54:00 Martijn and Mark butcher PARA of Tiago Forte. "Research", second is "Archive" (Martijn), and more.

49:00 Voys heeft een company wiki. Hierbij hanteren ze het volgende: weet je iets niet, kijk dan in het "orakel". Zit het er niet in? Voeg het dan toe. ... Je kan ook een logboek bijhouden voor beslissingen.

https://start.digitalefitheid.nl/c/podcast-digitale-fitheid/podcast-3-mark-vletter-ik-dacht-dat-2020-een-k-jaar-was-maar-dat-was-het-helemaal-niet

what Samson has been through still lingers because he can no longer see. he says that death by opium's is the only way

Samson is useless now without his sight and says his hopes have all gone--

God can "cause light again"-- manoa thinks God will give Samson his sight back

how can Samson be of any help now" he is blind etc

spionage macht keinen sinn gegen eine opposition die mehr transparenz fordert.<br /> spionage macht nur sinn gegen eine regierung die von lügen und zensur lebt.<br /> spoiler: wenn AFD zur regierung wird, dann gehts genauso weiter mit lügen und zensur.

Hier auch in der Zeitgeschichte oft das Problem, wenn Akten o.ä. nur schlecht erhalten sind bzw. Mikrofilm/Mikrofiche keine "saubere" Grundlage bieten.

Hier finde ich den Faktor Zeit sehr wichtig. Je nach Umfang/Prioriät der Arbeit und zur Verfügung stehender Zeit kann die Möglichkeit auch schnell zur Unmöglichkeit werden.

mit einem Original in Größe und Ausführung genau übereinstimmende Nachbildung, Wiedergabe, besonders als fotografische Reproduktion (Duden)

Ist das vergleichbar damit, dass es vor Jahren gestattet war, dass man eine Kopie von DVD oder CD für den Privatgebrauch machen durfte?

Wie wird mit dem Urheberrecht verfahren? Es gab vor vielen Jahren Proteste als Google damit begann, Texte zu digitalisieren. Bei historischen Texten ist das Urheberrecht abgelaufen und zeitgenössische Verfasser:innen müssen sich oftmals einverstanden erklären, wenn ihre Werke im open access erhältlich sind. Was ist mit den Fällen, die dazwischen liegen? Oder greift 'schlicht' die Begründung, dass ein Text aus nicht-kommerziellen oder aus wissenschaftlichen Gründen genutzt werden soll?

effects of shock therapy, lobotomy and other backward procedures as it never really cured but simply quietened the patient down so they wouldnt be troublesome to care for

Karakurt, Gunnur, and Tamra Cumbie. “The Relationship between Egalitarianism, Dominance, and Violence in Intimate Relationships.” Journal of Family Violence 27, no. 2 (January 8, 2012): 115–22. https://doi.org/10.1007/s10896-011-9408-y.

only 1/r dependence?

No repulsive parts when greater than rmin?

What is sigma?

2° Guerra médica "A Batalha de Termópila (desfiladeiro) 7.000 espartanos contra 300.000 persas, no primeiro dia os espartas ate conseguem conter os persas, mas, no segundo dia, um informante fala um outro caminho para os Persas e os espartanos voltam pra cidade, deixando 300 soldados para defender o desfiladeiro

Sociedade : - Escravos: A base de sustentação da sociedade ateniense, prisioneiros de guerra ou endividados. - Metecos: artesãos e comerciantes estrangeiros que viviam em atenas e em seus arredores. - Mulheres: consideradas inferiores, serviam aos maridos e cuidavam da casa e dos filhos ate os 7 anos - Eupátridas (Cidadãos): Homem nascido em Atenas, de pais atenienses, maiores de 18 anos, geralmente com posse de terras.

Democracia direta os cidadõs participando ativamente da política

Após várias revoltas populares, a aristocracia com medo, incumbe o legislador Drácon

Em 621 a.C, Drácon elabora a primeira lei escrita da Grácia, em que qualquer crime cometido seria punido com morte

Em 594 a.C, Sólon inicia um processo de transformações em Atenas: - fim da escravidão por dívidas - criação da Eclésia: Assembleia com 43.000 cidadão atenienses que prestaram serviços militares por pelomenos 2 anos. - Se reuniam na Acrópole, que era o centro urbano mais alto da polis. - Cria a Bulé : 400 cidadãos com 30 anos ou mais, participando do corpo legislativo e eleitos por voto censitário (Restrito a algo, no caso, cidadões com propriedades)

I had this feeling a while back. However now I realized that books aren't really as valuable as people think.

This is something I experience after finishing movies and TV series. Feeling empty.

I've always enjoyed the idea of psychiatry but getting into it seems to be way too complicated

I also relate to this, I first joined some clubs that talked about the anatomy of the human body and medicine, I later transitioned to coding skills and now I'm studying to become a geologist.

contemplating suicide

Although this might give experience. Trying to do multiple things at once will never work

As this is my first semester here at the University of Arizona, I have noticed that I have changed to the better. Becoming more independent and able to manage things by myself.

I actually prefer small class sizes as opposed to large classes. It makes me learn more

Having small class sizes allows teachers to help students effectively.

Speed is important. If you quickly do things you will never fail

You must get out of your comfort zone to be sucessful

Shahbandeh, M. “U.S. Sugar Cane Production by State 2023.” Statista, January 30, 2024. https://www.statista.com/statistics/191975/sugarcane-production-in-the-us-by-state/.

still considered to have semi-divine status? sitting on golden seats?

He is taking responsibility for their misery, but they decided to join the revolt of their own FREE WILL, so he is not really responsible. Even in the pit, they still follow him...this is EGO, not so much heroism...

these are all examples of persons/entities who lost faith in God...had to see before believing...

Don't be passive; rise up! What a great motivator; no wonder they all follow him down to the pit.

He may be in Hell but he is still makes a "cool" impression and seems to have control over much of his environment down there.

immoral behavior - makes sense

He believes to be under God's rule is a weakness, therefor the worst fate.

Faith is believing in the Almighty without having to see. Now that Satan sees the power of God, he believes, on some level, that He is more powerful than Satan and his crew. He broke the first rule of a relationship with God.

high rank, an equal

Amazing how one makes the conscious decision to abandon being the brightest light and in union with God...such a rare position to be in, yet it has been betrayed.

Satan's pride and hate led to the worst thing of all: the mere thought of eternal pain and no happiness. He is weak to the core if not able to control his own feelings...for this he only has himself to blame. Man is nothing unless able to control his own feelings.

man having dominion over the "rest of creation" meaning all animals other than man, land and sea...

Je ne comprends pas bien dans la solution ce bout de code : .lien-conteneur-photo:hover.photo-hover { display: flex; } Pourquoi la class .photo-hover se trouve après la pseudo classe hover ?

je ne comprend pas pourquoi dans le corrigé la class ".photo-hover" est aprés le hover ?

. Following his death, Gatsby is all but forgotten by those who enjoyed his lavish parties and displays of wealth; “’Why, my God! they used to go there by the hundreds.”’ (Fitzgerald 134). By the time of Gatsby’s funeral, few are actually in attendance, and the only legacy Gatsby has are the rumors floating around regarding his sudden death. The people he had made connections with in life, such as Wolfsheim and Daisy, left him behind in his final hour; Nick is the only one there to stick with him until the end. Gatsby's life is a tragedy of a high order.

Mehta, Vishal et al. “Metabolic Urbanism and Environmental Justice: The Water Conundrum in Bangalore, India”. Environmental Justice Vol. 7 (5) (2014): 130-137

Goldman, Michael and Narayan, Devika “Water crisis through the analytic of urban transformation: an analysis of Bangalore’s hydrosocial regimes” In Rural-Urban Water Struggles edited by Lena Hommes et al. London: Routledge, 2020: 95-114

"Learning from intermittent water supply schedules: Visualizing equality, equity, and hydraulic capacity in Bengaluru and Delhi, India" Science of the Total Environment Vol. 892

nice use of numbers here

in Journal 1 you said: The project I will be mainly working on through Zooniverse is called, “Killer Whale Count” ---did you switch?

the only thing missing her is a refernece to the assessment you took (for those who don't know)

they have unique skills with caring for those who are grieving--I wonder if you you are high in empathy

Aeromagnetic data to pseudogravity

“Not A Drop To Drink': How Bengaluru Is Coping Up With Its Worst-Ever Water Crisis” Times Now, 2024,

a great way to honor his legacy!

“Bengaluru gated communities struggle with water shortage amidst govt seizes private tankers” Times Now, 2024

.

“Bengaluru water crisis: Deputy CM Shivakumar denies shortage, says ‘only 7,000 borewells have dried’” Mint, 2024

“The Bengaluru water crisis and how the Karnataka govt is tackling it” Mint, 2024

Anand, Nikhil Hydraulic City: Water and the Infrastructures of Citizenship in Mumbai. Duke University Press, 2017

Rogers, Dennis and O’Neill, Bruce “Infrastructural violence: Introduction to the special issue” Ethnography Vol. 13 (4) (2012): 401-412

Anand, Nikhil “Municipal disconnect: On abject water and its urban infrastructures.” Ethnography Vol. 13 (4) (2012): 487-509

Citation seems to be missing from reference list

Citation seems to be missing from reference list

“Water scarcity hits schools in Bengaluru; home school shut doors, government-run institute hunts for alternate options” The Indian Express, 2024

cite author and tell your reader (who's not familiar with it) what it is...

use APA style (author name, year) with citation

Komives, S. R., & Wagner, W. (Eds.). (2017). Leadership for a better world: Understanding the social change model of leadership development. John Wiley & Sons.

See underdocumented #15.

Thanks to this post on threads.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

During the last decades, extensive studies (mostly neglected by the authors), using in vitro and in vivo models, have elucidated the five-step mechanism of intoxication of botulinum neurotoxins (BoNTs). The binding domain (H chain) of all serotypes of BoNTs binds polysialogangliosides and the luminal domain of a synaptic vesicle protein (which varies among serotypes). When bound to the synaptic membrane of neurons, BoNTs are rapidly internalized by synaptic vesicles (SVs) via endocytosis. Subsequently, the catalytic domain (L chain) translocates, a process triggered by the acidification of these organelles. Following translocation, the disulfide bridge connecting the H chain with the L chain is reduced by the thioredoxin reductase/thioredoxin system, and it is refolded by the chaperone Hsp90 on SV's surface. Once released into the cytosol, the L chains of different serotypes cleave distinct peptide bonds of specific SNARE proteins, thereby disrupting neurotransmission. In this study, Yeo et al. extensively revise the neuronal intoxication model, suggesting that BoNT/A follows a more complex intracellular route than previously thought. The authors propose that upon internalization, BoNT/A-containing endosomes are retro-axonally trafficked to the soma. At the level of the neuronal soma, this serotype then traffics to the endoplasmic reticulum (ER) via the Golgi apparatus. The ER SEC61 translocon complex facilitates the translocation of BoNT/A's LC from the ER lumen into the cytosol, where the thioredoxin reductase/thioredoxin system and HSP complexes release and refold the catalytic L chain. Subsequently, the L chain diffuses and cleaves SNAP25 first in the soma before reaching neurites and synapses. Strengths:

I appreciate the authors' efforts to confirm that the newly established methods somehow recapitulate aspects of the BoNTs mechanism of action, such as toxin binding and uptake occurring at the level of active synapses. Furthermore, even though I consider the SNAPR approach inadequate, the genome-wide RNAi screen has been well executed and thoroughly analyzed. It includes well-established positive and negative controls, making it a comprehensive resource not only for scientists working in the field of botulinum neurotoxins but also for cell biologists studying endocytosis more broadly. Weaknesses:

I have several concerns about the authors' main conclusions, primarily due to the lack of essential controls and validation for the newly developed methods used to assess toxin cleavage and trafficking into neurons. Furthermore, there is a significant discrepancy between the proposed intoxication model and existing studies conducted in more physiological settings. In my opinion, the authors have omitted over 20 years of work done in several labs worldwide (Montecucco, Montal, Schiavo, Rummel, Binz, etc.). I want to emphasize that I support changes in biological dogma only when these changes are supported by compelling experimental evidence, which I could not find in the present manuscript.

We thank the reviewer for his reading and comments and for pointing out the discrepancy between our proposed model and the existing model. However, we respectfully disagree with the phrase of “extensive studies have elucidated the five-steps mechanism of intoxication…”. This sentence and the following imply that the model is well-established and demonstrated. It also highlights how the reviewer is convinced about this previous model.

We contest this model for theoretical reasons and contest the strength of evidences that support it. We previously included references to previous work showing that the model is also being challenged by others. In light of the reviewer’s comments, we incluced more references in the introduction and we also explicit our main theoretical concern in the introduction:

“Arguably, the main problem of the model is its failure to propose a thermodynamically consistent explanation for the directional translocation of a polypeptidic chain across a biologial membrane. Other known instances of polypeptide membrane translocation such as the co-translational translocation into the ER indicate that it is an unfavorable process, which consumes significant energy (Alder and Theg 2003). ”

We also added the following text in the Discussion to address with the reviewer’s concerns: “Our study contradicts the long-established model of BoNT intoxication, which is described in several reviews specifically dedicated to the subject 1–4. In short, these reviews support the notion that BoNT are molecular machines able to mediate their own translocation across membranes; this notion has convinced some cell biologists interested in toxins and retrograde traffic, who describe BoNT mode of translocation in their reviews 5,6.

But is this notion well supported by data? A careful examination of the primary literature reveals that early studies indeed report that BonTs form ion channels at low pH values 7,8. These studies have been extended by the use of patch-clamp 9,10. These works and others lead to various suppositions on how the toxin forms a channel and translocate the LC 1,11 .

However, only a single study claims to reconstitute in vitro the translocation of BonT LC across membranes 12. In this paper, the authors report using a system of artificial membranes separating two aqueous compartments. They load the toxin in the cis compartment and measure the protease activity in the trans compartment after incubation. However, when the experimental conditions described are actually converted in terms of molarity, it appears that the cis compartment was loaded at 10e-8M BonT and that the reported translocated protease activity is equivalent to 10e-17 M (Figure 3D, 12). Thus, in this experiment, about 1 LC molecule in 100 millions has crossed the membrane. Such extremely low transfert rate does not tally with the extreme efficiency of intoxication in vivo, even while taking into account the difference between artificial and biological membranes.

In sum, a careful analysis of the primary literature indicate that while there is ample evidence that BoNTs have the ability to affect membranes and possibly create ion channels, there is actually no credible evidence that these channels mediate translocation of the LC. As mentioned earlier, it is not clear how such a self-translocation mechanism would function thermodynamically. By contrast, our model proposes a mechanism without a thermodynamic problem, is consistent with current knowledge about other protein toxins, such as PE, Shiga and Ricin, and can help explain previously puzzling features of BonT effects. It is worth noting that a similar self-translocation model was proposed for other protein toxins such as Pseudomonas exotoxin, which have similar molecular organisation as BonT (68). However, it has since been demonstrated that the PE toxins require cellular machinery, in particular in the ER, for intoxication (21,69,70).”

Reviewer #2 (Public Review):

Summary:

The study by Yeo and co-authors addresses a long-lasting issue about botulinum neurotoxin (BoNT) intoxication. The current view is that the toxin binds to its receptors at the axon terminus by its HCc domain and is internalized in recycled neuromediator vesicles just after the release of the neuromediators. Then, the HCn domain assists the translocation of the catalytic light chain (LC) of the toxin through the membrane of these endocytic vesicles into the cytosol of the axon terminus. There, the LC cleaves its SNARE substrate and blocks neurosecretion. However, other views involving kinetic aspects of intoxication suggest that the toxin follows the retrograde axonal transport up to the nerve cell body and then back to the nerve terminus before cleaving its substrate.

In the current study, the authors claim that the BoNT/A (isotype A of BoNT) not only progresses to the cell body but once there, follows the retrograde transport trafficking pathway in a retromer-dependent fashion, through the Golgi apparatus, until reaching the endoplasmic reticulum. Next, the LC dissociates from the HC (a process not studied here) and uses the translocon Sec61 machinery to retro-translocate into the cytosol. Only then, does the LC traffic back to the nerve terminus following the anterograde axonal transport. Once there, LC cleaves its SNARE substrate (SNAP25 in the case of BoTN/A) and blocks neurosecretion.

To reach their conclusion, Yeo and co-authors use a combination of engineered tools: a cell line able to differentiate into neurons (ReNcell VN), a reporter dual fluorescent protein derived from SNAP25, the substrate of BoNT/A (called SNAPR), the use of either native BoNT/A or a toxin to which three fragment 11 of the reporter fluorescent protein Neon Green (mNG) are fused to the N-terminus of the LC (BoNT/A-mNG11x3), and finally ReNcell VN transfected with mNG1-10 (a protein consisting of the first 10 beta strands of the mNG).

SNAPR is stably expressed all over in the ReNcell VN. SNAPR is yellow (red and green) when intact and becomes red only when cleaved by BoNT/A LC, the green tip being degraded by the cell. When the LC of BoNT/A-mNG11x3 reaches the cytosol in ReNcell VN transfected by mNG1-10, the complete mNG is reconstituted and emits a green fluorescence.

In the first experiment, the authors show that the catalytic activity of the LC appears first in the cell body of neurons where SNAPR is cleaved first. This phenomenon starts 24 hours after intoxication and progresses along the axon towards the nerve terminus during an additional 24 hours. In a second experiment, the authors intoxicate the ReNcell VN transfected by mNG1-10 using the BoNT/A-mNG11x3. The fluorescence appears also first in the soma of neurons, then diffuses in the neurites in 48 hours. The conclusion of these two experiments is that translocation occurs first in the cell body and that the LC diffuses in the cytosol of the axon in an anterograde fashion.

In the second part of the study, the authors perform a siRNA screen to identify regulators of BoNT/A intoxication. Their aim is to identify genes involved in intracellular trafficking of the toxin and translocation of the LC. Interestingly, they found positive and negative regulators of intoxication. Regulators could be regrouped according to the sequential events of intoxication.

Genes affecting binding to the cell-surface receptor (SV2) and internalization. Genes involved in intracellular trafficking. Genes involved in translocation such as reduction of the disulfide bond linking the LC to the HC and refolding in the cytosol. Genes involved in signaling such as tyrosine kinases and phosphatases. All these groups of genes may be consistent with the current view of BoNT intoxication within the nerve terminus. However, two sets of genes were particularly significant to reach the main conclusion of the work and definitely constitute an original finding important to the field. One set of genes consists of those of the retromer, and the other relates to the Sec61 translocon. This should indicate that once endocytosed, the BoNT traffics from the endosomes to the Golgi apparatus, and then to the ER. Ultimately, the LC should translocate from the ER lumen to the cytosol using the Sec61 translocon. The authors further control that the SV2 receptor for the BoNT/A traffics along the axon in a retromer-dependent fashion and that BoNT/A-mNG11x3 traverses the Golgi apparatus by fusing the mNG1-10 to a Golgi resident protein.

Strengths:

The findings in this work are convincing. The experiments are carefully done and are properly controlled. In the first part of the study, both the activity of the LC is monitored together with the physical presence of the toxin. In the second part of the work, the most relevant genes that came out of the siRNA screen are checked individually in the ReNcell VN / BoNT/A reporter system to confirm their role in BoNT/A trafficking and retro-translocation.

These findings are important to the fields of toxinology and medical treatment of neuromuscular diseases by BoNTs. They may explain some aspects of intoxication such as slow symptom onset, aggravation, and appearance of central effects.

Weaknesses:

The findings antagonize the current view of the intoxication pathway that is sustained by a vast amount of observations. The findings are certainly valid, but their generalization as the sole mechanism of BoNT intoxication should be tempered. These observations are restricted to one particular neuronal model and engineered protein tools. Other models such as isolated nerve/muscle preparations display nerve terminus paralysis within minutes rather than days. Also, the tetanus neurotoxin (TeNT), whose mechanism of action involving axonal transport to the posterior ganglia in the spinal cord is well described, takes between 5 and 15 days. It is thus possible that different intoxication mechanisms co-exist for BoNTs or even vary depending on the type of neurons.

Although the siRNA experiments are convincing, it would be nice to reach the same observations with drugs affecting the endocytic to Golgi to ER transport (such as Retro-2, golgicide or brefeldin A) and the Sec61 retrotranslocation (such as mycolactone). Then, it would be nice to check other neuronal systems for the same observations.

We thank the reviewer for the careful reading and comments of our manuscript. The reference to “a vast amount of observation” is a similar argument to the Reviewer 1 and used to suggest that our study may not be applicable as a general mechanism.

We respectfully disagree as described above and posit on the contrary that the model we propose is much more likely to be general than the model presented in current reviews for the several reasons cited (see added text in Introduction and Discussion). While we agree that more work is needed to confirm the proposed mechanisms of BonT translocation in other models, these experiments fall outside the perimeter of our study.

The fact that nerve/muscle preparations of BonT activity have relatively fast kinetics does not pose a contradiction to our model. Our model reveals primarily the requirement for trafficking to the ER membranes. This ER targeting requires trafficking through the Golgi complex, in turn explaining the requirement for trafficking to the soma of neurons in the experimental system we used. However, in neuronal cells in vivo, Golgi bodies can be found along the lenght of the axon, thus BonT may not always require trafficking to the soma of the affected cells. The time required for intoxication could thus vary greatly depending on the neuronal structural organisation.

TenT is proposed to transfer from excitatory neurons into inhibitory neurons before exerting its action. While the detailed mechanism of this fascinating mechanism remain to be explored, it clearly falls beyond the purview of this manuscript.

Regarding the use of drugs, we agree that it would be a nice addition; unfortunately we are unable to perform such experiments at this stage. Setting up a large scale siRNA screen for BonT mechanism of action is challenging as it requires a special facility with controlled access and police authorisation (in Singapore) given the high toxicity of this molecule. Unfortunately, the authorisations have now lapsed.

Reviewer #3 (Public Review): Summary:

The manuscript by Yao et al. investigates the intracellular trafficking of Botulinum neurotoxin A (BoNT/A), a potent toxin used in clinical and cosmetic applications. Contrary to the prevailing understanding of BoNT/A translocation into the cytosol, the study suggests a retrograde migration from the synapse to the soma-localized Golgi in neurons. Using a genome-wide siRNA screen in genetically engineered neurons, the researchers identified over three hundred genes involved in this process. The study employs organelle-specific split-mNG complementation, revealing that BoNT/A traffics through the Golgi in a retromer-dependent manner before moving to the endoplasmic reticulum (ER). The Sec61 complex is implicated in the retro-translocation of BoNT/A from the ER to the cytosol. Overall, the research challenges the conventional model of BoNT/A translocation, uncovering a complex route from synapse to cytosol for efficient intoxication. The findings are based on a comprehensive approach, including the introduction of a fluorescent reporter for BoNT/A catalytic activity and genetic manipulations in neuronal cell lines. The conclusions highlight the importance of retrograde trafficking and the involvement of specific genes and cellular processes in BoNT/A intoxication.

Strengths:

The major part of the experiments are convincing. They are well-controlled and the interpretation of their results is balanced and sensitive.

Weaknesses:

To my opinion, the main weakness of the paper is in the interpretation of the data equating loss of tGFP signal (when using the Red SNAPR assay) with proteolytic cleavage by the toxin. Indeed, the first step for loss of tGFP signal by degradation of the cleaved part is the actual cleavage. However, this needs to be degraded (by the proteasome, I presume), a process that could in principle be affected (in speed or extent) by the toxin.

We thank the reviewer for his comments and careful reading of our manuscript.

Regarding the read-out of the assay, we agree that the assay could be sensitive to alteration in the protein degradation pathway. We have added the following sentence in the Discussion to take it into account:

“As noted by one reviewer, the assay may be sensitive to perturbation in the general rate of protein degradation, a consideration to keep in mind when evaluating the results of large scale screens.”

While this may be valid for some hits in the general list, it is important to note that the main hits have been shown to affect toxin trafficking by an independent, orthogonal assay based on the split GFP reconstitution.

Recommendations to authors:

Reviewer #1 (Recommendations For The Authors):

(1) To assess the activity of BoNT/A in neurons, Yeo et al. have generated a neuronal stem line referred to as SNAPR. This cell line stably expresses a chimeric reporter protein that consists of SNAP25 flanked at its N-terminus with a tagRFPT and at its C-terminus with a tagGFP. After exposure to BoNT/A, SNAP25 is cleaved and, the C-terminal tGFP-containing moiety is rapidly degraded. I have many doubts about the validity of the described method. Indeed, BoNT/A activity is analysed in an indirect way by quantifying the degradation of the GFP moiety generated after toxin cleavage (Fig. 2). In this regard, the authors should consider that their approach is dependent, not only on the toxin's metalloprotease activity but also on the functionality of the proteasome in neurons. Therefore, considering the current dataset, it is impossible to rule out the possibility that the progression of GFP signal loss from the soma to the neurite terminals may be attributed to the different proteasome activity in these compartments. Is it conceivable that the GFP fragment generated upon toxin cleavage degrades more rapidly in the soma in comparison to axonal terminals? This alternative explanation could challenge the conclusion drawn in Fig. 2.

The reviewer’s alternative explanation disregards the experiments performed with the split-GFP complementation approach, which indicate translocation in the soma first. The split GFP reporter is not dependent on the proteasome activity. It also disregard the genetic data implicating many genes involved in membrane retrograde traffic, which are also not consistent with the hypothesis of the reviewer. These genes depletions not only affect SNAPR degradation but also BoNT/A-mNG11 trafficking: thus, their effect cannot be attributed to an completely hypothetical spatial heterogeneous distribution of the proteasome.

For this reason, I strongly suggest using a more physiological approach that does not depend on proteasomal degradation or on the expression of the sensor in neurons. The authors should consider performing a time course experiment following intoxication and staining BoNT/A-cleaved SNAP25 by using specific antibodies (see Antonucci F. et al., Journal of Neuroscience, 2008 or Rheaume C. et al., Toxins 2015).

For the above reason, we do not agree with the pressing importance of confirming by a third method using specific antibodies; especially considering that BonT is very difficult to detect in cells when incubated at physiological levels. By the way, the cited paper, by Antonucci F; et al. documents long distance retrograde traffic of BonT/A, which is in line with our data.

An alternative approach could involve the use of microfluidic devices that physically separate axons from cell bodies. Such a separation will allow us to test the authors' primary conclusion that SNAP25 is initially cleaved in the soma. The suggested experiments will also rule out potential overexpression artifacts that could influence the authors' conclusions when using the newly developed SNAPR approach. Without these additional experiments, the authors' main conclusion that SNAP25 is cleaved first in the neuronal soma rather than at the nerve terminal is inadequate.

As discussed above we disagree about the doubts raised by the reviewer: we present three types of evidences (SNAPR, split GFP and genetic hits) and they all point in the same direction. Thus, we respectfully doubt that a fourth approach would convince this reviewer. To note, we have attempted to use microfluidics devices as suggested by the reviewer, however, the Ren-VM neurons were not able to extend axons long enough across the device.

(2) To detect BoNT/A translocation into the cytosol, the authors have used a complementation assay by intoxicating ReNcell VM cell expressing a cytosolic HA-tagged split monomeric NeonGreen (Cyt-mNG1-10) with an engineered BoNT/A, where the catalytic domain (LC) was fused to mNG1-11. When drawing conclusions regarding the detection of cytosolic LC in the neuronal soma, the authors should highlight the limitations of this assay and explicitly describe them to the readers. Firstly, the authors need to investigate whether the addition of mNG1-11 to the LC affects the translocation process itself (by comparing with a WT, not tagged, LC).

Additionally, from the data shown in Fig. 2C, it is evident that the Cyt-mNG1-10 is predominantly expressed in the cytosol and less detected in neurites. This raises the question of whether there might be a bias for the cell soma in this assay. To address this important concern, I suggest quantifying MFI per cell (Fig. 2D) taking into consideration the amount of HA-tagged Cyt-mNG1-10. Furthermore, I strongly suggest targeting mNG1-10 to synapses and performing a similar time course experiment to observe when LC translocation occurs at nerve terminals. Alternative experiments, to prove that BoNT/A requires retrograde trafficking before it can translocate, may be done to repeat the experiments shown in Fig. 2D in the presence of inhibitors (or by KD some of the hits identified as microtubule stabilizers) that should interfere with BoNT/A trafficking to the neuronal somata. Without these additional experiments, the authors' main conclusion that the BoNT/A catalytic domain is first detected in the neuronal soma rather than at the nerve terminal is very preliminary.

Similarly as for the SNAPR assay, the reviewer is raising the level of doubt to very high levels. We respect his thoroughness and eagerness to question the new model. However, we note that a similar level of scrutiny does not apply to the prevalent competitive model. Indeed, the data supporting the self-translocation model is based on a single in vitro experiment published in one panel as we have explain din the discussion (see above).

(3) In the genome-wide RNAi screening, rather than solely assessing SV2 surface levels, it would have been beneficial to directly investigate BoNT/A binding to the neuronal membrane. For instance, this could have been achieved by using a GFP-tagged HC domain of BoNT/A. At present, the authors cannot exclude the possibility that among the 135 hits that did not affect SV2 levels, some might still inhibit BoNT/A binding to the neuronal surface. These concerns, already exemplified by B4CALT4 (which is known to be involved in the synthesis of GT1b), should be explicitly addressed in the main text.

We agree with the reviewer that perturbation of binding of BonT is possible. We added the following text:

“Network analysis reveals regulators of signaling, membrane trafficking and thioreductase redox state involved in BoNT/A intoxication

Among the positive regulators of the screen, 135 hits did not influence significantly surface SV2 levels and are thus likely to function in post-endocytic processes (Supplementary Table 2). However, we cannot formerly exclude that they could affect binding of BonT to the cell surface independently of SV2.”

(4) The authors should clearly state which reagents they have tried to use in order to explain the challenges they faced when directly testing the trafficking of BoNT/A. The accumulation of Dendra-SV2 bulbous structures at the neurite tips in VPS35-depleted cells could be interpreted as a sign of neuronal stress/death. Have the authors investigated other proteins that do not undergo retro-axonal trafficking in a retromer-dependent manner? This control is essential. In this regard, the use of a GFP-tagged HC domain of BoNT/A could prove to be quite helpful.

We tried multiple commercially available antibodies against BonT but we could not get a very good signal. The postdoc in charge of this project has now gone to greener pastures and we are not in the capacity to provide the details corresponding to these antibodies. We di dnot observe significant cell death after VPS-35 knockdown at the time of the experiment, however longe rterm treatment might result in toxicity indeed.

(5) Considering my concerns related to the SNAPR system and the complementation assay to study SNAP25 cleavage and BoNT/A trafficking, I suggest validating some of their major hits (ex. VPS34 and Sec61) by performing WB or IF analysis to examine the cleavage of endogenous SNAP25. Furthermore, the authors should test VPS35 depletion in the context of the experiments performed in Fig. 6G-H, by validating that this protein is essential for BoNT/A retrograde trafficking.

The reviewer concerns are well noted but as discussed above, the two systems we used are completely orthogonal. Thus, for the reviewer’s concerns to be valid, it would have to be two completely independent artefacts giving rise to the same result. The alternative explanation is that BonT/A translocates in the soma. The Ockham razor principle dictates that the simplest explanation is the likeliest.

(6) The introduction and the discussion section of this paper completely disregard more than 20 years of research conducted by several labs worldwide (Montecucco, Montal, Schiavo, Rummel, Binz, etc). The authors should make an effort to contextualize their data within the framework of these studies and address the significant discrepancies between their proposed intoxication model and existing research that clearly demonstrates BoNTs translocating upon the endocytic retrieval of SVs at presynaptic sites. Nevertheless, even assuming that the model proposed by the authors is accurate, numerous questions emerge. One such question is: How can the authors explain the exceptional toxicity of botulinum neurotoxin in an ex vivo neuromuscular junction preparation devoid of neuronal cell bodies (see Cesare Montecucco and Andreas Rummel's seminal studies)?

Please see above in the answer to public reviews.

(7) Scale bars should be added to all representative pictures.

This has been done. Thank you for the thorough reading of our manuscript.

Reviewer #2(Recommendations For The Authors):*

(1) The title overstates the results. It may be indicated "in differenciated ReNcell VM".

Title changed to: “Botulinum toxin intoxication requires retrograde transport and membrane translocation at the ER in RenVM neurons”

(2) In the provided manuscript there are two Figure 2 and no Figure 3. This made the reading and understanding extremely difficult and should be corrected. As a result, the Figure legends do not fit the numbering. There are also discrepancies between some Figure panels (A, B, C, etc), the text, and the Legends. All this needs to be carefully checked.

We apologize for the confusion as the manuscript as followed multiple rounds of revisions. We have carefully verified labels and legends.

(3) The BoNT/A-mNG11x3 may introduce some bias that could be discussed. Would these additional peptides block LC translocation from synaptic vesicles in the nerve termini? In addition, the mNG peptides that are unfolded before complementation may direct LC towards Sec61. These aspects should be discussed.

The comment would be valid if BoNT/A-mNG11x3 was the only approach used in the paper, however the SNAPR reporter is used with native BonT and shows data consistent with the split GFP approach.

(4) In the Figure about SV2 (Fig 3 or 4): The authors did not locate SV2. The cells seem not to have the same differentiated phenotype as in Figure 1 and Figure 2/3A.

We apologized above for the mislabeling. It is not clear what is the question here.

(5) The authors should check whether BoNT/A wt cleaves the endogeneous SNAP25 by western blot for instance in the original ReNcell VN before SNAPR engineering. This should be compared with wt SNAP25 cleavage by the BoNT/A-LC-mNG.

It is likely that BoNT/A-LC-mNG11 should have similar activity as it is only adding a small peptide at the end of the LC. At any rate, it is not clear why this is so important since both molecules translocate in the cytosol, with the same kinetics and in the same subcellular locale.

(6) Perhaps I did not understand. How can the authors exclude that what is observed is the kinetic overproduction of the reporter substrate SNAPR?

The authors could use SLO toxin (PNAS 98, 3185-3190, 2001) to permeabilize the cells all along their body and axon to introduce BoNT/A or LC (wt) and observe synchronized SNAPR cleavage throughout the cells.

The concept mentioned here is not very clear to us. The reviewer is proposing that the SNAPR is produced much more efficiently at the tips of the neurites and thus its cleavage takes longer to be detected and is apparent first in the soma?? With all due respect, this is a strange hypothesis, at odds with what we know of protein dynamics in the neurons (i.e. most proteins are largely made in the soma and transported or diffuse into the neurites).

Again, the two orthogonal approaches: split GFP and SNAPR reporter use different constructs and methods, yet converge on similar results. Perhaps, the incredulity of the reviewer might be more productively directed at the current data “demonstrating” the translocation of LC in the synaptic button?

(7) The authors could also use an essay on neurotransmitter release monitoring by electrophysiology measurements to check the functional consequences of the kinetic diffusion of LC activity along the axon. Can the authors exclude that some toxin molecules translocate from the endocytic vesicles and block neurotransmission within minutes or a few hours?

It is well established that inhibition of neurotransmission does not occur within minutes in vivo and in vitro, but rather within hours or even days. This kinetic delay is experienced by many patients and is one of the key argument against the current model of self-translocation at the synaptic vesicle level.

Minor remarks

Thank you for pointing out all these.

(1) Please check typos. There are many. Check space before the parenthesis, between numbers and h (hours), reference style etc.

Thank you. We have reviewed the text and try to eliminate all these instances.

(2) Line 90: The C of HC should be capitalized.

Fixed

(3) Line 107: add space between "neurons(Donato".

Fixed

(4) Line 109: space "72 h".

Fixed

(5) Line 115: a word is missing ? ...to show retro-axonal... ? Please clarify this sentence.

Fixed

(6) Figure 1E: does nm refer to nM (nanomolar)? Please correct. No mention of panel F.

Fixed

(7) Line 161: do you mean ~16 µm/h? Please correct.

Fixed

(8) Line 168, words are missing.

Fixed, thank you

We verified that Cyt-mNG1-10 was expressed using the HA tag, the expression was homogeneously distributed in differentiated neurons and we observed no GFP signal (Figure2C).

(9) Line 171: Isn't mNG 11 the eleventh beta strand of the neon green fluorescent protein, not alpha helix? Otherwise, can the authors confirm it acquires the shape of an alpha helix? Same at line 326.

We have corrected the mistake; thanks for pointing it out.

(10) Figure 2 is doubled. The legend of Fig 2 refers to Figure 3. There is no legend for Figure 2. Then, some figures are shifted in their numbering.

Fixed

(11) The fluorescence in the cell body must appear before the fluorescence in the axon due to higher volume. Please discuss.

The fluorescence progresses in the neurites extensions in a centripetal fashion. The volume of the neurite near the cell body is not significantly different from the end of the neurite. Thus the fluorescence data is consistent with translocation in soma and not with an effect due to higher volume in the soma.

(12) Figure 2D, right: the term intoxication is improper for this experiment. Rather, it is the presence of the BoNT/A-mNG11 that is detected. I believe the authors should be particularly careful about the use of terms: intoxication means blockade of neurosecretion, SNAPR cleavage means activity etc.

While the reviewer is correct that it is the presence of BoNT/A-mNG11 that is detected, it remains that it is an active toxin, so the neurons are effectively intoxicated; as they are when we use the wild type toxin. We do not imply that we are measuring intoxication, but simply that the neurons are put into contact with a toxin.

(13) Line 196: Should we read TXNRD1 is required for BoNT/A LC translocation? TXNRD1 in the current model of translocation is located in the cytoplasm and is supposed to play a role in the cleavage of the disulfide bond linking LC to HC. In the model proposed by this study, LC is translocated through the Sec61 translocon. In this case, I would assume that the protein disulfide isomerase (PDI) in the endoplasmic reticulum would reduce the LC-HC disulfide bond. In that case, TXNRD1 would not be required anymore. Please discuss.

Why should we assume that a PDI is involved in the reduction of the LC-HC disulfide bond? In our previous studies on A-B toxins (PE and Ricin), different reduction systems seemed to be at play. There is no conceptual imperative to assume reduction in the ER because the Sec61 translocon is implicated. Reduction might occur on the cytosolic side by TXNRD1 or the effect of this reductase could be indirect.

(14) The legend of Figure 4 (in principle Figure 5?) is not matching with the panels and panel entries are missing (Figure 4F in particular).

Fixed

(15) Figure 6 panels E and H, please match colors with legend (grey and another color).

Not clear

(16) Please indicate BoNT/A construct concentrations in all Figure legends.

Done

(17) Line 416: isn't SV2 also involved in epilepsy?

Yes it is.

(18) Line 433: as above, shouldn't the disulfide bond linking LC to HC be cleaved by PDI in the ER in this model (as for other translocating bacterial toxins) rather than by thioredoxin reductases in the cytoplasm? Please discuss.

See above

(19) Identification of vATPase in the screen could be consistent with the endocytic vesicle acidification model of translocation.

Yes

(20) Did the authors add KCl in screening controls without toxins? This should be detailed in the Materials and Methods. Could there be a KCl effect on the cells? KCl exposure for 48 hours may be highly stressful for cells. The KCl exposure should last only several minutes for toxin entry.

We did not observe significant cell detah with the cell culture conditions used. Cell viability was controlled at multiple stages using nuclei number for instance

Reviewer #3 (Recommendations For The Authors):

Main comments: (1) In Figure 1B: could you devise a means to prevent proteosomal degradation of the tGFP cleaved part to assess whether this is formed?

