complementa lo anterior del mejor ajuste ya que son datos necesarios para el mejor ajuste y como dice en la parte de indicadores nos ayuda a saber que tan bueno o no es el mejor ajuste

nos facilitan demasiado a la hora de organizar todos los datos obtenidos y son grandes herramientas que uno puede utilizar

es importante saber el significado de las cosas para explicar lo que se esta haciendo y por que utilizas el método que elegiste por ejemplo como lo dice en el texto se puede utilizar el principio de parsimonia para utilizar la regresión lineal

es importante ya que es otro método en la que se puede hacer un calculo rápido donde en el texto explica en que consiste el capsotone de PASCO

nos da los datos necesarios para saber si el mejor ajuste es bueno o no dando también otros datos como la desviación absoluta máxima, desviación estándar, residual, coeficiente de Pearson que esta es algo nuevo que aprendí, coeficiente de correlación y la chi cuadrada

es importante ya que nos ayuda a tener una simplicidad, inmediatez y adaptabilidad para calcular el mejor ajuste

Genera automáticamente el identificador y lo agrega al contenido de referencia, lo cual deja la marca en caso de no usar una otra herramienta que no sea Obsidian.

Por una parte me parece una funcionalidad muy interesante, pero por otra, es contraria, en cierta manera, al sentido y filosofía de Markdown.

new word

Muchas veces los estudios, donde no se tiene en cuenta la participación de otros géneros, al no diferenciarlos, terminan perjudicando a la investigación con datos inconclusos. En cambio si esta perspectiva de género de implmenta podemos obtener muchos más datos y variables que enriquecen el análisis

Tener en cuenta el género para las investigaciones es muy importante, porque se necesita generar un cambio donde los estudios, de cualquier tipo, tengan en cuenta que no todos sus participantes son sujetes masculinos y que deberían adecuar sus preguntas e investigaciones a otros géneros, no solo para que estas personas se sientan incluidas, sino que así se genera un cambio en las persepciones enfocadas a la sociedad heteropatrialcal. Como lo podemos ver en el ejemplo muchas veces los cuestionarios no difieren del género y usan pronombres masculinos lo cúal solo provoca que en las investigaciones no se tenga en cuenta a los sujetos femenínos y sus experiencias y aportaciones

En este apartado podemos ver como combatir la cegera de género a la hora de realizar una investigación es realmente importante ya que, si lo lo hacemos corremos el resigo de que nuestra investigación solo englobe datos y experiencias de un grupo específico, en este caso el de los hombres y no nos deja ver que en realidad, dentro de muchos procesos sociales tenemos que contemplar como el género afecta las diferentes experiencias y desafios de los individuos.

Este estracto del texto refleja muy bien cómo en las investigaciones la ceguera de género afecta a los resultados, ya que muchas veces no se tienen en cuenta como el género pude influir en las respuestas, infromación y opiniones recabadas para las investigaciones. Provocando un sesgo que muchas es invisible ante los ojos de una sociedad heteropatrciarcal y que normalmente esta influenciado por los estereotipos sociales que nos persiguen hasta hoy en día

Author response:

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

Reviewer #1 (Public Review):

This paper aims to address the establishment and maintenance of neural circuitry in the case of a massive loss of neurons. The authors used genetic manipulations to ablate the principal projection neurons, the mitral/tufted cells, in the mouse olfactory bulb. Using diphtheria toxin (Tbx21-Cre:: loxP-DTA line) the authors ablated progressively large numbers of M/T cells postnatally. By injecting diphtheria toxin (DT) into the Tbx21-Cre:: loxP-iDTR line, the authors were able to control the timing of the ablation in the adult stage. Both methods led to the successful elimination of a majority of M/TCs by 4 months of age. The authors made a few interesting observations. First, they found that the initial pruning of the remaining M/T cell primary dendrite was unaffected. However, in adulthood, a significant portion of these cells extended primary dendrites to innervate multiple glomeruli. Moreover, the incoming olfactory sensory neuron (OSN) axons, as examined for those expressing the M72 receptor, showed a divergent innervation pattern as well. The authors conclude that M/T cell density is required to maintain the dendritic structures and the olfactory map. To address the functional consequences of eliminating a large portion of principal neurons, the authors conducted a series of behavioral assays. They found that learned odor discrimination was largely intact. On the other hand, mating and aggression were reduced. The authors concluded that learned behaviors are more resilient than innate ones.

The study is technically sound, and the results are clear-cut. The most striking result is the contrast between the normal dendritic pruning during early development and the expanded dendritic innervation in adulthood. It is a novel discovery that can lead to further investigation of how the single-glomerulus dendritic innervation is maintained. The authors conducted a

few experiments to address potential mechanisms, but it is inconclusive, as detailed below. It is also interesting to see that the massive neuronal loss did not severely impact learned odor discrimination. This result, together with previous studies showing nearly normal odor discrimination in the absence of large portions of the olfactory bulb or scrambled innervation patterns, attests to the redundancy and robustness of the sensory system. The discussion should take into account these other studies in a historical context.

Main comments:

(1) In previous studies, it has been concluded that dendritic pruning unfolds independently, regardless of the innervation pattern or activity of the OSNs. The new observation bolsters this conclusion by showing that a loss of neighboring M/T cells does not affect the developmental process. A more nuanced discussion comparing the results of these studies would strengthen the paper.

We thank the reviewer for the suggestion. We now include an extended discussion citing relevant previous works in the manuscript (Lines 351-374).

(2) The authors propose that a certain density of M/T is required to prevent the divergent innervation of primary dendrites, but the evidence is not sufficient to support this proposal. The experiment with low-dose DT injection to ablate a smaller portion of M/T cells did not change the percentage of cells innervating two or more glomeruli. The authors suggest that a threshold must be met, but this threshold is not determined.  

In our experiments using high-dose DT, we hypothesized that there may be many empty glomeruli (glomeruli not innervated by M/T cells), and as a result, that some of the remaining M/T cells could branch their apical dendrite tuft into multiple empty glomeruli. To test this hypothesis, we carried out another experiment using a lower dose of DT. In this experiment, the fraction of remaining M/T cells was 25% (~10,000 M/T cells), which was higher than with the high DT dose (5%, or around 2,000 M/T cells) , but still significantly lower than wild type mice (~40,000 cells M/T cells). With around 2,000 glomeruli and 10,000 M/T per bulb, it could be expected that each glomerulus would be innervated by ~5 M/T cells (on average). However, we found that the percentage of M/T cells projecting to multiple glomeruli (around 40%) was similar when either 10,000 or 2,000 of M/T remained in the bulb. In addition, it is important to emphasize that even in wt animals with a full set of M/T cells, a small percentage of M/T cells still innervate more than one glomerulus (Lin et al., 2000). Together, these observations suggest that the innervation of multiple glomeruli by M/T cells is not simply due to the presence of empty glomeruli, and that our hypothesis was not correct.

We have added a comment explaining this issue in the Results section (Lines 200-203).

(3) The authors suggest that neural activity is not required for this plasticity. The evidence was derived primarily from naris occlusion and neuronal silencing using Kir2.1. While the results are consistent with the notion, it is a rather narrow interpretation of how neural activity affects circuit configuration. Perturbation of neural activity also entails an increase in firing. Inducing the activity of the neurons may alter this plasticity. Silencing per se may induce a homeostatic response that expands the neurite innervation pattern to increase synaptic input to compensate for the loss of activity. Thus, further silencing the cells may not reduce multiglomerular innervation, but an increased activity may.

The experiments with Kir2.1 demonstrate that the structural plasticity observed after reducing the total number of M/T cells in an animal is not regulated by the firing action potentials in the remaining cells. Instead, this experiment indicates that the observed structural plasticity may be regulated by other types of mechanisms (including increased synaptic excitation as suggested by the reviewer) that do not require the firing of action potentials in M/T cells. 

We now have included a comment regarding this point (Lines 243-247).  

(4) There is a discrepancy between this study and the one by Fujimoto et al. (Developmental Cell; 2023), which shows that not only glutamatergic inputs to the primary dendrite can facilitate pruning of remaining dendrites but also Kir2.1 overexpression can significantly perturb dendritic pruning. This discrepancy is not discussed by the authors.

We agree that it would be useful to contrast these two works.

In our experiments, performed in adult animals, we blocked sensory input by performing naris occlusion before we induced ablation of M/T cells. In a separate experiment, also in adult animals, we expressed the Kir2.1 channel, to reduce the ability of neurons to fire action potentials. With both types of manipulations, we observed that the ablation of a large fraction of M/T cells still caused the remaining M/T cells to maintain a single apical dendrite that sprouts several new tufts towards multiple glomeruli. A recent paper (Fujimoto et al., 2023)) in which Kir2.1 was expressed in a large percentage of M/T starting during embryonic development showed that these “silent” M/T cells failed to prune their arbors to a single dendrite. In aggregate, these observations indicate that action potentials are necessary for the normal pruning that occurs during perinatal development (Fujimoto et al., 2023), but are not required for the expansion of dendritic trees caused by ablating a large fraction of M/T cells in adult animals (our current manuscript).

We have now explained the differences between both studies in the manuscript (Lines 427-439).

(5) An alternative interpretation of the discrepancy between the apparent normal pruning by p10 and expanded dendritic innervation in adulthood is that there are more cells before P10, when ~25% of M/T cells are present, but at a later date only 1-3% are present. 

The relationship between the number of M/T cells and single glomerulus innervation has not been explored during postnatal development. It would be important to test this hypothesis.

We agree with this comment, and in lines 375-381 we discuss the discrepancy between normal refinement during development, and dendritic sprouting in adults.

Cre is expressed in M/T cells and it induces DTA expression starting around P0. The elimination of M/T cells starts at this time, and continues until by P10, when more than 75% of M/T have been eliminated. At P21 more than 90% of M/T have been eliminated, and their number remains stable thereafter.

Pruning of the dendrites of M/T cells starts at P0 and it is mostly complete by P10. Therefore, it is possible that between P0 and P7, when dendrites are being pruned, the number of M/T cells remaining in the bulb is still over a threshold that does not interfere with the process of normal dendrite pruning. We agree that it would be very informative to perform additional experiments in the future where a large set of M/T cells could be ablated before pruning occurs (ideally before P0). 

(6) The authors attribute the change in the olfactory map to the loss of M/T cells. Another obvious possibility is that the diffused projection is a response to the change in the olfactory bulb size. With less space to occupy, the axons may be forced to innervate neighboring glomeruli. It is not known how the total number of glomeruli is affected. This question could be addressed by tracking developmental changes in bulb volume and glomerular numbers.

Certainly, this is a possibility, and we have now included a comment on this regard in the manuscript (Lines 473-480). 

We believe that there are three likely scenarios that could account for these observations:

(a) After ablating M/T cells, the tufts of the remaining M/T cells sprout into multiple glomeruli, and this causes the axons of OSNs to project into multiple glomeruli.

(b) Ablating M/T cells may cause changes in other OB cells that make synapses in the glomeruli (ETCs, PGCs, sAC, etc…), and the misrouting of OSN axons that we observed in our experiments may be a secondary effect caused by the elimination of M/T cells.

(c) After ablating the majority of M/T cells, the olfactory bulb gets reduced in size, and the axons of OSNs find it difficult to precisely converge on a target that now has become smaller. As a result, the axons of OSNs fail to converge on single glomeruli.

(7) The retained ability to discriminate odors upon reinforced training is not surprising in light of a number of earlier studies. For example, Slotnick and colleagues have shown that rats losing ~90% of the OB can retain odor discrimination. Weiss et al have shown that humans without an olfactory bulb can perform normal olfactory tasks. Gronowitz et al have used theoretical prediction and experimental results to demonstrate that perturbing the olfactory map does not have a major impact on olfactory discrimination. Fleischmann et al have shown that mice with a monoclonal nose can discriminate odors. The authors should discuss their results in these contexts.

We apologize for this important oversight - we now include a more elaborate discussion including the relevant references as suggested in the manuscript (Line 483-496).

(8) It should be noted that odor discrimination resulting from reinforcement training does not mean normal olfactory function. It is a highly artificial situation as the animals are overtrained. It should not be used as a measure of the robustness of the olfactory sense. Natural odor discrimination (without training), detection threshold, and innate appetitive/aversive response to certain odors may be affected. These experiments were not conducted.

We agree that the standard tests commonly used to measure olfactory function require substantial training, and thus, are quite artificial. However, these tests are used because they allow a more precise quantification of olfactory function than those relying on natural behaviors.  

We have now included a few sentences to address this point in the results (Lines 321322) and discussion sections (Lines 541-543).

(9) The social behaviors were conducted using relatively coarse measures (vaginal plug and display of aggression). Moreover, these behaviors are most likely affected by the disruption of the AOB mitral cells and have little to do with the dendritic pruning process described in the paper. It is misleading to lump social behaviors with innate responses to odors.

This point follows the same logic as the previous one. The olfactory tests that rely on natural behaviors are quite coarse and difficult to quantify. In contrast, the olfactory tests using apparatuses such as olfactometers can be quantified with precision, but they are artificial. We agree that some of the naturalistic behaviors that we studied such as mating or aggression may depend to a large extent on the AOB (although it is possible that the MOB may also be involved in these tasks to a degree). In our initial version of the manuscript, we commented on the anticipated relative involvement of the MOB and AOB in the studied tasks, but we have now added some additional sentences to make this point clearer. In addition, we now add a comment indicating that it is possible that the abnormal behaviors could simply be due to a reduction in the number of AOB M/T cells (~98.5% and ~ 85% elimination of M/T cells in the AOB in Tbx::DTA and Tbx::iDTR mice, respectively), regardless of the abnormal dendritic pruning of main OB M/T cells (Lines 530-534).

See Figure 5E - M/T cells in AOB (Lines 1238-1239). 

Reviewer #2 (Public Review):

The authors make the interesting observation that the developmental refinement of apical M/T cell dendrites into individual glomeruli proceeds normally even when the majority of neighboring M/T cells are ablated. At later stages, the remaining neurons develop additional dendrites that invade multiple glomeruli ectopically, and similarly, OSN inputs to glomeruli lose projection specificity as well. The authors conclude that the normal density of M/T neurons is not required for developmental refinement, but rather for maintaining specific connectivity in adults.

The observations are indeed quite striking; however, the authors' conclusions are not entirely supported by the data.

(1) It is unclear whether the expression of diphtheria toxin that eventually leads to the ablation of the large majority of M/T neurons compromises the cell biology of the remaining ones.

DT is an extremely potent toxin that kills cells by inhibiting proteins translation, and it has been demonstrated that the presence of a single DT molecule in a cell is sufficient to kill it, because of its highly efficient catalytic activity. Accordingly, previous experiments have shown that DT kills cells within a few hours after its appearance in the cytoplasm (Yamaizumi et al., 1978). In other words, all the published evidence suggests that if a cell is exposed to the action of DT, that cell will die shortly. There is no evidence that cells exposed to DT can survive and experience long-term effects. Finally, previous works have not observed any long-term changes in neurons directly caused by the actions of DT (Johnson et al., 2017).

(2) The authors interpret the growth of ectopic dendrites later in life as a lack of maintenance of dendrite structure; however, maybe the observed changes reflect actually adaptations that optimize wiring for extremely low numbers of M/T neurons. The finding that olfactory behavior was less affected than predicted supports this interpretation.

We do not know the cellular or molecular mechanisms that explain why reducing the density of M/T cells is followed by the growth of ectopic dendrites from the remaining M/T cells. We agree that the functional outcome of growing ectopic dendrites may result in an optimization of wiring in the bulb and could explain why olfactory function is relatively preserved. We now include a comment regarding this possibility (Lines 513-525).   

(3) The number of remaining M/T neurons is much higher at P10 than later. Can the relatively large number of remaining neurons (or their better health status) be the reason that dendrites refine normally at the early developmental stages rather than a (currently unknown) developmental capacity that preserves refinement?

We thank the reviewer for the suggestion, which was also raised by reviewer 1. 

We agree with this comment, and in lines 375-381 we discuss the discrepancy between normal refinement during development, and dendritic sprouting in adults.

Cre is expressed in M/T cells and it induces DTA expression starting around P0. The elimination of M/T cells starts at this time, and continues until by P10, when more than 75% of M/T have been eliminated. At P21 more than 90% of M/T have been eliminated, and their number remains stable thereafter.

Pruning of the dendrites of M/T cells starts at P0 and it is mostly complete by P10. Therefore, it is possible that between P0 and P7, when dendrites are being pruned, the number of M/T cells remaining in the bulb is still over a threshold that does not interfere with the process of normal dendrite pruning. We agree that it would be very informative to perform additional experiments in the future where a large set of M/T cells could be ablated before pruning occurs (ideally before P0). 

(4) While the effect of reduced M/T neuron density on both M/T dendrites and OSN axons is described well, the relationship between both needs to be characterized better: Is one effect preceding the other or do they occur simultaneously? Can one be the consequence of the other?

Previous works have demonstrated that disrupting the topographic projection of the OSN axons has no effect on the structure of the apical dendrite of M/T cells (Ma et al., 2014; Nishizumi et al., 2019). Our experiments ablating a large fraction of M/T cells suggest that they are necessary for the correct targeting of OSN axons into the bulb. However, our experiments do not allow us to tell apart these 2 scenarios: 

(a) the ablation of a large fraction of M/T cells directly causes the sprouting of the apical dendrite of M/T cells, and that this sprouting in turn causes the abnormal projection of OSN axons onto the bulb. 

(b) the ablation of a large fraction of M/T cells first causes the axons of OSN to project abnormally onto multiple glomeruli in the bulb, and this in turn causes the dendrite of remaining M/T cells to sprout onto multiple glomeruli. 

We now include a comment on the manuscript explaining this point. (Lines 473-492)

(5) Page 7: the observation that not all neurons develop additional dendrites is not a sign of differences between cell types, it may be purely stochastic.

This is correct, and we mention these 2 scenarios in the discussion (Line 407-408). 

(6) Page 8: the fact that activity blockade did not affect the formation of ectopic dendrites does not suggest that the process is not activity-dependent: both manipulations have the same effect and may just mask each other.

The experiments with Kir2.1 demonstrate that the structural plasticity observed after reducing the total number of M/T cells in an animal is not regulated by the firing action potentials in the remaining cells. Instead, this experiment indicates that the observed structural plasticity may be regulated by other types of mechanisms (including increased synaptic excitation as suggested by the reviewer) that do not require the firing of action potentials in M/T cells. 

We now have included a comment regarding this point (Lines 243-247).  

(7) It remains unclear how the observed structural changes can explain the behavioral effects.

We agree that the relationship between structural changes and behavior was not appropriately explained in our manuscript. Our manipulations cause two major changes in the olfactory system, one primary, and several secondary. The primary change is a large reduction in the number of M/T cells both in the MOB and AOB. This reduction in M/T cell number triggers significant secondary changes in the connectivity of the bulb, including an abnormal projection of OSNs onto the OB, and the growth of ectopic dendrites from the remaining M/T cells into multiple glomeruli.

The behavioral abnormalities displayed by these mice is ultimately caused by the reduction in the number of M/T cells, but it is likely that the secondary structural changes could regulate some of the behavioral phenomena that we observed. For example, in principle, it is possible that the ectopic dendrites innervating several glomeruli could help the bulb to perceive smells with a much reduced number of M/T cells. On the other hand, this promiscuous growth of dendrites into multiple glomeruli could make it more difficult for the animals to discriminate between smells. The same argument could be made about the fact that OSN axons project onto multiple glomeruli: we simply do not know if this change helps or makes it more difficult for the animal to detect smells.  

We now include a comment regarding this issue (Lines 513-525).   

Reviewer #1 (Recommendations For The Authors):

Additional experiments and a more thorough discussion of the results, as suggested in the public review, would significantly strengthen the paper. Below are some specific parts that need to be addressed.

There is a lack of information on how M/T cell numbers are quantified. Without the information, it is difficult to evaluate the claim. Using the tdTomato signal may miss cells that are not labeled due to the transgenic effect. 

Although we cannot conclude that we are identifying the complete set of M/T cells (because the transgenic lines may fail to label some M/T cells), the number of M/T cells that we observed is similar to that previously reported (Richard et al., 2010). This concern has been included in the Results section (Lines 121-124).

A more detailed description about M/T cells quantification has been added into the method section (Lines 627-632).

There is a lack of information on the timeline of treatment and how measurement of the olfactory bulb volume is conducted.

We now include a more detailed description of how the volume of the OB was measured in the methods (Lines 621-623).

The volume measurement is inconsistent with the pictures shown. In Figure 1, supplemental data 2 panels B and C, it appears that the bulbs in DTA and DTR mice are about half in length in each dimension. This would translate into ~1/8 of the volume of the control mice.

We measured the volume of the bulbs based on the Neurolucida reconstructions, and we observed that in both DTA and iDTR mice the volumes of their bulbs are roughly 50% compared to a wild type mouse. In Figure 1 - figure supplement 2 the sections that were shown for wild type, DTA and iDTR mice were not taken at the same position in the bulb, and this gave the impression that the bulbs from DTA and iDTR were much smaller than they really are. We now show sections for these three animals at equivalent positions in the bulb. 

Figure 1 E and F have no legend.

We apologize for this mistake - we have now added the legend for Figures 1E and F (Lines 1009-1013).

Figure 3, supplemental data 2, it is not clear what the readers should be looking at. The data is confusing even for experts in the field. The authors should describe the figures more clearly, pointing out what they are supposed to show.

We apologize for this, and we have now added a more detailed description of Figure3 – figure supplement 2 (Lines 1153-1167).

In several figures, it is not clearly written what the comparisons were for where there are indications of statistical significance above the bars.

We have now included a more detailed description of the statistics comparison in the figure legends.

AAV serotype should be specified.

The AAV serotype used to label M/T cells was the AAV-PHP.eB. We have added this information in the methods section of the manuscript. 

Reviewer #2 (Recommendations For The Authors):

Minor points

Page 5, para 2: "The decrease in neuronal plasticity with age": it is unclear what "the decrease" refers to.

We have changed this sentence in the text to make it clear:

“The decrease in structural plasticity of M/T cells after apical dendrite refinement (Mizrahi and Katz, 2003),….”

Line 146-148

Is there a quantification of the effect of Kir2.1 overexpression alone (example shown in Figure 3D)?

We did an experiment in IDTR animals in which a fraction of M/T cells expressed Kir2.1, and we split these animals in 2 groups: (a) animals that received an injection of DT, and (b) animals that did not receive any DT. We quantified the effect of Kir2.1 on M/T cells from animals that received DT injection (with an ablation of around of 90% of M/T cells) and we did not observe any clear statistically significant differences between cells expressing Kir2.1 or neurons that did not express Kir2.1 from other iDTR animals that also received DT injections. We did not quantify the possible effects of kir2.1 in the group of animals that did not receive DT because on a first inspection we did not observe any clear differences between Kir2.1 cells and neighboring wild type cells. 

References

Fujimoto S, Leiwe MN, Aihara S, Sakaguchi R, Muroyama Y, Kobayakawa R, Kobayakawa K, Saito T, Imai T. 2023. Activity-dependent local protection and lateral inhibition control synaptic competition in developing mitral cells in mice. Dev Cell S1534-5807(23)00237-X. doi:10.1016/j.devcel.2023.05.004

Johnson RE, Tien N-W, Shen N, Pearson JT, Soto F, Kerschensteiner D. 2017. Homeostatic plasticity shapes the visual system’s first synapse. Nat Commun 8:1220. doi:10.1038/s41467-017-01332-7

Lin DM, Wang F, Lowe G, Gold GH, Axel R, Ngai J, Brunet L. 2000. Formation of precise connections in the olfactory bulb occurs in the absence of odorant-evoked neuronal activity. Neuron 26:69–80. doi:10.1016/s0896-6273(00)81139-3

Ma L, Wu Y, Qiu Q, Scheerer H, Moran A, Yu CR. 2014. A developmental switch of axon targeting in the continuously regenerating mouse olfactory system. Science 344:194–197. doi:10.1126/science.1248805

Nishizumi H, Miyashita A, Inoue N, Inokuchi K, Aoki M, Sakano H. 2019. Primary dendrites of mitral cells synapse unto neighboring glomeruli independent of their odorant receptor identity. Commun Biol 2:1–12. doi:10.1038/s42003-018-0252-y

Richard MB, Taylor SR, Greer CA. 2010. Age-induced disruption of selective olfactory bulb synaptic circuits. Proc Natl Acad Sci U S A 107:15613–15618. doi:10.1073/pnas.1007931107

Yamaizumi M, Mekada E, Uchida T, Okada Y. 1978. One molecule of diphtheria toxin fragment A introduced into a cell can kill the cell. Cell 15:245–250. doi:10.1016/0092-8674(78)90099-5

iemand versterkt opzettelijk onjuiste info met de bedoeling om anderen te bedreigen of te manipuleren

Sería muy interesante. No me gustan los iconos y me gusta cómo se ven las aplicaciones en Dmenu y Rofi.

Author response:

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

eLife Assessment <br /> This valuable study is a companion to a paper introducing a theoretical framework and methodology for identifying Cancer Driving Nucleotides (CDNs). While the evidence that recurrent SNVs or CDNs are common in true cancer driver genes is solid, the evidence that many more undiscovered cancer driver mutations will have CDNs, and that this approach could identify these undiscovered driver genes with about 100,000 samples, is limited. 

Same criticism as in the eLife assessment of eLife-RP-RA-2024-99340 (https://elifesciences.org/reviewed-preprints/99340). Hence, please refer to the responses to the companion paper.

Public Reviews:

Reviewer #1 (Public Review):

The study investigates Cancer Driving Nucleotides (CDNs) using the TCGA database, finding that these recurring point mutations could greatly enhance our understanding of cancer genomics and improve personalized treatment strategies. Despite identifying 50-150 CDNs per cancer type, the research reveals that a significant number remain undiscovered, limiting current therapeutic applications, and underscoring the need for further larger-scale research.

Strengths:

The study provides a detailed examination of cancer-driving mutations at the nucleotide level, offering a more precise understanding than traditional gene-level analyses. The authors found a significant number of CDNs remain undiscovered, with only 0-2 identified per patient out of an expected 5-8, indicating that many important mutations are still missing. The study indicated that identifying more CDNs could potentially significantly impact the development of personalized cancer therapies, improving patient outcomes.

Weaknesses:

The study is constrained by relatively small sample sizes for each cancer type, which reduces the statistical power and robustness of the findings. ICGC and other large-scale WGS datasets are publicly available but were not included in this study.

Thanks. We indeed have used all public data, including GENIE (figure 7 of the companion paper), ICGC and other integrated resources such as COSMIC. The main study is based on TCGA because it is unbiased for estimating the probability of CDN occurrences. In many datasets, the numerators are given but the denominators are not (the number of patients with the mutation / the total number of patients surveyed). In GENIE, we observed that E(u) estimated upon given sequencing panels are much smaller than in TCGA, this might be due to the selective report of nonsynonymous mutations for synonymous mutations are generally considered irrelevant in tumorigenesis.

To be able to identify rare driver mutations, more samples are needed to improve the statistical power, which is well-known in cancer research. The challenges in direct functional testing of CDNs due to the complexity of tumor evolution and unknown mutation combinations limit the practical applicability of the findings.

We fully agree. We now add a few sentences, making clear that the theory allows us to see how much more can be gained by each stepwise increase in sample size. For example, when the sample size reaches 106, further increases will yield almost no gain in confidence of CDNs identified (see figures of eLife-RP-RA-2024-99340. As pointed out in our provisional responses, an important strength of this pair of studies is that the results are testable. The complexity is the combination of mutations required for tumorigenesis and the identification of such combinations is the main goal and strength of this pair of studies. We add a few sentences to this effect.

While the importance of large sample sizes in identifying cancer drivers is well-recognized, the analytical framework presented in the companion paper (https://elifesciences.org/reviewed-preprints/99340) goes a step further by quantitatively elucidating the relationship between sample size and the resolution of CDN detection.

The question is very general as it is about multigene interactions, or epistasis. The challenges are true in all aspects of evolutionary biology, for example, the genetics of reproductive isolation(Wu and Ting 2004). The issue of epistasis is difficult because most, if not all, of the underlying mutations have to be identified in order to carry out functional tests. While the full identification is rarely feasible, it is precisely the objective of the CDN project. When the sample size increases to 100,000 for a cancer type, all point mutations for that cancer type should be identifiable.

The QC of the TCGA data was not very strict, i.e, "patients with more than 3000 coding region point mutations were filtered out as potential hypermutator phenotypes", it would be better to remove patients beyond +/- 3*S.D from the mean number of mutations for each cancer type. Given some point mutations with >3 hits in the TCGA dataset, they were just false positive mutation callings, particularly in the large repeat regions in the human genome.

Thanks. The GDC data portal offers data calls from multiple pipelines, enabling us to select mutations detected by at least two pipelines. While including patients with hypermutator phenotypes could introduce potential noise, as shown in Eq. 10 of the main text, our method for defining the upper limit of i* is relative robust to the fluctuations in the E(u) of the corresponding cancer population. Since readers may often ask about this, we expand the Methods section somewhat to emphasize this point.

The codes for the statistical calculation (i.e., calculation of Ai_e, et al) are not publicly available, which makes the findings hard to be replicated.

We have now updated the section of “Data Availability” in both papers. The key scripts for generating the major results are available at: https://gitlab.com/ultramicroevo/cdn_v1.

Reviewer #2 (Public Review):

Summary:

The study proposes that many cancer driver mutations are not yet identified but could be identified if they harbor recurrent SNVs. The paper leverages the analysis from Paper #1 that used quantitative analysis to demonstrate that SNVs or CDNs seen 3 or more times are more likely to occur due to selection (ie a driver mutation) than they are to occur by chance or random mutation.

Strengths:

Empirically, mutation frequency is an excellent marker of a driver gene because canonical driver mutations typically have recurrent SNVs. Using the TCGA database, the paper illustrates that CDNs can identify canonical driver mutations (Figure 3) and that most CDNs are likely to disrupt protein function (Figure 2). In addition, CDNs can be shared between cancer types (Figure 4).

Weaknesses:

Driver alteration validation is difficult, with disagreements on what defines a driver mutation, and how many driver mutations are present in a cancer. The value proposed by the authors is that the identification of all driver genes can facilitate the design of patient-specific targeting therapies, but most targeted therapies are already directed towards known driver genes. There is an incomplete discussion of oncogenes (where activating mutations tend to target a single amino acid or repeat) and tumor suppressor genes (where inactivating mutations may be more spread across the gene). Other alterations (epigenetic, indels, translocations, CNVs) would be missed by this type of analysis.

The above paragraph has three distinct points. We shall respond one by one.

First, …  can facilitate the design of patient-specific targeting therapies, but most targeted therapies are already directed towards known driver genes…

We state in the text of Discussion the following that shows only a few best-known driving mutations have been targeted. It is accurate to say that < 5% of CDNs we have identified are on the current targeting list. Furthermore, this list we have compiled is < 10% of what we expect to find.

Direct functional test of CDNs would be to introduce putative cancer-driving mutations and observe the evolution of tumors. Such a task of introducing multiple mutations that are collectively needed to drive tumorigenesis has been done only recently, and only for the best-known cancer driving mutations (Ortmann et al. 2015; Takeda et al. 2015; Hodis et al. 2022). In most tumors, the correct combination of mutations needed is not known. Clearly, CDNs, with their strong tumorigenic strength, are suitable candidates.

Second, “There is an incomplete discussion of oncogenes (where activating mutations tend to target a single amino acid or repeat) and tumor suppressor genes (where inactivating mutations may be more spread across the gene).”

We sincerely thank the reviewer for this insightful comment. Below are two new paragraphs in the Discussion pertaining to the point:

In this context, we should comment on the feasibility of targeting CDNs that may occur in either oncogenes (ONCs) or tumor suppressor genes (TSGs). It is generally accepted that ONCs drive tumorigenesis thanks to the gain-of-function (GOF) mutations whereas TSGs derive their tumorigenic powers by loss-of-function (LOF) mutations. It is worthwhile to point out that, since LOF mutations are likely to be more widespread on a gene, CDNs are biased toward GOF mutations. The often even distribution of non-sense mutations along the length of TSGs provide such evidence. As gene targeting aims to diminish gene functions, GOF mutations are perceived to be targetable whereas LOF mutations are not. By extension, ONCs should be targetable but TSGs are not. This last assertion is not true because mutations on TSGs may often be of the GOF kind as well.

The data often suggest that mis-sense mutations on TSGs are of the GOF kind. If mis-sense mutations are far more prevalent than nonsense mutations in tumors, the mis-sense mutations cannot possibly be LOF mutations. (After all, it is not possible to lose more functions than nonsense mutations.) For example, AAA to AAC (K to Q) is a mis-sense mutation while AAA to AAT (K to stop) is a non-sense mutation. In a separate study (referred to as the escape-route analysis), we found many cases where the mis-sense mutations on TSGs are more prevalent (> 10X) than nonsense mutations. Another well-known example is the distribution of non-sense mutations TSGs. For example, on APC, a prominent TSG, non-sense mutations are far more common in the middle 20% of the gene than the rest (Zhang and Shay 2017; Erazo-Oliveras et al. 2023). The pattern suggests that even these non-sense mutations could have GOF properties. 

The following response is about the clinical implications of our CDN analysis. Canonical targeted therapy often relies on the Tyrosine Kinase Inhibitors (TKIs) (Dang et al. 2017; Danesi et al. 2021; Waarts et al. 2022). Theoretically, any intervention that suppresses the expression of gain-of-function (GOF) CDNs could potentially have therapeutic value in cancer treatment. This leads us to a discussion of oncogenes versus TSGs in the context of GOF / LOF (loss of function) mutations. Not all mutations on oncogenes have oncogenic effect, besides, truncated mutations in oncogenes are often subject to negative selection (Bányai et al. 2021), the identification of CDNs within oncogenes is therefore crucial for developing effective cancer treatment guidelines. Secondly, while TSGs are generally believed to promote cancer development via loss of function mutations, research suggests that certain mutations within TSGs can have GOF-like effect, such as the dominant negative effect of truncated TP53 mutations (Marutani et al. 1999; de Vries et al. 2002; Gerasimavicius et al. 2022). Characterizing driver mutations as GOF or LOF mutations could potentially expand the scope of targeted cancer therapy. We’ll address this issue in a third study in preparation.

The method could be more valuable when applied to the noncoding genome, where driver mutations in promoters or enhancers are relatively rare, or as yet to be discovered. Increasingly more cancers have had whole genome sequencing. Compared to WES, criteria for driver mutations in noncoding regions are less clear, and this method could potentially provide new noncoding driver CDNs. Observing the same mutation in more than one cancer specimen is empirically unusual, and the authors provide a solid quantitative analysis that indicates many recurrent mutations are likely to be cancer-driver mutations.

Again, we are grateful for the comments which prompt us to expand a paragraph in Discussion, reproduced below.

The CDN approach has two additional applications. First, it can be used to find CDNs in non-coding regions. Although the number of whole genome sequences at present is still insufficient for systematic CDN detection, the preliminary analysis suggests that the density of CDNs in non-coding regions is orders of magnitude lower than in coding regions. Second, CDNs can also be used in cancer screening with the advantage of efficiency as the targeted mutations are fewer. For the same reason, the false negative rate should be much lower too. Indeed, the false positive rate should be far lower than the gene-based screen which often shows a false positive rate of >50% (supplement File S1).

Again, we are grateful that Reviewer #2 have addressed the potential value of our study in finding cancer drivers in non-coding regions. A major challenge in this area lies in defining the appropriate L value as presented in Eq. 10. In the main text, we used a gamma distribution to account for the variability of mutation rates across sites in coding region. For the non-coding region, we will categorize these regions based on biological annotations. The goal is to set different i* cutoffs for different genomic regions (such as heterochromatin / euchromatin, GC-rich regions or centromeric regions), and avoid false positive calls for CDN in repeated regions (Elliott and Larsson 2021; Peña et al. 2023).

References

Bányai L, Trexler M, Kerekes K, Csuka O, Patthy L. 2021. Use of signals of positive and negative selection to distinguish cancer genes and passenger genes. Elife 10:e59629.

Danesi R, Fogli S, Indraccolo S, Del Re M, Dei Tos AP, Leoncini L, Antonuzzo L, Bonanno L, Guarneri V, Pierini A, et al. 2021. Druggable targets meet oncogenic drivers: opportunities and limitations of target-based classification of tumors and the role of Molecular Tumor Boards. ESMO Open 6:100040.

Dang CV, Reddy EP, Shokat KM, Soucek L. 2017. Drugging the “undruggable” cancer targets. Nat Rev Cancer 17:502–508.

Elliott K, Larsson E. 2021. Non-coding driver mutations in human cancer. Nat Rev Cancer 21:500–509.

Erazo-Oliveras A, Muñoz-Vega M, Mlih M, Thiriveedi V, Salinas ML, Rivera-Rodríguez JM, Kim E, Wright RC, Wang X, Landrock KK, et al. 2023. Mutant APC reshapes Wnt signaling plasma membrane nanodomains by altering cholesterol levels via oncogenic β-catenin. Nat Commun 14:4342.

Gerasimavicius L, Livesey BJ, Marsh JA. 2022. Loss-of-function, gain-of-function and dominant-negative mutations have profoundly different effects on protein structure. Nat Commun 13:3895.

Hodis E, Triglia ET, Kwon JYH, Biancalani T, Zakka LR, Parkar S, Hütter J-C, Buffoni L, Delorey TM, Phillips D, et al. 2022. Stepwise-edited, human melanoma models reveal mutations’ effect on tumor and microenvironment. Science 376:eabi8175.

Marutani M, Tonoki H, Tada M, Takahashi M, Kashiwazaki H, Hida Y, Hamada J, Asaka M, Moriuchi T. 1999. Dominant-negative mutations of the tumor suppressor p53 relating to early onset of glioblastoma multiforme. Cancer Res 59:4765–4769.

Ortmann CA, Kent DG, Nangalia J, Silber Y, Wedge DC, Grinfeld J, Baxter EJ, Massie CE, Papaemmanuil E, Menon S, et al. 2015. Effect of Mutation Order on Myeloproliferative Neoplasms. N Engl J Med 372:601–612.

Peña MV de la, Summanen PAM, Liukkonen M, Kronholm I. 2023. Chromatin structure influences rate and spectrum of spontaneous mutations in Neurospora crassa. Genome Res. 33:599–611.

Takeda H, Wei Z, Koso H, Rust AG, Yew CCK, Mann MB, Ward JM, Adams DJ, Copeland NG, Jenkins NA. 2015. Transposon mutagenesis identifies genes and evolutionary forces driving gastrointestinal tract tumor progression. Nat Genet 47:142–150.

de Vries A, Flores ER, Miranda B, Hsieh H-M, van Oostrom CThM, Sage J, Jacks T. 2002. Targeted point mutations of p53 lead to dominant-negative inhibition of wild-type p53 function. Proceedings of the National Academy of Sciences 99:2948–2953.

Waarts MR, Stonestrom AJ, Park YC, Levine RL. 2022. Targeting mutations in cancer. J Clin Invest 132:e154943.

Wu C-I, Ting C-T. 2004. Genes and speciation. Nat Rev Genet 5:114–122.

Zhang L, Shay JW. 2017. Multiple Roles of APC and its Therapeutic Implications in Colorectal Cancer. JNCI: Journal of the National Cancer Institute 109:djw332.

Author response:

We thank the editor and reviewers for their feedback. We believe we can address the substantive criticisms in full, first, by providing a more explicit theoretical basis for the method. Then, we believe criticism based on assumptions about phase consistency across time points are not well founded and can be answered. Finally, in response to some reviewer comments, we will improve the surrogate testing of the method.

We will enhance the theoretical justification for the application of higher-order singular value decomposition (SVD) to the problem of irregular sampling of the cortical area. The initial version of the manuscript was written to allow informal access to these ideas (if possible), but the reviewers find a more rigorous account appropriate. We will add an introduction to modern developments in the use of functional SVD in geophysics, meteorology & oceanography (e.g., empirical orthogonal functions) and quantitative fluid dynamics (e.g., dynamic mode decomposition) and computational chemistry. Recently SVD has been used in neuroscience studies (e.g., cortical eigenmodes). To our knowledge, our work is the first time higher-order SVD has been applied to a neuroscience problem. We use it here to solve an otherwise (apparently) intractable problem, i.e., how to estimate the spatial frequency (SF) spectrum on a sparse and highly irregular array with broadband signals.

We will clarify the methodological strategy in more formal terms in the next version of the paper. But essentially SVD allows a change of basis that greatly simplifies quantitative analysis. Here it allows escape from estimating the SF across millions of data-points (triplets of contacts, at each sample), each of which contains multiple overlapping signals plus noise (noise here defined in the context of SF estimation) and are inter-correlated across a variety of known and unknown observational dimensions. Rather than simply average over samples, which would wash out much of the real signal, SVD allows the signals to be decomposed in a lossless manner (up to the choice of number of eigenvectors at which the SVD is truncated). The higher-order SVD we have implemented reduces the size of problem to allow quantification of SF over hundreds of components, each of which is guaranteed certain desirable properties, i.e., they explain known (and largest) amounts of variance of the original data and are orthonormal. This last property allows us to proceed as if the observations are independent. SF estimates are made within this new coordinate system.

We will also more concretely formalise the relation between Fourier analysis and previous observations of eigenvectors of phase that are smooth gradients.

We will very briefly review Fourier methods designed to deal with non-uniform sampling. The problems these methods are designed for fall into the non-uniform part of the spectrum from uniform–non-uniform–irregular–highly-irregular–noise. They are highly suited to, for example, interpolating between EEG electrodes to produce a uniform array for application of the fast Fourier transform (Alamia et al., 2023). However, survey across a range of applied maths fields suggests that no method exists for the degree of irregular sampling found in the sEEG arrays at issue here. In particular, the sparseness of the contact coverage presents an insurmountable hurdle to standard methods. While there exists methods for sparse samples (e.g., Margrave & Fergusen, 1999; Ying 2009), these require well-defined oscillatory behavior, e.g., for seismographic analysis. Given the problems of highly irregular sampling, sparseness of sampling and broadband, nonstationary signals, we have attempted a solution via the novel methods introduced in the current manuscript. We were able to leverage previous observations regarding the relation between eigenvectors of cortical phase and Fourier analysis, as we outline in the manuscript.

We will extend the current 1-dimensional surrogate data to better demonstrate that the method does indeed correctly detect the ordinal relations in power on different parts of the SF spectrum. We will include the effects of a global reference signal. Simulations of cortical activity are an expensive way to achieve this goal. While the first author has published in this area, such simulations are partly a function of the assumptions put into them (i.e., spatial damping, boundary conditions, parameterization of connection fields). We will therefore use surrogate signals derived from real cortical activity to complete this task.

Some more specific issues raised:<br /> (1) Application of the method to general neuroscience problems:<br /> The purpose of the manuscript was to estimate the SF spectrum of phase in the cortex, in the range where it was previously not possible. The purpose was not specifically to introduce a new method of analysis that might be immediately applicable to a wide range of available data-sets. Indeed, the specifics of the method are designed to overcome an otherwise intractable disadvantage of sEEG (irregular spatial sampling) in order to take advantage of its good coverage (compared to ECoG) and low volume conduction compared to extra-cranial methods. On the other hand, the developing field of functional SVD would be of interest to neuroscientists, as a set of methods to solve difficult problems, and therefore of general interest. We will make these points explicit in the next version of the manuscript. In order to make the method more accessible, we will also publish code for the key routines (construction of triplets of contacts, Morlet wavelets, calculation of higher-order SVD, calculation of SF).

(2) Novelty:<br /> We agree with the third reviewer: if our results can convince, then the study will have an impact on the field. While there is work that has been done on phase interactions at a variety of scales, such as from the labs of Fries, Singer, Engels, Nauhaus, Logothetis and others, it does not quantify the relative power of the different spatial scales. Additionally, the research of Freeman et al. has quantified only portions of the SF spectrum of the cortex, or used EEG to estimate low SFs. We would appreciate any pointers to the specific literature the current research contributes to, namely, the SF spectrum of activity in the cortex.

(3) Further analyses:<br /> The main results of the research are relatively simple: monotonically falling SF-power with SF; this effect occurs across the range of temporal frequencies. We provide each individual participant’s curves in the supplementary Figures. By visual inspection, it can be seen that the main result of the example participant is uniformly recapitulated. One is rarely in this position in neuroscience research, and we will make this explicit in the text.

The research stands or falls by the adequacy of the method to estimate the SF curves. For this reason most statistical analyses and figures were reserved for ruling out confounds and exploring the limits of the methods. However, for the sake of completeness, we will now include the SF vs. SF-power correlations and significance in the next version, for each participant at each frequency.

Since the main result was uniform across participants, and since we did not expect that there was anything of special significance about the delayed free recall task, we conclude that more participants or more tasks would not add to the result. As we point out in the manuscript, each participant is a test of the main hypothesis. The result is also consistent with previous attempts to quantify the SF spectrum, using a range of different tasks and measurement modalities (Barrie et al., 1996; Ramon & Holmes 2015; Alexander et al., 2019; Alexander et al., 2016; Freeman et al., 2003; Freeman et al. 2000). The search for those rare sEEG participants with larger coverage than the maximum here is a matter of interest to us, but will be left for a future study.

(4) Sampling of phase and its meaningfulness:<br /> The wavelet methods used in the present study have excellent temporal resolution but poor frequency resolution. We additionally oversample the frequency range to produce visually informative plots (usually in the context of time by frequency plots, see Alexander et al., 2006; 2013; 2019). But it is not correct that the methods for estimating phase assume a narrow frequency band. Rather, the poor frequency resolution of short time-series Morlet wavelets means the methods are robust to the exact shape of the waveforms; the signal need be only approximately sinusoidal; to rise and fall. The reason for using methods that have excellent resolution in the time-domain is that previous work (Alexander et al., 2006; Patten et al. 2012) has shown that traveling wave events can last only one or two cycles, i.e., are not oscillatory in the strict sense but are non-stationary events. So while short time-window Morlet wavelets have a disadvantage in terms of frequency resolution, this means they precisely do not have the problem of assuming narrow-band sinusoidal waveforms in the signal. We strongly disagree that our analysis requires very strong assumptions about oscillations (see last point in this section).

Our hypothesis was about the SF spectrum of the phase. When the measurement of phase is noise-like at some location, frequency and time, then this noise will not substantially contribute to the low SF parts of the spectrum compared to high SFs. Our hypothesis also concerned whether it was reasonable to interpret the existing literature on low SF waves in terms of cortically localised waves or small numbers of localised oscillators. This required us to show that low SFs dominate, and therefore that this signal must dominate any extra-cranial measurements of apparent low SF traveling waves. It does not require us to demonstrate that the various parts of the SF spectrum are meaningful in the sense of functionally significant. This has been shown elsewhere (see references to traveling waves in manuscript, to which we will also add a brief survey of research on phase dynamics).

The calculation of phase can be bypassed altogether to achieve the initial effect described in the introduction to the methods (Fourier-like basis functions from SVD). The observed eigenvectors, increasing in spatial frequency with decreasing eigenvalues, can be reproduced by applying Gaussian windows to the raw time-series (D. Alexander, unpublished observation). For example, undertaking an SVD on the raw time-series windowed over 100ms reproduces much the same spatial eigenvectors (except that they come in pairs, recapitulating the real and imaginary parts of the signal). This reproducibility is in comparison to first estimating the phase at 10Hz using Morlet wavelets, then applying the SVD to the unit-length complex phase values.

(5) Other issues to be addressed and improved:<br /> clarity on which experiments were analyzed (starting in the abstract) discussion of frequencies above 60Hz and caution in interpretation due to spike-waveform artefact or as a potential index of multi-unit spiking discussion of whether the ad hoc, quasi-random sampling achieved by sEEG contacts somehow inflates the low SF estimates

References (new)<br /> Patten TM, Rennie CJ, Robinson PA, Gong P (2012) Human Cortical Traveling Waves: Dynamical Properties and Correlations with Responses. PLoS ONE 7(6): e38392. https://doi.org/10.1371/journal.pone.0038392<br /> Margrave GF, Ferguson RJ (1999) Wavefield extrapolation by nonstationary phase shift, GEOPHYSICS 64:4, 1067-1078<br /> Ying Y (2009) Sparse Fourier Transform via Butterfly Algorithm SIAM Journal on Scientific Computing, 31:3, 1678-1694

basicallly % calculates the remainder of the division

Author response:

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

Reviewer #1 (Public Review):

In this manuscript, Molnar, Suranyi and colleagues have probed the genomic stability of Mycobacterium smegmatis in response to several anti-tuberculosis drugs as monotherapy and in combination. Unlike the study by Nyinoh and McFaddden http://dx.doi.org/10.1002/ddr.21497 (which should be cited), the authors use a sub-lethal dose of antibiotic. While this is motivated by sound technical considerations, the biological and therapeutic rationale could be further elaborated.

