where SNPs are insufficient.

Reviewer #3 (Public Review):

I very much like this approach and the idea of incorporating hypervariable markers. The method is intriguing, and the ability to e.g. estimate recombination rates, the size of DMRs, etc. is a really nice plus. I am not able to comment on the details of the statistical inference, but from what I can evaluate it seems reasonable and in principle the inclusion of highly mutable sties is a nice advance. This is an exciting new avenue for thinking about inference from genomic data. I remain a bit concerned about how well this will work in systems where much less is understood about methylation,

The authors include some good caveats about applying this approach to other systems, but I think it would be helpful to empiricists outside of thaliana or perhaps mammalian systems to be given some indication of what to watch out for. In maize, for example, there is a non-bimodal distribution of CG methlyation (35% of sites are greater than 10% and less than 90%) but this may well be due to mapping issues. The authors solve many of the issues I had concerns with by using gene body methylation, but this is only briefly mentioned on line 659. I'm assuming the authors' hope is that this method will be widely used, and I think it worth providing some guidance to workers who might do so but who are not as familiar with these kind of data.

Reviewer #1 (Public Review):

Summary: This study examines the role of host blood meal source, temperature, and photoperiod on the reproductive traits of Cx. quinquefasciatus, an important vector of numerous pathogens of medical importance. The host use pattern of Cx. quinquefasciatus is interesting in that it feeds on birds during spring and shifts to feeding on mammals towards fall. Various hypotheses have been proposed to explain the seasonal shift in host use in this species but have provided limited evidence. This study examines whether the shifting of host classes from birds to mammals towards autumn offers any reproductive advantages to Cx. quinquefasciatus in terms of enhanced fecundity, fertility, and hatchability of the offspring. The authors found no evidence of this, suggesting that alternate mechanisms may drive the seasonal shift in host use in Cx. quinquefasciatus.

Strengths: Host blood meal source, temperature, and photoperiod were all examined together.

Weaknesses: The study was conducted in laboratory conditions with a local population of Cx. quinquefasciatus from Argentina. I'm not sure if there is any evidence for a seasonal shift in the host use pattern in Cx. quinquefasciatus populations from the southern latitudes.

Comments on the revision:

Overall, I am not quite convinced about the possible shift in host use in the Argentinian populations of Cx. quinquefasciatus. The evidence from the papers that the authors cite is not strong enough to derive this conclusion. Therefore, I think that the introduction and discussion parts where they talk about host shift in Cx. quinquefasciatus should be removed completely as it misleads the readers. I suggest limiting the manuscript to talking only about the effects of blood meal source and seasonality on the reproductive outcomes of Cx. quinquefasciatus.

Reviewer #2 (Public Review):

Summary:

Conceptually, this study is interesting and is the first attempt to account for the potentially interactive effects of seasonality and blood source on mosquito fitness, which the authors frame as a possible explanation for previously observed host-switching of Culex quinquefasciatus from birds to mammals in the fall. The authors hypothesize that if changes in fitness by blood source change between seasons, higher fitness on birds in the summer and on mammals in the autumn could drive observed host switching. To test this, the authors fed individuals from a colony of Cx. quinquefasciatus on chickens (bird model) and mice (mammal model) and subjected each of these two groups to two different environmental conditions reflecting the high and low temperatures and photoperiod experienced in summer and autumn in Córdoba, Argentina (aka seasonality). They measured fecundity, fertility, and hatchability over two gonotrophic cycles. The authors then used a generalized linear model to evaluate the impact of host species, seasonality, and gonotrophic cycle on fecundity, fertility, and hatchability. The authors were trying to test their hypothesis by determining whether there was an interactive effect of season and host species on mosquito fitness. This is an interesting hypothesis; if it had been supported, it would provide support for a new mechanism driving host switching. While the authors did report an interactive impact of seasonality and host species, the directionality of the effect was the opposite from that hypothesized. The authors have done a very good job of addressing many of the reviewer concerns, with several exception that continue to cause concern about the conclusions of the study.

Strengths:

(1) Using a combination of laboratory feedings and incubators to simulate seasonal environmental conditions is a good, controlled way to assess the potentially interactive impact of host species and seasonality on the fitness of Culex quinquefasciatus in the lab.<br /> (2) The driving hypothesis is an interesting and creative way to think about a potential driver of host switching observed in the field.<br /> (3) The manuscript has become a lot clearer and easier to read with the revisions - thank you to the authors for working hard to make many of the suggested changes.

Weaknesses:

(1) The authors have decided not to follow the suggestion of conducting experimental replicates of the study. This is understandable given the significant investment of resources and time necessary, however, it leaves the study lacking support. Experimental replication is an important feature of a strong study and helps to provide confidence that the observed patterns are real and replicable. Without replication, I continue to lack confidence in the conclusions of the study.<br /> (2) The authors have included some additional discussion about the counterintuitive nature of their results, but the paragraph discussing this in the discussion was confusing. I believe that this should be revised. This is a key point of the paper and needs to be clear to the reader.<br /> (3) There should be more discussion of the host switching observed in the two studies conducted in Argentina referenced by the authors. Since host switching is the foundation for the hypothesis tested in this paper, it is important to fully explain what is currently known in Argentina.<br /> (4) In some cases, the explanations of referenced papers are not entirely accurate. For example, when referencing Erram et al 2022, I think the authors misrepresented the paper's discussion regarding pre-diuresis- Erram et al. are suggesting that pre-diuresis might be the mechanism by which C. furens compensates for the lower nutritional value of avian blood, leading to no significant difference between avian/mammal blood on fecundity/fertility (rather than leading to higher fecundity on birds, as stated in this manuscript). The study performed by Erram et al. also didn't prove this phenomenon, they just suggest it as a possible mechanism to explain their results, so that should be made clear when referencing the paper.<br /> (5) In some cases, the conclusions continue to be too strongly worded for the evidence available. For example, lines 322-324: I don't think the data is sufficient to conclude that a different physiological state is induced, nor that they are required to feed on a blood source that results in higher fitness.<br /> (6) There is limited mention of the caveat that this experiment performed with simulated seasonality that does not perfectly replicate seasonality in the field. I think this caveat should be discussed in the discussion (e.g. that humidity is held constant).

Reviewer #1 (Public Review):

In their manuscript, Laporte et al. analyze the process of formation of the quiescent-cell nuclear microtubule (Q-nMT) bundle, a set of parallel MTs that emanate from the nuclear side of the spindle pole bodies (SPBs) upon the entry of Saccharomyces cerevisiae cells in quiescence. Based on their results, the authors propose that Q-nMT bundle formation is a multistep process that comprises three distinct sequential phases. The authors further evaluate the role of different factors during the growth of the Q-nMT bundle upon quiescence entry, as well as during the disassembly of this structure once the cells resume their proliferation.

The Q-nMT is an interesting cellular structure whose physiological function is still widely unknown. Hence, providing new insights into the dynamics of Q-nMT bundle formation and identifying new factors involved in this process is an interesting topic of relevance in the field. The authors made a substantial effort in order to evaluate Q-nMT bundle establishment and provide a considerable amount of data, mainly obtained from microscopy analyses. Overall, the experiments are mostly well described and properly executed, and the data in the manuscript are clearly presented. Despite the interest of the study, nonetheless, there are several issues that could affect the validity of some conclusions drawn. In this way, regarding the analysis of the dynamics of Q-nMT bundle formation, the described experimental setup raises certain concerns, which mostly derive from the use of the microtubule-depolymerizing agent nocodazole as the only approach to evaluate this process. Also, regarding the factors involved in Q-nMT formation, the differences in microtubule length with the wild type strain, despite being statistically significant, are really subtle for many of the mutants analyzed (e.g., bir1, slk19, etc.). Furthermore, it is also puzzling that an effect on Q-nMT formation is proposed for meiosis-specific factors such as Mam1, which might as well be present during quiescence, but seems to be also detected in proliferating cells. Lastly, the evidence shown are insufficient to provide a direct link between defects in cell viability and aberrant Q-nMT formation.

Reviewer #2 (Public Review):

Summary: The authors investigate the assembly of the Q-nMT, a stable microtubule structure that is assembled during quiescence. Notably, the authors show that the formation of the Q-nMT cannot be solely explained by changes in the physico-chemical properties of quiescent cells. The authors report that Q-nMT assembly occurs in three regulated steps and identify kinesin motor proteins involved in the assembly and disassembly of the structure.

Strengths: The findings provide new insight into the assembly and possible function of the Q-nMT with respect to the response of haploid budding yeast to glucose starvation.

Weaknesses: The manuscript would benefit from more precise language and requires additional clarification regarding how claims are supported by the evidence. Clear definitions are also required, for example "active process" is not defined. Some conclusions are not supported by the results, for example the claim that the Q-nMT functions as a checkpoint effector that inhibits re-entry into the cell cycle.

After reviewing the responses of the authors and the revised manuscript I am now satisfied with the study in its current form.

Reviewer #1 (Public Review):

The study presented in this manuscript presents very convincing evidence that purifying selection is the main force shaping the landscape of TE polymorphisms in B. distachyon, with only a few putatively adaptive variants detected, even though most conclusions are based on the 10% of polymorphisms contributed by retrotransposons. That first conclusion is not novel, however, as it had already been clearly established in natural A. thaliana strains (Baduel et al. Genome Biol 2021) and in experimental D. simulans lines (Langmüller et al. NAR 2023). In contrast to the conclusions reached in A. thaliana, however, Horvath et al. report here a seemingly deleterious effect of TE insertions even very far away from genes (>5kb), a striking observation for a genome of relatively similar size. However, SNPs within these regions show similar allele frequency deviations, suggesting this effect may be due to mapping quality artefacts in gene poor regions of the genome. An additional caveat of this study is the lack of orthogonal benchmarking of the TE polymorphisms calls by a pipeline known for a high rate of false positives (see detailed Private Recommendations #1). The authors note that their conclusions are still valid using only the highest covered samples (>20x), yet this coverage threshold is relatively low and higher coverage would mostly reduce the rate of false negatives.

Nonetheless, this set of observations makes an important addition to the knowledge of TE dynamics in the wild and questions our understanding of the main molecular mechanisms through which TEs can impact fitness.

Reviewer #2 (Public Review):

Transposable elements are known to have a strong potential to generate diversity and impact gene regulation, and they are thought to play an important role in plant adaptation to changing environments. Nevertheless, very few studies have performed genome-wide analyses to understand the global effect of selection on TEs in natural populations. Horvath et al., used available whole-genome re-sequencing data from a representative panel of B. distachyon accessions to detect TE insertion polymorphisms (TIPs) and estimate their time of origin. Using a thorough combination of population genomics approaches, the authors demonstrate that only a small amount of the TE polymorphisms are targeted by positive selection or potentially involved in adaptation. By comparing the age-adjusted population frequencies of TE polymorphisms and neutral SNPs, the authors found that retrotransposons are affected by purifying selection independently of their distance to genes. Finally, using forward simulations they were able to quantify the strength of selection acting on TE polymorphisms, finding that retrotransposons are mainly under moderate purifying selection, with only a minority of the insertions evolving neutrally.

Horvath et al., use a convincing set of strategies and their conclusions are well supported by the data. I think that incorporating polymorphism's age to the analysis of purifying selection is an interesting way to reduce the possible bias introduced by the fact that SNPs and TEs polymorphisms do not occur at the same pace. The fact that TE polymorphisms far from genes are also under purifying selection is an interesting result that reinforces the idea that trans-regulatory effect of TE insertions might not be a rare phenomenon, a matter that may be demonstrated in future studies.

Reviewer #1 (Public Review):

Summary:<br /> In this study, Wu et al. investigated the microbiome in the rhizosphere and roots of plant species along an elevational gradient. They found that: (i) plants with higher root nitrogen ("fast" strategy) were more likely to be associated with saprotrophic fungi, plant pathogenic fungi, and AM fungi, but plants with lower root nitrogen ("slow" strategy) were more likely to be associated with ectomycorrhizal fungi; (ii) bacterial functional guilds were associated with root-zone pH but not root traits.

Strengths:<br /> This study is novel in the sense that it revealed the associations between microbiome and trait dimensions of plants. This has been rarely explored even though we acknowledge the importance of plant-microbe interactions.

Weaknesses:<br /> The authors tried to include the relative abundances of bacterial and fungal guilds into the root economics framework, which I disagree with because they are just associated with the root economics framework. The title also states that the authors' aim is to link microbial functional guilds to root economics. Therefore, I would suggest that the analyses should be redone to elaborate on the relationships between microbiome and root functional traits.

Below I provide some critiques and comments that outline my concerns and provide recommendations to hopefully improve the current manuscript.

-Figures 2 and 3: The authors included soil properties, relative abundances of bacterial or fungal guilds, and root traits in the root economics spectrum. However, soil properties and relative abundances of bacterial or fungal guilds are not root traits, they are just associated with root traits. These bacterial or fungal guilds are the consequence of root traits. Also, the authors did not elaborate on the root trait dimensions of the plants. The only trait dimension they discussed is the "fast-slow" axis. Therefore, I would suggest the authors first analyze the trait dimensions of plants by only using the root traits (PCA), and then explore how the soil properties and relative abundances of bacterial or fungal guilds are associated with the trait dimensions (e.g., envfit in the vegan package).

-When exploring the associations between microbial functional guilds and root traits, it is unnecessary to analyze the bacterial and fungal functional guilds separately. The bacterial and fungal functional guilds can be included in the same models, and their relative importance and patterns can be compared.

-For fungi, the authors used FUNGuild to infer functional guilds from taxonomy. qPCR was also performed to validate the results of AMF. This is fantastic. For bacteria, the authors used FAPROTAX to infer functional guilds from taxonomy. However, archaea are also considered in some functions in FAPROTAX. For example, both bacteria (ammonia-oxidizing bacteria) and archaea (ammonia-oxidizing archaea) play critical roles in nitrification. I would assume the authors have removed archaea from the dataset because they stated that the functions of bacteria are inferred from FAPROTAX. Therefore, the importance of nitrification might be underestimated.

-Key methodological details are missing. First, maps of the sampling site and plots are missing. It would be great if the authors provided maps showing the location of the sampling site and the spatial distribution of the 11 plots. Second, in lines 304-306 the authors claimed that they sampled the most common species in the plots, but they did not provide the coverage or relative abundances of plant species in the plots.

Reviewer #2 (Public Review):

Summary:<br /> The authors aimed to determine to what extent root morphology, chemistry, and soil characteristics explained the relative abundance of functional groups of bacteria and fungi associated with roots. To do so, they sample roots and rhizhospheric soil of trees along an elevation gradient. This type of work is common in the field of microbial ecology. The main novelties I see are two: a) a focus on the functional groups of bacteria and fungi rather than just taxonomic abundance. I think this approach is valuable because it provides information about the potential functions of these microorganisms; b) using the root economic spectrum to frame the findings. The root economic spectrum reflects a gradient along which plant roots can be allocated from 'short-lived that provide fast investment return' to 'long-lived that provide a slow investment return'. It is logical to expect (as the authors did) that variation along this gradient will be an important factor in explaining the variation in functional groups.

Strengths:<br /> The main strength is using the root economic spectrum as a framework to interpret the data. There are countless studies addressing variation in the relative abundance of microbial communities along environmental gradients which tend to be more descriptive. I think using this framework advances the field by suggesting that while the root economic spectrum exists it is not a very important explanatory variable to predict changes in functional diversity. I also think the authors use state-of-the art methods to collect and process the sample (i.e. to obtain the data).

Weaknesses:<br /> The main weakness is with the presentation of statistical methods as it currently stands. The authors use distance-based redundancy analysis as the main statistical method. However, my understanding is that this method is not advised for a relative abundance of communities. At least not with Euclidean distances which is the default option of the functions dbrda in vegan. The use of this distance would group together communities with no species in common as close to each other (which is an incorrect interpretation). I think the authors should specify what distance they use. My guess is that they actually used bray-curtis in which case this weakness does not apply. However, as it stands it is not specified what metric they use and if they indeed use Euclidean distances it may lead to inaccurate conclusions. In addition, they also mention they use PCA on the relative abundance of functional groups. By definition, PCA is also based on Euclidean distances, which gives a similar problem as dbrda. Thus, I encourage the authors to use bray-curtis distance and specify it in the text.

Reviewer #3 (Public Review):

Summary:<br /> In this study, the authors collected a large set of data on root traits and root-associated microbes in the root endosphere and rhizosphere in order to integrate these important organisms in the root economics spectrum. By sampling a relatively large set of species from the subtropics along an elevation gradient, they tested whether microbial functions covary with root traits and root trait axes and if so, aimed to discuss what this could tell us about the (belowground) functioning of trees and forests.

Strengths:<br /> The strengths of this study lie mostly in the impressive dataset set the authors compiled: they sampled belowground properties of a relatively large number of tree species from an understudied region: i.e., the subtropics, where species-level root data are notoriously scarce. Secondly, their extensive sampling of associated microbes to integrate them in the root economics space is an important quality, because of the strong associations between roots and fungi and bacteria: soil microbes are directly related to root form (e.g., mycorrhizal fungi and root diameter and SRL), and function (e.g., taking up soil nutrients from various sources). Thirdly, the PCA figures (Figures 2 and 3) look very nice and intuitive and the paper is very well written.

Weaknesses:<br /> That said, this study also has several methodological weaknesses that make the results, and therefore the impact of this study difficult to evaluate and interpret.

(1) Design: The design of this study needs further explanation and justification in the Introduction and Methods sections in order to understand the ecological meaning of the results. Root traits and microbial community composition differ with their environment, and therefore (likely) also with elevation. Elevation is included in the redundancy analysis as a main effect, but without further environmental information, its impact is not ecologically meaningful. What is the rationale for including an elevation gradient in the design and as a main effect in the analyses? Do environmental conditions vary across altitudes and how, and if so, how would this impact the data?

What is the rationale behind sampling endosphere and rhizosphere microbial communities - why do both? And why also include pathogens - what are their expected roles in the RES? What do we know about this already? The introduction needs a more extensive literature review of these additional variables that are included in the analyses.

(2) Units of replication and analysis in the model: What are the units of replication and analyses, e.g., how many trees were sampled per species, how many species or trees per elevation, and how many plots per elevation? Were all 11 plots at different elevations and if so, which ones? The level of analysis for the redundancy analyses is not entirely clear: L. 404 mentions that the analyses were done 'across the rhizosphere and root tissue samples', but is that then at the individual-tree level? If so, it seems that these analyses should then also account for dependencies between trees from the same species and phylogeny (as (nested) covariates or random factors). With the information provided, I cannot tell whether there was sufficient replication for statistical interpretations.

(3) PCA: The results of the parallel analyses are not described: which components were retained? Because the authors aim to integrate microbial functions in a root economics space, I recommend first demonstrating the existence of a root economics space across the 52 subtropical species before running a PCA that includes the microbial traits. The PCA shown in this study does not exactly match the RES and this could be because traits of these species covary differently, but may also simply result from including additional traits to the PCA.

Also, the PCA's shown are carried out at the individual-tree level. I would recommend, however, including the species-level PCA's in the main text, because the individual-level PCA may not only reflect species-inherent ecological strategies (that e.g., the RES by Bergmann et al. 2020 describe) but also plasticity (Figures 2 and 3 both show an elevation effect that may be partly due to plasticity). While the results here are rather similar, intraspecific differences in root traits may follow different ecological principles and therefore not always be appropriate to compare with an interspecific RES (see for example Weemstra & Valverde-Barrantes, 2022, Annals of Botany).

I could not deduce whether tree species in the "fungal PCA" (Figure 2) were assigned as AM or EcM based on Table 1, or based on their observed fungal community composition. In the former case, the fungal functional guild gradient (from EcM to saprotrophs and AM) is partially an artificial one, because EcM tree species are not AM species (according to Table 1) and therefore, by definition, constitute a tradeoff or autocorrelation. And, as the authors also discuss, AM tree species may host EcM fungal species. Before I can evaluate the ecological meaning of PC1, and whether or not it really represents a mineral/organic nutrient gradient, information is needed on which data are used here.

I do not agree with the term 'gradient of bacterial guilds' (i.e., PC1 in Figure 3). All but 1 bacterial 'function' positively loaded on PC1 and 'fermentation' was only weakly negatively correlated with PC1. I do not think this constitutes a 'bacterial gradient'.

(4) Soil samples: Were they collected from the surrounding soil of each tree (L. 341), or from the root zone (L. 110). The former seems to refer to bulk soil samples, but the latter could be interpreted as rhizosphere soils. It is therefore not entirely clear whether these are the same soil samples, and if so, where they were sampled exactly.

Aims:<br /> The authors aimed to integrate endospheric and rhizospheric microbial and fungal community composition in the root economics space. Owing to statistical concerns (i.e., lacking parallel analysis results and the makeup of the PCs (AM versus EcM classification), I am not sure the authors succeeded in this. Besides that, the interpretation of the axes seems rather oversimplified and needs some consideration.

Root N is discussed as an important driver of fungal functional composition. Indeed, it was one of the significant variables in the redundancy models predicting microbial community composition, but its contribution to community composition was small (2 - 3 %), and the mechanistic interpretation was rather speculative. Specifically, the role of root N in root (and tree) functioning remains highly uncertain: the link with respiration and exudation is increasingly demonstrated but its actual meaning for nutrient uptake is not well understood (Freschet et al. 2021. New Phytologist). If and how root economics (represented by root N) and the fungal-driven nutrient economy (EcM versus AM, saprotrophs) can indeed be integrated into a unified framework (L. 223 - 224) seems a relevant question that is worth pursuing based on this paper, but in my opinion, this study does not clearly answer it, because the statistical analyses might need further work (or explanation) and underlying mechanisms are not well explained and supported by evidence.

In addition, the root morphology axis was indeed independent of the "fungal gradient", but this is in itself not an interesting finding. What is interesting, but not discussed is that, generally, AM species are expected to have thicker roots than EcM tree species (Gu et al. 2014 Tree Physiology; Kong et al. 2014 New Phytologist). I am therefore curious to see why this is not the case here? Did the few EcM species sampled just happen to have very thick roots? Or is there a phylogenetic effect that influences both mycorrhizal type and root thickness that is not accounted for here (Baylis, 1975; Guo et al., 2008 New Phytologist; Kubisch et al., 2015 Frontiers in Plant Science; Valverde-Barrantes et al., 2015 Functional Ecology; 2016 Plant and Soil)?

I also do not agree with the conclusion that this integrated framework 'explained' tree distributions along the elevation gradient. First of all, it is difficult to interpret because the elevation gradient is not well explained (e.g., in terms of environmental variation). Secondly, the framework might coincide with the framework, but the framework does not explain it: an environmental gradient probably underlies the elevation gradient that may be selected for species with certain root traits or mycorrhizal types, but this is not tested nor clearly demonstrated by the data. It thus remains rather speculative, and it should be more thoroughly explained based on the data observed. Similarly, I do not understand from this study how root traits like root N can influence the abundance of EcM and pathogenic fungi (L. 242 - 243). Which data show this causality? It seems a strong statement, but not well supported (or explained).

Impact:<br /> The data collected for this study are timely, valuable, and relevant. Soilborne microbes (fungi and bacteria; symbionts and pathogens) play important roles in root trait expressions (e.g., root diameter) and below-ground functioning (e.g., resource acquisition). They should therefore not be excluded from studies into the belowground functioning of forests, but they mostly are. This dataset therefore has the potential to improve our understanding of this subject. Making these data publicly available in large-scale datasets that have recently been initiated (e.g., FRED) will also allow further study in comparative (with other biomes) or global (across biomes) studies.

Technically, the methodology seems sound, although I lack the expertise to judge the Molecular Methods (L. 349 - 397). However, owing to some statistical uncertainties mentioned above (that the authors might well clarify or improve) and the oversimplified discussion, I am hesitant to determine the impact of the contents of this work. Statistical improvements and/or clearer explanation/justification of statistical choices made can make this manuscript highly interesting and impact, however.

Context:<br /> As motivated above, I am not sure to what extent the EcM - AM/saprotroph presents a true ecological tradeoff. However, if it does, this work would fit very well in the context of the mycorrhizal-associated nutrient economy (Phillips et al. 2013 New Phytology). This theory postulates that EcM trees generally produce low-quality litter (associated with 'slow traits') that can be more readily accessed by EcM but not AM fungi, thereby slowing down nutrient cycling rates at their competitive advantage, and vice versa for AM tree species. This study did not aim to test the MANE, so it was beyond its scope to study litter quality, and the number of EcM and AM species was unbalanced (8 EcM versus 44 AM species): nonetheless, the denser roots of EcM species and higher root N of AM species indicates that the MANE may also apply to this subtropical forest and may be an interesting impetus for future work on this topic. It might also offer one way to bridge the root economics space and the MANE.

What I also found interesting is the sparse observations of EcM fungal taxa in the root endosphere of species typically identified as AM hosts (L. 212 - 214). While their functionality remains to be tested (fungal structures in the endosphere were not studied here), this observation might call for renewed attention to classifying species as AM, EcM, or both.

Reviewer #4 (Public Review):

Summary:<br /> Recent progress in root economics has revealed global-scale axes of covaried root traits that reflect various root resource acquisition strategies. These covariance patterns are powerful tools for understanding root functional diversity. However, roots do not function in isolation for below-ground resource acquisition. Rather, symbiotic fungi and rhizosphere microorganisms often collaborate with plant roots, forming a root-microbial-soil continuum. This study seeks to provide novel insights into this continuum by extending the existing framework of root economics to include the structures of root-associated microorganisms. I find this topic highly relevant. Considering the role of soil microorganisms is undoubtedly crucial for a more comprehensive understanding of below-ground resource strategies.

Major comments:<br /> A key finding of this study is a relationship between root N and the tendency for roots to associate with particular types of mycorrhizal associations (Line 27, Fig. 2). The authors concluded that this indicates "a linkage from simple root traits to fungal-mediated carbon nutrient cycling" (line 27) and integrates "microbial functions into the root economics framework," (line 32). If substantiated, this correlation could represent a significant discovery about the connection between root functional traits and root-associated fungi. It suggests that low root N, indicative of low metabolic activity within the root economics framework, is linked with forming EcM associations. However, I am not fully convinced this is the case based on the current data presentation and interpretation.

First, there is no biological interpretation of this relationship between root N and mycorrhizal type. It merely noted that root N is indicative of root metabolic activity, and thus by relating root N to fungal composition, "the trait-related root economics and fungal-driven nutrient economics may be integrated into a unified framework" (lines 221-224). Why would roots with low N and low metabolic activity tend to favor EcM associations? What are the potential mechanisms? Biological interpretation is essential for understanding whether a statistical correlation reflects a causal and meaningful relationship or is coincidental.

I am also concerned that this relationship may be spurious, especially when it lacks biological interpretation. EcM is underrepresented in this study (8 EcM species, of which more than half are conifers and oaks vs. 44 AM) and seems to cluster at higher elevations (line 231). Thus, the tree species/individual data points are not independent, but phylogenetically and geographically clustered. The unique properties at higher elevations (e.g., distinct plant community structures, low levels of mineral N) may drive both the lower root N and the prevalence of EcM associations. This scenario aligns with the observation that at higher elevations, AM roots also exhibited low root N (Line 231). In this case, root N may not directly relate to mycorrhizal type but is characteristic of certain locations (or closely related species), and it would be misleading to suggest that low root N/metabolic activity, a proxy in fast-slow root economics, is directly linked to the preference for a particular mycorrhizal type (lines 27-28, 220 - 224). In summary, because the studied tree species appear to be clustered both phylogenetically and geographically, these factors need to be carefully taken into account in the statistical analysis and data interpretation to understand the underlying causes of the apparent relationship and prevent overinterpretation. I also recommend, if possible, providing a visual presentation of the geographical and phylogenetic distribution of the studied tree species.

That being said, this dataset is undoubtedly valuable in revealing the shifts in the compositional structures of root-associated soil microorganisms. However, integrating the traits of microbial composition to root trait economics would require more caution and careful examination of the potential driving causes.

Reviewer #1 (Public Review):

This study used a multi-day learning paradigm combined with fMRI to reveal neural changes reflecting the learning of new (arbitrary) shape-sound associations. In the scanner, the shapes and sounds are presented separately and together, both before and after learning. When they are presented together, they can be either consistent or inconsistent with the learned associations. The analyses focus on auditory and visual cortices, as well as the object-selective cortex (LOC) and anterior temporal lobe regions (temporal pole (TP) and perirhinal cortex (PRC)). Results revealed several learning-induced changes, particularly in the anterior temporal lobe regions. First, the LOC and PRC showed a reduced bias to shapes vs sounds (presented separately) after learning. Second, the TP responded more strongly to incongruent than congruent shape-sound pairs after learning. Third, the similarity of TP activity patterns to sounds and shapes (presented separately) was increased for non-matching shape-sound comparisons after learning. Fourth, when comparing the pattern similarity of individual features to combined shape-sound stimuli, the PRC showed a reduced bias towards visual features after learning. Finally, comparing patterns to combined shape-sound stimuli before and after learning revealed a reduced (and negative) similarity for incongruent combinations in PRC. These results are all interpreted as evidence for an explicit integrative code of newly learned multimodal objects, in which the whole is different from the sum of the parts.

The study has many strengths. It addresses a fundamental question that is of broad interest, the learning paradigm is well-designed and controlled, and the stimuli are real 3D stimuli that participants interact with. The manuscript is well written and the figures are very informative, clearly illustrating the analyses performed.

There are also some weaknesses. The sample size (N=17) is small for detecting the subtle effects of learning. Most of the statistical analyses are not corrected for multiple comparisons (ROIs), and the specificity of the key results to specific regions is also not tested. Furthermore, the evidence for an integrative representation is rather indirect, and alternative interpretations for these results are not considered.

Reviewer #2 (Public Review):

Li et al. used a four-day fMRI design to investigate how unimodal feature information is combined, integrated, or abstracted to form a multimodal object representation. The experimental question is of great interest and understanding how the human brain combines featural information to form complex representations is relevant for a wide range of researchers in neuroscience, cognitive science, and AI. While most fMRI research on object representations is limited to visual information, the authors examined how visual and auditory information is integrated to form a multimodal object representation. The experimental design is elegant and clever. Three visual shapes and three auditory sounds were used as the unimodal features; the visual shapes were used to create 3D-printed objects. On Day 1, the participants interacted with the 3D objects to learn the visual features, but the objects were not paired with the auditory features, which were played separately. On Day 2, participants were scanned with fMRI while they were exposed to the unimodal visual and auditory features as well as pairs of visual-auditory cues. On Day 3, participants again interacted with the 3D objects but now each was paired with one of the three sounds that played from an internal speaker. On Day 4, participants completed the same fMRI scanning runs they completed on Day 2, except now some visual-auditory feature pairs corresponded with Congruent (learned) objects, and some with Incongruent (unlearned) objects. Using the same fMRI design on Days 2 and 4 enables a well-controlled comparison between feature- and object-evoked neural representations before and after learning. The notable results corresponded to findings in the perirhinal cortex and temporal pole. The authors report (1) that a visual bias on Day 2 for unimodal features in the perirhinal cortex was attenuated after learning on Day 4, (2) a decreased univariate response to congruent vs. incongruent visual-auditory objects in the temporal pole on Day 4, (3) decreased pattern similarity between congruent vs. incongruent pairs of visual and auditory unimodal features in the temporal pole on Day 4, (4) in the perirhinal cortex, visual unimodal features on Day 2 do not correlate with their respective visual-auditory objects on Day 4, and (5) in the perirhinal cortex, multimodal object representations across Days 2 and 4 are uncorrelated for congruent objects and anticorrelated for incongruent. The authors claim that each of these results supports the theory that multimodal objects are represented in an "explicit integrative" code separate from feature representations. While these data are valuable and the results are interesting, the authors' claims are not well supported by their findings.

(1) In the introduction, the authors contrast two theories: (a) multimodal objects are represented in the co-activation of unimodal features, and (b) multimodal objects are represented in an explicit integrative code such that the whole is different than the sum of its parts. However, the distinction between these two theories is not straightforward. An explanation of what is precisely meant by "explicit" and "integrative" would clarify the authors' theoretical stance. Perhaps we can assume that an "explicit" representation is a new representation that is created to represent a multimodal object. What is meant by "integrative" is more ambiguous-unimodal features could be integrated within a representation in a manner that preserves the decodability of the unimodal features, or alternatively the multimodal representation could be completely abstracted away from the constituent features such that the features are no longer decodable. Even if the object representation is "explicit" and distinct from the unimodal feature representations, it can in theory still contain featural information, though perhaps warped or transformed. The authors do not clearly commit to a degree of featural abstraction in their theory of "explicit integrative" multimodal object representations which makes it difficult to assess the validity of their claims.

(2) After participants learned the multimodal objects, the authors report a decreased univariate response to congruent visual-auditory objects relative to incongruent objects in the temporal pole. This is claimed to support the existence of an explicit, integrative code for multimodal objects. Given the number of alternative explanations for this finding, this claim seems unwarranted. A simpler interpretation of these results is that the temporal pole is responding to the novelty of the incongruent visual-auditory objects. If there is in fact an explicit, integrative multimodal object representation in the temporal pole, it is unclear why this would manifest in a decreased univariate response.

(3) The authors ran a neural pattern similarity analysis on the unimodal features before and after multimodal object learning. They found that the similarity between visual and auditory features that composed congruent objects decreased in the temporal pole after multimodal object learning. This was interpreted to reflect an explicit integrative code for multimodal objects, though it is not clear why. First, behavioral data show that participants reported increased similarity between the visual and auditory unimodal features within congruent objects after learning, the opposite of what was found in the temporal pole. Second, it is unclear why an analysis of the unimodal features would be interpreted to reflect the nature of the multimodal object representations. Since the same features corresponded with both congruent and incongruent objects, the nature of the feature representations cannot be interpreted to reflect the nature of the object representations per se. Third, using unimodal feature representations to make claims about object representations seems to contradict the theoretical claim that explicit, integrative object representations are distinct from unimodal features. If the learned multimodal object representation exists separately from the unimodal feature representations, there is no reason why the unimodal features themselves would be influenced by the formation of the object representation. Instead, these results seem to more strongly support the theory that multimodal object learning results in a transformation or warping of feature space.

(4) The most compelling evidence the authors provide for their theoretical claims is the finding that, in the perirhinal cortex, the unimodal feature representations on Day 2 do not correlate with the multimodal objects they comprise on Day 4. This suggests that the learned multimodal object representations are not combinations of their unimodal features. If unimodal features are not decodable within the congruent object representations, this would support the authors' explicit integrative hypothesis. However, the analyses provided do not go all the way in convincing the reader of this claim. First, the analyses reported do not differentiate between congruent and incongruent objects. If this result in the perirhinal cortex reflects the formation of new multimodal object representations, it should only be true for congruent objects but not incongruent objects. Since the analyses combine congruent and incongruent objects it is not possible to know whether this was the case. Second, just because feature representations on Day 2 do not correlate with multimodal object patterns on Day 4 does not mean that the object representations on Day 4 do not contain featural information. This could be directly tested by correlating feature representations on Day 4 with congruent vs. incongruent object representations on Day 4. It could be that representations in the perirhinal cortex are not stable over time and all representations-including unimodal feature representations-shift between sessions, which could explain these results yet not entail the existence of abstracted object representations.

In sum, the authors have collected a fantastic dataset that has the potential to answer questions about the formation of multimodal object representations in the brain. A more precise delineation of different theoretical accounts and additional analyses are needed to provide convincing support for the theory that "explicit integrative" multimodal object representations are formed during learning.

Reviewer #3 (Public Review):

This paper uses behavior and functional brain imaging to understand how neural and cognitive representations of visual and auditory stimuli change as participants learn associations among them. Prior work suggests that areas in the anterior temporal (ATL) and perirhinal cortex play an important role in learning/representing cross-modal associations, but the hypothesis has not been directly tested by evaluating behavior and functional imaging before and after learning cross-modal associations. The results show that such learning changes both the perceived similarities amongst stimuli and the neural responses generated within ATL and perirhinal regions, providing novel support for the view that cross-modal learning leads to a representational change in these regions.

This work has several strengths. It tackles an important question for current theories of object representation in the mind and brain in a novel and quite direct fashion, by studying how these representations change with cross-modal learning. As the authors note, little work has directly assessed representational change in ATL following such learning, despite the widespread view that ATL is critical for such representation. Indeed, such direct assessment poses several methodological challenges, which the authors have met with an ingenious experimental design. The experiment allows the authors to maintain tight control over both the familiarity and the perceived similarities amongst the shapes and sounds that comprise their stimuli so that the observed changes across sessions must reflect learned cross-modal associations among these. I especially appreciated the creation of physical objects that participants can explore and the approach to learning in which shapes and sounds are initially experienced independently and later in an associated fashion. In using multi-echo MRI to resolve signals in ventral ATL, the authors have minimized a key challenge facing much work in this area (namely the poor SNR yielded by standard acquisition sequences in ventral ATL). The use of both univariate and multivariate techniques was well-motivated and helpful in testing the central questions. The manuscript is, for the most part, clearly written, and nicely connects the current work to important questions in two literatures, specifically (1) the hypothesized role of the perirhinal cortex in representing/learning complex conjunctions of features and (2) the tension between purely embodied approaches to semantic representation vs the view that ATL regions encode important amodal/crossmodal structure.

There are some places in the manuscript that would benefit from further explanation and methodological detail. I also had some questions about the results themselves and what they signify about the roles of ATL and the perirhinal cortex in object representation.

A) I found the terms "features" and "objects" to be confusing as used throughout the manuscript, and sometimes inconsistent. I think by "features" the authors mean the shape and sound stimuli in their experiment. I think by "object" the authors usually mean the conjunction of a shape with a sound---for instance, when a shape and sound are simultaneously experienced in the scanner, or when the participant presses a button on the shape and hears the sound. The confusion comes partly because shapes are often described as being composed of features, not features in and of themselves. (The same is sometimes true of sounds). So when reading "features" I kept thinking the paper referred to the elements that went together to comprise a shape. It also comes from ambiguous use of the word object, which might refer to (a) the 3D-printed item that people play with, which is an object, or (b) a visually-presented shape (for instance, the localizer involved comparing an "object" to a "phase-scrambled" stimulus---here I assume "object" refers to an intact visual stimulus and not the joint presentation of visual and auditory items). I think the design, stimuli, and results would be easier for a naive reader to follow if the authors used the terms "unimodal representation" to refer to cases where only visual or auditory input is presented, and "cross-modal" or "conjoint" representation when both are present.