We have also used a FRET assay after tintoxication and obtained similar results

(2) Line 152: Where it reads "was not surprising", maybe I missed something, but to me, this is indeed surprising. If the toxin is rapidly internalized and translocated (therefore, it is able to cleave SNAP25), the fact that tGFP requires 48 hours to be degraded seems surprising to me. Or does it mean that the toxin also slows down the degradation of the tGFP fragment? So, how can you differentiate between the effect being on cleavage of the fragment or in tGFP degradation?

The reviewer is correct, the “not” was a typo due to re-writting; the long delay between adding the toxin and observing cleavage was suprising indeed. Our interpretation is that it is trafficking that takes time, indeed, the split-GFP data kinetics indicates that the toxin takes about 48h to fill up the entire cytosol (Fig. 2D).

(3) Regarding the effect of Sec61G knockdown, is it possible that the observed effects are indirect and not due to the translocon being directly responsible for translocating the protein?

As discussed in the last part of the results,Sec61 knock-down results in block of intoxication, but does not prevent BonT from reaching the lumen of the ER (Figure 6G,H). Thus, Sec61 is “is instrumental to the translocation of BoNT/A LC into the neuronal cytosol at the soma.”

Minor comments:

(1) Fig. 3E: in the legend I think one of the NT3+ should be NT3-.

Yes, thanks for spotting it

(2) Would you consider adding Figure S4 as a main figure?

Thanks for the suggestion

(3) Please, check that all microscopy image panels have scale bars.

Done

(4) Figure 6B (bottom panes): why does it seem that there is a lot of mNeonGreen positive signal in regions that are not positive for HA? Shouldn't complementation keep HA in the complemented protein.

Our assumption i sthat there is an excess of receptor protein (HA tag) over reconstituted protein (GFP protein) given the relatively low concentration of toxin being internalized and translocated Refs: (1) Pirazzini M, Azarnia Tehran D, Leka O, Zanetti G, Rossetto O, Montecucco C. On the translocation of botulinum and tetanus neurotoxins across the membrane of acidic intracellular compartments. Biochim Biophys Acta. 2016 Mar;1858(3):467–474. PMID: 26307528

(2) Pirazzini M, Rossetto O, Eleopra R, Montecucco C. Botulinum Neurotoxins: Biology, Pharmacology, and Toxicology. Pharmacol Rev. 2017 Apr;69(2):200–235. PMCID: PMC5394922

(3) Dong M, Masuyer G, Stenmark P. Botulinum and Tetanus Neurotoxins. Annu Rev Biochem. Annual Reviews; 2019 Jun 20;88(1):811–837.

(4) Rossetto O, Pirazzini M, Fabris F, Montecucco C. Botulinum Neurotoxins: Mechanism of Action. Handb Exp Pharmacol. 2021;263:35–47. PMCID: 6671090

(5) Williams JM, Tsai B. Intracellular trafficking of bacterial toxins. Curr Opin Cell Biol. 2016 Aug;41:51–56. PMCID: PMC4983527

(6) Mesquita FS, van der Goot FG, Sergeeva OA. Mammalian membrane trafficking as seen through the lens of bacterial toxins. Cell Microbiol. 2020 Apr;22(4):e13167. PMCID: PMC7154709

(7) Hoch DH, Romero-Mira M, Ehrlich BE, Finkelstein A, DasGupta BR, Simpson LL. Channels formed by botulinum, tetanus, and diphtheria toxins in planar lipid bilayers: relevance to translocation of proteins across membranes. Proc Natl Acad Sci U S A. 1985 Mar;82(6):1692–1696. PMCID: PMC397338

(8) Donovan JJ, Middlebrook JL. Ion-conducting channels produced by botulinum toxin in planar lipid membranes. Biochemistry. 1986 May 20;25(10):2872–2876. PMID: 2424493

(9) Fischer A, Montal M. Single molecule detection of intermediates during botulinum neurotoxin translocation across membranes. Proc Natl Acad Sci U S A. 2007 Jun 19;104(25):10447–10452. PMCID: PMC1965533

(10) Fischer A, Nakai Y, Eubanks LM, Clancy CM, Tepp WH, Pellett S, Dickerson TJ, Johnson EA, Janda KD, Montal M. Bimodal modulation of the botulinum neurotoxin protein-conducting channel. Proc Natl Acad Sci U S A. 2009 Feb 3;106(5):1330–1335. PMCID: PMC2635780

(11) Fischer A, Montal M. Crucial role of the disulfide bridge between botulinum neurotoxin light and heavy chains in protease translocation across membranes. J Biol Chem. 2007Oct 5;282(40):29604–29611. PMID: 17666397

(12) Koriazova LK, Montal M. Translocation of botulinum neurotoxin light chain protease through the heavy chain channel. Nature structural biology. 2003. p. 13–18. PMID: 12459720

(13) Moreau D, Kumar P, Wang SC, Chaumet A, Chew SY, Chevalley H, Bard F.Genome-wide RNAi screens identify genes required for Ricin and PE intoxications. Dev Cell. 2011 Aug 16;21(2):231–244. PMID: 21782526

(14) Bassik MC, Kampmann M, Lebbink RJ, Wang S, Hein MY, Poser I, Weibezahn J, Horlbeck MA, Chen S, Mann M, Hyman AA, Leproust EM, McManus MT, Weissman JS. A systematic mammalian genetic interaction map reveals pathways underlying ricin susceptibility. Cell. 2013 Feb 14;152(4):909–922. PMCID: PMC3652613

(15) Tian S, Muneeruddin K, Choi MY, Tao L, Bhuiyan RH, Ohmi Y, Furukawa K, Furukawa K, Boland S, Shaffer SA, Adam RM, Dong M. Genome-wide CRISPR screens for Shiga toxins and ricin reveal Golgi proteins critical for glycosylation. PLoS Biol. 2018 Nov;16(11):e2006951. PMCID: PMC6258472

eLife assessment

In this valuable manuscript, Yeo et al. describe new methods for assessing the intracellular itinerary of Botulinum neurotoxin A (BoNT/A), a potent toxin used in clinical and cosmetic applications. The current manuscript challenges previously held views on how the catalytic portion of the toxin makes its way from the endocytic compartment to the cytosol, to meet its substrates. The approach taken is deemed innovative and the experiments are carefully performed, presenting solid evidence for some of the drawn conclusion; however, the conclusions one may draw from the experimental results are somewhat limited, as it is possible that the scope of their findings could be restricted to the specific neuron model and molecular tools that were used. This paper could be of interest to both cell biologists and physicians.

Reviewer #1 (Public Review):

As outlined in my previous public review, Yeo et al. revised the current neuronal intoxication model, common to all serotypes of botulinum neurotoxins. Using a combination of genetic and imaging approaches, they demonstrate that upon internalization, BoNT/A-containing endosomes undergo retro-axonally trafficking to the neuronal soma. Within the soma, this particular serotype then traffics to the endoplasmic reticulum (ER) via the Golgi apparatus. At the ER, the SEC61 translocon complex facilitates the translocation of BoNT/A's metalloprotease domain (light chain, LC) from the ER lumen into the cytosol, where the thioredoxin reductase/thioredoxin system and HSP complexes release and refold the catalytic LC. Subsequently, the LC diffuses and cleaves SNAP25 first in the soma before reaching neurites and synapses.

Although I still acknowledge the well-executed and thoroughly analyzed genome-wide RNAi screen, I must once again highlight significant pitfalls and weaknesses in the paper due to the lack of essential controls and validations. Consequently, I suggest readers to approach the authors' findings with caution, as they may be limited to the combination of one specific cellular model and genetic engineering tools. During the revision process, authors declined to conduct additional experiments that could have strengthened their main conclusions. These include, but are not limited to:

(1) Investigating weather in the newly generated cell line Red-SNAPR, the GFP fragment produced upon toxin cleavage degrades more rapidly in the soma compared to axon terminals, possibly due to differences in proteasome activity in these two compartments.

(2) Validating toxin cleavage activity in the soma before reaching synapses by conducting an additional and more physiological approach, a time course experiment using native BoNT/A and staining BoNT/A-cleaved SNAP25 with specific antibodies.

(3) Assessing whether the addition of mNG1-11 to the LC affects the translocation process itself and quantifying the mean fluorescence intensity (MFI) per cell, taking into consideration the amount of HA-tagged Cyt-mG1-10, which appears predominantly expressed in the cytosol and less detected in neurites. This raises the question of potential bias toward the cell soma in this assay.

(4) Validating major hits (e.g., VPS34 and Sec61) by performing WB or IF analysis to test the cleavage of endogenous SNAP25.

Additionally, during the revision process, the authors raised concerns about the level of scrutiny applied by this reviewer, particularly in comparison to the seminal study of Lilia K. Koriazova & Mauricio Montal published in Nature Structural Biology (PMID: 12459720). In this 2003 paper, Montal's lab pioneered the use of single-channel recordings and substrate proteolysis analysis to reconstitute the translocation of BoNT/A light chain protease across an artificial lipid bilayer via the channel formed by its heavy chain. The authors highlighted that, when converting the experimental conditions from the aforementioned paper into molarity, it appears that the cis compartment was loaded with 10−8 M BoNT/A, and the reported translocated protease activity (measured by substrate cleavage) is equivalent to 10−17 M. This implies that only about 1 LC molecule in 100 million has crossed the membrane. The calculation performed by authors is indeed accurate. However, readers should be informed about another piece of information present in the same paper that might help them to clarify this important point. Koriazova & Montal, by discussing this experiment, have pointed out that this value (10−17 M) corresponds to ≈3600 LC molecules, a number closed to the maximum number of channels that can be formed under the used experimental conditions. Indeed, from the same paper, quotation: 'This number is in close agreement with the maximum number of channels inserted in the bilayer under the assay condition, ≈2000 (Fig. 3a), as estimated from macroscopic membrane conductance ∼1 × 105 pS and γ = 50 pS measured in 0.1 M KCl'. Another aspect that Yeo et al. forgot to mention in their rebuttal letter is that the system used by Koriazova & Montal lacks any chaperones in the trans compartment. Nowadays, we know that upon translocation, the refolding of the L chain is aided by Hsp90 (Azarnia Tehran et al., Cellular microbiology, 2017). Keeping this in mind, is not unrealistic to hypothesize that the number of LC molecules calculated more than 22 years ago by Koriazova & Montal (in an indirect way by checking SNAP25 cleavage using an ELISA-based assay) might be an underestimation. Indeed, the addition of Hsp90 in their system might aid in the refolding of LC molecules that, even if they have successfully be translocated, might not cleave the substrate due to their unfolded state.

As active scientist, I understand the challenges of peer review and publication, which can often be slow and frustrating involving seemingly endless rounds of review. Therefore, I am in favor of the new eLife publishing model. Indeed, this paper has already been published as Reviewed Preprints and will soon be declared as the final Version of Record, accompanied by this public review. Having said that, I hope that the readers of this journal and future scientists will prove me wrong. I hope they will engage with this paper, providing comments, validations (which are currently missing), and citations as frequently as they did for the seminal works of Koriazova & Montal.

Reviewer #2 (Public Review):

Summary:

The study by Yeo and co-authors addresses a long-lasting issue about botulinum neurotoxin (BoNT) intoxication. The current view is that the toxin binds to its receptors at the axon terminus by its HCc domain and is internalized in recycled neuromediator vesicles just after release of the neuromediators. Then, the HCn domain assists the translocation of the catalytic light chain (LC) of the toxin through the membrane of these endocytic vesicles into the cytosol of the axon terminus. There, the LC cleaves its SNARE substrate and blocks neurosecretion. However, other views involving kinetic aspects of intoxication suggest that the toxin follows the retrograde axonal transport up to the nerve cell body and then back to the nerve terminus before cleaving its substrate.

In the current study, the authors claim that the BoNT/A (isotype A of BoNT) not only progresses to the cell body but once there, follows the retrograde transport trafficking pathway in a retromer-dependent fashion, through the Golgi apparatus, until reaching the endoplasmic reticulum. Next, the LC dissociates from the HC (a process not studied here) and uses the translocon Sec61 machinery to retro-translocate into the cytosol. Only then, the LC traffics back to the nerve terminus following the anterograde axonal transport. Once there, LC cleaves its SNARE substrate (SNAP25 in the case of BoTN/A) and blocks neurosecretion.

To reach their conclusion, Yeo and co-authors use a combination of engineered tools: a cell line able to differentiate into neurons (ReNcell VN), a reporter dual fluorescent protein derived from SNAP25, the substrate of BoNT/A (called SNAPR), the use of either native BoNT/A or a toxin to which three fragment 11 of the reporter fluorescent protein Neon Green (mNG) are fused to the N-terminus of the LC (BoNT/A-mNG11x3), and finally ReNcell VN transfected with mNG1-10 (a protein consisting of the first 10 beta strands of the mNG).

SNAPR is stably expressed all over in the ReNcell VN. SNAPR is yellow (red and green) when intact and becomes red only when cleaved by BoNT/A LC, the green tip being degraded by the cell. When the LC of BoNT/A-mNG11x3 reaches the cytosol in ReNcell VN transfected by mNG1-10, the complete mNG is reconstituted and emits a green fluorescence.

In the first experiment, the authors show that the catalytic activity of the LC appears first in the cell body of neurons where SNAPR is cleaved first. This phenomenon starts 24 h after intoxication and progresses along the axon towards the nerve terminus during an additional 24 h. In a second experiment, the authors intoxicate the ReNcell VN transfected by mNG1-10 using the BoNT/A-mNG11x3. The fluorescence appears also first in the soma of neurons, then diffuses in the neurites in 48 h. The conclusion of these two experiments is that translocation occurs first in the cell body and that the LC diffuses in the cytosol of the axon in an anterograde fashion.

In the second part of the study, the authors perform a siRNA screen to identify regulators of BoNT/A intoxication. Their aim is to identify genes involved in intracellular trafficking of the toxin and translocation of the LC. Interestingly, they found positive and negative regulators of intoxication. Regulators could be regrouped according to the sequential events of intoxication. Genes affecting binding to the cell-surface receptor (SV2) and internalization. Genes involved in intracellular trafficking. Genes involved in translocation such as reduction of the disulfide bond linking the LC to the HC and refolding in the cytosol. Genes involved in signaling such as tyrosine kinases and phosphatases. All these groups of genes may be consistent with the current view of BoNT intoxication within the nerve terminus. However, two sets of genes were particularly significant to reach the main conclusion of the work and definitely constitute an original finding important to the field. One set of genes consists in those of the retromer, the other relates to the Sec61 translocon. This should indicate that once endocytosed, the BoNT traffics from the endosomes to Golgi apparatus, then to the ER. Ultimately, the LC should translocate from the ER lumen to the cytosol using the Sec61 translocon. The authors further control that the SV2 receptor for the BoNT/A traffics along the axon in a retromer-dependent fashion and that BoNT/A-mNG11x3 traverses the Golgi apparatus by fusing the mNG1-10 to a Golgi resident protein.

Strengths:

The findings in this work are convincing. The experiments are carefully done and are properly controlled. In the first part of the study, both the activity of the LC is monitored together with the physical presence of the toxin. In the second part of the work, the most relevant genes that came out of the siRNA screen are checked individually in the ReNcell VN / BoNT/A reporter system to confirm their role in BoNT/A trafficking and retro-translocation.<br /> These findings are important to the fields of toxinology and medical treatment of neuromuscular diseases by BoNTs. They may explain some aspects of intoxication such as slow symptom onset, aggravation and appearance of central effects.

Weaknesses:

The findings antagonize the current view of the intoxication pathway that is sustained by a vast amount of observations. The findings are certainly valid, but their generalization as the sole mechanism of BoNT intoxication should be tempered. These observations are restricted to one particular neuronal model and engineered protein tools. Other models such as isolated nerve/muscle preparations display nerve terminus paralysis within minutes rather than days. Also, the tetanus neurotoxin (TeNT), which mechanism of action involving axonal transport to the posterior ganglia in the spinal cord is well described, takes between 5 and 15 days. It is thus possible that different intoxication mechanisms co-exist for BoNTs or even vary depending on the type of neurons.

Although the siRNA experiments are convincing, it would be nice to reach the same observations with drugs affecting the endocytic to Golgi to ER transport (such as Retro-2, golgicide or brefeldin A) and the Sec61 retrotranslocation (such as mycolactone). Then, it would be nice to check other neuronal systems for the same observations.

Reviewer #3 (Public Review):

Summary:

The manuscript by Yeo et al. investigates the intracellular trafficking of Botulinum neurotoxin A (BoNT/A), a potent toxin used in clinical and cosmetic applications. Contrary to the prevailing understanding of BoNT/A translocation into the cytosol, the study suggests a retrograde migration from the synapse to the soma-localized Golgi in neurons. Using a genome-wide siRNA screen in genetically engineered neurons, the researchers identify over three hundred genes involved in this process. The study employs organelle-specific split-mNG complementation, revealing that BoNT/A traffics through the Golgi in a retromer-dependent manner before moving to the endoplasmic reticulum (ER). The Sec61 complex is implicated in the retro-translocation of BoNT/A from the ER to the cytosol. Overall, the research challenges the conventional model of BoNT/A translocation, uncovering a complex route from synapse to cytosol for efficient intoxication. The findings are based on a comprehensive approach, including the introduction of a fluorescent reporter for BoNT/A catalytic activity and genetic manipulations in neuronal cell lines. The conclusions highlight the importance of retrograde trafficking and the involvement of specific genes and cellular processes in BoNT/A intoxication.

Strengths:

The major part of the experiments are convincing. They are well-controlled and the interpretation of their results is balanced and sensitive.

Weaknesses:

To my opinion, the main weakness of the paper is that all experiments are performed using a single cellular system (RenVM neurons), as stated in the title. It is therefore unclear at the moment to what extent the findings in this paper can be generalized to other neuronal cell models / in vivo situation.

This source dives into the experience of content going viral and underscores the complexities of virality, showing how what starts as an exhilarating rush can quickly turn overwhelming or even dangerous. This article also prompts questions about the responsibility of platforms and authors in dealing with the consequences of going viral, where online harassment is an important aspect.

While I didn't necessarily create viral content myself, I have participated in sharing videos and reposting posts. I think sharing and reposting helps the content be suggested to other users whom the algorithms think have similar interests to me. Additionally, I noticed that the watching completion rate for videos is also a big factor for video platforms' algorithms to evaluate the quality of the content and therefore determine if the system will suggest the video.

Manfaat memahami konsep data sebelum malakukan penelitan adalah bagian dari penelitian itu sendiri karena akan memudahkan peneliti menjalani alur penilitan yang akan dilaksanakan di lapangan.

Nazif Fatul Azizah PPG Prajab 1 A 2023

The author was charged over $1300 after two days of using an S3 bucket, because some OS tool stored a default bucket name in the config, which was the same as his bucket name.

Luckily, after everything AWS made an exception and he did not have to pay the bill.

Karya Ilmiah yang bermanfaat adalah karya ilmiah yang ditulis sesuai dengan kebutuhan peradaban dan perkembangan teknologi yang bermanfaat untuk mencari solusi atas permasalah yang ada dalam lingkup masyarakat nyata.

Nazif Fatul Azizah PPG Prajab 1 A 2023

Author response:

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

Reviewer #1:

(1) Importantly, it would be useful to have provided more detailed information on the structure and histological properties of the murine cysts and how such findings relate to human lung cysts. Also, the authors should examine whether there is any information on Bmpr1a in human cyst formation (i.e GWAS data).

We fully agree that it is important to examine Bmpr1a in human cyst pathology. Unfortunately, there is no GWAS data on this. From the published RNA-seq data, which were obtained from postnatal lung specimen of congenital pulmonary airway malformation (CPAM) patients, “integrated suppression of BMP signaling pathway” was reported although altered expression of BMPR1A was not presented. We speculate that (1) BMPR1A is critical in embryonic development and a germline deficiency of BMPR1A may lead to early embryonic lethality prior to lung formation as supported by mouse data; (2) As suggested by our previously published study related to TGF-beta signaling and prenatal pulmonary cysts (Miao et al., Am J Physiol Lung Cell Mol Physiol 2021), dysregulation of BMPR1A-mediated signaling in a particular time window of fetal lung development may be sufficient to cause cyst formation, so that BMPR1A alteration may not be persistent to postnatal lung specimens.

(2) Throughout the paper, there is a lack of quantification for the histological findings. Littermate controls should also be clearly defined genetically,

We thank the reviewer for this suggestion and acknowledge the importance of quantitative measurement for the changes. We now add quantitative data on branching number and size of the airway tips to define the difference between wild-type and Bmpr1a CKO mouse lungs in Fig.1. “The littermate controls were the mice without any gene deletion due to lack of transgenes Tbx4-rtTA and/or TetO-Cre”, which is now added in Materials and Methods.

(3) Figure 1 suppl: "Doxycycline" is misspelled.

This has been corrected.

(4) Figure1c Suppl: Hard to discern clear-cut expression of Bmpr1a protein in mesenchyme in WT. Comparable images with similar sizes of airways should be used.

To provide a clearer comparison of Bmpr1a expression patterns between Bmpr1a CKO and control lungs, we enlarge the fluorescent stained lungs presented in Supplemental Figure 1C as suggested by the editor. Additionally, dotted lines have been added to delineate the airway boundaries from the surrounding mesenchyme to better visualize the Bmpr1a distribution in lung mesenchyme. Bmpr1a expression in fetal lung mesenchyme is easily detected at E15.5 when significant dilation of airways is presented in Bmpr1a CKO lung. It is rare to have comparable sizes of peripheral airways in the Bmpr1a CKO lung at this point.

(5) Figure 2a: Expression of several genes studied and altered should be identified on scatter plot.

As suggested by the reviewer, we now highlight the related genes, including Acta2, Myocd, Eln, Bmp4, Sox2, etc., in the scatter plot. In addition, we also highlight these critical genes in the heatmap (Fig. 2B and Fig. 7B).

(6) Figure 2c: Authors should also consider staining for other smooth muscle markers.

We now include a panel of Myh11 immunostaining in Figure 2E. Myh11 is another common marker for smooth muscle cells. Lack of Myh11 staining in Bmpr1a CKO lung airways further supports our conclusion that loss of mesenchymal Bmpr1a leads to defective airway smooth muscle growth.

(7) Figure 3: ELN expression should be defined in a clear quantitative manner.

We have presented RNA-seq data, Real-time PCR results, immunostaining, and western blot data for in vivo samples. Additionally, we have included in vitro experiment illustrating that Bmp4 induces Eln expression, suggesting that BMP signaling regulates Eln expression. We believe that these datasets collectively support our conclusion.

(8) Figure 4: Additional information on p38 dependent signaling (Including in vivo studies) would potentially help to understand key molecular events and perhaps could help to address key mechanistic events, including their location and identity.

We sincerely appreciate the insightful suggestion from the reviewer. While the study of p38-dependent signaling is definitely important to dissect the entire mechanisms, we are not going to include such experiments in this manuscript due to time constraints associated with in vivo studies.

(9) Figure 6: Would be helpful to know whether Bmpr1a receptor is expressed in Myocd KO.

Bmpr1a expression is not changed in Myocd KO lungs, which is now included as Figure 6C. Together with other data, this suggests that Myocd is a downstream target directly mediating Bmpr1a-regulated airway smooth muscle development.

(10) Figure 7: Not clear how these findings, though interesting, relate to the body of studies and the pathogenesis of cyst formation. Other points: 1) The authors should re-examine/repeat co-staining in the KO mouse lung (right 2 images in the top group of 4) for Foxj1, Sox2, and CDH (right 2 images, Figure 7A). For one thing, the cadherin stain in the 2 KO images seems localized to the lumen. Secondly, the pattern of cadherin staining looks exactly the same in both KO images, suggesting an error and/or duplication 2) authors should place arrows on the heat map showing the location of SPC, Sox2, Sox9, and FoxJ1 bands 3) figure 7D graph needs numbers on y axis.

Fig.7 provides an additional potential mechanism by which deficient Bmp signaling leads to abnormally increased Bmp ligand expression, which disrupts the formation of epithelial proximal-distal axis, and results in cystic defects. Further in vivo experiments are needed to test this, which is beyond the scope of this paper.

The E-cadherin staining signal in the lumen is caused by the tissue section positioned at an interface between lumen and the apical membrane of the lining epithelial cells where the E-cadherin is localized.

Triple immunostaining of E-Cadherin, Sox2, and FoxJ1 was performed for the same tissue section (upper two panels of Figure 7A) as these antibodies were derived from different species, but the images are presented in two different combinations for simplicity and clarity. For the lower two panels of Figure 7A, double immunostaining of Sox9/E-Cadherin and Spc/E-Cadherin were performed separately on different tissue sections due to both anti-Sox9 and anti-Spc antibodies were produced from rabbits.

The genes listed in the heatmap are canonical and putative marker genes for differential lung epithelial cell lineages, such as Scgb1a1 for Clara cells and FoxJ1 for ciliated cells. Therefore, progenitor cell marker Sox2 and Sox9 were not included. In the updated heatmap, four widely acknowledged epithelial cell markers—Scgb1a1, FoxJ1, Sftpb, and Sftpc have been distinguished by utilizing a distinct font color (red) to enhance their visibility.

Label for the y axis of Fig.7D is now added.

Reviewer #2 (Public Review):

(1) The authors may be aware that a recent paper (https://doi.org/10.1038/s41598-022-24858-3) reported on transcriptional changes seen in human CPAM. It would seem that some of the molecular changes seen in human CPAM move in the opposite direction of what is reported in mice lacking mesenchymal Bmrp1a. Perhaps the authors could comment on these differences in the discussion and whether they potentially explain the etiology of CPAM or branching morphogenesis in general.

We thank the reviewer for referring this paper regarding human CPAM study. CPAM has a variety of histopathology. The type 1 CPAM is assumed to develop from more proximal bronchial/bronchiolar airways while type 2 CPAM is developed from relatively distal bronchiolar airways. In that publication, surgical resected lung specimens were collected from type 1 CPAM patients postnatally (0.5-1 year), in which the cysts were lined with ciliated pseudostratified columnar epithelial cells. Gene expression was compared between cystic lung tissues and adjacent non-cystic lung tissues. Interestingly, integrated suppression of BMP signaling pathway was shown by their data analysis. In our mouse model, the histopathology appears as human type 2 CPAM, such as back-to-back cysts lining with a simple layer of epithelial cells. Therefore, several factors could explain the differences between their published data and our study at the molecular level: (1) Different types of CPAM based on the histopathology; (2) Different sampling time points, developing cysts at fetal stage in mouse sample vs. developed cysts in postnatal huma samples; (3) Different comparison of diseased and normal tissues: separate normal lungs vs. cystic lungs in mice while in human cystic tissues vs. non-cystic tissues in the same lungs. We now include this reference in the Discussion.

(2) Figure 4 shows that BMP4 increases SMADs, p38, and several muscle genes in mesenchymal cells. Figure 5 extends this finding with a clever strategy to label airway and vascular smooth muscle with different fluorescent molecules used to isolate different types of mesenchymal cells. It shows that non-vascular smooth muscle cells but not perivascular smooth muscles are responsive to BMP4 signaling as defined by increased expression of Myh11. Are there cell-restricted responses to the other genes shown in Figure 4? Given the lack of SMAD signaling and the increase seen in p38 signaling, would blocking p38 signaling influence the BMP responsiveness of these nonvascular smooth muscle cells?

We thank the reviewer for this constructive comment. As we have addressed above, we will leave p38-mediated signaling and cyst formation to next step study due to time constraints associated with these studies.

(3) Figure 6 shows that mesenchymal loss of Myocd causes a deficiency of airway smooth muscle cells, but this was not sufficient to create cysts. Did the authors ever check to see if it changed Sox2-Sox9 staining in the airway epithelium?

There is no significant change in Sox2 expression in proximal airway epithelia of Myocd CKO lungs as detected by immunostaining. The result was not included in this manuscript.

(4) Figure 7 shows that mesenchymal loss of Bmpr1a proximalizes the distal airway as defined by loss of Sox2 and FoxJ1 (a ciliated marker) and gain in (Sox9 and SP-C) staining. But Club cells expressing Scgb1a1 and Cyp2F2 are the predominant epithelial cells in the distal airway. The transcriptomics data in panel B shows expression of these genes is less in the mutant mice. Does this mean they fail to generate Club cells or there is just less expression per cell? In other words, what are the primary epithelial cells present in the airways of mice with loss of mesenchymal Bmpr1a?

As shown in the heatmap of Fig.7b, the dysregulated gene expression in the Bmpr1a CKO extends beyond the featured epithelial cell markers, encompassing alterations in numerous putative marker genes. For example, several putative Club cell markers in addition to Scgb1a1 and Cyp2F2 were reduced in the Bmpr1a CKO lungs, suggesting a compromised differentiation of Club cells. Additionally, we observed upregulations of some molecular markers for distal progenitors and differentiated cells in the proximal region of airways, again suggesting a significant disruption in epithelial differentiation in the Bmpr1a CKO lungs. These abnormal cells can be further defined by a single cell transcriptomic approach in future.

Recommendations for Authors:

Reviewer #1 (Recommendations For The Authors):

As discussed above, there may be an issue with the histological images and staining in 2 images in Figure 7A. The precise images, problems and suggestions to resolve the issue are in the Review.

Please see our response to Reviewer 1 above.

Reviewer #2 (Recommendations For The Authors):

Minor Weaknesses:

(1) Please enlarge the fluorescent stained lungs presented in Supplemental Figure 1C.

We have revised this panel accordingly.

(2) Figure 1D and E show that loss of Bmpr1a does not change proliferation or apoptosis on E15.5. Was that also seen through E18.5?

We thank the reviewer for the thoughtful question about proliferation and apoptosis at later embryonic stages. Our focus here was to elucidate the mechanisms underlying abnormal branching morphogenesis and lung cyst initiation that occur prior to E15.5 in our model. Measuring the dynamic changes in cell proliferation and apoptosis at later timepoints will help to understand cyst progression, which will be our next focus.

(3) BMP inhibitors used in Figure 4 show that BMP signaling regulates mesenchymal myogenesis independent of SMAD. But the experiments don't show how the inhibitors impact the control cells.

We have examined the effects of the BMPR1 inhibitor LDN on the control cells. At the same dose (200 nM) and serum-free culture condition, LDN did not affect the basal level of BMP signaling (data not included) but blocked exogeneous BMP4-induced signaling elevation (Fig.4E).

(4) Bmpr1a was deleted by administering doxycycline to pregnant dams prior to lung bud formation. It caused cystic disorders by disrupting proximal airspace. Could the authors speculate on why it does not impact tracheal and bronchiolar development? In other words, does the TBX4 promoter not target these cells? Do these cells not express Bmpr1a?

The Tbx4 enhancer does target mesenchymal cells surrounding the trachea and bronchioles. Deletion of Bmpr1a in tracheal mesenchymal cells result in disruption of tracheal cartilage formation and smooth muscle differentiation. These phenotypes are evident in the gross view of lungs from E15.5 and later (Fig.1A). However, our manuscript is focusing on the phenotype of prenatal lung cysts, and we have chosen not to include complex data on tracheal development.

eLife assessment

This valuable paper characterizes a murine model for congenital cystic airway abnormalities (CPAM). In contrast to previous assumptions that only epithelial cells are involved in the formation of pulmonary cysts, the authors provide compelling new evidence that defective BMP signaling in lung mesenchymal cells can disrupt airway development. Knowing that proper BMP signaling in mesenchymal cells is required for normal cyst-free lungs could potentially pave the way to understanding and preventing CPAM in infants at risk for this common disorder, which can be fatal if untreated. The relevance of the murine model could be enhanced by further molecular and histological comparison with human cysts.

Reviewer #2 (Public Review):

Congenital cystic airway abnormalities (CPAM) are a common poorly understood disorder in airway lung development that can be fatal if not effectively treated at birth. This study by Luo and colleagues provides compelling new evidence that bone morphogenetic protein signaling in distal mesenchymal cells is required for normal mouse lung development. Genetic loss of BMP receptor in mice and in fetal mesenchymal cells causes type 2 or alveolar-like CPAM pathology. Furthermore, this is associated with changes in expression of Sox2-Sox9 suggesting defects in the proximal to distal cellularity of the lung. Interestingly, cysts are formed even when SMAD1 and 5, two major downstream effects of BMP signaling are deleted suggesting a role for non-canonical BMP signalling. Furthermore, they were independent of ablating BMP signaling in non-vascular mesenchymal cells. The findings are compelling and provide strong evidence that cystic lung development is caused by loss of non-canonical BMP signaling in mesenchymal cells. The main weakness of the paper is that it does not identify the downstream non-canonical effector of mesenchymal BMP signaling. The authors provide a plausible suggestion that it may be p38 MAPK that deserves further investigation. Despite this minor weakness, the overall findings are novel and considered important because they provide a foundation for new studies, including experiments that may produce drugs designed to prevent or treat newborn infants with CPAM.

I can definitely see why she felt this way. Especially if people at her school weren't her ethnicity, it is no surprise if she was left out or outcast, as I've seen other people get treated this way first hand. I wonder if she will be able to recover from this early trauma, or if it'll be something that will subconsciously impact her and lie hidden throughout her later years. Hopefully, she can rely on her family as her support system as they have a strong sense of commonality.

I think that this passage points out the importance of having supportive learning environments that foster trustworthy relationships between faculty and students. As a student, I know I get a lot of anxiety when I have to go ask my advisor a small question. For those that are undocumented, I completely understand why some are weary of reaching out for help from school faculty.

https://web.archive.org/web/20240430105622/https://garymarcus.substack.com/p/evidence-that-llms-are-reaching-a

Author suggests the improvement of LLMs is flattening. E.g. points to the closing gap between proprietary and open source models out there, while improvement of proprietary stuff is diminishing or no longer happening (OpenAI progress flatlined 13 months ago it seems). In comment someone points to https://arxiv.org/abs/2404.04125 which implies a hard upper limit in improvement

What seems zero-shot performance by an LLM may well be illusionary as it is unclear what was in training data.

Exponential increase in training data is needed for linear improvements in zero-shot results of LLMs. This implies a very near, more or less now, brick wall in improvement.

Author response:

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

Reviewer #1 (Public Review):

Summary:

This manuscript explores the impact of serotonin on olfactory coding in the antennal lobe of locusts and odor-evoked behavior. The authors use serotonin injections paired with an odor-evoked palp-opening response assay and bath application of serotonin with intracellular recordings of odor-evoked responses from projection neurons (PNs).

Strengths:

The authors make several interesting observations, including that serotonin enhances behavioral responses to appetitive odors in starved and fed animals, induces spontaneous bursting in PNs, and uniformly enhances PN responses to odors. Overall, I had no technical concerns. Weaknesses:

While there are several interesting observations, the conclusions that serotonin enhanced sensitivity specifically and that serotonin had feeding-state-specific effects, were not supported by the evidence provided. Furthermore, there were other instances in which much more clarification was needed for me to follow the assumptions being made and inadequate statistical testing was reported.

Major concerns.

• To enhance olfactory sensitivity, the expected results would be that serotonin causes locusts to perceive each odor as being at a relatively higher concentration. The authors recapitulate a classic olfactory behavioral phenomenon where higher odor concentrations evoke weaker responses which is indicative of the odors becoming aversive. If serotonin enhanced the sensitivity to odors, then the dose-response curve should have shifted to the left, resulting in a more pronounced aversion to high odor concentrations. However, the authors show an increase in response magnitude across all odor concentrations. I don't think the authors can claim that serotonin enhances the behavioral sensitivity to odors because the locusts no longer show concentration-dependent aversion. Instead, I think the authors can claim that serotonin induces increased olfactory arousal.

The reviewer makes a valid point. Bath application of serotonin increased POR behavioral responses across all odor concentrations, and concentration-dependent aversion was also not observed. Furthermore, the monotonic relationship between projection neuron responses and the intensity of current injection is altered when serotonin is exogenously introduced (see Author response image 1; see below for more explanation). Hence, our data suggests that serotonin alters the dose-response relationship between neural/behavioral responses and odor intensity. As recommended, we have followed what the reviewer has suggested and revised our claim to serotonin inducing increase in olfactory arousal. The new physiology data has been added as Supplementary Figure 3 to the revised manuscript.

• The authors report that 5-HT causes PNs to change from tonic to bursting and conclude that this stems from a change in excitability. However, excitability tests (such as I/V plots) were not included, so it's difficult to disambiguate excitability changes from changes in synaptic input from other network components.

To confirm that the PN excitability did indeed change after serotonin application, we performed a new set of current-clamp recordings. In these experiments, we monitored the spiking activities in individual PNs as we injected different levels of current injections (200 – 1000 pico Amperes). Note that locust LNs that provide recurrent inhibition arborize and integrate inputs from a large number of sensory neurons and projection neurons. Therefore, activating a single PN should not activate the local neurons and therefore the antennal lobe network.

We found that the total spiking activity monotonically increased with the magnitude of the current injection in all four PNs recorded (Author response image 1). However, after serotonin injection, we found that the spiking activity remained relatively stable and did not systematically vary with the magnitude of the current injection. While the changes in odor-evoked responses may incorporate both excitability changes in individual PNs and recurrent feedback inhibition through GABAergic LNs, these results from our current injection experiments unambiguously indicate that there are changes in excitability at the level of individual PNs. We have added this result to the revised manuscript.

Author response image 1.

Current-injection induced spiking activity in individual PNs is altered after serotonin application. (A) Representative intracellular recordings showing membrane potential fluctuations as a function of time for one projection neuron (PNs) in the locust antennal lobe. A two-second window when a positive 200-1000pA current was applied is shown. Firing patterns before (left) and after (right) serotonin application are shown for comparison. Note, the spiking activity changes after the 5HT application. The black bar represents the 20mV scale. (B) Dose-response curves showing the average number of action potentials (across 5 trials) during the 2second current pulse before (green) and after (purple) serotonin for each recorded PN. Note that the current intensity was systematically increased from 200 pA to 1000 pA. The (C) The mean number of spikes across the four recorded cells during current injection is shown. The color progression represents the intensity of applied current ranging 200pA (leftmost bar) to 1000pA (rightmost bar). The dose-response trends before (green) and after (purple) 5HT application are shown for comparison.. The error bars represent SEM across the four cells.

• There is another explanation for the theoretical discrepancy between physiology and behavior, which is that odor coding is further processing in higher brain regions (ie. Other than the antennal lobe) not studied in the physiological component of this study. This should at least be discussed.

This is a valid argument. For our model of neural mapping onto behavior to work, we only need the odorant that evokes or suppresses PORs to activate a distinct set of neurons. Having said that, our extracellular recording results (Fig. 6E) indicate that hexanol (high POR) and linalool (low POR) do activate highly non-overlapping sets of PNs in the antennal lobe. Hence, our results suggest that the segregation of neural activity based on behavioral relevance already begins in the antennal lobe. We have added this clarification to the discussion section.

• The authors cannot claim that serotonin underlies a hunger state-dependent modulation, only that serotonin impacts responses to appetitive odors. Serotonin enhanced PORs for starved and fed locusts, so the conclusion would be that serotonin enhances responses regardless of the hunger state. If the authors had antagonized 5-HT receptors and shown that feeding no longer impacts POR, then they could make the claim that serotonin underlies this effect. As it stands, these appear to be two independent phenomena.