In the mutation accumulation experiments, we needed to ensure continuous and reproducible growth of a small number of colonies across multiple passages. This technical requirement necessitated the use of sublethal drug concentrations. However, sublethal doses also have biological relevance. Noncompliance with prescribed antibiotic regimens and the presence of antibiotic residues in food due to the extensive use of antibiotics in agricultural mass production are two obvious sources of prolonged exposure to sublethal antibiotics.

The results the authors obtain are in line with papers examining the genomic mutation rate in vitro and from patient samples in Mycobacterium tuberculosis, in vitro in Mycobacterium smegmatis and in vitro in Mycobacterium tuberculosis (although the study by HL David (PMID: 4991927) is not cited). The results are confirmatory of previous studies.

The two cited studies, along with several others, did not distinguish between genetic mutations and phenotypic responses to drug exposure (the fluctuation test alone is not suitable for this). Therefore, their objectives are not comparable to ours, which specifically investigated whether resistant colonies carry adaptive mutations. Nevertheless, we acknowledge the relevance of these studies and have now cited them in the appropriate sections in the text.

It is therefore puzzling why the authors propose the opposite hypothesis in the paper (i.e antibiotic exposure should increase mutation rates) merely to tear it down later. This straw-man style is entirely unnecessary.  

The phenomenon of stress-inducible mutagenesis in bacterial evolution remains a topic of heated debate. The emergence of genetically encoded resistance may stem from either microevolution or the dissemination of pre-existing variants from polyclonal infections under drug pressure. We believe that the Introduction presents both of these hypotheses in a balanced manner to elucidate the rationale behind our mutation accumulation investigations.  

The results on the nucleotide pools are interesting, but the statistically significant data is difficult to identify as presented, and therefore the new biological insights are unclear.

We now indicate statistical significance in the figure, in addition to the detailed statistical analysis of all dNTP measurements provided in Table S5.

Finally, the authors show that a fluctuation assay generates mutations with higher frequencies that the genetic stability assays, confirming the well-known effect of phenotypic antibiotic resistance.

What we show is that the fluctuation assay generated bacteria that tolerated the applied antibiotic without developing mutations. Conclusions about mutation rates are often drawn from fluctuation assays without confirming genetic-level changes, a discrepancy that persists despite these assays accounting for both phenotypic and genotypic alterations. By combining genome sequencing with fluctuation assays, our approach emphasizes the importance of distinguishing between these changes. While fluctuation assays remain valuable, inexpensive, and simple tools for evaluating the response of bacterial populations to various selective environments, they should not be considered definitive indicators of genetic changes.

Recommendations For The Authors:

The quality of the figures can be significantly improved. In Figure 1, cell lengths can be shown on separate histograms or better still as violin plots to enable better comparisons.

Thank you for the suggestion. We have revised the data presentation accordingly.

Details for statistical tests should be provided in the figure legend.  

Statistical details are now added in the figure legend.

In Figure 2, the number of data points is not mentioned.

Statistical information is now added to the new Figure 2, which has been revised extensively based on suggestions from all Referees.

The data in Figure 3 would be much easier to comprehend as a heatmap.  

The figure we provided is a color gradient table representing different gene expression levels, along with numerical data and statistical significance indicated within the color boxes, expanding the information content of a traditional heatmap. In response to the Referee's suggestion, we also prepared a hierarchical clustering heatmap, demonstrating that the grouping of rows and columns based on functional information in the original figure is consistent with the clustering pattern observed in the heatmap (Figure S5). As the original figure is more informative and better structured, we have included the new figure in the supplementary materials.

No statistical tests are provided for Figure 4.

We now indicate statistical significance in the figure and describe the statistical analysis in the figure legend, as suggested. Additionally, Table S5 is dedicated to the statistical analysis of the dNTP data.  

Reviewer #2 (Public Review):

In this study, the authors assess whether selective pressure from drug chemotherapy influences the emergence of drug resistance through the acquisition of genetic mutations or phenotypic tolerance. I commend the authors on their approach of utilizing the mutation accumulation (MA) assay as a means to answer this and whole genome sequencing of clones from the assay convincingly demonstrates low mutation rates in Mycobacteria when exposed to sub-inhibitory concentrations of antibiotics. Also, quantitative PCR highlighted the upregulation of DNA repair genes in Mycobacteria following drug treatment, implying the preservation of genomic integrity via specific repair pathways.

Even though the findings stem from M. smegmatis exposure to antibiotics under in vitro conditions, this is still relevant in the context of the development of drug resistance so I can see where the authors' train of thought was heading in exploring this. However, I think important experiments to perform to more fully support the conclusion that resistance is largely associated with phenotypic rather than genetic factors would have been to either sequence clones from the ciprofloxacin tolerance assay (to show absence/ minimal genetic mutations) or to have tested the MIC of clones from the MA assay (to show an increase in MIC).

Thank you for acknowledging the values of the manuscript and for the insightful suggestions for improvement. We agree on the necessity to directly connect the mutation accumulation experiments with the tolerance assay, and we have performed both suggested additional experiments.  

(1) We repeated the ciprofloxacin tolerance assay (Figure S6) using a large number of plates to gather enough cells for genomic DNA extraction and whole genome sequencing. The sequencing confirmed the absence of mutations in bacteria grown in both 0.3 and 0.5 ug/ml ciprofloxacin. We integrated this result in the revised manuscript text, while the sequencing data are available at the European Nucleotide Archive (ENA) with PRJEB71590 project number.

(2) We resuscitated three different clones from the MA assays stored at -80°C and tested the MIC of the respective drugs. The results are presented in Figure 2C. Except for EMB, we observed an increase in MIC values across the treatments.

There seems to be a disconnect between making these conclusions from experiments conducted under different conditions, or perhaps the authors can clarify why this was done.  

Molecular biology analysis methods are not easily compatible with long-term mutation accumulation experiments, or at least we could not establish the necessary conditions. When DNA or RNA extraction was required, we had to adjust the experimental scale for further analysis, which could be done in liquid culture. We believe that the suggested critical back-and-forth control experiments have significantly improved the comparability of the results.

With regards to the sub-inhibitory drug concentration applied, there is significant variation in the viability as calculated by CFUs following the different treatments and there is evidence that cell death greatly affects the calculation of mutation rate (PMCID: PMC5966242). For instance, the COMBO treatment led to 6% viability whilst the INH treatment led to 80% cell viability. Are there any adjustments made to take this into account?

We agree with and have been aware of the notion that cell death affects the calculation of the mutation rate. We included treatment optimization data on agar plates (Table 1 and Figure S2), which now demonstrate that the applied subinhibitory drug concentrations resulted in ≤10% viability across all treatments in the MA assay. This minimizes the potential discrepancy in the mutation rate calculation caused by variable cell death.  

It would also be useful to the reader to include a supplementary table of the SNPs detected from the lineages of each treatment - to determine if at any point rifampicin treatment led to mutations in rpoB, isoniazid to katG mutations, etc.  

Overall, while this study is tantalizingly suggestive of phenotypic tolerance playing a leading role in drug resistance (and perhaps genetic mutations a sub-ordinate role) a more substantial link is needed to clarify this.

The SNPs identified from the lineages of each treatment are compiled in the 'unique_muts.xls' file within the Figshare document bundle that was originally enclosed with the manuscript. In response to your suggestion, we have now added a simplified version of this data set in Table S2, listing the detected SNPs. Notably, no confirmed adaptive mutation developed in our experiments; rifampicin treatment did not result in mutations in rpoB, nor did isoniazid lead to mutations in katG.

Recommendations For The Authors:

I would suggest moving Figure 1 to the supplementary - it shows that cell wall targeting drugs cause cell shortening and DNA replication targeting drugs cause cell elongation as would be expected and this is simply a secondary observation, not one that is central to the paper.  

We agree that this is not a novel or unexpected observation. However, we used it as an indicator of drug effectiveness, particularly for bacteriostatic cell wall-targeting drugs in liquid culture that induced moderate cell death. Following Reviewer 1's suggestions, we extensively revised the figure to better convey our intended message. We believe the updated version now more clearly demonstrates the drugs' impact, and for this reason, we have opted to keep it in the main text.

Figure 2 and Table 2 show the same data so this can be combined as a paneled figure or one moved to the supplementary. It would be useful to include a diagram of how the MA assay was conducted, similar to the CIP tolerance assay figure.

Thank you for the suggestions. We have added a diagram to Figure 2 explaining the MA assay (Figure 2A), as well as the MIC experiment conducted on the MA cells (Figure 2C). To avoid redundancy, Table 2 has been removed.

Reviewer #3 (Public Review):

Summary:

This manuscript describes how antibiotics influence genetic stability and survival in Mycobacterium smegmatis. Prolonged treatment with first-line antibiotics did not significantly impact mutation rates. Instead, adaptation to these drugs appears to be mediated by upregulation of DNA repair enzymes. While this study offers robust data, findings remain correlative and fall short of providing mechanistic insights.

Strengths:

The strength of this study is the use of genome-wide approaches to address the specific question of whether or not mycobacteria induce mutagenic potential upon antibiotic exposure.

Weaknesses:

The authors suggest that the upregulation of DNA repair enzymes ensures a low mutation rate under drug pressure. However, this suggestion is based on correlative data, and there is no mechanistic validation of their speculations in this study.

Furthermore, as detailed below, some of the statements made by the authors are not substantiated by the data presented in the manuscript.

Finally, some clarifications are needed for the methodologies employed in this study. Most importantly, reduced colony growth should be demonstrated on agar plates to indicate that the drug concentrations calculated from liquid culture growth can be applied to agar surface growth. Without such validations, the lack of induced mutation could simply be due to the fact that the drug concentrations used in this study were insufficient.

Thank you for appreciating the manuscript's merits and for the instructive suggestions. We agree that demonstrating reduced colony growth on agar plates is important to validate the relevance of the drug concentrations used in the study. In response, we have added the treatment optimization data on agar plates in Figure S2 and reorganized Table 1 to show the decrease in CFU achieved with the applied subinhibitory drug concentrations.

We acknowledge that the observed upregulation of DNA repair enzymes and the low mutation rates under drug pressure represent correlative data. We removed the reference to mechanism from the abstract and avoided presenting the qPCR results as a mechanistic explanation in the text. We have only raised the possibility that correlation could be a causal relationship: "The observed upregulation of the relevant DNA repair enzymes might account for the low mutation rate even under drug pressure." We recognize the necessity for a new series of targeted experiments to provide mechanistic explanations. We added the following text to the Discussion:

“The observed activation of DNA repair processes likely mitigates mutation pressure, ensuring genome stability. However, to confirm this hypothesis, these investigations should be conducted using genetically modified DNA repair mutant strains.”

In the current manuscript, we aim to convincingly demonstrate that long-term antibiotic pressure did not induce the occurrence of new adaptive mutations.

Recommendations For The Authors:

Additional specific comments are:

Page 2. Do not italicize "Mycobacteria", which is not considered a scientific name.

Corrected.

Page 4. "Bacto pepcone" is a typo.

Corrected.

Page 6. "Quiagen" is a typo.

Corrected.

Page 9. In Table 1, RIF being described as a protein synthesis inhibitor is misleading.

Corrected.

Page 9. The statement "Specifically, following RIF, CIP, and MMC treatments, we observed cells elongating by more than twofold, whereas INH and EMB treatments led to a reduction in cell length." cannot be justified by Figure 1, as the cell length information is not conveyed in this figure.

Thank you for pointing this out, the revised Figure 1 conveys the cell length information.

Page 10. If the experiment shown in Figure S1 was done in an acidic growth condition, the figure legend should clearly indicate the fact. Additionally, the assay condition should be described in detail in the Methods section.

Thank you, the required information is now included in both the figure legend and the Methods section.

Page 10. If PZA does not work against M. smegmatis, it seems pointless to add it to the COMBO treatment. Please clarify why it was included in the drug combination experiment.

We added the following text to clarify the use of PZA: “Regardless of its inefficacy as a monotherapy, we included PZA in the combination treatment, as we could not rule out the possibility that PZA interacts with the other three drugs or that PZA elimination mechanisms are equally active in M. smegmatis under this regimen.”

Page 10. Generation times calculated from liquid culture cannot be applied to colony growth on an agar plate. The growth behaviors on a solid surface will be totally different from planktonic suspension growth. The numbers of generations indicated here will be inaccurate.

You are absolutely right. We conducted an experiment to calculate the number of generations on plates under the same conditions as used in the MA assay. We found, indeed, a different (doubled) generation time from what was determined in liquid culture. We have adjusted the mutation rates accordingly.

Page 12. Was the experiment shown in Figure 3 done in a liquid culture? If so, the transcriptional profile could be different from the experiment shown in Figure 2, which was done on an agar plate.

Yes, the experiment shown in Figure 3 was conducted in liquid culture. We acknowledge that the transcriptional profile could differ from the experiment shown in Figure 2, which was performed on an agar plate. However, technical limitations required us to use liquid cultures for these experiments.

Page 14. Regarding the statement "INH and EMB coincided with a decreased concentration of these [dCTP and dTTP] nucleotides", by examining Table S5, I do not see any statistical reductions in dCTP and dTTP levels.

Thank you for bringing this to our attention. We have made the necessary corrections to ensure that the text and data are now aligned.

Page 14. Similarly to the comment above, the statement "RIF, CIP and MMC treatments promoted an increase in the dCTP and dTTP pools" is misleading as each drug seems to increase either dCTP or dTTP, not both.

Same as above.

Page 14. The authors state, "a larger overall dNTP pool size coincides with a larger cell size and vice versa (Figure 4H)". Please indicate the unit of the pool size for the graph shown in Figure 4H. According to the legend, I assume that it refers to the concentration. The term "pool size" may be misleading as it implies quantity rather than concentration.

Page 15. Figure 4H is impossible to understand. The left y-axis label looks as if it is a ratio of cell length to volume. There is no point in having these three data on a single graph. Please separate them into individual graphs. Also, what is the spacing between the tick marks? The data also seem inconsistent with the values given in Table S1. For example, the mean volume of COMBO is larger than the control (according to Table S1), and yet the graph in Figure 4H indicates that COMBO's relative length is less than 1.

Thank you for your feedback. We have corrected these and created what we hope is a clearer figure.

Figure S1. Clarify what the gray shade in the graph represents.

The gray shade was unnecessary, so we removed it when recoloring the figure to ensure a more coherent color scheme across the different treatments.

Figure S1. Relative viability cannot be determined by OD600. CFU needs to be determined to assess cell viability.

Thank you. We changed the incorrect term viability to growth inhibition.

documentDatabase pages

Carga todos los documentos Para exportar varias páginas

No funciona para exportar varias páginas

Me muestra el documento/destino como tal

Que el documento se borre del destino donde se encontraba si lo encuentra con la línea de arriba

Se está convirtiendo el documento en un formato de exportación

Localiza la carpeta que se establece

Que se copie al destino

Establecer el destino de donde está el documento que se quiere exportar

Especifíca la subcarpeta en donde queremos estar de forma más específica

Ya se define la acción que requiere el usuario, en este caso copiar a el destino previamente establecido

En caso de que el documento ya esté guardado eliminarlo y reemplazarlo con el nuevo

define la ruta de destino del documento a copiar

Recolecta el documento que requiere ser copiado

Define la subcarpeta (a disposición personal) en donde se copiará el documento

Localiza la carpeta donde quiere que se copien los documentos

zkmAKZKMakmzkAMZXZQW

BIOciencias

BIOherramientas

IDpaper:20239

Descripción: Base de datos abierta de información científica y técnica esencial sobre herramientas de software, bases de datos y servicios para bioinformática y ciencias de la vida.

Reviewer #3 (Public review):

Summary:

The authors used the model organism Drosophila melanogaster to show that the neurotrophin Toll-6 and its ligands, DNT-2 and kek-6, play a role in maintaining the number of dopaminergic neurons and modulating their synaptic connectivity. This supports previous findings on the structural plasticity of dopaminergic neurons and suggests a molecular mechanism underlying this plasticity.

Strengths:

The experiments are overall very well designed and conclusive. Methods are in general state-of-the-art, the sample sizes are sufficient, the statistical analyses are sound, and all necessary controls are in place. The data interpretation is straightforward, and the relevant literature is taken into consideration. Overall, the manuscript is solid and presents novel, interesting, and important findings.

Weaknesses:

There are three technical weaknesses that could perhaps be improved.

First, the model of reciprocal, inhibitory feedback loops (Figure 2F) is speculative. On the one hand, glutamate can act in flies as an excitatory or inhibitory transmitter (line 157), and either situation can be the case here. On the other hand, it is not clear how an increase or decrease in cAMP level translates into transmitter release. One can only conclude that two types of neurons potentially influence each other.

Second, the quantification of bouton volumes (no y-axis label in Figure 5 C and D!) and dendrite complexity are not convincingly laid out. Here, the reader expects fine-grained anatomical characterizations of the structures under investigation, and a method to precisely quantify the lengths and branching patterns of individual dendritic arborizations as well as the volume of individual axonal boutons.

Third, Figure 1C shows two neurons with the goal of demonstrating between-neuron variability. It is not convincingly demonstrated that the two neurons are actually of the very same type of neuron in different flies or two completely different neurons.

Author response:

Reviewer #1 (Public review):

Summary:

Sun et al. are interested in how experience can shape the brain and specifically investigate the plasticity of the Toll-6 receptor-expressing dopaminergic neurons (DANs). To learn more about the role of Toll-6 in the DANs, the authors examine the expression of the Toll-6 receptor ligand, DNT-2. They show that DNT-2 expressing cells connect with DANs and that loss of function of DNT-2 in these cells reduces the number of PAM DANs, while overexpression causes alterations in dendrite complexity. Finally, the authors show that alterations in the levels of DNT-2 and Toll-6 can impact DAN-driven behaviors such as climbing, arena locomotion, and learning and long-term memory.

Strengths:

The authors methodically test which neurotransmitters are expressed by the 4 prominent DNT-2 expressing neurons and show that they are glutamatergic. They also use Trans-Tango and Bac-TRACE to examine the connectivity of the DNT-2 neurons to the dopaminergic circuit and show that DNT-2 neurons receive dopaminergic inputs and output to a variety of neurons including MB Kenyon cells, DAL neurons, and possibly DANS.

We are very pleased that Reviewer 1 found our connectivity analysis a strength.

Weaknesses:

(1) To identify the DNT-2 neurons, the authors use CRISPR to generate a new DN2-GAL4. They note that they identified at least 12 DNT-2 plus neurons. In Supplementary Figure 1A, the DNT-2-GAL4 driver was used to express a UAS-histoneYFP nuclear marker. From these figures, it looks like DNT-2-GAL4 is labeling more than 12 neurons. Is there glial expression?

Indeed, we claimed that DNT-2 is expressed in at least 12 neurons (see line 141, page 6 of original manuscript), which means more than 12 could be found. The membrane tethered reporters we used – UAS-FlyBow1.1, UASmcD8-RFP, UAS-MCFO, as well as UAS-DenMark:UASsyd-1GFP – gave a consistent and reproducible pattern. However, with DNT-2GAL4>UAS-Histone-YFP more nuclei were detected that were not revealed by the other reporters. We have found also with other GAL4 lines that the patterns produced by different reporters can vary. This could be due to the signal strength (eg His-YFP is very strong) and perdurance of the reporter (e.g. the turnover of His-YFP may be slower than that of the other fusion proteins).

We did not test for glial expression, as it was not directly related to the question addressed in this work.

(2) In Figure 2C the authors show that DNT-2 upregulation leads to an increase in TH levels using q-RT-PCR from whole heads. However, in Figure 3H they also show that DNT-2 overexpression also causes an increase in the number of TH neurons. It is unclear whether TH RNA increases due to expression/cell or the number of TH neurons in the head.

Figure 3H shows that over-expression of DNT-2 FL increased the number of Dcp1+ apoptotic cells in the brain, but not significantly (p=0.0939). The ability of full-length neurotrophins to induce apoptosis and cleaved neurotrophins promote cell survival is well documented in mammals. We had previously shown that DNT-2 is naturally cleaved, and that over-expression of DNT-2 does not induce apoptosis in the various contexts tested before (McIlroy et al 2013 Nature Neuroscience; Foldi et al 2017 J Cell Biol; Ulian-Benitez et al 2017 PLoS Genetics). Similarly, throughout this work we did not find DNT-2FL to induce apoptosis.

Instead, in Figure 3G we show that over-expression of DNT-2FL causes a mild yet statistically significant increase in the number of TH+ cells. This is an important finding that supports the plastic regulation of PAM cell number. We thank the Reviewer for highlighting this point, as we had forgotten to add the significance star in the graph. In this context, we cannot rule out the possibility that the increase in TH mRNA observed when we over-express DNT-2FL could not be due to an increase in cell number instead. Unfortunately, it is not possible for us to separate these two processes at this time. Either way, the result would still be the same: an increase in dopamine production when DNT-2 levels rise.

(3) DNT-2 is also known as Spz5 and has been shown to activate Toll-6 receptors in glia (McLaughlin et al., 2019), resulting in the phagocytosis of apoptotic neurons. In addition, the knockdown of DNT-2/Spz5 throughout development causes an increase in apoptotic debris in the brain, which can lead to neurodegeneration. Indeed Figure 3H shows that an adult-specific knockdown of DNT-2 using DNT2-GAL4 causes an increase in Dcp1 signal in many neurons and not just TH neurons.

Indeed, we did find Dcp1+ cells in TH-negative cells too (although not widely throughout the brain). This is not surprising, as DNT-2 neurons have large arborisations that can reach a wide range of targets; DNT-2 is secreted, and could reach beyond its immediate targets; Toll-6 is expressed in a vast number of cells in the brain; DNT-2 can bind promiscuously at least also Toll-7 and other Keks, which are also expressed in the adult brain (Foldi et al 2017 J Cell Biology; Ulian-Benitez et al 2017 PLoS Genetics; Li et al 2020 eLife). Together with the findings by McLaughlin et al 2019, our findings further support the notion that DNT-2 is a neuroprotective factor in the adult brain. It will be interesting to find out what other neuron types DNT-2 maintains.

We would like to thank Reviewer 1 for their positive comments on our work and their interesting and valuable feedback.

Reviewer #2 (Public review):

This paper examines how structural plasticity in neural circuits, particularly in dopaminergic systems, is regulated by Drosophila neurotrophin-2 (DNT-2) and its receptors, Toll-6 and Kek-6. The authors show that these molecules are critical for modulating circuit structure and dopaminergic neuron survival, synaptogenesis, and connectivity. They show that loss of DNT-2 or Toll-6 function leads to loss of dopaminergic neurons, dendritic arborization, and synaptic impairment, whereas overexpression of DNT-2 increases dendritic complexity and synaptogenesis. In addition, DNT-2 and Toll-6 modulate dopamine-dependent behaviors, including locomotion and long-term memory, suggesting a link between DNT-2 signaling, structural plasticity, and behavior.

A major strength of this study is the impressive cellular resolution achieved. By focusing on specific dopaminergic neurons, such as the PAM and PPL1 clusters, and using a range of molecular markers, the authors were able to clearly visualize intricate details of synapse formation, dendritic complexity, and axonal targeting within defined circuits. Given the critical role of dopaminergic pathways in learning and memory, this approach provides a good opportunity to explore the role of DNT-2, Toll-6, and Kek-6 in experience-dependent structural plasticity. However, despite the promise in the abstract and introduction of the paper, the study falls short of establishing a direct causal link between neurotrophin signaling and experience-induced plasticity.

Simply put, this study does not provide strong evidence that experience-induced structural plasticity requires DNT-2 signaling. To support this idea, it would be necessary to observe experience-induced structural changes and demonstrate that downregulation of DNT-2 signaling prevents these changes. The closest attempt to address this in this study was the artificial activation of DNT-2 neurons using TrpA1, which resulted in overgrowth of axonal arbors and an increase in synaptic sites in both DNT-2 and PAM neurons. However, this activation method is quite artificial, and the authors did not test whether the observed structural changes were dependent on DNT-2 signaling. Although they also showed that overexpression of DNT-2FL in DNT-2 neurons promotes synaptogenesis, this phenotype was not fully consistent with the TrpA1 activation results (Figures 5C and D).

In conclusion, this study demonstrates that DNT-2 and its receptors play a role in regulating the structure of dopaminergic circuits in the adult fly brain. However, it does not provide convincing evidence for a causal link between DNT-2 signaling and experience-dependent structural plasticity within these circuits.

We would like to thank Reviewer 2 for their very positive assessment of our approach to investigate structural circuit plasticity. We are delighted that this Reviewer found our cellular resolution impressive. We are also very pleased that Reviewer 2 found that our work demonstrates that DNT-2 and its receptors regulate the structure of dopaminergic circuits in the adult fly brain. This is already a very important finding that contributes to demonstrating that, rather than being hardwired, the adult fly brain is plastic, like the mammalian brain.

We are very pleased that this Reviewer acknowledges that this work provides a good opportunity to explore the role of DNT-2, Toll-6, and Kek-6 in experience-dependent structural plasticity. We provide a molecular mechanism and proof of principle, and we demonstrate a direct link between the function of DNT-2 and its receptors in circuit plasticity, and a suggestive link to neuronal activity. Finding out the direct link to lived experience is a big task, beyond the scope of this manuscript, and we will be testing this with future projects. Nevertheless, it is important to place our findings within this context, as it opens opportunities for discovery by the neuroscience community.

We would like to thank Reviewer 2 for the positive and thoughtful evaluation of our work, and for their feedback.

Reviewer #3 (Public review):

Summary:

The authors used the model organism Drosophila melanogaster to show that the neurotrophin Toll-6 and its ligands, DNT-2 and kek-6, play a role in maintaining the number of dopaminergic neurons and modulating their synaptic connectivity. This supports previous findings on the structural plasticity of dopaminergic neurons and suggests a molecular mechanism underlying this plasticity.

Strengths:

The experiments are overall very well designed and conclusive. Methods are in general state-of-the-art, the sample sizes are sufficient, the statistical analyses are sound, and all necessary controls are in place. The data interpretation is straightforward, and the relevant literature is taken into consideration. Overall, the manuscript is solid and presents novel, interesting, and important findings.

We are delighted that Reviewer 3 found our work solid, novel, interesting and with important findings. We are also very pleased that this Reviewer found that all necessary controls have been carried out.

Weaknesses:

There are three technical weaknesses that could perhaps be improved.

First, the model of reciprocal, inhibitory feedback loops (Figure 2F) is speculative. On the one hand, glutamate can act in flies as an excitatory or inhibitory transmitter (line 157), and either situation can be the case here. On the other hand, it is not clear how an increase or decrease in cAMP level translates into transmitter release. One can only conclude that two types of neurons potentially influence each other.

Thank you for pointing out that glutamate can be inhibitory. In mammals, the neurotrophin BDNF has an important function in glutamatergic synapses, thus we were intrigued by a potential evolutionary conservation. Our evidence that DNT-2A neurons could be excitatory is indirect, yet supportive: exciting DNT-2 neurons with optogenetics resulted in an increase in GCaMP in PAMs (data not shown); over-expression of DNT-2 in DNT-2 neurons increased TH mRNA levels; optogenetic activation of DNT-2 neurons results in the Dop2R-dependent downregulation of cAMP levels in DNT-2 neurons. Dop2R signals in response to dopamine, which would be released only if dopaminergic neurons had been excited. Accordingly, glutamate released from DNT-2 neurons would have been rather unlikely to inhibit DANs.

cAMP is a second messenger that enables the activation of PKA. PKA phosphorylates many target proteins, amongst which are various channels. This includes the voltage gated calcium channels located at the synapse, whose phosphorylation increases their opening probability. Thus, a rise in cAMP could facilitate neurotransmitter release, and a downregulation would have the opposite effect. Other targets of PKA include CREB, leading to changes in gene expression. Conceivably, a decrease in PKA activity could result in the downregulation of DNT-2 expression in DNT-2 neurons. This negative feedback loop would restore the homeostatic relationship between DNT-2 and dopamine levels.

Our data indeed demonstrate that DNT-2 and PAM neurons influence each other, not potentially, but really. We have provided data that: DNT-2 and PAMs are connected through circuitry; that the DNT-2 receptors Toll-6 and kek-6 are expressed in DANs, including in PAMs; that alterations in the levels of DNT-2 (both loss and gain of function) and loss of function for the DNT-2 receptors Toll-6 and Kek-6 alter PAM cell number, alter PAM dendritic complexity and alter synaptogenesis in PAMs; alterations in the levels of DNT-2, Toll-6 and kek-6 in adult flies alters dopamine dependent behaviours of climbing, locomotion in an arena and learning and long-term memory. These data firmly demonstrate that the two neuron types DNT-2 and PAMs influence each other.

We have also shown that over-expression of DNT-2 in DNT-2 neurons increases TH mRNA levels, whereas activation of DNT-2 neurons decreases cAMP levels in DNT-2 neurons in a dopamine/Dop2R-dependent manner. These data show a functional interaction between DNT-2 and PAM neurons.

Second, the quantification of bouton volumes (no y-axis label in Figure 5 C and D!) and dendrite complexity are not convincingly laid out. Here, the reader expects fine-grained anatomical characterizations of the structures under investigation, and a method to precisely quantify the lengths and branching patterns of individual dendritic arborizations as well as the volume of individual axonal boutons.

Figure 5C, D do contain Y-axis labels, all our graphs in main manuscript and in supplementary files contain Y-axis labels.

In fact, we did use a method to precisely quantify the lengths and branching patterns of individual dendritic arborisations, volume of individual boutons and bouton counting. These analyses were carried out using Imaris software. For dendritic branching patterns, the “Filament Autodetect” function was used. Here, dendrites were analysed by tracing semi-automatically each dendrite branch (ie manual correction of segmentation errors) to reconstruct the segmented dendrite in volume. From this segmented dendrite, Imaris provides measurements of total dendrite volume, number and length of dendrite branches, terminal points, etc. For bouton size and number, we used the Imaris “Spot” function. Here, a threshold is set to exclude small dots (eg of background) that do not correspond to synapses/boutons. All samples and genotypes are treated with the same threshold, thus the analysis is objective and large sample sizes can be analysed effectively. We have already provided a description of the use of Imaris in the methods section.

Third, Figure 1C shows two neurons with the goal of demonstrating between-neuron variability. It is not convincingly demonstrated that the two neurons are actually of the very same type of neuron in different flies or two completely different neurons.

We thank Reviewer 3 for raising this interesting point. It is not possible to prove which of the four DNT-2A neurons per hemibrain, which we visualised with DNT-2>MCFO, were the same neurons in every individual brain we looked at. This is because in every brain we have looked at, the soma of the neurons were not located in exactly the same location. Furthermore, the arborisation patterns are also different and unique, for each individual brain. Thus, there is natural variability in the position of the soma and in the arborisation patterns. Such variability presumably results from the combination of developmental and activity-dependent plasticity.

We would like to thank Reviewer 3 for the very positive evaluation of our work and the interesting and valuable feedback.

LAS NK- ATACAN A LAS QUE CARECEN DE MHC I X MEDIO DE AUTO.MHC QUE AL ACTIVARSE LAS INACTIVAN(SENSOR) SI DISMINUYE SU ACTIVIDAD (AL HACERSE HUMORAL DISMINUYE (VUELVE A HACERSE MALOTA)

ANTES DE LA PUBERTAD- LA MAYOR PARTE DE LOS HUESOS SON HEMATOPOYETICAMENTE ACTIVOS

A LOS 18 AÑOS: -VERTENRAS -COSTILLAS -ESTERNON -CRANEO - PELVIS - PARTES DEL HUMERO Y FEMUR

LAS CELULAS ESTROMALES QUE REGULAN LA QUIESCENCIA, PROLIFERACION, TRAFICO Y DIFERENCIACION SON:

CELS ENDOTELIALES QUE RECUBREN LOS VASOS SANGUINEOS -CELS PERIVASCULARES E INTERACTUAN CON LAS ENDOTELIALES -NV SIMPATICOS QUE TRANSMITEN SEÑALES A LOS NIÑOS CELULARES -MACROFAGOS -OSTEOBLASTOS- REGULAN LA DIFERENCIACION DE LAS CELS LINFOIDES

EN EL TIMO- CONFORME SE ACERCAN ,MAS A LA MEDULA SON MAS MADUROS, ES DECIR EN LA MEDULA

ES PARA LA RESPUESTA CONTRA HONGOS Y OTROS TIPOS

ACTIVA A LA RESPUESTA INMUNITARIA TIPO 2. EN EL CASO DE LAS ALERGIAS Y HELMINTOS

knknjknjk

mkmkmkm m

Author response:

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

Reviewer #1 (Public Review):

Summary:

The report describes the control of the activity of the RNA-activated protein kinase, PKR, by the Vaccinia virus K3 protein. Repressive binding of K3 to the kinase prevents phosphorylation of its recognised substrate, EIF2α (the α subunit of the Eukaryotic Initiation Factor 2). The interaction of K3 is probed by saturation mutation within four regions of PKR chosen by modelling the molecules' interaction. They identify K3-resistant PKR variants that recognise that the K3/EIF2α-binding surface of the kinase is malleable. This is reasonably interpreted as indicating the potential adaptability of this antiviral protein to combat viral virulence factors.

Strengths:

This is a well-conducted study that probes the versatility of the antiviral response to escape a viral inhibitor. The experimentation is very diligent, generating and screening a large number of variants to recognise the malleability of residues at the interface between PKR and K3.

Weaknesses:

(1) These are minor. The protein interaction between PKR and K3 has been previously well-explored through phylogenetic and functional analyses and molecular dynamics studies, as well as with more limited site-directed mutational studies using the same experimental assays.

Accordingly, these findings largely reinforce what had been established rather than making major discoveries.

First, thank you for your thoughtful feedback. We agree that our results are concordant with previous findings and recognize the importance of emphasizing what we find novel in our results. We have revised the introduction (lines 65-74 of the revised_manuscript.pdf) to emphasize three findings of interest: (1) the PKR kinase domain is largely pliable across its substrate-binding interface, a remarkable quality that is most fully revealed through a comprehensive screen, (2) we were able to differentiate variants that render PKR nonfunctional from those that are susceptible to Vaccinia K3, and (3) we observe a strong correlation between PKR variants that are resistant to K3 WT and K3-H47R.

There are some presumptions:

(2) It isn't established that the different PKR constructs are expressed equivalently so there is the contingency that this could account for some of the functional differences.

This is an excellent point. We have revised the manuscript to raise this caveat in the discussion (lines 247-251). One indirect reason to suppose that expression differences among our PKR variants are not a dominant source of variation is that we did not observe much variation in kinase activity in the absence of K3.

(3) Details about the confirmation of PKR used to model the interaction aren't given so it isn't clear how accurately the model captures the active kinase state. This is important for the interaction with K3/EIF2α.

We have expanded on Supplemental Figure 12 and our description of the AlphaFold2 models in the Materials and Methods section (lines 573-590). We clarify that these models may not accurately capture the phosphoacceptor loop of eIF2α (residues Glu49-Lys60) and the PKR β4-5 linker (Asp338-Asn350) as these are highly flexible regions that are absent in the existing crystal structure complex (PDB 2A1A) and have low AlphaFold2 confidence scores (pLDDT < 50). We also noted, in the Materials and Methods section and in the caption of Figure 1, that the modeled eIF2α closely resembles the crystal structure of standalone yeast eIF2α, which places the Ser51 phosphoacceptor site far from the PKR active site. Thus, we expect there are additional undetermined PKR residues that contact eIF2α.

(4) Not all regions identified to form the interface between PKR and K3 were assessed in the experimentation. It isn't clear why residues between positions 332-358 weren't examined, particularly as this would have made this report more complete than preceding studies of this protein interaction.

Great questions. We designed and generated the PKR variant library based on the vaccinia K3 crystal structure (PDB 1LUZ) aligned to eIF2α in complex with PKR (PDB 2A1A), in which PKR residues 338-350 are absent. After the genesis of the project, we generated the AlphaFold2-predicted complex of PKR and vaccinia K3, and have become very interested in the β4-β5 linker, a highly diverse region across PKR homologs which includes residues 332-358. However, this region remains unexamined in this manuscript.

Reviewer #2 (Public Review):

Chambers et al. (2024) present a systematic and unbiased approach to explore the evolutionary potential of the human antiviral protein kinase R (PKR) to evade inhibition by a poxviral antagonist while maintaining one of its essential functions.

The authors generated a library of 426 single-nucleotide polymorphism (SNP)-accessible non-synonymous variants of PKR kinase domain and used a yeast-based heterologous virus-host system to assess PKR variants' ability to escape antagonism by the vaccinia virus pseudo-substrate inhibitor K3. The study identified determinant sites in the PKR kinase domain that harbor K3-resistant variants, as well as sites where variation leads to PKR loss of function. The authors found that multiple K3-resistant variants are readily available throughout the domain interface and are enriched at sites under positive selection. They further found some evidence of PKR resilience to viral antagonist diversification. These findings highlight the remarkable adaptability of PKR in response to viral antagonism by mimicry.

Significance of the findings:

The findings are important with implications for various fields, including evolutionary biology, virus-host interfaces, genetic conflicts, and antiviral immunity.

Strength of the evidence:

Convincing methodology using state-of-the-art mutational scanning approach in an elegant and simple setup to address important challenges in virus-host molecular conflicts and protein adaptations.

Strengths:

Systematic and Unbiased Approach:

The study's comprehensive approach to generating and characterizing a large library of PKR variants provides valuable insights into the evolutionary landscape of the PKR kinase domain. By focusing on SNP-accessible variants, the authors ensure the relevance of their findings to naturally occurring mutations.

Identification of Key Sites:

The identification of specific sites in the PKR kinase domain that confer resistance or susceptibility to a poxvirus pseudosubstrate inhibition is a significant contribution.

Evolutionary Implications:

The authors performed meticulous comparative analyses throughout the study between the functional variants from their mutagenesis screen ("prospective") and the evolutionarily-relevant past adaptations ("retrospective").

Experimental Design:

The use of a yeast-based assay to simultaneously assess PKR capacity to induce cell growth arrest and susceptibility/resistance to various VACV K3 alleles is an efficient approach. The combination of this assay with high-throughput sequencing allows for the rapid characterization of a large number of PKR variants.

Areas for Improvement:

(5) Validation of the screen: The results would be strengthened by validating results from the screen on a handful of candidate PKR variants, either using a similar yeast heterologous assay, or - even more powerfully - in another experimental system assaying for similar function (cell translation arrest) or protein-protein interaction.

Thank you for your thoughtful feedback. We agree that additional data to validate our findings would strengthen the manuscript. We have individually screened a handful of PKR variants in duplicate using serial dilution to measure yeast growth, and found that the results generally support our original findings. We have revised the manuscript to include these validation experiments (lines 117-119 of the revised_manuscript.pdf, Supplemental Figure 4).

(6) Evolutionary Data: Beyond residues under positive selection, the screen would allow the authors to also perform a comparative analysis with PKR residues under purifying selection. Because they are assessing one of the most conserved ancestral functions of PKR (i.e. cell translation arrest), it may also be of interest to discuss these highly conserved sites.

This is a great point. We do find that there are regions of the PKR kinase domain that are not amenable to genetic perturbation, namely in the glycine rich loop and active site. We contrast the PKR functional scores at conserved residues under purifying selection with those under positive selection in Figure 2E (lines 141-143).

(7) Mechanistic Insights: While the study identifies key sites and residues involved in vaccinia K3 resistance, it could benefit from further investigation into the underlying molecular mechanisms. The study's reliance on a single experimental approach, deep mutational scanning, may introduce biases and limit the scope of the findings. The authors may acknowledge these limitations in the Discussion.

We agree that further investigation into the underlying molecular mechanisms is warranted and we have revised the manuscript to acknowledge this point in the discussion (lines 284-288).

(8) Viral Diversity: The study focuses on the viral inhibitor K3 from vaccinia. Expanding the analysis to include other viral inhibitors, or exploring the effects of PKR variants on a range of viruses would strengthen and expand the study's conclusions. Would the identified VACV K3-resistant variants also be effective against other viral inhibitors (from pox or other viruses)? or in the context of infection with different viruses? Without such evidence, the authors may check the manuscript is specific about the conclusions.

This is a fantastic question that we are interested in exploring in our future studies. In the manuscript we note a strong correlation between PKR variants that evade vaccinia wild-type K3 and the K3-H47R enhanced allele, but we are curious to know if this holds when tested against other K3 orthologs such as variola virus C3. That said, we have revised the manuscript to clarify this limitation to our findings and specify vaccinia K3 where appropriate.

Reviewer #3 (Public Review):

Summary:

-  This study investigated how genetic variation in the human protein PKR can enable sensitivity or resistance to a viral inhibitor from the vaccinia virus called K3.

-  The authors generated a collection of PKR mutants and characterized their activity in a high-throughput yeast assay to identify 1) which mutations alter PKR's intrinsic biochemical activity, 2) which mutations allow for PKR to escape from viral K3, and 3) which mutations allow for escape from a mutant version of K3 that was previously known to inhibit PKR more efficiently.

-  As a result of this work, the authors generated a detailed map of residues at the PKR-K3 binding surface and the functional impacts of single mutation changes at these sites.

Strengths:

-  Experiments assessed each PKR variant against three different alleles of the K3 antagonist, allowing for a combinatorial view of how each PKR mutant performs in different settings.

-  Nice development of a useful, high-throughput yeast assay to assess PKR activity, with highly detailed methods to facilitate open science and reproducibility.

-  The authors generated a very clean, high-quality, and well-replicated dataset.

Weaknesses:

(9) The authors chose to focus solely on testing residues in or near the PKR-K3 predicted binding interface. As a result, there was only a moderately complex library of PKR mutants tested. The residues selected for investigation were logical, but this limited the potential for observing allosteric interactions or other less-expected results.

First, we greatly appreciate all your feedback on the manuscript, as well as raising this particular point. We agree that this is a moderately complex library of PKR variants, from which we begin to uncover a highly pliable domain with a few specific sites that cannot be altered. We have revised the manuscript to raise this limitation (lines 284-288 of the revised_manuscript.pdf) and encourage additional exploration of the PKR kinase domain.

(10) For residues of interest, some kind of independent validation assay would have been useful to demonstrate that this yeast fitness-based assay is a reliable and quantitative readout of PKR activity.

We agree that additional data to validate our findings would strengthen the manuscript. We have individually screened a handful of PKR variants in duplicate using serial dilution to measure yeast growth, and generally found that the results support our original findings. We have revised the manuscript to include this validation experiment (lines 117-119, Supplemental Figure 4).

(11) As written, the current version of the manuscript could use more context to help a general reader understand 1) what was previously known about these PKR and K3 variants, 2) what was known about how other genes involved in arms races evolve, or 3) what predictions or goals the authors had at the beginning of their experiment. As a result, this paper mostly provides a detailed catalog of variants and their effects. This will be a useful reference for those carrying out detailed, biochemical studies of PKR or K3, but any broader lessons are limited.

Thank you for bringing this to our attention. We have revised the introduction of the manuscript to provide more context regarding previous work demonstrating an evolutionary arms race between PKR and K3 and how single residue changes alter K3 resistance (lines 51-64).

(12) I felt there was a missed opportunity to connect the study's findings to outside evolutionary genetic information, beyond asking if there was overlap with PKR sites that a single previous study had identified as positively selected. For example, are there any signals of balancing selection for PKR? How much allelic diversity is there within humans, and are people typically heterozygous for PKR variants? Relatedly, although PKR variants were tested in isolation here, would the authors expect their functional impacts to be recessive or dominant, and would this alter their interpretations? On the viral diversity side, how much variation is there among K3 sequences? Is there an elevated evolutionary rate, for example, in K3 at residues that contact PKR sites that can confer resistance? None of these additions are essential, but some kind of discussion or analysis like this would help to connect the yeast-based PKR phenotypic assay presented here back to the real-world context for these genes.

We appreciate this suggestion to extend our findings to a broader evolutionary context. There is little allelic diversity of PKR in humans, with all nonsynonymous variation listed in gnomAD being rare. (PKR shows sequence diversity in comparisons across species, including across primates.) Thus, barring the possibility of variation being present in under-studied populations, there is unlikely to be balancing selection on PKR in humans. Our expectation is that beneficial mutations in PKR for evading a pseudosubstrate inhibitor would be dominant, as a small amount of eIF2α phosphorylation is capable of halting translation (Siekierka, PNAS, 1984). There is a recent report citing PKR missense variants associated with dystonia that can be dominantly or recessively inherited (Eemy et al. 2020 PMID 33236446). Elde et al. 2009 (PMID 19043403) notes that poxvirus K3 homologs are under positive selection but no specific residues have been cited to be under positive selection. The lack of allelic diversity in PKR in humans notwithstanding, PKR could experience future selection in the human population as evidenced by its rapid evolution in primates, so we fully agree that a connection to the real-world context is useful. We have noted these topics in the discussion section (lines 289-294).

Reviewer #1 (Recommendations For The Authors):

I have no major criticisms but ask for some clarifications and make some comments about the perceived weaknesses.

(13)  If the authors disagree with my summation that the findings largely replicate what was known, could they detail how the findings differ from what was known about this protein interaction and the major new insights stemming from the study? Currently, the abstract is a little philosophical rather than listing the explicit discoveries of the study.

Thank you again for raising the need for us to clearly convey the novelty of our findings. We have revised the final paragraph in our introduction as described in comment #1.

(14) As the experimental approach is well reported it is unnecessary to confirm the proposed activity by, for instance, measures of Sui2 phosphorylation. However, previous reports have recognised that point mutants of PKR can be differentially expressed. The impact of this potential effect is unknown in the current experimentation as there are no measures of the expression of the different mutant PKR constructs. The large number of constructs used makes this verification onerous. The potential impact could be ameliorated by redundant replacing each residue (hoping different residues have different effects on expression). Still, this limitation of the study should be acknowledged in the text.

We greatly appreciate this comment and agree that this should be made clear in the text, which we have added to the discussion of the manuscript (lines 247-251).

(15) Preceding findings and the modeling in this report recognise an involvement in the kinase insert region (residues 332 to 358) in PKR's interaction with K3 but this region is excluded from the analysis. These residues have been largely disregarded in the preceding analysis (it is absent from the molecular structure of the kinase) so its inclusion here might have lent a more novel aspect or delivered a more complete investigation. Is there a justification for excluding this flexible loop?

The PKR variant library was designed based on the crystal structure of K3 (PDB 1LUZ) aligned to eIF2α in complex with PKR (PDB 2A1A). After the library was designed and made we attained complete predicted structures of PKR in complex with eIF2α and K3, which largely agrees with the predicted crystal structures but contain the additional flexible loops that were not captured in the crystal structures. Though the library studied here does not explore variation in the kinase insert region, we are very interested in doing so in our future studies.

(16)  Could the explanation of the 'PKR functional score' be clarified? The description given within the legend of SF1 was helpful, so could this be replicated earlier in the main body of the text when introducing these experiments? e.g. As PKR activity is toxic to yeast, the number of cells in the pool expressing the functional PKR will decrease over time. Thus the associated barcode read count will also decrease, while the read count for the nonfunctional PKR will increase. This is termed the PKR function score, which will be relatively lower for cells transformed with less active PKR than those with more active PKR.

Thank you for suggesting this clarification, we have revised the manuscript to clarify our definition of the PKR functional score (lines 106-109).

(17)  Another suggestion to clarify this term is to modify the figures. Currently, the intent of the first simulated graph in Fig 1E is clear but the inversion of the response (shown by the transposition of the colours) in the next graph (to the right) is less immediately obvious. Accordingly, the orientation of the 'PKR functional score' is uncertain. Could the authors add text to the rightmost graphic in Figure 1E by, for instance, indicating the PKR activity in the vertical column with text such as 'less active' (at the bottom), 'WT' (in the centre), and 'more activity' (at the top)? Also, the position of the inactive K296R mutant might be added to Figure 2A complementing the positioning of the active WT kinase in the first data graph of this kind.

We appreciate your specific feedback to improve the figures of the manuscript, we have made adjustments to Figure 1E to clarify how we derive the PKR functional scores.

(18) The authors don't use existing structures of PKR in their modelling. However, there is no information about the state of the PKR molecule used for modelling. Specific elements of the kinase domain affect its interaction with K3 so it would be informative to know the orientation of these elements in the model. Could the authors detail the state of pivotal kinase elements in their models? This could involve the alignment of the N- and C-lobes, the orientation of kinase spines (C- and R-spines), and the phosphorylation stasis of residues in the activation loop, or at least the position of this loop in relationship to that adopted in the active dimeric kinase (e.g. PDB-2A1A, 3UIU or 6D3L). Alternatively, crystallographic structures of active inactive PKR could be overlayed with the theoretical structure used for modelling (as supplementary information).