B) There are a few places where I wasn't sure what exactly was done, and where the methods lacked sufficient detail for another scientist to replicate what was done. Specifically:

(1) The behavioral study assessing perceptual similarity between visual and auditory stimuli was unclear. The procedure, stimuli, number of trials, etc, should be explained in sufficient detail in methods to allow replication. The results of the study should also minimally be reported in the supplementary information. Without an understanding of how these studies were carried out, it was very difficult to understand the observed pattern of behavioral change. For instance, I initially thought separate behavioral blocks were carried out for visual versus auditory stimuli, each presented in isolation; however, the effects contrast congruent and incongruent stimuli, which suggests these decisions must have been made for the conjoint presentation of both modalities. I'm still not sure how this worked. Additionally, the manuscript makes a brief mention that similarity judgments were made in the context of "all stimuli," but I didn't understand what that meant. Similarity ratings are hugely sensitive to the contrast set with which items appear, so clarity on these points is pretty important. A strength of the design is the contention that shape and sound stimuli were psychophysically matched, so it is important to show the reader how this was done and what the results were.

(2) The experiences through which participants learned/experienced the shapes and sounds were unclear. The methods mention that they had one minute to explore/palpate each shape and that these experiences were interleaved with other tasks, but it is not clear what the other tasks were, how many such exploration experiences occurred, or how long the total learning time was. The manuscript also mentions that participants learn the shape-sound associations with 100% accuracy but it isn't clear how that was assessed. These details are important partly b/c it seems like very minimal experience to change neural representations in the cortex.

(3) I didn't understand the similarity metric used in the multivariate imaging analyses. The manuscript mentions Z-scored Pearson's r, but I didn't know if this meant (a) many Pearson coefficients were computed and these were then Z-scored, so that 0 indicates a value equal to the mean Pearson correlation and 1 is equal to the standard deviation of the correlations, or (b) whether a Fisher Z transform was applied to each r (so that 0 means r was also around 0). From the interpretation of some results, I think the latter is the approach taken, but in general, it would be helpful to see, in Methods or Supplementary information, exactly how similarity scores were computed, and why that approach was adopted. This is particularly important since it is hard to understand the direction of some key effects.

C) From Figure 3D, the temporal pole mask appears to exclude the anterior fusiform cortex (or the ventral surface of the ATL generally). If so, this is a shame, since that appears to be the locus most important to cross-modal integration in the "hub and spokes" model of semantic representation in the brain. The observation in the paper that the perirhinal cortex seems initially biased toward visual structure while more superior ATL is biased toward auditory structure appears generally consistent with the "graded hub" view expressed, for instance, in our group's 2017 review paper (Lambon Ralph et al., Nature Reviews Neuroscience). The balance of visual- versus auditory-sensitivity in that work appears balanced in the anterior fusiform, just a little lateral to the anterior perirhinal cortex. It would be helpful to know if the same pattern is observed for this area specifically in the current dataset.

D) While most effects seem robust from the information presented, I'm not so sure about the analysis of the perirhinal cortex shown in Figure 5. This compares (I think) the neural similarity evoked by a unimodal stimulus ("feature") to that evoked by the same stimulus when paired with its congruent stimulus in the other modality ("object"). These similarities show an interaction with modality prior to cross-modal association, but no interaction afterward, leading the authors to suggest that the perirhinal cortex has become less biased toward visual structure following learning. But the plots in Figures 4a and b are shown against different scales on the y-axes, obscuring the fact that all of the similarities are smaller in the after-learning comparison. Since the perirhinal interaction was already the smallest effect in the pre-learning analysis, it isn't really surprising that it drops below significance when all the effects diminish in the second comparison. A more rigorous test would assess the reliability of the interaction of comparison (pre- or post-learning) with modality. The possibility that perirhinal representations become less "visual" following cross-modal learning is potentially important so a post hoc contrast of that kind would be helpful.

E) Is there a reason the authors did not look at representation and change in the hippocampus? As a rapid-learning, widely-connected feature-binding mechanism, and given the fairly minimal amount of learning experience, it seems like the hippocampus would be a key area of potential import for the cross-modal association. It also looks as though the hippocampus is implicated in the localizer scan (Figure 3c).

F) The direction of the neural effects was difficult to track and understand. I think the key observation is that TP and PRh both show changes related to cross-modal congruency - but still it would be helpful if the authors could articulate, perhaps via a schematic illustration, how they think representations in each key area are changing with the cross-modal association. Why does the temporal pole come to activate *less* for congruent than incongruent stimuli (Figure 3)? And why do TP responses grow less similar to one another for congruent relative to incongruent stimuli after learning (Figure 4)? Why are incongruent stimulus similarities *anticorrelated* in their perirhinal responses following cross-modal learning (Figure 6)?

This work represents a key step in our advancing understanding of object representations in the brain. The experimental design provides a useful template for studying neural change related to the cross-modal association that may prove useful to others in the field. Given the broad variety of open questions and potential alternative analyses, an open dataset from this study would also likely be a considerable contribution to the field.

Reviewer #1 (Public Review):

The hypothesis that the MA myristyl switch is a trigger for M-PMV maturation is derived from previously published findings, and is well supported by the data presented in this manuscript. The results suggest a new function for the myristyl switch, one that could perhaps be relevant for other proteins. Below are some suggestions for improving the MS.

Reviewer #2 (Public Review):

This manuscript presents measurements of proteolytic digestion and structural studies using both hydrogen-deuterium exchange and NMR. The data test the idea that membrane association leads to a rearrangement of the MA domain of the MPMV Gag protein, as the myristate chain at the N-terminus of the protein is "switched" from a hydrophobic pocket within the protein into lipid layers, finally rendering the protein efficiently digestible by the viral protease. In my opinion, the data are highly convincing, and the underlying hypothesis is a useful contribution to the field, providing for this retrovirus a solution to the long-standing problem of how proteolytic maturation is activated.

Reviewer #3 (Public Review):

D-type retroviruses, which include M-PMV assemble in the cytosol, however, do not efficiently start their maturation before membrane binding. There is very little known about the structural changes leading to maturation of D-type retroviruses and this manuscript presents compelling structural changes of the M-PMV matrix domain in mutations abrogating the myristol exposure or mutation which reasonably argue that myristol group is exposed (The relationship between these mutants and myristol exposure is argued based on structure of the matrix and liposome binding, however is not directly shown in structure). Assuming that the authors are correct about their mutations affect on myristol exposure, they have measured very interesting M-PMV matrix domain conformational changes which exposes the MAPP site to the protease.

Oligomerization of the matrix is probed by formation of disulfide bridges in a matrix mutant on liposomes with engineered cystine where authors suspect monomers of the matrix would be touching each other. The oligomerization data is very weak, does not directly support trimer formation and since 2D diffusion on liposomes would increase matrix-matrix interactions, can be non-specific, a point supported by presence of a stronger dimer band than trimer and tetramer. The main issue with the manuscript is that the authors do not show any evidence that the proposed mechanism actually works in the context of full M-PMV assembled particles.

Reviewer #1 (Public Review):

This study demonstrates that Langerhans ADAM17 is lower in nonlesional skin and type I interferons have effects on ADAM17. ADAM17 releases EGFR ligands that preserve epidermal integrity. LC ROS is lower with high type I interferons, accompanied by reduced epidermal EGFR phosphorylation in nonlesional skin in SLE. The authors did an outstanding job with data from 3 animal models and human lupus skin to demonstrate their findings.

Reviewer #2 (Public Review):

Many of the questions about type I interferon and photosensitivity have already been studied in murine lupus models but most importantly in skin biopsies from both lesional and non-lesional cutaneous lupus. The authors should try to link their data to the existing literature and validate their results by using human samples, as not all murine lupus models have a strong interferon-mediated disease. Other important aspects of the work include whether or not the authors have considered knocking out the mice for ADAM17 and reassessing the function of the Langerhans cells? Last but not least, some of the data presented will need to be validated by more in vitro work that will shed more light on the functional properties of ADAM17 in Langerhans cells and inflammatory response in cutaneous lupus.

Reviewer #3 (Public Review):

The study by Li et al investigates the role of type I interferon in suppressing ADAM17-mediated release of EGFR, the pathway previously implicated by this group in photosensitive skin reactions. Understanding the relevance of lupus murine models to the human disease is very important and the studies address this important gap in the knowledge. The most significant findings are: 1) the same high IFN and low Langerhans cell (LC) signatures seen in a lupus patient's skin, exist in the non-lesional skin of lupus mouse models; 2) IFN-Is and IFN-I signaling suppress ADAM17 activity in LCs in vitro and in vivo; 3) Blocking IFN-I signaling ameliorates photosensitive reactions, in an EGFR-dependent manner. These three conclusions are largely supported by the presented evidence but could be distilled as well as strengthened by additional data.

One of the strengths of the study is that the authors defined the relevance of lupus skin mouse models to human disease in the context of the Interferon-LC axis. The extensive computational approaches represent useful tools to compare cellular and molecular signatures across samples as well as species. This is highly relevant to the studies of lupus, a highly complex disease, for which the relevance of murine models has remained undefined. Major strengths related to the Aims of the study are that the authors established a role of interferon in suppressing Adam17 activity in the skin and showed that blocking interferon can reduce sunlight-induced skin inflammation in the lupus murine models. Interestingly, the authors observed that blocking IFN signaling in the absence of a high IFN-signature worsened sunlight-induced skin injury. The specificity of Adam17 in LCs for TNFR1 shedding provides an elegant approach to probing Adam17 activity in these cells.

While the three conclusions stated above are largely supported by the presented evidence, the data supporting a direct role of ADAM17 in IFN-triggered photosensitive reactions could be strengthened. Some of the concerns are outlined below:<br /> (1) Computational analyses in Figures 1 and 2 emphasize the co-occurrence of a high IFN-I signature and a low LC and/or DC signatures. It is not clear if the downregulation of the DC gene set indicates diminished presence of LCs in the non-lesional skin of the lupus mouse models or "reflects decreased LC function" as the authors suggest.

(2) Given the hypothesis that IFN-I may be the cause of a decreased DC signature in the mouse skin, it would be relevant to ask if this signature is also decreased in the IMQ model, which is a known model of IFN-induction as confirmed by the authors. Likewise, asking how anti-IFNAR treatment affects the DC signature / LC numbers would be important, in the absence and presence of UV. The authors indicate in Fig. 5I that IMQ reduces LC numbers.

(3) Decreased inflammation in LCad17 mice in the IMQ+UV model is unexpected. Previous studies by this group showed increased UV-induced inflammation in the absence of LC-ADAM17 (Shipman et al 2018). Therefore, it is not surprising that anti-IFNAR did not have an impact in these mice as ADAM17 deficiency appears to have normalized the response. These results are not addressed in the context of the previously published findings.

(4) Including the data that demonstrate the specificity of LCs for Adam17 expression in the epidermis and shedding of TNFR1 as a readout of LC-ADAM17-specific activity in the main figures would be important.

(5) UV light is an important inducer of IFN. Authors have previously shown that UV also induces Adam17 expression. Therefore, the question remains whether a high baseline IFN signature in lupus skin suppresses UV-induced Adam17 expression?

(6) A direct mechanistic link between high IFN-I and loss of Adam17 activity driving photosensitive reactions could be strengthened. Would blocking Adam17 with a blocking antibody suppress photosensitive reactions in lupus mouse models? Would treating LCAd17 mice with IFN fail to enhance or diminish UV-induced inflammation?

Reviewer #1 (Public Review):

The work by Porciello and colleagues provides scientific evidence that the acidic content of the stomach covaries with the experienced level of disgust and fear evoked by disgusting videos. The working of the inside of the gut during cognitive or emotional processes have remained elusive due to the invasiveness of the methods to study it. The major strength of the paper is the use of the non-invasive smart pill technology, which senses changes in Ph, pressure and temperature as it travels through the gut, allowing authors to investigate how different emotions induced with validated video clips modulate the state of the gut. The experimental paradigm used to evoke distinct emotions was also successful, as participants reported the expected emotions after each emotion block. While the reported evidence is correlational in nature, I believe these results open up new avenues for studying brain-body interactions during emotions in cognitive neuroscience, and future causal manipulations will shed more insight on this phenomena. Indeed, this is the first study to provide evidence for a link between gastric acidity and emotional experience beyond single patient studies, and it has major implications for the advancement of our understanding of disorders with psycho-somatic influences, such as stress and it's influence of gastritis.

As for the limitations, little insight is provided on the mechanisms, time scales, and inter-individual variability of the link between gastric Ph and emotional induction. Since this is a novel phenomena, it would be important to further validate and characterize this finding. On this line, one of the most well known influences of disgust on the gut is tachygastria, the acceleration of the gastric rhythm. It would be important to understand how acid secretion by disgusting film is related to tachygastria, but authors only examine the influence of disgusting film on the normogastric frequency range. Additionally, only one channel of the electrogastrogram (EGG) was used to measure the gastric rhythm, and no information is provided on the quality of the recordings. With only one channel of EGG, it is often impossible to identify the gastric rhythm as the position of the stomach varies from person to person, yielding inaccurate estimates of the frequency of the gastric rhythm. Finally, I believe that the results do not show evidence in favor of the discrete nature of emotions theory as they claim in the discussion. Authors chose to use stimuli inducing discrete emotions, and only asked subjective reports of these same discrete emotions, so these results shed no light on whether emotions are represented discretely vs continuously in the brain.

Reviewer #2 (Public Review):

To measure the role of gastric state in emotion, the authors used an ingestible smart pill to measure pH, pressure, and temperature in the gastrointestinal tract (stomach, small bowel, large bowel) while participants watched videos that induced disgust, fear, happiness, sadness, or a control (neutral). The study has a number of strengths, including the novelty of the measurement (very few studies have ever measured these gut properties during emotion processing) and the apparent robustness of their main finding (that during disgusting video clips, participants who experienced more feelings of disgust (and to a lesser degree which might not survive more stringent multiple comparison correction, fear) had more acidic stomach measurements, while participants who experienced more happiness during the disgusting video clips had a less acidic (more basic) stomach pH. Although the study is correlational (which all discussion should carefully reflect) and is restricted to a moderately-sized, homogenous sample, the results support their general conclusion that stomach pH is related to emotion experience during disgust induction. There may be additional analyses to conduct in order for the authors to claim this effect is specific to the stomach. Nevertheless, this work is likely to have a large impact on the field, which currently tends to rely on noninvasive measures of gastric activity such as electrogastrography (which the authors also collect for comparison); the authors' minimally-invasive approach yields new and useful measurements of gastric state. These new measures could have relevance beyond emotion processing in understanding the role of gut pH (and perhaps temperature and pressure) in cognitive processes (e.g. interoception) as well as mental and physical health.

Reviewer #3 (Public Review):

This study used novel ingestible pills to measure pH and other gastric signals, and related these measures to self-report ratings of emotions induced by video clips. The main finding was that when participants viewed videos of disgust, there was an association between gastric pH and feelings of disgust and fear, and (in the opposite direction) happiness. These findings may be the first to relate objective measures of gastric physiology to emotional experience. The methods open up many new questions that can be addressed by future studies and are thus likely to have an impact on the field.

My main concern is with the reliability of the results. The study associates many measures (pH, temperature, pressure, EGG) in stomach, small bowel, and large bowel with multiple emotion ratings. This amounts to many statistical tests. Only one of these measures (pH in the stomach) shows a significant effect. Furthermore, the key findings, as displayed in Figure 4 do not look particularly convincing. Perhaps this is a display issue, but the relations between stomach pH and Vas ratings of disgust, fear, and happiness were not apparent from the scatter plot and may be influenced by outliers (e.g., happiness).

Reviewer #1 (Public Review):

This manuscript describes the results of closed-loop SWR disruption in rats experiencing a short-term memory task that they previously acquired successfully. The authors aim to show that SWRs are dispensable for STM tasks requiring multiple match-to-sample trial rules, single-trial non-match-to-sample rules, and spatial sequence memory. In all cases, the analysis and intervention were performed at the higher standards, providing a clear proof-of-principle of appropriate detection and the necessary control. I found the experiments well executed and analyzed. Results may help to advance our understanding of the role of awake SWRs in STM. However, since the results consist of a lack of evidence there is a need for some additional positive controls to fully support the main claim of the study.

Reviewer #2 (Public Review):

This study aims to test the role of awake replay in short-term memory, a type of memory that operates on the timescale of seconds and minutes. Replay refers to a time-compressed burst of neuronal population activity during a particular oscillatory local field potential event in the hippocampus, called the sharp-wave ripple (SWR). SWRs are found during sleep and in the awake state and are always associated with the animal being quiescent. The paper compares results from three different behavioral tasks ranging in memory requirements and memory timescales. First, rats were trained on either a spatial match-to-sample task (MTS), a non-match-to-sample task (NMTS), or a task requiring the memorization of sequences (maze arms to be visited in a specific temporal order). In this initial training phase, the animals were allowed to learn the maze structure and the rules governing these tasks for all these behavioral paradigms. Then, awake sharp-SWRs were disrupted as the animal performed these tasks (both during instruction and test phases) via an online detection system combined with closed-loop electrical stimulation of the ventral hippocampal commissure. Notably, this manipulation appeared not to affect performance in all three tasks, as determined using various behavioral parameters. Trials with no stimulation or delayed stimulation serve as controls. Thus, the authors conclude that awake SWRs are not involved in these short-term memory-guided behaviors. I do have a few comments that the authors should discuss or address:

(1) This study adds to a large number of studies investigating the role of awake SWRs in spatial learning and memory tasks. The results of these previous studies are quite contradictory and range from awake SWRs are not crucial in guiding decisions at all to SWRs are only essential during task rule learning to SWRs do guide behavior. Could the authors comment on these seemingly contradictory results? Why are these experiments now the right ones?<br /> (2) None of the experiments presented here test the role of replay. I suggest making this distinction in the paper and the title clear. As the results are presented now, is it possible that the SWR content is not affected sufficiently to have a behavioral effect or that there is a bias towards detecting specific SWRs, e.g., longer SWRs?

Reviewer #3 (Public Review):

In this manuscript, the authors seek to shed light on the role of awake hippocampal replay during memory tasks that are claimed to be short-term memory. For this, they make use of a real-time detection and disruption system of awake hippocampal ripples, which are used as a proxy for awake neuronal replay. The manuscript describes extensively the tasks as well as the disruption system and controls used during the experiments. The authors present numerous and solid analyses of the behavioral data acquired during the tasks. Nonetheless, the current version of the manuscript is lacking a more complete discussion in which the results are contrasted to previous similar findings, as well as mentioning the role of the awake ripple in the stabilization of hippocampal maps. Some extra analyses are also suggested below. The manuscript would also be enriched if the authors suggested alternative mechanisms for memory rehearsal. Finally, some claims of "we are first" seem inappropriate when compared to the previous literature.

Major comments:

How does one define short-term memory (STM) in rodents? The examples and papers cited in the first paragraphs refer mostly to human working memory tasks, from which it is known that a non-rehearsed STM lasts typically 20-30 seconds. Could the authors mention how this concept is translated to rodents? Could you clarify until what point memory is considered STM and what is the criteria to consider it has turned into long-term memory or when is it simply working memory or habit/skill? Further, why should these tasks be classified as testing STM while Jadhav et al. tasks are working memory or as they now mention in this article rule learning? In humans, the retention of memory after a certain time is achieved by retrieving a long-term memory. How do we know if the considerable training the rats received has not allowed the use of a long-term memory strategy which allows the rats to perform well even in the absence of rehearsal (replay)? These are conceptual explanations that would help understand the key concept of STM in greater detail.

Further, claims of "first" should be adjusted, since I do not see a large difference between the w (m) maze of Jadhav and these tasks. The main difference between the two projects would rather be that Jadhav tests when animals are still newer to the task while here overtrained animals are used. In Jadhav, it's unlikely that just rule learning is affected since the inbound component is not affected by disruption, which also tests rule learning. Therefore, it is still likely that the effect seen in Jadhav et al is a deficit in working memory/short-term memory. And here it is more likely, that no effect was seen since with overtrained animals other strategies (cortical, striatal, etc) were used. The authors should compare in more detail how overtrained animals were in these different projects as well as in the articles they cite for replay analysis.

The main conclusion of the authors is that hippocampal replay is not the rehearsal mechanism expected in STM given that its disruption doesn't lead to behavioral changes. Could the authors hypothesize in their discussion what other neural mechanisms different from hippocampal replay may be involved in this rehearsal? The discussion also lacks closure with respect to how the findings fit in the study of STM in human memory. This would make the article more interesting to a larger audience and highlight its translational aspect.

The results describe deeply the behavioral performance of the rats and the validation of the ripple detection/disruption system. However, one important aspect missing is how the hippocampal activity and its encoding of space may be affected by the awake ripple disruption. The authors don't cite the work by Roux et al., Nature Neuroscience. 2017 where optogenetic stimulation of hippocampal neurons provided evidence that neuronal activity associated with awake hippocampal ripples during goal-directed behavior is required for both stabilizing and refining hippocampal place fields, while memory performance was not affected during ripple-locked stimulations compared to a ripple-delayed stimulation control (See supplementary Figure 7 of the mentioned article). I would like the authors to comment on their own findings and contrast them with those of Roux et al.

Line 64: Could the authors clarify what they mean by "indirect" causal evidence when discussing the contribution of papers by Jadhav, Igata, and Fernandez? Is it the fact that rodents' learning speed changed instead of showing a complete absence of learning? Or is it the fact that the disruption/prolongation is done on the hippocampal ripple and not strictly in the replay sequence? I would also highlight this latter difference, given that the above-mentioned authors describe their methodological approaches in terms of ripples and not in terms of replay content. For example, the use of "replay" instead of "ripple" in Line 61 results in methodological inaccurate terms such as replay disruption and replay prolongation.

Despite its apparent lack of statistical significance, the reported mean ripple detection rate during the trial and non-trial periods tend to be always higher in the disruption condition of all tasks by observing the median of the boxplots in Figure 1J, Figure 2H, and Figure 3J. It is worth investigating this further using the same linear regression method as Girardeau et al. Journal of Neuroscience, 2014 which may reduce the variability and allow comparing slopes of a cumulative number of ripples over time. This may reveal a compensatory homeostatic-like increase in the rate of ripples during the disrupted sessions, which may suggest a need for the ripple/replay occurrence in spite of it not having an effect on the rats' performance during the task.

In line 425, the authors report a median relative delay of 52.9 of their disruption system. Such a value would indicate that only around 47% of the ripple is being blocked. Is there any data from the authors or others that could reassure the reader that the 52.9% of the ripple that "leaks" is not enough for the replay phenomenon to occur? Considering the findings of Fernandez-Ruiz et al. 2019 on large-duration ripples, could the authors report the relative delay for both short and long ripples (>100 ms) separately? Line 494: The authors define long ripples as (>120 ms) but this doesn't coincide with the 100ms threshold from Fernandez Ruiz et al. 2019.

The online ripple detector used filtered the traces in the 135-255 Hz range. This is a narrower frequency range compared to online detectors used by Jadhav et al. 2012 (100-400 Hz) and Fernandez-Ruiz et al. 2019 (80-300 Hz). What motivated the use of this narrow range? Would the omittance of ripples below 135 Hz have implications in the results? Could the authors add to the supplement a figure similar to Figure 4B (FDR vs TPR) using a wider frequency range similar to the authors above in the offline detection of ripples?

It is unclear what criterion was used to train the rats in the NMTS task. Line 216 specifies a learning criterion of 80% fully correct trials in one session for three days in a row, while the methods in line 852 mention an average performance below 50% for at least three days in a row.

In the methods section, it is not mentioned if there was a specific region in the cortex where the tetrode was placed (Line 908). The detections in this tetrode were used to mark events as "false positives". The authors should be careful in line 933 when they make the statement "ripples are not present in the cortex". There have been recent publications that challenge this affirmation. See Khodagholy, Science. 2017, Nitzan, Nature Comm. 2020.

Reviewer #1 (Public Review):

Summary: Zai et al test if songbirds can recover the capacity to sing auditory targets without singing experience or sensory feedback. Past work showed that after the pitch of targeted song syllables are driven outside of birds' preferred target range with external reinforcement, birds revert to baseline (i.e. restore their song to their target). Here the authors tested the extent to which this restoration occurs in muted or deafened birds. If these birds can restore, this would suggest an internal model that allows for sensory-to-motor mapping. If they cannot, this would suggest that learning relies entirely on feedback dependent mechanisms, e.g. reinforcement learning (RL). The authors find that deafened birds exhibit moderate but significant restoration, consistent with the existence of a previously under-appreciated internal model in songbirds.

Strengths:

The experimental approach of studying vocal plasticity in deafened or muted birds is innovative, technically difficult and perfectly suited for the question of feedback-independent learning. The finding in Figure 4 that deafened birds exhibit subtle but significant plasticity toward restoration of their pre-deafening target is surprising and important for the songbird and vocal learning fields, in general.

In this revision, the authors suitably addressed the confusion about some statistical methods related to Fig. 4, where the main finding of vocal plasticity in deafened birds was presented.

There remain minor issues in the presentation early in the results section and in Fig. 4 that should be straightforward to clarify in revision.

Reviewer #3 (Public Review):

Summary:

Zai et al. test whether birds can modify their vocal behavior in a manner consistent with planning. They point out that while some animals are known to be capable of volitional control of vocalizations, it has been unclear if animals are capable of planning vocalizations-that is, modifying vocalizations towards a desired target without the need to learn this modification by practicing and comparing sensory feedback of practiced behavior to the behavioral target. They study zebra finches that have been trained to shift the pitch of song syllables away from their baseline values. It is known that once this training ends, zebra finches have a drive to modify pitch so that it is restored back to its baseline value. They take advantage of this drive to ask whether birds can implement this targeted pitch modification in a manner that looks like planning, by comparing the time course and magnitude of pitch modification in separate groups of birds who have undergone different manipulations of sensory and motor capabilities. A key finding is that birds who are deafened immediately before the onset of this pitch restoration paradigm, but after they have been shifted away from baseline, are able to shift pitch partially back towards their baseline target. In other words, this targeted pitch shift occurs even when birds don't have access to auditory feedback, which argues that this shift is not due to reinforcement-learning-guided practice, but is instead planned based on the difference between an internal representation of the target (baseline pitch) and current behavior (pitch the bird was singing immediately before deafening).

The authors present additional behavioral studies arguing that this pitch shift requires auditory experience of song in its state after it has been shifted away from baseline (birds deafened early on, before the initial pitch shift away from baseline, do not exhibit any shift back towards baseline), and that a full shift back to baseline requires auditory feedback. The authors synthesize these results to argue that different mechanisms operate for small shifts (planning, which does not need auditory feedback) and large shifts (through a mechanism that requires auditory feedback).

The authors also make a distinction between two kinds of planning: covert-not requiring any motor practice-and overt-requiring motor practice, but without access to auditory experience from which target mismatch could be computed. They argue that birds plan overtly, based on these deafening experiments as well as an analogous experiment involving temporary muting, which suggest that indeed motor practice is required for pitch shifts.

Strengths:

The primary finding (that partially restorative pitch shift occurs even after deafening) rests on strong behavioral evidence. It is less clear to what extent this shift requires practice, since their analysis of pitch after deafening takes the average over within the first two hours of singing. If this shift is already evident in the first few renditions then this would be evidence for covert planning. Technical hurdles, such as limited sample sizes and unstable song after surgical deafening, make this difficult to test. (Similarly, the authors could test whether the first few renditions after recovery from muting already exhibit a shift back towards baseline.)

This work will be a valuable addition to others studying birdsong learning and its neural mechanisms. It documents features of birdsong plasticity that are unexpected in standard models of birdsong learning based on reinforcement and are consistent with an additional, perhaps more cognitive, mechanism involving planning. As the authors point out, perhaps this framework offers a reinterpretation of the neural mechanisms underlying a prior finding of covert pitch learning in songbirds (Charlesworth et al., 2012).

A strength of this work is the variety and detail in its behavioral studies, combined with sensory and motor manipulations, which on their own form a rich set of observations that are useful behavioral constraints on future studies.

Weaknesses:

The argument that pitch modification in deafened birds requires some experience hearing their song in its shifted state prior to deafening (Fig. 4) is solid, but has an important caveat. Their argument rests on comparing two experimental conditions: one with and one without auditory experience of shifted pitch. However, these conditions also differ in the pitch training paradigm: the "with experience" condition was performed using white noise training, while the "without experience" condition used "lights off" training (Fig. 4A). It is possible that the differences in ability for these two groups to restore pitch to baseline reflects the training paradigm, not whether subjects had auditory experience of the pitch shift. Ideally, a control study would use one of the training paradigms for both conditions, which would be "lights off" or electrical stimulation (McGregor et al. 2022), since WN training cannot be performed in deafened birds. In the Discussion, in response to this point the authors point out that birds are known to recover their pitch shift if those shifts are driven using electrical stimulation as reinforcement (McGregor et al. 2022); however, it is arguably still relevant to know whether a similar recovery occurs for the "lights off" paradigm used here.

Reviewer #1 (Public Review):

Summary:<br /> By examining the prevalence of interactions with ancient amino acids of coenzymes in ancient versus recent folds, the authors noticed an increased interaction propensity for ancient interactions. They infer from this that coenzymes might have played an important role in prebiotic proteins.

Strengths:<br /> (1) The analysis, which is very straightforward, is technically correct. However, the conclusions might not be as strong as presented.

(2) This paper presents an excellent summary of contemporary thought on what might have constituted prebiotic proteins and their properties.

(3) The paper is clearly written.

Weaknesses:<br /> (1) The conclusions might not be as strong as presented. First of all, while ancient amino acids interact less frequently in late with a given coenzyme, maybe this just reflects the fact that proteins that evolved later might be using residues that have a more favorable binding free energy.

(2) What about other small molecules that existed in the probiotic soup? Do they also prefer such ancient amino acids? If so, this might reflect the interaction propensity of specific amino acids rather than the inferred important role of coenzymes.

(3) Perhaps the conclusions just reflect the types of active sites that evolved first and nothing more.

Reviewer #2 (Public Review):

I enjoyed reading this paper and appreciate the careful analysis performed by the investigators examining whether 'ancient' cofactors are preferentially bound by the first-available amino acids, and whether later 'LUCA' cofactors are bound by the late-arriving amino acids. I've always found this question fascinating as there is a contradiction in inorganic metal-protein complexes (not what is focused on here). Metal coordination of Fe, Ni heavily relies on softer ligands like His and Cys - which are by most models latecomer amino acids. There are no traces of thiols or imidazoles in meteorites - although work by Dvorkin has indicated that could very well be due to acid degradation during extraction. Chris Dupont (PNAS 2005) showed that metal speciation in the early earth (such as proposed by Anbar and prior RJP Williams) matched the purported order of fold emergence.

As such, cofactor-protein interactions as a driving force for evolution has always made sense to me and I admittedly read this paper biased in its favor. But to make sure, I started to play around with the data that the authors kindly and importantly shared in the supplementary files. Here's what I found:

Point 1: The correlation between abundance of amino acids and protein age is dominated by glycine.

There is a small, but visible difference in old vs new amino acid fractional abundance between Ancient and LUCA proteins (Figure 3, Supplementary Table 3). However, the bias is not evenly distributed among the amino acids - which Figure 4A shows but is hard to digest as presented. So instead I used the spreadsheet in Supplement 3 to calculate the fractional difference FDaa = F(old aa)-F(new aa). As expected from Figure 3, the mean FD for Ancient is greater than the mean FD for LUCA. But when you look at the same table for each amino acid FDcofactor = F(ancient cofactor) - F(LUCA cofactor), you now see that the bias is not evenly distributed between older and newer amino acids at all. In fact, most of the difference can be explained by glycine (FDcofactor = 3.8) and the rest by also including tryptophan (FDcofactor = -3.8). If you remove these two amino acids from the analysis, the trend seen in Figure 3 all but disappears.

Troubling - so you might argue that Gly is the oldest of the old and Trp is the newest of the new so the argument still stands. Unfortunately, Gly is a lot of things - flexible, small, polar - so what is the real correlation, age, or chemistry? This leads to point 2.

Point 2 - The correlation is dominated by phosphate.

In the ancient cofactor list, all but 4 comprise at least one phosphate (SAM, tetrahydrofolic acid, biopterin, and heme). Except for SAM, the rest have very low Gly abundance. The overall high Gly abundance in the ancient enzymes is due to the chemical property of glycine that can occupy the right-hand side of the Ramachandran plot. This allows it to make the alternating alphaleft-alpharight conformation of the P-loop forming Milner-White's anionic nest. If you remove phosphate binding folds from the analysis the trend in Figure 3 vanishes.

Likewise, Trp is an important functional residue for binding quinones and tuning its redox potential. The LUCA cofactor set is dominated by quinone and derivatives, which likely drives up the new amino acid score for this class of cofactors.

In summary, while I still believe the premise that cofactors drove the shape of peptides and the folds that came from them - and that Rossmann folds are ancient phosphate-binding proteins, this analysis does not really bring anything new to these ideas that have already been stated by Tawfik/Longo, Milner-White/Russell, and many others.

I did this analysis ad hoc on a slice of the data the authors provided and could easily have missed something and I encourage the authors to check my work. If it holds up it should be noted that negative results can often be as informative as strong positive ones. I think the signal here is too weak to see in the noise using the current approach.

Reviewer #1 (Public Review):

In this study, Hunt et al investigated the role of the ubiquitin-conjugating enzyme UBE2D/effete (eff) in maintaining proteostasis during aging. Utilizing Drosophila as a model, the researchers observed diverse roles of E2 ubiquitin-conjugating enzymes in handling the aggregation-prone protein huntingtin-polyQ in the retina. While some E2s facilitated aggregate assembly, UBE2D/eff and other E2s were crucial for degradation of htt-polyQ. The study also highlights the significance of UBE2D/eff in skeletal muscle, showing that declining levels of eff during aging correlate with proteostasis disruptions. Knockdown of eff in muscle led to accelerated accumulation of poly-ubiquitinated proteins, shortened lifespan, and mirrored proteomic changes observed in aged muscles. The introduction of human UBE2D2, analogous to eff, partially rescued the deficits in lifespan and proteostasis caused by eff-RNAi expression in muscles.

The conclusions of this paper are mostly well supported by data, although a more precise mechanistic explanation of phenotypes associated with UBE2D/eff deficiency would have strengthened the study. Additionally, some aspects of image quantification and data analysis need to be clarified and/or extended.

Reviewer #2 (Public Review):

Important findings:

• Knockdown of UBE2D increases HTT aggregation.

• Knockdown of UBE2D leads to an accumulation of ubiquitinated proteins and reduces the lifespan of Drosophila, which is rescued by an ectopic expression of the human homolog.

• UBE2D protein levels decline with aging.

• UBE2D knockdown is associated with an up- and downregulation of several different cellular pathways, including proteostasis components.

Caveats:

• The readout of HTT aggregation (with methods that are not suitable) as a proxy for the role of UBE2D in proteostasis is not convincing. It would probably improve the manuscript to start with the proteomic analysis of UBE2D to demonstrate that its protein levels decrease with aging. The authors could then induce UBE2D in aged animals to assess the role of UBE2D in the proteome with aging.

• UBE2D knockdown increases the number of HTT foci (Figure 1A), but the quantification is less convincing as depicted in Figure 1B, and other E2 enzymes show a stronger effect (e.g. Ubc6 that is only studied in Figures 1 and 2 without an explanation and Ubc84D). The graph is hard to interpret. What is the sample size and which genetic conditions show a significant change? P values and statistical analyses are missing.

• The quantification of the HTT fluorescence cannot be used as a proxy for HTT aggregation. The authors should assess HTT aggregation by e.g. SDD-AGE, FRAP, filter retardation, etc. The quantification of the higher MW species of HTT in the SDS-PAGE is not ideal either as this simply reflects material that is stuck in the wells that could not enter the gel. Aggregation and hence high MW size could be one reason, but it can also be HTT trapped in cell debris, etc.

• Does UBE2D ubiquitinate HTT? And thus, is HTT accumulation a suitable readout for the functional assessment of the E2 enzyme UBE2D?

• The proteomic analyses could help to identify potential substrates for UBE2D.

• Are there mutants available for UBE2D or conditional mutants? One caveat of RNAi is: first not complete knockdown and second, variable knockdown efficiencies that increase variability.

• The analysis of the E3 enzymes does not add anything to this manuscript.

• Figure 2B: the fluorescence intensities in images 2 and 4 are rather similar, yet the quantification shows significant differences.

• The proteomic analyses could provide insights into the functional spectrum of UBE2D or even the identification of substrates. Yet apart from a DAVID analysis, none of the hits were followed up. In addition, only a few hits were labelled in the volcano plots (Figure 5). On what basis did the authors select those?

• The manuscript remains at this stage rather descriptive.

Reviewer #3 (Public Review):

This is a potentially quite interesting paper that defines E2 and E3 genes in Drosophila that can impact the accumulation of the Q72-GFP protein in the fly eye. The authors then focus on the eff gene, showing which human homolog can rescue fly knockdown. They extend to skeletal muscle, from the htt protein, to show that eff by TMT mass spec decreases with age normally in the fly muscle and that there is a significant overlap of proteins that are disrupted with eff knockdown in young animals in muscle vs aged animals normally in muscle.

Overall these data suggest eff decrease with age may contribute to the increase in ubiquitinated proteins in muscle with age, and that upregulation of eff activity might be of interest to extending lifespan. Because eff function can be performed by a human homologue, the findings may also apply to human situations of aging.

These data are overall interesting and are of relevance for those interested in neurodegenerative disease and aging, although a number of points from the figures seem confusing and need more explanation or clarity.

Reviewer #1 (Public Review):

This study explores the relationship between neurodegeneration's most common spatial patterns and the density of different cell types in the cerebral cortex. The authors present data showing that atrophy patterns in Alzheimer's disease and Frontotemporal dementia (FTD) strongly associated with the abundance of astrocytes and microglia. This work (the original manuscript and the revision) takes a step in the right direction by emphasizing the critical role that cells other than neurons play in the degeneration patterns observable with neuroimaging.

Comments on revised version:

I appreciate the revisions the authors made to address my main comments:<br /> - adding whole-brain maps showing cellular abundance and atrophy<br /> - stratifying the FTD group into the three clinically defined categories bvFTD (behavior-variant), nfvPPA (nonfluent/agrammatic-variant primary progressive aphasia), and svPPA (semantic-variant primary progressive aphasia).

I reiterate my agreement with the authors that this work demonstrates the need to "surpass the current neuro-centric view of brain diseases and the imperative for identifying cell-specific therapeutic targets in neurodegeneration".

