INVESTIGAR FUENTE

This emphasizes the significant role of economic capital for student achievement. Economic capital provides students with good education, opportunities for extracurricular activities and security that could be applied towards improving their learning. Social capital comes into play, too because connecting with influencers or belonging to networks can enable you to get access to opportunities that one may not have had access to otherwise. Involvement can translate into mentorship, mentorship, knowledge and these things can motivate students.

This feels a little main character-y? People can have different opinions and surprise you, just like you can do the same.

Author response:

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

Reviewer #1 (Public Review):

Question 1: The experiment that utilizes lactose or glucose supplementation to infer the importance of carbohydrate recognition by galectin-9 cannot be interpreted unequivocally owing to the growth-enhancing effect of lactose supplementation on Mtb during liquid culture in vitro.

Thank you for this very constructive comment. We repeated the experiments by lowering the concentration of lactose or AG from 10 μg/mL to 1 μg/mL. We found that low concentration of lactose or AG showed neglectable effect on Mtb growth, however, they still reversed the inhibitory effect of galectin-9 on mycobacterial growth (revised Fig. 2A, C). Therefore, we consider that the supplementation of lactose or AG reverse galectin-9 mediated inhibition of Mtb growth largely through its carbohydrate recognition rather than their growth-enhancing effect.

Question 2: Similar to the comment above, the apparent dose-independent effect of galectin-9 on Mtb growth in vitro is difficult to reconcile with the interpretation that galectin is functioning as claimed.

We thank the reviewer for the correction. Indeed, as the reviewer pointed out, galectin-9 inhibits Mtb growth in dose-independent manner. We had corrected the claim in the revised manuscript (Line 114).

Question 3: The claimed differences in galectin-9 concentration in sera from tuberculin skin test (TST)-negative or TST-positive non-TB cases versus active TB patients are not immediately apparent from the data presented.

We appreciate your concern. Previous samples are from a cohort set up in Max Plank Institute for Infection Biology. We have performed the detection of galectin-9 in sera in another independent cohort of active TB patients and healthy donors in China. And we found higher abundance of galectin-9 in serum from TB patients than that from heathy donors (revised Fig. 1E).

Question 4: Neither fluorescence microscopy nor electron microscopy analyses are supported by high-quality, interpretable images which, in the absence of supporting quantitative data, renders any claims of anti-AG mAb specificity (fluorescence microscopy) or putative mAb-mediated cell wall swelling (electron microscopy) highly speculative.

We appreciate your concern. We have improved the procedure of the immunofluorescence assay and obtained high-quality and interpretable images with quantitative data (revised Fig. 4F). As for electron microscopy analyses, we added clearer label indicating cell wall in revised manuscript (revised Fig. 7C).

Question 5: Finally, the absence of any discussion of how anti-AG antibodies (similarly, galectin-9) gain access to the AG layer in the outer membrane of intact Mtb bacilli (which may additionally possess an extracellular capsule/coat) is a critical omission - situating these results in the context of current knowledge about Mtb cellular structure (especially the mycobacterial outer membrane) is essential for plausibility of the inferred galectin-9 and anti-AG mAb activities.

Exactly, AG is hidden by mycolic acids in the outer layer of Mtb cell wall. As we have discussed in the Discussion part of previous manuscript (line 285), we speculate that during Mtb replication, cell wall synthesis is active and AG becomes exposed, thereby facilitating its binding to galectin-9 or AG antibody and leading to Mtb growth arrest. It’s highly possible that galectin-9 or AG antibody targets replicating Mtb.

To Reviewer #2 (Public Review):

Question 1: In light of other observations that cleaved galectin-9 levels in the plasma is a biomarker for severe infection (Padilla A et al Biomolecules 2021 and Iwasaki-Hozumi H et al. Biomoleucles 2021) it is difficult to reconcile the author's interpretation that the elevated gal-9 in Active TB patients (Figure 1E) contributes to the maintenance of latent infection in humans. The authors should consider incorporating these observations in the interpretation of their own results.

Thank you for these very insightful comments. We observed elevated levels of galectin-9 in the serum of active TB patients, consistent with reports indicating that cleaved galectin-9 levels in the serum serve as a biomarker for severe infection (Iwasaki-Hozumi et al., 2021; Padilla et al., 2020). We consider that the elevated levels of galectin-9 in the serum of active TB may be an indicator of the host immune response to Mtb infection, however, the magnitude of elevated galectin-9 is not sufficient to control Mtb infection and maintain latent infection. This is highly similar to other protective immune factors such as interferon gamma, which is elevated in active TB as well (El-Masry et al., 2007; Hasan et al., 2009). We have included the discussion in the revised manuscript (line 298).

Question 2: The anti-AG titers were measured only in individuals with active TB (Figure 3C), generally thought to be a less protective immunological state. The speculation that individuals with anti-AG titers have some protection is not founded. Further only 2 mAbs were tested to demonstrate restriction of Mtb in culture. It is possible that clones of different affinities for AG present within a patient's polyclonal AG-antibody responses may or may not display a direct growth restriction pressure on Mtb in culture. The authors should soften the claims about the presence of AG-titers in TB patients being indicative of protection.

We appreciate your concern. As per your suggestion, we have softened the claim to that “We speculate that during Mtb infection, anti-AG IgG antibodies are induced, which potentially contribute to protection against TB by directly inhibiting Mtb replication albeit seemingly in vain.”

References

El-Masry, S., Lotfy, M., Nasif, W.A., El-Kady, I.M., and Al-Badrawy, M. (2007). Elevated serum level of interleukin (IL)-18, interferon (IFN)-gamma and soluble Fas in patients with pulmonary complications in tuberculosis. Acta microbiologica et immunologica Hungarica 54, 65-77.

Hasan, Z., Jamil, B., Khan, J., Ali, R., Khan, M.A., Nasir, N., Yusuf, M.S., Jamil, S., Irfan, M., and Hussain, R. (2009). Relationship between circulating levels of IFN-gamma, IL-10, CXCL9 and CCL2 in pulmonary and extrapulmonary tuberculosis is dependent on disease severity. Scandinavian journal of immunology 69, 259-267.

Iwasaki-Hozumi, H., Chagan-Yasutan, H., Ashino, Y., and Hattori, T. (2021). Blood Levels of Galectin-9, an Immuno-Regulating Molecule, Reflect the Severity for the Acute and Chronic Infectious Diseases. Biomolecules 11.

Padilla, S.T., Niki, T., Furushima, D., Bai, G., Chagan-Yasutan, H., Telan, E.F., Tactacan-Abrenica, R.J., Maeda, Y., Solante, R., and Hattori, T. (2020). Plasma Levels of a Cleaved Form of Galectin-9 Are the Most Sensitive Biomarkers of Acquired Immune Deficiency Syndrome and Tuberculosis Coinfection. Biomolecules 10.

Sin embargo, aunque sea el apartado central y que expone de manera práctica el enfoque adoptado, presenta solamente 28 ítems didácticos frente a al total de ítems del libro que es de 145. Los ítems de las tareas representan un 19,31%, porcentaje este muy pequeño para un apartado nombrado de acuerdo al enfoque que se propone. Obviamente los apartados anteriores a este están en razón de las tareas que se propondrá, pero los que están pospuestos a “tareas” no tienen relación con las tareas que se propusieron

sera?

individual as unit of analysis from which we start the empirical investigation- considering the whole is just a starting point- Not necessarily non-Durkheimien?

Method involves finding purely rational course of action and seeing irrational influences to work out their impact.

I think this is the hard part of reality that students will hide their true colors so they won't get judged from reality or have their school life be based on that. Students come to school for it to be a safe place and feel comfortable or put on a fake persona because the moment they go home they go back to reality. It's hard living a double life especially when you're able to see others do things or get things you've always wanted. I can say that i've experienced the we don't have enough for that and just shop for necessities, but i've been able to see others not being able to afford other things. It is a sad reality and wishing we can help everyone.

DOI: 10.1186/s13041-024-01145-y

Resource: (IMSR Cat# JAX_017688,RRID:IMSR_JAX:017688)

Curator: @dhovakimyan1

SciCrunch record: RRID:IMSR_JAX:017688

What is this?

C'est une petite magazine Il parle de communication, il aide les enterprises C'est un texte informatif

not a circular argument at all!

La medición nos permite valorar el cumplimiento de los objetivos. La data es clave para todo proyecto. Su valor radica en los fríos números, que no toman posiciones. Si algo va bien, se reflejará en cifras sólidas. Caso contrario, se debe tomar medidas.

Comentario de Santiago Sarango

Descubrimiento

ESTO ES INTERESANTE

Humildad aceptando la corrección, hacer los ajustes y adorar libremente

Significa adorarlo por puro amor y devoción, y no simplemente por obligación o porque nos parezca lo correcto. También implica mantener vivo ese amor durante toda la vida

Los reyes que Jehová consideraba fieles lo amaban con todo su corazón. Nota La Biblia a menudo utiliza la palabra corazón para describir todo lo que una persona es por dentro, lo que incluye sus deseos, pensamientos, inclinaciones, actitudes, habilidades, motivos y metas. Ejemplos, - Jehosafat “buscó a Jehová con todo su corazón” (2 Crón. 22:9). - Josías: “Antes de él no hubo ningún rey como él, que volviera a Jehová con todo su corazón” (2 Rey. 23:25). - Salomón, “no sirvió a Jehová su Dios con un corazón completo” (1 Rey. 11:4). - Abiyam, “No sirvió a Jehová su Dios con un corazón completo” (1 EhempliRey. 15:3).

si todos los reyes hacían cosas buenas y cosas malas, ¿por qué Jehová decía que algunos eran fieles y otros no? ¿Cuál era la diferencia? Tres aspectos claves - amor - arrepentimiento - adoración pura

Que hubieron Reyes buenos y Reyes malos, y que incluso Reyes buenos hicieron cosas malas, cómo David, y también que reyes malos hicieron cosas buenas como Rehoboam - (1 Rey. 14:8). - (2 Sam. 11:4, 14,15). - (2 Crón. 12:14).<br /> - (1 Rey. 12:21-24; - (2Crón. 11:5-12).

Pero no explicas porque w([v]) = 0. Recuerda que en lugar de considerar un v∈E arbitrario era mejor estudiar los vᵢ de la base de E. Así podías seguirle el rastro y ver que todos llegaban a 0.

This is where the preposterousness of what's being proposed really comes through. Experts are too expert-y, so the solution is to rely upon anti-experts (not novices).

Esta parte del artículo me pareció muy interesante; intuitivamente se piensa que la fotosíntesis a sido un gran regulador del CO2 y oxígeno en los primeros años de la Tierra, no obstante, este párrafo da brinda otra explicación interesante. Considero que no debe ser descartable esta posibilidad ya que la Tierra y todas las interaciones que hoy conocemos eran totalmente diferentes, hace 500 millones de años, en el Cambrico. Lo importante es no cegarse ante una o dos posibles explicaciones, sino más bien, tomar los mejor de ellas, e intentar dar explicación de estos sucesos para entender procesos actuales.

DOI: 10.1038/s41523-024-00690-y

Resource: University of Pennsylvania Penn Vet Comparative Pathology Core Facility (RRID:SCR_022438)

Curator: @scibot

SciCrunch record: RRID:SCR_022438

What is this?

Is the y-axis showing intensity normalized?

Author response:

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

eLife assessment

This valuable study revisits the effects of substitution model selection on phylogenetics by comparing reversible and non-reversible DNA substitution models. The authors provide evidence that 1) non time-reversible models sometimes perform better than general time-reversible models when inferring phylogenetic trees out of simulated viral genome sequence data sets, and that 2) non time-reversible models can fit the real data better than the reversible substitution models commonly used in phylogenetics, a finding consistent with previous work. However, the methods are incomplete in supporting the main conclusion of the manuscript, that is that non time-reversible models should be incorporated in the model selection process for these data sets.

The non-reversible models should be incorporated in the selection model process not because the significantly perform better but only because the do not perform worse than the reversible models and that true biochemical processes of nucleotide substitution does support the science of non-reversibility.

Reviewer #1 (Public Review):

The study by Sianga-Mete et al revisits the effects of substitution model selection on phylogenetics by comparing reversible and non-reversible DNA substitution models. This topic is not new, previous works already showed that non-reversible, and also covarion, substitution models can fit the real data better than the reversible substitution models commonly used in phylogenetics. In this regard, the results of the present study are not surprising. Specific comments are shown below.

True.

Major comments

It is well known that non-reversible models can fit the real data better than the commonly used reversible substitution models, see for example,

https://academic.oup.com/sysbio/article/71/5/1110/6525257

https://onlinelibrary.wiley.com/doi/10.1111/jeb.14147?af=R

The manuscript indicates that the results (better fitting of non-reversible models compared to reversible models) are surprising but I do not think so, I think the results would be surprising if the reversible models provide a better fitting.

I think the introduction of the manuscript should be increased with more information about non-reversible models and the diverse previous studies that already evaluated them. Also I think the manuscript should indicate that the results are not surprising, or more clearly justify why they are surprising.

The surprise in the findings is in NREV12 performing better than NREV6 for double stranded DNA viruses as it was expected that NREV6 would perform better given the biochemical processes discussed in the introduction.

In the introduction and/or discussion I missed a discussion about the recent works on the influence of substitution model selection on phylogenetic tree reconstruction. Some works indicated that substitution model selection is not necessary for phylogenetic tree reconstruction, https://academic.oup.com/mbe/article/37/7/2110/5810088 https://www.nature.com/articles/s41467-019-08822-w https://academic.oup.com/mbe/article/35/9/2307/5040133

While others indicated that substitution model selection is recommended for phylogenetic tree reconstruction, https://www.sciencedirect.com/science/article/pii/S0378111923001774 https://academic.oup.com/sysbio/article/53/2/278/1690801 https://academic.oup.com/mbe/article/33/1/255/2579471

The results of the present study seem to support this second view. I think this study could be improved by providing a discussion about this aspect, including the specific contribution of this study to that.

In our conclusion we have stated that: The lack of available data regarding the proportions of viral life cycles during which genomes exist in single and double stranded states makes it difficult to rationally predict the situations where the use of models such as GTR, NREV6 and NREV12 might be most justified: particularly in light of the poor over-all performance of NREV6 and GTR relative to NREV12 with respect to describing mutational processes in viral genome sequence datasets. We therefore recommend case-by-case assessments of NREV12 vs NREV6 vs GTR model fit when deciding whether it is appropriate to consider the application of non-reversible models for phylogenetic inference and/or phylogenetic model-based analyses such as those intended to test for evidence of natural section or the existence of molecular clocks.

The real data was downloaded from Los Alamos HIV database. I am wondering if there were any criterion for selecting the sequences or if just all the sequences of the database for every studied virus category were analysed. Also, was any quality filter applied? How gaps and ambiguous nucleotides were considered? Notice that these aspects could affect the fitting of the models with the data.

We selected varying number of sequences of the database for every studied virus type. Using the software aliview we did quality filter by re-aligning the sequences per virus type.

How the non-reversible model and the data are compared considering the non-reversible substitution process? In particular, given an input MSA, how to know if the nucleotide substitution goes from state x to state y or from state y to state x in the real data if there is not a reference (i.e., wild type) sequence? All the sequences are mutants and one may not have a reference to identify the direction of the mutation, which is required for the non-reversible model. Maybe one could consider that the most abundant state is the wild type state but that may not be the case in reality. I think this is a main problem for the practical application of non-reversible substitution models in phylogenetics.

True.

Reviewer #2 (Public Review):

The authors evaluate whether non time reversible models fit better data presenting strand-specific substitution biases than time reversible models. Specifically, the authors consider what they call NREV6 and NREV12 as candidate non time-reversible models. On the one hand, they show that AIC tends to select NREV12 more often than GTR on real virus data sets. On the other hand, they show using simulated data that NREV12 leads to inferred trees that are closer to the true generating tree when the data incorporates a certain degree of non time-reversibility. Based on these two experimental results, the authors conclude that "We show that non-reversible models such as NREV12 should be evaluated during the model selection phase of phylogenetic analyses involving viral genomic sequences". This is a valuable finding, and I agree that this is potentially good practice. However, I miss an experiment that links the two findings to support the conclusion: in particular, an experiment that solves the following question: does the best-fit model also lead to better tree topologies?

By NREV12 leading to inferred trees that are closer to the true generating tree as compared to GTR, it then shows that the best-fit model in this case being NREV12 leads to better tree topologies.

On simulated data, the significance of the difference between GTR and NREV12 inferences is evaluated using a paired t test. I miss a rationale or a reference to support that a paired t test is suitable to measure the significance of the differences of the wRF distance. Also, the results show that on average NREV12 performs better than GTR, but a pairwise comparison would be more informative: for how many sequence alignments does NREV12 perform better than GTR?

We have used the popular paired t-test as it is the most widely used when comparing means values between two matched samples where the difference of each mean pair is normally distributed. And the wRF distances do match the guidelines above.

The paired t-test contains the pairwise comparison and the boxplots side by side show the pairwise wRF comparisions..

Reviewer #1 (Recommendations For The Authors):

Minor comments

The reversible and non-reversible models used in this study assume that all the sites evolve under the same substitution matrix, which can be unrealistic. This aspect could be mentioned.

Done.

The manuscript indicates that "a phylogenetic tree was inferred from an alignment of real sequences (Avian Leukosis virus) with an average sequence identity (API) of ~90%.". I was wondering under which substitution model that phylogenetic tree reconstruction was performed? could the use of that model bias posterior results in terms of favoring results based on such a model?

We have stated on page ….. that the GTR+G model was used to reconstruct the tree. The use of the GTR+G model could yes bias the posterior results as we have stated on page ….

I was wondering which specific R function was used to calculate the weighted Robinson-Foulds metric. I think this should be included in the manuscript.

We stated that We used the weighted Robinson-Foulds metric (wRF; implemented in the R phangorn package (Schliep, 2011)⁠)

Despite a minority, several datasets fitted better with a reversible model than with a non-reversible model. I think that should be clearly indicated.

In addition, in my opinion the AIC does not enough penalizes the number of parameters of the models and favors the non-reversible models over the reversible models, but this is only my opinion based on the definition of AIC and it is not supported. Thus, I think the comparison between phylogenetic trees reconstructed under different substitution models was a good idea (but see also my second major comment).

Noted.

When comparing phylogenetic trees I was wondering if one should consider the effect of the estimation method and quality of the studied data? For example, should bootstrap values be estimated for all the ancestral nodes and only ancestral nodes with high support be evaluated in the comparison among trees?

Yes the estimation method and quality of the studied data should be considered. When using RF unlike wRF this will not matter but for weighted RF it does. When building the trees, using RaxML only high support nodes are added to the tree.

In Figure 3, I do not see (by eye) significant differences among the models. I see in the legend that the statistical evaluation was based on a t test but I am not much convinced. Maybe it is only my view. Exactly, which pairs of datasets are evaluated with the t test? Next, I would expect that the influence of the substitution model on the phylogenetic tree reconstruction is higher at large levels of nucleotide diversity because with more substitution events there is more information to see the effects of the model. However, the t test seems to show that differences are only at low levels of nucleotide diversity (and large DNR), what could be the cause of this?

The paired T-tests compares the wRF distances of the inferred tree real tree and the trees simulated using the GTR model verses the wRF distances of the inferred true tree from the trees simulated using the NREV12 model.

The reason why the influence of the NREV12 model on the tree reconstructed is not significantly higher at large levels of nucleotide diversity could be because at a certain level the DNR are simply unrealistic.

Can the user perform substitution model selection (i.e., AIC) among reversible and non-reversible substitution models with IQTREE? If yes, then doing that should be the recommendation from this study, correct?

But, can DNR be estimated from a real dataset? DNR seems to be the key factor (Figure 3) for the phylogenetic analysis under a proper model.

Substitution model selection can be performed among reversible and non-reversible using both HyPhy and IQTREE. And we have recommended that model tests should be done as a first step before tree building. Estimating DNR from real datasets requires a substation rate matrix of a non-reversible.

The manuscript has many text errors (including typos and incorrect citations). For example, many citations in page 20 show "Error! Reference source not found.". I think authors should double check the manuscript before submitting. Also, some text is not formally written. For example, "G represents gamma-distributed rates", rates of what? The text should be clear for readers that are not familiar with the topic (i.e., G represents gamma-distributed substitution rates among sites). In general, I recommend a detailed revision of the whole text of the manuscript.

Done.

Reviewer #2 (Recommendations For The Authors):

The authors reference Baele et al., 2010 for describing NREV6 and NREV12. I suggest using the same name used in the referenced paper: GNR-SYM and GNR respectively. Although I do not think there is a standard name for these models, I would use a previously used one.

We have built studies based on the names NREV6 and NREV12. We would like to keep the naming as standard for our studies.

GTR and NREV12 models are already described in many other papers. I do not see the need to include such an extensive description. Also, a reference should be included to the discrete Gamma rate categories [1]

We included the extensive description to enable other readers who are not super familiar with these models better understanding since we have given the models our own naming different from those used in other papers.

We have added referencing for the discrete gamma rate as recommended. (Yang, 1994)

To evaluate the exhaustiveness and correctness of the results, I would recommend publishing as supplementary material the simulated data sets or the scripts for generating the data set, the scripts or command lines for the analysis, and the versions of the software used (e.g., IQTREE). Also, to strongly support the main conclusion of the manuscript, I suggest adding to the simulations section results the RF-distances of the best-fit selected model under AIC, AICc, and BIC as well.

We can go ahead and submit all the needed datasets. The simulated data RF-Distances results are available and will be submitted. We cannot however add them to the main document as this will create very long data tables.

In some instances, it is mentioned that the selection criterion used is AIC, while in others, AIC-c is referenced. Even in the table captions, both terms are mixed. It should be made clearer which criterion is being employed, as AIC is not suitable for addressing the overparameterization of evolutionary models, given that it does not account for the sample size. A previous pre-print of this article [2] does not mention AIC-c, but also explicitly includes the formulas for AIC that do not take the sample size into account, and reports the same results as this manuscript, what indicates that AIC and not AIC-c was used here. This should be clarified. It is recommended to use AIC-c instead of AIC, especially if the sample size to model parameters ratio is low [3]. Two things may be appointed here: some authors consider tree branch lengths as model free parameters and others do not. In this paper it is not specified how the model parameters are counted. AIC tends to select more parameterized models than AIC-c, and overparameterization can lead to different tree inferences, as evidenced in Hoff et al., 2016. Therefore, it is expected that NREV12 is more frequently selected than NREV6 and GTR.

In my opinion, a pairwise comparison between GTR and NREV12 performance is of great interest here, and the whiskers plots are not useful. Scatterplots would display the results better.

Boxplots are meant to offer a simplified view of the results as the paired t-tests does all of the comparisons. We shall provide the scatter plots as supplementary information so that readers can get full detailed plots as recommended.

Some references are missing

Missing references added

Author response:

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

Reviewer 1:

Summary

This study has as its goal to determine how the structure and function of the circuit that stabilizes gaze in the larval zebrafish depends on the presence of the output cells, the motor neurons. A major model of neural circuit development posits that the wiring of neurons is instructed by their postsynaptic cells, transmitting signals retrogradely on which cells to contact and, by extension, where to project their axons. Goldblatt et al. remove the motor neurons from the circuit by generating null mutants for the phox2a gene. The study then shows that, in this mutant that lacks the isl1-labelled extraocular motor neurons, the central projection neurons have 1) largely normal responses to vestibular input; 2) normal gross morphology; 3) minimally changed transcriptional profiles. From this, the authors conclude that the wiring of the circuit is not instructed by the output neurons, refuting the major model.

Strengths

I found the manuscript to be exceptionally well-written and presented, with clear and concise writing and effective figures that highlight key concepts. The topic of neural circuit wiring is central to neuroscience, and the paper's findings will interest researchers across the field, and especially those focused on motor systems. 

The experiments conducted are clever and of a very high standard, and I liked the systematic progression of methods to assess the different potential effects of removing phox2a on circuit structure and function. Analyses (including statistics) are comprehensive and appropriate and show the authors are meticulous and balanced in most of the conclusions that they draw. Overall, the findings are interesting, and with a few tweaks, should leave little doubt about the paper's main conclusions. 

We are grateful for the Reviewer’s enthusiasm for our manuscript and recognition of the advance to the vestibular and motor systems fields. We particularly appreciate their suggestions for experiments to improve the characterization of our phox2a mutant line. We hope the Reviewer finds the results of the added experiment adequately address the points they raise. 

Weaknesses/Recommendations

(1) The main point is the incomplete characterisation of the effects of removing phox2a on the extra-ocular motor neurons. Are these cells no longer there, or are they there but no longer labelled by isl1:GFP? If they are indeed removed, might they have developed early on, and subsequently lost? These questions matter as the central focus of the manuscript is whether the presence of these cells influences the connectivity and function of their presynaptic projection neurons. Therefore, for the main conclusions to be fully supported by the data, the authors would need to test whether 1) the motor neurons that otherwise would have been labelled by the isl1:GFP line are physically no longer there; 2) that this removal (if, indeed, it is that) is developmental. If these experiments are not feasible, then the text should be adjusted to take this into account. 

Show (e.g., with DAPI or some other staining) whether there are still cells where you would have expected to see nIII/nIV extraocular motor neurons. If this is done in a developmental timeline both main "concerns" are addressed in one go. If this doesn't work for some reason, then I'd suggest adjusting the discussion section to note this caveat. I realise it is commonplace in zebrafish and rodent papers to equate the two, but it should also be considered that the isl1:GFP does not report which cells are isl1+ 100% faithfully. 

We thank the Reviewer for their suggestion. We’ve included the results of this experiment in (new) Supplemental Figure 1 and have updated the Results accordingly (text lines 69-72). 

Briefly: We performed fluorescent in situ hybridization for vachta, a marker for cholinergic motor neurons, when nIII/nIV differentiation is complete at 2 dpf and prior to synaptogenesis with both their pre- and postsynaptic partners. We included a DAPI stain. We find that while phox2a does not physically remove neurons from the region that contains nIII/nIV motor neurons, neurons in this region no longer express vachta. The presence of neurons at an early stage (2dpf) that have lost expression of both a transcription factor (isl1) and motor neuron marker (vachta) supports our contention that, while cells are there, they should not be considered motor neurons.

While the reviewer did not suggest it directly, we note that there is a more laborious way to determine “what happens to cells that would have been phox2a+ but no longer express phox2a?” Specifically, one could target a reporter transgene to the endogenous phox2a locus on the phox2a mutant background. Regrettably, generating such a knock-in reporter is difficult and success is far from assured.

Previously (Greaney et. al. 2017, 10.1002/cne.24042 ), we compared expression patterns in nIV to those observed after retro-orbital dye fills. We never saw neurons labeled by dye that were not also GFP+. However, it was not possible to perform a similar analysis for nIII, so we acknowledge the limits of the isl1:GFP reporter.

(2) A further point to address is the context of the manipulation. If the phox2a removal does indeed take out the extra-ocular motor neurons, what percentage of postsynaptic neurons to the projection neurons are still present?

In other words, how does the postsynaptic nMLF output relate to the motor neurons? If, for instance, the nMLF (which, as the authors state, are likely still innervated by the projection neurons) are the main output of the projections neurons, then this would affect the interpretation of the results.

Is there quantitative information on the projection neuron outputs to address the second point (i.e., how much of the projection neurons' output is the extra-ocular motor neurons)? If not, it should be discussed how this could affect the conclusions. 

Qualitatively, projection neurons form more robust arbors to the nMLF than to their nIII/nIV partners (see: Schoppik et al. 2017, DOI: 10.1523/JNEUROSCI.1711-17.2017 ). We expect this is proportional to the size of each downstream target. 

The Reviewer makes an interesting point here. These projection neurons innervate several downstream nuclei that could potentially influence their development; we’ve considered this in the Discussion based on existing literature and in the context of our own findings. A precise dissection of each target population’s contribution would be interesting and important for larger questions about neural circuits for balance (see Sugioka et. al. 2023 10.1038/s41467-023-36682-y ). However, we feel this analysis is outside our study’s scope, given that our aim here was to evaluate a standing hypothesis restricted to the contribution of nIII/nIV motor neurons. 

Less important, but still useful: 

- Figure 4C/D: I found these panels difficult to interpret. Perhaps split them up so each panel does a little less heavy lifting? Do the main panels in C show all axons? Where are the "two remaining nIII/nIV neurons" in D? 

We’ve split the panels in 4C as suggested and adjusted the caption text in 4D to clarify the “remaining neurons” were simply not eliminated following phox2a knockout. We presume they are instead phox2b+. 4C shows all axons labeled by our transgenic line that follow the medial longitudinal fasciculus.  

Extremely minor: 

- line 28: "tantamount" --> "paramount"? 

- some figure legends say DeltaFF, instead of DeltaF/F 

- line 192: "the any" 

These have been corrected; we thank the reviewer for their attention to detail. 

Reviewer 2:

Summary

This study was designed to test the hypothesis that motor neurons play a causal role in circuit assembly of the vestibulo-ocular reflex circuit, which is based on the retrograde model proposed by Hans Straka. This circuit consists of peripheral sensory neurons, central projection neurons, and motor neurons. The authors hypothesize that loss of extraocular motor neurons, through CRISPR/Cas9 mutagenesis of the phox2a gene, will disrupt sensory selectivity in presynaptic projection neurons if the retrograde model is correct. 

Strengths

The work presented is impressive in both breadth and depth, including the experimental paradigms. Overall, the main results were that the loss of function paradigm to eliminate extraocular motor neurons did not 1) alter the normal functional connections between peripheral sensory neurons and central projection neurons, 2) affect the position of central projection neurons in the sensorimotor circuit, or 3) significantly alter the transcriptional profiles of central projection neurons. Together, these results strongly indicate that retrograde signals from motor neurons are not required for the development of the sensorimotor architecture of the vestibulo-ocular circuit. 

We are grateful for the excellent summary of our manuscript and support for our aim, which was indeed to evaluate Hans Straka’s model for the development of the vestibulo-ocular reflex circuit.  

Appraisal of whether the authors achieved their aims, and whether the results support their conclusions The results of this study showed that extraocular motor neurons were not required for central projection neuron specification in the vestibulo-ocular circuit, which countered the prevailing retrograde hypothesis proposed for circuit assembly. A concern is that the results presented may be limited to this specific circuit and may not be generalizable to other circuit assemblies, even to other sensorimotor circuits. 

Impact

As mentioned above, this study sheds valuable new insights into the developmental organization of the vestibulo-ocular circuit. However, different circuits likely utilize various mechanisms, extrinsic or intrinsic (or both), to establish proper functional connectivity. So, the results shown here, although begin to explain the developmental organization of the vestibulo-ocular circuit, are not likely to be generalizable to other circuits; though this remains to be seen. At a minimum, this study provides a starting point for the examination of patterning of connections in this and other sensorimotor circuits.

Weaknesses/Recommendations

A concern is that the results presented may be limited to this specific circuit and may not be generalizable to other circuit assemblies, even to other sensorimotor circuits. However, different circuits likely utilize various mechanisms, extrinsic or intrinsic (or both), to establish proper functional connectivity. So, the results shown here, although begin to explain the developmental organization of the vestibulo-ocular circuit, are not likely to be generalizable to other circuits; though this remains to be seen. 

We agree with the Reviewer that — of course — a diverse array of developmental mechanisms shape sensorimotor circuit architecture. However, prior findings in the spinal cord (Wang & Scott 2000, Sürmeli et al. 2011, Bikoff et al. 2016, Sweeney et al. 2018, Shin et al. 2020) support our primary conclusion that motor neurons are dispensable for specification of premotor partners. The Recommendation ends with “though this remains to be seen.” We infer that the Reviewer does not have a counterexample at hand for a circuit where motor neurons determine the fate of their partners. Therefore, the preponderance of evidence argues that our work is likely to generalize to other circuits. However, we acknowledge the limitations of our work and we have tempered any claims to generality in the text.

Lines 156-57: The authors should consider and discuss explicitly the potential of compensatory mechanisms in the CRISPR/Cas9 mutants that may permit synaptogenesis of the projection neurons even though MNs partners are absent. 

We agree with the Reviewer that careful consideration of compensation is merited when using mutants. There are two synapses that the comment might refer to: those between projection neurons and motor neurons, and those between sensory afferents and projection neurons. Projection neurons fail to form any synapses at the region that would contain their motor neuron (nIII/nIV) partners (see Fig. 4C), so there is no question of compensation there. Figure 1B shows that there is no phox2a expression in sensory or central projection neurons. Consequentially, even if there were a gene that perfectly compensated for the loss of phox2a it wouldn’t be active in sensory or central projection neurons. We therefore do not believe that compensatory expression of other genes plays any role here. 

Line 162: Is this an accurate global statement or should it be restricted to the evidence provided in this report?

We’ve clarified this line, which summarizes findings described in previous results sections of this report.

Reviewer 3:

Summary

In this manuscript by Goldblatt et al. the authors study the development of a well-known sensorimotor system, the vestibulo-ocular reflex circuit, using Danio rerio as a model. The authors address whether motor neurons within this circuit are required to determine the identity, upstream connectivity and function of their presynaptic partners, central projection neurons. They approach this by generating a CRISPR-mediated knockout line for the transcription factor phox2a, which specifies the fate of extraocular muscle motor neurons. After showing that phox2a knockout ablates these motor neurons, the authors show that functionally, morphologically, and transcriptionally, projection neurons develop relatively normally.

Overall, the authors present a convincing argument for the dispensability of motor neurons in the wiring of this circuit, although their claims about the generalizability of their findings to other sensorimotor circuits should be tempered. The study is comprehensive and employs multiple methods to examine the function, connectivity and identity of projection neurons.

We appreciate the Reviewer’s support for our manuscript and have implemented their thoughtful suggestions on how to improve the clarity and presentation of our conclusions. We acknowledge the shared consideration with Reviewer 2 as to the generalizability of our findings, and have tempered the language in our revision. 

In the introduction the authors set up the controversy on whether or not motor neurons play an instructive role in determining "pre-motor fate". This statement is somewhat generic and a bit misleading as it is generally accepted that many aspects of interneuron identity are motor neuron-independent. The authors might want to expand on these studies and better define what they mean by "fate", as it is not clear whether the studies they are citing in support of this hypothesis actually make that claim. 

We appreciate the Reviewer’s attention to this important consideration. We agree that there are numerous, and often ambiguous ways to define cell fate. We’ve modified our manuscript to read  “…for and against an instructive role in establishing connectivity” (line 19) to reflect that connectivity is the most pertinent readout of cell fate in (most) studies cited there, as well as in our model system (lines 25-26: “Subtype fate, anatomical connectivity, and function are inextricably linked: directionally-tuned sensory neurons innervate nose-up/nosedown subtypes of projection neurons, which in turn innervate specific motor neurons…”). We’ve expanded on the prior studies mentioned above in relevant sections of the Results and Discussion. 

Although it appears unchanged from their images, the authors do not explicitly quantitate the number of total projection neurons in phox2a knockouts. 

We have added this quantification (text lines 95-96); the number of projection neurons per hemisphere is unchanged in control and mutant larvae.

For figures 2C and 3C, please report the proportion of neurons in each animal, either showing individual data points here or in a separate supplementary figure; and please perform and report the results of an appropriate statistical test. 