We have revised the manuscript to describe the alignment of the predicted PKR-K3 complex with active and inactive PKR, and we have extended Supplemental Figure 12 with an overlay of the predicted structures with existing structures. We have also added a supplemental data file containing the RMSD values of PKR (from the predicted PKR-K3 complex) aligned to active (PDB 2A1A) and inactive (PDB 3UIU) or unphosphorylated (PDB 6D3L) PKR (5_Structure-Alignment-RMSD-Values.xlsx). We have also provided the AlphaFold2 best model predictions for the PKR-eIF2α complex (6_AF2_PKR-KD_eIF2a.pdb) and PKR-K3 complex (7_AF2_PKR-KD_VACV-K3.pdb). Looking across the RMSD values, the AlphaFold2 model of PKR most closely resembles unphosphorylated PKR (PDB 6D3L) though we note the activation loop is absent from PDB 6D3L and 3UIU. We also aligned the Ser51 phosphoacceptor loop of AlphaFold2 eIF2α model to PDB 1Q46 and we see that the model reflects the pre-phosphorylation state. This loop is expected to interact with the PKR active site, which is not captured in our model and we state this explicitly in the caption of Figure 1 (lines 665-668).

(19) Could some specific residue in Figure 7 be labelled (numbered) to orient the findings? Also, the key in this figure doesn't title the residues coloured white (RE red/black/blue). The white also isn't distinguished from the green (outside the regions targeted for mutagenesis).

Excellent suggestion, we have revised this figure to include labels for the sites to orient the reader and clarify our categorization of PKR residues in the kinase domain.

(20)  Regarding the discussion, the authors adopt the convention of describing K3 as a pseudosubstrate. Although I realize it is common to refer to K3 as a pseudosubstrate, it isn't phosphorylated and binds slightly differently to PKR so alternative descriptors, such as 'a competitive binder', would more accurately present the protein's function. Possibly for this reason, the authors declared an expectation that evolution pressures should shift K3 to precisely mimic EIF2α. However, closer molecular mimicry shouldn't be expected for two reasons. The first is a risk of disrupting other interactions, such as the EIF2 complex. Secondly, equivalent binding to PKR would demote K3 to merely a stoichiometric competitor of EIF2α. In this instance, effective inhibition would require very high levels of K3 to compete with equivalent binding by EIF2α. This would be demanding particularly upon induction of PKR during the interferon response. To be an effective inhibitor K3 has to bind more avidly than EIF2α and merely requires a sufficient overlap with the EIF2α interface on PKR to disrupt this alternative association. This interpretation predicts that K3 is under pressure to bind PKR by a different mechanism than EIF2α.

We appreciate your thoughtful point about the usage of the term pseudosubstrate. Ultimately, we’ve decided to continue using the term due to its historical usage in the field. The question of the optimal extent of mimicry in K3 is a fascinating one, and we greatly appreciate your thoughts. We wholly agree that the possibility of K3 having superior PKR binding relative to eIF2α would be preferable to perfect mimicry. In our Ideas and Speculation section, we propose that benefits towards increasing PKR affinity may need to be balanced against potential loss of host range resulting from overfitting to a given host’s PKR. However, the possibility that reduced mimicry could be selected to avoid disruption of eIF2 function had not occurred to us; thank you for pointing it out!

(21) The discussion of the 'positive selection' of sites is also interesting in this context. To what extent has the proposed positive selection been quantified? My understanding is that all of the EIF2α kinases are conserved and so demonstrate lower levels of residue change that might be expected by random mutagenesis i.e. variance is under negative selection. The relatively higher rate of variance in PKR orthologs compared to other EIF2α kinases could reflect some relaxation of these constraints, rather than positive selection. Greater tolerance of change may stem from PKR 's more sporadic function in the immune response (infrequent and intermittent presence of its activating stimuli) rather than the ceaseless control of homeostasis by the other EIF2α kinases. Also, induction of PKR during the immune response might compensate for mutations that reduce its activity. I believe that the entire clade of extant poxviruses is young relative to the divergence between their hosts. Accordingly, genetic variance in PKR predates these viruses. Although a change in PKR may become fixed if it affords an advantage during infection, such an advantage to the host would be countered by the much higher mutation rates of the virus. This would appear to diminish the opportunity for a specific mutation to dominate a host population and, thereby, to differentiate host species. Rather, pressure to elude control by a rapidly evolving viral factor would favour variation at sites where K3 binds. This speculation offers an alternative perspective to the current discussion that the variance in PKR orthologs stems from positive selection driven by viral infection.

We appreciate this stimulating feedback for discussion. Three of the four eIF2α kinases (HIR, PERK, and GCN2) appear to be under purifying selection (Elde et al. 2009, PMID 19043403), which stand in contrast to PKR. Residues under positive selection have been found throughout PKR, including the dsRNA binding domains, linker region, and the kinase domain. Importantly, the selection analysis from Elde et al. and Rothenburg et al. concluded that positive selection at these sites is more likely than relaxed selection. We agree that poxviruses are young, though we would guess that viral pseudosubstrate inhibition of PKR is ancient. Many viral proteins have been reported to directly interact with PKR, including herpes virus US11, influenza A virus NS1A, hepatitis C virus NS5A, and human immunodeficiency virus Tat. The PKR kinase domain does contain residues under purifying selection that are conserved among all four eIF2α kinases, but it also contains residues under positive selection that interface with the natural substrate eIF2α. Our work suggests that PKR is genetically pliable across several sites in the kinase domain, and we are curious to know if this pliability would hold at the same sites across the other three eIF2α kinases.

(22) The manuscript is very well written but has a small number of typos; e.g. an aberrant 'e' ln 7 of the introduction, capitalise the R in ranavirus on the last line of the fourth paragraph of the discussion, and eIF2α (EIF2α?) is occasionally written as eIFα in the materials&methods.

Thank you for bringing these typos to our attention! We’ve deleted the aberrant ‘e’ in the introduction, capitalized ‘Ranavirus’ in the discussion (line 265), and corrected ‘eIFα’ to ‘eIF2α’ throughout the manuscript.

Reviewer #2 (Recommendations For The Authors):

Additional minor edits or revisions:

(23) Paragraph 3 of the Introduction gives the impression that most of the previous work on the PKR-virus arms race is speculative. However, it is one of the best-described and most convincing examples of virus-host arms races. Can the authors edit the paragraph accordingly?

Thank you for bringing this to our attention. We have revised the third paragraph and strengthened the description of the evolutionary arms race between PKR and viral pseudosubstrate antagonists.

(24) Introduction: PKR has "two" double-stranded RNA binding domains. Can the authors update the text accordingly?

We have updated the manuscript to clarify PKR has two dsRNA binding domains (lines 44-45).

(25) The authors test here for one of the key functions of PKR: cell growth/translation arrest. Because of PKR pleiotropy, the manuscript may be edited accordingly: For example, statements such as "We found few genetic variants render the PKR kinase domain nonfunctional" are too speculative as they may retain other (not tested here) functions.

This is a great suggestion, we have revised the manuscript to specify our definition of nonfunction in the context of our experimental screen (lines 86-92 and 106-109) and acknowledge this limitation in our experimental screen (lines 304-307).

(26) The authors should specify "vaccinia" K3 whenever appropriate.

We appreciate this comment and have revised the manuscript to specify vaccinia K3 where appropriate (e.g. lines 62,66, 70, 80, 108, and 226).

(27) Ref for ACE2 diversification may include Frank et al 2022 PMID: 35892217.

Thank you for pointing us to this paper, we have included it as a reference in the manuscript (line 277).

(28) Positive selection of PKR as referred to by the authors corresponds to analyses performed in primates. As shown by several studies, the sites under positive selection may vary according to host orders. Can the authors specify this ("primate") in their manuscript? And/or shortly discuss this aspect.

Thank you for raising this point. In the manuscript we performed our analysis using vertebrate sites under positive selection as identified in Rothenburg et al. 2009 PMID 19043413 (lines 51 and figure legends). We performed the same analysis using sites under positive selection in primates (as identified by Elde et al. 2009 PMID 19043403) and again found a significant difference in PKR functional scores versus K3. We have revised the manuscript to clarify our use of vertebrate sites under positive selection (line 80-81).

(29) We view deep mutational scanning experiments as a complementary approach to positive selection": The authors should edit this and acknowledge previous and similar work of other antiviral factors, in particular one of the first studies of this kind on MxA (Colon-Thillet et al 2019 PMID: 31574080), and TRIM5 (Tenthorey et al 2020 PMID: 32930662).

Thank you for raising up these two papers, which we acknowledge in the revised manuscript (line 299).

(30) We believe Figure S7 brings important results and should be placed in the Main.

We appreciate this suggestion, and have moved the contents of the former supplementary Figure 7 to the main text, in Figure 6.

(31) The title may specify "poxvirus".

Thank you for the suggestion to specify the nature of our experiment, we have adjusted the title to: Systematic genetic characterization of the human PKR kinase domain highlights its functional malleability to escape a poxvirus substrate mimic (line 3).

Reviewer #3 (Recommendations For The Authors):

(32) No line numbers or page numbers are provided, which makes it difficult to comment.

We sincerely apologize for this oversight and have included line numbers in our revised manuscript as well as the tracked changes document.

(33) In the introduction, I recommend defining evolutionary arms races more clearly for a broad audience.

Thank you for this suggestion. We have revised the manuscript in the first and third paragraphs to more clearly introduce readers to the concept of an evolutionary arms race.

(34) The introduction could use a clearer statement of the question being considered and the gap in knowledge this paper is trying to address. Currently, the third paragraph includes many facts about PKR and the fourth paragraph jumps straight into the approach and results. Some elaboration here would convey the significance of the study more clearly. As is, the introduction reads a bit like "We wanted to do deep mutational scanning. PKR seemed like an ok protein to look at", rather than conveying a scientific question.

This is a great suggestion to improve the introduction section. We have heavily revised the third and fourth paragraphs of the introduction to clarify the motivation, approach, and significance of our work.

(35) Relatedly, did the authors have any hypotheses at the start of the experiment about what kinds of results they expected? e.g. What parts of PKR would be most likely to generate escape mutants? Would resistant mutants be rare or common? etc? This would help the reader to understand which results are expected vs. surprising.

These are all great questions. We have revised the introduction of the manuscript to point out that previous studies have characterized a handful of PKR variants that evade vaccinia K3, and these variants were made at sites found to be under positive selection (lines 60-64).

(36) A description of the different K3 variants and information about why they were chosen for study should also be added to the Introduction. It was not until Figure 5 that the reader was told that K3-H47R was the same as the 'enhanced' K3 allele you are testing.

Thank you for bringing this to our attention, we have revised the introduction to clarify the experimental conditions (lines 65-67) and specify K3-H47R as the enhanced allele earlier in the manuscript (line 100).

(37) Does every PKR include just a single point mutation? It would be nice to see data about the number and types of mutations in each PRK window added to Supplemental Figure 1.

Thank you for the suggestion to improve this figure. Every PKR variant that we track has a single point mutation that generates a nonsynonymous mutation. In our PacBio sequencing of the PKR variant library we identified a few off-target variants or sequences with multiple variants, but we identified the barcodes linked to those constructs and discarded those variants in our analysis. We have revised Supplemental Figure 1 to include the number and types of mutations made at each PKR window.

(38) In terms of the paper's logical flow, personally, I would expect to begin by testing which variants break PKR's function (Figure 3) and then proceeding to see which variants allow for K3 escape (Figure 2). Consider swapping the order of these sections.

Thank you for this suggestion, and we can appreciate how the flow of the manuscript may be improved by swapping Figures 2 and 3. We have decided to maintain the current order of the figures because we use Figure 3 to emphasize the distinction of PKR sites that are nonfunctional versus susceptible to vaccinia K3.

(39) Figure 3A seems like a less-informative version of Figure 4A, recommend combining these two. Same comment with Figure 5A and Figure 6A.

We appreciate this specific feedback for the figures. Though there are similarities between figure panels (e.g. 3A and 4A) we use them to emphasize different points in each figure. For example, in Figure 3 we emphasize the general lack of variants that impair PKR kinase activity, and in Figure 4 we distinguish kinase-impaired variants from K3-susceptible variants. For this reason, and given space constraints, we have chosen to maintain the figures separately. We did decide to move the former Figure 6 to the supplement.

(40) In general, it felt like there was a lot of repetition/re-graphing of the same data in Figures 3-6. I recommend condensing some of this, and/or moving some of the panels to supplemental figures.

Thank you for your suggestion, we have revised the manuscript and have moved Figure 6 to Supplemental Figure 7.

(41) In contrast, Supplemental Figure 7 is helpful for understanding the distribution of the data. Recommend moving to the main text.

This is a great recommendation, and we have moved Supplemental Figure 7 into Figure 6.

(42) How do the authors interpret an enrichment of positively selected sites in K3-resistant variants, but not K3-H74R-resistant variants? This seems important. Please explain.

Thank you for this suggestion to improve the manuscript; we agree that this observation warranted further exploration. We found a strong correlation in PKR functional scores between K3 WT and K3-H47R, and with that we find sites under positive selection that are resistant to K3 WT are also resistant to K3-H47R. The lack of enrichment at positively selected sites appears to be caused by collapsed dynamic range between PKR wild-type-like and nonfunctional variants in the K3-H47R screen. We have revised the manuscript to clarify this point (line 202-204).

(43) Discussion: The authors compare and contrast between PKR and ACE2, but it would be worth mentioning other examples of genes involved in antiviral arms races wherein flexible, unstructured loops are functionally important and are hotspots of positive selection (e.g. MxA, NLRP1, etc).

We greatly appreciate this suggestion to improve the discussion. We note this contrast between the PKR kinase domain and the flexible linkers of MxA and NLRP1 in the revised manuscript (lines 273-274).

(44) Speculation section: What is the host range of the vaccinia virus? Is it likely to be a generalist amongst many species' PKRs (and if so, how variable are those PKRs)? Would be worth mentioning for context if you want to discuss this topic.

Thank you for raising this question. Vaccinia virus is the most well studied of the poxviruses, having been used as a vaccine to eradicate smallpox, and serves as a model poxvirus. Vaccinia virus has a broad host range, and though the name vaccinia derives from the Latin word “vacca” for cow the viruses origin remains uncertain (Smith 2007 https://doi.org/10.1007/978-3-7643-7557-7_1). has been used to eradicate smallpox as a vaccine and serves as a model poxvirus. Thought the natural host is unknown, it appears to be a general inhibitor of vertebrate PKRs The natural host of vaccinia virus is unknown, though there is some evidence to suggest it may be native to rabbits and does appear to be generalist.

(45) Many papers in this field discuss interactions between PKR and K3L, rather than K3. I understand that this is a gene vs. protein nomenclature issue, but consider matching the K3L literature to make this paper easier to find.

Thank you for bringing this to our attention. We have revised the manuscript to specify that vaccinia K3 is expressed from the K3L gene in both the abstract (line 26) and the introduction (line 56) to help make this paper easier to find when searching for “K3L” literature.

(46) Which PKR sequence was used as the wild-type background?

This is a great question. We used the predominant allele circulating in the human population represented by Genbank m85294.1:31-1686. We cite this sequence in the Methods (line 421) and have added it to the results section as well (lines 84).

(47) Figure 1C: the black dashed line is difficult to see. Recommend changing the colors in 1A-1C.

Thank you for this suggestion, we have changed the dashed lines from black to white to make them more distinguishable.

(48) Figure 1D: Part of the point of this figure is to convey overlaps between sites under selection, K3 contact sites, and eIF2alpha contact sites, but at this scale, many of the triangles overlap. It is therefore impossible to tell if the same sites are contacted vs. nearby sites. Perhaps the zoomed-in panels showing each of the four windows in the subsequent figures are sufficient?

Thank you for bringing this to our attention. We have scaled the triangles down to reduce their overlap in Figure 1D and list all sites of interest (predicted eIF2α and vaccinia contacts, conserved sites, and positive selection sites) in the Materials and Methods section “Predicted PKR complexes and substrate contacts”.

(49) Figure 1E: under "1,293 Unique Combinations", there is a line between the PKR and K3 variants, which makes it look like they are expressed as a fusion protein. I believe these proteins were expressed from the same plasmid, but not as a fusion, so I recommend re-drawing. Then in the graph, the y-axis says "PKR abundance", but from the figure, it is not clear that this refers to relative abundance in a yeast pool. Perhaps "yeast growth" or similar would be clearer?

Thank you for the specific feedback to improve Figure 1. We have made the suggested edits to clarify that PKR and vaccinia K3 are not fused but each is expressed from their own promoter. We have also changed the y-axis from “PKR Abundance” to “Yeast Growth”.

Underscores? Also thanks to the person who commented what this is!

traditional garment consisting of a skirt and veil to disguise the wearer's identity

Si estas disposiciones deben interpretarse de manera que las normas constitucionales de un Estado miembro, que impiden examinar la validez del nombramiento de un juez, puedan impedir la declaración de falta de independencia de un tribunal o juez.

Considera si, según el Derecho de la Unión, es posible que las circunstancias mencionadas en cuestiones anteriores puedan afectar la independencia judicial.

Esencialmente, la pregunta es si la interpretación de estas disposiciones puede permitir que las normas constitucionales de un Estado miembro que obstaculizan la revisión de nombramientos judiciales impidan determinar la falta de independencia e imparcialidad de un juez o tribunal.

pregunta 6 = Artículos Citados:

Artículo 19 TUE, apartado 1, párrafo segundo: Relacionado con la independencia de los tribunales.

Artículo 2 TUE: Valores de la Unión, como la democracia y el estado de derecho.

Artículo 4 TUE, apartado 3: Cooperación leal entre los Estados miembros y la Unión.

Artículo 6 TUE, apartado 3: Derecho a la tutela judicial efectiva.

Artículo 47 de la Carta: Derecho a un juicio justo y a una tutela judicial efectiva.

Artículo 267 TFUE, párrafo tercero: Referencia prejudicial al Tribunal de Justicia.

Artículo 38 de la Carta: Protección del consumidor.

Artículo 7 de la Directiva 93/13/CEE: Cláusulas abusivas en los contratos.

Obligaciones del Tribunal Supremo (Sąd Najwyższy): a) Se pregunta si el Tribunal Supremo está obligado a examinar de oficio si un órgano jurisdiccional cumple con los criterios de independencia, imparcialidad y calificación adecuada, independientemente de cómo esta valoración afecte la resolución sobre la abusividad de una cláusula contractual. b) También se pregunta si debe examinar de oficio la validez del procedimiento ante el órgano jurisdiccional en cuestión.

pregunta 5 = La pregunta plantea si deben interpretarse ciertos artículos del Tratado de la Unión Europea (TUE), la Carta de Derechos Fundamentales de la Unión Europea (Carta), el Tratado de Funcionamiento de la Unión Europea (TFUE) y la Directiva 93/13/CEE para considerar si un órgano jurisdiccional es independiente e imparcial.

La pregunta se enfoca en un juez cuya candidatura fue propuesta en un procedimiento ante el Consejo Nacional del Poder Judicial (Krajowa Rada Sądownictwa). Se cuestiona si este procedimiento, al no cumplir con los requisitos de publicidad y transparencia, puede socavar la confianza en la judicatura en una sociedad democrática.

En resumen, la cuestión es si, dadas las faltas de transparencia y publicidad en el nombramiento, se puede considerar que el juez forma parte de un órgano independiente e imparcial bajo el derecho de la Unión Europea.

pregunta 1 = según esos artículos;;; es adminisible considerar al juez democrático

Artículos del Tratado de la Unión Europea (TUE): La pregunta menciona varios artículos del TUE que se refieren a la independencia judicial y la tutela judicial efectiva:

Artículo 2 TUE: Valores de la Unión, incluyendo la democracia y el respeto a los derechos humanos.

Artículo 4 TUE, apartado 3: Competencias de los Estados miembros en relación con la justicia y la seguridad.

Artículo 6 TUE, apartados 1 y 3: Independencia de los tribunales y la tutela judicial efectiva.

Artículo 19 TUE, apartado 1, párrafo segundo: Procedimiento de referencia prejudicial al Tribunal de Justicia de la Unión Europea (CJEU).

Artículos de la Carta de Derechos Fundamentales de la Unión Europea (Carta):

Artículo 47, párrafos primero y segundo: Derecho a un juicio justo y a la tutela judicial efectiva.

Artículos del Tratado de Funcionamiento de la Unión Europea (TFUE):

Artículo 267 TFUE, párrafo tercero: Procedimiento de referencia prejudicial al CJEU.

Artículo 38 de la Carta: Derecho a la protección de datos personales.

Artículos de la Directiva 93/13/CEE:

Artículo 7, apartados 1 y 2: Cláusulas abusivas en los contratos.

La pregunta plantea si estos artículos deben interpretarse de manera que un juez nombrado por una autoridad política en un sistema totalitario y no democrático pueda ser considerado un órgano jurisdiccional independiente e imparcial según el Derecho de la Unión. La falta de transparencia en el proceso de nombramiento, la posibilidad de cesar al juez en cualquier momento, y la ausencia de participación del autogobierno del poder judicial y autoridades públicas democráticamente elegidas son factores que podrían socavar la confianza en la judicatura.

ARGUMENTO PARTE 1

mapping 資料 成為 美學的表現

其中 color為"線"的顏色，若geometry有填色可能的話，填色的美學為fill.

labs = labels<br /> 經濟學人習慣會有

subtitle = "..." 用來說明Y軸的單位，

caption = "..." 用來說明資料的出處。

What is the physical significance of \ddot{v}_y

I can see it looks like \ddot{x} = -\omega^2 x

.h2

1 Corintios 5:13

"mientras que Dios juzga a los de afuera?+ “Saquen a la persona malvada que está entre ustedes”

1 Juan 2:1

Nuestro Dios es tan amoroso que les tiende la mano a los pecadores y los anima a arrepentirse (Zac. 1:3; Rom. 2:4; Sant. 4:8)

Poliosis (despigmentación de pestañas), vitiligo, manifestaciones neuro, cutáneas y oftalmologicas

Hipopion frecuente, úlceras, vasculitis en venas y arterias de pequeño mediano y gran calibre

Author response:

Reviewer #1 (Public Review):

Padilha et al. aimed to find prospective metabolite biomarkers in serum of children aged 6-59 months that were indicative of neurodevelopmental outcomes. The authors leveraged data and samples from the cross-sectional Brazilian National Survey on Child Nutrition (ENANI-2019), and an untargeted multisegment injection-capillary electrophoresis-mass spectrometry (MSI-CE-MS) approach was used to measure metabolites in serum samples (n=5004) which were identified via a large library of standards. After correlating the metabolite levels against the developmental quotient (DQ), or the degree of which age-appropriate developmental milestones were achieved as evaluated by the Survey of Well-being of Young Children, serum concentrations of phenylacetylglutamine (PAG), cresol sulfate (CS), hippuric acid (HA) and trimethylamine-N-oxide (TMAO) were significantly negatively associated with DQ. Examination of the covariates revealed that the negative associations of PAG, HA, TMAO and valine (Val) with DQ were specific to younger children (-1 SD or 19 months old), whereas creatinine (Crtn) and methylhistidine (MeHis) had significant associations with DQ that changed direction with age (negative at -1 SD or 19 months old, and positive at +1 SD or 49 months old). Further, mediation analysis demonstrated that PAG was a significant mediator for the relationship of delivery mode, child's diet quality and child fiber intake with DQ. HA and TMAO were additional significant mediators of the relationship of child fiber intake with DQ.

Strengths of this study include the large cohort size and study design allowing for sampling at multiple time points along with neurodevelopmental assessment and a relatively detailed collection of potential confounding factors including diet. The untargeted metabolomics approach was also robust and comprehensive allowing for level 1 identification of a wide breadth of potential biomarkers. Given their methodology, the authors should be able to achieve their aim of identifying candidate serum biomarkers of neurodevelopment for early childhood. The results of this work would be of broad interest to researchers who are interested in understanding the biological underpinnings of development and also for tracking development in pediatric populations, as it provides insight for putative mechanisms and targets from a relevant human cohort that can be probed in future studies. Such putative mechanisms and targets are currently lacking in the field due to challenges in conducting these kind of studies, so this work is important.

However, in the manuscript's current state, the presentation and analysis of data impede the reader from fully understanding and interpreting the study's findings.

Particularly, the handling of confounding variables is incomplete. There is a different set of confounders listed in Table 1 versus Supplementary Table 1 versus Methods section Covariates versus Figure 4. For example, Region is listed in Supplementary Table 1 but not in Table 1, and Mode of Delivery is listed in Table 1 but not in Supplementary Table 1. Many factors are listed in Figure 4 that aren't mentioned anywhere else in the paper, such as gestational age at birth or maternal pre-pregnancy obesity.

We thank the reviewer for their comment. We would like to clarify that initially, the tables had different variables because they have different purposes. Table 1 aims to characterize the sample on variables directly related to the children’s and mother’s features and their nutritional status. Supplementary File 1(previously named supplementary table 1) summarizes the sociodemographic distribution of the development quotient. Neither of the tables concerned the metabolite-DQ relationships and their potential covariates, they only provide context for subsequent analyses by characterizing the sample and the outcome. Instead, the covariates included in the regression models were selected using the Direct Acyclic Graph presented in Figure 1.

To avoid this potential confusion however, we included the same variables in Table 1 and Supplementary File 1(page 38) and we discussed the selection of model covariates in Figure 4 in more detail here in the letter and in the manuscript.

The authors utilize the directed acrylic graph (DAG) in Figure 4 to justify the further investigation of certain covariates over others. However, the lack of inclusion of the microbiome in the DAG, especially considering that most of the study findings were microbial-derived metabolite biomarkers, appears to be a fundamental flaw. Sanitation and micronutrients are proposed by the authors to have no effect on the host metabolome, yet sanitation and micronutrients have both been demonstrated in the literature to affect microbiome composition which can in turn affect the host metabolome.

Thank you for your comment. We appreciate that the use of DAG and lack of the microbiome in the DAG are concerns. This has been already discussed in reply #1 to the editor that has been pasted below for convenience:

Thank you for the comment and suggestions. It is important to highlight that there is no data on microbiome composition. We apologize if there was an impression such data is available. The main goal of conducting this national survey was to provide qualified and updated evidence on child nutrition to revise and propose new policies and nutritional guidelines for this demographic. Therefore, collection of stool derived microbiome (metagenomic) data was not one of the objectives of ENANI-2019. This is more explicitly stated as a study limitation in the revised manuscript on page 17, lines 463-467:

“Lastly, stool microbiome data was not collected from children in ENANI-2019 as it was not a study objective in this large population-based nutritional survey. However, the lack of microbiome data does not reduce the importance/relevance, since there is no evidence that microbiome and factors affecting microbiome composition are confounders in the association between serum metabolome and child development.”

Besides, one must consider the difficulties and costs in collecting and analyzing microbiome composition in a large population-based survey. In contrast, the metabolome data has been considered a priority as there was already blood specimens collected to inform policy on micronutrient deficiencies in Brazil. However, due to funding limitations we had to perform the analysis in a subset of our sample, still representative and large enough to test our hypothesis with adequate study power (more details below).

We would like to argue that there is no evidence that microbiome and factors affecting microbiome composition are confounders on the association between serum metabolome and child development. First, one should revisit the properties of a confounder according to the epidemiology literature that in short states that confounding refers to an alternative explanation for a given conclusion, thus constituting one of the main problems for causal inference (Kleinbaum, Kupper, and Morgenstern, 1991; Greenland & Robins, 1986; VanderWeele, 2019). In our study, we highlight that certain serum metabolites associated with the developmental quotient (DQ) in children were circulating metabolites (e.g., cresol sulfate, hippuric acid, phenylacetylglutamine, TMAO) previously reported to depend on dietary exposures, host metabolism and gut microbiota activity. Our discussion cites other published work, including animal models and observational studies, which have reported how these bioactive metabolites in circulation are co-metabolized by commensal gut microbiota, and may play a role in neurodevelopment and cognition as mediated by environmental exposures early in life.

In fact, the literature on the association between microbiome and infant development is very limited. We performed a search using terms ‘microbiome’ OR ‘microbiota’ AND ‘child development’ AND ‘systematic’ OR ‘meta-analysis’ and found only one study: ‘Associations between the human immune system and gut microbiome with neurodevelopment in the first 5 years of life: A systematic scoping review’ (DOI 10.1002/dev.22360). The authors conclude: ‘while the immune system and gut microbiome are thought to have interactive impacts on the developing brain, there remains a paucity of published studies that report biomarkers from both systems and associations with child development outcomes.’ It is important to highlight that our criteria to include confounders on the directed acyclic graph (DAG) was based on the literature of systematic reviews or meta-analysis and not on single isolated studies.

In summary, we would like to highlight that there is no microbiome data in ENANI-2019 and in the event such data was present, we are confident that based on the current stage of the literature, there is no evidence to consider such construct in the DAG, as this procedure recommends that only variables associated with the exposure and the outcome should be included. Please find more details on DAG below.

Moreover, we would like to clarify that we have not stated that sanitation and micronutrients have no effect on the serum metabolome, instead, these constructs were not considered on the DAG.

To make it clearer, we have modified the passage about DAG in the methods section. New text, page 9, lines 234-241:

“The subsequent step was to disentangle the selected metabolites from confounding variables. A Directed Acyclic Graph (DAG; Breitling et al., 2021) was used to more objectively determine the minimally sufficient adjustments for the regression models to account for potentially confounding variables while avoiding collider variables and variables in the metabolite-DQ causal pathways, which if controlled for would unnecessarily remove explained variance from the metabolites and hamper our ability to detect biomarkers. To minimize bias from subjective judgments of which variables should and should not be included as covariates, the DAG only included variables for which there was evidence from systematic reviews or meta-analysis of relationships with both the serum metabolome and DQ (Figure 1). Birth weight, breastfeeding, child's diet quality, the child's nutritional status, and the child's age were the minimal adjustments suggested by the DAG. Birth weight was a variable with high missing data, and indicators of breastfeeding practice data (referring to exclusive breastfeeding until 6 months and/or complemented until 2 years) were collected only for children aged 0–23 months. Therefore, those confounders were not included as adjustments. Child's diet quality was evaluated as MDD, the child's nutritional status as w/h z-score, and the child's age in months.”

Additionally, the authors emphasized as part of the study selection criteria the following, "Due to the costs involved in the metabolome analysis, it was necessary to further reduce the sample size. Then, samples were stratified by age groups (6 to 11, 12 to 23, and 24 to 59 months) and health conditions related to iron metabolism, such as anemia and nutrient deficiencies. The selection process aimed to represent diverse health statuses, including those with no conditions, with specific deficiencies, or with combinations of conditions. Ultimately, through a randomized process that ensured a balanced representation across these groups, a total of 5,004 children were selected for the final sample (Figure 1)."

Therefore, anemia and nutrient deficiencies are assumed by the reader to be important covariates, yet, the data on the final distribution of these covariates in the study cohort is not presented, nor are these covariates examined further.

Thank you for the comments. We apologize for the misunderstanding and will amend the text to make our rationale clearer in the revised version of the manuscript.

We believed the original text was clear enough in stating that the sampling process was performed aiming to maintain the representativeness of the original sample. This sampling process considered anemia and nutritional deficiencies, among other variables. However, we did not aim to include all relevant covariates of the DQ-metabolome relationship; these were decided using the DAG, as described in the manuscript and other sessions of this letter. Therefore, we would like to emphasize that our description of the sampling process does not assumes anemia and nutritional deficiencies are important covariates for the DQ-metabolome relationship.

We rewrote this text part, page 11, lines 279-285:

“Due to the costs involved in the metabolome analysis, it was necessary to reduce the sample size that is equivalent to 57% of total participants from ENANI-2019 with stored blood specimens. Therefore, the infants were stratified by age groups (6 to 11, 12 to 23, and 24 to 59 months) and health conditions such as anemia and micronutrient deficiencies. The selection process aimed to represent diverse health statuses to the original sample. Ultimately, 5,004 children were selected for the final sample through a random sampling process that ensured a balanced representation across these groups (Figure 2).”

The inclusion of specific covariates in Table 1, Supplementary Table 1, the statistical models, and the mediation analysis is thus currently biased as it is not well justified.

We appreciate the reviewer comment. However, it would have been ideal to receive a comment/critic with a clearer and more straightforward argumentation, so we could try to address it based on our interpretation.

Please refer to our response to item #1 above regarding the variables in the tables and figures. The covariates in the statistical models were selected using the DAG, which is a cutting-edge procedure that aims to avoid bias and overfitting, a common situation when confounders are adjusted for without a clear rationale. We elaborate on the advantages of using the DAG in response to item #6 and in page 9 of the manuscript. The statistical models we use follow the best practices in the field when dealing with a large number of collinear predictors and a continuous outcome (see our response to the editor’s 4th comment). Finally, the mediation analyses were done to explore a few potential explanations for our results from the PLSR and multiple regression analyses. We only ran mediation analyses for plausible mechanisms for which the variables of interest were available in our data. Please see our response to reviewer 3’s item #1 for a more detailed explanation on the mediation analysis.

Finally, it is unclear what the partial-least squares regression adds to the paper, other than to discard potentially interesting metabolites found by the initial correlation analysis.

Thank you for the question. As explained in response to the editor’s item #4, PLS-based analyses are among the most commonly used analyses for parsing metabolomic data (Blekherman et al., 2011; Wold et al., 2001; Gromski et al. 2015). This procedure is especially appropriate for cases in which there are multiple collinear predictor variables as it allows us to compare the predictive value of all the variables without relying on corrections for multiple testing. Testing each metabolite in separate correlations corrected for multiple comparisons is less appropriate because the correlated nature of the metabolites means the comparisons are not truly independent and would cause the corrections (which usually assume independence) to be overly strict. As such, we only rely on the correlations as an initial, general assessment that gives context to subsequent, more specific analyses. Given that our goal is to select the most predictive metabolites, discarding the less predictive metabolites is precisely what we aim to achieve. As explained above and in response to the editor’s item #4, the PLSR allows us to reach that goal without introducing bias in our estimates or losing statistical power.  

Reviewer #2 (Public Review):

A strength of the work lies in the number of children Padilha et al. were able to assess (5,004 children aged 6-59 months) and in the extensive screening that the Authors performed for each participant. This type of large-scale study is uncommon in low-to-middle-income countries such as Brazil.

The Authors employ several approaches to narrow down the number of potentially causally associated metabolites.

Could the Authors justify on what basis the minimum dietary diversity score was dichotomized? Were sensitivity analyses undertaken to assess the effect of this dichotomization on associations reported by the article? Consumption of each food group may have a differential effect that is obscured by this dichotomization.

Thank you for the observation. We would like to emphasize that the child's diet quality was assessed using the minimum dietary diversity (MDD) indicator proposed by the WHO (World Health Organization & United Nations Children’s Fund (UNICEF), 2021). This guideline proposes the cutoff used in the present study. We understand the reviewer’s suggestion to use the consumption of healthy food groups as an evaluation of diet quality, but we chose to follow the WHO proposal to assess dietary diversity. This indicator is widely accepted and used as a marker and provides comparability and consistency with other published studies.

Could the Authors specify the statistical power associated with each analysis?

To the best of our knowledge, we are not aware of power calculation procedures for PLS-based analyses. However, given our large sample size, we do not believe power was an issue with the analyses. For our regression analyses, which typically have 4 predictors, we had 95% power to detect an f-squared of 0.003 and an r of 0.05 in a two-sided correlation test considering an alpha level of 0.05.

New text, page 11, lines 296-298:

“Given the size of our sample, statistical power is not an issue in our analyses. Considering an alpha of 0.05 for a two-sided test, a sample size of 5000 has 95% power to detect a correlation of r = 0.05 and an effect of f2 = 0.003 in a multiple regression model with 4 predictors.”

Could the Authors describe in detail which metric they used to measure how predictive PLSR models are, and how they determined what the "optimal" number of components were?

We chose the model with the fewest number of components that maximized R2 and minimized root mean squared error of prediction (RMSEP). In the training data, the model with 4 components had a lower R2 but a lower RMSEP, therefore we chose the model with 3 components which had a higher R2 than the 4-component model and lower RMSEP than the model with 2 components. However, the number of components in the model did not meaningfully change the rank order of the metabolites on the VIP index.

New text, page 8, lines 220-224:

“To better assess the predictiveness of each metabolite in a single model, a PLSR was conducted. PLS-based analyses are the most commonly used analyses when determining the predictiveness of a large number of variables as they avoid issues with collinearity, sample size, and corrections for multiple-testing (Blekherman et al., 2011; Wold et al., 2001; Gromski et al. 2015).”

New text, page 12, lines 312-314:

“In PLSR analysis, the training data suggested that three components best predicted the data (the model with three components had the highest R2, and the root mean square error of prediction (RMSEP) was only slightly lower with four components). In comparison, the test data showed a slightly more predictive model with four components (Figure 3—figure supplement 2).”

The Authors use directed acyclic graphs (DAG) to identify confounding variables of the association between metabolites and DQ. Could the dataset generated by the Authors have been used instead? Not all confounding variables identified in the literature may be relevant to the dataset generated by the Authors.

Thank you for the question. The response is most likely no, the current dataset should not be used to define confounders as these must be identified based on the literature. The use of DAGs has been widely explored as a valid tool for justifying the choice of confounding factors in regression models in epidemiology. This is because DAGs allow for a clear visualization of causal relationships, clarify the complex relationships between exposure and outcome. Besides, DAGs demonstrate the authors' transparency by acknowledging factors reported as important but not included/collected in the study. This has been already discussed in reply #1 to the editor that has been pasted below for convenience.

Thank you for the comment and suggestions. It is important to highlight that there is no data on microbiome composition. We apologize if there was an impression such data is available. The main goal of conducting this national survey was to provide qualified and updated evidence on child nutrition to revise and propose new policies and nutritional guidelines for this demographic. Therefore, collection of stool derived microbiome (metagenomic) data was not one of the objectives of ENANI-2019. This is more explicitly stated as a study limitation in the revised manuscript on page 17, lines 463-467:

“Lastly, stool microbiome data was not collected from children in ENANI-2019 as it was not a study objective in this large population-based nutritional survey. However, the lack of microbiome data does not reduce the importance/relevance, since there is no evidence that microbiome and factors affecting microbiome composition are confounders in the association between serum metabolome and child development.”

Besides, one must consider the difficulties and costs in collecting and analyzing microbiome composition in a large population-based survey. In contrast, the metabolome data has been considered a priority as there was already blood specimens collected to inform policy on micronutrient deficiencies in Brazil. However, due to funding limitations we had to perform the analysis in a subset of our sample, still representative and large enough to test our hypothesis with adequate study power (more details below).

We would like to argue that there is no evidence that microbiome and factors affecting microbiome composition are confounders on the association between serum metabolome and child development. First, one should revisit the properties of a confounder according to the epidemiology literature that in short states that confounding refers to an alternative explanation for a given conclusion, thus constituting one of the main problems for causal inference (Kleinbaum, Kupper, and Morgenstern, 1991; Greenland & Robins, 1986; VanderWeele, 2019). In our study, we highlight that certain serum metabolites associated with the developmental quotient (DQ) in children were circulating metabolites (e.g., cresol sulfate, hippuric acid, phenylacetylglutamine, TMAO) previously reported to depend on dietary exposures, host metabolism and gut microbiota activity. Our discussion cites other published work, including animal models and observational studies, which have reported how these bioactive metabolites in circulation are co-metabolized by commensal gut microbiota, and may play a role in neurodevelopment and cognition as mediated by environmental exposures early in life.

In fact, the literature on the association between microbiome and infant development is very limited. We performed a search using terms ‘microbiome’ OR ‘microbiota’ AND ‘child development’ AND ‘systematic’ OR ‘meta-analysis’ and found only one study: ‘Associations between the human immune system and gut microbiome with neurodevelopment in the first 5 years of life: A systematic scoping review’ (DOI 10.1002/dev.22360). The authors conclude: ‘while the immune system and gut microbiome are thought to have interactive impacts on the developing brain, there remains a paucity of published studies that report biomarkers from both systems and associations with child development outcomes.’ It is important to highlight that our criteria to include confounders on the directed acyclic graph (DAG) was based on the literature of systematic reviews or meta-analysis and not on single isolated studies.

In summary, we would like to highlight that there is no microbiome data in ENANI-2019 and in the event such data was present, we are confident that based on the current stage of the literature, there is no evidence to consider such construct in the DAG, as this procedure recommends that only variables associated with the exposure and the outcome should be included. Please find more details on DAG below.

Moreover, we would like to clarify that we have not stated that sanitation and micronutrients have no effect on the serum metabolome, instead, these constructs were not considered on the DAG.

To make it clearer, we have modified the passage about DAG in the methods section. New text, page 9, lines 234-241:

“The subsequent step was to disentangle the selected metabolites from confounding variables. A Directed Acyclic Graph (DAG; Breitling et al., 2021) was used to more objectively determine the minimally sufficient adjustments for the regression models to account for potentially confounding variables while avoiding collider variables and variables in the metabolite-DQ causal pathways, which if controlled for would unnecessarily remove explained variance from the metabolites and hamper our ability to detect biomarkers. To minimize bias from subjective judgments of which variables should and should not be included as covariates, the DAG only included variables for which there was evidence from systematic reviews or meta-analysis of relationships with both the serum metabolome and DQ (Figure 1). Birth weight, breastfeeding, child's diet quality, the child's nutritional status, and the child's age were the minimal adjustments suggested by the DAG. Birth weight was a variable with high missing data, and indicators of breastfeeding practice data (referring to exclusive breastfeeding until 6 months and/or complemented until 2 years) were collected only for children aged 0–23 months. Therefore, those confounders were not included as adjustments. Child's diet quality was evaluated as MDD, the child's nutritional status as w/h z-score, and the child's age in months.”

Were the systematic reviews or meta-analyses used in the DAG performed by the Authors, or were they based on previous studies? If so, more information about the methodology employed and the studies included should be provided by the Authors.

Thank you for the question. The reviews or meta-analyses used in the DAG have been conducted by other authors in the field. This has been laid out more clearly in our methods section.

New text, page 9, lines 234-241:

“The subsequent step was to disentangle the selected metabolites from confounding variables. A Directed Acyclic Graph (DAG; Breitling et al., 2021) was used to more objectively determine the minimally sufficient adjustments for the regression models to account for potentially confounding variables while avoiding collider variables and variables in the metabolite-DQ causal pathways, which if controlled for would unnecessarily remove explained variance from the metabolites and hamper our ability to detect biomarkers. To minimize bias from subjective judgments of which variables should and should not be included as covariates, the DAG only included variables for which there was evidence from systematic reviews or meta-analysis of relationships with both the metabolome and DQ (Figure 1). Birth weight, breastfeeding, child's diet quality, the child's nutritional status, and the child's age were the minimal adjustments suggested by the DAG. Birth weight was a variable with high missing data, and indicators of breastfeeding practice data (referring to exclusive breastfeeding until 6 months and/or complemented until 2 years) were collected only for children aged 0–23 months. Therefore, those confounders were not included as adjustments. Child's diet quality was evaluated as MDD, the child's nutritional status as w/h z-score, and the child's age in months.”

Approximately 72% of children included in the analyses lived in households with a monthly income superior to the Brazilian minimum wage. The cohort is also biased towards households with a higher level of education. Both of these measures correlate with developmental quotient. Could the Authors discuss how this may have affected their results and how generalizable they are?

Thank you for your comment. This has been already discussed in reply #6 to the editor and that has been pasted below for convenience.

Thank you for highlighting this point. The ENANI-2019 is a population-based household survey with national coverage and representativeness for macroregions, sex, and one-year age groups (< 1; 1-1.99; 2-2.99; 3-3.99; 4-5). Furthermore, income quartiles of the census sector were used in the sampling. The study included 12,524 households 14,588 children, and 8,829 infants with blood drawn.

Due to the costs involved in metabolome analysis, it was necessary to further reduce the sample size to around 5,000 children that is equivalent to 57% of total participants from ENANI-2019 with stored blood specimens. To avoid a biased sample and keep the representativeness and generability, the 5,004 selected children were drawn from the total samples of 8,829 to keep the original distribution according age groups (6 to 11 months, 12 to 23 months, and 24 to 59 months), and some health conditions related to iron metabolism, e.g., anemia and nutrient deficiencies. Then, they were randomly selected to constitute the final sample that aimed to represent the total number of children with blood drawn. Hence, our efforts were to preserve the original characteristics of the sample and the representativeness of the original sample.

The ENANI-2019 study does not appear to present a bias towards higher socioeconomic status. Evidence from two major Brazilian population-based household surveys supports this claim. The 2017-18 Household Budget Survey (POF) reported an average monthly household income of 5,426.70 reais, while the Continuous National Household Sample Survey (PNAD) reported that in 2019, the nominal monthly per capita household income was 1,438.67 reais. In comparison, ENANI-2019 recorded a household income of 2,144.16 reais and a per capita income of 609.07 reais in infants with blood drawn, and 2,099.14 reais and 594.74 reais, respectively, in the serum metabolome analysis sample.

In terms of maternal education, the 2019 PNAD-Education survey indicated that 48.8% of individuals aged 25 or older had at least 11 years of schooling. When analyzing ENANI-2019 under the same metric, we found that 56.26% of ≥25 years-old mothers of infants with blood drawn had 11 years of education or more, and 51.66% in the metabolome analysis sample. Although these figures are slightly higher, they remain within a reasonable range for population studies.

It is well known that higher income and maternal education levels can influence child health outcomes, and acknowledging this, ENANI-2019 employed rigorous sampling methods to minimize selection biases. This included stratified and complex sampling designs to ensure that underrepresented groups were adequately included, reducing the risk of skewed conclusions. Therefore, the evidence strongly suggests that the ENANI-2019 sample is broadly representative of the Brazilian population in terms of both socioeconomic status and educational attainment.

Further to this, could the Authors describe how inequalities in access to care in the Brazilian population may have affected their results? Could they have included a measure of this possible discrepancy in their analyses?

Thank you for the concern.

The truth is that we are not in a position to answer this question because our study focused on gathering data on infant nutritional status and there is very limited information on access to care to allow us to hypothesize. Another important piece of information is that this national survey used sampling procedures that aimed to make the sample representative of the 15 million Brazilian infants under 5 years. Therefore, the sample is balanced according to socio-economic strata, so there is no evidence to make us believe inequalities in access to health care would have played a role.

The Authors state that the results of their study may be used to track children at risk for developmental delays. Could they discuss the potential for influencing policies and guidelines to address delayed development due to malnutrition and/or limited access to certain essential foods?

The point raised by the reviewer is very relevant. Recognizing that dietary and microbial derived metabolites involved in the gut-brain axis could be related to children's risk of developmental delays is the first step to bringing this topic to the public policy agenda. We believe the results can contribute to the literature, which should be used to accumulate evidence to overcome knowledge gaps and support the formulation and redirection of public policies aimed at full child growth and development; the promotion of adequate and healthy nutrition and food security; the encouragement, support, and protection of breastfeeding; and the prevention and control of micronutrient deficiencies.  

Reviewer #3 (Public Review):

The ENANI-2019 study provides valuable insights into child nutrition, development, and metabolomics in Brazil, highlighting both challenges and opportunities for improving child health outcomes through targeted interventions and further research.

Readers might consider the following questions:

(1) Should investigators study the families through direct observation of diet and other factors to look for a connection between food taken in and gut microbiome and child development?

As mentioned before, the ENANI-2019 did not collect data on stool derived microbiome. However, there is data on child dietary intake with 24-hour recall that can be further explored in other studies.

(2) Can an examination of the mother's gut microbiome influence the child's microbiome? Can the mother or caregiver's microbiome influence early childhood development?

The questions raised by the reviewer are interesting and has been explored by other authors. However, we do not have microbiota data from the child nor from the mother/caregiver.

(3) Is developmental quotient enough to study early childhood development? Is it comprehensive enough?

Yes, we are confident it is comprehensive enough.

According to the World Health Organization, the term Early Childhood Development (ECD) refers to the cognitive, physical, language, motor, social and emotional development between 0 - 8 years of age. The SWCY milestones assess the domains of cognition, language/communication and motor. Therefore, it has enough content validity to represent ECD.

The SWYC is recommended for screening ECD by the American Society of Pediatrics. Furthermore, we assessed the internal consistency of the SWYC milestones questionnaire using ENANI-2019 data and Cronbach's alpha. The findings indicated satisfactory reliability (0.965; 95% CI: 0.963–0.968).

The SWCY is a screening instrument and indicates if the ECD is not within the expected range. If one of the above-mentioned domains are not achieved as expected the child may be at risk of ECD delay. Therefore, DQ<1 indicates that a child has not reached the expected ECD for the age group. We cannot say that children with DQ≥1 have full ECD, since we do not assess the socio-emotional domains. However, DQ can track the risk of ECD delay.

References

Blekherman, G., Laubenbacher, R., Cortes, D. F., Mendes, P., Torti, F. M., Akman, S., ... & Shulaev, V. (2011). Bioinformatics tools for cancer metabolomics. Metabolomics, 7, 329-343.

