La idea y el uso de las libretas computacionales es menos generales que la de (Inter) Personal Knowledge Management, como era de esperarse y ha evidenciado nuestras prácticas en la comunidad de Grafoscopio, donde sus libretas interactivas fueron usadas extensivamente y de acuerdo a las necesidades descubiertas con la comunidad en la creación y articulación de flujos documentales a medida. Algo similar se puede decir en las ciencias sociales y humanas, donde también se escribe, pero no simulaciones de sistemas complejos.

En estos contextos comunitarios y de las ciencias amplias y estudios críticos, el hipertexto y la interactividad puede servir, pero más para explorar la memoria propia, imaginar y enactuar otras formas de comunicarla y construirla.

El énfasis actual en Cardumem, en lugar de Grafoscopio, y los posibles vínculos entre ambos reconoce estas otras posibilidades de interacción y computación desde esa memoria interpersonal e interactiva.

Lo he notado con las Ciencias Archivisticas Computacionales, o CAS, por su sigla en inglés y mi rol dentro del departamento de Ciencias de la Información de la PUJ, alentando dicha transición

Bret Victor, en Media for thinking the unthikable, compara a Leibnitz con Steve Jobs, diciendo que el primero era un gran inventor de interfaces de su época, en la forma de nuevas notaciones.

Cardumem, de hecho, empezó como un ejercicio mental pensando una nueva notación para expresar "álgebras hipertextuales" que pudieran ser embebidas en un motor wiki (al comienzo vía Lua en JavaScript y luego del lado del servidor, con sistemas hipermedia). Y dicha notación fue concebida en la medida en que las herramientas externas para manipular hipertexto, como TiddlyWiki Pharo generaban mucha fricción en los miembros de la comunidad de Grafoscopio, al punto que su uso era marginal. Una nueva herramienta con una nueva notación alentaría usos y personalizaciones compartidas que con herramientas separadas eran muy esporpadicos y más bien solitarios, entre un puñado de personas, a lo sumo.

Dado que las piezas para armar el wiki (Djot y YueScript) ya estaban integradas al ecosistema Lua, y que originalmente se pensaba en integrarlo directamente a TiddlyWiki, del lado del navegador web, en lugar del servidor, Lua este fue elegido en lugar de Pharo para los procesos de prototipo, dando muy buenos resultados iniciales hasta el momento.

O tener transiciones multiformato, entre sistemas más análogos y más digitales, dependiendo de dónde el artículo es compartido como decía en este otro comentario

Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public review):

Summary:

Here, the authors have addressed the recruitment and firing patterns of motor units (MUs) from the long and lateral heads of the triceps in the mouse. They used their newly developed Myomatrix arrays to record from these muscles during treadmill locomotion at different speeds, and they used template-based spike sorting (Kilosort) to extract units. Between MUs from the two heads, the authors observed differences in their firing rates, recruitment probability, phase of activation within the locomotor cycle, and interspike interval patterning. Examining different walking speeds, the authors find increases in both recruitment probability and firing rates as speed increases. The authors also observed differences in the relation between recruitment and the angle of elbow extension between motor units from each head. These differences indicate meaningful variation between motor units within and across motor pools and may reflect the somewhat distinct joint actions of the two heads of triceps.

Strengths:

The extraction of MU spike timing for many individual units is an exciting new method that has great promise for exposing the fine detail in muscle activation and its control by the motor system. In particular, the methods developed by the authors for this purpose seem to be the only way to reliably resolve single MUs in the mouse, as the methods used previously in humans and in monkeys (e.g. Marshall et al. Nature Neuroscience, 2022) do not seem readily adaptable for use in rodents.

The paper provides a number of interesting observations. There are signs of interesting differences in MU activation profiles for individual muscles here, consistent with those shown by Marshall et al. It is also nice to see fine-scale differences in the activation of different muscle heads, which could relate to their partially distinct functions. The mouse offers greater opportunities for understanding the control of these distinct functions, compared to the other organisms in which functional differences between heads have previously been described.

The Discussion is very thorough, providing a very nice recounting of a great deal of relevant previous results.

We thank the Reviewer for these comments.

Weaknesses:

The findings are limited to one pair of muscle heads. While an important initial finding, the lack of confirmation from analysis of other muscles acting at other joints leaves the general relevance of these findings unclear.

The Reviewer raises a fair point. While outside the scope of this paper, future studies should certainly address a wider range of muscles to better characterize motor unit firing patterns across different sets of effectors with varying anatomical locations. Still, the importance of results from the triceps long and lateral heads should not be understated as this paper, to our knowledge, is the first to capture the difference in firing patterns of motor units across any set of muscles in the locomoting mouse.

While differences between muscle heads with somewhat distinct functions are interesting and relevant to joint control, differences between MUs for individual muscles, like those in Marshall et al., are more striking because they cannot be attributed potentially to differences in each head's function. The present manuscript does show some signs of differences for MUs within individual heads: in Figure 2C, we see what looks like two clusters of motor units within the long head in terms of their recruitment probability. However, a statistical basis for the existence of two distinct subpopulations is not provided, and no subsequent analysis is done to explore the potential for differences among MUs for individual heads.

We agree with the Reviewer and have revised the manuscript to better examine potential subpopulations of units within each muscle as presented in Figure 2C. We performed Hartigan’s dip test on motor units within each muscle to test for multimodal distributions. For both muscles, p > 0.05, so we cannot reject the null hypothesis that the units in each muscle come from a multimodal distribution. However, Hartigan’s test and similar statistical methods have poor statistical power for the small sample sizes (n=17 and 16 for long and lateral heads, respectively) considered here, so the failure to achieve statistical significance might reflect either the absence of a true difference or a lack of statistical resolution.

Still, the limited sample size warrants further data collection and analysis since the varying properties across motor units may lead to different activation patterns. Given these results, we have edited the text as follows:

“A subset of units, primarily in the long head, were recruited in under 50% of the total strides and with lower spike counts (Figure 2C). This distribution of recruitment probabilities might reflect a functionally different subpopulation of units. However, the distribution of recruitment probabilities were not found to be significantly multimodal (p>0.05 in both cases, Hartigan’s dip test; Hartigan, 1985). However, Hartigan’s test and similar statistical methods have poor statistical power for the small sample sizes (n=17 and 16 for long and lateral heads, respectively) considered here, so the failure to achieve statistical significance might reflect either the absence of a true difference or a lack of statistical resolution.”

The statistical foundation for some claims is lacking. In addition, the description of key statistical analysis in the Methods is too brief and very hard to understand. This leaves several claims hard to validate.

We thank the Reviewer for these comments and have clarified the text related to key statistical analyses throughout the manuscript, as described in our other responses below.

Reviewer #2 (Public review):

The present study, led by Thomas and collaborators, aims to describe the firing activity of individual motor units in mice during locomotion. To achieve this, they implanted small arrays of eight electrodes in two heads of the triceps and performed spike sorting using a custom implementation of Kilosort. Simultaneously, they tracked the positions of the shoulder, elbow, and wrist using a single camera and a markerless motion capture algorithm (DeepLabCut). Repeated one-minute recordings were conducted in six mice at five different speeds, ranging from 10 to 27.5 cm·s^-1.

From these data, the authors reported that:

(1) a significant portion of the identified motor units was not consistently recruited across strides,

(2) motor units identified from the lateral head of the triceps tended to be recruited later than those from the long head,

(3) the number of spikes per stride and peak firing rates were correlated in both muscles, and

(4) the probability of motor unit recruitment and firing rates increased with walking speed.

The authors conclude that these differences can be attributed to the distinct functions of the muscles and the constraints of the task (i.e., speed).

Strengths:

The combination of novel electrode arrays to record intramuscular electromyographic signals from a larger muscle volume with an advanced spike sorting pipeline capable of identifying populations of motor units.

We thank the Reviewer for this comment.

Weaknesses:

(1) There is a lack of information on the number of identified motor units per muscle and per animal.

The Reviewer is correct that this information was not explicitly provided in the prior submission. We have therefore added Table 1 that quantifies the number of motor units per muscle and per animal.

(2) All identified motor units are pooled in the analyses, whereas per-animal analyses would have been valuable, as motor units within an individual likely receive common synaptic inputs. Such analyses would fully leverage the potential of identifying populations of motor units.

Please see our answer to the following point, where we address questions (2) and (3) together.

(3) The current data do not allow for determining which motor units were sampled from each pool. It remains unclear whether the sample is biased toward high-threshold motor units or representative of the full pool.

We thank the Reviewer for these comments. To clarify how motor unit responses were distributed across animals and muscle targets, we updated or added the following figures:  

Figure 2C

Figure 4–figure supplement 1

Figure 5–figure supplement 2

Figure 6–figure supplement 2

These provide a more complete look at the range of activity within each motor pool, suggesting that we do measure from units with different activation thresholds within the same motor pool, rather than this variation being due to cross-animal differences. For example, Figure 2C illustrates that motor units from the same muscle and animal show a wide variety of recruitment probabilities. However, the limited number of motor units recorded from each individual animal does not allow a statistically rigorous test for examining cross-animal differences.

(4) The behavioural analysis of the animals relies solely on kinematics (2D estimates of elbow angle and stride timing). Without ground reaction forces or shoulder angle data, drawing functional conclusions from the results is challenging.

The Reviewer is correct that we did not measure muscular force generation or ground reaction forces in the present study. Although outside the scope of this study, future work might employ buckle force transducers as used in larger animals (Biewener et al., 1988; Karabulut et al., 2020) to examine the complex interplay between neural commands, passive biomechanics, and the complex force-generating properties of muscle tissue.

Major comments:

(1) Spike sorting

The conclusions of the study rely on the accuracy and robustness of the spike sorting algorithm during a highly dynamic task. Although the pipeline was presented in a previous publication (Chung et al., 2023, eLife), a proper validation of the algorithm for identifying motor unit spikes is still lacking. This is particularly important in the present study, as the experimental conditions involve significant dynamic changes. Under such conditions, muscle geometry is altered due to variations in both fibre pennation angles and lengths.

This issue differs from electrode drift, and it is unclear whether the original implementation of Kilosort includes functions to address it. Could the authors provide more details on the various steps of their pipeline, the strategies they employed to ensure consistent tracking of motor unit action potentials despite potential changes in action potential waveforms, and the methods used for manual inspection of the spike sorting algorithm's output?

This is an excellent point and we agree that the dynamic behavior used in this investigation creates potential new challenges for spike sorting. In our analysis, Kilosort 2.5 provides key advantages in comparing unit waveforms across multiple channels and in detecting overlapping spikes. We modified this version of Kilosort to construct unit waveform templates using only the channels within the same muscle (Chung et al., 2023), as clarified in the revised Methods section (see “Electromyography (EMG)”):

“A total of 33 units were identified across all animals. Each unit’s isolation was verified by confirming that no more than 2% of inter-spike intervals violated a 1 ms refractory limit. Additionally, we manually reviewed cross-correlograms to ensure that each waveform was only reported as a single motor unit.”

The Reviewer is correct that our ability to precisely measure a unit’s activity based on its waveform will depend on the relationship between the embedded electrode and the muscle geometry, which alters over the course of the stride. As a follow-up to the original text, we have included new analyses to characterize the waveform activity throughout the experiment and stride (also in Methods):

“We further validated spike sorting by quantifying the stability of each unit’s waveform across time (Figure 1–figure supplement 1). First, we calculated the median waveform of each unit across every trial to capture long-term stability of motor unit waveforms. Additionally, we calculated the median waveform through the stride binned in 50 ms increments using spiking from a single trial. This second metric captures the stability of our spike sorting during the rapid changes in joint angles that occur during the burst of an individual motor unit. In doing so, we calculated each motor unit’s waveforms from the single channel in which that unit’s amplitude was largest and did not attempt to remove overlapping spikes from other units before measuring the median waveform from the data. We then calculated the correlation between a unit’s waveform over either trials or bins in which at least 30 spikes were present. The high correlation of a unit waveform over time, despite potential changes in the electrodes’ position relative to muscle geometry over the dynamic task, provides additional confidence in both the stability of our EMG recordings and the accuracy of our spike sorting.”

(2) Yield of the spike sorting pipeline and analyses per animal/muscle

A total of 33 motor units were identified from two heads of the triceps in six mice (17 from the long head and 16 from the lateral head). However, precise information on the yield per muscle per animal is not provided. This information is crucial to support the novelty of the study, as the authors claim in the introduction that their electrode arrays enable the identification of populations of motor units. Beyond reporting the number of identified motor units, another way to demonstrate the effectiveness of the spike sorting algorithm would be to compare the recorded EMG signals with the residual signal obtained after subtracting the action potentials of the identified motor units, using a signal-to-residual ratio.

Furthermore, motor units identified from the same muscle and the same animal are likely not independent due to common synaptic inputs. This dependence should be accounted for in the statistical analyses when comparing changes in motor unit properties across speeds and between muscles.

We thank the Reviewer for this comment. Regarding motor unit yield, as described above the newly-added Table 1 displays the yield from each animal and muscle.

Regarding spike sorting, while signal-to-residual is often an excellent metric, it is not ideal for our high-resolution EMG signals since isolated single motor units are typically superimposed on a “bulk” background consisting of the low-amplitude waveforms of other motor units. Because these smaller units typically cannot be sorted, it is challenging to estimate the “true” residual after subtracting (only) the largest motor unit, since subtracting each sorted unit’s waveform typically has a very small effect on the RMS of the total EMG signal. To further address concerns regarding spike sorting quality, we added Figure 1–figure supplement 1 that demonstrates motor units’ consistency over the experiment, highlighting that the waveform maintains its shape within each stride despite muscle/limb dynamics and other possible sources of electrical noise or artifact.

Finally, the Reviewer is correct that individual motor units in the same muscle are very likely to receive common synaptic inputs. These common inputs may reflect in sparse motor units being recruited in overlapping rather than different strides. Indeed, in the following added to the Results, we identified that motor units are recruited with higher probability when additional units are recruited.

“Probabilistic recruitment is correlated across motor units

Our results show that the recruitment of individual motor units is probabilistic even within a single speed quartile (Figure 5A-C) and predicts body movements (Figure 6), raising the question of whether the recruitment of individual motor units are correlated or independent. Correlated recruitment might reflect shared input onto the population of motor units innervating the muscle (De Luca, 1985; De Luca & Erim, 1994; Farina et al., 2014). For example, two motor units, each with low recruitment probabilities, may still fire during the same set of strides. To assess the independence of motor unit recruitment across the recorded population, we compared each unit’s empirical recruitment probability across all strides to its conditional recruitment probability during strides in which another motor unit from the same muscle was recruited (Figure 7). Doing this for all motor unit pairs revealed that motor units in both muscles were biased towards greater recruitment when additional units were active (p<0.001, Wilcoxon signed-rank tests for both the lateral and long heads of triceps). This finding suggests that probabilistic recruitment reflects common synaptic inputs that covary together across locomotor strides.”

(3) Representativeness of the sample of identified motor units

However, to draw such conclusions, the authors should exclusively compare motor units from the same pool and systematically track violations of the recruitment order. Alternatively, they could demonstrate that the motor units that are intermittently active across strides correspond to the smallest motor units, based on the assumption that these units should always be recruited due to their low activation thresholds.

One way to estimate the size of motor units identified within the same muscle would be to compare the amplitude of their action potentials, assuming that all motor units are relatively close to the electrodes (given the selectivity of the recordings) and that motoneurons innervating more muscle fibres generate larger motor unit action potentials.

We thank the Reviewer for this comment. Below, we provide more detailed analyses of the relationships between motor unit spike amplitude and the recruitment probability as well as latency (relative to stride onset) of activation.

We generated the below figures to illustrate the relationship between the amplitude of motor units and their firing properties. As suspected, units with larger-amplitude waveforms fired with lower probability and produced their first spikes later in the stride. If we were comfortable assuming that larger spike amplitudes mean higher-force units, then this would be consistent with a key prediction of the size principle (i.e. that higher-force units are recruited later). However, we are hesitant to base any conclusions on this assumption or emphasize this point with a main-text figure, since EMG signal amplitude may also vary due to the physical properties of the electrode and distance from muscle fibers. Thus it is possible that a large motor unit may have a smaller waveform amplitude relative to the rest of the motor pool.

Author response image 1.

Relation between motor unit amplitude and (A) recruitment probability and (B) mean first spike time within the stride. Colored lines indicate the outcome of linear regression analyses.

Currently, the data seem to support the idea that motor units that are alternately recruited across strides have recruitment thresholds close to the level of activation or force produced during slow walking. The fact that recruitment probability monotonically increases with speed suggests that the force required to propel the mouse forward exceeds the recruitment threshold of these "large" motor units. This pattern would primarily reflect spatial recruitment following the size principle rather than flexible motor unit control.

We thank the Reviewer for this comment. We agree with this interpretation, particularly in relation to the references suggested in later comments, and have added the following text to the Discussion to better reflect this argument:

“To investigate the neuromuscular control of locomotor speed, we quantified speed-dependent changes in both motor unit recruitment and firing rate. We found that the majority of units were recruited more often and with larger firing rates at faster speeds (Figure 5, Figure5–figure supplement 1). This result may reflect speed-dependent differences in the common input received by populations of motor neurons with varying spiking thresholds (Henneman et al., 1965). In the case of mouse locomotion, faster speeds might reflect a larger common input, increasing the recruitment probability as more neurons, particularly those that are larger and generate more force, exceed threshold for action potentials (Farina et al., 2014).”

(4) Analysis of recruitment and firing rates

The authors currently report active duration and peak firing rates based on spike trains convolved with a Gaussian kernel. Why not report the peak of the instantaneous firing rates estimated from the inverse of the inter-spike interval? This approach appears to be more aligned with previous studies conducted to describe motor unit behaviour during fast movements (e.g., Desmedt & Godaux, 1977, J Physiol; Van Cutsem et al., 1998, J Physiol; Del Vecchio et al., 2019, J Physiol).

We thank the Reviewer for this comment. In the revised Discussion (see ‘Firing rates in mouse locomotion compared to other species’) we reference several examples of previous studies that quantified spike patterns based on the instantaneous firing rate. We chose to report the peak of the smoothed firing rate because that quantification includes strides with zero spikes or only one spike, which occur regularly in our dataset (and for which ISI rate measures, which require two spikes to define an instantaneous firing rate, cannot be computed). Regardless, in the revised Figure 4B, we present an analysis that uses inter-spike intervals as suggested, which yielded similar ranges of firing rates as the primary analysis.

(5) Additional analyses of behaviour

The authors currently analyse motor unit recruitment in relation to elbow angle. It would be valuable to include a similar analysis using the angular velocity observed during each stride, re broadly, comparing stride-by-stride changes in firing rates with changes in elbow angular velocity would further strengthen the final analyses presented in the results section.

We thank the Reviewer for this comment. To address this, we have modified Figure 6 and the associated Supplemental Figures, to show relationships in unit activation with both the range of elbow extension and the range of elbow velocity for each stride. These new Supplemental Figures show that the trends shown in main text Figure 6C and 6E (which show data from all speed quartiles on the same axes) are also apparent in both the slower and faster quartiles individually, although single-quartile statistical tests (with smaller sample size than the main analysis) not reach statistical significance in all cases.

Reviewer #3 (Public review):

Summary:

Using the approach of Myomatrix recording, the authors report that:

(1) Motor units are recruited differently in the two types of muscles.

(2) Individual units are probabilistically recruited during the locomotion strides, whereas the population bulk EMG has a more reliable representation of the muscle.

(3) The recruitment of units was proportional to walking speed.

Strengths:

The new technique provides a unique data set, and the data analysis is convincing and well-performed.

We thank the Reviewer for the comment.

Weaknesses:

The implications of "probabilistical recruitment" should be explored, addressed, and analyzed further.

Comments:

One of the study's main findings (perhaps the main finding) is that the motor units are "probabilistically" recruited. The authors do not define what they mean by probabilistically recruited, nor do they present an alternative scenario to such recruitment or discuss why this would be interesting or surprising. However, on page 4, they do indicate that the recruitment of units from both muscles was only active in a subset of strides, i.e., they are not reliably active in every step.

If probabilistic means irregular spiking, this is not new. Variability in spiking has been seen numerous times, for instance in human biceps brachii motor units during isometric contractions (Pascoe, Enoka, Exp physiology 2014) and elsewhere. Perhaps the distinction the authors are seeking is between fluctuation-driven and mean-driven spiking of motor units as previously identified in spinal motor networks (see Petersen and Berg, eLife 2016, and Berg, Frontiers 2017). Here, it was shown that a prominent regime of irregular spiking is present during rhythmic motor activity, which also manifests as a positive skewness in the spike count distribution (i.e., log-normal).

We thank the Reviewer for this comment and have clarified several passages in response. The Reviewer is of course correct that irregular motor unit spiking has been described previously and may reflect motor neurons’ operating in a high-sensitivity (fluctuation-driven) regime. We now cite these papers in the Discussion (see ‘Firing rates in mouse locomotion compared to other species’). Additionally, the revision clarifies that “probabilistically” - as defined in our paper - refers only to the empirical observation that a motor unit spikes during only a subset of strides, either when all locomotor speeds are considered together (Figure 2) or separately (Figure 5A-C):

“Motor units in both muscles exhibited this pattern of probabilistic recruitment (defined as a unit’s firing on only a fraction of strides), but with differing distributions of firing properties across the long and lateral heads (Figure 2).”

“Our findings (Figure 4) highlight that even with the relatively high firing rates observed in mice, there are still significant changes in firing rate and recruitment probability across the spikes within bursts (Figure 4B) and across locomotor speeds (Figure 5F). Future studies should more carefully examine how these rapidly changing spiking patterns derive from both the statistics of synaptic inputs and intrinsic properties of motor neurons (Manuel & Heckman, 2011; Petersen & Berg, 2016; Berg, 2017).”

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

As mentioned above, there are several issues with the statistics that need to be corrected to properly support the claims made in the paper.

The authors compare the fractions of MUs that show significant variation across locomotor speeds in their firing rate and recruitment probability. However, it is not statistically founded to compare the results of separate statistical tests based on different kinds of measurements and thus have unconstrained differences in statistical power. The comparison of the fractional changes in firing rates and recruitment across speeds that follow is helpful, though in truth, by contemporary standards, one would like to see error bars on these estimates. These could be generated using bootstrapping.

The Reviewer is correct, and we have revised the manuscript to better clarify which quantities should or should not be compared, including the following passage (see “Motor unit mechanisms of speed control” in Results):

“Speed-dependent increases in peak firing rate were therefore also present in our dataset, although in a smaller fraction of motor units (22/33) than changes in recruitment probability (31/33). Furthermore, the mean (± SE) magnitude of speed-dependent increases was smaller for spike rates (mean rate_fast/rate_slow of 111% ± 20% across all motor units) than for recruitment probabilities (mean p(recruitment) _fast/p(recruitment) _slow of 179% ± 3% across all motor units). While fractional changes in rate and recruitment probability are not readily comparable given their different upper limits, these findings could suggest that while both recruitment and peak rate change across speed quartiles, increased recruitment probability may play a larger role in driving changes in locomotor speed.”

The description in the Methods of the tests for variation in firing rates and recruitment probability across speeds are extremely hard to understand - after reading many times, it is still not clear what was done, or why the method used was chosen. In the main text, the authors quote p-values and then state "bootstrap confidence intervals," which is not a statistical test that yields a p-value. While there are mathematical relationships between confidence intervals and statistical tests such that a one-to-one correspondence between them can exist, the descriptions provided fall short of specifying how they are related in the present instance. For this reason, and those described in what follows, it is not clear what the p-values represent.

Next, the authors refer to fitting a model ("a Poisson distribution") to the data to estimate firing rate and recruitment probability, that the model results agree with their actual data, and that they then bootstrapped from the model estimates to get confidence intervals and compute p-values. Why do this? Why not just do something much simpler, like use the actual spike counts, and resample from those? I understand that it is hard to distinguish between no recruitment and just no spikes given some low Poisson firing rate, but how does that challenge the ability to test if the firing rates or the number of spiking MUs changes significantly across speeds? I can come up with some reasons why I think the authors might have decided to do this, but reasoning like this really should be made explicit.

In addition, the authors would provide an unambiguous description of the model, perhaps using an equation and a description of how it was fit. For the bootstrapping, a clear description of how the resampling was done should be included. The focus on peak firing rate instead of mean (or median) firing rate should also be justified. Since peaks are noisier, I would expect the statistical power to be lower compared to using the mean or median.

We thank the Reviewer for the comments and have revised and expanded our discussion of the statistical tests employed. We expanded and clarified our description of these techniques in the updated Methods section:

“Joint model of rate and recruitment

We modeled the recruitment probability and firing rate based on empirical data to best characterize firing statistics within the stride. Particularly, this allowed for multiple solutions to explain why a motor unit would not spike within a stride. From the empirical data alone, strides with zero spikes would have been assumed to have no recruitment of a unit. However, to create a model of motor unit activity that includes both recruitment and rate, it must be possible that a recruited unit can have a firing rate of zero. To quantify the firing statistics that best represent all spiking and non-spiking patterns, we modeled recruitment probability and peak firing rate along the following piecewise function:

where y denotes the observed peak firing rate on a given stride (determined by convolving motor unit spike times with a Gaussian kernel as described above), p denotes the probability of recruitment, and λ denotes the expected peak firing rate from a Poisson distribution of outcomes. Thus, an inactive unit on a given stride may be the result of either non-recruitment or recruitment with a stochastically zero firing rate. The above equations were fit by minimizing the negative log-likelihood of the parameters given the data.

“Permutation test for joint model of rate and recruitment and type 2 regression slopes

To quantify differences in firing patterns across walking speeds, we subdivided each mouse’s total set of strides into speed quartiles and calculated rate (𝜆, Eq. 1 and 2, Fig. 5A-C) and recruitment probability terms (p, Eq. 1 and 2, Fig. 5D-F) for each unit in each speed quartile. Here we calculated the difference in both the rate and recruitment terms across the fastest and slowest speed quartiles (p_fast-p_slow and 𝜆_fast-𝜆_slow). To test whether these model parameters were significantly different depending on locomotor speed, we developed a null model combining strides from both the fastest and slowest speed quartiles. After pooling strides from both quartiles, we randomly distributed the pooled set of strides into two groups with sample sizes equal to the original slow and fast quartiles. We then calculated the null model parameters for each new group and found the difference between like terms. To estimate the distribution of possible differences, we bootstrapped this result using 1000 random redistributions of the pooled set of strides. Following the permutation test, the 95% confidence interval of this final distribution reflects the null hypothesis of no difference between groups. Thus, the null hypothesis can be rejected if the true difference in rate or recruitment terms exceeds this confidence interval.

We followed a similar procedure to quantify cross-muscle differences in the relationship between firing parameters. For each muscle, we estimated the slope across firing parameters for each motor unit using type 2 regression. In this case, the true difference was the difference in slopes between muscles. To test the null hypothesis that there was no difference in slopes, the null model reflected the pooled set of units from both muscles. Again, slopes were calculated for 1000 random resamplings of this pooled data to estimate the 95% confidence interval.”

The argument for delayed activation of the lateral head is interesting, but I am not comfortable saying the nervous system creates a delay just based on observations of the mean time of the first spike, given the potential for differential variability in spike timing across muscles and MUs. One way to make a strong case for a delay would be to show aggregate PSTHs for all the spikes from all the MUs for each of the two heads. That would distinguish between a true delay and more gradual or variable activation between the heads.

This is a good point and we agree that the claim made about the nervous system is too strong given the results. Even with Author response image 2 below that the Reviewer suggested, there is still not enough evidence to isolate the role of the nervous system in the muscles’ activation.

Author response image 2.

Aggregate peristimulus time histogram (PSTH) for all motor unit spike times in the long head (top) and lateral head (bottom) within the stride.

In the ideal case, we would have more simultaneous recordings from both muscles to make a more direct claim on the delay. Still, within the current scope of the paper, to correct this and better describe the difference in timing of muscle activity, we edited the text to the following:

“These findings demonstrate that despite the synergistic (extensor) function of the long and lateral heads of the triceps at the elbow, the motor pool for the long head becomes active roughly 100 ms before the motor pool supplying the lateral head during locomotion (Figure 3C).”

The results from Marshall et al. 2022 suggest that the recruitment of some MUs is not just related to muscle force, but also the frequency of force variation - some of their MUs appear to be recruited only at certain frequencies. Figure 5C could have shown signs of this, but it does not appear to. We do not really know the force or its frequency of variation in the measurements here. I wonder whether there is additional analysis that could address whether frequency-dependent recruitment is present. It may not be addressable with the current data set, but this could be a fruitful direction to explore in the future with MU recordings from mice.

We agree that this would be a fruitful direction to explore, however the Reviewer is correct that this is not easily addressable with the dataset. As the Reviewer points out, stride frequency increases with increased speed, potentially offering the opportunity to examine how motor unit activity varies with the frequency, phase, and amplitude of locomotor movements. However, given our lack of force data (either joint torques or ground reaction forces), dissociating the frequency/phase/amplitude of skeletal kinematics from the frequency/phase/amplitude of muscle force. Marshall et al. (2022) mitigated these issues by using an isometric force-production task (Marshall et al., 2022). Therefore, while we agree that it would be a major contribution to extend such investigations to whole-body movements like locomotion, given the complexities described above we believe this is a project for the future, and beyond the scope of the present study.

Minor:

Page 5: "Units often displayed no recruitment in a greater proportion of strides than for any particular spike count when recruited (Figures 2A, B)," - I had to read this several times to understand it. I suggest rephrasing for clarity.

We have changed the text to read:

“Units demonstrated a variety of firing patterns, with some units producing 0 spikes more frequently than any non-zero spike count (Figure 2A, B),...”

Figure 3 legend: "Mean phase ({plus minus} SE) of motor unit burst duration across all strides.": It is unclear what this means - durations are not usually described as having a phase. Do we mean the onset phase?

We have changed the text to read:

“Mean phase ± SE of motor unit burst activity within each stride”

Page 9: "suggesting that the recruitment of individual motor units in the lateral and long heads might have significant (and opposite) effects on elbow angle in strides of similar speed (see Discussion)." I wouldn't say "opposite" here - that makes it sound like the authors are calling the long head a flexor. The authors should rephrase or clarify the sense in which they are opposite.

This is a fair point and we agree we should not describe the muscles as ‘opposite’ when both muscles are extensors. We have removed the phrase ‘and opposite’ from the text.

Page 11: "in these two muscles across in other quadrupedal species" - typo.

We have corrected this error.

Page 16: This reviewer cannot decipher after repeated attempts what the first two sentences of the last paragraph mean. - “Future studies might also use perturbations of muscle activity to dissociate the causal properties of each motor unit’s activity from the complex correlation structure of locomotion. Despite the strong correlations observed between motor unit recruitment and limb kinematics (Fig. 6, Supplemental Fig. 3), these results might reflect covariations of both factors with locomotor speed rather than the causal properties of the recorded motor unit.”

For better clarity, we have changed the text to read:

“Although strong correlations were observed between motor unit recruitment and limb kinematics during locomotion (Figure 6, Figure 6–figure supplement 1), it remains unclear whether such correlations actually reflect the causal contributions that those units make to limb movement. To resolve this ambiguity, future studies could use electrical or optical perturbations of muscle contraction levels (Kim et al., 2024; Lu et al., 2024; Srivastava et al., 2015, 2017) to test directly how motor unit firing patterns shape locomotor movements. The short-latency effects of patterned motor unit stimulation (Srivastava et al., 2017) could then reveal the sensitivity of behavior to changes in muscle spiking and the extent to which the same behaviors can be performed with many different motor commands.”

Reviewer #2 (Recommendations for the authors):

Minor comments:

Introduction:

(1) "Although studies in primates, cats, and zebrafish have shown that both the number of active motor units and motor unit firing rates increase at faster locomotor speeds (Grimby, 1984; Hoffer et al., 1981, 1987; Marshall et al., 2022; Menelaou & McLean, 2012)." I would remove Marshall et al. (2022) as their monkeys performed pulling tasks with the upper limb. You can alternatively remove locomotor from the sentence and replace it with contraction speed.

Thank you for the comment. While we intended to reference this specific paper to highlight the rhythmic activity in muscles, we agree that this deviates from ‘locomotion’ as it is referenced in the other cited papers which study body movement. We have followed the Reviewer’s suggestion to remove the citation to Marshall et al.

(2) "The capability and need for faster force generation during dynamic behavior could implicate motor unit recruitment as a primary mechanism for modulating force output in mice."

The authors could add citations to this sentence, of works that showed that recruitment speed is the main determinant of the rate of force development (see for example Dideriksen et al. (2020) J Neurophysiol; J. L. Dideriksen, A. Del Vecchio, D. Farina, Neural and muscular determinants of maximal rate of force development. J Neurophysiol 123, 149-157 (2020)).

Thank you for pointing out this important reference. We have included this as a citation as recommended.

Results:

(3) "Electrode arrays (32-electrode Myomatrix array model RF-4x8-BHS-5) were implanted in the triceps brachii (note that Figure 1D shows the EMG signal from only one of the 16 bipolar recording channels), and the resulting data were used to identify the spike times of individual motor units (Figure 1E) as described previously (Chung et al., 2023)."

This sentence can be misleading for the reader as the array used by the researchers has 4 threads of 8 electrodes. Would it be possible to specify the number of electrodes implanted per head of interest? I assume 8 per head in most mice (or 4 bipolar channels), even if that's not specifically written in the manuscript.

Thank you for the suggestion. As described above, we have added Table 1, which includes all array locations, and we edited the statement referenced in the comment as follows:

“Electrode arrays (32-electrode Myomatrix array model RF-4x8-BHS-5) were implanted in forelimb muscles (note that Figure 1D shows the EMG signal from only one of the 16 bipolar recording channels), and the resulting data were used to identify the spike times of individual motor units in the triceps brachii long and lateral heads (Table 1, Figure 1E) as described previously (Chung et al., 2023).“

(4) "These findings demonstrate that despite the overlapping biomechanical functions of the long and lateral heads of the triceps, the nervous system creates a consistent, approximately 100 ms delay (Figure 3C) between the activation of the two muscles' motor neuron pools. This timing difference suggests distinct patterns of synaptic input onto motor neurons innervating the lateral and long heads."

Both muscles don't have fully overlapping biomechanical functions, as one of them also acts on the shoulder joint. Please be more specific in this sentence, saying that both muscles are synergistic at the elbow level rather than "have overlapping biomechanical functions".

We agree with the above reasoning and that our manuscript should be clearer on this point. We edited the above text in accordance with the Reviewer suggestion as follows:

"These findings demonstrate that despite the synergistic (extensor) function of the long and lateral heads of the triceps at the elbow, …”  

(5) "Together with the differences in burst timing shown in Figure 3B, these results again suggest that the motor pools for the lateral and long heads of the triceps receive distinct patterns of synaptic input, although differences in the intrinsic physiological properties of motor neurons innervating the two muscles might also play an important role."

It is difficult to draw such an affirmative conclusion on the synaptic inputs from the data presented by the authors. The differences in firing rates may solely arise from other factors than distinct synaptic inputs, such as the different intrinsic properties of the motoneurons or the reception of distinct neuromodulatory inputs.

To better explain our findings, we adjusted the above text in the Results (see “Motor unit firing patterns in the long and lateral heads of the triceps”):

“Together with the differences in burst timing shown in Figure 3B, these results again suggest that the motor pools for the lateral and long heads of the triceps receive distinct patterns of synaptic input, although differences in the intrinsic physiological properties of motor neurons innervating the two muscles might also play an important role.”

We also included the following distinction in the Discussion (see “Differences in motor unit activity patterns across two elbow extensors”) to address the other plausible mechanisms mentioned.

“The large differences in burst timing and spike patterning across the muscle heads suggest that the motor pools for each muscle receive distinct inputs. However, differences in the intrinsic physiological properties of motor units and neuromodulatory inputs across motor pools might also make substantial contributions to the structure of motor unit spike patterns (Martínez-Silva et al., 2018; Miles & Sillar, 2011).”

(6) "We next examined whether the probabilistic recruitment of individual motor units in the triceps and elbow extensor muscle predicted stride-by-stride variations in elbow angle kinematics."

I'm not sure that the wording is appropriate here. The analysis does not predict elbow angle variations from parameters extracted from the spiking activity. It rather compares the average elbow angle between two conditions (motor unit active or not active).

We thank the Reviewer for this comment and agree that the wording could be improved here to better reflect our analysis. To lower the strength of our claim, we replaced usage of the word ‘predict’ with ‘correlates’ in the above text and throughout the paper when discussing this result.

Methods:

(7) "Using the four threads on the customizable Myomatrix array (RF-4x8-BHS-5), we implanted a combination of muscles in each mouse, sometimes using multiple threads within the same muscle. [...] Some mice also had threads simultaneously implanted in their ipsilateral or contralateral biceps brachii although no data from the biceps is presented in this study."

A precise description of the localisation of the array (muscles and the number of arrays per muscle) for each animal would be appreciated.

(8) "A total of 33 units were identified and manually verified across all animals." A precise description of the number of motor units concurrently identified per muscle and per animal would be appreciated. Moreover, please add details on the manual inspection. Does it involve the manual selection of missing spikes? What are the criteria for considering an identified motor unit as valid?

As discussed earlier, we added Table 1 to the main text to provide the details mentioned in the above comments.

Regarding spike sorting, given the very large number of spikes recorded, we did not rely on manual adjusting mislabeled spikes. Instead, as described in the revised Methods section, we verified unit isolation by ensuring units had >98% of spikes outside of 1ms of each other. Moreover, as described above we have added new analyses (Figure 1–figure supplement 1) confirming the stability of motor unit waveforms across both the duration of individual recording sessions (roughly 30 minutes) and across the rapid changes in limb position within individual stride cycles (roughly 250 msec).

Reviewer #3 (Recommendations for the authors):

Figure 2 (and supplement) show spike count distributions with strong positive skewness, which is in accordance with the prediction of a fluctuation-driven regime. I suggest plotting these on a logarithmic x-axis (in addition to the linear axis), which should reveal a bell-shaped distribution, maybe even Gaussian, in a majority of the units.

We thank the Reviewer for the suggestion. We present the requested analysis below, which shows bell-shaped distributions for some (but not all) distributions. However, we believe that investigating why some replotted distributions are Gaussian and others are not falls beyond the scope of this paper, and likely requires a larger dataset than the one we were able to obtain.

Author response image 3.

Spike count distributions for each motor unit on a logarithmic x-axis.

Why not more data? I tried to get an overview of how much data was collected.

Supplemental Figure 1 has all the isolated units, which amounts to 38 (are the colors the two muscle types?). Given there are 16 leads in each myomatrix, in two muscles, of six mice, this seems like a low yield. Could the authors comment on the reasons for this low yield?

Regarding motor unit yield, even with multiple electrodes per muscle and a robust sorting algorithm, we often isolated only a few units per muscle. This yield likely reflects two factors. First, because of the highly dynamic nature of locomotion and high levels of muscle contraction, isolating individual spikes reliably across different locomotor speeds is inherently challenging, regardless of the algorithm being employed. Second, because the results of spike-train analyses can be highly sensitive to sorting errors, we have only included the motor units that we can sort with the highest possible confidence across thousands of strides.

Minor:

Figure captions especially Figure 6: The text is excessively long. Can the text be shortened?

We thank the Reviewer for this comment. Generally, we seek to include a description of the methods and results within the figure captions, but we concede that we can condense the information in some cases. In a number of cases, we have moved some of the descriptive text from the caption to the Methods section.

References

Berg, R. W. (2017). Neuronal Population Activity in Spinal Motor Circuits: Greater Than the Sum of Its Parts. Frontiers in Neural Circuits, 11. https://doi.org/10.3389/fncir.2017.00103

Biewener, A. A., Blickhan, R., Perry, A. K., Heglund, N. C., & Taylor, C. R. (1988). Muscle Forces During Locomotion in Kangaroo Rats: Force Platform and Tendon Buckle Measurements Compared. Journal of Experimental Biology, 137(1), 191–205. https://doi.org/10.1242/jeb.137.1.191

Chung, B., Zia, M., Thomas, K. A., Michaels, J. A., Jacob, A., Pack, A., Williams, M. J., Nagapudi, K., Teng, L. H., Arrambide, E., Ouellette, L., Oey, N., Gibbs, R., Anschutz, P., Lu, J., Wu, Y., Kashefi, M., Oya, T., Kersten, R., … Sober, S. J. (2023). Myomatrix arrays for high-definition muscle recording. eLife, 12, RP88551. https://doi.org/10.7554/eLife.88551

De Luca, C. J. (1985). Control properties of motor units. Journal of Experimental Biology, 115(1), 125–136. https://doi.org/10.1242/jeb.115.1.125

De Luca, C. J., & Erim, Z. (1994). Common drive of motor units in regulation of muscle force. Trends in Neurosciences, 17(7), 299–305. https://doi.org/10.1016/0166-2236(94)90064-7

Farina, D., Negro, F., & Dideriksen, J. L. (2014). The effective neural drive to muscles is the common synaptic input to motor neurons. The Journal of Physiology, 592(16), 3427–3441. https://doi.org/10.1113/jphysiol.2014.273581

Hartigan, P. M. (1985). Algorithm AS 217: Computation of the Dip Statistic to Test for Unimodality. Applied Statistics, 34(3), 320. https://doi.org/10.2307/2347485

Henneman, E., Somjen, G., & Carpenter, D. O. (1965). FUNCTIONAL SIGNIFICANCE OF CELL SIZE IN SPINAL MOTONEURONS. Journal of Neurophysiology, 28(3), 560–580. https://doi.org/10.1152/jn.1965.28.3.560

Karabulut, D., Dogru, S. C., Lin, Y.-C., Pandy, M. G., Herzog, W., & Arslan, Y. Z. (2020). Direct Validation of Model-Predicted Muscle Forces in the Cat Hindlimb During Locomotion. Journal of Biomechanical Engineering, 142(5), 051014. https://doi.org/10.1115/1.4045660

Kim, J. J., Wyche, I. S., Olson, W., Lu, J., Bakir, M. S., Sober, S. J., & O’Connor, D. H. (2024). Myo-optogenetics: Optogenetic stimulation and electrical recording in skeletal muscles. https://doi.org/10.1101/2024.06.21.600113

Lu, J., Zia, M., Baig, D. A., Yan, G., Kim, J. J., Nagapudi, K., Anschutz, P., Oh, S., O’Connor, D., Sober, S. J., & Bakir, M. S. (2024). Opto-Myomatrix: μLED integrated microelectrode arrays for optogenetic activation and electrical recording in muscle tissue. https://doi.org/10.1101/2024.07.01.601601

Manuel, M., & Heckman, C. J. (2011). Adult mouse motor units develop almost all of their force in the subprimary range: A new all-or-none strategy for force recruitment? Journal of Neuroscience, 31(42), 15188–15194. https://doi.org/10.1523/JNEUROSCI.2893-11.2011

Marshall, N. J., Glaser, J. I., Trautmann, E. M., Amematsro, E. A., Perkins, S. M., Shadlen, M. N., Abbott, L. F., Cunningham, J. P., & Churchland, M. M. (2022). Flexible neural control of motor units. Nature Neuroscience, 25(11), 1492–1504. https://doi.org/10.1038/s41593-022-01165-8

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Srivastava, K. H., Holmes, C. M., Vellema, M., Pack, A. R., Elemans, C. P. H., Nemenman, I., & Sober, S. J. (2017). Motor control by precisely timed spike patterns. Proceedings of the National Academy of Sciences of the United States of America, 114(5), 1171–1176. https://doi.org/10.1073/pnas.1611734114

ver por ejemplo el capitulo 3 de bertsekas, el Luenberger, el bazara y el nocedal

mediante el uso de la implementacion numerica presentada en el capitulo 6 y el uso de las herramientas computacionales descritas en el capitulo 7

/hyperpost/🧊/me/experiment/~/peer.suite.space.instant-co-laboration/

DOI: 10.1093/g3journal/jkab202

Resource: Bloomington Drosophila Stock Center (RRID:SCR_006457)

Curator: @bdscstockkeepers

SciCrunch record: RRID:SCR_006457

What is this?

DOI: 10.1016/j.ccell.2025.12.013

Resource: (Cell Signaling Technology Cat# 3724, RRID:AB_1549585)

Curator: @scibot

SciCrunch record: RRID:AB_1549585

What is this?

Reviewer #3 (Public review):

The paper presents a synaptic mechanism for chunking in working memory, extending previous work of the last author by introducing specialized "chunking clusters", neural populations that can dynamically segment incoming items into chunks. The idea is that this enables hierarchical representations that increase the effective capacity of working memory. They also derive a theoretical bound for working memory capacity based on this idea, suggesting that hierarchical chunking expands the number of retrievable items beyond the basic WM capacity. Finally, they present neural and behavioral data related to their hypothesis.

Strengths

A major strength of the paper is its clear theoretical ambition of developing a mechanistic model of working memory chunking.

Weaknesses

Despite the inspiration in biophysical mechanisms (short-term synaptic plasticity with different time constants), the model is "cartoonish". It is unclear whether the proposed mechanism would work reliably in the presence of noise and non-zero background activity or in a more realistic implementation (e.g., a spiking network).

As far as I know, there is no evidence for cyclic neural activation patterns, which are supposed to limit WM capacity (such as in Figure 1d). In fact, I believe there is no evidence for population bursts in WM, which are a crucial ingredient of the model. For example, Panicello et al. 2024 have found evidence for periods during which working memory decoding accuracy decreases, but no population bursts were observed in their data. In brief, my critique is that including some biophysical mechanism in an abstract model does not make the model plausible per se.

It is claimed that "our proposed chunking mechanism applies to both the persistent-activity and periodic-activity regimes, with chunking clusters serving the same function in each", but this is not shown. If the results and model predictions are the same, irrespective of whether WM is activity-silent or persistent, I suggest highlighting this more and including the corresponding simulations.

The empirical validations of the model are weak. The single-unit analysis is purely descriptive, without any statistical quantification of the apparent dip-ramp pattern. I agree that the dip-ramp pattern may be consistent with the proposed model, but I don't believe that this pattern is a specific prediction of the proposed model. It seems just to be an interesting observation that may be compatible with several network mechanisms involving some inhibition and a rebound.

Moreover, the reanalyses of n-gram behavioral data do not constitute a mechanistic test of the model. The "new magic number" depends strongly on structural assumptions about how chunking operates, and it is unclear whether human working memory uses the specific hierarchical scheme required to achieve the predicted limit.

The presentation of the modeling results is highly compressed in two figures and is rather hard to follow. Plotting the activity of different neural clusters in separate subplots or as heatmaps (x-axis time, y-axis neural population, color = firing rate) would help to clarify (Figure 1d). Also, control signals that activate the chunking clusters should be shown.

Overall, the theoretical proposal is interesting, but its empirical grounding and biological plausibility need to be substantially reinforced.

Una solución serían los artículos de fuente única y salidas distintas (PDF y Web estática/interactiva) en los que la marginalia puede ser usada para colocar notas extendidas en forma de enlaces abreviados, AprilTags o códigos QR que apunten a las versiones expandidas de esos códigos en los formatos estáticos. Esto haría que esos códigos extendidos se presenten por demanda si la lectora/exploradora los desea, en el medio (impreso, web) donde acceda al artículo.

Afortunadamente, para las ciencias humanas y sociales, esta computación está aún en desuso y se pueden explorar otras dinámicas de escritura en digital mientras las acá criticadas maduran y mientras se trabaja el problema de reproducibilidad desdes otros lugares, como Cardumem

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

I read the paper by Parrotta et al with great interest. The authors are asking an interesting and important question regarding pain perception, which is derived from predictive processing accounts of brain function. They ask: If the brain indeed integrates information coming from within the body (interoceptive information) to comprise predictions about the expected incoming input and how to respond to it, could we provide false interoceptive information to modulate its predictions, and subsequently alter the perception of such input? To test this question, they use pain as the input and the sounds of heartbeats (falsified or accurate) as the interoceptive signal.

Strengths:

I found the question well-established, interesting, and important, with important implications and contributions for several fields, including neuroscience of prediction-perception, pain research, placebo research, and health psychology. The paper is well-written, the methods are adequate, and the findings largely support the hypothesis of the authors. The authors carried out a control experiment to rule out an alternative explanation of their finding, which was important.

Weaknesses:

I will list here one theoretical weakness or concern I had, and several methodological weaknesses.

The theoretical concern regards what I see as a misalignment between a hypothesis and a result, which could influence our understanding of the manipulation of heartbeats, and its meaning: The authors indicate from prior literature and find in their own findings, that when preparing for an aversive incoming stimulus, heartbeats *decrease*. However, in their findings, manipulating the heartbeats that participants hear to be slower than their own prior to receiving a painful stimulus had *no effect* on participants' actual heartbeats, nor on their pain perceptions. What authors did find is that when listening to heartbeats that are *increased* in frequency - that was when their own heartbeats decreased (meaning they expected an aversive stimulus) and their pain perceptions increased.

