Historiography in weirmar germany....duh

Here is a test for a public note about this page.

This is the origin of "Rhetorical Density" as a quantitative linguistics metric.

https://doi.org/10.48550/arXiv.2507.21106

This is the origin of "Rhetorical Density" as a quantitative linguistics metric.

https://doi.org/10.48550/arXiv.2507.21106

Page xxxv (b), Section 5: Page Sections

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Full PDF at https://doi.org/10.64393/speaker-tips.1 (free)

Ik denk dat dit een risicovolle actie is. Alhoewel ChatGPT spelling/grammaticafouten in een tekst zou kunnen weergeven/verbeteren, lijkt me dat het herschrijven van een stuk tekst voor betere flow snel de "personal touch" van de journalist uit het stuk kan halen. Daarnaast denk ik dat dit soort gebruik ook sneller kan leiden tot een mate van luiheid waar sneller gegrepen wordt naar automatische herschrijving in plaats van zelf de tekst herschrijven en je stem als schrijver te behouden.

Also described in English [here]

https://doi.org/10.64393/balagha.Q111

I totally agree with this. In high school, most of the kids are still very immature, and that they have to rely on other peoples judgment as a standard to judge himself. Therefore, choosing the right group to get along with would be very important, because you will use a better standard to judge yourself since you’re imitating them or seeing yourself in a mirrorI totally agree with this. In high school, most of the kids are still very immature, and that they have to rely on other peoples judgment as a standard to judge himself. Therefore, choosing the right group to get along with would be very important, because you will use a better standard to judge yourself since you’re imitating them or seeing yourself in a mirror

Personally speaking, I have never encounter very severe struggles since my school has a very good environment. However, I did hear a story from my dad. He grew up in an environment that is relatively poor. Once the school gets dismissed, he was blocked by a group of 4 high school kids, who tried to rob him. In order to save his money, he resisted them. But they got knives in their hands, and poked to my dad‘s back. Fortunately, it only left a scar instead of killing him. Therefore, I just want to say that school is very much a war zone, where people have to try their best to survive.

8 It connects the central canals to different osteons, allowing blood vessels and nerves to pass from the bones outer surface into the interior and vice versa.

16 This acts as a biomechanical sensor. It regulates the bone and remodel it by influencing the osteoblast and osteoclast activity. lt obtains mineral homeostasis by controlling calcium and phosphate levels in blood.

15 This house the osteocytes and chondrocytes. It enables nutrients and waste exchange through canaliculi

14 This is the drainage channel that transports fluids or nutrients into various parts of the body.

10 Produce blood cells storing marrow, protecting organs, reducing the weight of the bone, strengthens bone, and act as a a shock absorber.

9 Transport and circulate Cerebrospinal Fluid through the center of the spinal cord, which provides protective cushioning and aids with transporting nutrients and waste removal.

6 This gives bone formation , repair, protection, and sensation.

5 Transports blood through out the body and ensuring the delivery of oxygen, nutrients, and other essential substances to tissue while removing waste.

2 This is a thin, concentric layers of bone tissue that form the basic structural unit of the compact bone. This gives bone strength, rigidity, and growth.

4 Forms the dense outer layer of bones which resist stress, bending, and fractures. It also provides strength, protection, and support to the skeletal system.

1 This is the structural units of compact bones. It remains strength, provide nutrients, and remodel the bone.

Het nieuwsvergaren door middel van AI kan ook best problematisch zijn. De meeste reguliere taalmodellen hebben geen toegang tot betrouwbare data uit bijvoorbeeld onderzoeken omdat die vaak achter een betaalmuur zitten. Dit geldt ook voor analyses uit bijvoorbeeld kwaliteitskranten. De data die het heeft, komt dus niet van de meest betrouwbare websites, die waarschijnlijk toegang hebben tot de meest betrouwbare data. Het gebruiken voor trendanalyses of verwachtingsanalyses vormt dus daarom wel een risico. Bovendien zal een taalmodel nooit iets nieuws ‘verzinnen’, en dus nooit een nieuwe, interessante invalshoek belichten. De chatbots baseren zich immers op de informatie die al op het web staat. Je kunt het dus altijd gebruiken als een brainstorm, maar een echt creatieve invalshoek ligt toch bij de journalist zelf.

ook voor freelancers

interessant als er een scheidslijn komt tussen onderwerpen, waar ligt die grens en hoe wordt dit bepaald? worden bepaalde onderwerpen als belangrijker gezien dan andere en hoe wordt dit bepaald?

interessant, binnen deze opleiding wordt het strict uitgesloten om AI te gebruiken maar als we het journalistieke werkveld ingaan wordt het dus wel gebruikt

ik vraag me af of kleinere journalistieke organisaties de capaciteit hebben om zo een task force te hebben

maar gaan de standaarden hierdoor niet ook hoger liggen, waardoor de werkdruk alleen maar omhoog gaat? (vicieuze cirkel)

hoe betrouwbaar is een search engine

Hoe vermijden we dit, hoe zorgen we dat journalisten dit scherp controleren als zij deze zelf ook op een bepaalde manier hebben geïnternaliseerd, en hier misschien vooral minder scherp op zijn als ze onder hoge druk werken?

Interessant - soms zitten journalisten naar mijn idee te diep in een onderwerp waardoor ze het niet begrijpelijk overbrengen op het algemene publiek en dan zou AI en handig tweede paar ogen kunnen zijn, maar het moet hier dus niet te ver in gaan

Interessant - door bepaalde processen efficiënter te maken, kunnen journalisten meer tijd besteden aan het 'echte' journalistieke werk wat journalistieke items beter en betrouwbaarder maakt

Interessant, veel mensen zeggen AI te gebruiken maar het wel altijd zelf te verifiëren/aan te passen, maar wellicht kunnen we ook steeds minder kritisch ernaar kijken als we minder zelf maken

Interessant, aan de ene kant lijkt AI diversiteit negatief te beïnvloeden, maar aan de andere kant dus ook positief

Interessant, dit lijkt het mogelijk te maken voor de journalist om buiten diens eigen kaders en subjectiviteit te treden - AI maakt journalistiek ergens ook juist minder subjectief?

Journalistiek komt kijken met een hoge werkdruk, en AI lijkt ook verlichting te kunnen bieden hieraan (efficienter werken) maar dit zal de werkdruk misschien alleen maar weer hoger maken

Useful overview with some potential ways of using AI that I had not thought of before

Journalistiek is al afhankelijk van big tech en een onzekere sector, en wordt nu nog afhankelijker van bepaalde partijen - wat voor overheidsingrepen zijn hiervoor nodig?

Belangrijk - de journalistiek en mediawereld is al niet heel divers/representatief, en het lijkt er nu op dat AI dit alleen maar dreight te versterken

Al vrij bekend dat dit wordt gebruikt en handig is om journalistieke producties te maken die de doelgroep daadwerkelijk aanspreken, maar roept ook de vraag op hoe ver we willen gaan in het 'aantrekkelijk' maken van nieuws en of dit ten koste gaat van de kwaliteit informatie die wordt overgebracht

Ik vraag me of hoe die fact checking dan plaatsvindt, en of je de andere bronnen kunt bekijken die het bevestigen. Het doet me denken aan gewoon iets opzoeken via Google om iets te factchecken, wat als journalist niet genoeg is.

Ik ben benieuwd wanneer de tool nog meer bepaald wanneer iets gefactcheckt moet worden dan wanneer er een getal wordt genoemd, dit lijkt me een kwetsbaar systeem en gevaarlijk als journalisten alleen factchecken wat de AI tool aangeeft

Voor het publiek?

Interessant - we leren in deze opleiding nieuws erkennen, ik wist niet dat er zulke digitale tools voor bestaan, en ben benieuwd wat hierbij wordt gebruikt als criteria

incase of dilectric metal boundaries

eLife Assessment

Davies et al. present a valuable study proposing that Shot can act as a molecular linker between microtubules and actin during dendrite pruning, suggesting an intriguing role in non-centrosomal microtubule organization. However, the experimental evidence is incomplete and does not robustly support these claims, and the lack of a cohesive model connecting the findings weakens the overall impact. While the data suggest that Shot, actin, and microtubule nucleation contribute to dendritic pruning, their precise interplay remains unresolved.

Reviewer #1 (Public review):

Summary:

The Neuronal microtubule cytoskeleton is essential long long-range transport in axons and dendrites. The axon-specific plus-end out microtubule organization vs the dendritic-specific plus-end in organization allows for selective transport into each neurite, setting up neuronal polarity. In addition, the dendritic microtubule organization is thought to be important for dendritic pruning in Drosophila during metamorphosis. However, the precise mechanisms that organize microtubules in neurons are still incompletely understood.

In the current manuscript, the authors describe the spectraplakin protein Shot as important in developmental dendritic pruning. They find that Shot has dendritic microtubule polarity defects, which, based on their rescues and previous work, is likely the reason for the pruning defect.

Since Shot is a known actin-microtubule crosslinker, they also investigate the putative role of actin and find that actin is also important for dendritic pruning. Finally, they find that several factors that have been shown to function as a dendritic MTOC in C. elegans also show a defect in Drosophila upon depletion.

Strengths:

Overall, this work was technically well-performed, using advanced genetics and imaging. The author reports some interesting findings identifying new players for dendritic microtubule organization and pruning.

Weaknesses:

The evidence for Shot interacting with actin for its functioning is contradictory. The Shot lacking the actin interaction domain did not rescue the mutant; however, it also has a strong toxic effect upon overexpression in wildtype (Figure S3), so a potential rescue may be masked. Moreover, the C-terminus-only construct, which carries the GAS2-like domain, was sufficient to rescue the pruning. This actually suggests that MT bundling/stabilization is the main function of Shot (and no actin binding is needed). On the other hand, actin depolymerization leads to some microtubule defects and subtle changes in shot localization in young neurons (not old ones). More importantly, it did not enhance the microtubule or pruning defects of the Shot domain, suggesting these act in the same pathway. Interesting to note is that Mical expression led to microtubule defects but not to pruning defects. This argues that MT organization effects alone are not enough to cause pruning defects. This may be be good to discuss. For the actin depolymerization, the authors used overexpression of the actin-oxidizing Mical protein. However, Mical may have another target. It would be good to validate key findings with better characterized actin targeting tools.

In analogy to C. elegans, where RAB-11 functions as a ncMTOC to set up microtubules in dendrites, the authors investigated the role of these in Drosophila. Interestingly, they find that rab-11 also colocalizes to gamma tubulin and its depletion leads to some microtubule defects. Furthermore, they find a genetic interaction between these components and Shot; however, this does not prove that these components act together (if at all, it would be the opposite). This should be made more clear. What would be needed to connect these is to address RAB-11 localization + gamma-tubulin upon shot depletion.

All components studied in this manuscript lead to a partial reversal of microtubules in the dendrite. However, it is not clear from how the data is represented if the microtubule defect is subtle in all animals or whether it is partially penetrant stronger effect (a few animals/neurons have a strong phenotype). This is relevant as this may suggest that other mechanisms are also required for this organization, and it would make it markedly different from C. elegans. This should be discussed and potentially represented differently.

Reviewer #2 (Public review):

Summary:

In their manuscript, the authors reveal that the spectraplakin Shot, which can bind both microtubules and actin, is essential for the proper pruning of dendrites in a developing Drosophila model. A molecular basis for the coordination of these two cytoskeletons during neuronal development has been elusive, and the authors' data point to the role of Shot in regulating microtubule polarity and growth through one of its actin-binding domains. The authors also propose an intriguing new activity for a spectraplakin: functioning as part of a microtubule-organizing center (MTOC).

Strengths:

(1) A strength of the manuscript is the authors' data supporting the idea that Shot regulates dendrite pruning via its actin-binding CH1 domain and that this domain is also implicated in Shot's ability to regulate microtubule polarity and growth (although see comments below); these data are consistent with the authors' model that Shot acts through both the actin and microtubule cytoskeletons to regulate neuronal development.

(2) Another strength of the manuscript is the data in support of Rab11 functioning as an MTOC in young larvae but not older larvae; this is an important finding that may resolve some debates in the literature. The finding that Rab11 and Msps coimmunoprecipitate is nice evidence in support of the idea that Rab11(+) endosomes serve as MTOCs.

Weaknesses:

(1) A significant, major concern is that most of the authors' main conclusions are not (well) supported, in particular, the model that Shot functions as part of an MTOC. The story has many interesting components, but lacks the experimental depth to support the authors' claims.

(2) One of the authors' central claims is that Shot functions as part of a non-centrosomal MTOC, presumably a MTOC anchored on Rab11(+) endosomes. For example, in the Introduction, last paragraph, the authors summarize their model: "Shot localizes to dendrite tips in an actin-dependent manner where it recruits factors cooperating with an early-acting, Rab11-dependent MTOC." This statement is not supported. The authors do not show any data that Shot localizes with Rab11 or that Rab11 localization or its MTOC activity is affected by the loss of Shot (or otherwise manipulating Shot). A genetic interaction between Shot and Rab11 is not sufficient to support this claim, which relies on the proteins functioning together at a certain place and time. On a related note, the claim that Shot localization to dendrite tips is actin-dependent is not well supported: the authors show that the CH1 domain is needed to enrich Shot at dendrite tips, but they do not directly manipulate actin (it would be helpful if the authors showed the overexpression of Mical disrupted actin, as they predict).

(3) The authors show an image that Shot colocalizes with the EB1-mScarlet3 comet initiation sites and use this representative image to generate a model that Shot functions as part of an MTOC. However, this conclusion needs additional support: the authors should quantify the frequency of EB1 comets that originate from Shot-GFP aggregates, report the orientation of EB1 comets that originate from Shot-GFP aggregates (e.g., do the Shot-GFP aggregates correlate with anterogradely or retrogradely moving EB1 comets), and characterize the developmental timing of these events. The genetic interaction tests revealing ability of shot dsRNA to enhance the loss of microtubule-interacting proteins (Msps, Patronin, EB1) and Rab11 are consistent with the idea that Shot regulates microtubules, but it does not provide any spatial information on where Shot is interacting with these proteins, which is critical to the model that Shot is acting as part of a dendritic MTOC.

(4) It is unclear whether the authors are proposing that dendrite pruning defects are due to an early function of Shot in regulating microtubule polarity in young neurons (during 1st instar larval stages) or whether Shot is acting in another way to affect dendrite pruning. It would be helpful for the authors to present and discuss a specific model regarding Shot's regulation of dendrite pruning in the Discussion.

(5) The authors argue that a change in microtubule polarity contributes to dendrite pruning defects. For example, in the Introduction, last paragraph, the authors state: "Loss of Shot causes pruning defects caused by mixed orientation of dendritic microtubules." The authors show a correlative relationship, not a causal one. In Figure 4, C and E, the authors show that overexpression of Mical disrupts microtubule polarity but not dendrite pruning, raising the question of whether disrupting microtubule polarity is sufficient to cause dendrite pruning defects. The lack of an association between a disruption in microtubule polarity and dendrite pruning in neurons overexpressing Mical is an important finding.

(6) The authors show that a truncated Shot construct with the microtubule-binding domain, but no actin-binding domain (Shot-C-term), can rescue dendrite pruning defects and Khc-lacZ localization, whereas the longer Shot construct that lacks just one actin-binding domain ("delta-CH1") cannot. Have the authors confirmed that both proteins are expressed at equivalent levels? Based on these results and their finding that over-expression of Shot-delta-CH1 disrupts dendrite pruning, it seems possible that Shot-delta-CH1 may function as a dominant-negative rather than a loss-of-function. Regardless, the authors should develop a model that takes into account their findings that Shot, without any actin-binding domains and only a microtubule-binding domain, shows robust rescue.

(7) The authors state that: "The fact that Shot variants lacking the CH1 domain cannot rescue the pruning defects of shot[3] mutants suggested that dendrite tip localization of Shot was important for its function." (pages 10-11). This statement is not accurate: the Shot C-term construct, which lacks the CH1 domain (as well as other domains), is able to rescue dendrite pruning defects.

(8) The authors state that: "In further support of non-functionality, overexpression of Shot[deltaCH1] caused strong pruning defects (Fig. S3)." (page 8). Presumably, these results indicate that Shot-delta-CH1 is functioning as a dominant-negative since a loss-of-function protein would have no effect. The authors should revise how they interpret these results. This comment is related to another comment about the ability of Shot constructs to rescue the shot[3] mutant.

Author response:

We thank the reviewers for their comments. We are paraphrasing their three main criticisms below and provide responses and outlines of how we are going to address them.

Criticism 1: Actin binding by Shot may not be required for Shot's function in dendritic microtubule organization (Point 1 by Reviewer 1, points 6-8 by reviewer 2).

This criticism is mainly based on our finding that, while a version of Shot lacking just the high affinity actin binding site cannot rescue the pruning and orientation defects of shot³ mutants, expression of a construct harboring just the microtubule and EB1 binding sites can. The reviewers also point out that a Shot construct lacking one of its actin binding domains (deltaCH1), causes pruning defects when overexpressed in wild type cells.

We thank the reviewers for this comment. We concede that we did not properly explain our reasoning and conclusions regarding the role of actin binding in Shot dendritic function. From the literature, there is evidence that Shot fragments containing the C-terminal microtubule binding domain alone have positive effects on neuronal microtubule stability and organization by a gain-of-function mechanism. This is likely due to two reasons: firstly, the activity of these constructs is unrestrained by localization. For example, in axons, full length Shot localizes adjacent to the membrane and to growth cones, while a Shot C-terminal construct (lacking the actin-binding and spectrin-repeat domains) decorates axonal microtubules [1]. Secondly, the actin binding site appears to inhibit microtubule binding by an intramolecular mechanism that is relieved by actin binding [2]. Overexpression of such a construct also dramatically improves axonal microtubule defects in aged neurons [3]. Thus, actin recruitment may locally activate Shot's microtubule binding activity.

To address this criticism, we will test if other UAS-Shot transgenes lacking the actin binding or microtubule binding domains can rescue the defects of Shot mutants. We will also try to provide more evidence that the C-terminal Shot construct exerts a gain-of-function effect on microtubules. We will adjust our interpretation accordingly.

Criticism 2: The relationship between reversal of dendritic microtubule orientation and dendrite pruning defects could be correlative rather than causal (paragraph 1 by Reviewer 1, point 5 by reviewer 2).

This criticism is based on our finding that Mical overexpression causes a partial reversal of dendritic microtubule orientation but no apparent dendrite pruning defects.

We thank the reviewers for this comment. In fact, knockdown of EB1, which affects dendritic microtubule organisation via kinesin-2 [4], does not cause dendrite pruning defects by itself either, but strongly enhances the pruning defects caused by other microtubule manipulations [5]. This is likely because loss of EB1 destabilizes the dendritic cytoskeleton and thus also promotes dendrite degeneration. All other conditions that cause dendritic microtubule reversal also cause dendrite pruning defects [5 - 9]. As Mical is a known pruning factor [10], its overexpression may actually also destabilize dendrites, e. g., by severing actin filaments. However, we showed in the current manuscript that Mical overexpression causes a partial reversal of dendritic microtubule polarity and strongly enhances the dendrite pruning defects caused by Shot knockdown.

To address this criticism, we will rephrase the corresponding section of our manuscript and specify that conditions that cause reversal of dendritic microtubule orientation either cause dendrite pruning defects, or act as genetic enhancers of pruning defects caused by other microtubule regulators. This wording better explains the relationship between dendritic microtubule orientation and dendrite pruning and also includes the Mical overexpression condition.

Criticism 3: The presented data do not prove that Shot, Rab11 and Patronin act in a common pathway to establish dendritic plus end-in microtubule orientation (paragraphs 2-3 by Reviewer 1, point 1-4 by reviewer 2).

While these factors genetically interact with each other during dendrite pruning, it is not clear whether (1) they colocalize at the tips of growing dendrites during early growth stages; (2) their respective localizations depend on each other; (3) they act at the same developmental stage in microtubule orientation.  

We thank the reviewers for this comment. For technical reasons (e. g., incompatible transgenes, GAL4 drivers too weak), we could only partially address these questions at the time. We have now expanded our toolkit with additional drivers and fluorescently tagged transgenes. We will therefore test whether Shot and Rab11 or Patronin and Rab11 colocalize in growing dendrites during the early L1 stage, and if loss of Shot affects the localization or the activity of Patronin and Rab11 in dendrites. We will adapt our interpretation accordingly, and also add a comprehensive model.