This is also a valid point. We have clarified this in the revised manuscript.

Reviewer #2 (Public Review):

Summary:

The authors investigate the influence of serotonin on feeding behavior and electrophysiological responses in the antennal lobe of locusts. They find that serotonin injection changes behavior in an odorspecific way. In physiology experiments, they can show that antennal lobe neurons generally increase their baseline firing and odor responses upon serotonin injection. Using a modeling approach the authors propose a framework on how a general increase in antennal lobe output can lead to odorspecific changes in behavior. The authors finally suggest that serotonin injection can mimic a change in a hunger state.

Strengths:

This study shows that serotonin affects feeding behavior and odor processing in the antennal lobe of locusts, as serotonin injection increases activity levels of antennal lobe neurons. This study provides another piece of evidence that serotonin is a general neuromodulator within the early olfactory processing system across insects and even phyla. Weaknesses:

I have several concerns regarding missing control experiments, unclear data analysis, and interpretation of results.

A detailed description of the behavioral experiments is lacking. Did the authors also provide a mineral oil control and did they analyze the baseline POR response? Is there an increase in baseline response after serotonin exposure already at the behavioral output level? It is generally unclear how naturalistic the chosen odor concentrations are. This is especially important as behavioral responses to different concentrations of odors are differently modulated after serotonin injection (Figure 2: Linalool and Ammonium).

POR protocol: Sixth instar locusts (Schistocera americana) of either sex were starved for 24-48 hours before the experiment or taken straight from the colony and fed blades of grass for the satiated condition. Locusts were immobilized by placing them in the plastic tube and securing their body with black electric tape (see Author response image 2). Locusts were given 20 - 30 minutes to acclimatize after placement in the immobilization tube. As can be noted, the head of the locusts along with the antenna and maxillary palps protruded out of this immobilization tube so they can be freely moved by the locusts. Note that the maxillary palps are sensory organs close to the mouth parts that are used to grab food and help with the feeding process.

It is worth noting that our earlier studies had shown that the presentation of ‘appetitive odorants’ triggers the locust to open their maxillary palps even when no food is presented (Saha et al., 2017; Nizampatnam et al., 2018; Nizampatnam et al., 2022; Chandak and Raman, 2023.) Furthermore, our earlies results indicate that the probability of palp opening varies across different odorants (Chandak and Raman, 2023). We chose four odorants that had a diverse range of palp-opening: supra-median (hexanol), median (benzaldehyde), and sub-median (linaool). Therefore, each locust in our experiments was presented with one concentration of four odorants (hexanol, benzaldehyde, linalool, and ammonium) in a pseudorandomized order. The odorants were chosen based on our physiology results such that they evoked different levels of spiking activities.

The odor pulse was 4 s in duration and the inter-pulse interval was set to 60 s. The experiments were recorded using a web camera (Microsoft) placed right in front of the locusts. The camera was fully automated with the custom MATLAB script to start recording 2 seconds before the odor pulse and end recording at odor termination. An LED was used to track the stimulus onset/offset. The POR responses were manually scored offline. Responses to each odorant were scored a 0 or 1 depending on if the palps remained closed or opened. A positive POR was defined as a movement of the maxillary palps during the odor presentation time window as shown on the locust schematic (Main Paper Figure 1).

Author response image 2.

Pictures showing the behavior experiment setup and representative palp-opening responses in a locust.

As the reviewer inquired, we performed a new series of POR experiments, where we explored POR responses to mineral oil and hexanol, before and after serotonin injection. For this study, we used 10 locusts that were starved 24-48 hours before the experiment. Note that hexanol was diluted at 1% (v/v) concentration in mineral oil. Our results reveal that locusts PORs to hexanol (~ 50% PORs) were significantly higher than those triggered by mineral oil (~10% PORs). Injection of serotonin increased the POR response rate to hexanol but did not alter the PORs evoked by mineral oil (Author response image 3).

Author response image 3.

Serotonin does not alter the palp-opening responses evoked by paraffin oil. The PORs before and after (5HT) serotonin injection are summarized and shown as a bar plot for hexanol and paraffin oil. Striped bars signify the data collected after 5HT injection. Significant differences are identified in the plot (one-tailed paired-sample t-test; (*p<0.05).

Regarding recordings of potential PNs - the authors do not provide evidence that they did record from projection neurons and not other types of antennal lobe neurons. Thus, these claims should be phrased more carefully.

In the locust antennal lobe, only the cholinergic projection neurons fire full-blown sodium spikes. The GABAergic local neurons only fire calcium ‘spikelets’ (Laurent, TINS, 1996; Stopfer et al., 2003; see Author response image 4 for an example). Hence, we are pretty confident that we are only recording from PNs. Furthermore, due to the physiological properties of the LNs, their signals being too small, they are also not detected in the extracellular recordings from the locust antennal lobe. Hence, we are confident with our claims and conclusion.

Author response image 4.

PN vs LN physiological differences: Left: A representative raw voltage traces recorded from a local neuron before, during, and after a 4-second odor pulse are shown. Note that the local neurons in the locust antennal lobe do not fire full-blown sodium spikes but only fire small calcium spikelets. On the right: A representative raw voltage trace recorded from a representative projection neuron is shown for comparison. Clear sodium spikes are clearly visible during spontaneous and odor-evoked periods. The gray bar represents 4 seconds of odor pulse. The vertical black bar represents the 40mV.

The presented model suggests labeled lines in the antennal lobe output of locusts. Could the presented model also explain a shift in behavior from aversion to attraction - such as seen in locusts when they switch from a solitarious to a gregarious state? The authors might want to discuss other possible scenarios, such as that odor evaluation and decision-making take place in higher brain regions, or that other neuromodulators might affect behavioral output. Serotonin injections could affect behavior via modulation of other cell types than antennal lobe neurons. This should also be discussed - the same is true for potential PNs - serotonin might not directly affect this cell type, but might rather shut down local inhibitory neurons.

There are multiple questions here. First, regarding solitary vs. gregarious states, we are currently repeating these experiments on solitary locusts. Our preliminary results (not included in the manuscript) indicate that the solitary animals have increased olfactory arousal and respond with a higher POR but are less selective and respond similarly to multiple odorants. We are examining the physiology to determine whether the model for mapping neural responses onto behavior could also explain observations in solitary animals.

Second, this reviewer makes the point raised by Reviewer 1. We agree that odor evaluation and decisionmaking might take place in higher brain regions. All we could conclude based on our data is that a segregation of neural activity based on behavioral relevance might provide the simplest approach to map non-specific increase in stimulus-evoked neural responses onto odor-specific changes in behavioral outcome. Furthermore, our results indicate that hexanol and linalool, two odorants that had an increase and decrease in PORs after serotonin injection, had only minimal neural response overlap in the antennal lobe. These results suggest that the formatting of neural activity to support varying behavioral outcomes might already begin in the antennal lobe. We have added this to our discussion.

Third, regarding serotonin impacting PNs, we performed a new set of current-clamp experiments to examine this issue (Author response image 1). Our results clearly show that projection neuron activity in response to current injections (that should not incorporate feedback inhibition through local neurons) was altered after serotonin injection. Therefore, the observed changes in the odor-evoked neural ensemble activity should incorporate modulation at both individual PN level and at the network level. We have added this to our discussion as well.

Finally, the authors claim that serotonin injection can mimic the starved state behavioral response. However, this is only shown for one of the four odors that are tested for behavior (HEX), thus the data does not support this claim.

We note that Hex is the only appetitive odorant in the panel. But, as reviewer 1 has also brought up a similar point, we have toned down our claims and will investigate this carefully in a future study.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

• Was the POR of the locusts towards linalool and ammonium higher than towards a blank odor cartridge? I ask because the locusts appear to be less likely to respond to these odors and so I am concerned that this assay is not relevant to the ecological context of these odors. In other words, perhaps serotonin did not enhance the responses to these odors in this assay, because this is not a context in which locusts would normally respond to these odors.

The POR response to linalool and ammonium is lower and comparable to that of paraffin oil. Serotonin does not increase POR responses to paraffin oil but does increase response to hexanol (an appetitive odorant). We have clarified this using new data (Author response image 5).

• It seems to me that Figure 5C is the crux for understanding the potential impact of 5-HT on odor coding, but it is somewhat confusing and underutilized. Is the implication that 5-HT decorrelates spontaneous activity such that when an odor stimulus arrives, the odor-evoked activity deviates to a greater degree? The authors make claims about this figure that require the reader to guess as to the aspect of the figure to which they are referring.

The reviewer makes an astute observation. Yes, the spontaneous activity in the antennal lobe network before serotonin introduction is not correlated with the ensemble spontaneous activity after serotonin bath application. Remarkably, the odor-evoked responses were highly similar, both in the reduced PCA space and when assayed using high-dimensional ensemble neural activity vectors. Whether the changes in network spontaneous activity have a function in odor detection and recognition is not fully understood and cannot be convincingly answered using our data. But this is something that we had pondered.

• The modeling component summarized in Figure 6 needs clarification and more detail. Perhaps example traces associated with positive weighting within neural ensemble 1 relative to neural ensemble 2? I struggled to understand conceptually how the model resolved the theoretical discrepancy between physiology and behavior.

As recommended, here is a plot showing the responses of four PNs that had positive weights to hexanol and linalool. As can be expected, each PN in this group had higher responses to hexanol and no response to linalool. Further, the four PNs that received negative weights had response only to linalool.

Author response image 5.

Odor-evoked responses of four PNs that received positive weights in the model (top panel), and four PNs that were assigned negative weights in the model (bottom).

• Was there a significant difference between the PORs of hungry vs. fed locusts? The authors state that they differ and provide statistics for the comparisons to locusts injected with 5-HT, but then don't provide any statistical analyses of hungry vs. fed animals.

The POR responses to HEX (an appetitive odorant) were significantly different between the hungry and starved locusts.

Author response image 6.

A bar plot summarizing PORs to all four odors for satiated locust (highlighted with stripes), before (dark shade), and after 5HT injection (lighter shade). To allow comparison before 5HT injection for starved locust plotted as well (without stripes). The significance was determined using a one-tailed paired-sample ttest(*p<0.05).

• Were any of the effects of 5-HT on odor-evoked PN responses significant? No statistics are provided.

We examined the distribution of odor-evoked responses in PNs before and after 5HT introduction. We found that the overall distribution was not significantly different between the two (one-tailed pairedsample t-test; p = 0.93).

Author response image 7.

Comparison of the distribution of odor-evoked PN responses before (green) and after (purple) 5HT introduction. One-tailed paired sample t-test was used to compare the two distributions.

• The authors interchangeably use "serotonin", "5HT" and "5-HT" throughout the manuscript, but this should be consistent.

This has been fixed in the revised manuscript.

• On page 2 the authors provide an ecological relevance for linalool as being an additive in pesticides, however, linalool is a common floral volatile chemical. Is the implication that locusts have learned to associate linalool with pesticides?

Linalool is a terpenoid alcohol that has a floral odor but has also been used as a pesticide and insect repellent [Beier et al., 2014]. As shown in Author response image 2, it evoked the least POR responses amongst a diverse panel of 22 odorants that were tested. We have clarified how we chose odorants based on the prior dataset in the Methods section.

• In Figure 1, there should be a legend in the figure itself indicating that the black box indicates the absence of POR and the white box indicates presence, rather than just having it in the legend text.

Done.

• In Figure 2, the raw data from each animal can be moved to the supplements. The way it is presented is overwhelming and the order of comparisons is difficult to follow.

Done.

• For the induction of bursting in PNs by the application of 5-HT, were there any other metrics observed such as period, duration of bursts, or peak burst frequency? The authors rely on ISI, but there are other bursting metrics that could also be included to understand the nature of this observation. In particular, whether the bursts are likely due to changes in intrinsic biophysical properties of the PNs or polysynaptic effects.

We could use other metrics as the reviewer suggests. Our main point is that the spontaneous activity of individual PNs changed. We have added a new current-injection experiments to show that the PNs output to square pulses of current becomes different after serotonin application (Author response image 1)

• Were 4-vinyl anisole, 1-nonanol, and octanoic acid selected as additional odors because they had particular ecological relevance, or was it for the diversity of chemical structure?

These odorants were selected based on both, chemical structure and ecological relevance. The logic behind this was to have a very diverse odor panel that consisted of food odorant – Hexanol, aggregation pheromone – 4-vinyl anisole, sex pheromone – benzaldehyde, acid – octanoic acid, base – ammonium, and alcohol – 1-nonanol. Additionally, we selected these odors based on previous neural and behavioral data on these odorants (Chandak and Raman, 2023, Traner and Raman, 2023, Nizampatnam et al, 2022 & 2018; Saha et al., 2017 & 2013).

Reviewer #2 (Recommendations For The Authors):

The electrophysiology dataset combines all performed experiments across all tested different PN-odor pairs. How many odors have been tested in a single PN and how many PNs have been tested for a single odor? This information is not present in the current manuscript. Can the authors exclude that there are odor-specific modulations?

In total, our dataset includes recordings from 19 PNs. Seven PNs were tested on a panel of seven odorants (4-vinyl anisole, 1-nonanol, octanoic acid, Hex, Bza, Lool, and Amn), and the remaining twelve were tested with the four main odorants used in the study (Hex, Bza, Lool, and Amn). This information has been added to the Methods section

How did the authors choose the concentrations of serotonin injections and bath applications - is this a naturalistic amount?

The serotonin concentration for ephys experiments was chosen based on trial-error experiments:

0.01mM was the highest concentration that did not cause cell death. For the behavioral experiments, we increased the concentration (0.1 M) due to the presence of anatomical structures in the locust's head such as air sacks, sheath as well as hemolymph which causes some degree of dilution that we cannot control.

Behavior experiments were performed 3 hours after injection - ephys experiments 5-10 minutes following bath application. Can the authors exclude that serotonin affects neural processing differently on these different timescales?

We cannot exclude this possibility. We did ePhys experiments 5-10 minutes after bath application as it would be extremely hard to hold cells for that long.

A longer delay was required for our behavioral experiments as the locusts tended to be a bit more agitated with larger spontaneous movements of palps as well as exhibited unprompted vomiting. A 3hour period allowed the locust to regain its baseline level movements after 5HT introduction. [This information has been added to the methods section of the revised manuscript]

Concerning the analysis of electrophysiological data. The authors should correct for changes in the baseline before performing PCA analysis. And how much of the variance is explained by PC1 and PC2?

We did not correct for baseline changes or subtract baseline as we wanted to show that the odor-evoked neural responses still robustly encoded information about the identity of the odorant.

The authors should perform dye injections after recordings to visualize the cell type they recorded from. Serotonin might affect also other cell types in the antennal lobe.

As mentioned above, in the locust antennal lobe only PNs fire full-blown sodium spikes, and LNs only fire calcium spikelets (Author response image 4). Since these signals are small, they will be buried under the noise floor when using extracellular recording electrodes for monitoring responses in the AL antennal lobe.

Hence we are pretty certain what type of cells we are recording from.

There were several typos in the manuscript, please check again.

We have fixed many of the grammatical errors and typos in the revised version.

Reviewer #1 (Public Review):

Summary:

This manuscript explores the impact of serotonin on olfactory coding in the antennal lobe of locusts and odor-evoked behavior. The authors use serotonin injections paired with an odor-evoked palp-opening response assay and bath application of serotonin with intracellular recordings of odor-evoked responses from projection neurons (PNs).

Strengths:

The authors make several interesting observations, including that serotonin enhances behavioral responses to appetitive odors in starved and fed animals, induces spontaneous bursting in PNs, directly impacts PN excitability, and uniformly enhances PN responses to odors.

Weakness:

The one remaining issue to be resolved is the theoretical discrepancy between the physiology and the behavior. The authors provide a computational model that could explain this discrepancy and provide the caveat that while the physiological data was collected from the antennal lobe, but there could be other olfactory processing stages involved. Indeed other processing stages could be the sites for the computational functions proposed by the model. There is an additional caveat which is that the physiological data were collected 5-10 minutes after serotonin application whereas the behavioral data were collected 3 hours after serotonin application. It is difficult to link physiological processes induced 5 minutes into serotonin application to behavioral consequences 3 hours subsequent to serotonin application. The discrepancy between physiology and behavior could easily reflect the timing of action of serotonin (i.e. differences between immediate and longer-term impact).

Overall, the study demonstrates the impact of serotonin on odor-evoked responses of PNs and odor guided behavior in locust. Serotonin appears to have non-linear effects including changing the firing patterns of PNs from monotonic to bursting and altering behavioral responses in an odor-specific manner, rather than uniformly across all stimuli presented.

Reviewer #2 (Public Review):

Summary:

The authors investigate the influence of serotonin on feeding behavior and electrophysiological responses in the antennal lobe of locusts. They find that serotonin injection changes behavior in an odor-specific way. In physiology experiments, they can show that projection neurons in the antennal lobe generally increase their baseline firing and odor responses upon serotonin injection. Using a modeling approach the authors propose a framework on how a general increase in antennal lobe output can lead to odor-specific changes in behavior.

Strengths:

This study shows that serotonin affects feeding behavior and odor processing in the antennal lobe of locusts, as serotonin injection increases activity levels of projection neurons. This study provides another piece of evidence that serotonin is a general neuromodulator within the early olfactory processing system across insects and even phyla.

Weaknesses:

I still have several concerns regarding the generalizability of the model and interpretation of results. The authors cannot provide evidence that serotonin modulation of projection neurons impacts behavior.

The authors show that odor identity is maintained after 5-HT injection, however, the authors do not show if PN responses to different odors were differently affected after serotonin exposure.

Regarding the model, the authors show that the model works for odors with non-overlapping PN activation. However, only one appetitive, one neutral, and one aversive odor has been tested and modeled here. Can the fixed-weight model also hold for other appetitive and aversive odors that might share more overlap between active PNs? How could the model generate BZA attraction in 5-HT exposed animals (as seen in behavior data in Figure 1) if the same PNs just get activated more?

The authors should still not exclude the possibility that serotonin injections could affect behavior via modulation of other cell types than projection neurons. This should still be discussed, serotonin might rather shut down baseline activation of local inhibitory neurons - and thus lead to the interesting bursting phenotypes, which can also be seen in the baseline response, due to local PN-to-LN feedback.

The authors did not fully tone down their claims regarding causality between serotonin and starved state behavioral responses.<br /> There is no proof that serotonin injection mimics starved behavioral responses.

BBC highly critical of Humane AI Pin, just like [[Humane AI Pin review not even close]] I noted earlier. Explicitly ties this to the expectations of [[rabbit — home]] too, which is a similar device. Issue here is I think similar to other devices like voice devices in your home. Not smart enough at the edge, too generic to be of use as [[small band AI personal assistant]] leading to using it for at most 2 or 3 very basic things (weather forecast, time, start playlist usually, and at home perhaps switching on a light), that don't justify the price tag .

Author response:

We would like to thank the reviewers for their helpful comments. We note that both reviews are strongly supportive with comments including, “a biophysical tour de force” (rev #1), “the study is exemplary” (rev #2), and “represents a roadmap for future work” (rev #2). Below we respond to each reviewer comment.

Reviewer #1

This study provides a detailed and quantitative description of the allosteric mechanisms resulting in the paradoxical activation of BRAF kinase dimers by certain kinase inhibitors. The findings provide a much needed quantiative basis for this phenomenon and may lay the foundation for future drug development efforts aimed at the important cancer target BRAF. The study builds on very evidence obtained by multiple independent biophysical methods.

Summary:

The authors quantitatively describe the complex binding equilibria of BRAF and its inhibitors resulting in some cases in the paradoxical activation of BRAF dimer when bound to ATP competitive inhibitors. The authors use a biophysical tour de force involving FRET binding assays, NMR, kinase activity assays and DEER spectroscopy.

We are gratified by the reviewer’s supportive summary.

Strengths:

The strengths of the study are the beautifully conducted assays that allow for a thorough characterization of the allostery in this complex system. Additionally, the use of F-NMR and DEER spectroscopy provide important insights into the details of the process. The resulting model for binding of inhibitors and dimerization (Fig.4) is very helpful.

Weaknesses:

This is a complex system and its communication is inherently challenging. It might be of interest to the broader readership to understand the implications of the model for drug development and therapy.

We agree with the reviewer that this is a complicated system. With regard to inhibitor development, a key insight is that designing aC-in state inhibitors that avoid paradoxical activation may be non-trivial because these molecules not only induce dimers but also tend to bind the second dimer subunit more weakly than the first, due to allosteric asymmetry and/or inherently different affinities for each RAF isoform. We feel the full implications for future therapeutic development are an extensive topic that is beyond the scope of our work, which is focused on the properties of current inhibitors.

Recommendations for the author:

The experimental work, analysis and resulting model are excellent. I had some difficulty following the complex model in some instances and it may be useful to review the description of the model and see whether it can be made more palatable to the broader readership. I think it would be useful to discuss the model presented in reference 40 (Kholodenko) and to compare it to the presented model here.

We regret any confusion with regards to the nature of the model. Our analysis was built upon the model developed by Boris Kholodenko as reported in his 2015 Cell Reports paper. This formed the theoretical framework that combined with our experimental data allowed us to parameterize this model to obtain experimental values for the equilibrium constants and allosteric coupling factors.

Reviewer #2

This manuscript combines elegant biophysical solution measurements to address paradoxical kinase activation by Type II BRAF inhibitors. The novel findings challenge prevailing models, through experiments that are rigorous and carefully controlled. The study is exemplary in the breadth of strategies it uses to address protein kinase dynamics and inhibitor allostery.

Summary:

This manuscript uses FRET, 19F-NMR and DEER/EPR solution measurements to examine the allosteric effects of a panel of BRAF inhibitors (BRAFi). These include first-generation aC-out BRAFi, and more recent Type I and Type II aC-in inhibitors. Intermolecular FRET measurements quantify Kd for BRAF dimerization and inhibitor binding to the first and second subunits. Distinct patterns are found between aC-in BRAFi, where Type I BRAFi bind equally well to the first and second subunits within dimeric BRAF. In contrast, Type II BRAFi show stronger affinity for the first subunit and weaker affinity for the second subunit, an effect named "allosteric asymmetry". Allosteric asymmetry has the potential for Type II inhibitors to promote dimerization while favoring occupancy of only one subunit (BBD form), leading to enrichment of an active dimer.

Measurements of in vitro BRAF kinase activity correlate amazingly well with the calculated amounts of the half site-inhibited BBD forms with Type II inhibitors. This suggests that the allosteric asymmetry mechanism explains paradoxical activation by this class of inhibitors. DEER/EPR measurements further examine the positioning of helix aC. They show systematic outward movement of aC with Type II inhibitors, relative to the aC-in state with Type I inhibitors, and further show that helix aC adopts multiple states and is therefore dynamic in apo BRAF. This makes a strong case that negative cooperativity between sites in the BRAF dimer can account for paradoxical kinase activation by Type II inhibitors by creating a half site-occupied homodimer, BBD. In contrast, Type I inhibitors and aC-out inhibitors do not fit this model, and are therefore proposed to be explained by previous proposed models involving negative allostery between subunits in BRAF-CRAF heterodimers, RAS priming, and transactivation.

Strengths:

This study integrates orthogonal spectroscopic and kinetic strategies to characterize BRAF dynamics and determine how it impacts inhibitor allostery. The unique combination of approaches presented in this study represents a road map for future work in the important area of protein kinase dynamics. The work represents a worthy contribution not only to the field of BRAF regulation but protein kinases in general.

Weaknesses:

Some questions remain regarding the proposed model for Type II inhibitors and its comparison to Type I and aC-out inhibitors that would be useful to clarify. Specifically, it would be helpful to address whether the activation of BRAF by Type II inhibitors, while strongly correlated with BBD model predictions in vitro, also depends on CRAF via BRAF-CRAF in cells and therefore overlaps with the mechanisms of paradoxical activation by Type I and aC-out inhibitors.

We agree with the reviewer that this is a worthy question to be pursued. However, given the substantial experimental effort required for such an endeavor, and the highly supportive nature of the reviewer comments, including that “This is a strong manuscript that I feel is well above the bar for publication”, we believe this effort is more appropriate for a future study.

This is a strong manuscript that I feel is well above the bar for publication. Nevertheless, it is recommended that the authors consider addressing the following points in order to support their major conclusions.

(1) Fig 3D shows similar effects of Type II and Type I inhibitors in the biphasic increase of cellular pMEK/pERK. From this, the authors argue that Type II inhibitors are explained by negative allostery in the BRAF homodimer (based on Fig 2E), while Type I inhibitors are not. But it seems possible that despite the terrific correlation between BBD and BRAF kinase activities measured in vitro, CRAF is still important to explain pathway activation in cells. It also seems conceivable that the calculated %BBD between different Type II inhibitors may not correlate as well with their effects on pathway activation in cells. These possibilities should be addressed.

We agree with the reviewer that it is likely that CRAF contributes to paradoxical activation by type II inhibitors in cells. It is also likely that other cellular factors such as RAS-priming and membrane recruitment play a role in activation. However, we note that for the type II inhibitors there is good agreement between the biophysical predictions and the concentration regimes in which activation is observed in cells, suggesting that these predictions are capturing a key part of the activation process that occurs in cells.

(2) In Fig 2A, is it possible to report the activity of dimeric BRAF-WT in the absence of inhibitor? This would help confirm that the maximal activity measured after titrating inhibitor is indeed consistent with the predicted %BBD population, which would be expected to have half of the specific activity of BB.

In principle, it is possible to determine the catalytic activity of apo dimers (BB) by combining our model predictions for the concentration of BB dimers and our activity measurements. However, because the activity assays are performed at nanomolar kinase concentrations, whereas the baseline dimerization affinity of BRAF is in the micromolar range, the observed activity of apo BRAF arises from a small subpopulation of dimers (on the order of 4 percent under the conditions of our experiments) and is therefore difficult to define accurately. As a result, we deemed it more suitable to compare our results to published activity measurements derived from 14-3-3-activated dimers which should represent fully dimerized BRAF. This analysis, as reported in Figure 2E, suggests that the BBD activity is approximately half of that of BB.

(3) The 19F-NMR experiments make a good case for broadening of the helix aC signal in the BRAF dimer. From this, the study proposes that after inhibitor binds one subunit, the second unoccupied subunit retains dynamics. It would be useful to address this experimentally, if possible. For example, can the 19F-NMR signal be measured in the presence of inhibitor, to support the prediction that the unoccupied subunit is indeed dynamic and samples multiple conformations as in apo BRAF?

We agree with the reviewer that it would be interesting to determine the dynamic response of BRAF to inhibitor binding. However, this is a challenging undertaking due to the biochemical heterogeneity that occurs at sub saturating inhibitor concentrations. For example, at any given inhibitor concentration, BRAF exists as a mixture of monomers, apo dimers, dimers with one inhibitor molecule, and dimers with two inhibitor molecules bound. This makes it challenging to relate the 19F NMR signal to a single biochemical state. Addressing this would require a substantial experimental effort that we feel is beyond the scope of this study.

eLife assessment

This valuable paper describes innovative force measurements of the bending modulus of gliding cyanobacteria, along with measurements of the critical buckling length of the cells, which in combination lead to insight into how these cells produce the force necessary to move. Quantitative analysis convincingly shows that the propulsive force and resistive friction coefficient are strongly coupled, which supports propulsion based on adhesion forces rather than slime extrusion.

Reviewer #1 (Public Review):

The paper combines experiments on freely gliding cyanobacteria, buckling experiments using two-dimensional V shaped corners, and micropipette force measurements with theoretical models to study gliding forces in these organisms. The aim is to quantify these forces and use the results to perhaps discriminate between competing mechanisms by which these cells move. A large data set of possible collision events are analyzed, bucking events evaluated, and critical buckling lengths estimated. A line elasticity model is used to analyze the onset of buckling and estimate the effective (viscous type) friction/drag that controls the dynamics of the rotation that ensues post-buckling. This value of the friction/drag is compared to a second estimate obtained by consideration of the active forces and speeds in freely gliding filaments. The authors find that these two independent estimates of friction/drag correlate with each other and are comparable in magnitude. The experiments are conducted carefully, the device fabrication is novel, the data set is interesting, and the analysis is solid. The authors conclude that the experiments are consistent with the propulsion being generated by adhesion forces rather than slime extrusion. While consistent with the data, this conclusion is inferred.

Summary:

The paper addresses important questions on the mechanisms driving the gliding motility of filamentous cyanobacteria. The authors aim to understand these by estimating the elastic properties of the filaments, and by comparing the resistance to gliding under a) freely gliding conditions, and b) in post-buckled rotational states. Experiments are used to estimate the propulsion force density on freely gliding filaments (assuming over damped conditions). Experiments are combined with a theoretical model based on Euler beam theory to extract friction (viscous) coefficients for filaments that buckle and begin to rotate about the pinned end. The main results are estimates for the bending stiffness of the bacteria, the propulsive tangential force density, the buckling threshold in terms of the length, and estimates of the resistive friction (viscous drag) providing the dissipation in the system and balancing the active force. It is found that experiments on the two bacterial species yield nearly identical value of 𝑓 (albeit with rather large variations). The authors conclude that the experiments are consistent with the propulsion being generated by adhesion forces rather than slime extrusion.

Strengths of the paper:

The strengths of the paper lie in the novel experimental setup and measurements that allow for the estimation of the propulsive force density, critical buckling length, and effective viscous drag forces for movement of the filament along its contour - the axial (parallel) drag coefficient, and the normal (perpendicular) drag coefficient (I assume this is the case, since the post-buckling analysis assumes the bent filament rotates at a constant frequency). These direct measurements are important for serious analysis and discrimination between motility mechanisms.

Weaknesses:

There are aspects of the analysis and discussion that may be improved. I suggest that the authors take the following comments into consideration while revising their manuscript.

The conclusion that adhesion via focal adhesions is the cause for propulsion rather than slime protrusion, is consistent with the experimental results that the frictional drag correlates with propulsion force. At the same time, it is hard to rule out other factors that may result in this (friction) viscous drag - (active) force relationship while still being consistent with slime production. More detailed analysis aiming to discriminate between adhesion vs slime protrusion may be outside the scope of the study, but the authors may still want to elaborate on their inference. It would help if there was a detailed discussion on the differences in terms of the active force term for the focal adhesion-based motility vs the slime motility.

Can the authors comment on possible mechanisms (perhaps from the literature) that indicate how isotropic friction may be generated in settings where focal adhesions drive motility. A key aspect here would probably be estimating the extent of this adhesion patch and comparing it to a characteristic contact area. Can lubrication theory be used to estimate characteristic areas of contact (knowing the radius of the filament, and assuming a height above substrate)? If the focal adhesions typically cover areas smaller than this lubrication area, it may suggest the possibility that bacteria essentially present a flat surface insofar as adhesion is concerned, leading to transversely isotropic response in terms of the drag. Of course, we will still require the effective propulsive force to act along the tangent.

I am not sure why the authors mention that the power of the gliding apparatus is not rate limiting. The only way to verify this would be to put these in highly viscous fluids where the drag of the external fluid comes into the picture as well (if focal adhesions are on the substrate facing side, and the upper side is subject to ambient fluid drag). Also, the friction referred to here has the form of a viscous drag (no memory effect, and thus not viscoelastic or gel-like), and it is not clear if forces generated by adhesion involve other forms of drag such as chemical friction via temporary bonds forming and breaking. In quasi-static settings and under certain conditions such as separation of chemical and elastic time scales, bond friction may yield overall force proportional to local sliding velocities.

For readers from a non-fluids background, some additional discussion of the drag forces, and the forms of friction would help. For a freely gliding filament if 𝑓 is the force density (per unit length), then steady gliding with a viscous frictional drag would suggest (as mentioned in the paper) 𝑓 ∼ 𝑣! 𝐿 𝜂∥. The critical buckling length is then dependent on 𝑓 and on 𝐵 the bending modulus. Here the effective drag is defined per length. I can see from this that if the active force is fixed, and the viscous component resulting from the frictional mechanism is fixed, the critical buckling length will not depend on the velocity (unless I am missing something in their argument), since the velocity is not a primitive variable, and is itself an emergent quantity.

Reviewer #2 (Public Review):

In the presented manuscript, the authors first use structured microfluidic devices with gliding filamentous cyanobacteria inside in combination with micropipette force measurements to measure the bending rigidity of the filaments. The distribution of bending rigidities is very broad.

Next, they use triangular structures to trap the bacteria with the front against an obstacle. Depending on the length and rigidity, the filaments buckle under the propulsive force of the cells. The authors use theoretical expressions for the buckling threshold to infer propulsive force, given the measured length and (mean-) stiffnesses. They find nearly identical values for both species, 𝑓 ∼ (1.0 {plus minus} 0.6) nN∕µm, nearly independent of the velocity. These measurements have to be taken with additional care, as then inferred forces depend strongly on the bending rigidity, which already shows a broad distribution.

Finally, they measure the shape of the filament dynamically to infer friction coefficients via Kirchhoff theory. In this section they report a strong correlation with velocity and report propulsive forces that vary over two orders of magnitude.

From a theoretical perspective, not many new results are presented. The authors repeat the the well-known calculation for filaments buckling under propulsive load and arrive at the literature result of buckling when the dimensionless number (f L^3/B) is larger than 30.6 as previously derived by Sekimoto et al in 1995. In my humble opinion, the "buckling theory" section belongs to methods.<br /> Finally, the Authors use molecular dynamics type simulations similar to other models to reproduce the buckling dynamics from the experiments.

Data and source code are available via trusted institutional or third-party repositories that adhere to policies that make data discoverable, accessible and usable.

Reviewer #3 (Public Review):

Summary:

This paper presents novel and innovative force measurements of the biophysics of gliding cyanobacteria filaments. These measurements allow for estimates of the resistive force between the cell and substrate and provide potential insight into the motility mechanism of these cells, which remains unknown.

Strengths:

The authors used well-designed microfabricated devices to measure the bending modulus of these cells and to determine the critical length at which the cells buckle. I especially appreciated the way the authors constructed an array of pillars and used it to do 3-point bending measurements and the arrangement the authors used to direct cells into a V-shaped corner in order to examine at what length the cells buckled at. By examining the gliding speed of the cells before buckling events, the authors were able to determine how strongly the buckling length depends on the gliding speed, which could be an indicator of how the force exerted by the cells depends on cell length; however, the authors did not comment on this directly.

Weaknesses:

There are no major weaknesses in the paper.

Author response:

Reviewer 1:

The paper “Quantifying gliding forces of filamentous cyanobacteria by self-buckling” combines experiments on freely gliding cyanobacteria, buckling experiments using two-dimensional V-shaped corners, and micropipette force measurements with theoretical models to study gliding forces in these organisms. The aim is to quantify these forces and use the results to perhaps discriminate between competing mechanisms by which these cells move. A large data set of possible collision events are analyzed, bucking events evaluated, and critical buckling lengths estimated. A line elasticity model is used to analyze the onset of buckling and estimate the effective (viscous type) friction/drag that controls the dynamics of the rotation that ensues post-buckling. This value of the friction/drag is compared to a second estimate obtained by consideration of the active forces and speeds in freely gliding filaments. The authors find that these two independent estimates of friction/drag correlate with each other and are comparable in magnitude. The experiments are conducted carefully, the device fabrication is novel, the data set is interesting, and the analysis is solid. The authors conclude that the experiments are consistent with the propulsion being generated by adhesion forces rather than slime extrusion. While consistent with the data, this conclusion is inferred.

We thank the reviewer for the positive evaluation of our work.

Summary:

The paper addresses important questions on the mechanisms driving the gliding motility of filamentous cyanobacteria. The authors aim to understand these by estimating the elastic properties of the filaments, and by comparing the resistance to gliding under a) freely gliding conditions, and b) in post-buckled rotational states. Experiments are used to estimate the propulsion force density on freely gliding filaments (assuming over-damped conditions). Experiments are combined with a theoretical model based on Euler beam theory to extract friction (viscous) coefficients for filaments that buckle and begin to rotate about the pinned end. The main results are estimates for the bending stiffness of the bacteria, the propulsive tangential force density, the buckling threshold in terms of the length, and estimates of the resistive friction (viscous drag) providing the dissipation in the system and balancing the active force. It is found that experiments on the two bacterial species yield nearly identical values of f (albeit with rather large variations). The authors conclude that the experiments are consistent with the propulsion being generated by adhesion forces rather than slime extrusion.

We appreciate this comprehensive summary of our work.

Strengths of the paper:

The strengths of the paper lie in the novel experimental setup and measurements that allow for the estimation of the propulsive force density, critical buckling length, and effective viscous drag forces for movement of the filament along its contour – the axial (parallel) drag coefficient, and the normal (perpendicular) drag coefficient (I assume this is the case, since the post-buckling analysis assumes the bent filament rotates at a constant frequency). These direct measurements are important for serious analysis and discrimination between motility mechanisms.

We thank the reviewer for this positive assessment of our work.

Weaknesses:

There are aspects of the analysis and discussion that may be improved. I suggest that the authors take the following comments into consideration while revising their manuscript.

The conclusion that adhesion via focal adhesions is the cause for propulsion rather than slime protrusion is consistent with the experimental results that the frictional drag correlates with propulsion force. At the same time, it is hard to rule out other factors that may result in this (friction) viscous drag - (active) force relationship while still being consistent with slime production. More detailed analysis aiming to discriminate between adhesion vs slime protrusion may be outside the scope of the study, but the authors may still want to elaborate on their inference. It would help if there was a detailed discussion on the differences in terms of the active force term for the focal adhesion-based motility vs the slime motility.

We appreciate this critical assessment of our conclusions. Of course we are aware that many different mechanisms may lead to similar force/friction characteristics, and that a definitive conclusion on the mechanism would require the combination of various techniques, which is beyond the scope of this work. Therefore, we were very careful in formulating the discussion of our findings, refraining, in particular, from a singular conclusion on the mechanism but instead indicating “support” for one hypothesis over another, and emphasizing “that many other possibilities exist”.

The most common concurrent hypotheses for bacterial gliding suggest that either slime extrusion at the junctional pore complex [A1], rhythmic contraction of fibrillar arrays at the cell wall [A2], focal adhesion sites connected to intracellular motor-microtubule complexes [A3], or modified type-IV pilus apparati [A4] provide the propulsion forces. For the slime extrusion hypothesis, which is still abundant today, one would rather expect an anticorrelation of force and friction: more slime extrusion would generate more force, but also enhance lubrication. The other hypotheses are more conformal to the trend we observed in our experiments, because both pili and focal adhesion require direct contact with a substrate. How contraction of fibrilar arrays would micromechanically couple to the environment is not clear to us, but direct contact might still facilitate force transduction. Please note that these hypotheses were all postulated without any mechanical measurements, solely based on ultra-structural electron microscopy and/or genetic or proteomic experiments. We see our work as complementary to that, providing a mechanical basis for evaluating these hypotheses.