Gromski, P. S., Muhamadali, H., Ellis, D. I., Xu, Y., Correa, E., Turner, M. L., & Goodacre, R. (2015). A tutorial review: Metabolomics and partial least squares-discriminant analysis–a marriage of convenience or a shotgun wedding. Analytica chimica acta, 879, 10-23.

Wold, S., Sjöström, M., & Eriksson, L. (2001). PLS-regression: a basic tool of chemometrics. Chemometrics and intelligent laboratory systems, 58(2), 109-130.

LUIZ, RR., and STRUCHINER, CJ. Inferência causal em epidemiologia: o modelo de respostas potenciais [online]. Rio de Janeiro: Editora FIOCRUZ, 2002. 112 p. ISBN 85-7541-010-5. Available from SciELO Books http://books.scielo.org.

GREENLAND, S. & ROBINS, J. M. Identifiability, exchangeability, and epidemiological Confounding. International Journal of Epidemiolgy, 15(3):413-419, 1986.

Freitas-Costa NC, Andrade PG, Normando P, et al. Association of development quotient with nutritional status of vitamins B6, B12, and folate in 6–59-month-old children: Results from the Brazilian National Survey on Child Nutrition (ENANI-2019). The American journal of clinical nutrition 2023;118(1):162-73. doi: https://doi.org/10.1016/j.ajcnut.2023.04.026

Sheldrick RC, Schlichting LE, Berger B, et al. Establishing New Norms for Developmental Milestones. Pediatrics 2019;144(6) doi: 10.1542/peds.2019-0374 [published Online First: 2019/11/16]

Drachler Mde L, Marshall T, de Carvalho Leite JC. A continuous-scale measure of child development for population-based epidemiological surveys: a preliminary study using Item Response Theory for the Denver Test. Paediatric and perinatal epidemiology 2007;21(2):138-53. doi: 10.1111/j.1365-3016.2007.00787.x [published Online First: 2007/02/17]

VanderWeele, TJ Princípios de seleção de fatores de confusão. Eur J Epidemiol 34, 211–219 (2019). https://doi.org/10.1007/s10654-019-00494-6

David G. Kleinbaum, Lawrence L. Kupper; Hal Morgenstern. Epidemiologic Research: Principles and Quantitative Methods. 1991

Yan R, Liu X, Xue R, Duan X, Li L, He X, Cui F, Zhao J. Association between internet exclusion and depressive symptoms among older adults: panel data analysis of five longitudinal cohort studies. EClinicalMedicine 2024;75. doi: 10.1016/j.eclinm.2024.102767.

Zhong Y, Lu H, Jiang Y, Rong M, Zhang X, Liabsuetrakul T. Effect of homemade peanut oil consumption during pregnancy on low birth weight and preterm birth outcomes: a cohort study in Southwestern China. Glob Health Action. 2024 Dec 31;17(1):2336312.

Aristizábal LYG, Rocha PRH, Confortin SC, et al. Association between neonatal near miss and infant development: the Ribeirão Preto and São Luís birth cohorts (BRISA). BMC Pediatr. 2023;23(1):125. Published 2023 Mar 18. doi:10.1186/s12887-023-03897-3

Al-Haddad BJS, Jacobsson B, Chabra S, et al. Long-term risk of neuropsychiatric disease after exposure to infection in utero. JAMA Psychiatry. 2019;76(6):594-602. doi:10.1001/jamapsychiatry.2019.0029

Chan, A.Y.L., Gao, L., Hsieh, M.HC. et al. Maternal diabetes and risk of attention-deficit/hyperactivity disorder in offspring in a multinational cohort of 3.6 million mother–child pairs. Nat Med 30, 1416–1423 (2024).

Hernan MA, Robins JM (2020). Causal Inference: What If. Boca Raton: Chapman & Hall/CRC.

Greenland S; Pearl J; Robins JM. Confounding and collapsibility in causal inference. Statist Sci. 14 (1) 29 - 46 1999. https://doi.org/10.1214/ss/1009211805

Emergencia es una situación en la que la vida de una persona está en peligro o existe un riesgo significativo de daño permanente . Urgencia medica es una situación en la que una persona requiere atención médica inmediata para prevenir daños graves o la perdida de vida Importancia de emergencia y urgencia medica. estar preparado para responder y conocer los protocolos y procedimientos establecidos y trabajar en equipo con otros profesionales para salvar vidas y prevenir daños problemas cardíacos. Ejemplos de urgencia medica 1. Prevenir daños 2. Accidentes y traumas Ejemplos de emergencia 1. Emergencia médica 2.emergencia quirúrgica

Several Mayors and Courts around France say they can prohibit access to anyone not having a suitable outfit respectful of good values, secularism, hygiene and safety on pubic beaches. Three women were ticketed on the beaches of Cannes last weekend and forced to pay a 8 euro fine.

a few days ago

Author response:

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

Reviewer #1 (Public Review):

Summary:

Previous work demonstrated a strong bias in the percept of an ambiguous Shepard tone as either ascending or descending in pitch, depending on the preceding contextual stimulus. The authors recorded human MEG and ferret A1 single-unit activity during presentation of stimuli identical to those used in the behavioral studies. They used multiple neural decoding methods to test if context-dependent neural responses to ambiguous stimulus replicated the behavioral results. Strikingly, a decoder trained to report stimulus pitch produced biases opposite to the perceptual reports. These biases could be explained robustly by a feed-forward adaptation model. Instead, a decoder that took into account direction selectivity of neurons in the population was able to replicate the change in perceptual bias.

Strengths:

This study explores an interesting and important link between neural activity and sensory percepts, and it demonstrates convincingly that traditional neural decoding models cannot explain percepts. Experimental design and data collection appear to have been executed carefully. Subsequent analysis and modeling appear rigorous. The conclusion that traditional decoding models cannot explain the contextual effects on percepts is quite strong.

Weaknesses:

Beyond the very convincing negative results, it is less clear exactly what the conclusion is or what readers should take away from this study. The presentation of the alternative, "direction aware" models is unclear, making it difficult to determine if they are presented as realistic possibilities or simply novel concepts. Does this study make predictions about how information from auditory cortex must be read out by downstream areas? There are several places where the thinking of the authors should be clarified, in particular, around how this idea of specialized readout of direction-selective neurons should be integrated with a broader understanding of auditory cortex.

While we have not used the term "direction aware", we think the reviewer refers generally to the capability of our model to use a cell's direction selectivity in the decoding. In accordance with the reviewer's interpretation, we did indeed mean that the decoder assumes that a neuron does not only have a preferred frequency, but also a preferred direction of change in frequency (ascending/descending), which is what we use to demonstrate that the decoding in this way aligns with the human percept. We have adapted the text in several places to clarify this, in particular expanding the description in the Methods substantially.

Reviewer #2 (Public Review):

The authors aim to better understand the neural responses to Shepard tones in auditory cortex. This is an interesting question as Shepard tones can evoke an ambiguous pitch that is manipulated by a proceeding adapting stimulus, therefore it nicely disentangles pitch perception from simple stimulus acoustics.

The authors use a combination of computational modelling, ferret A1 recordings of single neurons, and human EEG measurements.

Their results provide new insights into neural correlates of these stimuli. However, the manuscript submitted is poorly organized, to the point where it is near impossible to review. We have provided Major Concerns below. We will only be able to understand and critique the manuscript fully after these issues have been addressed to improve the readability of the manuscript. Therefore, we have not yet reviewed the Discussion section.

Major concerns

Organization/presentation

The manuscript is disorganized and therefore difficult to follow. The biggest issue is that in many figures, the figure subpanels often do not correspond to the legend, the main body, or both. Subpanels described in the text are missing in several cases.

We have gone linearly through the text and checked that all figure subpanels are referred to in the text and the legend. As far as we can tell, this was already the case for all panels, with the exception of two subpanels of Fig. 5.

Many figure axes are unlabelled.

We have carefully checked the axes of all panels and all but two (Fig. 5D) were labeled. As is customary, certain panels inherit the axis label from a neighboring panel, if the label is the same, e.g. subpanels in Fig. 6F or Fig. 5E, which helps to declutter the figure. We hope that with this clarification, the reviewer can understand the labels of each panel.

There is an inconsistent style of in-text citation between figures and the main text. The manuscript contains typos and grammatical errors. My suggestions for edits below therefore should not be taken as an exhaustive list. I ask the authors to consider the following only a "first pass" review, and I will hopefully be able to think more deeply about the science in the second round of revisions after the manuscript is better organized.

While we are puzzled by the severity of issues that R2 indicates (see above, and R3 qualifies it as "well written", and R1 does not comment on the writing negatively), we have carefully gone through all specific issues mentioned by R2 and the other reviewers. We hope that the revised version of the paper with all corrections and clarifications made will resolve any remaining issues.

Frequency and pitch

The terms "frequency" and "pitch" seem to be used interchangeably at times, which can lead to major misconceptions in a manuscript on Shepard tones. It is possible that the authors confuse these concepts themselves at times (e.g. Fig 5), although this would be surprising given their expertise in this field. Please check through every use of "frequency" and "pitch" in this manuscript and make sure you are using the right term in the right place. In many places, "frequency" should actually be "fundamental frequency" to avoid misunderstanding.

Thanks for pointing this out. We have checked every occurrence and modified where necessary.

Insufficient detail or lack of clarity in descriptions

There seems to be insufficient information provided to evaluate parts of these analysis, most critically the final pitch-direction decoder (Fig 6), which is a major finding. Please clarify.

Thanks for pointing this out. We have extended the description of the pitch-direction decoder and highlighted its role for interpreting the results.

Reviewer #3 (Public Review):

Summary:

This is an elegant study investigating possible mechanisms underlying the hysteresis effect in the perception of perceptually ambiguous Shepard tones. The authors make a fairly convincing case that the adaptation of pitch direction sensitive cells in auditory cortex is likely responsible for this phenomenon.

Strengths:

The manuscript is overall well written. My only slight criticism is that, in places, particularly for non-expert readers, it might be helpful to work a little bit more methods detail into the results section, so readers don't have to work quite so hard jumping from results to methods and back.

Following this excellent suggestion, we have added more brief method sketches to the Results section, hopefully addressing this concern.

The methods seem sound and the conclusions warranted and carefully stated. Overall I would rate the quality of this study as very high, and I do not have any major issues to raise.

Thanks for your encouraging evaluation of the work.

Weaknesses:

I think this study is about as good as it can be with the current state of the art. Generally speaking, one has to bear in mind that this is an observational, rather than an interventional study, and therefore only able to identify plausible candidate mechanisms rather than making definitive identifications. However, the study nevertheless represents a significant advance over the current state of knowledge, and about as good as it can be with the techniques that are currently widely available.

Thanks for your encouraging evaluation of our work. The suggestion of an interventional study has also been on our minds, however, this appears rather difficult, as it would require a specific subset of cells to be inhibited. The most suitable approach would likely be 2p imaging with holographic inhibition of a subset of cells (using ArchT for example), that has a preference for one direction of pitch change, which should then bias the percept/behavior in the opposite direction.

Reviewer #1 (Recommendations For The Authors):

MAJOR CONCERNS

(1) What is the timescale used to compute direction selectivity in neural tuning? How does it compare to the timing of the Shepard tones? The basic idea of up versus down pitch is clear, the intuition for the role of direction tuning and its relation to stimulus dynamics could be laid out more clearly. Are the authors proposing that there are two "special" populations of A1 neurons that are treated differently to produce the biased percept? Or is there something specific about the dynamics of the Shepard stimuli and how direction selective neurons respond to them specifically? It would help if the authors could clarify if this result links to broader concepts of dynamic pitch coding in general or if the example reported here is specific (or idiosyncratic) to Shepard tones.

We propose that the findings here are not specific to Shepard tones. To the contrary, only basic properties of auditory cortex neurons, i.e. frequency preference, frequency-direction (i.e. ascending or descending) preference, and local adaptation in the tuning curve, suffice. Each of these properties have been demonstrated many times before and we only verified this in the lead-up to the results in Fig. 6. While the same effects should be observable with pure tones, the lack of ambiguity in the perception of direction of a frequency step for pure tone pairs, would make them less noticeable here. Regarding the time-scale of the directional selectivity, we relied on the sequencing of tones in our paradigm, i.e. 150 ms spacing. The SSTRFs were discretized at 50 ms, and include only the bins during the stimulus, not during the pause. The directional tuning, i.e. differences in the SSTRF above and below the preferred pitchclass for stimuli before the last stimulus, typically extended only one stimulus back in time. We have clarified this in more detail now, in particular in the added Methods section on the directional decoder.

(2) (p. 9) "weighted by each cell's directionality index ... (see Methods for details)" The direction-selective decoder is interesting and appears critical to the study. However, the details of its implementation are difficult to locate. Maybe Fig. 6A contains the key concepts? It would help greatly if the authors could describe it in parallel with the other decoders in the Methods.

We have expanded the description of the decoder in the Methods as the reviewer suggests.

LESSER CONCERNS

p. 1. (L 24) "distances between the pitch representations...." It's not obvious what "distances" means without reading the main paper. Can some other term or extra context be provided?

We have added a brief description here.

p. 2. (L 26) "Shepard tones" Can the authors provide a citation when they first introduce this class of stimuli?

Citation has been added.

p. 3 (L 4) "direction selective cells" Please define or provide context for what has a direction. Selective to pitch changes in time?

Yes, selective to pitch changes in time is what is meant. We have further clarified this in the text.

p. 4 (L 9-19). This paragraph seems like it belongs in the Introduction?

Given the concerns raised by R2 about the organization of the manuscript we prefer to keep this 'road-map' in the manuscript, as a guidance for the reader.

p. 4 (L 32) "majority of cells" One might imagine that the overlap of the bias band and the frequency tuning curve of individual neurons might vary substantially. Was there some criterion about the degree of overlap for including single units in the analysis? Does overlap matter?

We are not certain which analysis the reviewer is referring to. Generally, cells were not excluded based on their overlap between a particular Bias band and their (Shepard) tuning curve. There are several reasons for this: The bias was located in 4 different, overlapping Shepard tone regions, and all sounds were Shepard tones. Therefore, all cells overlapped with their (Shepard) tuning curve with one or multiple of the Biases. For decoding analysis, all cells were included as both a response and lack of a response is contributing to the decoding. If the reviewer is referring only to the analysis of whether a cell adapts, then the same argument applies as above, i.e. this was an average over all Bias sequences, and therefore every responding cell was driven to respond by the Bias, and therefore it was possible to also assess whether it adapted its response for different positions inside the Bias. We acknowledge that the limited randomness of the Bias sequences in combination with the specific tuning of the cells could in a few cases create response patterns over time that are not indicative of the actual behavior for repeated stimulation, however, since the results are rather clear with 91% of cells adapting, we do not think this would significantly change the conclusions.

p. 5 (L 17) "desynchronization ... behaving conditions" The logic here is not clear. Is less desynchronization expected during behavior? Typically, increased attention is associated with greater desynchronization.

Yes, we reformulated the sentence to: While this difference could be partly explained by desynchronization which is typically associated with active behavior or attention [30], general response adaptation to repeated stimuli is also typical in behaving humans [31].

p. 7 (L 5) "separation" is this a separation in time?

Yes, added.

p. 7 (L 33) "local adaptation" The idea of feedforward adaptation biasing encoding has been proposed before, and it might be worth citing previous work. This includes work from Nelken specifically related to SSA. Also, this model seems similar to the one described in Lopez Espejo et al (PLoS CB 2019).

Thanks for pointing this out. We think, however, that neither of these publications suggested this very narrow way of biasing, which we consider biologically implausible. We have therefore not added either of these citations.

p. 11 (L. 17) The cartoon in Fig. 6G may provide some intuition, but it is quite difficult to interpret. Is there a way to indicate which neuron "votes" for which percept?

This is an excellent idea, and we have added now the purported perceptual relation of each cell in the diagram.

p. 12 (L. 8). "classically assumed" This statement could benefit from a citation. Or maybe "classically" is not the right word?

We have changed 'classically' to 'typically', and now cite classical works from Deutsch and Repp. We think this description makes sense, as the whole concept of bistable percepts has been interpreted as being equidistant (in added or subtracted semitone steps) from the first tone, see e.g. Repp 1997, Fig.2.

p. 12 (L. 12) "...previous studies" of Shepard tone percepts? Of physiology?

We have modified it to 'Relation to previous studies of Shepard tone percepts and their underlying physiology", since this section deals with both.

p. 12 (L. 25) "compatible with cellular mechanisms..." This paragraph seems key to the study and to Major Concern 1, above. What are the dynamics of the task stimuli? How do they compare with the dynamics of neural FM tuning and previously reported studies of bias? And can the authors be more explicit in their interpretation - should direction selective neurons respond preferentially to the Shepard tone stimuli themselves? And/or is there a conceptual framework where the same neurons inform downstream percepts of both FM sweeps and both normal (unbiased) and biased Shepard tones?

The reviewer raises a number of different questions, which we address below:

- Dynamics of the task stimuli in relation to previously reported cellular biasing: The timescales tested in the studies mentioned are similar to what we used in our bias, e.g. Ye et al 2010 used FM sweeps that lasted for up to 200ms, which is quite comparable to our SOA of 150ms.

- Preferred responses to Shepard tones: no, we do not think that there should be preferred responses to Shepard tones, but rather that responses to Shepard tones can be thought of as the combined responses to the constituent tones.

- Conceptual framework where the same neurons inform about FM sweeps and both normal (unbiased) and biased Shepard tones: Our perspective on this question is as follows: To our knowledge, the classical approach to population decoding in the auditory system, i.e. weighted based on preferred frequency, has not been directly demonstrated to be read out inside the brain, and certainly not demonstrated to be read out in only this way in all areas of the brain that receive input from the auditory cortex. Rather it has achieved its credibility by being linked directly with animal performance or match with the presented stimuli. However, these approaches were usually geared towards a representation that can be estimated based on constituent frequencies. Additional response properties of neurons, such as directional selectivity have been documented and analyzed before, however, not been used for explaining the percept. We agree that our use of this cellular response preference in the decoding implicitly assumes that the brain could utilize this as well, however, this seems just as likely or unlikely as the use of the preferred frequency of a neuron. Therefore we do not think that this decoding is any more speculative than the classical decoding. In both cases, subsequent neurons would have to implicitly 'know' the preference of the input neuron, and weigh its input correspondingly.

We have added all the above considerations to the discussion in an abbreviated form.

p. 15 (L. 15). Is there a citation for the drive system?

There is no publication, but an old repository, where the files are available, which we cite now: https://code.google.com/archive/p/edds-array-drive/

p. 16 (L. 24) "position in an octave" It is implied but not explicitly stated that the Shepard tones don't contain the fundamental frequency. Can the authors clarify the relationship between the neural tuning band and the bands of the stimulus. Did a single stimulus band typically fall in a neuron's frequency tuning curve? If not 1, how many?

Yes, it is correct that the concept of fundamental frequency does not cleanly apply to Shepard tones, because it is composed of octave spaced pure tones, but the lowest tone is placed outside the hearing range of the animal and amplitude envelope (across frequencies). Therefore one or more constituent tones of the Shepard tone can fall into the tuning curve of a neuron and contribute to driving the neuron (or inhibiting it, if they fall within an inhibitory region of the tuning curve). The number of constituent tones that fall within the tuning curve depends on the tuning width of the neurons. The distribution of tuning widths to Shepard tones is shown in Fig. S1E, which indicated that a lot of neurons had rather narrow tuning (close to the center), but many were also tuned widely, indicated that they would be stimulated by multiple constituent tones of the Shepard tone. As the tuning bandwidth (Q30: 30dB above threshold) of most cortical neurons in the ferret auditory cortex (see e.g. Bizley et al. Cerebral Cortex, 2005, Fig.12) is below 1, this means that typically not more than 1 tone fell into the tuning curve of a neuron. However, we also observed multimodal tuning-curves w.r.t. to Shepard tones, which suggests that some neurons were stimulated by more than 2 or more constituent tones (again consistent with the existence of more broadly tuned neurons (see same citation). We have added this information partly to the manuscript in the caption of Fig. S1E.

p. 17 (L. 32). "Fig 4" Correct figure ref? This figure appears to be a schematic rather than one displaying data.

Thanks for pointing this out, changed to Fig. 5.

p. 18 (L. 25). "assign a pitchclass" Can the authors refer to a figure illustrating this process?

Added.

p. 19 (L. 17). Is mu the correct symbol?

Thanks. We changed it to phi_i, as in the formula above.

p. 19 (L 19). "convolution" in time? Frequency?

Thanks for pointing this out, the term convolution was incorrect in this context. We have replaced it by "weighted average" and also adapted and simplified the formula.

p. 19 (L 25) "SSTRF" this term is introduced before it is defined. Also it appears that "SSTRF" and "STRF" are sometimes interchanged.

Apologies, we have added the definition, and also checked its usage in each location.

p. 23 (Fig 2) There is a mismatch between panel labels in the figure and in the legend. Bottom right panel (B3), what does time refer to here?

Thanks for pointing these out, both fixed.

p. 24 (L 23) "shifts them away" away from what?

We have expanded the sentence to: "After the bias, the decoded pitchclass is shifted from their actual pitchclass away from the biased pitchclass range ... "

p. 25 (L 7) "individual properties" properties of individual subjects?

Thanks for pointing this out, the corresponding sentence has been clarified and citations added.

p. 26 (L 20) What is plotted in panel D? The average for all cells? What is n?

Yes, this is an average over cells, the number of cells has now been added to each panel.

p. 28 (L 3) How to apply the terms "right" "right" "middle" to the panel is not clear. Generally, this figure is quite dense and difficult to interpret.

We have changed the caption of Panel A and replaced the location terms with the symbols, which helps to directly relate them to the figure. We have considered different approaches of adding or removing content from the figure to help make it less dense, but that all did not seem to help. For lack of better options we have left it in its current form.

MINOR/TYPOS

p. 3 (L 1) "Stimulus Specific Adaptation" Capitalization seems unnecessary

Changed.

p. 4 (L 14) "Siple"

Corrected.

p. 9 (L 10) "an quantitatively"

Corrected

p. 9 (L 20) "directional ... direction ... directly ... directional" This is a bit confusing as directseems to mean several different things in its different usages.

We have gone through these sentences, and we think the terms are now more clearly used, especially since the term 'direction' occurs in several different forms, as it relates to different aspects (cells/percept/hypothesis). Unfortunately, some repetition is necessary to maintain clarity.

Reviewer #2 (Recommendations For The Authors):

Detailed critique

Stimuli

It would be very useful if the authors could provide demos of their stimuli on a website. Many readers will not be familiar with Shepard tones and the perceptual result of the acoustical descriptions are not intuitive. I ended up coding the stimuli myself to get some intuition for them.

We have created some sample tones and sequences and uploaded them with the revision as supplementary documents.

Abstract

P1 L27 'pitch and...selective cells' - The authors haven't provided sufficient controls to demonstrate that these are "pitch cells" or "selective" to pitch direction. They have only shown that they are sensitive to these properties in their stimuli. Controls would need to be included to ensure that the cells aren't simply responding to one frequency component in the complex sound, for example. This is not really critical to the overall findings, but the claim about pitch "selectivity" is not accurate.

Fair point. We have removed the word 'selective' in both occurrences.

Introduction

P2 L14-17: I do not follow the phonetic example provided. The authors state that the second syllable of /alga/ and /arda/ are physically identical, but how is this possible that ga = da? The acoustics are clearly different. More explanation is needed, or a correction.

Apologies for the slightly misleading description, it has now been corrected to be in line with the original reference.

P2,L26-27: Should the two uses of "frequency" be "F0" and "pitch" here? The tones are not separated in frequency by half and octave, but "separated in [F0]" by half an octave, correct? Their frequency ranges are largely overlapping. And the second 'frequency', which refers to the percept, should presumably be "pitch".

Indeed. This is now corrected.

P3 L2-6: Unclear at this point in the manuscript what is the difference between the 3 percepts mentioned: perceived pitch-change direction, Shepard tone pitches, and "their respective differences". (It becomes clear later, but clarification is needed here).

We have tried a few reformulations, however, it tends to overload the introduction with details. We believe it is preferable to present the gist of the results here, and present the complete details later in the MS.

P3 L6-7 What does it mean that the MEG and single unit results "align in direction and dynamics"? These are very different signals, so clarification is needed.

We have phrased the corresponding sentence more clearly.

Results

Throughout: Choose one of 'pitch class', 'pitchclass', or 'pitch-class' and use it consistently.

Done.

P4L12 - would be helpful at this point to define 'repulsive effect'

We have added another sentence to clarify this term.

P4, L14 "simple"

Done

P4, L12 - not clear here what "repulsive influence" means

See above.

P4, L17 - alternative to which explanation? Please clarify. In general, this paragraph is difficult to interpret because we do not yet have the details needed to understand the terms used and the results described. In my opinion, it would be better to omit this summary of the results at the very beginning, and instead reveal the findings as they come, when they can be fully explained to the Reader.

We agree, but we also believe that a rather general description here is useful for providing a roadmap to the results. However, we have added a half-sentence to clarify what is meant by alternative.

P4 L30 - text says that cells adapt in their onset, sustained and offset responses, but only data for onset responses are shown (I think - clarification needed for fig 2A2). Supp figure shows only 1 example cell of sustained and offset, and in fact there is no effect of adaptation in the sustained response shown there.

Regarding the effect of adaptation and whether it can be discerned from the supplementary figure: the shown responses are for 10 repetitions of one particular Bias sequence. Since the response of the cell will depend on its tuning and the specific sequence of the Shepard tones in this Bias, it is not possible to assess adaptation for a given cell. We assess the level of adaptation, by averaging all biases (similar to what is shown in Fig. 2A2) per cell, and then fit an exponential to it, separately by response type. The step direction of the exponential, relative to the spontaneous rate is then used to assess the kind of adaptation. The vast majority of cells show adaptation. We have added this information to the Methods of the manuscript.

P4, L32 - please state the statistical test and criterion (alpha) used to determine that 91% of cells decreased their responses throughout the Bias sequence. Was this specifically for onset responses?

Thanks for pointing this out, test and p-value added. Adaptation was observed for onset, sustained and offset responses, in all cases with the vast majority showing an adapting behavior, although the onset responses were adapting the most.

P4 L36 - "response strength is reduced locally". What does "locally" mean here? Nearby frequencies?

We have added a sentence here to clarify this question.

Figure 1 - this appears to be the wrong version of the figure, as it doesn't match the caption or results text. It's not possible to assess this figure until these things are fixed. Figure 1A schematic of definition of f(diff) does not correspond to legend definition.

As far as we can tell, it is all correct, only the resolution of the figure appears to be rather low. This has been improved now.

Fig 2 A2 - is this also onset responses only?

Yes, added to the caption.

Fig 2 A3 - add y-axis label. The authors are comparing a very wide octave band (5.5 octaves) to a much narrower band (0.5 octaves). Could this matter? Is there something special about the cut-off of 2.5 octaves in the 2 bands, or was this an arbitrary choice?

Interesting question.... essentially our stimulus design left us only with this choice, i.e. comparing the internal region of the bias with the boundary region of the bias, i.e. the test tones. The internal region just corresponds to the bias, which is 5 st wide, and therefore the range is here given as 2.5 st relative to its center, while the test tones are at the boundary, as they are 3 st from the center. The axis for the bias was mislabelled, and has now been corrected. The y-axis label is matched with the panel to the left, but has now been added to avoid any confusion.

Fig 2A4 - does not refer to ferret single unit data, as stated in the text (p5L8). Nor does supp Fig2, as stated. Also, the figure caption does not match the figure.

Apologies, this was an error in the code that led to this mislabelling. We have corrected the labels, which also added back the recovery from the Bias sequence in the new Panel A4.

P5 l9 - Figure 3 is not understandable at this point in the text, and should not be referred to here. There is a lot going on in Fig 3, and it isn't clear what you are referring to.

Removed.

P5 L12 - by Fig 2 B1, I assume you mean A4? Also, F2B1 shows only 1 subject, not 2.

Yes, mislabeled by mistake, and corrected now.

Fig2B2 -What is the y-axis?

Same as in the panel to its left, added for clarity.

Stimuli: why are tones presented at a faster rate to ferrets than to humans?

The main reason is that the response analysis in MEG requires more spacing in time than the neuronal analysis in the ferret brain.

P5 L6 - there is no Fig 5 D2? I don't think it is a good idea to get the reader to skip so far ahead in the figures at this stage anyway, even if such a figure existed. It is confusing to jump around the manuscript

Changed to 'see below'

P5 L8 - There is no Figure 2A4, so I don't know whether this time constant is accurate.

This was in reference to a panel that had been removed before, but we have added it back now.

P5 L16: "in humans appears to be more substantial (40%) than for the average single units under awake conditions". One cannot directly compare magnitude of effects in MEG and single unit signals in this way and assume it is due to behavioural state. You are comparing different measures of neural activity, averaged over vastly different numbers of numbers, and recorded from different species listening to different stimuli (presentation rates).

Yes, that's why the next sentence is: "However, comparisons between the level of adaptation in MEG and single neuron firing rates may be misleading, due to the differences in the signal measured and subsequent processing.", and all statements in the preceding sentences are phrased as 'appears' and 'may'. We think we have formulated this comparison with an appropriate level of uncertainty. Further, the main message here is that adaptation is taking place in both active and passive conditions.

P5 L25 -I do not see any evidence regarding tuning widths in Fig s2, as stated in the text.

Corrected to Fig. S1.

P5 l26 - Do not skip ahead to Fig 5 here. We aren't ready to process that yet.

OK, reference removed.

P5 l27 - Do you mean because it could be tuning to pitch chroma, not height?

Yes, that is a possible interpretation, although it could also arise from a combination of excitatory and inhibitory contributions across multiple octaves.

P5 l33 - remove speculation about active vs passive for reasons given above.

Removed.

P6L2-6 'In the present...5 semitone step' - This is an incorrect interpretation of the minimal distance hypothesis in the context of the Shepard tone ambiguity. The percept is ambiguous because the 'true' F0 of the Shepard tones are imperceptibly low. Each constituent frequency of a single tone can therefore be perceived either as a harmonic of some lower fundamental frequency or as an independent tone. The dominant pitch of the second tone in the tritone pair may therefore be biased to be perceived at a lower constituent frequency (when the bias sequence is low) or at a higher constituent frequency (when the bias sequence is high). The text states that the minimal distance hypothesis would predict that an up-bias would make a tritone into a perfect fourth (5 semitones). This is incorrect. The MDH would predict that an up-bias would reduce the distance between the 1st tone in the ambiguous pair and the upper constituent frequency of the 2nd tone in the pair, hence making the upper constituent frequency the dominant pitch percept of the 2nd tone, causing an ascending percept.

The reviewer here refers to a “minimal distance hypothesis”, which without a literature reference,is hard for us to fully interpret. However, some responses are given below:

- "The percept is ambiguous because the 'true' F0 of the Shepard tones are imperceptibly low." This statement appears to be based on some misconception: due to the octave spacing (rather than multiple/harmonics of a lowest frequency), the Shepard tones cannot be interpreted as usual harmonic tones would be. It is correct that the lowest tone in a Shepard tone is not audible, due to the envelope and the fact that it could in principle be arbitrarily small... hence, speaking about an F0 is really not well-defined in the case of a Shepard tone. The closest one could get to it would be to refer to the Shepard tone that is both in the audible range and in the non-zero amplitude envelope. But again, since the envelope is fading out the highest and lowest constituent tones, it is not as easy to refer to the lowest one as F0 (as it might be much quieter than the next higher constituent.

- "The dominant pitch of the second tone in the tritone pair may therefore be biased to be perceived at a lower constituent frequency (when the bias sequence is low) or at a higher constituent frequency (when the bias sequence is high)." This may relate to some known psychophysics, but we are unable to interpret it with certainty.

- "The text states that the minimal distance hypothesis would predict that an up-bias would make a tritone into a perfect fourth (5 semitones). This is incorrect." We are unsure how the reviewer reaches this conclusion.

- "The MDH would predict that an up-bias would reduce the distance between the 1st tone in the ambiguous pair and the upper constituent frequency of the 2nd tone in the pair, hence making the upper constituent frequency the dominant pitch percept of the 2nd tone, causing an ascending percept." Again, in the absence of a reference to the MDH, we are unsure of the implied rationale. We agree that this is a possible interpretation of distance, however, we believe that our interpretation of distance (i.e. distances between constituent tones) is also a possible interpretation.

Fig 4: Given that it comes before Figure 3 in the results text, these should be switched in order in the paper.

Switched.

PCA decoder: The methods (p18) state that the PCA uses the first 3 dimensions, and that pitch classes are calculated from the closest 4 stimuli. The results (P6), however, state that the first 2 principal components are used, and classes are computed from the average of 10 adjacent points. Which is correct, or am I missing something?

Thanks for pointing this out, we have made this more concrete in the Methods to: "The data were projected to the first three dimensions, which represented the pitch class as well as the position in the sequence of stimuli (see Fig. 43A for a schematic). As the position in the Bias sequence was not relevant for the subsequent pitch class decoding, we only focussed on the two dimensions that spanned the pitch circle." Regarding the number of stimuli that were averaged: this might be a slight misunderstanding: Each Shepard tone was decoded/projected without averaging. However, to then assign an estimated pitch class, we first had to establish an axis (here going around the circle), where each position along the axis was associated with a pitch class. This was done by stepping in 0.5 semitone steps, and finding the location in decoded space that corresponded to the median of the Shepard tones within +/- 0.25st. To increase the resolution, this circular 'axis' of 24 points was then linearly interpolated to a resolution of 0.05st. We have updated the text in the Methods accordingly. The mentioning of 10 points for averaging in the Results was correct, as there were 240 tones in all bias stimuli, and 24 bins in the pitch circle. The mentioning of an average over 4 tones in the Methods was a typo.

Fig 3A: axes of pink plane should be PC not PCA

Done.

Fig 3B: the circularity in the distribution of these points is indeed interesting! But what do the authors make of the gap in the circle between semitones 6-7? Is this showing an inherent bias in the way the ambiguous tone is represented?

While we cannot be certain, we think that this represents an inhomogeneous sampling from the overall set of neural tuning preferences, and that if we had recorded more/all neurons, the circle would be complete and uniformly sampled (which it already nearly is, see Fig.4C, which used to be Fig. 3C).

Fig 3B (lesser note): It'd be preferable to replace the tint (bright vs. dark) differentiation of the triangles to be filled vs. unfilled because such a subtle change in tint is not easily differentiable from a change in hue (indicating a different variable in this plot) with this particular colour palette

We have experimented with this suggestion, and it didn't seem to improve the clarity. However, we have changed the outline of the test-pair triangles to white, which now visually separates them better.

P6 l32 - Please indicate if cross-validation was used in this decoder, and if so, what sort. Ideally, the authors would test on a held-out data set, or at least take a leave-one-out approach. Otherwise, the classifier may be overfit to the data, and overfitting would explain the exceptional performance (r=.995) of the classifier.

Cross-validation was not used, as the purpose of the decoder is here to create a standard against which to compare the biased responses in the ambiguous pair, which were not used for training of the decoder. We agree that if we instead used a cross-validated decoder (which would only apply to the local average to establish the pitch class circle) the correlation would be somewhat lower, however, this is less relevant for the main question, i.e. the influence of the Bias sequence on the neural representation of the ambiguous pair. We have added this information to the corresponding section.

Fig 3D: I understood that these pitch classifications shown by the triangles were carried out on the final ambiguous pair of stimuli. I thought these were always presented at the edges of the range of other stimuli, so I do not follow how they have so many different pitchclass values on the x-axis here.

There were 4 Biases, centered at 0,3,6 or 9 semitones, and covering [-2.5,2.5]st relative to this center. Therefore the edges of the bias ranges (3st away from their centers) happen to be the same as the centers, e.g. for the Bias centered at 3, the ambiguous pair would be a 0-6 or 6-0 step. Therefore there are 4 locations for the ambiguous tones on the x-axis of Fig. 4D (previously 3D).

Figure 4: This demonstration of the ambiguity of Shepard pairs may be misleading. The actual musical interval is never ambiguous, as this figure suggests. Only the ascending vs descending percept is ambiguous. Therefore the predictions of the ferret A1 decoding (Fig 3D) and the model in Fig 5 are inconsistent with perception in two ways. One (which the authors mention) is the direction of the bias shift (up vs down). Another (not mentioned here) is that one never experiences a shift in the shepard tone at a fraction of a semitone - the musical note stays the same, and changes only in pitch height, not pitch chroma.

We are unsure of the reviewer’s direction with this question. In particular the second point is not clear to us: "...one (who?) never (in this experiment? in real life?) experiences a bias shift in the Shepard tone at a fraction of a semitone" (why is this relevant in the current experiment?). Pitch chrome would actually be a possible replacement for pitch class, but somehow, the previous Shepard tone literature has referred to it as pitch class.

P7 l12 - omit one 'consequently'

Changed to 'Therefore'.

P7 l24 - I encourage the authors to not use "local" and "global" without making it clear what space they refer to. One tends to automatically think of frequency space in the auditory system, but I think here they mean f0 space? What is a "cell close to the location of the bias"? Cells reside in the brain. The bias is in f0 space. The use of "local" and "global" throughout the manuscript is too vague.

Agreed, the reference here was actually to the cell's preferred pitch class, not its physical location (which one might arguably be able to disambiguate, given the context). We have changed the wording, and also checked the use of global/local throughout the manuscript. The main use of 'global/local' is now in reference to the range of adaptation, and is properly introduced on first mention.

P7 L26 -there is no Fig 5D1. Do you mean the left panel of 5D?

Thanks. Changed.

FigS3 is referred to a lot on p7-8. Should this be moved to the main text?

The main reason why we kept it in the supplement is that it is based on a more static model, which is intended to illustrate the consequences of different encoding schemes. In order to not confuse the reader about these two models, we prefer to keep it in the supplement, which - for an online journal - makes little difference since the reader can just jump ahead to this figure in the same way as any other figure.

Fig 5C, D - label x-axis.

Added.

Fig 5E - axis labels needed. I don't know what is plotted on x and y, and cannot see red and green lines in left plot

Thanks for noticing this, colors corrected, axes labeled.

Page 8 L3-15 - If I follow this correctly, I think the authors are confusing pitch and frequency here in a way that is fundamental to their model. They seem to equate tonotopic frequency tuning to pitch tuning, leading to confused implications of frequency adaptation on the F0 representation of complex sounds like Shepard tones. To my knowledge, the authors do not examine pure tone frequency tuning in their neurons in this study. Please clarify how you propose that frequency tuning like that shown in Fig 5A relates to representation of the F0 of Shepard tones. Or...are the authors suggesting these neural effects have little to do with pitch processing and instead are just the result of frequency tuning for a single harmonic of the Shepard tones?

We agree that it is not trivial to describe this well, while keeping the text uncluttered, in particular, because often tuning properties to stimulus frequency contribute to tuning properties of the same neuron for pitch class, although this can be more or less straightforward: specifically, for some narrowly tuned cells, the Shepard tuning is simply a reflection of their tuning to a single octave range of the constituent tones (see Fig. S1). For more broadly tuned cells, multiple constituent tones will contribute to the overall Shepard tuning, which can be additive, subtractive, or more complex. The assumption in our approach is that we can directly estimate the Shepard tuning to evaluate the consequence for the percept. While this may seem artificial, as Shepard tones do not typically occur in nature, the same argument could be made against pure tones, on which classical tuning curves and associated decodings are often based. Relating the Shepard tuning to the classical tuning would be an interesting study in itself, although arguably relating the tuning of one artificial stimulus to another. Regarding the terminology of pitch, pitch class and frequency: The term pitch class is commonly used in the field of Shepard tones, and - as we indicated in the beginning of the results: "the term pitch is used interchangeably with pitch class as only Shepard tones are considered in this study". We agree that the term pitch, which describes the perceptual convergence/construction of a tone-height from a range of possible physical stimuli, needs to be separated from frequency as one contributor/basis for the perception of a pitch. However, we think that the term pitch can - despite its perceptual origin - also be associated with neuron/neural responses, in order to investigate the neural origin of the pitch percept. At the same time, the present study is not targeted to study pitch encoding per se, as this would require the use of a variety of stimuli leading to consistent pitch percepts. Therefore, pitch (class) is here mainly used as a term to describe the neural responses to Shepard tones, based on the previous literature, and the fact that Shepard tones are composite stimuli that lead to a pitch percept. The last sentence has been added to the manuscript for clarity.

P7-9: I wasn't left with a clear idea of how the model works from this text. I assume you have layers of neurons tuned to frequency or f0 (based on the real data?), which are connected in some way to produce some sort of output when you input a sound? More detail is needed here. How is the dynamic adaptation implemented?

The detailed description of the model can be found in the Methods section. We have gone through the corresponding paragraph and have tried to clarify the description of the model by introducing a high-level description and the reference to the corresponding Figure (Fig. 5A) in the Results.

Fig6A: Figure caption can't be correct. In any case, these equations cannot be understood unless you define the terms in them.

We have clarified the description in the caption.

Fig 6/directionality analysis: Assuming that the "F" in the STRFs here is Shepard tone f0, and not simple frequency?

We have changed the formula in the caption and the axis labels now.

Fig 6C - y-axis values

In the submission, these values were left out on purpose, as the result has an arbitrary scale, but only whether it is larger or smaller than 0 counts for the evaluation of the decoded directionality (at the current level of granularity). An interesting refinement would be to relate the decoded values to animal performance. We have now scaled the values arbitrarily to fit within [-1,1], but we would like to emphasize that only their relative scale matters here, not their absolute scale.

Fig 6E - can't both be abscissa (caption). I might be missing something here, but I don't see the "two stripes" in the data that are described in the caption.

Thank you. The typo is fixed. The stripes are most clearly visible in the right panel of Fig. 6E, red and blue, diagonally from top left to bottom right.

Fig 6G -I have no idea what this figure is illustrating.

This panel is described in the text as follows: "The resulting distribution of activities in their relation to the Bias is, hence, symmetric around the Bias (Fig. 6G). Without prior stimulation, the population of cells is unadapted and thus exhibits balanced activity in response to a stimulus. After a sequence of stimuli, the population is partially adapted (Fig. 6G right), such that a subsequent stimulus now elicits an imbalanced activity. Translated concretely to the present paradigm, the Bias will locally adapt cells. The degree of adaptation will be stronger, if their tuning curve overlaps more with the biased region. Adaptation in this region should therefore most strongly influence a cell’s response. For example, if one considers two directional cells, an up- and a down-selective cell, cocentered in the same frequency location below the Bias, then the Bias will more strongly adapt the up-cell, which has its dominant, recent part of the SSTRF more inside the region of the Bias (Fig. 6G right). Consistent with the percept, this imbalance predicts the tone to be perceived as a descending step relative to the Bias. Conversely, for the second stimulus in the pair, located above the Bias, the down-selective cells will be more adapted, thus predicting an ascending step relative to the previous tone."

I might be just confused or losing steam at this point, but I do not follow what has been done or the results in Fig 6 and the accompanying text very well at all. Can this be explained more clearly? Perhaps the authors could show spike rate responses of an example up-direction and down-direction neuron? Explain how the decoder works, not just the results of it.

We agree that we are presenting something new here. However, it is conceptually not very different from decoding based on preferred frequencies. We have attempted to provide two illustrations of how the decoder works (Fig. 6A) and how it then leads to the percept using prototypical examples of cellular SSTRFs (Fig. 6G). We have added a complete, but accessible description to the Methods section. Showing firing rates of neurons would unfortunately not be very telling, given the usual variability in neural response and the fact that our paradigm did not have a lot of repetitions (but instead a lot of conditions), which would be able to average out the variability on a single neuron level.

Discussion - I do not feel I can adequately critique the author's interpretation of the results until I understand their results and methods better. I will therefore save my critique of the discussion section for the next round of revisions after they have addressed the above issues of disorganization and clarity in the manuscript.

We hope that the updated version of the manuscript provides the reviewer now with this possibility.

Methods

P15L7 - gender of human subjects? Age distribution? Age of ferrets?

We have added this information.

P16L21 - What is the justification for randomizing the phase of the constituent frequencies?

The purpose of the randomization was to prevent idiosyncratic phase relationships for particular Shepard tones, which would depend in an orderly fashion on the included base-frequencies if non-randomized, and could have contributed to shaping the percept for each Shepard tone in a way that was only partly determined by the pitch class of the Shepard tone. Added to the section.

P17L6 - what are the 2 randomizations? What is being randomized?

Pitch classes and position in the Bias sequence. Added to the section.

P16 Shepard Tuning section - What were the durations of the tones and the time between tones within a trial?

Thanks, added!

Equations - several undefined terms in the equations throughout the manuscript.

Thanks. We have gone through the manuscript and all equations and have introduced additional definitions where they had been missing.

Reviewer #3 (Recommendations For The Authors):

P3L10: "passive" and "active" conditions come totally out of the blue. Need introducing first. (Or cut. If adaptation is always seen, why mention the two conditions if the difference is not relevant here?)

We have added an additional sentence in the preceding paragraph, that should clarify this. The reason for mentioning it is that otherwise a possible counter-argument could be made that adaptation does not occur in the active condition, which was not tested in ferrets (but presents an interesting avenue for future research).

P3L14 "siple" typo

Corrected.

P4L1 "behaving humans" you should elaborate just a little here on what sort of behavior the participants engaged in.

Thanks for pointing this out. We have clarified this by adding an additional sentence directly thereafter.

P4 adaptation: I wonder whether it would be useful to describe the Bias condition a bit more here before going into the observations. The reader cannot know what to expect unless they jump ahead to get a sense of what the Bias looks like in the sense of how many stimuli are in it, and how similar they are to each other. Observations such as "the average response strength decreases as a function of the position in the Bias sequence" are entirely expected if the Bias is made up of highly repetitive material, but less expected if it is not. I appreciate that it can be awkward to have Methods after Results, but with a format like that, the broad brushstroke Methods should really be incorporated into the Results and only the tedious details should be reserved for the Methods to avoid readers having to jump back and forth.

Agreed, we have inserted a corresponding description before going into the details of the results.

Related to this (perhaps): Bottom of P4, top of P5: "significantly less reduced (33%, p=0.0011, 2 group t-test) compared to within the bias (Fig. 2 A3, blue vs. red), relative to the first responses of the bias" ... I am at a loss as to what the red and blue symbols in Fig 2 A3 really show, and I wonder whether the "at the edges" to "within the Bias" comparison were to make sense if at this stage I had been told more about the composition of the Bias sequence. Do the ambiguous ('target') tones also occur within the Bias? As I am unclear about what is compared against what I am also not sure how sound that comparison is.

We have added an extended description of the Bias to the beginning of this section of the manuscript. For your reference: the Shepard tones that made up the ambiguous tones were not part of the Bias sequence, as they are located at 3st distance from the center of the Bias (above and below), while the Bias has a range of only +/- 2.5st.

Fig 2: A4 B1 B2 labels should be B1 B2 B3

Corrected.

Fig 2 A2, A3: consider adjusting y-axis range to have less empty space above the data. In A3 in particular, the "interesting bit" is quite compressed.

Done, however, while still matching the axes of A2 and A3 for better comparability.

I am under the strong impression that the human data only made it into Fig 2 and that the data from Fig 3 onwards are animal data only. That is of course fine (MEG may not give responses that are differentiated enough to perform the sort of analyses shown in the later figures. But I do think that somewhere this should be explicitly stated.

Yes, the reviewer's observation is correct. The decoding analyses could not be conducted on the human MEG data and was therefore not further pursued. Its inclusion in the paper has the purpose of demonstrating that even in humans and active conditions, the local adaptation is present, which is a key contributor to the two decoding models. We now state this explicitly when starting the decoding analysis.

P5L2 "bias" not capitalized. Be consistent.

All changed to capitalized.

P5L8 reference to Fig 2 A4: something is amiss here. From legend of Fig 2 it seems clear that panel A4 label is mislabeled B1. Maybe some panels are missing to show recovery rates?

Apologies for this residual text from a previous version of the manuscript. We have gone through all references and corrected them.

P6L7 comma after "decoding".

Changed.

Fig 3, I like this analysis. What would be useful / needed here though is a little bit more information about how the data were preprocessed and pooled over animals. Did you do the PCA separately for each animal, then combine, or pool all units into a big matrix that went into the PCA? What about repeat, presentations? Was every trial a row in the matrix, or was there some averaging over repeats? (In fact, were there repeats??)

Thanks for bringing up these relevant aspects, which were partly insufficiently detailed in the manuscript. Briefly, cells were pooled across animals and we only used cells that could meaningfully contribute to the decoding analysis, i.e. had auditory responses and different responses to different Shepard tones. Regarding the responses, as stated in the Methods, "Each stimulus was repeated 10 times", and we computed average responses across these repetitions. Single trials were not analyzed separately. We have added this information in the Methods, and refer to it in the Results.