Generally, we agree that per-animal sampling can provide important metrics. We’ve added a line in the appropriate Methods section with the mean/standard deviation number of neurons sampled per animal for each genotype (lines 408-410). However, our extensive prior work using this transgenic line (Goldblatt et al. 2023, DOI: 10.1016/j.cub.2023.02.048 ) argued that a per-animal breakdown can be misleading. Due to occasional technical aberrations, variation in transgenic line expression, and limitations of our registration method, we cannot sample 100% of the projection nucleus (~50 neurons/hemisphere) in each animal. Likewise, the topography of the nucleus in WT animals, both for up/down subtypes (Fig. 2) and impulse responsive/unresponsive neurons (Fig. 3), means that subtypes may not be proportionally sampled on a peranimal basis. While such problems would likely resolve if we took data from ~50-75 animals for each condition, at a throughput of ~2 animals/day and 1-2 experimental days / week on shared instrumentation the throughput simply isn’t there. We therefore believe the data is best represented as an aggregate.

In the topographical mapping of calcium responses (figures 2D, E and 3D), the authors say they see no differences but this is hard to appreciate based on the 3D plotting of the data. Quantitating the strength of the responses across the 3-axes shown individually and including statistical analyses would help make this point, especially since the plots look somewhat qualitatively different. 

We have added a supplemental table (new Table 2) with statistical comparisons of projection neuron topography (both to tonic and impulse stimuli) across genotypes for additional clarification. Quantitatively, we find that differences in projection neuron position (max observed: approx. 5 microns) are within the limits of our expected error in registering neurons across larvae to a standardized framework, given the small size of the nucleus (approx. 40 microns in each spatial axis) and each individual neuron (approx. 5 microns in diameter).

The transcriptional analysis is very interesting, however, it is not clear why it was performed at 72 hpf, while functional experiments were performed at 5 days. Is it possible that early aspects of projection neuron identity are preserved, while motor neuron-dependent changes occur later? The authors should better justify and discuss their choice of timepoint. 

As suggested, we have updated the manuscript to justify the choice of timepoint (text lines 176-177). We agree with the Reviewer that choosing the “right” timepoint for transcriptional analysis is key. The comment underscores the challenges in balancing the amount of time past neurogenesis (24-54 hpf) when potential fate markers could change, with the timecourse of synaptogenesis (2-4 dpf) and functional maturation (5 dpf). We hypothesized that selecting an intermediate timepoint (72 hpf, during peak synaptogenesis), would enable the highest resolution of both fate markers expressed at the end of neurogenesis (54 hpf) and wiring specificity molecules. We point the Reviewer to recent studies in comparable systems that proposed subtype diversity is most resolvable during synaptogenesis as further justification (see: Ozel et al. 2022, DOI: 10.1038/s41586-020-2879-3 and Li et al. 2017, DOI: 10.1016/j.cell.2017.10.019). However, we acknowledge that the ideal experiment would have been a transcriptional timecourse that would have directly addressed the question. 

The inclusion of heterozygotes as controls is problematic, given that the authors show there are notable differences between phox2a+/+ and phox2a+/- animals; pooling these two genotypes could potentially flatten differences between controls and phox2a-/-. 

We agree that this is an important limitation on our interpretations and have noted this more explicitly in the appropriate Results section (line 204).  

Projection neurons appear to be topographically organized and this organization is maintained in the absence of motor neurons. Are there specific genes that delineate ventral and dorsal projection neurons? If so, the authors should look at those as candidate genes as they might be selectively involved in connectivity. Showing that generic synaptic markers (Figure 4E) are maintained in the entire population is not convincing evidence that these neurons would choose the correct synaptic partners.

We agree with the Reviewer that Figure 4E is limited and that the most convincing molecular probe would be against a subtype-specific marker gene, ideally the one(s) that establish subtype-specific connectivity. To date, few such markers have been identified in any system, and, to the best of our knowledge, no reported markers differentiate dorsal (nose-up) from ventral (nose-down) projection neurons. We are currently evaluating candidates, but will not include that data here until the relevant genes are established as veridical subtype markers with defined roles in subtype fate specification and connectivity.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

In this study, the authors describe the participation of the Hes4-BEST4-Twist axis in controlling the process of epithelial-mesenchymal transition (EMT) and the advancement of colorectal cancers (CRC). They assert that this axis diminishes the EMT capabilities of CRC cells through a variety of molecular mechanisms. Additionally, they propose that reduced BEST4 expression within tumor cells might serve as an indicator of an adverse prognosis for individuals with CRC.

Strengths:

• Exploring the correlation between the Hes4-BEST4-Twist axis, EMT, and the advancement of CRC is a novel perspective and gives readers a fresh standpoint.<br /> • The whole transcriptome sequence analysis (Figure 5) showing low expression of BEST4 in CRC samples will be of broad interest to cancer specialists as well as cell biologists although further corroborative data is essential to strengthen these findings (See Weaknesses).

Weaknesses:

(1) The authors employed three kinds of CRC cell lines, but not untransformed cells such as intestinal epithelial organoids which are commonly used in recent research.

Sincerely thanks for catching this issue. While we acknowledge the potential of intestinal epithelial organoids as a valuable model for this study and will consider establishing this system in future research, which falls outside the scope of our current work.

(2) The authors use three different human CRC cell lines with a lack of consistency in the selection of them. Please clarify 1) how these lines are different from each other, 2) why they pick up one or two of them for each experiment. To be more convincing, at least two lines should be employed for each in vitro experiment.

We apologize for any confusion caused to the reviewer. In our study, we employed HCT116 and Caco2 cell lines to investigate the overexpression of BEST4 in the biological functions of CRC and its involvement in EMT. The selection of HCT116, a human CRC cell line, was based on its relatively lower expression level of BEST4 compared to other CRC cell lines. Conversely, Caco2 is a human colon adenocarcinoma cell line that closely resembles differentiated intestinal epithelial cells and exhibits microvilli structures. Given that BEST4 serves as a marker for intestinal epithelial cells, these two cell lines were chosen for investigating the in vitro effects of overexpressing BEST4 on proliferation, clonality, invasion, migration of colon cancer tumor cells and expression of downstream EMT-related markers. Similarly, we selected the HCT-15 cell line derived from human CRC for BEST4 knockout due to its comparatively higher expression level of BEST4 among other CRC cell lines. We employed the CRISPR/Cas9 gene-editing technology to knockout BEST4 instead of utilizing shRNA for downregulating BEST4 expression, thereby limiting our selection to a single cell line.

(3) The authors demonstrated associations between BEST4 and cell proliferation/ viability as well as migration/invasion, utilizing CRC cell lines, but it should be noted that these findings do not indicate a tumor-suppressive role of BEST4 as mentioned in line 120. Furthermore, while the authors propose that "BEST4 functions as a tumor suppressor in CRC" in line 50, there seems no supporting data to suggest BEST4 as a tumor suppressor gene.

We apologize for these inaccurate expressions, and we have made the necessary modifications to the corresponding parts in the manuscript.

(4) The HES4-BEST4-Twist1 axis likely plays a significant role in CRC progression via EMT but not CRC initiation. Some sentences could lead to a misunderstanding that the axis is important for CRC initiation.

We apologize for these inaccurate expressions, and we have made the necessary modifications to the corresponding parts in the manuscript.

(5) The authors mostly focus on the relationship of the HES4-BEST4-Twist1 axis with EMT, but their claims sometimes appear to deviate from this focus.

We apologize for confusing the reviewer. The objectives of our study are as follows: (1) to establish the role of BEST4 in CRC growth both in vitro and in vivo; (2) to determine the underlying molecular mechanisms by which BEST4 interacts with Hes4 and Twist1, thereby regulating EMT; and (3) to investigate the correlation between BEST4 expression and prognosis of CRC. We have made the necessary modifications to the corresponding parts in the manuscript.

(6) Some experiments do not appear to have a direct relevance to their claims. For example, the analysis using the xenograft model in Figure 2E-J is not optimal for analyzing EMT. The authors should analyze metastatic or invasive properties of the transplanted tumors if they intend to provide some supporting evidence for their claims.

Sincerely thanks for catching this issue. The process of EMT transforms epithelial cells exhibiting a spindle fibroblast-like morphology, leading to the acquisition of mesenchymal characteristics and morphology, enabling these cells to acquire invasive and migratory abilities, with expression switching epithelial E-cadherin and Zo-1 to mesenchymal vimentin (Dongre and Weinberg, 2019)..The whole process is regulated by transcriptional factors of the Snail family and Twist1(Dongre and Weinberg, 2019). We utilized the xenograft model with overexpressed BEST4 to analyze the lysates of tumor tissue, revealing that BEST4 upregulated E-cadherin and downregulated vimentin and Twist1 (Figures 2I). These findings indicate that BEST4 inhibits EMT in vivo. Deletion of BEST4 may enable these cells to acquire invasive and migratory abilities, leading to metastasis in vivo. Therefore, we subsequently evaluated the metastatic potential of BEST4 in a CRC liver metastasis model by intrasplenically injecting HCT-15 cell lines with knockout of BEST4 (BEST4gRNA), wild-type control (gRNA), or knockout with rescue (BEST4-Rescued) into BALB/c nude mice. Our observations revealed a twofold increase in liver metastatic nodules in the absence of BEST4 compared to the control group (Fig. 2J-L). Although further in vivo experiments are required for confirmation, our research suggests a potential role for BEST4 in counteracting EMT induction in vivo.

(7) In Figure 4H, ZO-1 and E-cad expression looks unchanged in the BEST4 KD.

Sincerely thanks for catching this issue. We have implemented the necessary modifications to the corresponding sections in the manuscript and performed a comprehensive quantification of all Western Blot data to ensure statistically significant differences, including those presented in the supplementary file.

(8) The in vivo and in vitro data supporting the whole transcriptome sequence analysis (Figure 5) is mostly insufficient. Including the following experiments will substantiate their claims: 1) BEST4 and HES4 immunostaining of human surgical tissue samples, 2) qPCR data of HES4, Twist1, Vimentin, etc. as shown in Figure 5C, 5D.

Sincerely thanks for catching this issue.

(1) Due to the substandard quality of the BEST4 antibody, we opted to evaluate the clinical significance of BEST4 in CRC by assessing mRNA results instead of protein levels using immunohistochemistry (IHC). After testing multiple antibodies for western blotting, only one (1:800; LsBio, LS-C31133) accurately indicated BEST4 protein expression while still exhibiting some non-specific bands. Consequently, we decided to transfect a HA-tagged BEST4 plasmid into the CRC cell line and used HA as a marker for BEST4 expression. Unfortunately, none of the antibodies employed for IHC were suitable as they failed to accurately distinguish between positive or negative staining for BEST4 and showed significant non-specific staining (data not shown). The challenge in detecting BEST4 protein in colorectal cancer tissues may be attributed to its low expression levels. Our findings are consistent with previous reports from the Human Protein Atlas database (https://www.proteinatlas.org/ENSG00000142959-BEST4/pathology), which also did not detect any BEST4 protein expression in colorectal cancer tissues through IHC analysis.

(2) The qPCR data of E-cadherin, Twist1, and Vimentin mRNA expression in CRC tissue has already been published in other studies(Christou et al., 2017; Lazarova and Bordonaro, 2016; Zhu et al., 2015). It was found that E-cadherin is downregulated, while Twist1 and Vimentin are upregulated in CRC tissue compared to the adjacent normal tissues. The qPCR data of E-cadherin, Twist1, and Vimentin mRNA expression in CRC tissue has already been published in other studies(Christou et al., 2017; Lazarova and Bordonaro, 2016; Zhu et al., 2015). It was found that E-cadherin is downregulated, while Twist1 and Vimentin are upregulated in CRC tissue compared to the adjacent normal tissues. The analysis of mRNA expression data obtained from colorectal cancer samples and normal samples in the publicly available databases TCGA and GTEx also revealed a significant downregulation of _Hes_4 expression in colorectal cancer tissues, which will be our next research objective.

(9) Some statements are inconsistent probably due to grammatical errors. (For example, some High/low may be reversed in lines 234-244.)

We apologize for these mistakes. We have made corrections to this section in the manuscript.

Reviewer #2 (Public Review):

Summary:

Using in vitro and in vivo approaches, the authors first demonstrate that BEST4 inhibits intestinal tumor cell growth and reduces their metastatic potential, possibly via downstream regulation of TWIST1.

They further show that HES4 positively upregulates BEST4 expression, with direct interaction with BEST4 promoter region and protein. The authors further expand on this with results showing that negative regulation of TWIST1 by HES4 requires BEST4 protein, with BEST4 required for TWIST1 association with HES4. Reduction of BEST1 expression was shown in CRC tumor samples, with correlation of BEST4 mRNA levels with different clinicopathological factors such as sex, tumor stage, and lymph node metastasis, suggesting a tumor-suppressive role of BEST4 for intestinal cancer.

Strengths:

• Good quality western blot data.

• Multiple approaches were used to validate the findings.

• Logical experimental progression for readability.

• Human patient data / In vivo murine model / Multiple cell lines were used, which supports translatability / reproducibility of findings.

We sincerely thank Reviewer #2 for constructive feedback on this work

Weaknesses:

(1) Interpretation of figures and data (unsubstantiated conclusions).

We apologize for this confusing presentation. We have made corrections to this section in the manuscript.

(2) Figure quality.

We apologize for the poor quality of the figures. The figure resolution was drastically reduced during the conversion of the manuscript to pdf on publisher web site. The figures have been re-uploaded and we have once again confirmed that each image has a resolution exceeding 300dpi.

(3) Figure legends lack information.

Sincere thanks for catching this issue. We have provided detailed figure legends including supplementary figure legends on pages 36-43 of the manuscript. We have rechecked this section and made improvements and additions.

(4) Lacking/shallow discussion.

We apologize for our shallow discussion. We have supplemented and improved some parts of the discussion

(5) Requires more information for reproducibility regarding materials and methods.

Sincere thanks for catching this issue. We have provided detailed information for reproducibility regarding materials and methods on pages 18-29; 43-47 of the manuscript. We have rechecked this section and made improvements and additions.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

We sincerely thank Reviewer #1 for constructive feedback on this work.

Minor comments:

(1) Line 73: "Variant 4" is not precise. The term "variant" should mean mutation in the gene or different transcription.

We apologize for using an inaccurate expression. We have now changed Variant 4 to Bestrophin 4.

(2) Line 78. Is it correct that BEST4+ cells coexist with Hes4+ cells?

According to the previous study that published in Nature (Parikh et al., 2019), BEST4+ cells originate from the absorptive lineage and express the transcription factors Hes4. Additionally, we also observed the nuclear co-localization of BEST4 and Hes4 in HCT116 cells through immunofluorescence staining (Figure 3E)

(3) Line 85. The reason "Best4 may be associated with Twist1" is unclear.

We apologize for the lack of clarity in our previous statement. In a recent analysis utilizing single cell RNA-sequencing, it was discovered that a subset of mature colonocytes expresses BEST4 (Parikh et al., 2019). Additionally, this subset coexists with hairy/enhancer of split 4 positive (Hes4+) cells (Parikh et al., 2019). Previous research has demonstrated the antagonistic role of Hes4 in regulating Twist1 through protein-protein interaction, which governs the differentiation of bone marrow stromal/stem cell lineage (Cakouros et al., 2015). Based on these findings, we speculate that there may be an interactive regulation between BEST4/Hes4/Twist1, potentially influencing the process of cell polarity during epithelial-mesenchymal transition in colorectal cancer. We have made corrections to this section in the manuscript.

(4) Line 87. Grammatical error (Establishing the role BEST4).

We apologize for the grammatical error of this section. We have rectified the issue in the manuscript.

(5) Please clarify the reason the authors do not show any data of BEST4-overexpressing Caco2 cells in Figure 2?

We apologize for our negligence in not adding this data to in Figure 2. It has now been fully supplemented.

(6) In line 145, the authors did not show any tumorigenic properties.

We apologize for this confusing presentation. We have made corrections to this section in the manuscript.

(7) Figure 3 shows 1) HES4 regulates BEST4 promotor activity, and 2) HES4 and BEST4 colocalized in nuclei, but these are very different biological processes. Please clarify how these two relate to each other.

Trajectory analysis identifies the basic helix-loop-helix (bHLH) transcription factors Hairy/enhancer of split 4 (Hes4)-expressing colonocytes (Hes4+) in BEST4-expressing colonic epithelial lineage (BEST4+). Although they are very different biological processes, the recent identification of a heterogeneous BEST4+ and Hes4+ subgroup in a human colonic epithelial lineage (Parikh et al., 2019) led us to consider their potential role in regulating CRC progression. We firstly observed a responsive upregulation of both endogenous BEST4 mRNA and protein levels in Hes4 overexpression cells compared to the control transfectant, indicating that Hes4 is a potential upstream activator regulating BEST4 functional. We then confirmed that Hes4 interacted with BEST4, binding directly to its upstream promoter at the region of 1470-1569 bp enhancing its promoter activity as analysed by Co-IP, dual-luciferase assay and ChIP-qPCR, respectively. Essentially, they were co-localized in the nucleus, as shown in immunofluorescence staining after the transient co-transfection of Hes4 and BEST4 into HCT116, therefore indicating that BEST4 interacts with Hes4 at both transcriptional and translational levels (Figure 3; Figure 3-supplemental figure 1)

(8) In line 182-185, please clarify the reason BEST4 mediates the inhibition of the Twist 1 promotor activity by Hes4.

Because a step of Hes4 in committing to human bone marrow stromal/stem cell lineage-specific development is mediated by Twist1 downregulation (Cakouros et al., 2015), with evidence of direct interaction between BEST4 and Hes4 observed in HCT116, it is plausible that they could exploit Twist1 to regulate EMT. In the present study, we found that Twist1 colocalized with BEST4 in the nucleus, and their interaction destabilized Twist1 and significantly inhibited EMT induction. Hes4 caused the same effect; however, it required intermediation through BEST4. Although the mechanistic insights of their intercorrelation remain to be elucidated, the present study identified the axis of Hes4-BEST4-Twist1 governing the development of CRC, at least partially by counteracting EMT induction

(9) In line 205, please rephrase "BEST4-mediated upstream Hes4" to be clearer.

We apologize for this confusing presentation. We have made corrections to this section in the manuscript.

Reviewer #2 (Recommendations For The Authors):

We sincerely thank Reviewer #2 for constructive feedback on this work

Major Comments:

(1) The general quality of the figures requires improvement (text in some figures is illegible, and the resolution of the images is low) with more proofreading of the text for clarity. In addition, the resolution of the histology in Fig 2K does not allow a proper evalution of the data.

We apologize for the poor quality of the figures. The figure resolution was drastically reduced during the conversion of the manuscript to pdf on publisher web site. The figures have been re-uploaded and we have once again confirmed that each image has a resolution exceeding 300dpi. Meanwhile, the Figure 2K was further enhanced and expanded.

(2) While the authors show that the HES4/BEST4 complex interacts with the TWIST1 protein, they do not expand on the mechanisms underpinning the post-translational or transcriptional regulation of TWIST1. We would like the authors to prove or further speculate on the mechanisms behind this regulation in the discussion.

Our present study showed that BEST4 inhibited EMT in conjunction with downregulation of Twist1 in both HCT116 and Caco2 CRC cell lines. A previous study has shown an antagonist role of Hes4 in regulating Twist1 via protein-protein interaction that controls the bone marrow stromal/stem cell lineage differentiation (Cakouros et al., 2015). We speculate a possible interactive regulation between Hes4/BEST4/Twist1 by which they deter the process of cell polarity during EMT in CRC. In the present study, we found that BEST4 mediates the inhibition of the Twist1 both in transcription and translation level by Hes4. Twist1 colocalized with BEST4 in the nucleus, and their interaction destabilized Twist1 and significantly inhibited EMT induction. Hes4 caused the same effect; however, it required intermediation through BEST4. The present study identified the axis of Hes4-BEST4-Twist1 governing the development of CRC, at least partially by counteracting EMT induction. We agree that further studies to elucidate mechanistic insights of their intercorrelation are needed that are beyond the scope of the current work.

(3) The authors need to show or argue that why TWIST1 is necessary for the phenotypes observed, e.g. metastasis/proliferation.

We apologize for the lack of clarity in articulating this question. The process of EMT transforms epithelial cells exhibiting a spindle fibroblast-like morphology, leading to the acquisition of mesenchymal characteristics and morphology, enabling these cells to acquire invasive and migratory abilities, with expression switching epithelial E-cadherin and Zo-1 to mesenchymal vimentin (Dongre and Weinberg, 2019). When diagnosed in advanced stages, EMT may occur as CRC metastasize to distal organs (Pastushenko and Blanpain, 2019; Sunlin Yong, 2021; Yeung and Yang, 2017; Zhang et al., 2021).The whole process is regulated by transcriptional factors of the Snail family and Twist1(Dongre and Weinberg, 2019). Twist1 (a basic helix-loop-helix transcription factor) reprograms EMT by repressing the expression of E-cadherin and ZO-1 (Nagai et al., 2016; Yang et al., 2004) and simultaneously inducing several mesenchymal markers, typically vimentin (Bulzico et al., 2019; Meng et al., 2018; Nagai et al., 2016; Yang et al., 2004), which is a pivotal predictor of CRC progression (Vesuna et al., 2008; Yang et al., 2004; Yusup et al., 2017; Zhu et al., 2015).Overexpression of Twist1 significantly enhances the migration and invasion capabilities of colorectal cancer cells; furthermore, it is closely associated with metastasis and poor prognosis in patients with colorectal cancer(Yusup et al., 2017; Zhu et al., 2015). We have supplemented and improved these parts of the introduction and discussion.

(4) The authors sufficiently prove that HES4/BEST4 regulates TWIST1 downregulation, however, we believe the findings are not enough to show *direct* regulation (refer also to line 273). At least rephrasing the conclusions would be adequate, also while referring to the working model depicted in Fig. 5G.

We apologize for this inaccurate presentation. Although the interaction may not be direct, our co-immunoprecipitation (CO-IP) results demonstrated nuclear colocalization of Twist1 and BEST4 (Figure 4D; Figure 4-supplemental figure 1A). Furthermore, their interaction destabilized Twist1 and significantly inhibited the induction of EMT. We have made corrections to this section in the manuscript.

(5) The discussion is very short and not satisfactory; is BEST4 an evolutionary conserved protein (besides the channel region)? Any speculation on which domain(s) is(are) important for the interaction with HES4 and TWIST1? How do the findings in the current study compare with recent, potentially contradicting data indicating a pro-tumorigenic function of BEST4 for CRC, including its upregulation (and not downregulation) in malignant intestinal tissues, and activation of PI3K/AKT signaling (PMID: 35058597)?

We apologize for our shallow discussion. We have supplemented and improved some parts of the discussion. The bestrophins are a highly conserved family of integral membrane proteins initially discovered in Caenorhabditis elegans(Sonnhammer and Durbin, 1997). Homologous sequences can be found across animals, fungi, and prokaryotes, while they are absent in protozoans or plants(Hagen et al., 2005). Conservation is primarily observed within the N-terminal 350–400 amino acids, featuring an invariant motif arginine-phenylalanine-proline (RFP) with unknown functional properties (Milenkovic et al., 2008). Mutations in this region can lead to the development of vitelliform macular dystrophy. However, the C-terminus is a potential site for protein modification and function(Marmorstein et al., 2002; Miller et al., 2019). There is currently no further literature research on the functional roles of different domains of BEST4. Although the crucial domain for the interaction with HES4 and TWIST1 is yet to be determined, requiring further investigation for clarification, our findings demonstrate that Hes4 directly binds to the upstream promoter region of BEST4 at 1470-1569 bp, thereby enhancing its promoter activity. These results provide valuable insights for future research.

Sincere thanks for catching this publication to us. We carefully read this study and would like to point out a few things.

a) Firstly, the study demonstrated that BEST4 expression is upregulated in clinical CRC samples, which contradicts the results of other published studies except for our own research. RNA-seq of tissue samples from 95 human individuals representing 27 different tissues was performed to determine the tissue specificity of all protein-coding genes, and the results indicated that the BEST4 gene is predominantly expressed in the colon (Fagerberg et al., 2014). In addition, BEST4 was reported to be exclusively expressed by human absorptive cells and could be induced during the process of human absorptive cell differentiation(Ito et al., 2013). Recently, the research from Simmons’s group that published in Nature further proved that human absorptive colonocytes distinctly express BEST4 by single-cell profiling of healthy human colonic epithelial cells, and is dysregulated in colorectal cancer patients(Parikh et al., 2019). Furthermore, the analysis of RNA-seq expression data obtained from colorectal cancer samples and normal samples in the publicly available databases TCGA and GTEx also revealed a significant downregulation of BEST4 expression in colorectal cancer tissues, which is consistent with our research findings. The literature above demonstrates a close relationship between BEST4 and the normal function of the human colon, and provide evidence for their loss in colorectal cancer patients.

b) Their study showed an increased expression of BEST4 protein levels in colorectal cancer patients through Western Blot. However, the antibody they used was only suitable for IHC-P and not for Western Blot (Abcam , ab188823); . In our study, we also utilized WB technology to detect the expression of BEST4 in colorectal cancer tissues and adjacent normal tissues. The results revealed a decreased expression of BEST4 protein levels in colorectal cancer patients. The antibody we used was specifically designed for WB detection (1:800; LsBio, LS-C31133 https://www.lsbio.com/antibodies/best4-antibody-n-terminus-wb-western-ls-c31133/29602).

c) The study demonstrated an upregulation of BEST4 protein levels in colorectal cancer patients using immunohistochemistry (IHC). However, the expression of BEST4 was assessed in colorectal cancer tissues through IHC utilizing publicly available protein expression databases such as the Human Protein Atlas. Interestingly, this analysis revealed a minimal presence of BEST4 protein in colorectal cancer tissues (https://www.proteinatlas.org/ENSG00000142959-BEST4/pathology), contradicting their research findings but aligning with our own observations.

d) Literature based on single-cell genomics analysis reports that only OTOP2 and BEST4 genes are expressed in a subset of the normal colorectal epithelial cells but not the rest(Parikh et al., 2019). An inhibitory effect of OTOP2 on CRC has been recently shown BEST4, and the Otopetrin 2 (OTOP2), which encodes proton‐selective ion channel protein were reported to distinct expressed in normal absorptive colonocytes and colocalized with each other (Drummond et al., 2017; Ito et al., 2013; Parikh et al., 2019). OTOP2 has been recently demonstrated to have an inhibitory effect on the development of CRC via being regulated by wide-type p53(Qu et al., 2019), while the role of BEST4 in CRC is less well studied, that indicate the potential of BEST4 to inhibit colorectal cancer. The Gene set enrichment analysis (GSEA) conducted by them revealed a significant enrichment of gene signatures associated with oncogenic signaling and metastasis, such as the PI3K/Akt signaling pathway, in patients exhibiting higher BEST4 expression compared to those with lower BEST4 expression. However, our GSEA did not show any significant enrichment of the PI3K/Akt signaling pathway in patients with higher BEST4 expression compared to those with lower BEST4 expression. In contrast to their findings, our BEST4 overexpression cell line did not exhibit a significant increase in phosphorylated Akt levels. The present study concludes that our findings align with previous literature and public database analyses, providing evidence for the downregulation of BEST4 in colorectal cancer tissues and its potential as an anticancer agent. Discrepancies observed in other studies may be attributed to difference in experimental model, protocols, preparations or experimental conditions.

Minor Comments:

(1) Western blot data should be quantified.

Sincere thanks for catching this point to us. We have conducted a comprehensive quantification of all the Western Blot data and included the results in the supplementary file.

(2) Errors in labelling figures in the text should be corrected (Line 214 and more).

We apologize for these mistakes. We have made corrections to this section in the manuscript.

(3) The authors used the human HES4 gene, which is indicated with the incorrect nomenclature. The gene and protein nomenclature should be correctly used.

We apologize for these mistakes. We have made corrections to this section in the manuscript.

(4) Methods and Materials for certain assays should be further clarified; e.g transwell migration/invasion assays (reference to previous publication? transwell inserts used, etc.)

Sincerely thanks for catching this issue. We have implemented enhancements and updates to the respective sections.

(5) Figure 2K: Quality of histology is insufficient.

We apologize for the poor quality of the figures. The quality of Figure 2K was further enhanced and expanded.

(6) Figure 2K: Can the authors speculate on whether there is any increase in proliferation through BEST4-ko in HCT15 cells (with overexpression of BEST4 leading to reduced proliferation) and how this may impact the metastatic assay or engraftment/seeding onto the liver?

Our in vitro experiment demonstrated that the ablation of BEST4 in HCT-15 cells resulted in increased cell proliferation, clonogenesis, migration and invasion (Figures 1 and Figure 1-supplemental figure 1). These findings suggest that BEST4 knockout may potentially contribute to tumor proliferation in vivo; however, further research is required for confirmation. EMT transforms epithelial cells exhibiting a spindle fibroblast-like morphology, leading to the acquisition of mesenchymal characteristics and morphology, enabling these cells to acquire invasive and migratory abilities (Dongre and Weinberg, 2019). When diagnosed in advanced stages, EMT may occur as CRC metastasize to distal organs (Pastushenko and Blanpain, 2019; Sunlin Yong, 2021; Yeung and Yang, 2017; Zhang et al., 2021).  Our study demonstrated that BEST4 inhibits EMT in colorectal cancer (CRC) both in vitro and in vivo. Conversely, ablation of BEST4 promotes EMT by upregulating the expression of EMT-related genes, thereby facilitating the metastasis of colorectal cancer cells to the liver.

(7) Figure 2L: Authors should indicate in the figure that the BEST4-rescued is at 0 (and not blank).

Sincerely thanks for catching this issue. We have made corrections to this section in the manuscript.

(8) Figure 3B: Authors should introduce the usage of the new LS174T cell line in the text.

Sincerely thanks for catching this issue. The human colorectal cancer cell line, LS174T, was selected for Hes4 knockdown due to its comparatively higher expression of Hes4 in comparison to other CRC cell lines. We have made corrections to this section in the manuscript.

(9) Figure 3F: Why is there less FLAG in the input, compared to the IP?

Sincerely thanks for catching this issue. Cell lysates (20 µg) were used for input, and 500ug for IP according to the manufacturer's protocols.

(10) Figure 5F-G: the quality of the figure is not good enough for interpretation.

Again, we apologize for poor quality of pictures due to manuscript conversion. We have made corrections to this section in the manuscript.

(11) Table 1: Conclusions made by the authors are wrong (lines 237-239); instead "high BEST4 expression more prevalent in females" and "low BEST4 expression more prevalent among CRC patients with advanced tumor stage". And how are low and high BEST4 expressions defined (the same applies to the data in Fig. 5F)?

We apologize for these mistakes, we set cutoff-high (50%) and cutoff-low (50%) values to split the high-expression and low-expression cohorts. We have made corrections to this section in the manuscript.

(12) In all Figure legends, there should be an indication of the type of statistical tests that were applied, as well as information on the number of independent experiments that were performed and provided the same results

Sincerely thanks for catching this issue. The types of statistical tests applied in the Materials and Method- Statistical analysis section are indicated. Information on the number of independent experiments used is provided in the figure legend section.

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this is the art of words, writing and reading

Author response:

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

Reviewer 1：

One major issue arises in Figure 4, the recording of VLPO Ca2+ activity. In Lines 211-215, they stated that they injected AAV2/9-DBH-GCaMP6m into the VLPO, while activating LC NE neurons. As they claimed in line 157, DBH is a specific promoter for NE neurons. This implies an attempt to label NE neurons in the VLPO, which is problematic because NE neurons are not present in the VLPO. This raises concerns about their viral infection strategy since Ca activity was observed in their photometry recording. This means that DBH promoter could randomly label some non-NE neurons. Is DBH promoter widely used? The authors should list references. Additionally, they should quantify the labeling efficiency of both DBH and TH-cre throughout the paper.

In Figure 5, we found that the VLPO received the noradrenergic projection from LC, indicating the recorded Ca2+ activity may come from the axon fibers corresponding to the projection. Similarly, Gunaydin et al. (2014) demonstrated that fiber photometry can be used to selectively record from neuronal projection.

We appreciate the reviewer's insightful suggestion to elaborate on the DBH promoter, we have now expanded our discussion to address the DBH (pg. 18): “DBH (Dopamine-beta-hydroxylase), located in the inner membrane of noradrenergic and adrenergic neurons, is an enzyme that catalyzes the conversion of dopamine to norepinephrine, and therefore plays an important role in noradrenergic neurotransmission. DBH is a marker of noradrenergic neurons. Zhou et al. (2020) clarified the probe specifically labeled noradrenergic neurons by immunolabeling for DBH. Recently, DBH promoter have been used in several studies (e.g., Han et al., 2024; Lian et al., 2023). The DBH-Cre mice are widely used to specifically labeled noradrenergic neurons (e.g., Li et al., 2023; Breton-Provencher et al., 2022; Liu et al., 2024). It is difficult to distinguish the role of NE or DA neurons when using the TH promoter in VLPO. Therefore, we used DBH promoter with more specific labeling. LC is the main noradrenergic nucleus of the central nervous system. In our study, we injected rAAV-DBH-GCaMP6m-WPRE (Figure 2 and 8) and rAAV-DBH-EGFP-S'miR-30a-shRNA GABAA receptor)-3’-miR30a-WPRES (Figure 9) into the LC. The results showed that DBH promoter could specifically label noradrenergic neurons in the LC, while non-specific markers outside the LC were almost absent.”

As suggested, we have quantified the labeling efficiency of both DBH and TH-cre throughout the revised manuscript (Fig.2D; Fig.3D, N-O; Fig.4E-F, J, L; Fig.5E, L; Fig.6L, S, X; Fig.7G).

A similar issue arises with chemogenetic activation in Fig. 5 L-R, the authors used TH-cre and DIO-Gq virus to label VLPO neurons. Were they labelling VLPO NE or DA neurons for recording? The authors have to clarify this.

As previously addressed in response to Comment #1, we agree that it is difficult to distinguish the role of NE or DA neurons when using the TH promoter in the VLPO. Therefore, we injected the mixture of DBH-Cre-AAV and AAV-EF1a-DIO-hChR2(H134R)-eYFP/AAV-Ef1a-DIO-hM3Dq-mCherry viruses into bilateral LC and AAV-EF1a-DIO-hChR2(H134R)-eYFP/AAV-Ef1a-DIO-hM3Dq-mCherry virus into bilateral VLPO. Moreover, we quantified the labeling efficiency of DBH in the LC to demonstrate that this promoter can specifically label NE neurons (Fig. 5). Importantly, these corrections did not alter the outcomes of our results. Both photogenetic and chemogenetic activation of LC-NE terminals in the VLPO can effectively promote midazolam recovery (Fig. 5G, N).

Another related question pertains to the specificity of LC NE downstream neurons in the VLPO. For example, do they preferentially modulate GABAergic or glutamatergic neurons?