We agree with the referee that narrowing down this discussion to focal adhesion should have been avoided. We rewrote the concluding paragraph (page 8):

“…it indicates that friction and propulsion forces, despite being quite vari able, correlate strongly. Thus, generating more force comes, inevitably, at the expense of added friction. For lubricated contacts, the friction coefficient is proportional to the thickness of the lubricating layer (Snoeijer et al., 2013 ), and we conjecture active force and drag both increase due to a more intimate contact with the substrate. This supports mechanisms like focal adhesion (Mignot et al., 2007 ) or a modified type-IV pilus (Khayatan et al., 2015 ), which generate forces through contact with extracellular surfaces, as the underlying mechanism of the gliding apparatus of filamentous cyanobacteria: more contacts generate more force, but also closer contact with the substrate, thereby increasing friction to the same extent. Force generation by slime extrusion (Hoiczyk and Baumeister, 1998 ), in contrast, would lead to the opposite behavior: More slime generates more propulsion, but also reduces friction. Besides fundamental fluid-mechanical considerations (Snoeijer et al., 2013 ), this is rationalized by two experimental observations: i. gliding velocity correlates positively with slime layer thickness (Dhahri et al., 2013 ) and ii. motility in slime-secretion deficient mutants is restored upon exogenous addition of polysaccharide slime. Still we emphasize that many other possibilities exist. One could, for instance, postulate a regulation of the generated forces to the experienced friction, to maintain some preferred or saturated velocity.”

Can the authors comment on possible mechanisms (perhaps from the literature) that indicate how isotropic friction may be generated in settings where focal adhesions drive motility? A key aspect here would probably be estimating the extent of this adhesion patch and comparing it to a characteristic contact area. Can lubrication theory be used to estimate characteristic areas of contact (knowing the radius of the filament, and assuming a height above the substrate)? If the focal adhesions typically cover areas smaller than this lubrication area, it may suggest the possibility that bacteria essentially present a flat surface insofar as adhesion is concerned, leading to a transversely isotropic response in terms of the drag. Of course, we will still require the effective propulsive force to act along the tangent.

We thank the referee for suggesting to estimate the dimensions of the contact region. Both pili and focal adhesion sites would be of sizes below one micron [A3, A4], much smaller than the typical contact region in the lubricated contact, which is on the order of the filament radius (few microns). So indeed, isotropic friction may be expected in this situation [A5] and is assumed frequently in theoretical work [A6–A8]. Anisotropy may then indeed be induced by active forces [A9], but we are not aware of measurements of the anisotropy of friction in bacterial gliding.

For a more precise estimate using lubrication theory, rheology and extrusion rate of the secreted polysaccharides would have to be known, but we are not aware of detailed experimental characterizations.

We extended the paragraph in the buckling theory on page 5 regarding the assumption of isotropic friction:

“We use classical Kirchhoff theory for a uniform beam of length L and bending modulus B, subject to a force density ⃗b = −f ⃗t− η ⃗v, with an effective active force density f along the tangent ⃗t, and an effective friction proportional to the local velocity ⃗v, analog to existing literature (Fily et al., 2020; Chelakkot et al., 2014; Sekimoto et al., 1995 ). Presumably, this friction is dominated by the lubrication drag from the contact with the substrate, filled by a thin layer of secreted polysaccharide slime which is much more viscous than the surrounding bulk fluid. Speculatively, the motility mechanism might also comprise adhering elements like pili (Khayatan et al., 2015 ) or foci (Mignot et al., 2007 ) that increase the overall friction (Pompe et al., 2015 ). Thus, the drag due to the surrounding bulk fluid can be neglected (Man and Kanso, 2019 ), and friction is assumed to be isotropic, a common assumption in motility models (Fei et al., 2020; Tchoufag et al., 2019; Wada et al., 2013 ). We assume…”

We also extended the discussion regarding the outcome of isotropic friction (page 7):

“…Thus we plot f/v over η in Figure 4 D, finding nearly identical values over about two decades. Since f and η are not correlated with v0, this is due to a correlation between f and η. This relation is remarkable in two aspects: On the one hand, it indicates that friction is mainly isotropic. This suggests that friction is governed by an isotropic process like bond friction or lubrication from the slime layer in the contact with the substrate, the latter being consistent with the observation that mutations deficient of slime secretion do not glide but exogenous addition of slime restores motility (Khayatan et al., 2015 ). In contrast, hydrodynamic drag from the surrounding bulk fluid (Man and Kanso, 2019 ), or the internal friction of the gliding apparatus would be expected to generate strongly anisotropic friction. If the latter was dominant, a snapping-like transition into the buckling state would be expected, rather than the continuously growing amplitude that is observed in experiments. On the other hand, it indicates that friction and propulsion forces…”

I am not sure why the authors mention that the power of the gliding apparatus is not rate-limiting. The only way to verify this would be to put these in highly viscous fluids where the drag of the external fluid comes into the picture as well (if focal adhesions are on the substrate-facing side, and the upper side is subject to ambient fluid drag). Also, the friction referred to here has the form of a viscous drag (no memory effect, and thus not viscoelastic or gel-like), and it is not clear if forces generated by adhesion involve other forms of drag such as chemical friction via temporary bonds forming and breaking. In quasi-static settings and under certain conditions such as the separation of chemical and elastic time scales, bond friction may yield overall force proportional to local sliding velocities.

We agree with the referee that the origin of the friction is not easily resolved. Lubrication yields an isotropic force density that is proportional to the velocity, and the same could be generated by bond friction. Importantly, both types of friction would be assumed to be predominantly isotropic. We explicitly referred to lubrication drag because it has been shown that mutations deficient of slime extrusion do not glide [A4].

Assuming, in contrast, that in free gliding, friction with the environment is not rate limiting, but rather the internal friction of the gliding apparatus, i.e., the available power, we would expect a rather different behavior during early-buckling evolution. During early buckling, the tangential motion is stalled, and the dynamics is dominated by the growing buckling amplitude of filament regions near the front end, which move mainly transversely. For geometric reasons, in this stage the (transverse) buckling amplitude grows much faster than the rear part of the filament advances longitudinally. Thus that motion should not be impeded much by the internal friction of the gliding apparatus, but by external friction between the buckling parts of the filament and the ambient. The rate at which the buckling amplitude initially grows should be limited by the accumulated compressive stress in the filament and the transverse friction with the substrate. If the latter were much smaller than the (logitudinal) internal friction of the gliding apparatus, we would expect a snapping-like transition into the buckled state, which we did not observe.

In our paper, we do not intend to evaluate the exact origin of the friction, quantifying the gliding force is the main objective. A linear force-velocity relation agrees with our observations. A detailed analysis of friction in cyanobacterial gliding would be an interesting direction for future work.

To make these considerations more clear, we rephrased the corresponding paragraph on page 7 & 8:

“…Thus we plot f/v over η in Figure 4 D, finding nearly identical values over about two decades. Since f and η are not correlated with v0, this is due to a correlation between f and η. This relation is remarkable in two aspects: On the one hand, it indicates that friction is mainly isotropic. This suggests that friction is governed by an isotropic process like bond friction or lubrication from the slime layer in the contact with the substrate, the latter being consistent with the observation that mutations deficient of slime secretion do not glide but exogenous addition of slime restores motility (Khayatan et al., 2015 ). In contrast, hydrodynamic drag from the surrounding bulk fluid (Man and Kanso, 2019 ), or the internal friction of the gliding apparatus would be expected to generate strongly anisotropic friction. If the latter was dominant, a snapping-like transition into the buckling state would be expected, rather than the continuously growing amplitude that is observed in experiments. On the other hand, it indicates that friction and propulsion forces…”

For readers from a non-fluids background, some additional discussion of the drag forces, and the forms of friction would help. For a freely gliding filament if f is the force density (per unit length), then steady gliding with a viscous frictional drag would suggest (as mentioned in the paper) f ∼ v! L η||. The critical buckling length is then dependent on f and on B the bending modulus. Here the effective drag is defined per length. I can see from this that if the active force is fixed, and the viscous component resulting from the frictional mechanism is fixed, the critical buckling length will not depend on the velocity (unless I am missing something in their argument), since the velocity is not a primitive variable, and is itself an emergent quantity.

We are not sure what “f ∼ v! L η||” means, possibly the spelling was corrupted in the forwarding of the comments.

We assumed an overdamped motion in which the friction force density ff (per unit length of the filament) is proportional to the velocity v0, i.e. ff ∼ η v0, with a friction coefficient η. Overdamped means that the friction force density is equal and opposite to the propulsion force density, so the propulsion force density is f ∼ ff ∼ η v0. The total friction and propulsion forces can be obtained by multiplication with the filament length

L, which is not required here. In this picture, v0 is an emergent quantity and f and η are assumed as given and constant. Thus, by observing v0, f can be inferred up to the friction coefficient η. Therefore, by using two descriptive variables, L and v0, with known B, the primitive variable η can be inferred by logistic regression, and f then follows from the overdamped equation of motion.

To clarify this, we revised the corresponding section on page 5 of the paper:

“The substrate contact requires lubrication from polysaccharide slime to enable bacteria to glide (Khayatan et al., 2015 ). Thus we assume an over- damped motion with co-linear friction, for which the propulsion force f and the free gliding velocity v0 of a filament are related by f = η v0, with a friction coefficient η. In this scenario, f can be inferred both from the observed Lc ∼ (f/B)−1/3 and, up to the proportionality coefficient η, from the observed free gliding velocity. Thus, by combining the two relations, one may expect also a strong correlation between Lc and v0. In order to test this relation for consistency with our data, we include v0 as a second regressor, by setting x = (L−Lc(v0))/∆Lc in Equation 1, with Lc(v0) = (η v0/(30.5722 B))−1/3, to reflect our expectation from theory (see below). Now, η rather than f is the only unknown, and its ensemble distribution will be determined in the regression. Figure 3 E,F show the buckling behavior…”

Reviewer 2:

In the presented manuscript, the authors first use structured microfluidic devices with gliding filamentous cyanobacteria inside in combination with micropipette force measurements to measure the bending rigidity of the filaments.

Next, they use triangular structures to trap the bacteria with the front against an obstacle. Depending on the length and rigidity, the filaments buckle under the propulsive force of the cells. The authors use theoretical expressions for the buckling threshold to infer propulsive force, given the measured length and stiffnesses. They find nearly identical values for both species, f ∼ (1.0 ± 0.6) nN/µm, nearly independent of the velocity.

Finally, they measure the shape of the filament dynamically to infer friction coefficients via Kirchhoff theory. This last part seems a bit inconsistent with the previous inference of propulsive force. Before, they assumed the same propulsive force for all bacteria and showed only a very weak correlation between buckling and propulsive velocity. In this section, they report a strong correlation with velocity, and report propulsive forces that vary over two orders of magnitude. I might be misunderstanding something, but I think this discrepancy should have been discussed or explained.

We regret the misunderstanding of the reviewer regarding the velocity dependence, which indicates that the manuscript should be improved to convey these relations correctly.

First, in the Buckling Measurements section, we did not assume the same propulsion force for all bacteria. The logistic regression yields an ensemble median for Lc (and thus an ensemble median for f ), along with the width ∆Lc of the distribution (and thus also the width of the distribution of f ). Our result f ∼ (1.0 ± 0.6) nN/µm indicates the median and the width of the distribution of the propulsion force densities across the ensemble of several hundred filaments used in the buckling measurements. The large variability of the forces found in the second part is consistently reflected by this very wide distribution of active forces detected in the logistic regression in the first part.

We did small modifications to the buckling theory paragraph to clarify that in the first part, a distribution of forces rather than a constant value is inferred (page 6)

“Inserting the population median and quartiles of the distributions of bending modulus and critical length, we can now quantify the distribution of the active force density for the filaments in the ensemble from the buckling measurements. We obtain nearly identical values for both species, f ∼ (1.0±0.6) nN/µm, where the uncertainty represents a wide distribution of f across the ensemble rather than a measurement error.”

The same holds, of course, when inferring the distribution of the friction coefficients (page 5):

“The substrate contact requires lubrication from polysaccharide slime to enable bacteria to glide (Khayatan et al., 2015 ). Thus we assume an over- damped motion with co-linear friction, for which the propulsion force f and the free gliding velocity v0 of a filament are related by f = η v0, with a friction coefficient η. In this scenario, f can be inferred both from the observed Lc ∼ (f/B)−1/3 and, up to the proportionality coefficient η, from the observed free gliding velocity. Thus, by combining the two relations, one may expect also a strong correlation between Lc and v0. In order to test this relation for consistency with our data, we include v0 as a second regressor, by setting x = (L−Lc(v0))/∆Lc in Equation 1, with Lc(v0) = (η v0/(30.5722 B))−1/3, to reflect our expectation from theory (see below). Now, η rather than f is the only unknown, and its ensemble distribution will be determined in the regression. Figure 3 E,F show the buckling behavior…”

The (naturally) wide distribution of force (and friction) leads to a distribution of Lc as well. However, due to the small exponent of 1/3 in the buckling threshold Lc ∼ f 1/3, the distribution of Lc is not as wide as the distributions of the individually inferred f or η. This is visualized in panel G of Figure 3, plotting Lc as a function of v0 (v0 is equivalent to f , up to a proportionality coefficient η). The natural length distribution, in contrast, is very wide. Therefore, the buckling propensity of a filament is most strongly characterized by its length, while force variability, which alters Lc of the individual, plays a secondary role.

In order to clarify this, we edited the last paragraph of the Buckling Measurements section on page 5 of the manuscript:

“…Within the characteristic range of observed velocities (1 − 3 µm/s), the median Lc depends only mildly on v0, as compared to its rather broad distribution, indicated by the bands in Figure 3 G. Thus a possible correlation between f and v0 would only mildly alter Lc. The natural length distribution (cf. Appendix 1—figure 1 ), however, is very broad, and we conclude that growth rather than velocity or force distributions most strongly impacts the buckling propensity of cyanobacterial colonies. Also, we hardly observed short and fast filaments of K. animale, which might be caused by physiological limitations (Burkholder, 1934 ).”

Second, in the Profile analysis section, we did not report a correlation between force and velocity. As can be seen in Figure 4—figure Supplement 1, neither the active force nor the friction coefficient, as determined from the analysis of individual filaments, show any significant correlation with the velocity. This is also written in the discussion (page 7):

We see no significant correlation between L or v0 and f or η, but the observed values of f and η cover a wide range (Figure 4 B, C and Figure 4—figure Supplement 1 ).

Note that this is indeed consistent with the logistic regression: Using v0 as a second regressor did not significantly reduce the width of the distribution of Lc as compared to the simple logistic regression, indicating that force and velocity are not strongly correlated.

In order to clarify this in the manuscript, we modified that part (page 7):

“…We see no significant correlation between L or v0 and f or η, but the observed values of f and η cover a wide range (Figure 4 B,C and Figure 4— figure Supplement 1 ). This is consistent with the logistic regression, where using v0 as a second regressor did not significantly reduce the width of the distribution of critical lengths or active forces. The two estimates of the friction coefficient, from logistic regression and individual profile fits, are measured in (predominantly) orthogonal directions: tangentially for the logistic regression where the free gliding velocity was used, and transversely for the evolution of the buckling profiles. Thus we plot f/v over η in Figure 4 D, finding nearly identical values over about two decades. Since f and η are not correlated with v0, this is due to a correlation between f and η. This relation is remarkable in two aspects: On the one hand, it indicates that friction is mainly isotropic…”

From a theoretical perspective, not many new results are presented. The authors repeat the well-known calculation for filaments buckling under propulsive load and arrive at the literature result of buckling when the dimensionless number (f L3/B) is larger than 30.6 as previously derived by Sekimoto et al in 1995 [1] (see [2] for a clamped boundary condition and simulations). Other theoretical predictions for pushed semi-flexible filaments [1–4] are not discussed or compared with the experiments. Finally, the Authors use molecular dynamics type simulations similar to [2–4] to reproduce the buckling dynamics from the experiments. Unfortunately, no systematic comparison is performed.

[1]        Ken Sekimoto, Naoki Mori, Katsuhisa Tawada, and Yoko Y Toyoshima. Symmetry breaking instabilities of an in vitro biological system. Physical review letters, 75(1):172, 1995.

[2]       Raghunath Chelakkot, Arvind Gopinath, Lakshminarayanan Mahadevan, and Michael F Hagan. Flagellar dynamics of a connected chain of active, polar, brownian particles. Journal of The Royal Society Interface, 11(92):20130884, 2014.

[3]       Rolf E Isele-Holder, Jens Elgeti, and Gerhard Gompper. Self-propelled worm-like filaments: spontaneous spiral formation, structure, and dynamics. Soft matter, 11(36):7181–7190, 2015.

[4]       Rolf E Isele-Holder, Julia J¨ager, Guglielmo Saggiorato, Jens Elgeti, and Gerhard Gompper. Dynamics of self-propelled filaments pushing a load. Soft Matter, 12(41):8495–8505, 2016.

We thank the reviewer for pointing us to these publications, in particular the work by Sekimoto we were not aware of. We agree with the referee that the calculation is straight forward (basically known since Euler, up to modified boundary conditions). Our paper focuses on experimental work, the molecular dynamics simulations were included mainly as a consistency check and not intended to generate the beautiful post-buckling patterns observed in references [2-4]. However, such shapes do emerge in filamentous cyanobacteria, and with the data provided in our manuscript, simulations can be quantitatively matched to our experiments, which will be covered by future work.

We included the references in the revision of our manuscript, and a statement that we do not claim priority on these classical theoretical results.

Introduction, page 2:

“…Self-Buckling is an important instability for self-propelling rod-like micro-organisms to change the orientation of their motion, enabling aggregation or the escape from traps (Fily et al., 2020; Man and Kanso, 2019; Isele-Holder et al., 2015; Isele-Holder et al., 2016 ). The notion of self-buckling goes back to work of Leonhard Euler in 1780, who described elastic columns subject to gravity (Elishakoff, 2000 ). Here, the principle is adapted to the self-propelling, flexible filaments (Fily et al., 2020; Man and Kanso, 2019; Sekimoto et al., 1995 ) that glide onto an obstacle. Filaments buckle if they exceed a certain critical length Lc ∼ (B/f)1/3, where B is the bending modulus and f the propulsion force density…”

Buckling theory, page 5:

“…The buckling of gliding filaments differs in two aspects: the propulsion forces are oriented tangentially instead of vertically, and the front end is supported instead of clamped. Therefore, with L < Lc all initial orientations are indifferently stable, while for L > Lc, buckling induces curvature and a resultant torque on the head, leading to rotation (Fily et al., 2020; Chelakkot et al., 2014; Sekimoto et al., 1995 ). Buckling under concentrated tangential end-loads has also been investigated in literature (de Canio et al., 2017; Wolgemuth et al., 2005 ), but leads to substantially different shapes of buckled filaments. We use classical Kirchhoff theory for a uniform beam of length L and bending modulus B, subject to a force density ⃗b = −f ⃗t − η ⃗v, with an effective active force density f along the tangent ⃗t, and an effective friction proportional to the local velocity ⃗v, analog to existing literature (Fily et al., 2020; Chelakkot et al., 2014; Sekimoto et al., 1995 )…”

Further on page 6:

“To derive the critical self-buckling length, Equation 5 can be linearized for two scenarios that lead to the same Lc: early-time small amplitude buckling and late-time stationary rotation at small and constant curvature (Fily et al., 2020; Chelakkot et al., 2014 ; Sekimoto et al., 1995 ). […] Thus, in physical units, the critical length is given by Lc = (30.5722 B/f)1/3, which is reproduced in particle based simulations (Appendix Figure 2 ) analogous to those in Isele-Holder et al. (2015, 2016).”

Discussion, page 7 & 8:

“…This, in turn, has dramatic consequences on the exploration behavior and the emerging patterns (Isele-Holder et al., 2015, 2016; Abbaspour et al., 2021; Duman et al., 2018; Prathyusha et al., 2018; Jung et al., 2020 ): (L/Lc)3 is, up to a numerical prefactor, identical to the flexure number (Isele-Holder et al., 2015, 2016; Duman et al., 2018; Winkler et al., 2017 ), the ratio of the Peclet number and the persistence length of active polymer melts. Thus, the ample variety of non-equilibrium phases in such materials (Isele-Holder et al., 2015, 2016; Prathyusha et al., 2018; Abbaspour et al., 2021 ) may well have contributed to the evolutionary success of filamentous cyanobacteria.”

Reviewer 3:

Summary:

This paper presents novel and innovative force measurements of the biophysics of gliding cyanobacteria filaments. These measurements allow for estimates of the resistive force between the cell and substrate and provide potential insight into the motility mechanism of these cells, which remains unknown.

We thank the reviewer for the positive evaluation of our work. We have revised the manuscript according to their comments and detail our replies and modifications next to the individual points below.

Strengths:

The authors used well-designed microfabricated devices to measure the bending modulus of these cells and to determine the critical length at which the cells buckle. I especially appreciated the way the authors constructed an array of pillars and used it to do 3-point bending measurements and the arrangement the authors used to direct cells into a V-shaped corner in order to examine at what length the cells buckled at. By examining the gliding speed of the cells before buckling events, the authors were able to determine how strongly the buckling length depends on the gliding speed, which could be an indicator of how the force exerted by the cells depends on cell length; however, the authors did not comment on this directly.

We thank the referee for the positive assessment of our work. Importantly, we do not see a significant correlation between buckling length and gliding speeds, and we also do not see a correlation with filament length, consistent with the assumption of a propulsion force density that is more or less homogeneously distributed along the filament. Note that each filament consists of many metabolically independent cells, which renders cyanobacterial gliding a collective effort of many cells, in contrast to gliding of, e.g., myxobacteria.

In response also to the other referees’ comments, we modified the manuscript to reflect more on the absence of a strong correlation between velocity and force/critical length. We modified the Buckling measurements section on page 5 of the paper:

“The substrate contact requires lubrication from polysaccharide slime to enable bacteria to glide (Khayatan et al., 2015 ). Thus we assume an over-damped motion with co-linear friction, for which the propulsion force f and the free gliding velocity v0 of a filament are related by f = η v0, with a friction coefficient η. In this scenario, f can be inferred both from the observed Lc ∼ (f/B)−1/3 and, up to the proportionality coefficient η, from the observed free gliding velocity. Thus, by combining the two relations, one may expect also a strong correlation between Lc and v0. In order to test this relation for consistency with our data, we include v0 as a second regressor, by setting x = (L−Lc(v0))/∆Lc in Equation 1, with Lc(v0) = (η v0/(30.5722 B))−1/3, to reflect our expectation from theory (see below). Now, η rather than f is the only unknown, and its ensemble distribution will be determined in the regression. Figure 3 E, F show the buckling behavior…”

Further, we edited the last paragraph of the Buckling measurements section on page 5 of the manuscript:

“Within the characteristic range of observed velocities (1 − 3 µm/s), the median Lc depends only mildly on v0, as compared to its rather broad distribution, indicated by the bands in Figure 3 G. Thus a possible correlation between f and v0 would only mildly alter Lc. The natural length distribution (cf. Appendix 1—figure 1 ), however, is very broad, and we conclude that growth rather than velocity or force distributions most strongly impacts the buckling propensity of cyanobacterial colonies. Also, we hardly observed short and fast filaments of K. animale, which might be caused by physiological limitations (Burkholder, 1934 ).”

We also rephrased the corresponding discussion paragraph on page 7:

“…Thus we plot f/v over η in Figure 4 D, finding nearly identical values over about two decades. Since f and η are not correlated with v0, this is due to a correlation between f and η. This relation is remarkable in two aspects: On the one hand, it indicates that friction is mainly isotropic. This suggests that friction is governed by an isotropic process like bond friction or lubrication from the slime layer in the contact with the substrate, the latter being consistent with the observation that mutations deficient of slime secretion do not glide but exogenous addition of slime restores motility (Khayatan et al., 2015 ). In contrast, hydrodynamic drag from the surrounding bulk fluid (Man and Kanso, 2019 ), or the internal friction of the gliding apparatus would be expected to generate strongly anisotropic friction. If the latter was dominant, a snapping-like transition into the buckling state would be expected, rather than the continuously growing amplitude that is observed in experiments. On the other hand, it indicates that friction and propulsion forces…”

Weaknesses:

There were two minor weaknesses in the paper.

First, the authors investigate the buckling of these gliding cells using an Euler beam model. A similar mathematical analysis was used to estimate the bending modulus and gliding force for Myxobacteria (C.W. Wolgemuth, Biophys. J. 89: 945-950 (2005)). A similar mathematical model was also examined in G. De Canio, E. Lauga, and R.E Goldstein, J. Roy. Soc. Interface, 14: 20170491 (2017). The authors should have cited these previous works and pointed out any differences between what they did and what was done before.

We thank the reviewer for pointing us to these references. The paper by Wolgemuth is theoretical work, describing A-motility in myxobacteria by a concentrated propulsion force at the rear end of the bacterium, possibly stemming from slime extrusion. This model was a little later refuted by [A3], who demonstrated that focal adhesion along the bacterial body and thus a distributed force powers A-motility, a mechanism that has by now been investigated in great detail (see [A10]). The paper by Canio et al. contains a thorough theoretical analysis of a filament that is clamped at one end and subject to a concentrated tangential load on the other. Since both models comprise a concentrated end-load rather than a distributed propulsion force density, they describe a substantially different motility mechanism, leading also to substantially different buckling profiles. Consequentially, these models cannot be applied to cyanobacterial gliding.

We included both citations in the revision and pointed out the differences to our work in the introduction (page 2):

“…A few species appear to employ a type-IV-pilus related mechanism (Khayatan et al., 2015; Wilde and Mullineaux, 2015 ), similar to the better- studied myxobacteria (Godwin et al., 1989; Mignot et al., 2007; Nan et al., 2014; Copenhagen et al., 2021; Godwin et al., 1989 ), which are short, rod-shaped single cells that exhibit two types of motility: S (social) motility based on pilus extension and retraction, and A (adventurous) motility based on focal adhesion (Chen and Nan, 2022 ) for which also slime extrusion at the trailing cell pole was earlier postulated as mechanism (Wolgemuth et al., 2005 ). Yet, most gliding filamentous cyanobacteria do not exhibit pili and their gliding mechanism appears to be distinct from myxobacteria (Khayatan et al., 2015 ).”

And in Buckling theory, page 5:

“….The buckling of gliding filaments differs in two aspects: the propulsion forces are oriented tangentially instead of vertically, and the front end is supported instead of clamped. Therefore, with L < Lc all initial orientations are indifferently stable, while for L > Lc, buckling induces curvature and a resultant torque on the head, leading to rotation (Fily et al., 2020; Chelakkot et al., 2014; Sekimoto et al., 1995 ). Buckling under concentrated tangential end-loads has also been investigated in literature (de Canio et al., 2017; Wolgemuth et al., 2005 ), but leads to substantially different shapes of buckled filaments.”

The second weakness is that the authors claim that their results favor a focal adhesion-based mechanism for cyanobacterial gliding motility. This is based on their result that friction and adhesion forces correlate strongly. They then conjecture that this is due to more intimate contact with the surface, with more contacts producing more force and pulling the filaments closer to the substrate, which produces more friction. They then claim that a slime-extrusion mechanism would necessarily involve more force and lower friction. Is it necessarily true that this latter statement is correct? (I admit that it could be, but is it a requirement?)

We thank the referee for raising this interesting question. Our claim regarding slime extrusion is based on three facts: i. mutations deficient of slime extrusion do not glide, but start gliding as soon as slime is provided externally [A4]. ii. A positive correlation between speed and slime layer thickness was observed in Nostoc [A11]. iii. The fluid mechanics of lubricated sliding contacts is very well understood and predicts a decreasing resistance with increasing layer thickness.

We included these considerations in the revision of our manuscript (page 8):

“…it indicates that friction and propulsion forces, despite being quite variable, correlate strongly. Thus, generating more force comes, inevitably, at the expense of added friction. For lubricated contacts, the friction coefficient is proportional to the thickness of the lubricating layer (Snoeijer et al., 2013 ), and we conjecture active force and drag both increase due to a more intimate contact with the substrate. This supports mechanisms like focal adhesion (Mignot et al., 2007 ) or a modified type-IV pilus (Khayatan et al., 2015 ), which generate forces through contact with extracellular surfaces, as the underlying mechanism of the gliding apparatus of filamentous cyanobacteria: more contacts generate more force, but also closer contact with the substrate, thereby increasing friction to the same extent. Force generation by slime extrusion (Hoiczyk and Baumeister, 1998 ), in contrast, would lead to the opposite behavior: More slime generates more propulsion, but also reduces friction. Besides fundamental fluid-mechanical considerations (Snoeijer et al., 2013 ), this is rationalized by two experimental observations: i. gliding velocity correlates positively with slime layer thickness (Dhahri et al., 2013 ) and ii. motility in slime-secretion deficient mutants is restored upon exogenous addition of polysaccharide slime. Still we emphasize that many other possibilities exist. One could, for instance, postulate a regulation of the generated forces to the experienced friction, to maintain some preferred or saturated velocity.”

Related to this, the authors use a model with isotropic friction. They claim that this is justified because they are able to fit the cell shapes well with this assumption. How would assuming a non-isotropic drag coefficient affect the shapes? It may be that it does equally well, in which case, the quality of the fits would not be informative about whether or not the drag was isotropic or not.

The referee raises another very interesting point. Given the typical variability and uncertainty in experimental measurements (cf. error Figure 4 A), a model with a sightly anisotropic friction could be fitted to the observed buckling profiles as well, without significant increase of the mismatch. Yet, strongly anisotropic friction would not be consistent with our observations.

Importantly, however, we did not conclude on isotropic friction based on the fit quality, but based on a comparison between free gliding and early buckling (Figure 4 D). In early buckling, the dominant motion is in transverse direction, while longitudinal motion is insignificant, due to geometric reasons. Thus, independent of the underlying model, mostly the transverse friction coefficiont is inferred. In contrast, free gliding is a purely longitudinal motion, and thus only the friction coefficient for longitudinal motion can be inferred. These two friction coefficients are compared in Figure 4 D. Still, the scatter of that data would allow to fit a certain anisotropy within the error margins. What we can exclude based on out observation is the case of a strongly anisotropic friction. If there is no ab-initio reason for anisotropy, nor a measurement that indicates it, we prefer to stick with the simplest

assumption. We carefully chose our wording in the Discussion as “mainly isotropic” rather

than “isotropic” or “fully isotropic”.

We added a small statement to the Discussion on page 7 & 8:

“... Thus we plot f/v over η in Figure 4 D, finding nearly identical values over about two decades. Since f and η are not correlated with v0, this is due to a correlation between f and η. This relation is remarkable in two aspects: On the one hand, it indicates that friction is mainly isotropic. This suggests that friction is governed by an isotropic process like bond friction or lubrication from the slime layer in the contact with the substrate, the latter being consistent with the observation that mutations deficient of slime secretion do not glide but exogenous addition of slime restores motility (Khayatan et al., 2015 ). In contrast, hydrodynamic drag from the surrounding bulk fluid (Man and Kanso, 2019 ), or the internal friction of the gliding apparatus would be expected to generate strongly anisotropic friction. If the latter was dominant, a snapping-like transition into the buckling state would be expected, rather than the continuously growing amplitude that is observed in experiments. On the other hand, it indicates that friction and propulsion forces ...”

Recommendations for the authors

The discussion regarding how the findings of this paper imply that cyanobacteria filaments are propelled by adhesion forces rather than slime extrusion should be improved, as this conclusion seems questionable. There appears to be an inconsistency with a buckling force said to be only weakly dependent on the gliding velocity, while its ratio with the velocity correlates with a friction coefficient. Finally, data and source code should be made publicly available.

In the revised version, we have modified the discussion of the force generating mechanism according to the reviewer suggestions. The perception of inconsistency in the velocity dependence of the buckling force was based on a misunderstanding, as we detailed in our reply to the referee. We revised the corresponding section to make it more clear. Data and source code have been uploaded to a public data repository.

Reviewer #2 (recommendations for the authors)

Despite eLife policy, the authors do not provide a Data Availability Statement. For the presented manuscript, data and source code should be provided “via trusted institutional or third-party repositories that adhere to policies that make data discoverable, accessible and usable.” https://elifesciences.org/inside-elife/51839f0a/for-authors-updates- to-elife-s-data-sharing-policies

Most of the issues in this reviewer’s public review should be easy to correct, so I would strongly support the authors to provide an amended manuscript.

We added the Data Availability Statement in the amended manuscript.

References

[A1] E. Hoiczyk and W. Baumeister. “The junctional pore complex, a prokaryotic secretion organelle, is the molecular motor underlying gliding motility in cyanobacteria”. In: Curr. Biol. 8.21 (1998), pp. 1161–1168. doi: 10.1016/s0960-9822(07)00487-3.

[A2] N. Read, S. Connell, and D. G. Adams. “Nanoscale Visualization of a Fibrillar Array in the Cell Wall of Filamentous Cyanobacteria and Its Implications for Gliding Motility”. In: J. Bacteriol. 189.20 (2007), pp. 7361–7366. doi: 10.1128/jb.00706- 07.

[A3] T. Mignot, J. W. Shaevitz, P. L. Hartzell, and D. R. Zusman. “Evidence That Focal Adhesion Complexes Power Bacterial Gliding Motility”. In: Science 315.5813 (2007), pp. 853–856. doi: 10.1126/science.1137223.

[A4] Behzad Khayatan, John C. Meeks, and Douglas D. Risser. “Evidence that a modified type IV pilus-like system powers gliding motility and polysaccharide secretion in filamentous cyanobacteria”. In: Mol. Microbiol. 98.6 (2015), pp. 1021–1036. doi: 10.1111/mmi.13205.

[A5] Tilo Pompe, Martin Kaufmann, Maria Kasimir, Stephanie Johne, Stefan Glorius, Lars Renner, Manfred Bobeth, Wolfgang Pompe, and Carsten Werner. “Friction- controlled traction force in cell adhesion”. In: Biophysical journal 101.8 (2011), pp. 1863–1870.

[A6] Hirofumi Wada, Daisuke Nakane, and Hsuan-Yi Chen. “Bidirectional bacterial gliding motility powered by the collective transport of cell surface proteins”. In: Physical Review Letters 111.24 (2013), p. 248102.

[A7] Jo¨el Tchoufag, Pushpita Ghosh, Connor B Pogue, Beiyan Nan, and Kranthi K Mandadapu. “Mechanisms for bacterial gliding motility on soft substrates”. In: Proceedings of the National Academy of Sciences 116.50 (2019), pp. 25087–25096.

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[A11] Samia Dhahri, Michel Ramonda, and Christian Marliere. “In-situ determination of the mechanical properties of gliding or non-motile bacteria by atomic force microscopy under physiological conditions without immobilization”. In: PLoS One 8.4 (2013), e61663.

AI hype in material science. Google shows an allergy to being pointed to fundamental issues. Another example of pointing out obvious mistakes or issues is not only not welcomed but actively ignored (vgl examples of AI as search engine, where pointing out the first three of the top ten results were wrong resulted in being shouted at, or the blogpost writing video in which presented 'facts' were 1 wikipedia click away from being shown made-up. False citations etc.) It's not bad per se that AI can be wrong, no tool is infallible, but the problem is n:: the extreme asymmetry between the machine effort needed to make stuff up in heaps, and the human effort needed to wade through all the crap and point that out. Vgl [[Spammy handelings assymmetrie 20201220072726]] [[It is easier to F things up than fix things 20180610073041]] Creating entropy is way easier than reducing it, always. We don't need our tools to create ever more entropy on purpose, if only we can reduce it again. Our tools need to help decrease entropy. Decreasing entropy is the definition of life, increasing it should be anathema. Esp if it is unclear where a tool is increasing entropy.

dont sleep

Author response:

We extend our gratitude to the two reviewers and the editors at eLife for their meticulous examination of our manuscript, as well as for their valuable feedback and positive assessment. We are particularly pleased to observe in both the reviews and the editorial evaluation the recognition of the importance of our findings. Through this provisional response, we wish to convey to the editors, reviewers, and the readership of eLife our intention to enhance the paper by incorporating a detailed description of the sections pertaining to MAD analysis, data interpretation with combined HS-AFM and PCA methods, and specific portions of the discussions. This will involve editing the manuscript accordingly and providing separate explanations in the "author response”. We acknowledge that such additions will strengthen the comprehensiveness of our work and render it more self-contained.

Moreover, in alignment with the recommendations from the review team, we will provide a thorough discussion of published data and offer a clearer explanation of our utilized methods, thereby providing a more robust foundation for our conclusions.

eLife assessment

In this manuscript the authors present high-speed atomic force microscopy (HSAFM) to analyze real-time structural changes in actin filaments induced by cofilin binding. This important study enhances our understanding of actin dynamics which plays a crucial role in a broad spectrum of cellular activities. However, some technical questions remain, making the data interpretation incomplete.

Reviewer #1 (Public Review):

The authors provided a detailed analysis of the real-time structural changes in actin filaments resulting from cofilin binding, using High-Speed Atomic Force Microscopy (HSAFM). The cofilin family controls the lifespan of actin filaments in the cells by severing the filament and promoting depolymerization. Understanding the effects of cofilin on actin filament structure is critical. It is widely acknowledged that cofilin binding significantly shortens the pitch of the actin helix. The authors previously reported (1) that this shortening extends to the unbound region of the actin filament on the pointed end side of the cluster. In this study, the authors presented substantially improved AFM images and provide detailed accounts of the dynamics observed. It was found that a minimal cofilin-binding cluster, consisting of 2-4 molecules, could induce changes in the helical parameters over one or more actin crossover repeats. Adjacent to the cofilin-binding clusters, the actin crossovers were observed to shortened within seconds, and this shortening was limited to one side of the cluster. Additionally, the phosphate binding to the actin filament was observed to stabilize the helical twist, suggesting a mechanism in which cofilin preferentially binds to ADP-bound actin filaments. These findings significantly advance our understanding of actin filament dynamics which is essential for a wide of cellular processes.<br /> However, I propose that the sections about MAD and certain parts of the discussions need substantial revisions.

MAD analysis<br /> The authors have presented findings that the mean axial distance (MAD) within actin filaments exhibits a significant dependency on the helical twist, a conclusion not previously derived despite extensive analyses through electron microscopy (EM) and molecular dynamics (MD) simulations. Notably, the MAD values span from 4.5 nm (8.5 pairs per half helical pitch, HHP) to 6.5 nm (4.5 pairs/HHP) as depicted in Figure 3C. The inner domain (ID) of actin remains very similar across C, G, and F forms(2, 3), maintaining similar ID-ID interactions in both cofilactin and bare actin filaments, keeping the identical axial distance between subunits in the both states. This suggests that the ID is unlikely to undergo significant structural changes, even with fluctuations in the filament's twist, keeping the ID-ID interactions and the axial distances. The broad range of MAD values reported poses a challenge for explanation. A careful reassessment of the MAD analysis is recommended to ensure accuracy.<br /> In determining axial distances, the authors extracted measurements from filament line profiles. It is advised to account for potential anomalies such as missing peaks or pseudo peaks, which could arise from noise interference. An example includes the observation of three peaks in HHP6 of Figure Supplement 5C, corresponding to 4.5 pairs. Peak intervals measured from the graph were 5, 11.8, 8.7, and 5.7 nm. The second region (11.8 nm) appears excessively long. If one peak is hidden in the second region, the MAD becomes 5.5 nm.

Compiling histograms of axial distances (ADs) rather than focusing solely on MAD may provide deeper insights. If the AD is too long or too short, the authors should suspect the presence of missing peaks or pseudo-peaks due to noise. If 4.4 or 5.5 pairs/HHP regions tend to contain missing peaks and 7.5-8.5 pairs/HHP regions tend to contain pseudo peaks, this may explain the MAD dependency on the helical twist.