This is quite complex - but here is my concern: If the assumption is that the brain is collecting evidence from both outside and inside the body to prepare for an upcoming stimulus, and we know that *slowing down* of heartbeats predicts an aversive stimulus, why is it that participants responded in a change in pain perception and physiological response when listened to *increased heartbeats* and not decreased? My interpretation is that the manipulation did not fool the interoceptive signals that the brain collects, but rather the more conscious experience of participants, which may then have been translated to fear/preparation for the incoming stimulus. As the authors indicate in the discussion (lines 704-705), participants do not *know* that decreased heartbeats indicate upcoming aversive stimulus, and I would even argue the opposite - the common knowledge or intuitive response is to increase alertness when we hear increased heartbeats, like in horror films or similar scenarios. Therefore, the unfortunate conclusion is that what the authors assume is a manipulation of interoception - to me seems like a manipulation of participants' alertness or conscious experience of possible danger. I hope the (important) distinction between the two is clear enough because I find this issue of utmost importance for the point the paper is trying to make. If to summarize in one sentence - if it is decreased heartbeats that lead the brain to predict an approaching aversive input, and we assume the manipulation is altering the brain's interoceptive data collection, why isn't it responding to the decreased signal? --> My conclusion is, that this is not in fact a manipulation of interoception, unfortunately

We thank the reviewer for their comment, which gives us the opportunity to clarify what we believe is a theoretical misunderstanding that we have not sufficiently made clear in the previous version of the manuscript. The reviewer suggests that a decreased heart rate itself might act as an internal cue for a forthcoming aversive stimulus, and questions why our manipulation of slower heartbeats then did not produce measurable effects.

The central point is this: decreased heart rate is not a signal the brain uses to predict a threat, but is a consequence of the brain having already predicted the threat. This distinction is crucial. The well-known anticipatory decrease of heartrate serves an allostatic function: preparing the body in advance so that physiological responses to the actual stressor (such as an increase in sympathetic activation) do not overshoot. In other words, the deceleration is an output of the predictive model, not an input from which predictions are inferred. It would be maladaptive for the brain to predict threat through a decrease in heartrate, as this would then call for a further decrease, creating a potential runaway cycle.

Instead, increased heart rate is a salient and evolutionarily conserved cue for arousal, threat, and pain. This association is reinforced both culturally - for example, through the use of accelerating heartbeats in films and media to signal urgency, as R1 mentions - and physiologically, as elevated heart rates reliably occur in response to actual (not anticipated) stressors. Decreased heartrates, in contrast, are reliably associated with the absence of stressors, for example during relaxation and before (and during) sleep. Thus, across various everyday experiences, increased (instead of decreased) heartrates are robustly associated with actual stressors, and there is no a priori reason to assume that the brain would treat decelerating heartrates as cue for threat. As we argued in previous work, “the relationship between the increase in cardiac activity and the anticipation of a threat may have emerged from participants’ first-hand experience of increased heart rates to actual, not anticipated, pain” (Parrotta et al., 2024). The changes in heart rate and pain perception that we hypothesize (and observe) are therefore fully in line with the prior literature on the anticipatory compensatory heartrate response (Bradley et al., 2008, 2005; Colloca et al., 2006; Lykken et al., 1972; Taggart et al., 1976; Tracy et al., 2017; Skora et al., 2022), as well as with Embodied Predictive Coding models (Barrett & Simmons, 2015; Pezzulo, 2014; Seth, 2013; Seth et al., 2012), which assume that our body is regulated through embodied simulations that anticipate likely bodily responses to upcoming events, thereby enabling anticipatory or allostatic regulation of physiological states (Barrett, 2017).

We now add further explanation to this point to the Discussion (lines 740-758) and Introduction (lines 145-148; 154-156) of our manuscript to make this important point clearer.

Barrett, L. F., & Simmons, W. K. (2015). Interoceptive predictions in the brain. Nature reviews neuroscience, 16(7), 419-429.

Barrett, L. F. (2017). The theory of constructed emotion: An active inference account of interoception and categorization. Social cognitive and affective neuroscience, 12(1), 1-23.

Bradley, M. M., Moulder, B., & Lang, P. J. (2005). When good things go bad: The reflex physiology of defense. Psychological science, 16(6), 468-473.

Bradley, M. M., Silakowski, T., & Lang, P. J. (2008). Fear of pain and defensive activation. PAIN®, 137(1), 156-163.

Colloca, L., Petrovic, P., Wager, T. D., Ingvar, M., & Benedetti, F. (2010). How the number of learning trials affects placebo and nocebo responses. Pain®, 151(2), 430-439.

Lykken, D., Macindoe, I., & Tellegen, A. (1972). Preception: Autonomic response to shock as a function of predictability in time and locus. Psychophysiology, 9(3), 318-333.

Taggart, P., Hedworth-Whitty, R., Carruthers, M., & Gordon, P. D. (1976). Observations on electrocardiogram and plasma catecholamines during dental procedures: The forgotten vagus. British Medical Journal, 2(6039), 787-789.

Tracy, L. M., Gibson, S. J., Georgiou-Karistianis, N., & Giummarra, M. J. (2017). Effects of explicit cueing and ambiguity on the anticipation and experience of a painful thermal stimulus. PloS One, 12(8), e0183650.

Parrotta, E., Bach, P., Perrucci, M. G., Costantini, M., & Ferri, F. (2024). Heart is deceitful above all things: Threat expectancy induces the illusory perception of increased heartrate. Cognition, 245, 105719.

Pezzulo, G. (2014). Why do you fear the bogeyman? An embodied predictive coding model of perceptual inference. Cognitive, Affective & Behavioral Neuroscience, 14(3), 902-911.

Seth, A., Suzuki, K., & Critchley, H. (2012). An Interoceptive Predictive Coding Model of Conscious Presence. Frontiers in Psychology, 2. https://www.frontiersin.org/articles/10.3389/fpsyg.2011.00395

Seth, A. K. (2013). Interoceptive inference, emotion, and the embodied self. Trends in Cognitive Sciences, 17(11), 565-573.

Skora, L. I., Livermore, J. J. A., & Roelofs, K. (2022). The functional role of cardiac activity in perception and action. Neuroscience & Biobehavioral Reviews, 104655.

I will add that the control experiment - with an exteroceptive signal (knocking of wood) manipulated in a similar manner - could be seen as evidence of the fact that heartbeats are regarded as an interoceptive signal, and it is an important control experiment, however, to me it seems that what it is showing is the importance of human-relevant signals to pain prediction/perception, and not directly proves that it is considered interoceptive. For example, it could be experienced as a social cue of human anxiety/fear etc, and induce alertness.

The reviewer asks us to consider whether our measured changes in pain response happen not because the brain treats the heartrate feedback in Experiment 1 as interoceptive stimulus, but because heartbeat sounds could have signalled threat on a more abstract, perhaps metacognitive or affective, level, in contrast to the less visceral control sounds in Experiment 2. We deem this highly unlikely for several reasons.

First, as we point out in our response to Reviewer 3 (Point 3), if this were the case, the different sounds in both experiments should have induced overall (between-experiment) differences in pain perception and heart rate, induced by the (supposedly) generally more threatening heart beat sounds. However, when we added such comparisons, no such between-experiment differences were obtained (See Results Experiment 2, and Supplementary Materials, Cross-experiment analysis between-subjects model). Instead, we only find a significant interaction between experiment and feedback (faster, slower). Thus, it is not the heartbeat sounds per se that induce the measured changes to pain perception, but the modulation of their rate, and that identical changes to the rate of non-heartrate sounds produce no such effects. In other words, pain perception is sensitive to a change in heart rate feedback, as we predicted, instead of the overall presence of heartbeat sounds (as one would need to predict if heart beat sounds had more generally induced threat or stress).

Second, one may suspect that it is precisely the acceleration of heartrate feedback that could act as cue to arousal, while accelerated exteroceptive feedback would not. However, if this were the case, one would need to predict a general heart rate increase with accelerated feedback, as this is the general physiological marker of increasing alertness and arousal (e.g. Tousignant-Laflamme et al., 2005; Terkelsen et al., 2005; for a review, see Forte et al., 2022). However, the data shows the opposite, with real heartrates decreasing when the heartrate feedback increases. This result is again fully in line with the predicted interoceptive consequences of accelerated heartrate feedback, which mandates an immediate autonomic regulation, especially when preparing for an anticipated stressor.

Third, our view is further supported by neurophysiological evidence showing that heartbeat sounds, particularly under the belief they reflect one’s own body, are not processed merely as generic aversive or “human-relevant” signals. For instance, Vicentin et al. (2024) showed that simulated faster heartbeat sounds elicited stronger EEG alpha-band suppression, indicative of increased cortical activation  over frontocentral and right frontal areas, compatible with the localization of brain regions contributing to interoceptive processes (Kleint et al., 2015). Importantly, Kleint et al. also demonstrated via fMRI that heartbeat sounds, compared to acoustically matched tones, selectively activate bilateral anterior insula and frontal operculum, key hubs of the interoceptive network. This suggests that the semantic identity of the sound as a heartbeat is sufficient to elicit internal body representations, despite its exteroceptive nature. Further evidence comes from van Elk et al. (2014), who found that heartbeat sounds suppress the auditory N1 component, a neural marker of sensory attenuation typically associated with self-generated or predicted stimuli. The authors interpret this as evidence that the brain treats heartbeat sounds as internally predicted bodily signals, supporting interoceptive predictive coding accounts in which exteroceptive cues (i.e., auditory cardiac feedback) are integrated with visceral information to generate coherent internal body representations.

Finally, it is worth noting that the manipulation of heartrate feedback in our study elicited measurable compensatory changes in participants’ actual heart rate. This is striking compared to our previous work (Parrotta et al., 2024), wherein we used a highly similar design as here, combined with a very strong threat manipulation. Specifically, we presented participants with highly salient threat cues (knives directed at an anatomical depiction of a heart), which predicted forthcoming pain with 100% validity (compared to flowers that did predict the absence of pain with 100%). In other words, these cues perfectly predicted actual pain, through highly visceral stimuli. Nevertheless, we found no measurable decrease in actual heartrate. From an abstract threat perspective, it is therefore striking that the much weaker manipulation of slightly increased or decreased heartrates we used here would induce such a change. The difference therefore suggests that what caused the response here is not due to an abstract feeling of threat, but because the brain indeed treated the increased heartrate feedback as an interoceptive signal for (stressor-induced) sympathetic activation, which would then be immediately down-regulated.

Together, we hope you agree that these considerations make a strong case against a non-specific, arousal or alertness-related explanation of our data. We now make this point clearer in the new paragraph of the Discussion (Accounting for general unspecific contributionslines 796-830), and have added the relevant between experiment comparisons to the Results of Experiment 2.

Forte, G., Troisi, G., Pazzaglia, M., Pascalis, V. D., & Casagrande, M. (2022). Heart rate variability and pain: a systematic review. Brain sciences, 12(2), 153.

Vicentin, S., Guglielmi, S., Stramucci, G., Bisiacchi, P., & Cainelli, E. (2024). Listen to the beat: behavioral and neurophysiological correlates of slow and fast heartbeat sounds. International Journal of Psychophysiology, 206, 112447.

Kleint, N. I., Wittchen, H. U., & Lueken, U. (2015). Probing the interoceptive network by listening to heartbeats: an fMRI study. PloS one, 10(7), e0133164.

Parrotta, E., Bach, P., Perrucci, M. G., Costantini, M., & Ferri, F. (2024). Heart is deceitful above all things: Threat expectancy induces the illusory perception of increased heartrate. Cognition, 245, 105719.

Terkelsen, A. J., Mølgaard, H., Hansen, J., Andersen, O. K., & Jensen, T. S. (2005). Acute pain increases heart rate: differential mechanisms during rest and mental stress. Autonomic Neuroscience, 121(1-2), 101-109.

Tousignant-Laflamme, Y., Rainville, P., & Marchand, S. (2005). Establishing a link between heart rate and pain in healthy subjects: a gender effect. The journal of pain, 6(6), 341-347.

van Elk, M., Lenggenhager, B., Heydrich, L., & Blanke, O. (2014). Suppression of the auditory N1-component for heartbeat-related sounds reflects interoceptive predictive coding. Biological psychology, 99, 172-182.

Several additional, more methodological weaknesses include the very small number of trials per condition - the methods mention 18 test trials per participant for the 3 conditions, with varying pain intensities, which are later averaged (and whether this is appropriate is a different issue). This means 6 trials per condition, and only 2 trials per condition and pain intensity. I thought that this number could be increased, though it is not a huge concern of the paper. It is, however, needed to show some statistics about the distribution of responses, given the very small trial number (see recommendations for authors). The sample size is also rather small, on the verge of "just right" to meet the required sample size according to the authors' calculations.

We provide detailed responses to these points in the “Recommendations for The Authors” section, where each of these issues is addressed point by point in response to the specific questions raised.

Finally, and just as important, the data exists to analyze participants' physiological responses (ECG) after receiving the painful stimulus - this could support the authors' claims about the change in both subjective and objective responses to pain. It could also strengthen the physiological evidence, which is rather weak in terms of its effect. Nevertheless, this is missing from the paper.

This is indeed an interesting point, and we agree that analyzing physiological responses such as ECG following the painful stimulus could offer additional insights into the objective correlates of pain. However, it is important to clarify that the experiment was not designed to investigate post-stimulus physiological responses. Our primary focus was on the anticipatory processes leading up to the pain event. Notably, in the time window immediately following the stimulus - when one might typically expect to observe physiological changes such as an increase in heart rate - participants were asked to provide subjective ratings of their nociceptive experience. It is therefore not a “clean” interval that would lend itself for measurement, especially as a substantial body of evidence indicates that one’s heart rate is strongly modulated by higher-order cognitive processes, including attentional control, executive functioning, decision-making and action itself (e.g., Forte et al., 2021a; Forte et al., 2021b; Luque-Casado et al., 2016).

This limitation is particularly important as the induced change in pain ratings by our heart rate manipulation is substantially smaller than the changes in heart rate induced by actual pain (e.g., Loggia et al., 2011). To confirm this for our study, we simply estimated how much change in heart rate is produced by a change in actual stimulus intensity in the initial no feedback phase of our experiment. There, we find that a change between stimulus intensities 2 and 4 induces a NPS change of 32.95 and a heart rate acceleration response of 1.19 (difference in heart rate response relative to baseline, Colloca et al., 2006), d = .52, p < .001. The change of NPS induced by our implicit heart rate manipulation, however, is only a seventh of this (4.81 on the NPS). This means that the expected effect size of heart rate acceleration produced by our manipulation would only be d = .17. A power analysis, using GPower, reveals that a sample size of n = 266 would be required to detect such an effect, if it exists. Thus, while we agree that this is an exciting hypothesis to be tested, it requires a specifically designed study, and a much larger sample than was possible here.

Colloca, L., Benedetti, F., & Pollo, A. (2006). Repeatability of autonomic responses to pain anticipation and pain stimulation. European Journal of Pain, 10(7), 659-665.

Forte, G., Morelli, M., & Casagrande, M. (2021a). Heart rate variability and decision-making: Autonomic responses in making decisions. Brain sciences, 11(2), 243.

Forte, G., Favieri, F., Oliha, E. O., Marotta, A., & Casagrande, M. (2021b). Anxiety and attentional processes: the role of resting heart rate variability. Brain sciences, 11(4), 480.

Loggia, M. L., Juneau, M., & Bushnell, M. C. (2011). Autonomic responses to heat pain: Heart rate, skin conductance, and their relation to verbal ratings and stimulus intensity. PAIN®, 152(3), 592-598.

Luque-Casado, A., Perales, J. C., Cárdenas, D., & Sanabria, D. (2016). Heart rate variability and cognitive processing: The autonomic response to task demands. Biological psychology, 113, 83-90

I have several additional recommendations regarding data analysis (using an ANOVA rather than multiple t-tests, using raw normalized data rather than change scores, questioning the averaging across 3 pain intensities) - which I will detail in the "recommendations for authors" section.

We provide detailed responses to these points in the “Recommendations for The Authors” section, where each of these issues is addressed point by point in response to the specific questions raised.

Conclusion:

To conclude, the authors have shown in their findings that predictions about an upcoming aversive (pain) stimulus - and its subsequent subjective perception - can be altered not only by external expectations, or manipulating the pain cue, as was done in studies so far, but also by manipulating a cue that has fundamental importance to human physiological status, namely heartbeats. Whether this is a manipulation of actual interoception as sensed by the brain is - in my view - left to be proven.

Still, the paper has important implications in several fields of science ranging from neuroscience prediction-perception research, to pain and placebo research, and may have implications for clinical disorders, as the authors propose. Furthermore, it may lead - either the authors or someone else - to further test this interesting question of manipulation of interoception in a different or more controlled manner.

I salute the authors for coming up with this interesting question and encourage them to continue and explore ways to study it and related follow-up questions.

We sincerely thank the reviewer for the thoughtful and encouraging feedback. We hope our responses to your points below convince you a bit more that what we are measuring does indeed capture interoceptive processes, but we of course fully acknowledge that additional measures - for example from brain imaging (or computational modelling, see Reviewer 3) - could further support our interpretation, and highlights in the Limitations and Future directions section.

Reviewer #2 (Public Review):

In this manuscript, Parrotta et al. tested whether it is possible to modulate pain perception and heart rate by providing false HR acoustic feedback before administering electrical cutaneous shocks. To this end, they performed two experiments. The first experiment tested whether false HR acoustic feedback alters pain perception and the cardiac anticipatory response. The second experiment tested whether the same perceptual and physiological changes are observed when participants are exposed to a non-interoceptive feedback. The main results of the first experiment showed a modulatory effect for faster HR acoustic feedback on pain intensity, unpleasantness, and cardiac anticipatory response compared to a control (acoustic feedback congruent to the participant's actual HR). However, the results of the second experiment also showed an increase in pain ratings for the faster non-interoceptive acoustic feedback compared to the control condition, with no differences in pain unpleasantness or cardiac response.

The main strengths of the manuscript are the clarity with which it was written, and its solid theoretical and conceptual framework. The researchers make an in-depth review of predictive processing models to account for the complex experience of pain, and how these models are updated by perceptual and active inference. They follow with an account of how pain expectations modulate physiological responses and draw attention to the fact that most previous studies focus on exteroceptive cues. At this point, they make the link between pain experience and heart rate changes, and introduce their own previous work showing that people may illusorily perceive a higher cardiac frequency when expecting painful stimulation, even though anticipating pain typically goes along with a decrease in HR. From here, they hypothesize that false HR acoustic feedback evokes more intense and unpleasant pain perception, although the actual HR actually decreases due to the orienting cardiac response. Furthermore, they also test the hypothesis that an exteroceptive cue will lead to no (or less) changes in those variables. The discussion of their results is also well-rooted in the existing bibliography, and for the most part, provides a credible account of the findings.

Thank you for the clear and thoughtful review. We appreciate your positive comments on the manuscript’s clarity, theoretical framework, and interpretation of results.

The main weaknesses of the manuscript lies in a few choices in methodology and data analysis that hinder the interpretation of the results and the conclusions as they stand.

The first peculiar choice is the convoluted definition of the outcomes. Specifically, pain intensity and unpleasantness are first normalized and then transformed into variation rates (sic) or deltas, which makes the interpretation of the results unnecessarily complicated. This is also linked to the definitions of the smallest effect of interest (SESOI) in terms of these outcomes, which is crucial to determining the sample size and gauging the differences between conditions. However, the choice of SESOI is not properly justified, and strangely, it changes from the first experiment to the second.

We thank the reviewer for this important observation. In the revised manuscript, we have made substantial changes and clarifications to address both aspects of this concern: (1) the definition of outcome variables and their normalization, and (2) the definition of the SESOI.

First, As explained in our response to Reviewer #1, we have revised the analyses and removed the difference-based change scores from the main results, addressing concerns about interpretability. However, we retained the normalization procedure: all variables (heart rate, pain intensity, unpleasantness) are normalized relative to the no-feedback baseline using a standard proportional change formula (X−bX)/bX(X - bX)/bX(X−bX)/bX, where X is the feedback-phase mean and bX is the no-feedback baseline. This is a widely used normalization procedure (e.g., Bartolo et al., 2013; Cecchini et al., 2020). This method controls for interindividual variability by expressing responses relative to each participant’s own baseline. The resulting normalized values are then used directly in all analyses, and not further transformed into deltas.

To address potential concerns about this baseline correction approach and its interpretability, we also conducted a new set of supplementary analyses (now reported in the supplementary materials) that include the no-feedback condition explicitly in the models, rather than treating it as a baseline for normalization. These models confirm that our main effects are not driven by the choice of normalization and hold even when no-feedback is analyzed as an independent condition. The new analyses and results are now reported in the Supplementary Materials.

Second, concerning the SESOI values and their justification: The difference in SESOI values between Experiment 1 and Experiment 2 reflects the outcome of sensitivity analyses conducted for each dataset separately, rather than a post-hoc reinterpretation of our results. Specifically, we followed current methodological recommendations (Anderson, Kelley & Maxwell, 2017; Albers & Lakens, 2017; Lakens, 2022), which advise against estimating statistical power based on previously published effect sizes, especially when working with novel paradigms or when effect sizes in the literature may be inflated or imprecise. Instead, we used the sensitivity analysis function in G*Power (Version 3.1) to determine the smallest effect size our design was capable of detecting with high statistical power (90%), given the actual sample size, test type, and alpha level used in each experiment. This is a prospective, design-based estimation rather than a post-hoc analysis of observed effects. The slight differences in SESOI are due to more participants falling below our exclusions criteria in Experiment 2, leading to slightly larger effect sizes that can be detected (d = 0.62 vs d = 0.57). Importantly, both experiments remain adequately powered to detect effects of a size commonly reported in the literature on top-down pain modulation. For instance, Iodice et al. (2019) reported effects of approximately d = 0.7, which is well above the minimum detectable thresholds of our designs.

We have now clarified the logic in the Participant section of Experiment 1 (193-218).

Anderson, S. F., Kelley, K., & Maxwell, S. E. (2017). Sample-Size Planning for More Accurate Statistical Power: A Method Adjusting Sample Effect Sizes for Publication Bias and Uncertainty. Psychological Science, 28(11), 1547-1562.

Bartolo, M., Serrao, M., Gamgebeli, Z., Alpaidze, M., Perrotta, A., Padua, L., Pierelli, F., Nappi, G., & Sandrini, G. (2013). Modulation of the human nociceptive flexion reflex by pleasant and unpleasant odors. PAIN®, 154(10), 2054-2059.

Cecchini, M. P., Riello, M., Sandri, A., Zanini, A., Fiorio, M., & Tinazzi, M. (2020). Smell and taste dissociations in the modulation of tonic pain perception induced by a capsaicin cream application. European Journal of Pain, 24(10), 1946-1955.

Lakens, D. (2022). Sample size justification. Collabra: psychology, 8(1), 33267.

Albers, C., & Lakens, D. (2018). When power analyses based on pilot data are biased: Inaccurate effect size estimators and follow-up bias. Journal of experimental social psychology, 74, 187-195.

Furthermore, the researchers propose the comparison of faster vs. slower delta HR acoustic feedback throughout the manuscript when the natural comparison is the incongruent vs. the congruent feedback.

We very much disagree that the natural comparison is congruent vs incongruent feedback. First, please note that congruency simply refers to whether the heartrate feedback was congruent with (i.e., matched) the participant’s heartrate measurements in the no feedback trials, or whether it was incongruent, and was therefore either faster or slower than this baseline frequency. As such, simply comparing congruent with incongruent feedback could only indicate that pain ratings change when the feedback does not match the real heart rate, irrespective of whether it is faster or slower. Such a test can therefore only reveal potential general effects of surprise or salience, when the feedback heartrate does not match the real one.

We therefore assume that the reviewer specifically refers to the comparison of congruent vs incongruent faster feedback. However, this is not a good test either, as this comparison is, by necessity, confounded with the factor of surprise described above. In other words, if a difference would be found, it would not be clear if it emerges because, as we assume, that faster feedback is represented as an interoceptive signal for threat, or simply because participants are surprised about heartrate feedback that diverges from their real heartrate. Note that even a non-significant result in the analogous comparison of congruent vs incongruent slower feedback would not be able to resolve this confound, as in null hypothesis testing the absence of a significant effect does, per definition, not indicate that there is no effect - only that it could not be detected here.

Instead, the only possible test of our hypothesis is the one we have designed our experiment around and focussed on with our central t-test: the comparison of incongruent faster with incongruent slower feedback. This keeps any possible effects of surprise/salience from generally altered feedback constant and allows us to test our specific hypothesis: that real heart rates will decrease and pain ratings will increase when receiving false interoceptive feedback about increased compared to decreasing heartrates. Note that this test of faster vs slower feedback is also statistically the most appropriate, as it collapses our prediction onto a single and highest-powered hypothesis test: As faster and slower heartrate feedback are assumed to induce effects in the opposite direction, the effect size of their difference is, per definition, double than the averaged effect size for the two separate tests of faster vs congruent feedback and slower vs congruent feedback.

That being said, we also included comparisons with the congruent condition in our revised analysis, in line with the reviewer’s suggestion and previous studies. These analyses help explore potential asymmetries in the effect of false feedback. While faster feedback (both interoceptive and exteroceptive) significantly modulated pain relative to congruent feedback, the slower feedback did not, consistent with previous literature showing stronger effects for arousal-increasing cues (e.g., Valins, 1966; Iodice et al., 2019). To address this point, in the revised manuscript we have added a paragraph to the Data Analysis section of Experiment 1 (lines 405-437) to make this logic clearer.

Valins, S. (1966). Cognitive effects of false heart-rate feedback. Journal of personality and social psychology, 4(4), 400.

Iodice, P., Porciello, G., Bufalari, I., Barca, L., & Pezzulo, G. (2019). An interoceptive illusion of effort induced by false heart-rate feedback. Proceedings of the National Academy of Sciences, 116(28), 13897-13902.

This could be influenced by the fact that the faster HR exteroceptive cue in experiment 2 also shows a significant modulatory effect on pain intensity compared to congruent HR feedback, which puts into question the hypothesized differences between interoceptive vs. exteroceptive cues. These results could also be influenced by the specific choice of exteroceptive cue: the researchers imply that the main driver of the effect is the nature of the cue (interoceptive vs. exteroceptive) and not its frequency. However, they attempt to generalize their findings using knocking wood sounds to all possible sounds, but it is possible that some features of these sounds (e.g., auditory roughness or loomingness) could be the drivers behind the observed effects.

We appreciate this thoughtful comment. We agree that low-level auditory features can potentially introduce confounds in the experimental design, and we acknowledge the importance of distinguishing these factors from the higher-order distinction that is central to our study: whether the sound is perceived as interoceptive (originating from within the body) or exteroceptive (perceived as external). To this end, the knocking sound was chosen not for its specific acoustic profile, but because it lacked bodily relevance, thus allowing us to test whether the same temporal manipulations (faster, congruent, slower) would have different effects depending on whether the cue was interpreted as reflecting an internal bodily state or not. In this context, the exteroceptive cue served as a conceptual contrast rather than an exhaustive control for all auditory dimensions.

Several aspects of our data make it unlikely that the observed effects are driven by unspecific acoustic characteristics of the sounds used in the exteroceptive and interoceptive experiments (see also our responses to Reviewer 1 and Reviewer 3 who raised similar points).

First, if the knocking sound had inherent acoustic features that strongly influenced perception or physiological responses, we would expect it to have produced consistent effects across all feedback conditions (Faster, Slower, Congruent), regardless of the interpretive context. This would have manifested as an overall difference between experiments in the between-subjects analyses and in the supplementary mixed-effects models that included Experiment as a fixed factor. Yet, we observed no such main effects in any of our variables. Instead, significant differences emerged only in specific theoretically predicted comparisons (e.g., Faster vs. Slower), and critically, these effects depended on the cue type (interoceptive vs. exteroceptive), suggesting that perceived bodily relevance, rather than a specific acoustic property, was the critical modulator. In other words, any alternative explanation based on acoustic features would need to be able to explain why these acoustic properties would induce not an overall change in heart rate and pain perception (i.e., similarly across slower, faster, and congruent feedback), but the brain’s response to changes in the rate of this feedback – increasing pain ratings and decreasing heartrates for faster relative to slower feedback. We hope you agree that a simple effect of acoustic features would not predict such a sensitivity to the rate with which the sound was played.

Please refer to our responses to Reviewers 1 and 2 for further aspects of the data, arguing strongly against other features associated with the sounds (e.g., alertness, arousal) could be responsible for the results, as the data pattern again goes in the opposite direction than that predicted by such accounts (e.g., faster heartrate feedback decreased real heartrate, instead of increasing them, as would be expected if accelerated heartrate feedback increased arousal).

Finally, to further support this interpretation, we refer to neurophysiological evidence showing that heartbeat sounds are not processed as generic auditory signals, but as internal, bodily relevant cues especially when believed to reflect one’s own physiological state. For instance, fMRI research (Kleint et al., 2015) shows that heartbeat sounds engage key interoceptive regions such as the anterior insula and frontal operculum more than acoustically matched control tones. EEG data (Vicentin et al., 2024) showed that faster heartbeat sounds produce stronger alpha suppression over frontocentral areas, suggesting enhanced processing in networks associated with interoceptive attention. Moreover, van Elk et al. (2014) found that heartbeat sounds attenuate the auditory N1 response, a neural signature typically linked to self-generated or predicted bodily signals. These findings consistently demonstrate that heartbeats sounds are processed as interoceptive and self-generated signals, which is in line with our rationale that the critical factor at play concern whether it is semantically perceived as reflecting one’s own bodily state, rather than the physical properties of the sound.

We now explicitly discuss these issues in the revised Discussion section (lines 740-758).

Kleint, N. I., Wittchen, H. U., & Lueken, U. (2015). Probing the interoceptive network by listening to heartbeats: an fMRI study. PloS one, 10(7), e0133164.

van Elk, M., Lenggenhager, B., Heydrich, L., & Blanke, O. (2014). Suppression of the auditory N1-component for heartbeat-related sounds reflects interoceptive predictive coding. Biological psychology, 99, 172-182.

Vicentin, S., Guglielmi, S., Stramucci, G., Bisiacchi, P., & Cainelli, E. (2024). Listen to the beat: behavioral and neurophysiological correlates of slow and fast heartbeat sounds. International Journal of Psychophysiology, 206, 112447.

Finally, it is noteworthy that the researchers divided the study into two experiments when it would have been optimal to test all the conditions with the same subjects in a randomized order in a single cross-over experiment to reduce between-subject variability. Taking this into consideration, I believe that the conclusions are only partially supported by the evidence. Despite of the outcome transformations, a clear effect of faster HR acoustic feedback can be observed in the first experiment, which is larger than the proposed exteroceptive counterpart. This work could be of broad interest to pain researchers, particularly those working on predictive coding of pain.

We appreciate the reviewer’s suggestion regarding a within-subject crossover design. While such a design indeed offers increased statistical power by reducing interindividual variability (Charness, Gneezy, & Kuhn, 2012), we intentionally opted for a between-subjects design due to theoretical and methodological considerations specific to studies involving deceptive feedback. Most importantly, carryover effects are a major concern in deception paradigms. Participants exposed to one type of feedback initially (e.g., interoceptive), and then the other (exteroceptive) would be more likely to develop suspicion or adaptive strategies that would alter their responses. Such expectancy effects could contaminate results in a crossover design, particularly when participants realize that feedback is manipulated. In line with this idea, past studies on false cardiac feedback (e.g., Valins, 1966; Pennebaker & Lightner, 1980) often employed between-subjects or blocked designs to mitigate this risk.

Pennebaker, J. W., & Lightner, J. M. (1980). Competition of internal and external information in an exercise setting. Journal of personality and social psychology, 39(1), 165.

Valins, S. (1966). Cognitive effects of false heart-rate feedback. Journal of personality and social psychology, 4(4), 400.

Reviewer #3 (Public Review):

In their manuscript titled "Exposure to false cardiac feedback alters pain perception and anticipatory cardiac frequency", Parrotta and colleagues describe an experimental study on the interplay between false heart rate feedback and pain experience in healthy, adult humans. The experimental design is derived from Bayesian perspectives on interoceptive inference. In Experiment 1 (N=34), participants rated the intensity and unpleasantness of an electrical pulse presented to their middle fingers. Participants received auditory cardiac feedback prior to the electrical pulse. This feedback was congruent with the participant's heart rate or manipulated to have a higher or lower frequency than the participant's true heart rate (incongruent high/ low feedback). The authors find heightened ratings of pain intensity and unpleasantness as well as a decreased heart rate in participants who were exposed to the incongruent-high cardiac feedback. Experiment 2 (N=29) is equivalent to Experiment 1 with the exception that non-interoceptive auditory feedback was presented. Here, mean pain intensity and unpleasantness ratings were unaffected by feedback frequency.

Strengths:

The authors present interesting experimental data that was derived from modern theoretical accounts of interoceptive inference and pain processing.

(1) The motivation for the study is well-explained and rooted within the current literature, whereas pain is the result of a multimodal, inferential process. The separation of nociceptive stimulation and pain experience is explained clearly and stringently throughout the text.

(2) The idea of manipulating pain-related expectations via an internal, instead of an external cue, is very innovative.

(3) An appropriate control experiment was implemented, where an external (non-physiological) auditory cue with parallel frequency to the cardiac cue was presented.

(4) The chosen statistical methods are appropriate, albeit averaging may limit the opportunity for mechanistic insight, see weaknesses section.

(5) The behavioral data, showing increased unpleasantness and intensity ratings after exposure to incongruent-high cardiac feedback, but not exteroceptive high-frequency auditory feedback, is backed up by ECG data. Here, the decrease in heart rate during the incongruent-high condition speaks towards a specific, expectation-induced physiological effect that can be seen as resulting from interoceptive inference.

We thank the reviewer for their positive feedback. We are glad that the study’s theoretical foundation, innovative design, appropriate control conditions, and convergence of behavioral and physiological data were well received.

Weaknesses:

Additional analyses and/ or more extensive discussion are needed to address these limitations:

(1) I would like to know more about potential learning effects during the study. Is there a significant change in ∆ intensity and ∆ unpleasantness over time; e.g. in early trials compared to later trials? It would be helpful to exclude the alternative explanation that over time, participants learned to interpret the exteroceptive cue more in line with the cardiac cue, and the effect is driven by a lack of learning about the slightly less familiar cue (the exteroceptive cue) in early trials. In other words, the heartbeat-like auditory feedback might be "overlearned", compared to the less naturalistic tone, and more exposure to the less naturalistic cue might rule out any differences between them w.r.t. pain unpleasantness ratings.

We thank the reviewer for raising this important point. Please note that the repetitions in our task were relatively limited (6 trials per condition), which limits the potential influence of such differential learning effects between experiments. To address this concern, we performed an additional analysis, reported in the Supplementary Materials, using a Linear Mixed-Effects Model approach. This method allowed us to include "Trial" (the rank order of each trial) as a variable to account for potential time-on-task effects such as learning, adaptation, or fatigue (e.g., Möckel et al., 2015). All feedback conditions (no-feedback, congruent, faster, slower) and all stimulus intensity levels were included.

Specifically, we tested the following models:

Likert Pain Unpleasantness Ratings ~ Experiment × Feedback × StimInt × Trial + (StimInt + Trial | Subject)

Numeric Pain Scale of Intensity Ratings ~ Experiment × Feedback × StimInt × Trial + (StimInt + Trial | Subject)

In both models, no significant interactions involving Trial × Experiment or Trial × Feedback × Experiment were found. Instead, we just find generally larger effects in early trials compared to later ones (Main effect of Trial within each Experiment), similar to other cognitive illusions where repeated exposure diminishes effects. Thus, although some unspecific changes over time may have occurred (e.g., due to general task exposure), these changes did not differ systematically across experimental conditions (interoceptive vs. exteroceptive) or feedback types. However, we are fully aware that the absence of significant higher-order interactions does not conclusively rule out the possibility of learning-related effects. It is possible that our models lacked the statistical power to detect more subtle or complex time-dependent modulations, particularly if such effects differ in magnitude or direction across feedback conditions.

We report the full description of these analyses and results in the Supplementary materials 1. Cross-experiment analysis (between-subjects model).

(2) The origin of the difference in Cohen's d (Exp. 1: .57, Exp. 2: .62) and subsequently sample size in the sensitivity analyses remains unclear, it would be helpful to clarify where these values are coming from (are they related to the effects reported in the results? If so, they should be marked as post-hoc analyses).

Following recommendations (Anderson, Kelley & Maxwell, 2017; Albers &  Lakens, 2017), we do not report theoretical power based on previously reported effect sizes as this neglects uncertainty around effect size measurements, especially for new effects for which no reliable expected effect size estimates can be derived across the literature. Instead, the power analysis is based on a sensitivity analysis, conducted in G*Power (Version 3.1). Importantly, these are not post-hoc analyses, as they are not based on observed effect sizes in our study, but derived a priori. Sensitivity analyses estimate effect sizes that our design is well-powered (90%) to detect (i.e. given target power, sample size, type of test), for the crucial comparison between faster and slower feedback in both experiments (Lakens, 2022). Following recommendations, we also report the smallest effect size this test can in principle detect in our study (SESOI, Lakens, 2022). This yields effect sizes of d = .57 in Experiment 1 and d = .62 in Experiment 2 at 90% power and SESOIs of d = .34 and .37, respectively. Note that values are slightly higher in Experiment 2, as more participants were excluded based on our exclusion criteria. Importantly, detectable effect sizes in both experiments are smaller than reported effect sizes for comparable top-down effects on pain measurements of d = .7 (Iodice et al., 2019).  We have now added more information to the power analysis sections to make this clearer (lines 208-217).

Albers, C., & Lakens, D. (2018). When power analyses based on pilot data are biased: Inaccurate effect size estimators and follow-up bias. Journal of experimental social psychology, 74, 187-195.

Anderson, S. F., Kelley, K., & Maxwell, S. E. (2017). Sample-Size Planning for More Accurate Statistical Power: A Method Adjusting Sample Effect Sizes for Publication Bias and Uncertainty. Psychological Science, 28(11), 1547-1562.

Lakens, D. (2022). Sample size justification. Collabra: psychology, 8(1), 33267.

(3) As an alternative explanation, it is conceivable that the cardiac cue may have just increased unspecific arousal or attention to a larger extent than the exteroceptive cue. It would be helpful to discuss the role of these rather unspecific mechanisms, and how it may have differed between experiments.

We thank the reviewer for raising this important point. We agree that, in principle, unspecific mechanisms such as increased arousal or attention driven by cardiac feedback could be an alternative explanation for the observed effects. However, several aspects of our data indicate that this is unlikely:

(1) No main effect of Experiment on pain ratings:

If the cardiac feedback had simply increased arousal or attention in a general (non-specific) way, we would expect a main effect of Experiment (i.e., interoceptive vs exteroceptive condition) on pain intensity or unpleasantness ratings, regardless of feedback frequency. However, such a main effect was never observed when we compared between experiments (see between-experiment t-tests in results, and in supplementary analyses). Instead, effects were specific to the manipulation of feedback frequency.

(2) Heart rate as an arousal measure:

Heart rate (HR) is a classical physiological index of arousal. If there had been an unspecific increase in arousal in the interoceptive condition, we would expect a main effect of Experiment on HR. However, no such main effect was found. Instead, our HR analyses revealed a significant interaction between feedback and experiment, suggesting that HR changes depended specifically on the feedback manipulation rather than reflecting a general arousal increase.

(3) Arousal predicts faster, not slower, heart rates

In Experiment 1, faster interoceptive cardiac feedback led to a slowdown in heartrates both when compared to slower feedback and to congruent cardiac feedback. This is in line with the predicted compensatory response to faster heart rates. In contrast, if faster feedback would have only generally increased arousal, heart rates should have increased instead of decreased, as indicated by several prior studies (Tousignant-Laflamme et al., 2005; Terkelsen et al., 2005; for a review, see Forte et al., 2022), predicting the opposite pattern of responses than was found in Experiment 1.

Taken together, these findings indicate that the effects observed are unlikely to be driven by unspecific arousal or attention mechanisms, but rather are consistent with feedback-specific modulations, in line with our interoceptive inference framework.

We have now integrated these considerations in the revised discussion (lines 796-830), and added the relevant between-experiment comparisons to the Results of Experiment 2 and the supplementary analysis.

Terkelsen, A. J., Mølgaard, H., Hansen, J., Andersen, O. K., & Jensen, T. S. (2005). Acute pain increases heart rate: differential mechanisms during rest and mental stress. Autonomic Neuroscience, 121(1-2), 101-109.

Tousignant-Laflamme, Y., Rainville, P., & Marchand, S. (2005). Establishing a link between heart rate and pain in healthy subjects: a gender effect. The journal of pain, 6(6), 341-347.

Forte, G., Troisi, G., Pazzaglia, M., Pascalis, V. D., & Casagrande, M. (2022). Heart rate variability and pain: a systematic review. Brain sciences, 12(2), 153.

(4) The hypothesis (increased pain intensity with incongruent-high cardiac feedback) should be motivated by some additional literature.

We thank the reviewer for this helpful suggestion. Please note that the current phenomenon was tested in this experiment for the first time. Therefore, there is no specific prior study that motivated our hypotheses; they were driven theoretically, and derived from our model of interoceptive integration of pain and cardiac perception. The idea that accelerated cardiac feedback (relative to decelerated feedback) will increase pain perception and reduce heart rates is grounded on Embodied Predictive coding frameworks. Accordingly, expectations and signals from different sensory modalities (sensory, proprioceptive, interoceptive) are integrated both to efficiently infer crucial homeostatic and physiological variables, such as hunger, thirst, and, in this case, pain, and regulate the body’s own autonomic responses based on these inferences.

Within this framework, the concept of an interoceptive schema (Tschantz et al., 2022; Iodice et al., 2019; Parrotta et al., 2024; Schoeller et al., 2022) offers the basis for understanding interoceptive illusions, wherein inferred levels of interoceptive states (i.e., pain) deviate from the actual physiological state. Cardiac signals conveyed by the feedback manipulation act as a misleading prior, shaping the internal generative model of pain. Specifically, an increased heart rate may signal a state of threat, establishing a prior expectation of heightened pain. Building on predictive models of interoception, we predict that this cardiac prior is integrated with interoceptive (i.e., actual nociceptive signal) and exteroceptive inputs (i.e., auditory feedback input), leading to a subjective experience of increased pain even when there is no corresponding increase in the nociceptive input.

This idea is not completely new, but it is based on our previous findings of an interoceptive cardiac illusion driven by misleading priors about anticipated threat (i.e., pain). Specifically, in Parrotta et al. (2024), we tested whether a common false belief that heart rate increases in response to threat lead to an illusory perception of accelerated cardiac activity when anticipating pain. In two experiments, we asked participants to monitor and report their heartbeat while their ECG was recorded. Participants performed these tasks while visual cues reliably predicted a forthcoming harmless (low-intensity) vs. threatening (high-intensity) cutaneous electrical stimulus. We showed that anticipating a painful vs. harmless stimulus causes participants to report an increased cardiac frequency, which does not reflect their real cardiac response, but the common (false) belief that heart rates would accelerate under threat, reflecting the hypothesised integration of prior expectations and interoceptive inputs when estimating cardiac activity.

Here we tested the counterpart of such a cardiac illusion. We reasoned that if cardiac interoception is shaped by expectations about pain, then the inverse should also be true: manipulating beliefs about cardiac activity (via cardiac feedback) in the context of pain anticipation should influence the perception of pain. Specifically, we hypothesized that presenting accelerated cardiac feedback would act as a misleading prior, leading to an illusory increase in pain experience, even in the absence of an actual change in nociceptive input.

Moreover, next to the references already provided in the last version of the manuscript, there is ample prior research that provides more general support for such relationships. Specifically, studies have shown that providing mismatched cardiac feedback in contexts where cardiovascular changes are typically expected (i.e. sexual arousal, Rupp & Wallen, 2008; Valins, 1996; physical exercise, Iodice et al., 2019) can enhance the perception of interoceptive states associated with those experiences. Furthermore, findings that false cardiac feedback can influence emotional experience suggest that it is the conscious perception of physiological arousal, combined with the cognitive interpretation of the stimulus, that plays a key role in shaping emotional responses (Crucian et al., 2000).

This point is now addressed in the revised Introduction, wherein additional references have been integrated (lines 157-170).

Crucian, G. P., Hughes, J. D., Barrett, A. M., Williamson, D. J. G., Bauer, R. M., Bowers, D., & Heilman, K. M. (2000). Emotional and physiological responses to false feedback. Cortex, 36(5), 623-647.

Iodice, P., Porciello, G., Bufalari, I., Barca, L., & Pezzulo, G. (2019). An interoceptive illusion of effort induced by false heart-rate feedback. Proceedings of the National Academy of Sciences, 116(28), 13897-13902.

Parrotta, E., Bach, P., Perrucci, M. G., Costantini, M., & Ferri, F. (2024). Heart is deceitful above all things: Threat expectancy induces the illusory perception of increased heartrate. Cognition, 245, 105719.

Rupp, H. A., & Wallen, K. (2008). Sex differences in response to visual sexual stimuli: A review. Archives of sexual behavior, 37(2), 206-218.

Schoeller, F., Horowitz, A., Maes, P., Jain, A., Reggente, N., Moore, L. C., Trousselard, M., Klein, A., Barca, L., & Pezzulo, G. (2022). Interoceptive technologies for clinical neuroscience.

Tschantz, A., Barca, L., Maisto, D., Buckley, C. L., Seth, A. K., & Pezzulo, G. (2022). Simulating homeostatic, allostatic and goal-directed forms of interoceptive control using active inference. Biological Psychology, 169, 108266.

Valins, S. (1966). Cognitive effects of false heart-rate feedback. Journal of personality and social psychology, 4(4), 400.

(5) The discussion section does not address the study's limitations in a sufficient manner. For example, I would expect a more thorough discussion on the lack of correlation between participant ratings and self-reported bodily awareness and reactivity, as assessed with the BPQ.

We thank the reviewer for this valuable observation. In response, we have revised the Discussion section to explicitly acknowledge and elaborate on the lack of significant correlations between participants’ pain ratings and their self-reported bodily awareness and reactivity as assessed with the BPQ.

We now clarify that the inclusion of this questionnaire was exploratory. While it would be theoretically interesting to observe a relationship between subjective pain modulation and individual differences in interoceptive awareness, detecting robust correlations between within-subject experimental effects and between-subjects trait measures such as the BPQ typically requires much larger sample sizes (often exceeding N = 200) due to the inherently low reliability of such cross-level associations (see Hedge, Powell & Sumner, 2018; the “reliability paradox”). As such, the absence of a significant correlation in our study does not undermine the conclusions we draw from our main findings. Future studies with larger samples will be needed to systematically address this question. We now acknowledge this point explicitly in the revised manuscript (lines 501-504; 832-851).

Hedge, C., Powell, G., & Sumner, P. (2018). The reliability paradox: Why robust cognitive tasks do not produce reliable individual differences. Behavior Research Methods, 50(3), 1166-1186. https://doi.org/10.3758/s13428-017-0935-1

(a) Some short, additional information on why the authors chose to focus on body awareness and supradiaphragmatic reactivity subscales would be helpful.

We chose to focus on the body awareness and supradiaphragmatic reactivity subscales because these aspects are closely tied to emotional and physiological processing, particularly in the context of interoception. Body awareness plays a critical role in how individuals perceive and interpret bodily signals, which in turn affects emotional regulation and self-awareness. Supradiaphragmatic reactivity refers specifically to organs located or occurring above the diaphragm (i.e., the muscle that separates the chest cavity from the abdomen), which includes the heart, compared to subdiaphragmatic reactivity subscales further down. Our decision to include these subscales is further motivated by recent research, including the work by Petzschner et al. (2021), which demonstrates that the focus of attention can modulate the heartbeat-evoked potential (HEP), and that this modulation is predicted by participants’ responses on the supradiaphragmatic reactivity subscales. Thus, this subscale, and the more general body awareness scale, allows us to explore the interplay between bodily awareness, physiological reactivity, and emotional processing in our study. We now clarify this point in the revised version of the Methods - Body Perception Questionnaire (lines 384-393).

(6) The analyses presented in this version of the manuscript allow only limited mechanistic conclusions - a computational model of participants' behavior would be a very strong addition to the paper. While this may be out of the scope of the article, it would be helpful for the reader to discuss the limitations of the presented analyses and outline avenues towards a more mechanistic understanding and analysis of the data. The computational model in [7] might contain some starting ideas.

Thank you for your valuable feedback. We agree that a computational model would enhance the mechanistic understanding of our findings. While this is beyond the current scope, we now discuss the limitations of our analysis in the Limitations and Future directions section (lines 852-863). Specifically, we acknowledge that future studies could use computational models to better understand the interactions between physiological, cognitive, and perceptual factors.

Some additional topics were not considered in the first version of the manuscript:

(1) The possible advantages of a computational model of task behavior should be discussed.

We agree that a computational model of task behavior could provide several advantages. By formalizing principles of predictive processing and active inference, such a model could generate quantitative predictions about how heart rate (HR) and feedback interact, providing a more precise understanding of their respective contributions to pain modulation. However, this is a first demonstration of a theoretically predicted phenomenon, and computationally modelling it is currently outside the scope of the article. We would be excited to explore this in the future. We have added a brief discussion of these potential advantages in the revised manuscript and suggest that future work could integrate computational modelling to further deepen our understanding of these processes (lines 852-890).

(2) Across both experiments, there was a slightly larger number of female participants. Research suggests significant sex-related differences in pain processing [1,2]. It would be interesting to see what role this may have played in this data.

Thank you for your insightful comment. While we acknowledge that sex-related differences in pain processing are well-documented in the literature, we do not have enough participants in our sample to test this in a well-powered way. As such, exploring the role of sex differences in pain perception will need to be addressed in future studies with more balanced samples. It would be interesting if more sensitive individuals, with a more precise representation of pain, also show smaller effects on pain perception. We have noted this point in the revised manuscript (lines 845-851) and suggest that future research could specifically investigate how sex differences might influence the modulation of pain and physiological responses in similar experimental contexts.

(3) There are a few very relevant papers that come to mind which may be of interest. These sources might be particularly useful when discussing the roadmap towards a mechanistic understanding of the inferential processes underlying the task responses [3,4] and their clinical implications.