Also, there doesn't appear to be a preselection of units. We would not necessarily expect all cortical neurons to have a meaningful "best pitch" as they may be coding for things other than pitch. Intuitively I suspect that, perhaps, the PCA may take care of that by simply not assigning much weight to units that don't contribute much to explained variance? In any event I think it should be possible, and would be of some interest, to pull out of this dataset some descriptive statistics on what proportion of units actually "care about pitch" in that they have a lot (or at least significantly more than zero) of response variance explained by pitch. Would it make sense to show a distribution of %VE by pitch? Would it make sense to only perform the analysis in Fig 3 on units that meet some criterion? Doing so is unlikely to change the conclusion, but I think it may be useful for other scientists who may want to build on this work to get a sense of how much VE_pitch to expect.

We fully agree with the reviewer, which is why this information is already presented in Supplementary Fig.1, which details the tuning properties of the recorded neurons. Overall, we recorded from 1467 neurons across all ferrets, out of which 662 were selected for the decoding analysis based on their driven firing rate (i.e. whether they responded significantly to auditory stimulation) and whether they showed a differential response to different Shepard tones The thresholds for auditory response and tuning to Shepard tones were not very critical: setting the threshold low, led to quantitatively the same result, however, with more noise. Setting the thresholds very high, reduced the set of cells included in the analysis, and eventually that made the results less stable, as the cells did not cover the entire range of preferences to Shepard tones. We agree that the PCA based preprocessing would also automatically exclude many of the cells that were already excluded with the more concrete criteria beforehand. We have added further information on this issue in the Methods section under the heading 'Unit selection'.

P9 "tones This" missing period.

Changed.

P10L17 comma after "analysis"

Changed.

Porque tenían fe en ella y fueron obedientes

Poner en práctica la sabiduría de Dios, para que quede en evidencia la falsedad de Satanás y Jehová cumple sus promesas

Porque no tienen una visión espiritual, su visión es física y se basa en el conocimiento del hombre, no en el conocimiento del secreto sagrado de Dios. Que de hecho consideran una tontería ese secreto.

Porque también Jesús fue revelando nuevo entendimiento poco a poco, acerca del pecado, de la justicia y del juicio, y dijo que vendría un ayudante, que les iría revelando poco a poco la información.

.h2

Como para un ser humano la vida en los cielos era algo nuevo, a estos cristianos ungidos se les llama “una nueva creación”

Lo que sí heredó de sus padres humanos fue el derecho a ocupar el trono de David, pues ellos eran descendientes de este rey (Hechos 13:22, 23). Cuando Jesús se bautizó, en el año 29 de nuestra era, Jehová lo ungió con espíritu santo y dijo: “Este es mi Hijo amado” (Mateo 3:16, 17). ¡Al fin había llegado la Descendencia! (Gálatas 3:16).

: “De veras haré que tu descendencia sea tan numerosa como las estrellas de los cielos [...]. Y todas las naciones de la tierra conseguirán una bendición para ellas mismas mediante tu descendencia*, porque tú has escuchado mi voz” (Génesis 22:17, 18).

Génesis 3:15

•C’est un article que traite sur la structure, la fonction du systeme cardiovasculaire. •C’est un article que traite sur beaucoup sujects de la médecine •C’est un article que traite sur la recherche medicale. •C’est un article que traits de beaucoup sujects mais il y a une section qui traite du cancer.  •C’est article intéressant qui traite des traitements de la cardiologie

Résumé de la Vidéo

La vidéo présente une discussion avec Emmanuel Pic, fondateur des centres Chagrin Scolaire, qui explore les méthodes de l'école de Palo Alto pour transformer les relations dysfonctionnelles en fonctionnelles, en particulier dans le contexte de l'éducation et de la parentalité. Elle souligne l'importance de responsabiliser les enfants et les adolescents en leur permettant de gérer leurs propres apprentissages et de faire face aux conséquences de leurs choix.

Points Forts: 1. Introduction à Chagrin Scolaire [00:00:01][^1^][1] * Présentation d'Emmanuel Pic * Discussion sur le harcèlement scolaire * L'impact des centres Chagrin Scolaire 2. L'école de Palo Alto [00:01:46][^2^][2] * Fondements et philosophie * Approche de la communication et des relations * Importance de la responsabilisation 3. Méthodes thérapeutiques [00:03:33][^3^][3] * Techniques pour résoudre les problèmes * Exemples de situations familiales * Stratégies pour changer les comportements 4. Responsabilisation des enfants [00:09:00][^4^][4] * Encourager l'autonomie dans les devoirs * Conséquences de l'implication parentale * Importance de l'envie d'apprendre Résumé de la vidéo

Cette vidéo aborde la manière dont les étiquettes et les diagnostics affectent la perception et l'interaction avec les enfants, en particulier ceux ayant des besoins spéciaux. Elle souligne l'importance de voir au-delà des étiquettes pour reconnaître les ressources individuelles de chaque enfant. La vidéo discute également des défis auxquels les enseignants sont confrontés lorsqu'ils essaient de s'adapter à chaque élève tout en suivant le programme scolaire, et comment cela peut conduire à des situations paradoxales et à un sentiment d'échec.

Points saillants : 1. Perception des enfants avec des besoins spéciaux [00:19:01][^1^][1] * Impact des étiquettes sur l'interaction * Importance de reconnaître les ressources des enfants * Effet des diagnostics sur les relations 2. Défis pour les enseignants [00:20:17][^2^][2] * Adaptation à divers besoins dans la classe * Gestion des attentes contradictoires * Sentiment d'échec face à l'impossibilité de répondre à tous les besoins 3. Conséquences des attentes sociétales [00:22:00][^3^][3] * Pression sur les enfants pour qu'ils s'adaptent au monde * Réalité de l'entreprise et attentes de performance * Nécessité d'aider les enfants à développer leurs propres ressources 4. Approche de l'école de Palo Alto [00:22:57][^4^][4] * Focus sur les relations et la résolution de problèmes * Expansion des centres 180 degrés en France * Difficultés rencontrées pour introduire des méthodes alternatives en France Résumé de la vidéo

Cette vidéo aborde la souffrance liée au harcèlement scolaire et les outils pour y faire face. L'oratrice discute de l'importance de la prévention et de l'intervention, soulignant que malgré les efforts, les statistiques sur le harcèlement restent alarmantes. Elle partage son expérience en tant que psycho-praticienne et auteure, mettant en lumière les stratégies pour aider les enfants et les adolescents à surmonter le harcèlement et à s'épanouir.

Points saillants : 1. Outils contre le harcèlement [00:38:35][^1^][1] * Importance des outils pour gérer la souffrance * Stratégies pour faire face au harcèlement collectif et individuel * Nécessité d'une action face aux statistiques stagnantes 2. Expérience professionnelle [00:39:16][^2^][2] * Transition de généraliste à spécialiste du harcèlement scolaire * Révélation de la fréquence du harcèlement à travers les consultations * Modélisation d'une méthode pour résoudre la souffrance scolaire 3. Publications et conseils [00:41:46][^3^][3] * Livres écrits pour aider les parents et les professionnels * Approche pragmatique et stratégique pour le bien-être des adolescents * Importance de l'éducation des parents pour soutenir les adolescents 4. La méthode de Palo Alto [00:44:22][^4^][4] * Utilisation de la méthode de Palo Alto pour introduire de la souplesse * Approche stratégique pour traiter le harcèlement et les conflits * Importance de la communication entre parents et enseignants

Résumé de la Vidéo

La partie 4 de la vidéo aborde les conférences revigorantes organisées pour célébrer les 10 ans de l'école de Palo Alto au service de l'enfant dyslexique. Ces micro-conférences, qui auront lieu à Paris le 14 juin, visent à partager des connaissances et des expériences pour aider au développement de l'enfant.

Points Forts : 1. Conférences revigorantes [00:58:34][^1^][1] * Une dizaine d'intervenants * Thème : l'école de Palo Alto au service de l'enfant dyslexique * Format : mini-conférences de dix minutes 2. Célébration des 10 ans [00:59:40][^2^][2] * Événement pour marquer une décennie * Objectif : partager des expériences et des connaissances * Importance de l'accessibilité et de la communication 3. Organisation et accès [00:59:27][^3^][3] * Les conférences seront filmées * Accessibles même pour ceux qui ne sont pas à Paris * Encouragement à la participation et à l'interaction 4. Conclusion et remerciements [01:00:09][^4^][4] * Appréciation pour l'engagement envers les enfants * Invitation à poser des questions et à interagir après l'événement * Reconnaissance de l'importance de l'accessibilité éducative

Se destaca la importancia de establecer buenos hábitos dentales desde el embarazo y en los primeros años de vida del niño

La caries dental es una enfermedad multifactorial que implica la interacción de varios factores como el huésped, los microorganismos y la dieta. Factores socioeconómicos y el acceso a servicios de salud también juegan un papel importante.

La malnutrición puede afectar negativamente la salud dental, causando problemas como la mala calidad de los tejidos dentales y un mayor riesgo de caries y enfermedades periodontales.

En general, se ha restado importancia a la dieta como factor de riesgo para la caries dental, favoreciendo la promoción de una buena higiene y el uso de flúor. En niños y ancianos, la dieta sigue siendo clave para prevenir la caries

Durante mucho tiempo, la odontología se enfocó en procedimientos mecánicos como extraer o reparar dientes. En los últimos años, ha evolucionado hacia una ciencia enfocada en la prevención y conservación de los tejidos bucales.

La odontología preventiva se define como el conjunto de esfuerzos para promover, mantener y restaurar la salud bucodental. Se centra en la prevención de enfermedades dentales, evitando complicaciones y tratamientos invasivos. Los objetivos incluyen mantener la salud bucal, reducir factores de riesgo y prevenir la aparición de enfermedades.

La salud dental incluye la salud y el funcionamiento de la boca, dientes y encías. Es esencial para actividades como comer, hablar y sonreír, además de prevenir problemas como caries, inflamación de encías, pérdida de dientes y mal aliento.

En el sistema complemento de los reptiles requiere metales divalentes, temperaturas de 5 y 40 grados, superiores e inferiores lo inactivan . In a subsequent study, Merchant and Britton (Merchant and Britton, 2006) found that the lysis of sheep red blood cells by serum from saltwater (Crocodylus porosus) and freshwater (Crocodylus johnstoni) crocodiles required divalent metal ions, was heat sensitive and was unaffected by methylamine; therefore, suggesting that the alternative pathway was responsible for the lysis. Despite reptiles’ key place in evolutionary history, reptilian immune responses have received relatively little attention. Like all jawed vertebrates, reptiles possess both an innate and adaptive immune system. The innate system, which includes components such as non-specific leukocytes, antimicrobial peptides and the complement system, responds quickly as a non-specific first line of defense against a broad range of pathogens. In many cases the responses are stronger than those of mammals. Much less is known about the adaptive branch, and studies of the cell-mediated and humoral responses have been generally hampered by a lack of appropriate reagents.

moleculas del sistema inmune en tortugas: Lisozymas con actividad antibacteriana y otrs moleculas son catelicidinas con actividad antifungica yactividad antibacteriana, incluso mas potente que farmacos ampicilina y bencilpenicilina. En el cocodrilo siames se aislo una pequeña proteina cationica 2008). A small cationic protein was isolated from the Siamese crocodile (Crocodylus siamensis), which demonstrated antibacterial activity against S. typhi, E. coli, S. aureus, Staphylococcus epidermidis, K. pneumoniae, P. aeruginosa and Vibrio chorelae (Preecharram et al., 2008). These antimicrobial peptides offer potent protection for reptiles against infection as well as provide exciting opportunities in the search for new clinical or agricultural antibiotics.

El timo y el bazo se mantienen activos durante el verano e involucionan en en invierno cuando se encuentran en hibernación

Royal Quiet De Luxe Typewriter Adjustment Print Quality Height Balance On-Feet Shift Motion by [[Phoenix Typewriter]]

He made sure the carriage isn't out of alignment which can cause on feet issues as well.

Adjust the basket stops higher or lower as necessary. Try 1/2 to full turn and test each

The adjustment points are between the body and the carriage about an inch inside the body shell.

Do upper case first. The first set of screws/nuts just next to the outside of the typewriter are for lower case and the second set just inside of those are for upper case.

Turning the adjustment screws clockwise should push the carriage stops down just a bit.

Some good characters to check are H, h, p, y, and 8.

Welcome back and in this demo lesson you're going to learn how to install the Docker engine inside an EC2 instance and then use that to create a Docker image.

Now this Docker image is going to be running a simple application and we'll be using this Docker image later in this section of the course to demonstrate the Elastic Container service.

So this is going to be a really useful demo where you're going to gain the experience of how to create a Docker image.

Now there are a few things that you need to do before we get started.

First as always make sure that you're logged in to the I am admin user of the general AWS account and you'll also need the Northern Virginia region selected.

Now attached to this lesson is a one-click deployment link so go ahead and click that now.

This is going to deploy an EC2 instance with some files pre downloaded that you'll use during the demo lesson.

Now everything's pre-configured you just need to check this box at the bottom and click on create stack.

Now that's going to take a few minutes to create and we need this to be in a create complete state.

So go ahead and pause the video wait for your stack to move into create complete and then we're good to continue.

So now this stack is in a create complete state and we're good to continue.

Now if you're following along with this demo within your own environment there's another link attached to this lesson called the lesson commands document and that will include all of the commands that you'll need to type as you move through the demo.

Now I'm a fan of typing all commands in manually because I personally think that it helps you learn but if you are the type of person who has a habit of making mistakes when typing along commands out then you can copy and paste from this document to avoid any typos.

Now one final thing before we finish at the end of this demo lesson you'll have the opportunity to upload the Docker image that you create to Docker Hub.

If you're going to do that then you should pre sign up for a Docker Hub account if you don't already have one and the link for this is included attached to this lesson.

If you already have a Docker Hub account then you're good to continue.

Now at this point what we need to do is to click on the resources tab of this stack and locate the public EC2 resource.

Now this is a normal EC2 instance that's been provisioned on your behalf and it has some files which have been pre downloaded to it.

So just go ahead and click on the physical ID next to public EC2 and that will move you to the EC2 console.

Now this machine is set up and ready to connect to and I've configured it so that we can connect to it using Session Manager and this avoids the need to use SSH keys.

So to do that just right-click and then select connect.

You need to pick Session Manager from the tabs across the top here and then just click on connect.

Now that will take a few minutes but once connected you should see this prompt.

So it should say SH- and then a version number and then dollar.

Now the first thing that we need to do as part of this demo lesson is to install the Docker engine.

The Docker engine is the thing that allows Docker containers to run on this EC2 instance.

So we need to install the Docker engine package and we'll do that using this command.

So we're using shudu to get admin permissions then the package manager DNF then install then Docker.

So go ahead and run that and that will begin the installation of Docker.

It might take a few moments to complete it might have to download some prerequisites and you might have to answer that you're okay with the install.

So press Y for yes and then press enter.

Now we need to wait a few moments for this install process to complete and once it has completed then we need to start the Docker service and we do that using this command.

So shudu again to get admin permissions and then service and then the Docker service and then start.

So type that and press enter and that starts the Docker service.

Now I'm going to type clear and then press enter to make this easier to see and now we need to test that we can interact with the Docker engine.

So the most simple way to do that is to type Docker space and then PS and press enter.

Now you're going to get an error.

This error is because not every user of this EC2 instance has the permissions to interact with the Docker engine.

We need to grant permissions for this user or any other users of this EC2 instance to be able to interact with the Docker engine and we're going to do that by adding these users to a group and we do that using this command.

So shudu for admin permissions and then user mod -a -g for group and then the Docker group and then EC2 -user.

Now that will allow a local user of this system, specifically EC2 -user, to be able to interact with the Docker engine.

Okay so I've cleared the screen to make it slightly easier to see now that we've added EC2 -user the ability to interact with Docker.

So the next thing is we need to log out and log back in of this instance.

So I'm going to go ahead and type exit just to disconnect from session manager and then click on close and then I'm going to reconnect to this instance and you need to do the same.

So connect back in to this EC2 instance.

Now once you're connected back into this EC2 instance we need to run another command which moves us into EC2 user so it basically logs us in as EC2 -user.

So that's this command and the result of this would be the same as if you directly logged in to EC2 -user.

Now the reason we're doing it this way is because we're using session manager so that we don't need a local SSH client or to worry about SSH keys.

We can directly log in via the console UI we just then need to switch to EC2 -user.

So run this command and press enter and we're now logged into the instance using EC2 -user and to test everything's okay we need to use a command with the Docker engine and that command is Docker space ps and if everything's okay you shouldn't see any output beyond this list of headers.

What we've essentially done is told the Docker engine to give us a list of any running containers and even though we don't have any it's not erred it's simply displayed this empty list and that means everything's okay.

So good job.

Now what I've done to speed things up if you just run an LS and press enter the instance has been configured to download the sample application that we're going to be using and that's what the file container.zip is within this folder.

I've configured the instance to automatically extract that zip file which has created the folder container.

So at this point I want you to go ahead and type cd space container and press enter and that's going to move you inside this container folder.

Then I want you to clear the screen by typing clear and press enter and then type ls space -l and press enter.

Now this is the web application which I've configured to be automatically downloaded to the EC2 instance.

It's a simple web page we've got index.html which is the index we have a number of images which this index.html contains and then we have a docker file.

Now this docker file is the thing that the docker engine will use to create our docker image.

I want to spend a couple of moments just stepping you through exactly what's within this docker file.

So I'm going to move across to my text editor and this is the docker file that's been automatically downloaded to your EC2 instance.

Each of these lines is a directive to the docker engine to perform a specific task and remember we're using this to create a docker image.

This first line tells the docker engine that we want to use version 8 of the Red Hat Universal base image as the base component for our docker image.

This next line sets the maintainer label it's essentially a brief description of what the image is and who's maintaining it in this case it's just a placeholder of animals for life.

This next line runs a command specifically the yum command to install some software specifically the Apache web server.

This next command copy copies files from the local directory when you use the docker command to create an image so it's copying that index.html file from this local folder that I've just been talking about and it's going to put it inside the docker image in this path so it's going to copy index.html to /var/www/html and this is where an Apache web server expects this index.html to be located.

This next command is going to do the same process for all of the jpegs in this folder so we've got a total of six jpegs and they're going to be copied into this folder inside the docker image.

This line sets the entry point and this essentially determines what is first run when this docker image is used to create a docker container.

In this example it's going to run the Apache web server and finally this expose command can be used for a docker image to tell the docker engine which services should be exposed.

Now this doesn't actually perform any configuration it simply tells the docker engine what port is exposed in this case port 80 which is HTTP.

Now this docker file is going to be used when we run the next command which is to create a docker image.

So essentially this file is the same docker file that's been downloaded to your EC2 instance and that's what we're going to run next.

So this is the next command within the lesson commands document and this command builds a container image.

What we're essentially doing is giving it the location of the docker file.

This dot at the end contains the working directory so it's here where we're going to find the docker file and any associated files that that docker file uses.

So we're going to run this command and this is going to create our docker image.

So let's go ahead and run this command.

It's going to download version 8 of UBI which it will use as a starting point and then it's going to run through every line in the docker file performing each of the directives and each of those directives is going to create another layer within the docker image.

Remember from the theory lesson each line within the docker file generally creates a new file system layer so a new layer of a docker image and that's how docker images are efficient because you can reuse those layers.

Now in this case this has been successful.

We've successfully built a docker image with this ID so it's giving it a unique ID and it's tagged this docker image with this tag colon latest.

So this means that we have a docker image that's now stored on this EC2 instance.

Now I'll go ahead and clear the screen to make it easier to see and let's go ahead and run the next command which is within the lesson commands document and this is going to show us a list of images that are on this EC2 instance but we're going to filter based on the name container of cats and this will show us the docker image which we've just created.

So the next thing that we need to do is to use the docker run command which is going to take the image that we've just created and use it to create a running container and it's that container that we're going to be able to interact with.

So this is the command that we're going to use it's the next one within the lesson commands document.

It's docker run and then it's telling it to map port 80 on the container with port 80 on the EC2 instance and it's telling it to use the container of cats image and if we run that command docker is going to take the docker image that we've got on this EC2 instance run it to create a running container and we should be able to interact with that container.

So if you go back to the AWS console if we click on instances so look for a4l-public EC2 that's in the running state.

I'm just going to go ahead and select this instance so that we can see the information and we need the public IP address of this instance.

Go ahead and click on this icon to copy the public IP address into your clipboard and then open that in a new tab.

Now be sure not to use this link to the right because that's got a tendency to open the HTTPS version.

We just need to use the IP address directly.

So copy that into your clipboard open a new tab and then open that IP address and now we can see the amazing application if it fits i sits in a container in a container and this amazing looking enterprise application is what's contained in the docker image that you just created and it's now running inside a container based off that image.

So that's great everything's working as expected and that's running locally on the EC2 instance.

Now in the demo lesson for the elastic container service that's coming up later in this section of the course you have two options.

You can either use my docker image which is this image that I've just created or you can use your own docker image.

If you're going to use my docker image then you can skip this next step.

You don't need a docker hub account and you don't need to upload your image.

If you want to use your own image then you do need to follow these next few steps and I need to follow them anyway because I need to upload this image to docker hub so that you can potentially use it rather than your own image.

So I'm going to move back to the session manager tab and I'm going to control C to exit out of this running container and I'm going to type clear to clear the screen and make it easier to see.

Now to upload this to docker hub first you need to log in to docker hub using your credentials and you can do that using this command.

So it's docker space login space double hyphen username equals and then your username.

So if you're doing this in your own environment you need to delete this placeholder and type your username.

I'm going to type my username because I'll be uploading this image to my docker hub.

So this is my docker hub username and then press enter and it's going to ask for the corresponding password to this username.

So I'm going to paste in my password if you're logging into your docker hub you should use your password.

Once you've pasted in the password go ahead and press enter and that will log you in to docker hub.

Now you don't have to worry about the security message because whilst your docker hub password is going to be stored on the EC2 instance shortly we're going to terminate this instance which will remove all traces of this password from this machine.

Okay so again we're going to upload our docker image to docker hub so let's run this command again and you'll see because we're just using the docker images command we can see the base image as well as our image.

So we can see red hat UBI 8.

We want the container of cats latest though so what you need to do is copy down the image ID of the container of cats image.

So this is the top line in my case container of cats latest and then the image ID.

So then we need to run this command so docker space tag and then the image ID that you've just copied into your clipboard and then a space and then your docker hub username.

In my case it's actrl with 1L if you're following along you need to use your own username and then forward slash and then the name of the image that you want this to be stored as on docker hub so I'm going to use container of cats.

So that's the command you need to use so docker tag and then your image ID for container of cats and then your username forward slash container of cats and press enter and that's everything we need to do to prepare to upload this image to docker hub.

So the last command that we need to run is the command to actually upload the image to docker hub and that command is docker space push so we're going to push the image to docker hub then we need to specify the docker hub username so again this is my username but if you're doing this in your environment it needs to be your username and then forward slash and then the image name in my case container of cats and then colon latest and once you've got all that go ahead and press enter and that's going to push the docker image that you've just created up to your docker hub account and once it's up there it means that we can deploy from that docker image to other EC2 instances and even ECS and we're going to do that in a later demo in this section of the course.

Now that's everything that you need to do in this demo lesson you've essentially installed and configured the docker engine you've used a docker file to create a docker image from some local assets you've tested that docker image by running a container using that image and then you've uploaded that image to docker hub and as I mentioned before we're going to use that in a future demo lesson in this section of the course.

Now the only thing that remains to do is to clear up the infrastructure that we've used in this demo lesson so go ahead and close down all of these extra tabs and go back to the cloud formation console this is the stack that's been created by the one click deployment link so all you need to do is select this stack it should be called EC2 docker and then click on delete and confirm that deletion and that will return the account into the same state as it was at the start of this demo lesson.

Now that is everything you need to do in this demo lesson I hope it's been useful and I hope you've enjoyed it so go ahead and complete the video and when you're ready I look forward to you joining me in the next.

Kết quả chính: Được thể hiện ở bảng 1 và bảng 2. Chú ý rằng, thực nghiệm trong 2 bảng này đã thực hiện quá trình infer mà không có few-shot, lý do bởi việc infer này đem lại sự tăng tiến bền vững trong kết quả.

Kết quả cho thấy RA-IT đạt kết quả tốt nhất. Lý do cho sự tăng tiến này được cho là nhờ ngữ cảnh được truy xuất đã làm tăng cường khả năng hiểu đầu vào của mô hình, từ đó thể hiện sự cần thiết của các mẫu instruction có tăng cường ngữ cảnh.

Ý tưởng chính: Sử dụng phương pháp instruction tuning có tăng cường truy hồi (RA-IT) cho bài toán IE, tập trung vào bài toán Open NER. Cụ thể, với mỗi mẫu huấn luyện, các mẫu khác có tính tương đồng về mặt ngữ nghĩa sẽ được truy xuất ra từ bộ dữ liệu và thêm các mẫu này vào đầu vào của mẫu huấn luyện ban đầu.

yes exactly that thank you

can help clean pollutted area

numbers

important for soil health and plant growth

QUOTE

holy grail of quotes

quote?

microbes role in climate change

why it sequesters carbon

main ideas in my paper

Presicion

Résumé de la vidéo [00:00:08][^1^][1] - [00:23:40][^2^][2]:

Cette vidéo traite de l'évaluation des forces des jeunes contrevenants et de l'outil utilisé pour cela. Elle présente les raisons et les méthodes pour évaluer ces forces, ainsi que les résultats obtenus.

Moments forts: + [00:00:08][^3^][3] Introduction et équipe * Présentation de l'équipe derrière l'outil * Importance du travail d'équipe * Contexte de l'implantation en Belgique et au Québec + [00:01:27][^4^][4] Plan de la conférence * Quatre sections principales * Évaluation théorique des forces * Propriétés psychométriques de l'outil + [00:03:35][^5^][5] Pourquoi évaluer les forces * Importance de l'évaluation des forces * Modèle risque-besoin-réceptivité * Outil d'évaluation standardisé K Y LSMI + [00:09:54][^6^][6] Psychologie et criminologie positives * Importance des expériences positives * Effets des émotions positives sur le changement * Différence entre force et facteur de protection + [00:15:38][^7^][7] Évaluation structurée des forces * Développement de l'outil SA * Indicateurs concrets et objectivés * Collaboration avec les professionnels

Résumé de la vidéo [00:23:42][^1^][1] - [00:44:49][^2^][2]:

Cette vidéo explore l'évaluation des forces et des risques chez les jeunes contrevenants, en se concentrant sur la validité sociale et psychométrique des outils utilisés.

Moments forts : + [00:23:42][^3^][3] Introduction des données et outils * Données belges et thèse de doctorat * Outils d'évaluation comme SAPR et YLS * Stratégies analytiques classiques + [00:25:48][^4^][4] Résultats des évaluations * Scores moyens des domaines du WS * Coexistence des forces et des risques * Différences entre le Québec et la Belgique + [00:27:00][^5^][5] Fidélité et cohérence * Cohérence interne et accord interjuge * Comparaison des résultats entre régions * Explications des différences observées + [00:32:00][^6^][6] Validité convergente et divergente * Corrélation avec d'autres outils * Validité prédictive et ses limites * Importance de la motivation et de l'encadrement clinique + [00:37:00][^7^][7] Validité sociale et perception des professionnels * Accord sur les objectifs et procédures * Effets perçus des évaluations * Importance de l'utilité clinique et de la fidélité des cotations

Il y a une petite erreur avec l'article défini, ils l'ont utilisé au singulier, alors que son adjectif qualificatif est au feminin.

Author response:

Public Reviews: 

Reviewer #1 (Public review): 

Summary: 

This paper provides a computational model of a synthetic task in which an agent needs to find a trajectory to a rewarding goal in a 2D-grid world, in which certain grid blocks incur a punishment. In a completely unrelated setup without explicit rewards, they then provide a model that explains data from an approach-avoidance experiment in which an agent needs to decide whether to approach or withdraw from, a jellyfish, in order to avoid a pain stimulus, with no explicit rewards. Both models include components that are labelled as Pavlovian; hence the authors argue that their data show that the brain uses a Pavlovian fear system in complex navigational and approach-avoid decisions. 

We thank the reviewer for their thoughtful comments. To clarify, the grid-world setup was used as a didactic tool/testbed to understand the interaction between Pavlovian and instrumental systems (lines 80-81) [Dayan et al., 2006], specifically in the context of safe exploration and learning. It helps us delineate the Pavlovian contributions during learning, which is key to understanding the safety-efficiency dilemma we highlight. This approach generates a hypothesis about outcome uncertainty-based arbitration between these systems, which we then test in the approach-withdrawal VR experiment based on foundational studies studying Pavlovian biases [Guitart-Masip et al., 2012, Cavanagh et al., 2013].

Although the VR task does not explicitly involve rewards, it provides a specific test of our hypothesis regarding flexible Pavlovian fear bias, similar to how others have tested flexible Pavlovian reward bias without involving punishments (e.g., Dorfman & Gershman, 2019). Both the simulation and VR experiment models are derived from the same theoretical framework and maintain algebraic mapping, differing only in task-specific adaptations (e.g., differing in action sets and temporal difference learning for multi-step decisions in the grid world vs. Rescorla-Wagner rule for single-step decisions in the VR task). This is also true for Dayan et al. [2006] who bridge Pavlovian bias in a Go-No Go task (negative auto-maintenance pecking task) and a grid world task. Therefore, we respectfully disagree that the two setups are completely unrelated and that both models include components merely labelled as Pavlovian.

We will rephrase parts of the manuscript to prevent the main message of our manuscript from being misconveyed. Particularly in the Methods and Discussion, to clarify that our main focus is on Pavlovian fear bias in safe exploration and learning (as also summarised by reviewers #2 and #3), rather than on its role in complex navigational decisions. We also acknowledge the need for future work to capture more sophisticated safe behaviours, such as escapes and sophisticated planning which span different aspects of the threat-imminence continuum [Mobbs et al., 2020], and we will highlight these as avenues for future research.

In the first setup, they simulate a model in which a component they label as Pavlovian learns about punishment in each grid block, whereas a Q-learner learns about the optimal path to the goal, using a scalar loss function for rewards and punishments. Pavlovian and Q-learning components are then weighed at each step to produce an action. Unsurprisingly, the authors find that including the Pavlovian component in the model reduces the cumulative punishment incurred, and this increases as the weight of the Pavlovian system increases. The paper does not explore to what extent increasing the punishment loss (while keeping reward loss constant) would lead to the same outcomes with a simpler model architecture, so any claim that the Pavlovian component is required for such a result is not justified by the modelling. 

Thank you for this comment. We acknowledge that our paper does not compare the Pavlovian fear system to a purely instrumental system with varying punishment sensitivity. Instead, our model assumes the coexistence of these two systems and demonstrates the emergent safety-efficiency trade-off from their interaction. It is possible that similar behaviours could be modelled using an instrumental system alone. In light of the reviewer’s comment, we will soften our claims regarding the necessity of the Pavlovian system, despite its known existence.

We also encourage the reviewer to consider the Pavlovian system as a biologically plausible implementation of punishment sensitivity. Unlike punishment sensitivity (scaling of the punishments), which has not been robustly mapped to neural substrates in fMRI studies, the neural substrates for the Pavlovian fear system (e.g., the limbic loop) are well known (see Supplementary Fig. 16).

Additionally, we point out that varying reward sensitivities while keeping punishment sensitivity constant allows our PAL agent to differentiate from an instrumental agent that combines reward and punishment into a single feedback signal. As highlighted in lines 136-140 and the T-maze experiment (Fig. 3 A, B, C), the Pavlovian system maintains fear responses even under high reward conditions, guiding withdrawal behaviour when necessary (e.g., ω = 0.9 or 1), which is not possible with a purely instrumental model if the punishment sensitivities are fixed. This is a fundamental point.

We will revise our discussion and results sections to reflect these clarifications.

In the second setup, an agent learns about punishments alone. "Pavlovian biases" have previously been demonstrated in this task (i.e. an overavoidance when the correct decision is to approach). The authors explore several models (all of which are dissimilar to the ones used in the first setup) to account for the Pavlovian biases. 

Thank you, we respectfully disagree with the statement that our models used in the experimental setup are dissimilar to the ones used in the first setup. Due to differences in the nature of the task setup, the action set differs, but the model equations and the theory are the same and align closely, as described in our response above. The only additional difference is the use of a baseline bias in human experiments and the RLDDM model, where we also model reaction times with drift rates which is not a behaviour often simulated in grid world simulations. We will improve our Methods section to ensure that model similarity is highlighted.

Strengths: 

Overall, the modelling exercises are interesting and relevant and incrementally expand the space of existing models. 

We thank reviewer #1 for acknowledging the relevance of our models in advancing the field. We would like to further highlight that, to the best of our knowledge, this is the first time reaction times in Pavlovian-Instrumental arbitration tasks have been modelled using RLDDM, which adds a novel dimension to our approach.

Weaknesses: 

I find the conclusions misleading, as they are not supported by the data. 

First, the similarity between the models used in the two setups appears to be more semantic than computational or biological. So it is unclear to me how the results can be integrated. 

We acknowledge the dissimilarity between the task setups (grid-world vs. approach-withdrawal). However, we believe these setups are computationally similar and may be biologically related, as suggested by prior work like Dayan et al. [2006], which integrates Go-No Go and grid-world tasks. Just as that work bridged findings in the appetitive domain, we aim to integrate our findings in the aversive domain. We will provide a more integrated interpretation in the discussion section of the revised manuscript.

Dayan, P., Niv, Y., Seymour, B., and Daw, N. D. (2006). The misbehavior of value and the discipline of the will. Neural networks, 19(8):1153–1160.

Secondly, the authors do not show "a computational advantage to maintaining a specific fear memory during exploratory decision-making" (as they claim in the abstract). Making such a claim would require showing an advantage in the first place. For the first setup, the simulation results will likely be replicated by a simple Q-learning model when scaling up the loss incurred for punishments, in which case the more complex model architecture would not confer an advantage. The second setup, in contrast, is so excessively artificial that even if a particular model conferred an advantage here, this is highly unlikely to translate into any real-world advantage for a biological agent. The experimental setup was developed to demonstrate the existence of Pavlovian biases, but it is not designed to conclusively investigate how they come about. In a nutshell, who in their right mind would touch a stinging jellyfish 88 times in a short period of time, as the subjects do on average in this task? Furthermore, in which real-life environment does withdrawal from a jellyfish lead to a sting, as in this task? 

Thank you for your feedback. As mentioned above, we invite the reviewer to potentially think of Pavlovian fear systems as a way how the brain might implement punishment sensitivity. Secondly, it provides a separate punishment memory that cannot be overwritten with higher rewards (see also Elfwing and Seymour 2017, and Wang et al, 2021)

Elfwing, S., & Seymour, B. (2017, September). Parallel reward and punishment control in humans and robots: Safe reinforcement learning using the MaxPain algorithm. In 2017 Joint IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob) (pp. 140-147). IEEE. 

Wang, J., Elfwing, S., & Uchibe, E. (2021). Modular deep reinforcement learning from reward and punishment for robot navigation. Neural Networks, 135, 115-126.

The simulation setups such as the following grid-worlds are common test-beds for algorithms in reinforcement learning [Sutton and Barto, 2018].

Any experimental setup faces the problem of having a constrained experiment designed to test and model a specific effect versus designing a lesser constrained exploratory experiment which is more difficult to model. Here we chose the former, building upon previous foundational experiments on Pavlovian bias in humans [Guitart-Masip et al., 2012, Cavanagh et al., 2013].  The condition where withdrawal from a jellyfish leads to a sting, though less realistic, was included for balancing the four cue-outcome conditions. Overall the task was designed to isolate the effect we wanted to test - Pavlovian fear bias in choices and reaction times, to the best of our ability. In a free operant task, it is very well likely that other components not included in our model could compete for control.

Crucially, simplistic models such as the present ones can easily solve specifically designed lab tasks with low dimensionality but they will fail in higher-dimensional settings. Biological behaviour in the face of threat is utterly complex and goes far beyond simplistic fight-flight-freeze distinctions (Evans et al., 2019). It would take a leap of faith to assume that human decision-making can be broken down into oversimplified sub-tasks of this sort (and if that were the case, this would require a meta-controller arbitrating the systems for all the sub-tasks, and this meta-controller would then struggle with the dimensionality j). 

We agree that safe behaviours, such as escapes, involve more sophisticated computations. We do not propose Pavlovian fear bias as the sole computation for safe behavior, but rather as one of many possible contributors. Knowing about the existence about the Pavlovian withdrawal bias, we simply study its possible contribution. We will include in our discussion that such behaviours likely occupy different parts of the threat-imminence continuum [Mobbs et al., 2020].

Dean Mobbs, Drew B Headley, Weilun Ding, and Peter Dayan. Space, time, and fear: survival computations along defensive circuits. Trends in cognitive sciences, 24(3):228–241, 2020.

On the face of it, the VR task provides higher "ecological validity" than previous screen-based tasks. However, in fact, it is only the visual stimulation that differs from a standard screen-based task, whereas the action space is exactly the same. As such, the benefit of VR does not become apparent, and its full potential is foregone. 

We thank the reviewer for their comment. We selected the action space to build on existing models [Guitart-Masip et al., 2012, Cavanagh et al., 2013] that capture Pavlovian biases and we also wanted to minimize participant movement for EEG data collection. Unfortunately, despite restricting movement to just the arm, the EEG data was still too noisy to lead to any substantial results. We will explore more free-operant paradigms in future works.

On the issue of the difference between VR and lab-based tasks, we note the reviewer's point. Note however that desktop monitor-based tasks lack the sensorimotor congruency between the action and the outcome. Second, it is also arguable, that the background context is important in fear conditioning, as it may help set the tone of the fear system to make aversive components easier to distinguish.

If the authors are convinced that their model can - then data from naturalistic approach-avoidance VR tasks is publicly available, e.g. (Sporrer et al., 2023), so this should be rather easy to prove or disprove. In summary, I am doubtful that the models have any relevance for real-life human decision-making. 

We thank the reviewers for their thoughtful inputs. We do not claim our model is the best fit for all naturalistic VR tasks, as they require multiple systems across the threat-imminence continuum [Mobbs et al., 2020] and are currently beyond the scope of the current work. However, we believe our findings on outcome-uncertainty-based arbitration of Pavlovian bias could inform future studies and may be relevant for testing differences in patients with mental disorders, as noted by reviewer #2. At a general level, it can be said that most well-controlled laboratory-based tasks need to bridge a sizeable gap to applicabilty in real-life naturalistic behaviour; although the principle of using carefully designed tasks to isolate individual factors is well established

Finally, the authors seem to make much broader claims that their models can solve safety-efficiency dilemmas. However, a combination of a Pavlovian bias and an instrumental learner (study 1) via a fixed linear weighting does not seem to be "safe" in any strict sense. This will lead to the agent making decisions leading to death when the promised reward is large enough (outside perhaps a very specific region of the parameter space). Would it not be more helpful to prune the decision tree according to a fixed threshold (Huys et al., 2012)? So, in a way, the model is useful for avoiding cumulatively excessive pain but not instantaneous destruction. As such, it is not clear what real-life situation is modelled here. 

We thank the reviewer for their comments and ideas. In our discussion lines 257-264, we discuss other works which identify similar safety-efficiency dilemmas, in different models. Here, we simply focus on the safety-efficiency trade-off arising from the interactions between Pavlovian and instrumental systems. It is important to note that the computational argument for the modular system with separate rewards and punishments explicitly protects (up to a point, of course) against large rewards leading to death because the Pavlovian fear response is not over-written by successful avoidance in recent experience. Note also that in animals, reward utility curves are typically convex. We will clarify this in the discussion section.

We completely agree that in certain scenarios, pruning decision trees could be more effective, especially with a model-based instrumental agent. Here we utilise a model-free instrumental agent, which leads to a simpler model - which is appreciated by some readers such as reviewer #2. Future work can incorporate model-based methods.

A final caveat regarding Study 1 is the use of a PH associability term as a surrogate for uncertainty. The authors argue that this term provides a good fit to fear-conditioned SCR but that is only true in comparison to simpler RW-type models. Literature using a broader model space suggests that a formal account of uncertainty could fit this conditioned response even better (Tzovara et al., 2018). 

We thank the reviewer for bringing this to our notice. We will discuss Tzovara et al., 2018 in our discussion in our revised manuscript.

Reviewer #2 (Public review): 

Summary: 

The authors tested the efficiency of a model combining Pavlovian fear valuation and instrumental valuation. This model is amenable to many behavioral decision and learning setups - some of which have been or will be designed to test differences in patients with mental disorders (e.g., anxiety disorder, OCD, etc.). 

Strengths: 

(1) Simplicity of the model which can at the same time model rather complex environments. 

(2) Introduction of a flexible omega parameter. 

(3) Direct application to a rather advanced VR task. 

(4) The paper is extremely well written. It was a joy to read. 

Weaknesses: 

Almost none! In very few cases, the explanations could be a bit better. 

We thank reviewer #2 for their positive feedback and thoughtful recommendations. We will ensure that, in our revision, we clarify the explanations in the few instances where they may not be sufficiently detailed, as noted.

Reviewer #3 (Public review): 

Summary: 

This paper aims to address the problem of exploring potentially rewarding environments that contain the danger, based on the assumption that an independent Pavlovian fear learning system can help guide an agent during exploratory behaviour such that it avoids severe danger. This is important given that otherwise later gains seem to outweigh early threats, and agents may end up putting themselves in danger when it is advisable not to do so. 

The authors develop a computational model of exploratory behaviour that accounts for both instrumental and Pavlovian influences, combining the two according to uncertainty in the rewards. The result is that Pavlovian avoidance has a greater influence when the agent is uncertain about rewards. 

Strengths: 

The study does a thorough job of testing this model using both simulations and data from human participants performing an avoidance task. Simulations demonstrate that the model can produce "safe" behaviour, where the agent may not necessarily achieve the highest possible reward but ensures that losses are limited. Interestingly, the model appears to describe human avoidance behaviour in a task that tests for Pavlovian avoidance influences better than a model that doesn't adapt the balance between Pavlovian and instrumental based on uncertainty. The methods are robust, and generally, there is little to criticise about the study. 

Weaknesses: 

The extent of the testing in human participants is fairly limited but goes far enough to demonstrate that the model can account for human behaviour in an exemplar task. There are, however, some elements of the model that are unrealistic (for example, the fact that pre-training is required to select actions with a Pavlovian bias would require the agent to explore the environment initially and encounter a vast amount of danger in order to learn how to avoid the danger later). The description of the models is also a little difficult to parse. 

We thank reviewer #3 for their thoughtful feedback and useful recommendations, which we will take into account while revising the manuscript.

We acknowledge the complexity of specifying Pavlovian bias in the grid world and appreciate the opportunity to elaborate on how this bias is modelled. In the human experiment, the withdrawal action is straightforwardly biased, as noted, while in the grid world, we assume a hardwired encoding of withdrawal actions for each state/grid. This innate encoding of withdrawal actions could be represented in the dPAG [Kim et. al., 2013]. We implement this bias using pre-training, which we assume would be a product of evolution. Alternatively, this could be interpreted as deriving from an appropriate value initialization where the gradient over initialized values determines the action bias. Such aversive value initialization, driving avoidance of novel and threatening stimuli, has been observed in the tail of the striatum in mice, which is hypothesized to function as a Pavlovian fear/threat learning system [Menegas et. al., 2018].

Additionally, we explored the possibility of learning the action bias on the fly by tracking additional punishment Q-values instead of pre-training, which produced similar cumulative pain and step plots. While this approach is redundant, and likely not how the brain operates, it demonstrates an alternative algorithm.

We thank the reviewer for pointing out these potentially unrealistic elements, and we will revise the manuscript to clarify and incorporate these explanations and improve the model descriptions.

Eun Joo Kim, Omer Horovitz, Blake A Pellman, Lancy Mimi Tan, Qiuling Li, Gal Richter-Levin, and Jeansok J Kim. Dorsal periaqueductal gray-amygdala pathway conveys both innate and learned fear responses in rats. Proceedings of the National Academy of Sciences, 110(36):14795–14800, 2013

William Menegas, Korleki Akiti, Ryunosuke Amo, Naoshige Uchida, and Mitsuko Watabe-Uchida. Dopamine neurons projecting to the posterior striatum reinforce avoidance of threatening stimuli. Nature neuroscience, 21(10): 1421–1430, 2018

no entiendo por que Esposito hace eso, el hecho que en el personalismo no se tenga en cuenta el cuerpo, la verdad derrumba esta crítica de Esposito, la persona es cuerpo, pero no se reduce al cuerpo únicamente

I'd then follow it with a paragraph that addresses the right now and how they're currently approaching their art. The frustration of doubting themselves, not knowing where to start, lacking feedback so they know how they can improve, etc...

And then I would introduce your BB Art Journals Masterclass as the solution in the next section.

Author response:

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

Reviewer #1 (Recommendations For The Authors):

To hopefully contribute to more strongly support the conclusions drawn by the authors, I am including a series of concerns regarding the manuscript, as well as some suggestions that could be useful to address these issues:

(1) The main results of this study derive from the use of auxin-inducible degron (AID)-tagged proteins. Despite the great advantages of the AID strategy to conditionally deplete proteins, the AID tag can affect the normal function of a protein. In fact, some of the AID-labeled DDC components generated in this work are shown to be hypomorphic. Hence, the manuscript would have benefited from the additional confirmation of some of the observations using a different way to eliminate the proteins (e.g., temperature-sensitive mutants).

Most ts mutants are also hypomorphic; hence we don’t see there is much advantage to their use. The addition of the AID to these proteins alone does not interfere with the ability to sustain checkpoint arrest as demonstrated in Figure S1. Instead we found that by overexpressing Rad9-AID we could demonstrate that inactivating Rad9 after 15 h behaved the same way as the inactivation of Ddc2, significantly strengthening our finding that the DDC checkpoint becomes dispensable while the SAC takes over. 

(2) In cells depleted of Rad53-AID, the deletion of CHK1 stimulates an earlier release from a mitotic arrest induced by two DSBs (Figures 2D and 3C). Likewise, the authors claim that a faster escape from the cell cycle block can also be observed when upstream factors such as Ddc2, Rad9, or Rad24 are depleted in the absence of CHK1 (Figures 2A-C and Figures 3D-F). However, this earlier release from the cell cycle arrest, if at all, is only slightly noticeable in a Rad9-AID background (Figures 2B and 3E). In this sense, it is also worth pointing out that Rad9-AID chk1Δ (Figure 3E) and Rad24-AID chk1Δ (Figure 3F) cells were only evaluated up to 7 h, while in all other instances, cells were followed for 9 h, which hinders a fair assessment of the differences in the release from the cell cycle arrest.

As noted above, we have now been able to examine Rad9 over the long-time frame.

(3) Although only 25% of the cells depleted for Dun1 remained in G2/M arrest 7 h following the induction of two DSBs, it is shocking that Rad53 was nonetheless still phosphorylated after the cells had escaped the cell cycle blockage (Figure 4A).

This persistence of Rad53 phosphorylation is also seen with the inactivation of Mad2, allowing escape in spite of continued Rad53 phosphorylation.

(4) Generation of Rad9-AID2 and Rad24-AID2 strains did not fully restore the function of these proteins, since most cells had adapted 24 h after induction of two DSBs (Figure S1C). Nonetheless, Rad9-AID2 and Rad24-AID2 are still likely more stable than their AID counterparts, and hence the authors could have instead used the AID2 proteins for the experiments in Figure 2 to better evaluate the role of Rad9 and Rad24 in the maintenance of the DDC-dependent arrest.

We note again that we have found a way to study Rad9 up to 24 h. 

(5) Deletion of BFA1 has been shown to promote the escape from a cell cycle arrest triggered by telomere uncapping (Wang et al. 2000, Hu et al. 2001, Valerio-Santiago et al. 2013). Likewise, while cells carrying the cdc5-T238A allele cannot adapt to a checkpoint arrest induced by one irreparable DSB, BFA1 deletion rescues the adaptation defect of this mutant CDC5 allele (Rawal et al., 2016). The authors show how, using AID-degrons of Bfa1 and Bub2, that only Bub2, but not Bfa1, is required to maintain a prolonged cell cycle arrest after the induction of two DSBs. To reinforce this point, and as shown for mad2Δ cells (Figure S6A), the authors could perform a complete time course using both the Bfa1-AID and a bfa1Δ mutant to demonstrate that they do indeed show the same behavior in terms of the adaptation to a two DSB-induced cell cycle arrest.

We thank the reviewer for noting these other instances where bfa1D promoted an escape from arrest. We tested a 2-DSB bfa1 deletion, data has been added to Figure S9E-F. We did not observe a difference in the percentage of cells escaping arrest between the 2-DSB bfa1 deletion and the 2-DSB BFA1-AID strains.

(6) Bypass or adaptation of a checkpoint-induced cell cycle arrest in S. cerevisiae often leads to cells entering a new cell cycle without doing cytokinesis and, hence, to the accumulation of rebudded cells. However, the experiments shown in the manuscript only account for G1 or budded cells with either one or two nuclei. Do any of the mutants show cytokinesis problems and subsequent rebudding of the cells? If so, this should have been also noted and quantified in the corresponding assays.