Additionally, Figure 3E indicates a first decay constant of 0.14 seconds, substantially shorter than the frame rate (0.5 sec/frame). This suggests significant variations in line profiles between frames, attributable either to overly rapid dynamics or a low signal-to-noise ratio. Implementing running frame averages (of 2-3 frames) is recommended to distinguish between these scenarios. If the dynamics are indeed fast, the averaged frame's line profile may degrade, complicating peak identification. Conversely, if poor signal-to-noise ratio is the cause, averaging frames could facilitate peak detection. In the latter case, the authors can find the optimal number of frame averages and obtain better line profiles with fewer missing and pseudo-peaks.

Discussions<br /> The authors suggest a strong link between the C-form of actin and the formation of a short pitch helix. However, Oda et al. (3) have demonstrated that the C-form is highly unstable in the absence of cofilin binding, casting doubt on the possibility of the C-form propagating without cofilin binding. Moreover, in one strand of the cofilactin, interactions between actin subunits are limited to those between the inner domains (ID-ID interactions), which are quite similar to the interactions observed in bare actin filaments. This similarity implies that ID-ID interactions alone are insufficient to determine the helical parameters, suggesting that the presence of cofilin is essential for the formation of the short pitch helix in the cofilactin filament. Thus, crossover repeats are not necessarily shortened even if the actin form is C-form.

Narita (4) proposes that the facilitation of cofilin binding may occur through a shortening in the helix pitch, independent of a change to the C-form of actin. Furthermore, the dissociation of the D-loop from an adjacent actin subunit leads directly to the transition of actin to the G-form, which is considered the most stable configuration for the actin molecule (3).

The mechanism by which the shortened pitch propagates remains a critical and unresolved issue. It appears that this propagation is not a result of the C-form's propagation but likely involves an unidentified mechanism. Identifying and understanding this mechanism represents an essential direction for future research.

(1) K. X. Ngo et al., a, Cofilin-induced unidirectional cooperative conformational changes in actin filaments revealed by high-speed atomic force microscopy. eLife 4, (2015).<br /> (2) K. Tanaka et al., Structural basis for cofilin binding and actin filament disassembly. Nature communications 9, 1860 (2018).<br /> (3) T. Oda et al., Structural Polymorphism of Actin. Journal of molecular biology 431, 3217-3228 (2019).<br /> (4) A. Narita, ADF/cofilin regulation from a structural viewpoint. Journal of muscle research and cell motility 41, 141-151 (2020).

Reviewer #2 (Public Review):

Summary:

This study by Ngo et al. uses mostly high-speed AFM to estimate conformational changes within actin filaments, as they get decorated by cofilin. The authors build on their earlier study (Ngo et al. eLife 2015) where they used the same technique to monitor the expansion of cofilin clusters on actin filaments, and the propagation of the associated conformational changes in the filament (reduction of the helical pitch). Here, they propose a higher-resolution description of the binding of cofilin to actin filaments.

Strengths:

The high speed AFM technique used here is quite original to address this question, compared to classical light and electron microscopy techniques. It can certainly bring valuable information as it provides a high spatial resolution while monitoring live events. Also, in this paper, a nice effort was made to make the 3D structures and conformational changes clear and understandable.

Weaknesses:

The paper also has a number of limitations, which I detail below.

In addition to AFM, the authors also propose a Principal Component Analysis (PCA) of exisiting structural data on actin protomers. However, this part seems very similar to another published work by others (Oda et al. JMB 2019), which is not even cited.

The asymmetrical growth of cofilin clusters has so far only been seen using AFM, by the same authors (Ngo et al. eLife 2015). Using fluorescent microscopy, others have reported a very symmetrical expansion of cofilin clusters (Wioland et al. Curr Biol 2017). This is not mentioned at all, here. It should be discussed, and explanations for this discrepancy could be proposed.

Regarding the AFM technique, I have the following concerns.

The filaments appear densely packed on the surface, and even clearly in register in some images (if not most images, e.g., Figs 3A, 4BC, 5A). Why is that? Isn't there a risk that this could affect the result? This suggests there is some interaction between the filaments.

The properties of the lipid layer and its interaction with the actin filaments are not clear at all. A poor control of these interactions is a problem if one aims to measure conformational changes at high resolution. The strength of the interaction appears tuned by the ratio of lipids put on the surface to change its electrostatic charge. A strong attachement likely does more than suppress torsional motion (as claimed in Fig 8A). It may also hinder cofilin binding in several ways (lower availability of binding sites on the filament facing the surface, electrostatic interactions between cofilin and the surface, etc.)

How do we know that the variations over time are not mostly experimental noise, i.e. variations between repeats of the same measurement? As shown in Fig 3, correlation is mostly lost from one image to the next, and rather stable after that.

The identification of cofilactin regions relies on the additional height of the "peaks", due to the presence of cofilin. It thus seems that cofilin is detected every half helical pitch (HHP), but not in between, thereby setting the resolution for the localization of cluster borders to one HHP. It thus seems difficult to claim that there is a change in helicity without cofilin decoration over this distance. In Fig 7, the change in helicity could be due to cofilin decoration that is undetected because cofilins have not yet reached the next peak.

move working Virtual Machine

Author response:

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

eLife assessment

This study provides valuable insights into how chromatin-bound PfMORC controls gene expression in the asexual blood stage of Plasmodium falciparum. By interacting with key nuclear proteins, PfMORC appears to affect expression of genes important for host invasion and subtelomeric var genes. Correlating transcriptomic data with in vivo chromatin insights, the study provides solid evidence for the central role of PfMORC in epigenetic transcriptional regulation through modulation of chromatin compaction.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The study provides valuable insights into the role of PfMORC in Plasmodium's epigenetic regulation, backed by a comprehensive methodological approach. The overarching goal was to understand the role of PfMORC in epigenetic regulation during asexual blood stage development, particularly its interactions with ApiAP2 TFs and its potential involvement in the regulation of genes vital for Plasmodium virulence. To achieve this, they conducted various analyses. These include a proteomic analysis to identify nuclear proteins interacting with PfMORC, a study to determine the genome-wide localization of PfMORC at multiple developmental stages, and a transcriptomic analysis in PfMORCHA-glmS knockdown parasites. Taken together, this study suggests that PfMORC is involved in chromatin assemblies that contribute to the epigenetic modulation of transcription during the asexual blood stage development.

Strengths:

The study employed a multi-faceted approach, combining proteomic, genomic, and transcriptomic analyses, providing a holistic view of PfMORC's role. The proteomic analysis successfully identified several nuclear proteins that may interact with PfMORC. The genome-wide localization offered valuable insights into PfMORC's function, especially its predominant recruitment to subtelomeric regions. The results align with previous findings on PfMORC's interaction with ApiAP2 TFs. Notably, the authors meticulously contextualized their findings with prior research, including pre-prints, adding credibility to their work.

Weaknesses:

While the study identifies potential interacting partners and loci of binding, direct functional outcomes of these interactions remain an inference. The authors heavily rely on past research for some of their claims. While it strengthens some assertions, it might indicate a lack of direct evidence in the current study for particular aspects. The declaration that PfMORC may serve as an attractive drug target is substantial. While the data suggests its involvement in essential processes, further studies are required to validate its feasibility as a drug target.

Reviewer #2 (Public Review):

Summary:

This is a paper entitled "Plasmodium falciparum MORC protein modulates gene expression through interaction with heterochromatin" describes the role of PfMORC during the intra-erythrocytic cycle of Plasmodium falciparum. Garcia et al. investigated the PfMORC-interacting proteins and PfMORC genomic distribution in trophozoites and schizonts. They also examined the transcriptome of the parasites after partial knockdown of the transcript.

Strengths:

This study is a significant advance in the knowledge of the role of PfMORC in heterochromatin assembly. It provides an in-depth analysis of the PfMORC genomic localization and its correlation with other chromatin marks and ApiAP2 transcription factor binding.

Weaknesses:

However, most of the conclusions are based on the function of interacting proteins and the genomic localization of the protein. The authors did not investigate the direct effects of PfMORC depletion on heterochromatin marks. Furthermore, the results of the transcriptomic analysis are puzzling as 50% of the transcripts are downregulated, a phenotype not expected for a heterochromatin marker.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Suggestions for improved or additional experiments, data, or analyses.

• Figure 1A and Table 1: the authors should incorporate a volcano plot in their proteomic results presentation. This graphical representation can provide a more intuitive grasp of the most relevant proteins associated with PfMORC in terms of both their abundance and significance. It will aid in swiftly pinpointing proteins with the most notable differential associations. This will complement the comprehensive overview provided by the authors, referencing past research where PfMORC was detailed.

We thank the reviewer for the suggestion. We agree with the reviewer that the volcano plot we now provide does indeed bring comprehensive information on associations between PfMORC and other cellular proteins. The volcano plot presented in the revised manuscript as Figure 1A, was generated using the normalized MS/MS counts from the anti-GFP and 3D7 (control) proteomics datasets (n=3). The potential PfMORC interacting proteins were determined using the fold changes and p-values between the two datasets, as provided in Table 1.

Several protein interactors were strongly supported by statistical analysis (p-value), while others showed weaker p-value due to variability between replicates. Indeed, the total number of proteins identified in the three replicates, shown in the Venn diagram (Supplemental Figure 1D), exhibits a good overlap between the replicates but a lower number of identified proteins in the GFP-E1 sample. This variability was observed also in the statistical analysis. Indeed, by analyzing the GFP/3D7 ratios, some proteins have a significant difference in abundance (fold change greater than 1.5x) in one of the groups but do not meet the statistical threshold. For more clarity, we have included the -log p-value for the proteins listed in Table 1.

Overall, these results demonstrate that many ApiAP2 proteins and several chromatin-associated factors interact with PfMORC.

• Given the plethora of proteins detected in the PfMORC eluate, it raises the question of how many are genuine MORC interactors versus those that are merely nearby molecules acting adjacently. These might incidentally end up in the immunoprecipitate due to unintended interactions with DNA or chromatin. While the M&M section mentions that the beads were thoroughly washed, there is no specification about the washing buffer or its stringency (i.e., salinity level). At higher salinities, one could isolate core complexes of interactors associated with DNA or even RNA carryover.

We apologize for this omission and have now added the buffer composition used to wash the beads. This section now reads "To perform the co-immunoprecipitation we followed the manufacturer protocol (ChromoTek, gta-20). Samples were lysed in modified RIPA buffer (50 mM Tris, pH 7.5, 150 mM NaCl, 0.5% sodium deoxycholate, 1% Nonidet P-40, 10 µg/ml aprotinin, 10 µg/ml leupeptin, 10 µg/ml, 1 mM phenylmethylsulfonyl fluoride, benzamidine) for 30 min on ice. The lysate was precleared with 50 µl of protein A/G-Agarose beads at 4°C for 1 h and clarified by centrifugation at 10,000 × g for 10 min. The precleared lysate was incubated overnight with an anti-GFP antibody using anti-GFP-Trap-A beads (ChromoTek, gta-20). The magnetic beads were then pelleted using a magnet (Invitrogen) and washed 3 times with wash buffer (10 mM Tris/Cl pH 7.5, 150 mM NaCl, 0.05 % Nonidet™ P40 Substitute, 0.5 mM EDTA)."

We used the same salt concentration for immunoprecipitation as was used in the lysis buffer to minimize the binding of non-specific proteins. The wash buffer composition is updated in the revised manuscript. The immunoprecipitations were done in biological triplicates to ensure reproducibility and statistical support. A number of proteins are common across all three replicates. We also used wild-type parasites (non-GFP) as a negative control to eliminate non-specific hits, and we used a log2-fold change ≥1.5 relative to wild type parasites as our cutoff between the comparison groups.

We believe that these conditions provide the stringency required to identify high confidence PfMORC interacting proteins, although this still leaves a possibility for additional lower affinity interactions. Future studies will certainly follow up candidate interaction partners to better define this complex. However, the complexity of the complex resembles that reported previously in Toxoplasma gondii (Farhat et al. 2020, Nat Microbiol) as well another report on the PfMORC complexes: https://elifesciences.org/reviewed-prepri nts/92499

• The authors demonstrate that PfMORC creates distinct peaks in and around HP1-bound areas (Figure 2F), hinting at a specific role for PfMORC in heterochromatin compaction, boundary definition, and gene silencing. This pattern is clearly depicted in an example in Figure 2F. It would be beneficial to know if this enrichment profile is replicated elsewhere and, if so, it would be worthwhile to quantify it.

This is an excellent point. Yes, this pattern is seen across the entire genome, where PfMORC is apposed to PfHP1-bound heterochromatic regions. As indicated in the manuscript, we have quantified this effect genome-wide; however, since we already display compiled data for Chromosome 2 (at both chromosome ends) pertaining to the position of PfMORC relative to PfHP1 we do not feel it is essential to provide such a figure for the entire genome as it does not alter the central message of our manuscript. Figure 2F is representative of the genome-wide distribution of PfMORC relative to PfHP1. The raw genome-wide data are available in Supplementary Information for further inspection of specific loci on other chromosomes.

Recommendations for improving the writing and presentation.

MAIN TEXT

Panel e, referenced both in the main text and legend, is missing from Figure 4. This missing panel represents a significant finding of the study, highlighting according to the authors a low correlation between ChIP-seq gene targets and RNA-seq DEGs. This observation implies that PfMORC's global occupancy is more aligned with shaping chromatin architecture than directly regulating specific gene targets. In light of this, the authors should rephrase parts of their manuscript (including abstract and title) to avoid suggesting that PfMORC acts primarily (directly) as a gene regulator, emphasizing instead its role in influencing the topological structure of chromosomes.

We have modified the title as suggested by the reviewer to more accurately reflect that PfMORC modulates chromatin architecture rather than acting as a direct regulator of specific genes. Our new title is: A Plasmodium falciparum MORC protein complex modulates epigenetic control of gene expression through interaction with heterochromatin

We apologize for the omission of Figure 4e, which is now included in the revised manuscript. We found PfMORC occupancy on all chromosomes at subtelomeric regions, which are known to harbor genes related to immune evasion and antigenic variation (including most of the var genes). This study is also in agreement with Bryant et al. (PMID 32816370) which reported PfMORC occupancy along with PfISW1 at var gene promoters. PfMORC has also been identified in complexes with various ApiAP2 proteins in a proteome-wide study (Hillier et al. Cell Rep, PMID 31390575), as well as in immunoprecipitations of PfAP2-G2 (Singh et al., Mol Micro, PMID 33368818) and PfAP2-P (Subudhi et al., Nat Microbiol, PMID 37884813). The recent study by Subudhi et al. reports that PfAP2-P is involved in the regulation of var gene expression, antigenic variation, trophozoite development and parasite egress. It is therefore possible that PfMORC may have different effects on transcriptional regulation through interactions with different ApiAP2 transcription factors. Our comparison of PfMORC with known ApiAP2 protein occupancy reveals a high level of overlap, indicating that PfMORC may affect gene expression in various ways throughout the asexual cycle. Additionally, Hillier et al. show that PfMORC interaction is not limited to ApiAP2 but also implicates several other chromatin remodellers, which is consistent with our own results. We do not imply direct regulation of transcription via PfMORC in our manuscript. To the contrary, we suggest that it interacts with heterochromatin and thereby plays a role in the epigenetic control of asexual blood stage transcriptional regulation which is also clarified in the revised abstract.

Another limitation of differential gene expression was use of the glmS ribozyme system, which resulted in only 50% depletion of the PfMORC transcript. There may still be enough PfMORC to rescue the gene expression we could not detect correctly. Therefore, it is challenging to interpret the function of PfMORC in only chromatin architecture but not in gene expression.

If we believe that PfMORC in Plasmodium isn't mainly adjusting gene expression, the authors' suggestion that MORC is targeted by some AP2s becomes puzzling. How do we make sense of these different ideas? The authors need to clarify this to maintain consistency in their findings.

Based on our data, we hypothesize that PfMORC acts as an accessory protein for ApiAP2 transcription factors. In a number of studies, including ours and the concurrent publication in eLife (https://elifesciences.org/reviewed-preprints/92499), PfMORC co-IPed with several ApiAP2 proteins, suggest it has multiple functions. In our previous study we showed that PfMORC expression is highest in mid and late asexual stages. A comparison of the PfMORC occupancy with 6 ApiAP2 (having different expression profile) suggest plasticity in PfMORC function. We have revised our discussion to make this hypothesis more transparent for the readers.

The authors should cite Farhat et al. 2020 (Extended Data Fig. 1a), as it similarly identified 3 different ELM2-containing proteins in Toxoplasma MORC-associated complexes. This previous work provides context and supports the observations made with PfMORC in this study.

Thank you for the suggestion and pointing out this omission. We have indeed cited the work of the Farhat group in the original manuscript and have now included this additional reference to corroborate the text and provide further support to our conclusions.

Minor corrections to the text and figures.

• Panel e is missing from Figure 4.

As mentioned above Panel e is now included in Figure 4.

• The captions are very minimally detailed. An effort must be made to better describe the panels as well as which statistical tests were used. As it stands, this is not really up to standard.

We have elaborated the captions with more detailed descriptions, and we now provide additional information where further clarification was necessary.

Reviewer #2 (Recommendations For The Authors):

• The study lacks a direct correlation between the inferred function of PfMORC and the heterochromatin state of the genome after its depletion. It would be interesting to perform chip-seq on known heterochromatin markers such as H3K9me3, HP1 or H3K36me2/3 to measure the consequences of PfMORC depletion on global heterochromatin and its boundaries.

While the proposed experiments are certainly interesting, they are beyond the scope of this study. The current manuscript is focused on PfMORC occupancy, its interacting partners, and its impact on differential gene regulation after PfMORC depletion in asexual parasites. Nonetheless, we did in fact compared the PfMORC occupancy with that of various heterochromatin markers (H2A.Z, H3K9ac, H3K4me3, H3K27ac, H3K18ac, H3K9me3, H3K36me2/3, H4K20me3, and H3K4me1) at 30hpi and 4hpi time points. These data are presented in Supplemental Figure 9. We did not find any significant colocalization, but documented the presence of PMORC in H3K36me2 depleted regions.

• The PfMORC depletion was performed using a glms-based genetic system and the reviewer did not find any quantification of the depletion level at 24h or 36h. This is particularly important as the authors present RNA-seq data at these time points.

We would like to clarify that RNA-seq was performed on 32hpi parasites after approximately 48 h treatment with 2.5 mM GlcN. At the trophozoite and schizont stage, PfMORC expression is high, which is why we selected these time points for RNA-seq (32hpi) and ChIP-seq (30hpi and 40hpi). PfMORC protein expression after GlcN treatment is analyzed in our previous paper (Singh et al., Sci Rep, PMID 33479315), where treatment with 2.5 mM GlcN leads to 50% reduction in PfMORC transcript at 32hpi. This is referenced in the Results section; we decided not to repeat the same experiment in the current manuscript.

• The authors performed a thorough analysis of the correlations between ApiAP2 binding, histone modification and genomic localization of PfMORC (their chip-seq data). However, they found an inverse relationship between H3K36me2, a known histone repressive mark, and PfMORC genomic localization. This is particularly surprising when PfMORC itself is presented as a heterochromatin marker. The wording of this data is confusing in the results section (lines 257-258) and never discussed further. This important data should at least be discussed to make sense of this apparent contradiction.

H3K36me2 indeed acts as a global repressive mark in P. falciparum. However, our hypothesis implies that PfMORC not only overlaps with H3K36me2 depleted region, but also interacts with other epigenetic regulators. Therefore, we propose that PfMORC is part of chromatin remodeling complexes involved in heterochromatin dynamics. Moreover, we did not see any overlap between several other heterochromatin markers, suggesting it has a unique binding preference not shared with other heterochromatin markers. Based on this study and parallel work submitted by Chahine et al. (https://elifesciences.org/reviewed-preprints/92499#abstract), it is evident that PfMORC is crucial for gene regulation and chromatin structure maintenance as shown in other organisms. Currently, we do not know what the apparent mutual exclusion between H3K36me2 and PfMORC implies mechanistically or how PfMORC interaction with heterochromatin aids in chromatin integrity. In Arabidopsis thaliana, MORC binding leads to chromatin compaction and reduces DNA accessibility to transcription factors, thereby repressing gene expression. In P. falciparum, overlap in the binding region of PfMORC with different transcription factors suggests several possibilities that require further investigation. Since there is only one gene encoding a PfMORC protein in P. falciparum, it is possible that PfMORC function is not limited to chromatin integrity, but it may also function to modulate gene expression at different stages. To fully explore the function of PfMORC will require investigating the functional role of the other interacting partners we and others have identified.

We have modified the result section per the reviewer's suggestion, and we now also discuss this finding in more detail in the discussion section.

• The ChIP-seq data are central to this manuscript. However, the presentation of this data in Figure 2A suggests that it is very noisy (particularly for Chr1). It would be of interest to present the called peaks together with the normalized data so that the reader can assess the quality of the ChIP-seq data.

Our results clearly demonstrate the enrichment of PfMORC in sub-telomeric regions and internal heterochromatic islands. These results are consistent across all of our replicates taken at two independent time points of parasite asexual blood stage development and correlate well with the results of Le Roch: https://elifesciences.org/reviewed-preprints/92499. The raw data files have been provided and can be re-analyzed by any user.

• The RNA-seq data showed that only a few genes are affected after 24 h of PfMORC depletion. Furthermore, there is an equal number of up- and down-regulated genes. It is not clear why depletion of a heterochromatin marker would induce down-regulation of genes. How these data relate to the partial depletion of PfMORC is not discussed.

We would like to clarify that RNA-seq experiment was performed at 32hpi after GlcN following knockdown as previously described (Singh et al., Sci Rep, PMID 33479315). Briefly, synchronous, early trophozoites stage (24hpi) PfMORCglmS-HA parasites were treated with 2.5 mM GlcN until they reached the trophozoite stage (32 hpi) in the next cycle. These parasites were then collected for analysis by RNA-seq. We did not detect a substantial log-fold change at this point because only 50% of the transcripts were depleted in the glmS-based PfMORC knockdown system. However, we have seen a distinctive pattern of up (60) and down (103) regulated DEGs that are comprised of egress-related genes or surface antigens. We believe that PfMORC interacts with different ApiAP2 proteins, as shown in Figure 3A, and consequently exhibits multiple functions. This finding has now been corroborated in several other recent studies (See response to Reviewer 1 above).

eLife assessment

This study provides valuable insights into how chromatin-bound PfMORC controls gene expression in the asexual blood stage of Plasmodium falciparum. By interacting with key nuclear proteins, PfMORC is predicted to affect expression of genes important for host invasion and variable subtelomeric gene families. Correlating transcriptomic data with in vivo chromatin analysis, the study provides convincing evidence for the role of PfMORC in epigenetic transcriptional regulation.

Reviewer #1 (Public Review):

Summary:

The study provides valuable insights into the role of PfMORC in Plasmodium's epigenetic regulation, backed by a comprehensive methodological approach. The overarching goal was to understand the role of PfMORC in epigenetic regulation during asexual blood stage development, particularly its interactions with ApiAP2 TFs and its potential involvement in the regulation of genes vital for Plasmodium virulence. To achieve this, they conducted various analyses. These include a proteomic analysis to identify nuclear proteins interacting with PfMORC, a study to determine the genome-wide localization of PfMORC at multiple developmental stages, and a transcriptomic analysis in PfMORCHA-glmS knockdown parasites. Taken together, this study suggests that PfMORC is involved in chromatin assemblies that contribute to the epigenetic modulation of transcription during the asexual blood stage development.

Strengths:

The study employed a multi-faceted approach, combining proteomic, genomic, and transcriptomic analyses, providing a holistic view of PfMORC's role. The proteomic analysis successfully identified several nuclear proteins that may interact with PfMORC. The genome-wide localization offered valuable insights into PfMORC's function, especially its predominant recruitment to subtelomeric regions. The results align with previous findings on PfMORC's interaction with ApiAP2 TFs. Notably, the authors meticulously contextualized their findings with prior research adding credibility to their work.

Weaknesses:

While the study identifies potential interacting partners and loci of binding, direct functional outcomes of these interactions remain an inference. The use of the glmS ribozyme system to achieve a 50% reduction in PfMORC transcript levels makes it difficult to understand the role of PfMORC solely in terms of chromatin architecture without considering its impact on gene expression. Although assessing the overall impact of acute MORC depletion was beyond the scope of the study, it would have been informative.

Author response:

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

Reviewer #1:

I will summarize my comments and suggestions below.

(1) Abstract:

"Non-catalytic (pseudo)kinase signaling mechanisms have been described in metazoans, but information is scarce for plants." To the best of my understanding EFR is an active protein kinase in vitro and in vivo and cannot be considered a pseudokinase. Consider rephrasing.

We rephrased to: “Non-catalytic signaling mechanisms of protein kinase domains have been described in metazoans, but information is scarce for plants.”

(2) Page 4: It should be noted, that while membrane associated Rap-RiD systems have been used in planta to activate receptor kinase intracellular domains by promoting interaction with a co-receptor kinase domain, this system does not resemble the actual activation mechanism in the plasma membrane. This would be worth discussing when introducing the system. For example, the first substrates of the RK signaling complex may also be membrane associated and not freely diffuse in solution, which may be important for enzyme-substrate interaction.

We inserted on page 4: “The RiD system was previously applied in planta, maintaining membrane-association by N-terminal myristoylation (Kim et al., 2021). For the in vitro experiments, the myristoylation sites were excluded to facilitate the production of recombinant protein.”

(3) Page 4 and Fig 1: The catalytic Asp in BRI1 is D1027 and not D1009 (https://pubmed.ncbi.nlm.nih.gov/21289069/). Please check and prepare the correct mutant protein if needed.

We clarified this in the text by stating that we mutated the HRD-aspartate to asparagine in all our catalytic-dead mutants: “Kinase-dead variants with the catalytic residue (HRD-aspartate) replaced by asparagine (EFRD849N and BRI1D1009N), had distinct effects […]”. D1027 in BRI1 is the DFG-Asp, which was not mutated in our study.

(4) Page 4 and Fig 1: Is BIK1 a known component of the BR signaling pathway and a direct BRI1 substrate? Or in other words how specific is the trans-phosphorylation assay? In my opinion, a more suitable substrate for BRI1/BAK1 would be BSK1 or BSK3 (for example https://pubmed.ncbi.nlm.nih.gov/30615605/).

Kinase-dead BIK1 is a reported substrate of BRI1. We clarified this in the results section by inserting: “BIK1 was chosen as it is reported substrate of both, EFR/BAK1 and BRI1/BAK1 complexes (Lin et al., 2013).”

(5) Fig. 1B Why is BIK1 D202N partially phosphorylated in the absence of Rap? I would suggest to add control lanes showing BRI1, EFR, FLS2, BAK1 and BIK1 in isolation. Given that a nice in vitro activation system with purified components is available, why not compare the different enzyme kinetics rather than band intensities at only 1 enzyme : substrate ratio?

BIK1 D202N is partially phosphorylated due to the presence of active BAK1 that is capable of transphosphorylating BIK1 D202N as it has been reported in a previous study: (DOI: 10.1038/s41586-018-0471-x).

(6) Page 4 and Fig 1: Is the kinase dead variant of EFR indeed kinase dead? I could still see a decent autorad signal for this mutant when expressed in E. coli (Fig 1 A in Bender et al., 2021; https://pubmed.ncbi.nlm.nih.gov/34531323/)? If this mutant is not completely inactive, could this change the interpretation of the experiments performed with the mutant protein in vitro and in planta in the current manuscript? In my opinion, it could be possible that a partially active EFR mutant can be further activated by BAK1, and in turn can phosphorylate BIK1 D202N. The differences in autorad signal for BRI1D1009?N and EFRD849N is very small, and the entire mechanism hinges on this difference.

We would like to emphasize that the mechanism hinges on the difference between non-dimerized and dimerized kinase domains in the in vitro kinase assay. BRI1 D1009N fails to enhance BIK1 D202N trans-phosphorylation compared to the non-dimerized sample, while EFR D849N is still capable of enhancing BIK1 transphosphorylation upon dimerization as indicated by quantification of autorads (Figure 1B/C). We have also addressed this point in a section on the limitations of our study.

(7) Fig 1B. "Our findings therefore support the hypothesis that EFR increases BIK1 phosphorylation by allosterically activating the BAK1 kinase domain." To the best of my understanding presence of wild-type EFR in the EFR-BAK1 signaling complex leads to much better phosphorylation of BIK1D202N when compared to the EFRD849N mutant. How does that support the allosteric mechanism? By assuming that the D849N mutant is in an inactive conformation and fully catalytically inactive (see above)? Again, I think the data could also be interpreted in such a way that the small difference in autorad signal for BIK1 between BRI1 inactive (but see above) and ERF inactive are due to EFR not being completely kinase dead (see above), rather than EFR being an allosteric regulator. To clarify this point I would suggest to a) perform quantitative auto- and trans-(generic substrate) phosphorylation assays with wt and D849N EFR to derive enzyme kinetic parameters, to (2) include the EFRD849 mutant in the HDX analysis and (3) to generate transgenic lines for EFRD489N/F761H/Y836F // EFRD489N/F761H/SSAA and compare them to the existing lines in Fig. 3.

Mutations of proteins, especially those that require conformational plasticity for their function can have pleiotropic effects as the mutation may affect the conformational plasticity and consequently catalytic and non-catalytic functions that depend on the conformational plasticity. In such cases, it is difficult to fully untangle catalytic and non-catalytic functions. Coming back to EFR D849N, the D849N mutation may also impact the non-catalytic function by altering the conformational plasticity, explaining the difference observed in EFR vs EFR D849N. As you rightly suggested, HDX would be a way to address this but would still not clarify whether catalytic activity contributes to activation. We instead attempted to produce analog sensitive EFR variants for in vivo characterization of EFR-targeted catalytic inhibition. Unfortunately, we failed in producing an analog-sensitive variant for which we could show ATP-analog binding. To address your concern, we inserted a section on limitations of the study.

(8) Fig. 2B,C, supplement 3 C,D. Has it been assessed if the different EFR versions were expressed to similar protein levels and still localized to the PM?

Localization of the mutant receptors has not been explicitly evaluated by confocal microscopy. However, the selected mutation EFRF761H is shown to accumulate in stable Arabidopsis lines (Figure 3 – Supplement 1C) and BAK1 could be coIPed by all EFR variants upon elf18-treatment (Figure 3 B), indicating plasma membrane localization.

(9) How the active-like conformation of EFR is in turn activating BAK1 is poorly characterized, but appears to be the main step in the activation of the receptor complex. Extending the HDX analyses to resting and Rap-activated receptor complexes could be a first step to address this question. I tried to come up with an experimental plan to test if indeed the kinase activity of BAK1 and not of EFR is essential for signal propagation, but this is a complex issue. You would need to be able to mimic an activated form of EFR (which you can), to make sure its inactive (possibly, see above) and likewise to engineer a catalytically inactive form of BAK1 in an active-like state (difficult). As such a decisive experiment is difficult to implement, I would suggest to discuss different possible interpretations of the existing data and alternative scenarios in the discussion section of the manuscript.

We addressed your concern whether BAK1 kinase activity is essential for signaling propagation by pairing EFRF761H and BAK1D416N (Figure 4 Supplement 2 C) which fails to induce signaling. In this case, EFRF761H is in its activated conformation but cannot activate downstream signaling. We also attempted to address your concern by an in vitro kinase assay by pairing EFR and BAK1D416N and using a range of concentrations of the substrate BIK1D202N. We observed that catalytic activity of BAK1 but not EFR was essential for BIK1 phosphorylation. However, this experiment does not address whether activated EFR can efficiently propagate signaling in the absence of BAK1 catalytic activity. In the limitations of the study section, we now discuss the catalytic importance of EFR for signaling activation.

Author response image 1.

BIK1 trans-phosphorylation depends on BAK1 catalytic activity. Increasing concentrations of BIK1 D202N were used as substrate for Rap-induced dimers of EFR-BAK1, EFR D849N-BAK1, and EFR-BAK1 D416N respectively. BIK1 trans-phosphorylation depended on the catalytic activity of BAK1. Proteins were purified from E. coli λPP cells. Three experiments yielded similar results of which a representative is shown here.

Reviewer #2:

All of my suggestions are minor.

Figure 1B, I think it would be more useful to readers to explain the amino acid in the D-N change, rather than just call it D-to-N? Also, please label the bands on the stained gel; the shift on FKBP-BRI1 and FKBP-EFR are noticeable on the Coomassie stain.

We implemented your suggestions.

Figure 1-Supplement 1. There is still a signal in pS612 BAK1 (it states 'also failed to induce BAK1 S612 phosphorylation' in the text, which is not quite correct). Also, could mention the gel shift seen in BAK1, which appears absent in Y836F.

We corrected the text which now states: “To test whether the requirement for Y836 phosphorylation is similar, we immunoprecipitated EFR-GFP and EFRY836F-GFP from mock- or elf18-treated seedlings and probed co-immunoprecipitated BAK1 for S612 phosphorylation. EFRY836F also obstructed the induction of BAK1 S612 phosphorylation (Figure 1 – Supplement 1), indicating that EFRY836F and EFRSSAA impair receptor complex activation.” The gel shift of BAK1 you pointed out was not observed in replications and thus we prefer not to comment on it.

Figure 2 and 3 are full of a, b, c,d's, which I don't understand. Sorry

We used uppercase letters to indicate subpanels and lowercase letters to indicate the results of the statistical testing. In the figure caption, we have clarified that the lowercase letters refer to statistical comparisons.

Figure 2 A. If each point on the x-axis is one amino acid, I think it would again be useful to name the amino acids that the gold or purple or blue colored lines extend through.

Each point stands for a peptide which are sorted by position of their starting amino acid from N-terminus to C-terminus. We now added plots of HDX for individual peptides that correspond to the highlighted region in subpanel A.

Figure Supplement 1 is very small for what it is trying to show, even on the printed page. If this residue were to be phosphorylated, what would happen to the H-bond?

We suppose that VIa-Tyr phosphorylation would break the H-bond and causes displacement of the aC-b4 loop. Recent studies, published after our submission, highlight the importance of this loop for substrate coordination and ATP binding. Thus, phosphorylation of VIa-Tyr and displacing this loop may render the kinase rather unproductive. We have expanded the discussion to include this point.

Figure 2B: Tyr 836 is not present in any of the alignments in Figure 2A. This should be rectified, because the text talks about the similarity to Tyr 156 in PKA.

We have adjusted the alignments such that they now contain the VIa-Tyr residues of EFR and PKA.

Figure 4D. Is there any particular reason that these Blots are so hard to compare or FKBP and BAK1?

We assume it is referred to Figure 4 – Supplement 2 D. FKBP-EFR and FRB-BAK1 both are approximately the size of RubisCo, the most abundant protein in plant protein samples and which overlay the FKBP- and FRB-tagged kinase. Thus, it is difficult to detect these proteins.

Reviewer #3:

(1) The paper reporting the allosteric activation mechanism of EGFR should be cited.

Will be included.

(2)The authors showed that "Rap addition increased BIK1 D202N phosphorylation when the BRI1 or EFR kinase domains were dimerized with BAK1, but no such effect was observed with FLS2". Please explain why FLS2 failed to enhance BIK1 transphosphorylation by Rap treatment?

Even though BIK1 is a reported downstream signaling component of FLS2/BAK1, it might be not the most relevant downstream signaling component and rather related RLCKs, like PBL1, might be better substrates for dimerized FLS2/BAK1. We haven’t tested this, however. Alternatively, the purified FLS2 kinase domain might be labile and quickly unfolds even though it was kept on ice until the start of the assay, or the N-terminal FKBP-tag may disrupt function. As the reason for our observation is not clear, we have removed FLS2 in vitro dimerization experiments from the manuscript.

(3) Based solely on the data presented in Figure 1, it can be concluded that EFR's kinase activity is not required to facilitate BIK1 transphosphorylation. Therefore, the title of Figure 1, "EFR Allosterically Activates BAK1," may be inappropriate.

We have changed the figure title to: “EFR facilitates BIK1 trans-phosphorylation by BAK1 non-catalytically.”

(4) In Figure 1- Supplement 1, I could not find any bands in anti-GFP and anti-BAK1 pS612 of input. Please redo it.

Indeed, we could not detect protein in the input samples of this experiment. BAK1 S612 phosphorylation is an activation mark and not necessarily expected to be abundant enough for detection in input samples. EFR-GFP, however, is usually detected in input samples and is reported in Macho et al. 2014 from which manuscript these lines come. Why EFR-GFP is not detected in this set of experiments is unclear but, in our opinion, does not detract from the conclusions drawn since similar amounts of EFR-GFP are pulled-down across all samples.

(5) For Figure 2A, please mark the structure represented by each color directly in the figure.

We have made the suggested change.

(6) Please modify "EFRF761/Y836F and EFRF761H/SSAA restore BIK1 trans-phosphorylation" to "EFRF761H/Y836F and EFRF761H/SSAA restore BIK1 trans-phosphorylation".

Thank you for spotting this. We changed it.

(7) The HDX-MS analysis demonstrated that the EFR (Y836F) mutation inhibits the formation of the active-like conformation. Conversely, the EFR (F761H) mutation serves as a potent intragenic suppressor, significantly stabilizing the active-like conformation. Confirming through HDX-MS conformational testing that the EFR (Y836F F761H) double mutation does not hinder the formation of the active-like EFR kinase conformation would greatly strengthen the conclusions of the article.

Response: We agree that this is beneficial, and we attempted to do it but failed to produce enough protein for HDX-MS analysis. We stated this now in an extra section of the paper (“Limitations of the study”).

eLife assessment

This manuscript reports important in vitro biochemical and in planta experiments to study the receptor activation mechanism of plant membrane receptor kinase complexes with non-catalytic intracellular kinase domains. Several lines of evidence convincingly show that one such putative pseudokinase, the immune receptor EFR achieves an active conformation following phosphorylation by a co-receptor kinase, and then in turn activates the co-receptor kinase allosterically to enable it to phosphorylate down-stream signaling components. This manuscript will be of interest to scientists focusing on cell signalling and allosteric regulation.

Reviewer #1 (Public Review):

Summary

The authors use an elegant but somewhat artificial heterodimerisation approach to activate the isolated cytoplasmic domains of different receptor kinases (RKs) including the receptor kinase BRI1 and EFR. The developmental RK BRI1 is known to be activated by the co-receptor BAK1. Active BRI1 is then able to phosphorylate downstream substrates. The immune receptor EFR is also an active protein kinase also activated by the co-receptor BAK1. EFR however appears to have little or no kinase activity but seems to use an allosteric mechanism to in turn enable BAK1 to phosphorylate the substrate kinase BIK1. EFR tyrosine phosphorylation by BAK1 appears to trigger a conformational change in EFR, activating the receptor. Likewise, kinase activating mutations can cause similar conformational transitions in EFR and also in BAK1 in vitro and in planta.

Strengths:

I particularly liked The HDX experiments coupled with mutational analysis (Fig. 2) and the design and testing of the kinase activating mutations (Fig. 3), as they provide novel mechanistic insights into the activation mechanisms of EFR and of BAK1. These findings are nicely extended by the large-scale identification of EFR-related RKs from different species with potentially similar activation mechanisms (Fig. 5).