Thank you for highlighting these relevant papers. We appreciate your suggestion and have now cited them in the Limitations and Future directions paragraph (lines 852-863).

(4) In this version of the paper, we only see plots that illustrate ∆ scores, averaged across pain intensities - to better understand participant responses and the relationship with stimulus intensity, it would be helpful to see a more descriptive plot of task behavior (e.g. stimulus intensity and raw pain ratings)

To directly address the reviewer’s request, we now provide additional descriptive plots in the supplementary material of the revised manuscript, showing raw pain ratings across different stimulus intensities and feedback conditions. These plots offer a clearer view of participant behavior without averaging across pain levels, helping to better illustrate the relationship between stimulus intensity and reported pain.

Mogil, J. S. (2020). Qualitative sex differences in pain processing: emerging evidence of a biased literature. Nature Reviews Neuroscience, 21(7), 353-365. https://www.nature.com/articles/s41583-020-0310-6

Sorge, R. E., & Strath, L. J. (2018). Sex differences in pain responses. Current Opinion in Physiology, 6, 75-81. https://www.sciencedirect.com/science/article/abs/pii/S2468867318300786?via%3Dihub

Unal, O., Eren, O. C., Alkan, G., Petzschner, F. H., Yao, Y., & Stephan, K. E. (2021). Inference on homeostatic belief precision. Biological Psychology, 165, 108190.

Allen, M., Levy, A., Parr, T., & Friston, K. J. (2022). In the body's eye: the computational anatomy of interoceptive inference. PLoS Computational Biology, 18(9), e1010490.

Stephan, K. E., Manjaly, Z. M., Mathys, C. D., Weber, L. A., Paliwal, S., Gard, T., ... & Petzschner, F. H. (2016). Allostatic self-efficacy: A metacognitive theory of dyshomeostasis-induced fatigue and depression. Frontiers in human neuroscience, 10, 550.

Friston, K. J., Stephan, K. E., Montague, R., & Dolan, R. J. (2014). Computational psychiatry: the brain as a phantastic organ. The Lancet Psychiatry, 1(2), 148-158.

Eckert, A. L., Pabst, K., & Endres, D. M. (2022). A Bayesian model for chronic pain. Frontiers in Pain Research, 3, 966034.

We thank the reviewer for highlighting these relevant references which have now been integrated in the revised version of the manuscript.

Recommendations For The Authors: 

Reviewer #1 (Recommendations For The Authors):

At the time I was reviewing this paper, I could not think of a detailed experiment that would answer my biggest concern: Is this a manipulation of the brain's interoceptive data integration, or rather a manipulation of participants' alertness which indirectly influences their pain prediction?

One incomplete idea that came to mind was delivering this signal in a more "covert" manner (though I am not sure it will suffice), or perhaps correlating the effect size of a participant with their interoceptive abilities, as measured in a different task or through a questionnaire.... Another potential idea is to tell participants that  this is someone else's HR that they hear and see if that changes the results (though requires further thought). I leave it to the authors to think further, and perhaps this is to be answered in a different paper - but if so, I am sorry to say that I do not think the claims can remain as they are now, and the paper will need a revision of its arguments, unfortunately. I urge the authors to ask further questions if my point about the concern was not made clear enough for them to address or contemplate it.

We thank the reviewer for raising this important point. As detailed in our previous response, this point invites an important clarification regarding the role of cardiac deceleration in threat processing. Rather than serving as an interoceptive input from which the brain infers the likelihood of a forthcoming aversive event, heart rate deceleration is better described as an output of an already ongoing predictive process, as it reflects an allostatic adjustment of the bodily state aimed at minimizing the impact of the predicted perturbation (e.g., pain) and preventing sympathetic overshoot. It would be maladaptive for the brain to use a decelerating heart rate as evidence of impending threat, since this would paradoxically trigger further parasympathetic activation, initiating a potentially destabilizing feedback loop. Conversely, increased heart rate represents an evolutionarily conserved cue for arousal, threat, and pain. Our results therefore align with the idea that the brain treats externally manipulated increases in cardiac signals as congruent with anticipated sympathetic activation, prompting a compensatory autonomic and perceptual response consistent with embodied predictive processing frameworks (e.g., Barrett & Simmons, 2015; Seth, 2013).

We would also like to re-iterate that our results cannot be explained by general differences induced by the different heart rate sounds relative to the exteroceptive (see also our detailed comments to your point above, and our response to a similar point from Reviewer 3), for three main reasons.

(1) No main effect of Experiment on pain ratings:

If the cardiac feedback had simply increased arousal or attention in a general (non-specific) way, we would expect a main effect of Experiment (i.e., interoceptive vs exteroceptive condition) on pain intensity or unpleasantness ratings, regardless of feedback frequency. However, such a main effect was never observed. Instead, effects were specific to the manipulation of feedback frequency.

(2) Heart rate as an arousal measure:

Heart rate (HR) is a classical physiological index of arousal. If there had been an unspecific increase in arousal in the interoceptive condition, we would expect a main effect of Experiment on HR. However, no such main effect was found. Instead, our HR analyses revealed a significant interaction between feedback and experiment, suggesting that HR changes depended specifically on the feedback manipulation rather than reflecting a general arousal increase.

(3) Arousal predicts faster, not slower, heart rates

In Experiment 1, faster interoceptive cardiac feedback led to a slowdown in heartrates both when compared to slower feedback and to congruent cardiac feedback. This is in line with the predicted compensatory response to faster heart rates. In contrast, if faster feedback would have only generally increased arousal, heart rates should have increased instead of decreased, as indicated by several prior studies (for a review, see Forte et al., 2022), predicting the opposite pattern of responses than was found in Experiment 1.

Taken together, these findings indicate that the effects observed are unlikely to be driven by unspecific arousal or attention mechanisms, but rather are consistent with feedback-specific modulations, in line with our interoceptive inference framework. We now integrate these considerations in the general discussion (lines 796-830).

Barrett, L. F., & Simmons, W. K. (2015). Interoceptive predictions in the brain. Nature reviews neuroscience, 16(7), 419-429.

Forte, G., Troisi, G., Pazzaglia, M., Pascalis, V. D., & Casagrande, M. (2022). Heart rate variability and pain: a systematic review. Brain sciences, 12(2), 153.

Seth, A. K. (2013). Interoceptive inference, emotion, and the embodied self. Trends in Cognitive Sciences, 17(11), 565-573.

Additional recommendations:

Major (in order of importance):

(1) Number of trials per participant, per condition: as I mentioned, having only 6 trials for each condition is very little. The minimum requirement to accept so few trials would be to show data about the distribution of participants' responses to these trials, both per pain intensity (which was later averaged across - another issue discussed later), and across pain intensities, and see that it allows averaging across and that it is not incredibly variable such that the mean is unreliable.

We appreciate the reviewer’s concern regarding the limited number of trials per condition. This choice was driven by both theoretical and methodological considerations.

First, as is common in body illusion paradigms (e.g., the Rubber Hand Illusion, Botvinick & Cohen, 1998; the Full Body Illusion, Ehrsson, 2007; the Cardio-visual full body illusion, Pratviel et al., 2022) only a few trials are typically employed due to the immediate effects these manipulations elicit. Repetition can reduce the strength of the illusion through habituation, increased awareness, or loss of believability.

Second, the experiment was already quite long (1.5h to 2h per participant) and cognitively demanding. It would not have been feasible to expand it further without compromising data quality due to fatigue, attentional decline, or participant disengagement.

Third, the need for a large number of trials is more relevant when using implicit measures such as response times or physiological indices, which are typically indirectly related to the psychological constructs of interest. In contrast, explicit ratings are often more sensitive and less noisy, and thus require fewer repetitions to yield reliable effects (e.g., Corneille et al., 2024).

Importantly, we also addressed your concern analytically. We ran therefore linear mixed-effects model analyses across all dependent variables (See Supplementary materials), with Trial (i.e., the rank order of each trial) included as a predictor to account for potential time-on-task effects such as learning, adaptation, or fatigue (e.g., Möckel et al., 2015). These models captured trial-by-trial variability and allowed us to test for systematic changes in heart rate (HR) and pain ratings including interactions with feedback conditions (e.g., Klieg et al., 2011; Baayen et al., 2010; Ambrosini et al., 2019). The consistent effects of Trial suggest that repetition dampens the illusion, reinforcing our decision to limit the number of exposures.

In the interoceptive experiment, these analyses revealed a significant Feedback × Trial interaction (F(3, 711.19) = 6.16, p < .001), indicating that the effect of feedback on HR was not constant over time. As we suspected, and in line with other illusion-like effects, the difference between Faster and Slower feedback, which was significant early on (estimate = 1.68 bpm, p = .0007), decreased by mid-session (estimate = 0.69 bpm, p = .0048), and was no longer significant in later trials (estimate = 0.30 bpm, p = .4775). At the end of the session, HR values in the Faster and Slower conditions even numerically converged (Faster: M = 74.4, Slower: M = 74.1), and the non-significant contrast confirms that the difference had effectively vanished (for further details about slope estimation, see Supplementary material).

The same pattern emerged for pain-unpleasantness ratings. A significant Feedback × Trial interaction (F (3, 675.33) = 3.44, p = .0165) revealed that the difference between Faster and Slower feedback was strongest at the beginning of the session and progressively weakened. Specifically, Faster feedback produced higher unpleasantness than Slower in early trials (estimate= -0.28, p = .0058) and mid-session (estimate = - 0.19, p = .0001), but this contrast was no longer significant in the final trials, wherein all the differences between active feedback conditions vanished (all ps > .55).

Finally, similar results were yielded for pain intensity ratings. A significant Feedback × Trial interaction (F (3, 669.15) = 9.86, p < .001) showed that the Faster vs Slower difference was greatest at the start of the session and progressively vanished over trials. In early trials Faster feedback exceeded Slower (estimate=-8.33, p = .0001); by mid-session this gap had shrunk to 4.48 points (p < .0001); and in the final trials it was no longer significant (all ps > .94).

Taken together, our results show that the illusion induced by Faster relative to slower feedback fades with repetition; adding further trials would likely have masked this key effect, confirming the methodological choice to restrict each condition to fewer exposures. To conclude, given that this is the first study to investigate an illusion of pain using heartbeat-based manipulation, we intentionally limited repeated exposures to preserve the integrity of the illusion. The use of mixed models as complementary analyses strengthens the reliability of our conclusions within these necessary design constraints. We now clarify this point in the Procedure paragraph (lines 328-335)

Ambrosini, E., Peressotti, F., Gennari, M., Benavides-Varela, S., & Montefinese, M. (2023). Aging-related effects on the controlled retrieval of semantic information. Psychology and Aging, 38(3), 219.

Baayen, R. H., & Milin, P. (2010). Analyzing reaction times. International Journal of Psychological Research, 3(2), 12-28.

Botvinick, M., & Cohen, J. (1998). Rubber hands ‘feel’touch that eyes see. Nature, 391(6669), 756-756.

Corneille, O., & Gawronski, B. (2024). Self-reports are better measurement instruments than implicit measures. Nature Reviews Psychology, 3(12), 835–846.

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Kliegl, R., Wei, P., Dambacher, M., Yan, M., & Zhou, X. (2011). Experimental effects and individual differences in linear mixed models: Estimating the relation of spatial, object, and attraction effects in visual attention. Frontiers in Psychology, 1, 238. https://doi.org/10.3389/fpsyg.2010.00238

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(2) Using different pain intensities: what was the purpose of training participants on correctly identifying pain intensities? You state that the aim of having 5 intensities is to cause ambiguity. What is the purpose of making sure participants accurately identify the intensities? Also, why then only 3 intensities were used in the test phase? The rationale for these is lacking.

We thank the reviewer for raising these important points regarding the use of different pain intensities. The purpose of using five levels during the calibration and training phases was to introduce variability and increase ambiguity in the participants’ sensory experience. This variability aimed to reduce predictability and prevent participants from forming fixed expectations about stimulus intensity, thereby enhancing the plausibility of the illusion. It also helped prevent habituation to a single intensity and made the manipulation subtler and more credible. We had no specific theoretical hypotheses about this manipulation. Regarding the accuracy training, although the paradigm introduced ambiguity, it was important to ensure that participants developed a stable and consistent internal representation of the pain scale. This step was essential to control for individual differences in sensory discrimination and to ensure that illusion effects were not confounded by participants’ inability to reliably distinguish between intensities.

As for the use of only three pain intensities in the test phase, the rationale was to focus on a manageable subset that still covered a meaningful range of the stimulus spectrum. This approach followed the same logic as Iodice et al. (2019, PNAS), who used five (rather than all seven) intensity levels during their experimental session. Specifically, they excluded the extreme levels (45 W and 125 W) used during baseline, to avoid floor and ceiling effects and to ensure that each test intensity could be paired with both a “slower” and a “faster” feedback from an adjacent level. This would not have been possible at the extremes of the intensity range, where no adjacent level exists in one direction. We adopted the same strategy to preserve the internal consistency and plausibility of our feedback manipulation.

We further clarified these points in the revised manuscript (lines 336-342).

Iodice, P., Porciello, G., Bufalari, I., Barca, L., & Pezzulo, G. (2019). An interoceptive illusion of effort induced by false heart-rate feedback. Proceedings of the National Academy of Sciences, 116(28), 13897-13902.

(3) Averaging across pain intensities: this is, in my opinion, not the best approach as by matching a participant's specific responses to a pain stimulus before and after the manipulation, you can more closely identify changes resulting from the manipulation. Nevertheless, the minimal requirement to do so is to show data of distributions of pain intensities so we know they did not differ between conditions per participant, and in general - as you indicate they were randomly distributed.

We thank the reviewer for this thoughtful comment. The decision to average across pain intensities in our main analyses was driven by the specific aim of the study: we did not intend to determine at which exact intensity level the illusion was most effective, and the limited number of trials makes such an analysis difficult. Rather, we introduced variability in nociceptive input to increase ambiguity and reduce predictability in the participants’ sensory experience. This variability was critical for enhancing the plausibility of the illusion by preventing participants from forming fixed expectations about stimulus strength. Additionally, using a range of intensities helped to minimize habituation effects and made the feedback manipulation subtler and more credible.

That said, we appreciate the reviewer’s point that matching specific responses before and after the manipulation at each intensity level could provide further insights into how the illusion operates across varying levels of nociceptive input. We therefore conducted supplementary analyses using linear mixed-effects models in which all three stimulus intensities were included as a continuous fixed factor. This allowed us to examine whether the effects of feedback were intensity-specific or generalized across different levels of stimulation

These analyses revealed that, in both the interoceptive and exteroceptive experiments, the effect of feedback on pain ratings was significantly modulated by stimulus intensity, as indicated by a Feedback × Stimulus Intensity interaction (Interoceptive: unpleasantness F(3, 672.32)=3.90, p=.0088; intensity ratings F(3, 667.07)=3.46, p=.016. Exteroceptive: unpleasantness F(3, 569.16)=8.21, p<.0001; intensity ratings F(3, 570.65)=3.00, p=.0301). The interaction term confirmed that the impact of feedback varied with stimulus strength, yet the pattern that emerged in each study diverged markedly.

In the interoceptive experiment, the accelerated-heartbeat feedback (Faster) systematically heightened pain relative to the decelerated version (Slower) at every level of noxious input: for low-intensity trials Faster exceeded Slower by 0.22 ± 0.08 points on the unpleasantness scale (t = 2.84, p = .0094) and by 3.87 ± 1.69 units on the numeric intensity scale (t = 2.29, p = .0448); at the medium intensity the corresponding differences were 0.19 ± 0.05 (t = -4.02, p = .0001) and 4.52 ± 1.06 (t = 4.28, p < .0001); and even at the highest intensity, Faster still surpassed Slower by 0.17 ± 0.08 on unpleasantness (t = 2.21, p = .0326) and by 5.16 ± 1.67 on intensity (t = 3.09, p = .0032). This uniform Faster > Slower pattern indicates that the interoceptive manipulation amplifies perceived pain in a stimulus-independent fashion.

The exteroceptive control experiment told a different story: the Faster-Slower contrast reached significance only at the most noxious setting (unpleasantness: estimate = 0.24 ± 0.07, t = -3.24, p = .0019; intensity: estimate = - 5.14 ± 1.82, t = 2.83, p = .0072) and was absent at the medium level (intensity , p=0.29; unpleasantness,  p=0.45), while at the lowest level Slower actually produced numerically higher unpleasantness (2.56 versus 2.40) and intensity ratings (44.7 versus 42.2).

Thus, although both studies show that feedback effects depend on the actual nociceptive level of the stimulus, the results suggest that the faster vs. slower interoceptive feedback manipulation delivers a robust and intensity-invariant enhancement of pain, whereas the exteroceptive cue exerts a sporadic influence that surfaces solely under maximal stimulation.

These new results are now included in the Supplementary Materials, where we report the detailed analyses for both the Interoceptive and Exteroceptive experiments on the Likert unpleasantness ratings and the numeric pain intensity ratings.

(4) Sample size: It seems that the sample size was determined after the experiment was conducted, as the required N is identical to the actual N. I would be transparent about that, and say that retrospective sample size analyses support the ability of your sample size to support your claims. In general, a larger sample size than is required is always recommended, and if you were to run another study, I suggest you increase the sample size.

As also addressed in our responses to your later comments (see our detailed reply regarding the justification of SESOI and power analyses), the power analyses reported here were not post-hoc power analyses based on obtained results. In line with current recommendations (Anderson, Kelley & Maxwell, 2017; Albers & Lakens, 2018), we did not base our analyses on previously reported effect sizes, as these can carry considerable uncertainty, particularly for novel effects where robust estimates are lacking. Instead, we used sensitivity analyses, conducted using the sensitivity analysis function in G*Power (Version 3.1). Sensitivity analyses allow us to report effect sizes that our design was adequately powered (90%) to detect, given the actual sample size, desired power level, and the statistical test used in each experiment (Lakens, 2022). Following further guidance (Lakens, 2022), we also report the smallest effect size of interest (SESOI) that these tests could reliably detect.

This approach indicated that our design was powered to detect effect sizes of d = 0.57 in Experiment 1 and d = 0.62 in Experiment 2, with corresponding SESOIs of d = 0.34 and d = 0.37, respectively. The slightly higher value in Experiment 2 reflects the greater number of participants excluded (from an equal number originally tested) based on pre-specified criteria. Importantly, both experiments were well-powered to detect effects smaller than those typically reported in similar top-down pain modulation studies, where effect sizes around d = 0.7 have been observed (Iodice et al., 2019).

We have now clarified this rationale in the revised manuscript, Experiment 1- Methods - Participants (lines 208-217).

Albers, C., & Lakens, D. (2018). When power analyses based on pilot data are biased: Inaccurate effect size estimators and follow-up bias. Journal of experimental social psychology, 74, 187-195.

Anderson, S. F., Kelley, K., & Maxwell, S. E. (2017). Sample-Size Planning for More Accurate Statistical Power: A Method Adjusting Sample Effect Sizes for Publication Bias and Uncertainty. Psychological Science, 28(11), 1547-1562. https://doi.org/10.1177/0956797617723724

Lakens, D. (2022). Sample size justification. Collabra: psychology, 8(1), 33267.

(5) Analysis: the use of change scores instead of the actual scores is not recommended, as it is a loss of data, but could have been ignored if it didn't have a significant effect on the analyses conducted. Instead of conducting an RM-ANOVA of conditions (faster, slower, normal heartbeats) across participants, finding significant interaction, and then moving on to specific post-hoc paired comparisons between conditions, the authors begin with the change score but then move on to conduct the said paired comparisons without ever anchoring these analyses in an appropriate larger ANOVA. I strongly recommend the use of an ANOVA but if not, the authors would have to correct for multiple comparisons at the minimum.

We thank the reviewer for their comment regarding the use of change scores. These were originally derived from the difference between the slower and faster feedback conditions relative to the congruent condition. In line with the reviewer’s recommendation, we have now removed these difference-based change scores from the main analysis. The results remain identical. Please note that we have retained the normalization procedure, relative to each participant’s initial baseline in the no feedback trials, as it is widely used in the interoceptive and pain literature (e.g., Bartolo et al., 2013; Cecchini et al., 2020; Riello et al., 2019). This approach helps to control for interindividual variability and baseline differences by expressing each participant’s response relative to their no-feedback baseline. As before, normalization was applied across all dependent variables (heart rate, pain intensity, and pain unpleasantness).

To address the reviewer’s concern about statistical validity, we now first report a 1-factor repeated-measures ANOVA (Greenhouse-Geisser corrected) for each dependent variable, with feedback condition (slower, congruent, faster) as the within-subject factor.

These show in each case a significant main effect, which we then follow with planned paired-sample t-tests comparing:

Faster vs. slower feedback (our main hypothesis, as these manipulations are expected to produce largest, most powerful, test of our hypothesis, see response to Reviewer 3),

Faster vs. congruent and slower vs. congruent (to test for potential asymmetries, as suggested  by previous false heart rate feedback studies).

The rationale of these analyses is further discussed in the Data Analysis of Experiment 1 (lines 405-437).

Although we report the omnibus one-factor RM-ANOVAs to satisfy conventional expectations, we note that such tests are not statistically necessary, nor even optimal, when the research question is fully captured by a priori, theory-driven contrasts. Extensive methodological work shows that, in this situation, going straight to planned contrasts maximises power without inflating Type I error and avoids the logical circularity of first testing an effect one does not predict (e.g., Rosenthal & Rosnow, 1985). In other words, an omnibus F is warranted only when one wishes to protect against unspecified patterns of differences. Here our hypotheses were precise (Faster ≠ Slower; potential asymmetry relative to Congruent), so the planned paired comparisons would have sufficed statistically. We therefore include the RM-ANOVAs solely for readers who expect to see them, but our inferential conclusions rest on the theoretically motivated contrasts.

Rosenthal, R., & Rosnow, R. L. (1985). Contrast analysis. New York: Cambridge.

(6) Correlations: were there correlations between subjects' own heartbeats (which are considered a predictive cue) and pain perceptions? This is critical to show that the two are in fact related.

We thank the reviewer for this thoughtful suggestion. While we agree that testing for a correlation between anticipatory heart rate responses and subjective pain ratings is theoretically relevant. However, we have not conducted this analysis in the current manuscript, as our study was not designed or powered to reliably detect such individual differences. As noted by Hedge, Powell, and Sumner (2018), robust within-subject experimental designs tend to minimize between-subject variability in order to detect clear experimental effects. This reduction in variance at the between-subject level limits the reliability of correlational analyses involving trait-like or individual response patterns. This issue, known as the reliability paradox, highlights that measures showing robust within-subject effects may not show stable individual differences, and therefore correlations with other individual-level variables (like subjective ratings used here) require much larger samples to produce interpretable results than available here (and commonly used in the literature), typically more than 200 participants. For these reasons, we believe that running such an analysis in our current dataset would not yield informative results and could be misleading.

We now explicitly acknowledge this point in the revised version of the manuscript (Limitations and future directions, lines 832-851) and suggest that future studies specifically designed to examine individual variability in anticipatory physiological responses and pain perception would be better suited to address this question.

Hedge, C., Powell, G., & Sumner, P. (2018). The reliability paradox: Why robust cognitive tasks do not produce reliable individual differences. Behavior Research Methods, 50(3), 1166-1186. https://doi.org/10.3758/s13428-017-0935-1

(7) The direct comparison between studies is great! and finally the use of ANOVA - but why without the appropriate post-hoc tests to support the bold claims in lines 542-544? This is needed. Same for 556-558.

We apologize if our writing was not clear here, but the result of the ANOVAs fully warrants the claims in 542-544 (now lines 616-618) and 556-558 (now lines 601-603).

In a 2x2 design, the interaction term is mathematically identical to comparing the difference induced by Factor 1 at one level of Factor 2 with the same difference induced at the other level of Factor 2. In our 2x2 analysis with the factors Experiment (Cardiac feedback, Exteroceptive feedback - between participants) and Feedback Frequency (faster, slower - within participants), the interaction therefore directly tests whether the effect of Feedback frequency differs statistically (i.e., is larger or smaller) in the participants in the interoceptive and exteroceptive experiments. Thus, the conclusion that “faster feedback affected the perceptual bias more strongly in the Experiment 1 than in Experiment 2” captures the outcome of the significant interaction exactly. Indeed, this test would be statistically equivalent (and would produce identical p values) to a simple between-group t-test between each participant’s difference between the faster and slower feedback in the interoceptive group and the analogous differences between the faster and slower feedback in the exteroceptive group, as illustrated in standard examples of factorial analysis (see, e.g., Maxwell, Delaney and Kelley, 2018).

Please note that, for the above reason, mathematically the conclusion of larger effects in one experiment than the other is licensed by the significant interaction even without follow-up t-tests. However, if the reader would like to see these tests, they are simply the main analysis results reported in each of the two experiment sections, where significant (t-test) differences between faster and slower feedback were induced with interoceptive cues (Experiment 1) but not exteroceptive cues (Experiment 2). Reporting them in the between-experiment comparison section again would therefore be redundant.

To avoid this lack of clarity, we have now re-written the results section of each experiment. First, as noted above, we now precede our main hypothesis test - the crucial t-test comparing heartrate and pain ratings after faster vs slower feedback - with an ANOVA including all three levels (faster, congruent, slower feedback). Moreover, we removed the separate between-experiment comparison section. Instead, in the Result section of the exteroceptive Experiment 2, we now directly compare the (absent or reversed) effects of faster vs slower feedback directly, with a between-groups t-test, with the present effects in the interoceptive Experiment 1. This shows conclusively, and hopefully more clearly, that the effects in both experiments differ. We hope that this makes the logic of our analyses clearer.

Maxwell, S. E., Delaney, H. D., & Kelley, K. (2017). Designing experiments and analyzing data: A model comparison perspective. Routledge.

(8) The discussion is missing a limitation paragraph.

Thank you for the suggestion. We have now added a dedicated limitations paragraph in the Discussion section (lines 832-890).

Additional recommendations:

Minor (chronological order):

(1) Sample size calculations for both experiments: what was the effect size based on? A citation or further information is needed. Also, clarify why the effect size differed between the two experiments.

Please see above

(2) "Participants were asked to either not drink coffee or smoke cigarettes" - either is implying that one of the two was asked. I suspect it is redundant as both were not permitted.

The intention was to restrict both behaviors, so we have corrected the sentence to clarify that participants were asked not to drink coffee or smoke cigarettes before the session.

(3) Normalization of ECG - what exactly was normalized, namely what measure of the ECG?

The normalized measure was the heart rate, expressed in beats per minute (bpm). We now clarify this in the Data Analysis section of Experiment 1 (Measures of the heart rate recorded with the ECG (beats per minute) in the feedback phase were normalized)

(4) Line 360: "Mean Δ pain unpleasantness ratings were analysed analogously" - this is unclear, if already described in methods then should be removed here, if not - should be further explained here.

Thank you for your observation. We are no longer using change scores.

(5) Lines 418-420: "Consequently, perceptual and cardiac modulations associated with the feedback manipulation should be reduced over the exposure to the faster exteroceptive sound." - why reduced and not unchanged? I didn't follow the logic.

We chose the term “reduced” rather than “unchanged” to remain cautious in our interpretation. Statistically, the absence of a significant effect in one experiment does not necessarily mean that no effect is present; it simply means we did not detect one. For this reason, we avoided using language that would suggest complete absence of modulation. It also more closely matches the results of the between experiment comparisons that we report in the Result section of Experiment 2, which can in principle only show that the effect in Experiment 2 was smaller than that of Experiment 1, not that it was absent. Even the TOST analysis that we utilize to show the absence of an effect can only show that any effect that is present is smaller than we could reasonably expect to detect with our experimental design, not its complete absence.

Also, on a theoretical level, pain is a complex, multidimensional experience influenced not only by sensory input but also by cognitive, emotional, social and expectancy factors. For this reason, we considered it important to remain open to the possibility that other mechanisms beyond the misleading cardiac prior induced by the feedback might have contributed to the observed effects. If such other influences had contributed to the induced differences between faster and slower feedback in Experiment 1, some remainder of this difference could have been observed in Experiment 2 as well.

Thus, for both statistical and theoretical reasons, we were careful to predict a reduction of the crucial difference, not its complete elimination. However, to warrant the possibility that effects could be completely eliminated we now write that “perceptual and cardiac modulations associated with the feedback manipulation should be reduced or eliminated with exteroceptive feedback”

(6) Study 2 generation of feedback - was this again tailored per participants (25% above and beyond their own HR at baseline + gradually increasing or decreasing), or identical for everyone?

Yes, in Study 2, the generation of feedback was tailored to each participant, mirroring the procedure or Experiment 1. Specifically, the feedback was set to be 25% above or below their baseline heart rate, with the feedback gradually increasing or decreasing. This individualized approach ensured that each participant experienced feedback relative to their own baseline heart rate. We now clarify this in the Methods section (lines 306-318).

(7) I did not follow why we need the TOST and how to interpret its results.

We thank the reviewer for raising this important point. In classical null hypothesis significance testing (NHST), a non-significant p-value (e.g., p > .05) only indicates that we failed to find a statistically significant difference, not that there is no difference. It therefore does not allow us to conclude that two conditions are equivalent – only that we cannot confidently say they are different. In our case, to support the claim that exteroceptive feedback does not induce perceptual or physiological changes (unlike interoceptive feedback), we needed a method to test for the absence of a meaningful effect, not just the absence of a statistically detectable one.

The TOST (Two One-Sided Tests) procedure reverses the logic of NHST by testing whether the observed effect falls within a predefined equivalence interval, called the smallest effect size of interest (SESOI) that is in principle measurable with our design parameters (e.g., type of test, number of participants). This approach is necessary when the goal is not to detect a difference, but rather to demonstrate that an observed effect is so small that it can be considered negligible – or at the least smaller than we could in principle expect to observe in the given experiment. We used the TOST procedure in Experiment 2 to test for statistical equivalence between the effects of faster and slower exteroceptive feedback on pain ratings and heart rate.

We hope that the clearer explanation now provided in data analysis of Experiment 2 section (lines 5589-563) fully addresses the reviewer’s concern.

(8) Lines 492-3: authors say TOST significant, while p value = 0.065

We thank the reviewer for spotting this inconsistency. The discrepancy was due to a typographical error in the initial manuscript. During the revision of the paper, we rechecked and fully recomputed all TOST analyses, and the results have now been corrected throughout the manuscript to accurately reflect the statistical outcomes. In particular, for the comparison of heart rate between faster and slower exteroceptive feedback in Experiment 2, the corrected TOST analysis now shows a significant equivalence, with the observed effect size being d = -0.19 (90% CI [-0.36, -0.03]) and both one-sided tests yielding p = .025 and p < .001. These updated results are reported in the revised Results section.

Reviewer #2 (Recommendations For The Authors):

I would suggest the authors revise their definition of pain in the introduction, since it is not always a protective experience. The new IASP definition specifically takes this into consideration.

We thank the reviewer for this suggestion. We have updated the definition of pain in the Introduction (lines 2-4) to align with the most recent IASP definition (2020), which characterizes pain as “an unpleasant sensory and emotional experience associated with, or resembling that associated with, actual or potential tissue damage” (lines 51-53).

The work on exteroceptive cues does not necessarily neglect the role of interoceptive sources of information, although it is true that it has been comparatively less studied. I suggest rephrasing this sentence to reflect this.

We thank the reviewer for pointing out this important nuance. We agree that studies employing exteroceptive cues to modulate pain perception do not necessarily neglect the role of interoceptive sources, even though these are not always the primary focus of investigation. Our intention was not to imply a strict dichotomy, but rather to highlight that interoceptive mechanisms have been comparatively under-investigated. We have revised the sentence in the Introduction accordingly to better reflect this perspective (Introduction, lines 110-112, “Although interoceptive processes may have contributed to the observed effects, these studies did not specifically target interoceptive sources of information within the inferential process.”).

The last paragraph of the introduction (lines 158-164) contains generalizations beyond what can be supported by the data and the results, about the generation of predictive processes and the origins of these predictions. The statements regarding the understanding of pain-related pathologies in terms of chronic aberrant predictions in the context of this study are also unwarranted.

We have deleted this paragraph now.

I could not find the study registration (at least in clinicaltrials.gov). This is curious considering that the hypothesis and the experimental design seem in principle well thought out, and a study pre-registration improves the credibility of the research (Nosek et al., 2018). I also find the choice for the smallest effect of interest (SESOI) odd. Besides the unnecessary variable transformations (more on that later), there is no justification for why that particular SESOI was chosen, or why it changes between experiments (Dienes, 2021; King, 2011), which makes the choice look arbitrary. The SESOI is a fundamental component of a priori power analysis (Lakens, 2022), and without rationale and preregistration, it is impossible to tell whether this is a case of SPARKing or not (Sasaki & Yamada, 2023).

We acknowledge that the study was not preregistered. Although our hypotheses and design were developed a priori and informed by established theoretical frameworks, the lack of formal preregistration is a limitation.

The SESOI values for Experiments 1 and 2 were derived from sensitivity analyses based on the fixed design parameters (type of test, number of participants, alpha level) of our study, not from any post-hoc interpretation based on observed results - they can therefore not be a case of SPARKing. Following current recommendations (Anderson, Kelley & Maxwell, 2017; Albers & Lakens, 2017; Lakens, 2022), we avoided basing power estimates on published effect sizes, as no such values exist for in novel paradigms, and are typically inflated due to publication and other biases. Instead, sensitivity analyses (using G*Power, v 3.1) allows us to calculate, prospectively, the smallest effect each design could detect with 90 % power, given the actual sample size, test type, and α level. Because more participants were excluded in Experiment 2, this design can detect slightly larger effects (d = 0.62) than Experiment 1 (d = 0.57). Please note that both studies therefore remain well-powered to capture effects of the magnitude typically reported in previous research using feedback manipulations to explore interoceptive illusions (e.g., Iodice et al., 2019, d ≈ 0.7).

We have added this clarification to the Participants section of Experiment 1 (Lines 208-217).

Anderson, S. F., Kelley, K., & Maxwell, S. E. (2017). Sample-Size Planning for More Accurate Statistical Power: A Method Adjusting Sample Effect Sizes for Publication Bias and Uncertainty. Psychological Science, 28(11), 1547-1562.

Lakens, D. (2022). Sample size justification. Collabra: psychology, 8(1), 33267.

Albers, C., & Lakens, D. (2018). When power analyses based on pilot data are biased: Inaccurate effect size estimators and follow-up bias. Journal of experimental social psychology, 74, 187-195.

In the Apparatus subsection, it is stated that the intensity of the electrical stimuli was fixed at 2 ms. I believe the authors refer to the duration of the stimulus, not its intensity.

You are right, thank you for pointing that out. The text should refer to the duration of the electrical stimulus, not its intensity. We have corrected this wording in the revised manuscript to avoid confusion.

It would be interesting to report (in graphical form) the stimulation intensities corresponding to the calibration procedure for the five different pain levels identified for all subjects.

That's a good suggestion. We have included a supplementary figure showing the stimulation intensities corresponding to the five individually calibrated pain levels across all participants (Supplementary Figure 11.)

It is questionable that researchers state that "pain and unpleasantness should be rated independently" but then the first level of the Likert scale for unpleasantness is "1=no pain". This is particularly relevant since simulation (and specifically electrical stimulation) can be unpleasant but non-painful at the same time. Since the experiments were already performed, the researchers should at least explain this choice.

Thank you for raising this point. You are right in that the label of “no pain” in the pain unpleasantness scale was not ideal, and we now acknowledge this in the text (lines 886-890). Please note that this was always the second rating that participants gave (after pain intensity), and the strongest results come from this first rating.

Discussion.

I did not find in the manuscript the rationale for varying the frequency of the heart rate by 25% (instead of any other arbitrary quantity).

We thank the Reviewer for this observation, which prompted us to clarify the rationale behind our choice of a ±25% manipulation of heart rate feedback. False feedback paradigms have historically relied on a variety of approaches to modulate perceived cardiac signals. Some studies have adopted non-individualised values, using fixed frequencies (e.g., 60 or 110 bpm) to evoke states of calm or arousal, independently of participants’ actual physiology (Valins, 1966; Shahidi & Baluch, 1991; Crucian et al., 2000; Tajadura-Jiménez et al., 2008). Others have used the participant’s real-time heart rate as a basis, introducing accelerations or decelerations without applying a specific percentage transformation (e.g., Iodice et al., 2019). More recently, a growing body of work has employed percentage-based alterations of the instantaneous heart rate, offering a controlled and participant-specific manipulation. These include studies using −20% (Azevedo et al., 2017), ±30% (Dey et al., 2018), and even ±50% (Gray et al., 2007).

These different methodologies - non-individualised, absolute, or proportionally scaled - have all been shown to effectively modulate subjective and physiological responses. They suggest that the impact of false feedback does not depend on a single fixed method, but rather on the plausibility and salience of the manipulation within the context of the task. We chose to apply a ±25% variation because it falls well within the most commonly used range and strikes a balance between producing a detectable effect and maintaining the illusion of physiological realism. The magnitude is conceptually justified as being large enough to shape interoceptive and emotional experience (as shown by Azevedo and Dey), yet small enough to avoid implausible or disruptive alterations, such as those approaching ±50%. We have now clarified this rationale in the revised Procedure paragraph of Experiment 1 (lines 306-318).

T. Azevedo, R., Bennett, N., Bilicki, A., Hooper, J., Markopoulou, F., & Tsakiris, M. (2017). The calming effect of a new wearable device during the anticipation of public speech. Scientific reports, 7(1), 2285.

Crucian, G. P., Hughes, J. D., Barrett, A. M., Williamson, D. J. G., Bauer, R. M., Bowers, D., & Heilman, K. M. (2000). Emotional and physiological responses to false feedback. Cortex, 36(5), 623-647.

Dey, A., Chen, H., Billinghurst, M., & Lindeman, R. W. (2018, October). Effects of manipulating physiological feedback in immersive virtual environments. In Proceedings of the 2018 Annual Symposium on Computer-Human Interaction in Play (pp. 101-111).

Gray, M. A., Harrison, N. A., Wiens, S., & Critchley, H. D. (2007). Modulation of emotional appraisal by false physiological feedback during fMRI. PLoS one, 2(6), e546.

Shahidi, S., & Baluch, B. (1991). False heart-rate feedback, social anxiety and self-attribution of embarrassment. Psychological reports, 69(3), 1024-1026.

Tajadura-Jiménez, A., Väljamäe, A., & Västfjäll, D. (2008). Self-representation in mediated environments: the experience of emotions modulated by auditory-vibrotactile heartbeat. CyberPsychology & Behavior, 11(1), 33-38.

Valins, S. (1966). Cognitive effects of false heart-rate feedback. Journal of personality and social psychology, 4(4), 400.

The researchers state that pain ratings collected in the feedback phase were normalized to the no-feedback phase to control for inter-individual variability in pain perception, as established by previous research. They cite three studies involving smell and taste, of which the last two contain the same normalization presented in this study. However, unlike these studies, the outcomes here require no normalization whatsoever, because there should be no (or very little) inter-individual variability in pain intensity ratings. Indeed, pain intensity ratings in this study are anchored to 30, 50, and 70 / 100 as a condition of the experimental design. The researchers go to extreme lengths to ensure this is the case, by adjusting stimulation intensities until at least 75% of stimulation intensities are correctly matched to their pain ratings counterpart in the pre-experiment procedure. In other words, inter-individual variability in this study is in stimulation intensities, and not pain intensity ratings. Even if it could be argued that pain unpleasantness and heart rate still need to account for inter-individual variability, the best way to do this is by using the baseline (no-feedback) measures as covariates in a mixed linear model. Another advantage of this approach is that all the effects can be described in terms of the original scales and are readily understandable, and post hoc tests between levels can be corrected for multiple comparisons. On the contrary, the familywise error rate for the comparisons between conditions in the current analysis is larger than 5% (since there is a "main" paired t-test and additional "simple" tests).

We disagree that there is little to no variability in the no feedback phase. Participants were tested in their ability to distinguish intensities in an initial pre-experiment calibration phase. In the no feedback phase, participants rated the pain stimuli in the full experimental context.

In the pre-experiment calibration phase, participants were tested only once in their ability to match five electrical‐stimulation levels to the 0-100 NPS scale, before any feedback manipulation started. During this pre-experiment calibration we required that each level was classified correctly on ≥ 75 % of the four repetitions; “correct” meant falling within ± 5 NPS units of the target anchor (e.g., a response of 25–35 was accepted for the 30/100 anchor). This procedure served one purpose only: to make sure that every participant entered the main experiment with three unambiguously distinguishable stimulation levels (30 / 50 / 70). We integrated this point in the revised manuscript lines 263-270.

Once the real task began, the context changed: shocks are unpredictable, attention is drawn to the heartbeat, and participants must judge both intensity and unpleasantness. In this full experimental setting the no-feedback block indeed shows considerable variability, even for the pain intensity ratings. Participants mean rating on the NPS scale was 46.4, with a standard deviation of 11.9 - thus participants vary quite strongly in their mean ratings (range 14.5 to 70). Moreover, while all participants show a positive correlation between actual intensities and their ratings (i.e., they rate the higher intensities as more intense than the lower ones), they vary in how much of the scale they use, with differences between reported highest and lowest intensities ranging between 8 and 91, for the participants showing the smallest and largest differences, respectively.

Thus, while we simplified the analysis to remove the difference scoring relative to the congruent trials and now use these congruent trials as an additional condition in the analysis, we retained the normalisation procedure to account for the in-fact-existing between-participant variability, and ensure consistency with prior research (Bartolo et al., 2013; Cecchini et al., 2020; Riello et al., 2019) and our a priori analysis plan.

However, to ensure we fully address your point here (and the other reviewers’ points about potential additional factors affecting the effects, like trial number and stimulus intensity), we also report an additional linear mixed-effects model analysis without normalization. It includes every feedback level as condition (No-Feedback, Congruent, Slower, Faster), plus additional predictors for actual stimulus intensity and trial rank within the experiment (as suggested by the other reviewers). This confirms that all relevant results remain intact once baseline and congruent trials are explicitly included in the model.

In brief, cross‐experiment analyses demonstrated that the Faster vs Slower contrast was markedly larger when the feedback was interoceptive than when it was exteroceptive. This held for heart-rate deceleration (b = 0.94 bpm, p = .005), for increases in unpleasantness (b = -0.16 Likert units, p = .015), and in pain-intensity ratings (b = -3.27 NPS points, p = .037).

These findings were then further confirmed by within-experiment analyses. Within the interoceptive experiment, the mixed-model on raw scores replicated every original effect: heart rate was lower after Faster than Slower feedback (estimate = –0.69 bpm, p = .005); unpleasantness was higher after Faster than Slower feedback (estimate = 0.19, p < .001); pain-intensity rose after Faster versus Slower (estimate=-4.285, p < .001). In the exteroceptive experiment, however, none of these Faster–Slower contrasts reached significance for heart rate (all ps > .33), unpleasantness (all ps > .43) or intensity (all ps > .10).  Because these effects remain significant even with No-Feedback and Congruent trials explicitly included in the model and vanish under exteroceptive control, the supplementary, non-normalised analyses confirm that the faster vs. slower interoceptive feedback uniquely lowers anticipatory heart rate while amplifying both intensity and unpleasantness of pain, independent of data transformation or reference conditions.  Please see Supplementary analyses for further details.

Bartolo, M., Serrao, M., Gamgebeli, Z., Alpaidze, M., Perrotta, A., Padua, L., Pierelli, F., Nappi, G., & Sandrini, G. (2013). Modulation of the human nociceptive flexion reflex by pleasant and unpleasant odors. PAIN®, 154(10), 2054-2059.

Cecchini, M. P., Riello, M., Sandri, A., Zanini, A., Fiorio, M., & Tinazzi, M. (2020). Smell and taste dissociations in the modulation of tonic pain perception induced by a capsaicin cream application. European Journal of Pain, 24(10), 1946-1955.

Riello, M., Cecchini, M. P., Zanini, A., Di Chiappari, M., Tinazzi, M., & Fiorio, M. (2019). Perception of phasic pain is modulated by smell and taste. European Journal of Pain, 23(10), 1790-1800.

I could initially not find a rationale for bringing upfront the comparison between faster vs. slower HR acoustic feedback when in principle the intuitive comparisons would be faster vs. congruent and slower vs. congruent feedback. This is even more relevant considering that in the proposed main comparison, the congruent feedback does not play a role: since Δ outcomes are calculated as (faster - congruent) and (slower - congruent), a paired t-test between Δ faster and Δ slower outcomes equals (faster - congruent) - (slower - congruent) = (faster - slower). I later realized that the statistical comparison (paired t-test) of pain intensity ratings of faster vs. slower acoustic feedback is significant in experiment 1 but not in experiment 2, which in principle would support the argument that interoceptive, but not exteroceptive, feedback modulates pain perception. However, the "simple" t-tests show that faster feedback modulates pain perception in both experiments, although the effect is larger in experiment 1 (interoceptive feedback) compared to experiment 2 (exteroceptive feedback).

The comparison between faster and slower feedback is indeed crucial, and we regret not having made this clearer in the first version of the manuscript. As noted in our response to your point in the public review, this comparison is both statistically most powerful, and theoretically the most appropriate, as it controls for any influence of salience or surprise when heart rates deviate (in either direction) from what is expected. It therefore provides a clean measure of how much accelerated heartrate affects pain perception and physiological response, relative to an equal change in the opposite direction. However, as noted above, in the new version of the manuscript we have now removed the analysis via difference scores, and directly compared all three relevant conditions (faster, congruent, slower), first via an ANOVA and then with follow-up planned t-tests.

Please refer to our previous response for further details (i.e., Furthermore, the researchers propose the comparison of faster vs. slower delta HR acoustic feedback throughout the manuscript when the natural comparison is the incongruent vs. the congruent feedback [..]).

The design of experiment two involves the selection of knocking wood sounds to act as exteroceptive acoustic feedback. Since the purpose is to test whether sound affects pain intensity ratings, unpleasantness, and heart rate, it would have made sense to choose sounds that would be more likely to elicit such changes, e.g. Taffou et al. (2021), Chen & Wang (2022), Zhou et al. (2022), Tajadura-Jiménez et al. (2010). Whereas I acknowledge that there is a difference in effect sizes between experiment 1 and experiment 2 for the faster acoustic feedback, I am not fully convinced that this difference is due to the nature of the feedback (interoceptive vs. exteroceptive), since a similar difference could arguably be obtained by exteroceptive sound with looming or rough qualities. Since the experiment was already carried out and this hypothesis cannot be tested, I suggest that the researchers moderate the inferences made in the Discussion regarding these results.

Please refer to our previous response for a previous detailed answer to this point in the Public Review (i.e., This could be influenced by the fact that the faster HR exteroceptive cue in experiment 2 also shows a significant modulatory effect [..]). As we describe there, we see little grounds to suspect such a non-specific influence of acoustic parameters, as it is specifically the sensitivity to the change in heart rate (faster vs slower) that is affected by our between-experiment manipulation, not the overall response to the different exteroceptive or interoceptive sounds. Moreover, the specific change induced by the faster interoceptive feedback - a heartrate deceleration - is not consistent with a change in arousal or alertness (which would have predicted an increase in heartrate with increasing arousal). See also Discussion-Accounting for general unspecific contributions.

Additionally, the fact that no significant effects were found for unpleasantness ratings or heart rate (absence of evidence) should not be taken as proof that faster exteroceptive feedback does not induce an effect on these outcomes (evidence of absence). In this case, it could be that there is actually no effect on these variables, or that the experiment was not sufficiently powered to detect those effects. This would depend on the SESOIs for these variables, which as stated before, was not properly justified.

We very much agree that the absence of significant effects should not be interpreted as definitive evidence of absence. Indeed, we were careful not to overinterpret the null findings for heart rate and unpleasantness ratings, and we conducted additional analyses to clarify their interpretation. First, the TOST analysis shows that any effects in Experiment 2 are (significantly) smaller than the smallest effect size that can possibly be detected in our experiment, given the experimental parameters (number of participants, type of test, alpha level). Second, and more importantly, we run between-experiments comparisons (see Results Experiment 2, and Supplementary materials, Cross-experiment analysis between-subjects model) of the crucial difference in the changes induced by faster and slower feedback. This showed that the differences were larger with interoceptive (Experiment 1) than exteroceptive cues (Experiment 2). Thus, even if a smaller than is in principle detectable effect is induced by the exteroceptive cues in Experiment 2, it is smaller than with interoceptive cues in Experiment 1.

To ensure we fully address this point, we have now simplified our main analysis (main manuscript), replicated it with a different analysis (Supplementary material), we motivate more clearly (Methods Experiment 1), why the comparison between faster and slower feedback is crucial, and we make clearer that the difference between these conditions is larger in Experiment 1 than Experiment 2 (Results Experiment 2). Moreover, we went through the manuscript and ensured that our wording does not over-interpret the absence of effects in Experiment 2, as an absence of a difference.

The section "Additional comparison analysis between experiments" encompasses in a way all possible comparisons between levels of the different factors in both experiments. My original suggestion regarding the use of a mixed linear model with covariates is still valid for this case. This analysis also brings into question another aspect of the experimental design: what is the rationale for dividing the study into two experiments, considering that variability and confounding factors would have been much better controlled in a single experimental session that includes all conditions?