In the cases we have studied we have not seen instances where the cells re-bud without completing mitosis (at least as assessed by the formation of budded cells with two distinct DAPI staining masses). In the morphological assays we have done, we score the continuation of the cell cycle by the appearance of multiple buds, G1, and small budded cells. In our adaptation assays when cells escaped G2/M arrest they formed microcolonies indicating no short-term deficiency in cell division.

(7) The location of the DSB relative to the centromere of a chromosome seems to be a factor that determines the capacity of the SAC to sustain a prolonged cell cycle arrest. The authors discuss the possibility that the DSB could somehow affect the structure of the kinetochore. Did they evaluate whether Mad1 or Mad2 were more actively recruited to kinetochores in those strains that more strongly trigger the SAC after induction of the DSBs?

We have not attempted to follow Mad1/2 recruitment. ChIP-seq could be used to monitor Mad1/2 localization at the 16 centromeres in response to DSBs and the spread of g-H2AX across the centromere. Our previous data showed that g-H2AX could spread across the centromere region and could create a change that would be detected by Mad1/2.  This change does not, however, affect the mitotic behavior of a strain in which the H2A genes have been modified to the possibly phosphomimetic H2A-S129E allele.

(8) The authors could speculate in the discussion about the reasons that could explain why the DDC is required for the maintenance of checkpoint arrest at early stages but then becomes dispensable for the preservation of a prolonged cell DNA DSB-induced cycle arrest, which is instead sustained at later stages by the SAC.

Our suggestion is that cells would have adapted, but modification of the centromere region engages SAC.

Finally, some minor issues are:

(1) The lines in the graphs that display the results from adaptation assays (e.g., Figures 1B and 1E) or cell and nuclear morphology (e.g., Figures 1D and 1G) are too thick. This makes it sometimes difficult to distinguish the actual percentages of cells in each category, particularly in the experiments monitoring nuclear division.

Fixed

(2) While both the adaptation assay and the analysis of nuclear division in Figures 1E and 1G, respectively, show a complete DDC-dependent arrest at 4h, the Western blot in Figure 1F suggests that Rad53 is not phosphorylated at that time point. Do these figures represent independent experiments? Ideally, the analysis of cell budding and nuclear division, which is performed in liquid cultures, and the Western blot displaying Rad53 phosphorylation should correspond to the same experiment.

Cell budding in liquid cultures and adaptation assays were performed in triplicate with 3 biological replicates and the collective results are shown in each graph showing the percentage of large-budded cells. Western blot samples were collected in each liquid culture experiment. The western blot in 1G is a representative western blot.

(3) It is somewhat confusing that the blots for the proteins are not displayed in the same order in Figures 2A (Rad53 at the top) and 2B or 2C (Rad53 in the middle).

Fixed.  We place Rad53 – the relevant protein - at the top.

Reviewer #2 (Recommendations For The Authors):

(1) Yeast with the two breaks responds to DNA damage checkpoint (DDC) until sometimes 4-15 h post DNA damage. Since the auxin-induced degradation does not completely deplete all the tagged proteins in cells, the results should be more carefully considered and not to interpret if the checkpoint entry or maintenance depends on each target protein's ability to induce Rad53 phosphorylation. It should be theoretically possible if checkpoint maintenance requires only a modest amount of checkpoint factors especially because the experiments involve the induction of one or two DSBs. The low levels of DDC factors may be insufficient for Rad53 activation but could still be effective for cell cycle arrest. Indeed, the Haber group showed that the mating type switch did not induce Rad53 phosphorylation but still invoked detectable DNA damage response. To test such possibilities, the authors might consider employing yet another marker for DDC such as H2A or Chk1 phosphorylation besides Rad53 autophosphorylation. Alternatively, the authors might check if auxin-induced depletion also disrupts break-induced foci formation for checkpoint maintenance or their enrichment at DNA breaks using ChIP assays at various points post-damage.

DAPI staining of Ddc2-AID cells show that when IAA is added 4 h after DSB induction (Figure S3A), cells escape G2/M arrest as evidenced by the increase in large-budded cells with 2 DAPI signals, small budded cells, and G1 cells. Overexpression of Ddc2 can sustain the checkpoint past 24 h, but without SAC proteins like Mad2 they will eventually adapt (Figure S6B).

That Rad9-AID or Rad24-AID in the absence of added auxin (but in the presence of TIR1) is unable to sustain arrest suggests to us that low levels of Rad9 or Rad24 are not sufficient to maintain arrest.  As the reviewer notes, normal MAT switching doesn’t cause Rad53 phosphorylation or arrest, though early damage-induced events such as H2A phosphorylation do occur.  But our point is that Rad9 or Ddc2 is needed to maintain arrest only up to a certain point, after which they become superfluous and a different checkpoint arrest is imposed. At that point apparently a low level of these proteins plays no obvious role.

(2) It is interesting that DDC no longer responds to the damage signaling after 15 h of DSB-induced prolonged checkpoint arrest after two DNA double-strand breaks. Is this also applicable to other adaptation mutants? The results might improve the broad impact of the current conclusions. It is also possible that the transition from DDC to SPC depends on simply the changes in signaling or in part due to the molecular changes in the status of DNA breaks or its flanking regions. Indeed, the proposed model suggests that the spreading of H2A phosphorylation to centromeric regions induces SAC and thus mitotic arrest. The authors could measure H2A phosphorylation near the centromere using ChIP assays at various intervals post-DNA damage. It is particularly interesting if depletion of Ddc2 at 15 h post DNA damage does not alter the level of H2A phosphorylation at or near centromere.

Our previous data have suggested that the involvement of the SAC in prolonging DSB-induced arrest involved post-translational modification of centromeric chromatin such as the Mec1- and Tel1-dependent phosphorylation of the histone H2A (Dotiwala). In budding yeast there is also a similar DSB-induced modification of histone H2B (Lee et al.). To ask if there is an intrinsic activation of the SAC if the regions around centromeres were modified by checkpoint kinase phosphorylation, we examined cell cycle progression in strains in which histone H2A or histone H2B was mutated to their putative phosphomimetic forms (H2A-S129E and H2B-T129E).  As shown in Figure S11, there was no effect on the growth rate of these strains, or of the double mutant, suggesting that cells did not experience a delay in entering mitosis because of these modifications. We note that although histone H2A-S129E is recognized by an antibody specific for the phosphorylation of histone H2A-S129, the mutation to S129E may not be fully phosphomimetic. 

(3) It is puzzling why Rad9-AID or Rad24-AID are proficient for DDC establishment but cannot sustain permanent arrest in the two break cells. It appears Rad53 phosphorylation for DDC is weaker in cells expressing Rad9-AID or Rad24-AID according to Fig.2B and C even though their protein level before IAA treatment is still robust. This might also explain why the results of depleting Rad53 and Rad9 are very different. It also raises concern if the effect of Rad24 depletion on checkpoint maintenance is in part due to the weaker checkpoint establishment. It might be necessary to use the AID2 system to redo Rad24 depletion to exclude such a possibility.

We believe that the AID mutants are very sensitive to the low level of IAA present in yeast.  The instability of the protein is entirely dependent on the TIR1 SCF factor, so the proteins themselves are not intrinsically defective; they are just subject to degradation.  Overexpressing Rad9 allowed us to evaluate its role at late time points. 

(4) It is intriguing that the switch from DDC to SAC might take place at around 12 h when yeasts with a single unrepairable break ignore DDC and resume cell cycling (so-called "adaptation"). Since 4h and 15h are far apart and the transition point from DDC to SAC likely takes place between these two points, it will be very helpful to analyze and compare cell cycle exit after 24 h by treating IAA at multiple points between 4-15h.

When we add IAA to Mad2-AID and Mad1-AID 4 h after DSB induction, cells remain arrested for up to 12 h after DSB induction. At 15 h cells begin to exit checkpoint arrest indicating that the handoff of checkpoint arrest must occur between 12 to 15 h after DSB induction. If we degraded DNA damage checkpoint proteins at any point before Mad2, Mad1, and Bub2 begin to contribute to checkpoint arrest, then arrested cells will likely adapt in a similar manner to when IAA was added 4 h after DSB induction.

(5) Some of the Western blot quality is poor. For instance, in Figure 6C, Mad1-AID level after IAA addition is not compelling especially because the TIR level (the loading control) is also very low.

In Figure 6C, while the relative levels of TIR1 are similar in the IAA treated and untreated samples, there is no detectable amount of Mad1-AID in the IAA treated samples indicating that Mad1-AID was successful degraded with the AID system.

(6) Fig. 8 is complex. It might be helpful to define the different types of arrows in the figure. The legend also has a spelling error, Rad23 should be Rad24.

We’ve defined what each arrow means in the legend and corrected the spelling error in the figure legend.

Reviewer #3 (Recommendations For The Authors):

Major concerns:

Much of the manuscript states that two unrepairable DSBs lead to a long and severe G2/M arrest. Two main cytological approaches are used to make this statement: bud size and number on plates after micromanipulation (microcolony assay), and cell and nuclear morphology in liquid cultures. While the latter gives a clear pattern that can be assigned to a G2/M block as expected by DDC, i.e. metaphase-like mononucleated cells with large buds, the former can only tell whether cells eventually reach a second S phase (large budded cells on the plate can be in a proper G2/M arrest, but can also be in an anaphase block or even in the ensuing G1). The authors always performed the microcolony assay, but there are several cases where the much more informative budding/DAPI assay is missing. These include Dun1-aid and others, but more importantly chk1D and its combinations with DDC proteins. Incidentally, for the microcolony assay, it is more accurate to label the y-axis of the corresponding graphs (and in the figure legends and main text) with something like "large budded cells"; "G2/M arrested cells" is misleading.

Figures have been updated to more accurately reflect what we are measuring.

The results obtained with the Bfa1/Bub2 partner are intriguing. These two proteins form a complex whose canonical function is to prevent exit from mitosis until the spindle is properly aligned, acting in a distinct subpathway within the SAC that blocks MEN rather than anaphase onset. The data presented by the authors suggest that, on the one hand, both SAC subpathways work together to block the cell cycle. However, why does canonical SAC (Mad1/Mad2) inactivation not lead to a transition from G2/M (metaphase-like) arrested cells to anaphase-like arrest maintained by Bfa1-Bub2? Since Bfa1-Bub2 is a target of DDC, is it possible that DDC knockdown also inactivates this checkpoint, allowing adaptation? On the other hand, can the authors provide more data to confirm and strengthen their claim of a Bfa1-independent Bub2 role in prolonged arrest? Perhaps long-term protein localization and PTM changes. Bub2-independent roles for Bfa1 have been reported, but not vice versa, to the best of my knowledge.

In the mitotic exit network Bfa1/Bub2 prime activation of the pathway by bringing Tem1 to spindle pole bodies. Phosphorylation of Bfa1 causes Tem1 to be released and phosphorylate Cdc5 to trigger exit by MEN. It has been shown that DNA damage, in a cdc13-1 ts mutant, phosphorylates Bfa1 in a Rad53 and Dun1 dependent manner. This phosphorylation of Bfa1 could release Tem1 and prime cells to exit checkpoint arrest when cells pass through anaphase. Looking at Tem1 localization to spindle pole bodies and interactions with Bfa1/Bub2 in response to DNA damage might give insight into why cells don’t experience an anaphase-like arrest when they are released by either deactivation of the DNA damage checkpoint or SAC.

We have previously shown that a deletion of bub2 in a 1-DSB background shortens DSB-induced checkpoint arrest. Deletion of bfa1 in a 2-DSB background showed ~80-70% of cells stuck in a large-budded state as measured through an adaptation assay tracking the morphology of G1 cells on a YP-Gal plate and DAPI staining. Deletion or degradation of bfa1 might not release cells from arrest because the Mad2/Mad1 prevent cells from transitioning into anaphase. Our DAPI data for Bub2-AID shows an increase in cells with 2 DAPI signals (transition into anaphase) and small budded cells indicating that degradation of Bub2 is releasing cells into anaphase and allowing cells to complete mitosis.

Further suggestions:

It would be richer if authors could provide more than one experimental replicate in some panels (e.g., S1A,B; S4A; and S6B).

S1C confirms that Rad9-AID and Rad24-AID will adapt by 24 h even with the point mutant TIR1(F74G) which has lower basal degradation than TIR1. S4A has been updated with additional experimental replicates. The 48 h timepoint after DSB induction was to show the importance of Mad2 even when Ddc2 is overexpressed.

Figure 1: Rearrange figure panels when they are first mentioned in the text. For example, it makes more sense to have the plate adaptation assay as panel B for both 1-DSB and 2-DSB strains, budding plus DAPI as panel C, and Rad53 as panel D.

These figures have been rearranged in the order that they are mentioned in the paper.

Figure 5: Correct Ph-5-IAA in the Rad53 WBs (it should be 5-Ph-IAA).

This has been corrected.

Figure S2: The straight line under the "+IAA" text box is misleading. I think it should also cover the "-2" time point, right? Also, check the figure legend. Information is missing and does not correspond to the figure layout.

This has been corrected.

Figure S3: Perhaps "Cell cycle profile as determined by budding and DAPI staining" is a better and more accurate legend title.

The legend title has been updated to “Cell cycle profile as determined by budding and DAPI staining in Ddc2-AID and Rad53-AID mutants ± IAA 4 h after galactose.”

Figure S5: Detection of both Rad53 and Ddc2 in the same blot could lead to misinterpretation as hyperphosphorylated Rad53 appears to coincide with Ddc2 migration.

Figure S5A-B are representative western blots where Rad53 was probed to show activation of the DNA damage checkpoint by Rad53 phosphorylation. When measuring the relative abundance of Ddc2 we did not probe all blots for Rad53.

Table S1: Include the post-hoc test used for comparisons after ANOVA.

A Sidak post-hoc test was used in PRISM for the one-way ANOVA test. PRISM listed the Sidak post-hoc test as the recommended test to correct for multiple comparisons. A column has been added to S. Table 1 to show which post-hoc test was used.

Page 10, line 4: The putative additive effect of chk1 knockout with Dun1 depletion should also be compared to chk1 alone (in Figure 3A).

We address the additive effect of chk1 knockout with Dun1-AID depletion in a later section on Page 11, line 6. Since we had not explored possible effects from downstream targets of Rad53 for prolonging checkpoint arrest when Rad53 was depleted, we did not mention the effect of the chk1 knockout on Dun1 depletion.

Page 14, second paragraph, line 4: "Figure 6A-D", is it not?

Figure S6A is measuring checkpoint arrest in a deletion of mad2 in a 2-DSB strain. Figure 6A-D shows how degradation of Mad2-AID and Mad1-AID after the handoff of arrest causes cells to exit the checkpoint in a Rad53 independent manner.

.h2

Que el carcelero y todos los de su casa fueron bautizados. Nos imaginamos la alegría que debieron haber sentido por el buen resultado que tuvo aquella injusticia.

Le dedicaron tiempo “le predicaron la palabra de Jehová”. Sin preocuparse por el dolor de las profundas heridas que tenían en la espalda. Pero el carcelero sí se fijó en ellas y se puso a limpiárselas.

En determinado momento de la noche mientras Pablo y Silas cantaban alabanzas, hubo un terremoto y las puertas de la prisión se abrieron. El carcelero al pensar que se habían escapado, intentó quitarse la vida para evitar el castigo que se imaginó. Pero Pablo le hablo y dijo que estaban ahí. Éso seguramente lo conmovió el mismo los saco y le realizó esa pregunta. Hechos 16:25-31

Las mismas que en aquel momento, usan mentiras para fomentar el odio y la Opcion, debido al nacionalismo. O tradiciones. Por lo que muchos hermanos terminan golpeados o en prisión. Lo bueno es que Jehová está al tanto de todo éso 1 Pedro 3:12 dice que nada está escondido a Jehová y que además el escucha el ruego de los justos.

Cómo una señal que Jehová estaba contento con su obra. ellos estaban advertidos y recordaban las palabras de Jesús acerca de que Satanás se enojaría, y los atacaría. Juan 15:20

Los amos de la Joven perdieron una fuente de ingresos, así que tomaron venganza, y empezaron hablar mal de Pablo y Silas, y los arrastraron ante jueces que tenían prejuicios, así que mediante acusaciones falsas, los encerraron en un calabozo muy oscuro y con cepo en los pies. Hechos 16:19-24

Hicieron lo posible para distraerlos intentando detener su predicación, en una ocasión, un demonio los molestaba diciendo que Pablo y sus compañeros eran enviados por Dios, aunque éso era verdad. Era una molestia para Pablo y por supuesto distraía a las personas, nos imaginamos a los hermanos hablando normal y está joven gritando cerca de ellos? Hechos 16:16-18

A una de ésas mujeres de nombre Lidia que era adoradora de Jehová, en la ocasión que escucharon a Pablo, dice que Dios le abrió el corazón, se bautizó junto con los de su casa y después les insistió para que se quedarán en su casa

Cómo nos explica la información, era cómo una Roma en miniatura en plena macedonia, sus habitantes se sentían orgullosos de su ciudadanía Romana, también contaba con un pequeño río que era utilizado por mujeres para lavar la ropa, y con un entorno que invitaba a orar a Jehová.

De que Jehová nos bendecirá, recompensando nuestro esfuerzo y nos abrirá una puerta grande de actividad. Porque de hecho nuestro trabajo lo hacemos por amor a el y a su propósito.

Notamos que Jesús utilizó el espíritu, para dirigir a Pablo, después que el llegaba hasta cierto lugar, Entonces desde ahí le indicaba por donde seguir. Éste relato nos enseña que con nosotros puede ser igual, si tenemos una meta o queremos hacer algo, debemos orar por guía y empezar a trabajar, y una vez que nos movemos, en el momento preciso, el espíritu nos indicará el mejor camino, y debemos ser pacientes, decididos para insistir una y otra vez cómo lo hizo Pablo.

!

La metacognición es aquel proceso que nos permite saber cómo sabemos lo que sabemos. La invitación sería a usar metacognición para indagar por esos procesos de interiorización y sus complejidades

• ¿Cuándo dirías que un conocimiento se encuentra plenamente interiorizado y cuánto tiempo requiere esto? ¿Cómo y cuántas de las horas de trabajo autónomo asignadas a una materia con esta cantidad de créditos están siendo aplicadas?
  • ¿Hay algún registro o bitácora que permita ver esos avances, aparte de los mecanismo de publicación ofrecidos en clase?
  • En un mundo de constante interrupción a través del celular y el computador, ¿cómo mantenemos a raya esas distracciones para, efectivamente, podernos concentrar en procesos que requieren dicha interiorización?
• ¿Dónde radican las dificultades que te impiden esa interiorización?

Respecto a los ritmos de la clase, estos presumen ese trabajo previo de, no más de 8 horas autónomas. ¿Se pueden invertir ese máximo de horas semanales y por qué sí o no? Si se piensa en esa cantidad de horas de trabajo autónomo, aún así el ritmo de la clase iría rápido? ¿Qué otras mediaciones (documentación, videos, software, etc) podemos cocrear para ayudar esa parte autónoma y mejorar los ritmos?

Todas estas son excelentes alternativas para el trabajo entre pares y aútónomo. ¿Cómo se dejaría huella/registro de esto que se haga y cómo se compartiría con el resto de la clase, incluyendo al profesor?

what's your take on this?

Author response:

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

Public Review:

Summary:

Bursicon is a key hormone regulating cuticle tanning in insects. While the molecular mechanisms of its function are rather well studied--especially in the model insect Drosophila melanogaster, its effects and functions in different tissues are less well understood. Here, the authors show that bursicon and its receptor play a role in regulating aspects of the seasonal polyphenism of Cacopsylla chinensis. They found that low temperature treatment activated the bursicon signaling pathway during the transition from summer form to winter form and affect cuticle pigment and chitin content, and cuticle thickness. In addition, the authors show that miR-6012 targets the bursicon receptor, CcBurs-R, thereby modulating the function of bursicon signaling pathway in the seasonal polyphenism of C. chinensis. This discovery expands our knowledge of the roles of neuropeptide bursicon action in arthropod biology.

However, the study falls short of its claim that it reveals the molecular mechanisms of a seasonal polyphenism. While cuticle tanning is an important part of the pear psyllid polyphenism, it is not the equivalent of it. First, there are other traits that distinguish between the two morphs, such as ovarian diapause (Oldfield, 1970), and the role of bursicon signaling in regulating these aspects of polyphenism were not measured. Thus, the phenotype in pear psyllids, whereby knockdown bursicon reduces cuticle tanning seems to simply demonstrate the phenotypes of Drosophila mutants for bursicon receptor (Loveall and Deitcher, 2010, BMC Dev Biol) in another species (Fig. 2I, 4H). Second, the study fails to address the threshold nature of cuticular tanning in this species, although it is the threshold response (specifically, to temperature and photoperiod) that distinguishes this trait as a part of a polyphenism. Whereas miR-6012 was found to regulate bursicon expression, there no evidence is provided that this microRNA either responds to or initiates a threshold response to temperature. In principle, miR-6012 could regulate bursicon whether or not it is part of a polyphenism. Thus, the impact of this work would be significantly increased if it could distinguish between seasonal changes of the cuticle and a bona fide reflection of polyphenism.

Thanks for your valuable suggestion. We concur with the review’s comment that cuticle tanning does not equate to the C. chinensis polyphenism. To better reflect the core focus of our research, we have revised the title to "Neuropeptide Bursicon and its receptor mediated the transition from summer-form to winter-form of Cacopsylla chinensis".

In response to the reviewer's inquiry regarding the threshold nature of cuticular tanning in C. chinensis, we have included a detailed analysis of the phenotypic changes (including nymph phenotypes, cuticle pigment absorbance, and cuticle thickness) during the transition from summer-form to winter-form in C. chinensis at distinct time intervals (3, 6, 9, 12, 15 days) under different temperature conditions (10°C and 25°C). As shown in Figure S1, nymphs exhibit a light yellow and transparent coloration at 3, 6, and 9 days, while nymphs at 12 and 15 days display shades of yellow-green or blue-yellow under 25°C conditions. At 10°C conditions, the abdomen end turns black at 3, 6, and 9 days. By the 12 days, numerous light black stripes appear on the chest and abdomen of nymphs at 10°C. At 15 days, nymphs exhibit an overall black-brown appearance, featuring dark brown stripes on the left and right sides of each chest and abdominal section. Furthermore, the end of the abdomen and back display a large black-brown coloration at 10°C (Figure S1A). The UV absorbance of the total pigment extraction at a 300 nm wavelength markedly increases following 10°C exposure for 6, 9, 12, and 15 days compared to the 25°C treatment group (Figure S1B). Cuticle thicknesses also increased following 10°C exposure for 6, 9, 12, and 15 days compared to the 25°C treatment group (Figure S1C). The detailed results (L122-143), materials and methods (L647-652), and discussion (L319-322) have been added in our revised manuscript.

Regarding the response of miR-6012 to temperature, we have already determined its expression at 3, 6, 10 days under different temperatures in the previous Figure 5E. We now included additional time intervals (9, 12, 15 days) in the updated Figure 5E. Our results indicate a significant decrease in the expression levels of miR-6012 after 10°C treatment for 3, 6, 9, 12, 15 days compared to the 25°C treatment group. Detailed information regarding this has been integrated into the Materials and Methods (Line 608-610) of our revised manuscript.

Strengths:

This study convincingly identifies homologs of the genes encoding the bursicon subunits and its receptor, showing an alignment with those of another psyllid as well as more distant species. It also demonstrates that the stage- and tissue-specific levels of bursicon follow the expected patterns, as informed by other insect models, thus validating the identity of these genes in this species. They provide strong evidence that the expression of bursicon and its receptor depend on temperature, thereby showing that this trait is regulated through both parts of the signaling mechanism.

Several parallel measurements of the phenotype were performed to show the effects of this hormone, its receptor, and an upstream regulator (miR-6012), on cuticle deposition and pigmentation (if not polyphenism per se, as claimed). Specifically, chitin staining and TEM of the cuticle qualitatively show difference between controls and knockdowns, and this is supported by some statistical tests of quantitative measurements (although see comments below). Thus, this study provides strong evidence that bursicon and its receptor play an important role in cuticle deposition and pigmentation in this psyllid.

The study identified four miRNAs which might affect bursicon due to sequence motifs. By manipulating levels of synthetic miRNA agonists, the study successfully identified one of them (miR-6012) to cause a cuticle phenotype. Moreover, this miRNA was localized (by FISH) to the cuticle, body-wide. To our knowledge, this is the first demonstrated function for this miRNA, and this study provides a good example of using a gene of known function as an entry point to discovering others influencing a trait. Thus, this finding reveals another level of regulation of cuticle formation in insects.

Weaknesses:

(1) The introduction to this manuscript does not accurately reflect progress in the field of mechanisms underlying polyphenism (e.g., line 60). There are several models for polyphenism that have been used to uncover molecular mechanisms in at least some detail, and this includes seasonal polyphenisms in Hemiptera. Therefore, the justification for this study cannot be predicated on a lack of knowledge, nor is the present study original or unique in this line of research (e.g., as reviewed by Zhang et al. 2019; DOI: 10.1146/annurev-ento-011118-112448). The authors are apparently aware of this, because they even provide other examples (lines 104-108); thus the introduction seems misleading as framed.

Thanks for your excellent suggestion. We have added the paper of Zhang et al. 2019 which recommended by reviewer (DOI: 10.1146/annurev-ento-011118-112448) in Line 57 of our revised manuscript. The statement has been revised to “However, the specific molecular mechanism underling temperature-dependent polyphenism still require further clarification” in Line 60-61 of our revised manuscript.

(2) The data in Figure 2H show "percent of transition." However, the images in 2I show insects with tanned cuticle (control) vs. those without (knockdown). Yet, based on the description of the Methods provided, there appears to be no distinction between "percent of transition" and "percent with tanning defects". This an important distinction to make if the authors are going to interpret cuticle defects as a defect in the polyphenism. Furthermore, there is no mention of intermediate phenotypes. The data in 2H are binned as either present or absent, and these are the phenotypes shown in 2I. Was the phenotype really an all-or-nothing response? Instead of binning, which masks any quantitative differences in the tanning phenotypes, the authors should objectively quantify the degree of tanning and plot that. This would show if and to what degree intermediate tanning phenotypes occurred, which would test how bursicon affects the threshold response. This comment also applies to the data in Figures 4G and 6G. Since cuticle tanning is present in more insect than just those with seasonal polyphenism, showing how this responds as a threshold is needed to make claims about polyphenism.

We appreciate your insightful comments. As shown in Figure 1 of our published paper (Zhang et al., 2013; doi.org/10.7554/eLife.88744.3) and Figure 2C-2I of the current manuscript, the transition from summer-form to winter-form entails not only external cuticular tanning but also alterations in internal cuticular chitin levels and cuticle thickness. While external cuticular tanning serves as a prominent and easily observable indicator of this transition, it is crucial to acknowledge that internal changes also play a significant role and should be taken into consideration. Therefore, we propose that the term "percent of transition" may be more suitable than "percent with tanning defects" to describe this process accurately.

In order to provide a more visually comprehensive understanding of the phenotypic changes during the transition from summer-form to winter-form, we have included images at different time points (3, 6, 9, 12, 15 days) under different temperature conditions in Figure S1A of our revised manuscript. Specifically, under the 10°C condition, nymphs exhibit abdomen tanning after 6 and 9 days of treatment, while the thorax remains untanned. By days 12 to 15, both the abdomen and thorax of the nymphs show tanning, resulting in the majority of summer-form nymphs transitioning into winter-form, as depicted in Figure 2I for comparison. This observation indicates the presence of a critical threshold for cuticle tanning of C. chinensis following exposure to 10°C. Nymphs that did not undergo the transition to winter-form succumbed to the cold, highlighting the absence of intermediate phenotypes at 12-15 days under the 10°C condition. The UV absorbance of the total pigment extraction at a 300 nm wavelength markedly increases following 10°C exposure for 6, 9, 12, and 15 days compared to the 25°C treatment group (Figure S1B). Additionally, cuticle thickness shows an increase following 10°C exposure for 6, 9, 12, and 15 days compared to the 25°C treatment group (Figure S1C). These results highlight the relationship between the threshold of cuticular tanning and the transition process. The detailed description and information have been added in Results (L122-143), Materials and Methods (L647-652), and Discussion (L319-322) of our manuscript.

(3) This study also does not test the threshold response of cuticle phenotypes to levels of bursicon, its receptor, or miR-6012. Hormone thresholds are the most widespread and, in most systems where polyphenism has been studied, the defining characteristic of a polyphenism (e.g., Nijhout, 2003, Evol Dev). Quantitative (not binned) measurements of a polyphenism marker (e.g., chitin) should be demonstrated to result as a threshold titer (or in the case of the receptor, expression level) to distinguish defects in polyphenism from those of its component trait.

Thanks for your valuable feedback. We have supplemented additional data on the phenotypes (Figure S1A), cuticle pigment absorbance (Figure S1B), cuticle thickness (Figure S1C), expression levels of bursicon (Figure 1E and 1F), its receptors (Figure 3G), and miR-6012 (Figure 5E) corresponding to nymphs treated over different time periods (3, 6, 9, 12, 15 days) under both 10°C and 25°C conditions in our revised manuscript.

While all these identified markers exhibit a strong correlation with the transition from summer-form to winter-form, it is important to note that they are not suitable as definitive thresholds due to the nature of relative gene expression quantification and chitin content assessment, rather than absolute quantitation. Further, given that tanning hormones are neuropeptides present in trace amounts in insects, unlike steroid hormones, determining their titers poses a considerable challenge.

(4) Cuticle issue:

(a) Unlike Fig. 6D and F, Figs. 2D and F do not correspond to each other. Especially the lack and reduction of chitin in ds-a+b! By fluorescence microscopy there is hardly any signal, whereas by TEM there is a decent cuticle. Additionally, the dsGFP control cuticle in 2D is cut obliquely with a thick and a thin chitin layer. This is misleading.

Thanks for your insightful feedback. We have replaced the previous WGA chitin staining images in the dsCcbursα+β treatment of Figure 2D with new representative images aligning with Figure 2F. Furthermore, the presence of both thin and thick chitin layers observed in the dsEGFP treatment of Figure 2D could potentially be ascribed to the chitin content in the insect midgut or fat body as previously discussed (Zhu et al., 2016). It is notable that during the process of cuticle staining, the chitin located in the midgut and fat body of C. chinensis may exhibit green fluorescence, leading to the appearance of a thin chitin layer. A detailed analysis and elucidation of these observations have been added in the discussion section (Lines 347-352) of our revised manuscript.

Zhu KY, Merzendorfer H, Zhang W, Zhang J, Muthukrishnan S. Biosynthesis, Turnover, and Functions of Chitin in Insects. Annu Rev Entomol. 2016;61:177-196. doi:10.1146/annurev-ento-010715-023933.

(b) In Figs. 2F and 4F, the endocuticle appears to be missing, a portion of the procuticle that is produced post-molting. As tanning is also occurring post-molting, there seems to be a general problem with cuticle differentiation at this time point. This may be a timing issue. Please clarify.

Thank you for your suggestion. The insect cuticle typically comprises three distinct layers (endocuticle, exocuticle, and epicuticle), with the thickness of each layer varying among different insect species. Cuticle differentiation is closely linked to the molting cycle of insects (Mrak et al., 2017). In our study, nymphal cuticles exhibited normal differentiation patterns, characterized by a thin epicuticle and comparable widths of the endocuticle and exocuticle following dsEGFP treatment, as illustrated in Figure 2F and 4F. Conversely, nymphs treated with dsCcBurs-α, dsCcBurs-β, and dsCcburs-R displayed impaired development, manifesting only the exocuticle without a discernible endocuticle layer. These findings suggest that bursicon genes and their receptor play a pivotal role in regulating insect cuticle development (Costa et al., 2016). We have added some discussion about these results in Lines 356-367 of our revised manuscript.

Mrak, P., Bogataj, U., Štrus, J., & Žnidaršič, N. (2017). Cuticle morphogenesis in crustacean embryonic and postembryonic stages. Arthropod structure & development, 46(1), 77–95. https://doi.org/10.1016/j.asd.2016.11.001

Costa, C. P., Elias-Neto, M., Falcon, T., Dallacqua, R. P., Martins, J. R., & Bitondi, M. (2016). RNAi-mediated functional analysis of Bursicon genes related to adult cuticle formation and tanning in the Honeybee, Apis mellifera. PloS one, 11(12), e0167421. https://doi.org/10.1371/journal.pone.0167421

(c) To provide background information, it would be useful analyze cuticle formation in the summer and winter morphs of controls separately by light and electron microscopy. More baseline data on these two morphs is needed.

Thanks for your valuable feedback. To provide more background information about cuticle formation, we supplied the results of nymph phenotypes, cuticle pigment absorbance, and cuticle thickness at distinct time intervals (3, 6, 9, 12, 15 days) under different temperatures of 10°C and 25°C in Figure S1 of our revised manuscript. Hope these results can help better understand the baseline data on these two morphs.

(d) For the TEM study, it is not clear whether the same part of the insect's thorax is being sectioned each time, or if that matters. There is not an obvious difference in the number of cuticular layers, but only the relative widths of those layers, so it is difficult to know how comparable those images are. This raises two questions that the authors should clarify. First, is it possible that certain parts of the thoracic cuticle, such as those closer to the intersegmental membrane, are naturally thinner than other parts of the body? Second, is the tanning phenotype based on the thickness or on the number of chitin layers, or both? The data shown later in Figure 4I, J convincingly shows that the biosynthesis pathway for chitin is repressed, but any clarification of what this might mean for deposition of chitin would help to understand the phenotypes reported. Also, more details on how the data in Fig. 2G were collected would be helpful. This also goes for the data in Fig. 4 (bursicon receptor knockdowns).

Thanks for your great comment. The TEM investigation adhered to a standardized protocol was used as previous description (Zhang et al., 2023), Initially, insect heads were uniformly excised and then fixed in 4% paraformaldehyde. Subsequently, a consistent cutting and staining procedure was executed at a uniform distance above the insect's thorax. The dorsal region of the thorax was specifically chosen for subsequent fluorescence imaging or transmission electron microscopy assessments with the specific objective of quantifying cuticle thickness. Regarding the measurement of cuticle thickness, use the built-in measuring ruler on the software to select the top and bottom of the same horizontal line on the cuticle. Measure the cuticle of each nymph at two close locations. Six nymphs were used for each sample. Randomly select 9 values and plot them. The related description has been added in the Materials and Methods (Line 660-668) of our revised manuscript.

Zhang, S.D., Li, J.Y., Zhang, D.Y., Zhang, Z.X., Meng, S.L., Li, Z., & Liu, X.X. (2023). MiR-252 targeting temperature receptor CcTRPM to mediate the transition from summer-form to winter-form of Cacopsylla chinensis. eLife, 12. https://doi.org/10.7554/eLife.88744

(5) Tissue issue:

The timed experiments shown in all figures were done in whole animals. However, we know from Drosophila that Bursicon activity is complex in different tissues. There is, thus, the possibility, that the effects detected on different days in whole animals are misleading because different tissues--especially the brain and the epidermis, may respond differentially to the challenge and mask each other's responses. The animal is small, so the extraction from single tissue may be difficult. However, this important issue needs to be addressed.

Thanks for your excellent suggestion. We express our heartfelt appreciation to the reviewer for their valuable input regarding the challenges involved in dissecting various tissue sections from the diminutive early instar nymphs of C. chinensis. In light of the metamorphic transition of C. chinensis across developmental stages, this study concentrated on examining the extensive phenotypic alterations. Consequently, intact samples of C. chinensis were specifically chosen for for qPCR analysis. The related descriptions have been added in the Materials and Methods (Line 513, 517, 553, 555, and 613) and Discussion (Line 327-329) of our revised manuscript.

(6) No specific information is provided regarding the procedure followed for the rescue experiments with burs-α and burs-β (How were they done? Which concentrations were applied? What were the effects?). These important details should appear in the Materials and Methods and the Results sections.

Thanks for your excellent suggestion. For the rescue experiments, the dsRNA of CcBurs-R and proteins of burs α-α, burs β-β homodimers, or burs α-β heterodimer (200 ng/μL) were fed together. The concentration of heterodimer protein of CcBurs-α+β was 200 ng/μL. The heterodimer protein of CcBurs-α+β fully rescued the effect of RNAi-mediated knockdown on CcBurs-R expression, while α+α or β+β homodimers did not (Figure 3F). Feeding the α+β heterodimer protein fully rescued the defect in the transition percent and morphological phenotype after CcBurs-R knockdown (Figure 4G-4H). We have added the detailed methods of rescued experiments and specific concentrations in the Materials and Methods (Line 561-563), and Results (Line 263) of our revised manuscript.

(7) Pigmentation

(a) The protocol used to assess pigmentation needs to be validated. In particular, the following details are needed: Were all pigments extracted? Were pigments modified during extraction? Were the values measured consistent with values obtained, for instance, by light microscopy (which should be done)?

Thanks for your excellent comment. Our protocol for pigment extracted as detailed in Bombyx mori, the cuticles were pulverized in liquid nitrogen and then dissolved in 30 milliliters of acidified methanol (Futahashi et al., 2012; Osanai-Futahashi et al., 2012). Thus, all cuticle pigments were dissected and treated with acidified methanol. Pigments were not modified during extraction.. The details description have been integrated into the Materials and Methods (Line 630-633) of our revised manuscript.

Futahashi, R., Kurita, R., Mano, H., & Fukatsu, T. (2012). Redox alters yellow dragonflies into red. Proceedings of the National Academy of Sciences of the United States of America, 109(31), 12626–12631. https://doi.org/10.1073/pnas.1207114109

Osanai-Futahashi, M., Tatematsu, K. I., Yamamoto, K., Narukawa, J., Uchino, K., Kayukawa, T., Shinoda, T., Banno, Y., Tamura, T., & Sezutsu, H. (2012). Identification of the Bombyx red egg gene reveals involvement of a novel transporter family gene in late steps of the insect ommochrome biosynthesis pathway. The Journal of biological chemistry, 287(21), 17706–17714. https://doi.org/10.1074/jbc.M111.321331

(b) In addition, pigmentation occurs post-molting; thus, the results could reflect indirect actions of bursicon signaling on pigmentation. The levels of expression of downstream pigmentation genes (ebony, lactase, etc) should be measured and compared in molting summer vs. winter morphs.

Thanks for your valuable suggestion. Actually, we already studied the function of some downstream pigmentation genes, including ebony, Lactase, Tyrosine hydroxylase, Dopa decarboxylase, and Acetyltransferase. The variations in the expression patterns of these genes are closely tied to the molting dynamics of nymphs undergoing transitions between summer-form and winter-form. These findings will put in another manuscript currently being prepared for submission, thus detailed outcomes are not suitable for inclusion in the current manuscript.

(8) L236: "while the heterodimer protein of CcBurs α+β could fully rescue the effect of CcBurs-R knockdown on the transition percent (Figure 4G 4H)". This result seems contradictory. If CcBurs-R is the receptor of bursicon, the heterodimer protein of CcBurs α+β should not be able to rescue the effect of CcBurs-R knockdown insects. How can a neuropeptide protein rescue the effect when its receptor is not there! If these results are valid, then the CcBurs-R would not be the (sole) receptor for CcBurs α+β heterodimer. This is a critical issue for this manuscript and needs to be addressed (also in L337 in Discussion).

Thanks for your insightful suggestion. Following the administration of dsCcBur-R to C. chinensis, the expression of CcBurs-R exhibited a reduction of approximately 66-82% as depicted in Figure 4A, rather than complete suppression. Activation of endogenous CcBurs-R through feeding of the α+β heterodimer protein results in an increase in CcBurs-R expression, with the effectiveness of the rescue effect contingent upon the dosage of the α+β heterodimer protein. Consequently, the capacity of the α+β heterodimer protein to effectively mitigate the impacts of CcBurs-R knockdown on the conversion rate is clearly demonstrated. We have added additional discussion in Line 396-403 of our revised manuscript.

(9) Fig. 5D needs improvement (the magnification is poor) and further explanation and discussion. mi6012 and CcBurs-R seem to be expressed in complementary tissues--do we see internal tissues also (see problem under point 2)? Again, the magnification is not high enough to understand and appreciate the relationships discussed.

Thanks for your valuable suggestion. In order to enhance the resolution of the magnified images, we conducted FISH co-localization of miR-6012 and CcBurs-R in 3rd instar nymphs and obtained detailed zoomed-in images. As shown in the magnified view of Figure 5D, miR-6012 and CcBurs-R appear to exhibit complementary expression patterns in tissues. During the FISH assays, epidermis transparency of C. chinensis was achieved via decolorization treatment. Noteworthy observations from Figure 3G and Figure 5E reveal an inverse correlation in the expression profiles of CcBurs-R and miR-6012. Consequently, the FISH results distinctly highlight a significant disparity in the expression levels of CcBurs-R and miR-6012 within the same tissue. We have added related explanation and discussion in Line 291-293 of our revised manuscript.

(10) The schematic in Fig. 7 is a useful summary, but there is a part of the logic that is unsupported by the data, specifically in terms of environmental influence on cuticle formation (i.e., plasticity). What is the evidence that lower temperatures influence expression of miR-6012? The study measures its expression over life stages, whether with an agonist or not, over a single temperature. Measuring levels of expression under summer form-inducing temperature is necessary to test the dependence of miR-6012 expression on temperature. Otherwise, this result cannot be interpreted as polyphenism control, but rather the control of a specific trait.

Thanks for your great suggestion. We actually conducted the assessment of miR-6012 expression at specific time intervals (3, 6, 9, 12, 15 days) under different temperatures of 10°C and 25°C. As depicted in Figure 5E, the expression levels of miR-6012 were notably reduced at 10°C compared to 25°C. Additionally, the evaluation of agomir-6012 expression level of C. chinensis under 25°C conditions at various time points (3, 6, 9, 12, 15 days) revealed no significant changes. Hence, we suggest that the impact of miR-6012 on the seasonal morphological transition is influenced upon temperature.

Recommendations for the authors:

The authors report a novel role of Bursicon and its receptor in regulating the seasonal polyphenism of Cacopsylla chinensis. They found that low temperature treatment (10°C) activated the Bursicon signaling pathway during the transition from summer-form to winter-form, which influences cuticle pigment content, cuticle chitin content, and cuticle thickness. Moreover, the authors identified miR-6012 and show that it targets CcBurs-R, thereby modulating the function of Bursicon signaling pathway in the seasonal polyphenism of C. chinensis. This discovery expands our knowledge of multiple roles of neuropeptide bursicon action in arthropod biology. However, the m

anuscript does have several major weaknesses, described under "Public review", which the authors need to address.

Major issues:

(1) L152-154 Fig S2E and S2F: Bursicon has been shown to be expressed in the CNS in a specific set of neurons. For example, In the larval CNS of Manduca sexta, bursicon expression is restricted to the subesophageal ganglion (SG), thoracic ganglia, and first abdominal ganglion. Pharate pupae and pharate adults show expression of this heterodimer in all ganglia. In Drosophila larvae, expression of a bursicon heterodimer is confined to abdominal ganglia. The additional neurons in the ventral nerve cord express only burs. In pharate adults, bursicon is produced by neurons in the SG and abdominal ganglia. I am wondering where bursicon subunits are expressed in the C. chinensis CNS? Since the authors have the antibodies, it would be useful to include immunocytochemical staining of bursicon alpha and beta in the CNS. The qPCR results from head or other tissues (Fig S2E and S2F) is not the most informative way to document localization of gene expression. Regarding the qPCR results, they show that the cuticle and the fat body express CcBurs-α and CcBurs-β. Can the authors confirm this unexpected results independently?

Thanks for your insightful comment. In this study, we did not directly used antibodies targeting bursicon subunits, instead, the bursicon subunits along with a histidine tag were integrated into the expression vector pcDNA3.1 using homologous recombination. The experimental procedures were executed as follows: initially, the histidine tag was fused to the pcDNA3.1-mCherry vector through homologous recombination to generate the recombinant plasmid pcDNA3.1-his-mCherry. Subsequently, the amino acid sequences of the two bursicon subunits were introduced into the pcDNA3.1-his-mCherry vector via homologous recombination to produce the recombinant plasmids pcDNA3.1-CcBurs-α-his-mCherry and pcDNA3.1-CcBurs-β-his-mCherry. Finally, the P2A sequence was incorporated into the vector using reverse PCR to yield the recombinant plasmids pcDNA3.1-CcBurs-α-his-P2A-mCherry and pcDNA3.1-CcBurs-β-his-P2A-mCherry. Consequently, the bursicon subunits, along with the histidine tag, were capable of generating fusion proteins with the histidine tag. Western blot analysis was conducted using antibodies targeting the histidine tag, enabling the detection of histidine expression, which corresponds to the expression of the bursicon subunits. However, they are not suitable to conduct the in vivo immunocytochemical staining of bursicon alpha and beta in the CNS.

Due to the diminutive size of the C. chinensis nymphs, dissection of the central nervous system (CNS) was unfeasible, precluding specific assessment of bursicon expression in the CNS. Prior literature has documented the expression of bursicon subunits in the epidermis and fat body of C. chinensis. Studies suggest that bursicon subunits not only play a role in the melanization and sclerotization processes of insect epidermis but also have significant roles in insect immunity (An et al., 2012). The presence of bursicon subunits in the epidermis, gut, and fat body of C. chinensis may indicate their crucial roles in the immune functions of these tissues. Further investigation is required to elucidate the specific immune functions they perform, hinting at the potential expression of these bursicon subunits in these two tissues.

An, S., Dong, S., Wang, Q., Li, S., Gilbert, L. I., Stanley, D., & Song, Q. (2012). Insect neuropeptide bursicon homodimers induce innate immune and stress genes during molting by activating the NF-κB transcription factor Relish. PloS one, 7(3), e34510. https://doi.org/10.1371/journal.pone.0034510

(2) L222: "CcBurs-R is the Bursicon receptor of C. chinensis". Is this statement supported by affinity binding assay results?

Thanks for your excellent suggestion. We employed a fluorescence-based assay to quantify calcium ion concentrations and investigate the binding affinities of bursicon heterodimers and homodimers to the bursicon receptor across varying concentrations. Our findings suggest that activation of the receptor by the burs α-β heterodimer leads to significant alterations in intracellular calcium ion levels, whereas stimulation with burs α-α and burs β-β homodimers, in conjunction with Adipokinetic hormone (AKH), maintains consistent intracellular calcium ion levels. Consequently, this research definitively identifies CcBurs-R as the bursicon receptor. For further details, please refer to the Materials and Methods (Lines 493-504), Results (Lines 231-239), and Discussion (Lines 377-384) of our revised manuscript.

(3) L245 Figure 4I-4J: Since knockdown of bursicon and its receptor cause a decrease pigment accumulation in the cuticle, it would be useful to examine 1-2 rate limiting enzyme-encoding genes in the bursicon regulated cuticle darkening process if possible (as was done for genes involved in cuticle thickening).

Thanks for your excellent comment. Following the further study, a thorough analysis was conducted to evaluate the impact of bursicon and its receptor on the expression levels of Lactase, Tyrosine hydroxylase, Dopa decarboxylase, Acetyltransferase, and the effects of RNA interference targeting these genes on the seasonal morphological transition. The findings underscored their role in the bursicon-mediated cuticle darkening process. However, as this section is slated for inclusion in an upcoming manuscript intended for submission, it is deemed unsuitable for incorporation into the current manuscript.

Minor issues:

(1) L75 "stronger resistance (Ge et al., 2019; Tougeron et al., 2021)". Stronger resistance to what? Stronger resistance to environmental stress or weather condition? Please clarify.

Thanks for your excellent suggestion. We have changed the statement to “stronger resistance to weather condition” in Line 75 of our revised manuscript.

(2) L132 Figure 1A and 1B: Bursicon sequence was first identified and functionally characterized in Drosophila melanogaster: is there any reason why Drosophila bursicon sequences were not included in the comparison?

Thanks for your excellent comment. We have added the sequence of Burs-α and Burs-β of D. melanogaster in the sequence alignment results of Figure 1A and 1B of our revised manuscript.

(3) Although the authors clearly identify and validate the function for the bursicon genes and its receptor's, there is no mention of whether duplicates of this gene are also present in the pear psyllid. This has been known to happen in otherwise conserved hormone pathways (e.g., insulin receptor in some insects), so a formal check of this should be done.

Thanks for your excellent comment. As shown in Figure S2A-S2B and 3B, there are two bursicon subunit genes and only one bursicon receptor gene in our selected insect species, for examples Drosophila melanogaster, Diaphorina citri, Bemisia tabaci, Nilaparvata lugens, and Sogatella furcifera. In our transcriptome database of C. chinensis, we also only identified two bursicon subunit genes and only one bursicon receptor gene.

(4) Line 41: Here, as in the title, "fascinating" is a subjective judgement that does not improve a study's presentation.