Our study primarily aimed to explore the role of the LC-VLPO NEergic neural circuit in modulating midazolam recovery. We acknowledge that our evidence for the role of LC NE downstream neurons in the VLPO, derived from activation of LC-NE terminals and pharmacological intervention in the VLPO (Fig.5, Fig.6, Fig.8, Fig.9) is limited. Accordingly, we now present the VLPO’s role as a promising direction for future research in the limitation section of our revised manuscript: “This study shows that the LC-VLPO NEergic neural circuit plays an important role in modulating midazolam recovery. However, the specificity of LC NE downstream neurons in the VLPO is not explained in this paper, which is our next research direction, VLPO neurons and their downstream regulatory mechanisms may be involved in other nervous systems except the NE nervous system, and the deeper and more complex mechanisms need to be further investigated.”

In Figure 1A-D, in the measurement of the dosage-dependent effect of Mida in LORR, were they only performed one batch of testing? If more than one batch of mice were used, error bar should be presented in 1B. Also, the rationale of testing TH expression levels after Mid is not clear. Is TH expression level change related to NE activation specifically? If so, they should cite references.

As recommended, we have supplemented error bar and modified the graph of LORR’s rate in the revised manuscript. (Fig. 1A-B; Fig. 9G-H).

We agree that the use of TH as a marker of NE activation is controversial, so in the revised manuscript, we directly determined central norepinephrine content to reflect the change of NE activity after midazolam administration (Fig. 1D).

Regarding the photometry recording of LC NE neurons during the entire process of midazolam injection in Fig. 2 and Fig. 4, it is unclear what time=0 stands for. If I understand correctly, the authors were comparing spontaneous activity during the four phases. Additionally, they only show traces lasting for 20s in Fig. 2F and Fig. 4L. How did the authors select data for analysis, and what criteria were used? The authors should also quantify the average Ca2+ activity and Ca2+ transient frequency during each stage instead of only quantifying Ca2+ peaks. In line 919, the legend for Figure 2D, they stated that it is the signal at the BLA; were they also recorded from the BLA?

In this study, we used optical fiber calcium signal recording, which is a fluorescence imaging based on changes in calcium. The fluorescence signal is usually divided into different segments according to the behavior, and the corresponding segments are orderly according to the specific behavior event as the time=0. The mean calcium fluorescence signal in the time window 1.5s or 1s before the event behavior is taken as the baseline fluorescence intensity (F0), and the difference between the fluorescence intensity of the occurrence of the behavior and the baseline fluorescence intensity is divided by the difference between the baseline fluorescence intensity and the offset value. That is, the value ΔF/F0 represents the change of calcium fluorescence intensity when the event occurs. The results of the analysis are commonly represented by two kinds of graphs, namely heat map and event-related peri-event plot (e.g., Cheng et al., 2022; Gan-Or et al., 2023; Wei et al., 2018). In Fig. 2, the time points for awake, midazolam injection, LORR and RORR in mice were respectively selected as time=0, while in Fig. 4, RORR in mice was selected as time=0. The selected traces lasting for 20s was based on the length of a complete Ca2+ signal. We have explained the Ca2+ recording experiment more specifically in the figure legends and methods sections of our revised manuscript.

To the BLA, we sincerely apologize for our carelessness, the signal we recorded were from the LC rather than the BLA. We have carefully checked and corrected similar problems in the revised manuscript.

Reviewer 2：

In figure legends, abbreviations in figure should be supplemented as much as possible. For example, "LORR" in Figure 1.

As suggested, we have supplemented abbreviations in figure as much as possible in the revised manuscript.

Additional recommendations:

The main conceptual issue in the paper is the inflation of the conclusion regarding the mechanism of sedation induced by midazolam. The authors did not reveal the full mechanism of this but rather the relative contribution of NE system. Several conclusions in the text should be edited to take into account this starting from the title. I think the following examples are more appropriate: "NE contribution to rebooting unconsciousness caused by midazolam' or 'NE contribution to reverse the sedation induced by midazolam'.

As suggested, we have moderated the assertions about the mechanism of sedation induced by midazolam in several conclusions starting from the title (Line 1,125,150,169,202,237,482), to present a more measured interpretation in the manuscript.

Line 178-179, the authors state 'these suggest that intranuclear ... suppresses recovery from midazolam administration'. In fact, this intervention prolonged or postponed recovery from midazolam.

In our revised manuscript, we have corrected this inappropriate term (Line 178).

Pharmacology part (page 12) that aimed to pinpoint which NE receptor is implicated would suffer from specificity issues.

In relation to the specificity issue, the focus on VLPO might be rational but again other areas are most likely involved given the pharmacological actions of midazolam.

In the revised manuscript, we have discussed those specificity issues of NE receptor and areas involved throughout the midazolam-induced altered consciousness: “In addition, given the pharmacological actions of midazolam, other areas may also be involved. Current studies suggest that the neural network involved in the recovery of consciousness consists of the prefrontal cortex, basal forebrain, brain stem, hypothalamus and thalamus. The role of these regions in midazolam recovery remains to be further investigated. Therefore, we will apply more specific experimental methods to determine the importance of LC-VLPO NEergic neural circuit and related NE receptors in the midazolam recovery, and conduct further studies on other relevant brain neural regions, hoping to more fully elucidate the mechanism of midazolam recovery in the future”.

Line 274, the authors used 'inhibitory EEG activity'. what does it mean? a description of which rhythm-related power density is affected would be more objective.

Example of conclusion inflation: in line 477, the word 'contributes' is better than 'mediates' if the specificity issue is taken into account.

As suggested, we have improved our expression of words in our revised manuscript (pg. 13-14).

References

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Liu Q, Luo X, Liang Z, et al. Coordination between circadian neural circuit and intracellular molecular clock ensures rhythmic activation of adult neural stem cells. Proc Natl Acad Sci U S A. 2024;121(8):e2318030121. doi:10.1073/pnas.2318030121

Cheng J, Ma X, Li C, et al. Diet-induced inflammation in the anterior paraventricular thalamus induces compulsive sucrose-seeking. Nat Neurosci. 2022;25(8):1009-1013. doi:10.1038/s41593-022-01129-y

Gan-Or B, London M. Cortical circuits modulate mouse social vocalizations. Sci Adv. 2023;9(39):eade6992. doi:10.1126/sciadv.ade6992

Wei YC, Wang SR, Jiao ZL, et al. Medial preoptic area in mice is capable of mediating sexually dimorphic behaviors regardless of gender. Nat Commun. 2018;9(1):279. Published 2018 Jan 18. doi:10.1038/s41467-017-02648-0

Reviewer #3 (Public review):

Summary:

This study used transcranial direct current stimulation administered using small 'high-definition' electrodes to modulate neural activity within the non-human primate prefrontal cortex during both wakefulness and anaesthesia. Functional magnetic resonance imaging (fMRI) was used to assess the neuromodulatory effects of stimulation. The authors report on the modification of brain dynamics during and following anodal and cathodal stimulation during wakefulness and following anodal stimulation at two intensities (1 mA, 2 mA) during anaesthesia. This study provides some possible support that prefrontal direct current stimulation can alter neural activity patterns across wakefulness and sedation in monkeys. However, the reported findings need to be considered carefully against several important methodological limitations.

Strengths:

A key strength of this work is the use of fMRI-based methods to track changes in brain activity with good spatial precision. Another strength is the exploration of stimulation effects across wakefulness and sedation, which has the potential to provide novel information on the impact of electrical stimulation across states of consciousness.

Weaknesses:

The lack of a sham stimulation condition is a significant limitation, for instance, how can the authors be sure that results were not affected by drowsiness or fatigue as a result of the experimental procedure?

In the anaesthesia condition, the authors investigated the effects of two intensities of stimulation (1 mA and 2 mA). However, a potential confound here relates to the possibility that the initial 1 mA stimulation block might have caused plasticity-related changes in neural activity that could have interfered with the following 2 mA block due to the lack of a sufficient wash-out period. Hence, I am not sure any findings from the 2 mA block can really be interpreted as completely separate from the initial 1 mA stimulation period, given that they were administered consecutively. Several previous studies have shown that same-day repeated tDCS stimulation blocks can influence the effects of neuromodulation (e.g., Bastani and Jaberzadeh, 2014, Clin Neurophysiol; Monte-Silva et al., J. Neurophysiology).

The different electrode placement for the two anaesthetised monkeys (i.e., Monkey R: F3/O2 montage, Monkey N: F4/O1 montage) is problematic, as it is likely to have resulted in stimulation over different brain regions. The authors state that "Because of the small size of the monkey's head, we expected that tDCS stimulation with these two symmetrical montages would result in nearly equivalent electric fields across the monkey's head and produce roughly similar effects on brain activity"; however, I am not totally convinced of this, and it really would need E-field models to confirm. It is also more likely that there would in fact be notable differences in the brain regions stimulated as the authors used HD-tDCS electrodes, which are generally more focal.

Given the very small sample size, I think it is also important to consider the possibility that some results might also be impacted by individual differences in response to stimulation. For instance, in the discussion (page 9, paragraph 2) the authors contrast findings observed in awake animals versus anaesthetised animals. However, different monkeys were examined for these two conditions, and there were only two monkeys in each group (monkeys J and Y for awake experiments [both male], and monkeys R and N [male and female] for the anaesthesia condition). From the human literature, it is well known that there is a considerable amount of inter-individual variability in response to stimulation (e.g., Lopez-Alonso et al., 2014, Brain Stimulation; Chew et al., 2015, Brain Stimulation), therefore I wonder if some of these differences could also possibly result from differences in responsiveness to stimulation between the different monkeys? At the end of the paragraph, the authors also state "Our findings also support the use of tDCS to promote rapid recovery from general anesthesia in humans...and suggest that a single anodal prefrontal stimulation at the end of the anesthesia protocol may be effective." However, I'm not sure if this statement is really backed-up by the results, which failed to report "any behavioural signs of awakening in the animals" (page 7)?

Reviewer #1 (Public review):

Summary:

In the abstract and throughout the paper, the authors boldly claim that their evidence, from the largest set of data ever collected on inattentional blindness, supports the views that "inattentionally blind participants can successfully report the location, color, and shape of stimuli they deny noticing", "subjects retain awareness of stimuli they fail to report", and "these data...cast doubt on claims that awareness requires attention." If their results were to support these claims, this study would overturn 25+ years of research on inattentional blindness, resolve the rich vs. sparse debate in consciousness research, and critically challenge the current majority view in cognitive science that attention is necessary for awareness.

Unfortunately, these extraordinary claims are not supported by extraordinary (or even moderately convincing) evidence. At best, the results support the more modest conclusion: If sub-optimal methods are used to collect retrospective reports, inattentional blindness rates will be overestimated by up to ~8% (details provided below in comment #1). This evidence-based conclusion means that the phenomenon of inattentional blindness is alive and well as it is even robust to experiments that were specifically aimed at falsifying it. Thankfully, improved methods already exist for correcting the ~8% overestimation of IB rates that this study successfully identified.

Comments:

(1) In experiment 1, data from 374 subjects were included in the analysis. As shown in figure 2b, 267 subjects reported noticing the critical stimulus and 107 subjects reported not noticing it. This translates to a 29% IB rate, if we were to only consider the "did you notice anything unusual Y/N" question. As reported in the results text (and figure 2c), when asked to report the location of the critical stimulus (left/right), 63.6% of the "non-noticer" group answered correctly. In other words, 68 subjects were correct about the location while 39 subjects were incorrect. Importantly, because the location judgment was a 2-alternative-forced-choice, the assumption was that if 50% (or at least not statistically different than 50%) of the subjects answered the location question correctly, everyone was purely guessing. Therefore, we can estimate that ~39 of the subjects who answered correctly were simply guessing (because 39 guessed incorrectly), leaving 29 subjects from the non-noticer group who may have indeed actually seen the location of the stimulus. If these 29 subjects are moved to the noticer group, the corrected rate of IB for experiment 1 is 21% instead of 29%. In other words, relying only on the "Y/N did you notice anything" question leads to an overestimate of IB rates by 8%. This modest level of inaccuracy in estimating IB rates is insufficient for concluding that "subjects retain awareness of stimuli they fail to report", i.e. that inattentional blindness does not exist.

In addition, this 8% inaccuracy in IB rates only considers one side of the story. Given the data reported for experiment 1, one can also calculate the number of subjects who answered "yes, I did notice something unusual" but then reported the incorrect location of the critical stimulus. This turned out to be 8 subjects (or 3% of the "noticer" group). Some would argue that it's reasonable to consider these subjects as inattentionally blind, since they couldn't even report where the critical stimulus they apparently noticed was located. If we move these 8 subjects to the non-noticer group, the 8% overestimation of IB rates is reduced to 6%.

The same exercise can and should be carried out on the other 4 experiments, however, the authors do not report the subject numbers for any of the other experiments, i.e., how many subjects answered Y/N to the noticing question and how many in each group correctly answered the stimulus feature question. From the limited data reported (only total subject numbers and d' values), the effect sizes in experiments 2-5 were all smaller than in experiment 1 (d' for the non-noticer group was lower in all of these follow-up experiments), so it can be safely assumed that the ~6-8% overestimation of IB rates was smaller in these other four experiments. In a revision, the authors should consider reporting these subject numbers for all 5 experiments.

(2) Because classic IB paradigms involve only one critical trial per subject, the authors used a "super subject" approach to estimate sensitivity (d') and response criterion (c) according to signal detection theory (SDT). Some readers may have issues with this super subject approach, but my main concern is with the lack of precision used by the authors when interpreting the results from this super subject analysis.

Only the super subject had above-chance sensitivity (and it was quite modest, with d' values between 0.07 and 0.51), but the authors over-interpret these results as applying to every subject. The methods and analyses cannot determine if any individual subject could report the features above-chance. Therefore, the following list of quotes should be revised for accuracy or removed from the paper as they are misleading and are not supported by the super subject analysis:

"Altogether this approach reveals that subjects can report above-chance the features of stimuli (color, shape, and location) that they had claimed not to notice under traditional yes/no questioning" (p.6)

"In other words, nearly two-thirds of subjects who had just claimed not to have noticed any additional stimulus were then able to correctly report its location." (p.6)

"Even subjects who answer "no" under traditional questioning can still correctly report various features of the stimulus they just reported not having noticed, suggesting that they were at least partially aware of it after all." (p.8)

"Why, if subjects could succeed at our forced-response questions, did they claim not to have noticed anything?" (p.8)

"we found that observers could successfully report a variety of features of unattended stimuli, even when they claimed not to have noticed these stimuli." (p.14)

"our results point to an alternative (and perhaps more straightforward) explanation: that inattentionally blind subjects consciously perceive these stimuli after all... they show sensitivity to IB stimuli because they can see them." (p.16)

"In other words, the inattentionally blind can see after all." (p.17)

(3) In addition to the d' values for the super subject being slightly above zero, the authors attempted an analysis of response bias to further question the existence of IB. By including in some of their experiments critical trials in which no critical stimulus was presented, but asking subjects the standard Y/N IB question anyway, the authors obtained false alarm and correct rejection rates. When these FA/CR rates are taken into account along with hit/miss rates when critical stimuli were presented, the authors could calculate c (response criterion) for the super subject. Here, the authors report that response criteria are biased towards saying "no, I didn't notice anything". However, the validity of applying SDT to classic Y/N IB questioning is questionable.

For example, with the subject numbers provided in Box 1 (the 2x2 table of hits/misses/FA/CR), one can ask, 'how many subjects would have needed to answer "yes, I noticed something unusual" when nothing was presented on the screen in order to obtain a non-biased criterion estimate, i.e., c = 0?' The answer turns out to be 800 subjects (out of the 2761 total subjects in the stimulus-absent condition), or 29% of subjects in this condition.

In the context of these IB paradigms, it is difficult to imagine 29% of subjects claiming to have seen something unusual when nothing was presented. Here, it seems that we may have reached the limits of extending SDT to IB paradigms, which are very different than what SDT was designed for. For example, in classic psychophysical paradigms, the subject is asked to report Y/N as to whether they think a threshold-level stimulus was presented on the screen, i.e., to detect a faint signal in the noise. Subjects complete many trials and know in advance that there will often be stimuli presented and the stimuli will be very difficult to see. In those cases, it seems more reasonable to incorrectly answer "yes" 29% of the time, as you are trying to detect something very subtle that is out there in the world of noise. In IB paradigms, the stimuli are intentionally designed to be highly salient (and unusual), such that with a tiny bit of attention they can be easily seen. When no stimulus is presented and subjects are asked about their own noticing (especially of something unusual), it seems highly unlikely that 29% of them would answer "yes", which is the rate of FAs that would be needed to support the null hypothesis here, i.e., of a non-biased criterion. For these reasons, the analysis of response bias in the current context is questionable and the results claiming to demonstrate a biased criterion do not provide convincing evidence against IB.

(4) One of the strongest pieces of evidence presented in the entire paper is the single data point in Figure 3e showing that in Experiment 3, even the super subject group that rated their non-noticing as "highly confident" had a d' score significantly above zero. Asking for confidence ratings is certainly an improvement over simple Y/N questions about noticing, and if this result were to hold, it could provide a key challenge to IB. However, this result hinges on a single data point, it was not replicated in any of the other 4 experiments, and it can be explained by methodological limitations. I strongly encourage the authors (and other readers) to follow up on this result, in an in-person experiment, with improved questioning procedures.

In the current Experiment 3, the authors asked the standard Y/N IB question, and then asked how confident subjects were in their answer. Asking back-to-back questions, the second one with a scale that pertains to the first one (including a tricky inversion, e.g., "yes, I am confident in my answer of no"), may be asking too much of some subjects, especially subjects paying half-attention in online experiments. This procedure is likely to introduce a sizeable degree of measurement error.

An easy fix in a follow-up study would be to ask subjects to rate their confidence in having noticed something with a single question using an unambiguous scale:

On the last trial, did you notice anything besides the cross?

(1) I am highly confident I didn't notice anything else<br /> (2) I am confident I didn't notice anything else<br /> (3) I am somewhat confident I didn't notice anything else<br /> (4) I am unsure whether I noticed anything else<br /> (5) I am somewhat confident I noticed something else<br /> (6) I am confident I noticed something else<br /> (7) I am highly confident I noticed something else

If we were to re-run this same experiment, in the lab where we can better control the stimuli and the questioning procedure, we would most likely find a d' of zero for subjects who were confident or highly confident (1-2 on the improved scale above) that they didn't notice anything. From there on, the d' values would gradually increase, tracking along with the confidence scale (from 3-7 on the scale). In other words, we would likely find a data pattern similar to that plotted in Figure 3e, but with the first data point on the left moving down to zero d'. In the current online study with the successive (and potentially confusing) retrospective questioning, a handful of subjects could have easily misinterpreted the confidence scale (e.g., inverting the scale) which would lead to a mixture of genuine high-confidence ratings and mistaken ratings, which would result in a super subject d' that falls between zero and the other extreme of the scale (which is exactly what the data in Fig 3e shows).

One way to check on this potential measurement error using the existing dataset would be to conduct additional analyses that incorporate the confidence ratings from the 2AFC location judgment task. For example, were there any subjects who reported being confident or highly confident that they didn't see anything, but then reported being confident or highly confident in judging the location of the thing they didn't see? If so, how many? In other words, how internally (in)consistent were subjects' confidence ratings across the IB and location questions? Such an analysis could help screen-out subjects who made a mistake on the first question and corrected themselves on the second, as well as subjects who weren't reading the questions carefully enough. As far as I could tell, the confidence rating data from the 2AFC location task were not reported anywhere in the main paper or supplement.

(5) In most (if not all) IB experiments in the literature, a partial attention and/or full attention trial (or set of trials) is administered after the critical trial. These control trials are very important for validating IB on the critical trial, as they must show that, when attended, the critical stimuli are very easy to see. If a subject cannot detect the critical stimulus on the control trial, one cannot conclude that they were inattentionally blind on the critical trial, e.g., perhaps the stimulus was just too difficult to see (e.g., too weak, too brief, too far in the periphery, too crowded by distractor stimuli, etc.), or perhaps they weren't paying enough attention overall or failed to follow instructions. In the aggregate data, rates of noticing the stimuli should increase substantially from the critical trial to the control trials. If noticing rates are equivalent on the critical and control trials one cannot conclude that attention was manipulated.

It is puzzling why the authors decided not to include any control trials with partial or full attention in their five experiments, especially given their online data collection procedures where stimulus size, intensity, eccentricity, etc. were uncontrolled and variable across subjects. Including such trials could have actually helped them achieve their goal of challenging the IB hypothesis, e.g., excluding subjects who failed to see the stimulus on the control trials might have reduced the inattentional blindness rates further. This design decision should at least be acknowledged and justified (or noted as a limitation) in a revision of this paper.

(6) In the discussion section, the authors devote a short paragraph to considering an alternative explanation of their non-zero d' results in their super subject analyses: perhaps the critical stimuli were processed unconsciously and left a trace such that when later forced to guess a feature of the stimuli, subjects were able to draw upon this unconscious trace to guide their 2AFC decision. In the subsequent paragraph, the authors relate these results to above-chance forced-choice guessing in blindsight subjects, but reject the analogy based on claims of parsimony.

First, the authors dismiss the comparison of IB and blindsight too quickly. In particular, the results from experiment 3, in which some subjects adamantly (confidently) deny seeing the critical stimulus but guess a feature at above-chance levels (at least at the super subject level and assuming the online subjects interpreted and used the confidence scale correctly), seem highly analogous to blindsight. Importantly, the analogy is strengthened if the subjects who were confident in not seeing anything also reported not being confident in their forced-choice judgments, but as mentioned above this data was not reported.

Second, the authors fail to mention an even more straightforward explanation of these results, which is that ~8% of subjects misinterpreted the "unusual" part of the standard IB question used in experiments 1-3. After all, colored lines and shapes are pretty "usual" for psychology experiments and were present in the distractor stimuli everyone attended to. It seems quite reasonable that some subjects answered this first question, "no, I didn't see anything unusual", but then when told that there was a critical stimulus and asked to judge one of its features, adjusted their response by reconsidering, "oh, ok, if that's the unusual thing you were asking about, of course I saw that extra line flash on the left of the screen". This seems like a more parsimonious alternative compared to either of the two interpretations considered by the authors: (1) IB does not exist, (2) super-subject d' is driven by unconscious processing. Why not also consider: (3) a small percentage of subjects misinterpreted the Y/N question about noticing something unusual. In experiments 4-5, they dropped the term "unusual" but do not analyze whether this made a difference nor do they report enough of the data (subject numbers for the Y/N question and 2AFC) for readers to determine if this helped reduce the ~8% overestimate of IB rates.

(7) The authors use sub-optimal questioning procedures to challenge the existence of the phenomenon this questioning is intended to demonstrate. A more neutral interpretation of this study is that it is a critique on methods in IB research, not a critique on IB as a manipulation or phenomenon. The authors neglect to mention the dozens of modern IB experiments that have improved upon the simple Y/N IB questioning methods. For example, in Michael Cohen's IB experiments (e.g., Cohen et al., 2011; Cohen et al., 2020; Cohen et al., 2021), he uses a carefully crafted set of probing questions to conservatively ensure that subjects who happened to notice the critical stimuli have every possible opportunity to report seeing them. In other experiments (e.g., Hirschhorn et al., 2024; Pitts et al., 2012), researchers not only ask the Y/N question but then follow this up by presenting examples of the critical stimuli so subjects can see exactly what they are being asked about (recognition-style instead of free recall, which is more sensitive). These follow-up questions include foil stimuli that were never presented (similar to the stimulus-absent trials here), and ask for confidence ratings of all stimuli. Conservative, pre-defined exclusion criteria are employed to improve the accuracy of their IB-rate estimates. In these and other studies, researchers are very cautious about trusting what subjects report seeing, and in all cases, still find substantial IB rates, even to highly salient stimuli. The authors should consider at least mentioning these improved methods, and perhaps consider using some of them in their future experiments.

DOI: 10.3389/fnmol.2024.1406708

Resource: (Cell Signaling Technology Cat# 2792, RRID:AB_2303165)

Curator: @scibot

SciCrunch record: RRID:AB_2303165

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DOI: 10.1158/1078-0432.CCR-24-1317

Resource: DIA-NN (RRID:SCR_022865)

Curator: @scibot

SciCrunch record: RRID:SCR_022865

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DOI: 10.1016/j.molcel.2024.09.009

Resource: RRID:Addgene_40896

Curator: @scibot

SciCrunch record: RRID:Addgene_40896

What is this?

Para generar una estrategia comunicacional adecuada, se debe hacer un análisis tanto interno como externo para encontrara falencias, aquí se audita de manera comunicacional para encontrar los puntos débiles y cambiarlos, este cambio genera una mejora en todos los niveles, desde los objetivos que tiene la empresa hasta la ejecución del talento humano.

La importancia de mejorar la gestión de las comunicaciones en la empresa. Esto es clave para asegurar que los presupuestos, los objetivos a largo plazo y los resultados estén bien coordinados. Desarrollar una estrategia de comunicación efectiva no solo optimiza el funcionamiento interno, sino que también puede impactar positivamente en las ganancias y fortalecer la unión del equipo.

Cómo explica el párrafo: Sin darse por vencidos, tomaron una decisión que podría parecer ilógica: se dirigieron hacia el oeste y caminaron unos 550 kilómetro (Cómo desde aquí hasta la frontera con Brasil en Rivera) saltándose una ciudad tras otra hasta llegar al puerto de Troas, desde donde podían embarcarse a Macedonia (Hech. 16:8). Y ahora sí ¡esta vez les abrieron la puerta de par en par!

Bueno cómo se les cerró el camino al éste, no se hicieron problema, decidieron seguir al norte rumbo a Bitinia, pero ahí también el espíritu Santo les cerró el paso. Aunque habían pasado días caminando por caminos difíciles para llegar a ese lugar.

Ellos salieron del sur de Galacia rumbo al este, con destino a Éfeso en el distrito de Asia y cuando llegaron a la calzada romana que les llevaría a ese destino, de alguna forma el espíritu Santo les impide continuar en esa dirección. Jesús es quien está utilizando el espíritu Santo para indicarles que camino seguir. que es atravesar Asia Menor, cruzar el mar Egeo y se dirigirse a las orillas del Gangites.

acá daría a conocer las preguntas en cuestión, puede ser una imagen del cuestionario, o redactarlas bien. Me refiero a las de conflicto y percepción de desigualdad

Je suggère une traduction:

Comment peut-on considérer les œuvres qui ne se préoccupent aucunement des sites web sur des serveurs, mais plutôt de l'aventure qui y débute et échappe à notre contrôle? Les œuvres qui se définissent exclusivement comme des œuvres d'art numérique contredisent la logique du modèle propriétaire. Contredisent l'ancienne logique du modèle propriétaire. Et contredisent l'ancienne logique en général.

Welcome back and in this lesson I want to talk in a little bit of detail about fiber optic cables.

If you're involved with networking in any way, then you need to be comfortable with how they work, their characteristics and the differences between the various types.

Now this matters for the real world and if you need to work with any physical networking services, including AWS Direct Connect.

Now let's just jump in and get started.

Fiber optic cables are an alternative way to transmit data versus copper cables.

Where copper cables use changes in electrical signals to transmit data over a copper medium, fiber optic cables use a thin glass or plastic core surrounded by various protective layers.

The core is about the diameter of a human hair.

The cable that you can see and touch is that core surrounded by a lot of protection.

If you just handle the core on its own, it would be pretty susceptible to damage.

Now fiber optic cables, as the name suggests, transmit light over the glass or plastic medium, so light over glass or plastic versus electrical signals over copper.

These unique elements mean that the cable can cover much larger distances and achieve much higher speeds versus copper.

At the time of creating this lesson, this can be in the regions of terabits per second.

Now fiber is also resistant to electromagnetic interference known as EMI and it's less prone to being impacted by water ingress into a space where the cables are being used.

In general, fiber offers a more consistent experience versus copper cable and so in modern networks, specifically those which require higher speeds and or larger distances, fiber is often preferred versus copper.

You're going to see over time fiber will gradually overtake copper in almost all wired networking situations as it becomes cheaper and easier to install.

It's already used for many global networking, metro networking and even many local area networking applications.

So that's important to understand.

It's going to be used more and more in the future.

Now in terms of physical makeup, this is what a typical fiber cable looks like externally.

There are two different things that you need to think about and this is common with any form of networking cable or any form of cable in general.

There's the cable choice which will influence the physical characteristics so how fast data can be transferred and over what distances.

Then you have the cable connectors and these generally affect what the cable can be connected to so linked to physical ports on the networking equipment but they can also influence some of the physical characteristics in terms of distance ability and speeds.

Now I'm not going to detail all the different fiber cable types in this lesson.

Instead I've included a link attached to this lesson which gives you a good overview of the common cable and connector types within the industry.

Now I want to spend a few minutes talking about the physical construction of fiber cables.

I've talked about how the connectors are different but it's important that you understand the different physical makeups of the cable itself.

Now when we're talking about fiber cable, you'll see it referred to using an X/Y notation.

For example, 9/125.

This defines two parts of the cable.

The first part is the diameter of the core in microns and the second part is the diameter of the cladding.

Now the first bit that surrounds the core.

Both of these are in microns and there are a thousand microns in a millimeter.

Now let's talk about the different components of a fiber cable and I'm mainly going to be covering the fiber core, the fiber cladding and the buffer.

And don't worry, the functions of each of these will make sense in a second.

Now we're going to start with the core and this is the part of the cable where the light is carried which allows for the transfer of data.

This part on a 9/125 cable is tinier.

It's only 9 microns across.

So if you look at the right of the screen, you have the core, then you have the transmitter receive optics at each side and the light flows through the core along the length of the cable.

I'll talk more about this in a moment but the light doesn't flow in a straight line.

Now we're bouncing off the inside edges of the core which is why the size of the core matters.

Now surrounding the core is the cladding and this is a material which has a lower refractive index versus the core.

And this means that it acts as a container to keep the light bouncing around inside the core.

Different types of fiber have different sizes of core and cladding and both of them radically impact the physical characteristics of the cable.

And this is where we move on to the less important parts of the cable.

The core and cladding were directly responsible for the physical transmission of data but now we're moving on to the protective balance.

So next we have the buffer and the buffer is the thing which adds strength to the cable.

The core and cladding are generally really good at helping to carry data but really bad at withstanding any physical shocks.

The buffer is a combination of coating and strengthening materials such as fibers made out of other materials.

Now don't confuse this type of fiber with fiber optic.

This is just a length of material which is designed to absorb shocks and give physical protection.

And this buffer is surrounded by the cable jacket which is the physical thing that you see when looking at the fiber cable.

It generally has a defined color such as green, orange or blue and this generally gives some indication on the overall capabilities of the cable.

Now I've included a link attached to this lesson which details the common colors and what they mean in terms of the fiber optic cable capabilities.

Now one more thing that I want to cover before we finish this lesson and that's the difference between single mode and multi mode fiber.

The difference might seem pretty nuanced but it's really important to understand.

Let's start with single mode.

Single mode generally has a really small core and it's often 8 to 9 microns in size.

And it generally uses a yellow jacket but this isn't always the case.

Now because of this tiny core, light generally follows a fairly single and straight path down at the core.

There's generally very little bounce and so very little distortion.

Single mode fiber generally uses lasers and so it's more expensive in terms of the optics versus multi mode.

Single mode because of this lack of distortion is great for long distances and it can achieve excellent speeds over these long distances.

Now it's not the fastest type of fiber cable but if you need a combination of high speeds and long distances then it's by far the best.

Single mode fiber can reach kilometers and can do really high speeds at those distances.

Generally in production usage this is 10 gig and above.

Single mode fiber cable itself is generally cheaper than multi mode fiber but the transceivers are things which send and receive light are more expensive versus multi mode.

But this is changing over time and this will probably mean more and more single mode usage within most business applications.

Now multi mode cable generally has a much bigger core and often uses either an orange and aqua or other coloured jacket.

The bigger core means that it can be used with a wider range of wavelengths of light generally at the same time.

For simplicity think of this as different colours of light travelling down the same fiber cable so these different colours can be sent at the same time and don't interfere with each other.

More light means more data so multi mode tends to be faster.

But that comes with a trade off because this leads to more distortion over the light over longer distances.

For that reason multi mode historically has been used for shorter cable runs where speed and cost effectiveness is required.

Now multi mode generally has cheaper LED based optics rather than the more expensive laser optics used within single mode fiber.

Multi mode fiber cable will generally use the prefix OM so OM2, OM3, OM4 and so on each improving the previous ones capabilities.

And multi mode as I mentioned before has a larger core.

At a high level the type of cable you decide on is determined by the distances that you need to transmit data and the speed.

So single mode is just more sturdy, there's less distortion and it can do better speeds over higher distances.

And as the optics prices come down I suspect more people will use single mode even for shorter distances.

Now one final thing I want to cover before we finish up with this lesson and that's fiber optic transceivers.

Now these are generally the things which you plug into networking equipment which allows the networking equipment to connect to fiber optic cables.

They're known as SFP transceiver modules also known as SFP or mini gibix and this stands for single form factor pluggable.

Now these are the things which generate and send or receives light to and from the fiber optic cable.

So these plug into networking equipment, these have optics inside which generate the light or can detect the light and these are used to translate from data to light and from lights to data that networking equipment can use.

Now these transceivers are either multi mode or single mode and they're optimised for a specific cable type.

So you generally buy a transceiver that's designed to be used with a certain type of cable and the transceivers will need to be the same type on both sides or both ends of the fiber optic cable.

Now when you're talking about the connector type and the cable you're generally going to see terms such as 1000 base LX, 10G base LR or 100G base LR4.

And these are often specified by vendors such as AWS to give you an idea on the type of cable and the connector that you need to use to plug into their equipment.

So in the case of AWS DirectConnect the supported types are 1000 base LX, 10G base LR and 100G base LR4.

Now at this point that's everything I wanted to cover at a high level about fiber optic cables and transceivers and once again I've included some links attached to this lesson which go into a little bit more detail if you're interested.

At this point that's everything I wanted to cover to go ahead and complete the video and when you're ready I'll look forward to you joining me in the next.

I agree with author’s opinion. As the time passes, the multicultural classroom is unavoidable in school. However, most teachers have not prepared for that. Even for experienced author in progressive politics and feminist movement, it would be a difficult beginning for author to teach in multiculturalism. This problem needs to be taken seriously in today’s society.

Yes!!! Some people have unrecognized biases and that is completely normal. Some people may grow up hearing things and just keep those thoughts in their unconscious mind, it may not be intentionally hurtful in their mind, but to others their may be a problem. Anyone can say something that seems completely normal to them, but their audiences can all interpret them completely different. I think that through these discussions with the professors will allow them to release their unconscious biases and learn from one another and transform their teaching/curriculum to better fit a multicultural classroom.

education should serve its original purpose, on this point I totally agree with this passage. We hope to see a world that everyone is free, and fullfilled with hope.

This highlights the challenges educators often face when entering diverse classroom environments. Even with a background in progressive politics and feminist engagement. This experience underscores the need for training and resources to help teachers develop skill necessary to effectively engage with and support diverse student populations. It emphasizes that many educators may share similar feelings of inadequacy when confronted with ' difference' pointing to a broader need for professional development in this area.

This indicates that many teachers may encounter similar difficulties, highlighting the importance of training to equip educators for diverse learning environments.

Réponse à la question b

Donc, si je comprends bien, avec le single source publishing, il y aurait des templates pour tous les formats de fichiers et donc on aurait pas a entrer manuellement les informations pour que la machine comprennent ce qu'on veut faire avec le document.

Économie de temps, mais est-ce qu'on y sauve de l'argent aussi?