References

(1) Alves Silva et al. (2012) J. Neurosci. 32:9143

(2) Applewhite et al. (2013) Mol. Biol. Cell 24:2885

(3) Okenve-Ramos et al. (2024) PLoS Biol. 22:e3002504

(4) Mattie et al. (2010) Curr. Biol. 20:2169

(5) Herzmann et al. (2018) Development 145:dev156950

(6) Wang et al. (2019) eLife 8:e39964

(7) Rui et al. (2020) EMBO Rep. 21:e48843

(8) Tang et al. (2020) EMBO J. 39:e103549

(9) Bu et al. (2022) Cell Rep. 39:110887

(10) Kirilly et al. (2009) Nat. Neurosci. 12:1497

Eliminar. Nunca reiterar lo que ya se dijo en los métodos

most basic

Deberíamos señalar en el marco teórico que el DigComp comprende tres dimensiones básicas y otras dos agregadas para que se entienda esta afirmación

Escribir fraseo completo del ítem, o bien ser más específico con el tipo de tarea, pues para el lector sin contexto no queda claro a que se refiere identify app (Ni siquiera los presentamos así en la tabla de métodos.

Essai annotation

eLife Assessment

This study presents experiments suggesting intriguing mesoscale reorganization of functional connectivity across distributed cortical and subcortical circuits during learning. The approach is technically impressive and the results are potentially of valuable significance. However, in its current form, the strength of evidence is incomplete. More in-depth analyses and the acquisition of data from additional animals in the primary experiment could bolster these findings.

Reviewer #1 (Public review):

Summary:

This study aims to address an important and timely question: how does the mesoscale architecture of cortical and subcortical circuits reorganize during sensorimotor learning? By using high-density, chronically implanted ultra-flexible electrode arrays, the authors track spiking activity across ten brain regions as mice learn a visual Go/No-Go task. The results indicate that learning leads to more sequential and temporally compressed patterns of activity during correct rejection trials, alongside changes in functional connectivity ranks that reflect shifts in the relative influence of visual, frontal, and motor areas throughout learning. The emergence of a more task-focused subnetwork is accompanied by broader and faster propagation of stimulus information across recorded regions.

Strengths:

A clear strength of this work is its recording approach. The combination of stable, high-throughput multi-region recordings over extended periods represents a significant advance for capturing learning-related network dynamics at the mesoscale. The conceptual framework is well motivated, building on prior evidence that decision-relevant signals are widely distributed across the brain. The analysis approach, combining functional connectivity rankings with information encoding metrics is well motivated but needs refinement. These results provide some valuable evidence of how learning can refine both the temporal precision and the structure of interregional communication, offering new insights into circuit reconfiguration during learning.

Weaknesses:

The technical approach is strong and the conceptual framing is compelling, but several aspects of the evidence remain incomplete. In particular, it is unclear whether the reported changes in connectivity truly capture causal influences, as the rank metrics remain correlational and show discrepancies with the manipulation results. The absolute response onset latencies also appear slow for sensory-guided behavior in mice, and it is not clear whether this reflects the method used to define onset timing or factors such as task structure or internal state. Furthermore, the small number of animals, combined with extensive repeated measures, raises questions about statistical independence and how multiple comparisons were controlled. The optogenetic experiments, while intended to test the functional relevance of rank-increasing regions, leave it unclear how effectively the targeted circuits were silenced. Without direct evidence of reliable local inhibition, the behavioral effects or lack thereof are difficult to interpret. Details on spike sorting are limited.

Reviewer #2 (Public review):

Summary:

Wang et al. measure from 10 cortical and subcortical brain as mice learn a go/no-go visual discrimination task. They found that during learning, there is a reshaping of inter-areal connections, in which a visual-frontal subnetwork emerges as mice gain expertise. Also visual stimuli decoding became more widespread post-learning. They also perform silencing experiments and find that OFC and V2M are important for the learning process. The conclusion is that learning evoked a brain-wide dynamic interplay between different brain areas that together may promote learning.

Strengths:

The manuscript is written well and the logic is rather clear. I found the study interesting and of interest to the field. The recording method is innovative and requires exceptional skills to perform. The outcomes of the study are significant, highlighting that learning evokes a widespread and dynamics modulation between different brain areas, in which specific task-related subnetworks emerge.

Weaknesses:

I had several major concerns:

(1) The number of mice was small for the ephys recordings. Although the authors start with 7 mice in Figure 1, they then reduce to 5 in panel F. And in their main analysis, they minimize their analysis to 6/7 sessions from 3 mice only. I couldn't find a rationale for this reduction, but in the methods they do mention that 2 mice were used for fruitless training, which I found no mention in the results. Moreover, in the early case, all of the analysis is from 118 CR trials taken from 3 mice. In general, this is a rather low number of mice and trial numbers. I think it is quite essential to add more mice.

(2) Movement analysis was not sufficient. Mice learning a go/no-go task establish a movement strategy that is developed throughout learning and is also biased towards Hit trials. There is an analysis of movement in Figure S4, but this is rather superficial. I was not even sure that the 3 mice in Figure S4 are the same 3 mice in the main figure. There should be also an analysis of movement as a function of time to see differences. Also for Hits and FAs. I give some more details below. In general, most of the results can be explained by the fact that as mice gain expertise, they move more (also in CR during specific times) which leads to more activation in frontal cortex and more coordination with visual areas. More needs to be done in terms of analysis, or at least a mention of this in the text.

(3) Most of the figures are over-detailed, and it is hard to understand the take-home message. Although the text is written succinctly and rather short, the figures are mostly overwhelming, especially Figures 4-7. For example, Figure 4 presents 24 brain plots! For rank input and output rank during early and late stim and response periods, for early and expert and their difference. All in the same colormap. No significance shown at all. The Δrank maps for all cases look essentially identical across conditions. The division into early and late time periods is not properly justified. But the main take home message is positive Δrank in OFC, V2M, V1 and negative Δrank in ThalMD and Str. In my opinion, one trio map is enough, and the rest could be bumped to the Supplementary section, if at all. In general, the figure in several cases do not convey the main take home messages. See more details below.

(4) The analysis is sometimes not intuitive enough. For example, the rank analysis of input and output rank seemed a bit over complex. Figure 3 was hard to follow (although a lot of effort was made by the authors to make it clearer). Was there any difference between the output and input analysis? Also, the time period seems redundant sometimes. Also, there are other network analysis that can be done which are a bit more intuitive. The use of rank within the 10 areas was not the most intuitive. Even a dimensionality reduction along with clustering can be used as an alternative. In my opinion, I don't think the authors should completely redo their analysis, but maybe mention the fact that other analyses exist.

Reviewer #3 (Public review):

Summary:

In the manuscript " Dynamics of mesoscale brain network during decision-making learning revealed by chronic, large-scale single-unit recording", Wang et al investigated mesoscale network reorganization during visual stimulus discrimination learning in mice using chronic, large-scale single-unit recordings across 10 cortical/subcortical regions. During learning, mice improved task performance mainly by suppressing licking on no-go trials. The authors found that learning induced restructuring of functional connectivity, with visual (V1, V2M) and frontal (OFC, M2) regions forming a task-relevant subnetwork during the acquisition of correct No-Go (CR) trials.

Learning also compressed sequential neural activation and broadened stimulus encoding across regions. In addition, a region's network connectivity rank correlated with its timing of peak visual stimulus encoding.

Optogenetic inhibition of orbitofrontal cortex (OFC) and high order visual cortex (V2M) impaired learning, validating its role in learning. The work highlights how mesoscale networks underwent dynamic structuring during learning.

Strengths:

The use of ultra-flexible microelectrode arrays (uFINE-M) for chronic, large-scale recordings across 10 cortical/subcortical regions in behaving mice represents a significant methodological advancement. The ability to track individual units over weeks across multiple brain areas will provide a rare opportunity to study mesoscale network plasticity.

While limited in scope, optogenetic inhibition of OFC and V2M directly ties connectivity rank changes to behavioral performance, adding causal depth to correlational observations.

Weaknesses:

The weakness is also related to the strength provided by the method. It is demonstrated in the original method that this approach in principle can track individual units for four months (Luan et al, 2017). The authors have not showed chronically tracked neurons across learning. Without demonstrating that and taking advantage of analyzing chronically tracked neurons, this approach is not different from acute recording across multiple days during learning. Many studies have achieved acute recording across learning using similar tasks. These studies have recorded units from a few brain areas or even across brain-wide areas.

Another weakness is that major results are based on analyses of functional connectivity that is calculated using the cross-correlation score of spiking activity (TSPE algorithm). Functional connection strengthen across areas is then ranked 1-10 based on relative strength. Without ground truth data, it is hard to judge the underlying caveats. I'd strongly advise the authors to use complementary methods to verify the functional connectivity and to evaluate the mesoscale change in subnetworks. Perhaps the authors can use one key information of anatomy, i.e. the cortex projects to the striatum, while the striatum does not directly affect other brain structures recorded in this manuscript.

Author response:

Reviewer #1 (Public review):

Weaknesses:

The technical approach is strong and the conceptual framing is compelling, but several aspects of the evidence remain incomplete. In particular, it is unclear whether the reported changes in connectivity truly capture causal influences, as the rank metrics remain correlational and show discrepancies with the manipulation results.

We agree that our functional connectivity ranking analyses cannot establish causal influences. As discussed in the manuscript, besides learning-related activity changes, the functional connectivity may also be influenced by neuromodulatory systems and internal state fluctuations. In addition, the spatial scope of our recordings is still limited compared to the full network implicated in visual discrimination learning, which may bias the ranking estimates. In future, we aim to achieve broader region coverage and integrate multiple complementary analyses to address the causal contribution of each region.

The absolute response onset latencies also appear slow for sensory-guided behavior in mice, and it is not clear whether this reflects the method used to define onset timing or factors such as task structure or internal state.

We believe this may be primarily due to our conservative definition of onset timing. Specifically, we required the firing rate to exceed baseline (t-test, p < 0.05) for at least 3 consecutive 25-ms time windows. This might lead to later estimates than other studies, such as using the latency to the first spike after visual stimulus onset (~50-60 ms, Siegle et al., Nature, 2023) or the time to half-max response (~65 ms, Goldbach et al., eLife, 2021).

Furthermore, the small number of animals, combined with extensive repeated measures, raises questions about statistical independence and how multiple comparisons were controlled.

We agree that a larger sample size would strengthen the robustness of the findings. However, as noted above, the current dataset has inherent limitations in both the number of recorded regions and the behavioral paradigm. Given the considerable effort required to achieve sufficient unit yields across all targeted regions, we wish to adjust the set of recorded regions, improve behavioral task design, and implement better analyses in future studies. This will allow us to both increase the number of animals and extract more precise insights into mesoscale dynamics during learning.

The optogenetic experiments, while intended to test the functional relevance of rank increasing regions, leave it unclear how effectively the targeted circuits were silenced. Without direct evidence of reliable local inhibition, the behavioral effects or lack thereof are difficult to interpret.

We appreciate this important point. Due to the design of the flexible electrodes and the implantation procedure, bilateral co-implantation of both electrodes and optical fibers was challenging, which prevented us from directly validating the inhibition effect in the same animals used for behavior. In hindsight, we could have conducted parallel validations using conventional electrodes, and we will incorporate such controls in future work to provide direct evidence of manipulation efficacy.

Details on spike sorting are limited.

We will provide more details on spike sorting, including the exact parameters used in the automated sorting algorithm and the subsequent manual curation criteria.

Reviewer #2 (Public review):

Weaknesses:

I had several major concerns:

(1) The number of mice was small for the ephys recordings. Although the authors start with 7 mice in Figure 1, they then reduce to 5 in panel F. And in their main analysis, they minimize their analysis to 6/7 sessions from 3 mice only. I couldn't find a rationale for this reduction, but in the methods they do mention that 2 mice were used for fruitless training, which I found no mention in the results. Moreover, in the early case, all of the analysis is from 118 CR trials taken from 3 mice. In general, this is a rather low number of mice and trial numbers. I think it is quite essential to add more mice.

We apologize for the confusion. As described in the Methods section, 7 mice (Figure 1B) were used for behavioral training without electrode array or optical fiber implants to establish learning curves, and an additional 5 mice underwent electrophysiological recordings (3 for visual-based decision-making learning and 2 for fruitless learning).

As we noted in our response to Reviewer #1, the current dataset has inherent limitations in both the number of recorded regions and the behavioral paradigm. Given the considerable effort required to achieve high-quality unit yields across all targeted regions, we wish to adjust the set of recorded regions, improve behavioral task design, and implement better analyses in future studies. These improvements will enable us to collect data from a larger sample size and extract more precise insights into mesoscale dynamics during learning.

(2) Movement analysis was not sufficient. Mice learning a go/no-go task establish a movement strategy that is developed throughout learning and is also biased towards Hit trials. There is an analysis of movement in Figure S4, but this is rather superficial. I was not even sure that the 3 mice in Figure S4 are the same 3 mice in the main figure. There should be also an analysis of movement as a function of time to see differences. Also for Hits and FAs. I give some more details below. In general, most of the results can be explained by the fact that as mice gain expertise, they move more (also in CR during specific times) which leads to more activation in frontal cortex and more coordination with visual areas. More needs to be done in terms of analysis, or at least a mention of this in the text.

Due to the limitation in the experimental design and implementation, movement tracking was not performed during the electrophysiological recordings, and the 3 mice shown in Figure S4 were from a separate group. We have carefully examined the temporal profiles of mouse movements and found it did not fully match the rank dynamics, and we will add these results and related discussion in the revised manuscript. However, we acknowledge that without synchronized movement recordings in the main dataset, we cannot fully disentangle movement-related neural activity from task-related signals. We will make this limitation explicit in the revised manuscript and discuss it as a potential confound, along with possible approaches to address it in future work.

(3) Most of the figures are over-detailed, and it is hard to understand the take-home message. Although the text is written succinctly and rather short, the figures are mostly overwhelming, especially Figures 4-7. For example, Figure 4 presents 24 brain plots! For rank input and output rank during early and late stim and response periods, for early and expert and their difference. All in the same colormap. No significance shown at all. The Δrank maps for all cases look essentially identical across conditions. The division into early and late time periods is not properly justified. But the main take home message is positive Δrank in OFC, V2M, V1 and negative Δrank in ThalMD and Str. In my opinion, one trio map is enough, and the rest could be bumped to the Supplementary section, if at all. In general, the figure in several cases do not convey the main take home messages. See more details below.

We thank the reviewer for this valuable critique. The statistical significance corresponding to the brain plots (Figure 4 and Figure 5) was presented in Figure S3 and S5, but we agree that the figure can be simplified to focus on the key results. In the revised manuscript, we will condense these figures to focus on the most important comparisons and relocate secondary plots to the Supplementary section. This will make the visual presentation more concise and the take-home message clearer.

(4) The analysis is sometimes not intuitive enough. For example, the rank analysis of input and output rank seemed a bit over complex. Figure 3 was hard to follow (although a lot of effort was made by the authors to make it clearer). Was there any difference between the output and input analysis? Also, the time period seems redundant sometimes. Also, there are other network analysis that can be done which are a bit more intuitive. The use of rank within the 10 areas was not the most intuitive. Even a dimensionality reduction along with clustering can be used as an alternative. In my opinion, I don't think the authors should completely redo their analysis, but maybe mention the fact that other analyses exist

We appreciate the reviewer’s comment. In brief, the input- and output-rank analyses yielded largely similar patterns across regions in CR trials, although some differences were observed in certain areas (e.g., striatum in Hit trials) where the magnitude of rank change was not identical between input and output measures. We agree that the division into multiple time periods sometimes led to redundant results; we will combine overlapping results in the revision to improve clarity.

We did explore dimensionality reduction applied to the ranking data. However, the results were not intuitive and required additional interpretation, which did not bring more insights. Still, we acknowledge that other analysis approaches might provide complementary insights. While we do not plan to completely reanalyze the dataset at this stage, we will include a discussion of these alternative methods and their potential advantages in the revised manuscript.

Reviewer #3 (Public review):

Weaknesses:

The weakness is also related to the strength provided by the method. It is demonstrated in the original method that this approach in principle can track individual units for four months (Luan et al, 2017). The authors have not showed chronically tracked neurons across learning. Without demonstrating that and taking advantage of analyzing chronically tracked neurons, this approach is not different from acute recording across multiple days during learning. Many studies have achieved acute recording across learning using similar tasks. These studies have recorded units from a few brain areas or even across brain-wide areas.

We appreciate the reviewer’s important point. We did attempt to track the same neurons across learning in this project. However, due to the limited number of electrodes implanted in each brain region, the number of chronically tracked neurons in each region was insufficient to support statistically robust analyses. Concentrating probes in fewer regions would allow us to obtain enough units tracked across learning in future studies to fully exploit the advantages of this method.

Another weakness is that major results are based on analyses of functional connectivity that is calculated using the cross-correlation score of spiking activity (TSPE algorithm). Functional connection strengthen across areas is then ranked 1-10 based on relative strength. Without ground truth data, it is hard to judge the underlying caveats. I'd strongly advise the authors to use complementary methods to verify the functional connectivity and to evaluate the mesoscale change in subnetworks. Perhaps the authors can use one key information of anatomy, i.e. the cortex projects to the striatum, while the striatum does not directly affect other brain structures recorded in this manuscript

We agree that the functional connectivity measured in this study relies on statistical correlations rather than direct anatomical connections. We plan to test the functional connection data with shorter cross-correlation delay criteria to see whether the results are consistent with anatomical connections and whether the original findings still hold.

eLife Assessment

This study investigates how sleep loss and circadian disruption affect whole-organ metabolism in flies (Drosophila melanogaster) and reports that wild-type flies align metabolism in anticipation of diurnal rhythm, while mutant flies with impaired sleep or circadian function shift to reactive or misaligned metabolism. The integration of chamber-based flow-through respirometry with LC-MS metabolomics is innovative, and the significance of the findings is valuable. However, the strength of evidence needed to support the conclusions is incomplete based on concerns regarding the inappropriate use of constant darkness to disrupt circadian rhythms and the lack of details justifying the methods used to correlate respirometry data with whole-body metabolomics.

Reviewer #1 (Public review):

Summary:

This study by Akhtar et al. aims to investigate the link between systemic metabolism and respiratory demands, and how sleep and the circadian clock regulate metabolic states and respiratory dynamics. The authors leverage genetic mutants that are defective in sleep and circadian behavior in combination with indirect respirometry and steady-state LC-MS-based metabolomics to address this question in the Drosophila model.

First, the authors performed respirometry (on groups of 25 flies) to measure oxygen consumption (VO2) and carbon dioxide production (VCO2) to calculate the respiratory quotient (RQ) across the 24-hour day (12h:12h light-dark cycle) and assess metabolic fuel utilization. They observed that among all the genotypes tested, wild type (WT) flies and per0 flies in LD and WT flies in DD exhibit RQ >1. They concluded the >1 RQ is consistent with active lipogenesis. In contrast, the short-sleep mutants fumin (fmn) and sleepless (sss) showed significantly different RQ; the fmn exhibits a slight reduction in RQ values, suggesting increased reliance on carbohydrate metabolism, while sss exhibits even lower RQ (0.94), consistent with a shift toward lipid and protein catabolism.

The authors then proceeded to bin these measurements in 12-hour partitions, ZT0-12 and ZT12-24, to assess diurnal differences in average values of VO2, VCO2, and RQ. They observed significant day-night differences in metabolic rates in WT-LD flies, with higher rates during the day. The diurnal differences remain in the short-sleep mutants, but the overall metabolic rates are higher. WT-DD flies exhibit the lowest respiratory activity, although the day-night differences remain in free-running conditions. Finally, per01 mutants exhibit no significant change in day-night respiratory rates, suggesting that a functional circadian clock is necessary for diurnal differences in metabolic rates.

They then performed finer-resolution 24-hour rhythmic analysis (RAIN and JTK) to determine if VO2, VCO2, and RQ exhibit 24-hour rhythmic and if there are genotype-specific differences. Based on their criteria, VCO2 is rhythmic in all conditions tested, while VO2 is rhythmic in all conditions except in fmn-LD. Finally, RQ is rhythmic in all 3 mutants but not in WT-LD and WT-DD. Peak phases for the rhythms were deduced using JTK lag values.