Weaknesses:

In my opinion, there are currently two major issues with the present manuscript. (1) The authors have previously reported that the EFR kinase activity is dispensible for immune signaling (https://pubmed.ncbi.nlm.nih.gov/34531323/) but the wild-type EFR receptor still leads to a much better phosphorylation of the BIK1 substrate when compared to the kinase inactive D849N mutant protein (Fig. 1). (2) How the active-like conformation of EFR is in turn activating BAK1 is poorly characterized, but appears to be the main step in the activation of the receptor complex. Extending the HDX analyses to resting and Rap-activated receptor complexes could be a first step to address this question, but these HDX studies were not carried out due to technical limitations.

Overall this is an interesting study that aims to advance our understanding of the activation mechanisms of different plant receptor kinases with important functions in plant immunity.

Reviewer #2 (Public Review):

Summary:

Transmembrane signaling in plants is crucial for homeostasis. In this study, the authors set out to understand to what extent catalytic activity in the EFR tyrosine kinase is required in order to transmit a signal. This work was driven by mounting data that suggest many eukaryotic kinases do not rely on catalysis for signal transduction, relying instead on conformational switching to relay information. The crucial findings reported here involve the realisation that a kinase-inactive EFR can still activate (ie lead to downstream phosphorylation) of its partner protein BAK1. Using a convincing set of biochemical, mass spectrometric (HD-exchange) and in vivo assays, the team suggest a model in which EFR is likely phosphorylated in the canonical activation segment (where two Ser residues are present), which is sufficient to generate a conformation that can activate BAK1 through dimersation. A model is put forward involving C-helix positioning in BAK1, and the model extended to other 'non-RD' kinases in Arabidopsis kinases that likely do not require kinase activity for signaling.

Strengths:

The work uses logical and well-controlled approaches throughout, and is clear and convincing in most areas, linking data from IPs, kinase assays (including clear 32P-based biochemistry), HD-MX data (from non-phosphorylated EFR) structural biology, oxidative burst data and infectivity assays. Repetitions and statistical analysis all appear appropriate.<br /> Overall, the work builds a convincing story and the discussion does a clear job of explaining the potential impact of these findings (and perhaps an explanation of why so many Arabidopsis kinases are 'pseudokinases', including XPS1 and XIIa6, where this is shown explicitly).

Weaknesses:

No major weaknesses are noted from reviewing the data and the paper follows a logical course built on solid foundations; the use of Tables to explain various experimental data pertinent to the reported studies is appreciated.

(1) The use of a, b,c, d in Figures 2C and 3C etc is confusing to this referee, and is now addressed in the latest version<br /> (2) The debate about kinase v pseudokinases is well over a decade old. For non-experts, the kinase alignments/issues raised are in PMID: 23863165 and might prove useful if cited.<br /> (3) Early on in the paper, the concept of kinases and pseudokinases related to R-spine (and extended R-spine) stability and regulation really needs to be more adequately introduced to explain what comes next; e.g. some of the key work in this area for RAF and Tyr kinases where mutual F-helix Phe amino acid changes are evaluated (conceptually similar to this study of the E-helix Tyr to Phe changes in EFR) should be cited (PMID: 17095602, 24567368 and 26925779).<br /> (4) In my version, some of the experimental text is also currently in the wrong order (and no page numbers, so hard for me to state exactly where in the manuscript); However, I am certain that Figure 2C is mentioned in the text when the data are actually shown in Figure 3C for the EFR-SSAA protein.<br /> (5) Tyr 156 in PKA is not shown in Supplement 1, 2A as suggested in the text; for readers, it will be important to show the alignment of the Tyr residue in other kinases; this has been updated in the second version. Although it is clearly challenging to generate phosphorylated EFR (seemingly through Codon-expansion here?), it appears unlikely that a phosphorylated EFR protein, even semi-pure, couldn't have been assayed to test the idea that the phosphorylation drives/supports downstream signaling. What about a DD or EE mutation, as commonly used (perhaps over-used) in MEK-type studies?

Impact:

The work is an important new step in the huge amount of follow-up work needed to examine how kinases and pseudokinases 'talk' to each other in (especially) the plant kingdom, where significant genetic expansions have occurred. The broader impact is that we might understand better how to manipulate signaling for the benefit of plants and mankind; as the authors suggest, their study is a natural progression both of their own work, and the kingdom-wide study of the Kannan group.

Reviewer #3 (Public Review):

The study presents strong evidence for allosteric activation of plant receptor kinases, which enhances our understanding of the non-catalytic mechanisms employed by this large family of receptors.

Plant receptor kinases (RKs) play a critical role in transducing extracellular signals. The activation of RKs involves homo- or heterodimerization of the RKs, and it is believed that mutual phosphorylation of their intracellular kinase domains initiates downstream signaling. However, this model faces a challenge in cases where the kinase domain exhibits pseudokinase characteristics. In their recent study, Mühlenbeck et al. reveal the non-catalytic activation mechanisms of the EFR-BAK1 complex in plant receptor kinase signaling. Specifically, they aimed to determine that the EFR kinase domain activates BAK1 not through its kinase activity, but rather by utilizing a "conformational toggle" mechanism to enter an active-like state, enabling allosteric trans-activation of BAK1. The study sought to elucidate the structural elements and mutations of EFR that affect this conformational switch, as well as explore the implications for immune signaling in plants. To investigate the activation mechanisms of the EFR-BAK1 complex, the research team employed a combination of mutational analysis, structural studies, and hydrogen-deuterium exchange mass spectrometry (HDX-MS) analysis. For instance, through HDX-MS analysis, Mühlenbeck et al. discovered that the EFR (Y836F) mutation impairs the accessibility of the active-like conformation. On the other hand, they identified the EFR (F761H) mutation as a potent intragenic suppressor capable of stabilizing the active-like conformation, highlighting the pivotal role of allosteric regulation in BAK1 kinase activation. The data obtained from this methodology strengthens their major conclusion. Moreover, the researchers propose that the allosteric activation mechanism may extend beyond the EFR-BAK1 complex, as it may also be partially conserved in the Arabidopsis LRR-RK XIIa kinases. This suggests a broader role for non-catalytic mechanisms in plant RK signaling.

The allosteric activation mechanism was demonstrated for receptor tyrosine kinases (RTKs) many years ago. A similar mechanism has been suggested for the activation of plant RKs, but experimental evidence for this conclusion is lacking. Data in this study represent a significant advancement in our understanding of non-catalytic mechanisms in plant RK signaling. By shedding light on the allosteric regulation of BAK1, the study provides a new paradigm for future research in this area.

for - clarification - how is this conclusion reached?

clarification - how is this conclusion reached? - How does the starting premise of 1014 operation per year lead to 10 billion people per year required to manage the economy?

for - question - what is the tragedy of human life?

for - quote - digital decentralised governance - implications of for civilization

quote - digital decentralised governance - implications of for civilization

• (see below)

• If that definition of civilization is accepted, that means that the creation of a non-local digital layer of infrastructure,

  • which allows for the massive self-organization and mutual coordination of trans-local projects,
• is in itself a fundamental challenge to the civilizational model as we have known it for the last five thousand years.

for - cosmolocal - nation state - alternative - decentralised governance

Article details - title - The Age of Trans-Local Self-Organized CoordiNations: What will it do to our Empire of Nation-States ? - author - Michel Bauwens - date - 2024 April 29 - source - https://4thgenerationcivilization.substack.com/p/the-age-of-trans-local-self-organized

Author response:

eLife assessment

This study investigates associations between retrotransposon element expression and methylation with age and inflammation, using multiple public datasets. The study is valuable because a systematic analysis of retrotransposon element expression during human aging has been lacking. However, the data provided are incomplete due to the sole reliance on microarray expression data for the core analysis of the paper.

Both reviewers found this study to be important. We have selected the microarray datasets of human blood adopted by a comprehensive study of ageing published in Nature Communications (DOI: doi: 10.1038/ncomms9570). We only included the datasets specifically collected for ageing studies. Therefore, the large RNA-seq cohorts for cancer, cardiovascular, and neurological diseases were not relevant to this study and cannot be included.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

Tsai and Seymen et al. investigate associations between RTE expression and methylation and age and inflammation, using multiple public datasets. The concept of the study is in principle interesting, as a systematic analysis of RTE expression during human aging is lacking.

We thank the reviewer for the positive comment.

Unfortunately, the reliance on expression microarray data, used to perform the core analysis of the paper places much of the study on shaky ground. The findings of the study would not be sufficiently supported until the authors validate them with more suitable methods.

In our discussion section in the manuscript, we have clarified that “we are aware of the limitations imposed by using microarray in this study, particularly the low number of intergenic probes in the expression microarray data. Our study can be enriched with the advent of large RNA-seq cohorts for aging studies in the future.” However, the application of microarray for RTE expression analysis was introduced previously. In fact, in a manuscript published by Reichmann et al. (DOI: 10.1371/journal.pcbi.1002486) which was cited 76 times, the authors showed and experimentally verified that cryptic repetitive element probes present in Illumina and Affymetrix gene expression microarray platforms can accurately and sensitively monitor repetitive element expression data. Inspired by this methodological manuscript with reasonable acceptance by other researchers, we trusted that the RTE microarray probes could accurately quantify RTE expression at class and family levels.

Strengths:

This is a very important biological problem.

Weaknesses:

RNA microarray probes are obviously biased to genes, and thus quantifying transposon analysis based on them seems dubious. Based on how arrays are designed there should at least be partial (perhaps outdated evidence) that the probe sites overlap a protein-coding or non-coding RNA.

We disagree with the reviewer that quantifying transposon analysis based on microarray data is dubious. As previously shown by Reichmann et al., the quantification is reliable as long as the probes do not overlap with annotated genes and they are in the correct orientation to detect sense repetitive element transcripts. Reichman et al. identified 1,400 repetitive element probes in version 1.0, version 1.1 and version 2.0 of the Illumina Mouse WG-6 Beadchips by comparing the genomic locations of the probes with the Repeatmasked regions of the mouse genome. We applied the same criteria for Illumina Human HT-12 V3 (29431 probes) and V4 (33963) to identify the RTE-specific probes.

The authors state they only used intergenic probes, but based on supplementary files, almost half of RTE probes are not intergenic but intronic (n=106 out of 264).

All our identified RTE probes overlap with intergenic regions. However, due to their repetitive natures, some probes overlap with intronic regions, too. We can replace "intergenic" with "noncoding" in our revision to show that they do not overlap with the exons of protein-coding genes. However, we do not rule out the possibility that some of our detected RTE probes might overlap noncoding RNAs. In fact, the border between coding and non-coding genomes has recently become very fuzzy with new annotations of the genome. RTE RNAs can be easily considered as non-coding RNAs if we challenge our junk DNA view.

This is further complicated by the fact that not all this small subset of probes is available in all analyzed datasets. For example, 232 probes were used for the MESA dataset but only 80 for the GTP dataset. Thus, RTE expression is quantified with a set of probes which is extremely likely to be highly affected by non-RTE transcripts and that is also different across the studied datasets. Differences in the subsets of probes could very well explain the large differences between datasets in multiple of the analyses performed by the authors, such as in Figure 2a, or 3a. It is nonetheless possible that the quantification of RTE expression performed by the authors is truly interpretable as RTE expression, but this must be validated with more data from RNA-seq. Above all, microarray data should not be the main type of data used in the type of analysis performed by the authors.

In this study, we did not compare MESA with GTP etc. We have analysed each dataset separately based on the available data for that dataset. Therefore, sacrificing one analysis because of the lack of information from the other does not make sense. We would do that if we were after comparing different datasets. Moreover, the datasets are not comparable because they were produced from different blood cell types.

Reviewer #2 (Public Review):

Summary:

Yi-Ting Tsai and colleagues conducted a systematic analysis of the correlation between the expression of retrotransposable elements (RTEs) and aging, using publicly available transcriptional and methylome microarray datasets of blood cells from large human cohorts, as well as single-cell transcriptomics. Although DNA hypomethylation was associated with chronological age across all RTE biotypes, the authors did not find a correlation between the levels of RTE expression and chronological age. However, expression levels of LINEs and LTRs positively correlated with DNA demethylation, and inflammatory and senescence gene signatures, indicative of "biological age". Gene set variation analysis showed that the inflammatory response is enriched in the samples expressing high levels of LINEs and LTRs. In summary, the study demonstrates that RTE expression correlates with "biological" rather than "chronological" aging.

Strengths:

The question the authors address is both relevant and important to the fields of aging and transposon biology.

We thank the reviewer for finding this study relevant and important.

Weaknesses:

The choice of methodology does not fully support the primary claims. Although microarrays can detect certain intergenic transposon sequences, the authors themselves acknowledge in the Discussion section that this method's resolution is limited. More critical considerations, however, should be addressed when interpreting the results. The coverage of transposon sequences by microarrays is not only very limited (232 unique probes) but also predetermined. This implies that any potential agerelated overexpression of RTEs located outside of the microarray-associated regions, or of polymorphic intact transposons, may go undetected. Therefore, the authors should be more careful while generalising their conclusions.

This is a bioinformatics study, and we have already admitted and discussed the limitations in the discussion section of this manuscript. All technologies have their own limitations, and this should not stop us from shedding light on scientific facts because of inadequate information. In the manuscript, we have discussed that all large and proper ageing studies were performed using microarray technology. Peters et al. (DOI: doi: 10.1038/ncomms9570) adopted all these microarray data in their transcriptional landscape of ageing manuscript. Our study essentially applies the Reichmann et al. method to the peripheral blood-related data from the Peters et al. manuscript. Since hypomethylation due to ageing is a well-established and broad epigenetic reprogramming, it is unlikely that only a fraction of RTEs is affected by this phenomenon. Therefore, the subsampling of RTEs should not affect the result so much. Indeed, this is supported in our study by the inverse correlation between DNA methylation and RTE expression for LINE and SINE classes despite having limited numbers of probes for LINE and SINE expressions.

Additionally, for some analyses, the authors pool signals from RTEs by class or family, despite the fact that these groups include subfamilies and members with very different properties and harmful potentials. For example, while sequences of older subfamilies might be passively expressed through readthrough transcription, intact members of younger groups could be autonomously reactivated and cause inflammation. The aggregation of signals by the largest group may obscure the potential reactivation of smaller subgroups. I recommend grouping by subfamily or, if not possible due to the low expression scores, by subgroup. For example, all HERV subfamilies are from the ERVL family.

We agree with the reviewer that different subfamilies of RTEs play different roles through their activation. However, we will lose our statistical power if we study RTE subfamilies with a few probes. Global epigenetic alteration and derepression of RTEs by ageing have been observed to be genome-wide. While our systematic analysis across RTE classes and families cannot capture alterations in subfamilies due to statistical power, it is still relevant to the research question we are addressing.

Next, Illumina arrays might not accurately represent the true abundance of TEs due to non-specific hybridization of genomic transposons. Standard RNA preparations always contain traces of abundant genomic SINEs unless DNA elimination is specifically thorough. The problem of such noise should be addressed.

We have checked the RNA isolation step from MESA, GTP, and GARP manuscripts. The total RNA was isolated using the Qiagen mini kit following the manufacturer’s recommendations. The authors of these manuscripts did not mention whether they eliminated genomics DNA, but we assumed they were aware of the DNA contamination and eliminated it based on the manufacturer’s recommendations. We have looked up the literature about non-specific hybridization of RTEs but could not find any evidence to support this observation. We would appreciate the reviewers providing more evidence about such RTE contaminations.

Lastly, scRNAseq was conducted using 10x Genomics technology. However, quantifying transposons in 10x sequencing datasets presents major challenges due to sparse signals.

Applying the scTE pipeline (https://www.nature.com/articles/s41467-021-21808-x), we have found that the statical power of quantifying RTE classes (LINE, SINE, and LTR) or RTE families (L1, L2, All, ERVK, etc.) are as good as each individual gene. However, our proposed method cannot analyse RTE subfamilies, and we did not do that.

Smart-seq single-cell technology is better suited to this particular purpose.

We agree with the reviewer that Smart-seq provides higher yield than 10x, but there is no Smart-seq data available for ageing study.

Anyway, it would be more convincing if the authors demonstrated TE expression across different clusters of immune cells using standard scRNAseq UMAP plots instead of boxplots.

Since the number of RTE reads per cell is low, showing the expression of RTEs per cell in UMAP may not be the best statistical approach to show the difference between the aged and young groups. This is why we chose to analyse with pseudobulk and displayed differential expression using boxplot rather than UMAP for each immune cell type.

I recommend validating the data by RNAseq, even on small cohorts. Given that the connection between RTE overexpression and inflammation has been previously established, the authors should consider better integrating their observations into the existing knowledge.

Until recently, there were no publicly-available, non-cancerous, large cohort of RNA-seq data for ageing studies. We tried to gain access to the two RNA-seq datasets suggested by reviewer 2: Marquez et al. 2020 (phs001934.v1.p1, controlled access) and Morandini et al. 2023 (GSE193141, public access).

Unfortunately, Marquez et al. 2020 data is not accessible because the authors only provide the data for projects related to cardiovascular diseases. However, we did analyse Morandini et al. 2023 data, and we can confirm that no association was observed between any class and family of RTEs with chronological ageing, which is the second strong piece of evidence supporting the statement in the manuscript. However, as expected, we found a positive correlation between RTE expression and IFNI signature score.

eLife assessment

This study investigates associations between retrotransposon element expression and methylation with age and inflammation, using multiple public datasets. The study is valuable because a systematic analysis of retrotransposon element expression during human aging ishas beenlacking. However, the data provided are incomplete due to the sole reliance on microarray expression data for the core analysis of the paper.

Reviewer #1 (Public Review):

Summary:

Tsai and Seymen et al. investigate associations between RTE expression and methylation and age and inflammation, using multiple public datasets. The concept of the study is in principle interesting, as a systematic analysis of RTE expression during human aging is lacking. Unfortunately, the reliance on expression microarray data, used to perform the core analysis of the paper places much of the study on shaky ground. The findings of the study would not be sufficiently supported until the authors validate them with more suitable methods.

Strengths:

This is a very important biological problem.

Weaknesses:

RNA microarray probes are obviously biased to genes, and thus quantifying transposon analysis based on them seems dubious. Based on how arrays are designed there should at least be partial (perhaps outdated evidence) that the probe sites overlap a protein-coding or non-coding RNA. The authors state they only used intergenic probes, but based on supplementary files, almost half of RTE probes are not intergenic but intronic (n=106 out of 264). This is further complicated by the fact that not all this small subset of probes is available in all analyzed datasets. For example, 232 probes were used for the MESA dataset but only 80 for the GTP dataset. Thus, RTE expression is quantified with a set of probes which is extremely likely to be highly affected by non-RTE transcripts and that is also different across the studied datasets. Differences in the subsets of probes could very well explain the large differences between datasets in multiple of the analyses performed by the authors, such as in Figure 2a, or 3a. It is nonetheless possible that the quantification of RTE expression performed by the authors is truly interpretable as RTE expression, but this must be validated with more data from RNA-seq. Above all, microarray data should not be the main type of data used in the type of analysis performed by the authors.

Reviewer #2 (Public Review):

Summary:

Yi-Ting Tsai and colleagues conducted a systematic analysis of the correlation between the expression of retrotransposable elements (RTEs) and aging, using publicly available transcriptional and methylome microarray datasets of blood cells from large human cohorts, as well as single-cell transcriptomics. Although DNA hypomethylation was associated with chronological age across all RTE biotypes, the authors did not find a correlation between the levels of RTE expression and chronological age. However, expression levels of LINEs and LTRs positively correlated with DNA demethylation, and inflammatory and senescence gene signatures, indicative of "biological age". Gene set variation analysis showed that the inflammatory response is enriched in the samples expressing high levels of LINEs and LTRs. In summary, the study demonstrates that RTE expression correlates with "biological" rather than "chronological" aging.

Strengths:

The question the authors address is both relevant and important to the fields of aging and transposon biology.

Weaknesses:

The choice of methodology does not fully support the primary claims. Although microarrays can detect certain intergenic transposon sequences, the authors themselves acknowledge in the Discussion section that this method's resolution is limited. More critical considerations, however, should be addressed when interpreting the results. The coverage of transposon sequences by microarrays is not only very limited (232 unique probes) but also predetermined. This implies that any potential age-related overexpression of RTEs located outside of the microarray-associated regions, or of polymorphic intact transposons, may go undetected. Therefore, the authors should be more careful while generalising their conclusions.

Additionally, for some analyses, the authors pool signals from RTEs by class or family, despite the fact that these groups include subfamilies and members with very different properties and harmful potentials. For example, while sequences of older subfamilies might be passively expressed through readthrough transcription, intact members of younger groups could be autonomously reactivated and cause inflammation. The aggregation of signals by the largest group may obscure the potential reactivation of smaller subgroups. I recommend grouping by subfamily or, if not possible due to the low expression scores, by subgroup. For example, all HERV subfamilies are from the ERVL family.

Next, Illumina arrays might not accurately represent the true abundance of TEs due to non-specific hybridization of genomic transposons. Standard RNA preparations always contain traces of abundant genomic SINEs unless DNA elimination is specifically thorough. The problem of such noise should be addressed.

Lastly, scRNAseq was conducted using 10x Genomics technology. However, quantifying transposons in 10x sequencing datasets presents major challenges due to sparse signals. Smart-seq single-cell technology is better suited to this particular purpose. Anyway, it would be more convincing if the authors demonstrated TE expression across different clusters of immune cells using standard scRNAseq UMAP plots instead of boxplots.

I recommend validating the data by RNAseq, even on small cohorts. Given that the connection between RTE overexpression and inflammation has been previously established, the authors should consider better integrating their observations into the existing knowledge.

https://web.archive.org/web/20240430091654/https://pdworkman.com/writing-a-novel-in-markdown/

A full description of PD Workman's workflow writing a book in markdown and Obsidian. Mentions using Canvas and Excalidraw to visualise plot development, as well as Kanban style boards. Mentions compiling tools to create manuscript from loose files. Seems similar to Scrivener except that has this baked in and thus less flexible?

Defensive and expansive reasons for learning, as defined by Holzkamp

a lack of instruction that appropriately supports the diagrams

the demonstrations of the use of this strategy by teachers

The poor, ineffective use of diagrams in problem solving

show

身份

This is now called MITRE ATT&CK drop down menu

if you are doing this from scratch you will have to create a vm1 in RG1

Author response:

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

eLife assessment

This study provides an important finding that the local abundance of metabolites impacts the biology of the tumor microenvironment by utilizing kidney tumors from patients and adjacent normal tissues. The evidence supporting the claims of the authors is convincing although certain caveats need to be taken into consideration as the authors acknowledged in the paper. The work will be of interest to the research community working on metabolism and on kidney cancer especially.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The present study addresses how the local abundance of metabolites impacts the biology of the tumor microenvironment. The authors enroll patients harboring kidney tumors and use freshly resected tumor material for metabolic studies. Specifically, the authors separate the adjacent normal kidney tissue from the tumor material and then harvest the interstitial fluid from the normal kidney (KIF) or the tumor (TIF) for quantitative metabolomics. The plasma samples from the patient are used for comparison. Additionally, the authors also compare metabolite levels in the plasma of patients with kidney versus lung cancer (or healthy donors) to address how specific tumor types might contribute to circulating levels of metabolites. Altogether, the authors find that the metabolite levels in the KIF and TIF, although vastly different than plasma, are largely overlapping. These findings indicate that tissue of origin appears to have a stronger role in determining the local metabolic environment of tumors than the genetics or biochemistry of the tumor itself.

Strengths:

The biggest strength of the current study is the use of human patient-derived samples. The cohort size (~50 patients) is relatively large, which adds to the rigor of the work. The work also relies on a small pool of metabolites that can be quantitatively measured using methods developed by the authors. Focusing on a smaller metabolic pool also likely increases the signal-to-noise ratio and enables the more rigorous determination of any underlying differences. The manuscript is well-written and highlights both the significance of the findings and also acknowledges many of the caveats. The recognition of the metabolic contributions of surrounding normal tissue as the primary driver of local nutrient abundance is a novel finding in the work, which can be leveraged in future studies.

We thank the Reviewer for their careful evaluation of the study and for their supportive comments.

Weaknesses:

The work has certain caveats, some of which have been already recognized by the authors. These include the use of steady-state metabolites and the possibility of cross-contamination of some TIF into the adjacent KIF. This study is also unable to distinguish the mechanisms driving the metabolic changes in KIF/TIF relative to circulating levels in plasma.

We agree with the Reviewer that these are important caveats to consider when interpreting the results of this study.

The relative similarity of KIF and TIF is quite surprising. However, this interpretation is presently based on a sampling of only ~100 polar metabolites and ~200 lipid molecules. It is, perhaps, possible that future technological developments that enable more comprehensive quantitative metabolic profiling might distinguish between KIF and TIF composition.

The Reviewer raises another important point that our interpretation of KIF vs TIF is limited to the ~300 metabolites we measured. We agree it would be worthwhile quantifying more metabolites where technically feasible to further characterize similarities and differences in nutrient availability between tumor and normal tissues.

In vitro, tissue culture is recognized to suffer from ‘non-physiological’ nutrient dependencies, which are impacted by the composition of culture media. Thus, in vivo studies remain our current gold-standard in mechanistic studies of tumor metabolism. It is presently unclear whether the findings of this work will be recapitulated in any of the kidney cancer in vivo models and thus be functionally testable.

We thank the Reviewer for calling attention to the limitations of cell culture media in studying tumor metabolism. While both in vitro and in vivo approaches have inherent limitations, formulating culture media based on metabolite concentrations measured here and in other studies provides a tool to study the influence of nutrient availability on kidney cell or kidney cancer cell phenotypes in vitro. We also agree with the Reviewer that determining whether the findings in our study are recapitulated in mouse models of kidney cancer, as this might enable investigation into the factors that modulate nutrient availability in this tissue context.

Reviewer #2 (Public Review):

The study employs quantitative metabolomic and lipidomic analyses to scrutinize tumor interstitial fluid (TIF), adjacent normal kidney interstitial fluid (KIF), and plasma samples from renal cell carcinoma (RCC) patients. The authors delve into the intricate world of renal cell carcinoma and its tumor microenvironment, shedding light on the factors that shape nutrient availability in both cancerous and adjacent normal tissues. The authors prove that non-cancer-driven tissue factors play a dominant role in shaping nutrient availability in RCC. This finding opens up new avenues for research, suggesting that the tumor microenvironment is profoundly influenced by factors beyond the presence of cancer cells. This study not only contributes valuable insights into RCC metabolism but also prompts a reevaluation of the factors governing nutrient availability in tumor microenvironments more broadly. Overall, it represents a significant step forward in our understanding of the intricate interplay between cancer and its surrounding milieu.

We thank the Reviewer for their evaluation of our work and for their supportive comments.

The study is overall well-constructed, including appropriate analysis. Likewise, the manuscript is written clearly and supported by high-quality figures. Since the authors exclusively employed samples from RCC patients and did not include kidney interstitial fluid and plasma samples from healthy individuals, we cannot accurately assess the true significance and applicability of the results until the role of cancer cells in reshaping KIF is understood. In essence, some metabolite levels in the tumor interstitial fluid did not show an increase or decrease compared to the adjacent normal kidney interstitial fluid. However, the levels of these metabolites in both TIF and KIF might be higher or lower than those in kidney interstitial fluid from healthy individuals, and the roles of these metabolites should not be overlooked. Similar concerns extend to plasma levels, emphasizing the importance of metabolites that synchronously change in RCC TIF, KIF, and plasma-whether elevated or reduced.

We agree with the Reviewer that an important caveat in considering the study findings is that we do not have KIF values from healthy individuals. Since resection of normal kidney is not a common procedure, obtaining KIF samples from healthy patients was not possible to complement our analysis. We further agree that the metabolite levels we measured in KIF or plasma are plausibly impacted by the presence of RCC. We did compare the composition of polar metabolites in the plasma from RCC, lung cancer, and healthy patients, highlighting how cystine is affected by tumor presence and/or sample collection methodology. We also point out that factors such as diet will impact metabolites in both blood and tissues.

Reviewer #3 (Public Review):

In this study, the authors utilized mass spectrometry-based quantification of polar metabolites and lipids in normal and cancerous tissue interstitial fluid and plasma. This showed that nutrient availability in tumor interstitial fluid was similar to that of interstitial fluid in adjacent normal kidney tissue, but that nutrients found in both interstitial fluid compartments were different from those found in plasma. This suggests that the nutrients in kidney tissue differ from those found in blood and that nutrients found in kidney tumors are largely dictated by factors shared with normal kidney tissue. Those data could be useful as a resource to support further study and modeling of the local environment of RCC and normal kidney physiology.

We thank the Reviewer for their time considering our paper and for their supportive comments.

In Figures 1D and 1E, there were about 30% of polar metabolites and 25% of lipids significantly different between TIF and KIF, which could be key factors for RCC tumors. This reviewer considers that the authors should make comments on this.

We agree with the Reviewer that the metabolites that significantly differ between TIF and KIF are of interest, particularly for those studying RCC tumor metabolism. We comment on some of the metabolites driving differences between TIF and KIF in our discussion of Figure 2, and in the revised manuscript we now include a new figure showing a heatmap that enables visualization of these metabolites (Figure 2-Supplement 1A-B).

Recommendations for the authors:

From the Reviewing Editor:

Figure 2 needs to plot heatmaps for both upregulated and downregulated metabolites in TIF.

We agree and now include heatmaps for significantly differing polar metabolites and lipids in TIF vs KIF as requested by Reviewer 3 (Figure 2-Supplement 1A-B). For completeness, we also include heatmaps for metabolites differing between healthy and RCC plasma (Figure 2-Supplement 2C) and for NSCLC and RCC plasma (Figure 2-Supplement 2D).

There is a need to show whether the differences in these metabolites between plasma and tissue interstitial fluid are specific to RCC patients or if they are also present in normal individuals.

Unfortunately, it has not been possible for us to collect KIF from healthy individuals. Since resection of normal kidney is not a common procedure, we have no way to obtain sufficient KIF samples from healthy patients for this measurement. We discuss this as a limitation of the study.

Reviewer #1 (Recommendations For The Authors):

a. The authors should provide additional details about the methodology to separate the KIF and TIF. Contaminating metabolites from surrounding tissue or the peritoneal fluids could impact interpretation and it would be helpful to understand how these challenges were addressed during tissue collection for this study. Additionally, was the collected tissue minced or otherwise dissociated? If so, could these procedures cause tissue lysis and contaminate the KIF/TIF with intracellular components?

We thank the Reviewer for the suggestions to include more information about the sampling methodology. Care was taken to minimize cell lysis incurred by the processing methodology as tissues were not minced, smashed, nor dissociated, however there is still a possibility of some level of tissue lysis that is pre-existing or occurs during the isolation procedure. We note this caveat in the text (lines 218-220) and have updated the Methods with more details of the sampling and processing of the samples.

b. Although the authors focus on metabolites that are elevated in TIF (relative to KIF and plasma), it would be equally relevant to consider the converse. Metabolites that are reduced in TIF, either due to underproduction or overconsumption, could render the tumors auxotrophic for some critical dependencies and identify some novel metabolic vulnerabilities. In this regard, Figure 2 could have a heatmap of the top metabolites that are elevated and depleted specifically in the TIF.

We agree with the Reviewer it is useful to include heatmaps to better display the metabolites that significantly differ between TIF and KIF and now include these in Figure 2-Supplement 1A-B.

c. The future utilization of this knowledge would depend on our ability to model these differences. Would interstitial tissue from a normal mouse kidney or tumor-bearing mouse kidney recapitulate the same differences relative to mouse plasma?

We agree with the Reviewer that it would be worth determining whether the findings in our study are recapitulated in mouse models of kidney cancer, which would support future investigation into the factors that modulate nutrient availability. This is an interesting question, but we did not have access to endogenously arising models of RCC, which have been a limitation for the field, and comparison of normal mouse kidney metabolite data to human metabolite data is problematic for obvious reasons. Thus, we had no choice but to discuss this as a limitation of the study.

Reviewer #2 (Recommendations For The Authors):

In this study, Abbott et al. investigated the metabolic profile of renal cell carcinoma (RCC) by analyzing the tumor interstitial fluid (TIF), adjacent normal kidney interstitial fluid (KIF), and plasma samples from patients. The results indicate that nutrient composition in TIF closely resembles that of KIF, suggesting that tissue-specific factors, rather than tumor-driven alterations, have a more significant impact on nutrient levels. These findings are interesting. The study is overall well-constructed, including appropriate analysis, and the manuscript is written clearly and supported by high-quality figures. However, some issues are raised which if addressed, would strengthen the paper.

We thank the Reviewer for their suggestions to improve the paper.

The authors found a difference in the number of metabolites when comparing TIF or KIF lipid composition with plasma. The discoveries are intriguing; however, I am keen to understand whether the differences in these metabolites between plasma and tissue interstitial fluid are specific to RCC patients or if they are also present in normal individuals. I am particularly interested in identifying which metabolites could serve as potential diagnostic markers, intervention targets, or potentially reshape the tumor microenvironment. Because, even though some metabolite levels show no difference between TIF and KIF in RCC patients, I wonder if these metabolite levels in KIF increase or decrease compared to the interstitial fluid in healthy individuals. I am intrigued by the metabolites that simultaneously increase or decrease in both TIF and KIF compared to the kidney interstitial fluid in healthy individuals.

We agree with the Reviewer that it would be interesting to measure kidney interstitial fluid from healthy patients to be able to compare metabolites changing due to the presence of RCC tumor. As we discuss in response to the public review, this was not possible as we could not obtain material from healthy individuals for analysis. Nevertheless we agree it warrants future study if material were available.

The analysis conducted using plasma from healthy donors, as applauded by the author, is noteworthy. The author seems to have found that cystine levels do not differ between RCC patient plasma and tissue interstitial fluid. However, considering that in patient plasma, the cystine concentration is approximately two-fold higher than in plasma from healthy individuals, likely, cystine levels in patient tissue fluid have also increased nearly two-fold compared to levels in the interstitial fluid of normal kidney tissues. This finding aligns with the discovery of elevated GSH levels in cancer cells.

We agree with the Reviewer that a higher cystine concentration in RCC patient plasma and interstitial fluid is interesting, and also considered this in relationship to past findings including reports of elevated GSH levels in RCC. However, we think this observation is driven at least in part by the fasting status of the patients pre-surgery. This does not rule out some part being related to the presence of the tumor, as this would be consistent with elevated GSH levels as noted by the Reviewer. Future studies will be needed to further delineate the factors that impact elevated cystine levels in both interstitial fluid and plasma.

Some minor typos, such as "HIF1􀀀-driven" should be corrected.

We thank the Reviewer for pointing out this typo and we have corrected it in the revised manuscript.

Reviewer #3 (Public Review):

In this study, the authors utilized mass spectrometry-based quantification of polar metabolites and lipids in normal and cancerous tissue interstitial fluid and plasma. This showed that nutrient availability in tumor interstitial fluid was similar to that of interstitial fluid in adjacent normal kidney tissue, but that nutrients found in both interstitial fluid compartments were different from those found in plasma. This suggests that the nutrients in kidney tissue differ from those found in blood and that nutrients found in kidney tumors are largely dictated by factors shared with normal kidney tissue. Those data could be useful as a resource to support further study and modeling of the local environment of RCC and normal kidney physiology.

eLife assessment

This study provides an important finding that the local abundance of metabolites impacts the biology of the tumor microenvironment by utilizing kidney tumors from patients and adjacent normal tissues. The evidence supporting the claims of the authors is convincing. The work will of interest to the research community working on metabolism and kidney cancer especially.

Reviewer #1 (Public Review):

(a) Summary: The present study addresses how the local abundance of metabolites impacts the biology of the tumor microenvironment. The authors enroll patients harboring kidney tumors and use freshly resected tumor material for metabolic studies. Specifically, the authors separate the adjacent normal kidney tissue from the tumor material and then harvest the interstitial fluid from the normal kidney (KIF) or the tumor (TIF) for quantitative metabolomics. The plasma samples from the patient are used for comparison. Additionally, the authors also compare metabolite levels in the plasma of patients with kidney versus lung cancer (or healthy donors) to address how specific tumor types might contribute to circulating levels of metabolites. Altogether, the authors find that the metabolite levels in the KIF and TIF, although vastly different than plasma, are largely overlapping. These findings indicate that tissue of origin appears to have a stronger role in determining the local metabolic environment of tumors than the genetics or biochemistry of the tumor itself.

(b) Strengths: The biggest strength of the current study is the use of human patient-derived samples. The cohort size (~50 patients) is relatively large, which adds to the rigor of the work. The work also relies on a small pool of metabolites that can be quantitatively measured using methods developed by the authors. Focusing on a smaller metabolic pool also likely increases the signal-to-noise ratio and enables the more rigorous determination of any underlying differences. The manuscript is well-written and highlights both the significance of the findings and also acknowledges many of the caveats. The recognition of the metabolic contributions of surrounding normal tissue as the primary driver of local nutrient abundance is a novel finding in the work, which can be leveraged in future studies.

(c) Weaknesses: The work has certain caveats, some of which have been already recognized by the authors. These include the use of steady-state metabolites and the possibility of cross-contamination of some TIF into the adjacent KIF. This study is also unable to distinguish the mechanisms driving the metabolic changes in KIF/TIF relative to circulating levels in plasma.

The relative similarity of KIF and TIF is quite surprising. However, this interpretation is presently based on sampling of only ~100 polar metabolites and ~200 lipid molecules. It is, perhaps, possible that future technological developments that enable more comprehensive quantitative metabolic profiling might distinguish between KIF and TIF composition.

In vitro tissue culture is recognized to suffer from 'non-physiological' nutrient dependencies, which are impacted by the composition of culture media. Thus, in vivo studies remain our current gold-standard in mechanistic studies of tumor metabolism. It is presently unclear whether the findings of this work will be recapitulated in any of the kidney cancer in vivo models and thus be functionally testable.

The authors have acknowledged these caveats and where possible provided textual clarifications and updated figures in their revised manuscript. Future work will be required to model these changes in animal models.

Reviewer #2 (Public Review):

The study employs quantitative metabolomic and lipidomic analyses to scrutinize tumor interstitial fluid (TIF), adjacent normal kidney interstitial fluid (KIF), and plasma samples from renal cell carcinoma (RCC) patients. The authors delve into the intricate world of renal cell carcinoma and its tumor microenvironment, shedding light on the factors that shape nutrient availability in both cancerous and adjacent normal tissues. The authors prove that non-cancer-driven tissue factors play a dominant role in shaping nutrient availability in RCC. This finding opens up new avenues for research, suggesting that the tumor microenvironment is profoundly influenced by factors beyond the presence of cancer cells. This study not only contributes valuable insights into RCC metabolism but also prompts a reevaluation of the factors governing nutrient availability in tumor microenvironments more broadly. Overall, it represents a significant step forward in our understanding of the intricate interplay between cancer and its surrounding milieu.

The study is overall well-constructed, including appropriate analysis. Likewise, the manuscript is written clearly and supported by high-quality figures. Since the authors exclusively employed samples from RCC patients and did not include kidney interstitial fluid and plasma samples from healthy individuals, we cannot accurately assess the true significance and applicability of the results until the role of cancer cells in reshaping KIF is understood. In essence, some metabolite levels in the tumor interstitial fluid did not show an increase or decrease compared to the adjacent normal kidney interstitial fluid. However, the levels of these metabolites in both TIF and KIF might be higher or lower than those in kidney interstitial fluid from healthy individuals, and the roles of these metabolites should not be overlooked. Similar concerns extend to plasma levels, emphasizing the importance of metabolites that synchronously change in RCC TIF, KIF, and plasma-whether elevated or reduced.