Thanks for your great comment. We have changed "fascinating" to "transformation" in Line 41 and also revised the title of our revised manuscript.

(5) Line 44: What makes some fields "cutting-edge" and others not?

Thanks for your excellent suggestion. The expression of "in cutting-edge fields" has been deleted in Line 44 of our revised manuscript.

(6) Line 97: This is a peculiar choice of reference for the concept of slower development in cold temperatures. The concept of degree-days and growth rates is old and widespread in entomology.

Thanks for your insightful comment. The reference of Nyamaukondiwa et al., 2011 in Line 95 has been deleted in our revised manuscript.

(7) Lines 149-150: What justifies the assumption that higher levels of expression mean a more important role? This gene might be just as necessary for development of the summer form, even if expressed at lower levels.

Thanks for your excellent suggestion. This sentence has been revised to “Increased gene expression levels may potentially contribute to the transition from summer-form to winter-form in C. chinensis.” in Line 168-169 of our revised manuscript.

(8) The blue arrow in Fig. 7 is confusing.

Thanks for your excellent suggestion. In Figure 7, the blue arrow represents the down-regulated expression of miR-6012. We have added a description about the blue arrow in Figure 7 of our revised manuscript.

sera??Bold

Author response:

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

Reviewer #1 (Public Review):

Summary:

The authors aimed to elucidate the cytological mechanisms by which conjugated linoleic acids (CLAs) influence intramuscular fat deposition and muscle fiber transformation in pig models. Utilizing single-nucleus RNA sequencing (snRNA-seq), the study explores how CLA supplementation alters cell populations, muscle fiber types, and adipocyte differentiation pathways in pig skeletal muscles.

Thanks!

Strengths:

Innovative approach: The use of snRNA-seq provides a high-resolution insight into the cellular heterogeneity of pig skeletal muscle, enhancing our understanding of the intricate cellular dynamics influenced by nutritional regulation strategy.

Robust validation: The study utilizes multiple pig models, including Heigai and Laiwu pigs, to validate the differentiation trajectories of adipocytes and the effects of CLA on muscle fiber type transformation. The reproducibility of these findings across different (nutritional vs genetic) models enhances the reliability of the results.

Advanced data analysis: The integration of pseudotemporal trajectory analysis and cell-cell communication analysis allows for a comprehensive understanding of the functional implications of the cellular changes observed.

Practical relevance: The findings have significant implications for improving meat quality, which is valuable for both the agricultural and food industry.

Thanks!

Weaknesses:

Model generalizability: While pigs are excellent models for human physiology, the translation of these findings to human health, especially in diverse populations, needs careful consideration.

Thanks!

Reviewer #2 (Public Review):

Summary:

This study comprehensively presents data from single nuclei sequencing of Heigai pig skeletal muscle in response to conjugated linoleic acid supplementation. The authors identify changes in myofiber type and adipocyte subpopulations induced by linoleic acid at depth previously unobserved. The authors show that linoleic acid supplementation decreased the total myofiber count, specifically reducing type II muscle fiber types (IIB), myotendinous junctions, and neuromuscular junctions, whereas type I muscle fibers are increased. Moreover, the authors identify changes in adipocyte pools, specifically in a population marked by SCD1/DGAT2. To validate the skeletal muscle remodeling in response to linoleic acid supplementation, the authors compare transcriptomics data from Laiwu pigs, a model of high intramuscular fat, to Heigai pigs. The results verify changes in adipocyte subpopulations when pigs have higher intramuscular fat, either genetically or diet-induced. Targeted examination using cell-cell communication network analysis revealed associations with high intramuscular fat with fibro-adipogenic progenitors (FAPs).  The authors then conclude that conjugated linoleic acid induces FAPs towards adipogenic commitment. Specifically, they show that linoleic acid stimulates FAPs to become SCD1/DGAT2+ adipocytes via JNK signaling. The authors conclude that their findings demonstrate the effects of conjugated linoleic acid on skeletal muscle fat formation in pigs, which could serve as a model for studying human skeletal muscle diseases.

Thanks!

Strengths:

The comprehensive data analysis provides information on conjugated linoleic acid effects on pig skeletal muscle and organ function. The notion that linoleic acid induces skeletal muscle composition and fat accumulation is considered a strength and demonstrates the effect of dietary interactions on organ remodeling. This could have implications for the pig farming industry to promote muscle marbling. Additionally, these data may inform the remodeling of human skeletal muscle under dietary behaviors, such as elimination and supplementation diets and chronic overnutrition of nutrient-poor diets. However, the biggest strength resides in thorough data collection at the single nuclei level, which was extrapolated to other types of Chinese pigs.

Thanks!

Weaknesses:

While the authors generated a sizeable comprehensive dataset, cellular and molecular validation needed to be improved. For example, the single nuclei data suggest changes in myofiber type after linoleic acid supplementation, yet these data are not validated by other methodologies. Similarly, the authors suggest that linoleic acid alters adipocyte populations, FAPs, and preadipocytes; however, no cellular and molecular analysis was performed to reveal if these trajectories indeed apply. Attempts to identify JNK signaling pathways appear superficial and do not delve deeper into mechanistic action or transcriptional regulation. Notably, a variety of single cell studies have been performed on mouse/human skeletal muscle and adipose tissues. Yet, the authors need to discuss how the populations they have identified support the existing literature on cell-type populations in skeletal muscle.Moreover, the authors nicely incorporate the two pig models into their results, but the authors only examine one muscle group. It would be interesting if other muscle groups respond similarly or differently in response to linoleic acid supplementation.Further, it was unclear whether Heigai and Laiwu pigs were both fed conjugated linoleic acid or whether the comparison between Heigai-fed linoleic acid and Laiwu pigs (as a model of high intramuscular fat). With this in mind, the authors do not discuss how their results could be implicated in human and pig nutrition, such as desirability and cost-effectiveness for pig farmers and human diets high in linoleic acid. Notably, while single nuclei data is comprehensive, there needs to be a statement on data deposition and code availability, allowing others access to these datasets. Moreover, the experimental designs do not denote the conjugated linoleic acid supplementation duration. Several immunostainings performed could be quantified to validate statements. This reviewer also found the Nile Red staining hard to interpret visually and did not appear to support the conclusions convincingly. Within Figure 7, several letters (assuming they represent statistical significance) are present on the graphs but are not denoted within the figure legend.

Thanks for your suggestions! We accepted your suggestion to revised our manuscript.

For changes in myofiber type, we performed qPCR to verify the changes of muscle fiber type related gene expression after CLA treatment (Figure 2E); for changes of adipocyte and preadipocyte populations, we also performed immunofluorescence staining, qPCR, and western blotting in LDM tissues and FAPs to verify the alterations of cell types after feeding with CLA (Figure 3D, 3E, 6G, 7C, and 7D). Hence, we think these cellular and molecular results could support our conclusions.

For JNK signaling pathway, we selected this signaling pathway based on snRNA-seq dataset and verified by activator in vitro experiment. However, we did not explore the mechanistic action and the downstream transcriptional regulators need to be further discussed. We have added these in the discussion part (line 443-448).

We have added the comparation between different cell-type populations in skeletal muscles (line 362-368 and 385-390).

For changes in myofiber type of Laiwu pigs, we have discussed in our previous study(Wang et al., 2023). Interestingly, we also found in high IMF content Laiwu pigs, the percentage of type IIa myofibers had an increased tendency (29.37% vs. 23.95%) while the percentage of type IIb myofibers had a decreased tendency (38.56% vs. 43.75%) in this study. We also added this discussion in the discussion part (line 392-395).

We have supplied the information of treatment in the materials and methods part (line 469-478). We also added the discussion about significance of our study for human and pig nutrition in the discussion part (line 375-376 and 446-447).

Our data will be made available on reasonable request (line 574-576).

We have supplied the information of the CLA supplementation duration in the materials and methods part (line 465).

Porcine FAPs have little lipid droplets and we improved the image quality (Figure 7A). In Figure 7, the Nile Red staining could be quantified and we have the quantification of Oil Red O staining (Figure 7B and 7J). We also added the statistical significance in figure legend.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Suggestions for Improved or Additional Experiments, Data, or Analyses

Cross-species analysis: To strengthen the generalizability of the results, it would be beneficial to include a comparative analysis with other species, such as human, bovine, or rodent models, using publicly available snRNA-seq datasets.

Thanks! Our previous study has compared the conserved and unique signatures in fatty skeletal muscles between different species(Wang, Zhou, Wang, & Shan, 2024). We mainly focused on the regulatory mechanism of CLAs in regulating intramuscular fat deposition. However, there is still a blank in the snRNA-seq or scRNA-seq datasets about the effects of CLAs on regulating fat deposition in muscles across other species, including human, bovine or rodent models. Hence, we only analyze the regulatory mechanisms of CLAs influencing intramuscular fat deposition in pigs.

Functional link: the authors should discuss in the manuscript how the muscles differ in terms of texture, flavor, aroma, etc. before and after CLA administration or between Heigai and Laiwu to provide context and help readers better understand how the observed high-resolution cellular changes relate to these functional properties of meat.

Thanks! We have added these in the introduction part (line 90-98).

Improve figures: some figures, particularly those involving Oil Red O and Nail Red, could be improved by including higher magnification images to assess the organization of lipid droplets of individual adipocytes (Figure 7A, I, and K).

Thanks! Porcine FAPs have little lipid droplets and we improved the image quality (Figure 7A).

Reviewer #2 (Recommendations For The Authors):

All of my comments are above. However, I would recommend improving the writing as several areas throughout the results needed clarity.

Thanks! We have revised our manuscript carefully after accepting your revisions.

Wang, L., Zhao, X., Liu, S., You, W., Huang, Y., Zhou, Y., . . . Shan, T. (2023) Single-nucleus and bulk RNA sequencing reveal cellular and transcriptional mechanisms underlying lipid dynamics in high marbled pork NPJ Sci Food 7: 23. https://doi.org/10.1038/s41538-023-00203-4

Wang, L., Zhou, Y., Wang, Y., & Shan, T. (2024) Integrative cross-species analysis reveals conserved and unique signatures in fatty skeletal muscles Sci Data 11: 290. https://doi.org/10.1038/s41597-024-03114-5

Reviewer #1 (Public review):

Summary:

The manuscript by Yao S. and colleagues aims to monitor the potential autosomal regulatory role of the master regulator of X chromosome inactivation, the Xist long non-coding RNA. It has recently become apparent that in the human system, Xist RNA can not only spread in cis on the future inactive X chromosome but also reach some autosomal regions where it recruits transcriptional repression and Polycomb marking. Previous work has also reported that Xist RNA can show a diffused signal in some biological contexts in FISH experiments.

In this study, the authors investigate whether Xist represses autosomal loci in differentiating female mouse embryonic stem cells (ESCs) and somatic mouse embryonic fibroblasts (MEFs). They perform a time course of ESC differentiation followed by Capture Hybridization of Associated RNA Targets (CHART) on both female and male ESCs, as well as pulldowns with sense oligos for Xist. The authors also examine transcriptional activity through RNA-seq and integrate this data with prior ChIP-seq experiments. Additional experiments were conducted in MEFs and Xist-ΔB repeat mutants, the latter fails to recruit Polycomb repressors.

Based on this experimental design, the authors make several bold claims:

(1) Xist binds to about a hundred specific autosomal regions.<br /> (2) This binding is specific to promoter regions rather than broad spreading.<br /> (3) Xist autosomal signal is inversely correlated with PRC1/2 marks but positively correlated with transcription.<br /> (4) Xist targeting results in the attenuation of transcription at autosomal regions.<br /> (5) The B-repeat region is important for autosomal Xist binding and gene repression.<br /> (6) Xist binding to autosomal regions also occurs in somatic cells but does not lead to gene repression.

Together, these claims suggest that Xist might play a role in modulating the expression of autosomal genes in specific developmental and cellular contexts in mice.

Strengths:

This paper deals with an interesting hypothesis that Xist ncRNA can also function at autosomal loci.

Weaknesses:

The claims reported in this paper are largely unsubstantiated by the data, with multiple misinterpretations, lacking controls, and inadequate statistics. Fundamental flaws in the experimental design/analysis preclude the validity of the findings. Major concerns are listed below:

(1) The entire paper is based on the CHART observation that Xist is specifically targeted to autosomal promoters. Overall, the data analysis is flawed and does not support such conclusions. Importantly the sense WT and the 0h controls are not used, nor are the biological replicates. Data is typically visualized without quantification, and when quantified, control loci/gene sets are erroneously selected. Firstly, CHART validation on the X in FigS1 is misleading and not based on any quantifications (e.g., see the scale on Kdm6a (0-190) compared to Cdkl5 (0-40)). If scaled appropriately, there is Xist signal on the escapee. All X-linked loci should have been quantified and classified based on escape status; sense control should also be quantified, and biological replicates should be shown separately. Secondly, and most importantly, Figure 1 does not convincingly show specific Xist autosomal binding. Panel A quantification is on extremely variable y-scales and actually shows that Xist is recruited globally to nearly all autosomal genes, likely indicating an unspecific signal. Again, the sense and 0h controls should have been quantified along with biological replicates. Upon inspecting genome browser tracks of all regions reported in the manuscript (Rbm14, Srp9, Brf1, Cand2, Thra, Kmt2c, Kmt2e, Stau2, and Bcl7b), the signal is unspecific on all sites with the possible exception of Kmt2e. On all other loci, there is either a strong signal in the 0h ESC controls or more signal in some of the sense controls. This implies that peak calling is picking up false positive regions. How many peaks would have been picked up if the sense or the 0h controls were used for peak calling? It is likely that there would be a lot since there are also possible "peaks" (e.g., Fzd9) in control tracks. Further inspection of the data was not possible as the authors did not provide access to the raw fastq files. When inspecting results from past published experiments {Engreitz, 2013 #1839} reported regions were not bound by Xist. Thirdly, contrary to the authors' claim, deleting the B repeat does not lead to a loss of autosomal signal. Indeed, comparing Fig1A and Fig2B side by side clearly shows no difference in the autosomal signal, likely because the autosomal signal is CHART background. Properly quantifying the signal with separate replicates as well as the sense and 0h controls is vital. Overall current data together with published results indicate that CHART peak calling on autosomes is due to technical noise or artefacts.

(2) The RNA-seq analysis is also flawed and precludes strong statements. Firstly, the analysis frequently lacks statistical analysis (Fig3B, FigS2B-C) and is often based on visualizations (Fig 3D-G) without quantifications. Day 4 B-repeat deletion does not lead to a significant change in the expression of genes close to Xist signal (Fig3H, d14 does not fully show). Secondly, for all transcriptional analysis, it is important to show autosomal non-target genes, which is not always done. Indeed, both males and B repeat deletion will lead to transcriptional changes on autosomes as a secondary effect from different X inactivation status. The control set, if used, is inappropriate as it compares one randomly selected set of ~100 genes. This introduces sampling error and compares different classes of genes. Since Xist signal targets more active genes, it is important to always compare autosomal target genes to all other autosomal genes with similar basal expression patterns.

(3) The ChIP-seq analysis also has some problems. The authors claim that there is no positive correlation between genes close to Xist autosomal binding (10kb) compared to those 50kb away (Fig 3C, S2D); however, this analysis is based entirely on metagene visualization. Signal within the Xist binding sites should be quantified (not genes close by) and compared to other types of genomic loci and promoters. Focusing on the 50kb group only as controls is misleading. Secondly, the authors only look at PRC mark signal upon differentiation; what about the 0h timepoint, i.e., is there pre-marking? Most worryingly, the data analysis is not consistent between figures (see Fig3C vs 5H-I). In Fig5, the group of Xist targets was chosen as those within 100kb of Xist binding, which would encompass all the control regions from Fig3C. In this analysis, the authors report that there is Xist-dependent H3K27me3 deposition, and in fact, here the Xist autosomal targets have more of it than the controls. Overall, all of this analysis is misleading, and clear conclusions cannot be made.

All in all, because the fundamental observation is not robust (see point 1), all subsequent analyses are also affected. There are also multiple other inconsistencies within the analysis; however, they have not been included here for brevity.

La comunicación estratégica es el uso intencionado de la comunicación por parte de una organización para cumplir su misión, en la que integra diversas formas de comunicación, para promover una identidad corporativa y apoyar los objetivos estratégicos.

La comunicación estratégica es un enfoque deliberado y planificado que permite a las organizaciones alcanzar sus objetivos a largo plazo de manera efectiva. A diferencia de la comunicación operativa, que tiende a ser reactiva y puntual, la comunicación estratégica está alineada con la visión y misión de la organización, asegurando que cada mensaje, campaña y esfuerzo comunicacional contribuya a la consecución de metas a nivel global.

La comunicación estratégica es el uso intencionado de la comunicación para que una organización cumpla sus objetivos a largo plazo. Integra relaciones públicas, marketing y comunicación en línea para crear una identidad coherente. Los elementos clave incluyen la planificación cuidadosa de campañas, el desarrollo de mensajes claros y consistentes, la participación activa de los interesados y la medición de resultados. A diferencia de la comunicación general, que es operativa y puntual, la comunicación estratégica es planificada y se alinea con la visión global de la organización.

*Katherine Granizo*

La comunicación se ha ido modificando conforme pasa el tiempo y se ajusta a las necesidades de los usuarios, ayudándonos a destacar nuestra empresa, al ser cambiante y adaptativa, la conoceremos ahora como comunicación estratégica.

Veery interesting form of art, use of materials for the time frame makes me want to see his work just through the readings

In September, employers in Poland published 12% more job offers y/y (288 thousand).

The increase has been going on for 4 months, and now it has reached the highest level since March 2022. The largest increase in the number of job offers is in medical professions (24%) and manual workers (13%). Decrease among financiers (-15%), HR specialists (-10%) and lawyers (-7%). In IT, the number of offers increased by 5%, and in marketing and sales by 1%.

La necesidad de la inversión en planificación de la comunicación a largo plazo, obliga a la empresa a exprimir la comunicación para generar nuevas capacidades y a acercar cada vez mas en lo posible a la empresa con perspectiva en comunicación aplicada a requerimientos del mundo actual, estratégicamente.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The authors investigated how the presence of interspecific introgressions in the genome affects the recombination landscape. This research was intended to inform about genetic phenomena influencing the evolution of introgressed regions, although it should be noted that the research itself is based on examining only one generation, which limits the possibility of drawing far-reaching evolutionary conclusions. In this work, yeast hybrids with large (from several to several dozen percent of the chromosome length) introgressions from another yeast species were crossed. Then, the products of meiosis were isolated and sequenced, and on this basis, the genome-wide distribution of both crossovers (COs) and noncrossovers (NCOs) was examined. Carrying out the analysis at different levels of resolution, it was found that in the regions of introduction, there is a very significant reduction in the frequency of COs and a simultaneous increase in the frequency of NCOs. Moreover, it was confirmed that introgressions significantly limit the local shuffling of genetic information, and NCOs are only able to slightly contribute to the shuffling, thus they do not compensate for the loss of CO recombination.

Strengths:

- Previously, experiments examining the impact of SNP polymorphism on meiotic recombination were conducted either on the scale of single hotspots or the entire hybrid genome, but the impact of large introgressed regions from another species was not examined. Therefore, the strength of this work is its interesting research setup, which allows for providing data from a different perspective.

- Good quality genome-wide data on the distribution of CO and NCO were obtained, which could be related to local changes in the level of polymorphism.

Weaknesses:

(1)  The research is based on examining only one generation, which limits the possibility of drawing far-reaching evolutionary conclusions. Moreover, meiosis is stimulated in hybrids in which introgressions occur in a heterozygous state, which is a very unlikely situation in nature. Therefore, I see the main value of the work in providing information on the CO/NCO decision in regions with high sequence diversification, but not in the context of evolution.

While we are indeed only examining recombination in a single generation, we respectfully disagree that our results aren't relevant to evolutionary processes. The broad goals of our study are to compare recombination landscapes between closely related strains, and we highlight dramatic differences between recombination landscapes. These results add to a body of literature that seeks to understand the existence of variation in traits like recombination rate, and how recombination rate can evolve between populations and species. We show here that the presence of introgression can contribute to changes in recombination rate measured in different individuals or populations, which has not been previously appreciated. We furthermore show that introgression can reduce shuffling between alleles on a chromosome, which is recognized as one of the most important determinants for the existence and persistence of sexual reproduction across all organisms. As we describe in our introduction and conclusion, we see our experimental exploration of the impacts of introgression on the recombination landscape as complementary to studies inferring recombination and introgression from population sequencing data and simulations. There are benefits and challenges to each approach, but both can help us better understand these processes. In regards to the utility of exploring heterozygous introgression, we point out that introgression is often found in a heterozygous state (including in modern humans with Neanderthal and/or Denisovan ancestry). Introgression will always be heterozygous immediately after hybridization, and depending on the frequency of gene flow into the population, the level of inbreeding, selection against introgression, etc., introgression will typically be found as heterozygous.

- The work requires greater care in preparing informative figures and, more importantly, re-analysis of some of the data (see comments below).

More specific comments:

(1) The authors themselves admit that the detection of NCO, due to the short size of conversion tracts, depends on the density of SNPs in a given region. Consequently, more NCOs will be detected in introgressed regions with a high density of polymorphisms compared to the rest of the genome. To investigate what impact this has on the analysis, the authors should demonstrate that the efficiency of detecting NCOs in introgressed regions is not significantly higher than the efficiency of detecting NCOs in the rest of the genome. If it turns out that this impact is significant, analyses should be presented proving that it does not entirely explain the increase in the frequency of NCOs in introgressed regions.

We conducted a deeper exploration of the effect of marker resolution on NCO detection by randomly removing different proportions of markers from introgressed regions of the fermentation cross in order to simulate different marker resolutions from non-introgressed regions. We chose proportions of markers that would simulate different quantiles of the resolution of non-introgressed regions and repeated our standard pipeline in order to compare our NCO detection at the chosen marker densities. More details of this analysis have been added to the manuscript (lines 188-199, 525-538). We confirmed the effect of marker resolution on NCO detection (as reported in the updated manuscript and new supplementary figures S2-S10, new Table S10) and decided to repeat our analyses on the original data with a more stringent correction. For this we chose our observed average tract size for NCOs in introgressed regions (550bp), which leads to a far more conservative estimate of NCO counts (As seen in the updated Figure 2 and Table 2). This better accounts for the increased resolution in introgressed regions, and while it's possible to be more stringent with our corrections, we believe that further stringency would be unreasonable. We also see promising signs that the correction is sufficient when counting our CO and NCO events in both crosses, as described in our response to comment 39 (response to reviewer #3).

(2) CO and NCO analyses performed separately for individual regions rarely show statistical significance (Figures 3 and 4). I think that the authors, after dividing the introgressed regions into non-overlapping windows of 100 bp (I suggest also trying 200 bp, 500 bp, and 1kb windows), should combine the data for all regions and perform correlations to SNP density in each window for the whole set of data. Such an analysis has a greater chance of demonstrating statistically significant relationships. This could replace the analysis presented in Figure 3 (which can be moved to Supplement). Moreover, the analysis should also take into account indels.

We're uncertain of what is being requested here. If the comment refers to the effect of marker density on NCO detection, we hope the response to comment 2 will help resolve this comment as well. Otherwise, we ask for some clarification so that we may correct or revise as appropriate.

(3) In Arabidopsis, it has been shown that crossover is stimulated in heterozygous regions that are adjacent to homozygous regions on the same chromosome (http://dx.doi.org/10.7554/eLife.03708.001, https://doi.org/10.1038/s41467-022-35722-3).

This effect applies only to class I crossovers, and is reversed for class II crossovers (https://doi.org/10.15252/embj.2020104858, https://doi.org/10.1038/s41467-023-42511-z). This research system is very similar to the system used by the authors, although it likely differs in the level of DNA sequence divergence. The authors could discuss their work in this context.

We thank the reviewer for sharing these references. We have added a discussion of our work in the context of these findings in the Discussion, lines 367-376.

Reviewer #2 (Public Review):

Summary:

Schwartzkopf et al characterized the meiotic recombination impact of highly heterozygous introgressed regions within the budding yeast Saccharomyces uvarum, a close relative of the canonical model Saccharomyces cerevisiae. To do so, they took advantage of the naturally occurring Saccharomyces bayanus introgressions specifically within fermentation isolates of S. uvarum and compared their behavior to the syntenic regions of a cross between natural isolates that do not contain such introgressions. Analysis of crossover (CO) and noncrossover (NCO) recombination events shows both a depletion in CO frequency within highly heterozygous introgressed regions and an increase in NCO frequency. These results strongly support the hypothesis that DNA sequence polymorphism inhibits CO formation, and has no or much weaker effects on NCO formation. Eventually, the authors show that the presence of introgressions negatively impacts "r", the parameter that reflects the probability that a randomly chosen pair of loci shuffles their alleles in a gamete.

The authors chose a sound experimental setup that allowed them to directly compare recombination properties of orthologous syntenic regions in an otherwise intra-specific genetic background. The way the analyses have been performed looks right, although this reviewer is unable to judge the relevance of the statistical tests used. Eventually, most of their results which are elegant and of interest to the community are present in Figure 2.

Strengths:

Analysis of crossover (CO) and noncrossover (NCO) recombination events is compelling in showing both a depletion in CO frequency within highly heterozygous introgressed regions and an increase in NCO frequency.

Weaknesses:

The main weaknesses refer to a few text issues and a lack of discussion about the mechanistic implications of the present findings.

- Introduction

(1) The introduction is rather long. | I suggest specifically referring to "meiotic" recombination (line 71) and to "meiotic" DSBs (line 73) since recombination can occur outside of meiosis (ie somatic cells).

We agree and have condensed the introduction to be more focused. We also made the suggested edits to include “meiotic” when referring to recombination and DSBs.

(2) From lines 79 to 87: the description of recombination is unnecessarily complex and confusing. I suggest the authors simply remind that DSB repair through homologous recombination is inherently associated with a gene conversion tract (primarily as a result of the repair of heteroduplex DNA by the mismatch repair (MMR) machinery) that can be associated or not to a crossover. The former recombination product is a crossover (CO), the latter product is a noncrossover (NCO) or gene conversion. Limited markers may prevent the detection of gene conversions, which erase NCO but do not affect CO detection.

We changed the language in this section to reflect the reviewer’s suggestions.

(3) In addition, "resolution" in the recombination field refers to the processing of a double Holliday junction containing intermediates by structure-specific nucleases. To avoid any confusion, I suggest avoiding using "resolution" and simply sticking with "DSB repair" all along the text.

We made the suggested correction throughout the paper.

(4) Note that there are several studies about S. cerevisiae meiotic recombination landscapes using different hybrids that show different CO counts. In the introduction, the authors refer to Mancera et al 2008, a reference paper in the field. In this paper, the hybrid used showed ca. 90 CO per meiosis, while their reference to Liu et al 2018 in Figure 2 shows less than 80 COs per meiosis for S. cerevisiae. This shows that it is not easy to come up with a definitive CO count per meiosis in a given species. This needs to be taken into account for the result section line 315-321.

This is an excellent point. We added this context in the results (lines 180-187).

(5) In line 104, the authors refer to S. paradoxus and mention that its recombination rate is significantly different from that of S. cerevisiae. This is inaccurate since this paper claims that the CO landscape is even more conserved than the DSB landscape between these two species, and they even identify a strong role played by the subtelomeric regions. So, the discussion about this paper cannot stand as it is.

We agree with the reviewer's point. We also found that the entire paragraph was unnecessary, so it and the sentence in question have been removed.

(6) Line 150, when the authors refer to the anti-recombinogenic activity of the MMR, I suggest referring to the published work from Martini et al 2011 rather than the not-yet-published work from Copper et al 2021, or both, if needed.

Added the suggested citation.

Results

(7) The clear depletion in CO and the concomitant increase in NCO within the introgressed regions strongly suggest that DNA sequence polymorphism triggers CO inhibition but does not affect NCO or to a much lower extent. Because most CO likely arises from the ZMM pathway (CO interference pathway mainly relying on Zip1, 2, 3, 4, Spo16, Msh4, 5, and Mer3) in S. uvarum as in S. cerevisiae, and because the effect of sequence polymorphism is likely mediated by the MMR machinery, this would imply that MMR specifically inhibits the ZMM pathway at some point in S. uvarum. The weak effect or potential absence of the effect of sequence polymorphism on NCO formation suggests that heteroduplex DNA tracts, at least the way they form during NCO formation, escape the anti-recombinogenic effect of MMR in S. uvarum. A few comments about this could be added.

We have added discussion and citations regarding the biased repair of DSB to NCO in introgression, lines 380-386.

(8) The same applies to the fact that the CO number is lower in the natural cross compared to the fermentation cross, while the NCO number is the same. This suggests that under similar initiating Spo11-DSB numbers in both crosses, the decrease in CO is likely compensated by a similar increase in inter-sister recombination.

Thank you to the reviewer for this observation. We agree that this could explain some differences between the crosses.

(9) Introgressions represent only 10% of the genome, while the decrease in CO is at least 20%. This is a bit surprising especially in light of CO regulation mechanisms such as CO homeostasis that tends to keep CO constant. Could the authors comment on that?

We interpret these results to reflect two underlying mechanisms. First, the presence of heterozygous introgression does reduce the number of COs. Second, we believe the difference in COs reflects variation in recombination rate between strains. We note that CO homeostasis need not apply across different genetic backgrounds. Indeed, recombination rate is appreciated to significantly differ between strains of S. cerevisiae (Raffoux et al. 2018), and recombination rate variation has been observed between strains/lines/populations in many different species including Drosophila, mice, humans, Arabidopsis, maize, etc. We reference S. cerevisiae strain variability in the Introduction lines 128-130, and have added context in the Results lines 180-187, and Discussion lines 343-350.

(10) Finally, the frequency of NCOs in introgressed regions is about twice the frequency of CO in non-introgressed regions. Both CO and NCO result from Spo11-initiating DSBs.

This suggests that more Spo11-DSBs are formed within introgressed regions and that such DSBs specifically give rise to NCO. Could this be related to the lack of homolog engagement which in turn shuts down Spo11-DSB formation as observed in ZMM mutants by the Keeney lab? Could this simply result from better detection of NCO in introgressed regions related to the increased marker density, although the authors claim that NCO counts are corrected for marker resolution?

The effect noted by the reviewer remains despite the more conservative correction for marker density applied to NCO counts (as described in the response to Reviewer 1, comment #2). Given that CO+NCO counts in introgressed regions are not statistically different between crosses, it is likely that these regions are simply predisposed to a higher rate of DSBs than the rest of the genome. This is an interesting observation, however, and one that we would like to further explore in future work.

(11) What could be the explanation for chromosome 12 to have more shuffling in the natural cross compared to the fermentation cross which is deprived of the introgressed region?

We added this text to the Results, lines 323-327, "While it is unclear what potential mechanism is mediating the difference in shuffling on chromosome 12, we note that the rDNA locus on chromosome 12 is known to differ dramatically in repeat content across strains of S. cerevisiae (22–227 copies) (Sharma et a. 2022), and we speculate that differences in rDNA copy number between strains in our crosses could impact shuffling."

Technical points:

(12) In line 248, the authors removed NCO with fewer than three associated markers.

What is the rationale for this? Is the genotyping strategy not reliable enough to consider events with only one or two markers? NCO events can be rather small and even escape detection due to low local marker density.

We trust the genotyping strategy we used, but chose to be conservative in our detection of NCOs to account for potential sequencing biases.

(13) Line 270: The way homology is calculated looks odd to this reviewer, especially the meaning of 0.5 homology. A site is either identical (1 homology) or not (0 homology).

We've changed the language to better reflect what we are calculating (diploid sequence similarity; see comment #28). Essentially, the metric is a probability that two randomly selected chromatids--one from each parent--will share the same nucleotide at a given locus (akin to calculating the probability of homozygous offspring at a single locus). We average it along a segment of the genome to establish an expected sequence similarity if/when recombination occurs in that segment.

(14) Line 365: beware that the estimates are for mitotic mismatch repair (MMR). Meiotic MMR may work differently.

We removed the citation that refers exclusively to mitotic recombination. The statement regarding meiotic recombination is otherwise still reflective of results from Chen & Jinks-Robertson

(15) Figure 1: there is no mention of potential 4:0 segregations. Did the authors find no such pattern? If not, how did they consider them?

The program we used to call COs and NCOs (ReCombine's CrossOver program) can detect such patterns, but none were detected in our data.

Reviewer #3 (Public Review):

When members of two related but diverged species mate, the resulting hybrids can produce offspring where parts of one species' genome replace those of the other. These "introgressions" often create regions with a much greater density of sequence differences than are normally found between members of the same species. Previous studies have shown that increased sequence differences, when heterozygous, can reduce recombination during meiosis specifically in the region of increased difference. However, most of these studies have focused on crossover recombination, and have not measured noncrossovers. The current study uses a pair of Saccharomyces uvarum crosses: one between two natural isolates that, while exhibiting some divergence, do not contain introgressions; the other is between two fermentation strains that, when combined, are heterozygous for 9 large regions of introgression that have much greater divergence than the rest of the genome. The authors wished to determine if introgressions differently affected crossovers and noncrossovers, and, if so, what impact that would have on the gene shuffling that occurs during meiosis.

(1) While both crossovers and noncrossovers were measured, assessing the true impact of increased heterology (inherent in heterozygous introgressions) is complicated by the fact that the increased marker density in heterozygous introgressions also increases the ability to detect noncrossovers. The authors used a relatively simple correction aimed at compensating for this difference, and based on that correction, conclude that, while as expected crossovers are decreased by increased sequence heterology, counter to expectations noncrossovers are substantially increased. They then show that, despite this, genetic shuffling overall is substantially reduced in regions of heterozygous introgression. However, it is likely that the correction used to compensate for the effect of increased sequence density is defective, and has not fully compensated for the ascertainment bias due to greater marker density. The simplest indication of this potential artifact is that, when crossover frequencies and "corrected" noncrossover frequencies are taken together, regions of introgression often appear to have greater levels of total recombination than flanking regions with much lower levels of heterology. This concern seriously undercuts virtually all of the novel conclusions of the study. Until this methodological concern is addressed, the work will not be a useful contribution to the field.

We appreciate this concern. Please see response to comments #2 and #38. We further note that our results depicted in Figure 3 and 4 are not reliant on any correction or comparison with non-introgressed regions, and thus our results regarding sequence similarity and its effect on the repair of DSBs and the amount of genetic shuffling with/without introgression to be novel and important observations for the field.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

(1) Line 149 - this sentence refers to a mixture of papers reporting somatic or meiotic recombination and as these processes are based on different crossover pathways, this should not be mixed. For example, it is known that in Arabidopsis MSH2 has a pro-crossover function during meiotic recombination.

Corrected

(2) What is unclear to me is how the crosses are planned. Line 308 shows that there were only two crosses (one "natural" and one "fermentation"), but I understand that this is a shorthand and in fact several (four?) different strains were used for the "fermentation cross". At least that's what I concluded from Fig. 1B and its figure caption. This needs to be further explained. Were different strains used for each fermentation cross, or was one strain repeated in several crosses? In Figure 1, it would be worth showing, next to the panel showing "fermentation cross", a diagram of how "natural cross" was performed, because as I understand it, panel A illustrates the procedure common to both types of crosses, and not for "natural cross".

We thank the reviewer for drawing our attention to confusion about how our crosses were created. We performed two crosses, as depicted in Figure 1A. The fermentation cross is a single cross from two strains isolated from fermentation environments. The natural cross is a single cross from two strains isolated from a tree and insect. Table S1 and the methods section "Strain and library construction" describe the strains used in more detail. We modified Figure 1 and the figure legend to help clarify this. See also response to comment #37.

(3) The authors should provide a more detailed characterization of the genetic differences between chromosomes in their hybrids. What is the level of polymorphism along the S. uvarum chromosomes used in the experiments? Is this polymorphism evenly distributed? What are the differences in the level of polymorphism for individual introgressions? Theoretically, this data should be visible in Figure 2D, but this figure is practically illegible in the present form (see next comment).

As suggested, we remade Figure 2D to only include chromosomes with an introgression present, and moved the remaining chromosomes to the supplements (Figure S11). The patterns of markers (which are fixed differences between the strains in the focal cross) should be more clear now. As we detail in the Methods line 507-508, we utilized a total of 24,574 markers for the natural cross and 74,619 markers for the fermentation cross (the higher number in the fermentation cross being due to more fixed differences in regions of introgression).

(4) Figure 2D should be prepared more clearly, I would suggest stretching the chromosomes, otherwise, it is difficult to see what is happening in the introgression regions for CO and NCO (data for SNPs are more readable). Maybe leave only the chromosomes with introgressions and transfer the rest to the supplement?

See previous comment.

(5) How are the Y scales defined for Figure 2D?

Figure 2D now includes units for the y-axis.

(6) Are increases in CO levels in fermentation cross-observed at the border with introgressions? This would indicate local compensation for recombination loss in the introgressed regions, similar to that often observed for chromosomal inversions.

We see no evidence of an increase in CO levels at the borders of introgressions, neither through visual inspection or by comparing the average CO rate in all fermentation windows to that of windows at the edges of introgressions. This is included in the Discussion lines 360-366, "While we are limited in our interpretations by only comparing two crosses (one cross with heterozygous introgression and one without introgression), these results are in line with findings in inversions, where heterozygotes show sharp decreases in COs, but the presence of NCOs in the inverted region (Crown et al., 2018; Korunes & Noor, 2019). However, unlike heterozygous inversions where an increase in COs is observed on freely recombining chromosomes (the inter-chromosomal effect), we do not see an increase in COs on the borders flanking introgression or on chromosomes without introgression."

(7) Line 336 - "We find positive correlations between CO counts..." - you should indicate here that between fermentation and natural crosses, it was quite hard for me to understand what you calculated.

We corrected the language as suggested.

(8) The term "homology" usually means "having a common evolutionary origin" and does not specify the level of similarity between sequences, thus it cannot be measured. It is used incorrectly throughout the manuscript (also in the intro). I would use the term "similarity" to indicate the degree of similarity between two sequences.

We corrected the language as suggested throughout the document.

(9) Paragraph 360 and Figure 3 - was the "sliding window" overlapping or non-overlapping?

We added clarifying language to the text in both places. We use a 101bp sliding window with 50bp overlaps.

(10) Line 369 - what is "...the proportion of bases that are expected to match between the two parent strains..."?

We clarified the language in this location, and hopefully changes associated with the comment about sequence similarity will make the comment even clearer in context.

(11) Line 378 - should it refer to Figure S1 and not Figure 4?

Corrected.

(12) Line 399 - should refer to Figure 4, not Figure 5.

Corrected

(13) Line 444-449 - the analysis of loss of shuffling in the context of the location of introgression on the chromosome should be presented in the result section.

We shifted the core of the analysis to the results, while leaving a brief summary in the discussion.

(14) The authors should also take into account the presence of indels in their analyses, and they should be marked in the figures, if possible.

We filtered out indels in our variant calling. However, we did analyze our crosses for the presence of large insertions and deletions (Table S2), which can obscure true recombination rates, and found that they were not an issue in our dataset.

Reviewer #2 (Recommendations For The Authors):

This reviewer suggests that the authors address the different points raised in the public review.

(1) This reviewer would like to challenge the relevance of the r-parameter in light of chromosome 12 which has no introgression and still a strong depletion in r in the fermentation cross.

We added this text to the Results, lines 377-381, "While it is unclear what potential mechanism is mediating the difference in shuffling on chromosome 12, we note that the rDNA locus on chromosome 12 is known to differ dramatically in repeat content across strains of S. cerevisiae (22–227 copies) (Sharma et a. 2022), and we speculate that differences in rDNA copy number between strains in our crosses could impact shuffling."

(2) This reviewer insists on making sure that NCO detection is unaffected by the marker density, notably in the highly polymorphic regions, to unambiguously support Figure 1C.

We've changed our correction for resolution to be more aggressive (see response to comment #2), and believe we have now adequately adjusted for marker density (see response to comment #38).

Reviewer #3 (Recommendations For The Authors):

I regret using such harsh language in the public review, but in my opinion, there has been a serious error in how marker densities are corrected for, and, since the manuscript is now public, it seems important to make it clear in public that I think that the conclusions of the paper are likely to be incorrect. I regret the distress that the public airing of this may cause. Below are my major concerns:

(1) The paper is written in a way that makes it difficult to figure out just what the sequence differences are within the crosses. Part of this is, to be frank, the unusual way that the crosses were done, between more than one segregant each from two diploids in both natural and fermentation cases. I gather, from the homology calculations description, that each of these four diploids, while largely homozygous, contained a substantial number of heterozygosities, so individual diploids had different patterns of heterology. Is this correct? And if so, why was this strategy chosen? Why not start with a single diploid where all of the heterologies are known? Why choose to insert this additional complication into the mix? It seems to me that this strategy might have the perverse effect of having the heterology due to the polymorphisms present in one diploid affect (by correction) the impact of a noncrossover that occurs in a diploid that lacks the additional heterology. If polymorphic markers are a small fraction of total markers, then this isn't such a great concern, but I could not find the information anywhere in the manuscript. As a courtesy to the reader, please consider providing at the beginning some basic details about the starting strains-what is the average level of heterology between natural A and natural B, and what fraction of markers are polymorphic; what is the average level of heterology between fermentation A and fermentation B in non-introgressed regions, in introgressed regions, and what fraction of markers are polymorphic? How do these levels of heterology compare to what has been examined before in whole-genome hybrid strains? It also might be worth looking at some of the old literature describing S. cerevisiae/S. carlsbergensis hybrids.

We thank the reviewer for drawing our attention to confusion about the cross construction. These crosses were conducted as is typical for yeast genetic crosses: we crossed 2 genetically distinct haploid parents to create a heterozygous diploid, then collected the haploid products of meiosis from the same F1 diploid. Because the crosses were made with haploid parents, it is not possible for other genetic differences to be segregating in the crosses. We have revised Figure 1 and its caption to clarify this. Further details regarding the crosses are in the Methods section "Strain and library construction" and in Supplemental Table S1. We only utilized genetic markers that are fixed differences between our parental strains to call CO and NCO. As we detail in the Methods line 507-508, we utilized a total of 24,574 markers for the natural cross and 74,619 markers for the fermentation cross (the higher number in the fermentation cross being due to more fixed differences in regions of introgression). We additionally revised Figure 2D (and Figure S11) to help readers better visualize differences between the crosses.

(2) There are serious concerns about the methods used to identify noncrossovers and to normalize their levels, which are probably resulting in an artifactually high level of calculated crossovers in Figure 2. As a primary indication of this, it appears in Figure 2 that the total frequency of events (crossovers + noncrossovers) in heterozygous introgressed regions are substantially greater than those in the same region in non-introgressed strains, while just shifting of crossovers to noncrossovers would result in no net increase. The simplest explanation for this is that noncrossovers are being undercounted in non-introgressed relative to introgressed heterozygous regions. There are two possible reasons for this: i. The exclusion of all noncrossover events spanning less than three markers means that many more noncrossovers in introgressed heterozygous regions than in non-introgressed. Assuming that average non-homology is 5% in the former and 1% in the latter, the average 3-marker event will be 60 nt in introgressed regions and 300 nt in non-introgressed regions - so many more noncrossovers will be counted in introgressed regions. A way to check on this - look at the number of crossover-associated markers that undergo gene conversion; use the fraction that involves < 3 markers to adjust noncrossover levels (this is the strategy used by Mancera et al.). ii. The distance used for noncrossover level adjustment (2kb) is considerably greater than the measured average noncrossover lengths in other studies. The effect of using a too-long distance is to differentially under-correct for noncrossovers in non-introgressed regions, while virtually all noncrossovers in heterozygous introgressed regions will be detected. This can be illustrated by simulations that reduce the density of scored markers in heterozygous introgressed regions to the density seen in non-introgressed regions. Because these concerns go to the heart of the conclusions of the paper, they must be addressed quantitatively - if not, the main conclusions of the paper are invalid.

We adjusted the correction factor (See also response to comment #2) and compared the average number of CO and NCO events in introgressed and non-introgressed regions between crosses (two comparisons: introgression CO+NCO in natural cross vs introgression CO+NCO in fermentation cross; non-introgression CO+NCO in natural cross vs non-introgression CO+NCO in fermentation cross). We found no significant differences between the crosses in either of the comparisons. This indicates that the distribution of total events is replicated in both crosses once we correct for resolution.

(3) It is important to distinguish the landscape of double-strand breaks from the landscape of recombination frequencies. Double-strand breaks, as measured by uncalibrated levels of Spo11-linked oligos, is a relative number - not an absolute frequency. So it is possible that two species could have a similar break landscape in terms of topography but have absolute levels higher in one species than in the other.

We agree with this statement, however, we have removed the relevant text to streamline our introduction.

(4) Lines 123-125. Just meiosis will produce mosaic genomes in the progeny of the F1; further backcrossing will reduce mosaicism to the level of isolated regions of introgression.

Adjusted the language to be more specific.

(5) Please provide actual units for the Y axes in Figure 2D.

We have corrected the units on the axes.

(6) Tables (general). Are the significance measures corrected for multiple comparisons?

In Table 3, the cutoff was chosen to be more conservative than a Bonferroni corrected alpha=0.01 with 9 comparisons (0.0011). In text, any result referred to as significant has an associated hypothesis test with a p-value less than its corresponding Bonferroni-corrected alpha of 0.05. This has been clarified in the caption for Table 3 and in the text where relevant.

Reviewer #1 (Public review):

Summary:

This manuscript seeks to estimate the causal effect of genes on disease. To do so, they introduce a novel algorithm, termed the Root Causal Strength using Perturbations (RCSP) algorithm. RCSP uses perturb-seq to first estimate the gene regulatory network structure among genes, and then uses bulk RNA-seq with phenotype data on the samples to estimate causal effects of genes on the phenotype conditional on the learned network structure. The authors assess the performance of RCSP in comparison to other methods via simulation. Next, they apply RCSP to two real human datasets: 513 individuals age-related macular degeneration and 137 individuals with multiple sclerosis.

Strengths:

The authors tackle an important and ambitious problem - the identification of causal contributors to disease in the context of a causal inference framework. As the authors point out, observational RNA-seq data is insufficient for this kind of causal discovery, since it is very challenging to recover the true underlying graph from observational data; interventional data are needed. However, little perturb-seq data has been generated with annotated phenotype data, and much bulk RNA-seq data has already been generated, so it is useful to propose an algorithm to integrate the two as the authors have done.

The authors also offer substantial theoretical exposition for their work, bringing to bear both the literature on causal discovery as well as literature on the genetic architecture of complex traits.

Weaknesses:

The notion of a "root" causal gene - which the authors define based on a graph theoretic notion of topologically sorting graphs - requires a graph that is directed and acyclic. It is the latter that constitutes an important weakness here - it simply is a large simplification of human biology to draw out a DAG including hundreds of genes and a phenotype Y and to claim that the true graph contains no cycles. This is briefly touched upon the discussion, but given the fundamental nature of this choice - the manuscript should devote at least some of the main results to exploring the consequence of mischaracterizing true cyclic graphs as DAGs in this framework. For example - consider the authors' analysis of T cell infiltration in multiple sclerosis (MS). CD4+ effector T cells have the interesting property that they are stimulated by IL2 as a growth factor; yet IL2 also stimulates the activation of (suppressive) regulatory T cells. What does it mean to analyze CD4+ regulation in disease with a graph that does not consider IL2 (or other cytokine) mediated feedback loops/cycles?

I also encourage the authors to consider more carefully when graph structure learned from perturb-seq can be ported over to bulk RNA-seq. Consider again the MS CD4+ example - the authors first start with a large perturb-seq experiment (Replogle et al., 2022) performed in K562 cells. To what extent are K562 cells, which are derived from a leukemia cell line, suitable for learning the regulatory structure of CD4+ cells from individuals with an MS diagnosis? Presumably this structure is not exactly correct - to what extent is the RCSP algorithm sensitive to false edges in this graph? This leap - from cell line to primary human cells - is also not modeled in the simulation. Although challenging - it would be ideal for the RCSP to model or reflect the challenges in correctly identifying the regulatory structure.

It should also be noted that in most perturb-seq experiments, the entire genome is not perturbed, and frequently important TFs (that presumably are very far "upstream" and thus candidate "root" causal genes) are not expressed highly enough to be detected with scRNA-seq. In that context - perhaps slightly modifying the language regarding RCSP's capabilities might be helpful for the manuscript - perhaps it would be better to describe it has an algorithm for causal discovery among a set of genes that were perturbed and measured, rather than a truly complete search for causal factors. Perhaps more broadly - it would also benefit the manuscript to devote slightly more text to describing the kinds of scenarios where RCSP (and similar ideas) would be most appropriately applied - perhaps a well-powered, phenotype annotated perturb-seq dataset performed in a disease relevant primary cell.