We thank the reviewer for their comment. We would like to note, first, that the between-experiment analyses did not encompass all possible comparisons between levels, as it just included faster and slower feedback for the within-experiment comparison Instead, they focus on the specific interaction between faster and slower feedback on the one hand, and interoceptive vs exteroceptive cues on the other. This interaction essentially compares, for each dependent measure (HR, pain unpleasantness, pain intensity), the difference between faster and slower feedback in Experiment 1 with that the same difference in Experiment 2 (and would produce identical p values to a between-experiment t-test). The significant interactions therefore indicate larger effects of interoceptive cues than exteroceptive ones for each of the measures. To make this clearer, we have now exchanged the analysis with between-experiment t-tests of the difference between faster and slower feedback for each measure (Results Experiment 2), producing identical results. Moreover, as suggested, we also now report linear mixed model analyses (see Supplementary Materials), which provide a comprehensive comparison across experiments.

Regarding the experimental design, we appreciate the reviewer’s suggestion regarding a within-subject crossover design. While such an approach indeed offers greater statistical power by reducing interindividual variability (Charness, Gneezy, & Kuhn, 2012), we intentionally chose a between-subjects design due to theoretical and methodological considerations specific to deceptive feedback paradigms. First, carryover effects are a major concern in deception studies. Participants exposed to one type of feedback could develop suspicion or adaptive strategies that would alter their responses in subsequent conditions (Martin & Sayette, 1993). Expectancy effects could thus contaminate results in a crossover design, particularly when feedback manipulation becomes apparent. In line with this idea, past studies on false cardiac feedback (e.g., Valins, 1966; Pennebaker & Lightner, 1980) often employed between-subjects or blocked designs to maintain the ecological validity of the illusion.

Charness, G., Gneezy, U., & Kuhn, M. A. (2012). Experimental methods: Between-subject and within-subject design. Journal of economic behavior & organization, 81(1), 1-8.

Martin, C. S., & Sayette, M. A. (1993). Experimental design in alcohol administration research: limitations and alternatives in the manipulation of dosage-set. Journal of studies on alcohol, 54(6), 750-761.

Pennebaker, J. W., & Lightner, J. M. (1980). Competition of internal and external information in an exercise setting. Journal of personality and social psychology, 39(1), 165.

Valins, S. (1966). Cognitive effects of false heart-rate feedback. Journal of personality and social psychology, 4(4), 400.

References

Chen ZS, Wang J. Pain, from perception to action: A computational perspective. iScience. 2022 Dec 1;26(1):105707. doi: 10.1016/j.isci.2022.105707.

Dienes Z. Obtaining Evidence for No Effect. Collabra: Psychology 2021 Jan 4; 7 (1): 28202. doi: 10.1525/collabra.28202

King MT. A point of minimal important difference (MID): a critique of terminology and methods. Expert Rev Pharmacoecon Outcomes Res. 2011 Apr;11(2):171-84. doi: 10.1586/erp.11.9.

Lakens D. Sample Size Justification. Collabra: Psychology 2022 Jan 5; 8 (1): 33267. doi: 10.1525/collabra.33267

Nosek BA, Ebersole CR, DeHaven AC, Mellor DT. The preregistration revolution. Proc Natl Acad Sci U S A. 2018 Mar 13;115(11):2600-2606. doi: 10.1073/pnas.1708274114.

Sasaki K, Yamada Y. SPARKing: Sample-size planning after the results are known. Front Hum Neurosci. 2023 Feb 22;17:912338. doi: 10.3389/fnhum.2023.912338.

Taffou M, Suied C, Viaud-Delmon I. Auditory roughness elicits defense reactions. Sci Rep. 2021 Jan 13;11(1):956. doi: 10.1038/s41598-020-79767-0.

Tajadura-Jiménez A, Väljamäe A, Asutay E, Västfjäll D. Embodied auditory perception: The emotional impact of approaching and receding sound sources. Emotion. 2010, 10(2), 216-229.https://doi.org/10.1037/a0018422

Zhou W, Ye C, Wang H, Mao Y, Zhang W, Liu A, Yang CL, Li T, Hayashi L, Zhao W, Chen L, Liu Y, Tao W, Zhang Z. Sound induces analgesia through corticothalamic circuits. Science. 2022 Jul 8;377(6602):198-204. doi: 10.1126/science.abn4663.

Reviewer #3 (Recommendations For The Authors):

The manuscript would benefit from some spelling- and grammar checking.

Done

Discussion:

The discussion section is rather lengthy and would benefit from some re-structuring, editing, and sub-section headers.

In response, we have restructured and edited the Discussion section to improve clarity and flow.

I personally had a difficult time understanding how the data relates to the rubber hand illusion (l.623-630). I would recommend revising or deleting this section.

We thank the reviewer for this valuable feedback. We have revised the paragraph and made the parallel clearer (lines 731-739).

Other areas are a bit short and might benefit from some elaboration, such as clinical implications. Since they were mentioned in the abstract, I had expected a bit more thorough discussion here (l. 718).

Thank you for this suggestion. We have expanded the discussion to more thoroughly address the clinical implications of our interoceptive pain illusion (See Limitations and Future Directions paragraph).

Further, clarification is needed for the following:

I would like some more details on participant instructions; in particular, the potential difference in instruction between Exp. 1 and 2, if any. In Exp. 1, it says: (l. 280) "Crucially, they were also informed that over the 60 seconds preceding the administration of the shock, they were exposed to acoustic feedback, which was equivalent to their ongoing heart rate". Was there a similar instruction for Exp. 2? If yes, it would suggest a more specific effect of cardiac auditory feedback; if no, the ramifications of this difference in instructions should be more thoroughly discussed.

Thank you for this suggestion. We have clarified this point in the Procedure of Experiment 2 (548-550).

DOI: 10.1073/pnas.2415361122

Resource: Bloomington Drosophila Stock Center (RRID:SCR_006457)

Curator: @maulamb

SciCrunch record: RRID:SCR_006457

What is this?

DOI: 10.1073/pnas.2415361122

Resource: Bloomington Drosophila Stock Center (RRID:SCR_006457)

Curator: @maulamb

SciCrunch record: RRID:SCR_006457

What is this?

DOI: 10.1073/pnas.2415361122

Resource: Bloomington Drosophila Stock Center (RRID:SCR_006457)

Curator: @maulamb

SciCrunch record: RRID:SCR_006457

What is this?

DOI: 10.1073/pnas.2415361122

Resource: RRID:BDSC_23260

Curator: @maulamb

SciCrunch record: RRID:BDSC_23260

What is this?

DOI: 10.7554/eLife.107758

Resource: PubMLST (RRID:SCR_012955)

Curator: @scibot

SciCrunch record: RRID:SCR_012955

What is this?

Reviewer #2 (Public review):

Summary:

The manuscript by Sy SKH. et al. on pallium encoded chemosensory impact of eye-body coordination describes how the valence of chemosensory stimuli can affect the coordination of eye saccades with tail flips. They show that aversive valence stimuli can increase both the strength and frequency of tail flips through a pallium-mediated circuit.

Overall, the manuscript is well-written and easy to follow, although the figures are quite dense, the methodology is mostly sound, and the improvement to the fish on chips system is very interesting. The methods description is thorough and welcome, making the experiments clear. The limited number of animals, and the spread between 5 and 6dpf is a concern as most of the statistics seem to have been done on the individual events, and not the number of biological samples.

The initial behavioural experiments are very promising. However, the conclusions surrounding the role of the pallium are a lot more speculative and not supported by the results.

Comments:

(1) The fish on chips 2.0 methods show a lot of promise for future studies of chemosensory stimuli, combined with whole-brain imaging. This will provide new avenues of research for zebrafish neuroscientists.

(2) Chemosensory cues would have a very different timing than visual cues; timing is very important for multisensory integration. How do the authors suggest those are integrated? How would they differentiate between an integration of various cues or a different arousal state, as they describe in the introduction?

(3) Studies have looked at chemosensation in Drosophila, including multisensory integration, which should be discussed by the authors (see the work of Mark Frye, amongst others).

(4) In the brain imaging methods, there is a mention of robustly behaving larvae. Does that mean that an exclusion criterion was used to select only 5 larvae? If so, this should be stated clearly. The authors also do not mention how they avoid the switch to a passive state that one of the coauthors has observed in closed closed-loop setup. The authors should comment on this point.

(5) Were the statistics in Figure 2 done with an n of 5, or do they assume that each tail flip and saccade is an independent event? I would imagine the latter would have inflated p-values and should be avoided.

(7) Page 7: Why do the authors think that the cumulative effect of these minor differences could lead to very different behavioural goals? Especially when comparing to actual startle responses, which are extremely strong and stereotypical. How do their observations compare to the thermosensory navigation of larval zebrafish observed by Martin Haesemeyer, for example, or the work of the RoLi lab?

(8) Page 8: Figure 5, I am confused by the y-axis of g, in e and f, the values are capped at 2, whereas in g they go up to 6, with apparently a number of cells whose preference is out of the y-axis limit (especially in Q2). Having the number of cells in each quadrant would also help to assess if indeed there is some preference in the pallium towards Q1.

(9) Figure 6: How is the onset of neuronal activity determined compared to the motor stimulus? Looking at Supplementary Figure 8, it is quite unclear how the pallium is different from the OB or subpallium. The label of onset delay is also confusing in this figure.

(10) Page 9: I do not think that the small differences observed in the pallium are as clear-cut as the authors make them out to be, or that they provide such strong evidence of their importance. As there are no interventions showing any causality in the presence of these pallium responses and the sensorimotor responses, these could represent different arousal states rather than any integration of sensory information.

这里是对概率建模？P(Y|X)?

origin of cottage industry

• maritime empires and commercial revolution led to Indian cotton imports

• before people spun their own fabric

• Indian import came in after than the other spun

• to compete with Indian cotton, Britain used slave labor in Americas to make thread and export it

• Gave women in Britain some little power because they did their own work weaving, how ever it's slow so the y need new technologies

Il semble y avoir une coupure dans la phrase. Est-ce que ce serait pas plutôt "d'organiser et de gérer les captures [...]" ?

Dans ma dernière version j'ai retiré l'italique pour toutes les citations directes pour ne laisser que les guillemets.

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

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Reply to the reviewers

We thank the Reviewers for their positive assessment of the quality and significance of our work, as well as for their insightful comments, which have helped us to further improve the manuscript. We have addressed the majority of the comments in the revised version and, for those that require additional time, we outline below a detailed plan of the experiments we intend to perform.

We agree with Reviewer #2 that a more detailed mechanistic understanding of the drug effects would further strengthen the study, and we are grateful to both reviewers for the constructive experimental suggestions provided to address this point. In particular, we are highly motivated to better define the causal role of C18 sphingolipid alterations in mediating the effects of the drugs, as suggested by Reviewer #2, as well as to investigate the involvement of the retromer complex in the lysosome-to-Golgi connection, as suggested by Reviewer #1.

Below, we provide a point-by-point description of the revisions already incorporated into the manuscript, along with the planned experiments that will address the remaining comments

REVIEWER #1:

VPS13B is a bridge-like lipid transfer protein, the loss or mutation of which is associated with Cohen syndrome (CS) involving Golgi fragmentation. In this study, the authors performed image-based chemical screens to identify compounds capable of rescuing the Golgi morphology in VPS13B-KO HeLa cells. They identified 50 compounds, the majority of which are lysosomotropic compounds or cationic amphiphilic drugs (CADs). Treatment of cells with several of these compounds causes lysosomal lipid storage, as assessed by BMP/LBPA staining, filipin staining, or LipidTOX staining. Interestingly, most LipidTOX puncta colocalized with transferrin receptor-positive compartments but not lysosomes. Similar to lysosomotropic compounds, knocking down NPC1 or SMPD1, mimicking lysosomal storage disease, also substantially rescued Golgi morphology. The authors show that VPS13B-KO cells have reduced C18 sphingolipids, which is reversed by treatment with CADs. Finally, the authors show that two CADs partially rescue neurite outgrowth in neuronal cultures. However, these drugs do not rescue the size of VPS13B KO organoids.

Overall, this is an impressive study identifying CADs as potential therapeutics for CS and suggesting sphingolipid upregulation as a general strategy for CS treatment. The morphological and lipidomics analyses unravel important molecular basis of CS pathology. This study will be of high interest to the field of lipid biology and organelle homeostasis. I have a few comments to help improve the quality of this study.

1. The reverse of lipid changes in VPS13B-KO cells by CADs is intriguing. Are CAD-mediated benefits such as Golgi morphology recovery permanent or only transient within 24 hours of treatment? How do the CADs affect the Golgi morphology in WT HeLa cells?

RESPONSE:

We thank the reviewer for this insightful question Indeed, the effects of CADs on Golgi organization are most evident in VPS13B KO cells, where the Golgi apparatus is severely fragmented and becomes more compact upon drug treatment, whereas the effect is much less apparent in wild-type cells. Nevertheless, a careful quantitative analysis of the images (now presented in the new Fig. S7) demonstrates that the impact of these compounds on Golgi morphology is not restricted to KO cells but is likely more general, supporting a link between lysosomal storage and Golgi organization. Although this observation indicates an indirect effect (consistent with the proposed mechanism of action), rather than a direct correction of VPS13B loss, it does not compromise in our opinion their potential beneficial effect for KO cells as shown also from the results obtained in organoid-derived neurons.

Under continuous treatment, azelastine keeps the Golgi in a compact state for 72 hours without any noticeable deleterious effect on the cells (see new Fig. S10) Raloxifene, on the contrary proved to be toxic over the same time period. We believe this difference reflects the mechanism of action of CADs, which progressively accumulate within acidic organelles and may eventually reach a toxic threshold upon prolonged exposure. For this reason, lower drug concentrations administered over longer treatment periods may represent a viable alternative strategy. In this regard, we also refer the reviewer to our response to the comment on brain organoids below.

1. Is it surprising that Azelastine-induced lipid storage in transferrin receptor compartments (early and recycling endosomes)? I suggest more controls to examine LipidTOX overlap with Golgi markers or other late endosome/lysosome markers such as LBPA and CD63.

RESPONSE:

We agree with the reviewer that this observation is somewhat unexpected. However, we would like to clarify that we do not intend to suggest that lipid storage occurs primarily in early or recycling endosomes, which would indeed contradict a substantial body of existing evidence. Rather, our data indicate that this particular dye (LipidTOX) labels recycling endosomes, at least in HeLa cells. This finding is consistent with the widely accepted view that lysosomal lipid storage exerts broader effects on intracellular trafficking, not limited to late endosomes/lysosomes. We corrected the text in order to clarify this concept.

LipidTOX was specifically developed to detect drug-induced phospholipidosis, and based on our data, it appears suitable for this purpose. To our knowledge, there is no published information detailing its intracellular localization, which motivated us to perform these control experiments. Unfortunately, the proprietary formulation of this product does not allow informed speculations to explain the observed localization or whether this could refer to the intact molecule or to a catabolite.

As suggested by the reviewer, we plan to perform co-staining with additional markers to further clarify this this point.

1. Does the LipidTOX/TFRC overlap suggest potential roles of retrograde transport in supplying sphingolipids to the Golgi? The authors can quickly test if the knockdown of a retromer subunit (VPS35) blocks Azelastine-induced recovery of Golgi morphology.

RESPONSE:

We thank the reviewer for this insightful suggestion. Indeed, the retromer complex represents one of the best-characterized trafficking pathways from the endosomal system to the Golgi, and this relatively straightforward experiment could help to mechanistically clarify our observations. We plan to test whether VPS35 knockdown interferes with the effects of the drugs.

What is the rationale to use 500 nM to 1 uM azelastine and raloxifene for neuronal cultures and organoids? At such concentrations, no obvious changes in Golgi morphology or lipid storage were observed (Fig 4). Also, the lipidomics analysis was performed after 10 uM compound treatment. It might be worth trying dose-response experiments in organoid tests.

RESPONSE:

We thank the reviewer for this question. The rationale about this choice was indeed missing from our previous version of the manuscript. The reason of lowering the concentrations comes indeed from toxicity tests, preliminarily performed over long-term treatment of both WT and VPS13B KO organoids. This information has now been explicitly included in the Results section of the revised manuscript, and the broader implications are also discussed in the Discussion section.

MINOR COMMENTS:

It is important to know whether the authors used TGN or cis-Golgi markers for Golgi morphology analysis. Please label the two channels in Fig. 2C and throughout all figures. In many cases, it is not clear what is stained in the green channel to show the Golgi morphology. It was not even stated in the legend.

RESPONSE:

We now included the antibody staining in all figure legends where it was previously missing.

The authors stated that Recovery of Golgi morphology is dependent on lysosomal lipid storage. However, while the data show positive correlation between the two, no causal relationship is established by the data. It seems true that in all conditions (CADs or genetic knockdown) where lysosomal lipid storage was observed, the authors detect the Recovery of Golgi morphology. However, budesonide did not depend on lysosomal lipid storage to recover the Golgi morphology. Thus, the recovery of Golgi morphology is NOT dependent on lysosomal lipid storage, but inducing lysosomal lipid storage appears sufficient to recover Golgi morphology in VPS13B-KO HeLa cells.

RESPONSE:

We thank the reviewer for this comment and we agree that the previous title of the paragraph could have been misleading. This has been now changed in: “Lysosomal lipid storage mediates the recovery of Golgi morphology” which is probably less prone to ambiguous interpretations.

Obviously, in the previous version of the title we wanted to mean that Golgi recovery is dependent on lipid storage “in the context of CAD treatment” and not as a general statement.

With respect to the cause–effect relationship, we believe that the strongest evidence supporting this link is the observation that genetically induced lipid storage phenocopies the effects of drug treatment. We hope that this conclusion is now sufficiently clear from the revised text.

Each figure needs a title before the detailed legends for specific panels.

RESPONSE:

Titles have now been included to all figure legends.

Fig 8. Y axis labeling is missing.

RESPONSE:

Axes labels have now been included

Does U18666A rescues Golgi morphology in VPS13B-KO cells?

RESPONSE:

We thank the reviewer for this comment. U18666A indeed also corrects Golgi morphology. The result is now included in the new figure S5.

Please do not repeat the result section in discussion. Focus on the most important points.

RESPONSE:

We thank the reviewer for this comment. We shortened the descriptive part of the discussion trying as much as possible to avoid repetitions with the result session and keeping only the more essential information for the flow of the discussion.

Reviewer #1 (Significance (Required)):

This is an impressive study that identifies Cationic Amphiphilic Drugs (CADs) as potential therapeutics for Cohen syndrome (CS) and suggests sphingolipid upregulation as a general strategy for diseases driven by VPS13B loss-of-function. The unbiased approaches, notably the chemical screen and lipidomics, provide novel mechanistic insights into the underlying pathology of CS. This study will be of high interest to researchers in the fields of lipid biology and organelle homeostasis. It will also be highly valuable for clinical pediatricians managing CS patients.

REVIEWER #2:

This manuscript describes a compound screening aimed at identifying molecules that can restore Golgi organization in VPS13B knockout (KO) cells. The authors identify several compounds, most of which are lysosomotropic, and analyze their effects on Golgi morphology and lipid composition using multiple approaches. They report that VPS13B KO cells exhibit a reduction in C18-N-acyl sphingolipids, which can be restored by several of the identified compounds. Furthermore, two of these compounds, azelastine and raloxifene, promote neurite outgrowth in VPS13B KO cortical organoids. These findings are interesting and could potentially contribute to a better understanding of the pathophysiology of Cohen syndrome and the development of therapeutic strategies. However, despite the large number of analyses presented, the study remains largely descriptive, and there is no coherent mechanistic explanation for how these compounds restore Golgi structure in VPS13B KO cells. In addition to the reduction in C18-N-acyl sphingolipids, the KO cells display alterations in several other lipid species (LPC, LPE, PC40:1, PE42:1, TG, etc.), and treatment with the selected compounds induces further lipid accumulations, including cholesterol and BMP/LBPA. The relationship between these diverse lipid changes and the observed Golgi recovery lacks clarity and mechanistic consistency.

MAJOR COMMENTS:

The finding that compounds cannot prevent Golgi fragmentation caused by brefeldin A or nocodazole but can suppress statin-induced fragmentation is intriguing, but the underlying mechanism is not addressed. It is not evident whether this difference results from changes in membrane lipid composition or restoration of Rab/SNARE trafficking. The authors should examine Rab prenylation and SNARE localization by immunofluorescence or Western blotting to support their interpretation.

RESPONSE:

We thank the reviewer for this suggestion and agree that the ability of these compounds to counteract statin-induced Golgi fragmentation is indeed intriguing. The primary reason we did not further explore this aspect is that we evaluated the effects of statins not to be a central focus of the present study. Nevertheless, we fully agree that this observation represents a valuable opportunity to gain additional insight into the mechanism underlying drug-induced Golgi recovery.

To address this point, we plan to analyze Rab prenylation by Western blot and Rab localization by microscopy, focusing on a Golgi-associated Rab protein such as Rab6. In addition, we will employ downstream inhibitors of Rab prenylation, such as 3-PEHPC (an inhibitor of type II protein geranylgeranyltransferase (GGTase-II)), which should allow us to formally distinguish effects related to impaired Rab prenylation from those arising from inhibition of cholesterol biosynthesis.

Although restoration of C18 sphingolipids (SM 36:1, CER 36:1) is observed upon compound treatment, its causal role in Golgi recovery or neurite outgrowth is not established. The authors should test whether blocking the increase of C18 SM/CER prevents the rescue of Golgi or neuronal phenotypes.

RESPONSE:

We sincerely thank the reviewer for this comment. We agree that, based on the current data, a definitive cause–effect relationship between Golgi recovery and the increase in C18 sphingolipids cannot be firmly established, and we acknowledge that a deeper understanding of this issue will require further investigation. Furthermore, we believe that addressing this would not only provide a better mechanistic understanding of the biological processes behind the effect of the drugs but provide a potential avenue for therapeutic intervention. For these reasons, we are strongly motivated to pursue this aspect further.

With respect to the reviewer’s specific suggestion, we agree that preventing the increase in C18 sphingolipids would be an ideal experimental approach. However, the limited understanding of the regulatory mechanisms controlling C18 sphingolipid homeostasis currently precludes a fully informed strategy. In principle, if the observed increase were due to enhanced synthesis, one could envisage blocking it by silencing ceramide synthases with C18 selectivity, such as CERS1. The experiment shown in Fig. 7E (azelastine treatment in the presence of sphingolipid synthesis inhibitors) was designed with this rationale in mind. However, these results suggest that azelastine-induced C18 sphingolipid accumulation is unlikely to result from increased synthesis, and is instead more consistent with reduced degradation, in line with the proposed mechanism of action of CADs.

Based on these considerations, we propose to invert the experimental approach and test whether cellular re-complementation with C18 sphingolipids is sufficient to recapitulate the drug-induced Golgi recovery. We are aware of the technical challenges associated with the targeted delivery of exogenously supplied lipids, particularly given the likelihood that effective rescue would require lipid access to the Golgi apparatus. Based on current knowledge, we anticipate that externally supplied lipids would primarily traffic either to the ER via non-vesicular routes or to endosomes/lysosomes through endocytic uptake. From both locations they could eventually reach to some extent the Golgi. The route from endosomes to Golgi in particular as been intensively studied in the past with the use of fluorescent sphingolipid analogs1,2 and may well work also with native lipids.

Since we are not able to predict in advance which lipid species would be more effective or the optimal delivery strategy, we plan to test re-complementation using C18 sphingomyelin and some of its potential precursors, including C18 ceramide as well as using alternative delivery strategies such as incorporation in liposomes of different formulations and delivery at the plasma membrane with bovine serum albumin or cyclodextrins as carriers.

1. Puri et al., (2001). J Cell Biol.154:535-47 (doi: 10.1083/jcb.200102084)
2. Koivusalo et al.,(2007). Mol Biol Cell. 18:5113-23 (doi: 10.1091/mbc.e07-04-0330)

   In Figure 7D, comparisons should include the LM and HM fractions isolated from WT cells.

RESPONSE:

Wild-type control were included in the figure as requested.

The subcellular fractionation experiment should be repeated using AZL and RAL, the compounds used in organoid experiments, rather than TFPZ, to assess whether similar results are obtained. The compounds used differ across experiments, making it difficult to draw consistent conclusions.

RESPONSE:

We thank the reviewer for this comment and apology for some inconsistencies in the selection of the compounds to highlight in the figures which are mostly remnants of the drug prioritization history over the progression of the project. We tried to make it more consistent in the current version.

In the new version of figure 7D, AZL is substituting TFPZ, while TFPZ data were moved to supplementary figure S19.

Golgi morphology in VPS13B KO cells is reported to recover in NPC1 KD and SMPD1 KD cells, but it is not shown whether SM 36:1, CER 36:1, or other lipid levels also increase or change in these conditions. If Golgi morphology recovery occurs via the same mechanism as with compound treatment, a similar lipid pattern should be observed.

RESPONSE:

We thank the reviewer for this question that allowed us to expand our study including new interesting findings. We agree that this is an important point to strengthen the link between CAD and genetic perturbation effects. Given the availability of several published lipidomic datasets modelling LDS in HeLa and in other cell lines, we decided to perform a re-analysis of those to specifically focus on C18 sphingolipids. We found a relative increase of 36:1 upon depletion of LSD genes in all analyzed datasets for NPC1 and SMPD1, but also for more than 15 other LSD genes including NPC2, recapitulating what we find with all the CAD molecules tested in our study. These changes, were not noticed or at least not discussed by most of the authors. This is not surprising since those studies are focused on different biological questions. We believe that these findings, besides reinforcing our hypothesis of a common mechanism between CAD and NPC1/SMPD1 KO, have of general interest for the regulation of C18 sphingolipids, which are among the relative few lipid species with a bona fide specific protein binding partner and proposed to play a crucial role in Golgi traffic.

MINOR POINTS:

The manuscript lacks sufficient information about the compound library used for screening (number and source of compounds, compound type).

RESPONSE:

We apologize if this information was not sufficiently visible in the original version of the manuscript. The data about source, catalog number, formulation and several additional identifiers is included in the File S1. This is now clearly indicated in the methods so that I can be more easily visible to the readers

Fig. 3A: a WT control image is required.

RESPONSE:

A WT control image is now included in the new version of Figure 3.

Fig. 4: include representative images at concentrations higher than 1.25 µM.

RESPONSE:

Representative images are now included for all concentrations higher than 1.25 µM, as requested.

Abbreviations such as BMP/LBPA should be defined when first mentioned.

RESPONSE:

The abbreviation of BMP/LBPA was already defined when first mentioned in the original version of the manuscript

The abbreviation for raloxifene is inconsistent (RLX vs RAL) and should be unified.

RESPONSE:

Raloxifene is now abbreviated as RLX all over the manuscript.

Fig. 5C: the meaning of the green and magenta bars is not explained.

RESPONSE:

Color code for figure 5C has been included.

The definitions and centrifugation parameters for light and heavy membrane fractions should be clearly stated in the Methods.

RESPONSE:

The centrifugation parameters were already defined in the original manuscript. It is not clear to us, which parameter the Referee is referring to. Below is the sentence in the methods section:

“Gradients were centrifuged at 165,000 g for 1.5 h at 4°C with a SW40Ti Swinging-Bucket rotor (Beckman-Coulter). The LM and HM fractions were collected at the 35%-HB and 35%-40.6% interfaces, respectively”

The concentration and incubation times for BFA and nocodazole should be included in the main text or figure legends.

RESPONSE:

Concentrations and incubation times of BFA and nocodazole were already present in the legend of figure 5.

Fig. 8C, D, G, H: y-axes lack labels and must be defined.

RESPONSE:

Axes labels have now been included

There are multiple typographical errors, including "VPS12" instead of "VPS13B", that should be corrected.

RESPONSE:

We corrected this specific mistake as well as others that we could identify after careful reading of the manuscript.

Reviewer #2 (Significance (Required)):

While the dataset is extensive and technically detailed, the manuscript lacks a clear mechanistic explanation connecting lipid changes to Golgi restoration. The choice and comparison of compounds are inconsistent across experiments, and the interpretation remains speculative. Substantial revision and additional experiments are required before the study can be considered for publication.

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

This manuscript describes a compound screening aimed at identifying molecules that can restore Golgi organization in VPS13B knockout (KO) cells. The authors identify several compounds, most of which are lysosomotropic, and analyze their effects on Golgi morphology and lipid composition using multiple approaches. They report that VPS13B KO cells exhibit a reduction in C18-N-acyl sphingolipids, which can be restored by several of the identified compounds. Furthermore, two of these compounds, azelastine and raloxifene, promote neurite outgrowth in VPS13B KO cortical organoids. These findings are interesting and could potentially contribute to a better understanding of the pathophysiology of Cohen syndrome and the development of therapeutic strategies. However, despite the large number of analyses presented, the study remains largely descriptive, and there is no coherent mechanistic explanation for how these compounds restore Golgi structure in VPS13B KO cells. In addition to the reduction in C18-N-acyl sphingolipids, the KO cells display alterations in several other lipid species (LPC, LPE, PC40:1, PE42:1, TG, etc.), and treatment with the selected compounds induces further lipid accumulations, including cholesterol and BMP/LBPA. The relationship between these diverse lipid changes and the observed Golgi recovery lacks clarity and mechanistic consistency.

Major comments

The finding that compounds cannot prevent Golgi fragmentation caused by brefeldin A or nocodazole but can suppress statin-induced fragmentation is intriguing, but the underlying mechanism is not addressed. It is not evident whether this difference results from changes in membrane lipid composition or restoration of Rab/SNARE trafficking. The authors should examine Rab prenylation and SNARE localization by immunofluorescence or Western blotting to support their interpretation.

Although restoration of C18 sphingolipids (SM 36:1, CER 36:1) is observed upon compound treatment, its causal role in Golgi recovery or neurite outgrowth is not established. The authors should test whether blocking the increase of C18 SM/CER prevents the rescue of Golgi or neuronal phenotypes.

In Figure 7D, comparisons should include the LM and HM fractions isolated from WT cells.

The subcellular fractionation experiment should be repeated using AZL and RAL, the compounds used in organoid experiments, rather than TFPZ, to assess whether similar results are obtained. The compounds used differ across experiments, making it difficult to draw consistent conclusions.

Golgi morphology in VPS13B KO cells is reported to recover in NPC1 KD and SMPD1 KD cells, but it is not shown whether SM 36:1, CER 36:1, or other lipid levels also increase or change in these conditions. If Golgi morphology recovery occurs via the same mechanism as with compound treatment, a similar lipid pattern should be observed.

Minor points

The manuscript lacks sufficient information about the compound library used for screening (number and source of compounds, compound type).

Fig. 3A: a WT control image is required. Fig. 4: include representative images at concentrations higher than 1.25 µM. Abbreviations such as BMP/LBPA should be defined when first mentioned. The abbreviation for raloxifene is inconsistent (RLX vs RAL) and should be unified. Fig. 5C: the meaning of the green and magenta bars is not explained. The definitions and centrifugation parameters for light and heavy membrane fractions should be clearly stated in the Methods. The concentration and incubation times for BFA and nocodazole should be included in the main text or figure legends. Fig. 8C, D, G, H: y-axes lack labels and must be defined. There are multiple typographical errors, including "VPS12" instead of "VPS13B", that should be corrected.

Significance

While the dataset is extensive and technically detailed, the manuscript lacks a clear mechanistic explanation connecting lipid changes to Golgi restoration. The choice and comparison of compounds are inconsistent across experiments, and the interpretation remains speculative. Substantial revision and additional experiments are required before the study can be considered for publication.

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

VPS13B is a bridge-like lipid transfer protein, the loss or mutation of which is associated with Cohen syndrome (CS) involving Golgi fragmentation. In this study, the authors performed image-based chemical screens to identify compounds capable of rescuing the Golgi morphology in VPS13B-KO HeLa cells. They identified 50 compounds, the majority of which are lysosomotropic compounds or cationic amphiphilic drugs (CADs). Treatment of cells with several of these compounds causes lysosomal lipid storage, as assessed by BMP/LBPA staining, filipin staining, or LipidTOX staining. Interestingly, most LipidTOX puncta colocalized with transferrin receptor-positive compartments but not lysosomes. Similar to lysosomotropic compounds, knocking down NPC1 or SMPD1, mimicking lysosomal storage disease, also substantially rescued Golgi morphology. The authors show that VPS13B-KO cells have reduced C18 sphingolipids, which is reversed by treatment with CADs. Finally, the authors show that two CADs partially rescue neurite outgrowth in neuronal cultures. However, these drugs do not rescue the size of VPS13B KO organoids.

Overall, this is an impressive study identifying CADs as potential therapeutics for CS and suggesting sphingolipid upregulation as a general strategy for CS treatment. The morphological and lipidomics analyses unravel important molecular basis of CS pathology. This study will be of high interest to the field of lipid biology and organelle homeostasis. I have a few comments to help improve the quality of this study.

1. The reverse of lipid changes in VPS13B-KO cells by CADs is intriguing. Are CAD-mediated benefits such as Golgi morphology recovery permanent or only transient within 24 hours of treatment? How do the CADs affect the Golgi morphology in WT HeLa cells?
2. Is it surprising that Azelastine-induced lipid storage in transferrin receptor compartments (early and recycling endosomes)? I suggest more controls to examine LipidTOX overlap with Golgi markers or other late endosome/lysosome markers such as LBPA and CD63.
3. Does the LipidTOX/TFRC overlap suggest potential roles of retrograde transport in supplying sphingolipids to the Golgi? The authors can quickly test if the knockdown of a retromer subunit (VPS35) blocks Azelastine-induced recovery of Golgi morphology.
4. What is the rationale to use 500 nM to 1 uM azelastine and raloxifene for neuronal cultures and organoids? At such concentrations, no obvious changes in Golgi morphology or lipid storage were observed (Fig 4). Also, the lipidomics analysis was performed after 10 uM compound treatment. It might be worth trying dose-response experiments in organoid tests.

Minor:

1. It is important to know whether the authors used TGN or cis-Golgi markers for Golgi morphology analysis. Please label the two channels in Fig. 2C and throughout all figures. In many cases, it is not clear what is stained in the green channel to show the Golgi morphology. It was not even stated in the legend.
2. The authors stated that Recovery of Golgi morphology is dependent on lysosomal lipid storage. However, while the data show positive correlation between the two, no causal relationship is established by the data. It seems true that in all conditions (CADs or genetic knockdown) where lysosomal lipid storage was observed, the authors detect the Recovery of Golgi morphology. However, budesonide did not depend on lysosomal lipid storage to recover the Golgi morphology. Thus, the recovery of Golgi morphology is NOT dependent on lysosomal lipid storage, but inducing lysosomal lipid storage appears sufficient to recover Golgi morphology in VPS13B-KO HeLa cells.
3. Each figure needs a title before the detailed legends for specific panels.
4. Fig 8. Y axis labeling is missing.
5. Does U18666A rescues Golgi morphology in VPS13B-KO cells?
6. Please do not repeat the result section in discussion. Focus on the most important points.

Significance

This is an impressive study that identifies Cationic Amphiphilic Drugs (CADs) as potential therapeutics for Cohen syndrome (CS) and suggests sphingolipid upregulation as a general strategy for diseases driven by VPS13B loss-of-function. The unbiased approaches, notably the chemical screen and lipidomics, provide novel mechanistic insights into the underlying pathology of CS. This study will be of high interest to researchers in the fields of lipid biology and organelle homeostasis. It will also be highly valuable for clinical pediatricians managing CS patients.

Il y a un tout petit problème : lorsque je rentre une adresse mail avec à la fin ".co" au lieu de ".com", une erreur n'est pas affichée.

Johann Georg Hamann

DOI: 10.26508/lsa.202101342

Resource: RRID:BDSC_25756

Curator: @areedewitt04

SciCrunch record: RRID:BDSC_25756

What is this?

English — alleles (thorough explanation)

1) What alleles are (core idea)

Alleles are different versions of the same gene. They control variations of a trait, such as eye color or flower color.

Allele = one version of a gene

Each gene can have two or more alleles, but an individual organism usually carries two alleles per gene (one from each parent).

2) Where alleles are found

• Genes are located on chromosomes
• Alleles sit at the same position (locus) on homologous chromosomes
• One allele comes from the mother
• One allele comes from the father

📌 This is why offspring show traits from both parents.

3) Example of alleles (simple)

Trait: Seed color

• Y = yellow
• y = green

Possible allele combinations:

• YY
• Yy
• yy

These combinations affect the trait that appears.

4) Dominant vs recessive alleles (Science 10 focus)

Dominant allele

• Shown with a capital letter (A)
• Expressed if at least one copy is present

Recessive allele

• Shown with a lowercase letter (a)
• Expressed only if two copies are present

📌 Example:

• Aa → dominant trait shows
• aa → recessive trait shows

5) Alleles, genotype, and phenotype (key relationship)

| Term | Meaning | Example | | --------- | ------------------ | ---------- | | Allele | Version of a gene | A or a | | Genotype | Allele combination | Aa | | Phenotype | Physical trait | Brown eyes |

Alleles determine the genotype, which determines the phenotype.

6) Alleles in Punnett squares

Punnett squares:

• Show how alleles from parents combine
• Predict possible offspring genotypes
• Estimate trait probabilities

📌 Example: Parents: Aa × Aa

• Possible offspring: AA, Aa, Aa, aa

7) Why alleles are important

Alleles:

• Explain variation within a species
• Help predict inheritance patterns
• Are the basis of genetics and evolution
• Allow populations to adapt over time

One-sentence exam summary

Alleles are different versions of the same gene that determine variations in traits.

中文 — alleles（等位基因） 详细解释

1) 什么是等位基因（核心概念）

等位基因（alleles）是指同一基因的不同版本，决定同一性状的不同表现。

等位基因 = 同一基因的不同形式

2) 等位基因在哪里

• 基因位于染色体上
• 等位基因位于同源染色体的相同位置
• 一个来自母亲，一个来自父亲

3) 等位基因举例

性状：豌豆高度

• T = 高
• t = 矮

组合可能是：

• TT
• Tt
• tt

4) 显性与隐性等位基因（必考）

• 显性等位基因：只要有一个就会表现
• 隐性等位基因：必须两个都有才表现

📌 Tt → 显性性状 📌 tt → 隐性性状

5) 等位基因与性状的关系

• 等位基因 → 基因型
• 基因型 → 表现型

一句话考试版总结

等位基因是控制同一性状的不同基因形式。

如果你愿意，我可以把 alleles → genotype → phenotype → Punnett squares 整理成 Science 10 中英对照闪卡或互动练习，直接用于复习或教学。

DOI: 10.1007/s44371-025-00381-y

Resource: (ECACC Cat# 90112714, RRID:CVCL_0035)

Curator: @dhovakimyan1

SciCrunch record: RRID:CVCL_0035

What is this?

DOI: 10.1007/s44371-025-00381-y

Resource: (RRID:CVCL_0062)

Curator: @dhovakimyan1

SciCrunch record: RRID:CVCL_0062

What is this?

DOI: 10.1007/s44371-025-00381-y

Resource: (NCI-DTP Cat# MCF7, RRID:CVCL_0031)

Curator: @dhovakimyan1

SciCrunch record: RRID:CVCL_0031

What is this?

DOI: 10.1007/s44371-025-00381-y

Resource: (RRID:CVCL_0062)

Curator: @dhovakimyan1

SciCrunch record: RRID:CVCL_0062

What is this?

DOI: 10.1007/s44371-025-00381-y

Resource: (CLS Cat# 300168/p708_DU-145, RRID:CVCL_0105)

Curator: @dhovakimyan1

SciCrunch record: RRID:CVCL_0105

What is this?

DOI: 10.1007/s44371-025-00381-y

Resource: (RRID:CVCL_0062)

Curator: @dhovakimyan1

SciCrunch record: RRID:CVCL_0062

What is this?

DOI: 10.1007/s44371-025-00381-y

Resource: (CLS Cat# 300168/p708_DU-145, RRID:CVCL_0105)

Curator: @dhovakimyan1

SciCrunch record: RRID:CVCL_0105

What is this?

DOI: 10.1007/s44371-025-00381-y

Resource: (NCI-DTP Cat# MCF7, RRID:CVCL_0031)

Curator: @scibot

SciCrunch record: RRID:CVCL_0031

What is this?

DOI: 10.1007/s44371-025-00381-y

Resource: (ECACC Cat# 90112714, RRID:CVCL_0035)

Curator: @scibot

SciCrunch record: RRID:CVCL_0035

What is this?

DOI: 10.1186/s43042-025-00828-y

Resource: OHSU Massively Parallel Sequencing Shared Resource Core Facility (RRID:SCR_009984)

Curator: @scibot

SciCrunch record: RRID:SCR_009984

What is this?

DOI: 10.1186/s40364-025-00867-y

Resource: None

Curator: @scibot

SciCrunch record: RRID:AB_3085095

What is this?

DOI: 10.1186/s40364-025-00867-y

Resource: None

Curator: @scibot

SciCrunch record: RRID:AB_3069632

What is this?

DOI: 10.1186/s40364-025-00867-y

Resource: None

Curator: @scibot

SciCrunch record: RRID:AB_2059358

What is this?

DOI: 10.1186/s40364-025-00867-y

Resource: None

Curator: @scibot

SciCrunch record: RRID:AB_11125161

What is this?

DOI: 10.1186/s40364-025-00867-y

Resource: (Proteintech Cat# 66515-1-Ig, RRID:AB_2881878)

Curator: @scibot

SciCrunch record: RRID:AB_2881878

What is this?

DOI: 10.1038/s41388-025-03670-z

Resource: (BCRJ Cat# 0331, RRID:CVCL_B260)

Curator: @scibot

SciCrunch record: RRID:CVCL_B260

What is this?

English (thorough explanation with images)

1) What a karyotype is

A karyotype is an organized picture of all the chromosomes in a cell, arranged in pairs, from largest to smallest, showing their number and structure.

In short:

Karyotype = a chromosome map of a cell

2) How a karyotype is made

To create a karyotype, scientists:

1. Take dividing cells (often blood cells)
2. Stop cell division at metaphase → chromosomes are most condensed and visible
3. Stain and photograph the chromosomes

4. Arrange them into pairs based on:

5. Size

6. Shape
7. Centromere position
8. Banding patterns

3) What information a karyotype shows

A karyotype reveals:

• Total chromosome number
• Sex chromosomes (XX or XY)
• Large chromosomal abnormalities

For humans:

• 46 chromosomes

• 22 pairs of autosomes

• 1 pair of sex chromosomes

4) Autosomes vs sex chromosomes

| Type | Description | | ------------------- | ------------------------------------------- | | Autosomes | Chromosomes 1–22, same in males and females | | Sex chromosomes | X and Y, determine biological sex |

Examples:

• XX → typically female
• XY → typically male

5) Detecting chromosomal disorders (key use)

Karyotypes are commonly used to detect numerical abnormalities, such as:

• Trisomy 21 (Down syndrome) → three copies of chromosome 21
• Turner syndrome → XO
• Klinefelter syndrome → XXY

⚠️ Note: karyotypes detect large-scale changes, not small gene mutations.

6) Karyotype vs genotype (do not confuse)

| Term | What it shows | | ------------- | ----------------------------- | | Karyotype | Chromosome number & structure | | Genotype | Allele combinations of genes |

7) One-sentence exam definition

A karyotype is a visual display of an organism’s complete set of chromosomes, arranged by size and shape.

中文（配图·深入讲解）

1）什么是核型（karyotype）

核型是把一个细胞中的全部染色体拍照后，按照大小、形态和着丝粒位置系统排列得到的图像。

一句话：

核型 = 染色体的“全家福”

2）核型是如何制作的

制作步骤包括：

1）取正在分裂的细胞 2）将细胞固定在分裂中期 3）染色并拍照 4）按以下标准配对和排序：

• 长短
• 形状
• 着丝粒位置
• 条带特征

3）核型能告诉我们什么

核型可以显示：

• 染色体总数
• 性染色体组成
• 明显的染色体异常

人类正常核型：

• 46 条染色体

• 22 对常染色体

• 1 对性染色体

4）常染色体 vs 性染色体

| 类型 | 含义 | | -------- | ----------- | | 常染色体 | 第 1–22 号染色体 | | 性染色体 | X、Y，决定生物性别 |

5）核型的核心用途（考试重点）

核型常用于检测染色体数目异常，例如：

• 21 三体综合征（唐氏综合征）
• 特纳综合征（XO）
• 克氏综合征（XXY）

⚠️ 核型只能看见大尺度异常，看不到基因层面的细小突变。

6）核型 vs 基因型（易混点）

| 概念 | 内容 | | ------- | -------- | | 核型 | 染色体数量和结构 | | 基因型 | 等位基因组合 |

7）一句话考试总结

核型是按大小和形态排列的完整染色体图像，用于分析染色体数目和结构。

如果你愿意，我可以把 chromatin → chromosome → homologous → allele → karyotype 整理成一张 Biology 10/11 中英对照总复习图表，非常适合考前快速回顾。

https://web.archive.org/web/20260105183931/https://moultano.wordpress.com/2025/12/30/children-and-helical-time/ At first glance this graph seems thought provoking. With E we regularly remark to Y that in our heads, our childhood and student years are much bigger than the period aftwards. More firsts. Vgl Gregory Bateson [[Informatie is verschil dat verschil maakt 20230905124229]], information is a difference that makes a difference, i.e. firsts, and make your time perception longer by doing new stuff [[Maak tijd langer met nieuwe dingen 20210418104515]] and Bateson's use of Korzybski's landscape as theory of mind: [[Steps to an Ecology of Mind by Gregory Bateson]] (1972):

Sperm（精子）— with appropriate images

English (thorough explanation)

1) What sperm is (core definition)

Sperm is the male gamete (sex cell) used in sexual reproduction. Its role is to deliver male genetic information to the egg during fertilization.

In simple terms: 👉 Sperm = male sex cell that carries DNA to the egg

2) Chromosome number

• Sperm cells are haploid (n)
• They contain half the normal number of chromosomes

Example (humans):

• Sperm: 23 chromosomes
• Egg: 23 chromosomes
• Fertilization → zygote with 46 chromosomes

3) Structure of a sperm cell

A sperm cell has three main parts, each with a specific function:

• Head

• Contains the nucleus (DNA)

• Covered by the acrosome, which has enzymes to penetrate the egg

• Midpiece

• Packed with mitochondria

• Produces energy (ATP) for movement

• Tail (flagellum)

• Enables the sperm to swim toward the egg

This streamlined shape is adapted for speed and mobility.

4) How sperm are produced

Sperm are produced in the testes through spermatogenesis:

• Starts from diploid cells
• Uses meiosis
• Produces four haploid sperm cells
• Occurs continuously after puberty

5) Role of sperm in fertilization

During fertilization:

1. Sperm swim toward the egg
2. One sperm penetrates the egg membrane
3. The nuclei fuse
4. A diploid zygote forms

Only one sperm normally fertilizes the egg.

6) Why sperm are important

Sperm:

• Enable sexual reproduction
• Contribute genetic diversity
• Determine the biological sex of offspring in humans (X or Y chromosome)

中文（详细解释）

1）什么是精子（Sperm）

精子是用于有性生殖的雄性配子。 它的主要作用是将父方的遗传信息传递给卵细胞。

一句话： 👉 精子 = 携带遗传信息的雄性性细胞

2）染色体数目

• 精子是单倍体（n）
• 只含一半染色体

人类例子：

• 精子：23 条
• 卵子：23 条
• 受精后：46 条（合子）

3）精子的结构

精子主要由三部分组成：

• 头部

• 含细胞核（DNA）

• 有顶体，释放酶帮助进入卵子

• 中段

• 富含线粒体

• 提供运动能量

• 尾部

• 帮助精子游动

4）精子的产生

精子在睾丸中通过精子发生（减数分裂）产生：

• 每个原始细胞可形成 4 个精子
• 从青春期后持续进行

5）精子在受精中的作用

受精过程：

• 精子进入卵细胞
• 细胞核融合
• 形成二倍体合子

6）精子的重要性

精子：

• 实现有性生殖
• 增加遗传多样性
• 决定后代的生理性别（X 或 Y）

一句话总结（双语）

• Sperm = the male haploid gamete
• 精子 = 单倍体的雄性配子

如果你需要，我可以继续帮你做 ✔️ sperm vs egg 对照表 ✔️ Biology 10/11 易错点速记 ✔️ 双语闪卡（结构 + 功能）

Carácterísticas de la competencia imperfecta.

La clasificación es incompleta.

Tanto los productores como los individuos tienen recursos limitados.

Estudiar cómo resolver el problema económico.

Esto es una prueba para que no quede huerfano

Acá se ganaría mucha claridad con una tabla tipo dfsummary

Describir también la fuente de los datos

Estructura básica de la sección metodológica: datos, variables y métodos. En variables, los descriptivos univariados clásicos.

Dossier de Synthèse : État Limite

Synthèse Exécutive

Ce document de synthèse analyse les thèmes centraux d'un corpus de textes décrivant le quotidien d'un service de psychiatrie au sein d'un hôpital public français.

Il met en lumière une crise systémique profonde, caractérisée par une pénurie critique de moyens et de personnel, entraînant l'épuisement des soignants et une déshumanisation des soins.

Face à ce système défaillant, un psychiatre, figure centrale du document, incarne une philosophie du soin basée sur la création de liens humains et la confiance, remettant en question les logiques institutionnelles de quantification et de contention.

À travers une série d'études de cas poignantes — patients suicidaires, polytoxicomanes, psychotiques — le document expose la complexité de la souffrance psychique et les dilemmes éthiques constants auxquels les praticiens sont confrontés.

En définitive, le corpus présente la psychiatrie publique non seulement comme un domaine médical, mais comme une métaphore des tensions de la société, où la question de l'inclusion, de la productivité et de l'interdépendance humaine est posée de manière cruciale.

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1. La Crise Systémique de la Psychiatrie Publique

Le thème le plus prégnant est celui de l'effondrement du système hospitalier public, en particulier dans le secteur psychiatrique.

Cette crise se manifeste à plusieurs niveaux interdépendants.