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

Learn more at Review Commons

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Reply to the reviewers

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

The manuscript by Xie et al investigates the role of efemp1 in mediating ocular growth. Efemp1, a secreted extracellular matrix glycoprotein, was previously identified as a myopia-risk gene in human GWAS studies. Given that myopia is linked to aberrant eye shape, the authors investigated whether and how this gene mediates eye growth. Using a CRISPR based approach in zebrafish the authors knocked out efemp1 specifically in the retina and established that a myopic eye results. They went further and investigated visual function in these mutant fish using the optomotor response and electroretinograms. As dark-rearing in many animal models has been linked to the induction of myopia, the authors examined the effects of a dark-rearing regimen in efemp1 mutants and found surprisingly that they did not show signs of myopia. Lastly, the expression and distribution of several myopia-associated genes was investigated in the retina of efemp1 mutants and following dark-rearing.

1. The starting point for this study was the generation of a "retina-specific knockout mutant of the efemp1 gene". However, evidence for a 'successful' knockout at the protein level is missing.

We have clarified the exact nature of our efemp12C-Cas9 model further. The mutants have mosaic genetic modification that do not simply lead to gene deletion (knockout). We have reworded throughout the manuscript to avoid statement indicating the efemp1 2C-Cas9 fish as a knockout model and instead used “genetic modification” or “genetic disruption”, etc:

“This gene editing system led to mosaic retinal mutations; each Cas9-expressing retinal cell that were driven by the rx2 promoter would perform its own CRISPR gene editing process, and as a result, even within an individual retina, there were different types of indels (e.g., loss- or gain-of-function mutations, or milder mutations that may cause mislocalization) in different cells.” (Line 104–108).

For the same reason, it is very challenging to show such a mosaic genetic editing in the protein level. First of all, we were not able to find commercial anti-EFEMP1 antibody for zebrafish that targets specifically the editing sites in our fish model. This means that mutated efemp1 DNAs that were transcribed and translated would produce mutant EFEMP1 protein that might still be recognized by an anti-EFEMP1 antibody, although their dysfunction might manifest as altered distribution and thus abnormal ocular development.

On the other hand, in this study we used a headloop PCR technique, a sensitive genotyping approach that specifically suppresses amplification of wild-type but not mutated efemp1 DNA to show that there were genetic modifications in our mutants. However, likely due to the patchy distribution of Cas9-expressing retinal cells (Fig 1A′) and the non-uniform nature of gene editing, our genotyping results showed weak mutant bands (Fig 1C–D), implicating low editing rates. The fact that only a proportion of mutations would result in loss of the protein would make it difficult to distinguish the gene editing in the retina via immunostaining or western blot.

We have added following in the Results section to indicate the difficulties in showing genetic modification at the protein level for the efemp12C-Cas9 model:

“On the other hand, due to the mosaic nature of the gene editing, the patchiness of Cas9-expressing retinal cells (Fig 1A′) and the potentially low editing rate, as well as the unavailability of commercial anti-EFEMP1 antibodies that targets specifically the CRISPR editing sites, efemp1 modification in our mutant model at the protein level is challenging to show.” (Line 125–128)

Immunostaining for Efemp1 in sections of the entire retina from control and mutant fish would have helped here. It is only in Figure 7 B, C that portions of the inner retina from control and efemp1 2c-Cas9 fish are shown with Efemp1 immunostaining. Control and mutant retinae show slight relative differences in Efemp1 fluorescence levels which are difficult to reconcile with a knock-out scenario.

As mentioned above, our model is not simply a knockout but a combination of a range of indels that may produce mutant proteins. At least some of them are therefore still likely to bind with the anti-EFEMP1 antibody used in the present study; the antibody does not bind to EFEMP1 regions corresponding to sgRNAs target sites on zebrafish efemp1 DNA. We have added this detail in the Methods to clarify.

“Noting that the anti-EFEMP1 antibody does not bind to EFEMP1 regions corresponding to sgRNAs target sites on zebrafish efemp1 DNA, thus mutant proteins (if any) may still be labeled by the antibody.” (Line 790–792)

Therefore, it makes sense that our result showed differences in relative EFEMP1 fluorescence between groups across the inner retina rather than complete loss of EFEMP1 immunostaining in mutant retinas.

resumably this phenotype is a result of the mosaic expression of Cas9 (GFP) shown in Fig 1? Can the authors explain the reason for this mosaicism?

We believe that the “mosaic expression of Cas9” the reviewer mentioned is the “patchy distribution of Cas9-expressing retinal cells” as we mentioned in the above response. Yes this is also partially the reason why mutant retinas still present EFEMP1 immunostaining. The patchy (or mosaic) Cas9 expression in the retina of our mutant model can be because we use the Gal4/UAS system to drive the 2C-Cas9 gene editing system. Mosaic expression has long been noticed as a drawback of the Gal4/UAS system. We have modified the manuscript to explain the mosaic Cas9 expression in the mutant retina:

“The patchiness of Cas9 expression in the mutant retina may attribute to the Gal4/UAS system (Halpern et al., 2008).” (Line 103–104)

Given this mosaic expression would one expect Efemp1 immunoreactive areas intermingled with areas devoid of Efemp1 in the mutant retina?

This happens only in cells that CRIPSR eliminates production of EFEMP1, but due to patchy Cas9 expression and perhaps only a little proportion of Cas9-positive cells will lose EFEMP1 protein, our immunostaining did not show apparent intermingling. Importantly, it is worth noting that as our explanation above, anti-EFEMP1 antibody may be able to bind with mutant EFEMP1 proteins and thus EFEMP1 immunostaining will still present in retinal cells with successful gene editing.

Further, do deficits in the various functional assays the authors perform correlate with the degree of mosaicism?

We appreciate the reviewer’s interesting idea. As primary goal of the present study is to determine whether retinal-specific efemp1 modification has any effect on ocular refraction, we aimed to use fish with as more Cas9-expressing cells as possible for functional analysis, and thus fish used were not expected to have discernible difference in degree of Cas9 expression mosaicism. Therefore, it is not known that whether there is a correlation between ocular deficits and Cas9 expression mosaicism. We thank the reviewer’s suggestion and will bear this idea in mind for future experimental design.

In the same vein, in Figure 2 the authors refer to variation in GFP levels in the efemp12c-Cas9. It is not clear whether the authors mean levels of GFP in individual cells or numbers of GFP+ cells. Presumably the latter. Could the authors clarify?

We have added details in the Methods of the manuscript to clarify:

“Post-hoc retinal histology indicated that intensity of eGFP fluorescence is corresponding to eGFP positive cell number; fish with higher eGFP fluorescence level had more eGFP positive cells.” (Line 723–725)

In my opinion understanding and characterizing the efemp12c-Cas9 fish thoroughly is key to interpreting the phenotypes the authors show subsequently.

We agree with the reviewer. Due to the characteristics of our 2C-Cas9 model mentioned above, headloop PCR, which is highly sensitive for determining occurrence of gene mutations regardless indel types, is so far the most practical approach for us to provide evidence of successful gene editing. Because there was limited means to show gene modification in the protein level for our mutant model (as mentioned above), we instead provided functional verification of gene modification using OMR. We showed that functionally our 2C-Cas9 model have comparable phenotype with efemp1-knockdown zebrafish that have robust gene disruption induced by morpholino. Overall, with this evidence we believe that there were efemp1 modification in our fish model. Given no other manipulations, the phenotypes are presumably due to the mosaic mutations generated here. We would speculate (though have no data to show this) that a more even and complete knockout of Efemp1 throughout all of the retinal neurons would increase the size of the phenotypic changes seen even more. It was important for us to target the eye to assess the role in the local emmetropisation processes rather than mixing it with possible other CNS defects confounding the phenotype. We were excited to be able to observe quantifiable phenotypes even with such a mosaic randomized mutation model shown here and believe it gives more strength to the role of Efemp1.

Reviewer #1 (Significance (Required)):

The wide range of assays the authors perform to assess visual deficits is commendable. Such a comprehensive approach ranging from anatomical, behavioral and electrophysiological assays is poised to identify changes that could otherwise be overlooked. Given the increasing use of zebrafish as models of ocular diseases, this study provides a solid roadmap of the types of analysis possible. This work should be interesting to researchers in the field of myopia research and to basic vision researchers interested in using the zebrafish as a model organism.

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

Summary: In this study, the authors used the zebrafish model to study efemp1, a gene that was previously found to be associated with myopia. They used CRISPR-Cas9 to create specific efemp1 knockout in the retina in a mosaic manner. They used a few histological and physiological techniques to evaluate the resulting mutant and found that the efemp1 mutants developed symptoms that are consistent with myopia. The authors further quantified the expression of a few potential target genes in the eye that are potentially implicated in myopia phenotype. The authors also evaluated the differential phenotype of the efemp1 mutant grown in different light conditions that might contribute to myopia.

Major comments:

Overall, the authors have provided convincing evidence of the phenotype created by their efemp1 perturbation. Their experiments were thoroughly done and extensively analyzed. They even discussed some potential shortcomings of their study. Their study is a nice first step towards a better understanding of the efemp1 gene function in ocular growth and in myopia. All my comments below should be addressed by clarifications and discussions and not by any new experiments or projects.

Minor comments:

1. Elaborate the rationale for choosing efemp1 from the original GWAS study for zebrafish investigation. The authors only mentioned that this gene is among the highest in the rank and its role in myopia is not clear. However, there are quite a few other genes in the GWAS study that were ranked as high, if not higher than efemp1.

We thank the reviewer for the suggestion. Firstly, in a previous study, we used the high-throughput zebrafish optomotor response assay coupled with morpholino gene knockdown to screen top-ranked myopia-risk genes from the GWAS study. To use zebrafish as a model, additionally we took into account several other factors including existence of zebrafish orthologues, gene expression in the eye, association of ocular phenotypes with risk genes shown in previous zebrafish studies, fatality of gene depletion and availability of characterized morpholinos to prioritize GWAS-associated risk genes for screening. With significant reduction of OMR responses in efemp1 morphants, efemp1 was selected as a gene of interest for investigation. As our pre-screen is currently an unpublished study, we were not showing the data in the manuscript, but we are happy to show relevant results to the reviewer if requested. To clarify the selection of this gene, we have added a brief statement in the Introduction:

“Our previous study (unpublished) screening GWAS-associated myopia-risk genes with high-throughput optomotor response measurement and morpholino gene knockdown indicated that knockdown of efemp1 in larval zebrafish reduced spatial-frequency tuning function, making efemp1 a candidate gene worth for further investigation for myopia development.” (Line 53–57)

On the other hand, in the Introduction of our manuscript, we indeed had covered that in humans, efemp1 disruptions, with either gain- or loss-of functions, would lead to visual disease, such as Malattia Leventinese, doyne honeycomb retinal dystrophy, juvenile-onset open-angle glaucoma, or high myopia. These also implicated the importance in understanding the role of efemp1 in ocular development.

Elaborate the rationale for choosing retina as the target tissue of efemp1 knockout, especially when the original GWAS study indicated the expression of EFEMP1 is in cornea, RPE, and sclera, but not in retinal cells.

Firstly, efemp1 is expressed in the retina as shown by our immunostaining in zebrafish and in situ hybridization in mouse in a previous study (PMID: 26162006). We have modified the manuscript to clarify this point:

“EFEMP1 is a secreted extracellular matrix glycoprotein widely expressed throughout the human body, especially in elastic fiber-rich tissues, for examples, the brain, lung, kidney and eye including the retina (Livingstone et al., 2020; Mackay et al., 2015).” (Line 51–53)

In future studies it will be interesting to perform similar somatic efemp1 manipulation in other ocular tissues to examine whether this gene has tissue-dependent functions for ocular growth. Nonetheless, our results demonstrated that at the very least retinal efemp1 is involved in ocular development.

Secondly, the rx2 gene is indeed also expressed in the RPE in zebrafish (PMID: 11180949), meaning that there were also RPE cells expressing Cas9 driven by rx2. We have added this detailed to the manuscript:

“In this transgenic zebrafish line, Tg(rx2:Gal4) is expressed specifically in the retina and the RPE (Chuang and Raymond, 2001), due to the retina-specific retinal homeobox gene 2 (rx2) promoter.” (Line 95–97)

Importantly, as myopia generally develops due to dysregulated gene-environment interactions, modification of efemp1 specifically in the light-sensing retina allowed us to investigate the interaction of efemp1 with visual environment. We have added this point to the manuscript:

“In order to investigate the role of the efemp1 gene and its interaction with visual environment, we first generated a zebrafish line with efemp1 modification specifically in the retina (efemp12C-Cas9; Fig 1A), the light-sensing tissue in the eye, using a 2C-Cas9 somatic CRISPR gene editing system (Di Donato et al., 2016).” (Line 92–95)

Discuss possible ways of modifying efemp1 gene in the retina that would be more uniform and would not create mosaicism and/or heterogenous mutations that can complicate downstream characterizations and interpretations as the authors currently experienced.

We appreciate the reviewer’s suggestion. One possible way of generating uniform tissue-specific gene modification is to use the Cre-loxP recombination system. We have modified the Discussion of manuscript as per reviewer’s suggestion:

“To avoid such heterogeneous tissue-specific gene editing, the Cre-LoxP system is an option­: using tissue-specific driven Cre recombination to delete LoxP flanked exons of the target gene.” (Line 482–486)

• Added to discussion –

The authors should elaborate further on the effect of the mosaicism and heterogenous mutations on efemp1, a presumably excreted protein, on regulating the ocular growth.

We appreciate the reviewer’s interesting point of view. However, it is very difficult to identify a regionalized effect of mosaicism and heterogenous mutations of efemp1 on ocular growth even with dissected eyes. It is likely that distribution of Cas9-expressing cells was mosaic but still overall even across the retina. Perhaps in other models that allow controlled regional efemp1 manipulation in the eye, for sample, using gene promoters that present dorsal to ventral gradient, comparisons between modified and unmodified regions in the same eye will help to unravel whether efemp1 regulates eye growth only around the location where it was produced.

How did the downstream genes they studied affect by the messing up of the extracellular Efemp1? Is it through altering the Egf signal transduction?

Throughout the Discussion we have tried to cover how efemp1 disruption affect myopia-associated genes where it is possible by linking our results with literature. However, there were not enough details from the literature showing direct pathways between efemp1 and the tested myopia-risk genes. These will be interesting topics for further investigation. To our knowledge, there is no evidence that myopia-associated genes we analyzed in the study are transduced by Egf signaling.

If possible, discuss the original SNP that was associated with efemp1 and the potential mechanisms through which the SNP affects human EFEMP1; Then, discuss how the study of zebrafish efemp1 mutant can aid our understanding of the human's SNP.

Unfortunately, this information is not available. In the meta-analysis our work is based on Efemp1 ranked highly based on biological and statistical evidence. In figure 5 of Tedje et al., 2018, we can see Efemp1 in the first place. Where available, the annotation (light blue column) would indicate whether the variant was found in exonic, UTR or transcribing RNA. Nothing was identified for Efemp1 – which could mean that it is expressed in regulatory sequencing further away.

Typo: Page 15, Line 299: Loss of this gene "promotes".

Thanks to the reviewer, we have corrected the typo.

Reviewer #2 (Significance (Required)):

This study is an interesting and potentially significant addition to the ophthalmology field, as it conducted an initial characterization of a candidate gene for myopia in zebrafish and observed a relevant phenotype after the gene knockout. Colleagues in the myopia field will find the results interesting. In addition, colleagues in the zebrafish field will find the in-depth characterizations and tools used in the paper very informative.

I have conducted research in the human genetics of ophthalmology, gene expression analysis, zebrafish eye development and diseases. I believe my background allows me to effectively appreciate and evaluate the findings of this manuscript.

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

Summary The authors use a retinal-specific promoter to target zebrafish efemp1 for inactivation to study its effects on the eye. Their use of the DiDonato/del Bene 2C-Cas9 system is a good method to target only cells that express a specific promoter i.e. rx2. Following this (mosaic and transient) targeting of efemp1, the authors describe enlarged eyes and myopia development, as well as reduced spatial visual sensitivity and altered retinal function by ERG analysis. Furthermore, expression levels of egr1, tgfb1a, vegfb, and rbp3 are altered, as well as Timp2 and Mmp2 proteins. Finally, dark-rearing of efemp1 mutant fish is reported to lead to emmetropization, rather than myopia.

Major comments

1. The data presented by the authors are interesting, and likely due to efemp1 disruption in the eye. However, the authors should clarify or explain several points, and improve on experimental rigor. Figure 1 C, D- PCRs are not convincing for loss of efemp1. The authors should consider PCR reactions that would show deletion driven by both CRISPRs, or an RFLP reaction based on conventional PCR that would show differences if individual CRISPRs were effective.

Our zebrafish model is not simply a complete knockout model (please see response to reviewer 1’s comment 1 for details). In our model, even in a retina, there will be different indels in cells that expresses Cas9, including gain- or loss-of-function mutations, or mutations that do not even influence its function. In some cases, even with CRISPR cutting, DNA will recover to be wildtype. Thus, even with FACS to sort for Cas9+ (GFP+) cells, it is not possible to provide evidence for such gene modification using conventional PCR, because as long as there is a unmutated target sequence there will be PCR production. Because of this, headloop PCR as a well-established, highly sensitive approach is specifically suitable for our case.

There needs to be better evidence that efemp1 is being edited (e.g. Western blot, or qPCR).

As described in our response to reviewer 1’s comment 1, due the way efemp1 gene was modified in the retina in our model and the unavailability of suitable commercial antibodies, western blot is currently not an option for us. For qPCR, theoretically it is a way to show genetic modification at the transcriptional level, if combined with FACS from dissected eyes and sgRNA target sites specific primers. However, in reality it is not very practical to perform. First of all, even in our model with more Cas9+ cells, due to the patchy expression, the number of these cells are in fact low in a retina. This means that the number of fish to get enough cells for RNA isolation would be much higher, likely to be hundreds of fish. Moreover, in each clutch the number of fish with higher Cas9+ cell number is generally low, estimated to be only ~5%. Overall, this indicates that a large number of fish are required to even just get one sample for such an experiment. With evidence from headloop PCR and visual phenotype verification (OMR; Fig 1E–H and Fig S1), we believe it is certain that efemp1 gene has been modified. As mentioned also, the ability to identify quantifiable phenotypic differences in this model despite the mosaic Cas9 activity and random indels in different cells is highly suggestive of a full knockout of Efemp1 in the eye causing an even larger phenotype.

The data in Figure 7 are not convincing that EFEMP1 protein levels are substantially reduced in mutants.

This is expected. Please see response to reviewer 1’s comment 2.

Why are efemp12C-Cas9 eyes smaller with normal lighting? (Figure S2)

Fig S2 showed that efemp1*2C-Cas9 fish have smaller eye size than control fish only at 2 weeks of age. As shown by our survival data (Fig 2C), fish with more severe gene modification (implicated by more GFP+ cells, GFP+++ fish) are possibly died by 4 weeks of age, likely due to severe deficits in visually driven predation and subsequently nutrition deficiency. These fish thus gradually develop smaller size of the body including the eye with age, compared to control fish. Therefore, it makes senses that overall mutant fish have smaller eyes at 2 weeks of age but as GFP+++ fish die by 4 weeks, the group averaged eye size returned to a level similar to control fish. The fish survived are likely the ones that have mild mutations, which allow them to remain some levels of vision for feeding and develop without discernibly smaller eye size. Because there was variability of eye size in zebrafish caused by either development or gene manipulation, we used a relative calculation (ratio of retinal radius to lens radius) as a myopia index for comparison.

The clustering of datapoints in Figure 2B, 4B, overlaps extensively between control and mutant, and it is not easy to be sure that the high significance scores (***) are accurate.

We thank the reviewer for pointing out this concern. Though data points overlapped to some levels, in general difference between group means were apparent and the range that they deviate (i.e., mean ± SEM) were barely overlap. We realise it was difficult to see the SEM error bars, as they were so close to the mean, that they were hard to distinguish. We have adjusted our figures for clearer visualization of the error bars. Hopefully this will better show how far apart the data are as reflected by the significance scores.

The authors should consider discussing whether loss of efemp1 is developmental only, or sustained. rx2 is likely to be switched off after development, and retinal cells that arise after the rx2:Gal4 ceases to be active will have a normal quotient of efemp1.

Genetic modification in our mutant model is sustained. It is true that rx2 is only transiently expressed during early development, but once gDNA in a cell was modified by a CRISPR editing event driven by the 2C-Cas9 system, it remains throughout cell divisions (the same mutation would be copied during DNA synthesis) and cell lifetime. In addition, it has been showed that in adult teleost activation of rx2 in retinal stem cells in the retinal ciliary marginal zone determines its fate to form retinal neurons (PMID: 25908840). Therefore, in new neurons derived from retinal stem cells in the adult zebrafish retina, there is expression of rx2 to drive the 2C-Cas9 system for genetic modification. We have added relevant details to the Result section:

“Despite the mosaicism, the mutations resulted from the 2C-Cas9 system in retinal cells is expected to be sustained. Also, in adult teleost, activation of rx2 in retinal stem cells in the retinal ciliary marginal zone determines its fate to form retinal neurons (Reinhardt et al., 2015). This suggested that in new neurons derived from retinal stem cells in the adult zebrafish retina, there may be expression of rx2 to drive the 2C-Cas9 system for genetic modification.” (Line 108–116)

The authors should also consider a more detailed discussion of the mechanism mediated by/through efemp1 that alters retinal function and expression of other genes.

We appreciate the reviewer’s suggestion. It is possible to add more detailed discussion to the manuscript for potential mechanistic links, but ultimately such content would be highly speculative and may lead to over-interpretation of the data. Moreover, a comprehensive overview of detailed mechanisms of how efemp1 may alter retinal function and expression of relevant genes will require space and significantly lengthen the manuscript, with however only minimal improvement. Therefore, we believe it is reasonable to only touch the most relevant as we did for the manuscript.

Finally, since a full mouse knockout of efemp1 exists (Daniel et al, 2020), it is not clear why a retinal-specific zebrafish model would give better insight into the phenotype.

There are several advantages of our 2C-Cas9 zebrafish model. Firstly, with a retinal specific modification of the efemp1 gene, we are able to rule out systematic effect. Essentially our focus is the role of efemp1 in specifically ocular development. Secondly, with their smaller size, rapid development, high reproductivity, and ease of genetic and environmental manipulations, zebrafish allow us to perform large-scale high-throughput investigation with different genetic and environmental combinations. Furthermore, by changing the promoter that drives Gal4 expression in our model, we can target precisely different retinal neuron subtypes to characterize which and how different visual circuits are involved.

Minor comments

"Myopia is the most common ocular disorder" is overly broad and needs qualifiers.

We appreciate the reviewer’s rigorousness. However, myopia is in fact the most common ocular disorder around the world. We have mentioned in the Introduction that “Myopia (short-sightedness) is now the most common visual disorder, and is predicted to impact approximately half of the world population by 2050 (Holden et al., 2016).” Therefore, we believe a qualifier is appropriate.

Line 36 - what ocular changes cannot be easily managed?

We thank the reviewer’s suggestion. We have modified the Introduction manuscript to add some examples:

“Although considered manageable with optical correction, the development of high levels of myopia (or pathological myopia) brings with it ocular changes that promote eye diseases that cannot be easily managed (glaucoma, cataract, myopic maculopathy, etc.) (Hayashi et al., 2010; Ikuno, 2017; Marcus et al., 2011).” (Line 34–37)

Why does loss of retinal efemp1 cause reduced OMR response? Unlikely to be refractive error at this stage.

We have modified the Discussion as per the reviewer’s concern:

“We noticed that although 5 dpf efemp12C-Cas9 fish overall were not myopic relative to efemp1+/+ fish (Fig 2B), they showed reduced spatial-frequency tuning function (Fig 1E–H). This phenotype, if not due to refractive error, can be a result of altered visual processing, as aberrant extracellular matrix caused by efemp1 disruption may lead to dysfunctional synapses (Dityatev and Schachner, 2006).” (Line 491–494)

Which Timp2 (Timp2a or Timp2b) is visualized in Figure 7?

Thanks to the reviewer for raising this point. We have added relevant details to the Methods:

“The anti-TIMP2 antibody was developed based on human TIMP2. In a previous study this antibody was showed to label for zebrafish TIMP2a (Zhang et al., 2003). As similarity of zebrafish TIMP2b to human TIMP2 is much lower than that of zebrafish TIMP2a (60.55% vs. 71.23%), labelling of zebrafish TIM2b is less likely. Yet, we are not able to completely rule out this possibility due to lack of information of the exact immunogen.” (Line 790–794)

Why is the inner retina studied for altered protein expression, but not the rest of the eye? Myopia is primarily driven by growth of the outer retinal/sclera.

The reason why we focused on the inner retina is that in our study, prominent expression differences of our proteins of interest between groups were mainly noticed on the inner but not outer retina. We agree that the outer retina is a key driver for visually regulated ocular growth, yet the inner retina also plays a crucial role. There is abundant evidence that the inner retina is involved in development of ocular refraction. For examples, Cx36, Egr1 and dopamine pathways in the inner retina have been reported to be associated with regulation of ocular refraction (PMID: 10412059; PMID: 28602573; PMID: 25052990; PMID: 32547367). We believe it is reasonable to focus on the inner retina, were we observed robust quantifiable expression for the tested proteins in our case.

Reviewer #3 (Significance (Required)):

• General assessment: This study uses retinal-specific inactivation of efemp1 with a clever methodology to study its effects on the eye. However, the necessity of these experiments is not well explained, as a full mouse knockout line exists. • Advance: There are some interesting observations about gene expression following efemp1 inactivation, and useful experiments that look at the combination of genetics with environmental conditions on refractive error. This builds on studies by the Hulleman group on efemp1's role in the eye by adding functional information. • Audience: This will be of interest to both basic researchers and clinicians who study genetic influencers of the eye.

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

Learn more at Review Commons

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Referee #3

Evidence, reproducibility and clarity

Summary

The authors use a retinal-specific promoter to target zebrafish efemp1 for inactivation to study its effects on the eye. Their use of the DiDonato/del Bene 2C-Cas9 system is a good method to target only cells that express a specific promoter i.e. rx2. Following this (mosaic and transient) targeting of efemp1, the authors describe enlarged eyes and myopia development, as well as reduced spatial visual sensitivity and altered retinal function by ERG analysis. Furthermore, expression levels of egr1, tgfb1a, vegfb, and rbp3 are altered, as well as Timp2 and Mmp2 proteins. Finally, dark-rearing of efemp1 mutant fish is reported to lead to emmetropization, rather than myopia.

Major comments

The data presented by the authors are interesting, and likely due to efemp1 disruption in the eye. However, the authors should clarify or explain several points, and improve on experimental rigor. Figure 1 C, D- PCRs are not convincing for loss of efemp1. The authors should consider PCR reactions that would show deletion driven by both CRISPRs, or an RFLP reaction based on conventional PCR that would show differences if individual CRISPRs were effective. There needs to be better evidence that efemp1 is being edited (e.g. Western blot, or qPCR). The data in Figure 7 are not convincing that EFEMP1 protein levels are substantially reduced in mutants. Why are efemp12C-Cas9 eyes smaller with normal lighting? (Figure S2) The clustering of datapoints in Figure 2B, 4B, overlaps extensively between control and mutant, and it is not easy to be sure that the high significance scores (***) are accurate. The authors should consider discussing whether loss of efemp1 is developmental only, or sustained. rx2 is likely to be switched off after development, and retinal cells that arise after the rx2:Gal4 ceases to be active will have a normal quotient of efemp1. The authors should also consider a more detailed discussion of the mechanism mediated by/through efemp1 that alters retinal function and expression of other genes. Finally, since a full mouse knockout of efemp1 exists (Daniel et al, 2020), it is not clear why a retinal-specific zebrafish model would give better insight into the phenotype.

Minor comments

"Myopia is the most common ocular disorder" is overly broad and needs qualifiers. Line 36 - what ocular changes cannot be easily managed? Why does loss of retinal efemp1 cause reduced OMR response? Unlikely to be refractive error at this stage. Which Timp2 (Timp2a or Timp2b) is visualized in Figure 7? Why is the inner retina studied for altered protein expression, but not the rest of the eye? Myopia is primarily driven by growth of the outer retinal/sclera.

Significance

• General assessment: This study uses retinal-specific inactivation of efemp1 with a clever methodology to study its effects on the eye. However, the necessity of these experiments is not well explained, as a full mouse knockout line exists.
• Advance: There are some interesting observations about gene expression following efemp1 inactivation, and useful experiments that look at the combination of genetics with environmental conditions on refractive error. This builds on studies by the Hulleman group on efemp1's role in the eye by adding functional information.
• Audience: This will be of interest to both basic researchers and clinicians who study genetic influencers of the eye.

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

Learn more at Review Commons

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Referee #2

Evidence, reproducibility and clarity

Summary:

In this study, the authors used the zebrafish model to study efemp1, a gene that was previously found to be associated with myopia. They used CRISPR-Cas9 to create specific efemp1 knockout in the retina in a mosaic manner. They used a few histological and physiological techniques to evaluate the resulting mutant and found that the efemp1 mutants developed symptoms that are consistent with myopia. The authors further quantified the expression of a few potential target genes in the eye that are potentially implicated in myopia phenotype. The authors also evaluated the differential phenotype of the efemp1 mutant grown in different light conditions that might contribute to myopia.

Major comments:

Overall, the authors have provided convincing evidence of the phenotype created by their efemp1 perturbation. Their experiments were thoroughly done and extensively analyzed. They even discussed some potential shortcomings of their study. Their study is a nice first step towards a better understanding of the efemp1 gene function in ocular growth and in myopia. All my comments below should be addressed by clarifications and discussions and not by any new experiments or projects.

Minor comments:

• Elaborate the rationale for choosing efemp1 from the original GWAS study for zebrafish investigation. The authors only mentioned that this gene is among the highest in the rank and its role in myopia is not clear. However, there are quite a few other genes in the GWAS study that were ranked as high, if not higher than efemp1.
• Elaborate the rationale for choosing retina as the target tissue of efemp1 knockout, especially when the original GWAS study indicated the expression of EFEMP1 is in cornea, RPE, and sclera, but not in retinal cells.
• Discuss possible ways of modifying efemp1 gene in the retina that would be more uniform and would not create mosaicism and/or heterogenous mutations that can complicate downstream characterizations and interpretations as the authors currently experienced.
• The authors should elaborate further on the effect of the mosaicism and heterogenous mutations on efemp1, a presumably excreted protein, on regulating the ocular growth. How did the downstream genes they studied affect by the messing up of the extracellular Efemp1? Is it through altering the Egf signal transduction?
• If possible, discuss the original SNP that was associated with efemp1 and the potential mechanisms through which the SNP affects human EFEMP1; Then, discuss how the study of zebrafish efemp1 mutant can aid our understanding of the human's SNP.
• Typo: Page 15, Line 299: Loss of this gene "promotes".

Significance

This study is an interesting and potentially significant addition to the ophthalmology field, as it conducted an initial characterization of a candidate gene for myopia in zebrafish and observed a relevant phenotype after the gene knockout. Colleagues in the myopia field will find the results interesting. In addition, colleagues in the zebrafish field will find the in-depth characterizations and tools used in the paper very informative.

I have conducted research in the human genetics of ophthalmology, gene expression analysis, zebrafish eye development and diseases. I believe my background allows me to effectively appreciate and evaluate the findings of this manuscript.

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

Learn more at Review Commons

---

Referee #1

Evidence, reproducibility and clarity

The manuscript by Xie et al investigates the role of efemp1 in mediating ocular growth. Efemp1, a secreted extracellular matrix glycoprotein, was previously identified as a myopia-risk gene in human GWAS studies. Given that myopia is linked to aberrant eye shape, the authors investigated whether and how this gene mediates eye growth. Using a CRISPR based approach in zebrafish the authors knocked out efemp1 specifically in the retina and established that a myopic eye results. They went further and investigated visual function in these mutant fish using the optomotor response and electroretinograms. As dark-rearing in many animal models has been linked to the induction of myopia, the authors examined the effects of a dark-rearing regimen in efemp1 mutants and found surprisingly that they did not show signs of myopia. Lastly, the expression and distribution of several myopia-associated genes was investigated in the retina of efemp1 mutants and following dark-rearing.

The starting point for this study was the generation of a "retina-specific knockout mutant of the efemp1 gene". However, evidence for a 'successful' knockout at the protein level is missing. Immunostaining for Efemp1 in sections of the entire retina from control and mutant fish would have helped here. It is only in Figure 7 B, C that portions of the inner retina from control and efemp12c-Cas9 fish are shown with Efemp1 immunostaining. Control and mutant retinae show slight relative differences in Efemp1 fluorescence levels which are difficult to reconcile with a knock-out scenario. Presumably this phenotype is a result of the mosaic expression of Cas9 (GFP) shown in Fig 1? Can the authors explain the reason for this mosaicism? Given this mosaic expression would one expect Efemp1 immunoreactive areas intermingled with areas devoid of Efemp1 in the mutant retina? Further, do deficits in the various functional assays the authors perform correlate with the degree of mosaicism? In the same vein, in Figure 2 the authors refer to variation in GFP levels in the efemp12c-Cas9. It is not clear whether the authors mean levels of GFP in individual cells or numbers of GFP+ cells. Presumably the latter. Could the authors clarify? In my opinion understanding and characterizing the efemp12c-Cas9 fish thoroughly is key to interpreting the phenotypes the authors show subsequently.

Significance

The wide range of assays the authors perform to assess visual deficits is commendable. Such a comprehensive approach ranging from anatomical, behavioral and electrophysiological assays is poised to identify changes that could otherwise be overlooked. Given the increasing use of zebrafish as models of ocular diseases, this study provides a solid roadmap of the types of analysis possible. This work should be interesting to researchers in the field of myopia research and to basic vision researchers interested in using the zebrafish as a model organism.

Padarius leako matavimą, tada Glassfish'a ir Newt'a parametru patikra, šitos vietos nereikia - tai bus daroma vėliau. Nustatom max energiją (pvz: 100kHz). Taip pat prie esamos srovės (kuri padaro išvadinę galią maksimalia (pvz 20W)) pridedame 2,5A. Taip pat juodą plokštelę padedame šalia grasshoperio, kad matavimai būtų tikslesni. P.S. Gustas is PP rms nedaro.

eLife assessment

This important study presents a novel pipeline for the large-scale genomic prediction of members of the non-ribosomal peptide group of pyoverdines based on a dataset from nearly 2000 Pseudomonas genomes. The advance presented in this study is largely based on solid evidence, although some main claims are only incompletely supported. This study on bacterial siderophores has broad theoretical and practical implications beyond a singular subfield.

Reviewer #1 (Public Review):

The manuscript introduces a bioinformatic pipeline designed to enhance the structure prediction of pyoverdines, revealing an extensive and previously overlooked diversity in siderophores and receptors. Utilizing a combination of feature sequence and phylogenetic approaches, the method aims to address the challenging task of predicting structures based on dispersed gene clusters, particularly relevant for pyoverdines.

Predicting structures based on gene clusters is still challenging, especially pyoverdines as the gene clusters are often spread to different locations in the genome. An improved method would indeed be highly useful, and the diversity of pyoverdine gene clusters and receptors identified is impressive.

However, so far the method basically aligns the structural genes and domains involved in pyoverdine biosynthesis and then predicts A domain specificity to predict the encoded compounds. Both methods are not particularly new as they are included in other tools such as PRISM (10.1093/nar/gkx320 ) or Sandpuma (https://doi.org/10.1093/bioinformatics/btx400) among others. The study claims superiority in A domain prediction compared to existing tools, yet the support is currently limited, relying on a comparison solely with AntiSMASH. A more extensive and systematic comparison with other tools is needed.

Additionally, in contradiction to the authors' claims, the method's applicability seems constrained to well-known and widely distributed gene clusters. The absence of predictions for new amino acids raises concerns about its generalizability to NRPS beyond the studied cases.

The manuscript lacks clarity on how the alignment of structural genes operates when dealing with multiple NRPS gene clusters on different genome contigs. How would the alignment of each BGC work?

Another critical concern is that a main challenge in NRPS structure prediction is not the backbone prediction but rather the prediction of tailoring reactions, which is not addressed in the manuscript at all, and this limitation extensively restricts the applicability of the method.

The manuscript presents a potentially highly useful bioinformatic pipeline for pyoverdine structure prediction, showcasing a commendable exploration of siderophore diversity. However, some of the claims made remain unsubstantiated. Overall, while the study holds promise, further validation and refinement are required to fulfill its potential impact on the field of bioinformatic structure prediction.

Reviewer #2 (Public Review):

Pyoverdines, siderophores produced by many Pseudomonads, are one of the most diverse groups of specialized metabolites and are frequently used as model systems. Thousands of Pseudomonas genomes are available, but large-scale analyses of pyoverdines are hampered by the biosynthetic gene clusters (BGCs) being spread across multiple genomic loci and existing tools' inability to accurately predict amino acid substrates of the biosynthetic adenylation (A) domains. The authors present a bioinformatics pipeline that identifies pyoverdine BGCs and predicts the A domain substrates with high accuracy. They tackled a second challenging problem by developing an algorithm to differentiate between outer membrane receptor selectivity for pyoverdines versus other siderophores and substrates. The authors applied their dataset to thousands of Pseudomonas strains, producing the first comprehensive overview of pyoverdines and their receptors and predicting many new structural variants.

The A domain substrate prediction is impressive, including the correction of entries in the MIBiG database. Their high accuracy came from a relatively small training dataset of A domains from 13 pyoverdine BGCs. The authors acknowledge that this small dataset does not include all substrates, and correctly point out that new sequence/structure pairs can be added to the training set to refine the prediction algorithm. The authors could have been more comprehensive in finding their training set data. For instance, the authors claim that histidine "had not been previously documented in pyoverdines", but the sequenced strain P. entomophila L48, incorporates His (10.1007/s10534-009-9247-y). The workflow cannot differentiate between different variants of Asp and OHOrn, and it's not clear if this is a limitation of the workflow, the training data, or both. The prediction workflow holds up well in Burkholderiales A domains, however, they fail to mention in the main text that they achieved these numbers by adding more A domains to their training set.

To validate their predictions, they elucidated structures of several new pyoverdines, and their predictions performed well. However, the authors did not include their MS/MS data, making it impossible to validate their structures. In general, the biggest limitation of the submitted manuscript is the near-empty methods section, which does not include any experimental details for the 20 strains or details of the annotation pipeline (such as "Phydist" and "Syndist"). The source code also does not contain the requisite information to replicate the results or re-use the pipeline, such as the antiSMASH version and required flags. That said, skimming through the source code and data (kindly provided upon request) suggests that the workflow itself is sound and a clear improvement over existing tools for pyoverdine BGC annotation.

Predicting outer membrane receptor specificity is likewise a challenging problem and the authors have made a promising achievement by finding specific gene regions that differentiate the pyoverdine receptor FpvA from FpvB and other receptor families. Their predictions were not tested experimentally, but the finding that only predicted FpvA receptors were proximate to the biosynthesis genes lends credence to the predictive power of the workflow. The authors find predicted pyoverdine receptors across an impressive 468 genera, an exciting finding for expanding the role of pyoverdines as public goods beyond Pseudomonas. However, whether or not these receptors can recognize pyoverdines (and if so, which structures!) remains to be investigated.