Reviewer #3 (Public review):

Summary:

The authors provide an interesting and novel approach, RCSP, to determining what they call the "root causal genes" for a disease, i.e. the most upstream, initial causes of disease. RCSP leverages perturbation (e.g. Perturb-seq) and observational RNA-seq data, the latter from patients. They show using both theory and simulations that if their assumptions hold then the method performs remarkably well, compared to both simple and available state-of-the-art baselines. Whether the required assumptions hold for real diseases is questionable. They show superficially reasonable results on AMD and MS.

Strengths:

The idea of integrating perturbation and observational RNA-seq dataset to better understand the causal basis of disease is powerful and timely. We are just beginning to see genome-wide perturbation assay, albeit in limited cell-types currently. For many diseases, research cohorts have at least bulk observational RNA-seq from a/the disease-relevant tissue(s). Given this, RCSP's strategy of learning the required causal structure from perturbations and applying this knowledge in the observational context is pragmatic and will likely become widely applicable as Perturb-seq data in more cell-types/contexts becomes available.

The causal inference reasoning is another strength. A more obvious approach would be to attempt to learn the causal network structure from the perturbation data and leverage this in the observational data. However, structure learning in high-dimensions is notoriously difficult, despite recent innovations such as differentiable approaches. The authors notice that to estimate the root causal effect for a gene X, one only needs access to a (superset of) the causal ancestors of X: much easier relationships to detect than the full network.

The applications are also reasonably well chosen, being some of the few cases where genome-scale perturb-seq is available in a roughly appropriate (see below) cell-type, and observational RNA-seq is available at a reasonable sample size.

Weaknesses:

Several assumptions of the method are problematic. The most concerning is that the observational expression changes are all causally upstream of disease. There is work using Mendelian randomization (MR) showing that the _opposite_ is more likely to be true: most differential expression in disease cohorts is a consequence rather than a cause of disease (https://www.nature.com/articles/s41467-021-25805-y). Indeed, the oxidative stress of AMD has known cellular responses including the upregulation of p53. The authors need to think carefully about how this impacts their framework. Can the theory say anything in this light? Simulations could also be designed to address robustness.

A closely related issue is the DAG assumption of no cycles. This assumption is brought to bear because it required for much classical causal machinery, but is unrealistic in biology where feedback is pervasive. How robust is RCSP to (mild) violations of this assumption? Simulations would be a straightforward way to address this.

The authors spend considerable effort arguing that technical sampling noise in X can effectively be ignored (at least in bulk). While the mathematical arguments here are reasonable, they miss the bigger picture point that the measured gene expression X can only ever be a noisy/biased proxy for the expression changes that caused disease: 1) Those events happened before the disease manifested, possibly early in development for some conditions like neurodevelopmental disorders. 2) bulk RNA-seq gives only an average across cell-types, whereas specific cell-types are likely "causal". 3) only a small sample, at a single time point, is typically available. Expression in other parts of the tissue and at different times will be variable.

My remaining concerns are more minor.

While there are connections to the omnigenic model, the latter is somewhat misrepresented. 1) The authors refer to the "core genes" of the omnigenic model as being at the end (longitudinally) of pathogenesis. The omnigenic model makes no statements about temporally ordering: in causal inference terminology the core genes are simply the direct cause of disease. 2) "Complex diseases often have an overwhelming number of causes, but the root causal genes may only represent a small subset implicating a more omnigenic than polygenic model" A key observation underlying the omnigenic model is that genetic heritability is spread throughout the genome (and somewhat concentrated near genes expressed in disease relevant cell types). This implies that (almost) all expressed genes, or their associated (e)SNPs, are "root causes".

The claim that root causal genes would be good therapeutic targets feels unfounded. If these are highly variable across individuals then the choice of treatment becomes challenging. By contrast the causal effects may converge on core genes before impacting disease, so that intervening on the core genes might be preferable. The jury is still out on these questions, so the claim should at least be made hypothetical.

The closest thing to a gold standard I believe we have for "root causal genes" is integration of molecular QTLs and GWAS, specifically coloc/MR. Here the "E" of RCSP are explicitly represented as SNPs. I don't know if there is good data for AMD but there certainly is for MS. The authors should assess the overlap with their results. Another orthogonal avenue would be to check whether the root causal genes change early in disease progression.

The available perturb-seq datasets have limitations beyond on the control of the authors. 1) The set of genes that are perturbed. The authors address this by simply sub-setting their analysis to the intersection of genes represented in the perturbation and observational data. However, this may mean that a true ancestor of X is not modeled/perturbed, limiting the formal claims that can be made. Additionally, some proportion of genes that are nominally perturbed show little to no actual perturbation effect (for example, due to poor guide RNA choice) which will also lead to missing ancestors.

The authors provide no mechanism for statistical inference/significance for their results at either the individual or aggregated level. While I am a proponent of using effect sizes more than p-values, there is still value in understanding how much signal is present relative to a reasonable null.

I agree with the authors that age coming out of a "root cause" is potentially encouraging. However, it is also quite different in nature to expression, including being "measured" exactly. Will RCSP be biased towards variables that have lower measurement error?

Finally, it's a stretch to call K562 cells "lymphoblasts". They are more myeloid than lymphoid.

1. Definición y Origen del Capital Financiero: El capital financiero surge de la transformación histórica de la usura en un sistema de crédito. Los financieros emergen como una clase distinta que organiza y gestiona la circulación monetaria, separándose de la producción directa. Se distingue del capital usurero por su función dentro de una economía basada en el trabajo asalariado y la producción capitalista.

2. Sistema de Crédito: El crédito se convierte en una herramienta esencial para la expansión del comercio, la producción y el consumo, penetrando todos los aspectos de la economía. Organizado en torno a la banca y la Bolsa de valores, el sistema de crédito facilita la acumulación de capital, pero también actúa como un catalizador de las crisis económicas.

3. Funciones del Dinero y del Crédito: El crédito reemplaza al dinero en ciertas funciones, permitiendo el intercambio diferido y la acumulación de deudas. Este sistema ha evolucionado hacia una estructura compleja que incluye diversos instrumentos financieros y mecanismos de especulación.

4. Relaciones con el Estado: El capital financiero influye significativamente en las políticas económicas del Estado, ya que la financiación de proyectos públicos y la estabilidad económica están vinculadas al sistema de crédito y la banca.

5. Impacto y Riesgos del Capital Financiero: La autonomía del capital financiero conlleva una tendencia a la especulación, con la Bolsa como escenario principal de las transacciones de capital ficticio. Esto puede llevar a crisis financieras graves, como la de 2008, cuando la bursatilización de créditos bancarios y la especulación desenfrenada provocaron un colapso global.

El texto subraya la importancia de la estrategia de comunicación como un proceso de largo plazo para construir una imagen empresarial sostenible y creíble. Este enfoque estratégico requiere coherencia entre los mensajes emitidos y la realidad de la organización, rescatando elementos diferenciadores ya existentes y potenciándolos. A pesar de que algunos directivos puedan cuestionar la necesidad de una visión a largo plazo, el autor argumenta que las empresas buscan permanencia en el tiempo, y por tanto, la consistencia de los mensajes es clave para generar confianza. De esta manera, la estrategia de comunicación se convierte en un pilar fundamental para el desarrollo organizacional sostenido.

La comunicación estratégica es una herramienta clave para el éxito organizacional y no debería ser vista como un área en la que se puede reducir costos sin consecuencias. La planificación a largo plazo permite que las empresas mantengan coherencia en sus mensajes y relaciones con los stakeholders, lo que a su vez refuerza su reputación y capacidad de respuesta ante crisis. Si bien es comprensible que los ejecutivos busquen eficiencias financieras, reducir el presupuesto de comunicación puede debilitar la imagen pública y generar costos mayores a largo plazo. Invertir de manera adecuada en estrategias de comunicación robustas es esencial para garantizar sostenibilidad y confianza.

presenta un enfoque integral sobre cómo las organizaciones deben gestionar su comunicación para lograr coherencia entre sus acciones y sus objetivos. Plantea la comunicación como una herramienta clave para fortalecer la reputación, relacionarse con los públicos de interés y enfrentar crisis de manera efectiva. Garrido subraya la necesidad de una planificación estratégica que integre todos los canales y mensajes. Además, resalta la importancia de medir los resultados para ajustar las tácticas y mejorar la percepción pública.

Si no existen patrones coherentes, la estrategia se pierde y a su vez se genera un vacio el cual puede llegar a ser una problemática, por ello la delimitación y definición clara de objetivos es primordial para que existan patrones adecuados y útiles.

Se trata de aportar al cumplimiento de las metas de la empresa, por ende su importancia al buscar la rentabilidad (subir las ventas y aumentar su reputación.)

La estrategia se convierte además en un selector de negocios de la organización, una especie de paradigma que imprime un avanzar a la empresa en el que ella escoge y opta por ciertos espacios y nichos de mercados actuales y mercados (en comunicación preferimos hablar de sociedad) a futuro. Cuando la estrategia actúa generando una manera selectiva de comportamiento en la organización, está generando una distinción o diferenciación de la compañía y en su modo de operar que impregnará las visiones de sus miembros y que trascenderá a su sólo ser social. Con la presencia de este componente la estrategia clarificará el “donde está operando o donde estará haciéndolo en el futuro” la empresa, tal como se evidencia en el discurso que construimos de la organización para decir “algo” respecto a nosotros (donde estamos, quienes somos y hacia donde vamos), donde el selector de negocios o visión paradigmática de la que hablamos es un componente conceptual que generará modos de ver que pueden y deben ser integrados en la cultura e identidad de la compañía.

Una empresa se sostiene mediante las estrategias y cuando estas crean un precedente se benefician cada integrante de la misma. El trabajo en equipo por parte de quienes forman parte de la institución deben manejar un mismo lenguaje en cuanto a la definición de metas, acuerdos y el cumplimiento de los mismos, prácticamente un acuerdo que se ejecuta a rajatabla.

El texto presenta dos definiciones complementarias de estrategia organizacional, aportadas por Quinn y Mintzberg. Según Quinn, la estrategia es un plan que integra objetivos, políticas y acciones, sirviendo como una guía para la ejecución. Por su parte, Mintzberg propone que la estrategia es un patrón de decisiones basado en el análisis del comportamiento pasado de la empresa, aunque reconoce que algunas estrategias pueden surgir sin planificación previa, adaptándose a circunstancias imprevistas. En conjunto, se considera que la estrategia puede ser tanto planificada como emergente, en un proceso continuo de adaptación y reconstrucción.

Pienso que la estrategia debe convertirse en un precedente que evidencie, antes que nada un trabajo en equipo, donde los integrantes de la compañía, empresa, institución manejen un mismo lenguaje y estén convencidos de que las etapas de cada proceso que lleven a cabo los va a llevar al cumplimiento de metas que beneficiarán a cada miembro de la empresa.

El que esté de pie cuide de no caer. El pueblo que Dios bendijo, el pueblo escogido. - Terminó adorando a otros dioses - cometieron inmoralidad sexual que les costó la vida a Miles de ellos - pusieron a prueba dudando de Dios - y se quejaron.

Estos son ejemplos de cómo podemos perder nuestra relación con Jehová si no nos mantenemos alerta 1 Corintios 10:6-12

Aquí Pablo describe muy bien su lucha contra el pecado, la misma lucha que nosotros sentimos, cuando por nuestra imperfección queremos hacer lo que sabemos que está mal. Nuestros pensamientos y deseos son los de Dios, pero nuestra cuerpo es esclavo del pecado. Romanos 7:15-25

Porque nos da capacidad de pensar en las consecuencias de nuestras acciones Antes de realizarlas, porque podremos diciernir lo bueno, de los engaños de de personas inmorales que buscan la maldad y están alejados de la sabiduría y el amor de Jehová. Proverbios 2:10-22

Esforzarnos cómo si buscáramos tesoros. Implica, - estar atento a que necesitamos su consejo - tener un corazón humilde para aceptar su guía y llegar a amarla - pedir con fuerza y determinación que necesitamos entendimiento Entonces si llegaremos a temer a Dios que es el principio de la sabiduría. Proverbios 2:1-6

Author response:

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

Many thanks to the editors for the reviewing of the revised manuscript.

We are very grateful to the Reviewers for their time and for the appreciation of the revision.

We thank the Reviewer 3 for acknowledging the use of sulforhodamine B (SRB) fluorescence as a real-time readout of astrocyte volume dynamics. Experimental data in brain slices were provided to validate this approach.<br /> The incomplete matching of our observation with early reported data in cultured astrocytes (e.g., Solenov et al., AJP-Cell, 2004), might reflect certain of their properties differing from the slice/in vivo counterparts as discussed in the manuscript.<br /> The study (T.R. Murphy et al., Front Cell Neurosci., 2017) showed that AQP4 knockout increased astrocyte swelling extent in response to hypoosmotic solution in brain slices (Fig 9), and discussed '... AQP4 can provide an efficient efflux pathway for water to leave astrocytes.’ Correspondingly, our data suggest that AQP4 mediate astrocyte water efflux in basal conditions.<br /> We have discussed the study (Igarashi et al., NeuroReport 2013); our current data would help to understand the cellular mechanisms underlying the finding of Igarashi et al.

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

Pham and colleagues provide an illuminating investigation of aquaporin-4 water flux in the brain utilizing ex vivo and in vivo techniques. The authors first show in acute brain slices, and in vivo with fiber photometry, SRB-loaded astrocytes swell after inhibition of AQP4 with TGN-020, indicative of tonic water efflux from astrocytes in physiological conditions. Excitingly, they find that TGN-020 increases the ADC in DW-MRI in a region-specific manner, potentially due to AQP4 density. The resolution of the DW-MRI cannot distinguish between intracellular or extracellular compartments, but the data point to an overall accumulation of water in the brain with AQP4 inhibition. These results provide further clarity on water movement through AQP4 in health and disease.

Overall, the data support the main conclusions of the article, with some room for more detailed treatment of the data to extend the findings.

Strengths:

The authors have a thorough investigation of AQP4 inhibition in acute brain slices. The demonstration of tonic water efflux through AQP4 at baseline is novel and important in and of itself. Their further testing of TGN-020 in hyper- and hypo-osmotic solutions shows the expected reduction of swelling/shrinking with AQP4 blockade.

Their experiment with cortical spreading depression further highlights the importance of water efflux from astrocytes via AQP4 and transient water fluxes as a result of osmotic gradients. Inhibition of AQP4 increases the speed of tissue swelling, pointing to a role in the efflux of water from the brain.

The use of DW-MRI provides a non-invasive measure of water flux after TGN-020 treatment.

We thank the reviewer for the insightful comments.

Weaknesses:

The authors specifically use GCaMP6 and light sheet microscopy to image their brain sections in order to identify astrocytic microdomains. However, their presentation of the data neglects a more detailed treatment of the calcium signaling. It would be quite interesting to see whether these calcium events are differentially affected by AQP4 inhibition based on their cellular localization (ie. processes vs. soma vs. vascular end feet which all have different AQP4 expressions).

Following the suggestion, we provide new data on the effect of AQP4 inhibition on spontaneous calcium signals in perivascular astrocyte end-feet. As shown now in Fig.S2, acute application of TGN020 induced Ca2+ oscillations in astrocyte end-feet regions where the GCaMP6 labeling lines the profile of the blood vessel. It is noted that on average, the strength of basal Ca2+ signals in the end-feet is higher than that observed across global astrocyte territories (4.65 ± 0.55 vs. 1.45 ± 0.79, p < 0.01), as does the effect of TGN (8.4 ± 0.62 vs. 6.35 ± 0.97, p < 0.05; Fig S2 vs. Fig 2B). This likely reflects the enrichment of AQP4 in astrocyte end-feet. We describe the data in Fig.S2, and on page 8, line 20 – 23.  

We now use the transgenic line GLAST-GCaMP6 for cytosolic GCaMP6 expression in astrocytes. Spontaneous calcium signals, reflected by transient fluorescence rises, occur in discrete micro-domains whereas the basal GCaMP6 fluorescence in the soma is weak. In the present condition, it is difficult to unambiguously discriminate astrocyte soma from the highly intermingled processes. 

The authors show the inhibition of AQP4 with TGN-020 shortens the onset time of the swelling associated with cortical spreading depression in brain slices. However, they do not show quantification for many of the other features of CSD swelling, (ie. the duration of swelling, speed of swelling, recovery from swelling).

Regarding the features of the CSD swelling, we have performed new analysis to quantify the duration of swelling, speed of swelling and the recovery time from swelling in control condition and in the presence of TGN-020. The new analysis is now summarized in Fig. S5. Blocking AQP4 with TGN-020 increases the swelling speed, prolongs the duration of swelling and slows down the recovery from swelling, confirming our observation that acute inhibition of AQP4 water efflux facilitates astrocyte swelling while restrains shrinking. We describe the result on page 11, line 19-21. 

Significance:

AQP4 is a bidirectional water channel that is constitutively open, thus water flux through it is always regulated by local osmotic gradients. Still, characterizing this water flux has been challenging, as the AQP4 channel is incredibly water-selective. The authors here present important data showing that the application of TGN-020 alone causes astrocytic swelling, indicating that there is constant efflux of water from astrocytes via AQP4 in basal conditions. This has been suggested before, as the authors rightfully highlight in their discussion, but the evidence had previously come from electron microscopy data from genetic knockout mice.

AQP4 expression has been linked with the glymphatic circulation of cerebrospinal fluid through perivascular spaces since its rediscovery in 2012 [1]. Further studies of aging[2], genetic models[3], and physiological circadian variation[4] have revealed it is not simply AQP4 expression but AQP4 polarization to astrocytic vascular endfeet that is imperative for facilitating glymphatic flow. Still, a lingering question in the field is how AQP4 facilitates fluid circulation. This study represents an important step in our understanding of AQP4's function, as the basal efflux of water via AQP4 might promote clearance of interstitial fluid to allow an influx of cerebrospinal fluid into the brain. Beyond glymphatic fluid circulation, clearly, AQP4-dependent volume changes will differentially alter astrocytic calcium signaling and, in turn, neuronal activity.

(1) Iliff, J.J., et al., A Paravascular Pathway Facilitates CSF Flow Through the Brain Parenchyma and the Clearance of Interstitial Solutes, Including Amyloid β. Sci Transl Med, 2012. 4(147): p. 147ra111.

(2) Kress, B.T., et al., Impairment of paravascular clearance pathways in the aging brain. Ann Neurol, 2014. 76(6): p. 845-61.

(3) Mestre, H., et al., Aquaporin-4-dependent Glymphatic Solute Transport in the Rodent Brain. eLife, 2018. 7.

(4) Hablitz, L., et al., Circadian control of brain glymphatic and lymphatic fluid flow. Nature Communications, 2020. 11(1).

We thank the reviewer in acknowledging the significance of our study and the functional implication in brain glymphatic system. We have now highlighted the mentioned studies as well as the potential implication glymphatic fluid circulation (page 4, line 9-10; page 5, line 1-3; and page 19, line 3-10). 

Reviewer #2 (Public Review):

Summary:

The paper investigates the role of astrocyte-specific aquaporin-4 (AQP4) water channel in mediating water transport within the mouse brain and the impact of the channel on astrocyte and neuron signaling. Throughout various experiments including epifluorescence and light sheet microscopy in mouse brain slices, and fiber photometry or diffusion-weighted MRI in vivo, the researchers observe that acute inhibition of AQP4 leads to intracellular water accumulation and swelling in astrocytes. This swelling alters astrocyte calcium signaling and affects neighboring neuron populations. Furthermore, the study demonstrates that AQP4 regulates astrocyte volume, influencing mainly the dynamics of water efflux in response to osmotic challenges or associated with cortical spreading depolarization. The findings suggest that AQP4-mediated water efflux plays a crucial role in maintaining brain homeostasis, and indicates the main role of AQP4 in this mechanism. However authors highlight that the report sheds light on the mechanisms by which astrocyte aquaporin contributes to the water environment in the brain parenchyma, the mechanism underlying these effects remains unclear and not investigated. The manuscript requires revision.

Strengths:

The paper elucidates the role of the astrocytic aquaporin-4 (AQP4) channel in brain water transport, its impact on water homeostasis, and signaling in the brain parenchyma. In its idea, the paper follows a set of complimentary experiments combining various ex vivo and in vivo techniques from microscopy to magnetic resonance imaging. The research is valuable, confirms previous findings, and provides novel insights into the effect of acute blockage of the AQP4 channel using TGN-020.

We thank the reviewer for the constructive comments.

Weaknesses:

Despite the employed interdisciplinary approach, the quality of the manuscript provides doubts regarding the significance of the findings and hinders the novelty claimed by the authors. The paper lacks a comprehensive exploration or mention of the underlying molecular mechanisms driving the observed effects of astrocytic aquaporin-4 (AQP4) channel inhibition on brain water transport and brain signaling dynamics. The scientific background is not very well prepared in the introduction and discussion sections. The important or latest reports from the field are missing or incompletely cited and missconcluded. There are several citations to original works missing, which would clarify certain conclusions. This especially refers to the basis of the glymphatic system concept and recently published reports of similar content. The usage of TGN-020, instead of i.e. available AER-270(271) AQP4 blocker, is not explained. While employing various experimental techniques adds depth to the findings, some reasoning behind the employed techniques - especially regarding MRI - is not clear or seemingly inaccurate. Most of the time the number of subjects examined is lacking or mentioned only roughly within the figure captions, and there are lacking or wrongly applied statistical tests, that limit assessment and reproducibility of the results. In some cases, it seems that two different statistical tests were used for the same or linked type of data, so the results are contradictory even though appear as not likely - based on the figures. Addressing these limitations could strengthen the paper's impact and utility within the field of neuroscience, however, it also seems that supplementary experiments are required to improve the report.

The current data hint at a tonic water efflux from astrocyte AQP4 in physiological condition, which helps to understand brain water homeostasis and the functional implication for the glymphatic system. The underlying molecular and cellular mechanisms appear multifaceted and functionally interconnected, as discussed (page 14 line 8 –page 15, line 3). We agree that a comprehensive exploration will further advance our understanding.

The introduction and discussion are now strengthened by incorporating the important advances in glymphatic system while highlighting the relevant studies. 

The use of TGN-020 was based on its validation by wide range of ex vivo and in vivo studies including the use of heterologous expression system and the AQP4 KO mice. The validation of AER-270(271, the water soluble prodrug) using AQP4 KO mice is reported recently (Giannetto et al., 2024). AER-271 was noted to impact brain water ADC (apparent diffusion coefficient evaluated by diffusion-weighted MRI) in AQP4 KO mice ~75 min after the drug application (Giannetto et al., 2024). This likely reflects that AER270(271) is also an inhibitor for κΒ nuclear factor (NF-κΒ) whose inhibition could reduce CNS water content independent of AQP4 targeting (Salman et al., 2022). In addition, the inhibition efficiency of AER-270(271) seems lower than TGN-020 (Farr et al., 2019; Giannetto et al., 2024; Huber et al., 2009; Salman et al., 2022). We have now supplemented this information in the manuscript (page 7, line 1-6 and page15, line 7-17).

The description on the DW-MRI is now updated (page 4, line 10-14). 

We also performed new experiments and data analysis as described in a point-to-point manner below in the section ‘Recommendations For The Authors’.

Reviewer #3 (Public Review):

Summary:

In this manuscript, the authors propose that astrocytic water channel AQP4 represents the dominant pathway for tonic water efflux without which astrocytes undergo cell swelling. The authors measure changes in astrocytic sulforhodamine fluorescence as the proxy for cell volume dynamics. Using this approach, they perform a technically elegant series of ex vivo and in vivo experiments exploring changes in astrocytic volume in response to AQP4 inhibitor TGN-020 and/or neuronal stimulation. The key finding is that TGN-020 produces an apparent swelling of astrocytes and modifies astrocytic cell volume regulation after spreading depolarizations. Additionally, systemic application of TGN-020 produced changes in diffusion-weighted MRI signal, which the authors interpret as cellular swelling. This study is perceived as potentially significant. However, several technical caveats should be strongly considered and perhaps addressed through additional experiments.

Strengths:

(1) This is a technically elegant study, in which the authors employed a number of complementary ex vivo and in vivo techniques to explore functional outcomes of aquaporin inhibition. The presented data are potentially highly significant (but see below for caveats and questions related to data interpretation).

(2) The authors go beyond measuring cell volume homeostasis and probe for the functional significance of AQP4 inhibition by monitoring Ca2+ signaling in neurons and astrocytes (GCaMP6 assay).

(3) Spreading depolarizations represent a physiologically relevant model of cellular swelling. The authors use ChR2 optogenetics to trigger spreading depolarizations. This is a highly appropriate and much-appreciated approach.

We thank the reviewer for the effort in evaluating our work.

Weaknesses:

(1) The main weakness of this study is that all major conclusions are based on the use of one pharmacological compound. In the opinion of this reviewer, the effects of TGN-020 are not consistent with the current knowledge on water permeability in astrocytes and the relative contribution of AQP4 to this process.

Specifically: Genetic deletion of AQP4 in astrocytes reduces plasmalemmal water permeability by ~two-three-fold (when measured a 37oC, Solenov et al., AJP-Cell, 2004). This is a significant difference, but it is thought to have limited/no impact on water distribution. Astrocytic volume and the degree of anisosmotic swelling/shrinkage are unchanged because the water permeability of the AQP4null astrocytes remains high. This has been discussed at length in many publications (e.g., MacAulay et al., Neuroscience, 2004; MacAulay, Nat Rev Neurosci, 2021) and is acknowledged by Solenov and Verkman (2004).

Keeping this limitation in mind, it is important to validate astrocytic cell volume changes using an independent method of cell volume reconstruction (diameter of sulforhodamine-labeled cell bodies? 3D reconstruction of EGFP-tagged cells? Else?)

Solenov and coll. used the calcein quenching assay and KO mice demonstrating AQP4 as a functional water channel in cultured astrocytes (Solenov et al., 2004). AQP4 deletion reduced both astrocyte water permeability and the absolute amplitude of swelling over comparable time, and also slowed down cell shrinking, which overall parallels our results from acute AQP4 blocking. Yet in Solenovr’s study, the time to swelling plateau was prolonged in AQP4 KO astrocytes, differing from our data from the pharmacological acute blocking. This discrepancy may be due to compensatory mechanisms in chronic AQP4 KO, or reflect the different volume responses in cultured astrocytes from brain slices or in vivo results as suggested previously (Risher et al., 2009). 

Soma diameter might be an indicator of cell volume change, yet it is challenging with our current fluorescence imaging method that is diffraction-limited and insufficient to clearly resolve the border of the soma in situ. In addition, the lateral diameter of cell bodies may not faithfully reflect the volume changes that can occur in all three dimensions. Rapid 3D imaging of astrocyte volume dynamics with sufficient high Z-axis resolution appears difficult with our present tools. 

We have now accordingly updated the discussion with relevant literatures being cited (page 17 line 14 – page 18, line 3).

(2) TGN-020 produces many effects on the brain, with some but not all of the observed phenomena sensitive to the genetic deletion of AQP4. In the context of this work, it is important to note that TGN020 does not completely inhibit AQP4 (70% maximal inhibition in the original oocyte study by Huber et al., Bioorg Med Chem, 2009). Thus, besides not knowing TGN-020 levels inside the brain, even

"maximal" AQP4 inhibition would not be expected to dramatically affect water permeability in astrocytes.

This caveat may be addressed through experiments using local delivery of structurally unrelated AQP4 blockers, or, preferably, AQP4 KO mice.

It is an important point that TGN-020 partially blocks AQP4, implying the actual functional impact of AQP4 per se might be stronger than what we observed. TGN provides a means to acutely probe AQP4 function in situ, still we agree, its limitation needs be acknowledged. We mention this now on page 15, line 7-9 and 14-17.

We agree that local delivery of an alternative blocker will provide additional information. Meanwhile, local delivery requires the stereotaxic implantation of cannula, which would cause inflammations to surrounding astrocytes (and neurons). The recently introduced AQP4 blocker AER-270(271) has received attention that it influences brain water dynamics (ADC in DW-MRI) in AQP4 KO mice (Giannetto et al., 2024), recalling that AER-270(271) is also an inhibitor for κΒ nuclear factor (NF-κΒ). This pathway can potentially perturb CNS water content and influence brain fluid circulation, in an AQP4independent manner (Salman et al., 2022). The inhibition efficiency on mouse AQP4 of AER-270 (~20%, Farr et al., 2019; Salman et al., 2022) appears lower than TGN-020 (~70%, Huber et al., 2009).

We chose to use the pharmacological compound to achieve acute blocking of AQP4 thereby avoiding the chronic genetics-caused alterations in brain structural, functional and water homeostasis. Multiple lines of evidence including the recent study (Gomolka et al., 2023), have shown that AQP4 KO mice alters brain water content, extracellular space and cellular structures, which raises concerns to use the transgenic mouse to pinpoint the physiological functions of the AQP4 water channel. 

We have now mentioned the concerns on AQP4 pharmacology by supplementing additional literatures in the field (page 15, line 8-18). 

(3) This reviewer thinks that the ADC signal changes in Figure 5 may be unrelated to cellular swelling. Instead, they may be a result of the previously reported TGN-020-induced hyphemia (e.g., H. Igarashi et al., NeuroReport, 2013) and/or changes in water fluxes across pia matter which is highly enriched in AQP4. To amplify this concern, AQP4 KO brains have increased water mobility due to enlarged interstitial spaces, rather than swollen astrocytes (RS Gomolka, eLife, 2023). Overall, the caveats of interpreting DW-MRI signal deserve strong consideration.

The previous observation show that TGN-020 increases regional cerebral blood flow in wild-type mice but not in AQP4 KO mice (Igarashi et al., 2013). Our current data provide a possible mechanism explanation that TGN-020 blocking of astrocyte AQP4 causes calcium rises that may lead to vasodilation as suggested previously (Cauli and Hamel, 2018). We now add updates to the discussion on page 15, line 3-7.

We are in line with the reviewer regarding the structural deviations observed with the AQP4 KO mice

(Gomolka et al., 2023), now mentioned on page 19, line 3-5. Following the Reviewer’s suggestion, we have also updated the interpretation of the DW-MRI signal and point that in addition to being related to the astrocyte swelling, the ADC signal changes may also be caused by indirect mechanisms, such as the transient upregulation of other water-permeable pathways in compensating AQP4 blocking. We now describe this alternative interpretation and the caveats of the DW-MRI signals (page 20, line 1-8). 

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Private recommendations

My more broad experimental suggestions are in the "weaknesses" section. Some minor points that would improve the manuscript are included below:

(1) A more detailed explanation for why SRB fluorescence reflects the astrocyte volume changes, whereas typical intracellular GFP does not.

As an engineered fluorescence protein, the GFP has been used to tag specific type of cells. Meanwhile, as a relatively big protein (MW, 26.9 kDa), the diffusion rate of EGFP is expected to be much less than SRB, a small chemical dye (MW, 558.7 Da). Also, the IP injection of SRB enables geneticsless labeling of brain astrocytes, so to avoid the influence of protein overexpression on astrocyte volume and water transport responses. We have now stated this point in the manuscript (page 13, line 21 – page 14, line 4).

(2) Figure 1 panel B should have clear labels on the figure and a description in the legend to delineate which part of the panel refers to hyper- or hypo-osmotic treatment.

We have now updated the figure and the legend.  

(3) For Figure 2, what is the rationale for analyzing the calcium signaling data between the cell types differently?

We analyzed calcium micro-domains for astrocytes as their spontaneous signals occur mainly in discrete micro-domains (Shigetomi et al., 2013). While for neurons, we performed global analysis by calculating the mean fluorescence of imaging field of view, because calcium signal changes were only observed at global level rather than in micro-domains. This information is now included (page 24, line1820).

(4) For Figure 3, the authors mention that TGN-020 likely caused swelling prior to the hypotonic solution administration. Do they have any measurements from these experiments prior to the TGN-020 application to use as a "true baseline" volume?

The current method detects the relative changes in astrocyte volume (i.e., transmembrane water transport), which nevertheless is blind to the absolute volume value. We have no readout on baseline volumes.  

(5) For Figures 3 and 4, did the authors see any evidence for regulatory volume decrease? And is this impaired by TGN-020? It is a well-characterized phenomenon that astrocytes will open mechanosensitive channels to extrude ions during hypo-osmotic induced swelling. This process is dependent on AQP4 and calcium signaling [5]

Mola and coll. provided important results demonstrating the role of AQP4 in astrocyte volume regulation (Mola et al., 2016). In the present study in acute brain slices, when we applied hypotonic solution to induce astrocyte swelling, our protocol did not reveal rapid regulatory volume decrease (e.g., Fig. 3D). When we followed the volume changes of SRB-labeled astrocytes during optogenetically induced CSD, we observed the phase of volume decrease following the transient swelling (Fig. 4F), where the peak amplitude and the degree of recovery were both reduced by inhibiting AQP4 with TGN020. These data imply that regulatory astrocyte volume decrease may occur in specific conditions, which intriguingly has been suggested to be absent in brain slices and in vivo (e.g., Risher et al., 2009). We have not specifically investigated this phenomenon, and now briefly discuss this point on page18 line 6-14.

(6) Figure 5 box plots do not show all data points, could the authors modify to make these plots show all the animals, or edit the legend to clarify what is plotted?

We have now updated the plot and the legend. This plot is from all animals (n = 7 per condition).

(7) pg. 9 line 6, there is a sentence that seems incomplete or otherwise unfinished. "We first followed the evoked water efflux and shrinking induced by hypertonic solution while."

Fixed (now, page 9 line 17-18). 

(8)  During the discussion on pg 13 line 11, it may be more clear to describe this as the cotransport of water into the cells with ions/metabolites as reviewed by Macaulay 2021 [6].

We agree; the text is modified following this suggestion (now page14, line 12-13).  

(1) Iliff, J.J., et al., A Paravascular Pathway Facilitates CSF Flow Through the Brain Parenchyma and the Clearance of Interstitial Solutes, Including Amyloid β. Sci Transl Med, 2012. 4(147): p. 147ra111.

(2) Kress, B.T., et al., Impairment of paravascular clearance pathways in the aging brain. Ann Neurol, 2014. 76(6): p. 845-61.

(3) Mestre, H., et al., Aquaporin-4-dependent Glymphatic Solute Transport in the Rodent Brain. eLife, 2018. 7.

(4) Hablitz, L., et al., Circadian control of brain glymphatic and lymphatic fluid flow. Nature Communications, 2020. 11(1).

(5) Mola, M., et al., The speed of swelling kinetics modulates cell volume regulation and calcium signaling in astrocytes: A different point of view on the role of aquaporins. Glia, 2016. 64(1).

(6) MacAulay, N., Molecular mechanisms of brain water transport. Nat Rev Neurosci, 2021. 22(6): p. 326-344.

We thank the reviewer. These important literatures are now supplemented to the manuscript together with the corresponding revisions.

Reviewer #2 (Recommendations For The Authors):

In its concept, the paper is interesting and provides additional value - however, it requires revision.

Below, I provide the following remarks for the following sections/ pages/lines:

ABSTRACT/page 2 (remarks here refer to the rest of the manuscript, where these sentences are repeated):

- It seems that the 'homeostasis' provides not only physical protection, but also determines the diffusion of chemical molecules...' Please correct the sentence as it is grammatically incorrect.

It is now corrected (page 2, line 1).

- The term 'tonic water' is not clear. I understand, after reading the paper, that it is about tonicity of the solutes injected into the mouse.

We use the term ‘tonic’ to indicate that in basal conditions, a constant water efflux occurs through the APQ4 channel.

- 'tonic aquaporin water efflux maintains volume equilibrium' - I believe it is about maintaining volume and osmotic equilibrium?

This description is now refined (now page 2, line 10).

- It is not clear whether the tonic water outflow refers to the cellular level or outflow from the brain parenchyma (i.e., glymphatic efflux)

It refers to the cellular level. 

INTRODUCTION/page 3:

- 'clearance of waste molecules from the brain as described in the glymphatic system' - The original papers describing the phenomena are not cited: Iliff et al. 2012, 2013, Mestre et al. 2018, as well as reviews by Nedergaard et al.

Indeed. We have now cited these key literatures (now page 4, line 10).

- 'brain water diffusion is the basis for diffusion-weighted magnetic resonance imaging (DW-MRI)' - The statement is wrong. it is the mobility of the water protons that DWI is based on, but not the diffusion of molecules in the brain. This should be clarified and based on the DW-MRI principle and the original works by Le Bihan from 1986, 1988, or 2015.

This sentence is now updated (page 4, line10-14).

- Similarly, I suggest correcting or removing the citations and the sentence part regarding the clinical use of DWI, as it has no value here. Instead, it would be worth mentioning what actually ADC reflects as a computational score, and what were the results from previous studies assessing glymphatic systems using DWI. This is especially important when considering the mislocalization of the AQP4 channel.

We now states recent studies using DW-MRI to evaluate glymphatic systems (page 4, line16-17).  

- 'In the brain, AQP4 is predominantly expressed in astrocytes'-please review the citations. I suggest reading the work by Nielsen 1997, Nagelhus 2013, Wolburg 2011, and Li and Wang from 2017. To my best knowledge, in the brain AQP4 is exclusively expressed in astrocytes.

Thanks for the reviewer. It is described that while enriched in astrocytes, AQP4 is also expressed in ependymal cells lining the ventricles (e.g., (Mayo et al., 2023; Verkman et al., 2006)). ‘predominantly’ is now removed (page 4, line 21).

- The conclusion: ' Our finding suggests that aquaporin acts as a water export route in astrocytes in physiological conditions, so as to counterbalance the constitutive intracellular water accumulation caused by constant transmitter and ion uptake, as well as the cytoplasmic metabolism processes. This mechanism hence plays a necessary role in maintaining water equilibrium in astrocytes, thereby brain water homeostasis' seems to be slightly beyond the actual findings in the paper. I suggest clarifying according to the described phenomena.

We have now refined the conclusion sticking to the experimental observations (page 5, line16-18).

- The introduction lacks important information on existing AQP4 blockers and their effects, pros and cons on why to use TGN-020. Among others, I would refer to recent work by Giannetto et al 2024, as well as previous work of Mestre et al. 2018 and Gomolka et al. 2023.

We initiated the study by using TGN-020 as an AQP4 blocker because it has been validated by wide range of ex vivo and in vivo studies as documented in the text (page 7, line 1-6). We also update discussions on the recent advances in validating the AQP4 blocker AER-270(271) while citing the relevant studies (page 15, line 7-17).  

RESULTS:

- Page 5, lines 19-20: '...transport, we performed fluorescence intensity translated (FIT) imaging.' - this term was never introduced in the methods so it is difficult for the reader to understand it at first sight. -'To this end,' - it is not clear which action refers to 'this'. (is it about previous works or the moment that the brain samples were ready for imaging? Please clarify, as it is only starting to be clear after fully reading the methods.

We now refine the description give the principle of our imaging method first, then explain the technical steps. To avoid ambiguity, the term ‘To this end’ is removed. The updated text is now on page 6, line 1-3.  

- From page 6 onwards - all references to Figures lack information to which part of the figure subpanel the information refers (top/middle bottom or left/middle/right).

We apologize. The complementary indication is now added for figure citations when applicable.  

- 'whereas water export and astrocyte shrinking upon hyperosmotic manipulation increased astrocyte fluorescence (Figure 1B). Hence, FIT imaging enables real-time recording of astrocyte transmembrane water transport and volume dynamics.' - this part seems to be undescribed or not clear in the methods.

We have now refined this description (page 6, line 19-20).

- Page 6, lines 17-22: TGN-020. In addition to the above, I suggest familiarizing also with the following works by Igarashi 2011. doi: 10.1007/s10072-010-0431-1, and by Sun 2022. doi: 10.3389/fimmu.2022.870029.

These studies are now cited (page 7, line 3-4).

- Page 7: ' AQP4 is a bidirectional channel facilitating... ' - AQP4 water channel is known as the path of least resistance for water transfer, please see Manley, Nature Medicine, 2000 and Papadopoulos, Faseb J, 2004.

This sentence is now updated (page 7, line 12-13).

- ' astrocyte AQP4 by TGN-020 caused a gradual decrease in SRB fluorescence intensity, indicating an intracellular water accumulation' - tissue slice experiment is a very valuable method. However it seems right, the experiment does not comment on the cell swelling that may occur just due to or as a superposition of tissue deterioration and the effect of TGN-020. The AQP4 channel is blocked, and the influx of water into astrocytes should be also blocked. Thus, can swelling be also a part of another mechanism, as it was also observed in the control group? I suggest this should be addressed thoroughly.

We performed this experiment in acute brain slices to well control the pharmacological environment and gain spatial-temporal information. Post slicing, the brain slices recovered > 1hr prior to recording, so that the slices were in a stable state before TGN-020 application as evidenced by the stable baseline. The constant decrease in the control trace is due to photobleaching which did not change its curve tendency in response to vehicle. TGN-020, in contrast, caused a down-ward change suggesting intracellular water accumulation and swelling. 

The experiment was performed at basal condition without active water influx; a decrease in SRB fluorescence hints astrocyteintracellular water buildup. This result shows that in basal condition, astrocyte aquaporin mediates a constant (i.e., tonic) water efflux; its blocking causes intracellular water accumulation and swelling. 

We have accordingly updated the description of this part (page 7, line 15-20).

- From the Figure 1 legend: Only 4 mice were subjected to the experiment, and only 1 mouse as a control. I suggest expanding the experiment and performing statistics including two-way ANOVA for data in panels B, C, and D, as no results of statistical tests confirm the significance of the findings provided.

The panel B confirms that cytosolic SRB fluorescence displays increasing tendency upon water efflux and volume shrinking, and vice versa. As for the panel C, the number of mice is now indicated. Also, the downward change in the SRB fluorescence was now respectively calculated for the phases prior and post to TGN (and vehicle) application, and this panel is accordingly updated. TGN-020 induced a declining in astrocyte SRB fluorescence, which is validated by t-test performed in MATLAB. To clarify, we now add cross-link lines to indicate statistical significance between the corresponding groups (Fig 1C, middle). As for panel D, we calculated the SRB fluorescence change (decrease) relative to the photobleaching tendency illustrated by the dotted line. The significance was also validated by t-test performed in MATLAB.  

- Figure 1: Please correct the figure - pictures in panel A are low quality and do not support the specificity of SRB for astrocytes. Panels B-D are easier to understand if plotted as normal X/Y charts with associated statistical findings. Some drawings are cut or not aligned.

In GFAP-EGFP transgenic, astrocytes are labeled by EGFP. SRB labeling (red fluorescence) shows colocalization with EGFP-positive astrocytes, meanwhile not all EGFP-positive astrocytes are labeled by SRB. The PDF conversion procedure during the submission may also somehow have compromised image quality. We have tried to update and align the figure panels.  

- Page 12: ' TGN-020 increased basal water diffusion within multiple regions including the cortex,

hippocampus and the striatum in a heterogeneous manner (Figure 5C).'

This sentence is updated now (page 12, line 12 – page13, line 2). It reads ‘The representative images reveal the enough image quality to calculate the ADC, which allow us to examine the effect of TGN-020 on water diffusion rate in multiple regions (Fig. 5C).’

- The expression of AQP4 within the brain parenchyma is known to be heterogenous. Please familiarize yourself with works by Hubbard 2015, Mestre 2018, and Gomolka 2023. A correlation between ADC score and AQP4 expression ROI-wise would be useful, but it is not substantial to conduct this experiment.

We thank the reviewer. This point is stressed on page 19, line 12-14.

DISCUSSION:

- Most of the issues are commented on above, so I suggest following the changes applied earlier. -Page 16: 'We show by DW-MRI that water transport by astrocyte aquaporin is critical for brain water homeostasis.' This statement is not clear and does not refer to the actual impact of the findings. DWI is allowed only to verify the changes of ADC fter the application of TGN-020. I suggest commenting on the recent report by Giannetto 2024 here.

This sentence is now refined (page 19, line 1-2), followed by the updates commenting on the recent studies employing DW-MRI to evaluate brain fluid transport, including the work of (Giannetto et al., 2024) (page 19, line 3-10). 

METHODS:

- Page 18: no total number of mice included in all experiments is provided, as well as no clearly stated number of mice used in each experiment. Please correct.

We have now double checked the number of the mice for the data presented and updated the figure legends accordingly (e.g., updates in legends fig1, fig5, etc).

-  Page 18, line 7: 'Axscience' is not a producer of Isoflurane, but a company offering help with scientific manuscript writing. If this company's help was used, it should be stated in the acknowledgments section. Reference to ISOVET should be moved from line 15 to line 7.

We apologize. We did not use external writing help, and now have removed the ‘Axcience’. The Isoflurane was under the mark ‘ISOVET’ from ‘Piramal’. This info is now moved up (page 21, line 11). 

- Page 18, line 9: ' modified artificial cerebrospinal fluid (aCSF)'. Additional information on the reason for the modified aCSF would be useful for the reader.

In this modified solution, the concentration of depolarizing ions (Na+, Ca2+) was reduced to lower the potential excitotoxicity during the tissue dissection (i.e., injury to the brain) for preparing the brain slices. Extra sucrose was added to balance the solution osmolarity. This solution has been used previously for the dissection and the slicing steps in adult mice (Jiang et al., 2016). We now add this justification in the text and quote the relevant reference (page 21, line14-16). 

- Page 19, line 6: a reasoning for using Tamoxifen would be helpful for the reader.

The Glast-CreERT2 is an inducible conditional mouse line that expresses Cre recombinase selectively in astrocytes upon tamoxifen injection. We now add this information in the text (page 22, line 10-11). 

- Line 8 - 'Sigma'

Fixed.

- Line 7/8: It is not clear if ethanol is of 10% solution or if proportions of ethanol+tamoxifen to oil were of 1:9. The reasoning for each performed step is missing.

We have now clarified the procedure (page 22, line 11-15).

- Line 10: '/' means 'or'?

Here, we mean the bigenic mice resulting from the crossing of the heterozygous Cre-dependent GCaMP6f and Glast-CreERT2 mouse lines. We now modify it to ‘Glast-CreERT2::Ai95GCaMP6f//WT’, in consistence with the presentation of other mouse lines in our manuscript (page 22, line 16).

- Lines 22-23: being in-line with legislation was already stated at the beginning of the Methods so I suggest combining for clearance.

Done. 

- Page 21, line 4: it is good to mention which printer was used, but it would be worth mentioning the material the chamber was printed from - was it ABS?

Yes. We add this info in the text now (page 24, line 5).

- Line 9 -'PI' requires spelling out.

It is ‘Physik Instrumente’, now added (page 24, line 10).

- Line 11-12: What is the reason for background subtraction - clearer delineation of astrocytes/ increasing SNR in post-processing, or because SRB signal was also visible and changing in the background over time? Was the background removed in each frame independently (how many frames)? How long was the time-lapse and was the F0 frame considered as the first frame acquired? The background signal should be also measured and plotted alongside the astrocytic signal, as a reference (Figure 1). This should be clarified so that steps are to be followed easily.

We sought to follow the temporal changes in SRB fluorescence signal. The acquired fluorescent images contain not only the SRB signals, but also the background signals consisting of for instance the biological tissue autofluorescence, digital camera background noise and the leak light sources from the environments. The value of the background signal was estimated by the mean fluorescence of peripheral cell-free subregions (15 × 15 µm²) and removed from all frames of time-lapse image stack. The traces shown in the figures reflect the full lengths of the time-lapse recordings. F0 was identified as the mean value of the 10 data points immediately preceding the detected fluorescence changes. The text is now updated (page 24 line 21 - page 25 line 5).

- Line 15: Was astrocyte image delineation performed manually or automatically? Where was the center of the region considered in the reference to the astrocyte image? It would be good to see the regions delineated for reference.

Astrocytes labeled by SRB were delineated manually with the soma taken as the center of the region of interest. We now exemplify the delineated region in Fig 1A, bottom.

- Page 22, line 2: 'x4 objective'.

Added (now, page 25, line 16). 

- Line 3: 'barrels' - reference to publication or the explanation missing.

The relevant reference is now added on barrel cortex (Erzurumlu and Gaspar, 2020) (page 25, line 19-20). 

- Line 19: were the coordinates referred to = bregma?

Yes. This info is now added (page 26, line 12). 

- Line 20: was the habituation performed directly at the acquisition date? It is rather difficult to say that it was a habituation, but rather acute imaging. I suggest correcting, that mice were allowed to familiarize themselves with the setup for 30 minutes prior to the imaging start.

In this context, although it is a very nice idea and experiment, the influence of acute stress in animals familiar with the setup only from the day of acquisition is difficult to avoid. It is a major concern, especially when considering norepinephrine as a master driver of neuronal and vascular activity through the brain, and strong activation of the hypothalamic-adrenal axis in response to acute stress. It is well known, that the response of monoamines is reduced in animals subjected to chronic v.s acute stress, but still larger than that if the stressor is absent.

Major remark: The animals should, preferably, be imaged at least after 3 days of habituation based on existing knowledge. I suggest exploring the topic of the importance of habituation. It is difficult though, to objectively review these findings without considering stress and associated changes in vascular dynamics.