El mostrar favoritismo, va en contra de la ley de Jehová y de Jesús. Por ese motivo debemos tener cuidado de no mostrar la misma clase de maldad en nuestros juicios o prejuicios.

Porque si fuera al revés nos gustaría que nos juzguen con verdad y justicia, sin hipocresía. Porque Jehová advirtió que del modo que nosotros juzguemos también seremos juzgados. Y somos imperfectos así que nadie está libre de errores, defectos y pecados

Advertencia a - ganancias deshonestas, - derramar sangre inocente - cometer fraude y extorsión’. Hay que Defender al inocente sea pobre o rico, con la justicia de Jehová.

Que debían juzgar con imparcialidad, al grande y al pequeño, y si el asunto es muy díficil o delicado, se lo debían presentar a Jehová, y ese es el modelo del cual aprenden los ancianos

Escuchar, consolar , fortalecer y orar (1 Tesalonicenses 5:14). Escúchenlos con empatía (Santiago 1:19). (Proverbios 12:25).

(1 Pedro 1:22; 5:6, 7).

(Santiago 5:14, 15)

Una fuente de ánimo para los hermanos. Isaías 32:1,2

Los ancianos tienen que tomar muchas decisiones pero regularmente ellos se reúnen para recomendar hermanos para siervos ministeriales o ancianos y estas recomendaciones tienen que estar libre de prejuicios 1 Timoteo 5:21

deben tener presente el ejemplo de justicia de Jehová al dar consejos bíblicos cuando es necesario. Y aunque no estan buscando defectos pueden percibir si algún miembro de la congregación puede dar “un paso en falso sin darse cuenta”.

Como los ancianos saben que Jehová no es cruel ni duro al aplicar la justicia, seguirán este consejo: “Traten de corregir al hombre con espíritu apacible” (Gálatas 6:1).

Aunque tengan que ser muy directos y advertirlo de las consecuencias, tendrán presente que es una ovejita de Jehová (Lucas 15:7).b Cuando un consejo se da por amor y con amor, es más probable que la persona recapacite y acepte la corrección.

Los ancianos saben muy bien que para reflejar la justicia de Jehová tienen que contribuir a mantener limpia la congregación.

y psra eso muchas veces tienen que juzgar el caso de alguien que ha cometido un pecado grave.

Al hacerlo, tienen presente que Jehová quiere que muestren misericordia siempre que sea posible. Por eso tratan de ayudar a la persona a arrepentirse de corazón.

perosi ésono sucede tienen la dirección de “Saquen a la persona malvada que está entre ustedes”. En otras palabras, se le expulsa de la congregación (1 Corintios 5:11-13; 2 Juan 9-11).

Aunque a los ancianos les duele tener que tomar esta decisión, comprenden que es necesario para mantener la pureza moral y espiritual de la congregación. Aun así, esperan que la persona recapacite y vuelva al pueblo de Dios algún día (Lucas 15:17, 18).

Que cómo los príncipes de Jehová, imiten la justicia de Jesús

“¡Mira! Un rey reinará con rectitud, y príncipes gobernarán con justicia” (Isaías 32:1).

Lamentablemente es muy común en las familias ver mucho maltrato y es bastante triste. Muchos esposos, esposas y padres se portan como jueces inflexibles con su familia. No paran de decirles cosas crueles e hirientes, y hasta los golpean.

Pero entre los siervos de Dios no debe haber palabras crueles, sarcasmo ni ningún tipo de maltrato (Efesios 4:29, 31; 5:33; 6:4). Y los mandatos de Jesús de dejar de juzgar y dejar de condenar también son para la familia.

Recordemos que practicar la justicia implica tratar a los demás como nos trata Jehová. En vez de ser áspero y duro con quienes lo aman, él “es muy cariñoso y misericordioso” (Santiago 5:11).

Este párrafo es muy importante

Primero, no nos corresponde el discípulo Santiago nos recuerda que “solo hay un Legislador y Juez”: Jehová. Luego hace una pregunta que nos hace reflexionar: “¿Quién eres tú para juzgar a tu prójimo?” (Santiago 4:12; Romanos 14:1-4).

Segumdo somos imperfectos, es fácil juzgar a otros de forma injusta. Muchos factores - los prejuicios, - el resentimiento, - los celos - sentimientos de superioridad pueden distorsionar nuestra forma de ver a los demás.

Tercero no podemos leer los corazones de nadie ni saber todos los detalles de su situación personal.

Cuarto no contamos con toda la informacion, y eso puede inducirnos a sacar concludiines equivocadas. En especial si nos apresuramos.

en conclusión sólo Jehová puede juzgar con justicia y nosotros no podemos juzgar a los demás y si nuestra imperfección ve fallas en ellos , podemos imitar a Jehová , concentrandonos mejor en sus virtudes

Una Idea muy clara que esta mal hacerlo, y si lo hacemos, debemos dejar de hacerlo.

Jesús dio esta advertencia: “Dejen de juzgar, para que no sean juzgados” (Mateo 7:1). Y, según el Evangelio de Lucas, Jesús también dijo: “Dejen de condenar y así nunca serán condenados” (Lucas 6:37).a según la nota se explica que el tiempo verbal utilizado, demuestra que debían dejar de hacer algo que ya estaban haciendo , el juzgar a otros

porqué Jehová nos pone un ejemplo perfecto al tratarnos con misericordia y bondad Salmo 130:3).

y si somos agradecidos , debemos tratar a los demás del mismo modo (Salmo

hacer todo lo posible por ayudar a los demás a salvarse es lo justo.

Además siendo imparciales.

Pero aunque no quieran recibir el mensaje, sigue siendo un acto de justicia insistir mientras Jehová no nos diga que ya es suficiente.

(2 Pedro 3:9).

(Hechos 10:34, 35).

(Romanos 10:11-13).

Porque con la predicación se advierte a las personas, acerca de su juicio, y mediante ésa obra no se podrá decir que Jehová es injusto a no darle oportunidad a los que se comportan mal Marcos 13:10; Mateo 24:3). (Mateo 24:14, 21, 22). (Jonás 3:1-10).

.h2

Claro para entender eso es porque se reconoce que Somos humanos imperfectos Entonces es difícil mantenernos todo el tiempo haciendo la justicia de Pablo lo explicó en su carta a los romanos y en la carta los colosenses se explica que dice que uno debe continuar rehaciendo eso implica constancia verdad no es algo que se hace una vez sino que es un proceso continuo Colosenses 3:9, 10).

Porque primero que nada amamos a Jehová y nosotros le obedecemos por amor y al mismo tiempo entendemos que lo que él nos pide es para nuestro beneficio, y al mismo al tiempo también aprendemos a odiar lo que él odia. (1 Juan 5:3). (Salmo 11:7). (Salmo 97:10).

¿Y qué es lo que Jehová espera de ti? ¡Solo que practiques la justicia, ames la lealtad y andes con modestia junto a tu Dios!” (Miqueas 6:8)

Estamos en deuda con Jehová porque él nos rescató del pecado y la muerte y debido la fe que tenemos en el sacrificio de su hijo Jesucristo, se perdona nuestros pecados y podemos tener vida eterna 1 Juan 1:7; 4:9

DOI: 10.1038/s41596-024-01056-1

Resource: (ATCC Cat# PTA-5077, RRID:CVCL_6911)

Curator: @scibot

SciCrunch record: RRID:CVCL_6911

What is this?

I would pick either the controlled for human RA or the not controlled for human RA numbers here.

I would add alpha here (i.e., make the symbols slightly transaprent).

I find it quite hard to quickly match the different symbols with the different studies.

I would add more y tick labels

The horizontal grid lines are slightly confusing, as they do not help in matching the symbols to the labels on the left.

I would cut down on the number of viruses covered. E.g., you could have a cut off of ten samples minimum for each virus.

This should also work right?

x = 10 y = 10

if x < y: result = "x is less than y" if x > y: result = "x is greater than y" else: result = "x and y must be equal"

Wet van Leibniz = als x en y hetzelfde zijn, moeten x en y al hun eigenschappen gemeen hebben * Als er een of meer eigenschappen zijn die je geest heeft en je lichaam mist (of andersom), betekent dat dus dat je geest en je lichaam verschillend zijn * Relevant voor substantiedualisme

Reviewer #2 (Public review):

The manuscript by Shibata proposed a potentially interesting idea that variation in methylcytosine across cells can inform cellular lineage in a way similar to single nucleotide variants (SNVs). The work builds on the hypothesis that the "replication" of methylcytosine, presumably by DNMT1, is inaccurate and produces stochastic methylation variants that are inherited in a cellular lineage. Although this notion can be correct to some extent, it does not account for other mechanisms that modulate methylcytosines, such as active gain of methylation mediated by DNMT3A/B activity and activity demethylation mediated by TET activity. In some cases, it is known that the modulation of methylation is targeted by sequence-specific transcription factors. In other words, inaccurate DNMT1 activity is only one of the many potential ways that can lead to methylation variants, which fundamentally weakens the hypothesis that methylation variants can serve as a reliable lineage marker. With that being said (being skeptical of the fundamental hypothesis), I want to be as open-minded as possible and try to propose some specific analyses that might better convince me that the author is correct. However, I suspect that the concept of methylation-based lineage tracing cannot be validated without some kind of lineage tracing experiment, which has been successfully demonstrated for scRNA-seq profiling but not yet for methylation profiling (one example is Delgado et al., nature. 2022).

(1) The manuscript reported that fCpG sites are predominantly intergenic. The author should also score the overlap between fCpG sites and putative regulatory elements and report p-values. If fCpG sites commonly overlap with regulatory elements, that would increase the possibility that these sites being actively regulated by enhancer mechanisms other than maintenance methyltransferase activity.

(2) The overlap between fCpG and regulatory sequence is a major alternative explanation for many of the observations regarding the effectiveness of using fCpG sites to classify cell types correctly. One would expect the methylation level of thousands of enhancers to be quite effective in distinguishing cell types based on the published single-cell brain methylome works.

(3) The methylation level of fCpG sites is higher in hindbrain structures and lower in forebrain regions. This observation was interpreted as the hindbrain being the "root" of the methylation barcodes and, through "progressive demethylation" produced the methylation states in the forebrain. This interpretation does not match what is known about methylation dynamics in mammalian brains, in particular, there is no data supporting the process of "progressive demethylation". In fact, it is known that with the activation of DNMT3A during early postnatal development in mice or humans (Lister et al., 2013. Science), there is a global gain of methylation in both CH and CG contexts. This is part of the broader issue I see in this manuscript, which is that the model might be correct if "inaccurate mC replication" is the only force that drives methylation dynamics. But in reality, active enzymatic processes such as the activation of DNMT3A have a global impact on the methylome, and it is unclear if any signature for "inaccurate mC replication" survives the de novo methylation wave caused by DNMT3A activity.

(3) Perhaps one way the author could address comment 3 is to analyze methylome data across several developmental stages in the same brain region, to first establish that the signal of "inaccurate mC replication" is robust and does not get erased during early postnatal development when DNMT3A deposits a large amount of de novo methylation.

(4) The hypothesis that methylation barcodes are homogeneous among progenitor cells and more polymorphic in derived cells is an interesting one. However, in this study, the observation was likely an artifact caused by the more granular cell types in the brain stem, intermediate granularity in inhibitory cells, and highly continuous cell types in cortical excitatory cells. So, in other words, single-cell studies typically classify hindbrain cell types that are more homogenous, and cortical excitatory cells that are much more heterogeneous. The difference in cell type granularity across brain structures is documented in several whole-brain atlas papers such as Yao et al. 2023 Nature part of the BICCN paper package.

(5) As discussed in comment 2, the author needs to assess whether the successful classification of cell types (brain lineage) using fCpG was, in fact, driven by fCpG sites overlapping with cell-type specific regulatory elements.

(6) In Figure 5E, the author tried to address the question of whether methylation barcodes inform lineage or post-mitotic methylation remodeling. The Y-axis corresponds to distances in tSNE. However, tSNE involves non-linear scaling, and the distances cannot be interpreted as biological distances. PCA distances or other types of distances computed from high-dimensional data would be more appropriate.

I appreciate the structured approach of this framework, as it ensures comprehensive reflection and feedback. However, I might get stuck on identifying support options that align perfectly with individual needs. Adding a section for setting SMART goals could further enhance its effectiveness, making it a valuable tool for mentoring others by providing clear, actionable steps. Reference: Smith, J. (2020). Effective Mentoring Practices.

EMBRIOLOGÍA El tracto gastrointestinal se forma a partir de la 4ta SDG. El intestino primitivo se deriva del endodermo y se divide: intestino anterior, medio y posterior. El medio y posterior contribuyen a formar el colon, recto y ano. El intestino medio desarrolla: intestino delgado y colon ascendente y transverso proximal, reciben irrigación de la A. mesentérica superior. En la 6ta semana, el intestino medio se hernia fuera de la cavidad abdominal y luego gira 270° en sentido contrario a las agujas del reloj alrededor de la arteria mesentérica superior para regresar a su posición final dentro de la cavidad abdominal en la semana 10. Durante el transcurso de la 6ta semana, el extremo distal del intestino posterior, la cloaca, es dividida por el tabique urorrectal entre el seno urogenital y el recto. El intestino posterior se desarrolla en el colon transverso distal, colon descendente, recto y ano proximal, todos los cuales reciben la sangre de la arteria mesentérica inferior. El canal anal distal se deriva del ectodermo y recibe la sangre desde la arteria posterior pudenda interna. La línea dentada divide el intestino posterior endodérmico del canal distal ectodérmico.

DOI: 10.1038/s41467-024-52709-4

Resource: RRID:BDSC_58492

Curator: @scibot

SciCrunch record: RRID:BDSC_58492

What is this?

DOI: 10.1186/s12866-024-03514-y

Resource: SPSS (RRID:SCR_002865)

Curator: @scibot

SciCrunch record: RRID:SCR_002865

What is this?

La forma en que se difunde la información determina la velocidad con la que los individuos pueden actuar y planificar sus actividades futuras. En particular, el correo electrónico se ha convertido en el medio de comunicación predominante en la sociedad de la información. Impregna los intercambios comerciales, sociales y científicos y, como tal, es un área muy relevante para la investigación sobre comunidades y redes sociales.

he is happy with his life

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The overall analysis and discovery of the common motif are important and exciting. Very few human/primate ribozymes have been published and this manuscript presents a relatively detailed analysis of two of them. The minimized domains appear to be some of the smallest known self-cleaving ribozymes.

Strengths:

The manuscript is rooted in deep mutational analysis of the OR4K15 and LINE1 and subsequently in modeling of a huge active site based on the closely-related core of the TS ribozyme. The experiments support the HTS findings and provide convincing evidence that the ribozymes are structurally related to the core of the TS ribozyme, which has not been found in primates prior to this work.

Weaknesses:

(1) Given that these two ribozymes have not been described outside of a single figure in a Science Supplement, it is important to show their locations in the human genome, present their sequence and structure conservation among various species, particularly primates, and test and discuss the activity of variants found in non-human organisms. Furthermore, OR4K15 exists in three copies on three separate chromosomes in the human genome, with slight variations in the ribozyme sequence. All three of these variants should be tested experimentally and their activity should be presented. A similar analysis should be presented for the naturally-occurring variants of the LINE1 ribozyme. These data are a rich source for comparison with the deep mutagenesis presented here. Inserting a figure (1) that would show the genomic locations, directions, and conservation of these ribozymes and discussing them in light of this new presentation would greatly improve the manuscript. As for the biological roles of known self-cleaving ribozymes in humans, there is a bioRxiv manuscript on the role of the CPEB3 ribozyme in mammalian memory formation (doi.org/10.1101/2023.06.07.543953), and an analysis of the CPEB3 functional conservation throughout mammals (Bendixsen et al. MBE 2021). Furthermore, the authors missed two papers that presented the discovery of human hammerhead ribozymes that reside in introns (by de la PeÃ{plus minus}a and Breaker), which should also be cited. On the other hand, the Clec ribozyme was only found in rodents and not primates and is thus not a human ribozyme and should be noted as such.

We thank this Reviewer for his/her input and acknowledgment of this work. To improve the manuscript, we have included the genomic locations in Figure 1A, Figure 6A and Figure 6C. And we have tested the activity of representative variants found in the human genome and discussed the activity of the variants in other primates. All suggested publications are now properly cited.

Line 62-66: It has been shown that single nucleotide polymorphism (SNP) in CPEB3 ribozyme was associated with an enhanced self-cleavage activity along with a poorer episodic memory (14). Inhibition of the highly conserved CPEB3 ribozyme could strengthen hippocampal-dependent long-term memory (15, 16). However, little is known about the other human self-cleaving ribozymes.

Line 474-501: Homology search of two TS-like ribozymes. To locate close homologs of the two TS-like ribozymes, we performed cmsearch based on a covariance model (38) built on the sequence and secondary structural profiles. In the human genome, we got 1154 and 4 homolog sequences for LINE-1-rbz and OR4K15-rbz, respectively. For OR4K15-rbz, there was an exact match located at the reverse strand of the exon of OR4K15 gene (Figure 6A). The other 3 homologs of OR4K15-rbz belongs to the same olfactory receptor family 4 subfamily K (Figure 6C). However, there was no exact match for LINE-1-rbz (Figure 6A). Interestingly, a total of 1154 LINE-1-rbz homologs were mapped to the LINE-1 retrotransposon according to the RepeatMasker (http://www.repeatmasker.org) annotation. Figure 6B showed the distribution of LINE-1-rbz homologs in different LINE-1 subfamilies in the human genome. Only three subfamilies L1PA7, L1PA8 and L1P3 (L1PA7-9) can be considered as abundant with LINE-1-rbz homologs (>100 homologs per family). The consensus sequences of all homologs obtained are shown in Figure 6D. In order to investigate the self-cleavage activity of these homologs, we mainly focused on the mismatches in the more conserved internal loops. The major differences between the 5 consensus sequences are the mismatches in the first internal loop. The widespread A12C substitution can be found in majority of LINE-1-rbz homologs, this substitution leads to a one-base pair extension of the second stem (P2) but almost no activity (RA’: 0.03) based on our deep mutational scanning result. Then we selected 3 homologs without A12C substitution for LINE-1-rbz for in vitro cleavage assay (Figure 6E). But we didn’t observe significant cleavage activity, this might be caused by GU substitutions in the stem region. For 3 homologs of OR4K15-rbz, we only found one homolog of OR4K15 with pronounced self-cleavage activity (Figure 6F). In addition, we performed similar bioinformatic search of the TS-like ribozymes in other primate genomes. Similarly, the majority (15 out of 18) of primate genomes have a large number of LINE-1 homologs (>500) and the remaining three have essentially none. However, there was no exact match. Only one homolog has a single mutation (U38C) in the genome assembly of Gibbon (Figure S15). The majority of these homologs have 3 or more mismatches (Figure S15). For OR4K15-rbz, all representative primate genomes contain at least one exact match of the OR4K15-rbz sequence.

Line 598-602: According to the bioinformatic analysis result, there are some TS-like ribozymes (one LINE-1-rbz homolog in the Gibbon genome, and some OR4K15-rbz homologs) with in vitro cleavage activity in primate genomes. Unlike the more conserved CPEB3 ribozyme which has a clear function, the function of the TS-like ribozymes is not clear, as they are not conserved, belong to the pseudogene or located at the reverse strand.

(2) The authors present the story as a discovery of a new RNA catalytic motif. This is unfounded. As the authors point out, the catalytic domain is very similar to the Twister Sister (or "TS") ribozyme. In fact, there is no appreciable difference between these and TS ribozymes, except for the missing peripheral domains. For example, the env33 sequence in the Weinberg et al. 2015 NCB paper shows the same sequences in the catalytic core as the LINE1 ribozyme, making the LINE1 ribozyme a TS-like ribozyme in every way, except for the missing peripheral domains. Thus these are not new ribozymes and should not have a new name. A more appropriate name should be TS-like or TS-min ribozymes. Renaming the ribozymes to lanterns is misleading.

Although we observed some differences in mutational effects, we agree with the reviewer that it is more appropriate to call them TS-like ribozymes. We have replaced all “lantern ribozyme” with “TS-like ribozyme” as suggested.

(3) In light of 2) the story should be refocused on the fact the authors discovered that the OR4K15 and LINE1 are both TS-like ribozymes. That is very exciting and is the real contribution of this work to the field.

We thank this Reviewer for their acknowledgement of this work. To improve the manuscript, we have re-named the ribozymes as suggested.

(4) Given the slow self-scission of the OR4K15 and LINE1 ribozymes, the discussion of the minimal domains should be focused on the role of peripheral domains in full-length TS ribozymes. Peripheral domains have been shown to greatly speed up hammerhead, HDV, and hairpin ribozymes. This is an opportunity to show that the TS ribozymes can do the same and the authors should discuss the contribution of peripheral domains to the ribozyme structure and activity. There is extensive literature on the contribution of a tertiary contact on the speed of self-scission in hammerhead ribozymes, in hairpin ribozyme it's centered on the 4-way junction vs 2-way junction structure, and in HDVs the contribution is through the stability of the J1/2 region, where the stability of the peripheral domain can be directly translated to the catalytic enhancement of the ribozymes.

We appreciate your question and the valuable suggestions provided. We have included the citations and discussion about the peripheral domains in other ribozymes.

Line 570-576: Thus, a more sophisticated structure along with long-range interactions involving the SL4 region in the twister sister ribozyme must have helped to stabilize the catalytic region for the improved catalytic activity. Similarly, previous studies have demonstrated that peripheral regions of hammerhead (49), hairpin (50) and HDV (51, 52) ribozymes could greatly increase their self-cleavage activity. Given the importance of the peripheral regions, absence of this tertiary interaction in the TS-like ribozyme may not be able to fully stabilize the structural form generated from homology modelling.

(5) The argument that these are the smallest self-cleaving ribozymes is debatable. LÃ1/4nse et al (NAR 2017) found some very small hammerhead ribozymes that are smaller than those presented here, but the authors suggest only working as dimers. The human ribozymes described here should be analyzed for dimerization as well (e.g., by native gel analysis) particularly because the authors suggest that there are no peripheral domains that stabilize the fold. Furthermore, Riccitelli et al. (Biochemistry) minimized the HDV-like ribozymes and found some in metagenomic sequences that are about the same size as the ones presented here. Both of these papers should be cited and discussed.

We apologize for any confusion caused by our previous statement. To clarify, we highlighted “35 and 31 nucleotides only” because 46 and 47 nt contain the variable hairpin loops which are not important for the catalytic activity. By comparing the conserved segments, the TS-like ribozyme discussed in this paper is the shortest with the simplest secondary structure. And we have replaced the terms “smallest” and “shortest” with “simplest” in our manuscript. The title has been changed to “Minimal twister sister (TS)-like self-cleaving ribozymes in the human genome revealed by deep mutational scanning”. All the publications mentioned have been cited and discussed. Regarding possible dimerization, we did not find any evidence but would defer it to future detailed structural analysis to be sure.  

Line 605-608: Previous studies also have revealed some minimized forms of self-cleaving ribozymes, including hammerhead (19, 53) and HDV-like (54) ribozymes. However, when comparing the conserved segments, they (>= 36 nt) are not as short as the TS-like ribozymes (31 nt) found here.

(6) The authors present homology modeling of the OR4K15 and LINE1 ribozymes based on the crystal structures of the TS ribozymes. This is another point that supports the fact that these are not new ribozyme motifs. Furthermore, the homology model should be carefully discussed as a model and not a structure. In many places in the text and the supplement, the models are presented as real structures. The wording should be changed to carefully state that these are models based on sequence similarity to TS ribozymes. Fig 3 would benefit from showing the corresponding structures of the TS ribozymes.

We thank the reviewer for pointing these out and we have already fixed them. We have replaced all “lantern ribozyme” with “TS-like ribozyme” as suggested. The term “Modelled structures” were used for representing the homology model. And we have included the TS ribozyme structure in Fig 3.

Reviewer #2 (Public Review):

Summary:

This manuscript applies a mutational scanning analysis to identify the secondary structure of two previously suggested self-cleaving ribozyme candidates in the human genome. Through this analysis, minimal structured and conserved regions with imminent importance for the ribozyme's activity are suggested and further biochemical evidence for cleavage activity are presented. Additionally, the study reveals a close resemblance of these human ribozyme candidates to the known self-cleaving ribozyme class of twister sister RNAs. Despite the high conservation of the catalytic core between these RNAs, it is suggested that the human ribozyme examples constitute a new ribozyme class. Evidence for this however is not conclusive.

Strengths:

The deep mutational scanning performed in this study allowed the elucidation of important regions within the proposed LINE-1 and OR4K15 ribozyme sequences. Part of the ribozyme sequences could be assigned a secondary structure supported by covariation and highly conserved nucleotides were uncovered. This enabled the identification of LINE-1 and OR4K15 core regions that are in essence identical to previously described twister sister self-cleaving RNAs.

Weaknesses:

I am skeptical of the claim that the described catalytic RNAs are indeed a new ribozyme class. The studied LINE-1 and OR4K15 ribozymes share striking features with the known twister sister ribozyme class (e.g. Figure 3A) and where there are differences they could be explained by having tested only a partial sequence of the full RNA motif. It appears plausible, that not the entire "functional region" was captured and experimentally assessed by the authors.

We thank this Reviewer for his/her input and acknowledgment of this work. Because a similar question was raised by reviewer 1, we decided to name the ribozymes as TS-like ribozymes. Regarding the entire regions, we conducted mutational scanning experiments at the beginning of this study. The relative activity distributions (Figure 1B, 1C) have shown that only parts of the sequence contributes to the self-cleavage activity. That is the reason why we decided to focus on the parts of the sequence afterwards.

They identify three twister sister ribozymes by pattern-based similarity searches using RNA-Bob. Also comparing the consensus sequence of the relevant region in twister sister and the two ribozymes in this paper underlines the striking similarity between these RNAs. Given that the authors only assessed partial sequences of LINE-1 and OR4K15, I find it highly plausible that further accessory sequences have been missed that would clearly reveal that "lantern ribozymes" actually belong to the twister sister ribozyme class. This is also the reason I do not find the modeled structural data and biochemical data results convincing, as the differences observed could always be due to some accessory sequences and parts of the ribozyme structure that are missing.

We appreciate the reviewer for raising this question. As we explained in the last question, we now called the ribozymes as TS-like ribozymes. We also emphasize that the relative activity data of the original sequences have indicated that the other part did not make any contribution to the activity of the ribozyme. The original sequences provided in the Science paper (Salehi-Ashtiani et al. Science 2006) were generated from biochemical selection of the genomic library. It did not investigate the contribution of each position to the self-cleavage activity.

Highly conserved nucleotides in the catalytic core, the need for direct contacts to divalent metal ions for catalysis, the preference of Mn2+ oder Mg2+ for cleavage, the plateau in observed rate constants at ~100mM Mg2+, are all characteristics that are identical between the proposed lantern ribozymes and the known twister sister class.

The difference in cleavage speed between twister sister (~5 min-1) and proposed lantern ribozymes could be due to experimental set-up (true single-turnover kinetics?) or could be explained by testing LINE-1 or OR4K15 ribozymes without needed accessory sequences. In the case of the minimal hammerhead ribozyme, it has been previously observed that missing important tertiary contacts can lead to drastically reduced cleavage speeds.

We thank the reviewer for this question. We now called the ribozymes as TS-like ribozymes. As we explained in the last question, the relative activity data of the original sequences have proven that the other part did not make any contribution to the activity of the ribozyme. Moreover, we have tested different enzyme to substrate ratios to achieve single turn-over kinetics (Figure S13). The difference in cleavage speed should be related to the absence of peripheral regions which do not exist in the original sequences of the LINE-1 and OR4K15 ribozyme. We have included the publications and discussion about the peripheral domains in other ribozymes.

Line 458-463: The kobs of LINE-1-core was ~0.05 min-1 when measured in 10mM MgCl2 and 100mM KCl at pH 7.5 (Figure S13). Furthermore, the single-stranded ribozymes exhibited lower kobs (~0.03 min-1 for LINE-1-rbz) (Figure S14) when comparing with the bimolecular constructs. This confirms that the stem loop region SL2 does not contribute much to the cleavage activity of the TS-like ribozymes.

Line 570-576: Thus, a more sophisticated structure along with long-range interactions involving the SL4 region in the twister sister ribozyme must have helped to stabilize the catalytic region for the improved catalytic activity. Similarly, previous studies have demonstrated that peripheral regions of hammerhead (49), hairpin (50) and HDV (51, 52) ribozymes could greatly increase their self-cleavage activity. Given the importance of the peripheral regions, absence of this tertiary interaction in the TS-like ribozyme may not be able to fully stabilize the structural form generated from homology modelling.

Reviewer 2: ( Recommendations For The Authors):

Major points

It would have made it easier to connect the comments to text passages if the submitted manuscript had page numbers or even line numbers.

We thank the reviewer for pointing this out and we have already fixed it.

In the introduction: "...using the same technique, we located the functional and base-pairing regions of..." The use of the adjective functional is imprecise. Base-paired regions are also important for the function, so what type of region is meant here? Conserved nucleotides?

We thank the reviewer for pointing this out. We were describing the regions which were essential for the ribozyme activity. And we have defined the use of “functional region” in introduction.

Line 95: we located the regions essential for the catalytic activities (the functional regions) of LINE-1 and OR4K15 ribozymes in their original sequences.

In their discussion, the authors mention the possible flaws in their 3D-modelling in the absence of Mg2+. Is it possible to include this divalent metal ion in the calculations?

We thank the reviewer for this question. Currently, BriQ (Xiong et al. Nature Communications 2021) we used for modeling doesn’t include divalent metal ion in modeling.

Xiong, Peng, Ruibo Wu, Jian Zhan, and Yaoqi Zhou. 2021. “Pairing a High-Resolution Statistical Potential with a Nucleobase-Centric Sampling Algorithm for Improving RNA Model Refinement.” Nature Communications 12: 2777. doi:10.1038/s41467-021-23100-4.

Abstract:

It is claimed that ribozyme regions of 46 and 47 nt described in the manuscript resemble the shortest known self-cleaving ribozymes. This is not correct. In 1988, hammerhead ribozymes in newts were first discovered that are only 40 nt long.

We apologize for any confusion caused by our previous statement. To clarify, we highlighted “35 and 31 nucleotides only” as 46 and 47 nt contain the variable hairpin loops which are not important for the catalytic activity. By comparing the conserved segments, the TS-like ribozyme discussed in this paper is the shortest with the simplest secondary structure. And we have replaced the terms “smallest” and “shortest” with “simplest” in our manuscript. The title has been changed to “Minimal TS-like self-cleaving ribozyme revealed by deep mutational scanning”.

The term "functional region" is, to my knowledge, not a set term when discussing ribozymes. Does it refer to the catalytic core, the cleavage site, the acid and base involved in cleavage, or all, or something else? Therefore, the term should be 1) defined upon its first use in the manuscript and 2) probably not be used in the abstract to avoid confusion to the reader.

We apologize for any confusion caused by our previous statement. To clarify, we have changed the term “functional region” in abstract. And we have defined the use of “functional region” in introduction.

Line 34-37: We found that the regions essential for ribozyme activities are made of two short segments, with a total of 35 and 31 nucleotides only. The discovery makes them the simplest known self-cleaving ribozymes. Moreover, the essential regions are circular permutated with two nearly identical catalytic internal loops, supported by two stems of different lengths.

Line 95: we located the regions essential for the catalytic activities (the functional regions) of LINE-1 and OR4K15 ribozymes in their original sequences.

The choice of the term "non-functional loop" in the abstract is a bit unfortunate. The loop might not be important for promoting ribozyme catalysis by directly providing, e.g. the acid or base, but it has important structural functions in the natural RNA as part of a hairpin structure.

We thank the reviewer for pointing this out and we have re-phrased the sentences.

Line 33-34: We found that the regions essential for ribozyme activities are made of two short segments, with a total of 35 and 31 nucleotides only.

Line 283: Removing the peripheral loop regions (Figures 1B and 1C) allows us to recognize that the secondary structure of OR4K15-rbz is a circular permutated version of LINE-1-rbz.

Results:

Please briefly explain CODA and MC analysis when first mentioned in the results (Figure (1) The more detailed explanation of these terms for Figure 2 could be moved to this part of the results section (including explanations in the figure legend).

We thank the reviewer for pointing this out and we included a brief explanation.

Line 150-154: CODA employed Support Vector Regression (SVR) to establish an independent-mutation model and a naive Bayes classifier to separate bases paired from unpaired (26). Moreover, incorporating Monte-Carlo simulated annealing with an energy model and a CODA scoring term (CODA+MC) could further improve the coverage of the regions under-sampled by deep mutations.

Please indicate the source of the human genomic DNA. Is it a patient sample, what type of tissue, or is it an immortalized cell line? It is not stated in the methods I believe.

We thank the reviewer for pointing this out. According to the original Science paper (Salehi-Ashtiani et al. Science 2006), the human genomic DNA (isolated from whole blood) was purchased from Clontech (Cat. 6550-1). In our study, we directly employed the sequences provided in Figure S2 of the Science paper for gene synthesis. Thus, we think it is unnecessary to mention the source of genomic DNA in the methods section of our paper.  

Please also refer to the methods section when the calculation of RA and RA' values is explained in the main text to avoid confusion.

We thank the reviewer for pointing this out and we have fixed it.

Line 207-208: Figure 2A shows the distribution of relative activity (RA’, measured in the second round of mutational scanning) (See Methods) of all single mutations

For OR4K15 it is stated that the deep mutational scanning only revealed two short regions as important. However, there is another region between approx. 124-131 nt and possibly even at positions 47 and 52 (to ~55), that could contribute to effective RNA cleavage, especially given the library design flaws (see below) and the lower mutational coverage for OR4K15. A possible correlation of the mutations in these regions is even visible in the CODA+MC analysis shown in Figure 1D on the left. Why are these regions ignored in ongoing experiments?

We thank the reviewer for this question. As shown in Table S1, although the double mutation coverage of OR4K15-ori was low (16.2 %), we got 97.6 % coverage of single mutations. The relative activity of these single mutations was enough to identify the conserved regions in this ribozyme. Mutations at the positions mentioned by the reviewer did not lead to large reductions in relative activity. Since the relative activity of the original sequence is 1, we presumed that only positions with average relative activity much lower than 1 might contribute to effective cleavage.

Regarding the corresponding correlation of mutations in CODA+MC, they are considered as false positives generated from Monte Carlo simulated annealing (MC), because lack of support from the relative activity results.

Have the authors performed experiments with their "functional regions" in comparison to the full-length RNA or partial truncations of the full-length RNA that included, in the case of OR4K15, nt 47-131? Also for LINE-1 another stem region was mentioned (positions 14-18 with 30-34) and two additional base pairs. Were they included in experiments not shown as part of this manuscript?

We appreciate the reviewer for raising this question. We only compared the full-length or partial truncations of the LINE-1 ribozyme. Since the secondary structure predicted from OR4K15-ori data was almost the same as LINE-1, we didn’t perform deep mutagenesis on the partial truncation of the OR4K15. However, the secondary structure of OR4K15 was confirmed by further biochemical experiments.   

