This quote really hits home for me because I used to be someone who was pretty regularly late for calculus. Because I was late, I would always be flustered and so lost when I got to class, and my grade suffered because of it. This year I have made an effort to be early to all my classes, and I feel like I have definitely understood more of what I have been learning.

i think that this is really insightful and provides a good critique on how a lot of people react when they hear the word presentation. A lot of people just tend to space out when they know they're going to have to listen to a presentation in whatever format it might be.

In life and i can speak for myself all i need sometimes is that push of guidance. Knowing that someone is there for you and willing to put time aside to help you out. Even when it comes down you just want that reassurance of the Hey im here for you or if theres anything they can do or even the you got this! Listening to this honestly support goes a long way.

Its sad that her realization was at a young age just like someone elses comments stated and its true, you feel the inequality based on the standards of society the moment youre forced to grow up young. Even today the people who have the privelege to get an education deny that route and the people who want that route can't have it because society doesnt provide or allow them to. Here is with undocumented and her knowing that without papers she won't have that and others is knowing now that can get protected by DACA will help them out a little more.

The American Dream is always something that stays in the air that people tend to take differently. The American dream has been implemented forcefully to say education is the way to go but everything comes down to perception of what you think success means to someone. For me success means going to school, graduating, and getting the job i've always strived for or having a degree to rely on. Success for others can be getting a job as long as they're able to support their family. Now i wonder what Flors ideal would be.

Despite this being a sentiment from 1937 this is still a very real battle against women and children. One demonstration of this battle that has stuck with me is the fashion exhibit "What Were You Wearing?" This exhibit consists of outfits that were worn by SA survivors when they were attacked. Many of the exhibitions are children's clothing and some are just diapers. The sex criminal is still a terrifying threat but has only been taken less seriously by men.

Does the white fear of violent slave uprisings contribute to inherent subconscious prejudice towards black people?

I believe this blase attitude towards major stories has worsened with the age of social media. Many people have forgotten, or worse, were never aware, that Russia and Ukraine are at war, and the same for Israel and Palestine now that the genocide has been ongoing for over a year. Social media has simultaneously made people more nosey and forgetful. Social media users go from one outrageous story to another with no real empathy or care for those involved, only interested in the drama they provide. Overstimulation has dulled people's sense of reality and empathy.

As someone who is not very artistic, I'm not sure that this strategy would be that effective for me. I might get too caught up in making it look recognizable or overly explaining what it is, which detracts from the goal. I also think it's difficult to come up with this many ideas that also have some kind of visual element, and it requires a lot more effort and energy. With words, it can be easy to generate lists of dozens of things, but with drawings, creativity might start to drain halfway through the 8 pictures.

I think storyboarding is a great way to get into the mind of your target audience and build on the empathy phase of the process. It can be easy to come up with an idea and then realize it doesn't actually fit into the situation you had hoped. Storyboarding helps give context as to why some things do/don't work and helps focus ideas.

Is it often that ideas start to get similar by the end if ideas are working off of each other?

I think this is really important because a lot of times people have really good ideas but they are too scared to speak up. That's why I think writing things out makes it easier for less confident people to share their ideas

Descartes is arguing that, just as we should be wary of accepting false things, so should we be of accepting doubtful things. Should one have any cause to question a belief, we should completely reject it. This method promotes absolutely certain knowledge building.

To those families who maintain their culture and certain traditions in their households can be a good thing to a certain extent. I think being open to learning different cultures is a good thing except for when people try to bash and change aspects and perceptions. People tend to bash the cultures that they believe are not the correct ones or what they dont expect, but at the end of the day the correct ones start from native americans.

Honestly, I don't really understand this strategy. In general, this one seems the most confusing, but I also think the other strategies (opportunity redefinition, triggered brainwalking, and worst idea) do what it does better and in a more clear way. I just don't see a need to use this one over the others.

I think that this can be a great way to keep the energy up when brainstorming. I often feel like my first few ideas have to be the "right" ones, so purposefully being wrong can help take off some pressure and be fun.

Is this the best way to go about it from the start or could it be more helpful if no one can really think of any good ideas?

I think that this is very helpful because you can write down anything without having to think hard about it being a perfect idea or what others might think about it!

the greater the extent that you too possess this equipment, the more likely you will be to succeed (in anything). This doesn't just apply to those in the "entrepreneurial field" -- but that said, acquiring (and then knowing how to use) this equipment is much easier said than done.

what are your thoughts on this?

the 'pro' and the 'con' side

ground that we've covered before ... should be very familiar...

!!

exploited, as a good thing (as it seems in capitalism and resource extraction models in general...)

a vision for not just the future (but a better future, an improved tomorrow based on your innovation(s), today)

This is insightful! I think that a coach that is given to a teacher almost negates the whole growing and learning process

ecclesial imagery

No, I hate the idea of boxing in any ideas of communication by putting them in our made up term used to identify concepts used within a community. I think communication is a much larger idea and language just try’s to narrow down and define. Communication is a living, breathing idea and the rules are loose- changing all the time.

client only data

hierarchical object store

that;s what Peergos is for IndyWeb

I believe creating is so powerful and I like to use Book creator to do that,

Great definition or explanation of why we use digital tools.

I definitely agree that it is not about the shiniest but about what accomplished the learning goal and sometimes it is not technology that does it.

Mark Schrad discusses his German typewriter in episode 31 of the Austin Typewriter Ink Podcast<br /> https://www.podomatic.com/podcasts/austintypewriterink/episodes/2021-02-01T21_49_07-08_00

This is some really neat work. This is a wonderful resource for the community! I really appreciated the logic and decision making highlighted throughout the paper leading to an optimal protocol for 4- or 5- color imaging in dicty! Very cool work and beautiful images!!!

Why did y'all shift from mNeonGreen comparisons to GFP?

Very neat & exciting finding!!!

Is there any data comparing Dox-GFP with a Dox-mNeonGreen FP? If so, are the two probes comparable in their response times?

Was the cytoplasmic background also brighter here (not just the localized signal)? I'm just naively wondering if the achilles tag is causing some stress that is increasing background autofluorescence in the cell (given the onset of uniform cytoplasmic fluorescence in Fig 1).

Interesting that there is a significant difference in the distribution of mNeonGreen (anterior vs posterior) but not with Achilles. Do you have any ideas why this would be the case? Are there any observable physiological defects caused by either of these FPs?

Like sentence structures and outlines??

Analyze, analyze, analyze!!

I don't understand.

1. build confidence and voice as a writer
2. connect with the reading audience

You gotta snatch up the audience so then they will read the writing or something.

Yes!! Imagine if William Shakespeare wrote with zero emotion. Then it would twice as boring as it already is.

100% agree and I just love this sentence.

The title "Leave Yourself Out of Your Writing" has me confused because I thought that writing was a form of self expression??

He specifically mentions teaching here.

Reflect on this. Why do we need to be aware of this as teachers?

What are some complications he points to here?

What does ontological mean? What is the relationship between history and identity? How does that influence ontological satisfaction?

Note this. It's a long established practiced, and the pagans mock it.

Do we know that? Source?

Agents personal servers

to Agents personal servers

Flip the Web

I know on an intellectual level that the cultural implications of this practice are incredibly complex but emotionally I find it completely shocking that only a fifth of the women offered it choose reconstructive surgery.

The paper describes direct addition of amines to alkenes.

The authors present a novel iridium-catalyzed borylation method for aryl C–H bonds, allowing direct functionalization of unactivated arenes.

The authors demonstrate a palladium-catalyzed method for the amination of aromatic C–H bonds, enabling direct incorporation of nitrogen.

The paper discusses mechanistic insights and strategies that have broadened applications for C–H activation in synthetic organic chemistry.

The authors present a detailed computational and experimental study on the mechanistic pathways of iridium-catalyzed borylation of C–H bonds.

The authors introduce a photoredox-catalyzed cross-coupling reaction enabling the formation of C–C bonds without traditional organometallic reagents.

The authors explore the mechanisms of selective C–H activation catalysis involving transition metals through detailed computational studies.

https://phys.org/news/2020-10-approach-chemistry-enables-boron-added.html

Researchers have developed a new method to add boron atoms to organic molecules using light-driven chemistry, eliminating the need for transition metals. This approach is expected to simplify synthesis processes and reduce environmental impact by making boron addition more accessible and eco-friendly.

https://cen.acs.org/synthesis/c-h-activation/Alkane-borylation-reaction-kicks-metals/98/i42

Researchers have developed a novel alkane borylation reaction that bypasses the need for transition metal catalysts, traditionally essential in such reactions. This approach utilizes photoredox chemistry to enable borylation with milder conditions and reduced environmental impact, making the process greener and more sustainable. The reaction opens new possibilities for functionalizing hydrocarbons in pharmaceutical and material science applications by avoiding the cost and toxicity associated with metal catalysts.

https://news.berkeley.edu/2020/05/21/scientists-finally-crack-natures-most-common-chemical-bond/ Scientists finally crack nature’s most common chemical bond

https://phys.org/news/2023-12-catalyst-electronically-ch-functionalization.html

The Chirik Lab at Princeton has developed a cobalt-based catalyst that enables electronically controlled C–H functionalization, allowing for precise borylation in fluoroarenes without the need for directing groups. The catalyst offers potential applications in pharmaceutical and materials synthesis by expanding the toolkit for site-selective modifications.

2.5 M butyl lithium was added to a solution of furan at -78 deg Celsius under inert atmosphere. The reaction mixture was stirred at room temperature for 1 h. The reaction mixture was cooled to -78 deg Celsius and 43 was added. Then NBS was added. The reaction was warmed to room temperature and 10 mL of water was added. Extraction with ethyl acetate and followed by vacuum evaporation afforded a residue. This was purified by silica gel column chromatography to give 48 as a colorless oil.

Copper-Catalyzed Oxidation: To a premixed 2.0 M NaOH and 30% hydrogen peroxide and 15 was added in THF at 0 deg Celsius. The mixture was stirred at room temperature for 11 hours. 1.0 M aqueous hydrochloric acid was added and reaction mixture was extracted with ethyl acetate. The organic layer was washed with brine. Volatiles were evaporated with rotary evaporator and the residue was purified by silica gel column chromatography. 45 was obtained as a yellow solid.

Suzuki Coupling Reaction: 15, boronic acid, potassium phosphate, Pd(dppf)Cl2 and dioxane were added to a vial in a glove box. Then, the vial was removed from the glove box and water was added. The reaction mixture was heated to 100 deg Celsius for 2.5 hours. Volatiles were rotary evaporated and the residue was purified by silica gel column chromatography to give 37 as a colorless oil.

Deboronation and Deuterium Exchange: To a reaction vial, 15, [Ir(cod)OMe]2 and THF were added. The vial was sealed, removed out of the dry box and D2O was syringed. The reaction mixture was heated to 80 deg Celsius for ~3 h. The volatiles were removed by rotary evaporator and the residue was purified by column chromatography. 39 was obtained as a colorless oil.

To a reaction vial, cyclopropane carboxylate, B2Pin2, 6.3 micromol of [Ir(cod)(OMe)]2, 2-mphen and cyclooctane (solvent) were added. The reaction mixture was heated at 100 degrees Celsius for 20 h. The product mixture was purified by silica gel column chromatography. CH2Br2 was added as the internal standard and the product was characetrized by H-NMR spectroscopy

Diisopropylbenzene, B2PIn2, [Ir(cod)(OMe)]2, 2.5 mg of 2-mphen and cyclooctane were added to a reaction vial. The reaction mixture was heated to 100 deg Celsius, cooled and then stirred for 20 h under inert atmosphere. The reaction progress was monitored with GC-MS. Crude product was purified by silica gel column chromatography.

Metallophotoredox Reaction: In a glove-box, 4,4’-di-tert-butyl-2-2`-bipyridine and NiCl2.DME were added to a reaction vial, followed by addition of THF. The mixture was heated on a heating plate for 10 min. The vial was taken back into the glove-box, and the volatile materials were removed, followed by addition of Ir[dFCF3ppy]2(bpy)PF6, 4-bromobenzonitrile, Cs2CO3, potassium triflluoroborate and dioxane. The vial was capped and stirred under irridation of a 34 W blue LED lamp for 48 h. The crude reaction mixture was extracted with ethyl acetate. The resulting solution was concentrated under reduced pressure, and the residue was purified by column chromatography to give the product 52 as a colorless oil.

0.250 mol of the substrate, 3 equivalents of B2Pin2, 6.3 micromol of [Ir(cod)(OMe)]2, 13 micromol of 2-mphen and 200 microliter of cycloctane were added to a vial. The reaction mixture was heated to 100 degrees Celsius for 20 hours. Product was purified by column chromatography and characterized by proton NMR spectroscopy.

Carbocycles underwent borylation at the most accessible bond which is the bond with least steric hindrance. The secondary C-H bond was found to undergo borylation.

Heat was applied outside of dry box in this procedure. Then, the reaction vial is taken back into the glove box for further manipulations.

To a vial, an alcohol of interest was added inside a dry box. This was followed by 1.3 equivalents of HBPin and stirred for ~ 10 minutes to convert the alcohol to the borate ester. The vial was fitted with a cap and heated to 100 degrees Celsius outside the dry box. The vial was cooled and then 2.5 mol % of [Ir(cod)(OMe)]2, B2Pin2 and cyclooctane (solvent) were added under inert conditions. Heated to 80 degree Celsius (outside dry box) to activate the catalyst. Cooled to room temperature and charged with remaining B2Pin2 and more cyclooctane under inert conditions. Reaction mixture was stirred for 20 hours. CH2Br2 was added as an internal standard. Product was analyzed by proton NMR spectroscopy.

In a nitrogen filled glove box, a vial was charged with (MsSH)IrBPin3, a ligand, B2Pin2 (bis-pinacolatodiboron) and THF. Ligands are phenanthroline derived. Dodecane was used as the internal standard. The reaction vial was sealed with a Teflon cap. The mixture was heated to 100 degrees Celsius. Reaction was monitored by GC.

The same reaction was repeated replacing THF with diethyl ether.

The ligands used are 2-methylphananthroline (mphen), 2,9-dimethylphenanthroline (2,9-dmphen) and 3,4,7,8-tetramethylphanathroline (tmphen).

Borylations of heterocycles occured at C-H bonds closer to the heteroatom.

Acyl groups attached to the nitrogen atom are derived from non-aromatic carbon chains

Although several studies were carried out, the exact reason for the high reactivity of m-phen ligand is unclear. However, it can be concluded that the change in the methyl goup leads to the higher activity of the catalyst in borylation.

Borylation of primary C-H bond is irreversible leading to exclusivity in the product formed.

Borylation enabled placement of bromine atom at the strogest C-H bond instead of the weaker C-H bond that is typically observed.

Exclusive borylation occurred at the position beta to nitrogen.

Reaction preferentially occurred at the equatorial C-H over the axial C-H bond because reaction at the equatorial C-H bond is irreversible whereas the reaction at the axial C-H bond occurs but is reversible.

Less reactive carbocycles also underwent borylation because of the high reactivity of mphen.

Primary, secondary and tertiary alcohols were borylated at primary C-H bonds. However, this occurred after initial borylation of the hydroxyl group.

to key concepts

only shared by explicit invite

no need for server

built for customization

Students learn best when they are active participants, constructing new knowledge rather than passively receiving information. Why It Matters: Learning becomes more meaningful when students are encouraged to explore, reflect, and build connections to what they already know.

Self-reflection: Teachers develop the ability to reflect on their own practice and make adjustments independently.

Clinical supervision is a powerful tool for promoting effective teaching by focusing on growth through observation and reflection. When implemented with trust and consistency, it helps teachers build the skills needed to enhance student learning and achieve their instructional goals. The collaborative nature of the process encourages ongoing self-improvement and strengthens the partnership between teachers and supervisors.

This is a very big deal. The mentor needs to remember that this is the teacher's space and to be very respectful when being in that space.

argument

argument

DXOS

https://hyp.is/3sQOPuHyEe6_fLPUjKK-Vw/dxos.org/

Trexler's brains exhaustive on Climata Change

pinterestish

Plex magic

Plex format

Fellowship of the Link

Chris Aldrich

Love this scale for measuring student progress. I don't think it's linear either. I think there are times where teachers are in and out of the continuum depending on new content, etc.

I feel like I definitely fall into filter bubbles, and I try to listen to other people's points of view. Sometimes I agree with what they say and other times I don't. The part I find annoying is once you open yourself up to hearing the other side you then get bombarded with that propaganda and it immediately pushes me back to the other side. there needs to be a slower filter.

A telling story

This right here confuses me and terrifies and somehow comforts me at the same time. This "feature " or whatever it is makes most people uncomfortable and paranoid. I understand that it is a program but I think a lot of people think its a team of people listening in on their life and making them chose things based off of what the listener wants. It's odd and eerie.

This is me. I have a few disabilities that I struggle with, and I think this is excellent to remind others of something they might take for granted. It's funny for quite a while I learned to just adapt before I went out and got diagnosed and was able to receive help for some of my disabilities. I believe human beings are like that and can make do when they need to that's what makes us so resilient.

I really appreciate that this reading gave me that view of this issue. You always look at things from a certain scope and never venture to look at things from a different viewpoint. The fact that tetrachromats exist is something I never knew, and I think there should absolutely be special accommodations made for them as well.

Argument

Problem??/ end of beginning

Problem?

should schools be rolling out zero tolerance policies without full consideration of how they might contradict with their "diversity and inclusion" related policies, too?