The authors proceeded to leverage a previously published steady-state metabolite dataset to investigate the potential association of RQ with metabolite profiles. Spearman correlation was performed to identify metabolites that exhibit coupling to respiratory output. Positive and negative lag analysis were subsequently performed to further characterize these associations based on the timing of the metabolite peak changes relative to RQ fluctuations. The authors suggest that a positive lag indicates that metabolite changes occur after shifts in RQ, and a negative lag signifies that metabolite changes precede RQ changes. To visualize metabolic pathways that exhibit these temporal relationships, a clustered heatmap and enrichment analysis were performed. Through these analyses, they concluded that both sleep and circadian systems are essential for aligning metabolic substrate selection with energy demands, and different metabolic pathways are misregulated in the different mutants with sleep and circadian defects.

Strength:

The research questions this study explores are significant, given that metabolism and respiratory demand are central to animal biology. The experimental methods used, including the well-characterized fly genetic mutants, the newly developed method for indirect calorimetry measurements, and LC-MS-based metabolomics, are all appropriate. This study provides insights into the impact of sleep and circadian rhythm disruption on metabolism and respiratory demand and serves as a foundation for future mechanistic investigations.

Weaknesses:

There are some conceptual flaws that the authors need to address regarding circadian biology, and some of the conclusions can be better supported by additional analysis to provide a stronger foundation for future functional investigation. At times, the methods, especially the statistical analysis, are not well articulated; they need to be better explained.

Reviewer #2 (Public review):

This is an innovative and technically strong study that integrates dual-gas respirometry with LC-MS metabolomics to examine how sleep and circadian disruption shape metabolism in Drosophila. The combination of continuous O₂/CO₂ measurements with high-temporal-resolution metabolite profiling is novel and provides fresh insight into how wild-type flies maintain anticipatory fuel alignment, while mutants shift to reactive or misaligned metabolism. The use of lag-shift correlation analysis is particularly clever, as it highlights temporal coordination rather than static associations. Together, the findings advance our understanding of how circadian clocks and sleep contribute to metabolic efficiency and redox balance.

However, there are several areas where the manuscript could be strengthened. The authors should acknowledge that their findings may be gene-specific. Because sleep deprivation was not performed, it remains uncertain whether the observed metabolic shifts generalize to sleep loss broadly or are restricted to the fmn and sss mutants. This concern also connects to the finding of metabolic misalignment under constant darkness despite an intact clock. The conclusion that external entrainment is essential for maintaining energy homeostasis in flies may not translate to mammals. It would help to reference supporting data for the finding and discuss differences across species. Ideally, complementary circadian (light-dark cycle disruption) or sleep deprivation (for several hours) experiments, or citation of comparable studies, would strengthen the generality of the findings. Figures 1-4 are straightforward and clear, but when the manuscript transitions to the metabolite-respiration correlations, there is little description of the metabolomics methods or datasets, which should be clarified. The Discussion is at times repetitive and could be tightened, with the main message (i.e., wild-type flies align metabolism in advance, while mutants do not) kept front and center. Terms such as "anticipatory" and "reactive" should be defined early and used consistently throughout.

Overall, this is a strong and novel contribution. With clarification of scope, refinement of presentation, and a more focused Discussion, the paper will make a significant impact.

Reviewer #3 (Public review):

Summary:

The authors investigate how sleep loss and circadian disruption affect whole-organism metabolism in Drosophila melanogaster. They used chamber-based flow-through respirometry to measure oxygen consumption and carbon dioxide production in wild-type flies and in mutants with impaired sleep or circadian function. These measurements were then integrated with a previously published metabolomics dataset to explore how respiratory dynamics align with metabolic pathways. The central claim is that wild-type flies display anticipatory coordination of metabolic processes with circadian time, while mutants exhibit reactive shifts in substrate use, redox imbalance, and signs of mitochondrial stress.

Strengths:

The study has several strengths. Continuous high-resolution respirometry in flies is challenging, and its application across multiple genotypes provides good comparative insight. The conceptual framework distinguishing anticipatory from reactive metabolic regulation is interesting. The translational framing helps place the work in a broader context of sleep, circadian biology, and metabolic health.

Weaknesses:

At the same time, the evidence supporting the conclusions is somewhat limited. The metabolomics data were not newly generated but repurposed from prior work, reducing novelty. The biological replication in the respirometry assays is low, with only a small number of chambers per genotype. Importantly, respiratory parameters in flies are strongly influenced by locomotor activity, yet no direct measurements of activity were included, making it difficult to separate intrinsic metabolic changes from behavioral differences in mutants. In addition, repeated claims of "mitochondrial stress" are not directly substantiated by assays of mitochondrial function. The study also excluded female flies entirely, despite well-documented sex differences in metabolism, which narrows the generality of the findings.

Author response:

We thank the reviewers for their thoughtful public feedback. Our revision will clarify scope and methods/statistics, as well as streamline the narrative so the central message is clear: wild-type flies exhibit anticipatory alignment of fuel selection with circadian time, whereas short-sleep and clock mutants show reactive or misaligned metabolism under our conditions.

Major conceptual and experimental revisions:

(1) We will define “anticipatory” (clock-aligned, pre-emptive substrate choice) and “reactive” (post-hoc substrate shifts) up front and use these terms consistently. We will clearly distinguish diurnal (LD) from circadian (DD) regulation and avoid implying that DD abolishes rhythmicity. Claims will be limited to the tested genotypes (fmn, sss, and per⁰¹) without generalizing to all forms of sleep loss or to mammals (although we will speculate in the discussion about translation and generalizability). We will temper language around external entrainment in DD to “contributes strongly under our conditions in flies.”

(2) We will expand the respirometry and rhythmicity sections (RAIN/JTK parameters, period/phase outputs, multiple-testing control). We will clarify that each measurement is an average of 300 flies per genotype (25 flies/chamber, 4 chambers/experiment, 3 experimental days) and specify the chamber as the experimental unit with n and error structure in each figure legend. For metabolomics–respirometry correlations, we will briefly describe dataset parameters, time-matching across ZT, normalization, Spearman correlations, and lag interpretation.

(3) We are performing additional experimental measurements through tissue respirometry of gut tissues and ROS staining to support our claims of “mitochondrial stress” in the short sleeping mutants. We note that this has already been shown for fmn in Vaccaro et al (Cell, 2020) and we will extend this to the other mutants studied in our work.

Reviewer-specific points

Reviewer #1.

We will clarify the circadian/diurnal framing, fully report rhythmicity analyses (parameters, n, q-values, phases), and better explain the metabolomics-respiration coupling with a concise workflow figure and supplementary table. The conclusion that sleep and clock systems align substrate selection with energy demand will be presented as supported under our tested conditions and positioned as groundwork for future mechanistic studies.

Reviewer #2.

We will state explicitly that findings may be gene-specific and avoid inferring generality to all sleep loss. We will soften cross-species language about external entrainment and add a brief note on species differences. For behavioral context (activity/feeding/sleep in fmn andsss), we will cite our related manuscript in revision (Malik et al, https://www.biorxiv.org/content/10.1101/2023.10.30.564837v2) in which we have measured both activity and feeding for fmn, sss, and wt flies. We will add a concise description of LC-MS processing and pathway analysis and define “anticipatory”/“reactive” early, using them consistently.

Reviewer #3.

We acknowledge that metabolomics were repurposed and emphasize the novelty of integrating continuous VCO2 and VO2 respirometry with temporal lag analysis. We will report replication clearly (chambers as the unit, n per genotype) and acknowledge locomotor activity as a potential confound, pointing to the related manuscript (Malik et al) for independent activity/feeding measurements and experimental measures of mitochondrial stress as outlined above. We will also further note that only males were studied, outlining this as a limitation and a future direction.

The Vietnam War’s unpopularity created political chaos in 1968, leading Johnson to step aside and leaving the Democratic Party divided and unstable. Kennedy’s assassination and the violence at the Chicago convention showed how deeply the nation was fractured during the election year.

How did Martin Luther King Jr.’s death affect the nation’s sense of hope and the direction of the civil rights movement?

Women’s experiences in other social movements made them recognize their own unequal treatment what specific events or issues pushed them to start the feminist movement?

Why was Alcatraz Island chosen, and what made it significant for protests or government use?

SNCC grew more militant in the late 1960s as leaders like Stokely Carmichael rejected nonviolence and interracial cooperation. The group shifted focus from southern civil rights to addressing racism and inequality in northern cities, reflecting growing frustration with slow progress through peaceful methods.

Diem’s weak leadership and lack of public support made South Vietnam unstable. Despite U.S. military aid, the Vietcong’s determination and connection with locals helped them gain the upper hand, showing the limits of American influence in Vietnam.

This shows how major laws like the Civil Rights Act, Voting Rights Act, and War on Poverty caused a backlash among conservatives, especially in the South. Many white voters who opposed these federal programs and civil rights reforms started supporting the Republican Party, leading to its rise in the South and West.

If AI is inherently fallible because of the complexity in its design, how much trust should we really place in its predictions when it comes to critical areas like healthcare?

How can we ensure that AI systems in healthcare don’t reinforce existing racial inequalities when the data they rely on already reflect those biases?

This point really makes me question the true purpose behind popular health technologies. While smartwatches and fitness apps are marketed as tools to improve ourselves, it’s unsettling to realize they also serve as data collection devices for large companies. I’ve noticed how these devices constantly encourage users to share more information, which makes me wonder if health improvement is just a cover for a way to make profit. It’s a clear example of how convenience and self-tracking can blur into surveillance, raising important ethical questions about consent and corporate transparency.

I think this point really emphasizes how personal and situational our interactions with AI can be. It makes sense that someone’s level of AI literacy or their psychological traits would affect how much they trust or understand an algorithm’s decision. I’ve noticed this myself when using health or fitness apps, if I don’t fully understand how the technology works, I tend to question its accuracy more. This idea also raises an ethical concern: if people have unequal access to AI education or information, then their ability to make informed choices could be unfairly limited.

AI affects everyone from patients to healthcare workers to society. When new evolutions are introduced too quickly, they can harm the social fabric. It reminds me of how the internet changed society after COVID hit. It became our main way to work and learn, but it also took away a lot of real human connection. Instead of hanging out or talking face-to-face, we started relying on screens and text messages for almost everything.

I find it interesting that the authors highlight how privacy can’t be defined by a single, universal meaning. From my perspective, that really shows how complex the issue is. What one culture or generation views as a right to privacy, another might see as unnecessary secrecy. It also makes me think about how technology has blurred these moral boundaries even more, especially with social media encouraging people to share so much of their personal lives. The idea that privacy is tied to both ethics and law suggests that our understanding of it changes depending on social norms and legal systems, which I think explains why it’s such a difficult issue to regulate fairly.

Normativity means to establish, relate to, or deriving from a standard or norm, especially of behavior.

Computational means relating to or using computers.

The term implemented means that something like a plan, policy, or idea has been put into practice or carried out.

The term autonomous when it comes to learning means a process where individuals take responsibility for their own learning by setting goals.

reflects on her previous article

Мяу-мяу

my annotation testing

круто! мечтаю об организованных в картотеку идеях

inclus tous les dossiers y compris ceux gérés par un auditeur

树脂1

树脂1结构

碱性中和剂

溶剂1

光酸1

树脂2结构

特征尺寸和临界应力数据 可转换为临界尺寸数据

https://lns.lu/de/prof-dr-andre-rosenthal-direktor-ad-interim-des-lns/

https://lns.lu/de/prof-dr-andre-rosenthal-direktor-ad-interim-des-lns/

https://web.archive.org/web/20100603225318/http://www.tagesschau.de/inland/genomforschung4.html Künstliches Leben noch Jahrhunderte entfernt. 2008.

This is some discipline!

公式ドキュメントによると reversed(object, /) です。 https://docs.python.org/ja/3/library/functions.html#reversed

公式ドキュメントによると、sorted(iterable, /, *, key=None, reverse=False)です。 https://docs.python.org/ja/3/library/functions.html#sorted

person-first

and

self-hosted

https://bafybeib5pmzg6e2x3nheoenudqovtie3t3gobm3xhumgita6ptjwjrsb5m.ipfs.inbrowser.link/

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Reply to the reviewers

Reviewer #1

(...) The study describes meticulously conducted and controlled experiments, showing the impressive biochemistry work consistently produced by this group. The statistical analysis and data presentation are appropriate, with the following major comments noted:

Response: We thank the reviewer for their thoughtful and constructive review of our manuscript. We appreciate the positive comments on our experimentation.

Major comments

1. Please clarify why K8ac/K12ac, K5ac/K16ac, K5ac/K12ac are not quantified (Figure 3). If undetected, state explicitly and annotate figures with "n.d." rather than leaving gaps. If detected but excluded, justify the exclusion.

Response: We restricted ourselves to mapping those diacetylated motifs that can be readily identified by MS2. The characteristic ions of the d3-labeled and endogenous acetylated peptides in the MS2 spectra could not differentiate the diacetylated forms mentioned by the reviewer. Rather than expanding the figure with non-informative rows we amended the legend of figure 3 accordingly "Diacetylated forms K8-K12, K5-K16, K5-K12 could not be distinguished from each other by MS2 and were thus not included in the analysis".

The statement "Nevertheless, combinations of di- and triacetylation were much more frequent if K12ac was included, suggesting that K12 is the primary target." is under-supported because only two non-K12ac combinations are shown, and only one is lower than K12ac-containing combinations. Either soften the claim ("trend toward ... in our dataset") or expand the analysis to all observed di/tri combinations with effect sizes, n, and statistical tests.

Response: The reviewer is right our statement does properly reflect the data. It rather seems that combinations lacking K12ac are considerably less frequent (K5K8K16 tri-ac, K5K8 di-ac). We now modified the sentence as follows: "Peptides lacking K12ac were less frequent, suggesting that K12 is a primary target".

Please provide a more detailed discussion about the known nature of NU9056 inhibition and how it fits or doesn't fit with your data. Are there any structural studies on this?

Response: Unfortunately, NU9056 is very poorly described, neither the mode of interaction with Tip60 nor the mechanism of inhibition are known. The specificity of the chemical has not really been shown, but nevertheless it is used as a selective Tip60 inhibitor in several papers which is why we picked it in the first place. Our conclusions on the inhibitor are in the last paragraph of the discussion: "The fact that acetylation of individual lysines is inhibited with different kinetics argues against a mechanism involving competition with acetyl-CoA, but for an allosteric distortion of the catalytic center." We think that any further interpretation would likely be considered an overstatement.

Why was the inhibitor experiment MS only performed for H2A.V and not H2A? Given the clear H2A vs H2A.V differences reported in Fig. 2, it would be useful to have the matched data for H2A.

Response: In these costly mass spec experiments we strive to balance limited resources and most informative output. Because H2A.V and H4 are the major functional targets of Tip60, we considered that documenting the effect of the inhibitor on these substrates would be most appropriate. In hindsight, including H2A would have been nice to have, but would not change our conclusions about the inhibitor.

The inhibitor observations are very interesting as they can highlight systems to study the loss of specific acetyl residues: can the authors perform WB/IF validation in treated cells? I understand it will not be possible with the H2A antibodies, but the difference in H4K5ac vs H4K12ac should be possible to validate in cells

Response: We attempted to monitor changes of histone modifications upon treatment of cells with NU9056 by immunoblotting. Probing H4K5 and K12, the results were variable. We also observed occasionally that acetylation of H4K5 and H4K12 was slightly diminished in whole cell extracts, but not in nuclear extracts. This reminded us that diacetylation of H4 at K5 and K12 is a feature of cytoplasmic H4 in complex with chaperones, a mark that is placed by HAT1 (Aguldo Garcia et al., DOI: 10.1021/acs.jproteome.9b00843; Varga et al., DOI: 10.1038/s41598-019-54497-0). The observed proliferation arrest by NU9056 may thus affect chromatin assembly and indirectly K5K12 acetylation. H4K12 is also acetylated by chameau (Chm).

We observed a reduction of acetylated H4K16 and H2A.V. H4K16 is not a preferred target of Tip60, but Tip60 acetylates MSL1 and MBDR2, two subunits of the NSL1 complex (Apostolou et al. DOI: 10.1101/2025.07.15.664872). We, therefore, consider that effects on H4 acetylation upon NU9056 treatment may at least partially be affected indirectly. Because we are not confident about the data and because our manuscript emphasizes the direct, intrinsic specificity of Tip60, we refrain from showing the corresponding Western blots.

You highlight that H2AK10 (a major TIP60 site here) is not conserved in human canonical H2A. Please expand the discussion of the potential function and physiological relevance. Maybe in relation to H2A.V being a fusion of different human variants?

Response: The reviewer noted an interesting aspect of the evolution of the histone H2A variants. It turns out that H2A.Z is the more ancient variant, from which H2A derived by mutation. H2A.Z/H2A.V sequences are more conserved than H2A sequences. We summarized these evolutionary notions in Baldi and Becker (DOI: 10.1007/s00412-013-0409-x). In the context of the question, this means that mammalian H2A.Z, Drosophila H2A.V and mammalian H2A still contain the ancient sequence (lacking K10), and Drosophila H2A acquired K10 by mutation. The evolutionary advantage associated with this mutation in unclear. We now added a small paragraph summarizing these ideas on page 13 of the (changes tracked in red).

To enable direct comparisons between variants and residues, please match y-axis scales where the biology invites comparison (e.g., H2A vs H2A.V; Figs. 2-3).

Response: We adjusted the Y-axes in Figure 2 and 3 to facilitate direct comparisons, where such comparison is informative.

Minor comments

1. Add 1-2 sentences in the abstract on the gap in the field being addressed by the study.

Response: We are grateful for this suggestion and have expanded the abstract accordingly (changes tracked in red).

Either in the introduction or discussion, comment on your prior Tip60 three-subunit data (Kiss et al.). The three-subunit complex was significantly less active on H4, as indicated in that publication, which is likely due to the absence of Eaf6.

Response: We thank the reviewer for the opportunity to emphasize this point. Motivated by findings in the yeast and mammalian systems that Eaf6 was important for acetylation, we added this subunit to our previously reconstituted 3-subunit 'piccolo' complex. As can be seen by the comparison of the older data (Kiss et al.) and the new data, the 4-subunit TIP60 core complex is a much more potent HAT. We amended the introduction (see marked text) accordingly. We also added a paragraph on what is known about the properties and function of Eaf6 to the discussion.

3a. Text references Fig.1E before Fig.1C, please reorder

Response: We deleted the premature mentioning of Figure 1E and added the following explanation to the relevant panels in Figure 1: "The blot was reprobed with an antibody detecting H3 as an internal standard for nucleosome input."

3b. Fig.1B/C legend labels appear swapped.

Response: We thank the reviewer for spotting the swap. We corrected the figure legend.

3c. Fig.1E, 4A, 4B: add quantification

Response: We quantified each acetylation level, and added to the relevant panel of Figure 1 and 4 the following phrase: "The quantified levels of each acetylation mark over H3 are shown below each plot." Notably, the difference in acetylation signal strength between the two antibodies highlights the inherent variability of antibody-based detection.

3d. Fig.2A: Note explicitly that K5-K10 and K8-K10 are unresolvable pairs to explain the shading scheme used.

Response: The legend of Figure 2A now includes the following sentence. "Peptides that are diacetylated at either K5/K10 or K8/K10 cannot be resolved by MS2. The last row reminds of this fact by the patterning of boxes and displays the combined values."

Ensure consistent KAT5/TIP60 naming.

Response: Our naming follows this logic: We use 'Tip60' for the Drosophila protein and 'TIP60' for the Drosophila 'piccolo' or 'core' complexes. The mammalian protein is referred to by the capital acronym TIP60, as is established in the literature. We use KAT5/TIP60 according to the unified nomenclature in the introduction and parts of the discussion, when we refer to the enzymes in more general terms, independent of species. We scrutinized the manuscript again and made a few changes to adhere to the above scheme.

Consider moving the first two Discussion paragraphs (field context and challenges in antibody-based detection) into the Introduction to better frame the significance.

Response: We thank the reviewer for this suggestion that improved the manuscript a lot. We incorporated the first two paragraphs of the discussion into the introduction.