Reviewer #3 (Public Review):

This study is a fine example of a recent productive trend in the integration of neuroimaging and molecular biology of the brain: in brief, overlaying some neuroimaging data (usually from a large cohort) onto the high spatial resolution gene expression in the Allen Human Brain Atlas data, derived from 6 individuals. By projecting structural MRI images over cell type proportions identified in the Allen data, the authors can represent various diseases in terms of their spatially-associated cell types. The result has implications for prioritizing the contributions of various cell types to each disease and creates an even-handed cell type profile through which the 11 diseases can be compared.

Reviewer #1 (Public Review):

Summary:

In this paper, Ruggiero, Leite and colleagues assess the effects of early life seizures on a large number of anatomical, physiological, behavioral and neurochemical measures. They find that prolonged early life seizures do not lead to obvious cell loss, but lead to astrogliosis, working memory deficits on the radial arm maze, increased startle response, decreased paired pulse inhibition, and increased hippocampal-PFC LTP. There was a U-shaped relationship between LTP and cognitive deficits. There is increased theta power during the awake state in ELS animals but reduced PFC theta-gamma coupling and reduced theta HPC-PFC coherence. Theta coherence seems to be similar in ACT and REM states in ELS animals while in decreases in active relative REM in controls.

Strengths:

The main strength of the paper is the number of convergent techniques used to understand how hippocampal PFC neural dynamics and behavior change after early life seizures. The sheer scale, breadth and reach of the experiments are praiseworthy. It is clear that the paper is a major contribution to the field as far as understanding the impact of early life seizures. The LTP findings are robust and provide an important avenue for future study. The experiments are performed carefully and the analysis is appropriate. The paper is well-written and the figures are clear.

Weaknesses:

The main weakness of the paper remains the lack of causal manipulations to determine whether prevention or augmentation of any of the findings have any impact on behavior or cognition. Alternatively, if other manipulations would enhance working memory in ELS animals, it would have been interesting to see the effects on any of these parameters measured in the paper. The authors now discuss the lack of causal manipulations in the discussion but have not performed new experiments to address this weakness. Also, I find the sections where correlations and dimensionality reduction techniques are used to compare all possible variables to each other less compelling than the rest of the paper (with the exception of the findings of U shaped relationship of cognition to LTP). In fact, I think these sections take away from the impact of the actual findings. The rationale for the apomorphine experiments are now explained more fully.

Reviewer #3 (Public Review):

Summary:

The manuscript examines an important question, namely how the brain associates events spaced in time. It uses a variety of neural methods including fiber photometry as well as area-specific and pathway-silencing methods with the exquisite dissociation of norepinephrine and dopamine. The data show that neurons in the locus coeruleus (LC) respond to auditory cue onset, offset, and shock. These responses are stronger if the cue is paired with shock in a trace procedure. Optogenetic stimulation similar to the neural response captured by fiber photometry enhances associative learning. LC terminals in the dorsal hippocampus also showed phasic responses during fear conditioning and drove dopamine and norepinephrine responses. Pharmacological methods revealed that dopamine and not norepinephrine are critical for fear learning.

Strengths:

The examination of the neural signal to different tone intensities, different shock intensities, repeated tone presentation (habituation), and conditioning, offers an unprecedented account of the neural signal to non-associative and associative processes. This kind of deconstruction of the elements of conditioning offers a strong account of how the brain processes the stimuli used and their interaction during learning.

Excellent use of data acquired with fiber photometry in the optogenetic interrogation study.

The use of pharmacology to disentangle dopamine and norepinephrine was excellent.

Comments on revised version:

The authors have thoroughly and thoughtfully addressed my prior concerns.

Reviewer #1 (Public Review):

Summary:

The authors conducted two tasks at 300 days separation. First, a social perception task, where Ps responded whether a pictured person either deserved or needed help. Second, an altruism task, where Ps are offered monetary allocations for themselves and a partner. Ps decide whether to accept, or a default allocation of 20 dollars each. The partners differed in perceived merit, such that they were highly deserving, undeserving or unknown. This categorisation was decided on the basis of a prisoners dilemma game the partner played beforehand. "Need" was also manipulated, by altering the probability that the partner must have their hand in cold water at the end of the experiment and this partner can use the money to buy themselves out. These two tasks were conducted to assess the perception of need/merit in the first instance, and how this relates to social behaviour in the second. fMRI data were collected alongside behavioural.

The authors present many analyses of behaviour (including DDM results) and fMRI. E.g., they demonstrate that they could decode across the mentalising network whether someone was making a need or deserving judgement vs control judgements but couldn't decode need vs deserving. And that brain responses during merit inferences (merit - control) systematically covaried with participants' merit sensitivity scores in the rTPJ. They also found relationships between behaviour and rTPJ in the altruism task. And that merit sensitivity in the perception task predicted influence of merit on social behaviour in the altruism task.

Strengths:

This manuscript represents a sensible model to predict social perceptions and behaviours, and a tidy study design with interesting findings. The introduction introduced the field especially brilliantly for a general audience.

Weaknesses:

These are small samples. This is especially the case for the correlational questions. The limitation is acknowledged, but does mean that we cannot conclude much from absent relationships, where the likelihood of Type II error is high.

Decoding analyses. The authors decode need vs merit, and need+merit vs control, not the content of these inferences. The logic of these analyses implies that there is a distributed representation of merit that does not relate to its content but is an abstracted version that applies to all merit judgements. However, these analyses are not central to the authors' aims and conclusions, so this is just a minor point.

Reviewer #2 (Public Review):

When people help others is an important psychological and neuroscientific question. It has received much attention from the psychological side, but comparatively less from neuroscience. The paper translates some ideas from a social Psychology domain to neuroscience using a neuroeconomically oriented computational approach. In particular, the paper is concerned with the idea that people help others based on perceptions of merit/deservingness, but also because they require/need help. To this end, the authors conduct two experiments with an overlapping participant pool:

(1) A social perception task in which people see images of people that have previously been rated on merit and need scales by other participants. In a blockwise fashion, people decide to whether the depicted person a) deserves help, b) needs help, and c) whether the person uses both hands (== control condition)<br /> (2) In an altruism task, people make costly helping decisions by deciding between giving a certain amount of money to themselves or another person. It is manipulated how much the other person needs and deserves the money.<br /> The authors use sound and robust computational modelling approach for both tasks using evidence accumulation models. They analyse behavioural data for both tasks, showing that the behaviour is indeed influenced, as expected, by the deservingness and the need of the shown people. Neurally, the authors use a block-wise analysis approach to find differences in activity levels across conditions of the social perception task. The authors do find large activation clusters in areas related to theory of mind. Interestingly, they also find that activity in TPJ that relates to the deservingness condition correlates with people's deservingness ratings while they do the task, but also with computational parameters related to helping others in the second task, the one that was conducted many months later. Also some behavioural parameters correlate across the two tasks, suggesting that how deserving of help others are perceived reflects a relatively stable feature that translates into concrete helping decisions later-on.

The conclusions of the paper are overall well supported by the data.

(1) I found that the modelling was done very thoroughly for both tasks. Overall, I had the impression that the methods are very solid with many supplementary analyses. The computational modelling is done very well.<br /> (2) A slight caveat, however, regarding this aspect, is that, in my view, the tasks are relatively simplistic, so that even the complex computational models do not as much as they can in the case of more complex paradigms. For example, the bias term in the model seems to correspond to the mean response rate in a very direct way (please correct me if I am wrong).<br /> (3) Related to the simple tasks: The fMRI data is analysed in a simple block-fashion. This is in my view not appropriate to discern the more subtle neural substrates of merit/need-based decision making or person perception. Correspondingly, the neural activation patterns (merit > control, need > control) are relatively broad and unspecific. They do not seem to differ in the classic theory of mind regions, that are the focus of the analyses.<br /> (4) However, the relationship between neural signal and behavioural merit sensitivity in TPJ is noteworthy.<br /> (5) The latter is even more the case, as the neural signal and aspects of the behaviour are correlated across subjects with the second task that is conducted much later. Such a correlation is very impressive and suggests that the tasks are sensitive for important individual differences in helping perception/behaviour.<br /> (6) That being said, the number of participants in the latter analyses are at the lower end of the number of participants that are these days used for across-participant correlations.

Reviewer #3 (Public Review):

Summary: The paper aims at providing a neurocomputational account on how social perception translates in prosocial behaviors. Participants first completed a novel social perception task during fMRI scanning, in which were asked to judge the merit or need of people depicted in different situations. Second , a separate altruistic choice task was used to examine how the perception of merit and need influences the weights people place on themselves, others and fairness when deciding to provide help. Finally, a link between perception and action was drawn in those participants who completed both tasks.

Strengths: The paper is overall very well written and presented, leaving the reader at ease when describing complex methods and results. The approach used by the author is very compelling, as it combines computational modeling of behavior and neuroimaging data analyses. Despite not being able to comment on the computational model, I find the approach used (to disentangle sensitivity and biases, for merit and need) very well described and derived from previous theoretical work. Results are also clearly described and interpreted.

Weaknesses: in the social perception task, merit and need are evaluated by means of very different cues that rely on different cognitive processes (more abstract thinking for merit than need). Despite this limitation of the task, the authors were able to argue convincingly in the revised version about the solidity of their findings. Sample size is quite small for study 2, nevertheless the results provide convincing evidence.

Reviewer #1 (Public Review):

Summary:<br /> A long literature in cognitive neuroscience studies how humans and animals adjudicate between conflicting goals. However, despite decades of research on the topic, a clear computational account of control has been difficult to pin down. In this project, Petri, Musslick, & Cohen attempt to formalize and quantify the problem of control in the context of toy neural networks performing conflicting tasks.

This manuscript builds on the formalism introduced in Petri et al (2021), "Topological limits to the parallel processing capability of network architectures", which describes a set of tasks as a graph in which input nodes (stimuli) are connected to output nodes (responses). Each edge in this graph links an input node to an output node, representing a "task"; i.e. a word reading task connects the input node "word" to the output node "read". Cleverly, patterns of interference and conflict between tasks can be quantified from this graph. In the current manuscript, the authors extend this framework by converting these graphs into neural networks and a) allowing edges to be continuous rather than binary; b) introducing "hidden layers" of units between input and output nodes; and c) introducing a "control" signal that modulates edge weights. The authors then examine how, in such a network, optimal behavior may involve serial versus parallel execution of different sets of tasks.

Strengths:<br /> There is a longstanding belief in cognitive neuroscience that "control" manages conflicts by scheduling tasks to be executed in parallel versus serially; I applaud the efforts of the authors to give these intuitions a more concrete computational grounding.

My main scientific concern is that the authors focus on what seems like an arbitrary set of network architectures. The networks considered here are derived by converting task graphs, which represent a multitasking problem, into networks for _performing_ that multitasking problem. Frankly, these networks do not look like any neural network a computer scientist would use to actually solve a problem, nor do they seem biologically realistic. Furthermore, adding hidden layers to these networks only ever seems to make performance worse (Figures 4, 11), introducing unnecessary noise and interference; it would seem more useful to study a network architecture in which hidden layers fulfilled some useful purpose (as they do in the brain and machine learning).

However, this scientific concern is secondary to the major problem with this paper, which is clarity.

Major problem: A lack of clarity

I found this paper extremely difficult to read. To illustrate my difficulty, I will describe a subset of my confusion.

The authors define the "entropy" of an action in equation 1, but the content of the equation gives what is sometimes referred to as the "surprisal" of the action. Conventionally (as per Wikipedia and any introductory textbook I am familiar with), entropy is the "expected surprisal" of a random variable, not the surprisal of a single action. This creates immediate confusion going into the results. Furthermore, defining "entropy" this way means that "information" is functionally equivalent to accuracy for the purposes of this paper, in which case I do not know what has been gained by this excursion into (non-standard) information-theoretic terminology.

They next assert that equation 1 is the information _cost_ of an action. No motivation is given for this statement and I do not know what it means. In what sense is a "cost" associated with the negative logarithm of a probability?

In the next section II.B, the authors introduce a new formalism in which responses are represented by task graph nodes _R_. What is the relationship between an action _a_ and the responses _R_? Later, in section II.C, edges _f_ in the task graph are used as seemingly drop-in replacements for actions _a_.

I simply have no idea what is going on in equations 31 through 33. Where are the functions _R_ (not to be confused with the response nodes _R_) and _S_ defined? Or how are they approximated? What does the variable _t_ mean and why does it appear and disappear from equations seemingly at random?

Response times seem to be important, but as far as I can tell, nowhere do the authors actually describe how response times are calculated for the simulated networks.

Similar issues persist through the rest of the paper: unconventional formalism is regularly introduced using under-explained notation and without a clear relationship to the scientific questions at hand. As a result, the content and significance of the findings are largely inscrutable to me, and I suspect also to the vast majority of readers.

Reviewer #2 (Public Review):

Summary:<br /> The authors develop a normative account of automaticity-control trade-offs using the mathematics of information theory, which they apply to abstract neural networks. They use this framework to derive optimal trade-off solutions under particular task conditions.

Strengths:<br /> On the positive side, I appreciate the effort to rigorously synthesize ideas about multi-tasking within an information-theoretic framework. There is potentially a lot of promise in this approach. The analyis is quite comprehensive and careful.

Weaknesses:<br /> Generally speaking, the paper is very long and dense. I don't in principle mind reading long and dense papers (though conciseness is a virtue); it becomes more of a slog when it's not clear what new insights are being gained from laboring through the math. For example, after reading the Stroop section, I wasn't sure what new insight was provided by the information-theoretic formalism which goes beyond earlier models. Is this just an elegant formalism for expressing previously conceived ideas, or is there something fundamentally new here that's not predicted by other frameworks? The authors cite multiple related frameworks addressing the same kinds of data, but there is no systematic comparison of predictions or theoretical interpretations. Even in the Discussion, where related work is directly addressed, I didn't see much in terms of explaining how different models made different predictions, or even what predictions any of them make.

After a discussion of the Stroop task early in the paper, the analysis quickly becomes disconnected from any empirical data. The analysis could be much more impactful if it was more tightly integrated with relevant empirical data.

Reviewer #1 (Public Review):

Summary:<br /> The manuscript describes a series of experiments using human intracranial neural recordings designed to evaluate the processing of self-generated speech in the setting of feedback delays. Specifically, the authors aim to address the question about the relationship between speech-induced suppression and feedback sensitivity in the auditory cortex, whose relationship has been conflicting in the literature. They found a correlation between speech suppression and feedback delay sensitivity, suggesting a common process. Additional controls were done for possible forward suppression/adaptation, as well as controlling for other confounds due to amplification, etc.

Strengths:<br /> The primary strength of the manuscript is the use of human intracranial recording, which is a valuable resource and gives better spatial and temporal resolution than many other approaches. The use of delayed auditory feedback is also novel and has seen less attention than other forms of shifted feedback during vocalization. Analyses are robust, and include demonstrating a scaling of neural activity with the degree of feedback delay, and more robust evidence for error encoding than simply using a single feedback perturbation.

Weaknesses:<br /> Some of the analyses performed differ from those used in past work, which limits the ability to directly compare the results. Notably, past work has compared feedback effects between production and listening, which was not done here. There were also some unusual effects in the data, such as increased activity with no feedback delay when wearing headphones, that the authors attempted to control for with additional experiments, but remain unclear. Confounds by behavioral results of delayed feedback are also unclear.

Overall the work is well done and clearly explained. The manuscript addresses an area of some controversy and does so in a rigorous fashion, namely the correlation between speech-induced suppression and feedback sensitivity (or lack thereof). While the data presented overlaps that collected and used for a previous paper, this is expected given the rare commodity these neural recordings represent. Contrasting these results to previous ones using pitch-shifted feedback should spawn additional discussion and research, including verification of the previous finding, looking at how the brain encodes feedback during speech over multiple acoustic dimensions, and how this information can be used in speech motor control.

Reviewer #2 (Public Review):

Summary:<br /> In "Speech-induced suppression and vocal feedback sensitivity in human cortex", Ozker and colleagues use intracranial EEG to understand audiomotor feedback during speech production using a speech production and delayed auditory feedback task. The purpose of the paper is to understand where and how speaker-induced suppression occurs, and whether this suppression might be related to feedback monitoring. First, they identified sites that showed auditory suppression during speech production using a single-word auditory repetition task and a visual reading task, then observed whether and how these electrodes show sensitivity to auditory feedback using a DAF paradigm. The stimuli were single words played auditorily or shown visually and repeated or read aloud by the participant. Neural data were recorded from regular- and high-density grids from the left and right hemispheres. The main findings were:<br /> • Speaker-induced suppression is strongest in the STG and MTG, and enhancement is generally seen in frontal/motor areas except for small regions of interest in the dorsal sensorimotor cortex and IFG, which can also show suppression.<br /> • Delayed auditory feedback, even when simultaneous, induces larger response amplitudes compared to the typical auditory word repetition and visual reading tasks. The authors presume this may be due to the effort and attention required to perform the DAF task.<br /> • The degree of speaker-induced suppression is correlated with sensitivity to delayed auditory feedback.<br /> • pSTG (behind TTS) is more strongly modulated by DAF than mid-anterior STG

Strengths:<br /> Overall, I found the manuscript to be clear, the methodology and statistics to be solid, and the findings mostly quite robust. The large number of participants with high-density coverage over both the left and right lateral hemispheres allows for a greater dissection of the topography of speaker-induced suppression and changes due to audiomotor feedback. The tasks were well-designed and controlled for repetition suppression and other potential caveats.

Weaknesses:<br /> (1) In Figure 1D, it would make more sense to align the results to the onset of articulation rather than the onset of the auditory or visual cue, since the point is to show that the responses during articulation are relatively similar. In this form, the more obvious difference is that there is an auditory response to the auditory stimulus, and none to the visual, which is expected, but not what I think the authors want to convey.<br /> (2) The DAF paradigm includes playing auditory feedback at 0, 50, 100, and 200 ms lag, and it is expected that some of these lags are more likely to induce dysfluencies than others. It would be helpful to include some analysis of whether the degree of suppression or enhancement varies by performance on the task, since some participants may find some lags more interfering than others.<br /> (3) Figure 3 shows data from only two electrodes from one patient. An analysis of how amplitude changes as a function of the lag across all of the participants who performed this task would be helpful to see how replicable these patterns of activity are across patients. Is sensitivity to DAF always seen as a change in amplitude, or are there ever changes in latency as well? The analysis in Figure 4 gets at which electrodes are sensitive to DAF but does not give a sense of whether the temporal profile is similar to those shown in Figure 3.<br /> (4) While the sensitivity index helps to show whether increasing amounts of feedback delay are correlated with increased response enhancement, it is not sensitive to nonlinear changes as a function of feedback delay, and it is not clear from Figure 3 or 4 whether such relationships exist. A deeper investigation into the response types observed during DAF would help to clarify whether this is truly a linear relationship, dependent on behavioral errors, or something else.

Reviewer #1 (Public Review):

Summary:<br /> I really enjoyed this manuscript from Torsekar et al on "Contrasting responses to aridity by different-sized decomposers cause similar decomposition rates across a precipitation gradient". The authors aimed to examine how climate interacts with decomposers of different size categories to influence litter decomposition. They proposed a new hypothesis: "The opposing climatic dependencies of macrofauna and that of microorganisms and mesofauna should lead to similar overall decomposition rates across precipitation gradients".

This study emphasizes the importance as well as the contribution of different groups of organisms (micro, meso, macro, and whole community) across different seasons (summer with the following characteristics: hot with no precipitation, and winter with the following characteristics: cooler and wetter winter) along a precipitation gradient. The authors made use of 1050 litter baskets with different mesh sizes to capture decomposers contribution. They proposed a new hypothesis that was aiming to understand the "dryland decomposition conundrum". They combined their decomposition experiment with the sampling of decomposers by using pittfall traps across both experiment seasons. This study was carried out in Israel and based on a single litter species that is native to all seven sites. The authors found that microorganism contribution dominated in winter while macrofauna decomposition dominated the overall decomposition in summer. These seasonality differences combined with the differences in different decomposers groups fluctuation along precipitation resulted in similar overall decomposition rates across sites.<br /> I believe this manuscript has a potential to advance our knowledge on litter decomposition.

Strengths:<br /> Well design study with combination of different approaches (methods) and consideration of seasonality to generalize pattern.<br /> The study expands to current understanding of litter decomposition and interaction between factors affecting the process (here climate and decomposers).

Weaknesses:<br /> The study was only based on a single litter species.

Reviewer #2 (Public Review):

Summary: Torsekar et al. use a leaf litter decomposition experiment across seasons, and in an aridity gradient, to provide a careful test of the role of different-sized soil invertebrates in shaping the rates of leaf litter decomposition. The authors found that large-sized invertebrates are more active in the summer and small-sized invertebrates in the winter. The summed effects of all invets then translated into similar levels of decomposition across seasons. The system breaks down in hyper-arid sites.

Strengths: This is a well-written manuscript that provides a complete statistical analysis of a nice dataset. The authors provide a complete discussion of their results in the current literature.

Weaknesses: I have only three minor comments. Please standardize the color across ALL figures (use the same color always for the same thing, and be friendly to color-blind people). Fig 1 may benefit from separating the orange line (micro and meso) into two lines that reflect your experimental setup and results. I would mention the dryland decomposition conundrum earlier in the Introduction. And the manuscript is full of minor grammatical errors. Some careful reading and fixing of all these minor mistakes here and there would be needed.

Reviewer #1 (Public Review):

Summary:<br /> In this article, the authors investigate whether the connectivity of the hippocampus is altered in individuals with aphantasia ¬- people who have reduced mental imagery abilities and where some describe having no imagery, and others describe having vague and dim imagery. The study investigated this question using a fMRI paradigm, where 14 people with aphantasia and 14 controls were tested, and the researchers were particularly interested in the key regions of the hippocampus and the visual-perceptual cortices. Participants were interviewed using the Autobiographical Interview regarding their autobiographical memories (AMs), and internal and external details were scored. In addition, participants were queried on their perceived difficulty in recalling memories, imagining, and spatial navigation, and their confidence regarding autobiographical memories was also measured. Results showed that participants with aphantasia reported significantly fewer internal details (but not external details) compared to controls; that they had lower confidence in their AMs; and that they reported finding remembering and imagining in general more difficult than controls. Results from the fMRI section showed that people with aphantasia displayed decreased hippocampal and increased visual-perceptual cortex activation during AM retrieval compared to controls. In contrast, controls showed strong negative functional connectivity between the hippocampus and the visual cortex. Moreover, resting state connectivity between the hippocampus and visual cortex predicted better visualisation skills. The authors conclude that their study provides evidence for the important role of visual imagery in detail-rich vivid AM, and that this function is supported by the connectivity between the hippocampus and visual cortex. This study extends previous findings of reduced episodic memory details in people with aphantasia, and enables us to start theorising about the neural underpinnings of this finding.

The data provided good support for the conclusion that the authors draw, namely that there is a 'tight link between visual imagery and our ability to retrieve vivid and detail-rich personal past events'. However, as the authors also point out, the exact nature of this relationship is difficult to infer from this study alone, as the slow temporal resolution of fMRI cannot establish the directionality between the hippocampus and the visual-perceptual cortex. This is an exciting future avenue to explore.

Weaknesses:<br /> A weakness of the study is that some of the questions used are a bit vague, and no objective measure is used, which could have been more informative. For example, the spatial navigation question (reported as 'How difficult is it typically for you to orient you spatially?' - a question which is ungrammatical, but potentially reflects a typo in the manuscript) could have been more nuanced to tap into whether participants relied mostly on cognitive maps (likely supported by the hippocampus) or landmarks. It would also have been interesting to conduct a spatial navigation task, as participants do not necessarily have insight into their spatial navigation abilities (they could have been overconfident or underconfident in their abilities). Secondly, the question 'how difficult is it typically for you to use your imagination?' could also be more nuanced, as imagination is used in a variety of ways, and we only have reason to hypothesise that people with aphantasia might have difficulties in some cases (i.e. sensory imagination involving perceptual details). It is unlikely that people with aphantasia would have more difficulty than controls in using their imagination to imagine counterfactual situations and engage in counterfactual thought (de Brigard et al., 2013, https://doi.org/10.1016%2Fj.neuropsychologia.2013.01.015) due to its non-sensory nature, but the question used does not distinguish between these types of imagination. Again, this is a ripe area for future research. The general phrasing of 'how difficult is [x]' could also potentially bias participants towards more negative answers, something which ought to be controlled for in future research.

Strengths:<br /> A great strength of this study is that it introduces a fMRI paradigm in addition to the autobiographical interview, paralleling work done on episodic memory in cognitive science (e.g. Addis and Schacter, 2007, https://doi.org/10.1016%2Fj.neuropsychologia.2006.10.016 ), which has examined episodic and semantic memory in relation to imagination (future simulation) in non-aphantasic participants as well as clinical populations. Future work could build on this study, and for example use the recombination paradigm (Addis et al. 2009, 10.1016/j.neuropsychologia.2008.10.026 ), which would shed further light on the ability of people with aphantasia to both remember and imagine events. Future work could also build on the interesting findings regarding spatial navigation, which together with previous findings in aphantasia (e.g. Bainbridge et al., 2021, https://doi.org/10.1016/j.cortex.2020.11.014 ) strongly suggests that spatial abilities in people with aphantasia are unaffected. This can shed further light on the different neural pathways of spatial and object memory in general. In general, this study opens up a multitude of new avenues to explore and is likely to have a great impact on the field of aphantasia research.

Reviewer #2 (Public Review):

Summary:<br /> This study investigates to what extent neural processing of autobiographical memory retrieval is altered in people who are unable to generate mental images ('aphantasia'). Self-report as well as objective measures were used to establish that the aphantasia group indeed had lower imagery vividness than the control group. The aphantasia group also reported fewer sensory and emotional details of autobiographical memories. In terms of brain activity, compared to controls, aphantasics had a reduction in activity in the hippocampus and an increase in activity in the visual cortex during autobiographical memory retrieval. For controls, these two regions were also functionally connected during autobiographical memory retrieval, which did not seem to be the case for aphantasics. Finally, resting-state connectivity between the visual cortex and hippocampus was positively related to autobiographical vividness in the control group but negatively in the aphantasia group. The results are in line with the idea that aphantasia is caused by an increase in noise within the visual system combined with a decrease in top-down communication from the hippocampus.

Recent years have seen a lot of interest in the influence of aphantasia on other cognitive functions and one of the most consistent findings is deficits in autobiographical memory. This is one of the first studies to investigate the neural correlates underlying this difference, thereby substantially increasing our understanding of aphantasia and the relationship between mental imagery and autobiographical memory.

Strengths:<br /> One of the major strengths of this study is the use of both self-report as well as objective measures to quantify imagery ability. Furthermore, the fMRI analyses are hypothesis-driven and reveal unambiguous results, with alterations in hippocampal and visual cortex processing seeming to underlie the deficits in autobiographical memory.

Weaknesses:<br /> In terms of weaknesses, the control task, doing mathematical sums, also differs from the autobiographical memory task in aspects that are unrelated to imagery or memory, such as self-relevance and emotional salience, which makes it hard to conclude that the differences in activity are reflecting only the cognitive processes under investigation.

Overall, I believe that this is a timely and important contribution to the field and will inspire novel avenues for further investigation.

Reviewer #1 (Public Review):

Summary:

This manuscript from Park et al examines the molecular, anatomical and functional properties of a subset of wide-field amacrine cell (WAC) types in mouse retina. More than 60 mouse amacrine cell types have been identified by single-cell transcriptomic studies (Yan et al., 2020, PMID: 32457074), but the functions of most of these are unknown and WACs are particularly understudied. The authors use intersectional genetics to target a subset of mouse WACs that co-express Bhlhe22 and the kappa opioid receptor (referred to as B/K WACs). They used electrophysiological and anatomical approaches to determine how WACs contribute to neural computations in the retina.

Strengths:

Overall, the paper presents a technically impressive set of experiments that build strong evidence for the presence of at least 3 discrete WAC types in the B/K transgenic line. These cells vary with respect to their morphology, dendritic stratification, response polarity (On vs Off) and resting membrane potentials. All types have long, monostratified dendrites and appear to lack axons. Electrophysiological recordings establish that these WACs are non-spiking, while calcium imaging revealed orientation selectivity in dendritic segments with tuning that correlates strongly with dendritic orientation. The authors go on to use optogenetics to show that WACs provide strong GABA-A receptor mediated inhibitory input to OFF and ON alpha sustained RGCs. This connectivity is further substantiated, at least for the OFF sustained alpha RGCs, by connectomic analyses from serial block face EM volumes. The use of the APEX2 reporter system to label the B/K cells in one of the EM volumes is particularly nice, making identification of the B/K WACs unambiguous. The conclusions are largely well supported by the experimental data. The study provides novel insights into the structure and function of specific WACs that will provide a foundation for further studies investigating the role of these amacrine cells in retinal circuits.

Weaknesses:

A limitation of the study is that the B/K WAC types described here could not be aligned to specific transcriptomic identities. The authors show more than 15 GABA expressing ACs express Bhlhe22 in the transcriptomic dataset, but it is unclear which of these also express the kappa opioid receptor (Opkr1).

The optogenetic evidence suggests that WACs provide GABA-A receptor mediated inhibitory input to both the sustained OFF and ON alpha RGCs. However, at least in the examples shown, there appears to be a dramatic difference in the timecourse of the rising phase of the inhibitory inputs to these two cell types, with the inputs to the ON sustained alpha RGCs appearing slower than those in the OFF sustained and OFF transient alpha RGCs. This apparent temporal difference was accompanied by a relatively lower sensitivity to light stimulation for the ON sustained cells. The slow timecourse seems unexpected for a direct GABA-A mediated synaptic connections between the WACs and ON alpha sustained cells. Moreover, since the connectomic analyses do not examine inputs to ON RGC types, the direct synaptic connection between B/K WACs and On alpha RGC is less well substantiated.

Reviewer #2 (Public Review):

Summary:

An important frontier in research on the mammalian retina is to understand the role of inhibitory amacrine cells in visual processing. These cell types have been found to play roles in tuning the output of the retina to specific visual features like motion and orientation. These cell types are understudied for two main reasons. First, there are many types of them-over 60 types in the mouse--, and second, they are quite unconventional as far as neurons go, as they have dendrites but often lack axons. The manuscript "Molecular identification of wide-field amacrine cells in mouse retina that encode stimulus orientation" by Park et al. provides a characterization of two (or possibly more) cell types within the amacrine cell class. Specifically, they characterize types of widefield amacrine cells (WACs), which they have gained genetic access to using an intersectional transgenic mouse strategy (Bhlhe22 x KOR). The authors used a broad range of experiments to characterize these WACs' anatomical properties, their stimulus tuning, and their wiring within the retina to their postsynaptic partners. These experiments include anatomy, electrophysiology, calcium imaging, and electron microscopy.

Strengths:

Overall, the manuscript presents strong evidence that the Bhlhe22 x KOR WACs represent multiple WAC types in the retina and that these cell types are orientation tuned. The most exciting finding is that their orientation tuning is correlated with the physical orientations of the dendrites, which suggests that this anatomical feature supports the tuning in these cells.

Weaknesses:

(1) The one common thought about widefield amacrine cells (WACs) is that these are spiking cells, which allows them to transmit signals along their long dendrites. The authors state that "none of the recorded cells fired conventional action potentials (spikes)." (p.7) This is a surprising finding, which leads to an interesting question: how do these cells integrate information from their presynaptic partners to generate the orientation tuning observed without the ability to conduct over long distances? However, the authors have not fully ruled out that the cells do spike.<br /> For instance, one possibility is that spiking requires a specific stimulus and the authors did not play that stimulus during their recordings. Most somatic recordings did not result in very large depolarizations, and the cell could still be below threshold. Depolarizing the cell to attempt to evoke spikes directly could be used to explore this possibility. A second possibility is that the dendrites spike, but these spikes are attenuated at the soma. Direct injections of current into the cells to evoke such spikes could be used to observe whether dendritic spiking occurs. A third possibility is that some important machinery for spiking is being washed out by the whole cell recordings. Cell attached recordings could be used to assess whether spiking occurs in an intact cell. The authors may wish to address these possibilities experimentally, but at least should qualify their statement about spiking in these cells and discuss these possibilities.

(2) It was unclear in this paper how many cell types are present in the intersectional cross. I think the paper would be stronger if they clarified that. For instance, in Fig. 1B: the authors show Bhlhe22 expression in amacrine cells from a previous study. They should also show the expression of the other gene they used in their intersectional strategy, the Kappa Opioid receptor (Oprk1), which is available in the same dataset. Another piece of analysis that could help would be clearer quantification of the anatomical features of the cells. For instance, the cells shown in Fig. 2 A2 vs. B2 have clear differences in number of dendrites and the relative angles of the dendrites. The On cells appear to have more dendrites evenly spread around the soma, while the Off cells appear to have more clumping along a line. Is this the case for all the cells recorded, or just these examples? The authors should present some population-level quantification.

(3) In Fig. 4E, the preferred orientation of calcium responses and physical orientation of the dendrites appears to clump around specific orientations. The Methods don't mention if the retinas were aligned to the body axis during the dissection. Is this clumping real, or is this an artifact of the analysis? If there are specific preferred orientations to these WAC cell types, that would be important to discuss in the paper - for instance how this relates to the preferred direction in the direction selectivity system or how it might relate to the function of these cells for behavior.

Reviewer #3 (Public Review):

Summary:

Amacrine cells are a heterogeneous collection of retinal interneurons. Most are inhibitory, and like inhibitory neurons in other neural circuits, strongly shape retinal function. With a few exceptions, the role of amacrine cells in retinal signaling is poorly understood. This paper introduces an approach to study a set of wide-field amacrine cells that extend processes over large regions of the retina.

Strengths:

A substantial strength of the paper is the combination of genetic manipulations, electrophysiology, optogenetics and electron microscopy used to study these cells. As a result of that broad set of techniques, the results cover many properties of how the cells work and provide a nice overview. The paper is also (with a few exceptions below) clearly presented and the experiments look to be carefully executed with clean results.

Weaknesses:

My largest concern with the paper is that overall the results provided an initial view of an interesting set of issues about the function of these cells, but the interesting initial results are not pursued in more depth.

Spatial spread of signals in neurites<br /> An immediate question about axonless WACs is the extent of spread of signals along their processes, and hence whether they act as a collection of independent or semi-independent elements. This bears directly on interpretation of the responses to oriented stimuli for example. Did you do any experiments that might provide additional information about this issue? For example, if you stimulate one of the WACs peripherally do you see a strong modulation of the somatic voltage? Or in the imaging experiments, if you mask a region of the processes so that it is not receiving a stimulus, do you see responses "leak" into that occluded region from surrounding stimulated regions?

Orientation tuning and connectivity<br /> The most developed functional results in the paper relate to the sensitivity of the WAC processes to oriented stimuli. Interpretation of these results depends on a few factors. First is the spread of signals in the WAC processes - as noted above. Second is connectivity. The paper shows that the B/K WAC activity increases inhibitory input to Off-delta and On-alpha ganglion cells. These cells, as noted in the paper, are not orientation tuned. But the orientation tuned ganglion cells stratify in a similar location within the IPL, and hence are situated in an appropriate place to receive input from the B/K WACs. Did you focus exclusively on connections to the Off-delta and On-alpha cells (along with the Off-alpha) or did you look at any other ganglion cell types? This should at least get discussed in more detail.

In several places it is unclear whether the paper intends to be a methods paper or a basic research paper. One example is the last sentence of the abstract. If it is intended to be a basic research paper (which is my overall impression) then I suggest shifting the emphasis in some of those key locations towards results and away from methods.

Reviewer #1 (Public Review):

Summary:<br /> The thalamus is a central subcortical structure that receives anatomical connections from various cortical areas, each displaying a unique pattern. Previous studies have suggested that certain cortical areas may establish more extensive connections within the thalamus, influencing distributed information flow. Despite these suggestions, a quantitative understanding of cortical areas' anatomical connectivity patterns within the thalamus is lacking. In this study, the researchers addressed this gap by employing diffusion magnetic resonance imaging (dMRI) on a large cohort of healthy adults from the Human Connectome Project. Using brain-wide probabilistic tractography, a framework was developed to measure the spatial extent of anatomical connections within the thalamus for each cortical area. Additionally, the researchers integrated resting-state functional MRI, cortical myelin, and human neural gene expression data to investigate potential variations in anatomical connections along the cortical hierarchy. The results unveiled two distinct cortico-thalamic tractography motifs: 1) a sensorimotor cortical motif featuring focused thalamic connections to the posterolateral thalamus, facilitating fast, feed-forward information flow; and 2) an associative cortical motif characterized by diffuse thalamic connections targeting the anteromedial thalamus, associated with slower, feed-back information flow. These motifs exhibited consistency across human subjects and were corroborated in macaques, underscoring cross-species generalizability. In summary, the study illuminates differences in the spatial extent of anatomical connections within the thalamus for sensorimotor and association cortical areas, potentially contributing to functionally distinct cortico-thalamic information flow.

Strengths:<br /> * Quantitative Approach: The study employs diffusion magnetic resonance imaging (dMRI) and probabilistic tractography on a substantial sample size of 828 healthy adults, providing a robust quantitative analysis of anatomical connectivity patterns within the thalamus.

* Multi-Modal Integration: By incorporating resting-state functional MRI, cortical myelin, and human neural gene expression data, the study offers a comprehensive approach to understanding anatomical connections, enriching the interpretation of findings and enhancing the study's overall validity.

* Cross-Species Generalizability: The identification of consistent cortico-thalamic tractography motifs in both human subjects and macaques demonstrates the robustness and cross-species generalizability of the findings, strengthening the significance and broader applicability of the study.

* Supplementary Analyses: There are numerous, excellent examples of clear surrogates used to test the major claims of the paper. This is exemplary work.

Weaknesses:<br /> * Indirect Estimates of White Matter Connections: While dMRI is a valuable tool, it inherently provides indirect and inferred information about neural pathways. The accuracy and specificity of tractography can be influenced by various factors, including fiber crossing, partial volume effects, and algorithmic assumptions. A potential limitation in the accuracy of indirect estimates might affect the precision of spatial extent measurements, introducing uncertainty in the interpretation of cortico-thalamic connectivity patterns. Addressing the methodological limitations associated with indirect estimates and considering complementary approaches could strengthen the overall robustness of the findings.