Regarding the second question, the additional base pairs were generated by Monte Carlo simulated annealing (MC). They are considered as false positives because of low probabilities and lack of support from the deep mutational scanning results. The appearance of false positives is likely due to the imperfection of the experiment-based energy function employed in current MC simulated annealing. 

Are there other examples in the literature, where error-prone PCR generates biases towards A/T nucleotides as observed here? Please cite!

We thank the reviewer for pointing this out and we have included the corresponding citation.

Line 161-162: The low mutation coverage for OR4K15-ori was due to the mutational bias (27, 28) of error-prone PCR (Supplementary Figures S1, S2, S3 and S4).

Line 170-171: whose covariations are difficult to capture by error-prone PCR because of mutational biases (27, 28).

The authors mention that their CODA analysis was based on the relative activities of 45,925 and 72,875 mutation variants. I cannot find these numbers in the supplementary tables. They are far fewer than the read numbers mentioned in Supplementary Table 2. How do these numbers (45,925 and 72,875) arise? Could the authors please briefly explain their selection process?

We apologize for any confusion caused by our previous statement. Our CODA analysis only utilized variants with no more than 3 mutations. The number listed in the supplementary tables is the total number of the variants. To clarify, we have included a brief explanation for these numbers.

Line 203-204: We performed the CODA analysis (26) based on the relative activities of 45,925 and 72,875 mutation variants (no more than 3 mutations) obtained for the original sequence and functional region of the LINE-1 ribozyme, respectively.

What are the reasons the authors assume their findings from LINE-1 can be used to directly infer the structure for OR4K15? (Third section in results, last paragraph)

We apologize for any confusion caused by our previous statement. We meant to say that the consistency between LINE-1-rbz and LINE-1-ori results suggested that our method for inferring ribozyme structure was reliable. Thus, we employed the same method to infer the structure of the functional region of OR4K15. To clarify, we have re-phrased the sentence.   

Line 259-261: The consistent result between LINE-1-rbz and LINE-1-ori suggested that reliable ribozyme structures could be inferred by deep mutational scanning. This allowed us to use OR4K15-ori to directly infer the final inferred secondary structure for the functional region of OR4K15.

There are several occasions where the authors use the differences between the proposed lantern ribozymes and twister sister data as reasons to declare LINE-1 and OR4K15 a new ribozyme class. As mentioned previously, I am not convinced these differences in structure and biochemical results could not simply result from testing incomplete LINE-1 and OR4K15 sequences.

We apologize for any confusion caused by our previous statement. Despite we observed some differences in mutational effects, we agree with the reviewer that it is not convincing to claim them as a new ribozyme class. We have replaced all “lantern ribozyme” with “TS-like ribozyme” as the reviewer 1 suggested.

The authors state, that "the result confirmed that the stem loop SL2 region in LINE-1 and OR4K15 did not participate in the catalytic activity". To draw such a conclusion a kinetic comparison between a construct that contains SL2 and does not contain SL2 would be necessary. The given data does not suffice to come to this conclusion.

We appreciate the reviewer for raising this question. To address this, we performed gel-based kinetic analysis of these two ribozymes (Figure S14).

Line 458-462: The kobs of LINE-1-core under single-turnover condition was ~0.05 min-1 when measured in 10mM MgCl2 and 100mM KCl at pH 7.5 (Figure S13). Only a slightly lower value of  kobs (~0.03 min-1) was observed for LINE-1-rbz (Figure S14). This confirms that the stem loop region SL2 does not contribute to the cleavage activity of the TS-like ribozymes.

Construct/Library design:

The last 31 bp in the OR4K15 ribozyme template sequence are duplicated (Supplementary Table 4). Therefore, there are 2 M13 fwd binding sites and several possible primer annealing sites present in this template. This could explain the lower yield for the mutational analysis experiments. Did the authors observe double bands in their PCR and subsequent analysis? The experiments should probably be repeated with a template that does not contain this duplication. Alternatively, the authors should explain, why this template design was chosen for OR4K15.

We apologize for this mistake during writing. Our construct design for OR4K15 contains only one M13F binding site. We thank the reviewer for pointing this out and we have fixed the error.

Figure 5B: Where are the bands for the OR4K15 dC-substrate? They are not visible on the gel, so one has to assume there was no substrate added, although the legend indicates otherwise.

Also this figure, please indicate here or in the methods section what kind of marker was used. In panels A and B, please label the marker lanes.

We apologize for this mistake and we have repeated the experiment. The marker lane was removed to avoid confusion caused by the inappropriate DNA marker. 

The authors investigated ribozyme cleavage speeds by measuring the observed rate constants under single-turnover conditions. To achieve single-turnover conditions enzyme has to be used in excess over substrate. Usually, the ratios reported in the literature range between 20:1 (from the authors citation list e.g.: for twister sister (Roth et al 2014) and hatchet (Li et al. 2015)) or even ~100:1 (for pistol: Harris et al 2015, or others https://www.sciencedirect.com/science/article/pii/S0014579305002061). Can the authors please share their experimental evidence that only 5:1 excess of enzyme over the substrate as used in their experiments truly creates single-turnover conditions?

We greatly appreciate the Reviewer for raising this question. To address this, we performed kinetic analysis using different enzyme to substrate ratios (Figure S13). There is not too much difference in kobs, except that kobs reach the highest value of 0.048 min-1 when using 100:1 excess of enzyme over the substrate. 

Line 458-460: The kobs of LINE-1-core under single-turnover condition was ~0.05 min-1 when measured in 10mM MgCl2 and 100mM KCl at pH 7.5 (Figure S13).

Citations:

In the introduction citation number 12 (Roth et al 2014) is mentioned with the CPEB3 ribozyme introduction. This is the wrong citation. Please also insert citations for OR4K15 and IGF1R and LINE-1 ribozyme in this sentence.

We thank the reviewer for pointing this out and we now have fixed it.

Also in the introduction, a hammerhead ribozyme in the 3' UTR of Clec2 genes is mentioned and reference 16 (Cervera et al 2014) is given, I think it should be reference 9 (Martick et al 2008)

We thank the reviewer for pointing this out and we now have fixed it.

In the results section it is stated that, "original sequences were generated from a randomly fragmented human genomic DNA selection based biochemical experiment" citing reference 12. This is the wrong reference, as I could not find that Roth et al 2014 describe the use of such a technique. The same sentence occurs in the introduction almost verbatim (see also minor points).

We thank the reviewer for pointing this out and we now have fixed it.

Minor points

Headline:

Either use caps for all nouns in the headline or write "self-cleaving ribozyme" uncapitalized

We thank the reviewer for pointing this out and we now have fixed it.

Abstract:

1st sentence: in "the" human genome

"Moreover, the above functional regions are..." - the word "above" could be deleted here

"named as lantern for their shape"- it should be "its shape"

"in term of sequence and secondary structure"- "in terms"

"the nucleotides at the cleavage sites" - use singular, each ribozyme of this class has only one cleavage site

We thank the reviewer for pointing these out and we now have fixed them.

Introduction:

Change to "to have dominated early life forms"

Change to "found in the human genome"

Please write species names in italics (D. melanogaster, B. mori)

Please delete "hosting" from "...are in noncoding regions of the hosting genome"

Please delete the sentence fragment/or turn it into a meaningful sentence: "Selection-based biochemical experiments (12).

Change to "in terms of sequence and secondary structure, suggesting a more"

Please reword the last sentence in the introduction to make clear what is referred to by "its", e.g. probably the homology model of lantern ribozyme generated from twister sister ribozymes?

Please refer to the appropriate methods section when explaining the calculation of RA and RA'.

We thank the reviewer for pointing these out and we now have fixed them.

The last sentence of the second paragraph in the second section of the results states that the authors confirmed functional regions for LINE-1 and OR4K15, however, until that point the section only presents data on LINE-1. Therefore, OR4K15 should not be mentioned at the end of this paragraph.

In response to the reviewer's suggestions, we have removed OR4K15 from this paragraph.

Line 225-228: The consistency between base pairs inferred from deep mutational scanning of the original sequences and that of the identified functional regions confirmed the correct identification of functional regions for LINE-1 ribozyme.

Change to "Both ribozymes have two stems (P1, P2), to internal loops ..."

We thank the reviewer for pointing this out and we now have fixed it.

The section naming the "functional regions" of LINE-1 and OR4K15 lantern ribozymes should be moved after the section in which the circular permutation is shown and explained. Therefore, the headline of section three should read "Consensus sequence of LINE-1 and OR4K15 ribozymes" or something along these lines.

We thank the reviewer for pointing this out and we now have fixed it.

Line 308-309: Given the identical lantern-shaped regions of the LINE-1-rbz and OR4K15-rbz ribozyme, we named them twister sister-like (TS-like) ribozymes.

The statement on the difference between C8 in OR4K15 and U38 in LINE-1 should be further classified. As U38 is only 95% conserved. Is it a C in those other instances or do all other nucleotide possibilities occur? Is the high conservation in OR4K15 an "artifact" of the low mutation rate for this RNA in the deep mutational scanning?

We thank the reviewer for this question. Yes, the high conservation in OR4K15 an "artifact" of the low mutation rate for this RNA in the deep mutational scanning. That is why RA’ value is more appropriate to describe the conservation level of each position. We also mentioned this in the manuscript:

Line 287-288: The only mismatch U38C in L1 has the RA’ of 0.6, suggesting that the mismatch is not disruptive to the functional structure of the ribozyme.

Section five, first paragraph: instead of "two-stranded LINE-1 core" use the term "bimolecular", as it is more commonly used.

We thank the reviewer for pointing this out and we now have changed it.

Figure caption 3 headline states "Homology modelled 3D structure..."but it also shows the secondary structures of LINE1, OR4K15 and twister sister examples.

We thank the reviewer for pointing this out and we now have removed “3D”.

In Figure 3C, we see a nucleobase labeled G37, however in the secondary structure and sequence and 3D structural model there is a C37 at this position. Please correct the labeling.

We thank the reviewer for pointing this out and we now have fixed it.

Section 7 "To address the above question..." please just repeat the question you want to address to avoid any confusion to the reader.

We thank the reviewer for pointing these out and we have re-phrased this sentence.

Line 364: Considering the high similarity of the internal loops, we further investigated the mutational effects on the internal loop L1s.

Please rephrase the sentence "By comparison, mutations of C62 (...) at the cleavage site did not make a major change on the cleavage activity...", e.g. "did not lead to a major change" etc.

Section 8, first paragraph: This result further confirms that the RNA cleavage in lantern...", please delete "further"

Change to "analogous RNAs that lacked the 2' oxygen atom in the -1 nucleotide"

Methods

Change to "We counted the number of reads of the cleaved and uncleaved..."

Change to "...to produce enough DNA template for in vitro transcription."

Change to "The DNA template used for transcription was used..." (delete while)

We thank the reviewer for pointing these out and we now have fixed them.

Supplement

All supplementary figures could use more detailed Figure legends. They should be self-explanatory.

Fig S1/S2: how is "mutation rate" defined/calculated?

We thank the reviewer for pointing this out and we now have added a short explanation. The mutation rate was calculated as the proportion of mutations observed at each position for the DNA-seq library.

Fig S3/S4: axis label "fraction", fraction of what? How calculated?

We thank the reviewer for pointing this out and we now have added a short explanation. The Y axis “fraction” represents the ratio of each mutation type observed in all variants.

Fig S5: RA and RA' are mentioned in the main text and methods, but should be briefly explained again here, or it should be clearly referred to the methods. Also, the axis label could be read as average RA' divided by average RA. I assume that is not the case. I assume I am looking at RA' values for LINE-1 rbz and RA values for LINE-1-ori? Also, mention that only part of the full LINE-1-ori sequence is shown...

We thank the reviewer for pointing this out and we have now added a short explanation. The Y axis represents RA’ for LINE-1-rbz, or RA for LINE-1-ori. The part shown is the overlap region between LINE-1-rbz and LINE-1-ori. We apologize for any confusion caused by our previous statement.

Fig S9 the magenta for coloring of the scissile phosphate is hard to see and immediately make out.

We thank the reviewer for pointing this out and we now have added a label to the scissile phosphate.

Fig S10: Why do the authors only show one product band here? Instead of both cleavage fragments as in Figure 5?

We thank the reviewer for this question. We purposely used two fluorophores (5’ 6-FAM, 3’ TAMRA) to show the two product bands in Figure 5. In Fig S10, long-time incubation was used to distinguish catalysis based self-cleavage from RNA degradation. This figure was prepared before the purchasing of the substrate used in Figure 5. The substrate strand used in Fig S10 only have one fluorophore (5’ 6-FAM) modification. And the other product was too short to be visualized by SYBR Gold staining.

Fig S13: please indicate meaning of colors in the legend (what is pink, blue, grey etc.)

Please change to "RtcB ligase was used to capture the 3' fragment after cleavage...."

We thank the reviewer for pointing this out and we now have fixed it.

Author response:

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

Reviewer #1 (Recommendations For The Authors):

Materials and Methods section:

Cell gating and FACS sorting strategies need to be explained. There is no figure legend of supplementary figure 4 which is supposed to explain the gating strategy. Please detail the strategy for each cell types.

Thank you for your suggestion. We have given a detailed description about the gating and FACS sorting strategies for different liver cell types in supplementary figure 1. In addition, flow cytometry plots of CD45+Ly6C-CD64+F4/80+ KCs from Bmp9fl/flBmp10fl/flLrat Cre mouse were also presented in supplementary figure 1.

The genetic background of the different mouse strains and the age of the mice should be noted on each figure.

All the mice used in our study are C57BL/6 background (method section). The age of the mice has been described on each figure.

The Mann Whitney test instead of the two-tailed student's t-test should be used for the different statistical analyses. Why are the expression counts statically analyzed by 2-tailed Student's t test as they were already identified as DE in RNAseq statistical analysis?

Thank you for your suggestion. Statical methods have been corrected in the revised manuscript.

What is the age of the mice and how many are used for each bulk RNAseq?

This information has been added on the corresponding figure legends.

Figure 1:

Figure 1a and c: The qPCR data would be much more interesting if presented as DDct and not as relative value as we do not see the mRNA levels of BMP9 and BMP10 in each Bmp9fl/flBmp10fl/flCre mouse. This would allow to compare the mRNA level of BMP9 versus BMP10. This should be changed in all figures.

The presentation of qPCR data in Figure 1a have been changed, which is allowed to compare the abundance of BMP9 versus BMP10 mRNA. Figure 1c only shows the expression of BMP10, so it is unnecessary to present qPCR data as DDct. In our bulk RNA sequencing data of liver tissues, we found that BMP9 expression counts is higher than that of BMP10, in line with the data from BioGPS.

Figure 1e (IF) and f (FACS), the quantification of these data should be added as shown in Fig2d. What is the difference between Fig1e and Fig2d as they both seem to show the quantification of F4/80 in CTL versus Bmp9fl/flBmp10fl/flLratCre mice. Are the cells sorted in Fig1f and 1e and suppl Fig1b? if yes please precise the strategy. If they are not gated how can the authors obtain 93% of KC? The reference Tillet et al., JBC 2018 should be added in the discussion of figure 1 as it is the first description of BMP10 in HSC.

The quantitative data of Figure 1e and 1f have been added in our revised manuscript. Compared with other tissue-resident macrophages, CLEC4F as a KC-specific marker exclusively expressed on KCs. In our previous report (PMID: 34874921), we demonstrated that BMP9/10-ALK1 signal induced the expression of CLEC4F. The data shown in Figure 1e repeated this phenotype that upon loss of BMP9/10-ALK1 signal, liver macrophages did not express CLEC4F. F4/80 in Figure 1e was used as an internal positive control. Fig2d showed the quantification of F4/80 and CD64, two pan-macrophage markers, which was more accurate to measure the number of liver macrophages, especially given that F4/80 mean fluorescence intensity was reduced in liver macrophages of Bmp9fl/flBmp10fl/flLrat Cre mice. Cells in Fig1f, 1e and suppl Fig1b were not sorted and the flow cytometry plots of these cells were pre-gated on live CD45+Ly6C-CD64+F4/80+ liver macrophages. The reference Tillet et al., JBC 2018 has been added in our revised manuscript.

Supplementary 4 should have a detailed figure legend and should appear before gating experiments. What cell subtype is used for each cell type gating. Please add the exact references of all the antibodies used and if they are fluorescently labeled antibodies. Why is the number of lymphocytes noted and how is it calculated? The gating strategy for the Bmp9fl/flBmp10fl/flLratCre mice should also be showed as the number of FA4/80+ and Tim4+ cells are decreased.

A detailed figure legend has been added in original supplementary figure 4 that has been moved to supplementary figure 1 in our revised manuscript. The antibodies used in our study were also used in our previous report (PMID: 34874921) and others (PMID: 31561945; PMID: 26813785). Lymphocytes number on flow cytometry plots will automatically appear when we analyze flow cytometry data, so it does not mean that these selected cells are lymphocytes. To avoid the misunderstanding, these words have been deleted. The gating strategy of CD45+Ly6C-CD64+F4/80+ liver macrophages for the Bmp9fl/flBmp10fl/flLrat Cre mice was showed in our revised manuscript (Supplementary Figure 1).

Figure 2:

Figure 2a: How many mice were used for bulk RNAseq at what age? Please describe the gating strategy for sorting liver macrophages. The PCA should be shown. The genes represented in Fig2c and cited in the text should be shown on the volcano plot and the heatmap (Timd4, Cdh5, Cd5l). A reference for these KC and monocytic markers should be added in the text.

Control and Bmp9fl/flBmp10fl/flLrat Cre mice at the age of 8-10 weeks (n=3/group) were used for bulk RNAseq. This information has been added in Figure 2a legend. The PCA, Timd4 gene and references for these KC and monocytic markers have been shown in our revised manuscript according to your suggestion.

Figure 2b: How are selected the genes represented in the heatmap? The top ones? If it is a KC signature the authors should give a reference for this signature.

These genes were KC signature genes. The reference (PMID: 30076102) has been given in our revised manuscript.

Fig2e: Please explain what is the Vav1 promoter and in which cells it will delete Alk1and Smad4? The authors also need to show that Alk1 and Smad4 are indeed deleted in these mice and in which cell subtype (EC and KC?). This is an important point as the authors conclude that other molecular mechanisms than Smad4 signaling may affect the phenotypes of liver macrophages in Bmp9fl/flBmp10fl/flLratCre.

Cre recombinase of Vav1Cre mice is expressed at high levels in hematopoietic stem cells (PMID: 27185381). This strain is widely used to target all hematopoietic cells with a high efficiency (PMID: 24857755). In our previous report (PMID: 34874921), we demonstrated that Alk1 (Supplemental Figure 6A) and Smad4 (Supplemental Figure 6G) were efficiently deleted in KCs from Alk1fl/flVav1Cre and Smad4fl/flVav1Cre mice, respectively. This sentence and reference have been added in our revised manuscript. Homozygous loss of ALK-1 causes embryonically lethality due to aberrant angiogenesis (PMID: 28213819). EC-specific ALK1 knockout in the mouse through deletion of the ALK1 gene from an Acvrl12loxP allele with the EC-specific L1-Cre line results in postnatal lethality at P5, and mice exhibiting hemorrhaging in the brain, lung, and gastrointestinal tract (PMID: 19805914). In contrast, Alk1fl/flVav1Cre mice generated in our lab did not observe this phenomenon or body weight loss, and still survived at the age of 16 weeks. Thus, we don’t think that ECs can be targeted by Vav1Cre strain, at least in our experimental system.

Supl Figure 3 (revised Supl Figure 4): The authors need to explain what cell types are affected by Csf1r-Cre and Clec4fDTR. Have the authors tried to perform a similar experiment in Bmp9fl/flBmp10fl/flLratCre? The legend of the Y axis is not clear, why is CD45+ used in the first bar graph while the other two graphs use F4/80+?

We (PMID: 34874921) and others (PMID: 31587991; PMID: 31561945; PMID: 26813785) have demonstrated that Clec4f specifically expressed on KCs and thus only KCs can be deleted in Clec4fDTR mice after DT injection. CSF1R, also known as macrophage colony-stimulating factor receptor (M-CSFR), is the receptor for the major monocyte/macrophage lineage differentiation factor CSF1. Thus, Csf1r-Cre strain can target monocyte, monocyte-derived macrophage and tissue-resident macrophage including liver, spleen, intestine, heart, kidney, and muscle with a high efficiency (PMID: 29761406). We did not perform a similar experiment in Bmp9fl/flBmp10fl/flLrat Cre mice as we have demonstrated that the differentiation of liver macrophages from Bmp9fl/flBmp10fl/flLrat Cre mice is inhibited. The other two graphs in Supl Figure 4C were obtained from Supl Figure 4B. Flow cytometry plots in Supl Figure 4B are pre-gated on CD45+Ly6C-CD64+F4/80+ liver macrophages, so it is appropriate to use F4/80+ as an internal control.

Figure 3: Same remarks as in Figure 2. How many mice were used for bulk RNAseq, at what age? The PCA should be shown. How were selected the genes represented in the heatmap? The top ones? A reference should be given for the sinusoidal EC and the continuous EC signatures and large artery signature. Maf and Gata4 should be shown on the volcano plot. A quantification for CD34 IF (Fig3e) as well as for the quantification of the FACS data (Fig 3f) should be added.

Control and Bmp9fl/flBmp10fl/flLrat Cre mice at the age of 8-10 weeks (n=3/group) were used for bulk RNAseq. According to your suggestion, other revisions have been made.

Figure 4: A quantification and statistical analysis of Prussian staining area and GS IF should be added not just number of mice which were affected.

A quantification and statistical analysis of Prussian staining area and GS IF has been added.

Minor points:

Few spelling mistakes that should be checked.

Figure 5a, some bar graphs are missing.

Spelling mistakes and missing bar graphs in Figure 5a have been corrected.

Reviewer #2 (Recommendations For The Authors):

The authors should provide some additional information:

- Did the single HSC-KO mice for either BMP9 or BMP10 already show partial phenotypes?

We think that under steady state, the phenotype of KCs and ECs, described in our manuscript, in the livers of single HSC-KO mice for either BMP9 or BMP10 was not altered. However, we don’t know whether the role of BMP9 and BMP10 is still redundant in liver diseases or inflammation, which is worth further studying.

- The authors should also stain Endomucin, Lyve1, CD32b on liver tissue to assess endothelial zonation/differentiation in addition to FACS analysis.

In our revised manuscript, we performed immunostaining for Endomucin and Lyve1 and found increased expression of Endomucin and decreased expression of Lyve1 (Figure 3g), suggesting that endothelial zonation/differentiation was disrupt in the liver of Bmp9fl/flBmp10fl/flLrat Cre mice compared to their littermates. We did not stain CD32b expression in the liver section as there is no good antibody against mouse CD32b for frozen sections.

- Did the authors assess BMP9/BMP10 effects individually and combined in vitro on KC and EC? Are these likely only direct effects or may they also involve each other (i.e. also cross talk between KC and EC in response to BMP9/10?). This could be assessed in co-culture models.

Using ALK1 reporter mice, we demonstrated that KCs and liver ECs express ALK1.We and others have shown that in vitro stimulation with BMP9/BMP10 can induce the expression of ID1/ID3 and GATA4/Maf in KCs and ECs (PMID: 34874921; PMID: 35364013; PMID: 30964206), respectively. These results suggested that BMP9/BMP10 can directly function on KCs and ECs. Indeed, we are also interested in the crosstalk between KCs and ECs. However, in vitro coculture system can not mimic the interaction between KCs and ECs in the liver as these cells will lose their identity upon their isolation from liver environment. Nevertheless, Bonnardel et al. applied Nichenet bioinformatic analysis to predict that liver ECs provide anchoring site, Notch and CSF1 signal for KCs (PMID: 31561945). Of course, this prediction still needs experimental validation.

- The abstract should be rephrased and more specific focus on BMP related intercellular crosstalk in the liver and its implications for liver health and disease. At the end of the abstract they should also emphasize for which specific fields/topics/diseases these findings are important.

Thank you for your suggestion. The abstract has been rephrased and we hope this abstract could satisfy you.

Es una manera de ayudar a personas de otros lugares que tienen poca accesibilidad a recursos físicos como la impresión de sus escritos, a que todo eso que piensan y los inspiran, se pueda ver reflejado de alguna manera y pueda llegar a otros.

Me interesa la manera de como esta pensado en personas que no son expertas en este tecnología o que no tienen un acercamiento a ella a diferencia de otros.

Es interesante el terma de que no solo se puede corregir o se le pueden añadir cosas al texto una vez este hecho, sino que se puede hacer en conjunto y prácticamente al instante en que surjan nuevas ideas o comentarios.

¿para co-diseñar y co-construir hay algunos acuerdos mínimos?

Un libro y escritura colectiva que permite cambiar las dinámicas individuales de producción textual, pero que a su vez genera inquietudes frente al respeto por las diversas formas de pensar y escribir, y en donde me imagino prima el respeto.

Se busca la forma de que la información sea visible de forma estructurada desde cualquier parte

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

This is a well-written and detailed manuscript showing important results on the molecular profile of 4 different cohorts of female patients with lung cancer.

The authors conducted comprehensive multi-omic profiling of air-pollution-associated LUAD to study the roles of the air pollutant BaP. Utilizing multi-omic clustering and mutation-informed interface analysis, potential novel therapeutic strategies were identified.

Strengths:

The authors used several different methods to identify potential novel targets for therapeutic interventions.

Weaknesses:

Statistical test results need to be provided in comparisons between cohorts.

We appreciate your recognition and valuable suggestions.. We have revised statistical test results in the panels including: Fig. 3b, e and g.

Reviewer #2 (Public Review):

Summary:

Zhang et al. performed a proteogenomic analysis of lung adenocarcinoma (LUAD) in 169 female never-smokers from the Xuanwei area (XWLC) in China. These analyses reveal that XWLC is a distinct subtype of LUAD and that BaP is a major risk factor associated with EGFR G719X mutations found in the XWLC cohort. Four subtypes of XWLC were classified with unique features based on multi-omics data clustering.

Strengths:

The authors made great efforts in performing several large-scale proteogenomic analyses and characterizing molecular features of XWLCs. Datasets from this study will be a valuable resource to further explore the etiology and therapeutic strategies of air-pollution-associated lung cancers, particularly for XWLC.

Weaknesses:

(1) While analyzing and interpreting the datasets, however, this reviewer thinks that authors should provide more detailed procedures of (i) data processing, (ii) justification for choosing methods of various analyses, and (iii) justification of focusing on a few target gene/proteins in the datasets for further validation in the main text.

We appreciate your valuable feedback. In response to the suggestions for enhancing the manuscript's clarity, we have provided more detailed procedures in the main text and methods sections.

(2) Importantly, while providing the large datasets, validating key findings is minimally performed, and surprisingly there is no interrogation of XWLC drug response/efficacy based on their findings, which makes this manuscript descriptive and incomplete rather than conclusive. For example, testing the efficacy of XWLC response to afatinib combined with other drugs targeting activated kinases in EGFR G719X mutated XWLC tumors would be one way to validate their datasets and new therapeutic options.

We appreciate your suggestion. In reference to testing the efficacy of XWLC response to afatinib combined with drugs targeting kinases, we have planned to establish PDX and organoid models to validate the effectiveness of our therapeutic approach. Due to the extended timeframe required, we intend to present these results in a subsequent study.

(3) The authors found MAD1 and TPRN are novel therapeutic targets in XWLC. Are these two genes more frequently mutated in one subtype than the other 3 XWLC subtypes? How these mutations could be targeted in patients?

Thank you for your question. We have investigated the TPRN and MAD1 mutations in our dataset, identifying five TPRN mutations and eight MAD1 mutations. Among the TPRN mutations, XWLC_0046 and XWLC_0017 belong to the MCII subtype, XWLC_0012 belongs to the MCI subtype, and the subtype of the other three samples is undetermined, resulting in mutation frequencies of 1/16, 2/24, 0/15, and 0/13, respectively. Similarly, for the MAD1 mutations, XWLC_0115, XWLC_0021, and XWLC_0047 belong to the MCII subtype, XWLC_0055 containing two mutations belongs to the MCI subtype, and the subtype of the other three samples is undetermined, resulting in mutation frequencies of 1/16, 3/24, 0/15, and 0/13 across subtypes, respectively. Fisher’s test did not reveal significant differences between the subtypes.

For targeting novel therapeutic targets such as MAD1 and TPRN, we propose a multi-step approach. Firstly, we advocate for conducting functional in vivo and in vitro experiments to verify their roles during cancer progression. Secondly, we suggest conducting small molecule drug screening based on the pharmacophore of these proteins, which may lead to the identification of potential therapeutic drugs. Lastly, we recommend testing the efficacy of these drugs to further validate their potential as effective treatments.

(4) In Figures 2a and b: while Figure 2a shows distinct genomic mutations among each LC cohort, Figure 2b shows similarity in affected oncogenic pathways (cell cycle, Hippo, NOTCH, PI3K, RTK-RAS, and WNT) between XWLC and TNLC/CNLC. Considering that different genomic mutations could converge into common pathways and biological processes, wouldn't these results indicate commonalities among XWLC, TNLC, and CNLC? How about other oncogenic pathways not shown in Figure 2b?

Thank you for your question. Based on the data presented in Fig. 2a, which encompasses all genomic mutations, it appears that the mutation landscape of XWLC bears the closest resemblance to TSLC (Fig. 2a). However, when considering oncogenic pathways (Fig. 2b) and genes (Fig. 2c), there is a notable disparity between the two cohorts. These findings suggest that while XWLC and TSLC exhibit similarities in terms of genomic mutations, they possess distinct characteristics in terms of oncogenic pathways and genes.

Regarding the oncogenic signaling pathways, we referred to ten well-established pathways identified from TCGA cohorts. These members of oncogenic pathways are likely to serve as cancer drivers (functional contributors) or therapeutic targets, as highlighted by Sanchez-Vega et al. in 2018(Sanchez-Vega et al., 2018).

(5) In Figure 2c, how and why were the four genes (EGFR, TP53, RBM10, KRAS) selected? What about other genes? In this regard, given tumor genome sequencing was done, it would be more informative to provide the oncoprints of XWLC, TSLC, TNLC, and CNLC for complete genomic alteration comparison.

Thank you for your question and good suggestion. Building upon our previous study (Zhang et al., 2021), we found that EGFR, TP53, RBM10, and KRAS were the top mutated genes in Xuanwei lung cancer cohorts. Furthermore, we have included the mutation frequency of cancer driver genes (Bailey et al., 2018) across XWLC, TSLC, TNLC, and CNLC in Supplementary Table 2b.

(6) Supplementary Table 11 shows a number of mutations at the interface and length of interface between a given protein-protein interaction pair. Such that, it does not provide what mutation(s) in a given PPI interface is found in each LC cohort. For example, it fails to provide whether MAD1 R558H and TPRN H550Q mutations are found significantly in each LC cohort.

We appreciate your careful review. In Supplementary Table 11, we have provided significant onco_PPI data for each LC cohort, focusing on enriched mutations at the interface of two proteins. Our emphasis lies on onco_PPI rather than individual mutations, as any mutation occurring at the interface could potentially influence the function of the protein complex. Thus, our Supplementary Table 11 exclusively displays the onco_PPI rather than mutations. MAD1 R558H and TPRN H550Q were identified through onco_PPI analysis, and subsequent extensive literature research led us to focus specifically on these mutations.

(7) Figure 7c and d are simulation data not from an actual binding assay. The authors should perform a biochemical binding assay with proteins or show that the mutation significantly alters the interaction to support the conclusion.

We appreciate your suggestion. The relevant experiments are currently in progress, and we anticipate presenting the corresponding data in a subsequent study.

Reviewer #3 (Public Review):

Summary:

The manuscript from Zhang et al. utilizes a multi-omics approach to analyze lung adenocarcinoma cases in female never smokers from the Xuanwei area (XWLC cohort) compared with cases associated with smoking or other endogenous factors to identify mutational signatures and proteome changes in lung cancers associated with air pollution. Mutational signature analysis revealed a mutation hotspot, EGFR-G719X, potentially associated with BaP exposure, in 20% of the XWLC cohort. This correlated with predicted MAPK pathway activations and worse outcomes relative to other EGFR mutations. Multi-omics clustering, including RNA-seq, proteomics, and phosphoproteomics identified 4 clusters with the XWLC cohort, with additional feature analysis pathway activation, genetic differences, and radiomic features to investigate clinical diagnostic and therapeutic strategy potential for each subgroup. The study, which nicely combines multi-modal omics, presents potentially important findings, that could inform clinicians with enhanced diagnosis and therapeutic strategies for more personalized or targeted treatments in lung adenocarcinoma associated with air pollution. The authors successfully identify four distinct clusters with the XWLC cohort, with distinct diagnostic characteristics and potential targets. However, many validating experiments must be performed, and data supporting BaP exposure linkage to XWLC subtypes is suggestive but incomplete to conclusively support this claim. Thus, while the manuscript presents important findings with the potential for significant clinical impact, the data presented are incomplete in supporting some of the claims and would benefit from validation experiments.

Strengths:

Integration of omics data from multimodalities is a tremendous strength of the manuscript, allowing for cross-modal comparison/validation of results, functional pathway analysis, and a wealth of data to identify clinically relevant case clusters at the transcriptomic, translational, and post-translational levels. The inclusion of phosphoproteomics is an additional strength, as many pathways are functional and therefore biologically relevant actions center around activation of proteins and effectors via kinase and phosphatase activity without necessarily altering the expression of the genes or proteins.

Clustering analysis provides clinically relevant information with strong therapeutic potential both from a diagnostic and treatment perspective. This is bolstered by the individual microbiota, radiographic, wound healing, outcomes, and other functional analyses to further characterize these distinct subtypes.

Visually the figures are well-designed and presented and for the most part easy to follow. Summary figures/histograms of proteogenomic data, and specifically highlighted genes/proteins are well presented.

Molecular dynamics simulations and 3D binding analysis are nice additions.

While I don't necessarily agree with the authors' interpretation of the microbiota data, the experiment and results are very interesting, and clustering information can be gleaned from this data.

Weaknesses:

(1) Statistical methods for assessing significance may not always be appropriate.

We appreciate your suggestion. We have revised statistical test results in the panels including: Fig. 3b,e and g.

(2) Necessary validating experiments are lacking for some of the major conclusions of the paper.

Thank you for raising this point. However, we respectfully choose not to comment on this matter at present.

(3) Many of the conclusions are based on correlative or suggestive results, and the data is not always substantive to support them.

Thank you for raising this point. However, we respectfully choose not to comment on this matter at present.

(4) Experimental design is not always appropriate, sometimes lacking necessary controls or large disparity in sample sizes.

Thank you for raising this point. However, we respectfully choose not to comment on this matter at present.

(5) Conclusions are sometimes overstated without validating measures, such as in BaP exposure association with the identified hotspot, kinase activation analysis, or the EMT function.

Thank you for raising this point. However, we respectfully choose not to comment on this matter at present.

Reviewer #1 (Recommendations For The Authors):

(1) Please provide a justification for why only females were included in the study. I am concerned that the results obtained in this study can not be generalized as only females were included.