Is full, complete tolerance and acceptance even possible in schools, if when in the context of making general safety measures, it could lead to contradicting values amongst differing beliefs, ideaologies and faith practices of students and staff?

Is this even achieveable without any form of discrimination?

Is discrimination inherently bad?

And what about the kinds of restrictions to other practicing faiths, where certain symbols and/or expressions of the faith could be deemed inappropriate for a public school setting with other students of varying faiths and value sets?

There are also other ways that Sikhs can express their faith through this specific symbol, without carrying a concealed weapon in public spaces. (ie. decorative, mini versions of the kirpan, kirpans made of wood or plastic instead of metal, focusing on the VALUES that the symbol represents instead of the weapon itself)

school admin learned about a sikh student wearing religious attire - what's kirpan?

• ceremonial dagger in the sikh religion
• symbol that's very important to their religious faith, that represents their commitment to the faith, but ALSO their commitment to PROTECTING and DEFENDING the weak, and upholding justice
• it's a SYMBOL, not meant to be used in the traditional sense
• only to be used for defense in the case of injustice (but who decides what is just and what is not, and how do you measure when and how those injustices are to be handled in response to them? Does this practice of taking matters of justice and injustice into anyone's hands align with the practices of the school measures in dealing with escalations of injustice? And what about simple matters of religious freedom in schools? Is that enough to protect and defend the entire ownership of the weapon altogether, nevermind it's implications? should this be an opportunity for the school administrator to learn more about the religion to be better informed about how they make their policies, especially if within those policies, freedom of religion is included?

MLE are not in the bayesian tradition

what is this mixing time ?

This page sold me on clay bricks; they are easier to build with, and provide better strength, and insulation, once construction is completed. These benefits outweigh the increased cost, for me.

You don't want your bricks to absorb water.

What does he mean while Blore Leave by himself？ Was Lambard implying Blore’s death？

In the previous chapters, Mr. Justice Wargrave acted suspicious, but it turned out that he died.

The "little figure," is a symbol foreshadowing their misfortune. Why does Vera regard them as part of we?

In And Then There Were None, Lombard's death is indeed caused by Vera Claythorne. In the final stages of the story, overwhelmed by psychological pressure and fear, Vera shoots Lombard. This act is a reflection of her complex inner turmoil and the story's tense atmosphere. This twist is shocking for readers and adds to the overall tragic feel of the narrative.

In the later part of the story, some characters still believe that there is hope for salvation, which is quite ironic for readers who already know the ending.

Scare factors played a huge part in the entire war.

Is this an example of another proxy war?

This is interesting.

How did the people feel about this????

Did the communism scare the people as badly as it scared he US and Uk governments?

**Questions in this section: ** 1. How did religious beliefs influence societal attitudes toward slavery? 2. Are there parallels between Quaker activism and modern movements against systemic injustice?

Answering Historical Questions The audience that would have become interested in reading accounts detailing the Stono Rebellion would have been primarily the colonial masters and the white settlers. The accounts were prescribed to cordon off rebellion and fear in the enslaved people. The rebellion’s importance was to show that the enslaved people would fight to be free and thereby show the moral of slavery.

Asking Questions 1. How does the response to the Stono Rebellion reflect the attitudes toward enslaved people at the time? 2. How do these events connect to the legal and social frameworks surrounding slavery in other colonies, such as Pennsylvania?

The mid-Atlantic colonies relied heavily on enslaved labor, especially in urban areas like New York City, where over 40% of the population was enslaved by 1700.

The Stono Rebellion involved around eighty enslaved individuals who sought freedom in Spanish Florida, highlighting a critical moment in colonial history. This uprising happened at a time when the anti-black slavery system had firmly taken root in the southern colonies, especially the Lowcountry of South Carolina.

Stono Rebellion refers to the slave revolt, which happened in South Carolina in September 1739. The rebellion is crucial in demonstrating how far enslaved people were willing to go to attain their liberation no matter the consequences they were going to face.

I love this section. Allowing teachers to grapple with their thoughts and practice is giving them the freedom to come to their own conclusions and therefore master their craft.

I'm noticing that there are a few differences in philosophies regarding what children can and can't do. It's a mindset really. The approach that I've taken is one where I mention the difference in our thinking and then ground that in child development or the assessment tool we utilize.

I am working on honing in on being curious and asking the right questions to provoke thought. In my role, I'm often in the position to 'have all the answers' and I am carefully moving away from that.

because theyre still considered objects

That's intresting

Honestly... smart move

I think he was faking his sickness to distract Delano from what was really happening on board the San Dominick

Is this saying Americans are strong-willed?

If Delano wasn't so racist he probably could've sniffed out the fact that Benito was a captive.

Superiorty complex

This is interesting.

?

Author response:

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

A summary of changes

(1) Line 93: “positive effect” to “positive contribution”, as suggested by reviewer 2.

(2) Line 147-148: the null hypothesis to test “equal interspecific and intraspecific interactions”, as indicated by reviewers 2 and 4.

(3) Lines 155-162: removed to reduce duplication with the additive partitioning, as suggested by reviewer 2.

(4) Lines 186-188: added “the estimated competitive growth response would also include the effects of density-dependent pests, pathogens, or microclimates”, as suggested by reviewer 3.  

(5) Lines 219-222: added “The community positive effect can be further partitioned by mechanisms of positive interactions (resource partitioning and facilitation), and facilitative effect can be classified as mutualism (+/+), commensalism (+/0), or parasitic (+/–) based on species specific assessments”.  

(6) Lines 377-386: added options for determining maximum competitive growth response in some extreme scenarios of species mixtures.

(7) Figure 1: modified to show the variations of competitive growth response with relative competitive ability from minimum (null expectation) to maximum (competitive exclusion).    

A summary of four reviewers’ questions and authors’ response

(1) A summary of authors’ responses. Reviewers did not seem to understand our work. They indicated that our model is inadequate for hypothesis testing. The fact is, as we note below, that our model allows for more hypothesis testing than the additive partitioning model. They suggested that one of our model components, the competitive growth response, needs to be further partitioned. However, this term represents only the competition effect and can not be split any further. Reviewers criticized us for misunderstanding the additive components while they suggested the same logic to test some intuitive ideas. They did not seem to know that the effects of competitive interactions vary with assessment methods, which differ between competition and biodiversity research. Our work seeks to harmonise definitions between these two fields and bridge the gap. The reviewers acknowledged that the additive components (i.e., the selection effect and complementarity effect) do not have clear biological meanings; however, they did not acknowledge that the additive components are used extensively for determining mechanisms of species interactions in biodiversity research. There is hardly any research that uses the additive partitioning model without linking the additive components to specific mechanisms of species interactions (i.e., positive SE to competition and positive CE to positive interactions).

(2) Additive partitioning and underlying mechanisms. Some reviewers acknowledged that additive partitioning is not meant for determining mechanisms of species interactions and therefore argued that the additive partitioning should not be criticized for lack of biological meanings with the additive components. However, they insisted that additive partitioning is useful in quantifying net biodiversity effects against the null hypothesis that there is no difference between intraspecific and interspecific interactions or testing the idea that “niche complementarity mitigates competition” or “competitively superior species dominate mixtures”. Are these views contradictory each other? How can the additive partitioning that is not designed for determining mechanisms of species interactions provide meaningful explanations for outputs of species interactions, e.g., “niche complementarity mitigates competition” or “competitively superior species dominate mixtures”?

Reviewers did not seem to realize that these ideas are equivalent to the suggestions that CE represents for the effects of positive interactions and SE for the effects of competitive interactions, that the quantification of net biodiversity effects does not require the two additive components, and that the null hypothesis exists long before the additive partitioning (see de Wit, 1960, de Wit et al., 1966). It is generally agreed that CE and SE result from mathematical calculations and do not have clear biological meanings in terms of linkages to specific mechanisms of species interactions responsible for observed net biodiversity effects or changes in ecosystem function (Loreau and Hector, 2012; Bourrat et al., 2023). Calling some mixed effects of species interactions as mechanisms (e.g., CE and SE) is misleading.        

Model structure: incomplete or inadequate for hypothesis testing. Other than positive, negative, and competition interactions, two reviewers wanted to have more specific interactions such as microclimate amelioration and negative feedback from species-specific pests and pathogens. The determination of these specific mechanisms requires more investigations and cannot be simply made through partitioning growth and yield data. However, the effects of these interactions will be captured in our definition of species interactions.  Reviewers did not seem to know that the additive partitioning would also not allow identifying these specific positive species interactions.

Inspired by the mathematical form of additive partitioning, two reviewers suggested that our model (presumably equation 4) is incomplete and the second term, i.e., competitive growth response needs to be further explored or partitioned. The second term represents deviations from the null expectation, due to species differences in growth and competitive ability or competition effect. We do not know why and how this term can be further partitioned and what any subcomponents would mean.   

Our competitive partitioning model is based on two hypotheses: first, the null hypothesis to test the equivalence of interspecific and intraspecific interactions. This hypothesis is the same as the additive partitioning model. Second, the competitive hypothesis, which tests the dominance of positive or negative species interactions in a community. Thus, our model allows for more hypothesis testing than the current additive partitioning model.     

(3) Types of species interactions. We follow the definition of species interactions generally used in biodiversity research (see Loreau and Hector, 2001), i.e., positive interactions (or complementarity) include resource partitioning and facilitation, negative interactions include interference competition, and competitive interactions include resource competition. One reviewer suggested that resource partitioning is byproduct of competition and should not be part of positive species interactions, which may be true for long-term evolution of species co-existence but not for biodiversity experiments of decade duration at most. Two reviewers suggested that positive interactions should also include microclimate amelioration or negative feedback from species-specific pests and pathogens. We agree and these are included in our definition. 

(4) Significance of partial density monocultures. We used partial and full density monocultures and species competitive ability to determine what species can possibly achieve in mixture under the competitive hypothesis that constituent species share an identical niche but differ in growth and competitive ability. We did not use partial monocultures to test the effects of density on biodiversity effects. As with the additive partitioning, the competitive partitioning model is not designed for comparing yields across different densities. We added at lines 186-188 to indicate that the estimated competitive growth response would also include the effects of density-dependent pests, pathogens, or microclimates.  

Similarly, we do not use the partial density monoculture to  supplant the replacement series design. Partial density monocultures only supplement the “replacement series” design that does not provides estimates of facilitative effects and competitive growth responses that would occur in mixtures. It is crucial to know that one experimental approach is simply not enough for determining underlying mechanisms of species interactions responsible for changes in ecosystem function.  

(5) Competition effect in competition and biodiversity research. Due to different methods used, competition effect in competition research has different ecological meanings from that in biodiversity research. In competition research, species performance in mixture are compared with their partial density monocultures and therefore competition effect is generally negative, as suggested by reviewer 4. In biodiversity research, comparison is between mixture and full density monocultures. The resulting competition effect can be positive or negative for both individual species and community productivity defined by species composition and full density monoculture yields.     

Therefore, we cannot use the results of competition research based on additive series design to describe effects of competitive interactions on ecosystem productivity based replacement series design.

Reviewer #1 (Public Review):

[Editors' note: this is an overall synthesis from the Reviewing Editor in consultation with the reviewers.]

The three reviews expand our critique of this manuscript in some depth and complementary directions. These can be synthesized in the following main points (we point out that there is quite a bit more that could be written about the flaws with this study; however, time constraints prevented us from further elaborating on the issues we see):

(1) It is unclear what the authors want to do.

As indicate by the title, our objective is to “partition changes in ecosystem productivity by effects of species interactions”, i.e., partitioning net biodiversity effects estimated from the null expectation into components associated with positive, negative, or competition interspecific interactions.

It seems their main point is that the large BEF literature and especially biodiversity experiments overstate the occurrence of positive biodiversity effects because some of these can result from competition.

We demonstrated through ecological theories and simulation/experiment data that competition is a major source of the net biodiversity effects estimated with additive partitioning model. We know that competition effect varies with mixture attributes. Future research will determine average effect of competitive interactions on biodiversity effects in large BEF literature.   

Because reduced interspecific relative to intraspecific competition in mixture is sufficient to produce positive effects in mixtures (if interspecific competition = 0 then RYT = S, where S is species richness in mixture -- this according to the reciprocal yield law = law of constant final yield), they have a problem accepting NE > 0 as true biodiversity effect (see additive partitioning method of Loreau & Hector 2001 cited in manuscript).

We have no problem to accept NE>0 as true positive biodiversity effect. However, NE>0 can also result from competitive interactions based on the null expectation and needs to be partitioned by effects of species interactions.

(2) The authors' next claim, without justification, that additive partitioning of NE is flawed and theoretically and biologically meaningless.

The additive partitioning model is based on Covariance equation (or Price equation) that has nothing to do with biodiversity partitioning (Bourrat et al., 2023). Biological meaning was arbitrarily assigned to CE and SE. We made clear that the additive partitioning model is mathematically sound but does not have biological meanings that it has been used for.   

They misinterpret the CE component as biological niche partitioning and the SE component as biological dominance.

We did not. Loreau and Hector (2001) clearly indicated positive CE for positive interactions and positive SE for competitive interactions, which is generally what has been used for in the last twenty years.

They do not seem to accept that the additive partitioning is a logically and mathematically sound derivation from basic principles that cannot be contested.

We do not have problem with mathematical form of additive partitioning but only oppose ecological meanings assigned to CE and SE, simply because CE and SE both result from all species interactions (see Loreau and Hector, 2001; Bourrat et al., 2023). The reviewer seemed to have a contradictory thinking that the additive components are biologically meaningless but derived from biological basic principles.       

(3) The authors go on to introduce a method to calculate species-level overyielding (RY > 1/S in replacement series experiments) as a competitive growth response and multiply this with the species monoculture biomass relative to the maximum to obtain competitive expectation. This method is based on resource competition and the idea that resource uptake is fully converted into biomass (instead of e.g. investing it in allelopathic chemical production).

Correct, but we did not assume “resource uptake is fully converted into biomass”.

(4) It is unclear which experiments should be done, i.e. are partial-density monocultures planted or simply calculated from full-density monocultures? At what time are monocultures evaluated? The framework suggests that monocultures must have the full potential to develop, but in experiments, they are often performing very poorly, at least after some time. I assume in such cases the monocultures could not be used.

Both partial and full density monocultures are needed, along with mixtures to separate NE by species interactions. Calculating competitive growth responses from density-size relationships can be an alternative, given the lack of partial density monocultures in current biodiversity experiments, but is not preferred.

Similar to additive partitioning, our model can (and should) be applied to all developmental stages of an experiment to examine how interactions evolve through time.   

(5) There are many reasons why the ideal case of only resource competition playing a role is unrealistic. This excludes enemies but also differential conversion factors of resources into biomass and antagonistic or facilitative effects. Because there are so many potential reasons for deviations from the null model of only resource competition, a deviation from the null model does not allow conclusions about underlying mechanisms.

The competitive expectation is only a hypothesis, just as the null expectation. The difference between competitive and null expectations represents a competitive effect resulting from species differences in growth and competitive ability, while the deviation of observed yields from the competitive expectation indicates positive or negative effect (see lines 201-219).

Furthermore, this is not a systematically developed partitioning, but some rather empirical ad hoc formulation of a first term that is thought to approximate competitive effects as understood by the authors (but again, there already are problems here). The second residual term is not investigated. For a proper partitioning approach, one would have to decompose overyielding into two (or more) terms and demonstrate (algebraically) that under some reasonable definitions of competitive and non-competitive interactions, these end up driving the respective terms.

The first term represents the null expectation assuming equal interspecific and intraspecific interactions, i.e., absence of positive, negative, and competition effects. The second residual term represents competition effect, due to species differences in growth and competitive ability. The meaning of second residual term is clear and does not need to be further partitioned or investigated.

In fact, our competitive partitioning also has several components including null expectation, competitive growth response, and observed yield, plus partial density monocultures for species assessment, or null expectations, competitive expectations, and observed yields for community level assessment, although different from the additive partitioning.

(6) Using a simplistic simulation to test the method is insufficient. For example, I do not see how the simulation includes a mechanism that could create CE in additive partitioning if all species would have the same monoculture yield. Similarly, they do not include mechanisms of enemies or antagonistic interactions (e.g. allelopathy).

The simulation model we used is developed from real world data and can only do what are available in the model in terms of species and their growth under different conditions. We can not go beyond data limitation. The model is empirical and has been shown to accurately estimate yield in the aspen-spruce forest condition. We would also note that we do also use experimental data (Table 2).  

(7) The authors do not cite relevant literature regarding density x biodiversity experiments, competition experiments, replacement-series experiments, density-yield experiments, additive partitioning, facilitation, and so on.

We cited literature relevant to biodiversity partitioning since we are not aiming to cover everything. The reviewer may not be aware that most of the research areas listed are actually included in our work, such as additive and replacement-series experiment designs, additive partitioning, facilitation, competition studies, and density-yield relationships. Our competitive model partitioning is based on biological principles, while the additive partitioning model is based only on a mathematical equation.   

Overall, this manuscript does not lead further from what we have already elaborated in the broad field of BEF and competition studies and rather blurs our understanding of the topic.

The results of competition studies based on additive series design are not really used in the broad field of BEF based on replacement series design. The effects of competitive interactions on BEF are never clearly defined using the results of competition studies. Our work is filling that gap.  