Reviewer #2 (Public Review):

Summary:<br /> This paper by Howell and colleagues focuses on describing macro patterns of anatomical connections between cortical areas and the thalamus in the human brain. This research topic poses significant challenges due to the inability to apply the gold standard of mapping anatomical connections, and viral tracing, to humans. Moreover, when applied to animal models, viral tracing often has limited scope and resolution. As a result, the field has thus far lacked a comprehensive and validated description of thalamocortical anatomical connectivity in humans.

The paper focuses on an intriguing question: whether anatomical connections from the cortex to the thalamus exhibit a diffuse pattern, targeting multiple thalamic sub-regions, or a more focal pattern, selectively targeting specific thalamic subregions. This novel and significant question holds substantial implications for our understanding of thalamocortical information processing. The authors have developed a sophisticated and innovative quantitative metric to address this question. The study revealed two main patterns: a focal pattern originating from sensorimotor cortical regions to the posterior thalamus and a more diffuse pattern from associative cortical regions to the anterior-medial thalamus. These findings are then framed within the context of thalamocortical motifs involved in feedforward versus feedback processing.

While this paper has several strengths, including its significance and methodological sophistication, its extension to non-human primates, and other forms of data for testing hierarchy, there are important limitations. These limitations, discussed in more detail below, primarily concern tracking accuracy and the known limitations of using diffusion data to track thalamocortical connections in humans. These limitations may potentially introduce systematic biases into the results, weakening their support. Addressing these limitations through better validation is crucial, though some may remain unresolved due to the fundamental constraints of diffusion imaging.

Strengths:<br /> This research holds significant basic, clinical, and translational importance as it contributes to our understanding of how thalamocortical anatomical connectivity is organized. Its relevance spans cognitive, systems, and clinical neuroscientists in various subfields.

The central question addressed in this study, concerning whether cortico-thalamic projections are focal or diffuse, is both novel and previously unexplored to the best of my knowledge. It offers valuable insights into the potential capabilities of the thalamocortical system in terms of parallel or integrative processing.

The development of quantitative metrics to analyze anatomical connectivity is highly innovative and suitable for addressing the research questions at hand.

The findings are not only interesting but also robust, aligning with data from other sources that suggest a hierarchical organization in the brain.

Using PCA to integrate results across a range of thresholds is innovative.

The study's sophisticated integration of a diverse range of data and methods provides strong, converging support for its main findings, enhancing the overall credibility of the research.

Weaknesses:<br /> Structural thalamocortical connectivity was estimated from diffusion imaging data obtained from the HCP dataset. Consequently, the robustness and accuracy of the results depend on the suitability of this data for such a purpose. Conducting tractography on the cortical-thalamic system is recognized as a challenging endeavor for several reasons. First, diffusion directions lose their clearly defined principal orientations once they reach the deep thalamic nuclei, rendering the tracking of structures on the medial side, such as the medial dorsal (MD) and pulvinar nuclei difficult. Somewhat concerning is those are regions that authors found to show diffuse connectivity patterns. Second, the thalamic radiata diverge into several directions, and routes to the lateral surface often lack the clarity necessary for successful tracking. It is unclear if all cortical regions have similar levels of accuracy, and some of the lateral associative regions might have less accurate tracking, making them appear to be more diffuse, biasing the results.

While the methodology employed by the authors appears to be state-of-the-art, there exists uncertainty regarding its appropriateness for validation, given the well-documented issues of false positives and false negatives in probabilistic diffusion tractography, as discussed by Thomas et al. 2014 PNAS. Although replicating the results in both humans and non-human primates strengthens the study, a more compelling validation approach would involve demonstrating the method's ability to accurately trace known tracts from established tracing studies or, even better, employing phantom track data. Many of the control analyses the authors presented, such as track density, do not speak to accuracy.

Because the authors included data from all thresholds, it seems likely that false positive tracks were included in the results. The methodology described seems to unavoidably include anatomically implausible pathways in the spatial extent analyses.

If tracking the medial thalamus is indeed less accurate, characterized by higher false positives and false negatives, it could potentially lead to increased variability among individual subjects. In cases where results are averaged across subjects, as the authors have apparently done, this could inadvertently contribute to the emergence of the "diffuse" motif, as described in the context of the associative cortex. This presents a critical issue that requires a more thorough control analysis and validation process to ensure that the main results are not artifacts resulting from limitations in tractography.

The thresholding approach taken in the manuscript aimed to control for inter-areal differences in anatomical connection strength that could confound the ED estimates. Here I am not quite clear why inter-areal differences in anatomical connection strength have to be controlled. A global threshold applied on all thalamic voxels might kill some connections that are weak but do exist. Those weak pathways are less likely to survive at high thresholds. In the meantime, the mean ED is weighted, with more conservative thresholds having higher weights. That being said, isn't it possible that more robust pathways might contribute more to the mean ED than weaker pathways?

Reviewer #3 (Public Review):

Summary:<br /> In the current work, Howell et al studied the connectivity between the cortex and thalamus using DTI tractorgraphy per parcel to all voxels in the thalamus. Following they performed various dimensional reduction techniques to uncover how differences in connectivity to the thalamus vary across cortical parcels. Following they explore the spatial correlation of these variations with cortical myelin and functional organization, thalamic nuclei, gene expression derived core-matrix cell differentiation, and extend the model towards macaques. Overall, the authors find a differentiation between sensory and association areas in terms of the association with the thalamus, which reflects differences in cortical microstructure and function, and links to core-matrix differences and can be replicated in macaques.

Strengths:<br /> A clear strength of the current work is the combination of different models and approaches to study the link between the cortex and the thalamus. This approach nicely bridges different approaches to describe the role of the thalamus in cortical organisation using a diffusion-based approach. Especially the extension of the model to the macaque is quite nice.

Weaknesses:<br /> Potential weaknesses of the study are that it seems to largely integrate aspects of the thalamus that have been already described before. The differentiation between sensory and association systems across thalamic subregions is something that has been described before (see: Oldham and Ball, 2023; Zheng et al., 2023; Yang et al., 2020 Mueller, 2020; Behrens, 2003).

Appraisal:<br /> However, the aim of the study: 'to investigate the spatial extent of anatomical connectivity patterns within the thalamus in both humans and non-human primates and determine if such patterns differ between sensorimotor and association cortical areas' has been met.

Discussion:<br /> Overall, I think the study is a nice addition to the growing literature studying the anatomical connectivity between the thalamus and cortex and its functional implications.

Reviewer #1 (Public Review):

Summary:<br /> Wilmes and colleagues present a computational model of a cortical circuit for predictive processing which tackles the issue of how to learn predictions when different levels of uncertainty are present for the predicted sensory stimulus. When a predicted sensory outcome is highly variable, deviations from the average expected stimulus should evoke prediction errors that have less impact on updating the prediction of the mean stimulus. In the presented model, layer 2/3 pyramidal neurons represent either positive or negative prediction errors, SST neurons mediate the subtractive comparison between prediction and sensory input, and PV neurons represent the expected variance of sensory outcomes. PVs therefore can control the learning rate by divisively inhibiting prediction error neurons such that they are activated less, and exert less influence on updating predictions, under conditions of high uncertainty.

Strengths:<br /> The presented model is a very nice solution to altering the learning rate in a modality and context-specific way according to expected uncertainty and, importantly, the model makes clear, experimentally testable predictions for interneuron and pyramidal neuron activity. This is therefore an important piece of modelling work for those working on cortical and/or predictive processing and learning. The model is largely well-grounded in what we know of the cortical circuit.

Weaknesses:<br /> Currently, the model has not been challenged with experimental data, presumably because data from an adequate paradigm is not yet available. I therefore only have minor comments regarding the biological plausibility of the model:

Beyond the fact that some papers show SSTs mediate subtractive inhibition and PVs mediate divisive inhibition, the selection of interneuron types for the different roles could be argued further, given existing knowledge of their properties. For instance, is a high PV baseline firing rate, or broad sensory tuning that is often interpreted as a 'pooling' of pyramidal inputs, compatible with or predicted by the model?

On a related note, SSTs are thought to primarily target the apical dendrite, while PVs mediate perisomatic inhibition, so the different roles of the interneurons in the model make sense, particularly for negative PE neurons, where a top-down excitatory predicted mean is first subtractively compared with the sensory input, s, prior to division by the variance. However, sensory input is typically thought of as arising 'bottom-up', via layer 4, so the model may match the circuit anatomy less in the case of positive PE neurons, where the diagram shows 's' arising in a top-down manner. Do the authors have a justification for this choice?

In cortical circuits, assuming a 2:8 ratio of inhibitory to excitatory neurons, there are at least 10 pyramidal neurons to each SST and PV neuron. Pyramidal neurons are also typically much more selective about the type of sensory stimuli they respond to compared to these interneuron classes (e.g., Kerlin et al., 2012, Neuron). A nice feature of the proposed model is that the same interneurons can provide predictions of the mean and variance of the stimulus in a predictor-dependent manner. However, in a scenario where you have two types of sensory stimulus to predict (e.g., two different whiskers stimulated), with pyramidal neurons selective for prediction errors in one or the other, what does the model predict? Would you need specific SST and PV circuits for each type of predicted stimulus?

Reviewer #2 (Public Review):

Summary:<br /> This computational modeling study addresses the observation that variable observations are interpreted differently depending on how much uncertainty an agent expects from its environment. That is, the same mismatch between a stimulus and an expected stimulus would be less significant, and specifically would represent a smaller prediction error, in an environment with a high degree of variability than in one where observations have historically been similar to each other. The authors show that if two different classes of inhibitory interneurons, the PV and SST cells, (1) encode different aspects of a stimulus distribution and (2) act in different (divisive vs. subtractive) ways, and if (3) synaptic weights evolve in a way that causes the impact of certain inputs to balance the firing rates of the targets of those inputs, then pyramidal neurons in layer 2/3 of canonical cortical circuits can indeed encode uncertainty-modulated prediction errors. To achieve this result, SST neurons learn to represent the mean of a stimulus distribution and PV neurons its variance.

The impact of uncertainty on prediction errors is an understudied topic, and this study provides an intriguing and elegant new framework for how this impact could be achieved and what effects it could produce. The ideas here differ from past proposals about how neuronal firing represents uncertainty. The developed theory is accompanied by several predictions for future experimental testing, including the existence of different forms of coding by different subclasses of PV interneurons, which target different sets of SST interneurons (as well as pyramidal cells). The authors are able to point to some experimental observations that are at least consistent with their computational results. The simulations shown demonstrate that if we accept its assumptions, then the authors' theory works very well: SSTs learn to represent the mean of a stimulus distribution, PVs learn to estimate its variance, firing rates of other model neurons scale as they should, and the level of uncertainty automatically tunes the learning rate, so that variable observations are less impactful in a high uncertainty setting.

Strengths:<br /> The ideas in this work are novel and elegant, and they are instantiated in a progression of simulations that demonstrate the behavior of the circuit. The framework used by the authors is biologically plausible and matches some known biological data. The results attained, as well as the assumptions that go into the theory, provide several predictions for future experimental testing.

Weaknesses:<br /> Overall, I found this manuscript to be frustrating to read and to try to understand in detail, especially the Results section from the UPE/Figure 4 part to the end and parts of the Methods section. I don't think the main ideas are so complicated, and it should be possible to provide a much clearer presentation.

For me, one source of confusion is the comparison across Figure 1EF, Figure 2A, Figure 3A, Figure 4AB, and Figure 5A. All of these are meant to be schematics of the same circuit (although with an extra neuron in Figure 5), yet other than Figures 1EF and 4AB, no two are the same! There should be a clear, consistent schematic used, with identical labeling of input sources, neuron types, etc. across all of these panels.

The flow of the Results section overall is clear until the ``Calculation of the UPE in Layer 2/3 error neurons' and Figure 4, where I find that things become significantly more confusing. The mention of NMDA and calcium spikes comes out of the blue, and it's not clear to me how this fits into the authors' theory. Moreover: Why would this property of pyramidal cells cause the PV firing rate to increase as stated? The authors refer to one set of weights (from SSTs to UPE) needing to match two targets (weights from s to UPE and weights from mean representation to UPE); how can one set of weights match two targets? Why do the authors mention ``out-of-distribution detection' here when that property is not explored later in the paper? (see also below for other comments on Figure 4)

Coming back to one of the points in the previous paragraph: How realistic is this exact matching of weights, as well as the weight matching that the theory requires in terms of the weights from the SSTs to the PVs and the weights from the stimuli to the PVs? This point should receive significant elaboration in the discussion, with biological evidence provided. I would not advocate for the authors' uncertainty prediction theory, despite its elegant aspects, without some evidence that this weight matching occurs in the brain. Also, the authors point out on page 3 that unlike their theory, "...SSTs can also have divisive effects, and PVs can have subtractive effects, dependent on circuit and postsynaptic properties". This should be revisited in the Discussion, and the authors should explain why these effects are not problematic for their theory. In a similar vein, this work assumes the existence of two different populations of SST neurons with distinct UPE (pyramidal) targets. The Discussion doesn't say much about any evidence for this assumption, which should be more thoroughly discussed and justified.

Finally, I think this is a paper that would have been clearer if the equations had been interspersed within the results. Within the given format, I think the authors should include many more references to the Methods section, with specific equation numbers, where they are relevant throughout the Results section. The lack of clarity is certainly made worse by the current state of the Methods section, where there is far too much repetition and poor ordering of material throughout.

Reviewer #3 (Public Review):

Summary:<br /> The authors proposed a normative principle for how the brain's internal estimate of an observed sensory variable should be updated during each individual observation. In particular, they propose that the update size should be inversely proportional to the variance of the variable. They then proposed a microcircuit model of how such an update can be implemented, in particularly incorporating two types of interneurons and their subtractive and divisive inhibition onto pyramidal neurons. One type should represent the estimated mean while another represents the estimated variance. The authors used simulations to show that the model works as expected.

Strengths:<br /> The paper addresses two important issues: how uncertainty is represented and used in the brain, and the role of inhibitory neurons in neural computation. The proposed circuit and learning rules are simple enough to be plausible. They also work well for the designated purposes. The paper is also well-written and easy to follow.

Weaknesses:<br /> I have concerns with two aspects of this work.

(1) The optimality analysis leading to Eq (1) appears simplistic. The learning setting the authors describe (estimating the mean of a stationary Gaussian variable from a stream of observations) is a very basic problem in online learning/streaming algorithm literature. In this setting, the real "optimal" estimate is simply the arithmetic average of all samples seen so far. This can be implemented in an online manner with \hat{\mu}_{t} = \hat{\mu}_{t-1} +(s_t-\hat{\mu}_{t-1})/t. This is optimal in the sense that the estimator is always the maximum likelihood estimator given the samples seen up to time t. On the other hand, doing gradient descent only converges towards the MLE estimator after a large number of updates. Another critique is that while Eq (1) assumes an estimator of the mean (\hat{mu}), it assumes that the variance is already known. However, in the actual model, the variance also needs to be estimated, and a more sophisticated analysis thus needs to take into account the uncertainty of the variance estimate and so on. Finally, the idea that the update should be inverse to the variance is connected to the well-established idea in neuroscience that more evidence should be integrated over when uncertainty is high. For example, in models of two-alternative forced choices it is known to be optimal to have a longer reaction time when the evidence is noisier.

(2) While the incorporation of different inhibitory cell types into the model is appreciated, it appears to me that the computation performed by the circuit is not novel. Essentially the model implements a running average of the mean and a running average of the variance, and gates updates to the mean with the inverse variance estimate. I am not sure about how much new insight the proposed model adds to our understanding of cortical microcircuits.

Reviewer #1 (Public Review):

Summary:

Heer and Sheffield used 2 photon imaging to dissect the functional contributions of convergent dopamine and noradrenaline inputs to the dorsal hippocampus CA1 in head-restrained mice running down a virtual linear path. Mice were trained to collect water rewards at the end of the track and on test days, calcium activity was recorded from dopamine (DA) axons originating in the ventral tegmental area (VTA, n=7) and noradrenaline axons from the locus coeruleus (LC, n=87) under several conditions. When mice ran laps in a familiar environment, VTA DA axons exhibited ramping activity along the track that correlated with distance to reward and velocity to some extent, while LC input activity remained constant across the track, but correlated invariantly with velocity and time to motion onset. A subset of recordings taken when the reward was removed showed diminished ramping activity in VTA DA axons, but no changes in the LC axons, confirming that DA axon activity is locked to reward availability. When mice were subsequently introduced to a new environment, the ramping to reward activity in the DA axons disappeared, while LC axons showed a dramatic increase in activity lasting 90 s (6 laps) following the environment switch. In the final analysis, the authors sought to disentangle LC axon activity induced by novelty vs. behavioral changes induced by novelty by removing periods in which animals were immobile and established that the activity observed in the first 2 laps reflected novelty-induced signal in LC axons.

Strengths:

The results presented in this manuscript provide insights into the specific contributions of catecholaminergic input to the dorsal hippocampus CA1 during spatial navigation in a rewarded virtual environment, offering a detailed analysis of the resolution of single axons. The data analysis is thorough and possible confounding variables and data interpretation are carefully considered.

Weaknesses:

Aspects of the methodology, data analysis, and interpretation diminish the overall significance of the findings, as detailed below.

The LC axonal recordings are well-powered, but the DA axonal recordings are severely underpowered, with recordings taken from a mere 7 axons (compared to 87 LC axons). Additionally, 2 different calcium indicators with differential kinetics and sensitivity to calcium changes (GCaMP6S and GCaMP7b) were used (n=3, n=4 respectively) and the data pooled. This makes it very challenging to draw any valid conclusions from the data, particularly in the novelty experiment. The surprising lack of novelty-induced DA axon activity may be a false negative. Indeed, at least 1 axon (axon 2) appears to be showing a novelty-induced rise in activity in Figure 3C. Changes in activity in 4/7 axons are also referred to as a 'majority' occurrence in the manuscript, which again is not an accurate representation of the observed data.

The authors conducted analysis on recording data exclusively from periods of running in the novelty experiment to isolate the effects of novelty from novelty-induced changes in behavior. However, if the goal is to distinguish between changes in locus coeruleus (LC) axon activity induced by novelty and those induced by motion, analyzing LC axon activity during periods of immobility would enhance the robustness of the results.

The authors attribute the ramping activity of the DA axons to the encoding of the animals' position relative to reward. However, given the extensive data implicating the dorsal CA1 in timing, and the remarkable periodicity of the behavior, the fact that DA axons could be signalling temporal information should be considered.

The authors should explain and justify the use of a longer linear track (3m, as opposed to 2m in the DAT-cre mice) in the LC axon recording experiments.

Reviewer #2 (Public Review):

Summary:

The authors used 2-photon Ca2+-imaging to study the activity of ventral tegmental area (VTA) and locus coeruleus (LC) axons in the CA1 region of the dorsal hippocampus in head-fixed male mice moving on linear paths in virtual reality (VR) environments.

The main findings were as follows:

- In a familiar environment, the activity of both VTA axons and LC axons increased with the mice's running speed on the Styrofoam wheel, with which they could move along a linear track through a VR environment.<br /> - VTA, but not LC, axons showed marked reward position-related activity, showing a ramping-up of activity when mice approached a learned reward position.<br /> - In contrast, the activity of LC axons ramped up before the initiation of movement on the Styrofoam wheel.<br /> - In addition, exposure to a novel VR environment increased LC axon activity, but not VTA axon activity.

Overall, the study shows that the activity of catecholaminergic axons from VTA and LC to dorsal hippocampal CA1 can partly reflect distinct environmental, behavioral, and cognitive factors. Whereas both VTA and LC activity reflected running speed, VTA, but not LC axon activity reflected the approach of a learned reward, and LC, but not VTA, axon activity reflected initiation of running and novelty of the VR environment.

I have no specific expertise with respect to 2-photon imaging, so cannot evaluate the validity of the specific methods used to collect and analyse 2-photon calcium imaging data of axonal activity.

Strengths:

(1) Using a state-of-the-art approach to record separately the activity of VTA and LC axons with high temporal resolution in awake mice moving through virtual environments, the authors provide convincing evidence that the activity of VTA and LC axons projecting to dorsal CA1 reflect partly distinct environmental, behavioral and cognitive factors.

(2) The study will help a) to interpret previous findings on how hippocampal dopamine and norepinephrine or selective manipulations of hippocampal LC or VTA inputs modulate behavior and b) to generate specific hypotheses on the impact of selective manipulations of hippocampal LC or VTA inputs on behavior.

Weaknesses:

(1) The findings are correlational and do not allow strong conclusions on how VTA or LC inputs to dorsal CA1 affect cognition and behavior. However, as indicated above under Strengths, the findings will aid the interpretation of previous findings and help to generate new hypotheses as to how VTA or LC inputs to dorsal CA1 affect distinct cognitive and behavioral functions.

(2) Some aspects of the methodology would benefit from clarification.<br /> First, to help others to better scrutinize, evaluate, and potentially to reproduce the research, the authors may wish to check if their reporting follows the ARRIVE (Animal Research: Reporting of In Vivo Experiments) guidelines for the full and transparent reporting of research involving animals (https://arriveguidelines.org/). For example, I think it would be important to include a sample size justification (e.g., based on previous studies, considerations of statistical power, practical considerations, or a combination of these factors). The authors should also include the provenance of the mice. Moreover, although I am not an expert in 2-photon imaging, I think it would be useful to provide a clearer description of exclusion criteria for imaging data.<br /> Second, why were different linear tracks used for studies of VTA and LC axon activity (from line 362)? Could this potentially contribute to the partly distinct activity correlates that were found for VTA and LC axons?<br /> Third, the authors seem to have used two different criteria for defining immobility. Immobility was defined as moving at <5 cm/s for the behavioral analysis in Figure 3a, but as <0.2 cm/s for the imaging data analysis in Figure 4 (see legends to these figures and also see Methods, from line 447, line 469, line 498)? I do not understand why, and it would be good if the authors explained this.

(3) In the Results section (from line 182) the authors convincingly addressed the possibility that less time spent immobile in the novel environment may have contributed to the novelty-induced increase of LC axon activity in dorsal CA1 (Figure 4). In addition, initially (for the first 2-4 laps), the mice also ran more slowly in the novel environment (Figure 3aIII, top panel). Given that LC and VTA axon activity were both increasing with velocity (Figure 1F), reduced velocity in the novel environment may have reduced LC and VTA axon activity, but this possibility was not addressed. Reduced LC axon activity in the novel environment could have blunted the novelty-induced increase. More importantly, any potential novelty-induced increase in VTA axon activity could have been masked by decreases in VTA axon activity due to reduced velocity. The latter may help to explain the discrepancy between the present study and previous findings that VTA neuron firing was increased by novelty (see Discussion, from line 243). It may be useful for the authors to address these possibilities based on their data in the Results section, or to consider them in their Discussion.

(4) Sensory properties of the water reward, which the mice may be able to detect, could account for reward-related activity of VTA axons (instead of an expectation of reward). Do the authors have evidence that this is not the case? Occasional probe trials, intermixed with rewarded trials, could be used to test for this possibility.

Reviewer #3 (Public Review):

Summary:

Heer and Sheffield provide a well-written manuscript that clearly articulates the theoretical motivation to investigate specific catecholaminergic projections to dorsal CA1 of the hippocampus during a reward-based behavior. Using 2-photon calcium imaging in two groups of cre transgenic mice, the authors examine the activity of VTA-CA1 dopamine and LC-CA1 noradrenergic axons during reward seeking in a linear track virtual reality (VR) task. The authors provide a descriptive account of VTA and LC activities during walking, approach to reward, and environment change. Their results demonstrate LC-CA1 axons are activated by walking onset, modulated by walking velocity, and heighten their activity during environment change. In contrast, VTA-CA1 axons were most activated during the approach to reward locations. Together the authors provide a functional dissociation between these catecholamine projections to CA1. A major strength of their approach is the methodological rigor of 2-photon recording, data processing, and analysis approaches. These important systems neuroscience studies provide solid evidence that will contribute to the broader field of learning and memory. The conclusions of this manuscript are mostly well supported by the data, but some additional analysis and/or experiments may be required to fully support the author's conclusions.

Weaknesses:

(1) During teleportation between familiar to novel environments the authors report a decrease in the freezing ratio when combining the mice in the two experimental groups (Figure 3aiii). A major conclusion from the manuscript is the difference in VTA and LC activity following environment change, given VTA and LC activity were recorded in separate groups of mice, did the authors observe a similar significant reduction in freezing ratio when analyzing the behavior in LC and VTA groups separately?

(2) The authors satisfactorily apply control analyses to account for the unequal axon numbers recorded in the LC and VTA groups (e.g. Figure 1). However, given the heterogeneity of responses observed in Figures 3c, 4b and the relatively low number of VTA axons recorded (compared to LC), there are some possible limitations to the author's conclusions. A conclusion that LC-CA1 axons, as a general principle, heighten their activity during novel environment presentation, would require this activity profile to be observed in some of the axons recorded in most all LC-CA1 mice. Additionally, if the general conclusion is that VTA-CA1 axons ramp activity during the approach to reward, it would be expected that this activity profile was recorded in the axons of most all VTA-CA1 mice. Can the authors include an analysis to demonstrate that each LC-CA1 mouse contained axons that were activated during novel environments and that each VTA-CA1 mouse contained axons that ramped during the approach to reward?

(3) A primary claim is that LC axons projecting to CA1 become activated during novel VR environment presentation. However, the experimental design did not control for the presentation of a familiar environment. As I understand, the presentation order of environments was always familiar, then novel. For this reason, it is unknown whether LC axons are responding to novel environments or environmental change. Did the authors re-present the familiar environment after the novel environment while recording LC-CA1 activity?

Reviewer #1 (Public Review):

This is a short but important study. Basically, the authors show that α-synuclein overexpression's negative impact on synaptic vesicle recycling is mediated by its interaction with E-domain containing synapsins. This finding is highly relevant for synuclein function as well as for the pathophysiology of synucleinopathies. The data is clear, functional analysis is highly adequate.

Reviewer #1 (Public Review):

Neurons are not static-their activity patterns change as the result of learning, aging, and disease. Reliable tracking of activity from individual neurons across long time periods would enable studies of these important dynamics. For this reason, the authors' efforts to track electrophysiological activity across days without relying on matching neural receptive fields (which can change due to learning, aging, and disease) is very important.

By utilizing the tightly-spaced electrodes on Neuropixels probes, they are able to measure the physical distance and the waveform shape 'distance' between sorted units recorded on different days. To tune the matching algorithm and to validate the results, they used the visual receptive fields of neurons in the mouse visual cortex (which tend to change little over time) as ground truth. Their approach performs quite well, with a high proportion of neurons accurately matched across multiple weeks.

This suggests that the method may be useable in other cases where the receptive fields can't be used as ground truth to validate the tracking. This potential extendibility to tougher applications is where this approach holds the most promise. However, the study only looks at one brain area (visual cortex), in one species (mouse), using one type of spike sorter (Kilosort), and one type of behavioral prep (head-fixed). While the authors suggest methods to generalize their technique to other experimental conditions, no validation of those generalizations was done using data from different experimental conditions. Anyone using this method under different conditions would therefore need to perform such validation themselves.

Reviewer #2 (Public Review):

The manuscript presents a method for tracking neurons recorded with neuropixels across days, based on the matching of cells' spatial layouts and spike waveforms at the population level. The method is tested on neuropixel recordings of the visual cortex carried over 47 days, with the similarity in visual receptive fields used to verify the matches in cell identity.

This is an important tool as electrophysiological recordings have been notoriously limited in terms of tracking individual neuron's fate over time, unlike imaging approaches. The method is generally sound and properly tested but I think some clarifications would be helpful regarding the implementation of the method and some of the results.

(1) Page 6: I am not sure I understand the point of the imposed drift and how the value of 12µm is chosen.<br /> Is it that various values of imposed drift are tried, the EMDs computed to produce histograms as in Fig2c, values of rigid drifts estimated based on the histogram modes, and then the value associated with minimum cost selected? The corresponding manuscript section would need some clarification regarding this aspect.

(2) The EMD is based on the linear sum, with identical weight, of cell distance and waveform similarity measures. How performance is affected from using a different weighting of the 2 measures (for instance, using only cell distance and no waveform similarity)? It is common that spike waveforms associated to a given neuron appear different on different channels of silicon probes (i.e. the spike waveform changes depending the position of recording sites relative to the neuron), so I wonder if that feature is helping or potentially impeding the tracking.

(3) Fig.5: I assume the dots are representing time gaps for which cell tracking is estimated. The 3 different groups of colors correspond to the 3 mice used. For a given mouse, I would expect to always see 3 dots (for ref, putative and mixed) for a given tracking gap. However, for mouse AL036 for instance, at tracking duration of 8 days, a dot is visible for mixed but not for ref and putative. How come this is happening?

(4) Matched visual responses are measured by the sum of correlation of visual fingerprints, which are vectors of cells' average firing rate across visual stimuli, and correlation of PSTHs, which are implemented over all visual stimuli combined. I believe that some information is lost from combining all stimuli in the implementation of PSTHs (assuming that PSTHs show specificity to individual visual stimuli). The authors might consider, as alternative measure of matched visual responses, a correlation of the vector concatenations of all stimulus PSTHs. Such simpler measure would contain both visual fingerprint and PSTH information, and would not lose the information of PSTH specificity across visual stimuli.

2nd revision

(1) From reading the authors' response, I could understand several of the points I had previously missed. I still think that some part of the results are not straightforward to understand, the way it is written. Adding a few introductory sentences to the paragraphs (for instance the one related to my previous point #1) would really help the reader comprehend this important work.

(2) Following on my point #2, the w value used is 1500 and the recovery rate doesn't seems to reach a peak but rather a plateau for larger w values. From such large w value and the absence of a downward trend for increasing values, it would seem that only the 'waveform distance' matter and that the 'location distance' doesn't contribute much to the EMD distance. Is this correct?

Reviewer #1 (Public Review):

Summary: Nuclear depletion and cytoplasmic mislocalization/aggregation of the DNA and RNA binding protein TDP-43 are pathological hallmarks of multiple neurodegenerative diseases. Prior work has demonstrated that depletion of TDP-43 from the nucleus leads to alterations in transcription and splicing. Conversely, cytoplasmic mislocalization/aggregation can contribute to toxicity by impairing mRNA transport and translation as well as miRNA dysregulation. However, to date, models of TDP-43 proteinopathy rely on artificial knockdown- or overexpression-based systems to evaluate either nuclear loss or cytoplasmic gain of function events independently. Few model systems authentically reproduce both nuclear depletion and cytoplasmic miscloalization/aggreagtion events. In this manuscript, the authors generate novel iPSC-based reagents to manipulate the localization of endogenous TDP-43. This is a valuable resource for the field to study pathological consequences of TDP-43 proteinopathy in a more endogenous and authentic setting. However, in the current manuscript, there are a number of weaknesses that should be addressed to further validate the ability of this model to replicate human disease pathology and demonstrate utility for future studies.

Strengths: The primary strength of this paper is the development of a novel in vitro tool.

Weaknesses: There are a number of weaknesses detailed below that should be addressed to thoroughly validate these new reagents as more authentic models of TDP-43 proteinopathy and demonstrate their utility for future investigations.

(1) The authors should include images of their engineered TDP-43-GFP iPSC line to demonstrate TDP-43 localization without the addition of any nanobodies (perhaps immediately prior to addition of nanobodies). Additionally, it is unclear whether simply adding a GFP tag to endogenous TDP-43 impact its normal function (nuclear-cytoplasmic shuttling, regulation of transcription and splicing, mRNA transport etc).

(2) Can the authors explain why there is a significant discrepancy in time points selected for nanobody transduction and immunostaining or cell lysis throughout Figure 1 and 2? This makes interpretation and overall assessment of the model challenging.

(3) The authors should further characterize their TDP-43 puncta. TDP-43 immunostaining is typically punctate so it is unclear if the puncta observed are physiologic or pathologic based on the analyses carried out in the current version of this manuscript. Additionally, do these puncta co-localize with stress granule markers or RNA transport granule markers? Are these puncta phosphorylated (which may be more reminiscent of end-stage pathologic observations in humans)?

(4) The authors should include multiple time points in their evaluation of TDP-43 loss of function events and aggregation. Does loss of function get worse over time? Is there a time course by which RNA misprocessing events emerge or does everything happen all at once? Does aggregation get worse over time? Do these neurons die at any point as a result of TDP-43 proteinopathy?

(5) Can the authors please comment on whether or not their model is "tunable"? In real human disease, not every neuron displays complete nuclear depletion of TDP-43. Instead there is often a gradient of neurons with differing magnitudes of nuclear TDP-43 loss. Additionally, very few neurons (5-10%) harbor cytoplasmic TDP-43 aggregates at end-stage disease. These are all important considerations when developing a novel authentic and endogenous model of TDP-43 proteinopathy which the current manuscript fails to address.

Reviewer #2 (Public Review):

Summary:<br /> TDP-43 mislocalization occurs in nearly all of ALS, roughly half of FTD, and as a co-pathology in roughly half of AD cases. Both gain-of-function and loss-of-function mechanisms associated with this mislocalization likely contribute to disease pathogeneisis.

Here, the authors describe a new method to induce TDP-43 mislocalization in cellular models. They endogenously-tagged TDP-43 with a C-terminal GFP tag in human iPSCs. They then expressed an intrabody - fused with a nuclear export signal (NES) - that targeted GFP to the cytosol. Expression of this intrabody-NES in human iPSC-derived neurons induced nuclear depletion of homozygous TDP-43-GFP, caused its mislocalization to the cytosol, and at least in some cells appeared to cause cytosolic aggregates. This mislocalization was accompanied by induction of cryptic exons in well characterized transcripts known to be regulated by TDP-43, a hallmark of functional TDP-43 loss and consistent with pathological nuclear TDP-43 depletion. Interestingly, in heterozygous TDP-43-GFP neurons, expression of intrabody-NES appeared to also induce the mislocalization of untagged TDP-43 in roughly half of the neurons, suggesting that this system can also be used to study effects on untagged endogenous TDP-43 as well as TDP-43-GFP fusion protein.

Strengths:<br /> A clearer understanding of how TDP-43 mislocalization alters cellular function, as well as pathways that mitigate clearance of TDP-43 aggregates, is critical. But modeling TDP-43 mislocalization in disease-relevant cellular systems has proven to be challenging. High levels of overexpression of TDP-43 lacking an NES can drive endogenous TDP-43 mislocalization, but such overexpression has direct and artificial consequences on certain cellular features (e.g. altered exon skipping) not seen in diseased patients. Toxic small molecules such as MG132 and arsenite can induce TDP-43 mislocalization, but co-induce myriad additional cellular dysfunctions unrelated to TDP-43 or ALS. TDP-43 binding oligonucleotides can cause cytosolic mislocalization as well. Each system has pros and cons, and additional ways to induce TDP-43 mislocalization would be useful for the field. The method described in this manuscript could provide researchers with a powerful way to study the combined biology of cytosolic TDP-43 mislocalization and nuclear TDP-43 depletion, with additional temporal control that is lacking in current method. Indeed, the authors see some evidence of differences in RNA splicing caused by pure TDP-43 depletion versus their induced mislocalization model. Finally, their method may be especially useful in determining how TDP-43 aggregates are cleared by cells, potentially revealing new biological pathways that could be therapeutically targeted.

Weaknesses:<br /> The method and supporting data have limitations in its current form, outlined below, and in its current form the findings are rather preliminary.

• Tagging of TDP-43 with a bulky GFP tag may alter its normal physiological functions, for example phase separation properties and functions within complex ribonucleoprotein complexes. In addition, alternative isoforms of TDP-43 (e.g. "short" TDP-43, would not be GFP tagged and therefore these species would not be directly manipulatable or visualizable with the tools currently employed in the manuscript.<br /> • The data regarding potential mislocalization of endogenous TDP-43 in the heterozygous TDP-43-GFP lines is especially intriguing and important, yet very little characterization was done. Does untagged TDP-43 co-aggregate with the tagged TDP-43? Is localization of TDP-43 immunostaining the same as the GFP signal in these cells?<br /> • The experiments in which dox was used to induce the nanobody-NES, then dox withdrawn to study potential longer-lasting or self-perpetuating inductions of aggregation is potentially interesting. However, the nanobody was only measured at the RNA level. We know that protein half lives can be very long in neurons, and therefore residual nanobody could be present at these delayed time points. The key measurement to make would be at the protein level of the nanobody if any conclusions are be made from this experiment.<br /> • Potential differences in splicing and microRNAs between TDP-43 knockdown and TDP-43 mislocalization are potentially interesting. However, different patterns of dysregulated RNA splicing can occur at different levels of TDP-knockdown, thus it is difficult to asses whether the changes observed in this paper are due to mislocalization per se, or rather just reflect differences in nuclear TDP-43 abundance.

Reviewer #1 (Public Review):

Summary:<br /> Functionally important alternative isoforms are gold nuggets found in a swamp of errors produced by the splicing machinery.

The architecture of eukaryotic genomes, when compared with prokaryotes, is characterised by a preponderance of introns. These elements, which are still present within transcripts, are rapidly removed during the splicing of messenger RNA (mRNA), thus not contributing to the final protein. The extreme rarity of introns in prokaryotes, and the elimination of these introns from mRNAs before translation into protein, raises questions about the function of introns in genomes. One explanation comes from functional biology: introns are thought to be involved in post-transcriptional regulation and in the production of translational variants. The latter function is possible when the positions of the edges of the spliced intron vary. While some light has been shed on specific examples of the functional role of alternative splicing, to what extent are they representative of all introns in metazoans?

In this study, the hypothesis of a functional role for alternative splicing, and therefore to a certain extent for introns, is evaluated against another explanation coming from evolutionary biology: isoforms are above all errors of imprecision by the molecular machinery at work during splicing. This hypothesis is based on a principle established by Motoo Mikura, which has become central to population genetics, explaining that the evolutionary trajectory of a mutation with a given effect is intimately linked to the effective population size (Ne) where this mutation emerges. Thus, the probability of fixation of a weakly deleterious mutation increases when Ne decreases, and the probability of fixation of a weakly advantageous mutation increases when Ne increases. The genomes of populations with low Ne are therefore expected to accumulate more weakly deleterious mutations and fewer weakly advantageous mutations than populations with high Ne. In this framework, if splicing errors have only small effects on the fitness of individuals, then natural selection cannot increase the precision of the splicing machinery, allowing tolerance for the production of alternative isoforms.