We appreciate your suggestion. Lung cancer in never smokers (LCINS) accounts for approximately 25% of lung cancer cases (15% of lung cancer in men and 53% in women) (Parkin et al., 2005). Currently, the etiology and mechanisms of LCINS are not clear. Globally, LCINS shows remarkable gender and geographic variations, occurring more frequently among Asian women (Bray et al., 2018). Indoor coal burning for heating and cooking has been implicated as a risk factor for Chinese women, as they spend more time indoors (Mumford et al., 1987). Among men, the proportion of never smokers is lower, with less regional variation, and lung cancer in males is frequently caused by smoking. Thus, to better reveal the etiology and molecular mechanisms of LCINS, we collected data exclusively from female LCINS patients in the Xuanwei area, excluding potential confounding factors such as hormonal or smoking status. Our study specifically aims to uncover the etiology and mechanisms of LCINS in female patients, with future research planned to verify whether our conclusions can be generalized to LCINS in male patients.

(2) "Therefore, the XWLC and TSLC cohorts are more explicitly influenced by environmental carcinogens, while the TNLC and CNLC cohorts may be more affected by age or endogenous risk factors." This statement in the results (starting line 142) does not have adequate support from the results. First, the average age in the 4 cohorts does not seem to be very different to me based on Figure 1b. if they are different, please provide statistical test results. Please make sure this statement is supported by other results, otherwise, I would recommend excluding it from the manuscript.

We appreciate your suggestion. To gain biological insights, we frequently associate mutational signatures with factors such as age, defective DNA mismatch repair, or environmental exposures. These remain associations rather than causation. Thus, we agree with the suggestion to weaken the conclusion as follows:

“Generally, exposure to tobacco smoking carcinogens (COSMIC signature 4) and chemicals such as BaP (Kucab signatures 49 and 20) were identified as the most significant contributing factors in both the XWLC and TSLC cohorts (Fig. 1f and 1g). In contrast, defective DNA mismatch repair (COSMIC signature ID: SBS6) was identified as the major contributor in both the TNLC and CNLC cohorts (Fig. 1h and 1i), with no potential chemicals identified based on signature similarities. Therefore, the XWLC and TSLC cohorts appear to be more explicitly associated with environmental carcinogens, while the TNLC and CNLC cohorts may be more associated with defective DNA mismatch repair processes.”

(3) Please provide statistical test results in this subsection "The EGFR-G719X mutation, which is a hotspot associated with BaP exposure, possesses distinctive biological features " (Line 203) showing that the number of G719X is significantly different in XWLC.

We appreciate your suggestion. Two-sided Fisher’s test was used to calculate p-values, which are labeled in Figure 3b.

(4) "Analysis of overall survival and progression-free interval (PFI) revealed that patients with the G719X mutation had worse outcomes compared to other EGFR mutation subtypes " This statement (starting Line 232) should be supported by literature data.

We appreciate your suggestion.

In the Watanabe et al. post-hoc analysis, patients with the G719 mutation had significantly shorter OS with gefitinib compared to patients with the common mutations (Watanabe et al., 2014). We revised the sentences as following:

“Analysis of overall survival and progression-free interval (PFI) revealed that patients with the G719X mutation had worse outcomes compared to other EGFR mutation subtypes (Fig. 3j and 3k) which was consistent with a previous study(Watanabe et al., 2014).”

(5) I would suggest changing this statement to a "suggestion" as there is no experimental support for this, and mentioning that this requires further experimental validation with the suggested drugs "Therefore, a promising approach to overcome resistance in tumors with this mutation could involve combining afatinib, which targets activated EGFR, with FDA-approved drugs that specifically target the activated kinases associated with G719X. " (Line 260).

We appreciate your suggestion. We change the sentences as following:

"Therefore, we propose a potential approach to overcoming resistance in tumors with this mutation, which could involve combining afatinib, targeting activated EGFR, with FDA-approved drugs that specifically target the activated kinases associated with G719X. "

(6) It is not clear to me how PPIs were integrated with missense. Please clarify the method.

We appreciate your suggestion. To identify interactions enriched with missense mutations, we constructed mutation-associated protein–protein interactomes (PPIs). Initially, we downloaded protein-protein interactomes from Interactome INSIDER (v.2018.2) (Meyer et al., 2018). Subsequently, we identified interfaces carrying missense mutations by mapping mutation sites to PPI interface genomic coordinates using bedtools (v2.25.0)(Quinlan and Hall, 2010). Finally, we defined oncoPPI as those PPIs significantly enriched in interface mutations in either of the two protein-binding partners across individuals. For more details, please refer to the methods sections “Building mutation-associated protein–protein interactomes” and “Significance test of PPI interface mutations.”

Reviewer #2 (Recommendations For The Authors):

Regarding the tumor microbiota composition, it is not clear what the significance of these results would be. Are the specific microbiota associated with MC-IV more pathogenic than other species found in other subtypes? What are the unique features of these MC-IV microbiota? If these are difficult to address, this section could be removed from the manuscript.

We appreciate your suggestion. This section is removed from the manuscript.

Regarding the radiomic data section (Figure 6d and Extended Figure 6d), more description about the eight and five features (that are different between MC-II and others) would be helpful to better understand the importance and significance of these data.

We appreciate your suggestion. We have added the description as following: “Features such as median and mean reflect average gray level intensity and Idmn and Gray Level Non-Uniformity measure the variability of gray-level intensity values in the image, with a higher value indicating greater heterogeneity in intensity values. These results suggest a denser and more heterogeneous image in the MC-II subtype.”

Other minor comments:

(1) If EGFR G719X is a known hotspot mutation associated with BaP, please cite previous literature.

We appreciate your suggestion. Upon careful retrieval using "G719X" and "BaP" as keywords, we did not find previous literature discussing G719X as a known hotspot mutation associated with BaP.

(2) In Figure 1d, it should be clearly written in the legend that tumor (T) and normal (N) tissue were analyzed.

We appreciate your suggestion. We have clarified the figure legend of Figure 1d.

(3) In Figure 1m, it is not obvious that EGFR pY1173 and pY1068 are more abundant in the Bap+S9 sample. Total EGFR bands are very faint. These western blots should be repeated and quantified.

We appreciate your suggestion. We have removed Fig. 1m. After identifying the antibody with satisfactory performance, we will provide the revised results.

(4) In Figure 2d, aren't the EGFR E746__A750del mutations more frequently found in CNLC, TSLC, and TNLC? (which is opposite to what the authors wrote in the text).

We appreciate your suggestion. This mistake has been corrected.

(5) In Figure 7f-i and Ext Figure 8, Does "CK" mean empty vector control? Then, it would be changed to "EV".

We appreciate your suggestion. This mistake has been corrected.

Reviewer #3 (Recommendations For The Authors):

Methods:

While previous work was referenced, a description of proteomics methods should still include: instrumentation, acquisition method, all software packages used, method for protein identification, method for protein quantification, how FDR was maintained for identification/quantification, definition of differentially expressed proteins, whether multiple testing correction was performed and if so what method.

We appreciate your suggestion. We revised the description of label-free mass spectrometry methods accordingly.

The paper would greatly benefit from brief methodological explanations throughout, as all methods are currently exclusively found in the supplementary information. This severely hampers the readability of the manuscript.

Thank you for raising this point. However, we respectfully choose not to comment on this matter at present.

Suggestions Throughout

The paper would greatly benefit from proofreading/editing

Line 157-158/Figure 1J for CYP1A1 displays protein concentrations while Figure 1K for AhR shows mRNA. Why this discrepancy? It would be preferable to show both mRNA and protein levels for both CYP1A1 and AhR. Also, there is a large discrepancy in the "n" between the normal and tumor groups, which makes the statistical comparison challenging. The AhR data is therefore unconvincing, and additional protein data is suggested. Thus the claim of significantly elevated AhR and CYP1A1 levels in tumors is not sufficiently supported and requires further investigation, both mRNA and protein, and with similarly sized sample groups.

We appreciate your suggestion. We have thoroughly edited the revised manuscript, with all changes marked accordingly. Compared to mRNA level assessment, protein abundance is a better indicator of gene expression. Therefore, we reanalyzed the protein level of AhR for comparison and found no significant differences (Figure 1k). Additionally, the samples sequenced by mRNA-seq were not entirely consistent with those sequenced by label-free proteomics. The samples analyzed by different methods are shown in Figure 1d.

Line 159 Figure 1I There is no control for the data serum data presented here. What are the serum levels for individuals not residing in the Xuanwei? It is unclear whether this represents elevated BPDE serum levels without appropriate controls. Thus nothing insightful can be derived from this data.

We appreciate your suggestion. We have deleted the results concerning BPDE serum detection in the revised manuscript.

Line 164 The statement "sites such as Y1173 and Y1068 of EGFR were more phosphorylated in BaP treated cells" is not sufficiently supported by the presented data and cannot be made. Figure 1M has no quantitation, no indication of "n" or whether this represents a single experiment or one validated with repeating. The western blot is also cropped with no indication of molecular weight or antibody specificity. This data is NOT convincing. The antibody signal is very weak, and not convincing with cropped blots. An updated figure, with an uncropped blot, and quantitation with multiple n's and statistical comparison is required. I am not sure the Wilcoxon rank sum test is appropriate to test significance in j-l. The null hypothesis should not be equal medians but equal means based on the experimental design.

We appreciate your suggestion. We have removed Fig. 1m. After identifying the antibody with satisfactory performance, we will provide the revised results.

Line 181 phrase "significant differences" should not be used unless making a claim about statistical significance.

We appreciate your suggestion. We change “significant differences” to “noticeable differences”.

Line 197: "The blood serum assay provided support..." As noted above this claim is not sufficiently supported by the presented data and requires more complete investigation.

We appreciate your suggestion. This conclusion has been deleted in the revised manuscript.

Line 219: Requires proofreading/editing.

We appreciate your suggestion. We have thoroughly edited the revised manuscript, with all changes marked accordingly.

Line 220: appears to have a typo and should read GGGC>GTGC

We appreciate your suggestion. This mistake has been corrected in the revised manuscript.

Line 223/224 Figure 3e-h. Again there is a large disparity between the n's of each group. Despite the WT having the highest frequency in the XWLC study population, it has only n=5 when comparing the protein and phosphosite for MAPKs. There is also no explanation for what the graph symbols indicate, what statistical test was performed to determine the statistical significance of the presented differences, and between which specific groups that significance exists. Thus, it is challenging to ascertain whether there are relevant differences in the MAPK signaling components.

We appreciate your suggestion. We added the description of “N, number of tumor samples containing corresponding EGFR mutation” to the figure legend. p-values were calculated with a two-tailed Wilcoxon rank sum test, and p<0.05 was labeled on Figures 3e-i.

Figure 3I Good figure. However, it would be beneficial to provide validation with Western Blotting for a few of these substrates using pospho-specific antibodies. It is suggested that this experiment be added.

We appreciate your suggestion. Figure 3I showed the comparison of patients’ ages among subtypes. I guess you mean Figure 3g and Figure 3h. The relevant experiments are currently underway, and we will provide the corresponding data in the next revised version.

Figure 4b. Very compelling figure.

We appreciate your suggestion.

Line 276: The AhR and CYP1A1 data presented earlier was not convincing, and CYP1A1 and AhR cannot be responsibly used as indicators of BaP activity based on potential. This is not an appropriate application.

We appreciate your suggestion. CYP1A1 and AhR are two key regulators involved in BaP metabolism and signaling transduction, respectively. However, after examining the protein expression of AhR between tumor and normal tissues, we found no significant differences (Fig. 1k) and CYP1A1 has been proven to be highly expressed in tumor samples (Fig. 1j). Thus, we mainly examined the expression of CYP1A1 among the four subgroups. We changed our description as follows:

“As CYP1A1 is a key regulator involved in BaP metabolism and has been proven to be highly expressed in tumor samples (Fig. 1j), we next examined the expression of CYP1A1 among the four subgroups to evaluate their associations with air pollution.”

Figure 4d. Here it is AhR protein used rather than mRNA measured earlier. What is the explanation for this change?

We appreciate your suggestion. As there was no significant differences of the protein expression of AhR between tumor and normal tissues (Fig. 1k), we deleted the expression comparison of AhR among subtypes.

Line 281 "Moderately elevated expression level of AhR" is not supported by the presented data and should be removed.

We appreciate your suggestion. We have deleted the result of comparison of AhR among subtypes.

Figure 4: There is no indication or explanation of how the protein abundance is being measured. Is this from the proteomics (MS) approaches, by ELISA or by Western? If it is simply by MS then validation by another method is preferable. The data presented in Figure 4 do not adequately support the claim that MC-II subtype is more strongly associated with BaP exposure. What statistical test is used in 4F? Why is the n in the MC-II group, which is the highlighted group of interest nearly double the other groups?

We appreciate your suggestion. Fig. 4e is derived from the proteomics data. The two-tailed Wilcoxon rank sum test was used to calculate p-values in panels c and e.

Figure 4g: At least one or two of these should be validated by Western Blot or targeted MS.

We appreciate your suggestion. The relevant experiments are currently underway, and we will provide the corresponding data in the next revised version.

Figure 5a: Assuming these were also measured via proteomic analysis, how do their expression patterns compare across the different omics modes?

Thank you for your suggestion. Figure 5 integrates transcriptomics (19182 genes), proteomics (9152 genes), and phosphoproteomics (5733 genes) data. In general, we utilized transcriptomics data to identify unique or distinct pathways among subgroups. Furthermore, proteomics and phosphoproteomics data were employed to validate key gene expressions, as they encompass fewer genes compared to transcriptomics data.

For instance, in Fig. 5a-d, we observed higher expression levels of mesenchymal markers such as VIM, FN1, TWIST2, SNAI2, ZEB1, ZEB2, and others in the MC-IV subtype using transcriptomics data (Fig. 5a). Additionally, we calculated epithelial-mesenchymal transition (EMT) scores using the ssGSEA enrichment method based on protein levels and conducted GSEA analysis using transcriptomics data (Fig. 5b). Furthermore, using proteomics data, we evaluated Fibronectin (FN1), an EMT marker that promotes the dissociation, migration, and invasion of epithelial cells, at the protein level (Fig. 5c), and β-Catenin, a key regulator in initiating EMT, also at the protein level (Fig. 5d). Overall, our findings indicate that the MC-IV subtype exhibits an enhanced EMT capability, which may contribute to the high malignancy observed in this subtype.

Line 314: Not compared with MCI, which appeared to be much lower at the mRNA level. Is there an explanation for this difference?

We appreciate your suggestion. FN1 expression is lowest in MCI at the protein level (Fig. 5c). However, at the transcriptome level, FN1 expression is lowest in the MCIII subtype (Fig. 5a). You may wonder why these results are inconsistent. Discrepancies between mRNA and protein expression levels are common, and previous study showed that about 20% genes had a statistically significant correlation between protein and mRNA expression in lung adenocarcinomas (Chen et al., 2002). Post-transcriptional mechanisms, including protein translation, post-translational modification, and degradation, may influence the level of a protein present in a given cell or tissue. In this situation, we focused on identifying distinct biological pathways in each subgroup, supported by multi-omics data.

Line 321: MC-IV *potentially* possesses an enhanced EMT capability. This statement cannot be conclusively made.

We appreciate your suggestion. We changed our description as: “Collectively, our findings demonstrate that the MC-IV subtype is associated with enhanced EMT capability, which may contribute to the high malignancy observed in this subtype.”

Lines 325 and 327 indicated dysregulation of cell cycle processes and activation of CDK1 and CDK2 pathways based on KSEA analysis which is closely linked to cell cycle regulation as two separate pieces of evidence. However, these are both drawn from the phosphoproteomics, and likely indicate conclusions drawn from the same phosphosite data. Said another way, if phosphosite data indicates differences in kinases linked to cell cycle regulation then you would also expect phosphosite data to indicate dysregulation of cell cycle.

We appreciate your suggestion. You mentioned that Fig. 4f and Fig. 5e redundantly prove that the CDK1 and CDK2 pathways are dysregulated. However, KSEA analysis in Fig. 4f estimates changes in kinase activity based on the collective phosphorylation changes of its identified substrates (Wiredja et al., 2017). In contrast, Fig. 5e directly evaluates the abundance of protein and phosphosite levels of CDK1 and CDK2 across subtypes. These analyses mutually confirm each other rather than being redundant.

Line 413/Figure 6b: While there may be a trend displayed by the figure, it is not convincing enough to state that MC-IV shows a conclusively distinguishable bacterial composition. Too much variability exists within groups MC-II and MC-III. However, it does show that MC-IV and MC-II have consistent composition within their groups, and that is interesting.

We appreciate your suggestion. We have deleted the analysis of bacterial composition across subtypes.

Figure 6: Overall very nice figure, with intriguing diagnostic potential. See the above note on 6a-b interpretation.

We appreciate your suggestion. We have deleted the analysis of bacterial composition across subtypes, including Fig. 6a-6c.

Figure 7c-f better labeling of the panels will aid reader comprehension.

We appreciate your suggestion. Necessary labeling has been added to Fig. 7c-f to enhance comprehension.

Figure 7 panel order is confusing, switching from right to left to vertical. Rearranging to either left to right or vertical would help orient readers.

We appreciate your suggestion. We have adjusted the order of Fig. 7 and extended Fig. 8 panel.

Figure 7 legend i: should read Cell colony* assay

We appreciate your suggestion. We have corrected this mistake in the revised manuscript.

The Discussion is very brief. While it includes a discussion of the potential impact of the study, it does not include an analysis of the caveats/drawbacks of the study. A more thorough discussion of other studies focusing on the impacts of BaP exposure is also suggested as this was a highlighted point by the authors.

We appreciate your suggestion. we have added discussion about the associations between BaP exposure and lung cancer and also talked about the shortcomings of our study as followings:

“Mechanistically, Qing Wang showed that BaP induces lung carcinogenesis, characterized by increased inflammatory cytokines, and cell proliferative markers, while decreasing antioxidant levels, and apoptotic protein expression(Wang et al., 2020). In our study, we used clinical samples and linked the mutational signatures of XWLC to the chemical compound BaP, which advanced the etiology and mechanism of air-pollution-induced lung cancer. In our study, several limitations must be acknowledged. Firstly, although our multi-omics approach provided a comprehensive analysis of the subtypes and their unique biological pathways, the sample size for each subtype was relatively small. This limitation may affect the robustness of the clustering results and the identified subtype-specific pathways. Larger cohort studies are necessary to confirm these findings and refine the subtype classifications. Secondly, although our study advanced the understanding of air-pollution-induced lung cancer by using clinical samples, the reliance on epidemiological data in previous studies introduces potential confounding factors. Our findings should be interpreted with caution, and further mechanistic studies are warranted to establish causal relationships more definitively. Thirdly, our in silico analysis suggested potential approach to drug resistence in G719X mutations. However, these predictions need to be validated through extensive in vitro and in vivo experiments. The reliance on computational models without experimental confirmation may limit the clinical applicability of these findings.”

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Meyer, M. J., Beltran, J. F., Liang, S., Fragoza, R., Rumack, A., Liang, J., Wei, X., and Yu, H. (2018). Interactome INSIDER: a structural interactome browser for genomic studies. Nat Methods 15, 107-114.

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Author response:

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

Public reports:

In the public reports there is only one point we would like to discuss. It concerns our use of a computational model to analyse spatial tumour growth. Citing from the eLife assessment, which reflects several comments of the referees:

The paper uses published data and a proposed cell-based model to understand how growth and death mechanisms lead to the observed data. This work provides an important insight into the early stages of tumour development. From the work provided here, the results are solid, showing a thorough analysis. However, the work has not fully specified the model, which can lead to some questions around the model’s suitability.

The observables we use to determine the (i) growth mode and the (ii) dispersion of cells are modelindependent. The method to determine the (iii) rate of cell death does not use a spatial model. Throughout, our computational model of spatial growth is not used to analyze data. Instead, it is used to check that the observables we use can actually discriminate between different growth modes given the limitations of the data. We have expanded the description of the computational model in the revised version, and have released our code on Github. However, the conclusions we reach do not rely on a computational model. Instead, where we estimate parameters, we use population dynamics as described in section S5. The other observables are parameter free and model-independent. We view this as a strength of our approach.

Recommendations for the authors:

Reviewer #1:

(1.1) In Figure 1, the data presented by Ling et al. demonstrate a distinctive “comb” pattern. While this pattern diverges from the conventional observations associated with simulated surface growth, it also differs from the simulated volume growth pattern. Is this discrepancy attributable to insufficient data? Alternatively, could the emergence of such a comb-like structure be feasible in scenarios featuring multiple growth centers, wherein clones congregate into spatial clusters?

We are unsure what you are referring to. One possibility is you refer to the honey-comb structure formed by the samples of the Ling et al. data shown in Fig. 1A of the main text. This is an artefact arising from the cutting of the histological cut into four quadrants, see Fig. S1 in the SI of Ling et al. The perceived horizontal and vertical “white lines” in our Fig. 1A stems from the lack of samples near the edges of these quadrants. We have added this information to the figure caption.

An alternative is you are referring to the peaks in Fig 2A of the main text. The three of these peaks indeed stem from individual clones. We have placed additional figures in the SI (S2 B and S2 C) to disentangle the contribution from different clones. The peaks have a simple explanation: each clone contributes the same weight to the histogram. If a clone only has few offspring, this statistical weight is concentrated on a few angles only, see SI Figure S2 B.

(1.2) I am not sure why there are two sections about “Methods” in the main text: Line 50 as well as Line 293. Furthermore, the methods outlined in the main paper lack the essential details necessary for readers to navigate through the critical aspects of their analysis. While these details are provided in the Supplementary Information, they are not adequately referenced within the methods section of the main text. I would recommend that the authors revise the method sections of the main text to include pertinent descriptions of key concepts or innovations, while also directing readers to the corresponding supplementary method section for further elucidation.

We have merged the Section “Materials and Methods” at the end of the main text with the SI description of the data in SI 4.2 and placed a reference to this material in the main body.

(1.3) The impact of the particular push method (proposed in the model) on the resultant spatial arrangement of clones remains unclear. For instance, it’s conceivable that employing a different pushing method (for example, with more strict constraints on direction) could yield a varied pattern of spatial diversity. Furthermore, there is ambiguity regarding the criteria for determining the sequence of the queue housing overlapping cells.

Regarding the off-lattice dynamics we use, there are indeed many variants one could use. In nonexhaustive trials, we found that the details of the off-lattice dynamics did not affect the results. The reason may be that at each computational step, each cell only moves a very small amount, and differences in the dynamics tend to average out over time.

We deliberately do not give constraints on the direction. Such constraints emerge in lattice-based models (when preferred directions arise from the lattice symmetry), but these are artifacts of the lattice.

At cell division the offspring is placed in a random direction next to the parent regardless of whether this introduces an overlap. Cells then push each other along the axis connecting their two centers of mass – unlike in lattice based models a sequence of pushes does not propagate through the tumor straight away but sets off of a cascade of pushes. Equal pushing of two cells (i.e. two initial displacements as opposed to pushing one of the two) results in the same patterns of directed, low dispersion surface and undirected, high dispersion volume growth but is much harder computationally as it reintroduces overlaps that have been resolved in the previous step.

We have rewritten the description of the pushing queue in the SI Section 1. The choice of the pushing sequence is somewhat arbitrary but we found that it also has no noticable effect on the growth mode. Maybe putting it in contrast to depth-first approaches helps to illustrate this: We tried two queueing schemes for iterating through overlapping cells, width-first and depth-first. In both cases, we begin by scanning a given cell’s (the root’s) neighborhood for overlaps and shuffle the list of overlapping neighbours. In a width-first approach we then add this list to the queue. Subsequent iterations append their lists of overlapping cells to the queue, such that we always resolve overlaps within the neighborhood of the root first. A depth-first approach follows a sequence of pushes by immediately checking a pushed cell’s neighborhood for new overlaps and adding these to the front of the queue (which works more like a stack then). This can be efficiently implemented by recursion but has no noticeable performance advantage and results in the same patterns of directed, low dispersion surface and undirected, high dispersion volume growth. In our opinion the width-first approach of first resolving overlaps in the immediate neighborhood is more intuitive, which is why we adopted it for our simulation model.

(1.4) For the example presented in S5.1, how can the author identify from genomic data that mutation 3 does not replace its ancestral clade mutation 2? In other words, if mutation 2, 3 and 4 are linked meaning clone 4 survives but 2 and 3 dies, how does one know if clone 3 dies before clone 2? I understand that this is a conceptual example, but if one cannot identify this situation from the real data, how can the clade turnover be computed?

Thank you for this comment, which points to an error of ours in the turnover example of the SI: Clade 3 does in fact replace 2 and contributes to the turnover! (The algorithm correctly annotated clade 3 as orphaned and computes a turnover of 3/15 for this example). We have corrected this.

In this example, it does not matter for the clade turnover whether clone 3 dies before clone 2. As long as its ancestor (clone 2) becomes extinct it adds to the clade turnover. The term “replaces” applies to the clade of 3 which has a surviving subclone and thereby eventually replaces clade 2. The clade turnover its solely based on the presence of the mutations (which define their clade) and not on the individual clones.

(1.5) After reviewing reference 24 (Li et al.), I noticed that the assertions made therein contradict the findings presented in S3 (Mutation Density on Rings). Specifically, Li et al. state that “peripheral regions not only accumulated more mutations, but also contained more changes in genes related to cell proliferation and cell cycle function” (Page 6) and “Phylogenetic trees show that branch lengths vary greatly with the long-branched subclones tending to occur in peripheral regions” (Page 4). However, upon re-analysis of their data, the authors demonstrated a decrease in mutation density near the surface. It is crucial to comprehend the underlying cause of such a disparity.

The reason for this disparity is the way Li et al. labelled samples as belonging to peripheral or central regions of the tumour. We have added a new figure in the SI to show this: Fig. S14 shows the number of mutations found in samples of Li et al. against their distances from the centre, along with the classification of samples as center/periphery given in Li et al. In the case of tumor T1, the classification of a sample in reference Li et al. does not agree with the distance from the center: samples classified as core are often more distant from the center than those classified as peripheral. Furthermore, Lewinsohn et al. (see below) show in their Fig. 5 that samples classified as ‘center’ by Li all fall into a single clade, and we believe this affects all results derived from this classification. For this reason, we do not consider the classification in reference 24 (Li et al.) further. We now briefly discuss this in Section S3.3.

(1.6) The authors consider coinciding mutations to occur when offspring clades align with an ancestral clade. Nevertheless, since multiple mutations can arise simultaneously in a single generation (such as kataegis), it becomes essential to discern its impact on clade turnover and, consequently, the estimation of d/b.

The mutational signatures found here show no sign of kataegis. Also, the number of polymorphic sites in the whole-exome data is small and the mutations are uniformly spread across the exome. The point is well taken, however, the method requires single mutations per generation. In practice, this can be achieved by subsampling a random part of the genome or exome (see [45]). We tested this point by processing the data from only a fraction of the exome; this did not change the results. In particular, Figure S30 shows the turnover-based inference for different subsampling rates L of the Ling et al. data. Subsampling of sites reduces the exome-wide mutation rate, the inferred rate scales linearly with L, as expected.

(1.7) I could not understand Step 2 in Section S2.1, an illustration may be helpful.

We have added figure S2 explaining the directional angle algorithm to Section S2.1 in the supplementary information.

(1.8) Figure S2, does a large rhoc lead to volume growth rather than surface growth, not the other way around?

Thank you for catching this mix-up!

Reviewer #2

I do have a few minor comments/questions, but I am confident the authors will be able to address them appropriately.

(2.1) Line 56: I am not sure what the units of “average read depth 74X” is in terms of SI units?

This number gives the number of sequence reads covering a particular nucleotide and is dimensionless. We have added this information.

(2.2) Lines 63 - 68: I am unsure what is meant by the terms “T1 of ca.” and “T2 of ca.”. Can these also be explained/defined please?

These refer to the approximate (circa) diameters of tumor 1 and tumor 2 in the data by Li et al. We have expanded the abbreviations.

(2.3) Line 69: I would like to see a more extensive description of the cell-based model here in the main text, such as how do the cells move. Moreover, do cells have a finite reach in space, do they have a volume/area?

We have expanded the model description in the main body of the paper and placed information there that previously was only in the SI.

(2.4) Line 76: You have said cells can “push” one another in your model. Do they also “pull” one another? Cell adhesion is know to contribute to tumour integrity - so this seems important for a model of this nature.

We have not implemented adhesive forces between pairs of cells so far. This would cause a higher pressure under cell growth (which can have important physiological consequences). However, the hard potential enforcing a distance between adjacent cells would still lead to cells pushing each other apart under population growth, so we expect to see the dispersion effect we discuss even when there is adhesion.

(2.5) Line 80-81: “due to lack of nutrient”. Is nutrient included in this work? It is my understanding it is not. No problem if so, it is just that this line makes it seem like it is and important. If it is not, the authors should mention this in the same sentence.

Thank you for pointing out this source of misunderstanding, your understanding is correct and we have modified the text to remove the ambiguity.

(2.6) Line 94-95: Since you are interested in tissue growth, recent work has indicated how the cell boundary (and therefore tissue boundary) description influences growth. Please also be sure to indicate this when you describe the model.

We presume you refer to the recent paper by Lewinsohn et al. (Nature Ecology and Evolution, 2023), which reports a phylogenetic analysis based on the Li et al. data. Lewinsohn et al. find that cells near the tumour boundary grow significantly faster than those in the tumour’s core. This is at variance with what we find; we were not aware of this paper at the time of submission. We now refer to this paper in the main text, and also have included a new section S3.4 in the SI accounting for this discrepancy. If you refer to a different paper, please let us know.

Briefly, we repeat the analysis of Lewinsohn et al., using their algorithm on artificial data generated by our model under volume growth. Samples were placed precisely like they were placed in the tumor analyzed by Li et al. We find that, even though the data was generated by volume growth, the algorithm of Lewinsohn et al. finds a signal of surface growth, in many cases even stronger compared to the signal which Lewinsohn et al. find in the empirical data. We have added subsection S3.4 with new figure S15 in the Supplementary Information.

(2.7) Line 107: “thus no evidence for enhanced cell growth near the edge of the tumour”. It is unclear to me how this tells us information relative to the tumour edge. It seems to me this is an artifact that at the edge of the tumour, there are less cells to compare with? Could you please expand on this a bit?

The direction angles tell us if new mutations arise predominantly radially outwards. With this observable, surface growth would lead to a non-uniform distribution of these angles even if we restrict the analysis to samples from the interior of the tumor (which, under surface growth, was once near the surface). So the effect is not linked to fewer cells for comparison. Also, we have checked the direction angles in simulations under different growth modes with the samples placed in the same way as in the data (see Figs. S3 and S4 right panels). We have expanded the text in the main text, section Results accordingly.

(2.8) I really enjoyed the clear explanation between lines 119 and 122 regarding cell dispersion!

Thank you!

(2.9) Figure 2B: Since you are looking at a periodic feature in theta, I would have expected the distribution to be periodic too, and therefore equal at theta=-180=180. Can you explain why it is different, please? Interestingly, you simulated data does seem to obey this!

The distribution of theta is periodic but the binning and midpoints of bins were chosen badly. We have replotted the diagram with bin boundaries that handle the edge-points -180/180 correctly. Thank you for pointing this out.

(2.10) Figure 3B: This plot does not have a title. Also, what do the red vertical lines in plots 3B, 3C and 3D indicate?

We have added the title. The red lines indicate the expectation values of the distributions.

(2.11) Figure 4: I am unsure how to read the plot in 4B. Also, what does the y-axis represent in 4C and 4D?

We have added explanations for 4B and have placed the labels for 4C and 4D in the correct position on the y-axes.

(2.12) Lines 194-199: you discuss your inferred parameters here, but you do not indicate how you inferred these parameters. May you please briefly mention how you inferred these, please?

These were inferred using the turnover method explained in the paragraph above, we have expanded the information. A full account is given in the SI Section S5.

(2.13) Line 258-260: “... mutagen (aristolochic acid) found in herbal traditional Chinese medicine and thought to cause liver cancer.” I do not see what this sentence adds to the work. Could you please be clearer with the claim you are making here?

Mutational signatures allow to infer underlying mutational processes. The strongest signature found in the data is associated with a mutagen that has in the past been used in traditional Chinese medicines. The patients from whom the tumours were biopsied were from China, so past exposure to this potent mutagen is possible. We are not making a big claim here, the mutational signature of aristolochic acid and its cancerogenic nature has been well studied and is referenced here. The result is interesting in our context because in one of the datasets (Li et al.) the signature is present in early (clonal) mutations but absent in later ones, allowing to make inferences from present data on the past. We have added the information that the patients were from China.

(2.14) In your Supplementary Information, S1, I believe your summation should not be over i, as you state in the following it is over cells within 7 cell radii. Please fix this by possibly defining a set which are those within 7 cell radii.

We have done this.

El trabajador parte de una posición muy inferior respecto al empresario para negociar. ¿Siempre? Supongo que dependerá de la especialización del trabajador y la formación que requiera su puesto de trabajo. Un ingeniero especializado en algo muy concreto, por lo que hay poca gente tan capacitada en ese trabajo, le da mucho poder negociador ante el empresario que necesita de su pericia. Si el trabajador no necesita formación y cualquier puede realizar el trabajo, su poder negociador es ínfimo. Su única opción es formar sindicatos para negociar en bloques. Un conjunto de trabajadores muy poco formados organizados pueden negociar "algo mejor" frente al empresario.

También está la cuestión de que las dos partes deben cumplir por igual el contrato. Los dos no serás iguales en el plano "ontológico" pero si iguales ante la ley.

Las ideas, las opiniones se tienen que defender con pasión y convicción.

Si eres propietario de tus acciones y el resultado de éstas (tu trabajo) que alguien te lo expropie para repartirla entre aquellos a quien les venga en gana (lo necesiten o no) es un robo. La solidaridad, la caridad, no se puede imponer.

Dependiendo de la decisión que tomes, el suceso que va a pasar sí o sí te afectará de una u otra manera.

tu pourrais éventuellement mettre en accroche un exemple de soliloque parallèle de la littérature, j'imagine qu'il y en a dans Beckett

Desde un panorama significativo la comunicación estratégica envuelve una buena organización y hablando desde sus puntos generales; las organizaciones toman esta posta para trabajar asertivamente con el proceso de comunicar.

Este enfoque garantiza que cada mensaje refuerce la misión y apoye los objetivos a largo plazo.

La Comunicación estratégica destaca que su finalidad principal es la difusión deliberada de información.

Usar métricas y herramientas analíticas es importante en el proceso de comunicación estratégica.

The fact that they managed to outsource their information to get a much more accurate comparison I feel helps strengthen the study overall. Being able to compare a larger sample sizes behavior over a longer period of time allows a better grasp over what is “normal” behavior and what can be classified as “aggression” towards other species.

Reviewer #3 (Public review):

Summary:

Day et al. introduced high-throughput expansion microscopy (HiExM), a method facilitating the simultaneous adaptation of expansion microscopy for cells cultured in a 96-well plate format. The distinctive features of this method include: 1) the use of a specialized device for delivering a minimal amount (~230 nL) of gel solution to each well of a conventional 96-well plate, and 2) the application of the photochemical initiator, Irgacure 2959, to successfully form and expand toroidal gel within each well.

Addition upon revision:

Overall, the authors have adequately addressed most of the concerns raised. There are a few minor issues that require attention.