Reviewer #2 (Public Review):

This manuscript is motivated by the question of what mechanisms cause overyielding in mixed-species communities relative to the corresponding monocultures. This is an important and timely question, given that the ultimate biological reasons for such biodiversity effects are not fully understood.

As a starting point, the authors discuss the so-called "additive partitioning" (AP) method proposed by Loreau & Hector in 2001. The AP is the result of a mathematical rearrangement of the definition of overyielding, written in terms of relative yields (RY) of species in mixtures relative to monocultures. One term, the so-called complementarity effect (CE), is proportional to the average RY deviations from the null expectations that plants of both species "do the same" in monocultures and mixtures. The other term, the selection effect (SE), captures how these RY deviations are related to monoculture productivity. Overall, CE measures whether relative biomass gains differ from zero when averaged across all community members, and SE, whether the "relative advantage" species have in the mixture, is related to their productivity. In extreme cases, when all species benefit, CE becomes positive. When large species have large relative productivity increases, SE becomes positive. This is intuitively compatible with the idea that niche complementarity mitigates competition (CE>0), or that competitively superior species dominate mixtures and thereby driver overyielding (SE>0).

The reviewer needs to know that these ideas are based on the same logic that positive CE represents the effects of positive interactions and positive SE represents the effects of competitive interactions. CE>0 or SE>0 can result from many different scenarios of species interactions, not necessarily “niche complementarity mitigates competition” or “competitively superior species dominate mixtures”. CE>0 and SE>0 can occur alone or together. We simply can not tell underlying mechanisms of overyielding from mathematical calculations (CE and SE), as suggested by this reviewer later.

The reviewer criticizes us while using the same logic themselves.

However, it is very important to understand that CE and SE capture the "statistical structure" of RY that underlies overyielding. Specifically, CE and SE are not the ultimate biological mechanisms that drive overyielding, and never were meant to be. CE also does not describe niche complementarity. Interpreting CE and SE as directly quantifying niche complementarity or resource competition, is simply wrong, although it sometimes is done. The criticism of the AP method thus in large part seems unwarranted. The alternative methods the authors discuss (lines 108-123) are based on very similar principles.

The reviewer actually supports our point. However, CE and SE have been largely used as biological mechanisms, positive CE as the results of complementary interactions and positive SE as the results of competitive interactions (see Loreau and Hector, 2001).  

We do not have problem with the "statistical structure" of AP; it is simply a covariance equation. It is important to know that CE and SE do not provide additional information on overyielding than NE in terms of underlying mechanisms of species interactions. Any attempt to investigate mechanism of overyielding with CE or SE can easily go wrong.

Our competitive partitioning model incorporates effects of competitive interactions into the conventional null expectation and allows for separating different effects of species interactions. In comparison, the additive partitioning model does not have this capacity, not even designed for this purpose, as suggested by this and other reviewers.         

The authors now set out to develop a method that aims at linking response patterns to "more true" biological mechanisms.

Assuming that "competitive dominance" is key to understanding mixture productivity, because "competitive interactions are the predominant type of interspecific relationships in plants", the authors introduce "partial density" monocultures, i.e. monocultures that have the same planting density for a species as in a mixture. The idea is that using these partial density monocultures as a reference would allow for isolating the effect of competition by the surrounding "species matrix".

Correct.

The authors argue that "To separate effects of competitive interactions from those of other species interactions, we would need the hypothesis that constituent species share an identical niche but differ in growth and competitive ability (i.e., absence of positive/negative interactions)." - I think the term interaction is not correctly used here, because clearly competition is an interaction, but the point made here is that this would be a zero-sum game.

We did not say that competition is not an interaction; we only want to separate the effect of competition from those of other species interactions.

The authors use the ratio of productivity of partial density and full-density monocultures, divided by planting density, as a measure of "competitive growth response" (abbreviated as MG). This is the extra growth a plant individual produces when intraspecific competition is reduced.

Correct.

We added at lines 377-386 to discuss options to determine MG in some uncommon scenarios of species mixtures.

Here, I see two issues: first, this rests on the assumption that there is only "one mode" of competition if two species use the same resources, which may not be true, because intraspecific and interspecific competition may differ. Of course, one can argue that then somehow "niches" are different, but such a niche definition would be very broad and go beyond the "resource set" perspective the authors adopt. Second, this value will heavily depend on timing and the relationship between maximum initial growth rates and competitive abilities at high stand densities.

First, the "competitive effect" focusses on resource competition and other forms of competition (presumably interference competition) are included in the negative interactions.

Second, competitive growth response varies over time and with density, and so do NE, CE, SE, and interspecific interactions.

The authors then progress to define relative competitive ability (RC), and this time simply uses monoculture biomass as a measure of competitive ability. To express this biomass in a standardized way, they express it as different from the mean of the other species and then divide by the maximum monoculture biomass of all species.

I have two concerns here: first, if competitive ability is the capability of a species to preempt resources from a pool also accessed by another species, as the authors argued before, then this seems wrong because one would expect that a species can simply be more productive because it has a broader niche space that it exploits. This contradicts the very narrow perspective on competitive ability the authors have adopted. This also is difficult to reconcile with the idea that specialist species with a narrow niche would outcompete generalist species with a broad niche. Second, I am concerned by the mathematical form. Standardizing by the maximum makes the scaling dependent on a single value.

First, growth conditions are controlled in biodiversity experiments, i.e., both monocultures and mixtures are the same in resource space. Species do not have opportunity to exploit resources outside experimental area. For example, if less productive species on normal soils outperform more competitive species on saline/alkaline soil, these “less productive species” are considered “more productive”.    

Second, as discussed in our paper (lines 367-376; Figure 1), more research is needed to determine relationships between species traits (biomass or height) and relative competitive ability. By then, scaling by the maximum would not be needed. There has been quite a lot of research on such relationships; we should leave this to subject experts to determine what would be mostly appropriate for species studied.

As a final step, the authors calculate a "competitive expectation" for a species' biomass in the mixture, by scaling deviations from the expected yield by the product MG ⨯ RC. This would mean a species does better in a mixture when (1) it benefits most from a conspecific density reduction, and (2) has a relatively high biomass.

Put simply, the assumption would be that if a species is productive in monoculture (high RC), it effectively does not "see" the competitors and then grows like it would be the sole species in the community, i.e. like in the partial density monoculture.

Correct, if species competitive ability differs substantially, the more competitive species in the mixture would grow like partial density monoculture. This extra growth should not be treated as sources of positive biodiversity effects, simply because it does not result from positive species interactions.   

Overall, I am not very convinced by the proposed method.

(1) The proposed method seems not very systematic but rather "ad hoc". It also is much less a partitioning method than the AP method because the other term is simply the difference. It would be good if the authors investigated the mathematical form of this remainder and explored its properties.. when does complementarity occur? Would it capture complementarity and facilitation?

AP is, by no means, systematic. Remember, AP is based on covariance equation (or Price equation) that has nothing to do with species interactions, other than nice-looking mathematical form (Bourrat et al., 2023). Ecological meanings are subjectively given to CE and SE. Therefore,  CE and SE reflect what we call them, not what they really mean.    

The remainder measures deviations from the null expectation, due to only competition effect, and can not be partitioned any further. The remainder would be positive for more competitive species and negative for less competitive species in mixture relative to their full density monoculture. The deviation of observed yields from competitive expectations indicates dominance of positive or negative species interactions. All these are clearly outlined at lines 201-221.   

(2) The justification for the calculation of MG and RC does not seem to follow the very strict assumptions of what competition (in the absence of complementarity) is. See my specific comments above.

We do not see why not.

(3) Overall, the manuscript is hard to read. This is in part a problem of terminology and presentation, and it would be good to use more systematic terms for "response patterns" and "biological mechanisms".

To help understand the variations of competitive growth response with relative competitive ability, the x axis of Figure 1 is labelled with null expectation, competitive expectation, and competitive exclusion from minimum to maximum deviation of competitive ability from community average.

We have followed terms used in biodiversity partitioning and changing terms can be confusing.  

Examples:

- on line 30, the authors write that CE is used to measure "positive" interactions and SE to measure "competitive interactions", and later name "positive" and "negative" interactions "mechanisms of species interactions". Here the authors first use "positive interaction" as any type of effect that results in a community-level biomass gain, but then they use "interaction" with reference to specific biological mechanisms (e.g. one species might attract a parasite that infests another species, which in turn may cause further changes that modify the growth of the first and other species).

There are some differences in meaning, but that is what CE and SE have been generally used for. Using different terms can be confusing and does not help understanding the problems with AP.

- on line 70, the authors state that "positive interaction" increases productivity relative to the null expectation, but it is clear that an interaction can have "negative" consequences for one interaction partner and "positive" ones for the other. Therefore, "positive" and "negative" interactions, when defined in this way, cannot be directly linked to "resource partitioning" and "facilitation", and "species interference" as the authors do. Also, these categories of mechanisms are still simple. For example, how do biotic interactions with enemies classify, see above?

We are explaining effects of competitive interactions on species yield, and ultimately on community yield that can be linked to “resource partitioning" and "facilitation", and "species interference".

More specific species interactions require detailed biological investigation and cannot be determined through partitioning of biomass production.  

- line 145: "Under the null hypothesis, species in the mixture are assumed to be competitively equivalent (i.e., absence of interspecific interactions)". This is wrong. The assumption is that there are interspecific interactions, but that these are the same as the intraspecific ones. Weirdly, what follows is a description of the AP method, which does not belong here. This paragraph would better be moved to the introduction where the AP method is mentioned. Or omitted, since it is basically a repetition of the original Loreau & Hector paper.

As suggested, “absence of interspecific interactions” was replaced with “equal interspecific and intraspecific interactions”.

We have removed lines 155-162 to reduce duplication. However, our method is based on null expectation that needs to be introduced, despite it is part of AP.

Other points:

- line 66: community productivity, not ecosystem productivity.

Both community productivity and ecosystem productivity are used in biodiversity research, although meaning can be slightly different. Comparatively, ecosystem productivity is more common.

- line 68: community average responses are with respect to relative yields - this is important!

- line 64: what are "species effects of species interactions"?

We searched and did not find “species effects of species interactions”.

- line 90: here "competitive" and "productive" are mixed up, and it is important to state that "suffers more" refers to relative changes, not yield changes.

It, in fact, refers to yield changes. For example, less productive species, at active growth, are more responsive to changes in competition, while more productive species, at inactive growth (i.e., aging), are less responsive to changes in competition.   

- line 92: "positive effect of competitive dominance": I don't understand what is meant here.

The phrase was modified to “positive contribution of competitive dominance to ecosystem productivity based on the null expectation”.

Reviewer #3 (Public Review):

Summary:

This manuscript by Tao et al. reports on an effort to better specify the underlying interactions driving the effects of biodiversity on productivity in biodiversity experiments. The authors are especially concerned with the potential for competitive interactions to drive positive biodiversity-ecosystem functioning relationships by driving down the biomass of subdominant species. The authors suggest a new partitioning schema that utilizes a suite of partial density treatments to capture so-called competitive ability. While I agree with the authors that understanding the underlying drivers of biodiversity-ecosystem functioning relationships is valuable - I am unsure of the added value of this specific approach for several reasons.

Strengths:

I can find a lot of value in endeavouring to improve our understanding of how biodiversity-ecosystem functioning relationships arise. I agree with the authors that competition is not well integrated into the complementarity and selection effect and interrogating this is important.

Weaknesses:

(1) The authors start the introduction very narrowly and do not make clear why it is so important to understand the underlying mechanisms driving biodiversity-ecosystem functioning relationships until the end of the discussion.

There are different ways to start introduction; we believe that starting with the problems of the current approach is the most effective for outlining the study’s objective.  

(2) The authors criticize the existing framework for only incorporating positive interactions but this is an oversimplification of the existing framework in several ways:

We did not criticize the existing framework for only incorporating positive interactions. We criticize the existing framework, because it is not based on mechanisms of species interactions, but is extensively used to determine underlying mechanisms driving biodiversity-ecosystem functioning relationships.

a. The existing partitioning scheme incorporates resource partitioning which is an effect of competition.

Resource partitioning means that species utilize resources differently, while competition means species use the same resources. “resource partitioning is an effect of competition” is not true in biodiversity experiments that are often short in duration and controlled in conditions.  

b. The authors neglect the potential that negative feedback from species-specific pests and pathogens can also drive positive BEF and complementarity effects but is not a positive interaction, necessarily. This is discussed in Schnitzer et al. 2011, Maron et al. 2011, Hendriks et al. 2013, Barry et al. 2019, etc.

We did not. The feedback effect will be reflected in the differences between observed yields and competitive expectations if species in mixtures have different pests and pathogens relative to monocultures. The additive partitioning does not identify these feedback effects either.

c. Hector and Loreau (and many of the other citations listed) do not limit competition to SE because resource partitioning is a byproduct of competition.

Positive SE has been largely interpreted as the result of competition including Hector and Loreau (2001) and many others. It needs to be clear that neither of the additive components can be linked to specific mechanisms of species interactions. 

Does “resource partitioning is a byproduct of competition” mean that species change their niche to avoid competition? If this is what the reviewer means, it may occur through long-term evolution, but not in short-term biodiversity experiments. Hector and Loreau (2001) clearly indicated that their complementarity effect includes both resource partitioning and facilitation.   

(3) It is unclear how this new measure relates to the selection effect, in particular. I would suggest that the authors add a conceptual figure that shows some scenarios in which this metric would give a different answer than the traditional additive partition. The example that the authors use where a dominant species increases in biomass and the amount that it increases in biomass is greater than the amount of loss from it outcompeting a subdominant species is a general example often used for a selection effect when exactly would you see a difference between the two?:<br /> a. Just a note - I do think you should see a difference between the two if the species suffers from strong intraspecific competition and has therefore low monoculture biomass but this would tend to also be a very low-density monoculture in practice so there would potentially be little difference between a low density and high-density monoculture because the individuals in a high-density monoculture would die anyway. So I am not sure that in practice you would really see this difference even if partial density plots were incorporated.

Linking new measure to SE or CE would be difficult (see many comparisons in Tables and Figures in our manuscript), as SE and CE are derived from mathematical equation and do not represent specific mechanisms of species interactions (Hector and Loreau 2012; Bourrat et al., 2023).

(4) One of the tricky things about these endeavors is that they often pull on theory from two different subfields and use similar terminology to refer to different things. For example - in competition theory, facilitation often refers to a positive relative interaction index (this seems to be how the authors are interpreting this) while in the BEF world facilitation often refers to a set of concrete physical mechanisms like microclimate amelioration. The truth is that both of these subfields use net effects. The relative interaction index is also a net outcome as is the complementarity effect even if it is only a piece of the net biodiversity effect. Trying to combine these two subfields to come up with a new partitioning mechanism requires interrogating the underlying assumptions of both subfields which I do not see in this paper.

Agree, microclimate amelioration is also part of positive effect and will be reflected in the difference between observed yield and competitive expectation. We can not separate the two mechanisms of positive species interactions without investigating influences of microclimate on growth and yield.

(5) The partial density treatment does not isolate competition in the way that the authors indicate. All of the interactions that the authors discuss are density-dependent including the mechanism that is not discussed (negative feedback from species-specific pests and pathogens). These partial density treatment effects therefore cannot simply be equated to competition as the authors indicate.:

We use partial density monoculture to determine maximum competitive growth response, effect of density-dependent intraspecific interactions, and species competitive ability to determine the level of maximum competitive growth response species can achieve in mixtures. There may be changes in species-specific pests and pathogens from partial to full density monocultures, which will be captured in competitive growth responses of individuals. We added at lines 186-188 to indicate that the maximum competitive growth response estimated would also include the effects of density-dependent pests, pathogens, or microclimates.   

a. Additionally - the authors use mixture biomass as a stand-in for competitive ability in some cases but mixture biomass could also be determined by the degree to which a plant is facilitated in the mixture (for example).

We used monoculture biomass, not mixture biomass, to assess competitive ability

(6) I found the literature citation to be a bit loose. For example, the authors state that the additive partition is used to separate positive interactions from competition (lines 70-76) and cite many papers but several of these (e.g. Barry et al. 2019) explicitly do not say this.

Barry et al. (2019) defined CE as overproduction from monocultures, an effect of positive interactions.  

(7) The natural take-home message from this study is that it would be valuable for biodiversity experiments to include partial density treatments but I have a hard time seeing this as a valuable addition to the field for two reasons:

a. In practice - adding in partial density treatments would not be feasible for the vast majority of experiments which are already often unfeasibly large to maintain.

The reviewer suggested that quantity is more important than quality. Without partial density monocultures no one can separate different effects of species interactions, as suggested by Loreau and Hector, reviewers, and many others that effects of species interactions can not be clearly differentiated with replacement series design. Unreliable scientific findings are not valuable.

b. The density effect would likely only be valuable during the establishment phase of the experiment because species that are strongly limited by intraspecific competition will die in the full-density plots resulting in low-density monocultures. You can see this in many biodiversity experiments after the first years. Even though they are seeded (or rarely planted) at a certain density, the density after several years in many monocultures is quite low.

True. High or low density also depends on individual size; if individuals do not get enough resources, density is high. Therefore, density effect can be strong even as density drops substantially from initial levels.  

Reviewer #4 (Public Review):

Summary:

This manuscript claims to provide a new null hypothesis for testing the effects of biodiversity on ecosystem functioning. It reports that the strength of biodiversity effects changes when this different null hypothesis is used. This main result is rather inevitable. That is, one expects a different answer when using a different approach. The question then becomes whether the manuscript’s null hypothesis is both new and an improvement on the null hypothesis that has been in use in recent decades.