In the past, the debate opposed one-off observations of effectively functional isoforms on the one hand, to global genomic quantities describing patterns without the possibility of interpreting them in detail. The authors here propose an elegant quantitative approach in line with the expected continuous variation in the effectiveness of selection, both between species and within genomes. The result describing the inter-specific pattern on a large scale confirms what was already known (there is a negative relationship between effective size and average alternative splicing rate). The essential novelty of this study lies in 1) the quantification, for each intron studied, of the relative abundance of each isoform, and 2) the analysis of a relationship between this abundance and the evolutionary constraints acting on these isoforms.

What is striking is the light shed on the general very low abundance of alternative isoforms. Depending on the species, 60% to 96% of cases of alternatively spliced introns lead to an isoform whose abundance is less than 5% of the total variants for a given intron.

In addition to the fact that 60%-96% of the total isoforms are more than 20 times less abundant than their majority form, this large proportion of alternative isoforms exhibit coding-phase shift at rates similar to what would be expected by chance, i.e. for a third of them, which reinforces the idea that there is no particular constraint on these isoforms.

The remaining 4%-40% of isoforms see their coding-phase shift rate decrease as their relative abundance increases. This result represents a major step forward in our understanding of alternative splicing and makes it possible to establish a quantitative model directly linking the relative abundance of an isoform with a putative functional role concerning only those isoforms produced in abundance. Only the (rare) isoforms which are abundantly produced are thought to be involved in a biological function.

Within the same genome, the authors show that only highly expressed genes, i.e. those that tend to be more constrained on average, are also the genes with the lowest alternative splicing rates on average.

The comparison between species in this study reveals that the smaller the effective size of a species, the more its genome produces isoforms that are low in abundance and low in constraint. Conversely, species with a large effective size relatively reduce rare isoforms, and increase stress on abundant isoforms.

To sum up:<br /> • the higher the effective size of a species, the fewer introns are spliced.<br /> • highly expressed genes are spliced less.<br /> • when splicing occurs, it is mainly to produce low-abundance isoforms.<br /> • low-abundance isoforms are also less constrained.

Taken together, these results reinforce a quantitative view of the evolution of alternative splicing as being mainly the product of imprecision in the splicing machinery, generating a great deal of molecular noise. Then, out of all this noise, a few functional gold nuggets can sometimes emerge. From the point of view of the reviewer, the evolutionary dynamics of genomes are depressing. The small effective population sizes are responsible for the accumulation of multiple slightly deleterious introns. Admittedly, metazoan genomes try to get rid of these introns during RNA maturation, but this mechanism is itself rendered imprecise by population sizes.

Strengths:<br /> • The authors simultaneously study the effects of effective population size, isoform abundance, and gene expression levels on the evolutionary constraints acting on isoforms. Within this framework, they clearly show that an isoform becomes functionally important only under certain rare conditions.<br /> • The authors rule out an effect putatively linked to variations in expression between different organs which could have biased comparisons between different species.

Weaknesses:<br /> • While the longevity of organisms as a measure of effective size seems to work overall, it may not be relevant for discriminating within a clade. For example, within Hymenoptera, we might expect them to have the same overall longevity, but that effective size would be influenced more by the degree of sociality: solitary bees/ants/wasps versus eusocial. I am therefore certain that the relationship shown in Figure 4D is currently not significant because the measure of effective size is not relevant for Hymenoptera. The article would have been even more convincing by contrasting the rates of alternative splicing between solitary versus social hymenopterans.<br /> • When functionalist biologists emphasise the role of the complexity of living things, I'm not sure they're thinking of the comparison between "drosophila" and "homo sapiens", but rather of a broader evolutionary scale. Which gives the impression of an exaggeration of the debate in the introduction.

Reviewer #2 (Public Review):

Summary:<br /> Two hypotheses could explain the observation that genes of more complex organisms tend to undergo more alternative splicing. On one hand, alternative splicing could be adaptive since it provides the functional diversity required for complexity. On the other hand, increased rates of alternative splicing could result through nonadaptive processes since more complex organisms tend to have smaller effective population sizes and are thus more prone to deleterious mutations resulting in more spurious splicing events (drift-barrier hypothesis). To evaluate the latter, B́enitiere et al. analyzed transcriptome sequencing data across 53 metazoan species. They show that proxies for effective population size and alternative splicing rates are negatively correlated. Furthermore, the authors find that rare, nonfunctional (and likely erroneous) isoforms occur more frequently in more complex species. Additionally, they show evidence that the strength of selection on splice sites increases with increasing effective population size and that the abundance of rare splice variants decreases with increased gene expression. All of these findings are consistent with the drift-barrier hypothesis.

This study conducts a comprehensive set of separate analyses that all converge on the same overall result and the manuscript is well organized. Furthermore, this study is useful in that it provides a modified null hypothesis that can be used for future tests of adaptive explanations for variation in alternative splicing.

Strengths:<br /> The major strength of this study lies in its complementary approach combining comparative and population genomics. Comparing evolutionary trends across phylogenetic diversity is a powerful way to test hypotheses about the origins of genome complexity. This approach alone reveals several convincing lines of evidence in support of the drift-barrier hypothesis. However, the authors also provide evidence from a population genetics perspective (using resequencing data for humans and fruit flies), making results even more convincing.

The authors are forward about the study's limitations and explain them in detail. They elaborate on possible confounding factors as well as the issues with data quality (e.g. proxies for Ne, inadequacies of short reads, heterogeneity in RNA-sequencing data).

Weaknesses:<br /> The authors primarily consider insects and mammals in their study. This only represents a small fraction of metazoan diversity. Sampling from a greater diversity of metazoan lineages would make these results and their relevance to broader metazoans substantially more convincing. Although the authors are careful about their tone, it is challenging to reconcile these results with trends across greater metazoans when the underlying dataset exhibits ascertainment bias and represents samples from only a few phylogenetic groups. Relatedly, some trends (such as Figure 1B-C) seem to be driven primarily by non-insect species, raising the question of whether some results may be primarily explained by specific phylogenetic groups (although the authors do correct for phylogeny in their statistics). How might results look if insects and mammals (or vertebrates) are considered independently?

Throughout the manuscript, the authors refer to infrequently spliced (mode <5%) introns as "minor introns" and frequently spliced (mode >95%) as "major introns". This is extremely confusing since "minor introns" typically represent introns spliced by the U12 spliceosome, whereas "major introns" are those spliced by the U2 spliceosome. Furthermore, it remains unclear whether the study only considers major introns or both major and minor introns. Minor introns typically have AT-AC splice sites whereas major introns usually have GT/GC-AG splice sites, although in rare cases the U2 can recognize AT-AC (see Wu and Krainer 1997 for example). The authors also note that some introns show noncanonical AT-AC splice sites while these are actually canonical splice sites for minor introns.

Reviewer #1 (Public Review):

Summary:<br /> Fang Huang et al found that RBM7 deficiency promotes metastasis by coordinating MFGE8 splicing switch and NF-kB pathway in breast cancer by utilizing clinical samples as well as cell and tail vein injection models.

Strengths:<br /> This study uncovers a previously uncharacterized role of MFGE8 splicing alteration in breast cancer metastasis, and provides evidence supporting RBM7 function in splicing regulation. These findings facilitate the mechanistic understanding of how splicing dysregulation contributes to metastasis in cancer, a direction that has increasingly drawn attention recently, and provides a potentially new prognostic and therapeutic target for breast cancer.

Weaknesses:<br /> This study can be strengthened in several aspects by additional experiments or at least by further discussions. First, how RBM7 regulates NF-kB, and how it coordinates splicing and canonical function as a component of NEXT complex should be clarified. Second, although the roles of MFGE8 splicing isoforms in cell migration and invasion have been demonstrated in transwell and wound healing assays, it would be more convincing to explore their roles in vivo such as the tail vein injection model. Third, the clinical significance would be considerably improved, if the therapeutic value of targeting MFGE8 splicing could be demonstrated.

Reviewer #2 (Public Review):

Summary:<br /> In this manuscript, the authors reported the biological role of RBM7 deficiency in promoting metastasis of breast cancer. They further used a combination of genomic and molecular biology approaches to discover a novel role of RBM7 in controlling alternative splicing of many genes in cell migration and invasion, which is responsible for the RBM7 activity in suppressing metastasis. They conducted an in-depth mechanistic study on one of the main targets of RBM7, MFGE8, and established a regulatory pathway between RBM7, MFGE8-L/MFGE8-S splicing switch, and NF-κB signaling cascade. This link between RBM7 and cancer pathology was further supported by analysis of clinical data.

Strengths:<br /> Overall, this is a very comprehensive study with lots of data, and the evidence is consistent and convincing. Their main conclusion was supported by many lines of evidence, and the results in animal models are pretty impressive.

Weaknesses:<br /> However, there are some controls missing, and the data presentation needs to be improved. The writing of the manuscript needs some grammatical improvements because some of the wording might be confusing.

Specific comments:<br /> (1) Figure 2. The figure legend is missing for Figure 2C, which caused many mislabels in the rest of the panels. The labels in the main text are correct, but the authors should check the figure legend more carefully. Also in Figure 2C, it is not clear why the authors choose to examine the expression of this subset of genes. The authors only refer to them as "a series of metastasis-related genes", but it is not clear what criteria they used to select these genes for expression analysis.

(2) Line 218-220. The comparison of PSI changes in different types of AS events is misleading. Because these AS events are regulated in different mechanisms, they cannot draw the conclusion that "the presence of RBM7 may promote the usage of alternative splice sites". For example, the regulators of SE and IR may even be opposite, and thus they should discuss this in different contexts. If they want to conclude this point, they should specifically discuss the SE and A5SS rather than draw an overall conclusion.

(3) In the section starting at line 243, they first referred to the gene and isoforms as "EFG-E8" or "EFG-E8-L", but later used "EFGE8" and "EFGE8-L". Please be consistent here. In addition, it will be more informative if the authors add a diagram of the difference between two EFGE8 isoforms in terms of protein structure or domain configuration.

(4) Figure 7B and 7C. The figures need quantification of the inclusion of MFGE exon7 (PSI value) in addition to the RT-PCR gel. The difference seems to be small for some patients.

Minor comments:<br /> The writing in many places is a little odd or somewhat confusing, I am listing some examples, but the authors need to polish the whole manuscript more to improve the writing.

(1) Line 169-170, "...followed by profiling high-throughput transcriptome by RNA sequencing", should be "followed by high-throughput transcriptome profiling with RNA sequencing".

(2) Line 170, "displayed a wide of RBM7-regulated genes were enriched...", they should add a "that" after the "displayed" as the sentence is very long.

(3) Line 213, "PSI (percent splicing inclusion)" is not correct, PSI stands for "percent spliced in".

(4) Line 216-217, the sentence is long and fragmented, they should break it into two sentences.

(5) Line 224, the "tethering" should be changed to "recognizing". There is a subtle difference in the mechanistic implication between these two words.

(6) Line 250, should be changed to "..in the ratio of two MFGE8 isoforms".

Reviewer #1 (Public Review):

Summary:<br /> The study by Valles-Marti et al. was aimed at elucidating mechanisms of high-dose vitamin C (Ascorbate) sensitivity using proteomics of a large panel of cancer cell lines. The study is primarily based on correlating protein expression to vitamin C sensitivity based on IC50 from cell viability studies. As expected, cancer type-specific proteome patterns emerge and the authors conclude that some pan-cancer pathways, such as proliferation correlate with high sensitivity to VitC. In a subset of PDAC cells proteomics and phospho proteomics were also carried out following vitamin C treatment, albeit those studies did not identify significant changes in response to treatment.

Strengths:<br /> The premise for the work is of interest as high dose vitamin C is in clinical trials and thus studies investigating mechanisms of sensitivity and potential resistance mechanisms to this therapy are of interest to the field. The authors have collected large proteomic datasets on some of the most common cancer cells used and these data may be a useful resource for others when made publicly available. Although this is not necessarily novel, since proteomics data sets for some of the included cell lines are already available.

Weaknesses:<br /> The title suggests that the proteomics data presented "underscores high-dose vitamin C as a potent anti-cancer agent" However, while the proteomic data are extensive, it is my assessment that without further validation there are no clear pathways identified by the presented proteomics data that conclusively determine vitamin C sensitivity.

A major question arising from this work is how specific the proteomics data reflect sensitivity to vitamin C over general sensitivity to other cytotoxic agents. It would be of interest to compare the correlation of proteomic data and ascorbate sensitivity to the sensitivity of cell lines to other cytotoxic agents. (e.g. comparison to NCI-60 growth inhibition data). In other words, do the proteomic data that correlate with ascorbate sensitivity simply reflect susceptibility to other cytotoxic agents? The comments that vitamin C toxicity is not dependent on underlying histological or genetic subtypes of cancers ("one size fits all") suggest this.

The genetic backgrounds of tumor cells have not been taken into consideration in the analysis and how this may influence VitC susceptibility. An example that comes to mind is KEAP1/Nrf2 aberrations in lung cancer.

The study would be significantly strengthened if some of the proteins identified were further validated in eliciting low or high sensitivity to Vitamin C. Of particular interest are proteins that have functions related to known mechanisms of action of Vitamin C toxicity, such as iron homeostasis. Some of the metabolic-related protein changes are also of interest. For example, HCCS expression is mentioned several times as being associated with lower sensitivity to ascorbate. Providing experimental evidence that this protein is of significance to Vitamin C sensitivity and if this is due to its effects on iron and subsequent generation of ROS in response to VitC would be of significance.

Similarly, an interesting aspect of the findings is the authors' conclusion that proliferation is associated with Vitamin C sensitivity. The authors propose in their discussion that Vitamin C may be an attractive alternative to treat heavily pretreated and chemoresistant cancers. Thus it would be important to know which of the highly proliferative cell lines tested have a chemoresistance phenotype and are also more susceptible to Vitamin C toxicity. Perhaps partitioning the cells further into chemoresistant and sensitive cell lines to standard chemotherapy and then assessing which protein signatures are associated with Vitamin C sensitivity will allow for better elucidation of sensitivity mechanisms that are more relevant to using Vitamin C as an alternate therapy for chemoresistant tumors.

Following on from this, there is an interesting mechanistic question as to why more proliferative cells are more sensitive to vitamin C, and whether this is related to changes in metabolism and underlying changes in their steady-state levels of ROS. Further investigating this mechanistically based on the identified proteomic signatures could make the findings more significant.

Vitamin C can also generate H2O2 extracellularly in the presence of iron. Thus, Vitamin C toxicity could be affected by different abilities of the tumor cells to scavenge extracellular H2O2, such as different expression levels of extracellular antioxidant enzymes. Judging from the methods section, it does not appear that proteomic data include secreted proteins. Can the authors comment on how this may be a potential caveat?

In light of this, the strong effects of exogenous catalase addition on cell viability suggest that H2O2 may be produced by ascorbate in the media.

Similarly, can the authors comment on the cell culture conditions used to compare IC50s between cell lines, specifically if different media and FBS batches were used, as these have the potential to vary in metal/iron concentrations that might influence the pro-oxidant generation by high dose ascorbate in media. Specifically, have the authors looked into the iron content and how these different conditions may be contributing to intracellular H2O2 and extracellular H2O2 (AmplexRed) production in response to Vitamin C.

Other comments relate to methods:

How was ascorbate prepared? There is no mention of degassing of H2O and ensuring that H2O does not have mental impurities, which can lead to auto-oxidation.

The OxiSelect probe is based on DCFDA, which is an oxidant-sensitive probe that has been described to be fraught with artifacts. Thus it is advised to mention the caveats associated with the use of this probe (as outlined in PMCID: PMC3911769) and consider backing up these experiments with additional Oxidant probes.

Reviewer #2 (Public Review):

Summary:<br /> The authors generated proteome profiles of 51 cancer cell lines treated with pharmacologic ascorbate. The idea was to identify players responsible for the sensitivity or relative resistance to ascorbate to delineate mechanisms of action of this potentially transformative new treatment.

Strengths:<br /> The proteomic profiles themselves. The identification of MAPK and mTOR as overrepresented proteomic elements and close correlations between proliferation, cell cycle mediators, and sensitivity to ascorbate indicate that rapidly proliferating cancer may be more sensitive to ascorbate. Also, the finding that sensitivity to ascorbate is correlated to different pathways in different types of cancer is interesting. For instance, in some pancreatic and lung cancers sensitivity seemed to be related to iron handling while in breast DNA damage/repair seemed to be most involved.

Weaknesses:<br /> The study is quite descriptive. Although the proteomes indicate what pathways are more or less represented after ascorbate challenge there is little mechanistic information about their relevance to the sensitivity to ascorbate. Since activity is not assessed, proteins may be present in higher or lower abundance but not necessarily at the peak of their activity. Also, many statements are made as "known facts" but no references are provided.

Reviewer #3 (Public Review):

Summary:<br /> In the manuscript titled "Large pan-cancer cell screen coupled to (phospho-)proteomics underscores high-dose vitamin C (VitC) as a potent anti-cancer agent," the authors use a combination of proteomics and cell viability assays to understand the effect of Vitamin C on different solid tumor models in 51 different cancer cell lines. They found that many cancer cell lines are sensitive to high-dose Vitamin C, with IC50 values in the micromolar to millimolar range. Given that Vitamin C, when administered intravenously, can reach 20mM, this suggests that Vitamin C could provide some benefits to patients. The authors also generate and analyze bulk proteomic data for all 51 cell lines. They perform statistical analysis of these data to identify proteins that are up or downregulated in sensitive vs resistant cell lines in the same tumor and commonly across tumors. They then focus on PDAC cell lines and measure bulk and phosphoproteomics of PDAC cell lines 2, 4 and 24 hours after Vitamin C treatment.

Strengths: The strengths of the study are the rather large datasets accumulated on bulk proteomics of 51 different cancer cell lines. The IC50 values of these cell lines in response to Vitamin C is also useful.

Weaknesses:<br /> Though identifying targets to sensitize cancer cells to Vitamin C treatment is interesting, I felt the manuscript delved too much into listing off genes they found, with speculation on why the particular protein would be enriched in sensitive or resistant cell lines without testing any key claims experimentally.

Major Issues

(1) The overall premise of the study is that proteins that are enriched in Vitamin C-sensitive cell lines point to mechanisms of sensitivity and those enriched in Vitamin C resistant lines underlie mechanisms of resistance. Yet this is never directly tested. To show that the authors would need to knockdown/knockout a gene enriched in resistant lines and show this sensitizes cells to Vitamin C treatment or overexpress a protein associated with resistance and show that this leads to resistance in an otherwise sensitive cell line.

(2) One of the key strengths of this study is the large datasets generated, namely the proteomics data for 51 different cell lines. Yet the data is not included as a supplement or uploaded to a public repository.

Reviewer #1 (Public Review):

This manuscript proposes a complex incoherent model involving Ca2+ signaling in inflammasome activation. The experimental approaches used to study the calcium dynamics are highly problematic and the results shown are of very poor quality.

Major concerns:

(1) The analysis of lysosomal Ca2+release is being carried out after many hours of treatment. Such evidence is not meaningful to claim that PA activates Ca2+ efflux from lysosome and even if this phenomenon was robust, it is not doubtful that such kinetics are meaningful for the regulation of inflammasome activation. Furthermore, the evidence for lysosomal Ca2+ release is indirect and relies on a convoluted process that doesn't make any conceptual sense to me. In addition to these major shortcomings, the indirect evidence of perilysosomal Ca2+ elevation is also of very poor quality and from the standpoint of my expertise in calcium signaling, the data are incredulous. The use of GCaMP3-ML1, *transiently transfected* into BMDMs is highly problematic. The efficiency of transfection in BMDMs is always extremely low and overexpression of the sensor in a few rare cells can lead to erroneous observations. The overexpression also results in gross mislocalization of such membrane-bound sensors. The accumulation of GCaMP3-ML1 in the ER of these cells would prevent any credible measurements of perilysosomal Ca2+ signals. A meaningful investigation of this process in primary macrophages requires the generation of a mouse line wherein the sensor is expressed at low levels in myeloid cells, and shown to be localized almost exclusively in the lysosomal membrane. The mechanistic framework built around these major conceptual and technical flaws is not especially meaningful and since these are foundational results, I cannot take the main claims of this study seriously.

A few transfected cells may overexpress the protein through a strong promoter but this is not ideal. For reliable Ca2+ measurements, one needs low expression of the sensor in a substantially high percentage of cells. This can only be demonstrated by showing the time lapse of Ca2+ responses in the macrophages. More generally, I have nearly 2 decades of experience working with primary BMDMs and it is widely known that primary BMDMs are incredibly difficult to transfect - it is the nature of these cells. The claim that they get high efficiency of transfection is frankly too incredulous to take seriously.

(2) The cytosolic Ca2+ imaging shown in figure 1C doesn't make any sense. It looks like a snapshot of basal Ca2+ many hours after PA treatment - calcium elevations are highly dynamic. Snapshot measurements are not helpful and analyses of Calcium dynamics requires a recording over a certain timespan. Unfortunately, this technical approach has been used throughout the manuscript. Also, BAPTA-AM abrogates IL-1b secretion because IL-1b transcription is Ca2+ dependent - the result shown in figure 1D does not shed light on anything to do with inflammasome activation and it is misleading to suggest that.

(3) Trpm2-/- macrophages are known to be hyporesponsive to inflammatory stimuli - the reduced secretion of IL-1b by these macrophages is not novel. From a mechanistic perspective, this study does not add much to that observation and the proposed role of TRPM2 as a lysosomal Ca2+ release channel is not substantiated by good quality Ca2+ imaging data (see point 3 above). Furthermore, the study assumes that TRPM2 is a lysosomal ion channel. One paper reported TRPM2 in the lysosomes but this is a controversial claim, with no replication or further development in the last 14 years. This core assumption can be highly misleading to readers unfamiliar with TRPM2 biology and it is necessary to present credible evidence that TRPM2 is functional in the lysosomal membrane of macrophages. Ideally, this line of investigation should rest on robust demonstration of TRPM2 currents in patch-clamp electrophysiology of lysosomes. If this is not technically feasible for the authors, they should at least investigate TRPM2 localization on lysosomal membranes of macrophages.

In the revised manuscript, authors showed TRPM2 localization but these results are problematic. The authors provide no information on what TRPM2 antibody they used for this study and whether it has been validated by use of knockouts. The staining shows very high amounts of TRPM2 all across the cell - even more than LAMP2. In reality, TRPM2 expression in macrophages is very low. Are the authors overexpressing TRPM2? These data only add to my concerns about this manuscript.

(4) Apigenin and Quercetin are highly non-specific and their effects cannot be attributed to CD38 inhibition alone. Such conclusions need strong loss of function studies using genetic knockouts of CD38 - or at least siRNA knockdown. Importantly, if indeed TRPM2 is being activated downstream of CD38, this should be easily evident in whole cell patch clamp electrophysiology. TRPM2 currents can be resolved using this technique and authors have Trpm2-/- cells for proper controls. Authors attempted these experiments but the results are of very poor quality. If the TRPM2 current is being activated through ADPR generated by CD38 (in response to PA stimulation), then it is very odd that authors need to include 200 uM cADPR to see TRPM2 current (Fig. 3A). Oddly, even these data cast great doubt on the technical quality of the electrophysiology experiments. Even with such high concentrations of cADPr, the TRPM2 current is tiny and Trpm2-/- controls are missing. The current-voltage relationship is not shown, and I feel that the results are merely reporting leak currents seen in measurements with substandard seals. Also 20 uM ACA is not a selective inhibitor of TRPM2 - relying on ACA as the conclusive diagnostic is problematic.

(5) TRPM2 is expressed in many different cell lines. The broad metabolic differences observed by the authors in the Trpm2-/- mice cannot be attributed to macrophage-mediated inflammation. Such a conclusion requires the study of mice wherein Trpm2 is deleted selectively in macrophages or at least in the cells of the myeloid lineage.

(6) The ER-Lysosome Ca2+ refilling experiments rely on transient transfection of organelle-targeted sensors into BMDMs. See point #1 to understand why I find this approach to be highly problematic. Furthermore the data procured are also not convincing and lack critical controls (localization of sensors has not been demonstrated and their response to acute mobilization of Ca2+ has not been shown inspire any confidence in these results).

(7) Authors claim that SCOE is coupled to K+ efflux. But there is no credible evidence that SOCE is activated in PA stimulated macrophages. The data shown in Fig 4 supp 1 do not investigate SOCE in a reliable manner - the conclusion is again based on snapshot measurements and crude non-selective inhibitors. The correct way to evaluate SOCE is to record cytosolic Ca2+ elevations over a period of time in absence and presence of extracellular Ca2+. However, even such recordings can be unreliable since the phenomenon is being investigated hours after PA stimulation. So, the only definitive way to demonstrate that Orai channels are indeed active during this process is through patch clamp electrophysiology of PA stimulated cells.

Authors failed to respond to these concerns in a credible manner and simply tried to obfuscate the matters with extraneous arguments and wild claims. The revised manuscript was not a significant improvement. I have major concerns with this manuscript and let it be on record that this is very poor-quality science.

Reviewer #2 (Public Review):

In this manuscript by Kang et. al., the authors investigated the mechanisms of K+-efflux-coupled SOCE in NLRP3 inflammasome activation by LP（LPS+PA, and identified an essential role of TRPM2-mediated lysosomal Ca2+ release and subsequent IP3Rs-mediated ER Ca2+ release and store depletion in the process. K+ efflux is shown to be mediated by a Ca2+-activated K+ channel (KCa3.1). LP-induced cytosolic Ca2+ elevation also induced a delayed activation of ASK1 and JNK, leading to ASC oligomerization and NLRP3 inflammasome activation. Overall, this is an interesting and comprehensive study that has identified several novel molecular players in metabolic inflammation. The manuscript can benefit if the following concerns could be addressed.

(1) The expression of TRPM2 in the lysosomes of macrophages needs to more definitively established. For instance, the cADPR-induced TRPM2 currents should be abolished in the TRPM2 KO macrophages. Can you show the lysosomal expression of TRPM2, either with an antibody if available or with a fluorescently-tagged TRPM2 overexpression construct?

In the revised manuscript, the authors did not perform the KO control experiment to support that cADPR-induced currents were indeed mediated by TRPM2. Additonally, the co-localization analyses failed to convincingly establish the lysosomal perimeter membrane residence of TRPM2.

(2) Can you use your TRPM2 inhibitor ACA to pharmacologically phenocopy some results, e.g., about [Ca2+]ER, [Ca2+]LY, and [Ca2+]i from the TRPM2 knockout?

In the revised manuscript, most suggested experiments were not performed. In the only experiment that was conducted, Figure 3-figure supplement 1A, the effect of ACA was marginal.

(3) In Fig. S4A, bathing the cells in zero Ca2+ for three hours might not be ideal. Can you use a SOCE inhibitor, e.g, YM-58483, to make the point?

The specific suggested experiment was not performed.

(4) In Fig. 1A, you need a positive control, e.g., ionomycin, to show that the GPN response was selectively reduced upon LP treatment.

Results in a previous study cannot be used to substitute the missing control experiments in the current study.

Reviewer #2 (Public Review):

Summary: The study is titled "Leading an urban invasion: risk-sensitive learning is a winning strategy", and consists of three different parts. First, the authors analyse data on initial and reversal learning in Grackles confronted with a foraging task, derived from three populations labeled as "core", "middle" and "edge" in relation to the invasion front. The suggested difference between study populations does not surface, but the authors do find support for a difference between male and female individuals. Secondly, the authors confirm that the proposed mechanism can actually generate patterns such as observed in the Grackle data through agent-based forward simulations. In the third part, the authors present an evolutionary model, in which they show that learning strategies, as observed in male Grackles, do evolve in simplified urban conditions including different levels of environmental stability and environmental stochasticity.

Strengths: The manuscript's strength is that it combines real learning data collected across different populations of the Great-tailed grackle (Quiscalus mexicanus) with theoretical approaches to better understand the processes with which grackles learn and how such learning processes might be advantageous during range expansion and invasion. Furthermore, the authors also take sex into account revealing that males, the dispersing sex, show better reversal learning through higher reward-payoff sensitivity. I also find it refreshing to see that the authors took the time to preregister their study to improve transparency especially regarding data analysis.

Weakness: The small sample size of grackles across populations increases uncertainty as to parameter estimates and the conclusions drawn from these estimates.

After revision, the introduction is appropriate, and in the methods, the authors take great care in explaining the rational behind decisions as to the selection of analysis methods and parameters. I very much appreciate that the authors took such care in revising their paper, the quality of which has now greatly improved.

Reviewer #1 (Public Review):

Assessment:

The manuscript titled 'Rab7 dependent regulation of goblet cell protein CLCA1 modulates gastrointestinal 1 homeostasis' by Gaur et al discusses the role of Rab7 in the development of ulcerative colitis by regulating the lysosomal degradation of Clca1, a mucin protease. The manuscript presents interesting data, and provides a potential molecular mechanism for the pathological alterations observed in ulcerative colitis.

Strengths:

The manuscript used a multi-pronged approach and compares patient samples, mouse models of DSS and protocols that allow differentiation of goblet cells. They also use a nanogel-based delivery system for siRNAs, which is ideal for knockdown of specific genes in the gut.

Weaknesses:

The manuscript should also mention the limitations of the study.

Reviewer #2 (Public Review):

Summary:

In this work, the authors report a role for the well-studied GTPase Rab7 in gut homeostasis. The study combines cell culture experiments with mouse models and human ulcerative colitis patient tissues to propose a model where, Rab7 by delivering a key mucous component CLCA1 to lysosomes, regulates its secretion in the goblet cells. This is important for the maintenance of mucous permeability and gut microbiota composition. In the absence of Rab7, CLCA1 protein levels are higher in tissues as well as the mucus layer, corroborating with the anti-correlation of Rab7 (reduced) and CLCA1 (increased) from ulcerative colitis patients. The authors conclude that Rab7 maintains CLCA1 level by controlling its lysosomal degradation, thereby playing a vital role in mucous composition, colon integrity, and gut homeostasis.

Strengths:

The biggest strength of this manuscript is the combination of cell culture, mouse model, and human tissues. The experiments are largely well done and in most cases, the results support their conclusions. The authors go to substantial lengths to find a link, such as alteration in microbiota, or mucus proteomics.

Weaknesses:

There are also some weaknesses that need to be addressed. The association of Rab7 with UC in both mice and humans is clear, however, claims on the underlying mechanisms are less clear. Does Rab7 regulate specifically CLCA1 delivery to lysosomes, or is it an outcome of a generic trafficking defect? CLCA1 is a secretory protein, how does it get routed to lysosomes, i.e. through Golgi-derived vesicles, or by endocytosis of mucous components? Mechanistic details on how CLCA1 is routed to lysosomes will add substantial value.

Why does the level of Rab7 fluctuate during DSS treatment (Fig 1B)? Does the reduction seen in Rab7 levels (by WB) also reflect in reduced Rab7 endosome numbers? Are other late endosomal (and lysosomal) populations also reduced upon DSS treatment and UC? Is there a general defect in lysosomal function?

While it is clear that the pattern of Muc2 in WT and Rab7-/- cells are different, how this corroborates with the in vivo data on alterations in mucus layer permeability - as claimed - is not clear.

The use of an in vivo intestine-specific Rab7 silencing model is good. Why does Rab7 KD itself not capitulate aspects of DSS treatment, rather it seems to exacerbate it.

The use of mucous proteomics to identify mechanisms of Rab7-mediated phenotype is a good approach. The replicates in the proteomics dataset (Fig 6F) do not seem to match. Detailing of methodology used for analysis will help to overcome these doubts.

The work shows a role for a well-studied GTPase, Rab7, in gut homeostasis. This is an important finding and could provide scope and testable hypotheses for future studies aimed at understanding in detail the mechanisms involved.

Reviewer #1 (Public Review):

Summary:<br /> The authors compared four types of hiPSCs and four types of hESCs at the proteome level to elucidate the differences between hiPSCs and hESCs. Semi-quantitative calculations of protein copy numbers revealed increased protein content in iPSCs. Particularly in iPSCs, proteins related to mitochondrial and cytoplasmic were suggested to reflect the state of the original differentiated cells to some extent. However, the most important result of this study is the calculation of the protein copy numbers per cell, and the validity of this result is problematic. In addition, several experiments need to be improved, such as using cells of different genders (iPSC: female, ESC: male) in mitochondrial metabolism experiments.

Strengths:<br /> The focus on the number of copies of proteins is exciting and appreciated if the estimated calculation result is correct and biologically reproducible.

Weaknesses:<br /> The proteome results in this study were likely obtained by simply looking at differences between clones, and the proteome data need to be validated. First, there were only a few clones for comparison, and the gender and number of cells did not match between ESCs and iPSCs. Second, no data show the accuracy of the protein copy number per cell obtained by the proteome data.

Reviewer #2 (Public Review):

Summary:<br /> Pluripotent stem cells are powerful tools for understanding development, differentiation, and disease modeling. The capacity of stem cells to differentiate into various cell types holds great promise for therapeutic applications. However, ethical concerns restrict the use of human embryonic stem cells (hESCs). Consequently, induced human pluripotent stem cells (ihPSCs) offer an attractive alternative for modeling rare diseases, drug screening, and regenerative medicine. A comprehensive understanding of ihPSCs is crucial to establish their similarities and differences compared to hESCs. This work demonstrates systematic differences in the reprogramming of nuclear and non-nuclear proteomes in ihPSCs.

Strengths:<br /> The authors employed quantitative mass spectrometry to compare protein expression differences between independently derived ihPSC and hESC cell lines. Qualitatively, protein expression profiles in ihPSC and hESC were found to be very similar. However, when comparing protein concentration at a cellular level, it became evident that ihPSCs express higher levels of proteins in the cytoplasm, mitochondria, and plasma membrane, while the expression of nuclear proteins is similar between ihPSCs and hESCs. A higher expression of proteins in ihPSCs was verified by an independent approach, and flow cytometry confirmed that ihPSCs had larger cell sizes than hESCs. The differences in protein expression were reflected in functional distinctions. For instance, the higher expression of mitochondrial metabolic enzymes, glutamine transporters, and lipid biosynthesis enzymes in ihPSCs was associated with enhanced mitochondrial potential, increased ability to uptake glutamine, and increased ability to form lipid droplets.

Weaknesses:<br /> While this finding is intriguing and interesting, the study falls short of explaining the mechanistic reasons for the observed quantitative proteome differences. It remains unclear whether the increased expression of proteins in ihPSCs is due to enhanced transcription of the genes encoding this group of proteins or due to other reasons, for example, differences in mRNA translation efficiency. Another unresolved question pertains to how the cell type origin influences ihPSC proteomes. For instance, whether ihPSCs derived from fibroblasts, lymphocytes, and other cell types all exhibit differences in their cell size and increased expression of cytoplasmic and mitochondrial proteins. Analyzing ihPSCs derived from different cell types and by different investigators would be necessary to address these questions.

Reviewer #3 (Public Review):

Summary:<br /> In this study, Brenes and colleagues carried out proteomic analysis of several human induced pluripotent (hiPSC) and human embryonic stem cell (hESC) lines. The authors found quantitative differences in the expression of several groups of cytoplasmic and mitochondrial proteins. Overall, hiPSC expressed higher levels of proteins such as glutamine transporters, mitochondrial metabolism proteins, and proteins related to lipid synthesis. Based on the protein expression differences, the authors propose that hiPSC lines differ from hESC in their growth and metabolism.

Strengths:<br /> The number of generated hiPSC and hESC lines continues to grow, but potential differences between hiPSC and hESC lines remain to be quantified and explained. This study is a promising step forward in understanding of the differences between different hiPSC and hESC lines.

Weaknesses:<br /> It is unclear whether changes in protein levels relate to any phenotypic features of cell lines used. For example, the authors highlight that increased protein expression in hiPSC lines is consistent with the requirement to sustain high growth rates, but there is no data to demonstrate whether hiPSC lines used indeed have higher growth rates.

The authors claim that the cell cycle of the lines is unchanged. However, no details of the method for assessing the cell cycle were included so it is difficult to appreciate if this assessment was appropriately carried out and controlled for.

Details and characterisation of iPSC and ESC lines used in this study were overall lacking. The lines used are merely listed in methods, but no references are included for published lines, how lines were obtained, what passage they were used at, their karyotype status, etc. For details of basic characterisation, the authors should refer to the ISSC Standards for the use of human stem cells in research. In particular, the authors should consider whether any of the changes they see may be attributed to copy number variants in different lines.

The expression data for markers of undifferentiated state in Figure 1a would ideally be shown by immunocytochemistry or flow cytometry as it is impossible to tell whether cultures are heterogeneous for marker expression.

TEM analysis should ideally be quantified.

All figure legends should explicitly state what graphs are representing (e.g. average/mean; how many replicates (biological or technical), which lines)? Some data is included in Methods (e.g. glutamine uptake), but not for all of the data (e.g. TEM).

Validation experiments were performed typically on one or two cell lines, but the lines used were not consistent (e.g. wibj_2 versus H1 for respirometry and wibj_2, oaqd_3 versus SA121 and SA181 for glutamine uptake). Can the authors explain how the lines were chosen?

The authors should acknowledge the need for further functional validation of the results related to immunosuppressive proteins.

Differences in H1 histone abundance were highlighted. Can the authors speculate as to the meaning of these differences?

Reviewer #1 (Public Review):

Summary:<br /> In this study by Zhou, Wang, and colleagues, the authors utilize biventricular electromechanical simulations to illustrate how different degrees of ionic remodeling can contribute to different ECG morphologies that are observed in either acute or chronic post-myocardial infarction (MI) patients. Interestingly, the simulations show that abnormal ECG phenotypes - associated with a higher risk of sudden cardiac death - are predicted to have almost no correspondence with left ventricular ejection fraction, which is conventionally used as a risk factor for arrhythmia.

Strengths:<br /> The numerical simulations are state-of-the-art, integrating detailed electrophysiology and mechanical contraction predictions, which are often modeled separately. The simulation provides mechanistic interpretation, down to the level of single-cell ionic current remodeling, for different types of ECG morphologies observed in post-MI patients. Collectively, these results demonstrate compelling and significant evidence for the need to incorporate additional risk factors for assessing post-MI patients.

Weaknesses:<br /> The study is rigorous and well-performed. However, some aspects of the methodology could be clearer, and the authors could also address some aspects of the robustness of the results. Specifically, does variability in ionic currents inherent in different patients, or the location/size of the infarct and surrounding remodeled tissue impact the presentation of these ECG morphologies?