Minor comments:

Figure S10: There appears to be a discrepancy in the panel labeling. The current labels are E-H, but it is unclear whether panels A-D exist. Also, this reviewer thought that panels G and H would benefit from statistical testing to strengthen the conclusions. As a general rule for scientific graph presentation, the y-axis of all graphs should start at zero unless there is a compelling reason not to do so.

Author response:

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

Reviewer #1 (Public Review):

Summary:

Understanding large-scale neural activity remains a formidable challenge in neuroscience. While several methods have been proposed to discover the assemblies from such large-scale recordings, most previous studies do not explicitly model the temporal dynamics. This study is an attempt to uncover the temporal dynamics of assemblies using a tool that has been established in other domains.

The authors previously introduced the compositional Restricted Boltzmann Machine (cRBM) to identify neuron assemblies in zebrafish brain activity. Building upon this, they now employ the Recurrent Temporal Restricted Boltzmann Machine (RTRBM) to elucidate the temporal dynamics within these assemblies. By introducing recurrent connections between hidden units, RTRBM could retrieve neural assemblies and their temporal dynamics from simulated and zebrafish brain data.

Strengths:

The RTRBM has been previously used in other domains. Training in the model has been already established. This study is an application of such a model to neuroscience. Overall, the paper is well-structured and the methodology is robust, the analysis is solid to support the authors' claim.

Weaknesses:

The overall degree of advance is very limited. The performance improvement by RTRBM compared to their cRBM is marginal, and insights into assembly dynamics are limited.

(1) The biological insights from this method are constrained. Though the aim is to unravel neural ensemble dynamics, the paper lacks in-depth discussion on how this method enhances our understanding of zebrafish neural dynamics. For example, the dynamics of assemblies can be analyzed using various tools such as dimensionality reduction methods once we have identified them using cRBM. What information can we gain by knowing the effective recurrent connection between them? It would be more convincing to show this in real data.

See below in the recommendations section.

(2) Despite the increased complexity of RTRBM over cRBM, performance improvement is minimal. Accuracy enhancements, less than 1% in synthetic and zebrafish data, are underwhelming (Figure 2G and Figure 4B). Predictive performance evaluation on real neural activity would enhance model assessment. Including predicted and measured neural activity traces could aid readers in evaluating model efficacy.

See below in the recommendations section.

Recommendations:

(1) The biological insights from this method are constrained. Though the aim is to unravel neural ensemble dynamics, the paper lacks in-depth discussion on how this method enhances our understanding of zebrafish neural dynamics. For example, the dynamics of assemblies can be analyzed using various tools such as dimensionality reduction methods once we have identified them using cRBM. What information can we gain by knowing the effective recurrent connection between them? It would be more convincing to show this in real data.

We agree with the reviewer that our analysis does not explore the data far enough to reach the level of new biological insights. For practical reasons unrelated to the science, we cannot further explore the data in this direction at this point, however, funding permitting, we will pick up this question at a later stage. The only change we have made to the corresponding figure at the current stage was to adapt the thresholds, which better emphasizes the locality of the resulting clusters.

(2) Despite the increased complexity of RTRBM over cRBM, performance improvement is minimal. Accuracy enhancements, less than 1% in synthetic and zebrafish data, are underwhelming (Figure 2G and Figure 4B). Predictive performance evaluation on real neural activity would enhance model assessment. Including predicted and measured neural activity traces could aid readers in evaluating model efficacy.

We thank the reviewer kindly for the comments on the performance comparison between the two models. We would like to highlight that the small range of accuracy values for the predictive performance is due to both the sparsity and stochasticity of the simulated data, and is not reflective of the actual percentage in performance improvement. To this end, we have opted to use a rescaled metric that we call the normalised Mean Squared Error (nMSE), where the MSE is equal to 1 minus the accuracy, as the visible units take on binary values. This metric is also more in line with the normalised Log-Likelihood (nLLH) metric used in the cRBM paper in terms of interpretability. The figure shows that the RTRBM can significantly predict the state of the visible units in subsequent time-steps, whereas the cRBM captures the correct time-independent statistics but has no predictive power over time.

We also thank the reviewer for pointing out that there is no predictive performance evaluation on the neural data. This has been chosen to be omitted for two reasons. First, it is clear from Fig. 2 that the (c)RBM has no temporal dependencies, meaning that the predictive performance is determined mostly by the average activity of the visible units. If this corresponds well with the actual mean activity per neuron, the nMSE will be around 0. This correspondence is already evaluated in the first panel of 3F. Second, as this is real data, we can not make an estimate of a lower bound on the MSE that is due to neural noise. Because of this, the scale of the predictive performance score will be arbitrary, making it difficult to quantitatively assess the difference in performance between both models.

(3) The interpretation of the hidden real variable $r_t$ lacks clarity. Initially interpreted as the expectation of $\mathbf{h}_t$, its interpretation in Eq (8) appears different. Clarification on this link is warranted.

We thank the reviewer kindly for the suggested clarification. However, we think the link between both values should already be sufficiently clear from the text in lines 469-470:

“Importantly, instead of using binary hidden unit states 𝐡[𝑡−1], sampled from the expected real valued hidden states 𝐫[𝑡−1], the RTRBM propagates these real-valued hidden unit states directly.”

In other words, both indeed are the same, one could sample a binary-valued 𝐡[𝑡-1] from the real-valued 𝐫[𝑡-1] through e.g. a Bernoulli distribution, where 𝐫[𝑡-1] would thus indeed act as an expectation over 𝐡[𝑡−1]. However, the RTRBM formulation keeps the real-valued 𝐫[𝑡-1] to propagate the hidden-unit states to the next time-step. The motivation for this choice is further discussed in the original RTRBM paper (Sutskever et al. 2008).

(4) In Figure 3 panel F, the discrepancy in x-axis scales between upper and lower panels requires clarification. Explanation regarding the difference and interpretation guidelines would enhance understanding.

Thank you for pointing out the discrepancy in x-axis scales between the upper and lower panels of Figure 3F. The reason why these scales are different is that the activation functions in the two models differ in their range, and showing them on the same scale would not do justice to this difference. But we agree that this could be unclear for readers. Therefore we added an additional clarification for this discrepancy in line 215:

“While a direct comparison of the hidden unit activations between the cRBM and the RTRBM is hindered by the inherent discrepancy in their activation functions (unbounded and bounded, respectively), the analysis of time-shifted moments reveals a stronger correlation for the RTRBM hidden units ($r_s = 0.92$, $p<\epsilon$) compared to the cRBM ($r_s = 0.88$, $p<\epsilon$)”

(5) Assessing model performance at various down-sampling rates in zebrafish data analysis would provide insights into model robustness.

We agree that we would have liked to assess this point in real data, to verify that this holds as well in the case of the zebrafish whole-brain data. The main reason why we did not choose to do this in this case is that we would only be able to further downsample the data. Current whole brain data sets are collected at a few Hz (here 4 Hz, only 2 Hz in other datasets), which we consider to be likely slower than the actual interaction speed in neural systems, which is on the order of milliseconds between neurons, and on the order of ~100 ms (~10 Hz) between assemblies. Therefore reducing the rate further, we expect to only see a reduction in quality, which we considered less interesting than finding an optimum. Higher rates of imaging in light-sheet imaging are only achievable currently by imaging only single planes (which defies the goal of whole brain recordings), but may be possible in the future when the limiting factors (focal plane stepping and imaging) are addressed. For completeness, we have now performed the downstepping for the experimental data, which showed the expected decrease in performance. The results have been integrated into Figure 4.

Reviewer #2 (Public Review):

Summary:

In this work, the authors propose an extension to some of the last author's previous work, where a compositional restricted Boltzmann machine was considered as a generative model of neuron-assembly interaction. They augment this model by recurrent connections between the Boltzmann machine's hidden units, which allow them to explicitly account for temporal dynamics of the assembly activity. Since their model formulation does not allow the training towards a compositional phase (as in the previous model), they employ a transfer learning approach according to which they initialise their model with a weight matrix that was pre-trained using the earlier model so as to essentially start the actually training in a compositional phase. Finally, they test this model on synthetic and actual data of whole-brain light-sheet-microscopy recordings of spontaneous activity from the brain of larval zebrafish.

Strengths:

This work introduces a new model for neural assembly activity. Importantly, being able to capture temporal assembly dynamics is an interesting feature that goes beyond many existing models. While this work clearly focuses on the method (or the model) itself, it opens up an avenue for experimental research where it will be interesting to see if one can obtain any biologically meaningful insights considering these temporal dynamics when one is able to, for instance, relate them to development or behaviour.

Weaknesses:

For most of the work, the authors present their RTRBM model as an improvement over the earlier cRBM model. Yet, when considering synthetic data, they actually seem to compare with a "standard" RBM model. This seems odd considering the overall narrative, and it is not clear why they chose to do that. Also, in that case, was the RTRBM model initialised with the cRBM weight matrix?

Thank you for raising the important point regarding the RTRBM comparison in the synthetic data section. Initially, we aimed to compare the performance of the cRBM with the cRTRBM. However, we encountered significant challenges in getting the RTRBM to reach the compositional phase. To ensure a fair and robust comparison, we opted to compare the RBM with the RTRBM.

A few claims made throughout the work are slightly too enthusiastic and not really supported by the data shown. For instance, when the authors refer to the clusters shown in Figure 3D as "spatially localized", this seems like a stretch, specifically in view of clusters 1, 3, and 4.

Thanks for pointing out this inaccuracy. When going back to the data/analyses to address the question about locality, we stumbled upon a minor bug in the implementation of the proportional thresholding, causing the threshold to be too low and therefore too many neurons to be considered.

Fixing this bug reduces the number of neurons, thereby better showing the local structure of the clusters. Furthermore, if one would lower the threshold within the hierarchical clustering, smaller, and more localized, clusters would appear. We deliberately chose to keep this threshold high to not overwhelm the reader with the number of identified clusters. We hope the reviewer agrees with these changes and that the spatial structure in the clusters presented are indeed rather localized.

Moreover, when they describe the predictive performance of their model as "close to optimal" when the down-sampling factor coincided with the interaction time scale, it seems a bit exaggerated given that it was more or less as close to the upper bound as it was to the lower bound.

We thank the reviewer for catching this error. Indeed, the best performing model does not lay very close to the estimated performance of an optimal model. The text has been updated to reflect this.

When discussing the data statistics, the authors quote correlation values in the main text. However, these do not match the correlation values in the figure to which they seem to belong. Now, it seems that in the main text, they consider the Pearson correlation, whereas in the corresponding figure, it is the Spearman correlation. This is very confusing, and it is not really clear as to why the authors chose to do so.

Thank you for identifying the discrepancy between the correlation values mentioned in the text and those presented in the figure. We updated the manuscript to match the correlation coefficient values in the figure with the correct values denoted in the text.

Finally, when discussing the fact that the RTRBM model outperforms the cRBM model, the authors state it does so for different moments and in different numbers of cases (fish). It would be very interesting to know whether these are the same fish or always different fish.

Thank you for pointing this out. Keeping track of the same fish across the different metrics makes sense. We updated the figure to include a color code for each individual fish. As it turns out each time the same fish are significantly better performing.

Recommendations:

Figure 1: While the schematic in A and D only shows 11 visible units ("neurons"), the weight matrices and the activity rasters in B and C and E and F suggest that there should be, in fact, 12 visible units. While not essential, I think it would be nice if these numbers would match up.

Thank you for pointing out the inconsistency in the number of visible units depicted in Figure 1. We agree that this could have been confusing for readers. The figure has been updated accordingly. As you suggested, the schematic representation now accurately reflects the presence of 12 visible units in both the RBM and RTRBM models.

Figure 3: Panel G is not referenced in the main text. Yet, I believe it should be somewhere in lines 225ff.

Thank you for mentioning this. We added in line 233 a reference to figure 3 panel G to refer to the performance of the cRBM and RTRBM on the different fish.

Line 637ff: The authors consider moments <v\_i h\_μ> and <v\_i h\_j>, and from the context, it seems they are not the same. However, it is not clear as to why because, judging from the notation, they should be the same.

The second-order statistic <v\_i h\_j> on line 639 was indeed already mentioned and denoted as <v\_i h\_μ> on line 638. It has now been removed accordingly in the updated manuscript.

I found the usage of U^ and U throughout the manuscript a bit confusing. As far as I understand, U^ is a learned representation of U. However, maybe the authors could make the distinction clearer.

We understand the usage of Û and U throughout the text may be confusing for the reader. However, we would like to notify the reviewer that the distinction between these two variables is explained in line 142: “in addition to providing a close estimate (̂Û) to the true assembly connectivity matrix U”. However, for added clarification to the reader, we added additional mentions of the estimated nature of Û throughout the text in the updated manuscript.

Equation 3: It would be great if the authors could provide some more explanation of how they arrived at the identities.

These identities have previously been widely described in literature. For this reason, we decided not to include their derivation in our manuscript. However, for completeness, we kindly refer to:

Goodfellow, I., Bengio, Y., & Courville, A. (2016). Chapter 20: Deep generative models [In Deep Learning]. MIT Press. https://www.deeplearningbook.org/contents/generative_models.html

Typos:

-  L. 196: "connectiivty" -> "connectivity"

-  L. 197: Does it mean to say "very strong stronger"?

-  L. 339: The reference to Dunn et al. (2016) should appear in parentheses.

-  L. 504f: The colon should probably be followed by a full sentence.

-  Eq. 2: In the first line, the potential V still appears, which should probably be changed to show the concrete form (-b * h) as in the second line.

-  L. 351: Is there maybe a comma missing after "cRBM"?

-  L. 271: Instead of "correlation", shouldn't it rather be "similarity"? - L. 218: "Figure 3D" -> "Figure 3F"

We thank the reviewer for pointing out these typos, which have all (except one) been fixed in the text. We do emphasize the potential V to show that there are alternative hidden unit potentials that can be chosen. For instance, the cRBM utilizes dReLu hidden unit potentials.

Reviewer #3 (Public Review):

With ever-growing datasets, it becomes more challenging to extract useful information from such a large amount of data. For that, developing better dimensionality reduction/clustering methods can be very important to make sense of analyzed data. This is especially true for neuroscience where new experimental advances allow the recording of an unprecedented number of neurons. Here the authors make a step to help with neuronal analyses by proposing a new method to identify groups of neurons with similar activity dynamics. I did not notice any obvious problems with data analyses here, however, the presented manuscript has a few weaknesses:

(1) Because this manuscript is written as an extension of previous work by the same authors (van der Plas et al., eLife, 2023), thus to fully understand this paper it is required to read first the previous paper, as authors often refer to their previous work for details. Similarly, to understand the functional significance of identified here neuronal assemblies, it is needed to go to look at the previous paper.

We agree that the present Research Advance has been written in a way that builds on our previous publication. It was our impression that this was the intention of the Research Advance format, as spelled out in its announcement "eLife has introduced an innovative new type of article – the Research Advance – that invites the authors of any eLife paper to present significant additions to their original research". In the previous formatting guidelines from eLife this was more evident with a strong limitation on the number of figures and words, however, also for the present, more liberal guidelines, place an emphasis on the relation to the previous article. We have nonetheless tried in several places to fill in details that might simplify the reading experience.

(2) The problem of discovering clusters in data with temporal dynamics is not unique to neuroscience. Therefore, the authors should also discuss other previously proposed methods and how they compare to the presented here RTRBM method. Similarly, there are other methods using neural networks for discovering clusters (assemblies) (e.g. t-SNE: van der Maaten & Hinton 2008, Hippocluster: Chalmers et al. 2023, etc), which should be discussed to give better background information for the readers.

The clustering methods suggested by the reviewer do not include modeling any time dependence, which is the crucial advance presented here by the introduction of the RTRBM, in extending the (c)RBM. In our previous publication on the cRBM (an der Plas et al., eLife, 2023), this comparison was part of the discussion, although it focussed on a different set of methods. While clustering methods like t-SNE, UMAP and others certainly have their value in scientific analysis, we think it might be misleading the reader to think that they achieve the same task as an RTRBM, which adds the crucial dimension of temporal dependence.

(3) The above point to better describe other methods is especially important because the performance of the presented here method is not that much better than previous work. For example, RTRBM outperforms the cRBM only on ~4 out of 8 fish datasets. Moreover, as the authors nicely described in the Limitations section this method currently can only work on a single time scale and clusters have to be estimated first with the previous cRBM method. Thus, having an overview of other methods which could be used for similar analyses would be helpful.

We think that the perception that the RTRBM performs only slightly better is based on a misinterpretation of the performance measure, which we have tried to address (see comments above) in this rebuttal and the manuscript. In addition we would like to emphasize that the structural estimation (which is still modified by the RTRBM, only seeded by the cRBMs output), as shown in the simulated data, makes improved structural estimates, which is important, even in cases where the performance is comparable (which can be the case if the RBM absorbs temporal dependencies of assemblies into modified structure of assemblies). We have clarified this now in the discussion.

Recommendations:

(1) Line 181: it is not explained how a reconstruction error is defined.

Dear reviewer, thanks for pointing this out. A definition of the (mean square) reconstruction error is added in this line.

(2) How was the number of hidden neurons chosen and how does it affect performance?

Thank you for pointing this out. Due to the fact that we use transfer learning, the number of hidden units used for the RTRBM is given by the number of hidden units used for training the cRBM. In further research, when the RTRBM operates in the compositional phase, we can exploit a grid search over a set of hyper parameters to determine the optimal set of hidden units and other parameters.

Table des Matières - Réflexions sur l'école inclusive et le nouveau système de notation Source : Extraits de "Radio Révolution scolaire : inclusion et nouveau système de notation 2024"

Section 1 : Le rôle du projet pédagogique (7:22 - 14:19) 1.1 Posture de l'enseignant et questionnements des élèves (7:22 - 8:21)

Cette section aborde l'importance de la posture de l'enseignant et comment elle influence les élèves. Les intervenants discutent du rôle de l'enseignant comme "miroir" pour les élèves, qui cherchent à se projeter et à se comprendre à travers lui.

1.2 Sens du projet pédagogique et apprentissage (8:26 - 8:51)

Ce passage explore l'importance de donner du sens à l'apprentissage à travers des projets concrets. L'accent est mis sur la compréhension et la mise en pratique plutôt que sur la simple théorie.

1.3 Débat sur la pédagogie par projet (9:30 - 13:38) Un débat s'engage sur la pertinence de la pédagogie par projet. Certains intervenants y voient une source de motivation et un moyen d'intégrer l'élève, tandis que d'autres s'interrogent sur son efficacité réelle et son origine idéologique.

1.4 Remise en question du groupe classe et des disciplines (14:13 - 16:51)

Ce passage questionne l'évolution de l'école avec la volonté de "faire exploser" le groupe classe et les disciplines traditionnelles. L'exemple de l'histoire-géographie illustre cette évolution et les difficultés rencontrées par les enseignants.

Section 2 : Perception de l'école et poids de la note (16:51 - 30:30)

2.1 L'école inclusive comme réponse aux besoins spécifiques (16:51 - 17:09) L'école inclusive est présentée comme une solution pour répondre aux besoins spécifiques des élèves, notamment ceux en situation de handicap. Les intervenants soulignent l'importance de la différenciation pédagogique.

2.2 Perception actuelle de l'école par les élèves (17:15 - 19:16) Ce passage s'intéresse à la vision qu'ont les élèves de l'école aujourd'hui. Il est question de la lourdeur du système, du manque de sens perçu et de la vision de l'école comme une obligation.

2.3 Influence des parents et orientation des élèves (21:16 - 23:28) La discussion se tourne vers l'influence des parents sur l'orientation des élèves et les problèmes que cela peut engendrer. L'exemple des filières professionnelles est cité pour illustrer le manque de liberté de choix des élèves.

2.4 Déconnexion entre les résultats scolaires et les tests internationaux (23:28 - 26:42) Ce passage met en lumière le décalage entre les résultats scolaires souvent élevés et les mauvais résultats aux tests internationaux. L'inflation des notes et le manque de fiabilité du système de notation sont pointés du doigt.

2.5 La notation : entre paix sociale et reflet du niveau réel (26:49 - 30:30) Un débat s'engage sur la notation et son rôle. Faut-il privilégier la paix sociale en surévaluant les élèves ou refléter le niveau réel au risque de démotiver ? La pression exercée sur les élèves pour avoir des bonnes notes est également abordée.

Section 3 : Alternatives à la notation traditionnelle et évolution des pratiques (30:30 - 42:31) 3.1 Évaluation par compétences : avantages et limites (30:30 - 40:06) L'évaluation par compétences est présentée comme une alternative à la notation traditionnelle. Si elle permet de mieux cerner les acquis des élèves, sa mise en place demande un travail conséquent de la part des enseignants et sa pertinence est remise en question si les compétences évaluées changent d'une année à l'autre.

3.2 Importance de l'appréciation et compréhension de l'évaluation (40:06 - 42:31) Au-delà de la note, l'importance de l'appréciation et la compréhension de l'évaluation par les élèves sont mises en avant. Il s'agit de les aider à comprendre leurs points forts et leurs points faibles pour progresser. La nécessité d'adapter les méthodes d'évaluation en fonction du public est également abordée.

Section 4 : L'inclusion en pratique : Témoignages, difficultés et solutions (42:31 - 1:03:10) 4.1 Importance de la confiance en soi et valorisation des réussites (42:31 - 49:05) Cette section met l'accent sur l'importance de donner confiance aux élèves et de valoriser leurs réussites. Un témoignage poignant illustre comment une approche bienveillante et encourageante peut transformer le parcours d'un élève en difficulté.

4.2 La tricherie : un symptôme du système et de la pression scolaire (49:05 - 59:14) La tricherie est abordée comme un symptôme du système scolaire actuel et de la pression exercée sur les élèves. L'utilisation des téléphones portables et de l'intelligence artificielle comme ChatGPT complexifie la question de la notation et de l'évaluation des connaissances.

4.3 L'adaptation des supports et des méthodes pour l'inclusion (1:03:10 - 1:39:51) Différents aménagements et outils pour faciliter l'inclusion des élèves en difficulté sont présentés, tels que les ordinateurs, les logiciels de traitement de texte et les supports visuels. La question de la formation des enseignants et des moyens mis à disposition est soulevée.

4.4 Limites de l'inclusion et questionnements économiques (1:39:51 - 1:47:43) Les intervenants s'interrogent sur les limites de l'inclusion, notamment pour les cas les plus lourds, et soulèvent la question des moyens financiers alloués. La dimension économique de l'inclusion est questionnée.

Section 5: Bilan et perspectives de l'école inclusive (1:47:43 - 1:51:57)

5.1 Nécessité d'une approche globale et concertée (1:47:43 - 1:51:57) La discussion se conclut sur la nécessité d'une approche globale et concertée de l'école inclusive, impliquant tous les acteurs de la communauté éducative. L'importance de la formation des enseignants, de l'adaptation des évaluations et de la collaboration entre les différents professionnels est soulignée.

Author response:

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

eLife Assessment 

This study is a detailed investigation of how chromatin structure influences replication origin function in yeast ribosomal DNA, with focus on the role of the histone deacetylase Sir2 and the chromatin remodeler Fun30. Convincing evidence shows that Sir2 does not affect origin licensing but rather affects local transcription and nucleosome positioning which correlates with increased origin firing. However, the evidence remains incomplete as the methods employed do not rigorously establish a key aspect of the mechanism, fully address some alternative models, or sufficiently relate to prior results. Overall, this is a valuable advance for the field that could be improved to establish a more robust paradigm. 

We have added extensive new results to the manuscript that, we believe, address all three criticisms above, namely that the methods employed do not (1) rigorously establish a key aspect of the mechanism; (2) fully address some alternative models; or (3) sufficiently relate to prior results.

Public Reviews: 

Reviewer #1 (Public Review): 

Summary: 

This paper presents a mechanistic study of rDNA origin regulation in yeast by SIR2. Each of the ~180 tandemly repeated rDNA gene copies contains a potential replication origin. Earlyefficient initiation of these origins is suppressed by Sir2, reducing competition with origins distributed throughout the genome for rate-limiting initiation factors. Previous studies by these authors showed that SIR2 deletion advances replication timing of rDNA origins by a complex mechanism of transcriptional de-repression of a local PolII promoter causing licensed origin proteins (MCMcomplexes) to re-localize (slide along the DNA) to a different (and altered) chromatin environment. In this study, they identify a chromatin remodeler, FUN30, that suppresses the sir2∆ effect, and remarkably, results in a contraction of the rDNA to about onequarter it's normal length/number of repeats, implicating replication defects of the rDNA. Through examination of replication timing, MCM occupancy and nucleosome occupancy on the chromatin in sir2, fun30, and double mutants, they propose a model where nucleosome position relative to the licensed origin (MCM complexes) intrinsically determines origin timing/efficiency. While their interpretations of the data are largely reasonable and can be interpreted to support their model, a key weakness is the connection between Mcm ChEC signal disappearance and origin firing.  

Criticism: The reviewer expressed concern about the connection between Mcm ChEC signal disappearance and origin firing.

To further support our claim that the disappearance of the MCM signal in our ChEC datasets reflects origin firing, we now present additional data using the well-established method of MCM Chromatin IP (ChIP).

(1) New Supporting Evidence:  ChIP at genome-wide origins. In Figure 5 figure supplement 2, we demonstrate that the Mcm2 ChIP signal in cells released into hydroxyurea (HU) is significantly reduced at early origins compared to late origins, which mirrors the pattern observed with the MCM2 ChEC signal. This reduction in the ChIP signal at early origins supports the interpretation that the MCM signal disappearance is associated with origin firing.

(2) New supporting based evidence:  ChIP at rDNA Origins. Our ChIP analysis also shows that the disappearance of the MCM signal at rDNA origins in sir2Δ cells released into HU is accompanied by signal accumulation at the replication fork barrier (RFB), indicative of stalled replication forks at this location (Figure 5 figure supplement 3). This pattern is consistent with the initiation of replication at these origins and fork stalling at the RFB.

(3) New supporting evidence:  2D gels with quantification. Furthermore, additional 2D gel electrophoresis results provide ample independent evidence of rDNA origin firing in HU in sir2Δ mutants and suppression of origin firing in sir2 fun30 cells. These new data include 1) quantification of 2D gels in Figure 4D and 2) new 2D gels presented in Figure 4C as described below in greater detail. Collectively, these results demonstrate that rDNA origins fire prematurely in HU in sir2 cells and that firing is suppressed by FUN30 deletion. These additional data reinforce our model and support the association between MCM signal disappearance and replication initiation.

While the cyclical chromatin association-dissociation of MCM proteins with potential origin sequences may be generally interpreted as licensing followed by firing, dissociation may also result from passive replication and as shown here, displacement by transcription and/or chromatin remodeling.

The reviewer raised a concern that the cyclical chromatin association-dissociation of MCM proteins could be interpreted as licensing followed by firing, but might also result from passive replication or displacement by transcription and chromatin remodeling.

Addressing Alternative Explanations:

(1) Selective Disappearance of MCM Complexes: While transcription and passive replication can indeed cause the MCM-ChEC signal to disappear, these processes cannot selectively cause the disappearance of the displaced MCM complex without also affecting the non-displaced MCM complex. Specifically, RNA polymerase transcribing C-pro would first need to dislodge the normally positioned MCM complex before reaching the displaced complex, which is not observed in our data.

(2) Role of FUN30 Deletion:  FUN30 deletion results in increased C-pro transcription and reduced disappearance of the displaced MCM complex. This observation supports our model, as transcription alone would not selectively affect the displaced MCM complex while leaving the normally positioned MCM complex unaffected.

(3) Licensing Restrictions: It is crucial to note that continuous replenishment of displaced MCMs with newly loaded MCMs is not possible in our experimental conditions, as the cells are in S phase and licensing is restricted to G1. This temporal restriction further supports our interpretation that the disappearance of the MCM signal reflects origin firing rather than alternative processes.

In summary, while alternative explanations such as transcription and passive replication could potentially account for MCM signal disappearance, our data indicate that these processes cannot selectively affect the displaced MCM complex without impacting the non-displaced complex. The selective disappearance observed in our experiments, along with the effects of FUN30 deletion and the temporal constraints on MCM loading, strongly support our interpretation that the disappearance of the MCM signal reflects origin firing.

Moreover, linking its disappearance from chromatin in the ChEC method with such precise resolution needs to be validated against an independent method to determine the initiation site(s). Differences in rDNA copy number and relative transcription levels also are not directly accounted for, obscuring a clearer interpretation of the results. 

The reviewer raised concerns about the need to validate the disappearance of MCM from chromatin observed using the ChEC method against an independent method to determine initiation sites. Additionally, they pointed out that differences in rDNA copy number and relative transcription levels are not directly accounted for, which may obscure the interpretation of the results.

(1) Reduced rDNA Copy Number promotes Early Replication: Copy number reduction of the magnitude caused by deletion of both SIR2 and FUN30 is not expected to suppress early rDNA replication in sir2, but rather to exacerbate it. Specifically, deletion of SIR2 and FUN30 causes the rDNA to shrink to approximately 35 copies. Kwan et al., 2023 (PMID: 36842087) have shown that a reduction in rDNA copy number to 35 copies results in a dramatic acceleration of rDNA replication in a SIR2+ strain. Therefore, the effect of rDNA size on replication timing reinforces our conclusion that deletion of FUN30 suppresses rDNA replication.

(2) New 2D Gels in sir2 and sir2 fun30 strains with equal number of rDNA repeats: To directly address the concern regarding differences in the number of rDNA repeats, we have included new 2D gel analyses in the revised manuscript. By using a fob1

background, we were able to equalize the repeat number between the sir2 and sir2 fun30 strains (Figure 4E). The 2D gels conclusively show that the suppression of rDNA origin firing upon FUN30 deletion is independent of both rDNA size and FOB1.

Nevertheless, this paper makes a valuable advance with the finding of Fun30 involvement, which substantially reduces rDNA repeat number in sir2∆ background. The model they develop is compelling and I am inclined to agree, but I think the evidence on this specific point is purely correlative and a better method is needed to address the initiation site question. The authors deserve credit for their efforts to elucidate our obscure understanding of the intricacies of chromatin regulation. At a minimum, I suggest their conclusions on these points of concern should be softened and caveats discussed. Statistical analysis is lacking for some claims. 

Strengths are the identification of FUN30 as suppressor, examination of specific mutants of FUN30 to distinguish likely functional involvement. Use of multiple methods to analyze replication and protein occupancies on chromatin. Development of a coherent model. 

Weaknesses are failure to address copy number as a variable; insufficient validation of ChEC method relationship to exact initiation locus; lack of statistical analysis in some cases. 

With regard to "insufficient validation of ChEC method relationship to exact initiation locus":  The two potential initiation sites that one would monitor (non-displaced and displaced) are separated by less than 150 base pairs, and other techniques simply do not have the resolution necessary to distinguish such differences. Indeed, our new ChIP results presented in Figure 5 figure supplement 3 clearly demonstrate that while the resolution of ChIP is adequate to detect the reduction of MCM signal at the replication initiation site and its relocation to the RFB ( ~2 kb away), it lacks the resolution required to differentiate closely spaced MCM complexes.

Furthermore, as we suggest in the manuscript, our results are consistent with a model in which it is only the displaced MCM complex that is activated, whether in sir2 or WT.  If no genotypedependent difference in initiation sites is even expected, it would be hard to interpret even the most precise replication-based assays.  

We appreciate the reviewer pointing out that some statistical analyses were lacking: we have added statistical analysis for 2D gels (Figures 4D and 4E),  EdU incorporation experiments in Figure 4F and disappearance of MCM ChEC and ChIP signal upon release of cells into HU (Figure 5 supplement 1 and Supplement 2).  

Additional background and discussion for public review: 

This paper broadly addresses the mechanism(s) that regulate replication origin firing in different chromatin contexts. The rDNA origin is present in each of ~180 tandem repeats of the rDNA sequence, representing a high potential origin density per length of DNA (9.1kb repeat unit). However, the average origin efficiency of rDNA origins is relatively low (~20% in wild-type cells), which reduces the replication load on the overall genome by reducing competition with origins throughout the genome for limiting replication initiation factors. Deletion of histone deacetylase SIR2, which silences PolII transcription within the rDNA, results in increased early activation or the rDNA origins (and reduced rate of overall genome replication). Previous work by the authors showed that MCM complexes loaded onto the rDNA origins (origin licensing) were laterally displaced (sliding) along the rDNA, away from a well-positioned nucleosome on one side. The authors' major hypothesis throughout this work is that the new MCM location(s) are intrinsically more efficient configurations for origin firing. The authors identify a chromatin remodeling enzyme, FUN30, whose deletion appears to suppress the earlier activation of rDNA origins in sir2∆ cells. Indeed, it appears that the reduction of rDNA origin activity in sir2∆ fun30∆ cells is severe enough to results in a substantial reduction in the rDNA array repeat length (number of repeats); the reduced rDNA length presumably facilitates it's more stable replication and maintenance. 

Analysis of replication by 2D gels is marginally convincing, using 2D gels for this purpose is very challenging and tricky to quantify. 

We address this criticism by carefuly quantifying 2 D gel results using single rARS signal for normalizing bubble arc as discussed below.

The more quantitative analysis by EdU incorporation is more convincing of the suppression of the earlier replication caused by SIR2 deletion. 

We have also added quantification of EdU results to strengthen our arguments.  

To address the mechanism of suppression, they analyze MCM positioning using ChEC, which in G1 cells shows partial displacement of MCM from normal position A to positions B and C in sir2∆ cells and similar but more complete displacement away from A to positions B and C in sir2fun30 cells. During S-phase in the presence of hydroxyurea, which slows replication progression considerably (and blocks later origin firing) MCM signals redistribute, which is interpreted to represent origin firing and bidirectional movement of MCMs (only one direction is shown), some of which accumulate near the replication fork barrier, consistent with their interpretation. They observe that MCMs displaced (in G1) to sites B or C in sir2∆ cells, disappear more rapidly during S-phase, whereas the similar dynamic is not observed in sir2∆fun30∆. This is the main basis for their conclusion that the B and C sites are more permissive than A. While this may be the simplest interpretation, there are limitations with this assay that undermine a rigorous conclusion (additional points below). The main problem is that we know the MCM complexes are mobile so disappearance may reflect displacement by other means including transcription which is high is the sir2∆ background. Indeed, the double mutant has greater level of transcription per repeat unit which might explain more displaced from A in G1. Thus, displacement might not always represent origin firing. Because the sir2 background profoundly changes transcription, and the double mutant has a much smaller array length associated with higher transcription, how can we rule out greater accessibility at site A, for example in sir2∆, leading to more firing, which is suppressed in sir2 fun30 due to greater MCM displacement away from A? 

I think the critical missing data to solidly support their conclusions is a definitive determination of the site(s) of initiation using a more direct method, such as strand specific sequencing of EdU or nascent strand analysis. More direct comparisons of the strains with lower copy number to rule out this facet. As discussed in detail below, copy number reduction is known to suppress at least part of the sir2∆ effect so this looms over the interpretations. I think they are probably correct in their overall model based on the simplest interpretation of the data but I think it remains to be rigorously established. I think they should soften their conclusions in this respect. 

Please see discussion below about these issues.