1.1. Pénurie de Moyens et de Personnel

Le manque de ressources est une plainte récurrente et fondamentale.

Les institutions sont décrites comme "très paupérisées" et fonctionnant avec des "moyens très faibles".

Cette situation a des conséquences directes sur la qualité des soins et la tension au sein des équipes.

• Sous-effectif chronique : Un soignant déplore que le sous-effectif soit devenu "une habitude", menant à une baisse continue des effectifs car le système "marche" malgré tout.

Il estime qu'il faudrait "5, 6, 7 psychiatres de plus" dans l'hôpital.

• Conséquences matérielles : Un exemple trivial mais révélateur est celui d'un robinet aux urgences qui reste cassé pendant deux mois, alors que des graffitis sont effacés immédiatement.

• Impact sur la prise en charge : Le manque de personnel et de brancards conduit à des situations où des "choix" doivent être faits, au détriment de patients vulnérables comme les SDF, ce qu'un soignant juge inacceptable :

« Je ne suis pas rentré dans ce métier moi pour faire des choix et pour pas m'occuper d'un mec SDF ».

1.2. La Logique de Quantification Contre le Soin Relationnel

Le psychiatre principal exprime une opposition farouche à la logique de gestion comptable qui s'impose à l'hôpital, notamment la tarification à l'activité (T2A), qu'il juge incompatible avec la nature même du soin psychiatrique.

• L'inquantifiable du soin : « Le problème c'est que moi je lutte contre une logique où on ne peut pas quantifier ce que je fais [...] le traitement c'est de la relation, c'est de bâtir des relations de confiance [...] ça n'a pas de prix. »

• La dévalorisation du temps : Il compare son travail à une opération de la cataracte qui dure 15 minutes, alors que son propre travail n'est "pas prévisible" et peut nécessiter une heure juste pour lire un dossier ou apaiser une famille.

Cette non-prévisibilité justifie difficilement le salaire d'un psychiatre aux yeux d'une administration focalisée sur la productivité.

1.3. L'Épuisement et le Burnout des Soignants

La pression systémique engendre une fatigue et une détresse profondes chez le personnel soignant, allant jusqu'au burnout.

• Épuisement généralisé : Le psychiatre sent les agents de police "épuisés" lors d'une intervention.

Lui-même admet être "assez anxieux", bien qu'il le cache.

• Le burnout comme symptôme : Une infirmière expérimentée, décrite comme "géniale" et "en or", se retrouve hospitalisée en tant que patiente suite à un burnout.

La cause identifiée est directement liée à "l'encadrement" et à la gestion du service, se sentant "plus utile" et "débordée".

• Impact sur les plus jeunes : Une stagiaire confie être "beaucoup plus anxieuse" et moins bien dormir depuis le début de son stage, le contact avec la souffrance réactivant ses propres angoisses.

1.4. Complicité et Déshumanisation

Le personnel se retrouve pris dans un dilemme moral, se sentant complice d'un système qui maltraite les patients par négligence structurelle.

• Le sentiment de complicité : Le psychiatre s'interroge : « Est-ce qu'on se rend pas un peu complice tu vois à faire tenir un truc qui conduit à ce que les gens viennent à l'hôpital et qui en fait ils meurent de notre négligence tu vois ? »

• Perte de sens : Cette situation mène à un découragement profond : « Si c'est pour faire le travail comme ça je te cache pas que moi ça me décourage aussi. »

• Déconstruction de l'idéal du service public : Un confrère assène au psychiatre une vérité crue :

« Ils en ont rien à faire tu sais si les patients meurent à l'hôpital public [...] on vit maintenant dans une société où les établissements publics ne remplissent plus du tout leur valeur d'intérêt général. »

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2. Une Philosophie du Soin Centrée sur l'Humain

En contrepoint à la faillite du système, le psychiatre principal développe et applique une philosophie du soin résolument humaniste, basée sur le lien et une critique de l'institution.

2.1. Le Lien Thérapeutique comme Fondement

Au cœur de sa pratique se trouve la conviction que le soin réside avant tout dans la relation interpersonnelle.

• L'interdépendance fondamentale : « Le seule chose qui me paraît être du soin tu vois c'est de se dire on a besoin les uns des autres on est absolument dépendant les uns des autres il faut pas le fuir. »

• Créer des conditions non nuisibles : Il définit son rôle comme celui de créer "des conditions non nuisibles, j'espère le moins nuisible possible et après j'espère qu'il va arriver quelque chose mais c'est pas moi qui décide si ça va arriver ou pas."

• Le suivi comme clé : Il insiste auprès d'un patient alcoolique que le seul conseil qu'il peut donner est que "ce qui va marcher, c'est le suivi".

2.2. Une Approche Critique de l'Institution Psychiatrique

Son parcours personnel et ses convictions le poussent à remettre en cause les fondements de la psychiatrie traditionnelle.

• Une vocation de "désingueur" : Il raconte avoir choisi cette spécialité pour "désinguer la psychiatrie", trouvant que "ça n'allait pas de soi [...] le fait d'enfermer les gens, le fait de les prendre un peu pour des idiots".

• Une vision politique et sociale : Il souhaitait s'inscrire dans une "dimension plus politique plus social", ce que la psychiatrie lui permet.

• L'utopie d'une psychiatrie dissoute dans la société : Son objectif ultime est qu'il n'y ait "plus de psychiatrie", non pas par absence de soin, mais parce que la société aurait appris à "prendre tous soin des autres", assignant une place et une existence sociale à chacun, même à ceux qui voient le monde de manière "un peu déraisonnable".

2.3. Les Dilemmes Éthiques de la Pratique

Le psychiatre est constamment confronté à des choix difficiles qui mettent en balance la liberté du patient, sa sécurité et le cadre légal.

• Liberté vs. Protection (Cas de Solange) : Avec une patiente psychotique qui souhaite rester en France contre l'avis de sa famille, il verbalise son dilemme :

« C'est la position impossible du psychiatre, il a envie de répondre a priori à votre liberté et en même temps mon pouvoir est tel que comme vous n'êtes pas dans le cadre de la raison je dois vous priver pour vous renvoyer chez vous et ça c'est un dilemme pour moi qui est très difficile. »

• Usage de la force et de la contention : Face à un patient intoxiqué, agité et refusant les soins, il est contraint de passer de la parole à la force. Après avoir tenté de négocier, il conclut :

« On a essayé de jouer les choses avec la parole je pense qu'on va pas y arriver [...] à mon avis on a pas le choix. »

Il ordonne une sédation et une contention physique, malgré l'opposition véhémente du patient.

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3. Portraits de la Souffrance Psychique : Études de Cas

Le document est jalonné de rencontres avec des patients dont les histoires illustrent la diversité et la profondeur de la détresse psychique.

| Patient(e) / Situation | Problématique Principale | Détails Clés | | --- | --- | --- | | Alienor | Tentative de suicide, polytraumatisme, relations familiales | A perdu ses deux jambes et un avant-bras après avoir chuté d'un pont et avoir été percutée par un train.

Sa sœur refuse de la voir tant qu'elle n'aura pas la "preuve exacte" qu'elle ne fera "aucune connerie pendant au moins 6 mois".

Le psychiatre nuance sa responsabilité en pointant une prescription précoce et massive de benzodiazépines. | | Patient avec phobies d'impulsion | Re-diagnostic de trouble bipolaire (au lieu de schizophrénie) | Décrit des "phobies d'impulsion" : une peur obsédante de se jeter sous le métro ou par une fenêtre ouverte, bien qu'il n'en ait pas l'envie.

Il met en place des stratégies d'évitement. | | Solange, "la théologienne" | Épisode psychotique ("voyage des fous"), autonomie | Se dit "en voyage", refuse les neuroleptiques.

Le psychiatre pèse le risque de la laisser "livrée à elle-même" contre son désir de rester en France au sein d'une communauté religieuse.

Il décide finalement de la laisser partir. | | Windy | Addictions, mal-être adolescent, échec thérapeutique | Jeune patient suivi par le psychiatre, participant à un atelier théâtre.

Malgré le lien créé, il est retrouvé mort d'une overdose.

Sa mort est un choc pour le psychiatre, symbolisant l'échec de sa mission : "on doit les empêcher de se buter avant qu'ils arrivent à trouver leur place". | | Jeune homme avec pancréatite | Douleur chronique, angoisse existentielle, idées noires | Souffre de douleurs intenses et exprime sa peur de tomber dans l'addiction aux opiacés.

Il déclare : "Je sais pas jusqu'où je serais prêt à aller pour les faire cesser \[les douleurs\]". Son regard est décrit comme empreint de "mélancolie". | | Patient kidnappé et torturé | Traumatisme psychique sévère | Raconte avoir été enlevé par 15 à 20 personnes, frappé, et enfermé dans un coffre de voiture.

Le psychiatre l'écoute et lui offre un cadre sécurisant pour la nuit. | | Patient intoxiqué et agité | Polytoxicomanie, refus de soins, mise en danger | Revendique son droit à se faire du mal (« Si ce n'est qu'à moi ça me regarde »).

Son état d'intoxication rend toute discussion impossible, forçant l'équipe à recourir à la contention physique et à la sédation pour le protéger. | | Patiente et son anniversaire | Traumatisme, deuil, culpabilité | La patiente se met en danger à l'approche de son anniversaire.

Elle révèle que c'est le jour où une amie est décédée dans un accident de voiture en venant la voir. Elle se sent responsable : « Je dis que c'est ma faute ». |

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4. La Psychiatrie comme Enjeu Social

Au-delà des cas cliniques, la pratique psychiatrique est présentée comme un miroir des valeurs et des dysfonctionnements de la société contemporaine.

4.1. L'Exclusion du "Fou" dans l'Environnement Urbain

Le psychiatre analyse comment l'organisation de la société moderne marginalise structurellement les personnes atteintes de troubles psychiques.

• La perte de "l'idiot du village" : Il oppose le village, où l'on peut avoir de la "sollicitude" pour la personne différente, à la "cité urbaine" qui est un facteur de risque pour les maladies psychiatriques.

• La logique de productivité : Dans un environnement urbain "réfléchi pour permettre à des populations d'être productives", le "fou" est exclu car "il n'est pas productif, il ne sert à rien, il ne produit rien et puis il a un coût en plus".

L'institution devient alors le lieu où on le met à l'écart sous prétexte de le soigner.

4.2. Le Soin comme Acte de Résistance

Face à ce constat, chaque acte de soin centré sur la relation devient un acte politique de résistance contre la déshumanisation et l'isolement.

L'organisation d'un atelier de théâtre avec des jeunes patients, où ils jouent Molière, est une illustration de cette démarche, visant à recréer du lien social, de la confiance en soi et une existence au-delà du statut de malade.

La finalité est de réaffirmer que, malgré la maladie, une place dans la communauté est non seulement possible, mais nécessaire.

I am struggling with the Y-axis '% herbivory per leaf'. Firstly, I am confused about the range. It seems to go from around -2% to 3% damage. However, in the methods, damage values are explained as being the percentage of the leaf surface damaged by herbivory, averaged across four leaves). How can there be negative herbivory damage? Also, perhaps the Y axis title should be something like '% leaf area damaged'

This work has been peer reviewed in GigaScience (see https://doi.org/10.1093/gigascience/giaf157), which carries out open, named peer-review. These reviews are published under a CC-BY 4.0 license and were as follows:

Reviewer 3: Yu Jiang

The present study entitled "Open RGB Imaging Workflow for Morphological and Morphometric Analysis of Fruits using AI: A Case Study on Almonds" reported the development of a Python-based image analysis pipeline that extract morphological traits of almond nut shells and kernels. A case study was conducted to use the developed pipeline to analyze breeding populations of 665 genotypes and extract both general morphology traits such as height, length, area, aspect ratio, etc. and specialized traits for almond such as width at three heights, vertical and horizontal symmetry, etc. Further, each nut shell or kernel was weighed, so models were established to use the weight and morphological traits to predict the thickness of each nut shell or kernel. In addition to morphological traits, morphometric (or shape) was extracted for each nut shell or kernel. Clustering analysis was performed on the morphometric traits to identify variability among genotypes. To further validate the efficacy of the extracted traits, broad-sense heritability was calculated and used as a criterion.

The major contribution of this study is the integration of different components (e.g., camera calibration, image segmentation, and morphological/morphometric trait extraction, etc.) as a user-accessible, open-source Python implementation for the plant breeding community, especially for almond breeders. However, there several aspects that could be further improved.

First, the present study showed the most number of samples that were phenotyped by the proposed pipeline among recent efforts on almond nut shell and/or kernel phenotyping. However, there was no clear evidence to demonstrate direct benefits to ongoing almond breeding. Certain traits (e.g., aspect ratio, tip/top/side curvatures) could be included in a breeding program, but what's the significance of including these traits in breeding programs. Are they crucial to either improve the productivity, quality, or other management practices or processing practices for the almond industry, especially given breeding context?

Second, the pipeline uses deep learning-based segmentation which is powerful to handle complex background. Based on the limited figures or example images in the GitHub repo, the background is mostly single colored (e.g., white or black) without appearances that may confuse even conventional segmentation, especially if image color is calibrated. Assuming most of the almond nut shell and kernel analyses would be done in a laboratory condition, it is not convincing why conventional segmentation methods may not be preferred if both illumination and camera configuration can be well controlled. Ultimately, the question is whether it is worthy the effort of labeling hundreds of images to fine-tune a deep learning segmentation model compared to a careful hardware-software design to make operation more efficient. Or with the simplified background, vision foundation models such as SAM will be sufficient.

Third, in the Introduction section, some technical statements should be revised to make them accurate. For example, image segmentation is a core computer vision task rather than relying on computer vision algorithms. One-stage and two-stage strategies are used to differentiate models for object detection not image segmentation. Further, Faster RCNN is an object detection model and cannot do image segmentation. It is highly recommended that the authors could find a computer science or engineering colleague to proofread the technical statements to ensure the accuracy.

Last, it is appreciated the authors effort on making an open-source software for the community. However, the dataset can be equally important to advance the scientific discovery and technology development. Is there any plan to make the dataset publicly available to help facilitate the development of additional computer vision algorithms for almond phenotyping?

This work has been peer reviewed in GigaScience (see https://doi.org/10.1093/gigascience/giaf157), which carries out open, named peer-review. These reviews are published under a CC-BY 4.0 license and were as follows:

Reviewer 2: Qi Wang

We would like to thank you for submitting your manuscript to our journal.The manuscript proposes an AI-powered open RGB imaging workflow for morphological and morphometric analysis of fruits or other plant organs, with almonds as a case study. The workflow, developed in Python, covers the full pipeline from image pre-processing, segmentation model development and deployment, to trait measurement and analysis. It aims to improve the efficiency and accuracy of phenotyping in breeding programs by addressing the limitations of traditional methods, such as their time-consuming and labor-intensive nature. However, there are still the following problems in this paper that need further improvement: 1.Table formatting: Some of the tables in the manuscript do not follow the formatting standards of the journal. The authors are encouraged to revise them accordingly to ensure clarity, consistency, and ease of understanding. 2.Formula presentation: Certain mathematical formulas are not clearly formatted and appear disorganized. The authors should re-typeset the equations to improve readability and provide clearer explanations for each formula. 3.Introduction: The introduction could be strengthened by more thoroughly explaining the relationship between phenotypic data and breeding. The authors may also discuss how phenotyping data supports genomic selection and accelerates breeding via high-throughput workflows. 4.Methods section: While the paper clearly explains how morphological traits and kernel thickness are measured, it does not sufficiently explain how this data contributes to breeding decisions. The authors should elaborate on how the extracted traits are applied in practical breeding or selection strategies. 5.Lack of algorithmic novelty: While the integration of existing tools is commendable, the core methods used (e.g., YOLO, SAHI) are based on publicly available models, without introducing new algorithmic components or comparative ablation studies. The authors are advised to clarify the unique contribution of their workflow, especially in terms of engineering integration or practical usability. 6.Limited evaluation metrics: The performance of segmentation models is only reported using error percentage. The inclusion of standard metrics such as IoU, Precision, Recall, and F1-score would allow for a more comprehensive evaluation and comparison across models (see LLRL methods). 7.Figures and captions: Currently, figure images and their descriptions are placed separately, which may reduce readability. It is recommended to place figure captions immediately beneath or alongside the figures to enhance the paper's coherence and user-friendliness. 8.Trait extension suggestions: In order to enhance the expressiveness and resolution of phenotypic trait modeling, authors are advised to refer to the relevant research on extracting fine-grained phenotypic features in plant images in recent years. For example, PlanText proposed a progressive visual guidance strategy to help improve the modeling quality of phenotypic traits in images. Therefore, I would like to give a "Major Revision" recommendation.

This work has been peer reviewed in GigaScience (see https://doi.org/10.1093/gigascience/giaf157), which carries out open, named peer-review. These reviews are published under a CC-BY 4.0 license and were as follows:

Reviewer 1: Yuvraj Chopra

The methods described in this article represent a useful tool for fast and reliable morphometric analysis of almonds with potential applications in fruits. The pipeline is technically sound, and publicly available workflow will advance the adoption of this technology. However, there are critical concerns which needs to be addressed before the manuscript could be further proceeded for publication in the journal - Major Comments - 1. Authors claim this technique as a new phenotyping tool with breakthrough implications; however, I object to this claim. Numerous studies have utilized this technique in plant phenotyping to the extent that labeling it as a new phenotyping tool may not be ideal. Additionally, for kernel or seed morphometrics, a wide array of user-friendly, open-source tools have already been developed and are readily available, for example, - SeedExtractor https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2020.581546/full#h3 - SmartGrain https://academic.oup.com/plphys/article/160/4/1871/6109568 - GrainScan https://link.springer.com/article/10.1186/1746-4811-10-23 - PlantCV https://peerj.com/articles/4088/ These tools and a lot of other options can be readily used for almond kernel morphometrics. Authors are requested to discuss/compare advantages/performance of their model with SeedExtractor, SmartGrain, and GrainScan. 2. Within horticultural crops, workflows and studies (not acknowledged in this article) are available that can be adapted or modified to do the same thing. For example - publications from as early as 2020 used machine learning models to measure size and mass of almonds, however, this relevant study was not acknowledged by the authors https://onlinelibrary.wiley.com/doi/full/10.1111/jfpe.13374 . Discuss how the presented method is better than the aforementioned article, justify the claim 'breakthrough'. 3. The authors claim successfully testing the pipeline for apples and strawberries. Information for fruit size can be extracted from 2D images; however, the example results show only length, width, circularity, and ellipse ratio. How do these parameters assist fruit breeders? Since it is segmentation based classification using a reference scale, the aforementioned tools particularly SeedExtractor can generate similar results. Does it qualify the tool for integration into fruit crops breeding pipeline? Moreover, fruit breeders require on-tree analysis, recent advancements have enabled 3D sensing for significantly better detection particularly using cost effective RGB-D cameras.

Minor Comments - 1. Change title - This study uses deep learning models which are a type of AI. AI is a broader term. Additionally, the potential for utility in fruit breeding pipeline appears to be limited. Suggested - Open RGB imaging workflow for morphological and morphometric analysis of almond kernels using deep learning. 2. The video tutorial showed using YOLOv8, mention it in the methods. Add information for all settings used in CVAT.

Este componente nomqueda tan claro como los otros, en términos de líneas o acciones concretas de desarrollo. Me parece que puede ayudar la distinción entre el desarrollo de protocolos o modelos de gobernanza, y el foco en uso de información orientada a la toma de desiciones, por medio del desarrollo de estudios estratégicos.

Esto está poco desarrollado. Acceso, desarrollo de manuales de uso, recomendaciones que aborden objetivos de estudios especificos, actividades de formación, etc.

Un aspecto a destacar es el desarrollo de estudios estrategicos para la toma de desiciones.

Por ejemplo, DAMA (https://dama.org/about-dama/what-is-data-management/) es una asocación sobre Data Managment y sistematiza algo de lo que se presenta aquí.

Citas

Si se va a mencionar el colaboratorio, esta sería la sección adecuada para hacerlo. Aunque no se me ocurre como.

Creo que los números me confunden, FACSO realizó 16 actividades y recibió 970 millones y FAGOB realizó 99 y recibió 2.4 millones. Tanto menos? y en que sentido sería una oportunidad para crecer? en el número de actividad, si, pero en $$ no?

Dar un par de ejemplos

Misma cosa sobre el orden de aparición

Mantener el orden en que se presentan los apartados posteriormente.

el concepto de producción de datos está muy vinculado a levantamiento. Para diferenciarse de esa actividad propongo matizarlo o sacarlo por ahora. Así, el foco estaría en: transformar la manera en que se gestionan, documentan y utilizan los datos sociales, asegurando que el conocimiento generado sea riguroso, transparente y socialmente relevante.

Este párrafo es muy general. Funciona mejor como contexto amplio. Además, aparecen temas un poco descolgados como: los populismos o las crisis democráticas. ¿Cómo se conectan estos temas amplios con lo que se propone?

antes de hacer el cruce, se requieren los descriptivos de notas por cada una de las variables y ahí evaluar qué cruces son relevantes. ¿Por qué es importante el cruce entre sexo y tipo de colegio? es decir, ambas son variables dependientes del estudio, y todo cruce debería incluir la dependiente. Llama la atención además que este sea el primer cruce.

Además, el eje x tiene que ir ordenado en un sentido para ver mejor las tendencias: municipal, subvencionado, privado

Y partir por un gráfico general, y luego ver si tiene sentido una comparación por carreras. Si es por comparar, un tabset no ayuda mucho, mejor algo tipo cleveland o algo más eficiente visualmente.

esto de todas maneras antes que el cruce ... y hacer un gráfico general donde esté el promedio general y los de las carreras representadas tipo el boxplot de picils

parte de esta sección debería ser algo como "chequeo de calidad de los datos" y debería ir en el apéndice. La secciónn 4 debería ser simplemente Resultados, para la audiencia dudo que su principal interés sea conocer discrepancias de las bases de datos, le quita mucho peso al análisis. En la reunión habíamos dicho que vamos a trabajar con la base que tiene las notas por curso; si esto calza o no con la otra base es un tema a resolver, y que como dije ya un par de veces se puede despejar con las notas efectivas de algún curso que me puedes solicitar a mí o a Daniel @tomas, y simplemente comparar con las de la base.

yo creo que es mucho más que esto: describir y analizar el rendimiento académico en las y los estudiantes de pregrado de FACSO entre 2020 y 2025, sus cambios en el tiempo y sus factores asociados.

diría varias problemáticas. Más que la distinción entre quienes necesitan apoyo, que se podría estar en una situación de inflación de notas. Esto podría ser problemático porque ... -> y aquí el problema principal de investigación

Este punto queda descontextualizado, parece asumir que la heterogeneidad tiene que ver con algo de las notas (?). Y la planificación de este estudio de notas no tiene que ver con esto. Ahora, si refiere al estudio de notas inicial desde la dirección de pregrado para el tema de las tutorías, hace falta contextualizar mucho mejor. Y con fechas, para poder entender bien.

En términos globales, a la introducción le falta desarrollo del problema desde la literatura y desde la documentación de la universidad (reglamentos, modelo educativo, políticas de acceso, etc,). Es importante hacer esa revisión para contextualizar el problema: el análisis de la distribución de notas, su variación en tiempo (cuando se pueda) y los factores asociados. Haciendo un mejor contexto, podemos definir mejor la pregunta y guíar los análisis.

En la prensa, durante estos días apareció algo sobre esta discusión: - https://www.lun.com/Pages/NewsDetail.aspx?dt=2025-12-30&NewsID=556972&BodyID=0&PaginaId=3

Además, hay fuentes internacionales interesantes con las que iniciar: - https://www.tandfonline.com/doi/full/10.1080/02602938.2020.1795617 - https://www.tandfonline.com/doi/full/10.1080/03075079.2025.2470297#:~:text=Generally%2C%20grade%20inflation%20is%20harmful,;%20Yeritsyan%20and%20Mjelde%202024).

• https://www.ed.gov/about/homeroom-blog/addressing-grade-inflation-collective-action-problem#:~:text=Grade%20inflation%20is%20a%20trend%20of%20declining,causing%20learning%20and%20skills%20acquisition%20to%20plummet**

• https://theconversation.com/the-customer-is-always-right-why-some-uni-teachers-give-higher-grades-than-students-deserve-258923

• https://www.thecollegefix.com/harvard-report-warns-of-damaging-grade-inflation-60-of-grades-are-as/#:~:text=The%20office%20sent%20a%2025%2Dpage%20report%20to,of%20all%20undergraduate%20grades%20are%20now%20A's.

• https://www.bestcolleges.com/research/grade-inflation-trends-and-causes/

Interesting metric. Is it bc of the chasing (capital, eagerness) or bc of the founders (ideas, surfing a new tech wave). AI people are younger I suppose.

immich storage template没有区分model的选项 当前模版: {{y}}/{{MM}}/{{y}}{{MM}}{{dd}}-{{HH}}{{mm}}{{ss}}_{{filename}} UPLOAD_LOCATION/library/admin/2022/02/20220203-045605_IMAGE_56437.jpg

English (thorough explanation)

1) What “orchids” literally means

Orchids is the plural noun of orchid. It refers to plants in the orchid family (Orchidaceae)—one of the largest and most diverse plant families on Earth. Orchids are flowering plants known for their complex, often beautiful flowers.

In simple terms: orchids = a very large group of flowering plants with specialized flowers

2) Key characteristics of orchids

• Flowers: Highly specialized shapes; often bilaterally symmetrical with a distinctive petal called the labellum (lip) that attracts pollinators.
• Pollination: Frequently insect-pollinated, sometimes by a single specific species (very precise adaptation).

• Growth forms:

• Epiphytic (grow on trees, not parasitic)

• Terrestrial (grow in soil)
• Roots: Many have aerial roots with a spongy layer (velamen) that absorbs moisture from air.
• Seeds: Extremely tiny and dust-like, produced in huge numbers.

3) Orchids in ecosystems

Orchids play important ecological roles:

• Form specialized relationships with pollinators (bees, moths, flies, birds).
• Many rely on mycorrhizal fungi to germinate and obtain nutrients when young.
• Serve as indicators of healthy, stable ecosystems, especially forests.

4) Where orchids are found

• Found worldwide, but most diverse in tropical rainforests.
• Common in forest canopies and understorey, where they use trees for support to reach light.
• Some species grow in temperate regions, grasslands, or even near wetlands.

5) Orchids and adaptation (science focus)

Orchids are classic examples of evolutionary adaptation:

• Flower shapes match specific pollinators
• Some mimic insects or produce strong scents
• High specialization increases pollination efficiency but also vulnerability

6) How the word is used if it appears alone

If you see “orchids” by itself, it usually functions as:

• a plant group name,
• an example of insect-pollinated plants,
• a label under a picture or diagram,
• a biology vocabulary term.

7) Pronunciation

• orchid /ˈɔːr.kɪd/
• orchids /ˈɔːr.kɪdz/

中文（详细解释）

1）“orchids”的基本意思

orchids 是 orchid（兰花） 的复数形式，指 兰科植物。 兰科是 地球上物种最多的植物科之一，以 结构复杂、形态多样的花朵 著称。

简单说： orchids = 兰花，一大类开花植物

2）兰花的主要特征

• 花结构特殊：左右对称，有明显的 唇瓣 用来吸引传粉者
• 授粉方式：多为 虫媒授粉，有时高度专一

• 生长方式：

• 附生兰（长在树上，但不寄生）

• 地生兰（长在土里）
• 根系：常有 气生根，可直接吸收空气中的水分
• 种子极小，数量巨大

3）生态系统中的作用

• 与昆虫形成 高度专化的授粉关系
• 幼苗依赖 真菌共生 才能发芽
• 常被视为 生态环境健康的指示植物

4）分布环境

• 全球都有，但以 热带雨林 最丰富
• 常见于 树冠层和林下层
• 也有耐寒、耐旱的品种分布在温带地区

5）适应性与进化（考试重点）

兰花是 自然选择与适应进化 的经典例子：

• 花形与传粉者高度匹配
• 有些会 拟态昆虫 或释放强烈气味
• 专一性高，效率高，但对环境变化敏感

6）单独出现 “orchids” 的含义

如果只看到 orchids，通常表示：

• 植物分类名称
• 虫媒授粉植物的例子
• 图片或图表标签
• 生物学词汇

7）发音

• orchids /ˈɔːrkɪdz/ 中文可近似理解为：“奥-克兹 / 兰花”

If you want, paste the full sentence, diagram, or exam question (for example about pollination, coevolution, or forest layers), and I’ll explain exactly why orchids are mentioned and what concept they illustrate（中英对照、直击考点）.

En general, me parece que la introducción está muy bien. Se sintetiza correctamente el problema y el objetivo del artículo; mi único reparo está en el último párrafo (donde dejé los comentarios específicos correspondientes).

Author response:

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

We would like to proceed with this paper as a Version of Record but we will correct the mistake that we made in the Key resources table. As the reviewer noted we had added the wrong guide RNA sequence here. We are super thankful to the reviewer and apologize for the mistake.

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

eLife Assessment 

This important study identifies a new key factor in orchestrating the process of glial wrapping of axons in Drosophila wandering larvae. The evidence supporting the claims of the authors is convincing and the EM studies are of outstanding quality.

We are thankful for this kind and very positive judgment.

However, the quantification of the wrapping index, the role of Htl/Uif/Notch signaling in differentiation vs growth/wrapping, and the mechanism of how Uif "stabilizes" a specific membrane domain capable of interacting with specific axons might require further clarification or discussion.

This is now addressed

Reviewer #1 (Public review):

Summary:

A central function of glial cells is the ensheathment of axons. Wrapping of larger-diameter axons involves myelin-forming glial classes (such as oligodendrocytes), whereas smaller axons are covered by non-myelin-forming glial processes (such as olfactory ensheathing glia). While we have some insights into the underlying molecular mechanisms orchestrating myelination, our understanding of the signaling pathways at work in non-myelinating glia remains limited. As non-myelinating glial ensheathment of axons is highly conserved in both vertebrates and invertebrates, the nervous system of Drosophila melanogaster, and in particular the larval peripheral nerves, have emerged as a powerful model to elucidate the regulation of axon ensheathment by a class of glia called wrapping glia. Using this model, this study seeks to specifically address the question, as to which molecular mechanisms contribute to the regulation of the extent of glial ensheathment focusing on the interaction of wrapping glia with axons. 

Strengths and Weaknesses:

For this purpose, the study combines state-of-the-art genetic approaches with high-resolution imaging, including classic electron microscopy. The genetic methods involve RNAi-mediated knockdown, acute Crispr-Cas9 knock-outs, and genetic epistasis approaches to manipulate gene function with the help of cell-type specific drivers. The successful use of acute Crispr-Cas9 mediated knockout tools (which required the generation of new genetic reagents for this study) will be of general interest to the Drosophila community. 

The authors set out to identify new molecular determinants mediating the extent of axon wrapping in the peripheral nerves of third-instar wandering Drosophila larvae. They could show that over-expressing a constitutive-active version of the Fibroblast growth factor receptor Heartless (Htl) causes an increase in wrapping glial branching, leading to the formation of swellings in nerves close to the cell body (named bulges). To identify new determinants involved in axon wrapping acting downstream of Htl, the authors next conducted an impressive large-scale genetic interaction screen (which has become rare, but remains a very powerful approach), and identified Uninflatable (Uif) in this way. Uif is a large single-pass transmembrane protein that contains a whole series of extracellular domains, including Epidermal growth factor-like domains. Linking this protein to glial branch formation is novel, as it has so far been mostly studied in the context of tracheal maturation and growth. Intriguingly, a knock-down or knock-out of uif reduces branch complexity and also suppresses htl over-expression defects. Importantly, uif over-expression causes the formation of excessive membrane stacks. Together these observations are in in line with the notion that htl may act upstream of uif. 

Further epistasis experiments using this model implicated also the Notch signaling pathway as a crucial regulator of glial wrapping: reduction in Notch signaling reduces wrapping, whereas over-activation of the pathway increases axonal wrapping (but does not cause the formation of bulges). Importantly, defects caused by the over-expression of uif can be suppressed by activated Notch signaling. Knock-down experiments in neurons suggest further that neither Delta nor Serrate act as neuronal ligands to activate Notch signaling in wrapping glia, whereas knock-down of Contactin, a GPI anchored Immunoglobulin domain-containing protein led to reduced axon wrapping by glia, and thus could act as an activating ligand in this context. 

Based on these results the authors put forward a model proposing that Uif normally suppresses Notch signaling, and that activation of Notch by Contactin leads to suppression of Htl, to trigger the ensheathment of axons. While these are intriguing propositions, future experiments would need to conclusively address whether and how Uif could "stabilize" a specific membrane domain capable of interacting with specific axons.

We absolutely agree with the reviewer that it would be fantastic to understand whether and how Uif could stabilize specific membrane domains that are capable of interacting with axons. To address this we need to be able to label such membrane domains and unfortunately we still cannot do so. We analyzed the distribution of PIP2/PIP3 but failed to detect any differences. Thus we still lack wrapping glial membrane markers that are able to label specific compartments.

Moreover, to obtain evidence for Uif suppression by Notch to inhibit "precocious" axon wrapping and for a "gradual increase" of Notch signaling that silences uif and htl, (1) reporters for N and Htl signaling in larvae, (2) monitoring of different stages at a time point when branch extension begins, and (3) a reagent enabling to visualize Uif expression could be important next tools/approaches. Considering the qualitatively different phenotypes of reduced branching, compared to excessive membrane stacks close to cell bodies, it would perhaps be worthwhile to explore more deeply how membrane formation in wrapping glia is orchestrated at the subcellular level by Uif.

In the revised version of the manuscript we have now included the use of Notch and RTK-signaling reporters.

(1) reporters for N and Htl signaling in larvae,

We had already employed the classic reporter generated by the Bray lab: Gbe-Su(H)-lacZ. This unfortunately failed to detect any activity in larval wrapping glia nuclei but was able to detect Notch activity in the adult wrapping glia (Figure S5C,F).

We did, as requested, the analysis of a RTK signaling reporter.  The activity of sty-lacZ that we had previously characterized in the lab (Sieglitz et al., 2013) increases by 22% when Notch is silenced. Given the normal distribution of the data points, this shows a trend which, however, is not in the significance range. We have not included this in the paper, but would be happy to do so, if requested.

Author response image 1.

(2) monitoring of different stages at a time point when branch extension begins,

The reviewer asks for an important question; however, this is extremely difficult to tackle experimentally. It would require a detailed electron microscopic analysis of early larval stages which cannot be done in a reasonable amount of time. We have however added additional information on wrapping glia growth summarizing recently published work from the lab (Kautzmann et al., 2025).

(3) a reagent enabling to visualize Uif expression could be important next tools/approaches.

The final comment of the reviewer also addresses an extremely relevant and important issue. We employed antibodies generated by the lab of R. Ward, but they did not allow detection of the protein in larval nerves. We also attempted to generate anti-Uif peptide antibodies but these antibodies unfortunately do not work in tissue. We are still trying to generate suitable reagents but for the current revision cannot offer any solution.

Lastly, we agree with the reviewer that it would be worthwhile to explore how Uif controls membrane formation at the subcellular level. This, however, is a completely new project and will require the identification of the binding partners of Uif in wrapping glia to start working on a link between Uif and membrane extension. The reduced branching phenotype might well be a direct consequence of excessive membrane formation as it likely blocks recourses needed for efficient growth of glial processes.

Finally, in light of the importance of correct ensheathment of axons by glia for neuronal function, this study will be of general interest to the glial biology community. 

We are very grateful for this very positive comment.

Reviewer #2 (Public review): 

The FGF receptor Heartless has previously been implicated in Drosophila peripheral glial growth and axonal wrapping. Here, the authors perform a large-scale screen of over 2600 RNAi lines to find factors that control the downstream signaling in this process. They identify a transmembrane protein Uninflatable to be necessary for the formation of plasma membrane domains. They further find that a Uif regulatory target, Notch, is necessary for glial wrapping. Interestingly, additional evidence suggests Notch itself regulates uif and htl, suggesting a feedback system. Together, they propose that Uif functions as a "switch" to regulate the balance between glial growl and wrapping of axons. 

Little is known about how glial cell properties are coordinated with axons, and the identification of Uif is a promising link to shed light on this orchestration. The manuscript is well-written, and the experiments are generally well-controlled. The EM studies in particular are of outstanding quality and really help to mechanistically dissect the consequences of Uif and Notch signaling in the regulation of glial processes. Together, this valuable study provides convincing evidence of a new player coordinating the interactions controlling the glial wrapping of axons.

Reviewer #1 (Recommendations for the authors): 

(1) To be reproducible and understandable, it would be important to provide detailed information about crosses and genotypes, as reagents are currently listed individually and genotypes are provided in rather simplified versions. 

We have added the requested information to the text.

(2) Neurons are inherently resistant to RNAi-mediated knockdown and it thus may be necessary to introduce the over-expression of UAS-dcr2 when assessing neuronal requirements and to specifically exclude Delta or Serrate as ligands. 

We agree with the reviewer and have repeated the knockdown experiments using UAS-dcr2 and obtained the same results. To use an RNAi independent approach we also employed sgRNA expression in the presence of Cas9. The neuron specific gene knockout also showed no glial wrapping phenotype. These results are now added to the manuscript.

(3) Throughout the manuscript, the authors use the terms "growth" and "differentiation" referring to the extent of branch formation versus axon wrapping. However glial differentiation and growth could have different meanings (for instance, growth could implicate changes in cell size or numbers, while differentiation could refer to a change from an immature precursor-like state to a mature cell identity). It may thus be useful to replace these general terms with more specific ones. 

This is a very good point. When we use the term “growth” we only infer on glial cell growth and thus, the increase in cell mass. Proliferation is excluded and this is now explicitly stated in the manuscript. The term “differentiation” is indeed difficult and therefore we changed it either directly addressing the morphology or to axon wrapping.

(4) Page 4. "remake" fibers should be Remak fibers. 

We have corrected this typo.

(5) Page 5. "Heartless controls glial growth but does promote axonal wrapping", this sentence is not clear in its message because of the "but".

We have corrected this sentence.

(6) Generally, many gene names are used as abbreviations without introductions (e.g. Sos, Rl, Msk on page 7). These would require an introduction.

All genetic elements are now introduced.

(7) Page 8. When Cas9 is expressed ubiquitously ... It would be helpful to add how this is done (nsyb-Gal4, nrv2-Gal4, or another Gal4 driver are used to express UAS-Cas9, as the listed Gal4 drivers seem to be specific to neurons or glia?).

This now added. We used the following genotype for ubiquitous knockout using the four different uif specific sgRNAs (UAS-uif^{sgRNA X}): [w; UAS-Cas9/ Df(2L)ED438; da-Gal4 /UAS-uif^{sgRNA X}]. We used the following genotype for a glial knockout in wrapping glia ([+/+; UAS-Cas9/+; nrv2-Gal4,UAS-CD8::mCherry/UAS-uif^{sgRNA X}].

We had previously shown that nrv2-Gal4 is a wrapping glia specific driver in the larval PNS (Kottmeier et al., 2020).

Moreover, the authors mention that "This indicates that a putatively secreted version of Uif is not functional". This conclusion would need to be explained in detail.

First, because it requires quite some detective work to understand the panels in Figure 1 on which this statement is based; second, since the acutely induced double-stranded breaks in the DNA and subsequent repair may cause variable defects, it may indeed be not certain what changes have been induced in each cell; and third considering that there is a putative cleavage site, would it be not be expected that the protein is not functional, when it is not cleaved, and there is no secreted extracellular part (unless the cleavage site is not required). The latter could probably only be addressed by rescue experiments with UAS transgenes with identified changes.

We agree with the reviewer. The rescue experiments are unfortunately difficult, since even expression of a full length uif construct does not fully rescue the uif mutant phenotype (Loubéry et al., 2014). We therefore explained the conclusion taken from the different sgRNA knockout experiments better and also removed the statement that secreted Uif forms are non-functional.

In the Star Method reagent table, it is not clear, why all 8 oligonucleotides are for "uif cleavage just before transmembrane domain" despite targeting different locations. 

We are very sorry for this mistake and corrected it now. Thank you very much for spotting this.

(8) Page 13. However, we expressed activated Notch,... the word "when" seems to be missing, and it would be helpful to specify how this was done (over-expression of N[ICD].

We corrected it now accordingly.

(9) To strengthen the point similarity of phenotypes caused by Htl pathway over-activation and Uif over-expression, it would be helpful to also show an EM electron micrograph of the former.

We now added an extensive description of the phenotype caused by activated Heartless. This is shown as new Figure 2.

(10) Figure 4C, the larval nerve seems to be younger, as many extracellular spaces between axons are detected.

This perception is a misunderstanding and we are sorry for not explaining this better. The third instar larvae are all age matched. The particular specimen in Figure 4C shows some fixation artifacts that result in the loss of material. Importantly, however, membranes are not affected. Similar loss of material is also seen in Figure 6C. For further examples please see a study on nerve anatomy by (Kautzmann et al., 2025).

(11) The model could be presented as a figure panel in the manuscript. To connect the recommendation section with the above public review, a step forward could be to adjust the model and the wording in the Result section and to move some of the less explored points and thoughts to the discussion.

We are thankful for this advice and have moved an updated model figure to the end of the main text (now Figure 7).

Reviewer #2 (Recommendations for the authors):

(1) Screen and the interest in Uif: Out of the ~62 genes that came out of the RNAi screen, why did the authors prioritize and focus on Uif? What were the other genes that came out of the screen, and did any of those impinge on Notch signaling? 

We have now more thoroughly described the results of the screen.  We selected Uif as it was the only transmembrane // adhesion protein identified and given the findings that Uif decorate apical membrane domains in epithelial cells, we hoped to identify a protein specific for a similar membrane domain in wrapping glia.

Notch as well as its downstream transcription factors were not included in the initial screen, and were only analyzed, once we had seen the contribution of Notch. Interestingly, here is one single hit in our screen linked to Notch signaling: Gp150. Here however, we have tested additional dsRNA expressing lines and were not able to reproduce the phenotype. This information is added to the discussion.

The authors performed a large-scale screen of 2600 RNAi lines, it seems more details about what came out of the screen and why the focus on Uif would benefit the manuscript. 

See above comment.

Relatedly, there would be a discussion of the limitations of the screen, and that it was really a screen looking to modify a gain-of-function phenotype from the activated Htl allele; it seems a screen of this design may lead to artifacts that may not reflect endogenous signaling.

We have now added a short paragraph on suppressor screens, employing gain of function alleles to the introduction.

“In Drosophila, such suppressor screens have been used successfully many times (Macagno et al., 2014; Rebay et al., 2000; Therrien et al., 2000). Possibly, such screens also uncover genes that are not directly linked to the signaling pathway under study but this can be tested in further experiments. Our screen led to the unexpected identification of the large transmembrane protein Uninflatable, which in epithelial cells localizes to the apical plasma membrane. Loss of uninflatable suppresses the phenotype caused by activated RTK signaling. In addition, we find that uif knockdown and uif knockout larvae show impaired glial growth while an excess of Uninflatable leads to the formation of ectopic wrapping membrane processes that, however, fail to interact with axons. uninflatable is also known to inhibit Notch.  “

(2) In general this study relies on RNAi knockdown, and is generally well controlled in using multiple RNAi lines giving the same phenotype, and also controlled for by tissue-specific gene knockout. However, there is little in the way of antibody staining to directly confirm the target of interest is lost/reduced, which would obviously strengthen the study. 

Lacking the tools or ability to assess RNAi efficiency (qPCR, antibody staining), some conclusions need to be tempered. For example, in the experiments in Figure S6 regarding canonical Notch signaling, the authors do not find a phenotype by Delta or Serrate knockdown, but there are no experiments that show Delta or Serrate are lost. Thus, if the authors cannot directly test for RNAi efficiency, these conclusions should be tempered throughout the manuscript. 

We agree with the reviewer and now provide information on the use of Dicer in our RNAi experiments and conducted new sgRNA/Cas9 experiments. In addition we tempered our wording stating that Dl and or Ser are still possible ligands.

(3) More description is needed regarding how the authors are measuring and calculating the "wrapping index". In principle, the approach seems sound. However, are there cases where axons are "partially" wrapped of various magnitudes, and how are these cases treated in the analysis? Are there additional controls of previously characterized mutants to illustrate the dynamic range of the wrapping index in various conditions?

This is now explained.

Further, can the authors quantify the phenotypes in the axonal "bulges" in Figures 1, 3, and 5?

This is a difficult question. Although we can easily quantify the number of bulges we cannot quantify the severity of the phenotype as this will require EM analysis. Sectioning nerves at a specific distance of the ventral nerve cord already requires very careful adjustments. Sectioning at the level of a bulge is way more difficult and it is not possible to get the number of sections needed to quantify the bulge phenotype.

The fact is that all wrapping glial cells develop swellings (bulges) at the position of the nucleus. As there are in general three wrapping glial cells per segmental nerve, the number of bulges is three.

(4) It seems difficult to clearly untangle the functions of Htl/Uif/Notch in differentiation itself vs subsequent steps in growth/wrapping. For example, if the differentiation steps are not properly coordinated, couldn't this give rise to some observed differences in growth or wrapping at later stages? I'm not sure of any obvious experiments to pursue here, but at least a brief discussion of these issues in the manuscript would be of use.

We have discussed this in our discussion now more carefully. To discriminate the function of the three genes in either differentiation or in a stepwise mode of growth and differentiation.

When comparing the different loss of function phenotypes they al appear the same, which would argue all three genes act in a common process.

However, when we look at gain of function phenotypes, Htl and Uif behave different compared to Notch. This would favor for two distinct processes.

We have now added activity markers for RTK signaling to directly show that Notch silences RTK activity. Unfortunately we were not able to do a similar reciprocal experiment.

Minor:

(1) The Introduction is too long, and would benefit from revisions to make it shorter and more concise.

We have shortened the introduction and hopefully made it more concise.

(2) A schematic illustrating the model the authors propose about Htl, Uif, and Notch in glial differentiation, growth, and wrapping would benefit the clarity of this work. 

We had previously added the graphical abstract below that we updated and included as a Figure in the main text.

References

Kautzmann, S., Rey, S., Krebs, A., and Klämbt, C. (2025). Cholinergic and glutamatergic axons differentially require glial support in the Drosophila PNS. Glia. 10.1002/glia.70011.

Kottmeier, R., Bittern, J., Schoofs, A., Scheiwe, F., Matzat, T., Pankratz, M., and Klämbt, C. (2020). Wrapping glia regulates neuronal signaling speed and precision in the peripheral nervous system of Drosophila. Nature communications 11, 4491-4417. 10.1038/s41467-020-18291-1.

Loubéry, S., Seum, C., Moraleda, A., Daeden, A., Fürthauer, M., and González-Gaitán, M. (2014). Uninflatable and Notch control the targeting of Sara endosomes during asymmetric division. Current biology : CB 24, 2142-2148. 10.1016/j.cub.2014.07.054.

Macagno, J.P., Diaz Vera, J., Yu, Y., MacPherson, I., Sandilands, E., Palmer, R., Norman, J.C., Frame, M., and Vidal, M. (2014). FAK acts as a suppressor of RTK-MAP kinase signalling in Drosophila melanogaster epithelia and human cancer cells. PLoS Genet 10, e1004262. 10.1371/journal.pgen.1004262.

Rebay, I., Chen, F., Hsiao, F., Kolodziej, P.A., Kuang, B.H., Laverty, T., Suh, C., Voas, M., Williams, A., and Rubin, G.M. (2000). A genetic screen for novel components of the Ras/Mitogen-activated protein kinase signaling pathway that interact with the yan gene of Drosophila identifies split ends, a new RNA recognition motif-containing protein. Genetics 154, 695-712. 10.1093/genetics/154.2.695.

Sieglitz, F., Matzat, T., Yuva-Adyemir, Y., Neuert, H., Altenhein, B., and Klämbt, C. (2013). Antagonistic Feedback Loops Involving Rau and Sprouty in the Drosophila Eye Control Neuronal and Glial Differentiation. Science signaling 6, ra96. 10.1126/scisignal.2004651.

Therrien, M., Morrison, D.K., Wong, A.M., and Rubin, G.M. (2000). A genetic screen for modifiers of a kinase suppressor of Ras-dependent rough eye phenotype in Drosophila. Genetics 156, 1231-1242.

Interesante a seguir la pista

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

We appreciate the reviewer for the critical review of the manuscript and the valuable comments. We have carefully considered the reviewer’s comments and have revised our manuscript accordingly.

The reviewer’s comments in this letter are in Bold and Italics.

Summary:

This study identified three independent components of glucose dynamics-"value," "variability," and "autocorrelation", and reported important findings indicating that they play an important role in predicting coronary plaque vulnerability. Although the generalizability of the results needs further investigation due to the limited sample size and validation cohort limitations, this study makes several notable contributions: validation of autocorrelation as a new clinical indicator, theoretical support through mathematical modeling, and development of a web application for practical implementation. These contributions are likely to attract broad interest from researchers in both diabetology and cardiology and may suggest the potential for a new approach to glucose monitoring that goes beyond conventional glycemic control indicators in clinical practice.

Strengths:

The most notable strength of this study is the identification of three independent elements in glycemic dynamics: value, variability, and autocorrelation. In particular, the metric of autocorrelation, which has not been captured by conventional glycemic control indices, may bring a new perspective for understanding glycemic dynamics. In terms of methodological aspects, the study uses an analytical approach combining various statistical methods such as factor analysis, LASSO, and PLS regression, and enhances the reliability of results through theoretical validation using mathematical models and validation in other cohorts. In addition, the practical aspect of the research results, such as the development of a Web application, is also an important contribution to clinical implementation.