Reviewer #2 (Public Review):

Summary:<br /> The authors constructed multi-scale modeling and simulation methods to investigate the electrical and mechanical properties of acute and chronic myocardial infarction (MI). They simulated three acute MI conditions and two chronic MI conditions. They showed that these conditions gave rise to distinct ECG characteristics that have been seen in clinical settings. They showed that the post-MI remodeling reduced ejection fraction up to 10% due to weaker calcium current or SR calcium uptake, but the reduction of ejection fraction is not sensitive to remodeling of the repolarization heterogeneities.

Strengths:<br /> The major strength of this study is the construction of computer modeling that simulates both electrical behavior and mechanical behavior for post-MI remodeling. The links of different heterogeneities due to MI remodeling to different ECG characteristics provide some useful information for understanding complex clinical problems.

Weaknesses:<br /> The rationale (e.g., physiological or medical bases) for choosing the 3 acute MI and 2 chronic MI settings is not clear. Although the authors presented a huge number of simulation data, in particular in the supplemental materials, it is not clearly stated what novel findings or mechanistic insights this study gained beyond the current understanding of the problem.

Reviewer #1 (Public Review):

Herzog and colleagues investigated the interactions between working memory (WM) task condition (updating, maintenance) and BMI (body-mass-index), while considering selected dopaminergic genes (COMT, Taq1A, C957T, DARPP-32). Emerging evidence suggests that there might be a specific negative association with BMI in the updating but not maintenance condition, with potential bearings to reversal reward learning in obesity. The inclusion of multiple dopaminergic genes is a strength in the present study, considering the complexity of the interactions between tonic and phasic dopamine across the brain that may distinctly associate with the component processes of WM. Here, the finding was that BMI was negatively associated with WM performance regardless of the condition (updating, maintenance), but in models including moderation by either Taq1A or DARPP-32 (but not by COMT and C957T) an interaction by task condition was observed. Furthermore, a two-way interaction effect between BMI and genotype was observed exclusively in the updating condition. These findings are in line with the accounts by which striatal dopamine as reflected by Taq1A and DARPP-32 play an important role in working memory updating, while cortical dopamine as reflected by COMT is mainly associated with maintenance. The authors conclude that the genetic moderation reflects a compound negative effect of having high BMI and a risk allele in Taq1A or DARPP-32 to working memory updating specifically.

These data increment the accumulating evidence that the dopamine system may play an important role in obesity, but some of the claims in the present work are not entirely supported by the data and analysis presented. In particular, theoretical analysis of the extant evidence and formulation of the hypothesis remains elusive in terms of the potential mechanisms of updating/maintaining balance in obesity, and as such the interpretation of the present findings in the light of dopaminergic moderation warrants some caution. The result that Taq1A and DARPP-32 moderated the interaction between WM condition and BMI requires intricate post hoc analysis to understand the bearings to update. The authors found that Taq1A or DARPP-32 genotype moderated the negative association between BMI and WM exclusively in the update condition (significant two-way interaction effect), suggesting that the BMI-WM associations in other conditions were similar across genotypes. Importantly, visual inspection of the relationship between WM and BMI (Fig 4 & 5) suggests more prevalent positive effects of the putatively advantageous Taq1A-A1 and DARPP-32-AA genotypes to the overall negative relationship between WM and BMI in updating, but not in the other conditions. Given that an overall negative relationship was statistically supported across all conditions (model 1), a plausible interpretation would be that the updating condition stands out in terms of a positive moderation by putative advantageous genotypes, rather than compound negative consequences of BMI and genotype in updating. Critically, this interpretation stands in stark contrast with the interpretation put forth by the authors suggesting a specifically negative association between BMI and WM updating.

In conclusion, in its current form the title of the present work is ambivalent in terms of 1) the use of the term "impaired" in the context of cognitively normal individuals, 2) a BMI group difference specifically in the updating condition, and 3) the dopaminergic mechanisms based on observational data.

Reviewer #2 (Public Review):

Summary:<br /> The authors investigated if obesity is associated with elevated working memory deficits. Prior theorizing would suggest that individuals with a higher BMI would be worse at working memory updating, potentially due to impaired dopaminergic signaling in the striatum. However, the authors find that higher BMI was associated with worse working memory performance, irrespective of having to ignore or update new information. To further explore the putative dopaminergic mechanisms, participants are stratified according to genetic polymorphisms in COMT, Taq1A, DARPP, and C957T and the ratio of the amino acids phenylalanine and tyrosine, all implicated in dopamine-signaling. They find that especially for working memory updating, carriers of a risk allele of Taq1A and DARPP, but not of COMT and C957T, performed worse with increasing BMI. The detrimental effects of these polymorphisms on updating only surfaced for individuals with high but not low BMI.

Although the authors allude to potential imbalances in the striatal go/no-go dopamine pathways to explain these findings, the dopaminergic mechanisms of the effects remain speculative.

Strengths:<br /> Differentiating between working memory maintenance (ignoring) and updating is a powerful way to get a deeper insight into specific working memory deficits in individuals with obesity. This way of assessing working memory could potentially be applied to various populations at risk for cognitive or working memory deficits.

By pooling data from three studies, the authors reached a relatively large sample of 320 participants, which enables the assessment of more subtle effects on working memory, including the differentiation between updating and ignoring.

Working memory gating has long implicated striatal dopamine signaling. This paper shows that specific combinations of risk factors, a high BMI and carrying a risk allele, can contribute to very selective working memory impairments. More insight into how these risk factors interact can ultimately lead to more tailor-made treatments.

Weaknesses:<br /> The majority of participants seem to fall within the normal BMI range, whereas the interaction between BMI and genetic variations or amino acid ratio particularly surfaces at higher BMI. As genetic variations are usually associated with small effect sizes, the effective sample size, although large for a behavioral analysis only, might have been too small to detect meaningful effects of risk alleles of COMT and C957T.

The relationships between genetic variations, BMI, and specific disturbances in dopamine signaling are complex, as compensating mechanisms might be at play to mitigate any detrimental effects. The results would therefore benefit from more direct measures or manipulations of dopaminergic processes.

The introduction could benefit from a more elaborate description of the predicted effects: into which direction (better or worse updating) would the authors predict each effect to go and why? This is clearly explained for COMT, but not for e.g. DARPP-32.

Reviewer #1 (Public Review):

Drawing on insights from preceding studies, the researchers pinpointed mutations within the spag7 gene that correlate with metabolic aberrations in mice. The precise function of spag7 has not been fully described yet, thereby the primary objective of this investigation is to unravel its pivotal role in the development of obesity and metabolic disease in mice. First, they generated a mice model lacking spag7 and observed that KO mice exhibited diminished birth size, which subsequently progressed to manifest obesity and impaired glucose tolerance upon reaching adulthood. This behaviour was primarily attributed to a reduction in energy expenditure. In fact, KO animals demonstrated compromised exercise endurance and muscle functionality, stemming from a deterioration in mitochondrial activity. Intriguingly, none of these effects was observed when using a tamoxifen-induced KO mouse model, implying that Spag7's influence is predominantly confined to the embryonic developmental phase. Explorations within placental tissue unveiled that mice afflicted by Spag7 deficiency experienced placental insufficiency, likely due to aberrant development of the placental junctional zone, a phenomenon that could impede optimal nutrient conveyance to the developing fetus. Overall, the authors assert that Spag7 emerges as a crucial determinant orchestrating accurate embryogenesis and subsequent energy balance in the later stages of life.

The study boasts several noteworthy strengths. Notably, it employs a combination of animal models and a thorough analysis of metabolic and exercise parameters, underscoring a meticulous approach. Furthermore, the investigation encompasses a comprehensive evaluation of fetal loss across distinct pregnancy stages, alongside a transcriptomic analysis of skeletal muscle, thereby imparting substantial value. Upon addressing the previously mentioned aspects, the study is poised to exert a substantial influence on the field, its significance reverberating significantly. The methodologies and data presented undoubtedly hold the potential to facilitate the community's deeper understanding of the ramifications stemming from disruptions during pregnancy, shedding light on their enduring impact on the metabolic well-being of subsequent generations.

Reviewer #1 (Public Review):

Summary:

Plant roots grow following the gravity vector. Changes in the direction of gravity can be sensed in the root tip by specialized cells that hold starch granules. These starch granules act as levels. Movement and settling of the granules at the bottom of these specialized cells initiates an imbalanced distribution of auxin, a key hormone for plant development. Consequently, this leads to a reorientation of root growth towards the newly established gravity vector. This work provides new insights into granules' relocalization, the proteins associated with them, and the molecular processes triggered downstream.

Comments on revised submission:

In the previous review round, the reviewers noted that the authors had missed an opportunity to discuss the results presented in two recently published articles closely related to the topic of their manuscript. The authors have now referenced these articles in the current version of the manuscript, but the discussion remains rather brief. It would have been beneficial to summarize, identify, and highlight the similarities among these studies in a more comprehensive manner.

In Figure 1, it would have been more informative if the authors had provided specific information concerning the key time-points described in the graphs to visually illustrate the dynamics of PIN3 localization, intracellular calcium transients, and auxin reporter DII Venus. Including these images would have perfectly complemented panels E, F, and G.

The authors expressed concerns about overcrowding the figure. If the aesthetics of the figure were their primary concern, they could have included essential image frames for the data represented in the graphs in a supplementary figure. Alternatively, a detailed description of supplementary movie 3, highlighting the specific frames quantified in the graphs (Figure 1), could have sufficed.

Reviewer #2 (Public Review):

Summary:

This manuscript addresses what rapid molecular events underly the earliest responses after gravity-sensing via the sedimentation of starch-enriched amyloplasts in columella cells of the plant root cap. The LAZY or NEGATIVE GRAVITROPIC RESPONSE OF ROOTS (NGR) protein family is involved in this process and localizes to both the amyloplast and to the plasma membrane (PM) of columella cells.

This manuscript complements and extends a very recent study, (Nishimura et al., Science, 2023, August 10, 2023) that reported that the LZY3 and LZY4 proteins translocate from amyloplasts to the PM and that this translocation is likely necessary for the root gravitropic response. Kulich and colleagues describe the role of the LZY2 protein, also called NGR1, during this process, imaging its fast relocation and addressing additional novel points such as molecular mechanisms underlying NGR1 plasma membrane association as well as revealing the requirement of NGR1/LZY2, 3,4 for the polar localization of the AGCVIII D6 protein kinase at the PM of columella cells, in which NGR1/LZY2 acts redundantly with LZY3 and LZY4.

The authors initially monitored relocalization of functional NGR1-GFP in columella cells of the ngr1 ngr2 ngr3 triple mutant after 180 degree reorientation of the roots. Within 10 -15 min NGR1-GFP signal disappeared from the upper PM after reorientation and reappeared at the lower PM of the reoriented cells in close proximity to the sedimented amyloplasts. Reorientation of NGR1-GFP occurred substantially faster than PIN3-GFP reorientation, at about the same time or slightly later than a rise in a calcium sensor (GCaMP3) just preceding a change in D2-Venus auxin sensor alterations. Reorientation of NGR1-GFP proved to be fast and not dependent on a brefeldin A-sensitive ARF GEF-mediated vesicle trafficking, unlike the trafficking of PIN proteins, like PIN3, or the AGCVIII D6 protein kinase. Strikingly, the PM association of NGR1-GFP was highly sensitive to pharmacological interference with sterol composition or concentration and phosphatidylinositol (4)kinase inhibition as well as dithiothreitol (DTT) treatment interfering with thioester bond formation e.g. during S-acylation. Indeed, combined mutation of a palmitoylation site and polybasic regions of NRG1 abolished its PM but not its amyloplast localization and rendered the protein non-functional during the gravitropic response, suggesting NRG1 PM localization is essential for the gravitropic response. Targeting the protein to the PM via an artificially introduced N-terminal myristoylation and a ROP2-derived polybasic region and geranylgeranylation site partially restored its functionality in the gravitropic response.

Strengths:

This timely work should be of broad interest to plant, cell and developmental biologists across the field as gravity sensing and signaling may well be of general interest. The point that NGR1 is rapidly responsive to gravistimulation, polarizes at the PM in the vicinity to amyloplast and that this is required for repolarization of D6 protein kinase, prior to PIN relocation is really compelling. The manuscript is generally well written and accessible to a general readership, except for very minor language errors. The figures are clear and of high quality, the methods are sufficiently explained for reproduction of the experiments.

Comments on revised submission:

The authors have addressed my comments to a large part, however, while they write they have updated the statistical analysis as requested, they only did this for the main figures, but NOT for the supplementary images (except for Fig. S2) and their legends. These issues need fixing in order to correctly describe the data and let the reader know, which distributions actually differed. Some specific examples of concerns are:

In Figs. 3F and D we now know that a one-way ANOVA test was performed and that letters designate the statistically significant difference between distributions with p smaller 0.0001, but we still do not know what "n" in the displayed distributions is e.g. how many PM/cytoplasm ratios were measured i.e. e.g 112? (from 112 cells?). It is said that 8-15 roots were quantified, but the data points in the distributions are not 8-15 .... . They are many more, so, "n" must be the number of cells derived from 8-15 roots but what is "n" in the displayed distributions and is that the same value that was used for the Anova test?

This must be clarified as it has very well been done for Fig. 2 and Fig. S2B, E in the legends and by inserting a lettering for significance differences in the figures.

Similar information is still lacking for Fig. S3D, no number "n" of cells from which the PM/cytoplasm ratios are analyzed is given, no lettering for differences, no p -value. This leaves one to guess which distributions differ from each other.

This also needs to be fixed for Figs. S4 E, F (for G and H one can see the differences where the SDs do not overlap and it is explained what they are derived from).

Reviewer #3 (Public Review):

This reviewer appreciates the responses to previous notes. The authors attempted to address concerns mostly in writing, avoiding performing some of the experiments suggested in my previous review. Although some of the points were clarified, and the revised manuscript presents valuable insights into the implications of YBR238C and RMD9 on cellular function and yeast aging, my major concern still needs to be addressed. The gene expression signature significantly changes under different metabolic conditions. The media condition under which samples are collected for RNAseq analyses should match the media condition under which the lifespans of those KO strains are tested. This is the major confounding effect, and the conclusions are not informative based on the analysis done in this study.

To avoid experiments, the authors responded that yeast culture results in low optical density and does not reach the stationary phase under rapamycin treatment conditions; however, the simple solution is to grow the yeast cells until they reach the stationary phase and then rapamycin treatment can be done for certain hours - collect the cells for transcriptomics analysis then it can be compared to the CLS gene set.

Another example is chromosome copy number alteration, which can be easily analyzed using transcriptome data, and it is an important aspect to understand whether observed expression changes are also affected by this alteration in YBR238C KO cells. However, the authors ignore this important point as well.

After all, this is an interesting study "limited by subfield" and will be of general interest in the yeast aging field, again considering the lack of homology of the genes of interest in higher eukaryotes.

Reviewer #1 (Public Review):

Summary

This fascinating paper by M. Alfatah et al. describes work to uncover novel genes affecting lifespan in the budding yeast S. cerevisiae, eventually identifying and further characterizing a gene, YBR238C, now named AAG1 by the authors.<br /> The authors began by considering published gene sets pulled from the Saccharomyces genome database that described increases or decreases in either chronological lifespan or replicative lifespan in yeast. They also began with gene sets known to be downregulated upon treatment with the lifespan-extending TOR inhibitor rapamycin.

YBR283C was unique in being largely uncharacterized, downregulated upon rapamycin treatment and linked to both increased replicative lifespan and increased chronological lifespan upon deletion.

The authors show that YBR283C may act to negatively regulate mitochondrial function, in ways that are both dependent on and independent of the stress-responsive transcription factor Hap4, largely by looking at relative expression levels of relevant mitochondrial genes.

In a hard to fully interpret but well documented series of experiments the authors not that the two paralogues YBR283C and RMD9 (which have ~66% similarity) (a) have opposite effects when acting alone, and (b) appear to interact in that some phenotypes of ybr283c are dependent on RMD9.

A particularly interesting finding in light of the current literature and of the authors' strategy in identifying YBR283C is that changes in electron transport chain genes upon rapamycin treatment appear to be effected via YBR283C.<br /> Based on a series of experiments the authors move to conclude the existence of "a feedback loop between TORC1 and mitochondria (the TORC1-Mitochondria-TORC1 (TOMITO) signaling process) that regulates cellular aging processes."

Strengths

Overall, this study describes a great deal of new data from a large number of experiments, that shed light on the potential specific roles of YBR238C and its paralog RMD9 in aging in yeast, and also underscore the potential of an approach looking for "dark matter" such as uncharacterized genes when seining the increasing deluge of published datasets for new hypotheses to test. This work when revised will become a valuable addition to the field.

Weaknesses

A paralog of YBR283C, RMD9, also exists in the yeast genome. While the authors indicate that part of their interest in YBR283C lies in its uncharacterized nature, its paralogue, RMD9, is not uncharacterized but is named due to its phenotype of Required for Meiotic nuclear Division, which is not mentioned or discussed anywhere in the manuscript currently.

In the context of the current work, in addition to the cited Hillen, H.S et al. and Nouet C. et al, the authors might be very interested in the 2007 Genetics paper "Translation initiation in Saccharomyces cerevisiae mitochondria: functional interactions among mitochondrial ribosomal protein Rsm28p, initiation factor 2, methionyl-tRNA-formyltransferase and novel protein Rmd9p" (PMID: 17194786), which does not appear to be cited or discussed in the current version of the manuscript.

Reviewer #2 (Public Review):

The effectors of cellular aging in yeast have not been fully elucidated. To address this, the authors curated gene expression studies to link genes influenced by rapamycin - a well-known mediator of longevity across model systems - to genes known to affect chronological and replicative lifespan (RLS) in yeast. Through their analyses, they find one gene, ybr238c, whose deletion increases both CLS and RLS upon deletion and that is downregulated by rapamycin. The authors follow up their cellular aging studies using CLS as a model throughout their study, demonstrating that deletion of ybr238c increases CLS across multiple yeast strains and through multiple assays. The authors also test the effects of YBR238C overexpression on lifespan and find the opposite effect, with overexpression yeast showing decreased survival relative to wild type cells, consistent with accelerated aging as the authors propose. The authors also note that ybr238c has a paralog, rmd9, whose deletion decreases CLS and seems to be epistatic to ybr238c, as a double ybr238c/rmd9 mutant has decreased CLS relative to a wild-type strain.

Collectively, the data presented by the authors convincingly demonstrate that ybr238c influences lifespan in a manner that is distinct from (and likely opposite to) rmd9. The authors then link the increased CLS in Δybr238c yeast to HAP4, a transcription factor that promotes mitochondrial biogenesis and oxidative phosphorylation. Through genetic studies, the authors suggest a model in which YBR238C negatively regulates HAP4 activity, and thus loss of HAP4 repression in Δybr238c yeast leads to elevated mitochondrial function. Notably, while the authors use various methods to test mitochondrial function, including the quantification of transcripts associated with oxidative phosphorylation, cellular ATP levels, and mtDNA, none of these fully test mitochondrial function. Thus, while the trends of these proxies are consistent with the model proposed by the authors, including data such as respirometry or assaying the activity of oxidative phosphorylation complexes would have bolstered these conclusions.

Finally, the authors tie the phenotypes of mitochondrial dysfunction caused by deletion of ybr238c to TORC1 signaling, as the gene is influenced by rapamycin. However, the data assaying mitochondrial function in these experiments, such as profiling the transcriptional changes in oxidative phosphorylation complexes or monitoring cellular ATP levels, do not directly measure mitochondrial function. Furthermore, many of the studies performed by the authors rely on genetic or pharmacological rescue of lifespan to establish the influence of YBR238C on TORC1 signaling and mitochondrial function. While valuable, these assays leave questions as to the molecular mechanisms by which YBR238C functions. As such, this manuscript establishes that ybr238c is rapamycin responsive and influences CLS, but the molecular mechanisms by which it affects mitochondrial activity and TORC1 signaling remain to be elucidated.

Reviewer #3 (Public Review):

Summary:<br /> Kobayashi et al identify MER21C as a common promoter of GPR1-AS/Liz in Euarchontoglires, which establishes a somatic DMR that controls ZFDB2 imprinting. In mice, MER21C appears to have diverged significantly from its primate counterparts and is no longer annotated as such.

Strengths:<br /> The authors used high-quality cross-species RNA-seq data to characterise GPR1-AS-like transcripts, which included generating new data in five different species. The association between MER21C/B elements and the promoter of GPR1-AS in most species is clear and convincing. The expression pattern of MER21C/B elements overall further supports their role in enabling correct temporal expression of GPR1-AS during embryonic development.

Weaknesses:<br /> A deeper comparison of syntenic regions to the GPR1-AS promoter could be performed to provide a clearer picture of how the MER21C/B element evolved. The use of alternative TE annotation software may also be helpful. These analyses would be particularly useful to drive home the conclusion that the mouse (Liz) promoter is derived from the same insertion.

Reviewer #1 (Public Review):

Summary:<br /> The study tests the conservation of imprinting of the ZBDF2 locus across mammals. ZDBF2 is known to be imprinted in mice, humans, and rats. The locus has a unique mechanism of imprinting: although imprinting is conferred by a germline DMR methylated in oocytes, the DMR is upstream to ZDBF2 (at GPR1) and monoallelic methylation of the gDMR does not persist beyond early developmental stages. Instead, a lncRNA (GPR1-AS, also known as Liz in mouse) initiating at the gDMR is expressed transiently in embryos and sets up a secondary DMR (by mechanisms not fully elucidated) that then confers monoallelic expression of ZDBF2 in somatic tissues.

In this study, the authors first interrogate existing placental RNA-seq datasets from multiple mammalian species, and detect GPR1-AS1 candidate transcripts in humans, baboons, macaques and mice, but not in about a dozen other mammals. Because of the varying depth, quality, and nature of these RNA-seq libraries, the ability to definitely detect the GPR1-AS1 lncRNA is not guaranteed; therefore, they generate their own deep, directional RNA-seq data from tissues/embryos from five species, finding evidence of GPR1-AS in rabbits and chimpanzees, but not bovine animals, pigs or opossums. From these surveys, the authors conclude that the lncRNA is present only in Euarchontoglires mammals. To test the association between GPR1-AS and ZDBF2 imprinting, they perform RT-PCR and sequencing in tissue from wallabies and cattle, finding biallelic expression of ZDBF2 in these species that also lack a detected GPR1-AS transcript. From inspection of the genomic location of the GPR1-AS first exon, the authors identify an overlap with a solo LTR of the MER21C retrotransposon family in those species in which the lncRNA is observed, except for some rodents, including mice. However, they do detect a degree of homology (46%) to the MER21C consensus at the first exon on Liz in mouse. Finally, the authors explore public RNA-seq datasets to show that GPR1-AS is expression transiently during human preimplantation development, an expression dynamic that would be consistent with the induction of monoallelic methylation of a somatic DMR at ZDBF2 and consequent monoallelic expression.

Strengths:<br /> -The analysis uncovers a novel mechanism by which a retrotransposon-derived LTR may be involved in genomic imprinting.<br /> -The genomic analysis is very well executed.<br /> -New directional and deeply-sequenced RNA-seq datasets from the placenta or the trophectoderm of five mammalian species and marsupial embryos, that will be of value to the community.

Weaknesses:<br /> Although the genomic analysis is very strong, the study remains entirely correlative. All of the data are descriptive, and much of the analysis is performed on RNA-seq and other datasets from the public domain; a small amount of primary data is generated by the authors.<br /> Evidence that the residual LTR in mouse is functionally relevant for Liz lncRNA expression is lacking.

Reviewer #2 (Public Review):

Summary:<br /> This work concerns the evolution of ZDBF2 imprinting in mammalian species via initiation of GPR1 antisense (AS) transcription from a lineage-specific long-terminal repeat (LTR) retrotransposon. It extends previous work describing the mechanism of ZDBF2 imprinting in mice and humans by demonstrating conservation of GPR1-AS transcripts in rabbits and non-human primates. By identifying the origin of GPR1-AS transcription as the LTR MER21C, the authors claim to account for how imprinting evolved in these species but not in those lacking the MER21C insertion. This illustrates the principle of LTR co-option as a means of evolving new gene regulatory mechanisms, specifically to achieve parent-of-origin allele specific expression (i.e., imprinting). Examples of this phenomenon have been described previously, but usually involve initiation of transcription during gametogenesis rather than post-fertilization, as in this work. The findings of this paper are therefore relevant to biologists studying imprinted genes or interested more generally in the evolution of gene regulatory mechanisms.

Strengths:<br /> (1) The authors convincingly demonstrate the existence of GPR1-AS orthologs in specific mammalian lineages using deeply sequenced, stranded, and paired-end RNA-seq libraries collected from diverse mammalian species.

Weaknesses:<br /> (1) The authors do not directly demonstrate imprinting of the ZDBF2 locus in rabbits and non-human primates, which would greatly strengthen their model linking ZDBF2 imprinting to transcription from MER21C.

(2) Experimental evidence linking GPR1-AS transcription to ZDBF2 imprinting in rabbits and non-human primates is currently lacking. Consideration should be given to the challenges associated with studying non-model species and manipulating repeat sequences, which may explain the absence of experimental evidence in this case. Further, this mechanism is established in humans and mice, so the authors' model is arguably sufficiently supported merely by the existence of GPR1-AS orthologs in other mammalian lineages.

Reviewer #1 (Public Review):

This work demonstrates a new technique to characterize the interaction between a crawling larva and the substrate on which it is crawling, at much higher temporal speed and spatial resolution than previously possible. While I have some questions about the interpretation of the data, both the demonstration of WARP microscopy to characterize small animal behavior and the discovery of new crawling-associated anatomical features and motor patterns make the paper worthy of attention.

I thank the authors for providing data underlying the figures. In these uncropped data sets, the deformation of the substrate due to the surface tension of an adhering water layer is visible. I would hope the authors would provide a subset of these images and some of the accompanying information (e.g. that the deformation of the gel due to the water layer cannot be accurately calculated due to too-rapid phase wrapping in the interferogram) as supplements to the text, to aid in interpretation and understanding of the data. It is also worth noting that in the data provided, under the larva, the integral of the stress on the gel is upward, despite the downward force exerted by the protopodia.

Future work using this exciting technique might address the role of surface tension and the balance of forces and might also produce direct evidence to show that the protopodia serve to "anchor" segments of the larva not in motion. Indeed, the most exciting aspect of this work is the number of new questions it both raises and provides a technological pathway towards resolving.

Reviewer #2 (Public Review):

The biology and dynamics is well-described. The ERISM and WARP methods are state-of-the-art. The most important new information is the highly accurate and detailed maps of displacement. The real achievements are the new locomotory dynamics uncovered with amazing displacement measurements. One key discovery is the broad but shallow anchoring of the posterior body when the anterior body undertakes a "head sweep". Another discovery is the tripod indentation at the tail at the beginning of peristalsis cycles. This paper describes the detailed dynamics of anchoring for the first time. Anchoring behavior now has to be included in the motor sequence for Drosophila larva locomotion in any comprehensive biomechanical or neural model.

Reviewer #1 (Public Review):

Understanding the ecology including the dietary ecology of enantiornithines is challenging by all means. This work explores the possible trophic diversity of the "opposite-bird" enantiornithines by referring to the body mass, jaw mechanical advantage, finite element analysis of the jaw bones, and morphometrics of the claws and skull of both fossil and extant avian species. By incorporation the dietary information of longipterygids and pengornithinds, the authors predicted a wide variety of foods for enantiornithine ancestors. This indicates the evolutionary successes of enantiornitine during Cretaceous is very likely to have been driven by the wide range of recipes. I believe this work represented the most comprehensive analysis of enantiornithines' diet and trophic diversity by far and the first systematic dietary analysis of bohaiornithids, though the analysis themselves are largely based on the indirect evidence including jaw bone morphologies and claw and skull morphometrics. Anyway, I believe the authors did most the paleontologists could do, and I do not know whether the conclusions could be further supported by incorporating some geochemical data, as most of the specimens the authors analyzed were recovered from a small geographic area. The results also indicate that the developmental trajectories of enantiornithines, at least for jaw bones, might also have been diverse to some extent in response to the diverse ecological niches they adapted. My only concern regarding the analysis is to what extent the conclusions are convincing by comparing specimens representing various ontogenetic stages. This concern has been addressed in the revised manuscript. I believe the authors have almost exhausted all available methods, and I congratulate the authors for the detailed study they conducted.

Reviewer #2 (Public Review):

Miller et al. take a variety of measurements and analytical techniques to assess the ecology of various species of the enantiornithine clade Bohaiornithidae. From this they suggest that the ancestral enantiornithine was a generalist and that the descendant clades occupied a breadth of niches similar to that of the radiation of derived birds after the K-Pg extinction.

Overall, I find the idea that enantiornithines had occupied a similar niche breadth to post-K-Pg derived birds to be a curious, thought-provoking proposal.

I am satisfied with the edits made by the authors and approve the revised version of the manuscript.

Reviewer #3 (Public Review):

Summary:<br /> The authors use several quantitative approaches to characterize the feeding ecologies of bohaiornithid enantiornithines, including allometric data, mechanical advantage and finite element analyses of the jaw, and morphometric analyses of the claws. The authors combine their results with data for other enantiornithines collected from the literature to shed new insight on the ecological evolution of Enantiornithes as a clade.

Although the authors have taken steps to improve their paper, I generally find improvements unsatisfying, especially regarding my comments.

My remaining concerns:

Teeth: My concern here is not whether having teeth limits available niche space compared to having a keratinous beak. Rather, my concern regards how exploitation of the same niche space might be differently reflected in parameter space between birds with teeth and birds with beaks. Can we reliably expect two species that both eat seeds to occupy the same parameter space if, for example, distribution of stress/strain is across a series of teeth vs. across a more uniform beak? In this manuscript, the authors are clearly making this assumption, but that assumption is not made explicit, let alone justified. The authors should discuss this.

Cranial kinesis: As with teeth, my concern here regards our ability to compare data between birds with and without a flexible beak to mitigate forces when foraging. I appreciate that the functional complexity of the kinetic neognath skull precludes our ability to account for it in analyses such as these, but when comparisons are made using these analyses *specifically among neognaths*, we can reliably assume that we are comparing like to like - that is, we can assume that both have kinetic skulls, and so kinesis is reflected similarly in the data for each bird. Similarly, even if a comparison between two neognaths focuses exclusively on the mandible - in which cranial kinesis is not directly reflected - we can assume that those mandibles serve as comparisons between functionally similar systems. However, we cannot necessarily make those same assumptions when comparing the kinetic skull of a neognath to the akinetic skull of an enantiornithine. Indeed, even when focusing just on the mandible, can we reliably assume that data collected from an akinetic enantiornithine reflect the same comparative context as data collected from kinetic neognaths? I appreciate that the authors added a call for better functional understanding of bird cranial kinesis - a call I enthusiastically endorse - but the authors should still discuss how that current lack of understanding impacts interpretations of the comparisons they draw.

Finally, I still find the discussion to be overly long and lacking clear focus and organization. I again urge the authors to minimally consider adding subheadings to better allow the reader to follow the flow of ideas, and I second Reviewer #1's suggestion to add a "Conclusions" section.

Reviewer #1 (Public Review):

In this work, the authors set out to ask whether the MYRF family of transcription factors, represented by myrf-1 and myrf-2 in C. elegans, have a role in the temporally controlled expression of the miRNA lin-4. The precisely timed onset of lin-4 expression in the late L1 stage is known to be a critical step in the developmental timing ("heterochronic") pathway, allowing worms to move from the L1 to the L2 stage of development. Despite the importance of this step of the pathway, the mechanisms that control the onset of lin-4 expression are not well understood.

Overall, the paper provides convincing evidence that MYRF factors have a role in the regulation of lin-4 expression. Using state-of-the-art techniques (knock-in reporters and conditional alleles), the authors show that MYRF factors are essential for lin-4 activation and act cell-autonomously. While there are some minor concerns about the use of unusual gain-of-function alleles, these are mitigated by consistent results using other approaches. The authors also provide evidence that MYRF factors activate lin-4 by directly activating its promoter. While their results are certainly consistent with this possibility, they rely on indirect measurements and are therefore not definitive. Further experiments will be necessary to determine whether this model is accurate.

Some details about the relative roles of the two C. elegans MYRF factors, myrf-1 and myrf-2, remain unclear. myrf-1 clearly seems to play the more important role lin-4 activation and the regulation of developmentally timed processes. However, there are numerous hints that myrf-2 may act in the opposite direction, either by inhibiting myrf-1 itself or its ability to activate its targets. Further work will be necessary to understand the genetic and mechanistic relationships between these two genes.

Overall, the findings in this paper are convincing, and the results will be of interest to a wide range of developmental biologists.

Reviewer #2 (Public Review):

Summary:<br /> In this manuscript, the authors examine how temporal expression of the lin-4 microRNA is transcriptionally regulated.

In the revised manuscript, the authors have suitably addressed my original concerns.

Aims achieved: The aims of the work are now achieved.

Impact: This study shows that a single transcription factor (MYRF-1) is important for the regulation of multiple microRNAs that are expressed early in development to control developmental timing.

Reviewer #2 (Public Review):

The authors suggest that the African trypanosome endomembrane system has unusual organisation, in that the entire system is a single reticulated structure. It is not clear if this is thought to extend to the lysosome or MVB. There is also a suggestion that this unusual morphology serves as a trans-(post)Golgi network rather than the more canonical arrangement.

The updated manuscript is significantly improved. I remain at slight odds with the author's push for the lack of generality as important, and the new cell biology that we have been on the verge of for decades. However, that is a scholarly issue and is not grounds for any further revision of the present manuscript.

Reviewer #3 (Public Review):

Summary:<br /> A key element in the ability of trypanosomes to evade the mammalian host's immune system is its high rate of endocytosis. This rapid turnover of its surface enables the trypanosome to 'clean' its surface removing antibodies and other immune effectors that are subsequently degraded. The high rate of endocytosis is likely reflected in the organisation of the endosomal system in these parasites. Here, Link et al., sought to address this question using a range of light and three-dimensional electron microscopy approaches to define the endosomal organisation in this parasite.

Before this study, the vast majority of our information about the make-up of the trypanosome endosomal system was from thin section electron microscopy and immunofluorescence studies, which did not provide the necessary resolution and 3D information to address this issue. Therefore, it was not known how the different structures observed by EM were related. Link et al., have taken advantage of the advances in technology and used an impressive combination of approaches at the LM and EM level to study the endosomal system in these parasites. This innovative combination has now shown the interconnected-ness of this network and demonstrated that there are no 'classical' compartments within the endosomal system, with instead different regions of the network enriched in different protein markers (Rab5a, Rab7, Rab11). Overall, the authors have achieved their aims, with results supporting their conclusions.

This is a well written manuscript in which the authors use an impressive range of approaches to address the organisation of the endosomal system. The authors have clearly demonstrated that trypanosomes have a large interconnected endosomal network, without defined compartments and instead shows enrichment for specific Rabs within this network. I appreciate their inclusion of how they used a range of different light microscopy approaches even though for instance the dSTORM approach did not turn out to be as effective as hoped.

The methodological impact of this work has the potential to be large, as the authors have introduced a range of advanced EM techniques for the study of trypanosomes. Moreover, the study of fundamental biological processes such as endosomal trafficking in divergent eukaryotes is important to define the limits within which this process operates.

Reviewer #1 (Public Review):

Summary:<br /> This important study nicely integrates a breadth of experimental and computational data to address fundamental aspects of RNA methylation by an important for biology and health RNA methyltransferases (MTases). 



Strengths: The authors offer compelling and strong evidence, based on carefully performed with appropriate and well-established techniques to shed light on aspects of the methyl transfer mechanism of the methyltransferase-like protein 3 (METTL3), which is part of the methyltransferase-like proteins 3 & 14 (METTL3-14) complex. 


There are no weaknesses that we identified in the revised version.

Reviewer #2 (Public Review):

Summary:<br /> Caflisch and coworkers investigate the methyltransferase activity of the complex of methyltransferase-like proteins 3 and 14 (METTL3-14). To obtain an high resolution description of the complete catalytic cycle they have carefully designed a combination of experiments and simulations. Starting from the identification of bisubstrate analogues (BAs) as binder to stabilise a putative transition state of the reaction they have determined multiple crystal structures and validated relevant interactions by mutagenesis and enzymatic assays.

Using the resolved structure and classical MD simulations they obtained a kinetic picture of the binding and release of the substrates. Of note, they accumulate very good statistics on these processes using 16 simulation replicates over a time scale of 500 ns. To compare the time scale of the release of the products with that of the catalytic step they performed state-of-the-art QM/MM free energy calculations (testing multiple levels of theory) and obtain a free energy barrier that indicates how the release of the product is slower than the catalytic step.

Strengths:<br /> All the work proceeds through clear hypothesis testing based on a combination of literature and new results. Eventually, this allows them to present in Figure 10 a detailed step-by-step description of the catalytic cycle. The work is very well crafted and executed.

Reviewer #3 (Public Review):

Summary:<br /> The manuscript by Coberski et al describes a combined experimental and computational study aimed to shed light on the catalytic mechanism in a methyltransferase that transfers a methyl group from S-adenosylmethionine (SAM) to a substrate adenosine to form N6-methyladenosine (m6A).

Strengths:<br /> The authors determine crystal structures in complex with so-called bi-substrate analogs that can bridge across the SAM and adenosine binding sites and mimic a transition state or intermediate of the methyl-transfer reaction. The crystal structures suggest dynamical motions of the substrate(s) that are examined further using classical MD simulations. The authors then use QM/MM calculations to study the methyl-transfer process. Together with biochemical assays of ligand/substrate binding and enzyme turnover, the authors use this information to suggest what the key steps are in the catalytic cycle. The manuscript is in most places easy to read.

Weaknesses:<br /> After revising the manuscript, there are few weaknesses beyond those listed in the paper.

Reviewer #1 (Public Review):

Summary:<br /> This paper provides strong evidence for the roles of JH in an ametabolous insect species. In particular, it demonstrates that:<br /> • JH shifts embryogenesis from a growth mode to a differentiation mode and is responsible for terminal differentiation during embryogenesis. This, and other JH roles, are first suggested as correlations, based on the timing of JH peaks, but then experimentally demonstrated using JH antagonists and rescue thereof with JH mimic. This is a robust approach and the experimental results are very convincing.<br /> • JH redirects ecdysone-induced molting to direct formation of a more mature cuticle<br /> • Kr-h1 is downstream of JH in Thermobia, as it is in other insects, and is a likely mediator of many JH effects<br /> • The results support the proposed model that an ancestral role of JH in promoting and maintaining differentiation was coopted during insect radiations to drive the evolution of metamorphosis. However, alternate evolutionary scenarios should also be considered.