Reviewer #2 (Public Review): 

Summary: 

In this manuscript, the authors follow up on their previous work showing that in the absence of the Sir2 deacetylase the MCM replicative helicase at the rDNA spacer region is repositioned to a region of low nucleosome occupancy. Here they show that the repositioned displaced MCMs have increased firing propensity relative to non-displaced MCMs. In addition, they show that activation of the repositioned MCMs and low nucleosome occupancy in the adjacent region depend on the chromatin remodeling activity of Fun30. 

Strengths: 

The paper provides new information on the role of a conserved chromatin remodeling protein in the regulation of origin firing and in addition provides evidence that not all loaded MCMs fire and that origin firing is regulated at a step downstream of MCM loading. 

Weaknesses: 

The relationship between the author's results and prior work on the role of Sir2 (and Fob1) in regulation of rDNA recombination and copy number maintenance is not explored, making it difficult to place the results in a broader context. Sir2 has previously been shown to be recruited by Fob1, which is also required for DSB formation and recombination-mediated changes in rDNA copy number. Are the changes that the authors observe specifically in fun30 sir2 cells related to this pathway? Is Fob1 required for the reduced rDNA copy number in fun30 sir2 double mutant cells? 

We have conducted additional studies in the fob1 background to address how FOB1 and the replication fork barrier (RFB) influence the kinetics of rDNA size reduction upon FUN30 deletion (Figure 2 - figure supplement 2), rDNA replication timing (Figure 2 - figure supplement 3), and rDNA origin firing using 2D gels (Figure 4C).

Strains lacking SIR2 exhibit unstable rDNA size, and FOB1 deletion stabilizes rDNA size in a sir2 background (and otherwise). Similarly, we found that FOB1 deletion influences the kinetics of rDNA size reduction in sir2 fun30 cells. Specifically, we were able to generate a fob1 sir2 fun30 strain with more than 150 copies. Nonetheless, and consistent with our model, this strain still exhibited delayed rDNA replication timing (Figure 2 - figure supplement 3), and its rDNA still shrank upon continuous culture (Figure 2 figure supplement 2). These results demonstrate that, although FOB1 affects the kinetics of rDNA size reduction in sir2 fun30 strains, the reduced rDNA array size or delayed replication timing upon FUN30 deletion size does not depend on FOB1.

The use of the fob1 background allowed us to compare the activation of rDNA origins in sir2 and sir2 fun30 strains with equally short rDNA sizes. 2D gels demonstrate robust and reproducible suppression of rDNA origin activity upon deletion of FUN30 in sir2 fob1 strains with 35 rDNA copies (Figure 4C). These results indicate that the main effect we are interested in—FUN30-induced reduction in origin firing—is independent of both FOB1 and rDNA size.

Our additional studies conclusively show that the FUN30-induced reduction in rDNA origin firing is independent of both FOB1 and rDNA size. These findings provide important insights into the mechanisms regulating rDNA copy number maintenance, placing our results within the broader context of existing knowledge on Sir2 and Fob1 functions.

Reviewer #3 (Public Review): 

Summary: 

Heterochromatin is characterized by low transcription activity and late replication timing, both dependent on the NAD-dependent protein deacetylase Sir2, the founding member of the sirtuins. This manuscript addresses the mechanism by which Sir2 delays replication timing at the rDNA in budding yeast. Previous work from the same laboratory (Foss et al. PLoS Genetics 15, e1008138) showed that Sir2 represses transcription-dependent displacement of the Mcm helicase in the rDNA. In this manuscript, the authors show convincingly that the repositioned Mcms fire earlier and that this early firing partly depends on the ATPase activity of the nucleosome remodeler Fun30. Using read-depth analysis of sorted G1/S cells, fun30 was the only chromatin remodeler mutant that somewhat delayed replication timing in sir2 mutants, while nhp10, chd1, isw1, htl1, swr1, isw2, and irc3 had not effect. The conclusion was corroborated with orthogonal assays including two-dimensional gel electrophoresis and analysis of EdU incorporation at early origins. Using an insightful analysis with an Mcm-MNase fusion (Mcm-ChEC), the authors show that the repositioned Mcms in sir2 mutants fire earlier than the Mcm at the normal position in wild type. This early firing at the repositioned Mcms is partially suppressed by Fun30. In addition, the authors show Fun30 affects nucleosome occupancy at the sites of the repositioned Mcm, providing a plausible mechanism for the effect of Fun30 on Mcm firing at that position. However, the results from the MNAse-seq and ChEC-seq assays are not fully congruent for the fun30 single mutant. Overall, the results support the conclusions providing a much better mechanistic understanding how Sir2 affects replication timing at rDNA, 

The observation that the MNase-seq plot in fun30 mutant shows a large signal at the +3 nucleosome and somewhat smaller at position +2, while the ChEC-seq plot exhibits negligible signals, is indeed an important point of consideration. This discrepancy arises because most of the MCM in fun30 mutant remains at its original site where it abuts +1 nucleosome. As a result, the MCM-MNase fusion protein fails to reach and “light up” the +3 nucleosome, which is, nonetheless, well-visualized with exogenous MNase.  The paucity of displaced MCMs, which is responsible for cutting +2 nucleosome, explains the discrepancy in the +2 nucleosome signal between exogenous MNase and CheC datasets in the fun30 mutant.  

Despite this apparent discrepancy, the overall results support our conclusions and provide a much better mechanistic understanding of how Sir2 affects replication timing at rDNA. The MNaseseq data reflect nucleosome positioning and chromatin structure, while the ChEC-seq data specifically highlights the locations where MCM is bound and active.  

Strengths 

(1) The data clearly show that the repositioned Mcm helicase fires earlier than the Mcm in the wild type position. 

(2) The study identifies a specific role for Fun30 in replication timing and an effect on nucleosome occupancy around the newly positioned Mcm helicase in sir2 cells. 

Weaknesses 

(1) It is unclear which strains were used in each experiment. 

(2) The relevance of the fun30 phospho-site mutant (S20AS28A) is unclear. 

We appreciate the reviewer pointing out places in which our manuscript omitted key pieces of information (items 1 and 3), we have included the strain numbers in our revision.  With regard to point 2, we had written:  

Fun30 is also known to play a role in the DNA damage response; specifically, phosphorylation of Fun30 on S20 and S28 by CDK1 targets Fun30 to sites of DNA damage, where it promotes DNA resection (Chen et al. 2016; Bantele et al. 2017). To determine whether the replication phenotype that we observed might be a consequence of Fun30's role in the DNA damage response, we tested non-phosphorylatable mutants for the ability to suppress early replication of the rDNA in sir2; these mutations had no effect on the replication phenotype (Figure 2B), arguing against a primary role for Fun30 in DNA damage repair that somehow manifests itself in replication. 

(3) For some experiments (Figs. 3, 4, 6) it is unclear whether the data are reproducible and the differences significant. Information about the number of independent experiments and quantitation is lacking. This affects the interpretation, as fun30 seems to affect the +3 nucleosome much more than let on in the description. 

We have provided replicas and quantitation for the results in these figures.

(Replica ChEC Southern blot with quantification (Figure 3 figure supplement 1), quantification and replicas for 2D gels in Figure 4 and replicas for nucleosome occupancy (Figure 6 supplement 1).

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors): 

Fig. 3-Examination of MCM occupancy at the rDNA ARS region using a variation of ChEC.

Presumably these are these G1-arrested cells but does not seem to be stated. Please confirm. 

The 2D gels results are not very convincing of their conclusions. We are asked to compare bubble to fork arcs at 30 minutes, but this is not feasible. It is the author's job to quantify the data from multiple replicates, but none is given. After much careful examination, comparing the relative intensities of ascending bubble and Y-arcs, I think I can accept that 4A shows highest early efficiency for sir2 over WT and fun30, which are similar to each other, and lowest for sir2 fun30, at 60 and 90 min. 

In the revision we provide a careful quantification of the 2D gels in Figure 4. For assessing rDNA origin activity, we normalized the bubble arc during the HU time course to a single rARS signal, that appears as large 24.4kb Nhe1I fragment originating from the  rightmost rDNA repeat (see Figures 4A and 4B). The description of the quantification in the text is provided below. 

“Prior to separation on 2D gels, DNA was digested with NheI, which releases a 4.7 kb rARScontaining linear DNA fragment at the internal rDNA repeats (1N) and a much larger, 24.5 kb single-rARS-containing fragment originating from the rightmost repeat. In 2D gels, active origins generate replication bubble arc signals, whereas passive replication of an origin appears as a y-arc. Having a signal emanating from a single ARS-containing fragment simplifies the comparison of rDNA origin activity in strains with different numbers of rDNA repeats, such as in sir2 vs sir2 fun30 mutants. Origin activity is expressed as a ratio of the bubble to the single-ARS signal, effectively measuring the number of active rDNA origins per cell at a given time point. 

As seen previously (Foss et al. 2019), deletion of SIR2 increased the number of activated rDNA origins, while deletion of FUN30 suppressed this effect. When analyzed in aggregate at 20, 30, 60 and 90 minutes following release into HU, the average number of activated rDNA origin activity in sir2 mutant was increased 6.3-fold compared to those in WT (5.0±2.3 in sir2 vs 0.8±0.4 in wt, p<0.05 by 2 tailed t-test), and the increased number was reduced upon FUN30 deletion (1.3±0.7 in sir2 fun30, p<0.05 by 2 tailed t-test vs sir2, NS for comparison to WT).”

However, for part 4B, they state (p. 11) that deletion of FUN30 in a SIR2 background had no perceptible effect (on ARS305) but I think the data appear otherwise: the FUN30 cells show more Y-arc than WT.

We now provide the assessment of ARS305 activity in HU cells as a ratio of bubble-arc to 1N signal. The reviewer is right that FUN30 has a more robust bubble arc signal compared to WT.

However, after normalization to 1N this difference did not appear significant (3.7 vs 5.1). Overall the analysis of activity or ARS305 origins demonstrates a reciprocity with the activity of rDNA origins in each of the four genotypes.  Furthermore, this observation is confirmed in our EdU-based analysis of 111 genomic origins, with statistical analysis showing a very high level of significance (see below).  

Ultimately, analysis of unsynchronized cells would give unambiguous results about origin efficiency. In this regard I note that analysis of rDNA origin firing by 2D gels with HU versus asynchronous gives different results in WT versus sir2∆, with no difference in unsynchronized cells (He et al. 2022). It would be interesting to test the strains here unsynchronized, though copy number size would still be a variable to address.

Origin activity in log cultures is typically assessed by comparing replication initiation within an origin, presenting as a bubble arc, to passively replicated DNA (Y-arc). However, such an analysis at tandemly arrayed origins, such as rDNA, is not feasible, as both active and passive replication are the result of activation of the same origins. This explains the lack of difference between WT and sir2 cells previously reported (He et al. 2022), which we have also observed. Differences in activation of rDNA origins in WT vs sir2 cells is clearly reflected in HU experiments, as was the case in the earlier report (He et al. 2022). 

To address the issue of differences in copy number between sir2 and sir2 fun30 cells we have now done experiments in a fob1 background where we can equalize the copy number among the two genotypes. These 2D gels are presented in Figure 4C. We address this issue in the revised manuscript as follows:

“The overall impact of FUN30 deletion on rDNA origin activity in a sir2 background is expected to be a composite of two opposing effects: a suppression of rDNA origin activation and increased rDNA origin activation due to reduced rDNA size (Kwan et al. 2023). To evaluate the effect FUN30 on rDNA origin activation independently of rDNA size, we generated an isogenic set of strains in a fob1 background, all of which contain 35 copies of the rDNA repeat.  (Deletion of FOB1 is necessary to stabilize rDNA copy number.)  Comparing rDNA origin activity in sir2 versus sir2 fun30 genotypes, we observed a robust and reproducible reduction in rDNA origin activity upon FUN30 deletion. This finding confirms that the FUN30 suppresses rDNA origin firing in sir2 background independently of both rDNA size and FOB1 status.”

-EdU analysis is more convincing regarding relative effects on genome versus rDNA, however, again, the effect of reduced rDNA array size in the sir2 fun30 cells may also be the proximal cause of the reduced effect on genome (early origins) replication rather than a direct effect on origin efficiency. No statistic provided to support that fun30 suppresses sir2 for rDNA activity. 

This comment raises three distinct, but related, issues: 

First, the reviewer is asking whether the reduced rDNA size, of the magnitude we observed in sir2 fun30 cells, could by itself be responsible for increased origin activity elsewhere in the genome, just because there is less rDNA that needs to be replicated. As noted earlier (Kwan et al. 2023), Kwan et al. examined the effect of rDNA size reduction and observed: 1) marked increased in rDNA origin activity and 2) reciprocal reduction in origin activity elsewhere in the genome. This counterintuitive finding suggests that a smaller rDNA size exerts more competition for limited replication resources compared to a larger rDNA size. In light of this, our findings with FUN30 deletion become even more compelling. The suppression of rDNA firing upon FUN30 deletion is so significant that it overrides the expected effects of rDNA size reduction.

Second, the reviewer points out our lack of statistical analysis to support our contention that fun30 suppresses sir2 with regard to rDNA origin activity. We have now addressed this issue as well, by quantifying 2D gel signals, as described above in the text that begins with "Prior to separation on 2D gels, DNA was digested with NheI ...". 

Third, we have now provided a statistical analysis to support our conclusion that EdU-based analysis of activity of 111 early origins shows suppression upon deletion of SIR2 that is largely reversed by additional deletion of FUN30. 

"Deletion of FUN30 in a sir2 background partially restored EdU incorporation at early origins, concomitant with reduced EdU incorporation at rDNA origins. In particular, the median value of log10 of read depths at 111 early origins, as the data are shown in Figure 4F, dropped from 6.5 for wild type to 6.2 for sir2 but then returned almost to wild type levels (6.4) in sir2 fun30.  The p value obtained by Student's t test, comparing the drop in 111 origins from wild type to sir2 with that from wild type to sir2 fun30 was highly significant (<< 10-16)  In contrast, FUN30 deletion in the WT background did not reduce EdU incorporation at genomic origins (median 6.6). These findings highlight that FUN30 deletion-induced suppression of rDNA origins in sir2 is accompanied by the activation of genomic origins."

Use loss of Mcm-ChEC signal as proxy for origin firing. Reasonably convincing that decrease correlates with origin firing on a one-to-one basis (Fig. 5B), though no statistic given. 

We provide the statistical analysis in Figure 5-figure supplement 1.

However, there is no demonstration of ability to observe this correlation with fine resolution as needed for the claims here. It seems equally possible that sir2 deletion causes more firing by repositioning MCMs to a better location or that the prior location, which still contains substantial MCM, becomes more permissive. The MCM signal appears to be mobile, so perhaps the role of FUN30 is to prevent to mobility of MCM away from the original site in WT cells; note that significantly less Mcm signal is at the original position in sir2 fun30. No accumulation of MCM occurs near the RFB in WT (and fun30) cells. I understand that origin firing is lower in WT but raises concerns about sensitivity and dynamic range of this assay and that MCM positions may reflect transcription versus replication. 

Please see the section above labeled "Addressing Alternative Explanations".  

Is Fig 6A Y-axis correctly labeled? I understand this figure to represent MNase-seq reads; is there any Mcm2-ChEC-seq in part A? 

We have corrected the labeling. 6A represent MNase-seq reads. Thank you for pointing this out.

I understand part B to represent nucleosome-sized fragments released by Mcm2-ChEC interpreted to be nucleosomes. But could they be large fragments potentially containing adjacent MCM-double hexamers?  

Our representation of ChEC-seq data in Figure 1 supplement 1, where we can see the entire spectrum of fragment sizes, demonstrates two distinct populations of fragments: nucleosome size and MCM-size fragments.

Reviewer #2 (Recommendations For The Authors): 

Suggestions for the authors to consider: 

(1) The authors make a good case for the importance of replication balance between rDNA and euchromatin in ensuring that the genome is replicated in a timely fashion. This seems to be clearly regulated by Sir2. However, Sir2 also affects rDNA copy number and suppresses unequal cross over events, which are stimulates by Fob1. Does Fun30 suppress Fob1-dependent recombination events in sir2D cells? 

It is unclear why FUN30 only affects rDNA repeat copy number in sir2 cells. Why doesn't Fun30 reduce copy number in wild-type cells? 

Deletion of SIR2 causes rightward repositioning of MCMs to a position where they are more prone to fire, as shown by our HU ChEC datasets in which we show that the repositioned MCMs are more prone to activation than the non-repositioned ones. FUN30 deletion suppresses activation of these, activation-prone repositioned MCMs, as shown by HU ChEC. This suppression of rDNA origin activation in sir2 cells causes rDNA to shrink. In fun30 single mutants, due to the paucity of non-repositioned MCMs, we do not observe significant suppression of rDNA origin firing, and consequently, there is no reduction in rDNA size in fun30 cells.

(2) The authors use Mcm-MNase to map the location of the MCM helicase. Can these results be confirmed using the more standard and direct ChIP assay to examine changes in MCM localization

We carried out suggested MCM ChIP experiments and present these results in Figure 5 supplement 2 and supplement 3. These ChIP data demonstrate that: 

(1) MCM signal disappears preferentially at early origins compared to late origins, as seen in our ChEC results.

(2) The disappearance of ChEC signal at rDNA origins in sir2 mutant is accompanied by the signal accumulation at the RFB, consistent with fork stalling at the RFB mirroring the results we obtained by ChEC. While these results indicate that that ChIP has adequate resolution to detect MCM repositioning at 2 kb, scale, its resolution was insufficient for fine scale discrimination of repositioned and non-repositioned MCMs.

In this regard, the specific role of Fun30 in regulation of MCM firing at rDNA is interesting. 

Does Fun30 localize to the ARS region of rDNA? How is Fun30 specifically recruited to rDNA?  

We carried out ChIP for Fun30 and observed, similarly to previous reports (Durand-Dubief et al. 2012), a wide distribution of Fun30 throughout the genome and at rDNA. We have elected not to include these results in the current manuscript.

(3) The 2D gels in Figure 4 are difficult to interpret. The bubble to arc ratios in fun30D seem different from both wild-type and sir2D. It may be helpful to the reader to quantify the bubble to arc ratios. fun30D also seems to be affecting ARS305 by itself.

We provide quantification of 2 D gels in Figure 4.

(4) Figure 5. 

(4.1) For examining origin firing based on the disappearance of the Mcm-MNase reads, is HU arrest necessary? HU may be causing indirect effects due to replication fork stalling. In principle, the authors should be able to perform this analysis without HU, since their cells are released from synchronized arrest in G1 (and at least for the first cell cycle should proceed synchronously on to S phase). In addition, validation of Mcm-ChEC results using ChIP for one of the subunits of the MCM complex would increase confidence in the results. 

The HU arrest allows us to examine early events in DNA replication at much finer spatial and temporal resolution than it would be possible without it.

We have now used Mcm2 ChIP to confirm that the signal disappears at the MCM loading site in HU in sir2 cells as discussed above (Figure 5 figure supplement 3). However, the resolution is inadequate to discriminate non-repositioned vs repositioned MCMs.

(4.2) The non-displaced Mcm-ChEC signal in sir2D seems like it's decreasing more than in wildtype cells. Explain. It would be helpful to quantify these results by integrating the area under each peek (or based on read numbers). It looks like one of the displaced Mcm signals (the one more distal from the non-displaced) is changing at a similar rate to the non-displaced.  

Integrating the area under each Mcm-ChEC peak or using read numbers is superfluous for the following reasons:  (1) The rectangular appearance of the peaks in Figure 5 clearly reflects signal intensity, making additional numerical integration redundant. (2) The visual differences between wild-type and sir2D cells are distinct and sufficient for drawing conclusions without further quantification.  (3) Keeping the analysis straightforward avoids unnecessary complexity and maintains clarity.

(4.3) Can the authors explain why fun30D seems to be suppressing only one of the 2 displaced Mcms from firing? 

We speculate that the local environment is more conductive for firing one of two displaced MCMs, but we do not understand why.

(5) Figure 6. Why would the deletion of SIR2, a silencing factor, results in increased nucleosome occupancy at rDNA? 

If we understand correctly, the reviewer is referring to a small increase in +2 and +3 signal in sir2 compared to the WT. In WT G1 cells, there is a single MCM between +1 and +3 nucleosome. This space cannot accommodate a +2 nucleosome in G1 cells because MCM is loaded at that position in most cells (in G2 cells however, this space is occupied by a nucleosome (Foss et al., 2019). MCM repositioning in sir2 mutant would displace MCM from this location making it possible for this space to be now occupied by a nucleosome.

The changes in nuc density seem modest. Also, nucleosome density is similarly increased in sir2D and fun30D cells, but sir2 has a dramatic effect on origin firing but fun30D does not. Explain. 

We believe that the FUN30 status makes most of the difference for firing of displaced MCMs.

Since there are few displaced MCMs in SIR2 cells, there is not large impact on origin firing. Furthermore, the rDNA already fires late in WT cells, so our ability to detect further delay upon  FUN30 deletion could be more difficult.

(6) Discussion. At rDNA Sir2 may simply act by deacetylating nucleosomes and decreasing their mobility. This is unrelated to compaction which is usually only invoked regarding the activities of the full SIR complex (Sir2/3/4) at telomeres and the mating type locus. The arguments regarding polymerase size, compaction etc may not be relevant to the main point since although the budding yeast Sir2 participates in heterochromatin formation at the mating type loci and telomeres, at rDNA it may act locally near its recruitment site at the RFB. 

This is a valid point. We have added this sentence in the discussion to highlight the differences between silencing at rDNA and those at the silent mating loci and telomeres that SIR-complex dependent.

“Steric arguments such as these are even less compelling when made for rDNA than for the silent mating type loci and telomeres, because chromatin compaction has been studied mostly in the context of the complete Sir complex (Sir1-4). In contrast, Sir1, 3, and 4 are not present at the rDNA.”

Minor 

It would be interesting to see if deletion of any histone acetyltranferases acts in a similar way to Fun30 to reduce rDNA copy number in sir2D cells. 

Thank you for this suggestion.

Reviewer #3 (Recommendations For The Authors): 

(1) The design of Figure 3 could be improved. A scheme could help understand the assay without flipping back to Figure 1. The numbers below the gel bands need definition. 

We have included the scheme describing the restriction and MCM-MNase cut sites and the location of the probe for the Southern blot.

(2) The design of Figure 4 could be improved by adding a scheme to help interpret the 2d gel picture. The figure also lacks quantitation. Are the results reproducible and the differences significant? 

We have added the scheme, quantification and statistics in Figure 4.

(3) Please list in each figure legend the exact strains from Table S1 which were used. 

We have included the strain numbers in the Figure legend.

Durand-Dubief M, Will WR, Petrini E, Theodorou D, Harris RR, Crawford MR, Paszkiewicz K, Krueger F, Correra RM, Vetter AT et al. 2012. SWI/SNF-like chromatin remodeling factor Fun30 supports point centromere function in S. cerevisiae. PLoS Genet 8: e1002974.

Foss EJ, Gatbonton-Schwager T, Thiesen AH, Taylor E, Soriano R, Lao U, MacAlpine DM, Bedalov A. 2019. Sir2 suppresses transcription-mediated displacement of Mcm2-7 replicative helicases at the ribosomal DNA repeats. PLoS Genet 15: e1008138.

He Y, Petrie MV, Zhang H, Peace JM, Aparicio OM. 2022. Rpd3 regulates single-copy origins independently of the rDNA array by opposing Fkh1-mediated origin stimulation. Proc Natl Acad Sci U S A 119: e2212134119.

Kwan EX, Alvino GM, Lynch KL, Levan PF, Amemiya HM, Wang XS, Johnson SA, Sanchez JC, Miller MA, Croy M et al. 2023. Ribosomal DNA replication time coordinates completion of genome replication and anaphase in yeast. Cell Rep 42: 112161.

En fråga: går det inte att komma med ett någorlunda kvalificerat påstående i stil med: "vi bedömer att en aktiv och datadriven övervakning av energicentraler som motsvarar Apotekaren och Bilia kan resultera i en förbättring av COP med X procentenheter, samt möjligheten att flytta en effektmängd om Y kW inköpt el och Z kW inköpt fjärrvärme på en axel om Å timmar, för att åstadkomma en plattare effektsignatur för den inköpta elen, såväl som för den inköpta fjärrvärmen."

Jag vet att det är en massa antaganden som behöver göra och att det behövs mycket data och analyser, men nu har jag lyxen att låtsas vara en IT-kille utan koll på byggnadsfysik... om det skulle gå att komma med en sådan hypotes som inte är tagen ur luften, utan tillräckligt väl underbyggd för att hålla för Thomas och Carolines granskning, tror jag att vi har goda förutsättningar för att fortsätta, eftersom de prioriterar effektfrågan inom Fabege.

Yo creo que sí tiene una base cultural que valora la objetividad y filosofía. Pero se limita y reduce a lo más básico y esencial para dar la mayor libertad de pensamiento y opinión posible al individuo en todos los sentido. Se reduce y limita a lo esencial. Por eso da pie a que haya muchas ideas y opiniones diferentes dentro de los grupos libertarios.

No se si es una idea muy extendida entre el liberalismo. Pero si fuera así el desarrollo lógico y la defensa argumentativa que muchos hacen no sería tan buena como es. Entiendo que esta postura será la típica de los menos formados o más conformistas que no se preocupan por defender públicamente sus ideas.

En el liberalismo se engloban muchas ideas diferentes y muchas veces contradictorias entre ellas. Ejemplo: minarquismo y anarquismo. Hay mucha disparidad de ideas y creencias que tienen como base el deseo de defender la libertad individual. En cambio, el objetivismo es un sistema más "cerrado" en ese sentido. Parte de un sistema filosófico bien definido y a partir de ahí se llegan a ciertas conclusiones si no iguales muy similares. Hay pocas discrepancias ideológicas entre los diferentes objetivistas (aunque las hay).

Las creencias están interrelacionadas y se siguen unas a otras. En otro caso no habría coherencia y en ciertos puntos se llega a contradicciones, lo cual es un error.

El escepticismo debería ser un estado intermedio temporal. O un estado que se toma respecto a ciertos temas sobre los que uno no ha investigado o analizado a fondo para formarse una opinión con base.

Vamos, que el Objetivismo sería minarquista. No anarcocapitalista, eso lo rechazaría tajantemente.

Résumé de la vidéo [00:00:01][^1^][1] - [00:22:57][^2^][2]:

Cette vidéo explore la distinction entre l'absentéisme scolaire et la phobie scolaire, également connue sous le nom de refus scolaire anxieux (RSA). Elle met en lumière les causes, les symptômes et les différentes catégories d'absentéisme.

Moments forts: + [00:00:14][^3^][3] Définition du refus scolaire anxieux * Trouble de santé mentale * Symptômes somatiques et anxiété * Profils variés selon l'âge et le contexte + [00:03:38][^4^][4] Responsabilité du RSA * Pas la faute de l'école, de l'enfant ou de la famille * Modèle bio-psychologique * Facteurs biologiques, psychologiques et sociaux + [00:07:10][^5^][5] Distinction entre RSA et absentéisme * Quatre catégories d'absentéisme * Refus scolaire, école buissonnière, retrait scolaire, exclusion scolaire * Importance de clarifier la catégorie + [00:12:04][^6^][6] Profils mixtes * Refus scolaire anxieux et école buissonnière * Refus scolaire anxieux et retrait scolaire * Importance de traiter les profils mixtes + [00:20:01][^7^][7] Rôle des enseignants * Identifier les symptômes du RSA * Collaborer avec les professionnels * Importance de l'accompagnement adapté

Introduction (0:00-1:10) : Marie Galet, psychologue et spécialiste du refus scolaire anxieux, présente le sujet de la vidéo. Définition du refus scolaire anxieux (1:10-2:10) : Marie Galet définit le refus scolaire anxieux comme un trouble en santé mentale qui se caractérise par une peur intense et persistante de l'école. Causes du refus scolaire anxieux (2:10-3:10) : Marie Galet explique que les causes du refus scolaire anxieux sont multiples et peuvent inclure des facteurs biologiques, psychologiques et sociaux. Traitement du refus scolaire anxieux (3:10-4:10) : Marie Galet explique que le traitement du refus scolaire anxieux peut inclure une variété de thérapies, y compris la thérapie cognitivo-comportementale, la thérapie familiale et la pharmacothérapie. Rôle des enseignants (4:10-5:10) : Marie Galet souligne le rôle important que les enseignants peuvent jouer dans le traitement du refus scolaire anxieux. Les enseignants peuvent aider les enfants à se sentir en sécurité à l'école et à développer des compétences sociales et émotionnelles.

Symptômes du refus scolaire anxieux (5:10-6:10) : Marie Galet explique que les symptômes du refus scolaire anxieux peuvent inclure des maux de tête, des maux de ventre, des nausées, des tremblements, des sueurs froides et des palpitations cardiaques. Diagnostic du refus scolaire anxieux (6:10-7:10) : Marie Galet explique que le diagnostic du refus scolaire anxieux peut être difficile, car les symptômes peuvent être similaires à ceux d'autres troubles, tels que l'anxiété de séparation ou la dépression. Pronostic du refus scolaire anxieux (7:10-8:10) : Marie Galet explique que le pronostic du refus scolaire anxieux est variable. Certains enfants peuvent surmonter leur peur de l'école et retourner à l'école à temps plein, tandis que d'autres peuvent avoir besoin d'un soutien à long terme. Traitement du refus scolaire anxieux (8:10-10:10) : Marie Galet explique que le traitement du refus scolaire anxieux peut inclure une variété de thérapies, y compris la thérapie cognitivo-comportementale, la thérapie familiale et la pharmacothérapie. Rôle des enseignants (10:10-11:10) : Marie Galet souligne le rôle important que les enseignants peuvent jouer dans le traitement du refus scolaire anxieux. Les enseignants peuvent aider les enfants à se sentir en sécurité à l'école et à développer des compétences sociales et émotionnelles.

I'm surprised that Poe, as the pioneer of detective literature, can come up with such a deliberate and coherent process of thinking.

I know that the author wants to create an image of Dupin as a detective who is good at reasoning; however, I wondered, how could he link all these details together and never miss one action or facial expression from our narrator? If the author had cut some of the details, would it be more convincing to most people? Since most of us could barely do that, we might not be able to think of it and resonate with it.

• porque pueden debilitar nuestra relación con Jehová
• y también pueden ser el primer paso que nos lleve a cometer pecados más graves

La realidad es que todos tenemos nuestros puntos débiles o tentaciones. Quizá sea comer o beber con exceso, el entrenamiento, el dinero, o quizá la inmoralidad sexual. El párrafo también menciona, espíritu de independencia, el temor al hombre o el mal carácter. “cada uno es probado al ser atraído y seducido por su propio deseo” (Sant. 1:14).

Tener un buen programa espiritual es de gran ayuda

Orar Estudiar y meditar Reuniones Predicación

Jehová nos ayudará, porque nos ama, puede y quiere hacerlo.

Jehová está ahí para ayudarnos (lea 2 Corintios 4:7). Eso sí, fijémonos en que él nos da el poder que va más allá de lo normal. Pero a nosotros nos corresponde poner el poder normal, es decir, los esfuerzos diarios para no caer en la tentación.

Una conciencia limpia, la alegría de poder hacer feliz a Jehová, por nuestra obediencia y la posibilidad de disfrutar de vida eterna.

Sería tonto poner en riesgo todo eso por no vencer una tentación que nos lleve a un pecado grave.

Seguir el consejo de Jesús de mantenernos en guardia para resistir la tentación rápidamente cuando quiera sorprendernos

Evitar situaciónes de riesgo que puedan llevarnos a caer en tentación

Y continuar aplicando los métodos que personalmente nos dan resultado con la bendición de Jehová

Con el miedo al hombre.

Recordemos que negó tres veces conocer a Jesús por temor.

Luego enfrentó al sanedrín y habló con valor

Pero después volvió a demostrar temor al hombre cuando dejó de comer con cristianos no judíos.

Parece que está debilidad lo persiguió por mucho y a veces era vencido por ella.

Como vimos nuestra imperfección puede llevarnos a dar pasos que terminen en pecado.

Pero estar ocupados dando pasos que agraden a Jehová, son la mejor protección

Participar en las reuniones y predicamos, se fortalece nuestra amistad con Dios, nos prepara cómo mejores maestro y estimulamos a nuestros hermanos (Mat. 28:19, 20; Heb. 10:24, 25).

Si estamos solos Leer y estudiar la Palabra de Dios y meditar en ella nos ayudará a alejarnos de lo malo y al mismo tiempo fortalecerá nuestro amor por la ley de Jehová (Jos. 1:8; Sal. 1:2, 3; 119:97, 101).

Y recordar que Jesús nos aconsejó orar constantemente para resistir las tentaciones (Mat. 26:41). Y nosotros sabemos que Jehová bendice ésas oraciones y al mismo tiempo lo hacen Feliz

Seguir un buen programa de actividades espirituales nos ayuda a rechazar las tentaciones

Tener la firme convicción de que cumplir las normas de Jehová, siempre al 100%.

Porque Jehová es santo y perfecto, entonces lo que nos pide siempre es para nuestro beneficio, muy diferente de nosotros que somos imperfectos y por ese motivo podemos desarrollar pensamientos y deseos diferente a los de él.

Porque podemos atraer lo que pensamos, en el sentido que. Con el tiempo se fortalece el deseo de cumplir esos pensamientos.

Por éso debemos resistir y evitar esos pensamientos cuando surjan. Para evitar que crezcan hasta convertirse en deseos intensos, y difíciles de resistir y que pueden llevarnos a cometer un pecado grave (Filip. 4:8; Col. 3:2; Sant. 1:13-15).

Dijo: “He hecho un pacto con mis ojos” (lea Job 31:1). Respetar ese pacto lo ayudaría a no cometer nunca adulterio. Nosotros también podemos tomar en nuestro interior la firme decisión de no hacer nada que pudiera llevarnos a caer en una tentación.

Nuestras debilidades,

Es cómo en las ciudades amurallada, los puntos más débiles, son las puertas, y por eso se ponía mayor énfasis en su protección, en nuestro caso es igual, nuestras debilidades, son la puerta o el punto más vulnerable que nos conduzca a cometer algún pecado.

Que tenemos puntos débiles in duda, nos conviene ser honrados con nosotros mismos y reconocer los campos en los que no somos fuertes (2 Cor. 13:5).

Primero porque somos imperfectos, y en cualquier momento podemos enfrentar a situacionais que nos pongan a prueba, por eso el consejo de mantenerse siempre en guardia. Pablo explico que tenía una lucha constante Romanos 7:21-23

Porque estaban desprevenidos. Jesús mismo les había advertido que se mantuvieran despiertos. Pero se durmieron y la situación los agarró tan de sorpresa que terminaron haciendo exactamente lo que habían dicho que nunca harían: abandonar a Jesús (Mat. 26:56).

“EL ESPÍRITU está dispuesto, pero la carne es débil” (Mat. 26:41b).a

Jesús tiene claro que somos imperfectos y aunque tengamos buenas intenciones, podemos caer en el error de pensar o actuar con exceso de confianza, cómo decir que a nosotros o a mi, nada nada me apartará de hacer siempre lo correcto. Porque amo a Jehová y Jesús.

DOI: 10.1038/s41598-024-70759-y

Resource: Addgene_69058

Curator: @olekpark

SciCrunch record: RRID:Addgene_69058

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