It needs to be clear that we use two hypotheses, null hypothesis that is currently used with AP, and competitive hypothesis that is new with this manuscript. The null hypothesis helps determine changes in ecosystem productivity from all species interactions, while the competitive hypothesis helps partition changes in ecosystem productivity by mechanisms of species interactions, i.e., positive, negative, or competitive interactions.    

Strengths:

In general, I appreciate studies like this that question whether we have been doing it all wrong and I encourage consideration of new approaches.

Weaknesses:

Despite many sweeping critiques of previous studies and bold claims of novelty made throughout the manuscript, I was unable to find new insights. The manuscript fails to place the study in the context of the long history of literature on competition and biodiversity and ecosystem functioning. The Introduction claims the new approach will address deficiencies of previous approaches, but after reading further I see no evidence that it addresses the limitations of previous approaches noted in the Introduction. Furthermore, the manuscript does not reproducibly describe the methods used to produce the results (e.g., in Table 1) and relies on simulations, claiming experimental data are not available when many experiments have already tested these ideas and not found support for them. Finally, it is unclear to me whether rejecting the ‘new’ null hypothesis presented in the manuscript would be of interest to ecologists, agronomists, conservationists, or others. I will elaborate on each of these points below.

First, there are many biodiversity experiments but those with partial density monocultures are rare. We found only one greenhouse experiment. We have to use simulation to illustrate different scenarios of species interactions to demonstrate how our approach works and how different it is from the AP.  

Because of different methods used, the results of long history competition research (generally based on additive series design) cannot be used to define effects of competitive interactions in biodiversity research (generally based on replacement series design). This may be the reason that few competition researchers were cited in Loreau and Hector (2001).

Our approach requires two hypotheses, null and competitive, and the meaning of deviation from these hypotheses are outlined at lines 201-221 for both individual species and community level assessments. Distinguishing changes in ecosystem productivity by species interactions would be of great interest to “ecologists, agronomists, conservationists, or others”.

The critiques of biodiversity experiments and existing additive partitioning methods are overstated, as is the extent to which this new approach addresses its limitations. For example, the critique that current biodiversity experiments cannot reveal the effects of species interactions (e.g., lines 37-39) isn't generally true, but it could be true if stated more specifically. That is, this statement is incorrect as written because comparisons of mixtures, where there are interspecific and intraspecific interactions, with monocultures, where there are only intraspecific interactions, certainly provide information about the effects of species interactions (interspecific interactions). These biodiversity experiments and existing additive partitioning approaches have limits, of course, for identifying the specific types of interactions (e.g., whether mediated by exploitative resource competition, apparent competition, or other types of interactions). However, the approach proposed in this manuscript gets no closer to identifying these specific mechanisms of species interactions. It has no ability to distinguish between resource and apparent competition, for example. Thus, the motivation and framing of the manuscript do not match what it provides. I believe the entire Introduction would need to be rewritten to clarify what gap in knowledge this proposed approach is addressing and what would be gained by filling this knowledge gap.

Our approach helps determine underlying mechanisms of species interactions, i.e., positive (resources partitioning or facilitation), negative, or competitive interactions. I am not sure how much we need to go further in identifying more specific mechanisms. If resource and apparent competition refers to resource and interference competition, our approach can tease apart them.

I recommend that the Introduction instead clarify how this study builds on and goes beyond many decades of literature considering how competition and biodiversity effects depend on density. This large literature is insufficiently addressed in this manuscript. This fails to give credit to previous studies considering these ideas and makes it unclear how this manuscript goes beyond the many previous related studies. For example, see papers and books written by de Wit, Harper, Vandermeer, Connolly, Schmid, and many others. Also, note that many biodiversity experiments have crossed diversity treatments with a density treatment and found no significant effects of density or interactions between density and diversity (e.g., Finn et al. 2013 Journal of Applied Ecology). Thus, claiming that these considerations of density are novel, without giving credit to the enormous number of previous studies considering this, is insufficient.

A misunderstanding here. Our approach is not designed to test density effect. The same density is held across full density monocultures and mixtures. We use partial density monocultures to determine what species may competitively achieve in full density mixture, without positive or negative interspecific interactions.  

Replacement series designs emerged as a consensus for biodiversity experiments because they directly test a relevant null hypothesis. This is not to say that there are no other interesting null hypotheses or study designs, but one must acknowledge that many designs and analyses of biodiversity experiments have already been considered. For example, Schmid et al. reviewed these designs and analyses two decades ago (2002, chapter 6 in Loreau et al. 2002 OUP book) and the overwhelming consensus in recent decades has been to use a replacement series and test the corresponding null hypothesis.

Some wrong impressions. We are not trying to supplant “replacement series” with “additive series”; we use “additive series” designs to supplement “replacement series” design for partitioning changes in ecosystem productivity by mechanisms of species interactions, which would not be possible with “replacement series” design alone, as suggested by many including reviewers.   

It is unclear to me whether rejecting the 'new' null hypothesis presented in the manuscript would be of interest to ecologists, agronomists, conservationists, or others. Most biodiversity experiments and additive partitions have tested and quantified diversity effects against the null hypothesis that there is no difference between intraspecific and interspecific interactions. If there was no less competition and no more facilitation in mixtures than in monocultures, then there would be no positive diversity effects. Rejecting this null hypothesis is relevant when considering coexistence in ecology, overyielding in agronomy, and the consequences of biodiversity loss in conservation (e.g., Vandermeer 1981 Bioscience, Loreau 2010 Princeton Monograph). This manuscript proposes a different null hypothesis and it is not yet clear to me how it would be relevant to any of these ongoing discussions of changes in biodiversity.

Our method begins with the null expectation: that intraspecific and interspecific interactions are equivalent. We then propose the competitive hypothesis as a second non-exclusive hypothesis which tests the dominance of positive or negative specific interactions. As shown by its name, the additive partitioning model has been advocated for partitioning biodiversity effects by some ecological mechanisms (CE and SE). The ecological meaning of deviation from the two hypotheses are outlined at lines 201-221 for both individual species and community level assessments.   

The claim that all previous methods 'are not capable of quantifying changes in ecosystem productivity by species interactions and species or community level' is incorrect. As noted above, all approaches that compare mixtures, where there are interspecific interactions, to monocultures, where there are no species interactions, do this to some extent. By overstating the limitations of previous approaches, the manuscript fails to clearly identify what unique contribution it is offering, and how this builds on and goes beyond previous work.

The reviewer implies that a partial truth equals the whole truth. The same argument can also be applied to the additive partitioning if relative yield total or response ratio provides a kind of comparison between mixture and monocultures. Our statement is correct in the way that previous approaches are not designed to separate changes in ecosystem productivity by species interactions, as indicated by other reviewers. The additive partitioning is built on Price equation (covariance equation) that has never been biologically demonstrated for relevance in biodiversity partitioning (Bourrat et al., 2023).  

We made clear that our work is built on and beyond the null expectation with addition of competitive expectation.

The manuscript relies on simulations because it claims that current experiments are unable to test this, given that they have replacement series designs (lines 128-131). There are, however, dozens of experiments where the replacement series was repeated at multiple densities, which would allow a direct test of these ideas. In fact, these ideas have already been tested in these experiments and density effects were found to be nonsignificant (e.g., Finn et al. 2013).

Out of point. Again, we are not testing density effect. Partial density is used to determine competitive growth responses that species may achieve in mixture based on their relative competitive ability. We used simulations, as partial density monocultures are used only in one experimental study that has been included in our study.  

It seems that the authors are primarily interested in trees planted at a fixed density, with no opportunity for changes in density, and thus only changes in the size of individuals (e.g., Fig. 1). In natural and experimental systems, realized density differs from the initial planted density, and survivorship of seedlings can depend on both intraspecific and interspecific interactions. Thus, the constrained conditions under which these ideas are explored in this manuscript seem narrow and far from the more complex reality where density is not fixed.

We use fixed density only for convenience. In biodiversity experiments, density can increase or decrease over time from initial levels. However, initial density is generally used in evaluation of species interactions. If interest is community productivity, density change does not need to be considered. Again, we are not testing density effects.    

Additional detailed comments:

It is unclear to me which 'effects' are referred to on line 36. For example, are these diversity effects or just effects of competition? What is the response variable?

It means the effect of competitive interactions on productivity and should be clear based on previous sentences.

The usefulness of the approach is overstated on line 52. All partitioning approaches, including the new one proposed here, give the net result of many types of species interactions and thus cannot 'disentangle underlying mechanisms of species interactions.'

Not sure how many types of species interactions the reviewer referred to. If mechanisms of species interactions are grouped in three categories (positive, negative, and competitive) as has been in biodiversity research, our approach can tease them apart.   

The weaknesses of previous approaches are overstated throughout the manuscript, including in lines 60-61. All approaches provide some, but not all insights. Sweeping statements that previous approaches are not effective, without clarifying what they can and can't do, is unhelpful and incorrect. Also, these statements imply that the approach proposed here addresses the limitations of these previous approaches. I don't yet see how it does so.

The weaknesses of previous approaches are not overstated in terms of separating changes in ecosystem productivity by species interactions. As pointed by other reviewers, none of the previous approaches are designed for quantifying changes in ecosystem productivity by species interactions.   

The definitions given for the CE and SE on line 71 are incorrect. Competition affects both terms and CE can be negative or have nothing to do with positive interactions, as noted in many of the papers cited.

We are not trying to define CE and SE but only point out how CE and SE have been generally used in biodiversity research (see recent publication by Feng et al., 2022).

The proposed approach does not address the limitations noted on lines 73 and 74.

It does in terms of sources of net biodiversity effect, whether from positive, negative or competitive interactions.

The definition of positive interactions in lines 77 and 78 seems inconsistent with much of the literature, which instead focuses on facilitation or mutualism, rather than competition when describing positive interactions.

Much of the literature supports our definition (see Loreau and Hector, 2001). In biodiversity research, positive interactions include resource partitioning and facilitation. What we are trying to point out is that competition affects species and community level assessments based on the null expectation and needs to be separated.

Throughout the manuscript, competition is often used interchangeably with resource competition (e.g., line 82) and complementarity is often attributed to resource partitioning (e.g., line 77). This ignores apparent competition and partitioning enemy-free niche space, which has been found to contribute to biodiversity effects in many studies.

If apparent competition refers to interference competition, it is included in negative interaction. Changes in species-specific pests and pathogens in mixture will be captured in positive or negative effects through facilitation or interference.  

In what sense are competitive interactions positive for competitive species (lines 82-83)? By definition, competition is an interaction that has a negative effect. Do you mean that interspecific competition is less than intraspecific competition? I am having a very difficult time following the logic.

I am glad the reviewer raised this question that may confuse many others and has never been clearly discussed. It all depends on how comparison is made. If species performance in mixture are compared with that in partial density monocultures, as is in competition research, competition effect is negative for all species. If comparison is made between mixture and full density monocultures, as is done in biodiversity research, competition effect should be positive for more competitive species and negative for less competitive species, with resources flowing from less to more competitive species in mixture relative to full density monocultures.   

Therefore, the definitions of competitive interactions based on additive series design in competition research cannot be used to describe competitive interactions based on replacement series design in biodiversity research. In biodiversity research, the effects of competitive interactions are never clearly defined at species or community level and mixed up with those of other species interactions.      

Results are asserted on lines 93-95, but I cannot find the methods that produced these results. I am unable to evaluate the work without a repeatable description of the methods.

We have added references on sources of these data.

The description of the null hypothesis in the common additive partitioning approach on lines 145-146 is incorrect. In the null case, it does not assume that there are no interspecific interactions, but rather that interspecific and intraspecific interactions are equivalent.

Correct, changes have been made as suggested.

Recommendations for the authors:

Reviewer #2 (Recommendations For The Authors):

I recommend to:

- re-organize the presentation of the material (see my concerns in the public review section). The manuscript is very difficult to read.

Changes have been made to help with understanding of our approach. Figure 1 was modified to show the variations of competitive growth response with relative competitive ability from minimum (null expectation) to maximum (competitive exclusion).

- explore the mathematical form the the remainder term. It seems important to understand that the remainder capture terms unrelated to competition as defined in the present scope.

The remainder measures deviations from the null expectation, due to species differences in growth and competitive ability or competition effect. The term has clear meaning, positive for more competitive species and negative for less competitive species (lines 202-204), and does not need to be further explored or partitioned. The deviations of observed yields from competitive expectations are outlined in lines 205-221.  

Reviewer #4 (Recommendations For The Authors):

The authors should be sure to include reproducible methods and share any data and code.

Both simulation and experimental data are shared through supplementary tables. Calculations are included in excel spreadsheets and do not require program coding.

eLife Assessment

The authors propose that positive biodiversity-ecosystem functioning relationships found in experiments have been exaggerated because commonly used statistical analyses are flawed. To remedy this, a new type of analysis based on a concept of "partial density monoculture yield" is proposed. However, the presented concept and analysis methods are not reproducibly described, do not appear to be complete, and are inadequate for hypothesis testing. The reviewers found that the authors misinterpret current research in the field and made limited efforts to understand or address the reviewer comments on a previous version of the study.

Reviewer #1 (Public review):

As a starting point, the authors discuss the so-called "additive partitioning" (AP) method proposed by Loreau & Hector in 2001. The AP is the result of a mathematical rearrangement of the definition of overyielding, written in terms of relative yields (RY) of species in mixtures relative to monocultures. One term, the so-called complementarity effect (CE), is proportional to the average RY deviations from the null expectations that plants of both species "do the same" in monocultures and mixtures. The other term, the selection effect (SE), captures how these RY deviations are related to monoculture productivity. Overall, CE measures whether relative biomass gains differ from zero when averaged across all community members, and SE, whether the "relative advantage" species have in the mixture, is related to their productivity. In extreme cases, when all species benefit, CE becomes positive. When large species have large relative productivity increases, SE becomes positive. This is intuitively compatible with the idea that niche complementarity mitigates competition (CE>0), or that competitively superior species dominate mixtures and thereby driver overyielding (SE>0).

However, it is very important to understand that CE and SE capture the "statistical structure" of RY that underlies overyielding. Specifically, CE and SE are not the ultimate biological mechanisms that drive overyielding, and never were meant to be. CE also does not describe niche complementarity. Interpreting CE and SE as directly quantifying niche complementarity or resource competition, is simply wrong, although it sometimes is done. The criticism of the AP method thus in large part seems unwarranted. The alternative methods the authors discuss (lines 108-123) are based on very similar principles.

The authors now set out to develop a method that aims at linking response patterns to "more true" biological mechanisms.

Assuming that "competitive dominance" is key to understanding mixture productivity, because "competitive interactions are the predominant type of interspecific relationships in plants", the authors introduce "partial density" monocultures, i.e. monocultures that have the same planting density for a species as in a mixture. The idea is that using these partial density monocultures as a reference would allow for isolating the effect of competition by the surrounding "species matrix".

The authors argue that "To separate effects of competitive interactions from those of other species interactions, we would need the hypothesis that constituent species share an identical niche but differ in growth and competitive ability (i.e., absence of positive/negative interactions)." - I think the term interaction is not correctly used here, because clearly competition is an interaction, but the point made here is that this would be a zero-sum game.

The authors use the ratio of productivity of partial density and full-density monocultures, divided by planting density, as a measure of "competitive growth response" (abbreviated as MG). This is the extra growth a plant individual produces when intraspecific competition is reduced.

Here, I see two issues: first, this rests on the assumption that there is only "one mode" of competition if two species use the same resources, which may not be true, because intraspecific and interspecific competition may differ. Of course, one can argue that then somehow "niches" are different, but such a niche definition would be very broad and go beyond the "resource set" perspective the authors adopt. Second, this value will heavily depend on timing and the relationship between maximum initial growth rates and competitive abilities at high stand densities.

The authors then progress to define relative competitive ability (RC), and this time simply uses monoculture biomass as a measure of competitive ability. To express this biomass in a standardized way, they express it as different from the mean of the other species and then divide by the maximum monoculture biomass of all species.

I have two concerns here: first, if competitive ability is the capability of a species to preempt resources from a pool also accessed by another species, as the authors argued before, then this seems wrong because one would expect that a species can simply be more productive because it has a broader niche space that it exploits. This contradicts the very narrow perspective on competitive ability the authors have adopted. This also is difficult to reconcile with the idea that specialist species with a narrow niche would outcompete generalist species with a broad niche. Second, I am concerned by the mathematical form. Standardizing by the maximum makes the scaling dependent on a single value.

As a final step, the authors calculate a "competitive expectation" for a species' biomass in the mixture, by scaling deviations from the expected yield by the product MG ⨯ RC. This would mean a species does better in a mixture when (1) it benefits most from a conspecific density reduction, and (2) has a relatively high biomass.

Put simply, the assumption would be that if a species is productive in monoculture (high RC), it effectively does not "see" the competitors and then grows like it would be the sole species in the community, i.e. like in the partial density monoculture.

Overall, I am not very convinced by the proposed method.

Comments on revised version:

Only minimal changes were made to the manuscript, and they do not address the main points that were raised.