Strengths:

Overall, this is a beautiful, in-depth student. The paper is well-written and clear. The background places the work in a broad context and shows its importance in understanding fundamental questions about insect biology. The researchers are leaders in the field, and a strength of this manuscript is their use of a variety of different approaches (enzymatic assays, gene expression, agonists & antagonists, analysis of morphology using different types of microscopy and detection, and more) to attack their research questions. The experimental data is clearly presented and carefully executed with appropriate controls and attention to detail. The 'multi-pronged' approach provides support for the conclusions from different angles, strengthening conclusions. In sum, the data presented are convincing and the conclusions about experimental outcomes are well-justified based on the results obtained.

Weaknesses:

This paper provides more detail than is likely needed for readers outside the field but also provides sufficient depth for those in the field. This is both a strength and a weakness. I would suggest the authors shorten some aspects of their text to make it more accessible to a broader audience. In particular, the discussion is very long and accompanied by two model figures. The discussion could be tightened up and much of the text used for a separate review article (perhaps along with Figure 11) that would bring more attention to the proposed evolution of JH roles.

Reviewer #2 (Public Review):

The authors have studied in detail the embryogenesis of the ametabolan insect Thermobia domestica. They have also measured the levels of the two most important hormones in insect development: juvenile hormone (JH) and ecdysteroids. The work then focuses on JH, whose occurrence concentrates in the final part (between 70 and 100%) of embryo development. Then, the authors used a precocene compound (7-ethoxyprecocene, or 7EP) to destroy the JH producing tissues in the embryo of the firebrat T. domestica, which allowed to unveil that this hormone is critically involved in the last steps of embryogenesis. The 7EP-treated embryos failed to resorb the extraembryonic fluid and did not hatch. More detailed observations showed that processes like the maturational growth of the eye, the lengthening of the foregut and posterior displacement of the midgut, and the detachment of the E2 cuticle, were impaired after the 7EP treatment. Importantly, a treatment with a JH mimic subsequent to the 7EP treatment restored the correct maturation of both the eye and the gut. It is worth noting that the timing of JH mimic application was essential for correcting the defects triggered by the treatment with 7EP.

This is a relevant result in itself since the role of JH in insect embryogenesis is a controversial topic. It seems to have an important role in hemimetabolan embryogenesis, but not so much in holometabolans. Intriguingly, it appears important for hatching, an observation made in hemimetabolan and in holometabolan embryos. Knowing that this role was already present in ametabolans is relevant from an evolutionary point of view, and knowing exactly why embryos do not hatch in the absence of JH, is relevant from the point of view of developmental biology.

Then, the authors describe a series of experiments applying the JH mimic in early embryogenesis, before the natural peak of JH occurs, and its effects on embryo development. Observations were made under different doses of JHm, and under different temporal windows of treatment. Higher doses triggered more severe effects, as expected, and different windows of application produced different effects. The most used combination was 1 ng JHm applied 1.5 days AEL, checking the effects 3 days later. Of note, 1.5 days AEL is about 15% embryonic development, whereas the natural peak of JH occurs around 85% embryonic development. In general, the ectopic application of JHm triggered a diversity of effects, generally leading to an arrest of development. Intriguingly, however, a number of embryos treated with 1 ng of JHm at 1.5 days AEL showed a precocious formation of myofibrils in the longitudinal muscles. Also, a number of embryos treated in the same way showed enhanced chitin deposition in the E1 procuticle and showed an advancement of at least a day in the deposition of the E2 cuticle.

While the experiments and observations are done with great care and are very exhaustive, I am not sure that the results reveal genuine JH functions. The effects triggered by a significant pulse of ectopic JHm when the embryo is 15% of the development will depend on the context: the transcriptome existing at that time, especially the cocktail of transcription factors. This explains why different application times produce different effects. This also explains why the timing of JHm application was essential for correcting the effects of 7EP treatment. In this reasoning, we must consider that the context at 85% development, when the JH peaks in natural conditions and plays its genuine functions, must be very different from the context at 15% development, when the JHm was applied in most of the experiments. In summary, I believe that the observations after the application of JHm reveal effects of the ectopic JHm, but not necessarily functions of the JH. If so, then the subsequent inferences made from the premise that these ectopic treatments with JHm revealed JH functions are uncertain and should be interpreted with caution.

Those inferences affect not only the "JH and the progressive nature of embryonic molts" section, but also, the "Modifications in JH function during the evolution of hemimetabolous and holometabolous life histories" section, and the entire "Discussion". In addition to inferences built on uncertain functions, the sections mentioned, especially the Discussion, I think suffer from too many poorly justified speculations. I love speculation in science, it is necessary and fruitful. But it must be practiced within limits of reasonableness, especially when expressed in a formal journal.

Finally, In the section "Modifications in JH function during the evolution of hemimetabolous and holometabolous life", it is not clear the bridge that connects the observations on the embryo of Thermobia and the evolution of modified life cycles, hemimetabolan and holometabolan.

Reviewer #3 (Public Review):

Summary:

In this manuscript, the authors use inhibitors and mimetics of juvenile hormone (JH) to demonstrate that JH has a key role in late embryonic development in Thermobia, specifically in gut and eye development but also resorption of the extraembryonic fluid and hatching. They then exogenously apply JH early in development (when it is not normally present) to examine the biological effects of JH at these stages. This causes a plethora of defects including developmental arrest, deposition of chitin, limb development, and enhanced muscle differentiation. The authors interpret these early effects on development as JH being important for the shift from morphogenetic growth to differentiation - a role that they speculate may have facilitated the evolution of metamorphosis (hemi- and holo-metaboly). This paper will be of interest to insect evo-devo researchers, particularly those with interests in the evolution of metamorphosis.

Strengths:

The experiments are generally conducted very well with appropriate controls and the authors have included a very detailed analysis of the phenotypes.<br /> The manuscript significantly advances our understanding of Thermobia development and the role of JH in Thermobia development.<br /> The authors interpret this data to present some hypotheses regarding the role of JH in the evolution of metamorphosis, some aspects of which can be addressed by future studies.

Weaknesses:

The results are based on using inhibitors and mimetics of JH and there was no attempt to discern immediate effects of JH from downstream effects. The authors show, for instance, that the transcription of myoglianin is responsive to JH levels, it would have been interesting to see if any of the phenotypic effects are due to myoglianin upregulation/suppression (using RNAi for example). These kinds of experiments will be necessary to fully work out if and how the JH regulatory network has been co-opted into metamorphosis.

The results generally support the authors' conclusions. However, the discussion contains a lot of speculation and some far-reaching conclusions are made about the role of JH and how it became co-opted into controlling metamorphosis. There are some interesting hypotheses presented and the author's speculations are consistent with the data presented. However, it is difficult to make evolutionary inferences from a single data point as although Thermobia is a basally branching insect, the lineage giving rise to Thermobia diverged from the lineages giving rise to the holo- and hemimetabolous insects approx.. 400 mya and it is possible that the effects of JH seen in Thermobia reflect lineage-specific effects rather than the 'ancestral state'. The authors ignore the possibility that there has been substantial rewiring of the networks that are JH responsive across these 400 my. I would encourage the authors to temper some of the discussion of these hypotheses and include some of the limitations of their inferences regarding the role of JH in the evolution of metamorphosis in their discussion.

Reviewer #1 (Public Review):

The basic approach is that the authors first train an RBM on all MprF sequences, and then use this analysis to identify a subset of the family that catalyzes the addition of amino acids to PG. Then a second RBM is trained on this subset.

In the initial RBM training a particular hidden unit is identified that has a sparse and bimodal activation in response to the input sequences. The contribution of individual resides is shown in Figure 3c, which highlights one of the strengths of this RBM implementation - it is interpretable in a physically meaningful way. However, there are several decisions here, the justification of which is not entirely clear.

i) Some of the residues in Fig 3c are stated as "relevant" for aminoacylated PG production. But is this the only such hidden unit? Or are there others that are sparse, bimodal, and involve "relevant" AA?<br /> ii) In order to filter the sequences for the second stage, only those that produce an activation over +2.0 in this particular hidden unit were taken. How was this choice made?<br /> iii) How many sequences are in the set before and after this filtering? On the basis of the strength of the results that follow I expect that there are good reasons for these choices, but they should be more carefully discussed.<br /> iv) Do the authors think that this gets all of the aminoacylated PG enzymes? Or are some missed?

The authors show that they can classify members of the family by training a second RBM on the filtered sequences. They do this by identifying two hidden unit activations in particular (Figure 5b) which seem to be useful for determining lipid substrate specificity, and they test several variants that obtain different responses of these two hidden units by experimentally determining what lipids they produce (Table 2). However, some similar criticisms from the last point occur here as well, namely the selection of which weights should be used to classify the enzymes' function. Again the approach is to identify hidden unit activations that are sparse (with respect to the input sequence), have a high overall magnitude, and "involve residues which could be plausibly linked to the lipid binding specificity."

i) Two hidden units are identified as useful for classification, but how many candidates are there that pass the first two criteria? Indeed, how many hidden units are there?<br /> ii) The criterion "involve residues which could be plausibly linked to the lipid binding specificity" is again vague. Do all of the other candidate hidden units *not* involve significant contributions from substrate-binding residues? Maybe one of the other units does a better job of discriminating substrate specificity. (As indicated in Figure 8, there are examples of enzymes that confound the proposed classification.) Why combine the activations of two units for the classification, instead of 1 or 3 or...?

Reviewer #2 (Public Review):

In "Lipid discovery enabled by sequence statistics and machine learning" Christensen et al. address an important question: how can bacteria modify lipid charges to produce cationic lipids, prone to confer resistance to cationic antibiotics? One of the enzymes involved in this process is MprF, which can, through the transfer of amino acids, in particular, lysine, from charged tRNA modify the charge of anionic membrane phospholipid from negative to positive. Recent works have shown that MprF can also modify another substrate, glycolipid glucosyl-diacylglycerol, which is neutral. These findings immediately raise two questions: what are the determinants in the MrpF sequence controlling the lipid substrates it can modify? Are there other substrates for MrpF, so far unknown?

Christensen et al. address both of these questions in an elegant way, combining sequence analysis with machine-learning methods and experimental characterisation of the enzymatic products through mass spectrometry. Using restricted Boltzmann machines (RBM), an unsupervised architecture extracting statistical features from the sequence data, they identify putative amino-acid motifs along the MprF sequences possibly related to the substrate identity, select some bacterial species whose wild-type sequence contains those motifs, and validate the biological role of the motifs by identifying the produced lipids. Remarkably, with this approach, the authors find a novel cationic lipid with two glucosyl groups.

Besides these new results on MrpF and its operation, the present work is appealing, as it shows that the functional characterisation of a very small number of proteins (here, three!) combined with the guided classification of homologous sequence data with appropriate machine-learning methods can lead to the discovery of new functionalities.

Reviewer #3 (Public Review):

Summary:<br /> After the previous identification that the Streptococcus agalactiae MprF enzyme can synthesize also lysyl-glucosyl-diacylglycerol (Lys-Glc-DAG), besides the already known lysyl-phosphatidylglycerol (Lys-PG), the authors aim for the current manuscript was to investigate the molecular determinants of MprF lipid substrate specificity in a variety of bacterial species.

Strengths:<br /> - In general, the manuscript is well constructed and easy to follow, especially taking into account the multidisciplinary aspect of it (computational machine learning combined with lipid biology).<br /> -The added value of the Restricted Boltzmann machines (RBM) approach, in comparison to standard computational pairwise sequence statistics, becomes evident. This is exemplified by a successful, although not perfect, classification and categorization of MprF activity.<br /> - The MS analysis (monoisotopic mass, plus fragmentation pattern), convincingly shows the identification of a novel lipid species Lys-Glc2-DAG.

Weaknesses:<br /> -In many of the analyzed strains, the presence of the lipid species Lys-PG, Lys-Glc-DAG, and Lys-Glc2-DAG is correlated to the presence of the MprF enzyme(s), but one should keep in mind that a multitude of other membrane proteins are present that in theory could be involved in the synthesis as well. Therefore, there is no direct evidence that the MprF enzymes are linked to the synthesis of these lipid species. Although, it is unlikely that other enzymes are involved, this weakens the connection between the observed lipids and the type of MprF.<br /> -Related to this, in a few cases MprF activity is tested, but the manuscript does not contain any information on protein expression levels. Heterologous expression of membrane proteins is in general challenging and due to various reasons, proteins end up not being expressed at all. As an example, the absence of activity for the E. faecalis MprF1 and E. faecium MprF2 could very well be explained by the entire absence of the protein.

Overall, the authors largely achieved their goals, as the applied RBM approach led to specific sequence determinants in MprF enzymes that could categorize the specificity of these enzymes. The experimental data could largely confirm this categorization, although a stronger connection between synthesized lipids and enzyme activity would have further strengthened the observations.

The work now focuses only on MprF enzymes, but could in theory be expanded to other categories of lipid-synthesizing enzymes. In other words, the RBM approach could have an impact on the lipid synthesis field, if it would be a tool that is easily applicable. Moreover, the lipids synthesized by MprF (Lys-PG, but also other cationic lipids) play an important role in bacterial resistance against certain antibiotics.

Reviewer #1 (Public Review):

This paper describes a new method for sequence-based remote homology detection. Such methods are essential for the annotation of uncharacterized proteins and for studies of protein evolution.

The main strength and novelty of the proposed approach lies in the idea of combining state-of-the-art sequence-based (HHpred and HMMER) and structure-based (Foldseek) homology detection methods with protein language models (the ESM2 model was used). The authors show that high-dimensional, information-rich representations extracted from the ESM2 model can be efficiently combined with the aforementioned tools.

The benchmarking of the new approach is convincing and shows that it is suitable for homology detection at very low sequence similarity. The method is also fast because it does not require the computation of multiple sequence alignments for profile calculation or structure prediction.

Overall, this is an interesting and useful paper that proposes an alternative direction for the problem of distant homology detection.

Reviewer #1 (Public Review):

The current manuscript focusses on the adenine phosphoribosyltransferase (Aprt) and how the lack of its function affects nervous system function. It puts it into the context of Lesch-Nyhan disease, a rare hereditary disease linked to hypoxanthine-guanine phosphoribosyltransferase (HGPRT). Since HGPRT appears absent in Drosophila, the study focusses initially on Aprt and shows that aprt mutants have a decreased life-span and altered uric acid levels (the latter can be attenuated by allopurinol treatment). Moreover, aprt mutants show defects in locomotor reactivity behaviors. A comparable phenotype can be observed when specifically knocking down aprt in dopaminergic cells (in an adult-specific fashion). Interestingly, also glia-specific knock-down caused a similar behavioral defect, which could not be restored when re-expressing UAS-aprt, while neuronal re-expression did restore the mutant phenotype. Moreover, mutants, pan-neuronal and glia-specific RNAi for aprt caused sleep-defects. Based on immunostainings Dopamine levels are increased; UPLC shows that adenosine levels are reduced and PCR showed in increase of Ent2 levels are increased (but not AdoR). Moreover, aprt mutants display seizure-like behaviros, which can be partly restored by purine feeding (adenosine and N6-methyladenosine). Finally, expression of the human HGPRT also causes locomotor defects.

The authors provide a wide range of genetic experimental data to assess behavior and some molecular assessment on how the defects may emerge. It is clearly written, and the arguments follow the experimental evidence that is provided.

The findings provide a new example of how manipulating specific genes in the fruit fly allow the study of fundamental molecular processes that are linked to a human disease.

Reviewer #2 (Public Review):

The manuscript by Petitgas et al demonstrates that loss of function for the only enzyme responsible for the purine salvage pathway in fruit-flies reproduces the metabolic and neurologic phenotypes of human patients with Lesch-Nyhan disease (LND). LND is caused by mutations in the enzyme HGPRT, but this enzyme does not exist in fruit-flies, which instead only have Aprt for purine recycling. They demonstrate that mutants lacking the Aprt enzyme accumulate uric acid, which like in humans can be rescued by feeding flies allopurinol, and have decreased longevity, locomotion and sleep impairments and seizures, with striking resemblance to HGPRT loss of function in humans. They demonstrate that both loss of function throughout development or specifically in the adult ubiquitously or in all neurons, or dopaminergic neurons, mushroom body neurons or glia, can reproduce the phenotypes (although knock-down in glia does not affect sleep). They show that the phenotypes can be rescued by over-expressing a wild-type form of the Aprt gene in neurons. They identify a decrease in adenosine levels as the cause underlying these phenotypes, as adenosine is a neurotransmitter functioning via the purinergic adenosine receptor in neurons. In fact, feeding flies throughout development and in the adult with either adenosine or m6A could prevent seizures. They also demonstrate that loss of adenosine caused a secondary up-regulation of ENT nucleoside transporters and of dopamine levels, that could explain the phenotypes of decreased sleep and hyperactivity and night. Finally, they provide the remarkable finding that over-expression of the human mutant HGPRT gene but not its wild-type form in neurons impaired locomotion and induced seizures. This means that the human mutant enzyme does not simply lack enzymatic activity, but it is toxic to neurons in some gain-of-function form. Altogether, these are very important and fundamental findings that convincingly demonstrate the establishment of a Drosophila model for the scientific community to investigate LND, to carry out drug testing screens and find cures.

The authors have dealt with my concerns satisfactorily and have explained the instances in which resolving experimentally the criticisms raised would require a work effort well beyond the scope of a revision for this manuscript.

Reviewer #3 (Public Review):

The revised study provides better evidence to suggest that loss of Aprt activity in Drosophila provides a model for the loss of HGPRT activity in humans, which is causative for LND. Analysis of Drosophila Aprt mutations and RNAi-mediated knockdown reveals similar phenotypes to LND, particularly neurological defects, reduced nighttime sleep, and potentially seizures. LND is currently resistant to treatments and screening of a limited number of compounds in Drosophila has not identified a compound that can reduce all of the associated phenotypes. It is appropriate, therefore, that claims to have developed a clinically exploitable model for human LND have been toned down. Future drug screening may well prove profitable, but currently the evidence that Drosophila Aprt will be a suitable model for LND remains speculative.

The second approach adopted is to express a 'humanised mutated' form of HGPRT in Drosophila, which holds more promise for the development of a pharmacological screen. In particular, the locomotor defect is recapitulated but the seizure-like activity, whilst reported as being recapitulated, is debatable. A recovery time of 2.3 seconds is very much less than timings for typical seizure mutants. Nevertheless, the SING behaviour could be sufficient to screen against. However, this is not explored. With respect the short seizure duration, the authors cite similar findings for porin loss of function, but the cited study similarly did not employ anti-seizure drug exposure to validate that this phenotype is seizure related.

In summary, this is a largely descriptive study reporting the behavioural effects of an Aprt loss-of-function mutation. RNAi KD and rescue expression studies suggest that a mix of neuronal (particularly dopaminergic and possibly adenosinergic signalling pathways) and glia are involved in the behavioural phenotypes affecting locomotion, sleep and seizure. There remains insufficient evidence to have full confidence that the Arpt fly model will prove valuable for understanding / treating LND.

Reviewer #1 (Public Review):

Summary:<br /> In this study, Clark et. al. used electrophysiology approaches to measure MEC neuron activity while mice performed spatial memory tasks in one-dimensional virtual tracks, where the mice must stop in a specific reward zone for a reward. The authors identified that grid cell activity could either be anchored to the track reference frame ('task-anchored') or can maintain a periodic firing pattern independent of the track reference frame ('task-independent'). They found that in the task that requires path integration, good task performance is specifically associated with task-anchored grid cell activity.

Strength:<br /> This study took advantage of the variation in neural activity and navigation task behaviors to answer an important question: how grid cell activity is associated with performance of spatial tasks. The mice performed individual trials where they must stop in a specific reward zone for a reward. Individual behavioral sessions could include three types of trials: (1) a visual cue at the reward location (beaconed trials), (2) no cue at the reward location (non-beaconed trials), and (3) no cue and no reward regardless of stopping (probe trials). The authors found that, interestingly, grid cell activity pattern could be anchored to task reference frame or maintain a periodic pattern independent of the reference frame. The anchoring of activity patterns could switch within a behavioral session. On the other hand, spatial firing of non-grid cells was either coherent with the grid population or was stably anchored to the task reference frame. Combining grid cell activity feature with task behaviors, they uncovered an association between the task-anchoring of grid cell activity with good performance in spatial navigation tasks that requires path integration (non-beaconed and probe trials). This work suggests the contribution of grid firing to path integration-dependent navigation.

Weakness:<br /> It would be interesting to find out that on the trial-by-trial basis, whether the activity anchoring switched first, or the task behaviors altered first, or whether they happened within the same trial. This will potentially determine whether the encoding is causal for the behavior, or the other way around. However, based the authors explanation, their experimental design lacks sufficient statistical power to address the timing of mode switches within a trial, because task mode switching is relatively infrequent (so the n for switching is low) and only a subset of trials are uncued (making the relevant n even lower), while at a trial level the behavioral outcome is variable (increasing the required n for adequate power).

In addition, the authors reported that the activity anchoring of some non-grid cells coherently switched with grid cells, while others do not. They propose that the MEC implement multiple coding schemes. However, it is unclear whether and how the coding scheme is associated with behavior. It would be interesting to further investigate this question.

Reviewer #2 (Public Review):

Clark and Nolan's study aims to test whether the stability of grid cell firing fields is associated with better spatial behavior performance on a virtual task. Mice were trained to stop at a rewarded location along a virtual linear track. The rewarded location could be marked by distinct visual stimuli or be unmarked. When the rewarded location was unmarked, the animal had to estimate its distance run from the beginning of the trial to know where to stop. When the mouse reached the end of the virtual track, it was teleported back to the start of the virtual track.

The authors found that grid cells could fire in at least two modes. In the "task-anchored" mode, grid firing fields had stable positions relative to the virtual track. In the "task-independent" mode, grid fields were decoupled from the virtual cues and appeared to be located as a function of distance run on the track. Importantly, on trials in which the rewarded location was unmarked, the behavioral performance of mice was better when grid cells fired in the "task-anchored" mode. When a unique visual cue marked the reward location, navigation performance was not correlated with the grid cells' firing mode.

This study is very timely as there is a pressing need to identify/delimit the contribution of grid cells to spatial behaviors. More studies are needed in which grid cell activity is linked to navigational abilities. The link proposed by Clark and Nolan between "task-anchored" coding by grid cells and navigational performance is a significant step toward better understanding how grid cell activity might support behavioral behavior. The results also highlight that some forms of navigation (approaching a location marked by a visual cue) might be less dependent on the anchoring of grid cells.

It should be noted that the study by Clark and Nolan is correlative. Therefore, the effect of selective manipulations of grid cell activity on the virtual task will be needed to evaluate whether the activity of grid cells is causally linked to the behavioral performance on this task. A previous study by the same research group showed that inactivating the synaptic output of stellate cells of the medial entorhinal cortex affected mice's performance of the same virtual task (Tennant et al., 2018). Although this manipulation likely affects non-grid cells, it is still one of the most selective manipulations of grid cells that are currently available.

It is interesting to consider how grid cells remain anchored to virtual cues. Recent work shows that grid cell activity spans the surface of a torus (Gardner et al., 2022). A run on the track can be mapped to a trajectory on the torus. Assuming that grid cell activity is updated primarily from self-motion cues on the track and that the grid cell period is unlikely to be an integer of the virtual track length, having stable firing fields on the virtual track likely requires a resetting mechanism taking place on each trial. During this resetting event, the active location on the torus is likely to jump to a new toroidal location, independently of self-motion cues. Future studies in which large numbers of grid cells are recorded could pinpoint at which moment such resetting event occurs on each trial.

Reviewer #1 (Public Review):

Summary:<br /> This paper presents a compelling and comprehensive study of decision-making under uncertainty. It addresses a fundamental distinction between belief-based (cognitive neuroscience) formulations of choice behaviour with reward-based (behavioural psychology) accounts. Specifically, it asks whether active inference provides a better account of planning and decision-making, relative to reinforcement learning. To do this, the authors use a simple but elegant paradigm that includes choices about whether to seek both information and rewards. They then assess the evidence for active inference and reinforcement learning models of choice behaviour, respectively. After demonstrating that active inference provides a better explanation of behavioural responses, the neuronal correlates of epistemic and instrumental value (under an optimised active inference model) are characterised using EEG. Significant neuronal correlates of both kinds of value were found in sensor and source space. The source space correlates are then discussed sensibly, in relation to the existing literature on the functional anatomy of perceptual and instrumental decision-making under uncertainty.

Strengths:<br /> The strengths of this work rest upon the theoretical underpinnings and careful deconstruction of the various determinants of choice behaviour using active inference. A particular strength here is that the experimental paradigm is designed carefully to elicit both information-seeking and reward-seeking behaviour; where the information-seeking is itself separated into resolving uncertainty about the context (i.e., latent states) and the contingencies (i.e., latent parameters), under which choices are made. In other words, the paradigm - and its subsequent modelling - addresses both inference and learning as necessary belief and knowledge-updating processes that underwrite decisions.

The authors were then able to model belief updating using active inference and then look for the neuronal correlates of the implicit planning or policy selection. This speaks to a further strength of this study; it provides some construct validity for the modelling of belief updating and decision-making; in terms of the functional anatomy as revealed by EEG. Empirically, the source space analysis of the neuronal correlates licences some discussion of functional specialisation and integration at various stages in the choices and decision-making.

In short, the strengths of this work rest upon a (first) principles account of decision-making under uncertainty in terms of belief updating that allows them to model or fit choice behaviour in terms of Bayesian belief updating - and then use relatively state-of-the-art source reconstruction to examine the neuronal correlates of the implicit cognitive processing.

Weaknesses:<br /> The main weaknesses of this report lies in the communication of the ideas and procedures. Although the language is generally excellent, there are some grammatical lapses that make the text difficult to read. More importantly, the authors are not consistent in their use of some terms; for example, uncertainty and information gain are sometimes conflated in a way that might confuse readers. Furthermore, the descriptions of the modelling and data analysis are incomplete. These shortcomings could be addressed in the following way.

First, it would be useful to unpack the various interpretations of information and goal-seeking offered in the (active inference) framework examined in this study. For example, it will be good to include the following paragraph:

"In contrast to behaviourist approaches to planning and decision-making, active inference formulates the requisite cognitive processing in terms of belief updating in which choices are made based upon their expected free energy. Expected free energy can be regarded as a universal objective function, specifying the relative likelihood of alternative choices. In brief, expected free energy can be regarded as the surprise expected following some action, where the expected surprise comes in two flavours. First, the expected surprise is uncertainty, which means that policies with a low expected free energy resolve uncertainty and promote information seeking. However, one can also minimise expected surprise by avoiding surprising, aversive outcomes. This leads to goal-seeking behaviour, where the goals can be regarded as prior preferences or rewarding outcomes.

Technically, expected free energy can be expressed in terms of risk plus ambiguity - or rearranged to be expressed in terms of expected information gain plus expected value, where value corresponds to (log) prior preferences. We will refer to both decompositions in what follows; noting that both decompositions accommodate information and goal-seeking imperatives. That is, resolving ambiguity and maximising information gain have epistemic value, while minimising risk or maximising expected value have pragmatic or instrumental value. These two kinds of values are sometimes referred to in terms of intrinsic and extrinsic value, respectively [1-4]."

The description of the modelling of choice behaviour needs to be unpacked and motivated more carefully. Perhaps along the following lines:

"To assess the evidence for active inference over reinforcement learning, we fit active inference and reinforcement learning models to the choice behaviour of each subject. Effectively, this involved optimising the free parameters of active inference and reinforcement learning models to maximise the likelihood of empirical choices. The resulting (marginal) likelihood was then used as the evidence for each model. The free parameters for the active inference model scaled the contribution of the three terms that constitute the expected free energy (in Equation 6). These coefficients can be regarded as precisions that characterise each subjects' prior beliefs about contingencies and rewards. For example, increasing the precision or the epistemic value associated with model parameters means the subject would update her beliefs about reward contingencies more quickly than a subject who has precise prior beliefs about reward distributions. Similarly, subjects with a high precision over prior preferences or extrinsic value can be read as having more precise beliefs that she will be rewarded. The free parameters for the reinforcement learning model included..."

In terms of the time-dependent correlations with expected free energy - and its constituent terms - I think the report would benefit from overviewing these analyses with something like the following:

"In the final analysis of the neuronal correlates of belief updating - as quantified by the epistemic and intrinsic values of expected free energy - we present a series of analyses in source space. These analyses tested for correlations between constituent terms in expected free energy and neuronal responses in source space. These correlations were over trials (and subjects). Because we were dealing with two-second timeseries, we were able to identify the periods of time during decision-making when the correlates were expressed.

In these analyses, we focused on the induced power of neuronal activity at each point in time, at each brain source. To illustrate the functional specialisation of these neuronal correlates, we present whole-brain maps of correlation coefficients and pick out the most significant correlation for reporting fluctuations in selected correlations over two-second periods. These analyses are presented in a descriptive fashion to highlight the nature and variety of the neuronal correlates, which we unpack in relation to the existing EEG literature in the discussion. Note that we did not attempt to correct for multiple comparisons; largely, because the correlations observed were sustained over considerable time periods, which would be almost impossible under the null hypothesis of no correlations."

There was a slight misdirection in the discussion of priors in the active inference framework. The notion that active inference requires a pre-specification of priors is a common misconception. Furthermore, it misses the point that the utility of Bayesian modelling is to identify the priors that each subject brings to the table. This could be easily addressed with something like the following in the discussion:

"It is a common misconception that Bayesian approaches to choice behaviour (including active inference) are limited by a particular choice of priors. As illustrated in our fitting of choice behaviour above, priors are a strength of Bayesian approaches in the following sense: under the complete class theorem [5, 6], any pair of choice behaviours and reward functions can be described in terms of ideal Bayesian decision-making with particular priors. In other words, there always exists a description of choice behaviour in terms of some priors. This means that one can, in principle, characterise any given behaviour in terms of the priors that explain that behaviour. In our example, these were effectively priors over the precision of various preferences or beliefs about contingencies that underwrite expected free energy."

(1) Oudeyer, P.-Y. and F. Kaplan, What is intrinsic motivation? a typology of computational approaches. Frontiers in Neurorobotics, 2007. 1: p. 6.<br /> (2) Schmidhuber, J., Formal Theory of Creativity, Fun, and Intrinsic Motivation (1990-2010). Ieee Transactions on Autonomous Mental Development, 2010. 2(3): p. 230-247.<br /> (3) Barto, A., M. Mirolli, and G. Baldassarre, Novelty or surprise? Front Psychol, 2013. 4: p. 907.<br /> (4) Schwartenbeck, P., et al., Computational mechanisms of curiosity and goal-directed exploration. Elife, 2019. 8: p. e41703.<br /> (5) Wald, A., An Essentially Complete Class of Admissible Decision Functions. Annals of Mathematical Statistics, 1947. 18(4): p. 549-555.<br /> (6) Brown, L.D., A Complete Class Theorem for Statistical Problems with Finite-Sample Spaces. Annals of Statistics, 1981. 9(6): p. 1289-1300.

Reviewer #2 (Public Review):

Summary:<br /> Zhang and colleagues use a combination of behavioral, neural, and computational analyses to test an active inference model of exploration in a novel reinforcement learning task.

Strengths:<br /> The paper addresses an important question (validation of active inference models of exploration). The combination of behavior, neuroimaging, and modeling is potentially powerful for answering this question.

Weaknesses:<br /> The paper does not discuss relevant work on contextual bandits by Schulz, Collins, and others. It also does not mention the neuroimaging study of Tomov et al. (2020) using a risky/safe bandit task.

The statistical reporting is inadequate. In most cases, only p-values are reported, not the relevant statistics, degrees of freedom, etc. It was also not clear if any corrections for multiple comparisons were applied. Many of the EEG results are described as "strong" or "robust" with significance levels of p<0.05; I am skeptical in the absence of more details, particularly given the fact that the corresponding plots do not seem particularly strong to me.

The authors compare their active inference model to a "model-free RL" model. This model is not described anywhere, as far as I can tell. Thus, I have no idea how it was fit, how many parameters it has, etc. The active inference model fitting is also not described anywhere. Moreover, you cannot compare models based on log-likelihood, unless you are talking about held-out data. You need to penalize for model complexity. Finally, even if active inference outperforms a model-free RL model (doubtful given the error bars in Fig. 4c), I don't see how this is strong evidence for active inference per se. I would want to see a much more extensive model comparison, including model-based RL algorithms which are not based on active inference, as well as model recovery analyses confirming that the models can actually be distinguished on the basis of the experimental data.

Another aspect of the behavioral modeling that's missing is a direct descriptive comparison between model and human behavior, beyond just plotting log-likelihoods (which are a very impoverished measure of what's going on).

The EEG results are intriguing, but it wasn't clear that these provide strong evidence specifically for the active inference model. No alternative models of the EEG data are evaluated.

Overall, the central claim in the Discussion ("we demonstrated that the active inference model framework effectively describes real-world decision-making") remains unvalidated in my opinion.

Reviewer #3 (Public Review):

Summary:<br /> This paper aims to investigate how the human brain represents different forms of value and uncertainty that participate in active inference within a free-energy framework, in a two-stage decision task involving contextual information sampling, and choices between safe and risky rewards, which promotes a shift from exploration to exploitation. They examine neural correlates by recording EEG and comparing activity in the first vs second half of trials and between trials in which subjects did and did not sample contextual information, and perform a regression with free-energy-related regressors against data "mapped to source space." Their results show effects in various regions, which they take to indicate that the brain does perform this task through the theorised active inference scheme.

Strengths:<br /> This is an interesting two-stage paradigm that incorporates several interesting processes of learning, exploration/exploitation, and information sampling. Although scalp/brain regions showing sensitivity to the active-inference-related quantities do not necessarily suggest what role they play, it can be illuminating and useful to search for such effects as candidates for further investigation. The aims are ambitious, and methodologically it is impressive to include extensive free-energy theory, behavioural modelling, and EEG source-level analysis in one paper.

Weaknesses:<br /> Though I could surmise the above general aims, I could not follow the important details of what quantities were being distinguished and sought in the EEG and why. Some of this is down to theoretical complexity - the dizzying array of constructs and terms with complex interrelationships, which may simply be part and parcel of free-energy-based theories of active inference - but much of it is down to missing or ambiguous details.

In general, an insufficient effort has been made to make the paper accessible to readers not steeped in the free energy principle and active inference. There are critical inconsistencies in key terminology; for example, the introduction states that aim 1 is to distinguish the EEG correlates of three different types of uncertainty: ambiguity, risk, and unexpected uncertainty. But the abstract instead highlights distinctions in EEG correlates between "uncertainty... and... risk" and between "expected free energy .. and ... uncertainty." There are also inconsistencies in mathematical labelling (e.g. in one place 'p(s|o)' and 'q(s)' swap their meanings from one sentence to the very next).

Some basic but important task information is missing, and makes a huge difference to how decision quantities can be decoded from EEG. For example:<br /> - How do the subjects press the left/right buttons - with different hands or different fingers on the same hand?<br /> - Was the presentation of the Stay/cue and safe/risky options on the left/right sides counterbalanced? If not, decisions can be formed well in advance especially once a policy is in place.<br /> - What were the actual reward distributions ("magnitude X with probability p, magnitude y with probability 1-p") in the risky option? 

The EEG analysis is not sufficiently detailed and motivated. For example,<br /> - why the high lower-filter cutoff of 1 Hz, and shouldn't it be acknowledged that this removes from the EEG any sustained, iteratively updated representation that evolves with learning across trials?<br /> - Since the EEG analysis was done using an array of free-energy-related variables in a regression, was multicollinearity checked between these variables?<br /> - In the initial comparison of the first/second half, why just 5 clusters of electrodes, and why these particular clusters? How many different variables are systematically different in the first vs second half, and how do you rule out less interesting time-on-task effects such as engagement or alertness? In what time windows are these amplitudes being measured? In the comparison of asked and not-asked trials, what trial stage and time window is being measured? Again, how many different variables, of the many estimated per trial in the active inference model, are different in the asked and not-asked trials, and how can you know which of these differences is the one reflected in the EEG effects? The authors choose to interpret that on not-asked trials the subjects are more uncertain because the cue doesn't give them the context, but you could equally argue that they don't ask because they are more certain of the possible hidden states.<br /> - The EEG regressors are not fully explained. For example, an "active learning" regressor is listed as one of the 4 at the beginning of section 3.3, but it is the first mention of this term in the paper and the term does not arise once in the methods.<br /> - In general, it is not clear how one can know that the EEG results reflect that the brain is purposefully encoding these very parameters while implementing this very mechanism, and not other, possibly simpler, factors that correlate with them since there is no engagement with such potential confounds or alternative models. For example, a model-free reinforcement learning model is fit to behaviour for comparison. Why not the EEG?

Reviewer #1 (Public Review):

The authors report here interesting data on the interactions mediated by the SH3 domain of BIN1 that expand our knowledge on the role of the SH3 domain of BIN1 in terms of mediating specific interactions with a potentially high number of proteins and how variants in this region alter or prevent these protein-protein interactions. These data provide useful information that will certainly help to further dissect the networks of proteins that are altered in some human myopathies as well as the mechanisms that govern the correct physiological activity of muscle cells.

The work is mostly based on improved biochemical techniques to measure protein-protein interaction and provide solid evidence that the SH3 domain of BIN1 can establish an unexpectedly high number of interactions with at least a hundred cellular proteins, among which the authors underline the presence of other proteins known to be causative of skeletal muscle diseases and not known to interact with BIN1. This represents an unexpected and interesting finding relevant to better define the network of interactions established among different proteins that, if altered, can lead to muscle disease. An interesting contribution is also the detailed identification of the specific sites, namely the Proline-Rich Motifs (PRMs) that in the interacting proteins mediate binding to the BIN1 SH3 domain. Less convincing, or too preliminary in my opinion, are the data supporting BIN1 co-localization with PRC1. Indeed, the affinity of PRC1 is significantly lower than that of DNM2, an established BIN1 interacting protein. Thus, this does not provide compelling evidence to support PRC1 as a significant interactor of BIN1. Similarly, the localization data appears somewhat preliminary to substantiate a role of BIN1 in mitotic processes. These findings may necessitate additional experimental work to be more convincing.