We appreciate reviewer #1 for the positive assessment and for the valuable and constructive comments on our manuscript.

Weaknesses:

The most significant weakness of this study is the relatively small sample size of 53 study subjects. This sample size limitation leads to a lack of statistical power, especially in subgroup analyses, and to limitations in the assessment of rare events. 

We appreciate the reviewer’s concern regarding the sample size. We acknowledge that a larger sample size would increase statistical power, especially for subgroup analyses and the assessment of rare events.

We would like to clarify several points regarding the statistical power and validation of our findings. Our sample size determination followed established methodological frameworks, including the guidelines outlined by Muyembe Asenahabi, Bostely, and Peters Anselemo Ikoha. “Scientific research sample size determination.” (2023). These guidelines balance the risks of inadequate sample size with the challenges of unnecessarily large samples. For our primary analysis examining the correlation between CGM-derived measures and %NC, power calculations (a type I error of 0.05, a power of 0.8, and an expected correlation coefficient of 0.4) indicated that a minimum of 47 participants was required. Our sample size of 53 exceeded this threshold and allowed us to detect statistically significant correlations, as described in the Methods section. Moreover, to provide transparency about the precision of our estimates, we have included confidence intervals for all coefficients. 

Furthermore, our sample size aligns with previous studies investigating the associations between glucose profiles and clinical parameters, including Torimoto, Keiichi, et al. “Relationship between fluctuations in glucose levels measured by continuous glucose monitoring and vascular endothelial dysfunction in type 2 diabetes mellitus.” Cardiovascular Diabetology 12 (2013): 1-7. (n=57), Hall, Heather, et al. “Glucotypes reveal new patterns of glucose dysregulation.” PLoS biology 16.7 (2018): e2005143. (n=57), and Metwally, Ahmed A., et al. “Prediction of metabolic subphenotypes of type 2 diabetes via continuous glucose monitoring and machine learning.” Nature Biomedical Engineering (2024): 1-18. (n=32).

Furthermore, the primary objective of our study was not to assess rare events, but rather to demonstrate that glucose dynamics can be decomposed into three main factors - mean, variance and autocorrelation - whereas traditional measures have primarily captured mean and variance without adequately reflecting autocorrelation. We believe that our current sample size effectively addresses this objective. 

Regarding the classification of glucose dynamics components, we have conducted additional validation across diverse populations including 64 Japanese, 53 American, and 100 Chinese individuals. These validation efforts have consistently supported our identification of three independent glucose dynamics components.

However, we acknowledge the importance of further validation on a larger scale. To address this, we conducted a large follow-up study of over 8,000 individuals (Sugimoto, Hikaru, et al. “Stratification of individuals without prior diagnosis of diabetes using continuous glucose monitoring” medRxiv (2025)), which confirmed our main finding that glucose dynamics consist of mean, variance, and autocorrelation. As this large study was beyond the scope of the present manuscript due to differences in primary objectives and analytical approaches, it was not included in this paper; however, it provides further support for the clinical relevance and generalizability of our findings.

To address the sample size considerations, we have added the following sentences in the Discussion section (lines 409-414): 

Although our analysis included four datasets with a total of 270 individuals, and our sample size of 53 met the required threshold based on power calculations with a type I error of 0.05, a power of 0.8, and an expected correlation coefficient of 0.4, we acknowledge that the sample size may still be considered relatively small for a comprehensive assessment of these relationships. To further validate these findings, larger prospective studies with diverse populations are needed.

We appreciate the reviewer’s feedback and believe that these clarifications improve the manuscript.

In terms of validation, several challenges exist, including geographical and ethnic biases in the validation cohorts, lack of long-term follow-up data, and insufficient validation across different clinical settings. In terms of data representativeness, limiting factors include the inclusion of only subjects with well-controlled serum cholesterol and blood pressure and the use of only short-term measurement data.

We appreciate the reviewer’s comment regarding the challenges associated with validation. In terms of geographic and ethnic diversity, our study includes validation datasets from diverse populations, including 64 Japanese, 53 American and 100 Chinese individuals. These datasets include a wide range of metabolic states, from healthy individuals to those with diabetes, ensuring validation across different clinical conditions. In addition, we recognize the limited availability of publicly available datasets with sufficient sample sizes for factor decomposition that include both healthy individuals and those with type 2 diabetes (Zhao, Qinpei, et al. “Chinese diabetes datasets for data-driven machine learning.” Scientific Data 10.1 (2023): 35.). The main publicly available datasets with relevant clinical characteristics have already been analyzed in this study using unbiased approaches.

However, we fully agree with the reviewer that expanding the geographic and ethnic scope, including long-term follow-up data, and validation in different clinical settings would further strengthen the robustness and generalizability of our findings. To address this, we conducted a large follow-up study of over 8,000 individuals with two years of follow-up (Sugimoto, Hikaru, et al. “Stratification of individuals without prior diagnosis of diabetes using continuous glucose monitoring” medRxiv (2025)), which confirmed our main finding that glucose dynamics consist of mean, variance, and autocorrelation. As this large study was beyond the scope of the present manuscript due to differences in primary objectives and analytical approaches, it was not included in this paper; however, it provides further support for the clinical relevance and generalizability of our findings.

Regarding the validation considerations, we have added the following sentences to the Discussion section (lines 409-414, 354-361): 

Although our analysis included four datasets with a total of 270 individuals, and our sample size of 53 met the required threshold based on power calculations with a type I error of 0.05, a power of 0.8, and an expected correlation coefficient of 0.4, we acknowledge that the sample size may still be considered relatively small for a comprehensive assessment of these relationships. To further validate these findings, larger prospective studies with diverse populations are needed.

Although our LASSO and factor analysis indicated that CGM-derived measures were strong predictors of %NC, this does not mean that other clinical parameters, such as lipids and blood pressure, are irrelevant in T2DM complications. Our study specifically focused on characterizing glucose dynamics, and we analyzed individuals with well-controlled serum cholesterol and blood pressure to reduce confounding effects. While we anticipate that inclusion of a more diverse population would not alter our primary findings regarding glucose dynamics, it is likely that a broader data set would reveal additional predictive contributions from lipid and blood pressure parameters.

In terms of elucidation of physical mechanisms, the study is not sufficient to elucidate the mechanisms linking autocorrelation and clinical outcomes or to verify them at the cellular or molecular level.

We appreciate the reviewer’s point regarding the need for further elucidation of the physical mechanisms linking glucose autocorrelation to clinical outcomes. We fully agree with the reviewer that the detailed molecular and cellular mechanisms underlying this relationship are not yet fully understood, as noted in our Discussion section.

However, we would like to emphasize the theoretical basis that supports the clinical relevance of autocorrelation. Our results show that glucose profiles with identical mean and variability can exhibit different autocorrelation patterns, highlighting that conventional measures such as mean or variance alone may not fully capture inter-individual metabolic differences. Incorporating autocorrelation analysis provides a more comprehensive characterization of metabolic states. Consequently, incorporating autocorrelation measures alongside traditional diabetes diagnostic criteria - such as fasting glucose, HbA1c and PG120, which primarily reflect only the “mean” component - can improve predictive accuracy for various clinical outcomes. While further research at the cellular and molecular level is needed to fully validate these findings, it is important to note that the primary goal of this study was to analyze the characteristics of glucose dynamics and gain new insights into metabolism, rather than to perform molecular biology experiments.

Furthermore, our previous research has shown that glucose autocorrelation reflects changes in insulin clearance (Sugimoto, Hikaru, et al. “Improved detection of decreased glucose handling capacities via continuous glucose monitoring-derived indices.” Communications Medicine 5.1 (2025): 103.). The relationship between insulin clearance and cardiovascular disease has been well documented (Randrianarisoa, Elko, et al. “Reduced insulin clearance is linked to subclinical atherosclerosis in individuals at risk for type 2 diabetes mellitus.” Scientific reports 10.1 (2020): 22453.), and the mechanisms described in this prior work may potentially explain the association between glucose autocorrelation and clinical outcomes observed in the present study.

Rather than a limitation, we view these currently unexplored associations as an opportunity for further research. The identification of autocorrelation as a key glycemic feature introduces a new dimension to metabolic regulation that could serve as the basis for future investigations exploring the molecular mechanisms underlying these patterns.

While we agree that further research at the cellular and molecular level is needed to fully validate these findings, we believe that our study provides a theoretical framework to support the clinical utility of autocorrelation analysis in glucose monitoring, and that this could serve as the basis for future investigations exploring the molecular mechanisms underlying these autocorrelation patterns, which adds to the broad interest of this study. Regarding the physical mechanisms linking autocorrelation and clinical outcomes, we have added the following sentences in the Discussion section (lines 331-339, 341-352): 

This study also provided evidence that autocorrelation can vary independently from the mean and variance components using simulated data. In addition, simulated glucose dynamics indicated that even individuals with high AC_Var did not necessarily have high maximum and minimum blood glucose levels. This study also indicated that these three components qualitatively corresponded to the four distinct glucose patterns observed after glucose administration, which were identified in a previous study (Hulman et al., 2018). Thus, the inclusion of autocorrelation in addition to mean and variance may improve the characterization of inter-individual differences in glucose regulation and improve the predictive accuracy of various clinical outcomes.

Despite increasing evidence linking glycemic variability to oxidative stress and endothelial dysfunction in T2DM complications (Ceriello et al., 2008; Monnier et al., 2008), the biological mechanisms underlying the independent predictive value of autocorrelation remain to be elucidated. Our previous work has shown that glucose autocorrelation is influenced by insulin clearance (Sugimoto et al., 2025), a process known to be associated with cardiovascular disease risk (Randrianarisoa et al., 2020). Therefore, the molecular pathways linking glucose autocorrelation to cardiovascular disease may share common mechanisms with those linking insulin clearance to cardiovascular disease. Although previous studies have primarily focused on investigating the molecular mechanisms associated with mean glucose levels and glycemic variability, our findings open new avenues for exploring the molecular basis of glucose autocorrelation, potentially revealing novel therapeutic targets for preventing diabetic complications.

Reviewer #2 (Public review):

We appreciate the reviewer for the critical review of the manuscript and the valuable comments. We have carefully considered the reviewer’s comments and have revised our manuscript accordingly. The reviewer’s comments in this letter are in Bold and Italics.

Sugimoto et al. explore the relationship between glucose dynamics - specifically value, variability, and autocorrelation - and coronary plaque vulnerability in patients with varying glucose tolerance levels. The study identifies three independent predictive factors for %NC and emphasizes the use of continuous glucose monitoring (CGM)-derived indices for coronary artery disease (CAD) risk assessment. By employing robust statistical methods and validating findings across datasets from Japan, America, and China, the authors highlight the limitations of conventional markers while proposing CGM as a novel approach for risk prediction. The study has the potential to reshape CAD risk assessment by emphasizing CGM-derived indices, aligning well with personalized medicine trends.

Strengths:

(1) The introduction of autocorrelation as a predictive factor for plaque vulnerability adds a novel dimension to glucose dynamic analysis.

(2) Inclusion of datasets from diverse regions enhances generalizability.

(3) The use of a well-characterized cohort with controlled cholesterol and blood pressure levels strengthens the findings.

(4) The focus on CGM-derived indices aligns with personalized medicine trends, showcasing the potential for CAD risk stratification.

We appreciate reviewer #2 for the positive assessment and for the valuable and constructive comments on our manuscript.

Weaknesses:

(1) The link between autocorrelation and plaque vulnerability remains speculative without a proposed biological explanation. 

We appreciate the reviewer’s point about the need for a clearer biological explanation linking glucose autocorrelation to plaque vulnerability. We fully agree with the reviewer that the detailed biological mechanisms underlying this relationship are not yet fully understood, as noted in our Discussion section.

However, we would like to emphasize the theoretical basis that supports the clinical relevance of autocorrelation. Our results show that glucose profiles with identical mean and variability can exhibit different autocorrelation patterns, highlighting that conventional measures such as mean or variance alone may not fully capture inter-individual metabolic differences. Incorporating autocorrelation analysis provides a more comprehensive characterization of metabolic states. Consequently, incorporating autocorrelation measures alongside traditional diabetes diagnostic criteria - such as fasting glucose, HbA1c and PG120, which primarily reflect only the “mean” component - can improve predictive accuracy for various clinical outcomes.

Furthermore, our previous research has shown that glucose autocorrelation reflects changes in insulin clearance (Sugimoto, Hikaru, et al. “Improved detection of decreased glucose handling capacities via continuous glucose monitoring-derived indices.” Communications Medicine 5.1 (2025): 103.). The relationship between insulin clearance and cardiovascular disease has been well documented (Randrianarisoa, Elko, et al. “Reduced insulin clearance is linked to subclinical atherosclerosis in individuals at risk for type 2 diabetes mellitus.” Scientific reports 10.1 (2020): 22453.), and the mechanisms described in this prior work may potentially explain the association between glucose autocorrelation and clinical outcomes observed in the present study. 

Rather than a limitation, we view these currently unexplored associations as an opportunity for further research. The identification of autocorrelation as a key glycemic feature introduces a new dimension to metabolic regulation that could serve as the basis for future investigations exploring the molecular mechanisms underlying these patterns.

While we agree that further research at the cellular and molecular level is needed to fully validate these findings, we believe that our study provides a theoretical framework to support the clinical utility of autocorrelation analysis in glucose monitoring, and that this could serve as the basis for future investigations exploring the molecular mechanisms underlying these autocorrelation patterns, which adds to the broad interest of this study. Regarding the physical mechanisms linking autocorrelation and clinical outcomes, we have added the following sentences in the Discussion section (lines 331-339, 341-352): 

This study also provided evidence that autocorrelation can vary independently from the mean and variance components using simulated data. In addition, simulated glucose dynamics indicated that even individuals with high AC_Var did not necessarily have high maximum and minimum blood glucose levels. This study also indicated that these three components qualitatively corresponded to the four distinct glucose patterns observed after glucose administration, which were identified in a previous study (Hulman et al., 2018). Thus, the inclusion of autocorrelation in addition to mean and variance may improve the characterization of inter-individual differences in glucose regulation and improve the predictive accuracy of various clinical outcomes.

Despite increasing evidence linking glycemic variability to oxidative stress and endothelial dysfunction in T2DM complications (Ceriello et al., 2008; Monnier et al., 2008), the biological mechanisms underlying the independent predictive value of autocorrelation remain to be elucidated. Our previous work has shown that glucose autocorrelation is influenced by insulin clearance (Sugimoto et al., 2025), a process known to be associated with cardiovascular disease risk (Randrianarisoa et al., 2020). Therefore, the molecular pathways linking glucose autocorrelation to cardiovascular disease may share common mechanisms with those linking insulin clearance to cardiovascular disease. Although previous studies have primarily focused on investigating the molecular mechanisms associated with mean glucose levels and glycemic variability, our findings open new avenues for exploring the molecular basis of glucose autocorrelation, potentially revealing novel therapeutic targets for preventing diabetic complications.

(2) The relatively small sample size (n=270) limits statistical power, especially when stratified by glucose tolerance levels. 

We appreciate the reviewer’s concern regarding sample size and its potential impact on statistical power, especially when stratified by glucose tolerance levels. We fully agree that a larger sample size would increase statistical power, especially for subgroup analyses.

We would like to clarify several points regarding the statistical power and validation of our findings. Our sample size followed established methodological frameworks, including the guidelines outlined by Muyembe Asenahabi, Bostely, and Peters Anselemo Ikoha. “Scientific research sample size determination.” (2023). These guidelines balance the risks of inadequate sample size with the challenges of unnecessarily large samples. For our primary analysis examining the correlation between CGM-derived measures and %NC, power calculations (a type I error of 0.05, a power of 0.8, and an expected correlation coefficient of 0.4) indicated that a minimum of 47 participants was required. Our sample size of 53 exceeded this threshold and allowed us to detect statistically significant correlations, as described in the Methods section. Moreover, to provide transparency about the precision of our estimates, we have included confidence intervals for all coefficients. 

Furthermore, our sample size aligns with previous studies investigating the associations between glucose profiles and clinical parameters, including Torimoto, Keiichi, et al. “Relationship between fluctuations in glucose levels measured by continuous glucose monitoring and vascular endothelial dysfunction in type 2 diabetes mellitus.” Cardiovascular Diabetology 12 (2013): 1-7. (n=57), Hall, Heather, et al. “Glucotypes reveal new patterns of glucose dysregulation.” PLoS biology 16.7 (2018): e2005143. (n=57), and Metwally, Ahmed A., et al. “Prediction of metabolic subphenotypes of type 2 diabetes via continuous glucose monitoring and machine learning.” Nature Biomedical Engineering (2024): 1-18. (n=32).

Regarding the classification of glucose dynamics components, we have conducted additional validation across diverse populations including 64 Japanese, 53 American, and 100 Chinese individuals. These validation efforts have consistently supported our identification of three independent glucose dynamics components.

However, we acknowledge the importance of further validation on a larger scale. To address this, we conducted a large follow-up study of over 8,000 individuals with two years of followup (Sugimoto, Hikaru, et al. “Stratification of individuals without prior diagnosis of diabetes using continuous glucose monitoring” medRxiv (2025)), which confirmed our main finding that glucose dynamics consist of mean, variance, and autocorrelation. As this large study was beyond the scope of the present manuscript due to differences in primary objectives and analytical approaches, it was not included in this paper; however, it provides further support for the clinical relevance and generalizability of our findings.

To address the sample size considerations, we have added the following sentences in the Discussion section (lines 409-414): 

Although our analysis included four datasets with a total of 270 individuals, and our sample size of 53 met the required threshold based on power calculations with a type I error of 0.05, a power of 0.8, and an expected correlation coefficient of 0.4, we acknowledge that the sample size may still be considered relatively small for a comprehensive assessment of these relationships. To further validate these findings, larger prospective studies with diverse populations are needed.

(3) Strict participant selection criteria may reduce applicability to broader populations. 

We appreciate the reviewer’s comment regarding the potential impact of strict participant selection criteria on the broader applicability of our findings. We acknowledge that extending validation to more diverse populations would improve the generalizability of our findings.

Our study includes validation cohorts from diverse populations, including 64 Japanese, 53 American and 100 Chinese individuals. These cohorts include a wide range of metabolic states, from healthy individuals to those with diabetes, ensuring validation across different clinical conditions. However, we acknowledge that further validation in additional populations and clinical settings would strengthen our conclusions. To address this, we conducted a large follow-up study of over 8,000 individuals (Sugimoto, Hikaru, et al. “Stratification of individuals without prior diagnosis of diabetes using continuous glucose monitoring” medRxiv (2025)), which confirmed our main finding that glucose dynamics consist of mean, variance, and autocorrelation. As this large study was beyond the scope of the present manuscript due to differences in primary objectives and analytical approaches, it was not included in this paper; however, it provides further support for the clinical relevance and generalizability of our findings.

We have added the following text to the Discussion section to address these considerations (lines 409-414, 354-361):

Although our analysis included four datasets with a total of 270 individuals, and our sample size of 53 met the required threshold based on power calculations with a type I error of 0.05, a power of 0.8, and an expected correlation coefficient of 0.4, we acknowledge that the sample size may still be considered relatively small for a comprehensive assessment of these relationships. To further validate these findings, larger prospective studies with diverse populations are needed.

Although our LASSO and factor analysis indicated that CGM-derived measures were strong predictors of %NC, this does not mean that other clinical parameters, such as lipids and blood pressure, are irrelevant in T2DM complications. Our study specifically focused on characterizing glucose dynamics, and we analyzed individuals with well-controlled serum cholesterol and blood pressure to reduce confounding effects. While we anticipate that inclusion of a more diverse population would not alter our primary findings regarding glucose dynamics, it is likely that a broader data set would reveal additional predictive contributions from lipid and blood pressure parameters.

(4) CGM-derived indices like AC_Var and ADRR may be too complex for routine clinical use without simplified models or guidelines. 

We appreciate the reviewer’s concern about the complexity of CGM-derived indices such as AC_Var and ADRR for routine clinical use. We acknowledge that for these indices to be of practical use, they must be both interpretable and easily accessible to healthcare providers. 

To address this concern, we have developed an easy-to-use web application that automatically calculates these measures, including AC_Var, mean glucose levels, and glucose variability (https://cgmregressionapp2.streamlit.app/). This tool eliminates the need for manual calculations, making these indices more practical for clinical implementation.

Regarding interpretability, we acknowledge that establishing specific clinical guidelines would enhance the practical utility of these measures. For example, defining a cut-off value for AC_Var above which the risk of diabetes complications increases significantly would provide clearer clinical guidance. However, given our current sample size limitations and our predefined objective of investigating correlations among indices, we have taken a conservative approach by focusing on the correlation between AC_Var and %NC rather than establishing definitive cutoffs. This approach intentionally avoids problematic statistical practices like phacking. It is not realistic to expect a single study to accomplish everything from proposing a new concept to conducting large-scale clinical trials to establishing clinical guidelines. Establishing clinical guidelines typically requires the accumulation of multiple studies over many years. Recognizing this reality, we have been careful in our manuscript to make modest claims about the discovery of new “correlations” rather than exaggerated claims about immediate routine clinical use.

To address this limitation, we conducted a large follow-up study of over 8,000 individuals in the next study (Sugimoto, Hikaru, et al. “Stratification of individuals without prior diagnosis of diabetes using continuous glucose monitoring” medRxiv (2025)), which proposed clinically relevant cutoffs and reference ranges for AC_Var and other CGM-derived indices. As this large study was beyond the scope of the present manuscript due to differences in primary objectives and analytical approaches, it was not included in this paper; however, by integrating automated calculation tools with clear clinical thresholds, we expect to make these measures more accessible for clinical use.

We have added the following text to the Discussion section to address these considerations (lines 415-419):

While CGM-derived indices such as AC_Var and ADRR hold promise for CAD risk assessment, their complexity may present challenges for routine clinical implementation. To improve usability, we have developed a web-based calculator that automates these calculations. However, defining clinically relevant thresholds and reference ranges requires further validation in larger cohorts.

(5) The study does not compare CGM-derived indices to existing advanced CAD risk models, limiting the ability to assess their true predictive superiority. 

We appreciate the reviewer’s comment regarding the comparison of CGMderived indices with existing CAD risk models. Given that our study population consisted of individuals with well-controlled total cholesterol and blood pressure levels, a direct comparison with the Framingham Risk Score for Hard Coronary Heart Disease (Wilson, Peter WF, et al. “Prediction of coronary heart disease using risk factor categories.” Circulation 97.18 (1998): 1837-1847.) may introduce inherent bias, as these factors are key components of the score.

Nevertheless, to further assess the predictive value of the CGM-derived indices, we performed additional analyses using linear regression to predict %NC. Using the Framingham Risk Score, we obtained an R² of 0.04 and an Akaike Information Criterion (AIC) of 330. In contrast, our proposed model incorporating the three glycemic parameters - CGM_Mean, CGM_Std, and AC_Var - achieved a significantly improved R² of 0.36 and a lower AIC of 321, indicating superior predictive accuracy. 

We have added the following text to the Result section (lines 115-122):

The regression model including CGM_Mean, CGM_Std and AC_Var to predict %NC achieved an R² of 0.36 and an Akaike Information Criterion (AIC) of 321. Each of these indices showed statistically significant independent positive correlations with %NC (Fig. 1A). In contrast, the model using conventional glycemic markers (FBG, HbA1c, and PG120) yielded an R² of only 0.05 and an AIC of 340 (Fig. 1B). Similarly, the model using the Framingham Risk Score for Hard Coronary Heart Disease (Wilson et al., 1998) showed limited predictive value, with an R² of 0.04 and an AIC of 330 (Fig. 1C).

(6) Varying CGM sampling intervals (5-minute vs. 15-minute) were not thoroughly analyzed for impact on results. 

We appreciate the reviewer’s comment regarding the potential impact of different CGM sampling intervals on our results. To assess the robustness of our findings across different sampling frequencies, we performed a down sampling analysis by converting our 5minute interval data to 15-minute intervals. The AC_Var value calculated from 15-minute intervals was significantly correlated with that calculated from 5-minute intervals (R = 0.99, 95% CI: 0.97-1.00). Furthermore, the regression model using CGM_Mean, CGM_Std, and AC_Var from 15-minute intervals to predict %NC achieved an R² of 0.36 and an AIC of 321, identical to the model using 5-minute intervals. These results indicate that our results are robust to variations in CGM sampling frequency. 

We have added this analysis to the Result section (lines 122-125):

The AC_Var computed from 15-minute CGM sampling was nearly identical to that computed from 5-minute sampling (R = 0.99, 95% CI: 0.97-1.00) (Fig. S1A), and the regression using the 15‑min features yielded almost the same performance (R² = 0.36; AIC = 321; Fig. S1B).

Reviewer #3 (Public review):

We appreciate the reviewer for the critical review of the manuscript and the valuable comments. We have carefully considered the reviewer’s comments and have revised our manuscript accordingly. The reviewer’s comments in this letter are in Bold and Italics.

Summary:

This is a retrospective analysis of 53 individuals over 26 features (12 clinical phenotypes, 12 CGM features, and 2 autocorrelation features) to examine which features were most informative in predicting percent necrotic core (%NC) as a parameter for coronary plaque vulnerability. Multiple regression analysis demonstrated a better ability to predict %NC from 3 selected CGM-derived features than 3 selected clinical phenotypes. LASSO regularization and partial least squares (PLS) with VIP scores were used to identify 4 CGM features that most contribute to the precision of %NC. Using factor analysis they identify 3 components that have CGM-related features: value (relating to the value of blood glucose), variability (relating to glucose variability), and autocorrelation (composed of the two autocorrelation features). These three groupings appeared in the 3 validation cohorts and when performing hierarchical clustering. To demonstrate how these three features change, a simulation was created to allow the user to examine these features under different conditions.

We appreciate reviewer #3 for the valuable and constructive comments on our manuscript.

The goal of this study was to identify CGM features that relate to %NC. Through multiple feature selection methods, they arrive at 3 components: value, variability, and autocorrelation. While the feature list is highly correlated, the authors take steps to ensure feature selection is robust. There is a lack of clarity of what each component (value, variability, and autocorrelation) includes as while similar CGM indices fall within each component, there appear to be some indices that appear as relevant to value in one dataset and to variability in the validation. 

We appreciate the reviewer’s comment regarding the classification of CGMderived measures into the three components: value, variability, and autocorrelation. As the reviewer correctly points out, some measures may load differently between the value and variability components in different datasets. However, we believe that this variability reflects the inherent mathematical properties of these measures rather than a limitation of our study.

For example, the HBGI clusters differently across datasets due to its dependence on the number of glucose readings above a threshold. In populations where mean glucose levels are predominantly below this threshold, the HBGI is more sensitive to glucose variability (Fig. S3A). Conversely, in populations with a wider range of mean glucose levels, HBGI correlates more strongly with mean glucose levels (Fig. 3A). This context-dependent behaviour is expected given the mathematical properties of these measures and does not indicate an inconsistency in our classification approach.

Importantly, our main findings remain robust: CGM-derived measures systematically fall into three components-value, variability, and autocorrelation. Traditional CGM-derived measures primarily reflect either value or variability, and this categorization is consistently observed across datasets. While specific indices such as HBGI may shift classification depending on population characteristics, the overall structure of CGM data remains stable.

To address these considerations, we have added the following text to the Discussion section (lines 388-396):

Some indices, such as HBGI, showed variation in classification across datasets, with some populations showing higher factor loadings in the “mean” component and others in the “variance” component. This variation occurs because HBGI calculations depend on the number of glucose readings above a threshold. In populations where mean glucose levels are predominantly below this threshold, the HBGI is more sensitive to glucose variability (Fig. S5A). Conversely, in populations with a wider range of mean glucose levels, the HBGI correlates more strongly with mean glucose levels (Fig. 3A). Despite these differences, our validation analyses confirm that CGM-derived indices consistently cluster into three components: mean, variance, and autocorrelation.

We are sceptical about statements of significance without documentation of p-values. 

We appreciate the reviewer’s concern regarding statistical significance and the documentation of p values.

First, given the multiple comparisons in our study, we used q values rather than p values, as shown in Figure 1D. Q values provide a more rigorous statistical framework for controlling the false discovery rate in multiple testing scenarios, thereby reducing the likelihood of false positives.

Second, our statistical reporting follows established guidelines, including those of the New England Journal of Medicine (Harrington, David, et al. “New guidelines for statistical reporting in the journal.” New England Journal of Medicine 381.3 (2019): 285-286.), which recommend that “reporting of exploratory end points should be limited to point estimates of effects with 95% confidence intervals” and that “replace p values with estimates of effects or association and 95% confidence intervals”. According to these guidelines, p values should not be reported in this type of study. We determined significance based on whether these 95% confidence intervals excluded zero - a method for determining whether an association is significantly different from zero (Tan, Sze Huey, and Say Beng Tan. "The correct interpretation of confidence intervals." Proceedings of Singapore Healthcare 19.3 (2010): 276-278.). 

For the sake of transparency, we provide p values for readers who may be interested, although we emphasize that they should not be the basis for interpretation, as discussed in the referenced guidelines. Specifically, in Figure 1A-B, the p values for CGM_Mean, CGM_Std, and AC_Var were 0.02, 0.02, and <0.01, respectively, while those for FBG, HbA1c, and PG120 were 0.83,

0.91, and 0.25, respectively. In Figure 3C, the p values for factors 1–5 were 0.03, 0.03, 0.03, 0.24, and 0.87, respectively, and in Figure S8C, the p values for factors 1–3 were <0.01, <0.01, and 0.20, respectively.

We appreciate the opportunity to clarify our statistical methodology and are happy to provide additional details if needed.

While hesitations remain, the ability of these authors to find groupings of these many CGM metrics in relation to %NC is of interest. The believability of the associations is impeded by an obtuse presentation of the results with core data (i.e. correlation plots between CGM metrics and %NC) buried in the supplement while main figures contain plots of numerical estimates from models which would be more usefully presented in supplementary tables. 

We appreciate the reviewer’s comment regarding the presentation of our results and recognize the importance of ensuring clarity and accessibility of the core data. 

The central finding of our study is twofold: first, that the numerous CGM-derived measures can be systematically classified into three distinct components-mean, variance, and autocorrelation-and second, that each of these components is independently associated with %NC. This insight cannot be derived simply from examining scatter plots of individual correlations, which are provided in the Supplementary Figures. Instead, it emerges from our statistical analyses in the main figures, including multiple regression models that reveal the independent contributions of these components to %NC.

We acknowledge the reviewer’s concern regarding the accessibility of key data. To improve clarity, we have moved several scatter plots from the Supplementary Figures to the main figures (Fig. 1D-J) to allow readers to more directly visualize the relationships between CGM-derived measures and %NC. We believe this revision improved the transparency and readability of our results while maintaining the rigor of our analytical approach.

Given the small sample size in the primary analysis, there is a lot of modeling done with parameters estimated where simpler measures would serve and be more convincing as they require less data manipulation. A major example of this is that the pairwise correlation/covariance between CGM_mean, CGM_std, and AC_var is not shown and would be much more compelling in the claim that these are independent factors.

We appreciate the reviewer’s feedback on our statistical analysis and data presentation. The correlations between CGM_Mean, CGM_Std, and AC_Var were documented in Figure S1B. However, to improve accessibility and clarity, we have moved these correlation analyses to the main figures (Fig. 1F). 

Regarding our modeling approach, we chose LASSO and PLS methods because they are wellestablished techniques that are particularly suited to scenarios with many input variables and a relatively small sample size. These methods have been used in the literature as robust approaches for variable selection under such conditions (Tibshirani R. 1996. Regression shrinkage and selection via the lasso. J R Stat Soc 58:267–288. Wold S, Sjöström M, Eriksson L. 2001. PLS-regression: a basic tool of chemometrics. Chemometrics Intellig Lab Syst 58:109–130. Pei X, Qi D, Liu J, Si H, Huang S, Zou S, Lu D, Li Z. 2023. Screening marker genes of type 2 diabetes mellitus in mouse lacrimal gland by LASSO regression. Sci Rep 13:6862. Wang C, Kong H, Guan Y, Yang J, Gu J, Yang S, Xu G. 2005. Plasma phospholipid metabolic profiling and biomarkers of type 2 diabetes mellitus based on high-performance liquid chromatography/electrospray mass spectrometry and multivariate statistical analysis.

Anal Chem 77:4108–4116.). 

Lack of methodological detail is another challenge. For example, the time period of CGM metrics or CGM placement in the primary study in relation to the IVUS-derived measurements of coronary plaques is unclear. Are they temporally distant or proximal/ concurrent with the PCI? 

We appreciate the reviewer’s important question regarding the temporal relationship between CGM measurements and IVUS-derived plaque assessments. As described in our previous work (Otowa‐Suematsu, Natsu, et al. “Comparison of the relationship between multiple parameters of glycemic variability and coronary plaque vulnerability assessed by virtual histology–intravascular ultrasound.” Journal of Diabetes Investigation 9.3 (2018): 610615.), all individuals underwent continuous glucose monitoring for at least three consecutive days within the seven-day period prior to the PCI procedure. To improve clarity for readers, we have added the following text to the Methods section (lines 440-441):

All individuals underwent CGM for at least three consecutive days within the seven-day period prior to the PCI procedure.

A patient undergoing PCI for coronary intervention would be expected to have physiological and iatrogenic glycemic disturbances that do not reflect their baseline state. This is not considered or discussed. 

We appreciate the reviewer’s concern regarding potential glycemic disturbances associated with PCI. As described in our previous work (Otowa‐Suematsu, Natsu, et al. “Comparison of the relationship between multiple parameters of glycemic variability and coronary plaque vulnerability assessed by virtual histology–intravascular ultrasound.” Journal of Diabetes Investigation 9.3 (2018): 610-615.), all CGM measurements were performed before the PCI procedure. This temporal separation ensures that the glycemic patterns analyzed in our study reflect the baseline metabolic state of the patients, rather than any physiological or iatrogenic effects of PCI. To avoid any misunderstanding, we have clarified this temporal relationship in the revised manuscript (lines 440-441):

All individuals underwent CGM for at least three consecutive days within the seven-day period prior to the PCI procedure.

The attempts at validation in external cohorts, Japanese, American, and Chinese are very poorly detailed. We could only find even an attempt to examine cardiovascular parameters in the Chinese data set but the outcome variables are unspecified with regard to what macrovascular events are included, their temporal relation to the CGM metrics, etc. Notably macrovascular event diagnoses are very different from the coronary plaque necrosis quantification. This could be a source of strength in the findings if carefully investigated and detailed but due to the lack of detail seems like an apples-to-oranges comparison. 

We appreciate the reviewer’s comment regarding the validation cohorts and the need for greater clarity, particularly in the Chinese dataset. We acknowledge that our initial description lacked sufficient methodological detail, and we have expanded the Methods section to provide a more comprehensive explanation.

For the Chinese dataset, the data collection protocol was previously documented (Zhao, Qinpei, et al. “Chinese diabetes datasets for data-driven machine learning.” Scientific Data 10.1 (2023): 35.). Briefly, trained research staff used standardized questionnaires to collect demographic and clinical information, including diabetes diagnosis, treatment history, comorbidities, and medication use. Physical examinations included anthropometric measurements, and body mass index was calculated using standard protocols. CGM was performed using the FreeStyle Libre H device (Abbott Diabetes Care, UK), which records interstitial glucose levels at 15-minute intervals for up to 14 days. Laboratory measurements, including metabolic panels, lipid profiles, and renal function tests, were obtained within six months of CGM placement. While previous studies have linked necrotic core to macrovascular events (Xie, Yong, et al. “Clinical outcome of nonculprit plaque ruptures in patients with acute coronary syndrome in the PROSPECT study.” JACC: Cardiovascular Imaging 7.4 (2014): 397-405.), we acknowledge the limitations of the cardiovascular outcomes in the Chinese data set. These outcomes were extracted from medical records rather than standardized diagnostic procedures or imaging studies. To address these concerns, we have added the following text to the Methods section (lines 496-504):

The data collection protocol for the Chinese dataset was previously documented (Zhao et al., 2023). Briefly, trained research staff used standardized questionnaires to collect demographic and clinical information, including diabetes diagnosis, treatment history, comorbidities, and medication use. CGM records interstitial glucose levels at 15-minute intervals for up to 14 days. Laboratory measurements, including metabolic panels, lipid profiles, and renal function tests, were obtained within six months of CGM placement. While previous studies have linked necrotic core to macrovascular events, we acknowledge the limitations of the cardiovascular outcomes in the Chinese data set. These outcomes were extracted from medical records rather than from standardized diagnostic procedures or imaging studies.

Finally, the simulations at the end are not relevant to the main claims of the paper and we would recommend removing them for the coherence of this manuscript. 

We appreciate the reviewer’s feedback regarding the relevance of the simulation component of our manuscript. The primary contribution of our study goes beyond demonstrating correlations between CGM-derived measures and %NC; it highlights three fundamental components of glycemic patterns-mean, variability, and autocorrelation-and their independent relationships with coronary plaque characteristics. The simulations are included to illustrate how glycemic patterns with identical means and variability can have different autocorrelation structures. Because temporal autocorrelation can be conceptually difficult to interpret, these visualizations were intended to provide intuitive examples for the readers. 

However, we agree with the reviewer’s concern about the coherence of the manuscript. In response, we have streamlined the simulation section by removing simulations that do not directly support our primary conclusions (old version of the manuscript, lines 239-246, 502526), while retaining only those that enhance understanding of the three glycemic components. Regarding reviewer 2’s minor comment #4, we acknowledge that autocorrelation can be challenging to understand intuitively. To address this, we kept Fig. 4A with a brief description.

Recommendations for the authors:

Reviewer 2# (Recommendations for the authors):

Summary:

The study by Sugimoto et. al. investigates the association between components of glucose dynamics-value, variability, and autocorrelation-and coronary plaque vulnerability (%NC) in patients with varying glucose tolerance levels. The research identifies three key factors that independently predict %NC and highlights the potential of continuous glucose monitoring (CGM)-derived indices in risk assessment for coronary artery disease (CAD). Using robust statistical methods and validation across diverse populations, the study emphasizes the limitations of conventional diagnostic markers and suggests a novel, CGMbased approach for improved predictive performance While the study demonstrates significant novelty and potential impact, several issues must be addressed by the authors.

Major Comments:

(1) The study demonstrates originality by introducing autocorrelation as a novel predictive factor in glucose dynamics, a perspective rarely explored in prior research. While the innovation is commendable, the biological mechanisms linking autocorrelation to plaque vulnerability remain speculative. Providing a hypothesis or potential pathways would enhance the scientific impact and practical relevance of this finding.

We appreciate the reviewer’s point about the need for a clearer biological explanation linking glucose autocorrelation to plaque vulnerability. Our previous research has shown that glucose autocorrelation reflects changes in insulin clearance (Sugimoto, Hikaru, et al. “Improved detection of decreased glucose handling capacities via continuous glucose monitoring-derived indices.” Communications Medicine 5.1 (2025): 103.). The relationship between insulin clearance and cardiovascular disease has been well documented (Randrianarisoa, Elko, et al. “Reduced insulin clearance is linked to subclinical atherosclerosis in individuals at risk for type 2 diabetes mellitus.” Scientific reports 10.1 (2020): 22453.), and the mechanisms described in this prior work may potentially explain the association between glucose autocorrelation and clinical outcomes observed in the present study. We have added the following sentences to the Discussion section (lines 341-352):

Despite increasing evidence linking glycemic variability to oxidative stress and endothelial dysfunction in T2DM complications (Ceriello et al., 2008; Monnier et al., 2008), the biological mechanisms underlying the independent predictive value of autocorrelation remain to be elucidated. Our previous work has shown that glucose autocorrelation is influenced by insulin clearance (Sugimoto et al., 2025), a process known to be associated with cardiovascular disease risk (Randrianarisoa et al., 2020). Therefore, the molecular pathways linking glucose autocorrelation to cardiovascular disease may share common mechanisms with those linking insulin clearance to cardiovascular disease. Although previous studies have primarily focused on investigating the molecular mechanisms associated with mean glucose levels and glycemic variability, our findings open new avenues for exploring the molecular basis of glucose autocorrelation, potentially revealing novel therapeutic targets for preventing diabetic complications.

(2) The inclusion of datasets from Japan, America, and China adds a valuable cross-cultural dimension to the study, showcasing its potential applicability across diverse populations. Despite the multi-regional validation, the sample size (n=270) is relatively small, especially when stratified by glucose tolerance categories. This limits the statistical power and applicability to diverse populations. A larger, multi-center cohort would strengthen conclusions.

We appreciate the reviewer’s concern regarding sample size and its potential impact on statistical power, especially when stratified by glucose tolerance levels. We fully agree that a larger sample size would increase statistical power, especially for subgroup analyses.

We would like to clarify several points regarding the statistical power and validation of our findings. Our study adheres to established methodological frameworks for sample size determination, including the guidelines outlined by Muyembe Asenahabi, Bostely, and Peters Anselemo Ikoha. “Scientific research sample size determination.” (2023). These guidelines balance the risks of inadequate sample size with the challenges of unnecessarily large samples. For our primary analysis examining the correlation between CGM-derived measures and %NC, power calculations with a type I error of 0.05, a power of 0.8, and an expected correlation coefficient of 0.4 indicated that a minimum of 47 participants was required. Our sample size of 53 exceeded this threshold and allowed us to detect statistically significant correlations, as described in the Methods section.

Furthermore, our sample size aligns with previous studies investigating the associations between glucose profiles and clinical parameters, including Torimoto, Keiichi, et al. “Relationship between fluctuations in glucose levels measured by continuous glucose monitoring and vascular endothelial dysfunction in type 2 diabetes mellitus.” Cardiovascular Diabetology 12 (2013): 1-7. (n=57), Hall, Heather, et al. “Glucotypes reveal new patterns of glucose dysregulation.” PLoS biology 16.7 (2018): e2005143. (n=57), and Metwally, Ahmed A., et al. “Prediction of metabolic subphenotypes of type 2 diabetes via continuous glucose monitoring and machine learning.” Nature Biomedical Engineering (2024): 1-18. (n=32). Moreover, to provide transparency about the precision of our estimates, we have included confidence intervals for all coefficients.

Regarding the classification of glucose dynamics components, we have conducted additional validation across diverse populations including 64 Japanese, 53 American, and 100 Chinese individuals. These validation efforts have consistently supported our identification of three independent glucose dynamics components. Furthermore, the primary objective of our study was not to assess rare events, but rather to demonstrate that glucose dynamics can be decomposed into three main factors - mean, variance and autocorrelation - whereas traditional measures have primarily captured mean and variance without adequately reflecting autocorrelation. We believe that our current sample size effectively addresses this objective. 

However, we acknowledge the importance of further validation on a larger scale. To address this, we conducted a large follow-up study of over 8,000 individuals with two years of followup (Sugimoto, Hikaru, et al. “Stratification of individuals without prior diagnosis of diabetes using continuous glucose monitoring” medRxiv (2025)), which confirmed our main finding that glucose dynamics consist of mean, variance, and autocorrelation. As this large study was beyond the scope of the present manuscript due to differences in primary objectives and analytical approaches, it was not included in this paper; however, it provides further support for the clinical relevance and generalizability of our findings.

To address the sample size considerations, we have added the following sentences to the Discussion section (lines 409-414):

Although our analysis included four datasets with a total of 270 individuals, and our sample size of 53 met the required threshold based on power calculations with a type I error of 0.05, a power of 0.8, and an expected correlation coefficient of 0.4, we acknowledge that the sample size may still be considered relatively small for a comprehensive assessment of these relationships. To further validate these findings, larger prospective studies with diverse populations are needed.

(3) The study focuses on a well-characterized cohort with controlled cholesterol and blood pressure levels, reducing confounding variables. However, this stringent selection might exclude individuals with significant variability in these parameters, potentially limiting the study's applicability to broader, real-world populations. The authors should discuss how this may affect generalizability and potential bias in the results.

We appreciate the reviewer’s comment regarding the potential impact of strict participant selection criteria on the broader applicability of our findings. We acknowledge that extending validation to more diverse populations would improve the generalizability of our findings.

Our validation strategy included multiple cohorts from different regions, specifically 64 Japanese, 53 American and 100 Chinese individuals. These cohorts represent a clinically diverse population, including both healthy individuals and those with diabetes, allowing for validation across a broad spectrum of metabolic conditions. However, we recognize that further validation in additional populations and clinical settings would strengthen our conclusions. To address this, we conducted a large follow-up study of over 8,000 individuals with two years of follow-up (Sugimoto, Hikaru, et al. “Stratification of individuals without prior diagnosis of diabetes using continuous glucose monitoring” medRxiv (2025)), which confirmed our main finding that glucose dynamics consist of mean, variance, and autocorrelation. As this large study was beyond the scope of the present manuscript due to differences in primary objectives and analytical approaches, it was not included in this paper; however, it provides further support for the clinical relevance and generalizability of our findings.

We have added the following text to the Discussion section to address these considerations (lines 409-414, 354-361):

Although our analysis included four datasets with a total of 270 individuals, and our sample size of 53 met the required threshold based on power calculations with a type I error of 0.05, a power of 0.8, and an expected correlation coefficient of 0.4, we acknowledge that the sample size may still be considered relatively small for a comprehensive assessment of these relationships. To further validate these findings, larger prospective studies with diverse populations are needed.

Although our LASSO and factor analysis indicated that CGM-derived measures were strong predictors of %NC, this does not mean that other clinical parameters, such as lipids and blood pressure, are irrelevant in T2DM complications. Our study specifically focused on characterizing glucose dynamics, and we analyzed individuals with well-controlled serum cholesterol and blood pressure to reduce confounding effects. While we anticipate that inclusion of a more diverse population would not alter our primary findings regarding glucose dynamics, it is likely that a broader data set would reveal additional predictive contributions from lipid and blood pressure parameters.

(4) The study effectively highlights the potential of CGM-derived indices as a tool for CAD risk assessment, a concept that aligns with contemporary advancements in personalized medicine. Despite its potential, the complexity of CGM-derived indices like AC_Var and ADRR may hinder their routine clinical adoption. Providing simplified models or actionable guidelines would facilitate their integration into everyday practice.

We appreciate the reviewer’s concern about the complexity of CGM-derived indices such as AC_Var and ADRR for routine clinical use. We recognize that for these indices to be of practical use, they must be both interpretable and easily accessible to healthcare providers.

To address this, we have developed an easy-to-use web application that automatically calculates these measures, including AC_Var, mean glucose levels, and glucose variability. By eliminating the need for manual calculations, this tool streamlines the process and makes these indices more practical for clinical use.

Regarding interpretability, we acknowledge that establishing specific clinical guidelines would enhance the practical utility of these measures. For example, defining a cut-off value for AC_Var above which the risk of diabetes complications increases significantly would provide clearer clinical guidance. However, given our current sample size limitations and our predefined objective of investigating correlations among indices, we have taken a conservative approach by focusing on the correlation between AC_Var and %NC rather than establishing definitive cutoffs. This approach intentionally avoids problematic statistical practices like phacking. It is not realistic to expect a single study to accomplish everything from proposing a new concept to conducting large-scale clinical trials to establishing clinical guidelines. Establishing clinical guidelines typically requires the accumulation of multiple studies over many years. Recognizing this reality, we have been careful in our manuscript to make modest claims about the discovery of new “correlations” rather than exaggerated claims about immediate routine clinical use.

To address this limitation, we conducted a large follow-up study of over 8,000 individuals in the next study (Sugimoto, Hikaru, et al. “Stratification of individuals without prior diagnosis of diabetes using continuous glucose monitoring” medRxiv (2025)), which proposed clinically relevant cutoffs and reference ranges for AC_Var and other CGM-derived indices. As this large study was beyond the scope of the present manuscript due to differences in primary objectives and analytical approaches, it was not included in this paper; however, by integrating automated calculation tools with clear clinical thresholds, we expect to make these measures more accessible for clinical use.

We have added the following text to the Discussion section to address these considerations (lines 415-419):

While CGM-derived indices such as AC_Var and ADRR hold promise for CAD risk assessment, their complexity may present challenges for routine clinical implementation. To improve usability, we have developed a web-based calculator that automates these calculations. However, defining clinically relevant thresholds and reference ranges requires further validation in larger cohorts.

(5) The exclusion of TIR from the main analysis is noted, but its relevance in diabetes management warrants further exploration. Integrating TIR as an outcome measure could provide additional clinical insights.

We appreciate the reviewer’s comment regarding the potential role of time in range (TIR) as an outcome measure in our study. Because TIR is primarily influenced by the mean and variance of glucose levels, it does not fully capture the distinct role of glucose autocorrelation, which was the focus of our investigation.

To clarify this point, we have expanded the Discussion section as follows (lines 380-388):

Although time in range (TIR) was not included in the main analyses due to the relatively small number of T2DM patients and the predominance of participants with TIR >70%, our results demonstrate that CGM-derived indices outperformed conventional markers such as FBG, HbA1c, and PG120 in predicting %NC. Furthermore, multiple regression analysis between factor scores and TIR revealed that only factor 1 (mean) and factor 2 (variance) were significantly associated with TIR (Fig. S8C, D). This finding confirms the presence of three distinct components in glucose dynamics and highlights the added value of examining AC_Var as an independent glycemic feature beyond conventional CGM-derived measures.

(6) While the study reflects a commitment to understanding CAD risks in a global context by including datasets from Japan, America, and China, the authors should provide demographic details (e.g., age, gender, socioeconomic status) and discuss how these factors might influence glucose dynamics and coronary plaque vulnerability.

We appreciate the reviewer’s comment regarding the potential influence of demographic factors on glucose dynamics and coronary plaque vulnerability. We examined these relationships and found that age and sex had minimal effects on glucose dynamics characteristics, as shown in Figure S8A and S8B. These findings suggest that our primary conclusions regarding glucose dynamics and coronary risk remain robust across demographic groups within our data set.