Reviewer #2 (Public review):

This manuscript by Tao et al. reports on an effort to better specify the underlying interactions driving the effects of biodiversity on productivity in biodiversity experiments. The authors are especially concerned with the potential for competitive interactions to drive positive biodiversity-ecosystem functioning relationships by driving down the biomass of subdominant species. The authors suggest a new partitioning schema that utilizes a suite of partial density treatments to capture so-called competitive ability. While I agree with the authors that understanding the underlying drivers of biodiversity-ecosystem functioning relationships is valuable - I am unsure of the added value of this specific approach for several reasons.

Comments on revised version:

The authors changed only one minor detail in response to the last round of reviews.

Reviewer #3 (Public review):

Summary:

This manuscript claims to provide a new null hypothesis for testing the effects of biodiversity on ecosystem functioning. It reports that the strength of biodiversity effects changes when this different null hypothesis is used. This main result is rather inevitable. That is, one expects a different answer when using a different approach. The question then becomes whether the manuscript's null hypothesis is both new and an improvement on the null hypothesis that has been in use in recent decades.

Strengths:

In general, I appreciate studies like this that question whether we have been doing it all wrong and I encourage consideration of new approaches.

Weaknesses:

Despite many sweeping critiques of previous studies and bold claims of novelty made throughout the manuscript, I was unable to find new insights. The manuscript fails to place the study in the context of the long history of literature on competition and biodiversity and ecosystem functioning. The Introduction claims the new approach will address deficiencies of previous approaches, but after reading further I see no evidence that it addresses the limitations of previous approaches noted in the Introduction. Furthermore, the manuscript does not reproducibly describe the methods used to produce the results (e.g., in Table 1) and relies on simulations, claiming experimental data are not available when many experiments have already tested these ideas and not found support for them. Finally, it is unclear to me whether rejecting the 'new' null hypothesis presented in the manuscript would be of interest to ecologists, agronomists, conservationists, or others.

Comments on revised version:

Please see review comments on the previous version of this manuscript. The authors have not revised their manuscript to address most of the issues previously raised by reviewers.

I wish we were able to know the best thing for our students before we met them. I feel like we can't truly know what is best for our students unless we meet them. It doesn't matter how much paperwork we read on them.

claim: local volumetric reductions in gray matter associated with certain psychiatric disorders may be reversible in adequate training. 1 march 2011 : no studies have examined this according to doi:10.1002/hbm.21143

suprising: the capacity of the nervous system for rapid morphological adjustments in response to environmental triggers

similar to US Constitution

What if this is unsafe

Religion is placed back into government

Restricting religion in this area

Author response:

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

Public Reviews:

Reviewer 1 (Public Review):

Summary:

The authors present a mean-field model that describes the interplay between (protein) aggregation and phase separation. Different classes of interaction complexity and aggregate dimensionality are considered, both in calculations concerning (equilibrium) phase behavior and kinetics of assembly formation.

Strengths:

The present work is, although purely theoretical, of high interest to understanding biological processes that occur as a result of a coupling between protein aggregation and phase separation. Of course, such processes are abundant, in the living cell as well as in in-vitro experiments. I appreciate the consideration of aggregates with various dimensionality, as well as the categorization into different ”interaction classes”, together with the mentioning of experimental observations from biology. The model is convincing and underlines the complexity associated with the distribution of proteins across phases and aggregates in the living cell.

Weaknesses:

There are a few minor weaknesses.

Reviewer 2 (Public Review):

This work deals with a very difficult physical problem: relating the assembly of building blocks on a molecular scale to the appearance of large, macroscopic assemblies. This problem is particularly difficult to treat, because of the large number of units involved, and of the complex way in which these units-monomers-interact with each other and with the solvent. In order to make the problem treatable, the authors recur to a number of approximations: Among these, there is the assumption that the system is spatially homogeneous, i.e., its features are the same in all regions of space. In particular, the homogeneity assumption may not hold in biologically relevant systems such as cells, where the behavior close to the cell membrane may strongly differ from the one in the bulk. As a result, this hypothesis calls for a cautious consideration and interpretation of the results of this work. Another notable simplification introduced by the authors is the assumption that the system can only follow two possible behaviors: In the first, each monomer interacts equally with the solvent; no matter the size of the cluster of which it is part. In the second case, monomers in the bulk of a cluster and monomers at the assembly boundary interact with the solvent in a different way. These two cases are considered not only because they simplify the problem, but also because they are inspired by biologically relevant proteins.

With these simplifications, the authors trace the phase diagram of the system, characterizing its phases for different fractions of the volume occupied by the monomers and solvent, and for different values of the temperature. The results qualitatively reproduce some features observed in recent experiments, such as an anomalous distribution of cluster sizes below the system saturation threshold, and the gelation of condensed phases above such threshold.

Reviewer 3 (Public Review):

Summary:

The authors combine classical theories of phase separation and self-assembly to establish a framework for explaining the coupling between the two phenomena in the context of protein assemblies and condensates. By starting from a mean-field free energy for monomers and assemblies immersed in solvent and imposing conditions of equilibrium, the authors derive phase diagrams indicating how assemblies partition into different condensed phases as temperature and the total volume fraction of proteins are varied. They find that phase separation can promote assembly within the protein-rich phase, providing a potential mechanism for spatial control of assembly. They extend their theory to account for the possibility of gelation. They also create a theory for the kinetics of self-assembly within phase separated systems, predicting how assembly size distributions change with time within the different phases as well as how the volumes of the different phases change with time.

Strengths:

The theoretical framework that the authors present is an interesting marriage of classic theories of phase separation and self-assembly. Its simplicity should make it a powerful general tool for understanding the thermodynamics of assembly coupled to phase separation, and it should provide a useful framework for analyzing experiments on assembly within biomolecular condensates.

The key advance over previous work is that the authors now account for how self-assembly can change the boundaries of the phase diagram.

A second interesting point is the explicit theoretical consideration for the possibility that gelation (i.e. self-assembly into a macroscopic aggregate) could account for widely observed solidification of condensates. While this concept has been broadly discussed, to date I have yet to see a rigorous theoretical analysis of the possibility.

The kinetic theory in sections 5 and 6 is also interesting as it extends on previous work by considering the kinetics of phase separation as well as those of self-assembly.

Weaknesses:

A key point the authors make about their theory is that it allows, as opposed to previous research, to study non-dilute limits. It is true that they consider gelation when the 3D assemblies become macroscopic. However, dilute solution theory assumptions seem to be embedded in many aspects of their theory, and it is not always clear where else the non-dilute limits are considered. Is it in the inter-species interaction χij? Why then do they never explore cases for which χij is nonzero in their analysis?

We explicitly consider that monomers and aggregates are non-dilute with respect to solvent. This is evident in accounting for the mixing entropy of all components, including the solvent. Moreover, we account for interactions among the monomers and the different aggregates with the solvent. We consider the case where each monomeric unit, independent in aggregate it is part of, interacts the same way with the solvent. Please note that this case corresponds to a non-dilute scenario where interactions indeed drive phase separation.

The connection between this theory and biological systems is described in the introduction but lost along the main text. It would be very helpful to point out, for instance, that the presence of phase separation might induce aggregation of proteins. This point is described formally at the end of Section 3, but a more qualitative connection to biological systems would be very useful here.

We thank the referee for the useful comment, we now mention this in the introduction (line 80) and point out the biological relevance of assembly formation and localization via the presence of phase separation (lines 268 and 283).

Building on the previous point, it would be helpful to give an intuitive sense of where the equations derived in the Appendices and presented in the main text come from and to spell out clear physical interpretations of the results. For example, it would be helpful to point out that Eq. 4 is a form of the law of mass action, familiar from introductory chemistry. It would be useful to better explain how the current work extends on existing previous work from these authors as well as others. Along these lines, closely related work by W. Jacobs and B. Rogers [O. Hedge et al. 2023, https://arxiv.org/abs/2301.06134; T. Li et al. 2023, https://arxiv.org/abs/2306.13198] should be cited in the introduction. The results discussed in the first paragraph of Section 3 on assembly size distributions in a homogeneous system are well-known from classic theories of self-assembly. This should be acknowledged and appropriate references should be added; see for instance, Rev. Mod. Phys. 93, 025008 and Statistical Thermodynamics Of Surfaces, Interfaces, And Membranes by Sam Safran. Equation 14 for the kinetic of volume fractions is given with reference to Bauermann et al. 2022, but it should be accompanied by a better intuitive interpretation of its terms in the main text. In particular, how should one understand the third term in this equation? Why does the change in volume impact the change of volume fraction in this way?

We thank the referee for the suggestions. We have included the missing references, with a particular emphasis on DNA nanostars that inhibit phase separation in DNA liquids in the definition of class II. We added intuitive explanations of the main equations, such as Eqs. (4),(8),(14), (17), and (18). Notice that, according to Mysels, Karol J., J. Chem. Educ., 33, 178 (1956) (https://pubs-acs-org.sire.ub.edu/doi/epdf/10.1021/ed033p178) we refer to (18) as the law of mass action.

The discussion in the last paragraph of Section 6 should be clarified. How can the total amount of protein in both phases decrease? This would necessarily violate either mass or volume conservation. Also, the discussion of why the volume is non-monotonic in time is not clear.

A decrease in the total amount of protein in both phases does not violate mass conservation, if the volume of the phases varies accordingly. In particular, the volume of the denser phase should grow. This given, in the case presented the total protein amount in the dense phase decreases, while in the dilute phase increases. For this reason, we revised the paragraph and now explain the results in more detail (see lines starting from 407). The nonmonotonic volume change is indeed a puzzling finding that, as we now state in the manuscript, requires further investigation. Given the lack of analytical approaches available to tackle the complex kinetics in the presence of coexisting phases, we believe that this analysis goes beyond the scope of the present paper.

Recommendations for the authors

Reviewer 1 (Recommendations For The Authors):

Line 96: I feel a mentioning/definition/explanation and perhaps some discussion on the parameter M (limiting aggregate size) would have been in place in the introduction of Equation (1). Furthermore, in the usual interpretation, Flory interaction parameters (symbolized χ) are dimensionless, as, classically, they represent an exchange energy (normalized by kT), defined on a monomeric basis. Here they seem to carry the dimension of energy.

We thank the reviewer for the observation. We have included a brief comment on M and mentioned that we use χ parameters that carry the dimension of energy such that, varying kBT, we scale at the same time the term containing interaction propensities (χ) and the one containing internal energies (_e_int). See the comment on line 127

Line 150: The choice of ρi \= i physically implies that a single protein is assumed to have the same as a solvent molecule. This may be a bit of a stretch. This assumption leads to an overestimation of the translational entropy of the aggregates (first term in Equation (1)). Acknowledging that ρ_1 >> ρs_ would give a pronounced desymmetrization of the phase diagram (I suspect).

Indeed, in the case of monomers only, the assumption leads to a symmetric phase diagram which may be unrealistic. Once assemblies form, however, the phase diagram becomes asymmetric and for this reason we decided to assume ρi \= i, simplifying the theoretical analysis. We have added a clarifying sentence in the manuscript, see line 163

Furthermore, the pictures in Figure 1a-c suggest the presence of a disordered residue, the degree of swelling of which might affect binding strength (see for instance: https://doi.org/10.3389/fnmol.2022.962526).

We added a comment on the possible coupling between internal free energies and interaction propensities, such as the swelling mechanism that affects binding sites, and included the reference above (line 215).

Line 154-156: It’s unclear what is meant with ”an internal bond that keeps each assembly together”. How should this be interpreted on an intuitive physical level?

We apologise for being unclear. We meant the internal bonds that lead to the formation of assemblies. We have now rephrased this sentence in the main text (lines starting from 169).

Line 254: The fact that ϕsg is defined below does not mean it does not fall out of the air here. The same holds for the consideration of the limit M →∞. Ideally, the main text should stand on its own, in particular with respect to physical intuitiveness, as well as the necessity and interest of discussion topics. Technical details, derivations and additional information can be in an appendix.

We agree with the referee and added some physical insights about the limit. We now also state clearly in the main text (line 298) that _ϕ_sg is affected by temperature and the free energy of internal bonds.

Line 257: ”Since we do not explicitly include the solvent in assembly formation we will consider the gel as a phase without solvent and thus ϕtot \= 1”. I’m not sure if I can agree with this. I would say, a gel, certainly in biological context, almost per definition contains a large fraction of solvent, i.e. here water. The situation ”ϕtot \= 1” would rather be a solid precipitate. Is gelation properly captured by this model?

We thank the referee for this very relevant observation. We now state in the main text that the model predicts a macroscopic assembly which we call ’the gel phase’, in agreement with previous literature. Then, to clarify, we added the sentence ”Please note that, since we do not explicitly include the solvent in assembly formation (see reaction scheme in Fig.1a), in our model the gel corresponds to a phase without solvent, _ϕ_tot \= 1. To account for biological gels that can be rich in water, our theory can be straightforwardly extended by incorporating the solvent into the reaction scheme.”, see main text line 300.

Line 268: Shouldn’t ”solvent” be ”solution”? If fsol is given by Equation (1), surely not only the solvent is considered.

Indeed, this is a typo, and we now use the term ’solution’ instead of ’solvent.’

Line 273: At this stage, the only information provided in the main text is that ω∞ is ”a constant that does not affect chemical nor phase equilibrium, except in the limit M →∞” (see lines 153-154). This is a little bit too abstract for me. Again, the main text should stand on its own, meaning the reader should not have to rely on an appendix to at least have an intuitive physical understanding of any modeling or input parameter discussed in the main text.

We thank the reviewer for pointing this out. We now comment on the physical interpretation of ω∞ in the main text, see lines from 320 on.

Figure 4. appears in Equation (39) but it is not defined.

We thank the reviewer for pointing this out. We have reshaped appendix 6A, making use of chemical activities and clarified the origin of the rate .

Line 317. I don’t fully understand the intention of the remark on the model being adaptable for ”primary and secondary nucleation”. How/in what way is this different from association and dissociation? For instance, classical nucleation theory is based on association and dissociation of monomeric units to and from clusters.

We agree that the kinetic rate coefficients kij (appearing in the association and dissociation rates ∆rij, Eq. 17) in our manuscript already depend on assembly length, see Appendix 6 B, where we now clarified their definition. Please note that, however, that secondary nucleation is a special kind of association, for which the kinetic rate coefficients corresponding to associations of small assemblies, i.e. kij with_i,j_ ≪ M, explicitly depend on the presence of large assemblies with sizes l ≫ 1. In our manuscript, we have not accounted for such a dependence. We now make this aspect clear in the manuscript, see Appendix 6 B.

Line 321. Why is ∆rij called the ”monomer exchange rate”? In line 318 the same parameter is defined as the ”reaction rate for the formation of a (i+j)-mer”. Why should these be the same?

We thank the reviewer for spotting this typo.

Line 323. Why do these calculations use M = 15?

The exploration of a 15-dimensional phase space is already numerically challenging. We are currently working on a generalization of the numerical scheme to work with larger values of M but, to discuss the fundamental physical principles, we kept M \= 15.

Reviewer 2 (Recommendations For The Authors):

The manuscript presents several issues, on both the scientific and presentational level, which need to be carefully addressed. Please find below a list of the points that need to be addressed by the authors, divided into major and minor points. Major issues:

• A general, major concern about the results in the paper is the homogeneity assumption. I do understand that repeating the whole analysis presented in the manuscript by allowing for spatial inhomogeneities partially goes beyond the scope of this paper. However, the authors should at least discuss how such inhomogeneities may alter the results in a qualitative way, and treat explicitly the presence of inhomogeneity in one prototypical case treated in the manuscript. Namely, what happens if the volume fractions and relative molecular volumes in the free energy (1) depend on space, e.g., ϕi → ϕi(x)?

We would like to stress that, in the present paper, we do account for spatial inhomogeneities. Indeed, in the case of phase separation, we consider systems which are divided into two phases, characterized by different values of the assemblies’ volume fractions ϕi. We do, however, consider the system to be homogeneous inside the phases, implying a jump in the value of the volume fraction at the interface between the two phases. In this sense, the analysis we carry out is valid in the thermodynamic limit, where gradients of the volume fractions ϕi(x) within the phases, can be neglected. On the other hand, considering the full spatial problem, i.e. solving the equations for M \= 15 spatially varying fields, would be numerically extremely challenging.

• The authors’ results relate molecular assembly- a phenomenon at the molecular scale-to phase separation-a mesoscopic or macroscopic phenomenon. The authors should stress the conceptual importance of this connection between scales, and present their results from the perspective of a multi-scale model.

We thank the reviewer for pointing this out. We now emphasize the multi-scale feature of our model in the introduction (line 80).

• Starting from Section 1, the reader is not well guided through the sections that follow. The authors should provide an outline of the line of though that they are going to follow in the following sections, and logically connect each section to the next one with a short paragraph at the end of each section. This paragraph should resume what has been addressed in the current section, and the connection with the topic that will be addressed in the next one.

We agree with the reviewer and have added a transitioning sentence at the end of each paragraph.

• ’We focus on linear assemblies (d = 1)’: Given the striking differences of the results between d = 1 and d > 1 shown above, the authors should discuss what happens for d > 1 as well.

• ’In figure Fig. 5a, we show the initial and final equilibrium binodals (black and coloured curve, respectively), for the case of linear assemblies (d = 1) belonging to class 1’: Again, show what happens for d > 1.

We agree with the reviewer, the kinetics in d > 1 would be definitely interesting. However, in this case, one assembly can become macroscopic (i.e. M must be set to ∞). This requires some substantial modification in the kinetic scheme, like introducing an absorbing boundary condition for monomers ’sucked in’ the gel. We prefer to leave this for future work, and now state it explicitly in the manuscript (line 383).