Significance

This is a valuable and timely study for the histone acetylation field. The substrate specificity of many individual HATs remains incompletely understood owing to (i) cross-reactivity and limited selectivity of many anti-acetyl-lysine antibodies, (ii) functional redundancy among KATs, (iii) variability across in-vitro assays (HAT domain vs full-length/complex; free histones vs oligonucleosomes), and (iv) incomplete translation of in-vitro specificity to in-vivo settings. These factors have produced conflicting reports in the literature. By combining quantitative mass spectrometry with carefully engineered oligonucleosomal arrays, the authors make a principal step toward deconvoluting TIP60 biology in a controlled yet close-to-physiologically relevant system. Conceptually, the work delineates intrinsic, site-specific preferences of the TIP60 core on variant versus canonical nucleosomes, consistent with largely distributive behaviour and site-dependent inhibitor sensitivity. The inhibitor-dependent shifts in acetylation patterns are particularly intriguing and could enable dissection of residue-specific functions, with potential translational implications for preclinical cancer research and biomarker development. Overall, this manuscript will be of interest to the chromatin community, and I am supportive of publication pending satisfactory resolution of the points raised above.

Response: Once more we thank the reviewer for their time and efforts devoted to help us improve the manuscript.

Reviewer #2

Major comments

(...) A central limitation of the study, noted by the authors, is the uncertainty regarding the biological relevance of the findings. While the in vitro system provides a controlled framework for analyzing residue specificity and kinetics, it does not address the functional significance of these results in a cellular or organismal context. This limitation is outside the scope of the current work but indicates potential directions for follow-up studies. Within its defined objectives, the study presents a methodological framework and dataset that contribute to understanding TIP60 activity in a biochemical setting.

Response: We agree with the referee.

Minor comments

While the manuscript is clearly presented overall, there are two minor issues that could be addressed:

1. In Figure 1, the panels are not ordered according to their appearance in the Results section. In addition, the legends for Figures 1B and 1C appear to be swapped.

Response: We thank the reviewer for spotting these oversights. We deleted the premature mentioning of Figure 1E and added the following explanation to the relevant panels in Figure 1: "The blot was reprobed with an antibody detecting H3 as an internal standard for nucleosome input." We also swapped the legends.

For the quantitative MS data (N = 2 biological replicates), the phrasing "Error bars represent the two replicate values" could be refined. With N = 2, showing individual data points or the range may convey the information more transparently than conventional error bars, which are typically associated with statistical measures (e.g., SEM) from larger sample sizes. Alternatively, a brief note explaining the choice to use two replicates and represent them with error bars could be added.

Response: We appreciate the reviewer's comment and have revised the figure to display individual data points for the two biological replicates instead of error bars, providing a clearer representation of the data distribution. We changed the phrasing 'Error bars represent...' to "Bars represent the mean of two biological replicates (each consisting of two TIP60 core complexes and two nucleosome arrays - each analyzed with two technical replicates), with individual replicate values shown as open circles." and hope that this describes the data better.

Significance

Krause and colleagues, using a clean in vitro system, define the substrate specificity of the Drosophila TIP60 core complex. They identify the main acetylation sites and their kinetic dynamics on H2A, H2A.V, and H4 tails, and further characterize the inhibitory activity of NU9056. This work addresses a longstanding question in the field and provides compelling evidence to support its conclusions. Future studies will be needed to establish the biological relevance of these findings.

Response: We thank the reviewer for a thoughtful and constructive review of our manuscript. We appreciate the suggestions that helped to improve the manuscript.

Reviewer #3

(...) However, the authors should revisit some additional points:

Major comments:

1. The Tip60 core complex is usually described as containing three subunits: Tip60, Ing3 and E(Pc). The authors also included Eaf6 in their analysis, however, their motivation to include Eaf6 specifically remains unclear. They should explain in the manuscript why Eaf6 was included and how this could affect the observed acetylation pattern.

Response: We thank the reviewer for the opportunity to emphasize this point. Motivated by findings in the yeast and mammalian systems that Eaf6 was important for acetylation, we added this subunit to our previously reconstituted 3-subunit piccolo complex. As can be seen by the comparison of the older data (ref Kiss) and the new data, the 4-subunit Tip60 core complex is a much more potent HAT. We amended the introduction accordingly. We also added a paragraph on what is known about the properties and function of Eaf6 to the discussion. Please see the amended text marked in red.

The authors investigated the effectiveness of two Tip60 inhibitors by testing their effects on H4K12ac using an antibody. They state that "TH1834 had no detectable effect on either complex [Tip60 or Msl], even at very high concentrations." However, the initial publication describing TH1834 also stated that this inhibitor particularly affected H2AX with not direct effect on H4 acetylation. The authors should revisit TH1834 and specifically investigate its effect on H2A and, in particular, on H2Av as H2Av is the corresponding ortholog of H2AX.

Response: The case of TH1834 is not very strong in the literature, which is why we discontinued the line of experimentation when we did not see any effect of TH1834 (2 different batches) on the preferred substrate. The reviewer's suggestion is very good, but given our limited resources we decided to remove the data and discussion of TH1834 from the manuscript (old Figure 4A). The deletion of these very minor data does not diminish the overall conclusion and significance of the manuscript.

The authors performed a detailed analysis of NU9056 effects. However, they did not include effects on H2A. H2A is distinct from H4 and H2Av as it is the only one containing K10 and this lysine also showed high levels of acetylation by Tip60. Therefore, a comprehensive analysis of Nu9056 effects should include analyzing its effects on H2A acetylation.

Response: In these costly mass spec experiments, we strive to balance limited resources and most informative output. Because H2A.V and H4 are the major functional targets of Tip60, we considered that documenting the effect of the inhibitor on these substrates would be most appropriate. In hindsight, including H2A would have been nice to have, but would not change our conclusions about the inhibitor.

The authors have previously reported non-histone substrates of Tip60. It would be interesting to test whether the two investigated Tip60 inhibitors affect acetylation of non-histone substrates of Tip60. This analysis would greatly increase the understanding of how selective these inhibitors are. (OPTIONAL)

Response: We agree with the reviewer that the proposed experiments may be an interesting extension of our current work. However, the Becker lab will be closed down by the end of this year due to retirement, precluding major follow-up studies at this point.

__ Minor comments: __

1. Fig. 1 a: instead of "blue residues", would be more accurate to refer to "blue arrows"?

Response: Yes of course - the text has been revised accordingly.

Fig.1 b-c: it would be helpful to include which staining (silver/Ponceau?) was performed here.

Response: The legends now contain the relevant information.

Fig. 2a: I did not understand the shading for the K5/K8-K10ac panel from the figure legend. The explanation is present in the main text but would be helpful in the figure legend to allow easy access for readers.

Response: We agree and revised text accordingly.

Fig. 4 c: bar graphs on the top: the X-values are missing.

Response: The figure has been revised accordingly.

This sentence in the discussion seems to require revision: "Whereas the replication-dependent H2A resides in most nucleosomes in the genome, H2A.V, the only H2A variant histone in Drosophila, is incorporated by exchange of H2A, independent of replication."

Response: We revised the sentence as follows to improve clarity. "While the replication-dependent H2A is present in most nucleosomes across the genome, H2A.V, the only H2A variant in Drosophila, is incorporated through replication-independent exchange of H2A."

In this sentence: "A comparison with the TIP60 core complex is instructive since both enzymes are MYST acetyltransferases and bear significant similarity in their catalytic center." do the authors mean "informative" rather than "instructive"?

Response: We replaced 'instructive' by 'informative.

Significance

The findings are novel and expand our knowledge of Tip60 histone tail acetylation dynamics and specificity. The manuscript does not address the biological relevance of distinct acetylation marks, which is clearly beyond the scope of the study, but discuss their relevance where possible. The analysis of NU9056 is informative and relevant in a broad context. Optionally, the authors could expand their analysis of NU9056 on its effects on non-histone Tip60 targets to increase impact further. Their analysis of TH1834, however, is currently insufficient as they focused on H4 acetylation alone, which has already been reported to not be affected by TH1834. The authors should include an analysis of TH1834 effects on H2A and H2A.V acetylation. The manuscript is well written, easy to follow and of appropriate length. The methods are elegant and the findings of the study are novel. The manuscripts targets researchers specifically interested in chromatin remodeling as well as a broader audience using the Tip60 inhibitor NU9056.

Response: We thank the reviewer for their profound assessment and the general appreciation of our work. We agree that the analysis of the TH1834 is not satisfactory at this point and have removed the corresponding data and description from figure 4. The deletion of these very minor data does not diminish the overall conclusion and significance of the manuscript.

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Referee #3

Evidence, reproducibility and clarity

In their manuscript Krause et al investigate Tip60 selectivity on histone tail acetylation. They use elegant mass spectrometry analysis to analyze lysine acetylation marks and combination of acetylation marks of histone tails of the Tip60 targets H2A, H2A.V and H4. They further consider distinct dynamics by performing a time course experiment and compare Tip60 to MOF. Using these methods, the authors describe interesting and previously undescribed selectivity, dynamics and di-acetylation patterns of Tip60 that will be the starting point of follow-up studies diving into the biological relevance of these findings. Lastly, they investigate the effects of two Tip60 inhibitors and characterize the effects of NU9056 on Tip60 histone tail acetylation in detail. These studies showed that NU9056 has selective effects, impacting some lysine acetylations with greater efficiency than others. As antibodies available to investigate histone acetylations affected by NU9056 are not selective enough, these findings are relevant for any applicant of NU9056.

However, the authors should revisit some additional points:

Major comments:

1. The Tip60 core complex is usually described as containing three subunits: Tip60, Ing3 and E(Pc). The authors also included Eaf6 in their analysis, however, their motivation to include Eaf6 specifically remains unclear. They should explain in the manuscript why Eaf6 was included and how this could affect the observed acetylation pattern
2. The authors investigated the effectiveness of two Tip60 inhibitors by testing their effects on H4K12ac using an antibody. They state that "TH1834 had no detectable effect on either complex [Tip60 or Msl], even at very high concentrations." However, the initial publication describing TH1834 also stated that this inhibitor particularly affected H2AX with not direct effect on H4 acetylation. The authors should revisit TH1834 and specifically investigate its effect on H2A and, in particular, on H2Av as H2Av is the corresponding ortholog of H2AX.
3. The authors performed a detailed analysis of NU9056 effects. However, they did not include effects on H2A. H2A is distinct from H4 and H2Av as it is the only one containing K10 and this lysine also showed high levels of acetylation by Tip60. Therefore, a comprehensive analysis of Nu9056 effects should include analyzing its effects on H2A acetylation.
4. The authors have previously reported non-histone substrates of Tip60. It would be interesting to test whether the two investigated Tip60 inhibitors affect acetylation of non-histone substrates of Tip60. This analysis would greatly increase the understanding of how selective these inhibitors are. (OPTIONAL)

Minor comments:

1. Fig. 1 a): instead of "blue residues", would be more accurate to refer to "blue arrows"?
2. Fig.1 b-c): it would be helpful to include which staining (silver/Ponceau?) was performed here
3. Fig. 2a): I did not understand the shading for the K5/K8-K10ac panel from the figure legend. The explanation is present in the main text but would be helpful in the figure legend to allow easy access for readers.
4. Fig. 4 c) bar graphs on the top: the X-values are missing.
5. This sentence in the discussion seems to require revision: "Whereas the replication-dependent H2A resides in most nucleosomes in the genome, H2A.V, the only H2A variant histone in Drosophila, is incorporated by exchange of H2A, independent of replication."
6. In this sentence: "A comparison with the TIP60 core complex is instructive since both enzymes are MYST acetyltransferases and bear significant similarity in their catalytic center." do the authors mean "informative" rather than "instructive"?

Significance

The findings are novel and expand our knowledge of Tip60 histone tail acetylation dynamics and specificity. The manuscript does not address the biological relevance of distinct acetylation marks, which is clearly beyond the scope of the study, but discuss their relevance where possible. The analysis of NU9056 is informative and relevant in a broad context. Optionally, the authors could expand their analysis of NU9056 on its effects on non-histone Tip60 targets to increase impact further. Their analysis of TH1834, however, is currently insufficient as they focused on H4 acetylation alone, which has already been reported to not be affected by TH1834. The authors should include an analysis of TH1834 effects on H2A and H2A.V acetylation.

The manuscript is well written, easy to follow and of appropriate length. The methods are elegant and the findings of the study are novel. The manuscripts targets researchers specifically interested in chromatin remodeling as well as a broader audience using the Tip60 inhibitor NU9056.

My expertise: I am a researcher working with Drosophila melanogaster and have published on the functions of the Tip60-p400 complex. I do not have extensive expertise in nucleosome arrays, the major method applied in this manuscript.

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Referee #2

Evidence, reproducibility and clarity

Summary:

In this study, Krause and colleagues investigate the intrinsic substrate selectivity of the four-subunit TIP60 core module from Drosophila melanogaster using synthetic nucleosome arrays. To quantitatively assess acetylation at individual lysines on histones H2A, the variant H2A.V, and H4, the authors employ targeted mass spectrometry, thereby overcoming the limitations of antibody-based approaches. Contrary to earlier reports, their results reveal that the TIP60 core complex displays a selective lysine acetylation pattern, with distinct kinetics toward specific residues on each histone tail. For example, H2A lysines K5, K8, and K10 were acetylated, with K10 exhibiting the highest modification levels. On H2A.V, K4 and K7 were modified, with K7 showing greater initial efficiency. For H4, K12 was identified as the primary target, and its acetylation was further enhanced in the presence of H2A.V. The study also examined the activity of the KAT5 inhibitor NU9056, uncovering variable inhibition across different acetylation sites. Overall, the authors conclude that intrinsic substrate selectivity is central to understanding the mechanism of Tip60 activity and that the presence of H2A variants can modulate both the efficiency and specificity of acetylation.

Major comments:

The study by Krause et al. examines the in vitro substrate selectivity of the Drosophila TIP60 core complex and the lysine-specific effects of the inhibitor NU9056. The authors use a defined in vitro system with recombinant proteins and nucleosome arrays, together with targeted mass spectrometry, to assess intrinsic enzyme activity while avoiding potential issues of antibody specificity and avidity. Heatmaps and bar plots derived from the MS data show site-specific acetylation patterns and the effects of the inhibitor. A comparative analysis with the MSL core complex, which has a well-characterized selectivity, is used as a reference point for interpreting the specificity of TIP60. The observation that NU9056 exhibits different levels of effectiveness on individual lysines, including residues within the same histone tail, is supported by the quantitative MS measurements. A central limitation of the study, noted by the authors, is the uncertainty regarding the biological relevance of the findings. While the in vitro system provides a controlled framework for analyzing residue specificity and kinetics, it does not address the functional significance of these results in a cellular or organismal context. This limitation is outside the scope of the current work but indicates potential directions for follow-up studies. Within its defined objectives, the study presents a methodological framework and dataset that contribute to understanding TIP60 activity in a biochemical setting.

Minor comments:

While the manuscript is clearly presented overall, there are two minor issues that could be addressed:

• In Figure 1, the panels are not ordered according to their appearance in the Results section. In addition, the legends for Figures 1B and 1C appear to be swapped.
• For the quantitative MS data (N = 2 biological replicates), the phrasing "Error bars represent the two replicate values" could be refined. With N = 2, showing individual data points or the range may convey the information more transparently than conventional error bars, which are typically associated with statistical measures (e.g., SEM) from larger sample sizes. Alternatively, a brief note explaining the choice to use two replicates and represent them with error bars could be added.

Significance

Krause and colleagues, using a clean in vitro system, define the substrate specificity of the Drosophila TIP60 core complex. They identify the main acetylation sites and their kinetic dynamics on H2A, H2A.V, and H4 tails, and further characterize the inhibitory activity of NU9056. This work addresses a longstanding question in the field and provides compelling evidence to support its conclusions. Future studies will be needed to establish the biological relevance of these findings.

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Referee #1

Evidence, reproducibility and clarity

Summary

This study uses defined, reconstituted nucleosome arrays (H2A- or H2A.V-containing) and the four-subunit Drosophila TIP60 core complex to map intrinsic substrate selectivity across time courses and in the presence of reported TIP60 inhibitors (NU9056, TH1834). Key findings are: (i) selective H2A-tail acetylation (K10 > K8 > K5) with negligible K12/K14; (ii) preferential H2A.V K4 and K7 acetylation with distinct kinetics and low co-occurrence on a single tail; (iii) H4K12 is strongly favoured over other H4 sites; (iv) acetylation patterns are consistent with a more distributive (non-processive) mechanism relative to MOF/MSL; (v) NU9056 inhibits TIP60 activity with site-specific differences suggestive of a non-competitive/allosteric component, whereas TH1834 shows no effect in this Drosophila system.

Major comments

The study describes meticulously conducted and controlled experiments, showing the impressive biochemistry work consistently produced by this group. The statistical analysis and data presentation are appropriate, with the following major comments noted:

1. Please clarify why K8ac/K12ac, K5ac/K16ac, K5ac/K12ac are not quantified (Figure 3). If undetected, state explicitly and annotate figures with "n.d." rather than leaving gaps. If detected but excluded, justify the exclusion.
2. The statement "Nevertheless, combinations of di- and triacetylation were much more frequent if K12ac was included, suggesting that K12 is the primary target." is under-supported because only two non-K12ac combinations are shown, and only one is lower than K12ac-containing combinations. Either soften the claim ("trend toward ... in our dataset") or expand the analysis to all observed di/tri combinations with effect sizes, n, and statistical tests.
3. Please provide a more detailed discussion about the known nature of NU9056 inhibition and how it fits or doesn't fit with your data. Are there any structural studies on this?
4. Why was the inhibitor experiment MS only performed for H2A.V and not H2A? Given the clear H2A vs H2A.V differences reported in Figure 2, it would be useful to have the matched data for H2A.
5. The inhibitor observations are very interesting as they can highlight systems to study the loss of specific acetyl residues: can the authors perform WB/IF validation in treated cells? I understand it will not be possible with the H2A antibodies, but the difference in H4K5ac vs H4K12ac should be possible to validate in cells.
6. You highlight that H2A K10 (a major TIP60 site here) is not conserved in human canonical H2A. Please expand the discussion of the potential function and physiological relevance. Maybe in relation to H2A.V being a fusion of different human variants?
7. To enable direct comparisons between variants and residues, please match y-axis scales where the biology invites comparison (e.g., H2A vs H2A.V; Figs. 2-3).

Minor comments

1. Add 1-2 sentences in the abstract on the gap in the field being addressed by the study.
2. Either in the introduction or discussion, comment on your prior Tip60 three-subunit data (Kiss et al.). The three-subunit complex was significantly less active on H4, as indicated in that publication, which is likely due to the absence of Eaf6.
3. Figure order/legends:

a. Text references Fig.1E before Fig.1C, please reorder

b. Fig.1B/C legend labels appear swapped.

c. Fig.1E, 4A, 4B: add quantification

d. Fig.2A: Note explicitly that K5-K10 and K8-K10 are unresolvable pairs to explain the shading scheme used 4. Ensure consistent KAT5/TIP60 naming. 5. Consider moving the first two Discussion paragraphs (field context and challenges in antibody-based detection) into the Introduction to better frame the significance.

Significance

This is a valuable and timely study for the histone acetylation field. The substrate specificity of many individual HATs remains incompletely understood owing to (i) cross-reactivity and limited selectivity of many anti-acetyl-lysine antibodies, (ii) functional redundancy among KATs, (iii) variability across in-vitro assays (HAT domain vs full-length/complex; free histones vs oligonucleosomes), and (iv) incomplete translation of in-vitro specificity to in-vivo settings. These factors have produced conflicting reports in the literature. By combining quantitative mass spectrometry with carefully engineered oligonucleosomal arrays, the authors make a principal step toward deconvoluting TIP60 biology in a controlled yet close-to-physiologically relevant system. Conceptually, the work delineates intrinsic, site-specific preferences of the TIP60 core on variant versus canonical nucleosomes, consistent with largely distributive behaviour and site-dependent inhibitor sensitivity. The inhibitor-dependent shifts in acetylation patterns are particularly intriguing and could enable dissection of residue-specific functions, with potential translational implications for preclinical cancer research and biomarker development. Overall, this manuscript will be of interest to the chromatin community, and I am supportive of publication pending satisfactory resolution of the points raised above.