To address the reviewer’s suggestion, we have added the following discussion (lines 361-368):

In our analysis of demographic factors, we found that age and gender had minimal influence on glucose dynamics characteristics (Fig. S8A, B), suggesting that our findings regarding the relationship between glucose dynamics and coronary risk are robust across different demographic groups within our dataset. Future studies involving larger and more diverse populations would be valuable to comprehensively elucidate the potential influence of age, gender, and other demographic factors on glucose dynamics characteristics and their relationship to cardiovascular risk.

(7) While the article shows CGM-derived indices outperform traditional markers (e.g., HbA1c, FBG, PG120), it does not compare these indices against existing advanced risk models (e.g., Framingham Risk Score for CAD). A direct comparison would strengthen the claim of superiority.

We appreciate the reviewer’s comment regarding the comparison of CGMderived indices with existing CAD risk models. Given that our study population consisted of individuals with well-controlled total cholesterol and blood pressure levels, a direct comparison with the Framingham Risk Score for Hard Coronary Heart Disease (Wilson, Peter WF, et al. “Prediction of coronary heart disease using risk factor categories.” Circulation 97.18 (1998): 1837-1847.) may introduce inherent bias, as these factors are key components of the score.

Nevertheless, to further assess the predictive value of the CGM-derived indices, we performed additional analyses using linear regression to predict %NC. Using the Framingham Risk Score, we obtained an R² of 0.04 and an Akaike Information Criterion (AIC) of 330. In contrast, our proposed model incorporating the three glycemic parameters - CGM_Mean, CGM_Std, and AC_Var - achieved a significantly improved R² of 0.36 and a lower AIC of 321, indicating superior predictive accuracy. We have updated the Result section as follows (lines 115-122):

The regression model including CGM_Mean, CGM_Std and AC_Var to predict %NC achieved an R² of 0.36 and an Akaike Information Criterion (AIC) of 321. Each of these indices showed statistically significant independent positive correlations with %NC (Fig. 1A). In contrast, the model using conventional glycemic markers (FBG, HbA1c, and PG120) yielded an R² of only 0.05 and an AIC of 340 (Fig. 1B). Similarly, the model using the Framingham Risk Score for Hard Coronary Heart Disease (Wilson et al., 1998) showed limited predictive value, with an R² of 0.04 and an AIC of 330 (Fig. 1C).

(8) The study mentions varying CGM sampling intervals across datasets (5-minute vs. 15minute). Authors should employ sensitivity analysis to assess the impact of these differences on the results. This would help clarify whether higher-resolution data significantly improves predictive performance.

We appreciate the reviewer’s comment regarding the potential impact of different CGM sampling intervals on our results. To assess the robustness of our findings across different sampling frequencies, we performed a down sampling analysis by converting our 5minute interval data to 15-minute intervals. The AC_Var value calculated from 15-minute intervals was significantly correlated with that calculated from 5-minute intervals (R = 0.99, 95% CI: 0.97-1.00). Consequently, the main findings remained consistent across both sampling frequencies, indicating that our results are robust to variations in temporal resolution. We have added this analysis to the Result section (lines 122-126):

The AC_Var computed from 15-minute CGM sampling was nearly identical to that computed from 5-minute sampling (R = 0.99, 95% CI: 0.97-1.00) (Fig. S1A), and the regression using the 15‑min features yielded almost the same performance (R²  = 0.36; AIC = 321; Fig. S1B).

(9) The identification of actionable components in glucose dynamics lays the groundwork for clinical stratification. The authors could explore the use of CGM-derived indices to develop a simple framework for stratifying risk into certain categories (e.g., low, moderate, high). This could improve clinical relevance and utility for healthcare providers.

We appreciate the reviewer’s suggestion regarding the potential for CGMderived indices to support clinical stratification. We completely agree with the idea that establishing risk categories (e.g., low, moderate, high) based on specific thresholds would enhance the clinical utility of these measures. However, given our current sample size limitations and our predefined objective of investigating correlations among indices, we have taken a conservative approach by focusing on the correlation between AC_Var and %NC rather than establishing definitive cutoffs. This approach intentionally avoids problematic statistical practices like p-hacking. It is not realistic to expect a single study to accomplish everything from proposing a new concept to conducting large-scale clinical trials to establishing clinical thresholds. Establishing clinical thresholds typically requires the accumulation of multiple studies over many years. Recognizing this reality, we have been careful in our manuscript to make modest claims about the discovery of new “correlations” rather than exaggerated claims about immediate routine clinical use.

To address this limitation, we conducted a large follow-up study of over 8,000 individuals in the next study (Sugimoto, Hikaru, et al. “Stratification of individuals without prior diagnosis of diabetes using continuous glucose monitoring” medRxiv (2025)), which proposed clinically relevant cutoffs and reference ranges for AC_Var and other CGM-derived indices. As this large study was beyond the scope of the present manuscript due to differences in primary objectives and analytical approaches, it was not included in this paper. However, we expect to make these measures more actionable in clinical use by integrating automated calculation tools with clear clinical thresholds.

We have added the following text to the Discussion section to address these considerations (lines 415-419):

While CGM-derived indices such as AC_Var and ADRR hold promise for CAD risk assessment, their complexity may present challenges for routine clinical implementation. To improve usability, we have developed a web-based calculator that automates these calculations. However, defining clinically relevant thresholds and reference ranges requires further validation in larger cohorts.

(10) While the study acknowledges several limitations, authors should also consider explicitly addressing the potential impact of inter-individual variability in glucose metabolism (e.g., age-related changes, hormonal influences) on the findings.

We appreciate the reviewer’s comment regarding the potential impact of interindividual variability in glucose metabolism, including age-related changes and hormonal influences, on our results. In our analysis, we found that age had minimal effects on glucose dynamics characteristics, as shown in Figure S8A. In addition, CGM-derived measures such as ADRR and AC_Var significantly contributed to the prediction of %NC independent of insulin secretion (I.I.) and insulin sensitivity (Composite index) (Fig. 2). These results suggest that our primary conclusions regarding glucose dynamics and coronary risk remain robust despite individual differences in glucose metabolism.

To address the reviewer’s suggestion, we have added the following discussion (lines 186-188, 361-368):

Conventional indices, including FBG, HbA1c, PG120, I.I., Composite index, and Oral DI, did not contribute significantly to the prediction compared to these CGM-derived indices.

In our analysis of demographic factors, we found that age and gender had minimal influence on glucose dynamics characteristics (Fig. S8A, B), suggesting that our findings regarding the relationship between glucose dynamics and coronary risk are robust across different demographic groups within our dataset. Future studies involving larger and more diverse populations would be valuable to comprehensively elucidate the potential influence of age, gender, and other demographic factors on glucose dynamics characteristics and their relationship to cardiovascular risk.

(11) It's unclear whether the identified components (value, variability, and autocorrelation) could serve as proxies for underlying physiological mechanisms, such as beta-cell dysfunction or insulin resistance. Please clarify.

We appreciate the reviewer’s comment regarding the physiological underpinnings of the glucose components we identified. The mean, variance, and autocorrelation components we identified likely reflect specific underlying physiological mechanisms related to glucose regulation. In our previous research (Sugimoto, Hikaru, et al. “Improved detection of decreased glucose handling capacities via continuous glucose monitoring-derived indices.” Communications Medicine 5.1 (2025): 103.), we explored the relationship between glucose dynamics characteristics and glucose control capabilities using clamp tests and mathematical modelling. These investigations revealed that autocorrelation specifically shows a significant correlation with the disposition index (the product of insulin sensitivity and insulin secretion) and insulin clearance parameters.

Furthermore, our current study demonstrates that CGM-derived measures such as ADRR and AC_Var significantly contributed to the prediction of %NC independent of established metabolic parameters including insulin secretion (I.I.) and insulin sensitivity (Composite index), as shown in Figure 2. These results suggest that the components we identified capture distinct physiological aspects of glucose metabolism beyond traditional measures of beta-cell function and insulin sensitivity. Further research is needed to fully characterize these relationships, but our results imply that these characteristics of glucose dynamics offer supplementary insight into the underlying beta-cell dysregulation that contributes to coronary plaque vulnerability.

To address the reviewer’s suggestion, we have added the following discussion to the Result section (lines 186-188):

Conventional indices, including FBG, HbA1c, PG120, I.I., Composite index, and Oral DI, did not contribute significantly to the prediction compared to these CGM-derived indices.

Minor Comments:

(1) The use of LASSO and PLS regression is appropriate, but the rationale for choosing these methods over others (e.g., Ridge regression) should be explained in greater detail.

We appreciate the reviewer’s comment and have added the following discussion to the Methods section (lines 578-585):

LASSO regression was chosen for its ability to perform feature selection by identifying the most relevant predictors. Unlike Ridge regression, which simply shrinks coefficients toward zero without reaching exactly zero, LASSO produces sparse models, which is consistent with our goal of identifying the most critical features of glucose dynamics associated with coronary plaque vulnerability. In addition, we implemented PLS regression as a complementary approach due to its effectiveness in dealing with multicollinearity, which was particularly relevant given the high correlation among several CGM-derived measures.

(2) While figures are well-designed, adding annotations to highlight key findings (e.g., significant contributors in factor analysis) would improve clarity.

We appreciate the reviewer’s suggestion to improve the clarity of our figures. In the factor analysis, we decided not to include annotations because indicators such as ADRR and J-index can be associated with multiple factors, which could lead to misleading or confusing interpretations. However, in response to the suggestion, we have added annotations to the PLS analysis, specifically highlighting items with VIP values greater than 1 (Fig. 2D, S2D) to emphasize key contributors.

(3) The term "value" as a component of glucose dynamics could be clarified. For instance, does it strictly refer to mean glucose levels, or does it encompass other measures?

We appreciate the reviewer’s question regarding the term “value” in the context of glucose dynamics. Factor 1 was predominantly influenced by CGM_Mean, with a factor loading of 0.99, indicating that it primarily represents mean glucose levels. Given this strong correlation, we have renamed Factor 1 to “Mean” (Fig. 3A) to more accurately reflect its role in glucose dynamics.

(4) The concept of autocorrelation may be unfamiliar to some readers. A brief, intuitive explanation with a concrete example of how it manifests in glucose dynamics would enhance understanding.

We appreciate the reviewer’s suggestion. Autocorrelation refers to the relationship between a variable and its past values over time. In the context of glucose dynamics, it reflects how current glucose levels are influenced by past levels, capturing patterns such as sustained hyperglycemia or recurrent fluctuations. For example, if an individual experiences sustained high glucose levels after a meal, the strong correlation between successive glucose readings indicates high autocorrelation. We have included this explanation in the revised manuscript (lines 519-524) to improve clarity for readers unfamiliar with the concept. Additionally, Figure 4A shows an example of glucose dynamics with different autocorrelation.

(5) Ensure consistent use of terms like "glucose dynamics," "CGM-derived indices," and "plaque vulnerability." For instance, sometimes indices are referred to as "components," which might confuse readers unfamiliar with the field.

We appreciate the reviewer’s comment about ensuring consistency in terminology. To avoid confusion, we have reviewed and standardized the use of terms such as “CGM-derived indices,” and “plaque vulnerability” throughout the manuscript. Additionally, while many of our measures are strictly CGM-derived indices, several “components” in our analysis include fasting blood glucose (FBG) and glucose waveforms during the OGTT. For these measures, we retained the descriptors “glucose dynamics” and “components” rather than relabelling them as CGM-derived indices.

(6) Provide a more detailed overview of the supplementary materials in the main text, highlighting their relevance to the key findings.

We appreciate the reviewer’s suggestion. We revised the manuscript by integrating the supplementary text into the main text (lines 129-160), which provides a clearer overview of the supplementary materials. Consequently, the Supplementary Information section now only contains supplementary figures, while their relevance and key details are described in the main text. 

Reviewer #3 (Recommendations for the authors):

Other Concerns:

(1) The text states the significance of tests, however, no p-values are listed: Lines 118-119: Significance is cited between CGM indices and %NC, however, neither the text nor supplementary text have p-values. Need p-values for Figure 3C, Figure S10. When running the https://cgm-basedregression.streamlit.app/ multiple regression analysis, a p-value should be given as well. Do the VIP scores (Line 142) change with the inclusion of SBP, DBP, TG, LDL, and HDL? Do the other datasets have the same well-controlled serum cholesterol and BP levels?

We appreciate the reviewer’s concern regarding statistical significance and the documentation of p values.

First, given the multiple comparisons in our study, we used q values rather than p values, as shown in Figure 1D. Q values provide a more rigorous statistical framework for controlling the false discovery rate in multiple testing scenarios, thereby reducing the likelihood of false positives.

Second, our statistical reporting follows established guidelines, including those of the New England Journal of Medicine (Harrington, David, et al. “New guidelines for statistical reporting in the journal.” New England Journal of Medicine 381.3 (2019): 285-286.), which recommend that “reporting of exploratory end points should be limited to point estimates of effects with 95% confidence intervals” and that “replace p values with estimates of effects or association and 95% confidence intervals”. According to these guidelines, p values should not be reported in this type of study. We determined significance based on whether these 95% confidence intervals excluded zero - a statistical method for determining whether an association is significantly different from zero (Tan, Sze Huey, and Say Beng Tan. “The correct interpretation of confidence intervals.” Proceedings of Singapore Healthcare 19.3 (2010): 276-278.).

For the sake of transparency, we provide p values for readers who may be interested, although we emphasize that they should not be the basis for interpretation, as discussed in the referenced guidelines. Specifically, in Figure 1A-B, the p values for CGM_Mean, CGM_Std, and AC_Var were 0.02, 0.02, and <0.01, respectively, while those for FBG, HbA1c, and PG120 were 0.83, 0.91, and 0.25, respectively. In Figure 3C, the p values for factors 1–5 were 0.03, 0.03, 0.03, 0.24, and 0.87, respectively, and in Figure S8C, the p values for factors 1–3 were <0.01, <0.01, and 0.20, respectively. We appreciate the opportunity to clarify our statistical methodology and are happy to provide additional details if needed.

We confirmed that the results of the variable importance in projection (VIP) analysis remained stable after including additional covariates, such as systolic blood pressure (SBP), diastolic blood pressure (DBP), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C). The VIP values for ADRR, MAGE, AC_Var, and LI consistently exceeded one even after these adjustments, suggesting that the primary findings are robust in the presence of these clinical variables. We have added the following sentences in the Results and Methods section (lines 188-191, 491-494):

Even when SBP, DBP, TG, LDL-C, and HDL-C were included as additional input variables, the results remained consistent, and the VIP scores for ADRR, AC_Var, MAGE, and LI remained greater than 1 (Fig. S2D).

Of note, as the original reports document, the validation datasets did not specify explicit cutoffs for blood pressure or cholesterol. Consequently, they included participants with suboptimal control of these parameters.

(2) Negative factor loadings have not been addressed and consistency in components: Figure 3, Figure S7. All the main features for value in Figure 3A are positive. However, MVALUE in S7B is very negative for value whereas the other features highlighted for value are positive. What is driving this difference? Please explain if the direction is important. Line 480 states that variables with factor loadings >= 0.30 were used for interpretation, but it appears in the text (Line 156, Figure 3) that oral DI was used for value, even though it had a -0.61 loading. Figure 3, Figure S7. HBGI falls within two separate components (value and variability). There is not a consistent component grouping. Removal of MAG (Line 185) and only MAG does not seem scientific. Did the removal of other features also result in similar or different Cronbach's ⍺? It is unclear what Figure S8B is plotting. What does each point mean?

We appreciate the reviewer’s comment regarding the classification of CGMderived measures into the three components: value, variability, and autocorrelation. As the reviewer correctly points out, some measures may load differently between the value and variability components in different datasets. However, we believe that this variability reflects the inherent mathematical properties of these measures rather than a limitation of our study.

For example, the HBGI clusters differently across datasets due to its dependence on the number of glucose readings above a threshold. In populations where mean glucose levels are predominantly below this threshold, the HBGI is more sensitive to glucose variability (Fig. S3A). Conversely, in populations with a wider range of mean glucose levels, HBGI correlates more strongly with mean glucose levels (Fig. 3A). This context-dependent behaviour is expected given the mathematical properties of these measures and does not indicate an inconsistency in our classification approach.

Importantly, our main findings remain robust: CGM-derived measures systematically fall into three components-value, variability, and autocorrelation. Traditional CGM-derived measures primarily reflect either value or variability, and this categorization is consistently observed across datasets. While specific indices such as HBGI may shift classification depending on population characteristics, the overall structure of CGM data remains stable.

With respect to negative factor loadings, we agree that they may appear confusing at first. However, in the context of exploratory factor analysis, the magnitude, or absolute value, of the loading is most critical for interpretation, rather than its sign. Following established practice, we considered variables with absolute loadings of at least 0.30 to be meaningful contributors to a given component. Accordingly, although the oral DI had a negative loading of –0.61, its absolute magnitude exceeded the threshold of 0.30, so it was considered in our interpretation of the “value” component. Regarding the reviewer’s observation that MVALUE in Figure S7B shows a strongly negative loading while other indices in the same component show positive loadings, we believe this reflects the relative orientation of the factor solution rather than a substantive difference in interpretation. In factor analysis, the direction of factor loadings is arbitrary: multiplying all the loadings for a given factor by –1 would not change the factor’s statistical identity. Therefore, the important factor is not whether a variable loads positively or negatively but rather the strength of its association with the latent component (i.e., the absolute value of the loading).

The rationale for removing MAG was based on statistical and methodological considerations. As is common practice in reliability analyses, we examined whether Cronbach’s α would improve if we excluded items with low factor loadings or weak item–total correlations. In the present study, we recalculated Cronbach’s α after removing the MAG item because it had a low loading. Its exclusion did not substantially affect the theoretical interpretation of the factor, which we conceptualize as “secretion” (without CGM). MAG’s removal alone is scientifically justified because it was the only item whose exclusion improved Cronbach's α while preserving interpretability. In contrast, removing other items would have undermined the conceptual clarity of the factor or would not have meaningfully improved α. Furthermore, the MAG item has a high factor 2 loading.

Each point in Figure S8B (old version) corresponds to an individual participant.

To address these considerations, we have added the following text to the Discussion, Methods, (lines 388-396, 600-601) and Figure S6B (current version) legend:

Some indices, such as HBGI, showed variation in classification across datasets, with some populations showing higher factor loadings in the “mean” component and others in the “variance” component. This variation occurs because HBGI calculations depend on the number of glucose readings above a threshold. In populations where mean glucose levels are predominantly below this threshold, the HBGI is more sensitive to glucose variability (Fig. S5A). Conversely, in populations with a wider range of mean glucose levels, the HBGI correlates more strongly with mean glucose levels (Fig. 3A). Despite these differences, our validation analyses confirm that CGM-derived indices consistently cluster into three components: mean, variance, and autocorrelation.

Variables with absolute factor loadings of ≥ 0.30 were used in interpretation.

Box plots comparing factors 1 (Mean), 2 (Variance), and 3 (Autocorrelation) between individuals without (-) and with (+) diabetic macrovascular complications. Each point corresponds to an individual. The boxes represent the interquartile range, with the median shown as a horizontal line. Mann–Whitney U tests were used to assess differences between groups, with P values < 0.05 considered statistically significant.

Minor Concerns:

(1) NGT is not defined.

We appreciate the reviewer for pointing out that the term “NGT” was not clearly defined in the original manuscript. We have added the following text to the Methods section (lines 447-451):

T2DM was defined as HbA1c ≥ 6.5%, fasting plasma glucose (FPG) ≥ 126 mg/dL or 2‑h plasma glucose during a 75‑g OGTT (PG120) ≥ 200 mg/dL. IGT was defined as HbA1c 6.0– 6.4%, FPG 110–125 mg/dL or PG120 140–199 mg/dL. NGT was defined as values below all prediabetes thresholds (HbA1c < 6.0%, FPG < 110 mg/dL and PG120 < 140 mg/dL).

(2) Is it necessary to list the cumulative percentage (Line 173), it could be clearer to list the percentage explained by each factor instead.

We appreciate the reviewer’s suggestion to list the percentage explained by each factor rather than the cumulative percentage for improved clarity. According to the reviewer’s suggestion, we have revised the results to show the individual contribution of each factor (39%, 21%, 10%, 5%, 5%) rather than the cumulative percentages (39%, 60%, 70%, 75%, 80%) that were previously listed (lines 220-221).

(3) Figure S10. How were the coefficients generated for Figure S10? No methods are given.

We conducted a multiple linear regression analysis in which time in range (TIR) was the dependent variable and the factor scores corresponding to the first three latent components (factor 1 representing the mean, factor 2 representing the variance, and factor 3 representing the autocorrelation) were the independent variables. We have added the following text to the figure legend (Fig. S8C) to provide a more detailed description of how the coefficients were generated:

Comparison of predicted Time in range (TIR) versus measured TIR using multiple regression analysis between TIR and factor scores in Figure 3. In this analysis, TIR was the dependent variable, and the factor scores corresponding to the first three latent components (factor 1 representing the mean, factor 2 representing the variance, and factor 3 representing the autocorrelation) were the independent variables. Each point corresponds to the values for a single individual.

(4) In https://cgm-basedregression.streamlit.app/, more explanation should be given about the output of the multiple regression. Regression is spelled incorrectly on the app.

We appreciate the reviewer for pointing out the need for a clearer explanation of the multiple regression analysis presented in the online tool

(https://cgmregressionapp2.streamlit.app/). We have added the description about the regression and corrected the typographical error in the spelling of “regression” within the app. 

(5) The last section of results (starting at line 225) appears to be unrelated to the goal of predicting %NC.

We appreciate the reviewer’s feedback regarding the relevance of the simulation component of our manuscript. The primary contribution of our study goes beyond demonstrating correlations between CGM-derived measures and %NC; it highlights three fundamental components of glycemic patterns-mean, variance, and autocorrelation-and their independent relationships with coronary plaque characteristics. The simulations are included to illustrate how glycemic patterns with identical means and variability can have different autocorrelation structures. As reviewer 2 pointed out in minor comment #4, temporal autocorrelation can be difficult to interpret, so these visualizations were intended to provide intuitive examples for readers.

However, we agree with the reviewer’s concern about the coherence of the manuscript. In response, we have streamlined the simulation section by removing technical simulations that do not directly support our primary conclusions (old version of the manuscript, lines 239-246, 502-526), while retaining only those that enhance understanding of the three glycemic components (Fig. 4A).

(6) Figure S2. The R2 should be reported.

We appreciate the reviewer for suggesting that we report R² in Figure S2. In the revised version, we have added the correlation coefficients and their 95% confidence intervals to Figure 1E.

(7) Multiple panels have a correlation line drawn with a slope of 1 which does not reflect the data or r^2 listed. this should be fixed.

We appreciate the reviewer’s concern that several panels included regression lines with a fixed slope of one that did not reflect the associated R² values. We have corrected Figures 1A–C and 3C to display regression lines representing the estimated slopes derived from the regression analyses.

lo interesante es el espacio que hay entre esta civilización y homero. teniendo en cuenta que sucedió en el siglo xviii a.c y homero escribió la ilíada en el 750, se deduce la importancia que tuvo la tradición oral para que se conservasen todas esas historias

Ariadna está obviamente ligada a Creta a través del mito de Teseo y el Minotauro. Sin embargo, Nilsson observa que ninguna otra heroína murió de tantas maneras diferentes, y que esto solo puede explicarse por un culto de la muerte de Ariadna. Los festivales de Ariadna en Naxos se asemejan a los cultos a la vegetación, bien conocidos por las religiones orientales, pero ausentes en la religión de la Grecia clásica. Sin embargo, en las religiones orientales, los cultos son a un dios en cuestión, mientras que en Naxos es a una diosa. Su muerte se celebraba cada año, consecuencia de la cultura del renacimiento anual. Esta forma divina no es griega u oriental, pudiendo haber sido heredada directamente de las tradiciones minoicas

Le lien vers le corrigé a été oublié. Vous pouvez y accéder en cliquant sur le lien "à onotre projet" plus haut. Il faut regarder le sommaire/menu déroulant.

Il y a une erreur je crois, car dans la correction vous ne parlez pas de ces deux variables.

By studying hard, you will be able to

?

lo reduce?

poner una cita sobre esta afirmación

Estos desafíos quedan desconectados de la primera parte del párrafo siguiente. Me parece que deberían dialogar mejor esas dos partes.

Me parece que debería ser algo del tipo: Tal contexto introduce desafíos significativos para las ciencias sociales: la necesidad de adaptar marcos teóricos y metodológicos para integrar la diversas fuentes de datos con explicaciones sociales robustas, transformando los datos en datos sociales.

es un poco redundante esta frase: masiva, grandes volumenes y big data. Queda mejor algo como: la disponibilidad de grandes volumenes de datos, comunmente llamado big data,...

Centrarse en lo que hay y no en lo que podría haber. No sabemos que de esto es posible de obtener, así es que sugiero no redundar en esto. La mismo elementos fueron mencionados en la sección final de la introducción.

Detallas variables

Falta uan sección detallada de variables: ¿qué variables son relevantes para cada base de datos? - Definir cada variable detalladamente (variables originales y variables creadas). - Es necesario construir el libro de códigos que ayudará a tener a la vista la información disponible.

Describir base de datos combinada, en los términos anteriores. Las variables se decriben en la sección de variables.

Describir los datos: n, distribución de sexo, edad u otra variable relevante; periodo de tiempo, procedimiento de generación, etc.

¿de qué se va a tratar esta sección?

esta lógica se refiere a la relación entre puntaje de ingreso y notas? Si es así, referirse a esto como un resultado más que como lógica.

Bien que vayamos pensando y haciendo estas distinciones desde el principios. Pero me parece que las definiciones sobre los tipos de variables no corresponden tanto a la introducción, sino a la metodología.

cita

are able to use

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Summary:

Wang, Po-Kai, et al., utilized the de novo polarization of MDCK cells cultured in Matrigel to assess the interdependence between polarity protein localization, centrosome positioning, and apical membrane formation. They show that the inhibition of Plk4 with Centrinone does not prevent apical membrane formation, but does result in its delay, a phenotype the authors attribute to the loss of centrosomes due to the inhibition of centriole duplication. However, the targeted mutagenesis of specific centrosome proteins implicated in the positioning of centrosomes in other cell types (CEP164, ODF2, PCNT, and CEP120) did not affect centrosome positioning in 3D cultured MDCK cells. A screen of proteins previously implicated in MDCK polarization revealed that the polarity protein Par-3 was upstream of centrosome positioning, similar to other cell types.

Strengths:

The investigation into the temporal requirement and interdependence of previously proposed regulators of cell polarization and lumen formation is valuable to the community. Wang et al., have provided a detailed analysis of many of these components at defined stages of polarity establishment. Furthermore, the generation of PCNT, p53, ODF2, Cep120, and Cep164 knockout MDCK cell lines is likely valuable to the community.

Weaknesses:

Additional quantifications would highly improve this manuscript, for example it is unclear whether the centrosome perturbation affects gamma tubulin levels and therefore microtubule nucleation, it is also not clear how they affect the localization of the trafficking machinery/polarity proteins. For example, in Figure 4, the authors measure the intensity of Gp134 at the apical membrane initiation site following cytokinesis, but there is no measure of Gp134 at the centrosome prior to this.

We thank the reviewer for this important suggestion. Previous studies have shown that genes encoding appendage proteins and CEP120 do not regulate γ-tubulin recruitment to centrosomes (Betleja, Nanjundappa, Cheng, & Mahjoub, 2018; Vasquez-Limeta & Loncarek, 2021). Although the loss of PCNT reduces γ-tubulin levels, this reduction is partially compensated by AKAP450. Even in the case of PCNT/AKAP450 double knockouts, low levels of γ-tubulin remain at the centrosome (Gavilan et al., 2018), suggesting that it is difficult to completely eliminate γ-tubulin by perturbing centrosomal genes alone.

To directly address this question, in the revised manuscript (Page 8, Paragraph 4; Figure 4—figure supplement 3), we employed a recently reported method to block γ-tubulin recruitment by co-expressing two constructs: the centrosome-targeting carboxy-terminal domain (C-CTD) of CDK5RAP2 and the γ-tubulin-binding domain of NEDD1 (N-gTBD). This approach effectively depleted γ-tubulin and abolished microtubule nucleation at the centrosome (Vinopal et al., 2023). Interestingly, despite the reduced efficiency of apical vesicle trafficking, these cells were still able to establish polarity, with centrioles positioned apically. These results suggest that microtubule nucleation at the centrosomes (centrosomal microtubules) facilitates—but is not essential for—polarity establishment.

Regarding Figure 4, we assume the reviewer was referring to Gp135 rather than Gp134. In the revised manuscript (Page 8, Paragraph 2; Figure 4I), we observed a slight decrease in Gp135 intensity near PCNT-KO centrosomes at the pre-Abs stage. However, its localization at the AMIS following cytokinesis remained unaffected. These results suggest that the loss of PCNT has a limited impact on Gp135 localization. 

Reviewer #2 (Public review):

Summary:

The authors decoupled several players that are thought to contribute to the establishment of epithelial polarity and determined their causal relationship. This provides a new picture of the respective roles of junctional proteins (Par3), the centrosome, and endomembrane compartments (Cdc42, Rab11, Gp135) from upstream to downstream.

Their conclusions are based on live imaging of all players during the early steps of polarity establishment and on the knock-down of their expression in the simplest ever model of epithelial polarity: a cell doublet surrounded by ECM.

The position of the centrosome is often taken as a readout for the orientation of the cell polarity axis. There is a long-standing debate about the actual role of the centrosome in the establishment of this polarity axis. Here, using a minimal model of epithelial polarization, a doublet of daugthers MDCK cultured in Matrigel, the authors made several key observations that bring new light to our understanding of a mechanism that has been studied for many years without being fully explained:

(1) They showed that centriole can reach their polarized position without most of their microtubule-anchoring structures. These observations challenge the standard model according to which centrosomes are moved by the production and transmission of forces along microtubules.

(2) However) they showed that epithelial polarity can be established in the absence of a centriole.

(3) (Somehow more expectedly) they also showed that epithelial polarity can't be established in the absence of Par3.

(4) They found that most other polarity players that are transported through the cytoplasm in lipid vesicles, and finally fused to the basal or apical pole of epithelial cells, are moved along an axis which is defined by the position of centrosome and orientation of microtubules.

(5) Surprisingly, two non-daughter cells that were brought in contact (for 6h) could partially polarize by recruiting a few Par3 molecules but not the other polarity markers.

(6) Even more surprisingly, in the absence of ECM, Par 3 and centrosomes could move to their proper position close to the intercellular junction after cytokinesis but other polarity markers (at least GP135) localized to the opposite, non-adhesive, side. So the polarity of the centrosome-microtubule network could be dissociated from the localisation of GP135 (which was believed to be transported along this network).

Strengths:

(1) The simplicity and reproducibility of the system allow a very quantitative description of cell polarity and protein localisation.

(2) The experiments are quite straightforward, well-executed, and properly analyzed.

(3) The writing is clear and conclusions are convincing.

Weaknesses:

(1) The simplicity of the system may not capture some of the mechanisms involved in the establishment of cell polarity in more physiological conditions (fluid flow, electrical potential, ion gradients,...).

We agree that certain mechanisms may not be captured by this simplified system. However, the model enables us to observe intrinsic cellular responses, minimize external environmental variables, and gain new insights into how epithelial cells position their centrosomes and establish polarity. 

(2) The absence of centriole in centrinone-treated cells might not prevent the coalescence of centrosomal protein in a kind of MTOC which might still orient microtubules and intracellular traffic. How are microtubules organized in the absence of centriole? If they still form a radial array, the absence of a centriole at the center of it somehow does not conflict with classical views in the field.

Previous studies have shown that in the absence of centrioles, centrosomal proteins can relocate to alternative microtubule-organizing centers (MTOCs), such as the Golgi apparatus (Gavilan et al., 2018). Furthermore, centriole loss leads to increased nucleation of non-centrosomal microtubules (Martin, Veloso, Wu, Katrukha, & Akhmanova, 2018). However, these microtubules typically do not form the classical radial array or a distinct star-like organization. 

While this non-centrosomal microtubule network can still support polarity establishment, it does so less efficiently—similar to what is observed in p53-deficient cells undergoing centriole-independent mitosis (Meitinger et al., 2016). Thus, although the absence of centrioles does not completely prevent microtubule-based organization or polarity establishment, it impairs their spatial coordination and reduces overall efficiency compared to a centriole-centered microtubule-organizing center (MTOC). 

(3) The mechanism is still far from clear and this study shines some light on our lack of understanding. Basic and key questions remain:

(a) How is the centrosome moved toward the Par3-rich pole? This is particularly difficult to answer if the mechanism does not imply the anchoring of MTs to the centriole or PCM.

Previous studies have shown that Par3 interacts with dynein, potentially anchoring it at the cell cortex (Schmoranzer et al., 2009). This interaction enables dynein, a minus-enddirected motor, to exert pulling forces on microtubules, thereby promoting centrosome movement toward the Par3-enriched pole.

In our experiments (Figure 4), we attempted to disrupt centrosomal microtubule nucleation by knocking out multiple genes involved in centrosome structure and function, including ODF2 and PCNT. Under these perturbations, γ-tubulin still remained detectable at the centrosome, and we were unable to completely eliminate centrosomal microtubules. 

To address this question more directly, we employed a strategy to deplete γ-tubulin from centrosomes by co-expressing the centrosome-targeting C-terminal domain (C-CTD) of CDK5RAP2 and the γ-tubulin-binding domain of NEDD1 (N-gTBD). As shown in the new data of the revised manuscript (Page 8, Paragraph 4; Figure 4—figure supplement 3), this approach effectively depleted γ-tubulin from centrosomes, thereby abolishing microtubule nucleation at the centrosome. 

Surprisingly, even under these conditions, centrioles remained apically positioned (Page 8, Paragraph 4; Figure 4—figure supplement 3), indicating that centrosomal microtubules are not essential for centrosome movement during polarization.

Given these findings, we agree that the precise mechanism by which the Par3-enriched cortex attracts or guides centrosome movement remains unclear. Although dynein–Par3 interactions may contribute, further studies are needed to elucidate how centrosome repositioning occurs in the absence of microtubule-based pulling forces from the centrosome itself.

(b) What happens during cytokinesis that organises Par3 and intercellular junction in a way that can't be achieved by simply bringing two cells together? In larger epithelia cells have neighbours that are not daughters, still, they can form tight junctions with Par3 which participates in the establishment of cell polarity as much as those that are closer to the cytokinetic bridge (as judged by the overall cell symmetry). Is the protocol of cell aggregation fully capturing the interaction mechanism of non-daughter cells?

We speculate that a key difference between cytokinesis and simple cell-cell contact lies in the presence or absence of actomyosin contractility during the process of cell division. Specifically, contraction of the cytokinetic ring generates mechanical forces between the two daughter cells, which are absent when two non-daughter cells are simply brought together. While adjacent epithelial cells can indeed form tight junctions and recruit Par3, the lack of shared cortical tension and contractile actin networks between non-daughter cells may lead to differences in how polarity is initiated. This mechanical input during cytokinesis may serve as an organizing signal for centrosome positioning. This idea is supported by recent work showing that the actin cytoskeleton can influence centrosome positioning (Jimenez et al., 2021), suggesting that contractile actin structures formed during cytokinesis may contribute to spatial organization in a manner that cannot be replicated by simple aggregation. 

In our experiments, we simply captured two cells that were in contact within Matrigel. We cannot say for sure that it captures all the interaction mechanisms of non-daughter cells, but it does provide a contrast to daughter cells produced by cytokinesis. 

Reviewer #3 (Public review):

Here, Wang et al. aim to clarify the role of the centrosome and conserved polarity regulators in apical membrane formation during the polarization of MDCK cells cultured in 3D. Through well-presented and rigorous studies, the authors focused on the emergence of polarity as a single MDCK cell divided in 3D culture to form a two-cell cyst with a nascent lumen. Focusing on these very initial stages, rather than in later large cyst formation as in most studies, is a real strength of this study. The authors found that conserved polarity regulators Gp135/podocalyxin, Crb3, Cdc42, and the recycling endosome component Rab11a all localize to the centrosome before localizing to the apical membrane initiation site (AMIS) following cytokinesis. This protein relocalization was concomitant with a repositioning of centrosomes towards the AMIS. In contrast, Par3, aPKC, and the junctional components E-cadherin and ZO1 localize directly to the AMIS without first localizing to the centrosome. Based on the timing of the localization of these proteins, these observational studies suggested that Par3 is upstream of centrosome repositioning towards the AMIS and that the centrosome might be required for delivery of apical/luminal proteins to the AMIS.

To test this hypothesis, the authors generated numerous new cell lines and/or employed pharmacological inhibitors to determine the hierarchy of localization among these components. They found that removal of the centrosome via centrinone treatment severely delayed and weakened the delivery of Gp135 to the AMIS and single lumen formation, although normal lumenogenesis was apparently rescued with time. This effect was not due to the presence of CEP164, ODF2, CEP120, or Pericentrin. Par3 depletion perturbed the repositioning of the centrosome towards the AMIS and the relocalization of the Gp135 and Rab11 to the AMIS, causing these proteins to get stuck at the centrosome. Finally, the authors culture the MDCK cells in several ways (forced aggregation and ECM depleted) to try and further uncouple localization of the pertinent components, finding that Par3 can localize to the cell-cell interface in the absence of cell division. Par3 localized to the edge of the cell-cell contacts in the absence of ECM and this localization was not sufficient to orient the centrosomes to this site, indicating the importance of other factors in centrosome recruitment.

Together, these data suggest a model where Par3 positions the centrosome at the AMIS and is required for the efficient transfer of more downstream polarity determinants (Gp135 and Rab11) to the apical membrane from the centrosome. The authors present solid and compelling data and are well-positioned to directly test this model with their existing system and tools. In particular, one obvious mechanism here is that centrosome-based microtubules help to efficiently direct the transport of molecules required to reinforce polarity and/or promote lumenogenesis. This model is not really explored by the authors except by Pericentrin and subdistal appendage depletion and the authors do not test whether these perturbations affect centrosomal microtubules. Exploring the role of microtubules in this process could considerably add to the mechanisms presented here. In its current state, this paper is a careful observation of the events of MCDK polarization and will fill a knowledge gap in this field. However, the mechanism could be significantly bolstered with existing tools, thereby elevating our understanding of how polarity emerges in this system.

We agree that further exploration of microtubule dynamics could strengthen the mechanistic framework of our study. In our initial experiments, we disrupted centrosome function through genetic perturbations (e.g., knockout of PCNT, CEP120, CEP164, and ODF2). However, consistent with previous reports (Gavilan et al., 2018; Tateishi et al., 2013), we found that single-gene deletions did not completely eliminate centrosomal microtubules. Furthermore, imaging microtubule organization in 3D culture presents technical challenges. Due to the increased density of microtubules during cell rounding, we were unable to obtain clear microtubule filament structures—either using α-tubulin staining in fixed cells or SiR-tubulin labeling in live cells. Instead, the signal appeared diffusely distributed throughout the cytosol.

To overcome this, we employed a recently reported approach by co-expressing the centrosome-targeting carboxy-terminal domain (C-CTD) of CDK5RAP2 and the γtubulin-binding domain (gTBD) of NEDD1 to completely deplete γ-tubulin and abolish centrosomal microtubule nucleation (Vinopal et al., 2023). In our new data presented in the revised manuscript (Page 8, Paragraph 4; Figure 4—figure supplement 3), we found that cells lacking centrosomal microtubules were still able to polarize and position the centrioles apically. However, the efficiency of polarized transport of Gp135 vesicles to the apical membrane was reduced. These findings suggest that centrosomal microtubules are not essential for polarity establishment but may contribute to efficient apical transport. 

Reference

Betleja, E., Nanjundappa, R., Cheng, T., & Mahjoub, M. R. (2018). A novel Cep120-dependent mechanism inhibits centriole maturation in quiescent cells. Elife, 7. doi:10.7554/eLife.35439

Gavilan, M. P., Gandolfo, P., Balestra, F. R., Arias, F., Bornens, M., & Rios, R. M. (2018). The dual role of the centrosome in organizing the microtubule network in interphase. EMBO Rep, 19(11). doi:10.15252/embr.201845942

Jimenez, A. J., Schaeffer, A., De Pascalis, C., Letort, G., Vianay, B., Bornens, M., . . . Thery, M. (2021). Acto-myosin network geometry defines centrosome position. Curr Biol, 31(6), 1206-1220 e1205. doi:10.1016/j.cub.2021.01.002

Martin, M., Veloso, A., Wu, J., Katrukha, E. A., & Akhmanova, A. (2018). Control of endothelial cell polarity and sprouting angiogenesis by non-centrosomal microtubules. Elife, 7. doi:10.7554/eLife.33864

Meitinger, F., Anzola, J. V., Kaulich, M., Richardson, A., Stender, J. D., Benner, C., . . . Oegema, K. (2016). 53BP1 and USP28 mediate p53 activation and G1 arrest after centrosome loss or extended mitotic duration. J Cell Biol, 214(2), 155-166. doi:10.1083/jcb.201604081

Schmoranzer, J., Fawcett, J. P., Segura, M., Tan, S., Vallee, R. B., Pawson, T., & Gundersen, G. G. (2009). Par3 and dynein associate to regulate local microtubule dynamics and centrosome orientation during migration. Curr Biol, 19(13), 1065-1074. doi:10.1016/j.cub.2009.05.065

Tateishi, K., Yamazaki, Y., Nishida, T., Watanabe, S., Kunimoto, K., Ishikawa, H., & Tsukita, S. (2013). Two appendages homologous between basal bodies and centrioles are formed using distinct Odf2 domains. J Cell Biol, 203(3), 417-425. doi:10.1083/jcb.201303071

Vasquez-Limeta, A., & Loncarek, J. (2021). Human centrosome organization and function in interphase and mitosis. Semin Cell Dev Biol, 117, 30-41. doi:10.1016/j.semcdb.2021.03.020

Vinopal, S., Dupraz, S., Alfadil, E., Pietralla, T., Bendre, S., Stiess, M., . . . Bradke, F. (2023). Centrosomal microtubule nucleation regulates radial migration of projection neurons independently of polarization in the developing brain. Neuron, 111(8), 1241-1263 e1216. doi:10.1016/j.neuron.2023.01.020.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

Figures:

(1) Figure 3 B+C - Although in comparison to Figure 2 it appears the p53 mutation does not affect θN-C, or Lo-c. the figure would benefit from direct comparison to control cells.

We appreciate your suggestion to improve the clarity of the figure. In response, we have revised Figure 3B+C to include control cell data, allowing for clearer side-by-side comparisons in the updated figures. 

(2) Figure 3D - Clarify if both were normalized to time point 0:00 of the p53 KO. The image used appears that Gp135 intensity increases substantially between 0:00 and 0:15 in the figure, but the graph suggests that the intensity is the same if not slightly lower.

Figure 3D – The data were normalized to the respective 0:00 time point for each condition. Because the intensity profile was measured along a line connecting the two nuclei, Gp135 signal could only be detected if it appeared along this line. However, the images shown are maximum-intensity projections, meaning that Gp135 signals from peripheral regions are projected onto the center of the image. This may create the appearance of increased intensity at certain time points (e.g., Figure 3A, p53-KO + CN, 0:00–0:15). 

(3) Figure 4A: The diagram does not accurately represent the effect of the mutations, for example, PCNT mutation likely doesn't completely disrupt PCM (given gamma-tubulin is still visible in the staining), but instead results in its disorganization, Cep164 also wouldn't be expected to completely ablate distal appendages.

Thank you for your comment. We have modified the figure in the revised manuscript (Figure 4A) to more clearly depict the defective DAs. 

(4) Figure 4 + Supplements: A more in-depth characterization of the mutations would help address the previous comment and strengthen the manuscript. Especially as these components have previously been implicated in centrosome transport.

Thank you for your valuable suggestion. As noted in previous studies, CEP164 is essential for distal appendage function and basal body docking, with its loss resulting in blocked ciliogenesis (Tanos et al., 2013); CEP120 is required for centriole elongation and distal appendage formation, and its loss also results in blocked ciliogenesis (Comartin et al., 2013; Lin et al., 2013; Tsai, Hsu, Liu, Chang, & Tang, 2019); ODF2 functions upstream in the formation of subdistal appendages, and its loss eliminates these structures and impairs microtubule anchoring (Tateishi et al., 2013); and PCNT functions as a PCM scaffold, necessary for the recruitment of PCM components and for microtubule nucleation at the centrosome (Fong, Choi, Rattner, & Qi, 2008; Zimmerman, Sillibourne, Rosa, & Doxsey, 2004). 

Given that the phenotypes of these mutants have been well characterized in the literature. Here, we further focus on their roles in centrosome migration and polarized vesicle trafficking within the specific context of our study. 

(5) Figure 4: It would be interesting to measure the Gp135 intensity at the centrosomes, given that the model proposes it is trafficked from the centrosomes to the AMIS.

Thank you for your suggestion. We have included measurements of Gp135 intensity at the centrosomes during the Pre-Abs stage in the revised figure (Figure 4I). Our data show no significant differences in Gp135 intensity between wild-type (WT) and CEP164-, ODF2-, or CEP120-knockout (KO) cell lines. However, a slight decrease in Gp135 intensity was observed in PCNT-KO cells. 

(6) Figure 6F shows that in suspension culture polarity is reversed, however, in Figure 6G gp135 still localizes to the cytokinetic furrow prior to polarity reversal. Given this paper demonstrates Par-3 is upstream of centrosome positioning, it would be important to have temporal data of how Par-3 localizes prior to the ring observed in 6F.

Thank you for your comment. We have included a temporal analysis of Par3 localization using fixed-cell staining in the revised figure (Figure 6—figure supplement 1D). This analysis shows that Par3 also localizes to the cytokinesis site during the Pre-Abs stage, prior to ring formation observed during the Post-CK stage (Figure 6F). Interestingly, during the Pre-Abs stage, the centrosomes also migrate toward the center of the cell doublets in suspension culture, and Gp135 surrounding the centrosomes is also recruited to a region near the center (Figure 6—figure supplement 1E). These data suggest that Par3 also is initially recruited to the cytokinesis site before polarity reversal, potentially promoting centrosome migration. The main difference from Matrigel culture is the peripheral localization of Par3 and Gp135 in suspension, which is likely due to the lack of external ECM signaling. 

Results:

(1) Page 7 Paragraph 1 - consistently use AMIS (Apical membrane initiation site) rather than "the apical site".

Thank you for your helpful comment. We have revised the manuscript (Page 7, Paragraph 1) and will now use "AMIS" (Apical Membrane Initiation Site) instead of "the apical site" throughout the text. 

(2) Page 7 Paragraph 4 - A single sentence explaining why the p53 background had to be used for the Cep120 deletion would be beneficial. Did the cell line have a reduced centrosome number? Does this effect apical membrane initiation similar to centrinone?

We have revised the text (Page 7, Paragraph 4) to clarify that we were unable to generate a CEP120 KO line in p53-WT cells for unknown reasons. CEP120-KO cells have a normal number of centrosome, but their centrioles are shorter. Because this KO line still contains centrioles, the effect is different from centrinone treatment, which results in a complete loss of centrioles. 

(3) Page 10 paragraph 4 - This paragraph is confusing to read. I understand that in the cysts and epithelial sheet the cytokinetic furrow is apical, therefore a movement towards the AMIS could be due to its coincidence with the furrow. However, the phrasing "....we found that centrosomes move towards the apical membrane initiation site direction before bridge abscission. Taken together these findings indicate the position is strongly associated with the site of cytokinesis but not with the apical membrane" is confusing to the reader.

We have revised the manuscript (Page 11, paragraph 4) to change the AMIS as the center of the cell doublet. During de novo epithelial polarization, the apical membrane has not yet formed at the Pre-Abs stage. However, at the Pre-Abs stage, the centrosome has already migrated toward the site of cytokinesis, suggesting that centrosome positioning is correlated with the site of cell division. A similar phenomenon occurs in fully polarized epithelial cysts and sheets, where the centrosomes also migrate before bridge abscission. Thus, we propose that the position of the centrosome is closely associated with the site of cytokinesis and is independent of apical membrane formation. 

Discussion

(1) Page 11, Paragraph 2 - citations needed when discussing previous studies.

Thank you for your suggestion. We have included the necessary references to the discussion of the previous studies in the revised manuscript (Page 12, Paragraph 2). 

(2) Page 12, Paragraph 2 - This section of the discussion would be strengthened by discussing the role of the actomyosin network in defining centrosome position (Jimenez et al., 2021). It seems plausible that the differences observed in the different conditions could be due to altered actomyosin architecture. Especially where the cells haven't undergone cytokinesis.

We appreciate the suggestion of a role for the actomyosin network in determining centrosome positioning. Recent studies have indeed highlighted the role of the actomyosin network in regulating centrosome centering and off-centering (Jimenez et al., 2021). During the pre-abscission stage of cell division, the actomyosin network undergoes significant dynamic changes, with the contractile ring forming at the center and actin levels decreasing at the cell periphery. In contrast, under aggregated cell conditions—meaning cells that have not undergone division—the actomyosin network does not exhibit such dynamic changes. The loss of actomyosin remodeling may therefore influence whether the centrosome moves. Thus, alterations in actomyosin architecture may contribute to the differences observed under various conditions, particularly when cells have not yet completed cytokinesis. We have revised Paragraph 2 on Page 13 to briefly mention the referenced study and to propose that the actomyosin network may influence centrosome positioning, contributing to our observed results. This addition strengthens the discussion and clarifies our findings. 