• ’This difference arises because, within class 2, monomers in the bulk of an assembly have reduced interaction propensity with respect to the boundary ones. As a consequence, the formation of large clusters shifts the onset of phase separation to higher ϕtot values.’: To prove this argument, the authors should show Fig. 2g and h for d > 1. In fact, by varying d, the effect of the boundary vs. bulk also varies.

We prefer to discuss the thermodynamics of d > 1 in section 4 on gelation. There we present only a single phase diagram so as not to blow up the discussion on equilibrium too much.

• ’referring for simplicity to systems belonging to Class 1’: The authors should do the same analysis for Class 2.

We agree with the reviewer. However, again not to blow up the discussion on equilibrium, we leave it for future work.

• ’other, implying that the corresponding Flory-Huggins parameter χij vanishes’: Why?

The explanation based on a lattice model is reported in Appendix 2, and is now more clearly referenced (line 185).

Minor issues:

• Eq. (10): Here the authors should explain in the main text, possibly in a simple and intuitive way, why the number of monomers i and the space dimension d enter the righthand side of this equation in this particular way.

We thank the reviewer for pointing this out. We added the physical origin of the scaling with dimension in Eq. (10) and in Eq. (8), as pointed out by reviewer 3.

• ’The second and fifth terms of fsol characterize the internal free energies’: What do you mean by ’characterize the internal free energies’? Please clarify.

As we now state more clearly (lines 114-120), these two contributions include the internal free energies ω_s and _ωi, stemming from the free energy of internal bonds that lead to assembly formation.

• ’depend on the scaling form of the’: Scaling with respect to what ? Please clarify.

We have now clarified that the scaling is with respect to the assembly size i.

• Figure 2 is way too dense: it should be split into two figures, and the legend of each of the two figures should be expanded to properly guide the reader to understand the figures.

We understand the reviewer’s point of view. To avoid altering the present flow, we decided not to split the figure, but we have included shaded boxes to better guide the reader.

• ’this is a consequence of the gelation transition’: Please clarify

• ’and this limitation can be dealt with by introducing explicitly the infinite-sized gel in the free energy’: Why? Please clarify.

We have now rephrased these sentences, hopefully in a clearer way. We now state: ’We know that this divergence is physical, and is caused by the gelation transition. This limitation can be dealt with by introducing explicitly a term in the free energy that accounts for an infinite-sized assembly (the gel)’, see lines 320-322.

• Figure 4: Add plots of panels d, e, h and i with log scale on the y axis to make explicit an eventual exponential behavior, and revise the text accordingly

Not to further complicate Figure 4, we preferred to display the logarithmic plots of the equilibrium distribution in the appendix, see Figure A3-1.

• ’... an equilibrium distribution which monotonously decreases with assembly size’: It is not the distributions that decreases but the cluster volume fraction, please rephrase.

We thank the reviewer for pointing this out and have now rephrased this sentence (line 394).

Reviewer 3 (Recommendations For The Authors):

I could not obtain the exact form of Eq 29 in App 3, can the authors elaborate on this calculation. App 3: What does it mean binodal agrees well with ϕsg? And doesn’t ϕsg depend on temperature through phi tilde? What temperature is this result for?

We apologise for the unclear explanation. We now state in detail that Eq. (29) is obtained by plugging the expression of ϕi given in Eq. (24) into Eq. (1), in the main text. The dependence of ϕ₁ on ϕ_tot is expressed in Eq. (26), and we have omitted linear terms in ϕ_tot, since they do not affect phase equilibrium (see lines 802-809). Moreover, ϕsg depends indeed on k_BT. We refer to the comparison between the full curve ϕsg in the k_BT−ϕ_tot plane, and the branch of the binodal between the triple point (indicated now with a cross) and ϕ_tot \= 1. The two curves are close, as expected since both correspond to the boundary between homogeneous mixtures and the gel state, obtained with different methods.

The references to Figures in the appendices are confusing. Please make it clear whether Figures in the main text or the appendices are being referenced. On a related note, the Appendix figures seem to be placed in appendices whose text describes something else - Appendix 2, Figure 1 should be moved to Appendix 3; Appendix 3, Figure 1 should be moved to Appendix 4; etc.

We revised the appendix, corrected the figure positions and clarified their references.

eLife Assessment

The authors present an important theoretical framework that describes the interplay between liquid-liquid phase separation and protein aggregation within a mean-field model. This work will be of high interest to the biophysics and molecular biology communities, as it will help understand and analyse assembly within biomolecular condensates in cells or in-vitro. Major strengths of this convincing work are the consideration of aggregates with various dimensionality and the possibility for protein gelation.

Reviewer #3 (Public review):

Summary:

The authors combine classical theories of phase separation and self-assembly to establish a framework for explaining the coupling between the two phenomena in the context of protein assemblies and condensates. By starting from a mean-field free energy for monomers and assemblies immersed in solvent and imposing conditions of equilibrium, the authors derive phase diagrams indicating how assemblies partition into different condensed phases as temperature and the total volume fraction of proteins are varied. They find that phase separation can promote assembly within the protein-rich phase, providing a potential mechanism for spatial control of assembly. They extend their theory to account for the possibility of gelation. They also create a theory for the kinetics of self-assembly within phase separated systems, predicting how assembly size distributions change with time within the different phases as well as how the volumes of the different phases change with time.

Review For Revision:

The revised manuscript provides better motivation and physical explanations for the equations, and the authors have addressed references, typos, and other minor technical issues identified in the review. These changes have significantly improved the manuscript.

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of waiting until marriage to cohabit

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there is progress in the way we think unlike in the past

eLife Assessment

This valuable study provides an experimental paradigm and state-of-the-art analysis method for studying the existence of call types and transition differences among Mongolian gerbil families in a naturalistic environment. The analyses are convincing, with a thorough treatment of the acoustic data and a demonstration of the robustness of the observed effect across days. The work will likely be of interest to the auditory neuroscience and neuroethology communities.

Reviewer #1 (Public review):

Summary:

This research offers an in-depth exploration and quantification of social vocalization within three families of Mongolian gerbils. In an enlarged, semi-natural environment, the study continuously monitored two parent gerbils and their four pups from P14 to P34. Through dimensionality reduction and clustering, a diverse range of gerbil call types was identified. Interestingly, distinct sets of vocalizations were used by different families in their daily interactions, with unique transition structures exhibited across these families. The primary results of this study are compelling, although some elements could benefit from clarification

Strengths:

Three elements of this study warrant emphasis. Firstly, it bridges the gap between laboratory and natural environments. This approach offers the opportunity to examine natural social behavior within a controlled setting (such as specified family composition, diet, and life stages), maintaining the social relevance of the behavior. Secondly, it seeks to understand short-timescale behaviors, like vocalizations, within the broader context of daily and life-stage timescales. Lastly, the use of unsupervised learning precludes the injection of human bias, such as pre-defined call categories, allowing the discovery of the diversity of vocal outputs.

Comments on the revised version:

(1) The authors have clarified the possible types of differences in the vocalizations of different families and discussed the potential contribution of the adult-pup difference.

(2) The authors have added the analysis in Figure 4 about the developmental changes in call types.

(3) The authors have analyzed the additional information in the 2-gram structure of the calls as evidence to apply the transition matrices to compare the families.

Reviewer #2 (Public review):

Peterson et al., perform a series of behavioral experiments to study the repertoire and variance of Mongolian gerbil vocalizations across social groups (families). A key strength of the study is the use of a behavioral paradigm which allows for long term audio recordings under naturalistic conditions. This new experimental set-up results in the identification of additional vocalization types, not previously described the literature. In combination with state-of-the-art methods for vocalization analysis, the authors demonstrate that the distribution of sound types and the transitions between these sound types across three gerbil families is different. This is a highly compelling finding which suggests that individual families may develop distinct vocal repertories. One potential limitation of the study lies in the cluster analysis used for identifying distinct vocalization types. The authors use a Gaussian Mixed Model (GMM) trained on variational auto Encoder derived latent representation of vocalizations to classify recorded sounds into clusters. Through the analysis the authors identify 70 distinct clusters and demonstrate a differential usage of these sound clusters across families. While the authors acknowledge the inherent challenges in cluster analysis and provide additional analyses (i.e. maximum mean discrepancy, MMD), additional analysis would increase the strength of the conclusions. In particular, analysis with different cluster sizes would be valuable. An additional limitation of the study is that due to the methodology that is used, the authors can not provide any information about the bioacoustic features that contribute to differences in sound types across families which limits interpretations about how the animals may perceive and react to these sounds in an ethologically relevant manner.

The conclusions of this paper are well supported by data.

• Can the authors comment on the potential biological significance of the 70 sound clusters? Does each cluster represent a single sound type? How many vocal clusters can be attributed to a single individual? Similarly, can the authors comment on the intra-individual and inter-individual variability of the sound types within and across families?<br /> • As a main conclusion of the paper rests on the different distribution of sound clusters across families, it is important to validate the robustness of these differences across different cluster parameters. Specifically, the authors state that "we selected 70 clusters as the most parsimonious fit". Could the authors provide more details about how this was fit? Specifically, could the authors expand upon what is meant by "prior domain knowledge about the number of vocal types...". If the authors chose a range of cluster values (i.e. 10, 30, 50, 90) does the significance of the results still hold?<br /> • While VAEs are powerful tools for analyzing complex datasets in this case they are restricted to analysis of spectrogram images. Have the authors identified any acoustic differences (i.e. in pitch, frequency, other sound components) across families?

Following a revision of the manuscript the authors have taken many of these points under consideration and as a result have significantly improved the manuscript. Critically, they have now provided additional quantification that differences across family repertories are robust against cluster selection size.

Reviewer #3 (Public review):

Summary:

In this study, Peterson et al. longitudinally record and document the vocal repertoires of three Mongolian gerbil families. Using unsupervised learning techniques, they map the variability across these groups, finding that while overall statistics of, e.g., vocal emission rates and bout lengths are similar, families differed markedly in their distributions of syllable types and the transitions between these types within bouts. In addition, the large and rich data are likely to be valuable to others in the field.

Strengths:

- Extensive data collection across multiple days in multiple family groups.<br /> - Thoughtful application of modern analysis techniques for analyzing vocal repertoires.<br /> - Careful examination of the statistical structure of vocal behavior, with indications that these gerbils, like naked mole rats, may differ in repertoire across families.<br /> - Estimation of the stability of the effects across days.

Weaknesses:

- The work is largely descriptive, documenting behavior rather than testing a specific hypothesis.<br /> - The number of families (N=3) is somewhat limited, though the authors have taken some care to examine the robustness of the findings.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

This research offers an in-depth exploration and quantification of social vocalization within three families of Mongolian gerbils. In an enlarged, semi-natural environment, the study continuously monitored two parent gerbils and their four pups from P14 to P34. Through dimensionality reduction and clustering, a diverse range of gerbil call types was identified. Interestingly, distinct sets of vocalizations were used by different families in their daily interactions, with unique transition structures exhibited across these families. The primary results of this study are compelling, although some elements could benefit from clarification

Strengths:

Three elements of this study warrant emphasis. Firstly, it bridges the gap between laboratory and natural environments. This approach offers the opportunity to examine natural social behavior within a controlled setting (such as specified family composition, diet, and life stages), maintaining the social relevance of the behavior. Secondly, it seeks to understand short-timescale behaviors, like vocalizations, within the broader context of daily and life-stage timescales. Lastly, the use of unsupervised learning precludes the injection of human bias, such as pre-defined call categories, allowing the discovery of the diversity of vocal outputs.

Weaknesses:

(1) While the notable differences in vocal clusters across families are convincing, the drivers of these differences remain unclear. Are they attributable to "dialect," call usage, or specific vocalizing individuals (e.g., adults vs. pups)? Further investigation, via a literature review or additional observation, into acoustic differences between adult and pup calls is recommended. Moreover, a consistent post-weaning decrease in the bottom-left cluster (Fig. S3) invites interpretation: could this reflect drops in pup vocalization?

Thank you for bringing up this point of clarification. Without knowledge of individual vocalizers, we are unable to rigorously assess pronunciation differences between individuals, however we can get a clear proxy for dialect through observing usage differences between families. We’ve added the following text (blue) in the Discussion to help clarify:

“To address whether gerbils also exhibit family specific vocal features, we compared GMM-labeled vocal cluster usages across the three recorded families and showed differences in vocal type usage (Figure 3). The differences in this study align with the definition of human vocal dialect, which is a regional or social variety of language that can differ in pronunciation, grammatical, semantic and/or language use differences (Henry et al., 2015). This definition of dialect is inclusive of both pronunciation differences (e.g. a Bostonian’s characteristic pronunciation of “car” as “cah”) and usage differences (e.g. a Bostonian’s preferential usage of the words “Go Red Sox” vs. a New Yorker’s preferential usage of the words “Go Yankees”). In our case, vocal clusters can be rarely observed in some families yet highly over-expressed in others (e.g. analogous to language usage differences in humans), or highly expressed in both families, but contain subtle spectrotemporal variations (Figure 3D, Family 1 cluster 11 vs. Family 3 clusters 2, 18, 30; e.g. analogous to pronunciation differences in humans).”

Indeed, our recordings obtained after pup removal could suggest that adults may use fewer low frequency calls (bottom left cluster in UMAP). However, this dataset does not permit a proper assessment of post-weaning pup calls. In fact, our results and the literature shows that adults are likely to use low frequency calls, but only during social interactions with pups or other adults. For example, Furuyama et al. 2022 describe a number of low frequency call types used by adults in agonistic social interactions, which look similar to a low frequency call type used by pups described in Silberstein et al. 2023. Similarly, Ter-Mikaelian et al. 2012 (their Figure 6) recorded several types of sonic vocalizations during adult social interaction. To our knowledge, it has not been shown whether gerbil pups and adults produce distinct call types. It is a challenging problem to solve, as animals placed in isolation (i.e. an experimental condition for which the identity of the vocalizer is known) vocalize infrequently and of the limited number they might emit, they do not use the full range of vocalizations described in the literature (RP personal observations). To properly address this question, one would need to elicit full use of the vocal repertoire through free social interaction, then attribute calls to individual vocalizers via sound source localization and/or head-mounted microphones — we are currently pursuing both of these technical challenges, but this is outside the scope of this manuscript.

Although the literature reflects the limitations discussed above, we have added a brief paragraph to the Discussion (limitations section) that addresses the reviewer’s question about the development of vocalizations:

“Although we were not able to attribute vocalizations to individual family members, we did seek to determine the importance of family structure by comparing audio recordings before and after removal of the pups at P30. The results show a clear effect of family integrity, and the sudden reduction of sonic calls following pup removal (Figure S3) could suggest that these vocalizations are produced selectively by pups.

However, there is ample evidence that adult gerbils also produce sonic vocalizations. For example, a number of low frequency call types are used by adults during a range of social interactions (Ter-Mikaelian et al., 2012; Furuyama et al., 2022), some of which are similar to a low frequency call type used by pups (Silberstein et al., 2023). Vocalization patterns of developing gerbils depend on isolation or staged interactions. Thus, when gerbil pups are recorded during isolation, ultrasonic vocalization rate declines and sonic vocalizations increase for animals that are in a high arousal state (De Ghett 1974, Silberstein et al., 2023). As gerbils progress from juvenile to adolescent development (P17-55) a significant increase in ultrasonic vocalization rate is observed during dyadic social encounters, with a distinct change in usage pattern that depends upon the sex of each animal (Holman & Seale 1991, Holman et al. 1995). The development of vocalization types has been assessed in another member of the Gerbillinae subfamily, called fat-tailed gerbils (Pachyuromys duprasi), during isolation and handling. Here, the number of ultrasonic vocalization syllable types increase from neonatal to adult animals (Zaytseva et al. 2019), while some very low frequency sonic call types were rarely observed after P20 (Zaytseva et al. 2020). By comparison, mouse syllable usage changes during development, but pups produced 10 of the 11 syllable types produced by adults (Grimsley et al. 2011). In summary, our understanding of the maturation of vocalization usage remains limited by our inability to obtain longitudinal data from individual animals within their natural social setting. For example, when recorded in their natural environment, chimpanzees display a prolonged maturation of vocalization complexity, such as the probability of a unique utterance in a sequence, with the greatest changes occuring when animals begin to experience non-kin social interactions (Bortolato et al. 2023).”

(2) Developmental progression, particularly during pre-weaning periods when pup vocal output remains unstable, might be another factor influencing cross-family vocal differences. Representing data from this non-stationary process as an overall density map could result in the loss of time-dependent information. For instance, were dominating call types consistently present throughout the recording period, or were they prominent only at specific times? Displaying the evolution of the density map would enhance understanding of this aspect.