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Reply to the reviewers

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

In this manuscript, Xiong and colleagues investigate the mechanisms operating downstream to TRIM32 and controlling myogenic progression from proliferation to differentiation. Overall, the bulk of the data presented is robust. Although further investigation of specific aspects would make the conclusions more definitive (see below), it is an interesting contribution to the field of scientists studying the molecular basis of muscle diseases.

We thank the Reviewer for appreciating our work and for their valuable suggestions to improve our manuscript. We have carefully addressed some of the concerns raised, as detailed here, while others, which require more experimental efforts, will be addressed as detailed in the Revision Plan.

In my opinion, a few aspects would improve the manuscript. Firstly, the conclusion that Trim32 regulates c-Myc mRNA stability could be expanded and corroborated by further mechanistic studies:

1. Studies investigating whether Tim32 binds directly to c-Myc RNA. Moreover, although possibly beyond the scope of this study, an unbiased screening of RNA species binding to Trim32 would be informative. Authors’ response. This point will be addressed as detailed in the Revision Plan

If possible, studies in which the overexpression of different mutants presenting specific altered functional domains (NHL domain known to bind RNAs and Ring domain reportedly involved in protein ubiquitination) would be used to test if they are capable or incapable of rescuing the reported alteration of Trim32 KO cell lines in c-Myc expression and muscle maturation.

Authors’ response. This point will be addressed as detailed in the Revision Plan

An optional aspect that might be interesting to explore is whether the alterations in c-Myc expression observed in C2C12 might be replicated with primary myoblasts or satellite cells devoid of Trim32.

Authors’ response. This point will be addressed as detailed in the Revision Plan

I also have a few minor points to highlight:

• • It is unclear if the differences highlighted in graphs 5G, EV5D, and EV5E are statistically significant.*

Authors’ response. We thank the Reviewer for raising this point. We now indicated the statistical analyses performed on the data presented in the mentioned figures (according also to a point of Reviewer #3). According to the conclusion that Trim32 is necessary for proper regulation of c-Myc transcript stability, using 2-way-ANOVA, the data now reported as Figure 5G show the statistically significant effect of the genotype at 6h (right-hand graph) but not at D0 (left-hand graph). In the graphs of Fig. EV5 D and E at D0 no significant changes are observed whereas at 6h the data show significant difference at the 40 min time point. We included this info in the graphs and in the corresponding legends.

- On page 10, it is stated that c-Myc down-regulation cannot rescue KO myotube morphology fully nor increase the differentiation index significantly, but the corresponding data is not shown. Could the authors include those quantifications in the manuscript?

Authors’ response. As suggested, we included the graph showing the differentiation index upon c-Myc silencing in the Trim32 KO clones and in the WT clones, as a novel panel in Figure 6 (Fig. 6D). As already reported in the text, a partial recovery of differentiation index is observed but the increase is not statistically significant. In contrast, no changes are observed applying the same silencing in the WT cells. Legend and text were modified accordingly.

Reviewer #1 (Significance (Required)):

The manuscript offers several strengths. It provides novel mechanistic insight by identifying a previously unrecognized role for Trim32 in regulating c-Myc mRNA stability during the onset of myogenic differentiation. The study is supported by a robust methodology that integrates CRISPR/Cas9 gene editing, transcriptomic profiling, flow cytometry, biochemical assays, and rescue experiments using siRNA knockdown. Furthermore, the work has a disease relevance, as it uncovers a mechanistic link between Trim32 deficiency and impaired myogenesis, with implications for the pathogenesis of LGMDR8. * * At the same time, the study has some limitations. The findings rely exclusively on the C2C12 myoblast cell line, which may not fully represent primary satellite cell or in vivo biology. The functional rescue achieved through c-Myc knockdown is only partial, restoring Myogenin expression but not the full differentiation index or morphology, indicating that additional mechanisms are likely involved. Although evidence supports a role for Trim32 in mRNA destabilization, the precise molecular partners-such as RNA-binding activity, microRNA involvement, or ligase function-remain undefined. Some discrepancies with previous studies, including Trim32-mediated protein degradation of c-Myc, are acknowledged but not experimentally resolved. Moreover, functional validation in animal models or patient-derived cells is currently lacking. Despite these limitations, the study represents an advancement for the field. It shifts the conceptual framework from Trim32's canonical role in protein ubiquitination to a novel function in RNA regulation during myogenesis. It also raises potential clinical implications by suggesting that targeting the Trim32-c-Myc axis, or modulating c-Myc stability, may represent a therapeutic strategy for LGMDR8. This work will be of particular interest to muscle biology researchers studying myogenesis and the molecular basis of muscle disease, RNA biology specialists investigating post-transcriptional regulation and mRNA stability, and neuromuscular disease researchers and clinicians seeking to identify new molecular targets for therapeutic intervention in LGMDR8. * * The Reviewer expressing this opinion is an expert in muscle stem cells, muscle regeneration, and muscle development.

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

Summary: * * In this study, the authors sought to investigate the molecular role of Trim32, a tripartite motif-containing E3 ubiquitin ligase often associated with its dysregulation in Limb-Girdle Muscular Dystrophy Recessive 8 (LGMDR8), and its role in the dynamics of skeletal muscle differentiation. Using a CRISPR-Cas9 model of Trim32 knockout in C2C12 murine myoblasts, the authors demonstrate that loss of Trim32 alters the myogenic process, particularly by impairing the transition from proliferation to differentiation. The authors provide evidence in the way of transcriptomic profiling that displays an alteration of myogenic signaling in the Trim32 KO cells, leading to a disruption of myotube formation in-vitro. Interestingly, while previous studies have focused on Trim32's role in protein ubiquitination and degradation of c-Myc, the authors provide evidence that Trim32-regulation of c-Myc occurs at the level of mRNA stability. The authors show that the sustained c-Myc expression in Trim32 knockout cells disrupts the timely expression of key myogenic factors and interferes with critical withdrawal of myoblasts from the cell cycle required for myotube formation. Overall, the study offers a new insight into how Trim32 regulates early myogenic progression and highlights a potential therapeutic target for addressing the defects in muscular regeneration observed in LGMDR8.

We thank the Reviewer for valuing our work and for their appreciated suggestions to improve our manuscript. We have carefully addressed some of the concerns raised as detailed here, while others, which require more laborious experimental efforts, will be addressed as reported in the Revision Plan.

Major Comments:

The work is a bit incremental based on this:

https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0030445 * * And this:

https://www.nature.com/articles/s41418-018-0129-0 * * To their credit, the authors do cite the above papers.

Authors’ response. We thank the Reviewer for this careful evaluation of our work against the current literature and for recognising the contribution of our findings to the understanding of myogenesis complex picture in which the involvement of Trim32 and c-Myc, and of the Trim32-c-Myc axis, can occur at several stages and likely in narrow time windows along the process, thus possibly explaining some reports inconsistencies.

The authors do provide compelling evidence that Trim32 deficiency disrupts C2C12 myogenic differentiation and sustained c-Myc expression contributes to this defective process. However, while knockdown of c-Myc does restore Myogenin levels, it was not sufficient to normalize myotube morphology or differentiation index, suggesting an incomplete picture of the Trim32-dependent pathways involved. The authors should qualify their claim by emphasizing that c-Myc regulation is a major, but not exclusive, mechanism underlying the observed defects. This will prevent an overgeneralization and better align the conclusions with the author's data.

Authors’ response. We agree with the Reviewer and we modified our phrasing that implied Trim32-c-Myc axis as the exclusive mechanism by explicitly indicated that other pathways contribute to guarantee proper myogenesis, in the Abstract and in Discussion.

The Abstract now reads: “… suggesting that the Trim32–c-Myc axis may represent an essential hub, although likely not the exclusive molecular mechanism, in muscle regeneration within LGMDR8 pathogenesis.”

The Discussion now reads: “Functionally, we demonstrated that c-Myc contributes to the impaired myogenesis observed in Trim32 KO clones, although this is clearly not the only factor involved in the Trim32-mediated myogenic network; realistically other molecular mechanisms can participate in this process as also suggested by our transcriptomic results.”

The authors provide a thorough and well-executed interrogation of cell cycle dynamics in Trim32 KO clones, combining phosphor-histone H3 flow cytometry of DNA content, and CFSE proliferation assays. These complementary approaches convincingly show that, while proliferation states remain similar in WT and KO cells, Trim32-deficient myoblasts fail in their normal withdraw from the cell cycle during exposure to differentiation-inducing conditions. This work adds clarity to a previously inconsistent literature and greatly strengthens the study.

Authors’ response. We thank the Reviewer for appreciating our thorough analyses on cell cycle dynamics in proliferation conditions and at the onset of the differentiation process.

The transcriptomic analysis (detailed In the "Transcriptomic analysis of Trim32 WT and KO clones along early differentiation" section of Results) is central to the manuscript and provides strong evidence that Trim32 deficiency disrupts normal differentiation processes. However, the description of the pathway enrichment results is highly detailed and somewhat compressed, which may make it challenging for readers to following the key biological 'take-homes'. The narrative quickly moves across their multiple analyses like MDS, clustering, heatmaps, and bubble plots without pausing to guide the reader through what each analysis contributes to the overall biological interpretation. As a result, the key findings (reduced muscle development pathways in KO cells and enrichment of cell cycle-related pathways) can feel somewhat muted. The authors may consider reorganizing this section, so the primary biological insights are highlighted and supported by each of their analyses. This would allow the biological implications to be more accessible to a broader readership.

Authors’ response. We thank the Reviewer for raising this point and apologise for being too brief in describing the data, leaving indeed some points excessively implicit. As suggested, we now reorganised this session and added the lists of enriched canonical pathways relative to WT vs KO comparisons at D0 and D3 (Fig. EV3B) as well as those relative to the comparison between D0 and D3 for both WT and Trim32 KO samples (Fig. EV3C), with their relative scores. We changed the Results section “Transcriptomic analysis of Trim32 WT and Trim32 KO clones along early differentiation” as reported here below and modified the legends accordingly.

The paragraph now reads: “Based on our initial observations, the absence of Trim32 already exerts a significant impact by day 3 (D3) of C2C12 myogenic differentiation. To investigate how Trim32 influences early global transcriptional changes during the proliferative phase (D0) and early differentiation (D3), we performed an unbiased transcriptomic profiling of WT and Trim32 KO clones (Fig. 2A). Multidimensional Scaling (MDS) analysis revealed clear segregation of gene expression profiles based on both time of differentiation (Dim1, 44% variance) and Trim32 genotype (Dim2, 16% variance) (Fig. 2A). Likewise, hierarchical clustering grouped WT and Trim32 KO clones into distinct clusters at both timepoints, indicating consistent genotype-specific transcriptional differences (Fig. EV3A). Differentially Expressed Genes (DEGs) were detected in the Trim32 KO transcriptome relative to WT, at both D0 and D3. In proliferating conditions, 72 genes were upregulated and 189 were downregulated whereas at D3 of differentiation, 72 genes were upregulated and 212 were downregulated. Ingenuity Pathway Analysis of the DEGs revealed the top 10 Canonical Pathways displayed in Fig. EV3B as enriched at either D0 or D3 (Fig. EV3B). Several of these pathways can underscore relevant Trim32-mediated functions though most of them represent generic functions not immediately attributable to the observed myogenesis defects.

Notably, the transcriptional divergence between WT and Trim32 KO cells is more pronounced at D3, as evidenced by a greater separation along the MSD Dim2 axis, suggesting that Trim32-dependent transcriptional regulation intensifies during early differentiation (Fig. 2A). Given our interest in the differentiation process, we therefore focused our analyses comparing the changes occurring from D0 to D3 in WT (WT D3 vs. D0) and in Trim32 KO (KO D3 vs. D0) RNAseq data.

Pathway enrichment analysis of D3 vs. D0 DEGs allowed the selection of the top-scored pathways for both WT and Trim32 KO data. We obtained 18 top-scored pathways enriched in each genotype (-log(p-value) ³ 9 cut-off): 14 are shared while 4 are top-ranked only in WT and 4 only in Trim32 KO (Fig. EV3C). For the following analyses, we employed thus a total of 22 distinct pathways and to better mine those relevant in the passage from the proliferation stage to the early differentiation one and that are affected by the lack of Trim32, we built a bubble plot comparing side-by-side the scores and enrichment of the 22 selected top-scored pathways above in WT and Trim32 KO (Fig. 2B). A heatmap of DEGs included within these selected pathways confirms the clustering of the samples considering both the genotypes and the timepoints highlighting gene expression differences (Fig. 2C). These pathways are mainly related to muscle development, cell cycle regulation, genome stability maintenance and few other metabolic cascades.

As expected given the results related to Figure 1, moving from D0 to D3 WT clones showed robust upregulation of key transcripts associated with the Inactive Sarcomere Protein Complex, a category encompassing most genes in the “Striated Muscle Contraction” pathway, while in Trim32 KO clones this pathway was not among those enriched in the transition from D0 to D3 (Fig. EV3C). Detailed analyses of transcripts enclosed within this pathway revealed that on the transition from proliferation to differentiation, WT clones show upregulation of several Myosin Heavy Chain isoforms (e.g., MYH3, MYH6, MYH8), α-Actin 1 (ACTA1), α-Actinin 2 (ACTN2), Desmin (DES), Tropomodulin 1 (TMOD1), and Titin (TTN), a pattern consistent with previous reports, while these same transcripts were either non-detected or only modestly upregulated in Trim32 KO clones at D3 (Fig. 2D). This genotype-specific disparity was further confirmed by gene set enrichment barcode plots, which demonstrated significant enrichment of these muscle-related transcripts in WT cells (FDR_UP = 0.0062), but not in Trim32 KO cells (FDR_UP = 0.24) (Fig. EV3D). These findings support an early transcriptional basis for the impaired myogenesis previously observed in Trim32 KO cells.

In addition to differences in muscle-specific gene expression, we observed that also several pathways related to cell proliferation and cell cycle regulation were more enriched in Trim32 KO cells compared to WT. This suggests that altered cell proliferation may contribute to the distinct differentiation behavior observed in Trim32 KO versus WT (Fig. 2B). Given that cell cycle exit is a critical prerequisite for the onset of myogenic differentiation and considering that previous studies on Trim32 role in cell cycle regulation have reported inconsistent findings, we further examined cell cycle dynamics under our experimental conditions to clarify Trim32 contribution to this process”

The work would be greatly strengthened by the conclusion of LGMDR8 primary cells, and rescue experiments of TRIM32 to explore myogenesis.

Authors’ response. This point will be addressed as detailed in the Revision Plan

Also, EU (5-ethynyl uridine) pulse-chase experiments to label nascent and stable RNA coupled with MYC pulldowns and qPCR (or RNA-sequencing of both pools) would further enhance the claim that MYC stability is being affected.

Authors’ response. This point will be addressed as detailed in the Revision Plan

"On one side, c-Myc may influence early stages of myogenesis, such as myoblast proliferation and initial myotube formation, but it may not contribute significantly to later events such as myotube hypertrophy or fusion between existing myotubes and myocytes. This hypothesis is supported by recent work showing that c-Myc is dispensable for muscle fiber hypertrophy but essential for normal MuSC function (Ham et al, 2025)." Also address and discuss the following, as what is currently written is not entirely accurate: https://www.embopress.org/doi/full/10.1038/s44319-024-00299-z and https://journals.physiology.org/doi/prev/20250724-aop/abs/10.1152/ajpcell.00528.2025

Authors’ response. We thank the Reviewer for bringing to our attention these two publications, that indeed, add important piece of data to recapitulate the in vivo complexity of c-Myc role in myogenesis. We included this point in our Discussion.

The Discussion now reads: “On one side, c-Myc may influence early stages of myogenesis, such as myoblast proliferation and initial myotube formation, but it may not contribute significantly to later events such as myotube hypertrophy or fusion between existing myotubes and myocytes. This hypothesis is supported by recent work showing that c-Myc is dispensable for muscle fiber hypertrophy but essential for normal MuSC function (Ham et al, 2025). Other reports, instead, demonstrated the implication of c-Myc periodic pulses, mimicking resistance-exercise, in muscle growth, a role that cannot though be observed in our experimental model (Edman et al., 2024; Jones et al., 2025).”

Minor Comments:

Z-score scale used in the pathway bubble plot (Figure 2C) could benefit from alternative color choices. Current gradient is a bit muddy and clarity for the reader could be improved by more distinct color options, particularly in the transition from positive to negative Z-score.

Authors’ response. As suggested, we modified the z-score-representing colors using a more distinct gradient especially in the positive to negative transition in Figure 2B.

Clarification on the rationale for selecting the "top 18" pathways would be helpful, as it is not clear if this cutoff was chosen arbitrarily or reflects a specific statistical or biological threshold.

Authors’ response. As now better explained (see comment regarding Major point: Transcriptomics), we used a cut-off of -log(p-value) above or equal to 9 for pathways enriched in DEGs of the D0 vs D3 comparison for both WT and Trim32 KO. The threshold is now included in the Results section and the pathways (shared between WT and Trim32 KO and unique) are listed as Fig. EV3C.

The authors alternates between using "Trim 32 KO clones" and "KO clones" throughout the manuscript. Consistent terminology across figures and text would improve readability.

Authors’ response. We thank the Reviewer for this remark, and we apologise for having overlooked it. We amended this throughout the manuscript by always using for clarity “Trim32 KO clones/cells”.

Cell culture methodology does not specify passage number or culture duration (only "At confluence") before differentiation. This is important, as C2C12 differentiation potential can drift with extended passaging.

Authors’ response. We agree with the Reviewer that C2C12 passaging can reduce the differentiation potential of this myoblast cell lines; this is indeed the main reason why we decided to employ WT clones, which underwent the same editing process as those that resulted mutated in the Trim32 gene, as reference controls throughout our study. We apologise for not indicating the passages in the first version of the manuscript that now is amended as per here below in the Methods section:

“The C2C12 parental cells used in this study were maintained within passages 3–8. All clonal cell lines (see below) were utilized within 10 passages following gene editing. In all experiments, WT and Trim32 KO clones of comparable passage numbers were used to ensure consistency and minimize passage-related variability.”

Reviewer #2 (Significance (Required)):

General Assessment:

This study provides a thorough investigation of Trim32's role the processes related to skeletal muscle differentiation using a CRISPR-Cas9 knockout C2C12 model. The strengths of this study lie in the multi-layered experimental approach as the authors incorporated transcriptomics, cell cycle profiling, and stability assays which collectively build a strong case for their hypothesis that Trim32 is a key factor in the normal regulation of myogenesis. The work is also strengthened by the use of multiple biological and technical replicates, particularly the independent KO clones which helps address potential clonal variation issues that could occur. The largest limitation to this study is that, while the c-Myc mechanism is well explored, the other Trim32-dependent pathways associated with the disruption (implicated by the incomplete rescue by c-Myc knockdown) are not as well addressed. Overall however, the study convincingly identifies a critical function for Trim32 during skeletal muscle differentiation. * * Advance: * * To my knowledge, this is the first study to demonstrate the mRNA stability level of c-Myc regulation by Trim32, rather than through the ubiquitin-mediated protein degradation. This work will advance the current understanding and provide a more complete understanding of Trim32's role in c-Myc regulation. Beyond c-Myc, this work highlights the idea that TRIM family proteins can influence RNA stability which could implicate a broader role in RNA biology and has potential for future therapeutic targeting. * * Audience: * * This research will be of interest to an audience that focuses on broad skeletal muscle biology but primarily to readers with more focused research such as myogenesis and neuromuscular disease (LGMDR8 in particular) where the defined Trim32 governance over early differentiation checkpoints will be of interest. It will also provide mechanistic insights to those outside of skeletal muscle that study TRIM family proteins, ubiquitin biology, and RNA regulation. For translational/clinical researchers, it identifies the Trim32/c-Myc axis as a potential therapeutic target for LGMDR8 and related muscular dystrophies.

Expertise: * * My expertise lies in skeletal muscle biology, gene editing, transgenic mouse models, and bioinformatics. I feel confident evaluating the data and conclusions as presented.