(3) Page 12 paragraph 3 - Given that centrosome translocation during cytokinesis in MDCK cells (this study) appears to be similar to that observed in HeLa cells and the zebrafish Kupffers vesicle (Krishnan et al., 2022) it would be interesting to discuss why Rab11a and PCNT may not be essential to centrosome positioning in MDCK cells.

Thank you for your insightful comment. We agree that it is interesting that centrosome translocation during cytokinesis in MDCK cells (as observed in our study) is similar to that observed in HeLa cells and zebrafish Kupffer's vesicle (Krishnan et al., 2022). However, there are notable differences between these systems that may help explain why Rab11a and PCNT are not essential for centrosome positioning in MDCK cells.

Our study used 3D culture of MDCK cells, while the reference study examined adherent culture of HeLa cells. In the adherent culture, cells attached to the culture surface form large actin stress fibers on their basal side, which weakens the actin networks in the apical and intercellular regions. In contrast, the 3D culture system used in our study better preserves cell polarity and the integrity of the actin network, which might contribute to centrosome positioning independent of Rab11a and PCNT. Differences in culture conditions and actin network architecture may explain why Rab11a and PCNT are not required for centrosome positioning in MDCK cells.

Furthermore, the referenced study focused on Rab11a and PCNT in zebrafish embryos at 3.3–5 hours post-fertilization (hpf), a time point before the formation of the Kupffer’s vesicle. At this stage, the cells they examined may not yet have become epithelial cells, which may also influence the requirement of Rab11a and PCNT for centrosome positioning. We hypothesize that during the pre-abscission stage, centrosome migration toward the cytokinetic bridge occurs primarily in epithelial cells, and that the polarity and centrosome positioning mechanisms in these cells may differ from those in other cell types, such as zebrafish embryos.

Furthermore, data from Krishnan et al. (2022) suggest that cytokinesis failure in pcnt+/- heterozygous embryos and Rab11a functional-blocked embryos may be due to the presence of supernumerary centrosomes. Consistent with this, our data show that blocking cytokinesis inhibits centrosome movement in MDCK cells. However, in our MDCK cell lines with PCNT or Rab11a knockdown, we did not observe significant cytokinesis failure, and centrosome migration proceeded normally. 

Reviewer #2 (Recommendations for the authors):

Suggestions for experiments:

(1) A description of the organization of microtubules in the absence of centriole, or in the absence of ECM would be interesting to understand how polarity markers end up where you observed them. This easy experiment may significantly improve our understanding of this system.

Previous studies have shown that in the absence of centrioles, microtubule organization undergoes significant changes. Specifically, the number of non-centrosomal microtubules increases, and these microtubules are not radially arranged, leading to the absence of focused microtubule organizing centers in centriolar-deficient cells (Martin, Veloso, Wu, Katrukha, & Akhmanova, 2018). This disorganized microtubule network reduces the efficiency of vesicle transport during de novo epithelial polarization at the mitotic preabscission stage. 

In contrast, the organization of microtubules under ECM-free conditions remains less well characterized. Here, we show that while the ECM plays a critical role in establishing the direction of epithelial polarity, it does not influence the positioning of the centrosome, the microtubule-organizing center (MTOC).  

(2) Would it be possible to knock down ODF2 and pericentrin to completely disconnect the centrosome from microtubules?

ODF2 is the base of subdistal appendages. When ODF2 is knocked out, it affects the recruitment of all downstream proteins to the subdistal appendages (Mazo, Soplop, Wang, Uryu, & Tsou, 2016). One study has shown that ODF2 knockout cells almost completely lost subdistal appendage structures and significantly reduced the microtubule asters surrounding the centrioles (Tateishi et al., 2013). However, although pericentrin (PCNT) is the main scaffold of the pericentriolar matrix (PCM) of centrosomes, the microtubule organization ability of centrosomes can be compensated by AKAP450, a paralog of PCNT, after PCNT knockout. A previous study has even shown that in cells with a double knockout of PCNT and AKAP450, γ-tubulin can still be recruited to the centrosomes, and centrosomes can still nucleate microtubules (Gavilan et al., 2018). This suggests that there are other proteins or pathways that promote microtubule nucleation on centrosomes. We are unsure whether the triple knockout of ODF2, PCNT, and AKAP450 can completely disconnect the centrosome from microtubules. However, a recent study reported a simpler approach involving the expression of dominant-negative fragments of the γ-tubulinbinding protein NEDD1 and the activator CDK5RAP2 at the centrosome (Vinopal et al., 2023). In our revised manuscript (Page 8, Paragraph 4; Figure 4—figure supplement 3), we applied this strategy, which resulted in the depletion of nearly all γ-tubulin from the centrosome. This indicates a strong suppression of centrosomal microtubule nucleation and an effective disconnection of the centrosome from the microtubule network. 

(3) The study does not distinguish the role of cytokinesis from the role of tight junctions, which form only after cytokinesis and not simply by bringing cells into contact. Would it be feasible and interesting to study the polarization after cytokinesis in cells that could not form tight junctions (due to the absence of Ecad or ZO1 for example)?

Studying cell polarization after cytokinesis in cells unable to form tight junctions is a promising area of research.

Recent studies have shown that mouse embryonic stem cells (mESCs) cultured in Matrigel can form ZO-1-labelled tight junctions at the midpoint of cell–cell contact even in the absence of cell division. However, in the absence of E-cadherin, ZO-1 localization is significantly impaired. Interestingly, despite the loss of E-cadherin, the Golgi apparatus and centrosomes remain oriented toward the cell–cell interface (Liang, Weberling, Hii, Zernicka-Goetz, & Buckley, 2022). These findings suggest that cell polarity can be maintained independently of tight junction formation, highlighting the potential value of studying cell polarization that lack tight junctions.

Furthermore, while studies have explored the effects of knocking down tight junction components such as JAM-A and Cingulin on lumen formation in MDCK 3D cultures (Mangan et al., 2016; Tuncay et al., 2015), the role of ZO-1 in this context remains underexplored. Cingulin knockdown has been shown to disrupt endosome targeting and the formation of the AMIS, while both JAM-A and Cingulin knockdown result in actin accumulation at multiple points, leading to the formation of multi-lumen structures rather than a reversal of polarity. However, previous research has not specifically investigated centrosome positioning in JAM-A and Cingulin knockdown cells, an area that could provide valuable insights into how polarity is maintained in the absence of tight junctions. 

Writing details:

(1) The migration of the centrosome in the absence of appendages or PCM is proposed to be ensured by compensatory mechanisms ensuring the robustness of microtubule anchoring to the centrosome. It could also be envisaged that the centrosome motion does not require this anchoring and that other yet unknown moving mechanisms, based on an actin network for example, might exist.

Thank you for your valuable comments. We agree that there may indeed be some unexpected mechanisms that allow centrosomes to move independently of microtubule anchoring to the centrosome, such as mechanisms based on actin filaments or noncentrosomal microtubules; these mechanisms are worth further investigation.

In response to your suggestion, in the Paragraph 5 of the discussion section, we further clarified that while a microtubule anchoring mechanism might be one explanation, other mechanisms could also influence centrosome movement in the absence of appendages or PCM. Additionally, we revised the Paragraph 4 regarding the possibility of actin network-driven centrosome movement and emphasized the importance of future research for a deeper understanding of these processes. 

(2) The actual conclusion of the study of Martin et al (eLife 2018) is not simply that centrosome is not involved in cell polarization but that it hinders cell polarization!

Thank you for your valuable feedback. We agree with the findings of Martin et al. (eLife 2018) that centrosome is not irrelevant to cell polarity, but rather they inhibit cell polarization. Therefore, we have revised the manuscript (Page 2, Paragraph 2) to more accurately reflect this viewpoint. 

(3) This study recalls some conclusions of the study by Burute et al (Dev Cell 2017), in particular the role of Par3 in driving centrosome toward the intercellular junction of daughter cells after cytokinesis. It would be welcome to comment on the results of this study in light of their work.

Thank you for your valuable feedback. The study by Burute et al. (Dev Cell, 2017) showed that in micropattern-cultures of MCF10A cells, the cells exhibit polarity and localize their centrosomes towards the intercellular junction, while downregulation of Par3 gene expression disrupts this centrosome positioning. This result is similar to our findings in 3D cultured MDCK cells and consistent with previous studies in C. elegans intestinal cells and migrating NIH 3T3 cells (Feldman & Priess, 2012; Schmoranzer et al., 2009), indicating that Par3 indeed influences centrosome positioning in different cellular systems. However, Par3 does not directly localize to the centrosome; rather, it localizes to the cell cortex or cell-cell junctions. Therefore, Par3 likely regulates centrosome positioning through other intermediary molecules or mechanisms, but the specific mechanism remains unclear and requires further investigation. 

(4) Could the term apico-basal be used in the absence of a basement membrane to form a basal pole?

We understand that using the term "apico-basal" in the absence of a basement membrane might raise some questions. Traditionally, the apico-basal axis refers to the polarity of epithelial cells, where the apical surface faces the lumen or external environment, and the basal surface is oriented toward the basement membrane. However, in the absence of a basement membrane, such as in certain in vitro systems or under specific experimental conditions, polarity along a similar axis can still be observed. In such cases, the term "apico-basal" can still be used to describe the polarity between the apical domain and the region where it contacts the substrate or adjacent cells. 

(5) The absence of centrosome movement to the intercellular bridge in spread cells in culture is not so surprising considering the work of Lafaurie-Janvore et al (Science 2018) about the role of cell spreading in the regulation of bridge tension and abscission delay.

Thank you for your valuable comment. Indeed, previous studies have shown that in some cell types, the centrosome does move toward the intercellular bridge in spread cells (Krishnan et al., 2022; Piel, Nordberg, Euteneuer, & Bornens, 2001), but other studies have suggested that this movement may not be significant and it may not occur in universally observed across all cell types (Jonsdottir et al., 2010). In our study, we aim to demonstrate that this phenomenon is more pronounced in 3D culture systems compared to 2D spread cell culture systems. Previous studies and our work have observed that centrosome migration occurs during the pre-abscission stage, but whether this migration is directly related to cytokinetic bridge tension or the time of abscission remains an open question. Further research is needed to explore the potential relationship between centrosome positioning, cytokintic bridge tension, and the timing of abscission. 

(6) GP135 (podocalyxin) has been proposed to have anti-adhesive/lubricant properties (hence its pro-invasive effect). Could it be possible that once localized at the cell surface it is systematically moved away from regions that are anchored to either the ECM or adjacent cells? So its localization away from the centrosome in an ECM-free experiment would not be a consequence of defective targeting but relocalization after reaching the plasma membrane?

Thank you for your valuable comment. We agree that GP135 may indeed move directly across the cell surface, away from the region where it interacts with the ECM or adjacent cells. This re-localization could be due to its anti-adhesive or lubricating properties, which may facilitate its displacement from these adhesive sites. To validate this, it is necessary to employ higher-resolution real-time imaging system to observe the dynamic behavior of GP135 on the cell surface.

However, this does not contradict our main conclusion. Under suspension culture conditions without ECM, the centrosome positioning in cell doublets is indeed decoupled from apical membrane orientation. This suggests that the localization of the centrosome and the apical membrane is regulated by different mechanisms. Specifically, the GP135 protein tends to accumulate away from areas of contact with the ECM or adjacent cells, possibly through movement within the cell membrane or by recycling endosome transport. In contrast, centrosome positioning is closely related to the cytokinesis site. Our study clearly elucidates the differences between these two polarity properties. 

Reviewer #3 (Recommendations for the authors):

Major:

(1) To me, a clear implication of these studies is that Gp135, Rab11, etc. are delivered to the AMIS on centrosomal microtubules. The authors do not explore this model except to say that depletion of SD appendage or pericentrin has no effect on the protein relocalization to the AMIS. However, the authors do not observe microtubule association with the centrosome in these KO conditions. This analysis is imperative to interpret existing results since these are new KO conditions in this cell/culture system and parallel pathways (e.g. CDK5RAP2) are known to contribute to microtubule association with the centrosome. An ability to comment on the mechanism by which the centrosome contributes to the efficiency of polarization would greatly enhance the paper.

Microtubule requirement could also be tested in numerous additional ways requiring varying degrees of new experiments:

(a) faster live cell imaging at abscission to see if the deposition of those components appears to traffic on MTs;

(b) live cell imaging with microtubules (e.g. SPY-tubulin) and/or EB1 to determine the origin and polarity of microtubules at the pertinent stages;

For (a) and (b), because the cells were cultured in Matrigel, they tended to be round up, with a dense internal structure that made observation difficult. In contrast, under adherent culture conditions, the cells were flattened with a more dispersed internal structures, making them easier to observe. We had previously used SPY-tubulin to label microtubules for live cell imaging; however, due to the dense microtubule structure in 3D culture, the image contrast was reduced, and we could not clearly observe the microtubule network within the cells. 

(c) acute nocodazole treatment at abscission to determine the effect on protein localization.

Regarding the method of using nocodazole to study microtubule requirements at the abscission stage, we believe that nocodazole treatment may lead to cytokinesis failure. Cell division failure results in the formation of binucleated cells, which are unable to establish cell polarity. Furthermore, nocodazole treatment cannot distinguish between centrosomal and non-centrosomal microtubules, making it unsuitable for studying the specific role of centrosomal microtubules in this process.

In our new data (Figure 4-figure supplementary 3) presented in the revised manuscript, we employed a recently reported method by co-expressing of the centrosome-targeting carboxy-terminal domain (C-CTD) of CDK5RAP2 and the γ-tubulin-binding domain (gTBD) of NEDD1 to completely deplete γ-tubulin and abolish centrosomal microtubule nucleation (Vinopal et al., 2023). We found that cells lacking centrosomal microtubules were still able to polarize and position the centrioles apically. However, the efficiency of polarized transport of Gp135 vesicles to the apical membrane was reduced. These findings suggest that centrosomal microtubules are not essential for polarity establishment but may contribute to facilitate efficient apical transport. 

(2) Similar to the expanded analysis of the role of microtubules in this system, it would be excellent if the author could expand on the role of Par3 and the centrosome, although this reviewer recognizes that the authors have already done substantial work. For example, what are the consequences of Gp135 and/or Rab11 getting stuck at the centrosome? Do the authors have any later images to determine when and if these components ever leave the centrosome? Existing literature focuses on the more downstream consequence of Par3 removal on single-lumen formation. 

Similarly, could the authors expand on the description of polarity disruption following centrinone treatment? It is clear that Gp135 recruitment is disrupted, but how and when do things get fixed and what else is disrupted at the very earliest stages of AMIS formation? The authors have an excellent opportunity to really expand on what is known about the requirements for these conserved components.

Regarding the use of centrinone in treatment, we speculate that Gp135 can still accumulate at the AMIS over time, although the efficiency of its recruitment may be reduced.

Furthermore, under similar conditions, other apical membrane components (such as the Crumbs3 protein) may exhibit similar characteristics to Gp135 protein. 

(3) Perhaps satisfying both of the above asks, could the authors do a faster time-lapse at the relevant time points, i.e. as proteins are being recruited to the AMIS (time points between 1Aiv and v)? This type of imaging again might help shed light on the mechanism.

We believe the above questions are very important and may require further experimental verification in the future. 

Minor:

(1) What is the green patch of Gp135 in Figure 2A that does not colocalize with the centrosome? Is this another source of Gp135 that is being delivered to the AMIS? This type of patch is also visible in Figure 3A 15 and 30-minute panels.

During mitosis, membrane-composed organelles such as the Golgi apparatus are typically dispersed throughout the cytoplasm. However, during the pre-abscission stage, these organelles begin to reassemble and cluster around the centrosome. Furthermore, they also accumulate in the region between the nucleus and the cytokinetic bridge, corresponding to the “patch” mentioned in Figure 2A. 

Live cell imaging results showed that this Gp135 patch initially appears in a region not associated with the centrosome. Subsequently, they were either directly transported to the AMIS or fused with the centrosome-associated Gp135 and transported together. Notably, this patch was only observed when Gp135 was overexpressed in cells. No such distinct protein patches were observed when staining endogenous Gp135 protein (Figure 1A), suggesting that overexpression of Gp135 protein may lead to a localized increase in its concentration in that region. 

(2) I am confused by the "polarity index" quantification as this appears to just be a nucleus centrosome distance measurement and wouldn't, for example, distinguish if the centrosomes separated from the nucleus but were on the basal side of the cell.

The position of the centrosome within the cell (i.e., its distance from the nucleus) can indeed serve as an indicator of cell polarity (Burute et al., 2017). We acknowledge that this quantitative method does not directly capture the specific direction in which the centrosome deviates from the cell center. To address this limitation, we have incorporated information about the angle between the nucleus and the centrosome, which allows for a more accurate description of changes in cell polarity (Rodriguez-Fraticelli, Auzan, Alonso, Bornens, & Martin-Belmonte, 2012). 

(3) How is GP135 "at AMIS" measured? Is an arbitrary line drawn? This is important later when comparing to centrinone treatment in Figure 3D where the quantification does not seem to accurately capture the enrichment of Gp135 that is seen in the images.

To measure the expression level of Gp135 in the "AMIS" region of the cell, we first connected the centers of the two cell nuclei in three-dimensional space to form a straight line. Then, we used the Gp135 expression intensity at the midpoint of this line as the representative value for the AMIS region. This method is based on the assumption that the AMIS region is most likely located between the centers of the two cell nuclei. Therefore, this quantitative method provides a standardized assessment tool for comparing Gp135 expression levels under different conditions. 

(4) The authors reference cell height (p.7) but no data for this measurement are shown

Thank you for the comment. Although we did not perform quantitative measurements, the differences in cell height are clearly visible in Figure 3E (p53-KO + CN), which visually illustrates this phenomenon. 

(5) Can the authors comment on the seeming reduction of Par3 in p53 KO cells?

We did not observe a reduction of Par3 in p53-KO cells in our experiments.

(6) Can the authors make sense of the E-cad localization: Figure 5, Supplement 2.

Our study revealed that E-cadherin begins to accumulate at the cell-cell contact sites during the pre-abscission stage. Its appearance is similar to that of ZO-1, which also appears near the cell division site during this phase. Therefore, the behavior of E-cadherin contrasts sharply with that of Gp135, further highlighting the unique trafficking mechanisms of apical membrane proteins during this process. 

(7) I find the results in Figure 6G puzzling. Why is ECM signaling required for Gp135 recruitment to the centrosome. Could the authors discuss what this means?

We appreciate the reviewer’s valuable comments and thank you for the opportunity to clarify this point. The data in Figure 6G do not indicate that ECM signaling is required for the recruitment of Gp135 to the centrosome. Rather, our findings suggest that even in the absence of ECM, the centrosomes can migrate to a polarized position similar to that in Matrigel culture. This suggests that centrosome migration and the orientation of the nucleus–centrosome axis may be independent of ECM signaling and are primarily driven by cytokinesis alone. 

Regarding the localization of Gp135, previous studies have shown that ECM signaling through integrin promotes endocytosis, which is crucial for the internalization of Gp135 from the cell membrane and its subsequent transport to the AMIS (Buckley & St Johnston, 2022). Our study found that, prior to its accumulation at the AMIS, Gp135 transiently localizes around the centrosome. In the absence of ECM, due to reduced endocytosis, Gp135 primarily remains on the cell membrane and does not undergo intracellular trafficking.  

(8) The authors end the Discussion stating that these studies may have implication for in vivo settings, yet do not discuss the striking similarities to the C. elegans and Drosophila intestine or the findings from any other more observational studies of tubular epithelial systems in vivo (e.g. mouse kidney polarization, zebrafish neuroepithelium, etc.). These models should be discussed.

Thank you for your valuable comment. Indeed, all types of epithelial tissues or tubular epithelial systems in vivo share some common features during cell division, which have been well-documented across various species. 

These features include: during interphase, the centrosome is located at the apical surface of the cells; after the cell enters mitosis, the centrosome moves to the lateral side of the cell to regulate spindle orientation; and during cytokinesis, the cleavage furrow ingresses asymmetrically from the basal to the apical side, with the cytokinetic bridge positioned at the apical surface. Our study using MDCK 3D culture and transwell culture systems successfully mimicked these key features, demonstrating that these in vitro models are of significant value for studying cell polarization dynamics. 

Based on our observations, we speculate that the centrosome may return to the apical surface after anaphase, just before bridge abscission. This is consistent with our findings from studies using MDCK 3D cultures and transwell systems, which showed that the centrosome relocates prior to the final stages of cytokinesis.

Additionally, we propose that de novo polarization of the kidney tubule in vivo may not solely depend on the aggregation and mesenchymal-epithelial transition (MET) of the metanephric mesenchyme. It may also be related to the cell division process, which triggers centrosome migration and polarized vesicle trafficking. These processes likely contribute to enhancing cell polarization, as we observed in our in vitro models.

We hope this will further clarity the potential implications of our findings for in vivo model studies, as well as and their broader impact on the field of tubular epithelial cell polarization research. 

(9) There are several grammatical issues/typos throughout the paper. A careful readthrough is required. For example:

this sentence makes no sense "that the centrosome acts as a hub of apical recycling endosomes and centrosome migration during cytokinetic pre-abscission before apical membrane components are targeted to the AMIS"

We carefully reviewed the paper and made necessary revisions to address the issues raised. In particular, we revised certain sentences to improve clarity and readability (Page 5, Paragraph 3). 

(10) P.8: have been previously reported [to be] involved in MDCK...

We appreciate the reviewer's valuable suggestions. We have revised the sentence accordingly (Page 9, Paragraph 2). 

(11) This sentence seems misplaced: "Cultured conditions influence cellular polarization preferences."

The sentence itself is fine, but to improve the coherence and clarity of the paragraph, we adjusted the paragraph structure and added some transitional phrases (Page 13, Paragraph 1).  

(12) "Play a downstream role in Par3 recruitment" doesn't make sense, this should just be downstream of Par3 recruitment.

Thank you for your suggestion. We have revised the wording accordingly, changing it to "downstream of Par3 recruitment" (Page 10, Paragraph 2).  

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Author response:

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

Public Reviews: 

Reviewer #1 (Public review): 

Summary: 

This paper focuses on understanding how covalent inhibitors of peroxisome proliferator-activated receptor-gamma (PPARg) show improved inverse agonist activities. This work is important because PPARg plays essential roles in metabolic regulation, insulin sensitization, and adipogenesis. Like other nuclear receptors, PPARg, is a ligand-responsive transcriptional regulator. Its important role, coupled with its ligand-sensitive transcriptional activities, makes it an attractive therapeutic target for diabetes, inflammation, fibrosis, and cancer. Traditional non-covalent ligands like thiazolininediones (TZDs) show clinical benefit in metabolic diseases, but utility is limited by off-target effects and transient receptor engagement. In previous studies, the authors characterized and developed covalent PPARg inhibitors with improved inverse agonist activities. They also showed that these molecules engage unique PPARg ligand binding domain (LBD) conformations whereby the c-terminal helix 12 penetrates into the orthosteric binding pocket to stabilize a repressive state. In the nuclear receptor superclass of proteins, helix 12 is an allosteric switch that governs pharmacologic responses, and this new conformation was highly novel. In this study, the authors did a more thorough analysis of how two covalent inhibitors, SR33065 and SR36708 influence the structural dynamics of PPARg LBD. 

Strengths: 

(1) The authors employed a compelling integrated biochemical and biophysical approach.  

(2) The cobinding studies are unique for the field of nuclear receptor structural biology, and I'm not aware of any similar structural mechanism described for this class of proteins.  

(3) Overall, the results support their conclusions.  

(4) The results open up exciting possibilities for the development of new ligands that exploit the potential bidirectional relationship between the covalent versus non-covalent ligands studied here. 

Weaknesses: 

(1) The major weakness in this work is that it is hard to appreciate what these shifting allosteric ensembles actually look like on the protein structure. Additional graphical representations would really help convey the exciting results of this study. 

We thank the review for the comments. In response to the specific recommendations below, we added two new figures—Figure 1 and Figure 8 in this resubmission—that hopefully address the weakness identified by the reviewer.

Reviewer #2 (Public review): 

Summary: 

The authors use ligands (inverse agonists, partial agonists) for PPAR, and coactivators and corepressors, to investigate how ligands and cofactors interact in a complex manner to achieve functional outcomes (repressive vs. activating). 

Strengths: 

The data (mostly biophysical data) are compelling from well-designed experiments. Figures are clearly illustrated. The conclusions are supported by these compelling data. These results contribute to our fundamental understanding of the complex ligand-cofactor-receptor interactions. 

Weaknesses: 

This is not the weakness of this particular paper, but the general limitation in using simplified models to study a complex system. 

We appreciate the reviewer’s comments. Breaking down a complex system into a simpler model system, when possible, provides a unique lens with which to probe systems with mechanistic insight. While simplified models may not always explain the complexity of systems in cells, for example, our recently published work showed that a simplified model system — biochemical assays using reconstituted PPARγ ligand-binding domain (LBD) protein and peptides derived from coregulator proteins (similar to the assays in this current work) and protein NMR structural biology studies using PPARγ LBD — can explain the activity of ligand-induced PPARγ activation and repression to a high degree (pearson/spearman correlation coefficients ~0.7-0.9):

MacTavish BS, Zhu D, Shang J, Shao Q, He Y, Yang ZJ, Kamenecka TM, Kojetin DJ. Ligand efficacy shifts a nuclear receptor conformational ensemble between transcriptionally active and repressive states. Nat Commun. 2025 Feb 28;16(1):2065. doi: 10.1038/s41467-025-57325-4. PMID: 40021712; PMCID: PMC11871303.

Recommendations for the authors

Reviewer #1 (Recommendations for the authors): 

(1) More set-up is needed in the results section. The first paragraph is unclear on what is new to this study versus what was done previously. Likewise, a brief description of the assays used and the meaning behind differences in signals would help the general reader along. 

We modified the last paragraph of the introduction and first results section to hopefully better set the stage for what was done previously vs. what is new/recollected in this study. In our results section, we also include more description about what the assays measure.

(2) Since this paper is building on previous work, additional figures are needed in the introduction and discussion. Graphical depictions of what was found in the first study on how these ligands uniquely influence PPARg LBD conformation. A new model/depiction in the discussion for what was learned and its context with the rest of the field. 

Our revised manuscript includes a new Figure 1 describing the possible allosteric mechanism by which a covalent ligand inhibits binding of other non-covalent ligands that was inferred from our previous study; and a new Figure 8 with a model for what has been learned.

(3) It is stated that the results shown are representative data for at least two biological replicates. However, I do not see the other replicates shown in the supplementary information. 

We appreciate the Reviewer’s emphasis on data reproducibility and rigor. We confirm that the biochemical and cellular assay data presented are indeed representative of consistent findings observed across two or more biological replicates—and we show representative data in our figures but not the extensive replicate data in supplementary information consistent with standard practices.

(4) Figure 1a could benefit from labels of antagonists, inverse agonist, etc., next to each chemical structure. Likewise, if any co-crystal or other models are available it would be helpful to include those for comparison. 

We added the pharmacological labels to Figure 2a (old Figure 1a).

(5) The figure legends don't seem to match up completely with the figures. For example, Figure 2b states that fitted Ki values +/- standard deviation. are stated in the legend, but it's shown as the log Ki. 

We revised the figure legends to ensure they display the appropriate errors as reported from the data fitting.

(6) EC50, IC50, Ki, and Kd values alongside reported errors and R2 values for the fits should be reported in a table. 

Our revised manuscript now includes a Source Data file (Figure 5—source data 1.xlsx) of the data (n=2) plotted in Figure 5 (old Figure 4) so that readers can regenerate the plots and calculate the errors and R2 values if desired. Otherwise, fitted values and errors are reported in figures when fitting in Prism permitted and reported errors; when Prism was unable to fit data or fit the error, n.d. (not determined) is specified.

(7) Statistical analysis is missing in some places, for example, Figure 1b. 

We revised Figure 2b (old Figure 1b) to include statistical testing.

Reviewer #2 (Recommendations for the authors): 

I suggest that the authors discuss the following points to broaden the significance of the results: 

(1) The two partial agonists MRL24 and nTZDpa) are "partial" in the coactivator and corepressor recruitment assays, but are "complete" in the TR-FRET ligand displacement assay (Figure 2). Please explain that a partial agonist is defined based on the functional outcome (cofactor recruitment in this study) but not binding affinity/efficacy. 

We added the following sentence to describe the partial agonist activity of these compounds: “These high affinity ligands are partial agonists as defined on their functional outcome in coregulator recruitment and cellular transcription; i.e., they are less efficacious than full agonists at recruiting peptides derived from coactivator proteins in biochemical assays (Chrisman et al., 2018; Shang et al., 2019; Shang and Kojetin, 2024) and increasing PPARγ-mediated transcription (Acton et al., 2005; Berger et al., 2003).“

(2) Will the discovery reported here be broadly applicable? 

(a) Applicable if other partial agonists and inhibitors are used? 

(b) Applicable if different coactivators/corepressors, or different segments of the same cofactor, are used?

(c) Applicable to other NRs (their AF-2 are similar but with sequence variation)?

(d) The term "allosteric" might mean different things to different people - many readers might think that it means a "distal and unrelated" binding pocket. It might be helpful to point out that in this study, the allosteric site is actually "proximal and related". 

We expanded our introduction and/or discussion sections to expand upon these concepts; specific answers as follows:

(a) Orthosteric partial agonists?—yes, because helix 12 would clash with an orthosteiric ligand; other covalent inhibitors?—it depends on whether the covalent inhibitor stabilizes helix 12 in the orthosteric pocket.

(b) yes with some nuanced exceptions where certain segments of the same coregulator protein bind with high affinity and others apparently do not bind or bind with low affinity

(c) it is not clear yet if other NRs share a similar ligand-induced conformational ensemble to PPARγ

(d) we addressed this point in the 4th paragraph of the introduction “...the non-covalent ligand binding event we previously described at the alternate/allosteric site, which is proximal to the orthosteric ligand-binding pocket, …”

Reviewer #2 (Public review):

Summary:

In this manuscript, the authors use various genomics approaches to examine nucleosome acetylation, phosphorylation, and PolII-CTD phosphorylation marks. The results are synthesized into a hypothesis that 'fragile' nucleosomes are associated with active regions of PolII transcription.

Strengths:

The manuscript contains a lot of genome-wide analyses of histone acetylation, histone phosphorylation, and PolII-CTD phosphorylation.

Weaknesses:

This reviewer's main research expertise is in the in vitro study of transcription and its regulation in purified, reconstituted systems. I am not an expert at the genomics approaches and their interpretation, and overall, I had a very hard time understanding and interpreting the data that are presented in this manuscript. I believe this is due to a problem with the manuscript, in that the presentation of the data is not explained in a way that's understandable and interpretable to a non-expert. For example:

(1) Figure 1 shows genome-wide distributions of H3K9ac, H4K12ac, Ser2ph-PolII, mRNA, H3S10ph, and H4S1ph, but does not demonstrate correlations/coupling - it is not clear from these data that pan-acetylation and pan-phosphorylation are coupled with Pol II transcription.

(2) Figure 2 - It's not clear to me what Figure 2 is supposed to be showing.

(A) Needs better explanation - what is the meaning of the labels at the top of the gel lanes?

(B) This reviewer is not familiar with this technique, its visualization, or its interpretation - more explanation is needed. What is the meaning of the quantitation graphs shown at the top? How were these calculated (what is on the y-axis)?

(3) To my knowledge, the initial observation of DRB effects on RNA synthesis also concluded that DRB inhibited initiation of RNA chains (pmid:982026) - this needs to be acknowledged.

(4) Again, Figures 4B, 4C, 5, and 6 are very difficult to understand - what is shown in these heat maps, and what is shown in the quantitation graphs on top?

Author response:

Public Reviews: 

Reviewer #1 (Public review): 

Summary: 

This study aims to explore how different forms of "fragile nucleosomes" facilitate RNA Polymerase II (Pol II) transcription along gene bodies in human cells. The authors propose that pan-acetylated, pan-phosphorylated, tailless, and combined acetylated/phosphorylated nucleosomes represent distinct fragile states that enable eFicient transcription elongation. Using CUT&Tagseq, RNA-seq, and DRB inhibition assays in HEK293T cells, they report a genome-wide correlation between histone pan-acetylation/phosphorylation and active Pol II occupancy, concluding that these modifications are essential for Pol II elongation. 

Strengths: 

(1) The manuscript tackles an important and long-standing question about how Pol II overcomes nucleosomal barriers during transcription. 

(2) The use of genome-wide CUT&Tag-seq for multiple histone marks (H3K9ac, H4K12ac, H3S10ph, H4S1ph) alongside active Pol II mapping provides a valuable dataset for the community. 

(3) The integration of inhibition (DRB) and recovery experiments oFers insight into the coupling between Pol II activity and chromatin modifications. 

(4) The concept of "fragile nucleosomes" as a unifying framework is potentially appealing and could stimulate further mechanistic studies. 

Really appreciate the positive or affirmative comments from the reviewer.

Weaknesses: 

(1)  Misrepresentation of prior literature 

The introduction incorrectly describes findings from Bintu et al., 2012. The cited work demonstrated that pan-acetylated or tailless nucleosomes reduce the nucleosomal barrier for Pol II passage, rather than showing no improvement. This misstatement undermines the rationale for the current study and should be corrected to accurately reflect prior evidence. 

What we said is according to the original report in the publication (Bintu et al., Cell, 2012). Here is the citation from the report:

Page 739,(Bintu, L. et al., Cell, 2012)(PMID: 23141536)

“Overall transcription through tailless and acetylated nucleosomes is slightly faster than through unmodified nucleosomes (Figure 1C), with crossing times that are generally under 1 min (39.5 ± 5.7 and 45.3 ± 7.6 s, respectively). Both the removal and acetylation of the tails increase eFiciency of NPS passage:71% for tailless nucleosomes and 63% for acetylated nucleosomes (Figures 1C and S1), in agreement with results obtained using bulk assays of transcription (Ujva´ ri et al., 2008).”

We will cite this original sentence in our revision.

(2) Incorrect statement regarding hexasome fragility

The authors claim that hexasome nucleosomes "are not fragile," citing older in vitro work. However, recent studies clearly showed that hexasomes exist in cells (e.g., PMID 35597239) and that they markedly reduce the barrier to Pol II (e.g., PMID 40412388). These studies need to be acknowledged and discussed. 

“hexasome” was introduced in the transcription field four decades ago. Later, several groups claimed that “hexasome” is fragile and could be generated in transcription elongation of Pol II. However, their original definition was based on the detection of ~100 bps DNA fragments (MNase resistant) in vivo by Micrococcal nuclease sequencing (MNase-seq), which is the right length to wrap up one hexasome histone subunit (two H3/4 and one H2A/2B) to form the sub-nucleosome of a hexasome. As we should all agree that acetylation or phosphorylation of the tails of histone nucleosomes will lead to the compromised interaction between DNA and histone subunits, which could lead to the intact naïve nucleosome being fragile and easy to disassemble, and easy to access by MNase. Fragile nucleosomes lead to better accessibility of MNase to DNA that wraps around the histone octamer, producing shorter DNA fragments (~100 bps instead of ~140 bps). In this regard, we believe that these ~100 bps fragments are the products of fragile nucleosomes (fragile nucleosome --> hexasome), instead of the other way around (hexasome --> fragile). 

Actually, two early reports from Dr. David J.  Clark’s group from NIH raised questions about the existence of hexasomes in vivo (PMID: 28157509) (PMID: 25348398).

From the report of PMID:35597239, depletion of INO80 leads to the reduction of “hexasome” for a group of genes, and the distribution of both “nucleosomes” and “hexasomes” with the gene bodies gets fuzzier (less signal to noise). In a recent theoretical model (PMID: 41425263), the corresponding PI found that chromatin remodelers could act as drivers of histone modification complexes to carry out different modifications along gene bodies. The PI found that INO80 could drive NuA3 (a H3 acetyltransferase) to carry out pan-acetylation of H3 and possibly H2B as well in the later runs of transcription of Pol II for a group of genes (SAGA-dependent). It suggests that the depletion of INO80 will affect (reduce) the pan-acetylation of nucleosomes, which leads to the drop of pan-acetylated fragile nucleosomes, subsequently the drop of “hexasomes”. This explains why depletion of INO80 leads to the fuzzier results of either nucleosomes or “hexasomes” in PMID: 35597239. The result of PMID: 35597239 could be a strong piece of evidence to support the model proposed by the corresponding PI (PMID: 41425263).

From a recent report: PMID:40412388, the authors claimed that FACT could bind to nucleosomes to generate “hexasomes”, which are fragile for Pol II to overcome the resistance of nucleosomes. It was well established that FACT enhances the processivity of Pol II in vivo via its chaperonin property. However, the exact working mechanism of FACT still remains ambiguous. A report from Dr. Cramer’s group showed that FACT enhances the elongation of regular genes but works just opposite for pausing-regulated genes (PMID: 38810649). An excellent review by Drs. Tim Formosa and Fred Winston showed that FACT is not required for the survival of a group of differentiated cells (PMID: 33104782), suggesting that FACT is not always required for transcription. It is quite tricky to generate naïve hexasomes in vitro according to early reports from the late Dr. Widom’s group. Most importantly, the new data (the speed of Pol II, the best one on bare DNA is ~27 bps/s) from the report of PMID: 40412388, which is much slower than the speed of Pol II in vivo: ~2.5 kbs/min or ~40 bps/s. From our recovering experiments (Fig. 4C, as mentioned by reviewer #3), in 20 minutes (the period between 10 minutes and 30 minutes, due to the property of CUT-&TAG-seq, of which Pol II still active after cells are collected, there is a big delay of complete stop of Pol II during the procedure of CUT&TAG experiments, so the first period of time does not actually reflect the speed of Pol II, which is ~5 kb/min), all Pol IIs move at a uniform speed of ~2.5 kbs/min in vivo. Interestingly, a recent report from Dr. Shixin Liu’s group (PMID: 41310264) showed that adding SPT4/5 to the transcription system with bare DNA (in vitro), the speed of Pol II reaches ~2.5kbs/min, exactly the same one as we derived in vivo. Similar to the original report (PMID: 23141536), the current report of PMID:40412388 does not mimic the conditions in vivo exactly.

There is an urgent need for a revisit of the current definition of “hexasome”, which is claimed to be fragile and could be generated during the elongation of Pol II in vivo. MNase is an enzyme that only works when the substrate is accessible. In inactive regions of the genome, due to the tight packing of chromatin, MNase is not accessible to individual nucleosomes within the bodies of a gene or upstream of promoters, which is why we only see phased/spacing or clear distribution of nucleosomes at the transcription start sites, but it becomes fuzzy downstream or upstream of promoters. On the other hand, for fragile nucleosomes, the accessibility to MNase should increase dramatically, which leads to the ~100 bps fragments. Based on the uniform rate (2.5 kbs/min) of Pol II for all genes derived from human 293T cells and the similar rate (2.5 kbs/min) of Pol II on bare DNA in vitro, it is unlikely for Pol II to pause in the middle of nucleosomes to generate “hexasomes” to continue during elongation along gene bodies. Similar to RNAPs in bacterial (no nucleosomes) and Archaea (tailless nucleosomes), there should be no resistance when Pol IIs transcribe along all fragile nucleosomes within gene bodies in all eukaryotes, as we characterized in this manuscript. 

(3)  Inaccurate mechanistic interpretation of DRB 

The Results section states that DRB causes a "complete shutdown of transcription initiation (Ser5-CTD phosphorylation)." DRB is primarily a CDK9 inhibitor that blocks Pol II release from promoter-proximal pausing. While recent work (PMID: 40315851) suggests that CDK9 can contribute to CTD Ser5/Ser2 di-phosphorylation, the manuscript's claim of initiation shutdown by DRB should be revised to better align with the literature. The data in Figure 4A indicate that 1 M DRB fully inhibits Pol II activity, yet much higher concentrations (10-100 ) are needed to alter H3K9ac and H4K12ac levels. The authors should address this discrepancy by discussing the differential sensitivities of CTD phosphorylation versus histone modification turnover. 

Yes, it was reported that DRB is also an inhibitor of CDK9. However, if the reviewer agrees with us and the current view in the field, the phosphorylation of Ser5-CTD of Pol II is the initiation of transcription for all Pol II-regulated genes in eukaryotes. CDK9 is only required to work on the already phosphorylated Ser5-CTD of Pol II to release the paused Pol II, which only happens in metazoans. From a series of works by us and others: CDK9 is unique in metazoans, required only for the pausing-regulated genes but not for regular genes. We found that CDK9 works on initiated Pol II (Ser5-CTD phosphorylated Pol II) and generates a unique phosphorylation pattern on CTD of Pol II (Ser2ph-Ser2ph-Ser5ph-CTD of Pol II), which is required to recruit JMJD5 (via CID domain) to generate a tailless nucleosome at +1 from TSS to release paused Pol II (PMID: 32747552). Interestingly, the report from Dr. Jesper Svejstrup’s group (PMID: 40315851) showed that CDK9 could generate a unique phosphorylation pattern (Ser2ph-Ser5ph-CTD of Pol II), which is not responsive to the popular 3E10 antibody that recognizes the single Ser2phCTD of Pol II.  This interesting result is consistent with our early report showing the unique phosphorylation pattern (Ser2ph-Ser2ph-Ser5ph-CTD of Pol II) is specifically generated by CDK9 in animals, which is not recognized by 3E10 either (PMID: 32747552). Actually, an early report from Dr. Dick Eick’s group (PMID: 26799765) showed the difference in the phosphorylation pattern of the CTD of Pol II between animal cells and yeast cells.  We have characterized how CDK9 is released from 7SK snRNP and recruited onto paused Pol II via the coupling of JMJD6 and BRD4 (PMID: 32048991), which was published on eLIFE. It is well established that CDK9 works after CDK7 or CDK8. From our PRO-seq data (Fig. 3) and CUT&TAG-seq data of active Pol II (Fig. 4), adding DRB completely shuts down all genes via inhibiting the initiation of Pol II (generation of Ser5ph-CTD of Pol II). Due to the uniqueness of CDK9 only in metazoans, it is not required for the activation of CDK12 or CDK13 (they are orthologs of CTK1 in yeast), as we demonstrated recently (PMID: 41377501). Instead, we found that CDK11/10 acts as the ortholog of Bur1 kinase from yeast, is essential for the phosphorylation of Spt5, the link of CTD of Pol II, and CDK12 (PMID: 41377501). 

(4) Insufficient resolution of genome-wide correlations 

Figure 1 presents only low-resolution maps, which are Insufficient o determine whether pan-acetylation and pan-phosphorylation correlate with Pol II at promoters or gene bodies. The authors should provide normalized metagene plots (from TSS to TTS) across different subgroups to visualize modification patterns at higher resolution. In addition, the genome-wide distribution of another histone PTM with a diFerent localization pattern should be included as a negative control. 

A popular view in the field is that the majority of genomes are inactive since they do not contain coding RNAs, which are responsible for ~20,000 protein candidates characterized in animals. However, our genomewide characterization using the four histone modification marks, active Pol II, and RNA-seq, shows a different story. Figure 1 shows that most of the human genome of HEK293T is active in producing not only protein-coding RNAs but also non-coding RNAs (the majority of them). We believe that Figure 1 could change our current view of the activity of the entire genome, and should be of great interest to general readers as well as researchers on genomics. Furthermore, it is a basis for Figure 2, which is a zoom-in of Figure 1.  

(5) Conceptual framing 

The manuscript frequently extrapolates correlative genome-wide data to mechanistic conclusions (e.g., that pan-acetylation/phosphorylation "generate" fragile nucleosomes). Without direct biochemical or structural evidence. Such causality statements should be toned down.  

The reviewer is right, we should tone down the strong sentences. However, we believe that our data is strong enough to derive the general conclusion. The reviewer may agree with us that the entire field of transcription and epigenetics has been stagnant in recent decades, but there is an urgent need for fresh ideas to change the current situation. Our novel discoveries, for sure, additional supporting data are needed, should open up a brand new avenue for people to explore. We believe that a new era of transcription will emerge based on our novel discoveries. We hope that this manuscript will attract more people to these topics. As Reviewer #3 pointed out, this story establishes the connection between transcription and epigenetics in the field. 

Reviewer #2 (Public review): 

Summary: 

In this manuscript, the authors use various genomics approaches to examine nucleosome acetylation, phosphorylation, and PolII-CTD phosphorylation marks. The results are synthesized into a hypothesis that 'fragile' nucleosomes are associated with active regions of PolII transcription. 

Strengths: 

The manuscript contains a lot of genome-wide analyses of histone acetylation, histone phosphorylation, and PolII-CTD phosphorylation. 

Weaknesses: 

This reviewer's main research expertise is in the in vitro study of transcription and its regulation in purified, reconstituted systems. 

Actually, the pioneering work of the establishment of in vitro transcription assays at Dr. Robert Roeder’s group led to numerous groundbreaking discoveries in the transcription field. The contributions of in vitro work in the transcription field are the key for us to explore the complexity of transcription in eukaryotes in the early times and remain important currently.

I am not an expert at the genomics approaches and their interpretation, and overall, I had a very hard time understanding and interpreting the data that are presented in this manuscript.  I believe this is due to a problem with the manuscript, in that the presentation of the data is not explained in a way that's understandable and interpretable to a non-expert.

Thanks for your suggestions. You are right, we have problems expressing our ideas clearly in this manuscript, which could confuse. We will make modifications accordingly per your suggestions.

For example: 

(1) Figure 1 shows genome-wide distributions of H3K9ac, H4K12ac, Ser2phPolII, mRNA, H3S10ph, and H4S1ph, but does not demonstrate correlations/coupling - it is not clear from these data that pan-acetylation and pan-phosphorylation are coupled with Pol II transcription. 

Figure 1 shows the overall distribution of the four major histone modifications, active Pol II, and mRNA genome-wide in human HEK293T cells. It tells general readers that the entire genome is quite active and far more than people predicted that most of the genome is inactive, since just a small portion of the genome expresses coding RNAs (~20,000 in animals). Figure 1 shows that the majority of the genome is active and expresses not only coded mRNA but also non-coding RNAs. After all, it is the basis of Figure 2, which is a zoom-in of Figure 1. However, it is beyond the scope of this manuscript to discuss the non-coding RNAs. 

(2) Figure 2 - It's not clear to me what Figure 2 is supposed to be showing. 

(A) Needs better explanation - what is the meaning of the labels at the top of the gel lanes? 

Figure 2 is a zoom-in for the individual gene, which shows how histone modifications are coupled with Pol II activity on the individual gene. We will give a more detailed explanation of the figure per the reviewer’s suggestions.

(B) This reviewer is not familiar with this technique, its visualization, or its interpretation - more explanation is needed. What is the meaning of the quantitation graphs shown at the top? How were these calculated (what is on the y-axis)? 

Good suggestions, we will do some modifications.

(3) To my knowledge, the initial observation of DRB eFects on RNA synthesis also concluded that DRB inhibited initiation of RNA chains (pmid:982026) - this needs to be acknowledged. 

Thanks for the reference, which is the first report to show the DRB inhibits initiation of Pol II in vivo. We will cite it in the revision.  

(4) Again, Figures 4B, 4C, 5, and 6 are very difficult to understand - what is shown in these heat maps, and what is shown in the quantitation graphs on top? 

Thanks for the suggestions, we will give a more detailed description of the Figures.  

Reviewer #3 (Public review): 

Summary: 

Li et al. investigated the prevalence of acetylated and phosphorylated histones (using H3K9ac, H4K12ac, H3S10ph & H4S1ph as representative examples) across the gene body of human HEK293T cells, as well as mapping elongating Pol II and mRNA. They found that histone acetylation and phosphorylation were dominant in gene bodies of actively transcribing genes. Genes with acetylation/phosphorylation restricted to the promoter region were also observed. Furthermore, they investigated and reported a correlation between histone modifications and Pol II activity, finding that inhibition of Pol II activity reduced acetylation/phosphorylation levels, while resuming Pol II activity restored them. The authors then proposed a model in which panacetylation or pan-phosphorylation of histones generates fragile nucleosomes; the first round of transcription is accompanied by panacetylation, while subsequent rounds are accompanied by panphosphorylation. 

Strengths: 

This study addresses a highly significant problem in gene regulation. The author provided riveting evidence that certain histone acetylation and/or phosphorylation within the gene body is correlated with Pol II transcription. The author furthermore made a compelling case that such transcriptionally correlated histone modification is dynamic and can be regulated by Pol II activity. This work has provided a clearer view of the connection between epigenetics and Pol II transcription. 

Thanks for the insightful comments, which are exactly what we want to present in this manuscript. 

Weaknesses: 

The title of the manuscript, "Fragile nucleosomes are essential for RNA Polymerase II to transcribe in eukaryotes", suggests that fragile nucleosomes lead to transcription. While this study shows a correlation between histone modifications in gene bodies and transcription elongation, a causal relationship between the two has not been demonstrated. 

Thanks for the suggestions. What we want to express is that the generation of fragile nucleosomes precedes transcription, or, more specifically, transcription elongation. The corresponding PI wrote a hypothetical model on how pan-acetylation is generated by the coupling of chromatin remodelers and acetyltransferase complexes along gene bodies, in which chromatin remodelers act as drivers to carry acetyltransferases along gene bodies to generate pan-acetylation of nucleosomes (PMID: 41425263). We have a series of work to show how “tailless nucleosomes” at +1 from transcription start sites are generated to release paused Pol II in metazoans (PMID: 28847961) (PMID: 29459673) (PMID: 32747552) (PMID: 32048991).   We still do not know how pan-phosphorylation along gene bodies is generated. It should be one of the focuses of our future research.

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