This is a great suggestion. Thank you for bringing it up. To address this, we have added an additional figure (Figure 4) to the main text (Note that the former Figure 4 is now Figure 5). New text associated with this new figure was added to the Results and Discussion sections:

Results

“Vocal usage differences remain stable across days of development It is possible that the observed vocal usage differences could result from varying developmental progression of vocal behavior or overexpression of certain vocal types during specific periods within the recording. To assess the potential effect of daily variation on family specific vocal usage, we visualized density maps of vocal usage across days for each of the families (Figure 4A). There are two noteworthy trends: 1.) the density map remains coarsely stable across days (rows) and 2.) the maps look distinct across families on any given day (columns). This is a qualitative approximation for the repertoire’s stability, but does not take into account variation of call type usage (as defined by GMM clustering of the latent space). Figure 4B, shows the normalized usage of each cluster type over development for each family. Cluster usages during the period of “full family, shared recording days” (postnatal days beneath the purple bars) are stable across days within families – as is apparent by the horizontal striations in the plot – though each family maintains this stability through using a unique set of call types. This is addressed empirically in Figure 4C, which shows clearly separable PCA projections of the cluster usages shown in Figure 4B (purple days). Finally, we computed the pairwise Mean Max Discrepancy (MMD) between latent distributions of vocalizations from individual recording days for each of the families (Figure 4D). This shows that across-family repertoire differences are substantially larger than within-family differences. This is visualized in a multidimensional scaling projection of the MMD matrix in Figure 4E.”

Discussion

“The described family differences collapse data from multiple days into a single comparison, however it’s possible that factors such as vocal development and/or high usage of particular vocal types during specific periods of the recording could explain family differences. Therefore, we took advantage of the longitudinal nature of our dataset to assess whether repertoire differences remain stable across time. First, we visualized vocal repertoire usage across days as either UMAP probability density maps (Figure 4A) or daily GMM cluster usages (Figure 4B). Though qualitative, one can appreciate that family repertoire usage remains stable across days and appears to differ on a consistent daily basis across families. To formally quantify this, we first projected GMM cluster usages from Figure 4B into PC space and show that family GMM cluster usage patterns are highly separable, regardless of postnatal day (Figure 4C). If families had used a more overlapping set of call types, then the projections would have appeared intermixed. Next, we performed a cluster-free analysis by computing the pairwise MMD distance between VAE latent distributions of vocalizations from each family and day (Figure 4D). This analysis shows very low MMD values across days within a family (i.e. the repertoire is highly consistent with itself), and high MMD values across families/days (greater than would be expected by chance; see shuffle control in Figure S2D). The relative differences in this matrix are made clear in Figure 4E, which provides additional evidence that family vocal repertoires remain stable across days and are consistently different from other families. Taken together, we believe that this is compelling evidence that differences in vocal repertoires between families are not driven by dominating call types during specific phases in the recording period; rather, families consistently emit characteristic sets of call types across days. This opens up the possibility to assess repertoire differences over much shorter time periods (e.g. 24 hours) in future studies.”

(3) Family-specific vocalizations were credited to the transition structure, a finding that may seem obvious if the 1-gram (i.e., the proportion of call types) already differs. This result lacks depth unless it can be demonstrated that, firstly, the transition matrix provides a robust description of the data, and secondly, different families arrange the same set of syllables into unique sequences.

Thank you for these important suggestions. We agree that it is true that the 2-gram transition structure must vary based on the 1-gram structure. To determine whether this influences the interpretation of the finding, we have added Figure S5 and the following text in the Results section:

“To determine whether differences in 1-gram structure contribute to differences in the transition (2-gram) structure, we performed a number of controls. Although subtle, vertical streaks are clearly present in shuffled transition matrices that correspond to 1-gram usages (Figure S5A-B). Given the shuffled data structure, we sought to determine whether the observed transition probabilities differed significantly from chance levels. We randomly shuffled label sequences 1000 times independently for each family to generate a null transition matrix distribution. Using these null distributions and the observed transition probabilities, we computed a p-value for each transition using a one-sample t-test and created a binary transition matrix indicating which transitions happen above chance levels (Figure S5C, black pixels, p <= 0.05 after post hoc Benjamini-Hochberg multiple comparisons correction). As is made clear in Figure S5C, most transitions for each family occur significantly above chance levels, despite the inherent 1-gram structure. Moreover, by looking at transitions from a highly usage cluster type used roughly the same proportion across families (cluster 12), we show that families arrange the same sets of vocal clusters into unique sequences (Figure S5D). We believe that this provides compelling evidence that the 1-gram structure does not change the interpretation of the main claim that transition structure varies by family. “””

To address your second point, we inspected frequent transitions from individual syllables to all other syllables using bigram transition probability graphs. This revealed a common trend that across all families, many shared and unshared transitions existed, suggesting that families use the same sets of syllables to make unique transition patterns. Figure S5D shows a single syllable example of the phenomenon, with red lines indicating the shared transition types between families and black showing transition patterns not shared between families (i.e. unique family-specific transitions, or lack thereof).”

Reviewer #2 (Public Review):

Peterson et al., perform a series of behavioral experiments to study the repertoire and variance of Mongolian gerbil vocalizations across social groups (families). A key strength of the study is the use of a behavioral paradigm which allows for long term audio recordings under naturalistic conditions. This experimental set-up results in the identification of additional vocalization types. In combination with state of the art methods for vocalization analysis, the authors demonstrate that the distribution of sound types and the transitions between these sound types across three gerbil families is different. This is a highly compelling finding which suggests that individual families may develop distinct vocal repertoires. One potential limitation of the study lies in the cluster analysis used for identifying distinct vocalization types. The authors use a Gaussian Mixed Model (GMM) trained on variational auto Encoder derived latent representation of vocalizations to classify recorded sounds into clusters. Through the analysis the authors identify 70 distinct clusters and demonstrate a differential usage of these sound clusters across families. While the authors acknowledge the inherent challenges in cluster analysis and provide additional analyses (i.e. maximum mean discrepancy, MMD), additional analysis would increase the strength of the conclusions. In particular, analysis with different cluster sizes would be valuable. An additional limitation of the study is that due to the methodology that is used, the authors can not provide any information about the bioacoustic features that contribute to differences in sound types across families which limits interpretations about how the animals may perceive and react to these sounds in an ethologically relevant manner.

The conclusions of this paper are well supported by data, but certain parts of the data analysis should be expanded and more fully explained.

• Can the authors comment on the potential biological significance of the 70 sound clusters? Does each cluster represent a single sound type? How many vocal clusters can be attributed to a single individual? Similarly, can the authors comment on the intra-individual and inter-individual variability of the sound types within and across families?

Previous work documenting the Mongolian gerbil repertoire (Ter-Mikaelian 2012, Kobayasi 2012) has revealed ~12 vocalization types that vary with social context. Our thinking is that we are capturing these ~12 (plus a few more, as illustrated in Figure 2C) as well as individual or family-specific variations of some call types. Although the number of discrete call types is likely less than 70, it’s plausible that variation due to vocalizer identity pushes some calls into unique clusters. This idea is supported by the fact that both naked mole rats and Mongolian gerbils have been shown to exhibit individual-specific variation in vocalizations, though only in single call types (Barker 2021, Figure 1; Nishiyama 2011, Table I). The current study is not ideal to test this prediction, as we cannot attribute each vocalization to individual family members. Using our 4-mic array, we attempted to apply established sound source localization techniques to assign vocalizations to individuals (Neunuebel 2015), but the technique failed, presumably due to high amounts of reverberation in the arena. We are currently developing a custom deep learning based sound localization algorithm, and had hoped to extract individual animal vocalizations from our data set (part of the reason why this manuscript has taken longer than expected to return!), but the performance is not yet satisfactory for large groups of animals. We have added text to the Methods sections with the context outlined above to further justify the use of ~70 clusters.

• As a main conclusion of the paper rests on the different distribution of sound clusters across families, it is important to validate the robustness of these differences across different cluster parameters. Specifically, the authors state that "we selected 70 clusters as the most parsimonious fit". Could the authors provide more details about how this was fit? Specifically, could the authors expand upon what is meant by "prior domain knowledge about the number of vocal types...". If the authors chose a range of cluster values (i.e. 10, 30, 50, 90) does the significance of the results still hold?

Thank you for the suggestion, this is an important point that we have addressed with new analyses in the revision (see GMM clustering methods and new Figure S4). The prior domain knowledge referenced is with respect to the information known about the Mongolian gerbil vocal types provided in the response above. We have made this more clear in the discussion.

We mainly based our selection of the number of clusters using the elbow method on GMM held-out log likelihood (Figure S2C). Around 70 clusters is when the likelihood begins to plateau, though it’s clear that there are a number of reasonable cluster sizes. To assess whether cluster size has an effect on interpretation of the family differences result, we added Figure S5, where we varied the number of GMM clusters used and compared cluster usage differences across families (Figure S4A). We quantified pairwise family differences in cluster usage by computing the sum of the absolute value of differential cluster usages, for each GMM cluster value (Figure S4B). We find that relative usage differences remain unchanged across the range of cluster values used, indicating that GMM cluster size does bias the finding.

• While VAEs are powerful tools for analyzing complex datasets in this case they are restricted to analysis of spectrogram images. Have the authors identified any acoustic differences (i.e. in pitch, frequency, and other sound components) across families?

Though it’s true that this VAE is limited to spectrograms, the VAE latent space has been shown to correspond to real acoustic features such as frequency and duration, and contain a higher representational capacity than traditional acoustic features (Goffinet 2021, Figure 2). Therefore, clustering of the latent space necessarily means that vocalizations with similar acoustic features are clustered together regardless of their family identity.

Despite this, your point is well taken that there could be systematic differences in certain acoustic features for specific call types. We are not able to ascertain this with the current dataset. This is addressed in Barker 2021 by recording a single call type (soft chirp) from individuals within and across families. Mongolian gerbils have been shown to exhibit individual differences in the initial, terminal, minimum, and maximum frequency of the ultrasonic up-frequency modulated call type (Figure 2, top right green; Nishiyama 2011, Figure 1A ). Therefore it’s possible that family-specific differences exist for that particular call type. To assess whether other call types show family or individual differences, it’s necessary to either 1.) elicit all call types from an animal in isolation or 2.) determine vocalizer identity in social-vocal interactions. The problem with the former idea is that gerbils only produce up-frequency modulated USVs in isolation and there is no known way to elicit the full vocal repertoire in single animals. The latter idea would allow for full use of the vocal repertoire, but requires invasive techniques (e.g., skull-implanted microphones, or awake-behaving laryngeal nerve recordings) that permit assignment of vocalizations to individuals during a natural social interaction. We are actively exploring solutions to both problems.

It’s likely that future studies will look deeper into acoustic differences between individuals and families. Therefore, we have added acoustic feature quantification of vocalizations in each of the GMM clusters as a reference (Figure S6).

Reviewer #3 (Public Review):

Summary:

In this study, Peterson et al. longitudinally record and document the vocal repertoires of three Mongolian gerbil families. Using unsupervised learning techniques, they map the variability across these groups, finding that while overall statistics of, e.g., vocal emission rates and bout lengths are similar, families differed markedly in their distributions of syllable types and the transitions between these types within bouts. In addition, the large and rich data are likely to be valuable to others in the field.

Strengths:

- Extensive data collection across multiple days in multiple family groups.

-  Thoughtful application of modern analysis techniques for analyzing vocal repertoires. - Careful examination of the statistical structure of vocal behavior, with indications that these gerbils, like naked mole rats, may differ in repertoire across families.

Weaknesses:

- The work is largely descriptive, documenting behavior rather than testing a specific hypothesis.

- The number of families (N=3) is somewhat limited.

We agree that the number of families is relatively small. However, our new analysis of vocal repertoire by postnatal day (Figure 4) demonstrates that the finding is quite robust. A high sample-size study was outside the scope of this initial observational study given the difficulty of obtaining and processing longitudinal data of this scale. In light of new analyses in Figure 4, we are confident that future studies will not need so much data to characterize family-specific differences. A single 24-hour recording should be sufficient, making comparison of many more families relatively straightforward.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Several minor concerns:

(1) The three thresholds used for vocalization segmentation lack explanation.

Figure 1C's first vocal event appears to define the first gap via the gray threshold (th_2, as the trace does not cross the black line) and the second gap via the black threshold (th_1 or th_3). And this is not addressed in the Methods section.

Thank you for bringing this to our attention. We agree, this is presented in an unnecessarily complicated way. We have updated the methods section describing the thresholding procedure.

“Sound onsets are detected when the amplitude exceeds 'th_3' (black dashed line, Figure 1C), and sound offset occurs when there is a subsequent local minimum e.g., amplitude less than 'th_2' (gray dashed line, Figure 1C), or 'th_1' (black dashed line, Figure 1C), whichever comes first. In this specific use case, th_2 (5) will always come before th_1 (2), therefore the gray dashed line will always be the offset. A subsequent onset will be marked if the sound amplitude crosses th_2 or th_3, whichever comes first. For example, the first sound event detected in Figure 1C shows the sound amplitude rising above the black dashed line (th_3) and marks an onset. Subsequently, the amplitude trace falls below the gray dashed line (th_2) and an offset is marked. Finally, the amplitude rises above th_2 without dipping below th_3 and an onset for a new sound event is marked. Had the amplitude dipped below th_3, a new sound event onset would be marked when the amplitude trace subsequently exceeded th_3 (e.g. between sound event 2 and 3, Figure 1C). The maximum and minimum syllable durations were selected based on published duration ranges of gerbil vocalizations (Ter-Mikaelian et al. 2012, Kobayasi & Riquimaroux, 2012).”

(2) The determination of multi-syllabic calls could be explained further. In Figure 1C, for instance, do syllables separated by short gaps (e.g., the first syllable and the rest of the first group, and the third group in this example) belong to the same call or different calls?

We have added an operational definition of mono vs. multisyllabic calls in the Results section:

“Vocalizations occur as either single syllables bounded by silence (monosyllabic) or consist of combinations of single syllables without a silent interval (multisyllabic).”

Under this definition, the examples you mentioned in Figure 1C are considered monosyllabic. One could reasonably expand the definition to include calls separated by less than X ms of silence for example, however we choose not to do that in this study. A deeper understanding of the phonation mechanisms for different gerbil vocalization types would be helpful to more rigorously determine the distinction between mono vs. multisyllabic vocalizations.

(3) Labeling the calls shown in Fig. 3D in the latent feature space would help highlight within-family diversity and between-family similarities.

Great suggestion. We have updated Figure 3 to include where in UMAP space each family’s preferred clusters are.

(4) In the introduction, the statement, "Therefore, our study considers the possibility that there is a diversity of vocalizations within the gerbil family social group" doesn't naturally follow from the previous example. This could be rephrased.

Agreed, thank you. We revised this section of the introduction to flow better.

Reviewer #2 (Recommendations For The Authors):

While outside the scope of the current study the authors may consider the following experiments and analysis for future studies:

• Do vocal repertories retain their family signatures across subsequent generations of pups? (i.e. if vocalizations are continually monitored during second or third litters of the same parents).

• Do the authors observe any long-term changes in family repertoires related to the developmental trajectory of the pups? Are there changes in individual pup vocal features or sound type usage throughout development?

Thank you for these great suggestions. Given that naked mole rats learn vocalizations through cultural transmission, it would be interesting to see whether other subterranean species with complex social structures (gerbils, voles, rats) have similar abilities. A straightforward way to assess this possibility could be as you suggest — are latent distributions of vocalizations from multi-generational families closer together than cross-family differences? If true, this would provide compelling evidence to investigate further.

We partially address your second suggestion in our response to Reviewer 1 and in Figure S4, which shows that the family repertoire remains stable throughout this particular period of development. This doesn’t rule out the possibility that there could be other phases of development that undergo more vocal change. Your final suggestion is an area that we are actively researching and eager to know the answer to. A follow-up question: could differences in pup vocal features contribute to differential care by parents?

Reviewer #3 (Recommendations For The Authors):

In all, I found the paper clearly written and the figures easy to follow. One small suggestion:

Figure 1: I can't see the black and gray thresholds described in the caption very well. Perhaps a zoom-in to the first 0.15s or so of the normalized amplitude plot would better display these.

Agreed, thank you. We added a zoom-in to Figure 1.

change cartoon logo to the left

change the cartoon van image to something that tells a story

Es geht vor allem darum, wie lange die unstabilen Bedingungen herrschen werden, die von anthropogenen Störungen des Erdsystems ausgelöst wurden und werden. Es verändern sich Atmosphäre, Hydrosphäre, Cryosphäre, Lithosphäre und Biosphäre. Zu stabileren Bedingungen kann es erst am Ende des Anthropozäns kommen. wenn sich die menschlichen Einflüsse nicht mehr auswirken.

Das Holozän ist durch relativ hohe klimatische Stabilität gekennzeichnet, im Gegensatz zum Anthropozän. Diese Stabilität lässt sich nicht wiederherstellen. Durch die weitere Zunahme des Treibhausgas-Gehalts der Erdatmosphäre wird sich der Unterschied zwischen Anthropozän und Holozän noch vergrößern.

eLife Assessment

This valuable study investigates evolutionary aspects around a single amino acid polymorphism, known to be under long-term balancing selection, in an immune peptide of Drosophila melanogaster. Using alleles with different substitutions, the investigators demonstrate that while one allele provides better survival after systemic infections by a bacterial pathogen, the alternative allele endows its carriers with a longer lifespan under certain conditions. The authors suggest that these contrasting fitness effects of the two alleles contribute to balancing their long-term evolutionary fate. While the work is very interesting, the strength of the provided evidence is still incomplete, and the study would benefit from more rigorous approaches.

Reviewer #1 (Public review):

Summary:

In this manuscript, Unckless and colleagues address the issue of the maintenance of genetic diversity of the gene diptericin A, which encodes an antimicrobial peptide in the model organism Drosophila melanogaster. This is an important question as the maintenance of different alleles in wild populations is not known.