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

• In this paper, the authors examine the role of TRIM32, implicated in limb girdle muscular dystrophy recessive 8 (LGMDR8), in the differentiation of C2C12 mouse myoblasts. Using CRISPR, they generate mutant and wild-type clones and compare their differentiation capacity in vitro. They report that Trim32-deficient clones exhibit delayed and defective myogenic differentiation. RNA-seq analysis reveals widespread changes in gene expression, although few are validated by independent methods. Notably, Trim32 mutant cells maintain residual proliferation under differentiation conditions, apparently due to a failure to downregulate c-Myc. Translation inhibition experiments suggest that TRIM32 promotes c-Myc mRNA destabilization, but this conclusion is insufficiently substantiated. The authors also perform rescue experiments, showing that c-Myc knockdown in Trim32-deficient cells alleviates some differentiation defects. However, this rescue is not quantified, was conducted in only two of the three knockout lines, and is supported by inappropriate statistical analysis of gene expression. Overall, the manuscript in its current form has substantial weaknesses that preclude publication. Beyond statistical issues, the major concerns are: (1) exclusive reliance on the immortalized C2C12 line, with no validation in primary/satellite cells or in vivo, (2) insufficient mechanistic evidence that TRIM32 acts directly on c-Myc mRNA, and (3) overinterpretation of disease relevance in the absence of supporting patient or in vivo data. Please find more details below:*

We thank the Reviewer for the in-depth assessment of our work and precious suggestions to improve the manuscript. We have carefully addressed some of the concerns raised, as detailed here, while others, which require more experimental efforts, will be addressed as detailed in the Revision Plan.

- TRIM32 complementation / rescue experiments to exclude clonal or off-target CRISPR effects and show specificity are lacking.

Authors’ response. This point will be addressed as detailed in the Revision Plan

- The authors link their in vitro findings to LGMDR8 pathogenesis and propose that the Trim32-c-Myc axis may serve as a central regulator of muscle regeneration in the disease. However, LGMDR8 is a complex disorder, and connecting muscle wasting in patients to differentiation assays in C2C12 cells is difficult to justify. No direct evidence is provided that the proposed mRNA mechanism operates in patient-derived samples or in mouse satellite cells. Moreover, the partial rescue achieved by c-Myc knockdown (which does not fully restore myotube morphology or differentiation index) further suggests that the disease connection is not straightforward. Validation of the TRIM32-c-Myc axis in a physiologically relevant system, such as LGMD patient myoblasts or Trim32 mutant mouse cells, would greatly strengthen the claim.

Authors’ response. This point will be addressed as detailed in the Revision Plan

-Some gene expression changes from the RNA-seq study in Figure 2 should be validated by qPCR

Authors’ response. We thank the reviewer for this suggestion. This point will be addressed as detailed in the Revision Plan. We have selected several transcripts that will be evaluated in independent samples in order to validate the RNAseq results.

- The paper shows siRNA knockdown of c-Myc in KO restores Myogenin RNA/protein but does not fully rescue myotube morphology or differentiation index. This suggests that Trim32 controls additional effectors beyond c-Myc; yet the authors do not pursue other candidate mediators identified in the RNA-seq. The manuscript would be strengthened by systematically testing whether other deregulated transcripts contribute to the phenotype.

Authors’ response. This point will be addressed as detailed in the Revision Plan

- There are concerns with experimental/statistical issues and insufficient replicate reporting. The authors use unpaired two-tailed Student's t-test across many comparisons; multiple testing corrections or ANOVA where appropriate should be used. In Figure EV5B and Figure 6B, the authors perform statistical analyses with control values set to 1. This method masks the inherent variability between experiments and artificially augments p values. Control sample values need to be normalized to one another to have reliable statistical analysis. Myotube morphology and differentiation index quantifications need clear description of fields counted, blind analysis, and number of biological replicates.

Authors’ response. We thank the Reviewer for raising this point.

Regarding the replicates, we clarified in the Methods and Legends that the Trim32 KO experiments have been performed on 3 biological replicates (independent clones) and the same for the reference control (3 independent WT clones), except for the Fig. 6 experiments that were performed on 2 Trim32 KO and 2 WT clones. All the Western Blots, immunofluorescence, qPCR data are representative of the results of at least 3 independent experiments unless otherwise stated. We reported the number and type of replicates as well as the microscope fields analyzed.

We repeated the statistical analyses of the data in Figure 5G, EV5D, EV5E, employing more appropriately the 2-way-ANOVA test, as suggested, and we now reported this info in the graphs and legends.

We thank the Reviewer for raising this point, we agree and substituted the graphs in Fig. EV5B and 6B showing the control values normalised as suggested. The statistical analyses now reflect this change.

-Some English mistakes require additional read-throughs. For example: "Indeed, Trim32 has no effect on the stability of c-Myc mRNA in proliferating conditions, but upon induction of differentiation the stability of c-Myc mRNA resulted enhanced in Trim32 KO clones (Fig. 5G, Fig. EV5D and 5E)."

Authors’ response. We re-edited this revised version of the manuscript as suggested.

-Results in Figure 5A should be quantified

Authors’ response. We amended this point by quantifying the results shown in Fig. 5A, we added the graph of the quantification of 3 experimental replicates to the Figure. Quantification confirms that no statistically significant difference is observed. The Figure and the relative legend are modified accordingly.

-Based on the nuclear marker p84, the separation of cytoplasmic and nuclear fractions is not ideal in Figure 5D

Authors’ response. We agree with the Reviewer that the presence of p84 also in the cytoplasmic fraction is not ideal. Regrettably, we observed this faint p84 band in all the experiments performed. We think however, that this is not impacting on the result that clearly shows that c-Myc and Trim32 are never detected in the same compartment.

-In Figure 6, it is not appropriate to perform statistical analyses on only two data points per condition.

Authors’ response. We agree with the Reviewer and we now show the graph of the results of the 3 technical replicates for 2 biological replicates and do not indicate any statistics (Fig. 6B). The graph was also modified according to a previous point raised.

-The nuclear MYOG phenotype is very interesting; could this be related to requirements of TRIM32 in fusion?

Authors’ response. We agree with the Reviewer that Trim32 might also be necessary for myoblast fusion. This point is however beyond the scope of the present study and will be addressed in future work.

- The hypothesis that TRIM32 destabilizes c-Myc mRNA is intriguing but requires stronger mechanistic support. This would be more convincing with RNA immunoprecipitation to test direct association with c-Myc mRNA, and/or co-immunoprecipitation to identify interactions between TRIM32 and proteins involved in mRNA stability. The study would also be strengthened by reporter assays, such as c-Myc 3′UTR luciferase constructs in WT and KO cells, to directly demonstrate 3′UTR-dependent regulation of mRNA stability.

Authors’ response. This point will be addressed as detailed in the Revision Plan

Reviewer #3 (Significance (Required)):

The manuscript presents a minor conceptual advance in understanding TRIM32 function in myogenic differentiation. Its main limitation is that all experiments were performed in C2C12 cells. While C2C12 are a classical system to study muscle differentiation, they are an immortalized, long-cultured, and genetically unstable line that represents a committed myoblast stage rather than bona fide satellite cells. They therefore do not fully model the biology of early regenerative responses. Several TRIM32 phenotypes reported in the literature differ between primary satellite cells and cell lines, and the authors themselves note such discrepancies. Extrapolating these findings to LGMDR8 pathogenesis without validation in primary human myoblasts, satellite cell assays, or in vivo regeneration models is therefore not justified. Previous work has already established clear roles for TRIM32 in mouse satellite cells in vivo and in patient myoblasts in vitro, whereas this study introduces a novel link to c-Myc regulation during differentiation. In addition, without mechanistic evidence, the central claim that TRIM32 regulates c-Myc mRNA stability remains descriptive and incomplete. Nevertheless, the results will be of interest to researchers studying LGMD and to those exploring TRIM32 biology in broader contexts. I review this manuscript as a muscle biologist with expertise in satellite cell biology and transcriptional regulation.

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

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I thank the Referees for their...

Referee #1

1. The authors should provide more information when...

Responses + The typical domed appearance of a hydrocephalus-harboring skull is apparent as early as P4, as shown in a new side-by-side comparison of pups at that age (Fig. 1A). + Though this is not stated in the MS 2. Figure 6: Why has only...

Response: We expanded the comparison

Minor comments:

1. The text contains several...

Response: We added...

Referee #2

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Reply to the Reviewers

I thank the Referees for their...

Referee #1

1. The authors should provide more information when...

Responses + The typical domed appearance of a hydrocephalus-harboring skull is apparent as early as P4, as shown in a new side-by-side comparison of pups at that age (Fig. 1A). + Though this is not stated in the MS 2. Figure 6: Why has only...

Response: We expanded the comparison

Minor comments:

1. The text contains several...

Response: We added...

Referee #2

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Referee #3

Evidence, reproducibility and clarity

In this paper, the authors examine the role of TRIM32, implicated in limb girdle muscular dystrophy recessive 8 (LGMDR8), in the differentiation of C2C12 mouse myoblasts. Using CRISPR, they generate mutant and wild-type clones and compare their differentiation capacity in vitro. They report that Trim32-deficient clones exhibit delayed and defective myogenic differentiation. RNA-seq analysis reveals widespread changes in gene expression, although few are validated by independent methods. Notably, Trim32 mutant cells maintain residual proliferation under differentiation conditions, apparently due to a failure to downregulate c-Myc. Translation inhibition experiments suggest that TRIM32 promotes c-Myc mRNA destabilization, but this conclusion is insufficiently substantiated. The authors also perform rescue experiments, showing that c-Myc knockdown in Trim32-deficient cells alleviates some differentiation defects. However, this rescue is not quantified, was conducted in only two of the three knockout lines, and is supported by inappropriate statistical analysis of gene expression. Overall, the manuscript in its current form has substantial weaknesses that preclude publication. Beyond statistical issues, the major concerns are: (1) exclusive reliance on the immortalized C2C12 line, with no validation in primary/satellite cells or in vivo, (2) insufficient mechanistic evidence that TRIM32 acts directly on c-Myc mRNA, and (3) overinterpretation of disease relevance in the absence of supporting patient or in vivo data. Please find more details below:

• TRIM32 complementation / rescue experiments to exclude clonal or off-target CRISPR effects and show specificity are lacking.
• The authors link their in vitro findings to LGMDR8 pathogenesis and propose that the Trim32-c-Myc axis may serve as a central regulator of muscle regeneration in the disease. However, LGMDR8 is a complex disorder, and connecting muscle wasting in patients to differentiation assays in C2C12 cells is difficult to justify. No direct evidence is provided that the proposed mRNA mechanism operates in patient-derived samples or in mouse satellite cells. Moreover, the partial rescue achieved by c-Myc knockdown (which does not fully restore myotube morphology or differentiation index) further suggests that the disease connection is not straightforward. Validation of the TRIM32-c-Myc axis in a physiologically relevant system, such as LGMD patient myoblasts or Trim32 mutant mouse cells, would greatly strengthen the claim. -Some gene expression changes from the RNA-seq study in Figure 2 should be validated by qPCR
• The paper shows siRNA knockdown of c-Myc in KO restores Myogenin RNA/protein but does not fully rescue myotube morphology or differentiation index. This suggests that Trim32 controls additional effectors beyond c-Myc; yet the authors do not pursue other candidate mediators identified in the RNA-seq. The manuscript would be strengthened by systematically testing whether other deregulated transcripts contribute to the phenotype.
• There are concerns with experimental/statistical issues and insufficient replicate reporting. The authors use unpaired two-tailed Student's t-test across many comparisons; multiple testing corrections or ANOVA where appropriate should be used. In Figure EV5B and Figure 6B, the authors perform statistical analyses with control values set to 1. This method masks the inherent variability between experiments and artificially augments p values. Control sample values need to be normalized to one another to have reliable statistical analysis. Myotube morphology and differentiation index quantifications need clear description of fields counted, blind analysis, and number of biological replicates. -Some English mistakes require additional read-throughs. For example: "Indeed, Trim32 has no effect on the stability of c-Myc mRNA in proliferating conditions, but upon induction of differentiation the stability of c-Myc mRNA resulted enhanced in Trim32 KO clones (Fig. 5G, Fig. EV5D and 5E)." -Results in Figure 5A should be quantified -Based on the nuclear marker p84, the separation of cytoplasmic and nuclear fractions is not ideal in Figure 5D -In Figure 6, it is not appropriate to perform statistical analyses on only two data points per condition. -The nuclear MYOG phenotype is very interesting; could this be related to requirements of TRIM32 in fusion?
• The hypothesis that TRIM32 destabilizes c-Myc mRNA is intriguing but requires stronger mechanistic support. This would be more convincing with RNA immunoprecipitation to test direct association with c-Myc mRNA, and/or co-immunoprecipitation to identify interactions between TRIM32 and proteins involved in mRNA stability. The study would also be strengthened by reporter assays, such as c-Myc 3′UTR luciferase constructs in WT and KO cells, to directly demonstrate 3′UTR-dependent regulation of mRNA stability.

Significance

The manuscript presents a minor conceptual advance in understanding TRIM32 function in myogenic differentiation. Its main limitation is that all experiments were performed in C2C12 cells. While C2C12 are a classical system to study muscle differentiation, they are an immortalized, long-cultured, and genetically unstable line that represents a committed myoblast stage rather than bona fide satellite cells. They therefore do not fully model the biology of early regenerative responses. Several TRIM32 phenotypes reported in the literature differ between primary satellite cells and cell lines, and the authors themselves note such discrepancies. Extrapolating these findings to LGMDR8 pathogenesis without validation in primary human myoblasts, satellite cell assays, or in vivo regeneration models is therefore not justified. Previous work has already established clear roles for TRIM32 in mouse satellite cells in vivo and in patient myoblasts in vitro, whereas this study introduces a novel link to c-Myc regulation during differentiation. In addition, without mechanistic evidence, the central claim that TRIM32 regulates c-Myc mRNA stability remains descriptive and incomplete. Nevertheless, the results will be of interest to researchers studying LGMD and to those exploring TRIM32 biology in broader contexts. I review this manuscript as a muscle biologist with expertise in satellite cell biology and transcriptional regulation.

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

Learn more at Review Commons

Referee #2

Evidence, reproducibility and clarity

Summary:

In this study, the authors sought to investigate the molecular role of Trim32, a tripartite motif-containing E3 ubiquitin ligase often associated with its dysregulation in Limb-Girdle Muscular Dystrophy Recessive 8 (LGMDR8), and its role in the dynamics of skeletal muscle differentiation. Using a CRISPR-Cas9 model of Trim32 knockout in C2C12 murine myoblasts, the authors demonstrate that loss of Trim32 alters the myogenic process, particularly by impairing the transition from proliferation to differentiation. The authors provide evidence in the way of transcriptomic profiling that displays an alteration of myogenic signaling in the Trim32 KO cells, leading to a disruption of myotube formation in-vitro. Interestingly, while previous studies have focused on Trim32's role in protein ubiquitination and degradation of c-Myc, the authors provide evidence that Trim32-regulation of c-Myc occurs at the level of mRNA stability. The authors show that the sustained c-Myc expression in Trim32 knockout cells disrupts the timely expression of key myogenic factors and interferes with critical withdrawal of myoblasts from the cell cycle required for myotube formation. Overall, the study offers a new insight into how Trim32 regulates early myogenic progression and highlights a potential therapeutic target for addressing the defects in muscular regeneration observed in LGMDR8.

Major Comments:

The work is a bit incremental based on this: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0030445 And this: https://www.nature.com/articles/s41418-018-0129-0 To their credit, the authors do cite the above papers.

The authors do provide compelling evidence that Trim32 deficiency disrupts C2C12 myogenic differentiation and sustained c-Myc expression contributes to this defective process. However, while knockdown of c-Myc does restore Myogenin levels, it was not sufficient to normalize myotube morphology or differentiation index, suggesting an incomplete picture of the Trim32-dependent pathways involved. The authors should qualify their claim by emphasizing that c-Myc regulation is a major, but not exclusive, mechanism underlying the observed defects. This will prevent an overgeneralization and better align the conclusions with the author's data. The authors provide a thorough and well-executed interrogation of cell cycle dynamics in Trim32 KO clones, combining phosphor-histone H3 flow cytometry of DNA content, and CFSE proliferation assays. These complementary approaches convincingly show that, while proliferation states remain similar in WT and KO cells, Trim32-deficient myoblasts fail in their normal withdraw from the cell cycle during exposure to differentiation-inducing conditions. This work adds clarity to a previously inconsistent literature and greatly strengthens the study.

The transcriptomic analysis (detailed In the "Transcriptomic analysis of Trim32 WT and KO clones along early differentiation" section of Results) is central to the manuscript and provides strong evidence that Trim32 deficiency disrupts normal differentiation processes. However, the description of the pathway enrichment results is highly detailed and somewhat compressed, which may make it challenging for readers to following the key biological 'take-homes'. The narrative quickly moves across their multiple analyses like MDS, clustering, heatmaps, and bubble plots without pausing to guide the reader through what each analysis contributes to the overall biological interpretation. As a result, the key findings (reduced muscle development pathways in KO cells and enrichment of cell cycle-related pathways) can feel somewhat muted. The authors may consider reorganizing this section, so the primary biological insights are highlighted and supported by each of their analyses. This would allow the biological implications to be more accessible to a broader readership.

The work would be greatly strengthened by the conclusion of LGMDR8 primary cells, and rescue experiments of TRIM32 to explore myogenesis. Also, EU (5-ethynyl uridine) pulse-chase experiments to label nascent and stable RNA coupled with MYC pulldowns and qPCR (or RNA-sequencing of both pools) would further enhance the claim that MYC stability is being affected.

"On one side, c-Myc may influence early stages of myogenesis, such as myoblast proliferation and initial myotube formation, but it may not contribute significantly to later events such as myotube hypertrophy or fusion between existing myotubes and myocytes. This hypothesis is supported by recent work showing that c-Myc is dispensable for muscle fiber hypertrophy but essential for normal MuSC function (Ham et al, 2025)." Also address and discuss the following, as what is currently written is not entirely accurate: https://www.embopress.org/doi/full/10.1038/s44319-024-00299-z and https://journals.physiology.org/doi/prev/20250724-aop/abs/10.1152/ajpcell.00528.2025

Minor Comments:

Z-score scale used in the pathway bubble plot (Figure 2C) could benefit from alternative color choices. Current gradient is a bit muddy and clarity for the reader could be improved by more distinct color options, particularly in the transition from positive to negative Z-score.

Clarification on the rationale for selecting the "top 18" pathways would be helpful, as it is not clear if this cutoff was chosen arbitrarily or reflects a specific statistical or biological threshold.

The authors alternates between using "Trim 32 KO clones" and "KO clones" throughout the manuscript. Consistent terminology across figures and text would improve readability.

Cell culture methodology does not specify passage number or culture duration (only "At confluence") before differentiation. This is important, as C2C12 differentiation potential can drift with extended passaging.

Significance

General Assessment:

This study provides a thorough investigation of Trim32's role the processes related to skeletal muscle differentiation using a CRISPR-Cas9 knockout C2C12 model. The strengths of this study lie in the multi-layered experimental approach as the authors incorporated transcriptomics, cell cycle profiling, and stability assays which collectively build a strong case for their hypothesis that Trim32 is a key factor in the normal regulation of myogenesis. The work is also strengthened by the use of multiple biological and technical replicates, particularly the independent KO clones which helps address potential clonal variation issues that could occur. The largest limitation to this study is that, while the c-Myc mechanism is well explored, the other Trim32-dependent pathways associated with the disruption (implicated by the incomplete rescue by c-Myc knockdown) are not as well addressed. Overall however, the study convincingly identifies a critical function for Trim32 during skeletal muscle differentiation.

Advance:

To my knowledge, this is the first study to demonstrate the mRNA stability level of c-Myc regulation by Trim32, rather than through the ubiquitin-mediated protein degradation. This work will advance the current understanding and provide a more complete understanding of Trim32's role in c-Myc regulation. Beyond c-Myc, this work highlights the idea that TRIM family proteins can influence RNA stability which could implicate a broader role in RNA biology and has potential for future therapeutic targeting.

Audience:

This research will be of interest to an audience that focuses on broad skeletal muscle biology but primarily to readers with more focused research such as myogenesis and neuromuscular disease (LGMDR8 in particular) where the defined Trim32 governance over early differentiation checkpoints will be of interest. It will also provide mechanistic insights to those outside of skeletal muscle that study TRIM family proteins, ubiquitin biology, and RNA regulation. For translational/clinical researchers, it identifies the Trim32/c-Myc axis as a potential therapeutic target for LGMDR8 and related muscular dystrophies.