In all, the authors have assembled a rich dataset that will enable large-scale comparative genomic analyses. This dataset could be used by a variety of researchers, including those studying natural product evolution, public good eco/evo dynamics, and NRPS engineering.

Reviewer #3 (Public Review):

Summary:

Secondary metabolites are produced by numerous microorganisms and have important ecological functions. A major problem is that neither the function of a secondary metabolite enzyme nor the resulting metabolite can be precisely predicted from gene sequence data.

In the current paper, the authors addressed this highly relevant question.

The authors developed a bioinformatic pipeline to reconstruct the complete secondary metabolism pathway of pyoverdines, a class of iron-scavenging siderophores produced by Pseudomonas spp. These secondary metabolites are biosynthesized by a series of non-ribosomal peptide synthetases and require a specific receptor (FpvA) for uptake. The authors combined knowledge-guided learning with phylogeny-based methods to predict with high accuracy encoding NRPSs, substrate specificity of A domains, pyoverdine derivatives, and receptors. After validation, the authors tested their pipeline with sequence data from 1664 phylogenetically distinct Pseudomonas strains and were able to determine 18,292 enzymatic A domains involved in pyoverdine synthesis, reliably predicted 97.8% of their substrates, identified 188 different pyoverdine molecule structures and 4547 FpvA receptor variants belonging to 94 distinct groups. All the results and predictions were clearly superior to predictions that are based on antiSMASH. Novel pyoverdine structures were elucidated experimentally by UHPLC-HR-MS/MS.

To assess the extendibility of the pipeline, the authors chose Burkholderiales as a test case which led to the results that the pipeline consistently maintains high prediction accuracy within Burkholderiales of 83% which was higher than for antiSMASH (67%).

Together, the authors concluded that supervised learning based on a few known compounds produced by species from the same genus probably outperforms generalized prediction algorithms trained on many products from a diverse set of microbes for NRPS substrate predictions. As a result, they also show that both pyoverdine and receptor diversity have been vastly underestimated.

Strengths:

The authors developed a very useful bioinformatic pipeline with high accuracy for secondary metabolites, at least for pyoverdines. The pipelines have several advantages compared to existing pipelines like the extensively used antiSMASH program, e.g. it can be applied to draft genomes, shows reduced erroneous gene predictions, etc. The accuracy was impressively demonstrated by the discovery of novel pyoverdines whose structures were experimentally substantiated by UHPLC-HR-MS/MS.

The manuscript is very well written, and the data and the description of the generation of pipelines are easy to follow.

Weaknesses:

The only major comment I have is the uncertainty of whether the pipeline can be applied to more complex non-ribosomal peptides. In the current study, the authors only applied their pipeline to a very narrow field, i.e., pyoverdines of Pseudomonas and Burkholderia strains.

Author response:

eLife assessment

This study provides valuable evidence indicating that Syngap1 regulates the synaptic drive and membrane excitability of parvalbumin- and somatostatin-positive interneurons in the auditory cortex. Since haplo-insufficiency of Syngap1 has been linked to intellectual disabilities without a well-defined underlying cause, the central question of this study is timely. However, the support for the authors' conclusions is incomplete in general and some parts of the experimental evidence are inadequate. Specifically, the manuscript requires further work to properly evaluate the impact on synaptic currents, intrinsic excitability parameters, and morphological features.

We are happy that the editors found that our study provides valuable evidence and that the central question is timely. We thank the reviewers for their detailed comments and suggestions. Below, we provide a point-by-point answer (in blue) to the specific comments and indicate the changes to the manuscript and the additional experiments we plan to perform to answer these comments.

Public Reviews:

Reviewer #1 (Public Review):

The study is designed to assess the role of Syngap1 in regulating the physiology of the MGE-derived PV+ and SST+ interneurons. Syngap1 is associated with some mental health disorders, and PV+ and SST+ cells are the focus of many previous and likely future reports from studies of interneuron biology, highlighting the translational and basic neuroscience relevance of the authors' work.

Strengths of the study are using well-established electrophysiology methods and the highly controlled conditions of ex vivo brain slice experiments combined with a novel intersectional mouse line, to assess the role of Syngap1 in regulating PV+ and SST+ cell properties. The findings revealed that in the mature auditory cortex, Syngap1 haploinsufficiency decreases both the intrinsic excitability and the excitatory synaptic drive onto PV+ neurons from Layer 4. In contrast, SST+ interneurons were mostly unaffected by Syngap1 haploinsufficiency. Pharmacologically manipulating the activity of voltage-gated potassium channels of the Kv1 family suggested that these channels contributed to the decreased PV+ neuron excitability by Syngap insufficiency. These results therefore suggest that normal Syngap1 expression levels are necessary to produce normal PV+ cell intrinsic properties and excitatory synaptic drive, albeit, perhaps surprisingly, inhibitory synaptic transmission was not affected by Syngap1 haploinsufficiency.

Since the electrophysiology experiments were performed in the adult auditory cortex, while Syngap1 expression was potentially affected since embryonic stages in the MGE, future studies should address two important points that were not tackled in the present study. First, what is the developmental time window in which Syngap1 insufficiency disrupted PV+ neuron properties? Albeit the embryonic Syngap1 deletion most likely affected PV+ neuron maturation, the properties of Syngap-insufficient PV+ neurons do not resemble those of immature PV+ neurons. Second, whereas the observation that Syngap1 haploinsufficiency affected PV+ neurons in auditory cortex layer 4 suggests auditory processing alterations, MGE-derived PV+ neurons populate every cortical area. Therefore, without information on whether Syngap1 expression levels are cortical area-specific, the data in this study would predict that by regulating PV+ neuron electrophysiology, Syngap1 normally controls circuit function in a wide range of cortical areas, and therefore a range of sensory, motor and cognitive functions. These are relatively minor weaknesses regarding interpretation of the data in the present study that the authors could discuss.

We agree with the reviewer on the proposed open questions, which we will certainly discuss in the revised manuscript we are preparing. We do have experimental evidence suggesting that Syngap1 mRNA is expressed by PV+ and SST+ neurons in different cortical areas, during early postnatal development and in adulthood; therefore, we agree that it will be important, in future experiments, to tackle the question of when the observed phenotypes arise.

Reviewer #2 (Public Review):

Summary:

In this manuscript, the authors investigated how partial loss of SynGap1 affects inhibitory neurons derived from the MGE in the auditory cortex, focusing on their synaptic inputs and excitability. While haplo-insufficiently of SynGap1 is known to lead to intellectual disabilities, the underlying mechanisms remain unclear.

Strengths:

The questions are novel

Weaknesses:

Despite the interesting and novel questions, there are significant concerns regarding the experimental design and data quality, as well as potential misinterpretations of key findings. Consequently, the current manuscript fails to contribute substantially to our understanding of SynGap1 loss mechanisms and may even provoke unnecessary controversies.

Major issues:

(1) One major concern is the inconsistency and confusion in the intermediate conclusions drawn from the results. For instance, while the sEPSC data indicates decreased amplitude in PV+ and SOM+ cells in cHet animals, the frequency of events remains unchanged. In contrast, the mEPSC data shows no change in amplitudes in PV+ cells, but a significant decrease in event frequency. The authors conclude that the former observation implies decreased excitability. However, traditionally, such observations on mEPSC parameters are considered indicative of presynaptic mechanisms rather than changes of network activity.‎ The subsequent synapse counting experiments align more closely with the traditional conclusions. This issue can be resolved by rephrasing the text. However, it would remain unexplained why the sEPSC frequency shows no significant difference. If the majority of sEPSC events were indeed mediated by spiking (which is blocked by TTX), the average amplitudes and frequency of mEPSCs should be substantially lower than those of sEPSCs. Yet, they fall within a very similar range, suggesting that most sEPSCs may actually be independent of action potentials. But if that was indeed the case, the changes of purported sEPSC and mEPSC results should have been similar.

We understand the reviewer’s perspective; indeed, we asked ourselves the very same question regarding why the sEPSC and mEPSC frequency fall within a similar range when we analysed neuron means (bar graphs). We have already recorded sEPSCs followed by mEPSCs from several PV neurons (control and cHet) and are in the process of analyzing the data. We will add this data to the revised version of the manuscript. We will also rephrase the manuscript to present multiple potential interpretations of the data.

We hope that we have correctly interpreted the reviewer's concern. However, if the question is why sEPSC amplitude but not frequency is affected in cHet vs ctrl then the reviewer’s comment is perhaps based on the assumption that the amplitude and frequency of miniature events should be lower for all events compared to those observed for spontaneous events. However, it's essential to note that changes in the mean amplitude of sEPSCs are primarily driven by alterations in large sEPSCs (>9-10pA, as shown in cumulative probability in Fig. 1b right), with smaller ones being relatively unaffected. Consequently, a reduction in sEPSC amplitude may not necessarily result in a significant decrease in frequency since their values likely remain above the detection threshold of 3 pA. This could explain the lack of a significant decrease in average inter-interval event of sEPSCs (as depicted in Fig. 1b left).

If the question is whether we should see the same parameters affected by the genetic manipulation in both sEPSC and mEPSC, then another critical consideration is the involvement of the releasable pool in mEPSCs versus sEPSCs. Current knowledge suggests that activity-dependent and -independent release may not necessarily engage the same pool of vesicles or target the same postsynaptic sites. This concept has been extensively explored (reviewed in Kavalali, 2015). Consequently, while we may have traditionally interpreted activity-dependent and -independent data assuming they utilize the same pool, this is no longer accurate. The current discussion in the field revolves around understanding the mechanisms underlying such phenomena. Therefore, comparisons between sEPSCs and mEPSCs may not yield conclusive data but rather speculative interpretations. For a rigorous analysis, particularly in this context involving thousands of events, it is essential to assess these data sets (mEPSCs vs sEPSCs) separately and provide cumulative probability curves. This approach allows for a more comprehensive understanding of the underlying distributions and helps to elucidate any potential differences between the two types of events. We will rephrase the text, and as mentioned above, add additional data, to better reflect these considerations.

(2) Another significant concern is the quality of synapse counting experiments. The authors attempted to colocalize pre- and postsynaptic markers Vglut1 and PSD95 with PV labelling. However, several issues arise. Firstly, the PV labelling seems confined to soma regions, with no visible dendrites. Given that the perisomatic region only receives a minor fraction of excitatory synapses, this labeling might not accurately represent the input coverage of PV cells. Secondly, the resolution of the images is insufficient to support clear colocalization of the synaptic markers. Thirdly, the staining patterns are peculiar, with PSD95 puncta appearing within regions clearly identified as somas by Vglut1, hinting at possible intracellular signals. Furthermore, PSD95 seems to delineate potential apical dendrites of pyramidal cells passing through the region, yet Vglut1+ partners are absent in these segments, which are expected to be the marker of these synapses here. Additionally, the cumulative density of Vglut2 and Vglut1 puncta exceeds expectations, and it's surprising that subcortical fibers labeled by Vglut2 are comparable in number to intracortical Vglut1+ axon terminals. Ideally, N(Vglut1)+N(Vglut2) should be equal or less than N(PSD95), but this is not the case here. Consequently, these results cannot be considered reliable due to these issues.

We apologize, as it appears that the images we provided have caused confusion. The selected images represent a single focal plane of a confocal stack, which was visually centered on the PV cell somata. We chose just one confocal plane because we thought it showed more clearly the apposition of presynaptic and postsynaptic immunolabeling around the somata. In the revised version of the manuscript, we will provide higher magnification images, which will clearly show how we identified and selected the region of interest for the quantification of colocalized synaptic markers. In our confocal stacks, we can also identify PV immunolabeled dendrites and colocalized vGlut1/PSD95 or vGlut2/PSD95 puncta on them; but these do not appear in the selected images because, as explained, only one focal plane, centered on the PV cell somata, was shown.

We acknowledge the reviewer's point that in PV+ cells the majority of excitatory inputs are formed onto dendrites; however, we focused on the somatic excitatory inputs to PV cells, because despite their lower number, they produce much stronger depolarization in PV neurons than dendritic excitatory inputs (Hu et al., 2010; Norenberg et al., 2010). Further, quantification of perisomatic putative excitatory synapses is more reliable since by using PV immunostaining, we can visualize the soma and larger primary dendrites, but smaller, higher order dendrites are not be always detectable. Of note, PV positive somata receive more excitatory synapses than SST positive and pyramidal neuron somata as found by electron microscopy studies in the visual cortex (Hwang et al., 2021; Elabbady et al., 2024).

Regarding the comment on the density of vGlut1 and vGlut2 puncta, the reason that the numbers appear high and similar between the two markers is because we present normalized data (cHet normalized to their control values for each set of immunolabelling) to clearly represent the differences between genotypes. This information is present in the legends but we apologize for not clearly explaining it the methods section. We will provide a more detailed explanation of our methods in the revised manuscript.

Briefly, immunostained sections were imaged using a Leica SP8-STED confocal microscope, with a 63x (NA 1.4) at 1024 X 1024, z-step =0.3 μm, stack size of ~15 μm. Images were acquired from the auditory cortex from at least 3 coronal sections per animal. All the confocal parameters were maintained constant throughout the acquisition of an experiment. All images shown in the figures are from a single confocal plane. To quantify the number of vGlut1/PSD95 or vGlut2/PSD95 putative synapses, images were exported as TIFF files and analyzed using Fiji (Image J) software. We first manually outlined the profile of each PV cell soma (identified by PV immunolabeling). At least 4 innervated somata were selected in each confocal stack. We then used a series of custom-made macros in Fiji as previously described (Chehrazi et al, 2023). After subtracting background (rolling value = 10) and Gaussian blur (σ value = 2) filters, the stacks were binarized and vGlut1/PSD95 or vGlut2/PSD95 puncta were independently identified around the perimeter of a targeted soma in the focal plane with the highest soma circumference. Puncta were quantified after filtering particles for size (included between 0-2μm2) and circularity (included between 0-1). Data quantification was done by investigators blind to the genotype, and presented as normalized data over control values for each experiment.

(3) One observation from the minimal stimulation experiment was concluded by an unsupported statement. Namely, the change in the onset delay cannot be attributed to a deficit in the recruitment of PV+ cells, but it may suggest a change in the excitability of TC axons.

We agree with the reviewer, please see answer to point below.

(‎4) The conclusions drawn from the stimulation experiments are also disconnected from the actual data. To make conclusions about TC release, the authors should have tested release probability using established methods, such as paired-pulse changes. Instead, the only observation here is a change in the AMPA components, which remained unexplained.

We agree with the reviewer and we will perform additional paired-pulse ratio experiments at different intervals. We will rephrase the discussion and our interpretation and potential hypothesis according to the data obtained from this new experiment.

(5) The sampling rate of CC recordings is insufficient ‎to resolve the temporal properties of the APs. Therefore, the phase-plots cannot be interpreted (e.g. axonal and somatic AP components are not clearly separated), raising questions about how AP threshold and peak were measured. The low sampling rate also masks the real derivative of the AP signals, making them apparently faster.

We acknowledge that a higher sampling rate could offer a more detailed analysis of the action potential waveform. However, in the context of action potential analysis, it is acceptable to use sampling rates ranging from 10 kHz to 20 kHz (Golomb et al., 2007; Stevens et al., 2021; Zhang et al., 2023), which are considered adequate in the context of the present study. Indeed, our study aims to evaluate "relative" differences in the electrophysiological phenotype when comparing groups following a specific genetic manipulation. A sampling rate of 10 kHz is commonly employed in similar studies, including those conducted by our collaborator and co-author S. Kourrich (e.g., Kourrich and Thomas 2009, Kourrich et al., 2013), as well as others (Russo et al., 2013; Ünal et al., 2020; Chamberland et al., 2023).

Despite being acquired at a lower sampling rate than potentially preferred by the reviewer, our data clearly demonstrate significant differences between the experimental groups, especially for parameters that are negligibly or not affected by the sampling rate used here (e.g., #spikes/input, RMP, Rin, Cm, Tm, AP amplitude, AP latency, AP rheobase).

Regarding the phase-plots, we agree that a higher sampling rate would have resulted in smoother curves and more accurate absolute values. However, the differences were sufficiently pronounced to discern the relative variations in action potential waveforms between the experimental groups.

A related issue is that the Methods section lacks essential details about the recording conditions, such as bridge balance and capacitance neutralization.

We indeed performed bridge balance and neutralized the capacitance before starting every recording. We will add the information in the methods.

(6) Interpretation issue: One of the most fundamental measures of cellular excitability, the rheobase, was differentially affected by cHet in BCshort and BCbroad. Yet, the authors concluded that the cHet-induced changes in the two subpopulations are common.

We are uncertain if we have correctly interpreted the reviewer's comment. While we observed distinct impacts on the rheobase (Fig. 7d and 7i), there seems to be a common effect on the AP threshold (Fig. 7c and 7h), as interpreted and indicated in the final sentence of the results section for Figure 7 (page 12). If our response does not address the reviewer's comment adequately, we would greatly appreciate it if the reviewer could rephrase their feedback.

(7) Design issue:

The Kv1 blockade experiments are disconnected from the main manuscript. There is no experiment that shows the causal relationship between changes in DTX and cHet cells. It is only an interesting observation on AP halfwidth and threshold. However, how they affect rheobase, EPSCs, and other topics of the manuscript are not addressed in DTX experiments.

Furthermore, Kv1 currents were never measured in this work, nor was the channel density tested. Thus, the DTX effects are not necessarily related to changes in PV cells, which can potentially generate controversies.

While we acknowledge the reviewer's point that Kv1 currents and density weren't specifically tested, an important insight provided by Fig. 5 is the prolonged action potential latency. This delay is significantly influenced by slowly inactivating subthreshold potassium currents, namely the D-type K+ current. It's worth noting that D-type current is primarily mediated by members of the Kv1 family. The literature supports a role for Kv1.1-containing channels in modulating responses to near-threshold stimuli in PV cells (Wang et al., 1994; Goldberg et al., 2008; Zurita et al., 2018). However, we recognize that besides the Kv1 family, other families may also contribute to the observed changes.

To address this concern, we will revise our interpretation. We will opt for the more accurate term "D-type K+ current" and only speculate about the involved channel family in the discussion. It is not our intention to open unnecessary controversy, but present the data we obtained. We believe this approach and rephrasing the discussion as proposed will prevent unnecessary controversy and instead foster fruitful discussions.

(8) Writing issues:

Abstract:

The auditory system is not mentioned in the abstract.

One statement in the abstract is unclear‎. What is meant by "targeting Kv1 family of voltage-gated potassium channels was sufficient..."? "Targeting" could refer to altered subcellular targeting of the channels, simple overexpression/deletion in the target cell population, or targeted mutation of the channel, etc. Only the final part of the Results revealed that none of the above, but these channels were blocked selectively.

We agree with the reviewer and we will rephrase the abstract accordingly.

Introduction:

There is a contradiction in the introduction. The second paragraph describes in detail the distinct contribution of PV and SST n‎eurons to auditory processing. But at the end, the authors state that "relatively few reports on PV+ and SST+ cell-intrinsic and synaptic properties in adult auditory cortex". Please be more specific about the unknown properties.

We agree with the reviewer and we will rephrase more specifically.

(9) The introduction emphasizes the heterogeneity of PV neurons, which certainly influences the interpretation of the results of the current manuscript. However, the initial experiments did not consider this and handled all PV cell data as a pooled population.

In the initial experiments, we handled all PV cell data together because we wanted to be rigorous and not make assumptions/biases on the different PV cells, which in later experiments we were to distinguish based on the intrinsic properties alone. We will make this point clear in the revised manuscript.

(10) The interpretation of the results strongly depends on unpublished work, which potentially provide the physiological and behavioral contexts about the role of GABAergic neurons in SynGap-haploinsufficiency. The authors cite their own unpublished work, without explaining the specific findings and relation to this manuscript.

We agree with the reviewer and apologize for the lack of clarity. Our unpublished work is in revision right now. We will provide more information and update references in the revised version of this manuscript.

(11) The introduction of Scholl analysis ‎experiments mentions SOM staining, however, there is no such data about this cell type in the manuscript.

We apologize for the error, we will change SOM with SST (SOM and SST are two commonly used acronyms for Somatostatin expressing interneurons).

Reviewer #3 (Public Review):

This paper compares the synaptic and membrane properties of two main subtypes of interneurons (PV+, SST+) in the auditory cortex of control mice vs mutants with Syngap1 haploinsufficiency. The authors find differences at both levels, although predominantly in PV+ cells. These results suggest that altered PV-interneuron functions in the auditory cortex may contribute to the network dysfunction observed in Syngap1 haploinsufficiency-related intellectual disability. The subject of the work is interesting, and most of the approach is direct and quantitative, which are major strengths. There are also some weaknesses that reduce its impact for a broader field.

(1) The choice of mice with conditional (rather than global) haploinsufficiency makes the link between the findings and Syngap1 relatively easy to interpret, which is a strength. However, it also remains unclear whether an entire network with the same mutation at a global level (affecting also excitatory neurons) would react similarly.

The reviewer raises an interesting and pertinent open question which we will address in the discussion of the revised paper.

(2) There are some (apparent?) inconsistencies between the text and the figures. Although the authors appear to have used a sophisticated statistical analysis, some datasets in the illustrations do not seem to match the statistical results. For example, neither Fig 1g nor Fig 3f (eNMDA) reach significance despite large differences.

We respectfully disagree, we do not think the text and figures are inconsistent. In the cited example, large apparent difference in mean values does not show significance due to the large variability in the data; further, we did not exclude any data points, because we wanted to be rigorous. In particular, for Fig.1g, statistical analysis shows a significant increase in the inter-mEPSC interval (*p=0.027, LMM) when all events are considered (cumulative probability plots), while there is no significant difference in the inter-mEPSCs interval for inter-cell mean comparison (inset, p=0.354, LMM). Inter-cell mean comparison does not show difference with Mann-Whitney test either (p=0.101, the data are not normally distributed, hence the choice of the Mann-Whitney test). For Fig. 3f (eNMDA), the higher mean value for the cHet versus the control is driven by two data points which are particularly high, while the other data points overlap with the control values. The Mann-Whitney test show also no statistical difference (p=0.174).

In the manuscript, discussion of the data is based on the results of the LMM analysis, which takes in account both the number of cells and the numbers of mice from which these cells are recorded. We chose this statistical approach because it does not rely on the assumption that cells recorded from same mouse are independent variables. In the supplemental tables, we provided the results of the statistical analysis done with both LMM and the most commonly used Mann Whitney (for not normally distributed) or t-test (for normally distributed), for each data set.

Also, the legend to Fig 9 indicates the presence of "a significant decrease in AP half-width from cHet in absence or presence of a-DTX", but the bar graph does not seem to show that.

We apologize for our lack of clarity. In legend 9, we reported the statistical comparisons between 1) cHET mice in absence of a-DTX and control mice and 2) cHET mice in presence of a-DTX and control mice. We will rephrase result description and the legend of the figure to avoid confusion.

(3) The authors mention that the lack of differences in synaptic current kinetics is evidence against a change in subunit composition. However, in some Figures, for example, 3a, the kinetics of the recorded currents appear dramatically different. It would be important to know and compare the values of the series resistance between control and mutant animals.

We agree with the reviewer that there appears to be a qualitative difference in eNMDA decay between conditions, although quantified eNMDA decay itself is similar between groups. We have used a cutoff of 15 % for the series resistance (Rs), which is significantly more stringent as compared to the cutoff typically used in electrophysiology, which are for the vast majority between 20 and 30%. To answer this concern, we re-examined the Rs, we compared Rs between groups and found no difference for Rs in eAMPA (13.2±0.5 in WT n=16 cells, 7 mice vs 13.7±0.3 in cHet n=14 cells, 7 mice, p=0.432 LMM) and eNMDA (12.7±0.7 in WT n=6 cells, 3 mice vs 13.8±0.7 in cHet n=6 cells, 5 mice, p=0.231, LMM). Thus, the apparent qualitative difference in eNMDA decay stems from inter-cell variability rather than inter-group differences. Notably, this discrepancy between the trace (Fig. 3a) and the data (Fig. 3f, right) is largely due to inter-cell variability, particularly in eNMDA, where a higher but non-significant decay rate is driven by a couple of very high values (Fig. 3f, right). In the revised manuscript, we will show traces that better represent our findings.

(4) A significant unexplained variability is present in several datasets. For example, the AP threshold for PV+ includes points between -50-40 mV, but also values at around -20/-15 mV, which seems too depolarized to generate healthy APs (Fig 5c, Fig7c).

We acknowledge the variability in AP threshold data, with some APs appearing too depolarized to generate healthy spikes. However, we meticulously examined each AP that spiked at these depolarized thresholds and found that other intrinsic properties (such as Rin, Vrest, AP overshoot, etc.) all indicate that these cells are healthy. Therefore, to maintain objectivity and provide unbiased data to the community, we opted to include them in our analysis. It's worth noting that similar variability has been observed in other studies (Bengtsson Gonzales et al., 2020; Bertero et al., 2020).

Further, we conducted a significance test on AP threshold excluding these potentially unhealthy cells and found that the significant differences persist. After removing two outliers from the cHet group with values of -16.5 and 20.6 mV, we obtain: -42.6±1.01 mV in control, n=33, 15 mice vs -36.2±1.1 mV in cHet, n=38 cells, 17 mice, ***p<0.001, LMM. Thus, whether these cells are included or excluded, our interpretations and conclusions remain unchanged.

We would like to clarify that these data have not been corrected with the junction potential. We will add this info in the revised version.

(5) I am unclear as to how the authors quantified colocalization between VGluts and PSD95 at the low magnification shown in Supplementary Figure 2.

We apologize for our lack of clarity. Although the analysis was done at high resolution, the figures were focused on showing multiple PV somata receiving excitatory inputs. We will add higher magnification figures and more detailed information in the methods of the revised version. Please also see our response to reviewer #2.

(6) The authors claim that "cHet SST+ cells showed no significant changes in active and passive membrane properties", but this claim would seem to be directly refused by the data of Fig 8f. In the absence of changes in either active or passive membrane properties shouldn't the current/#AP plot remain unchanged?

While we acknowledge the theoretical expectation that changes in intrinsic parameters should correlate with alterations in neuronal firing, the absence of differences in the parameters analyzed in this study should not overshadow the clear and significant decrease in firing rate observed in cHet SST+ cells. This decrease serves as a compelling indication of reduced intrinsic neuronal excitability. It's certainly possible that other intrinsic factors, not assessed in this study, may have contributed to this effect. However, exploring these mechanisms is beyond the scope of our current investigation. We will rephrase the discussion and add this limitation of our study in the revised version.

(7) The plots used for the determination of AP threshold (Figs 5c, 7c, and 7h) suggest that the frequency of acquisition of current-clamp signals may not have been sufficient, this value is not included in the Methods section.

This study utilized a sampling rate of 10 kHz, which is a standard rate for action potential analysis in the present context. We will describe more extensively the technical details in the method section of the revised manuscript we are preparing. While we acknowledge that a higher sampling rate could have enhanced the clarity of the phase plot, our recording conditions, as detailed in our response to Rev#2/comment#5, were suitable for the objectives of this study.

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eLife assessment

This study provides valuable evidence indicating that SynGap1 regulates the synaptic drive and membrane excitability of parvalbumin- and somatostatin-positive interneurons in the auditory cortex. Since haplo-insufficiency of SynGap1 has been linked to intellectual disabilities without a well-defined underlying cause, the central question of this study is timely. However, the support for the authors' conclusions is incomplete in general and some parts of the experimental evidence are inadequate. Specifically, the manuscript requires further work to properly evaluate the impact on synaptic currents, intrinsic excitability parameters, and morphological features.

Reviewer #1 (Public Review):

The study is designed to assess the role of Syngap1 in regulating the physiology of the MGE-derived PV+ and SST+ interneurons. Syngap1 is associated with some mental health disorders, and PV+ and SST+ cells are the focus of many previous and likely future reports from studies of interneuron biology, highlighting the translational and basic neuroscience relevance of the authors' work.

Strengths of the study are using well-established electrophysiology methods and the highly controlled conditions of ex vivo brain slice experiments combined with a novel intersectional mouse line, to assess the role of Syngap1 in regulating PV+ and SST+ cell properties. The findings revealed that in the mature auditory cortex, Syngap1 haploinsufficiency decreases both the intrinsic excitability and the excitatory synaptic drive onto PV+ neurons from Layer 4. In contrast, SST+ interneurons were mostly unaffected by Syngap1 haploinsufficiency. Pharmacologically manipulating the activity of voltage-gated potassium channels of the Kv1 family suggested that these channels contributed to the decreased PV+ neuron excitability by Syngap insufficiency. These results therefore suggest that normal Syngap1 expression levels are necessary to produce normal PV+ cell intrinsic properties and excitatory synaptic drive, albeit, perhaps surprisingly, inhibitory synaptic transmission was not affected by Syngap1 haploinsufficiency.

Since the electrophysiology experiments were performed in the adult auditory cortex, while Syngap1 expression was potentially affected since embryonic stages in the MGE, future studies should address two important points that were not tackled in the present study. First, what is the developmental time window in which Syngap1 insufficiency disrupted PV+ neuron properties? Albeit the embryonic Syngap1 deletion most likely affected PV+ neuron maturation, the properties of Syngap-insufficient PV+ neurons do not resemble those of immature PV+ neurons. Second, whereas the observation that Syngap1 haploinsufficiency affected PV+ neurons in auditory cortex layer 4 suggests auditory processing alterations, MGE-derived PV+ neurons populate every cortical area. Therefore, without information on whether Syngap1 expression levels are cortical area-specific, the data in this study would predict that by regulating PV+ neuron electrophysiology, Syngap1 normally controls circuit function in a wide range of cortical areas, and therefore a range of sensory, motor and cognitive functions. These are relatively minor weaknesses regarding interpretation of the data in the present study that the authors could discuss.

Reviewer #2 (Public Review):

Summary:

In this manuscript, the authors investigated how partial loss of SynGap1 affects inhibitory neurons derived from the MGE in the auditory cortex, focusing on their synaptic inputs and excitability. While haplo-insufficiently of SynGap1 is known to lead to intellectual disabilities, the underlying mechanisms remain unclear.

Strengths:

The questions are novel

Weaknesses:

Despite the interesting and novel questions, there are significant concerns regarding the experimental design and data quality, as well as potential misinterpretations of key findings. Consequently, the current manuscript fails to contribute substantially to our understanding of SynGap1 loss mechanisms and may even provoke unnecessary controversies.

Major issues:

(1) One major concern is the inconsistency and confusion in the intermediate conclusions drawn from the results. For instance, while the sEPSC data indicates decreased amplitude in PV+ and SOM+ cells in cHet animals, the frequency of events remains unchanged. In contrast, the mEPSC data shows no change in amplitudes in PV+ cells, but a significant decrease in event frequency. The authors conclude that the former observation implies decreased excitability. However, traditionally, such observations on mEPSC parameters are considered indicative of presynaptic mechanisms rather than changes of network activity.‎ The subsequent synapse counting experiments align more closely with the traditional conclusions. This issue can be resolved by rephrasing the text. However, it would remain unexplained why the sEPSC frequency shows no significant difference. If the majority of sEPSC events were indeed mediated by spiking (which is blocked by TTX), the average amplitudes and frequency of mEPSCs should be substantially lower than those of sEPSCs. Yet, they fall within a very similar range, suggesting that most sEPSCs may actually be independent of action potentials. But if that was indeed the case, the changes of purported sEPSC and mEPSC results should have been similar.

(2) Another significant concern is the quality of synapse counting experiments. The authors attempted to colocalize pre- and postsynaptic markers Vglut1 and PSD95 with PV labelling. However, several issues arise. Firstly, the PV labelling seems confined to soma regions, with no visible dendrites. Given that the perisomatic region only receives a minor fraction of excitatory synapses, this labeling might not accurately represent the input coverage of PV cells.<br /> Secondly, the resolution of the images is insufficient to support clear colocalization of the synaptic markers. Thirdly, the staining patterns are peculiar, with PSD95 puncta appearing within regions clearly identified as somas by Vglut1, hinting at possible intracellular signals. Furthermore, PSD95 seems to delineate potential apical dendrites of pyramidal cells passing through the region, yet Vglut1+ partners are absent in these segments, which are expected to be the marker of these synapses here.<br /> Additionally, the cumulative density of Vglut2 and Vglut1 puncta exceeds expectations, and it's surprising that subcortical fibers labeled by Vglut2 are comparable in number to intracortical Vglut1+ axon terminals. Ideally, N(Vglut1)+N(Vglut2) should be equal or less than N(PSD95), but this is not the case here. Consequently, these results cannot be considered reliable due to these issues.

(3) One observation from the minimal stimulation experiment was concluded by an unsupported statement. Namely, the change in the onset delay cannot be attributed to a deficit in the recruitment of PV+ cells, but it may suggest a change in the excitability of TC axons.

(‎4) The conclusions drawn from the stimulation experiments are also disconnected from the actual data. To make conclusions about TC release, the authors should have tested release probability using established methods, such as paired-pulse changes. Instead, the only observation here is a change in the AMPA components, which remained unexplained.

(5) The sampling rate of CC recordings is insufficient ‎to resolve the temporal properties of the APs. Therefore, the phase-plots cannot be interpreted (e.g. axonal and somatic AP components are not clearly separated), raising questions about how AP threshold and peak were measured. The low sampling rate also masks the real derivative of the AP signals, making them apparently faster.<br /> A related issue is that the Methods section lacks essential details about the recording conditions, such as bridge balance and capacitance neutralization.

(6) Interpretation issue: One of the most fundamental measures of cellular excitability, the rheobase, was differentially affected by cHet in BCshort and BCbroad. Yet, the authors concluded that the cHet-induced changes in the two subpopulations are common.

(7) Design issue:<br /> The Kv1 blockade experiments are disconnected from the main manuscript. There is no experiment that shows the causal relationship between changes in DTX and cHet cells. It is only an interesting observation on AP halfwidth and threshold. However, how they affect rheobase, EPSCs, and other topics of the manuscript are not addressed in DTX experiments.<br /> Furthermore, Kv1 currents were never measured in this work, nor was the channel density tested. Thus, the DTX effects are not necessarily related to changes in PV cells, which can potentially generate controversies.

(8) Writing issues:<br /> Abstract:<br /> The auditory system is not mentioned in the abstract.<br /> One statement in the abstract is unclear‎. What is meant by "targeting Kv1 family of voltage-gated potassium channels was sufficient..."? "Targeting" could refer to altered subcellular targeting of the channels, simple overexpression/deletion in the target cell population, or targeted mutation of the channel, etc. Only the final part of the Results revealed that none of the above, but these channels were blocked selectively.<br /> Introduction:<br /> There is a contradiction in the introduction. The second paragraph describes in detail the distinct contribution of PV and SST n‎eurons to auditory processing. But at the end, the authors state that "relatively few reports on PV+ and SST+ cell-intrinsic and synaptic properties in adult auditory cortex". Please be more specific about the unknown properties.

(9) The introduction emphasizes the heterogeneity of PV neurons, which certainly influences the interpretation of the results of the current manuscript. However, the initial experiments did not consider this and handled all PV cell data as a pooled population.

(10) The interpretation of the results strongly depends on unpublished work, which potentially provide the physiological and behavioral contexts about the role of GABAergic neurons in SynGap-haploinsufficiency. The authors cite their own unpublished work, without explaining the specific findings and relation to this manuscript.

(11) The introduction of Scholl analysis ‎experiments mentions SOM staining, however, there is no such data about this cell type in the manuscript.

Reviewer #3 (Public Review):

This paper compares the synaptic and membrane properties of two main subtypes of interneurons (PV+, SST+) in the auditory cortex of control mice vs mutants with Syngap1 haploinsufficiency. The authors find differences at both levels, although predominantly in PV+ cells. These results suggest that altered PV-interneuron functions in the auditory cortex may contribute to the network dysfunction observed in Syngap1 haploinsufficiency-related intellectual disability. The subject of the work is interesting, and most of the approach is direct and quantitative, which are major strengths. There are also some weaknesses that reduce its impact for a broader field.

(1) The choice of mice with conditional (rather than global) haploinsufficiency makes the link between the findings and Syngap1 relatively easy to interpret, which is a strength. However, it also remains unclear whether an entire network with the same mutation at a global level (affecting also excitatory neurons) would react similarly.

(2) There are some (apparent?) inconsistencies between the text and the figures. Although the authors appear to have used a sophisticated statistical analysis, some datasets in the illustrations do not seem to match the statistical results. For example, neither Fig 1g nor Fig 3f (eNMDA) reach significance despite large differences. Also, the legend to Fig 9 indicates the presence of "a significant decrease in AP half-width from cHet in absence or presence of a-DTX", but the bar graph does not seem to show that.

(3) The authors mention that the lack of differences in synaptic current kinetics is evidence against a change in subunit composition. However, in some Figures, for example, 3a, the kinetics of the recorded currents appear dramatically different. It would be important to know and compare the values of the series resistance between control and mutant animals.

(4) A significant unexplained variability is present in several datasets. For example, the AP threshold for PV+ includes points between -50-40 mV, but also values at around -20/-15 mV, which seems too depolarized to generate healthy APs (Fig 5c, Fig7c).

(5) I am unclear as to how the authors quantified colocalization between VGluts and PSD95 at the low magnification shown in Supplementary Figure 2.

(6) The authors claim that "cHet SST+ cells showed no significant changes in active and passive membrane properties", but this claim would seem to be directly refused by the data of Fig 8f. In the absence of changes in either active or passive membrane properties shouldn't the current/#AP plot remain unchanged?

(7) The plots used for the determination of AP threshold (Figs 5c, 7c, and 7h) suggest that the frequency of acquisition of current-clamp signals may not have been sufficient, this value is not included in the Methods section.

for - classification table - types of Buddhist practice - nondual vs classical

for - two types of mindfulness

Buddhist classifcation - two types of mindfulness - classical - requires - memory of specific Buddhist teachings - dhammas mental framework<br /> - ethical consideraions - think of these things - don't think of those things - nondual - not distracted by anything - nothing in particular to focus on - no objject of attention

for - John D. Dunne - Buddhist scholar - paper - Buddhist Styles of Mindfulness - A Heuristic Approach - to - citation - John Dunne

to - citation - John Dunne website and paper - citation - https://hyp.is/N348dga5Ee-vq5-ZnnVD9Q/docdrop.org/video/BNAVYglundg/

for - definition - mindfulness

definition - mindfulness - This is a 20th century Western, Buddhist psychology term which has two complimentary aspects - remembering / recollecting (smrti) - hold some mental object in mind and prevent it from drifting away - clear comprehension (samprajanya) - clear knowing through alert awareness - mental surveying / monitoring

for - definition - Bhavana - meditation - Sanskrit - samatha - vipassana

definition - Bhavana - meditation - Sanskrit - https://encyclopediaofbuddhism.org/wiki/Bh%C4%81van%C4%81 - cultivation - samatha-bhāvanā, the cultivation of calm-abiding - stabilizing attention leading to refined states of concentration - vipassanā-bhāvanā, the cultivation of insight<br /> - clearly noting what is arising from moment to moment

for . Evan Thompson - interview - Osher Center for Integrative Health - Harvard

to - Osher Center

Why I’m excited about Richard Polt’s book. by [[Scott K]]