Reviewer #2 (Public Review):

Summary:<br /> In this paper, Zambo and coworkers use a powerful technique, called native holdup, to measure the affinity of the SH3 domain of BIN1 for cellular partners. Using this assay, they combine data using cellular proteins and proline-containing fragments in these proteins to identify 97 distinct direct binding partners of BIN1. They also compare the binding interactome of the BIN1 SH3 domain to the interactome of several other SH3 domains, showing varying levels of promiscuity among SH3 domains. The authors then use pathway analysis of BIN1 binding partners to show that BIN1 may be involved in mitosis. Finally, the authors examine the impact of clinically relevant mutations of the BIN1 SH3 domain on the cellular interactome. The authors were able to compare the interactome of several different SH3 domains and provide novel insight into the cellular function of BIN1. Generally, the data supports the conclusions, although the reliance on one technique and the low number of replicates in each experiment is a weakness of the study.

Strengths:<br /> The major strength of this paper is the use of holdup and native holdup assays to measure the affinity of SH3 domains to cellular partners. The use of both assays using cell-derived proteins and peptides derived from identified binding partners allows the authors to better identify direct binding partners. This assay has some complexity but does hold the possibility of being used to measure the affinity of the cellular interactome of other proteins and protein domains. Beyond the utility of the technique, this study also provides significant insight into the cellular function of BIN1. The authors have strong evidence that BIN1 might have an undiscovered function in cellular mitosis, which potentially highlights BIN1 as a drug target. Finally, the study provides outstanding data on the cellular binding properties and partners of seven distinct SH3 domains, showing surprising differences in the promiscuity of these proteins.

Weaknesses:<br /> There are three major weaknesses of the study. First, the authors rely completely on a single technique to measure the affinity of the cellular interactome. The native holdup is a relatively new technique that is powerful yet relatively unproven. However, it appears to have the capacity to measure the relative affinity of proteins. Second, the authors appear to use a relatively small number of replicates for the holdup assays. There is no information in the legends about the number of replicates but the materials and methods suggest the native holdup data is from a single experimental replicate with multiple technical replicates. Finally, the authors' data using cellular proteins and fragments show that the affinity of the whole proteins is 5-20 fold lower than individual proline-containing fragments. The authors state that this difference suggests that there is cooperativity between different proline-rich sites of the binding partners of BIN1, yet BIN1 only has one SH3 domain. It is unclear what the molecular mechanism of the cooperative interaction would be exactly since there would be only one SH3 domain to bind the partner. An alternative interpretation would be that the BIN 1 SH3 domain requires sequences outside of the short proline-rich regions for high-affinity interactions with cellular partners, a hypothesis that is supported by other studies.

Reviewer #2 (Public Review):

Summary:

Chew et al describe interaction of the flavivirus protein NS1 with HDL using primarily cryoEM and mass spec. The NS1 was secreted from dengue virus infected Vero cells, and the HDL were derived from the 3% FBS in the culture media. NS1 is a virulence factor/toxin and is a biomarker for dengue infection in patients. The mechanisms of its various activities in the host are incompletely understood. NS1 has been seen in dimer, tetramer and hexamer forms. It is well established to interact with membrane surfaces, presumably through a hydrophobic surface of the dimer form, and the recombinant protein has been shown to bind HDL. In this study, cryoEM and crosslinking-mass spec are used to examine NS1 secreted from virus-infected cells, with the conclusion that the sNS1 is predominantly/exclusively HDL-associated through specific contacts with the ApoA1 protein.

Strengths: The experimental results are consistent with previously published data.

Weaknesses:

CryoEM:<br /> Some of the neg-stain 2D class averages for sNS1 in Fig S1 clearly show 1 or 2 NS1 dimers on the surface of a spherical object, presumably HDL, and indicate the possibility of high-quality cryoEM results. However, the cryoEM results are disappointing. The cryo 2D class averages and refined EM map in Fig S4 are of poor quality, indicating sub-optimal grid preparation or some other sample problem. Some of the FSC curves (2 in Fig S7 and 1 in Fig S6) have extremely peculiar shapes, suggesting something amiss in the map refinement. The sharp drop in the "corrected" FSC curves in Figs S5c and S6c (upper) indicate severe problems. The stated resolutions (3.42 & 3.82 Å) for the sNS1ts-Fab56.2 are wildly incompatible with the images of the refined maps in Figs 3 & S7. At those resolutions, clear secondary structural elements should be visible throughout the map. From the 2D averages and 3D maps shown in the figures, this does not seem to be the case. Local resolution maps should be shown for each structure.

The samples were clearly challenging for cryoEM, leading to poor quality maps that were difficult to interpret. None of the figures are convincing that NS1, Ab56.2 or Fab56.2 are correctly fit into EM maps. There is no indication of ApoA1 helices. Details of the fit of models to density for key regions of the higher-resolution EM maps should be shown and the models should be deposited in the PDB. An example of modeling difficulty is clear in the sNS1ts dimer with bound Fab56.2 (figs 3c & S7e). For this complex, the orientation of the Fab56.2 relative to the sNS1ts dimer in this submission (Fig 3c) is substantially different than in the bioRxiv preprint (Fig 3c). Regions of empty density in Fig 3c also illustrate the challenge of building a model into this map.

Mass spec:<br /> Crosslinking-mass spec was used to detect contacts between NS1 and ApoA1, providing strong validation of the sNS1-HDL association. As the crosslinks were detected in a bulk sample, they show that NS1 is near ApoA1 in many/most HDL particles, but they do not indicate a specific protein-protein complex. Thus, the data do not support the model of an NS1-ApoA1 complex in Fig 4d. Further, a specific NS1-ApoA1 interaction should have evidence in the EM maps (helical density for ApoA1), but none is shown or mentioned. If such exists, it could perhaps be visualized after focused refinement of the map for sNS1ts-HDL with Fab56.2 (Fig S7d). The finding that sNS1-ApoA1 crosslinks involved residues on the hydrophobic surface of the NS1 dimer confirms previous data that this NS1 surface engages with membranes and lipids.

Sample quality:<br /> The paper lacks any validation that the purified sNS1 retains established functions, for example the ability to enhance virus infectivity or to promote endothelial dysfunction. Peculiarities include the gel filtration profiles (Fig 2a), which indicate identical elution volumes (apparent MWs) for sNS1wt-HDL bound to Ab562 (~150 kDa) and to the ~3X smaller Fab56.2 (~50 kDa). There should also be some indication of sNS1wt-HDL pairs crosslinked by the full-length Ab, as can be seen in the raw cryoEM micrograph (Fig S5b).

Obtaining high quality structures is often more demanding of sample integrity than are activity assays. Given the low quality of the cryoEM maps, it's possible that the acidification step in immunoaffinity purification damaged the HDL complex. No validation of HDL integrity, for example with acid-treated HDL, is reported. Acid treatment is perhaps discounted by a statement (line 464) that another group also used immunoaffinity purification in a recent study (ref 20) reporting sNS1 bound to HDL. However the statement is incorrect; the cited study used affinity purification via a strep-tag on recombinant sNS1.

Discussion:<br /> The Discussion reflects a view that the NS1 secreted from virus-infected cells is a 1:1 sNS1dimer:HDL complex with the specific NS1-ApoA1 contacts detected by crosslinking mass spec. This is inconsistent with both the neg-stain 2D class average with 2 sNS1 dimers on an HDL (Fig S1c) and with the recent study of Flamand & co-workers showing 1-3 NS1 dimers per HDL (ref 20). It also ignores the propensity of NS1 to associate with membranes and lipids. It is far more likely that NS1 association with HDL is driven by these hydrophobic interactions than by specific protein-protein contacts. A lengthy Discussion section (lines 461-522) includes several chemically dubious or inconsistent statements, all based on the assumption that specific ApoA1 contacts are essential to NS1 association with HDL and that sNS1 oligomers higher than the dimer necessarily involve ApoA1 interaction, conclusions that are not established by the data in this paper.

Additional comments on the revised manuscript:

Comments on the structures:

The authors kindly provided their fitted atomic models for the 2 reported structures. The EM maps are available in the EMDB. Based on these materials, the derived structures are not well supported due to problems with the models, the maps, and the fit of models to maps.

Quick inspection revealed that the models for both structures are implausible due to a large steric clash of Fab56.2 and the end of the NS1. The Fab and NS1 protein backbones interpenetrate by nearly 20 Å. This substantial overlap exists for all 3 Fab56.2-NS1 interactions in the 2 structures, and is also visible in the perpendicular views of the NS1 dimer with 2 bound Fab56.2 in Fig. 2c. It appears that the Fab56.2 model was jammed into the NS1 model in order to bring all domains inside the density envelope at the threshold chosen to display the map. The poor fit of model to map is also clear in several protruding density regions without any model.

The fits of both atomic models to the maps are questionable because<br /> - The maps suffer from severe preferred orientation problems, as seen in the streaky tubes of density. The streaks in both maps do not match the NS1 beta strands of the fitted models.<br /> - The shape of the modeled ApoA1 helical ring surrounding the HDL does not match the shape of the EM density. In some regions, the ApoA1 helices are inside the rather strong density for the spherical HDL, but in other regions the helices are outside the density.<br /> - Both maps have regions of strong density that are adjacent to NS1 but lack any protein model, while other parts of the structure, including the beta-roll domain, lack density.<br /> - The claimed 4.3-Å resolution of the NS1-Fab56.2 complex is wildly overstated. The local resolution of ~2.5 Å for the "best" part of the structure (Supp Fig. 7E) appears to pertain to the beta strands at the center of the NS1 dimer. However, these density streaks do not match the beta strands of the fit model.<br /> - The manuscript lacks statistics on the fit of model to map. A standard cryo-EM "Table 1" should include more than is presented in Supp Table 1. The fitted model for at least the higher resolution structure should be deposited in the PDB.

Comments on the structure interpretation:

By now it should be abundantly clear that the oligomer state of NS1 is dynamic and highly sensitive to environmental conditions and to each sample's "history". For the reasons pointed out by reviewer 1, it is not clear that the immunoaffinity purification method captured all forms of sNS1 equally. Thus, the authors insistence that NS1 secreted from virus-infected cells is predominantly bound to HDL particles in a ratio of 1 NS1 dimer per HDL is not well supported. They employ similar arguments to challenge the discovery of sNS1 as a lipoprotein particle (PNAS 2011), contending that the 2011 finding was an artefact of recombinant NS1 production and is irrelevant to sNS1 from a virus infection. The several published structures of NS1 oligomers reveal a large degree of asymmetry in dimer-dimer interaction, consistent with the ability of NS1 to dynamically associate with a variety of hydrophobic entities.

Reviewer #1 (Public Review):

The authors of this study seek to visualize NS1 purified from dengue virus infected cells. They infect vero cells with DV2-WT and DV2 NS1-T164S (a mutant virus previously characterized by the authors). The authors utilize an anti-NS1 antibody to immunoprecipitate NS1 from cell supernatants and then elute the antibody/NS1 complex with acid. The authors evaluate the eluted NS1 by SDS-PAGE, Native Page, mass spec, negative-stain EM, and eventually Cryo-EM. SDS-PAGE, mas spec, and native page reveal a >250 Kd species containing both NS1 and the proteinaceous component of HDL (ApoA1). The authors produce evidence to suggest that this population is predominantly NS1 in complex with ApoA1. This contrasts with recombinantly produced NS1 (obtained from a collaborator) which did not appear to be in complex with or contain ApoA1 (Figure 1C). The authors then visualize their NS1 stock in complex with their monoclonal antibody by CryoEM. For NS1-WT, the major species visualized by the authors was a ternary complex of an HDL particle in complex with an NS1 dimer bound to their mAB. For their mutant NS1-T164S, they find similar structures, but in contrast to NS1-WT, they visualize free NS1 dimers in complex with 2 Fabs (similar to what's been reported previously) as one of the major species. This highlights that different NS1 species have markedly divergent structural dynamics. It's important to note that the electron density maps for their structures do appear to be a bit overfitted since there are many regions with electron density that do not have a predicted fit and their HDL structure does not appear to have any predicted secondary structure for ApoA1. The authors then map the interaction between NS1 and ApoA1 using cross-linking mass spectrometry revealing numerous NS1-ApoA1 contact sites in the beta-roll and wing domain. The authors find that NS1 isolated from DENV infected mice is also present as a >250 kD species containing ApoA1. They further determine that immunoprecipitation of ApoA1 out of the sera from a single dengue patient correlates with levels of NS1 (presumably COIPed by ApoA1) in a dose-dependent manner.

In the end, the authors make some useful observations for the NS1 field (mostly confirmatory) providing additional insight into the propensity of NS1 to interact with HDL and ApoA1. The study does not provide any functional assays to demonstrate activity of their proteins or conduct mutagenesis (or any other assays) to support their interaction predications. The authors assertion that higher-order NS1 exists primarily as a NS1 dimer in complex with HDL is not well supported as their purification methodology of NS1 likely introduces bias as to what NS1 complexes are isolated. While their results clearly reveal NS1 in complex with ApoA1, the lack of other NS1 homo-oligomers may be explained by how they purify NS1 from virally infected supernatant. Because NS1 produced during viral infection is not tagged, the authors use an anti-NS1 monoclonal antibody to purify NS1. This introduces a source of bias since only NS1 oligomers with their mAb epitope exposed will be purified. Further, the use of acid to elute NS1 may denature or alter NS1 structure and the authors do not include controls to test functionality of their NS1 stocks (capacity to trigger endothelial dysfunction or immune cell activation). The acid elution may force NS1 homo-oligomers into dimers which then reassociate with ApoA1 in a manner that is not reflective of native conditions. Conducting CryoEM of NS1 stocks only in the presence of full-length mAbs or Fabs also severely biases what species of NS1 is visualized since any NS1 oligomers without the B-ladder domain exposed will not be visualized. If the residues obscured by their mAb are involved in formation of higher-order oligomers then this antibody would functionally inhibit these species from forming. The absence of critical controls, use of one mAb, and acid elution for protein purification severely limits the interpretation of these data and do not paint a clear picture of if NS1 produced during infection is structurally distinct from recombinant NS1. Certainly there is novelty in purifying NS1 from virally infected cells, but without using a few different NS1 antibodies to purify NS1 stocks (or better yet a polyclonal population of antibodies) it's unclear if the results of the authors are simply a consequence of the mAb they selected.

Data produced from numerous labs studying structure and function of flavivirus NS1 proteins provide diverse lines of evidence that the oligomeric state of NS1 is dynamic and can shift depending on context and environment. This means that the methodology used for NS1 production and purification will strongly impact the results of a study. The data in this manuscript certainly capture one of these dynamic states and overall support the general model of a dynamic NS1 oligomer that can associate with both host proteins as well as itself but the assertions of this manuscript are overall too strong given their data, as there is little evidence in this manuscript, and none available in the large body of existing literature, to support that NS1 exists only as a dimer associated with ApoA1. More likely the results of this paper are a result of their NS1 purification methodology.

Comments on revised version:

The authors have not adequately addressed my concerns from the original review. My major concerns are that the binding modality of NS1 to ApoA1/HDL was not validated using a mutagenesis approach and that the overarching conclusion drawn by the authors, that the major species of NS1 in vivo is a dimer in complex with ApoA1, is not supported by the data in this study given the methodology of using a single monoclonal antibody to immunoprecipitate NS1. Certainly, the structures in this manuscript are valuable in confirming that NS1 interacts with HDL and captures a snapshot of NS1/HDL interaction dynamics, but the use of only a single antibody is a major source of bias that makes it challenging to draw conclusions about the oligomeric state of NS1. Further on this point, a critically important control that is missing from this study is to determine if the anti-NS1 mAb 56.2 prevents NS1 from interacting with cells, triggering the release of proinflammatory cytokines from immune cells, or mediating endothelial dysfunction of endothelial cells. If this antibody inhibits these NS1-triggered events (linked to pathogenesis), it would suggest that the NS1 within this ternary complex is not active. Presumably some protective anti-NS1 antibodies may function by modulating the oligomeric state of NS1.

Reviewer #1 (Public Review):

Summary:<br /> This technical report by Kugler et al., expands the application of a fluorescence-based reporter to study the conformational state of various kinases. This reporter, named KinCon (Kinase Conformation), interrogates the conformational state of a kinase (i.e., active vs. inactive) based on engineering complementary fusion proteins that fluoresce upon interaction. This assay has several advantages as it allows studying full-length kinases, that is, the kinase domain and regulatory domains, inside the cell and under various experimental conditions such as the presence of inhibitors or activator proteins, and in wildtype and mutants involved in disease states.

Strengths:

One major strength of this study is that it is quite comprehensive. The authors use KinCon for four different kinases, BRAF, LKB1, RIP, and CDK4/6. These kinases have very different regulatory elements and associated proteins, which the authors explore to study their conformational state. Moreover, they use small molecule inhibitors or mutations to further dissect how the conformational state of the kinase in disease states. The collective set of results strongly suggests that KinCon is a versatile tool that can be used to study many kinases of biomedical and fundamental importance. Given that kinases are extensively studied by researchers in academia or industry, KinCon could have a broad impact as well.

Weaknesses:<br /> This manuscript, however, also has several weaknesses. These include:

- The manuscript is exceedingly long. For instance, the introduction provides background information for each kinase that is further expanded in the results section. I think the background information for each kinase in the Introduction and Results sections could be significantly reduced to highlight the major points. Otherwise, not only does the manuscript become too long, but the main points get diluted.

- The figure legends are very long, providing information that is already in the main text or Methods. In the legend, the authors should provide only the essential information to understand the figure.

- A major concern throughout the manuscript is the use of the word "dynamics," which is used in the text in various contexts. The authors should clarify what they understand about the dynamics of conformation. Are they measuring how the time-dependent process by which the kinase is interconverting between active and inactive states? It seems to me that the assays in this report evaluate a population of kinases that are in an open or close conformation (i.e., a particular state in each experimental condition) but there is no direct information on how the kinase goes from one state to the other. In that sense, the use of the word dynamics is unclear. Also, the use of the word dynamics in different sentences is ambiguous.

- There are various other issues with terminology and presentation that also affect the overall level of impact of the manuscript.

Reviewer #2 (Public Review):

Summary:<br /> Protein kinases have been very successfully targeted with small molecules for several decades, with many compounds (including clinical drugs) bringing about conformational changes that are also relevant to broader interactions with the cellular signaling networks that they control. The authors set out to develop a targeted biosensor approach to evaluate distinct kinase conformations in cells for multiple kinases in the context of incoming signals, other proteins, and small molecule binding, with a broad goal of using the KinCon assay to confirm (and perhaps predict) how drug binding or signal perception changes conformations and outputs in the presence of cellular complexes. This work will likely impact on the field with cellular reporters of kinase conformations a useful addition to the toolbox.

Strengths:<br /> The KinCon reporter platform has previously been validated for well-known kinases; in this study, the team evaluates how to employ a full-length kinase (often containing a known pathlogical mutation). The sensitive detection method is based on a Renilla luciferase (RLuc)protein fragment complementation assay, where individual RLuc fragments are present at the N and the C terminus of the kinase. This report, which is both technical and practical in nature, co-expresses the kinase with known interactors (at low levels) in a high throughput format and then performs pharmacological evaluation with known small molecule kinase modulators. This is explained nicely in Figure 1, as are the signaling pathways that are being evaluated. Data demonstrate that V600E BRAF iexposed to vemurafenib is converted to the inactive conformation, as expected. In contrast, the more closed STRAD𝛼 and LKB1 KinCon conformations appear to represent the more active state of the complexed kinase, and a W308C mutation (evaluated alongside others) reverses this effect. The authors then evaluated necroptotic signaling in the context of RIPK1/3 under conditions where RIPK1 and RIPK3 are active, confirming that the reporters highlight the active states of both kinases. Exposure to compounds that are known to engage with the RIPK1 arm of the pathway induce bioluminescence changes consistent with the opening (inactivation) of the kinase. Finally, the authors move to an important drug target for which clinical drugs have arrived relatively recently; the CDK4/6 complexes. These are of additional importance because kinase-independent functions also exist for CDK6, and the effects of drugs in cells usually rely on a downstream marker, rather than demonstration of direct protein complex engagement. The data presented are interpreted as the formation of complexes with the CDK inhibitor p16INK4a; reducing the affinity of the interaction through mutations drives an inactive conformation, whilst the application of CDK4/6 inhibitors does not, implying binding to the active conformation.

Weaknesses:<br /> (1) The work is very solid, uses examples from the literature, and also extends into new experimental space. An obvious weakness is mentioned by the authors for the CKDK data, in that measurements with Cyclin D (the activating subunit) are not characterised, although Cyclin D might be assumed to be present.

(2) The work with the trimeric LKB1 complex involves pseudokinase, STRADalpha, whose conformation is also examined as a function of LKB1 status; since STRAD is an activator of LKB1. A future goal should be the evaluation of the complex in the presence of STRAD inhibitory/activating small molecules.

Reviewer #1 (Public Review):

Summary:<br /> This study provides convincing data showing that expression of the PIK3R1(delta Exon11) dominant negative mutation in Activated PI3K Delta Syndrome 1/2 (APDS1/2) patient-derived cells reduces AKT activation and p110δ protein levels. Using a 3T3-L1 model cell system, the authors show that overexpressed p85α delta Exon 11) displays reduced association with the p110α catalytic subunit but strongly interacts with Irs1/2. Overexpression of PIK3R1 dominant negative mutants inhibits AKT phosphorylation and reduces cellular differentiation of preadipocytes. The strength of this article is the clear results derived from Western blots analysis of cell signaling markers (e.g. pAKT1), and co-immunoprecipitation of PI3K holoenzyme complexes and associated regulatory factors (e.g. Irs1/2). The experimental design, interpretation, and quantification broadly support the authors' conclusions.

Strengths:<br /> The authors analyze a variety of PIK3R1 mutants (i.e. delta Exon11, E489K, R649W, and Y657X), which reveals a range of phenotypes that support the proposed model for dominant negative activity. The use of clonal cell lines with doxycycline-induced expression of the PIK3R1 mutants (Exon 11, R649W, and Y657X) provides convincing experimental data concerning the relationship between p85α mutant expression and AKT phosphorylation in vivo. The authors convincingly show that p85α delta Exon11, R649W, or Y657X) is unable to associate with p110α but instead more strongly associates with Irs1/2 compared to wild type p85α. This helps explain why the authors were unable to purify the recombinant p110α/p85α delta Exon 11) heterodimeric complex from insect cells.

Weaknesses:<br /> Future experimentation will be needed to reconcile the cell type specific differences (e.g. APDS2 patient-derived cells vs. the 3T3-L1 cell model system) in PIK3R1 mutant behavior reported by the authors. An unbiased proteomic study that broadly evaluates the cell signaling landscape could provide a more holistic understanding of the APDS2 and SHORT mutants compared to a candidate-based approach. Additional biochemical analysis of p110α/p85α delta Exon 11) complex is needed to explain why this mutant regulatory subunit does not strongly associate with the p110 catalytic subunit. It remains unclear why p85α delta Exon 11) expression reduces p110δ protein levels in APDS2 patient-derived dermal fibroblasts. This study would benefit from a more comprehensive biochemical analysis of the described p110α/p85α, p110β/p85α, and p110δ/p85α mutant protein complexes. The current limitation of this study to the use of a single endpoint assay to measure PI3K lipid kinase activity in the presence of a single regulatory input (i.e. RTK-derived pY peptide). A broader biochemical analysis of the mutant PI3K complexes across the canonical signaling landscape will be important for establishing how competition between wild-type and mutant regulatory subunits is regulated in different cell signaling pathways.

Reviewer #2 (Public Review):

Summary:<br /> Patsy R. Tomlinson et al; investigated the impact of different p85 alpha variants associated with SHORT syndrome or APDS2 on insulin-mediated signaling in dermal fibroblasts and preadipocytes. They find no evidence of hyperactive PI3K signalling monitored by pAKT in APDS2 patient-derived dermal fibroblast cells. In these cells p110 alpha protein levels were comparable to levels in control cells, however, the p110 delta protein levels were strongly reduced. Remarkably, the truncated APDS2-causal p85 alpha variant was less abundant in these cells than p85 alpha wildtype. Afterwards, they studied the impact of ectopically expressed p85 alpha variants on insulin-mediated PI3K signaling in 3T3-L1 preadipocytes. Interestingly they found that the truncated APDS2-causal p85 alpha variant impaired insulin-induced signaling. Using immunoprecipitation of p110 alpha they did not find truncated APDS2-causal p85 alpha variant in p110 alpha precipitates. Furthermore, by immunoprecipitating IRS1 and IRS2, they observed that the truncated APDS2-causal p85 alpha variant was very abundant in IRS1 and IRS2 precipitates, even in the absence of insulin stimulation. These important findings add in an interesting way possible mechanistic explanation for the growing number of APDS2 patients described with features of SHORT syndrome.

Strengths:<br /> Based on state-of-the-art functional investigation the authors propose indicating a loss-of-function activity of the APDS2-disease causing p85 alpha variant in preadipocytes providing a possible mechanistic explanation for the growing number of APDS2 patients described with features of SHORT syndrome.

Weaknesses:<br /> Related to Figure 1: PIK3R1 expression not only by Western blotting but also by quantifying the RNA transcripts, e.g. mutant and wildtype transcripts, was not performed. RNA expression analysis would further strengthen the suggested impaired stabilization/binding.

Related to Figure 2: As mentioned by the authors in the manuscript the expression of p110 delta but also p110 beta in 3T3-L1 preadipocytes ectopically expressing p85 alpha variants has not been analyzed.

Furthermore, a direct comparison of the truncated APDS2-causal p85 alpha variant with SHORT syndrome -causal p85 alpha variants in regard to pAKT level, and p85 alpha expression level has not been performed.

These investigations would further strengthen the data.

Related to Figure 3:<br /> The E489K and Y657X p85 alpha variants should be also tested in combination with p110 delta in the PI3K activity in vitro assay. This would help to further decipher the overall impact, especially of the E489K variant.

Reviewer #1 (Public Review):

Summary:<br /> This study is valuable in that it may lead to the discovery of future OA markers, etc., in that changes in glycan metabolism in chondrocytes are involved in the initiation of cartilage degeneration and early OA via hypertrophic differentiation of chondrocytes. However, more robust results would be obtained by analyzing the mechanisms and pathways by which changes in glycosylation lead to cartilage degeneration.

Strengths:<br /> This study is important because it indicates that glycan metabolism may be associated with pre-OA and may lead to the elucidation of the cause and diagnosis of pre-OA.

Weaknesses:<br /> More robust results would be obtained by analyzing the mechanism by which cartilage degeneration induced by changes in glycometabolism occurs.

Reviewer #2 (Public Review):

Summary:<br /> This paper consists of mostly descriptive data, judged from alpha-mannosidase-treated samples, in which they found an increase in core fucose, a product of Fut 8.

Strengths:<br /> This paper is interesting in the clinical field, but unfortunately the data is mostly descriptive and does not have a significant impact on the scientific community in general.

Weaknesses:<br /> If core fucose is increased, at least the target glycan molecules of core fucose should be evaluated. They also found an increase in NO, suggesting that inflammatory processes also play an important role in OA in addition to glycan changes.<br /> It has already been reported that core fucose is decreased by administration of alpha-mannosidase inhibitors. Therefore, it is expected that alphaa-mannosidase administration increases core fucose.

Review #1 (Public Review)

Single-molecule visualization of chromatin remodelers on long chromatin templates-a long sought-after goal-is still in its infancy. This work describes the behaviors of two remodelers RSC and ISW2, from SWI/SNF and ISWI families respectively, with well-conducted experiments and rigorous quantitative analysis, thus representing a significant advance in the field of chromatin biology and biophysics.

Review #2 (Public Review)

The authors use a dual optical trap instrument combined with 2-color fluorescence imaging to analyze the diffusion of RSC and ISW2 on DNA, both in the presence and absence of nucleosomes, as well as long-range nucleosome sliding by these remodelers. This allowed them to demonstrate that both enzymes can participate in 1D diffusion along DNA for rather long ranges, with ISW2 predominantly tracking the DNA strand, while RSC diffusion involves hopping. In an elegant two-color assay, the authors were able to analyze interactions of diffusing remodeler molecules, both of the same or different types, observing their collisions, co-diffusion and bypassing. The authors demonstrate that nucleosomes act as barriers for remodeler diffusion, either repelling or sequestering them upon collision. In the presence of ATP, they observed surprisingly processive unidirectional nucleosome sliding with a strong bias in the direction opposite to where the remodeler approached the nucleosome from for ISW2. These results have fundamentally important implications for the mechanism of nucleosome positioning at promoters in vivo, will be of great interest for the scientific community, and will undoubtedly spark exciting future research

Reviewer #1 (Public Review):

Summary: Here, the authors were attempting to use molecular simulation or probe the nature of how lipids, especially PIP lipids, bind to a medically-important ion channel. In particular, they look at how this binding impacts the function of the channel.

Strengths: The study is very well written and composed. The techniques are used appropriately, with plenty of sampling and analysis. The findings are compelling, and provide clear insights into the biology of the system.

Weaknesses: A few of the analyses are hard to understand/follow, and rely on "in house" scripts. This is particularly the case for the lipid binding events, which can be difficult to compute accurately. However the provision of these scripts on github means that these can be assessed by the reader if desired. Additionally, a lack of experimental validation, or coupling to existing experimental data, limits the study.

It is my view that the authors have achieved their aims, and their findings are compelling and believable. Their findings should have impacts on how researchers understand the functioning of the Nav1.4 channel, as well as on the study of other ion channels and how they interact with membrane lipids.

Reviewer #2 (Public Review):

Summary:<br /> Lin Y., Tao E., et al. used multiscale MD simulations to show that PI(4,5)P2 binds stably to an inactivated state of Nav channels at a conserved site within the DIV S4-S5 linker, which couples the voltage sensing domain (VSD) to the pore. The authors hypothesized that PI(4,5)P2 prolongs inactivation by binding to the same site where the C-terminal tail is proposed to bind during recovery from inactivation. They convincingly showed that PI(4,5)P2 reduces the mobility of both the DIV S4-S5 linker and the DIII-IV linker, thus slowing the conformational changes required for the channel to recover to the resting state. They also conducted MD simulations to show that phosphoinositides bind to VSD gating charges in the resting state of Nav channels. These interactions may anchor VDS at the resting state and impede its activation. Their results provide a mechanism by which phosphoinositides alter the voltage dependence of activation and the recovery rate from inactivation, an important step for developing novel therapies to treat Nav-related diseases. However, the study is incomplete lacks the expected confirmatory studies which are relevant to such proposals.

Strengths:<br /> The authors identified a novel binding between phosphoinositides and the VSD of Nav and showed that the strength of this interaction is state-dependent. Based on their work, the affinity of PIPs to the inactivated state is higher than the resting state. This work will help pave the way for designing novel therapeutics that may help relieve pain or treat diseases like arrhythmia, which may result from a leftward shift of the channel's activation.

Weaknesses:<br /> However, the study lacks the expected confirmatory studies relevant to such proposals. For example, one would expect that the authors would mutate the positive residues that they claim to make interactions with phosphoinositides to show that there are much fewer interactions once they make these mutations. Another point is that the authors found that the main interaction site of PIPs with Nav1.4 is the VSD-DIV and DIII-DIV linker. This interaction is expected to delay fast inactivation if it happens at the resting state. The authors should make a resting state model of the Nav1.4 channel to explain the recent experimental data showing that PIP2 delays the activation of Nav1.4, with almost no effect on the voltage dependence of fast inactivation.

The reviewers answered most of my concerns about the first version of the manuscript.

Reviewer #3 (Public Review):

Summary:<br /> This work uses multiscale molecular dynamics simulations to demonstrate molecular mechanism(s) for phosphatidylinositol regulation of voltage gated sodium channel (Nav1.4) gating. Recent experimental work by Gada et al. JGP 2023 showed altered Nav1.4 gating when Nav1.4 current was recorded with simultaneous application of PI(4,5)P2 dephosphorylate. Here the authors revealed probable molecular mechanism that can explain PI(4,5)P2 modulation of Nav1.4 gating. They found PIP lipids interacting with the gating charges - potentially making it harder to move the voltage sensor domain and altering the channels voltage sensitivity. They also found a stable PIP binding site that reaches the D_IV S4-S5 linker, reducing the mobility of the linker and potentially competing with the C-terminal domain.

Strengths:<br /> Using multiscale simulations with course-grained simulations to capture lipid-protein interactions and the overall protein lipid fingerprint and then all-atom simulations to verify atomistic details for specific lipid-protein interactions is extremely appropriate for the question at hand. Overall, the types of simulation and their length are suitable for the questions the authors pose and a thorough set of analysis was done which illustrates the observed PIP-protein interactions.

Weaknesses:<br /> Although the set of current simulations and analysis supports the conclusions drawn nicely, the course-grained simulations have further utility than that utilized by the authors. With the 4to1 heavy atoms bead mapping in Martini 2 some detailed chemical specificity is averaged out but parameters for different PIP family members do exist - including specific PIP(4,5)P2 vs PIP(3,4)P2, and could have been explored at the course-grained level. However, performing more detailed all-atom simulation, as done in this manuscript, is always advisable to extend and/or confirm course-grained results.

Reviewer #1 (Public Review):

Summary:<br /> Cyclic Nucleotide Binding (CNB) domains are pervasive structural components involved in signaling pathways across eukaryotes and prokaryotes. Despite their similar structures, CNB domains exhibit distinct ligand-sensing capabilities. The manuscript offers a thorough and convincing investigation that clarifies numerous puzzling aspects of nucleotide binding in Trypanosoma.

Reviewer #2 (Public Review):

Summary:<br /> This manuscript clearly shows that Trypanosoma PKA is controlled by nucleoside analogues rather than cyclic nucleotides, which are the primary allosteric effectors of human PKA and PKG. The authors demonstrate that the inosine, guanosine, and adenosine nucleosides bind with high affinity and activate PKA in the tropical pathogens T. brucei, T. cruzi and Leishmania. The underlying determinants of nucleoside binding and selectivity are dissected by solving the crystal structure of T. cruzi PKAR(200-503) and T. brucei PKAR(199-499) bound to inosine at 1.4 Å and 2.1 Å resolution and through comparative mutational analyses. Of particular interest is the identification of a minimal subset of 2-3 residues that controls nucleoside vs. cyclic nucleotide specificity.

Reviewer #1 (Public Review):

Summary of Author's Objectives:<br /> The authors aimed to explore JMJD6's role in MYC-driven neuroblastoma, particularly in the interplay between pre-mRNA splicing and cancer metabolism, and to investigate the potential for targeting this pathway.

Strengths:<br /> (1) The study employs a diverse range of experimental techniques, including molecular biology assays, next-generation sequencing, interactome profiling, and metabolic analysis. Moreover, the authors specifically focused on gained chromosome 17q in neuroblastoma, in combination with analyzing cancer dependency genes screened with Crispr/Cas9 library, analyzing the association of gene expression with prognosis of neuroblastoma patients with large clinical cohort. This comprehensive approach strengthens the credibility of the findings. The identification of the link between JMJD6-mediated pre-mRNA splicing and metabolic reprogramming in MYC-driven cancer cells is innovative.<br /> (2) The authors effectively integrate data from multiple sources, such as gene expression analysis, RNA splicing analysis, JMJD6 interactome assay, and metabolic profiling. This holistic approach provides a more complete understanding of JMJD6's role.<br /> (3) The identification of JMJD6 as a potential therapeutic target and its correlation with the response to indisulam have significant clinical implications, addressing an unmet need in cancer treatment.

Weaknesses:<br /> It would be beneficial to explore whether treatment with JMJD6 inhibitors, both in vitro and in vivo, can effectively target the enhanced pre-mRNA splicing of metabolic genes in MYC-driven cancer cells. However, the authors have noted that there are currently no potent and selective JMJD6 inhibitors available.

Appraisal of Achievement and Conclusion Support:<br /> The authors have effectively met their objectives by offering valuable insights into JMJD6's role in MYC-driven neuroblastoma. The results robustly underpin their conclusions about JMJD6's contribution to metabolic reprogramming through alternative splicing and its connection to the therapeutic response to indisulam.

Likely Impact on the Field and Utility of Methods/Data:<br /> The study's findings have the potential to significantly impact the field of cancer research by identifying JMJD6 as a promising therapeutic target for MYC-driven cancers. The methods and data presented in the manuscript offer valuable resources to the research community for further investigations into cancer metabolism and splicing regulation.

Additional Context for Interpretation:<br /> Understanding the complex interplay between cancer metabolism and splicing regulation is crucial for developing effective cancer treatments. This study sheds light on a previously poorly understood aspect of MYC-driven cancers and opens new avenues for targeted therapies. However, the transition from preclinical findings to clinical applications may face challenges, which should be considered in future research and clinical trials.

Reviewer #2 (Public Review):

Summary:

Jablonowski and colleagues explored altered pre-mRNA splicing and metabolism in MYC-driven neuroblastoma cell lines. They focused on the role of JMJD6 assessing cellular transformation, for example through interactions with RNA-binding proteins. Moreover, the study examined JMJD6's impact on the splicing of glutaminase (GLS), crucial in neuroblastoma cell metabolism. It also connected JMJD6 to the anti-proliferative effects of indisulam, a compound targeting RBM39 (splicing factor interacting with JMJD6).

Overall, the findings presented by Jablonowski et al. begin to illuminate a cancer-promoting metabolic, and potentially, a protein synthesis suppression program that may be linked to alternative pre-mRNA splicing through the action of JMJD6 - downstream of MYC. This discovery can provide further evidence for considering JMJD6 as a potential therapeutic target for the treatment of MYC-driven cancers.

Strengths:

Alternative Splicing Induced by JMJD6 Knockdown: the study presents evidence for the role of JMJD6 in alternative splicing in neuroblastoma cells. Specifically, the RNA immunoprecipitation experiments demonstrated a significant shift from the GAC to the KGA GLS isoform upon JMJD6 knockdown. Moreover, a significant correlation between JMJD6 levels and GAC/KGA isoform expression was identified in two distinct neuroblastoma cohorts. This suggests a causative link between JMJD6 activity and isoform prevalence.

Physical Interaction of JMJD6 in Neuroblastoma Cells: The paper provides preliminary insight into the physical interactome of JMJD6 in neuroblastoma cells. This offers a potential mechanistic avenue for the observed effects on metabolism and protein synthesis and could be exploited for a deeper investigation into the exact nature, and implications of neuroblastoma-specific JMJD6 protein-protein interactions.

Weaknesses:

There are several areas that would benefit from improvements with regards to the neuroblastoma modelling strategy, lack of in vivo data, and depth of mechanistic investigation. While the need for additional experimental evidence in these areas remains (as highlighted in the initial review), the authors have now acknowledged several relevant limitations and provided a paragraph discussing future experimental work.