Expertise:

My expertise lies in skeletal muscle biology, gene editing, transgenic mouse models, and bioinformatics. I feel confident evaluating the data and conclusions as presented.

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

Learn more at Review Commons

Referee #1

Evidence, reproducibility and clarity

In this manuscript, Xiong and colleagues investigate the mechanisms operating downstream to TRIM32 and controlling myogenic progression from proliferation to differentiation. Overall, the bulk of the data presented is robust. Although further investigation of specific aspects would make the conclusions more definitive (see below), it is an interesting contribution to the field of scientists studying the molecular basis of muscle diseases. In my opinion, a few aspects would improve the manuscript.

Firstly, the conclusion that Trim32 regulates c-Myc mRNA stability could be expanded and corroborated by further mechanistic studies:

1. Studies investigating whether Tim32 binds directly to c-Myc RNA. Moreover, although possibly beyond the scope of this study, an unbiased screening of RNA species binding to Trim32 would be informative.
2. If possible, studies in which the overexpression of different mutants presenting specific altered functional domains (NHL domain known to bind RNAs and Ring domain reportedly involved in protein ubiquitination) would be used to test if they are capable or incapable of rescuing the reported alteration of Trim32 KO cell lines in c-Myc expression and muscle maturation. An optional aspect that might be interesting to explore is whether the alterations in c-Myc expression observed in C2C12 might be replicated with primary myoblasts or satellite cells devoid of Trim32.

I also have a few minor points to highlight:

• It is unclear if the differences highlighted in graphs 5G, EV5D, and EV5E are statistically significant.
• On page 10, it is stated that c-Myc down-regulation cannot rescue KO myotube morphology fully nor increase the differentiation index significantly, but the corresponding data is not shown. Could the authors include those quantifications in the manuscript?

Significance

The manuscript offers several strengths. It provides novel mechanistic insight by identifying a previously unrecognized role for Trim32 in regulating c-Myc mRNA stability during the onset of myogenic differentiation. The study is supported by a robust methodology that integrates CRISPR/Cas9 gene editing, transcriptomic profiling, flow cytometry, biochemical assays, and rescue experiments using siRNA knockdown. Furthermore, the work has a disease relevance, as it uncovers a mechanistic link between Trim32 deficiency and impaired myogenesis, with implications for the pathogenesis of LGMDR8. At the same time, the study has some limitations. The findings rely exclusively on the C2C12 myoblast cell line, which may not fully represent primary satellite cell or in vivo biology. The functional rescue achieved through c-Myc knockdown is only partial, restoring Myogenin expression but not the full differentiation index or morphology, indicating that additional mechanisms are likely involved. Although evidence supports a role for Trim32 in mRNA destabilization, the precise molecular partners-such as RNA-binding activity, microRNA involvement, or ligase function-remain undefined. Some discrepancies with previous studies, including Trim32-mediated protein degradation of c-Myc, are acknowledged but not experimentally resolved. Moreover, functional validation in animal models or patient-derived cells is currently lacking.

Despite these limitations, the study represents an advancement for the field. It shifts the conceptual framework from Trim32's canonical role in protein ubiquitination to a novel function in RNA regulation during myogenesis. It also raises potential clinical implications by suggesting that targeting the Trim32-c-Myc axis, or modulating c-Myc stability, may represent a therapeutic strategy for LGMDR8. This work will be of particular interest to muscle biology researchers studying myogenesis and the molecular basis of muscle disease, RNA biology specialists investigating post-transcriptional regulation and mRNA stability, and neuromuscular disease researchers and clinicians seeking to identify new molecular targets for therapeutic intervention in LGMDR8.

The Reviewer expressing this opinion is an expert in muscle stem cells, muscle regeneration, and muscle development.

possible primary source to back up votes or liberal democracy

公式ドキュメントの定義に合わせて、引数の後ろにスラッシュを入れたほうがよさそうです。 https://docs.python.org/ja/3/library/functions.html#dir

公式ドキュメントの定義に合わせて、引数の後ろにスラッシュを入れたほうがよさそうです。 https://docs.python.org/ja/3/library/functions.html#issubclass

公式ドキュメントの定義に合わせて、引数の後ろにスラッシュを入れたほうがよさそうです。 https://docs.python.org/ja/3/library/functions.html#isinstance

公式ドキュメントの定義に合わせて、引数の後ろにスラッシュを入れたほうがよさそうです。 https://docs.python.org/ja/3/library/functions.html#type

公式ドキュメントの定義に合わせて、引数の後ろにスラッシュを入れたほうがよさそうです。 https://docs.python.org/ja/3/library/functions.html#id

このエラーが発生するところも下の実行例で見せてもいいのではと思いました。

読みました。LGTM。

読みました。LGTM。

読みました。LGTM。

Potential primary source for liberal democracy

possible primary source?

HyperPost - FeatherWiki Experiment

2024/12

eLife Assessment

This important work presents technical and conceptual advances with the release of MorphoNet 2.0, a versatile and accessible platform for 3D+T segmentation and analysis. The authors provide compelling evidence across diverse datasets, and the clarity of the manuscript together with the software's usability broadens its impact. Although the strength of some improvements is hard to fully gauge given sample complexity, the tool is a significant step forward that will likely impact many biological imaging fields.

Reviewer #2 (Public review):

Summary:

This article presents Morphonet 2.0, a software designed to visualise and curate segmentations of 3D and 3D+t data. The authors demonstrate its capabilities on five published datasets, showcasing how even small segmentation errors can be automatically detected, easily assessed and corrected by the user. This allows for more reliable ground truths which will in turn be very much valuable for analysis and training deep learning models. Morphonet 2.0 offers intuitive 3D inspection and functionalities accessible to a non-coding audience, thereby broadening its impact.

Strengths:

The work proposed in this article is expected to be of great interest for the community, by enabling easy visualisation and correction of complex 3D(+t) datasets. Moreover, the article is clear and well written making MorphoNet more likely to be used. The goals are clearly defined, addressing an undeniable need in the bioimage analysis community. The authors use a diverse range of datasets, successfully demonstrating the versatility of the software.

We would also like to highlight the great effort that was made to clearly explain which type of computer configurations are necessary to run the different dataset and how to find the appropriate documentation according to your needs. The authors clearly carefully thought about these two important problems and came up with very satisfactory solutions.

Weaknesses:

Sometimes, it can be a bit difficult to assess the strength of the improvements made by the proposed methods, but this is not something the authors could easily address, given the great complexity of the samples

Author response:

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

Reviewer #1 (Public review): 

The authors present a substantial improvement to their existing tool, MorphoNet, intended to facilitate assessment of 3D+t cell segmentation and tracking results, and curation of high-quality analysis for scientific discovery and data sharing. These tools are provided through a user-friendly GUI, making them accessible to biologists who are not experienced coders. Further, the authors have re-developed this tool to be a locally installed piece of software instead of a web interface, making the analysis and rendering of large 3D+t datasets more computationally efficient. The authors evidence the value of this tool with a series of use cases, in which they apply different features of the software to existing datasets and show the improvement to the segmentation and tracking achieved. 

While the computational tools packaged in this software are familiar to readers (e.g., cellpose), the novel contribution of this work is the focus on error correction. The MorphoNet 2.0 software helps users identify where their candidate segmentation and/or tracking may be incorrect. The authors then provide existing tools in a single user-friendly package, lowering the threshold of skill required for users to get maximal value from these existing tools. To help users apply these tools effectively, the authors introduce a number of unsupervised quality metrics that can be applied to a segmentation candidate to identify masks and regions where the segmentation results are noticeably different from the majority of the image. 

This work is valuable to researchers who are working with cell microscopy data that requires high-quality segmentation and tracking, particularly if their data are 3D time-lapse and thus challenging to segment and assess. The MorphoNet 2.0 tool that the authors present is intended to make the iterative process of segmentation, quality assessment, and re-processing easier and more streamlined, combining commonly used tools into a single user interface.   

We sincerely thank the reviewer for their thorough and encouraging evaluation of our work. We are grateful that they highlighted both the technical improvements of MorphoNet 2.0 and its potential impact for the broader community working with complex 3D+t microscopy datasets. We particularly appreciate the recognition of our efforts to make advanced segmentation and tracking tools accessible to non-expert users through a user-friendly and locally installable interface, and for pointing out the importance of error detection and correction in the iterative analysis workflow. The reviewer’s appreciation of the value of integrating unsupervised quality metrics to support this process is especially meaningful to us, as this was a central motivation behind the development of MorphoNet 2.0. We hope the tool will indeed facilitate more rigorous and reproducible analyses, and we are encouraged by the reviewer’s positive assessment of its utility for the community.

One of the key contributions of the work is the unsupervised metrics that MorphoNet 2.0 offers for segmentation quality assessment. These metrics are used in the use cases to identify low-quality instances of segmentation in the provided datasets, so that they can be improved with plugins directly in MorphoNet 2.0. However, not enough consideration is given to demonstrating that optimizing these metrics leads to an improvement in segmentation quality. For example, in Use Case 1, the authors report their metrics of interest (Intensity offset, Intensity border variation, and Nuclei volume) for the uncurated silver truth, the partially curated and fully curated datasets, but this does not evidence an improvement in the results. Additional plotting of the distribution of these metrics on the Gold Truth data could help confirm that the distribution of these metrics now better matches the expected distribution. 

Similarly, in Use Case 2, visual inspection leads us to believe that the segmentation generated by the Cellpose + Deli pipeline (shown in Figure 4d) is an improvement, but a direct comparison of agreement between segmented masks and masks in the published data (where the segmentations overlap) would further evidence this. 

We agree that demonstrating the correlation between metric optimization and real segmentation improvement is essential. We have added new analysis comparing the distributions of the unsupervised metrics with the gold truth data before and after curation. Additionally, we provided overlap scores where ground truth annotations are available, confirming the improvement. We also explicitly discussed the limitation of relying solely on unsupervised metrics without complementary validation.

We would appreciate the authors addressing the risk of decreasing the quality of the segmentations by applying circular logic with their tool; MorphoNet 2.0 uses unsupervised metrics to identify masks that do not fit the typical distribution. A model such as StarDist can be trained on the "good" masks to generate more masks that match the most common type. This leads to a more homogeneous segmentation quality, without consideration for whether these metrics actually optimize the segmentation 

We thank the reviewer for this important and insightful comment. It raises a crucial point regarding the risk of circular logic in our segmentation pipeline. Indeed, relying on unsupervised metrics to select “good” masks and using them to train a model like StarDist could lead to reinforcing a particular distribution of shapes or sizes, potentially filtering out biologically relevant variability. This homogenization may improve consistency with the chosen metrics, but not necessarily with the true underlying structures.

We fully agree that this is a key limitation to be aware of. We have revised the manuscript to explicitly discuss this risk, emphasizing that while our approach may help improve segmentation quality according to specific criteria, it should be complemented with biological validation and, when possible, expert input to ensure that important but rare phenotypes are not excluded.

In Use case 5, the authors include details that the errors were corrected by "264 MorphoNet plugin actions ... in 8 hours actions [sic]". The work would benefit from explaining whether this is 8 hours of human work, trying plugins and iteratively improving, or 8 hours of compute time to apply the selected plugins. 

We clarified that the “8 hours” refer to human interaction time, including exploration, testing, and iterative correction using plugins. 

Reviewer #2 (Public review):

Summary: 

This article presents Morphonet 2.0, a software designed to visualise and curate segmentations of 3D and 3D+t data. The authors demonstrate their capabilities on five published datasets, showcasing how even small segmentation errors can be automatically detected, easily assessed, and corrected by the user. This allows for more reliable ground truths, which will in turn be very much valuable for analysis and training deep learning models. Morphonet 2.0 offers intuitive 3D inspection and functionalities accessible to a non-coding audience, thereby broadening its impact. 

Strengths: 

The work proposed in this article is expected to be of great interest to the community by enabling easy visualisation and correction of complex 3D(+t) datasets. Moreover, the article is clear and well written, making MorphoNet more likely to be used. The goals are clearly defined, addressing an undeniable need in the bioimage analysis community. The authors use a diverse range of datasets, successfully demonstrating the versatility of the software. 

We would also like to highlight the great effort that was made to clearly explain which type of computer configurations are necessary to run the different datasets and how to find the appropriate documentation according to your needs. The authors clearly carefully thought about these two important problems and came up with very satisfactory solutions. 

We would like to sincerely thank the reviewer for their positive and thoughtful feedback. We are especially grateful that they acknowledged the clarity of the manuscript and the potential value of MorphoNet 2.0 for the community, particularly in facilitating the visualization and correction of complex 3D(+t) datasets. We also appreciate the reviewer’s recognition of our efforts to provide detailed guidance on hardware requirements and access to documentation—two aspects we consider crucial to ensuring the tool is both usable and widely adopted. Their comments are very encouraging and reinforce our commitment to making MorphoNet 2.0 as accessible and practical as possible for a broad range of users in the bioimage analysis community.

Weaknesses: 

There is still one concern: the quantification of the improvement of the segmentations in the use cases and, therefore, the quantification of the potential impact of the software. While it appears hard to quantify the quality of the correction, the proposed work would be significantly improved if such metrics could be provided. 

The authors show some distributions of metrics before and after segmentations to highlight the changes. This is a great start, but there seem to be two shortcomings: first, the comparison and interpretation of the different distributions does not appear to be trivial. It is therefore difficult to judge the quality of the improvement from these. Maybe an explanation in the text of how to interpret the differences between the distributions could help. A second shortcoming is that the before/after metrics displayed are the metrics used to guide the correction, so, by design, the scores will improve, but does that accurately represent the improvement of the segmentation? It seems to be the case, but it would be nice to maybe have a better assessment of the improvement of the quality. 

We thank the reviewer for this constructive and important comment. We fully agreed that assessing the true quality improvement of segmentation after correction is a central and challenging issue. While we initially focused on changes in the unsupervised quality metrics to illustrate the effect of the correction, we acknowledged that interpreting these distributions was not always straightforward, and that relying solely on the metrics used to guide the correction introduced an inherent bias in the evaluation.

To address the first point, we revised the manuscript to provide clearer guidance on how to interpret the changes in metric distributions before and after correction, with additional examples to make this interpretation more intuitive.

Regarding the second point, we agreed that using independent, external validation was necessary to confirm that the segmentation had genuinely improved. To this end, we included additional assessments using complementary evaluation strategies on selected datasets where ground truth was accessible, to compare pre- and post-correction segmentations with an independent reference. These results reinforced the idea that the corrections guided by unsupervised metrics generally led to more accurate segmentations, but we also emphasized their limitations and the need for biological validation in real-world cases.

Reviewer #3 (Public review): 

Summary: 

A very thorough technical report of a new standalone, open-source software for microscopy image processing and analysis (MorphoNet 2.0), with a particular emphasis on automated segmentation and its curation to obtain accurate results even with very complex 3D stacks, including timelapse experiments. 

Strengths: 

The authors did a good job of explaining the advantages of MorphoNet 2.0, as compared to its previous web-based version and to other software with similar capabilities. What I particularly found more useful to actually envisage these claimed advantages is the five examples used to illustrate the power of the software (based on a combination of

Python scripting and the 3D game engine Unity). These examples, from published research, are very varied in both types of information and image quality, and all have their complexities, making them inherently difficult to segment. I strongly recommend the readers to carefully watch the accompanying videos, which show (although not thoroughly) how the software is actually used in these examples. 

We sincerely thanked the reviewer for their thoughtful and encouraging feedback. We were particularly pleased that the reviewer appreciated the comparative analysis of MorphoNet 2.0 with both its earlier version and existing tools, as well as the relevance of the five diverse and complex use cases we had selected. Demonstrating the software’s versatility and robustness across a variety of challenging datasets was a key goal of this work, and we were glad that this aspect came through clearly. We also appreciated the reviewer’s recommendation to watch the accompanying videos, which we had designed to provide a practical sense of how the tool was used in real-world scenarios. Their positive assessment was highly motivating and reinforced the value of combining scripting flexibility with an interactive 3D interface.

Weaknesses: 

Being a technical article, the only possible comments are on how methods are presented, which is generally adequate, as mentioned above. In this regard, and in spite of the presented examples (chosen by the authors, who clearly gave them a deep thought before showing them), the only way in which the presented software will prove valuable is through its use by as many researchers as possible. This is not a weakness per se, of course, but just what is usual in this sort of report. Hence, I encourage readers to download the software and give it time to test it on their own data (which I will also do myself).   

We fully agreed that the true value of MorphoNet 2.0 would be demonstrated through its practical use by a wide range of researchers working with complex 3D and 3D+t datasets. In this regard, we improved the user documentation and provided a set of example datasets to help new users quickly familiarize themselves with the platform. We were also committed to maintaining and updating MorphoNet 2.0 based on user feedback to further support its usability and impact.

In conclusion, I believe that this report is fundamental because it will be the major way of initially promoting the use of MorphoNet 2.0 by the objective public. The software itself holds the promise of being very impactful for the microscopists' community. 

Reviewer #1 (Recommendations for the authors): 

(1) In Use Case 1, when referring to Figure 3a, they describe features of 3b? 

We corrected the mismatch between Figure 3a and 3b descriptions.

(2) In Figure 3g-I, columns for Curated Nuclei and All Nuclei appear to be incorrectly labelled, and should be the other way around. 

We corrected  the label swapped between “Curated Nuclei” and “All Nuclei.”

(3) Some mention of how this will be supported in the future would be of interest. 

We added a note on long-term support plans  

(4) Could Morphonet be rolled into something like napari and integrated into its environment with access to its plugins and tools? 

We thank the reviewer for this pertinent suggestion. We fully recognize the growing importance of interoperability within the bioimage analysis community, and we have been working on establishing a bridge between MorphoNet and napari to enable data exchange and complementary use of the two tools. As a platform, all new developments are first evaluated by our beta testers before being officially released to the user community and subsequently documented. The interoperability component is still under active development and will be announced shortly in a beta-testing phase. For this reason, we were not able to include it in the present manuscript, but we plan to document it in a future release.

(5) Can meshes be extracted/saved in another format? 

We agreed that the ability to extract and save meshes in standard formats was highly useful for interoperability with other tools. We implemented this feature in the new version of MorphoNet, allowing users to export meshes in commonly used formats such as OBJ or STL. Response: We thank the reviewer for this pertinent suggestion. We fully recognize the growing importance of interoperability within the bioimage analysis community, and we have been working on establishing a bridge between MorphoNet and napari to enable data exchange and complementary use of the two tools. As a platform, all new developments are first evaluated by our beta testers before being officially released to the user community and subsequently documented. The interoperability component is still under active development and will be announced shortly in a beta-testing phase. For this reason, we were not able to include it in the present manuscript, but we plan to document it in a future release.

Reviewer #2 (Recommendations for the authors): 

As a comment, since the authors mentioned the recent progress in 3D segmentation of various biological components, including organelles, it could be interesting to have examples of Morphonet applied to investigate subcellular structures. These present different challenges in visualization and quantification due to their smaller scale.

We thank the reviewer for this insightful suggestion. We fully agree that applying MorphoNet 2.0 to the analysis of sub-cellular structures is a promising direction, particularly given the specific challenges these datasets present in terms of resolution, visualization, and quantification. While our current use cases focus on cellular and tissue-level segmentation, we are actively interested in extending the applicability of the tool to finer scales. We are currently exploring plugins for spot detection and curation in single-molecule FISH data. However, this requires more time to properly validate relevant use cases, and we plan to include this functionality in the next release.

Another comment is that the authors briefly mention two other state-of-the-art softwares (namely FIJI and napari) but do not really position MorphoNet against them. The text would likely benefit from such a comparison so the users can better decide which one to use or not. 

We agreed that providing a clearer comparison between MorphoNet 2.0 and other widely used tools such as FIJI and Napari would greatly benefit readers and potential users. In response, we included a new paragraph in the supplementary materials of the revised manuscript, highlighting the main features, strengths, and limitations of each tool in the context of 3D+t segmentation, visualization, and correction workflows. This addition helped users better understand the positioning of MorphoNet 2.0 and make informed choices based on their specific needs.

Minor comments: 

L 439: The Deli plugin is mentioned but not introduced in the main text; it could be helpful to have an idea of what it is without having to dive into the supplementary material. 

We included a brief description in the main text and thoroughly revise the help pages to improve clarity

Figure 4: It is not clear how the potential holes created by the removal of objects are handled. Are the empty areas filled by neighboring cells, for example, are they left empty? 

We clarified in the figure legend of Figure 4.

Please remove from the supplementary the use cases that are already in the main text. 

We cleaned up redundant use case descriptions.

Typos: 

L 22: the end of the sentence is missing. 

L 51: There are two "."   

L 370: replace 'et' with 'and'.   

L 407-408, Figure 3: panels g-i, the columns 'curated nuclei' and 'all nuclei' seem to be inverted. 

L 549: "four 4". 

Reviewer #3 (Recommendations for the authors): 

Dear Authors, what follows are "minor comments" (the only sort of comment I have for this nice report): 

Minor issues: 

(1) Not being a user of MorphoNet, I found that reading the manuscript was a bit hard due to the several names of plugins or tools that are mentioned, many times without a clear explanation of what they do. One way of improving this could be to add a table, a sort of glossary, with those names, a brief explanation of what they are, and a link to their "help" page on the web. 

We understood that the manuscript might be difficult to follow for readers unfamiliar with MorphoNet, especially due to the numerous plugin and tool names referenced. To address this, we carried out a complete overhaul of the help pages to make them clearer, more structured, and easier to navigate.

(2) Figure 4d, orthogonal view: It is claimed that this segmentation is correct according to the original intensity image, but it is not clear why some cells in the border actually appear a lot bigger than other cells in the embryo. It does look like an incomplete segmentation due to the poor image quality at the border. Whether this is the case or if the authors consider the contrary, it should be somehow explained/discussed in the figure legend or the main text. 

We revised the figure legend and main text to acknowledge the challenge of segmenting peripheral regions with low signal-to-noise ratios and discussed how this affects segmentation.

Small writing issues I could spot:   

Line 247: there is a double point after "Sup. Mat..". 

Line 329: probably a diagrammation error of the pdf I use to review, there is a loose sentence apparently related to a figure: "Vegetal view ofwith smoothness". 

Line 393 (and many other places): avoid using numbers when it is not a parameter you are talking about, and the number is smaller than 10. In this case, it should be: "The five steps...". 

Line 459: Is "opposite" referring to "Vegetal", like in g? In addition, it starts with lower lowercase. 

Lines 540-541: Check if redaction is correct in "...projected the values onto the meshed dual of the object..." (it sounds obscure to me). 

Lines 548-549: Same thing for "...included two groups of four 4 nuclei and one group of 3 fused nuclei.". 

Line 637: Should it be "Same view as b"? 

Line 646: "The property highlights..."? 

Line 651: In the text, I have seen a "propagation plugin" named as "Prope", "Propa", and now "Propi". Are they all different? Is it a mistake? Please, see my first "Minor issue", which might help readers navigate through this sort of confusing nomenclature. 

Line 702: I personally find the use of the term "eco-system" inappropriate in this context. We scientists know what an ecosystem is, and the fact that it has now become a fashionable word for politicians does not make it correct in any context. 

We thank the reviewer for their careful reading of the manuscript and for pointing out these writing and typographic issues. We corrected all the mentioned points in the revised version, including punctuation, sentence clarity, consistent naming of tools (e.g., the propagation plugin), and appropriate use of terms such as “ecosystem.” We also appreciated the suggestion to avoid numerals for numbers under ten when not referring to parameters, and we ensured consistency throughout the text. These corrections improved the clarity and readability of the manuscript, and we were grateful for the reviewer’s attention to detail.

eLife Assessment

The study presents important insights into the regulation of muscle hypertrophy, regulated by Muscle Ankyrin Repeat Proteins (MARPs) and mTOR. The methods are overall solid and complementary, with only minor limitations. Overall, the findings will be of interest for both muscle-biology specialists and the broader mechanobiology community.

Reviewer #1 (Public review):

Summary:

In this manuscript, the authors employ diaphragm denervation in rats and mice to study titin‑based mechanosensing and longitudinal muscle hypertrophy. By integrating bulk RNA‑seq, proteomics, and phosphoproteomics, they map the stretch‑responsive signalling landscape, uncovering robust induction of the muscle‑ankyrin‑repeat proteins (MARP1‑3) together with enhanced phosphorylation of titin's N2A element. Genetic ablation of MARPs in mice amplifies longitudinal fibre growth and is accompanied by activation of the mTOR pathway, whereas systemic rapamycin treatment suppresses the hypertrophic response, highlighting mTORC1 as a key downstream effector of titin/MARP signalling.

Strengths:

The authors address a clear biological question: "how titin‑associated factors translate mechanical stretch into longitudinal fibre growth" using a unique and clinically relevant animal model of diaphragm denervation. Using a comprehensive multiomics approach, the authors identify MARPs as potential mediators of these effects and use a genetic mouse model to provide compelling evidence supporting causality. Additionally, connecting these findings to rapamycin, a drug widely used clinically, further increases the relevance and potential impact of the study.

Weaknesses:

There are several areas where the manuscript could be substantially improved.

(1) The statistical analysis of multi-omics data needs clarification. Typically, analyses across multiple experimental groups require controlling the false discovery rate (FDR) simultaneously to avoid reporting false-positive findings. It would be very helpful if the authors could specify whether adjusted p-values were calculated using a multi-factorial statistical model (e.g., ~group) or through separate pairwise contrasts.

(2) There are three separate points regarding MARP3 that could be improved. First, the authors report that MARP3-KO mice exhibit smaller increases in muscle mass after diaphragm denervation compared to wild-type mice (a -13% difference), indicating MARP3 likely promotes rather than attenuates hypertrophy. However, the manuscript currently states the opposite (lines 215-216); this interpretation should be revisited. Second, it would be valuable if the authors could provide data showing whether MARP3 transcript or protein levels change response to denervation - if they do not, discussing mechanisms behind the observed phenotype would help clarify the findings. Finally, given that some MARP-KO mice already exhibit baseline differences, employing and reporting the full two-way ANOVA ( including genotype × treatment interaction) would allow a direct statistical assessment of whether MARP deficiency modifies the muscle's response to stretch. This analysis would help clearly resolve any existing ambiguity.

(3) The current presentation of multi-omics data is somewhat difficult to follow, making it challenging to determine whether observed changes occur at the transcript or protein level due to inconsistent gene/protein naming and capitalization (e.g., proper forms are mTOR, p70 S6K, 4E-BP1). Clearly organizing and presenting transcript and protein-level changes side-by-side, especially for key molecules discussed in later experiments, would make the data more accessible and provide clearer insights into the biology of titin-mediated mechanosensing.

(4) The current analysis relies on total protein measurements downstream of mTOR, yet mTOR's primary mode of action is to change phosphorylation status. Because the authors have already generated a phosphoproteomic dataset, it would be very helpful to report - or at least comment on - whether known mTOR target phosphosites were detected and how they respond to denervation and rapamycin. Including even a brief summary of canonical sites such as S6K1 Thr389 or 4E‑BP1 Thr37/46 would make the link between mTOR activity and hypertrophy much clearer.

(5) Finally, since rapamycin blocks only a subset of mTOR signalling, a brief discussion that distinguishes rapamycin‑sensitive from rapamycin‑insensitive pathways would be valuable. Clarifying whether diaphragm stretch relies exclusively on the sensitive branch or also engages the resistant branch would place the results in a broader mTOR context and deepen the mechanistic narrative.

Reviewer #2 (Public review):

Summary:

Muscle hypertrophy is a major regulator of human health and performance. Here, van der Pilj and colleagues assess the role of the giant elastic protein, titin, in regulating the longitudinal hypertrophy of diaphragm muscles following denervation. Interestingly, the authors find an early hypertrophic response, with 30% new serial sarcomeres added within 6 days, followed by subsequent muscle atrophy. Using RBM20 mutant mice, which express a more compliant titin, the authors discovered that this longitudinal hypertrophy is mediated via titin mechanosensing. Through an omics approach, it is suggested that the Muscle ankyrin proteins may regulate this approach. Genetic ablation of MARPs 1-3 blocks the hypertrophic response, although single knockouts are more variable, suggesting extensive complementation between these titin binding proteins. Finally, it is found through the administration of rapamycin that the mTOR signalling pathway plays a role in longitudinal hypertrophic growth.

Strengths:

This paper is well written and uses an impressive suite of genetic mouse models to address this interesting question of what drives longitudinal muscle growth.

Weaknesses:

While the findings are of interest, they lack sufficient mechanistic detail in the current state to separate cross-sectional versus longitudinal hypertrophy. The authors have excellent tools such as the RBM20 model to functionally dissect mTOR signalling to these processes. It is also unclear if this process is unique to the diaphragm or is conserved across other muscle groups during eccentric contractions.

Even among the white slave owners, there was a hierarchy.

Despite of the terrible life they had working as slaves in a farm/plantation, they made light of it all through songs and music.

Seems like children of the slaves met even worse conditions because they only had shirts to cover themselves with. At least this was what the owners provided the parents with.

The very word and title are very contradictory.

A strong argument made by Douglass. It is beneficial for slaveholders to rape their enslaved women. To satisfy their carnal thirst and monetary needs.

Discover how SOLID principles improve iOS development in Swift. Learn to write clean, scalable, and maintainable code with real-world examples and best practices.

In Finnish NFI the distance between the SSUs is set to 300 meters, because the range of autocorrelation is shorter than that. Maybe you could mention that if there is info about the autocorrelation, then this effect can be diminished.

but the precision can be improved, if you can measure more plots or SSUs than with SRS or SYS.

My experience is that using the model is always better than not using the model, even when the model is very poor indeed. I have tested with very poor models. I am not sure what you mean by accounting for the uncertainty here. Since the formulas are based on the observed errors from the models, what else is there to account for?

I do not agree. In design-based setting, the working model does not need to be correct. In model-based setting the situation is obviously different. I remember (from way back when I was teaching from it) that there is mention of some kind of bias in Cochran's book regarding regression estimator, but generally I think we nowadays consider model-assisted estimation as design-unbiased, at least approximately design-unbiased. Need to check from my Yello book, but I anyway think it is not correct to state the validity of regression estimation depends on the validity of the model.

This part seems maybe overly simplistic, as everybody and their dog is nowadays using model-assisted estimation, and much more complicated models. Or that is how it feels. While the one-predictor model is good for getting familiar with the idea, I think it would be good to at least mention the term model-assisted, and explain that very much more complicated models are possible.

Göran Ståhl once gave me a lesson about this. I know I have written this sentence in my chapter in 2006, but Göran later proved me wrong. It is not biased, it is just increased variance. If you go through all the possible starting points, and calculate all the possible results, take a mean of them, it should be exactly unbiased.

You mean for periodic populations? Usually it is assumed to overestimate, as we generally assume a trend in the population. I think this should be made clear, the proof for this should be in Matern's paper from 1960.

even more like a cluster sampling with one cluster.

But they can overestimate the variance so much that they are not useful anymore.

This is not the best of approaches, as when there is a trend in the data, and systematic sample is truly more precise than SRS, using the SRS estimator does not show it. Instead, it overestimates the variance. There are estimators available that work better, depending on differences between neighbours. By Grafström. See Räty, M., Kuronen, M., Myllymäki, M., Kangas, A., Mäkisara, K., Heikkinen J. 2020. Comparison of the local pivotal method and systematic sampling for national forest inventories. Forest Ecosystems 7: 54.

I do not know if it is of any importance here, but it could also be a pseudo-systematic sample. So that make a grid of desired size, and make a simple random sample of one unit from each. That would also be more like a real random sample.

This is only true, if there is a trend in the population. If the units are completely randomly assigned, the accuracy is the same as in SRS.

really?? a link would be helpful.

will synthesize?

running in Colab it is: Treblinka GPE

before

before

before

I see that this book is meant to be very practice-oriented and directed for students not at all familiar with sampling beforehand. However, I think it would be very important to include also the concepts such as inclusion probability and its relation to the Cochranian notation style you have chosen to use. The Horwitz-Thompson estimator, I mean. I know that students in statistics in Finland do not learn the Cochranian style at all anymore, all they learn about sampling is the inclusion probability -based notation. It would be important (for the next level courses, such as model-assisted methods) to understand the inclusion probabilities, otherwise the step to next level will be quite high. I noted that you had explained the inclusion zone for trees, which is a related concept, and could benefit from the relations to the inclusion probabilities. Like in the book by Mandallaz.

But the systematic sample is still really one big cluster of plots, because when you select one plot, you select all of them. I noticed that you explained this later on, but I would prefer mentioning the problem also here. To avoid misunderstandings.

Do you mean within plot boundaries? Or that only boundary trees are measured to check whether they are in or out?

Astonishing, in a way, to see guidance so concretely dependent on the particular technology, given the abstract nature of most of this. Of course he really means duration, time, but it's like saying "a film should be 65 to 75 gigabytes, as not to exhaust the viewer".

Open - Source - Construct - Sauce

leading to emergent Open Standards =

not specification that can b implemented across platforms

but treating the Brwwser as a Universal Platform

allowing "implementations" across a whole variety

of non-functional requirements

constellations designed to expand/scale according to the number of

• participants
• sychronization
  • real time
  • asynchronous
• consitency
  • eventual
  • instantaneous
• reach
• discoerabiity
• privacy
• security
• availability

make assummed impossibiities ineviatable

Zooko's triangle

1.eles estão na praia 2,no domingo 3,é perta 4,perto da cadeira 5,mais tarde

DOI: 10.3389/fimmu.2025.1664463

Resource: (NIH Nonhuman Primate Reagent Resource Cat# PR-1547, RRID:AB_2716324)

Curator: @giovanni.decastro

SciCrunch record: RRID:AB_2716324

What is this?

DOI: 10.3389/fimmu.2025.1664463

Resource: (NIH Nonhuman Primate Reagent Resource Cat# PR-4047, RRID:AB_2716325)

Curator: @giovanni.decastro

SciCrunch record: RRID:AB_2716325

What is this?

DOI: 10.1038/s41467-025-63325-1

Resource: (NIH Nonhuman Primate Reagent Resource Cat# PR-0817, RRID:AB_2716320)

Curator: @giovanni.decastro

SciCrunch record: RRID:AB_2716320

What is this?

mutton comes up repeatedly in the diet, it would be interesting to see if this also comes up in the Han diet later on. My guess, is it wont be as central to the Han diet as it is to the Uyghur diet

I agree with this. I'm taking 380 as well, and we just did a lesson on User Stories and Personas. This is almost sounds like the One User Fallacy, where a product is designed with one "generic user" in mind, which doesn't exist. This leads to people having their needs missed or creating a solution to a problem that doesn't exist.

When thinking about this specific section within the text, this paragraph about consistency and standards stood out to me because it shows how something as simple as consistency can completely shape the user experience. The example makes the point really clear, as when shortcuts and commands stay the same across applications, it saves users from constantly having to relearn basic actions. It also shows how thoughtful design is not always about adding new features, but about creating familiarity and predictability. That kind of consistency builds trust. When a user has familiarity with something that expectation of the user knowing and controlling understandably where they are and being able to direct where they want to head to is super important in terms of comfortability.

I really agree with this because when a system doesn’t show what it’s doing, I get confused or . I hate when I click something and I’m not sure if it worked, if I need to wait, or if something broke. Clear system status makes the experience way easier and reduces a lot of stress. It reminded me that even small UI choices affect whether users feel confident or lost when using something.

I think usability tests are very useful for identifying interface breakdowns. However, they can’t show whether a design truly provides value in real life. Designers often focus on whether users can complete tasks in a controlled setting. They assume that success there means success in the real world. As Ko points out, task completion alone doesn’t measure whether someone would actually want to use the product or integrate it into their routine. I’ve seen prototypes work perfectly in tests but fail when deployed because the tasks felt artificial or didn’t meet real user needs. That’s why real-world testing is so important. It’s absolutely harder and takes a lot more time, but it truly shows how people actually use a design in their daily lives.

When thinking about designers perspectives and role this does not surprise me, however thinking from the users perspective it does as I always assumed user studies were the ultimate way to test a design, but considering what the author stated about them (at times) missing fundamental flaws which can change that perspective. It’s interesting how the author says that considering designers define the tasks, the results can’t show whether people would actually want to do those tasks in real life. It makes me realize how important it is to test a design’s real-world value, not just its usability from seeing if people would actually miss it if it were taken away.

I agree with this. You want to know if a user would be able to use your design without any explicit instructions, so you should test it by not giving your participant any specific instructions. The design should be so easy to use that anyone could pick it up and start using it.

I liked how this part because i think a lot of people, including me, tend to feel judged when we try something new in front of others. This kinda push the responsibility back onto us as designers, which I think is more fair and also encourages better feedback.

It also reminded me how easy it is to assume user are thing wrong but in reality, if they are confuse, the design and interface didnt not pass and failed. This is what I also talked about in my essay for informatics.

As there is no design schema - is this not to be connected to 5v? Instead of pin 5? Is this not an error?

1,a gente aprende a falar português na aula 2,Hoje é sábato ,nós irem para praia no sábato 3,Beto está na banco,ele chegará em casa mais tarde 4,Anita gosto de aprender da 5,De que cor é a bandeira no Estados Unidos

Não ,você não perto da parede Não,eu não gosta de ir ao clube Não,eu não portugês Não,ele não prepara a lição tudos os dias Não ele não pronuncia bem a palavra Não, ele não na igreja Não,vocês não alunos de inglês Não,nós não visitam os parentes Não, vocês não estão longe da famácia Não, eles não vão à loja Não,nós não somos bons alunos Não, eles não repetem o vocabulário com satisfação

sim,eles estão dentro da caixa

sim eles são bonitas

sim, eles escrevem no quadro

sim eles são dentistas

sim eles gostam da camiseta amarela

https://hyp.is/ZQzJ4rlPEfCuckNr506v0g/www.youtube.com/watch?v=KU_dz56Bu30

https://via.hypothes.is/https://hyperpost.peergos.me/%F0%9F%8E%AD/gyuri/do/web/snarf/%F0%9F%93%85/2025/11/2/Dienes.Istv%C3%A1n-Tudom%C3%A1ny-tudat-%C3%BAjrat%C3%B6ltve/

https://peergos.net/secret/z59vuwzfFDorrRTiasuWPoJP6QmenGbG1qqA9h8vKR3sb4TgaXVhMbT/1570054938#E7LNmB2YLape

https://peergos.net/secret/z59vuwzfFDorrRTiasuWPoJP6QmenGbG1qqA9h8vKR3sb4TgaXVhMbT/1570054938#%7B%22app%22:%22htmlviewer%22%2c%22path%22:%22hyperpost/%F0%9F%8C%90/%F0%9F%8E%AD/gyuri/do/web/snarf/%F0%9F%93%85/2025/11/2%22%2c%22args%22:%7B%22filename%22:%22(1143)%20Dienes%20Istv%C3%A1n-Tudom%C3%A1ny%20%C3%A9s%20tudat%20%C3%BAjrat%C3%B6ltve%20-%20YouTube%20(11_4_2025%207%EF%BC%9A45%EF%BC%9A47%20AM).html%22%7D%2c%22writable%22:false%2c%22secretLink%22:true%2c%22linkpassword%22:%22E7LNmB2YLape%22%7D

it seems hard to interpret exact results of what we want to see, we don't have an exact loss metric to look at, its more of our interpretation based on our own knowledge of the clustering quality

then we well use the clustering algorithm i know shocker

Then we create some sort of similarity metric, it requires a deeper understanding of our data, I assume this means choosing like euclidian, manhattan, cosine, etc.

Like usual we always need to normally prepare our data, if we accurately calculate a similarity then awesome sauce

It basically calculates a bunch of arithmetic means that finds the data closest to the mean, with means/clusters farthest from eachother

k-means is the main focus, it's O(n) so its much more efficient, I have yet to learn how right now

Algorithms that operate in O(n^2) are VERY INEFFICIENT, specifically ones that calculate the similarity between each individual object of data, out of MILLIONS of data

instead of multiple different sets of features for youtube videos, you can just use a cluster id that represents this data

Funny enough this is a great way to compress data to make features less complex

Clusters can help with imputations, by just infering what that data should have from the same cluster groups

each group has a unique cluster id, it can help us basically turn a bunch of data into a small amount of features

You would choose different similarity measures depending on the data you're handling

more features = more complex to compare similarity

we need a similarity measure, its a metric for comparing how close datapoints actually are

Clustering, unsupervised learning, no use of labels, tries to predict the grouping and relationship of the data