Strengths:

The data indicate that flies homozygous for the dptA S69 allele are better protected against some bacteria. By contrast, male flies homozygous for the R69 allele resist better to starvation than flies homozygous for the S69 allele. This provides an element of explanation.

Weaknesses:

(1) Some of the results are difficult to understand. The observation that R69 die more than the null Dpt mutant and the wild-type is strange. This could be due to background effect. The fact that the second chromosome was not isogenized after the CRISPR change is an issue. This issue may take too much time to fix, but should be acknowledged. The existence of background effect and the multiple tested conditions that may lead to the obtention of results that may not be reproduced in other contexts/labs.<br /> (2) Some lifespans are rather short and often in disagreement with other studies (Leulier, Iatsenko but also Hanson/Lemaitre). There are also disagreements inside the article itself for instance between Fig4C and 2A. This should be mentioned.<br /> (3) The shape of many lifespan analysis with abrupt decline contrast with classical lifespan studies, suggesting technical problems.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

In this manuscript, Unckless and colleagues address the issue of the maintenance of genetic diversity of the gene diptericin A, which encodes an antimicrobial peptide in the model organism Drosophila melanogaster.

Strengths:

The data indicate that flies homozygous for the dptA S69 allele are better protected against some bacteria. By contrast, male flies homozygous for the R69 allele better resist starvation than flies homozygous for the S69 allele.

Weaknesses:

-I am surprised by the inconsistency between the data presented in Fig. 1A and Fig. S2A for the survival of male flies after infection with P. rettgeri. I am not convinced that the data presented support the claim that females have lower survival rates than males when infected with P. rettgeri (lines 176-182).

The two figures are pasted above (1A left, S2A right). The reviewer is correct that the two experiments look different in terms of overall outcomes for males, though qualitatively similar. These two experiments were performed by different researchers, and as much as we attempt to infect consistently from researcher to researcher, some have heavier hands than others. It is true that the genotype that has the largest sex effect is the arginine line (blue) where females (in this experiment) are as bad as the null allele, and males are more intermediate. Also note that the experiments in S2A (male and female) were done in the same block so they are the better comparison. We’ve reflected this in the manuscript.

- The data in Fig. 2 do not seem to support the claim that female flies with either the dptA S69 or the R69 alleles have a longer lifespan than males (lines 211-215). A comment on the [delta] dpt line, which is one of the CRISPR edited lines, would be welcome.

We’ve reworded this section based on these comments.

- The data in Fig. 2B show that male flies with the dptA S69 or R69 alleles have the same lifespan when poly-associated with L. plantarum and A. tropicalis, which contradicts the claim of the authors (lines 256-260).

This is correct – the effect is only in females. It has been corrected.

Reviewer #2 (Public Review):

Summary: In this study, the authors delve into the mechanisms responsible for the maintenance of two diptericin alleles within Drosophila populations. Diptericin is a significant antimicrobial peptide that plays a dual role in fly defense against systemic bacterial infections and in shaping the gut bacterial community, contributing to gut homeostasis.

Strengths: The study unquestionably demonstrates the distinct functions of these two diptericin alleles in responding to systemic infections caused by specific bacteria and in regulating gut homeostasis and fly physiology. Notably, these effects vary between male and female flies.

Weaknesses: Although the findings are highly intriguing and shed light on crucial mechanisms contributing to the preservation of both diptericin alleles in fly populations, a more comprehensive investigation is warranted to dissect the selection mechanisms at play, particularly concerning diptericin's roles in systemic infection and gut homeostasis. Unfortunately, the results from the association study conducted on wild-caught flies lack conclusive evidence.

This is true that the wild fly association study is mostly a negative result. We’ve backed off the claim about the Morganella association.

Major Concerns:

Lines 120-134: The second hypothesis is not adequately defined or articulated. Please revise it to provide more clarity. Additionally, it should be explicitly stated that the first part of the first hypothesis (pathogen specificity), i.e., the superior survival of the S allele in Providencia infections compared to the R allele, has been previously investigated and supported by the results in the Unckless et al. 2016 paper. The current study aims to additionally investigate the opposite scenario: whether the R allele exhibits better survival in a different infection. Please consider revising to emphasize this point.

We’ve reworded this section and added references to both the Unckless et al. 2016 and Hanson et al. 2023 papers.

Figures and statistical analyses: It is essential to present the results of significant differences from the statistical analyses within Figures 1B, 2B, and 3. Additionally, please include detailed descriptions of the statistical analysis methods in the figure legends. Specify whether the error bars represent standard error or standard deviation, particularly in Figure 3, where assays were conducted with as few as 3 flies.

We have added statistical details as requested.

Lines 317-318 (as well as 320-328): The data related to P. rettgeri appear somewhat incomplete, and the authors acknowledge that bacterial load varies significantly, and this bacterium establishes poorly in the gut. These data may introduce more noise than clarity to the study. Please consider revising these sections by either providing more data, refining the presentation, or possibly removing them altogether.

The fact that P. rettgeri establishes poorly in the gut in wildtype flies is the result of several unpublished experiments in the Lazzaro and Unckless labs. We don’t have this as a figure because it was not directly tested in these experiments. We’ve added a note that it is personal observation and we’ve reworked the discussion in the second section.

Lines 335-387 and Figure 4: Although these results are intriguing and suggest interactions between functional diptericin and fly physiology, some mediated by the gut microbiome, they remain descriptive and do not significantly contribute to our understanding of the mechanism that maintains the diptericin alleles.

While the reviewer is correct that these experiments do not elucidate mechanism, they do strongly suggest (based on the controlled nature of the experiments) that the physiological tradeoffs are due to Diptericin genotype. The disagreement is the level of “mechanism”. At the evolutionary level, the demonstration of a physiological cost of a protective immune allele is sufficient to explain the maintenance of alleles. However, we have not determined (and did not attempt to determine) why Diptericin genotype influences these traits. That will have to wait for future experiments.

Lines 399-400: The contrast between this result and statement and the highly reproducible data presented in Figures 2-4 should be discussed.

We’ve added some discussion to this section including a reference to the “inconstancy” of the Drosophila gut microbiome.

Lines 422-429 and Figure 5D: The conclusion regarding an association between diptericin alleles and Morganellaceae bacteria is not clearly supported by Figure 5D and lacks statistical evidence.

We’ve changed this to just be suggestive.

Reviewer #3 (Public Review):

Summary:

This paper investigates the evolutionary aspects around a single amino acid polymorphism in an immune peptide (the antimicrobial peptide Diptericin A) of Drosophila melanogaster. This polymorphism was shown in an earlier population genetic study to be under long-term balancing selection. Using flies with different AA at this immune peptide it was found that one allelic form provides better survival of systemic infections by a bacterial pathogen, but that the alternative allele provides its carriers a longer lifespan under certain conditions (depending on the microbiota). It is suggested that these contrasting fitness effects of the two alleles contribute to balance their long-term evolutionary fate.

Strengths:

The approach taken and the results presented are interesting and show the way forward for studying such polymorphisms experimentally.

Weaknesses:

(1) A clear demonstration (in one experiment) that the antagonistic effect of the two selection pressures isolated is not provided.

The study is overwhelming with many experiments and countless statistical tests. The overall conclusion of the many experiments and tests suggests that "dptS69 flies survive systemic infection better, while dptS69R flies survive some opportunistic gut infections better." (line 444-446). Given the number of results, different experiments, and hundreds of tests conducted, how can we make sure that the result is not just one of many possible combinations? I suggest experimentally testing this conclusion in one experiment (one may call this the "killer-experiment") with the relevant treatments being conducted at the same time, side by side, and the appropriate statistical test being conducted by a statistical test for a treatment x genotype interaction effect.

This is a nice idea but would not work in practice since the fly lines used are different (gnotobiotic vs conventional) and gnotobiotics have to be derived from axenic lines that need a few generations to recover from the bleaching treatment.

(2) The implication that the two forms of selection acting on the immune peptide are maintained by balancing selection is not supported.

The picture presented about how balancing selection is working is rather simplistic and not convincing. In particular, it is not distinguished between fluctuating selection (FL) and balancing selection (BL). BL is the result of negative frequency-dependent selection. It may act within populations (e.g. Red Queen type processes, mating types) or between populations (local adaptation). FL is a process that is sometimes suggested to produce BL, but this is only the case when selection is negative frequency dependent. In most cases, FL does not lead to BL.

The presented study is introduced with a framework of BL, but the aspects investigated are all better described as FL (as the title says: "A suite of selective pressures ..."). The two models presented in the introduction (lines 62 to 69; two pathogens, cost of resistance) are both examples for FL, not for BL.

We’ve added a discussion of how fluctuating selection and balancing selection relate at the end of the discussion.

Finally, no evidence is presented that the different selection pressures suggested to select on the different allelic forms of the immune peptide are acting to produce a pattern of negative frequency dependence.

We are not arguing for negative frequency dependent selection. We assume throughout that Dpt allele does not drive overall frequency of P. rettgeri in populations since it is a ubiquitous microbe. So evolution within D. melanogaster therefore has little to no effect on density of the pathogen.

Recommendations for the authors:

Reviewer #2 (Recommendations For The Authors):

Minor Comments:

Line 31: Rewrite the sentence mentioning "homozygous serine" for improved clarity, especially since the S/R polymorphism of Diptericin has not been introduced yet.

This has been changed to be vague in terms of specific alleles and just refers to “one allele” vs the other.

Lines 87-94: Consider reorganizing this paragraph to maintain a logical flow of the discussion on the Drosophila immune system and the IMD pathway.

We explored other orders, but we think that as is (IMD to AMPs in general to AMPs in Drosophila) makes the most sense here.

Line 99: Provide an explanation of balancing selection for a broader readership, differentiating it from other modes of selection.

We added a brief discussion but note that the intro has significant discussion of balancing selection.

Lines 105-106: Please provide a proper reference. Additionally, ensure that the Unkless et al. 2016 paper is correctly referenced, both in lines 111 and 138-141.

This has been added.

Lines 138-141: It would be beneficial to state that the previous study by Unkless et al. 2016 did not control for genetic background, which is why the assay was redone with gene editing.

This has been added.

Lines 296-303: Clarify the source of the survival observations and consider incorporating this data into Figure 2 for improved visualization.

We’ve clarified that this is Figure 2.

Lines 390-394: Explain the distinctions between vials and cages, particularly in terms of food consumption, exposure to bacteria, etc., which can be relevant to gut homeostasis.

We’ve added a discussion of why these two approaches are complementary.

Reviewer #3 (Recommendations For The Authors):

Statistics

Statistical results are limited to the presentation of p-values (several hundred of them!). For a proper assessment of the statistical analyses, one would also want to see the models used and the test statistics obtained.

The statistical tests done are often unclear. For example, in several experiments, pools of 3 trials (blocs) of multiple animals were tested. The blocs need to be included in the model. Likewise, it seems that multiple delta-dpt fly genotypes were produced. Apparently, they were not distinguished later. Were they considered in the statistical analyses? By contrast, two lines of dptS69R flies were reported to show differences. What concept was applied to test for line difference in some cases and not in others?

In the same dataset (i.e. data resulting from one experiment), it seems that mostly multiple tests were done. For example, in one case each treatment was contrasted to the dptS69 flies. It is generally not acceptable to break down one dataset in multiple subsets and conduct tests with each subtest. One single model for each experiment should be done. This may then be followed by post-hoc tests to see which treatments differ from each other.

We’ve attempted to clarify these statistical approaches throughout.

Minor points

In the legend of Figure 3 it says: "A) monoassociations where each plot represents a different experiment,". This is unclear to me. First, how many plots are there: 3 or 12? Second, what means "experiment"? Are these treatments, or entirely different experiments? How was this statistically taken into account?

We’ve changed this to “different condition” which is clearer. We performed statistical analysis independently for each condition and we’ve now discussed that.

Fig. 5D. It is suggested in the text ("Most intriguing", line 426) and the figure legend that the abundance of Morganellaceae in wild-caught flies differs among genotypes. This is not visible in the figure and not convincingly shown in the text. No stats are given.

We’ve now added that these differences are not significant.

Line 458-461: This sentence is unclear.

We’ve attempted to clarify.

What is a "a traditional adaptive immune system"?

We’ve reworded to “an adaptive immune system”.

There are several typos in the manuscript. Please correct.

We’ve attempted to fix typos throughout.

Bold statements are often without references.

We’ve attempted to add appropriate references throughout.

Reviewer #1 (Public review):

This paper introduces a new transgenic mouse line that allows the labelling of the AIS and nodes of Ranvier by tagging Ank-G with GFP in a Cre-dependent manner. The authors characterise the properties of the AIS and nodes of Ranvier when labelled with GFP to show that it has no adverse effects on the properties of the AIS and nodes of Ranvier, nor on most measures of intrinsic excitability in neurons. They also show that this mouse line can be used to follow AIS plasticity in vitro and to visualise the AIS of neurons in vivo. This is a very useful and timely tool that will make an important impact in the field.

Reviewer #2 (Public review):

The axon initial segment (AIS) is the axonal domain where most neurons integrate inputs and generate action potentials. Though structural and electrophysiological studies have allowed to better understand the mechanisms of assembly and maintenance of this domain, as well as its functions, there is still a need for efficient tools to study its structural organization and plasticity in vivo.

In this article, the authors describe the generation of a knock-in mouse reporter line allowing the conditional expression of a GFP-tagged version of AnkyrinG (Ank-G), which is a major protein of the axon initial segment and the nodes of Ranvier in neurons. This reporter line can in particular be used to study axon initial segment assembly and plasticity, by combining it with mouse lines or viruses expressing the Cre recombinase under the control of a neuronal promoter. Furthermore, the design of the line should allow to preserve the expression of the main Ank-G isoforms observed in neurons and could thus allow to study Ank-G related mechanisms in various neuronal subcompartments.

Some mouse lines allowing the neuronal expression of AIS/node of Ranvier markers coupled to a fluorescent protein exist, however they correspond to transgenic lines leading to potential overexpression of the tagged protein. Depending on the promoter used, their expression can vary and be absent in some neuronal populations (in particular, the Thy-1 promoter can lead to variable expression depending on the transgene insertion locus). Furthermore, these lines do not allow conditional expression of the protein regarding neuronal subtypes nor controlled temporal expression. Finally, a thorough description of the in vivo expression of the tagged protein at the AIS, and its impact on the structural and electrophysiological properties of the AIS are missing for these lines.

The present reporter line is thus definitely of interest, as the authors convincingly show that it can be used in various contexts (from in vitro to in vivo). It could in particular be used to study the assembly and plasticity of the domains where Ank-G is expressed. The strength of this work is that it thoroughly characterizes the reporter line expression and shows that it does not alter the structural nor the electrophysiological properties of the labeled neurons. The additional data presented by the authors in the revised version adequately complete the previously shown data and address the questions raised by the reviewers.

Author response:

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

Reviewer #1:

R1-01 - Does ank-G-GFP label all isoforms (190, 270 and 480kDa) of ankG? From the images of the AIS and noR it appears that the large forms (270 and 480 kDa) are probably tagged with GFP. Did the authors check for puncta along dendrites and in dendritic spines, which are thought to be formed by the small (190 kDa) isoform? Perhaps a western blot to show that Ank-G-GFP labels all isoforms would be a useful addition to this study.

We believe that AnkG-GFP indeed labels the major Ank3 transcripts in the brain, including the 190, 270, and 480 kDa isoforms, based both on known mRNA exon usage and on Western blot analysis (data not shown). Thus, theoretically, this model would be useful for examining the localization of 190 kD ankyrin-G to dendritic spines. While we attempted to examine this in sections from tissue, it was difficult to separate punctate ankyrinG-GFP labeling from the background. However, these experiments were done in genetic crosses that would label most pyramidal neurons in a given area (i.e. CaMKIIa-Cre). Given the Cre-dependence of this model, future experiments could utilize sparse transduction with a Cre virus that also fills neurons with soluble fluorophores (i.e. mCherry or tdTomato) to mark isolated neurons and identify dendritic spines, as exemplified in Fig. 2D. This would allow examination of subcellular localization of ankyrin-G within single pyramidal cells before and after induction of synaptic plasticity.

R1-02 - In Figure 2, does all the native Ank-G get replaced by Ank-G-GFP? In Fig. 2E the GFP signal along the AIS of CamKII +ve neurons does not appear to be very homogeneous compared to the BIV-spectrin label. Have the authors carried out more experiments like those in 2F, using antibodies that label AnkG together with the GFP fluorescence of the labeled AnkG? It would also be informative to know if, as one might expect, the total levels of ankG-GFP correlate with the levels of ankG at the AIS.

We agree that this is an important point and conducted additional experiments to address your concerns. Of course, we cannot exclude that some unmodified ankyrin-G remains in the AIS or other structures. We expect the turnover of the protein to be rather slow, and native ankyrin-G likely remains to some degree. However, our quantification demonstrates that the ankyrin-G-GFP labeling is sufficiently homogeneous to accurately represent AIS size, indicating proportional levels of GFP to native ankyrin-G. Animals were crossed with a CaMKIIa-Cre driver line and ex vivo slices were imaged live and after immunolabeling. We found a strong correlation between live ankyrin-G-GFP (patch clamp chamber), postfix ankyrin-G-GFP, postfix ankyrin-G, and βIV-spectrin immunosignals of the same AIS. Furthermore, our measurements of AIS length using the intrinsic GFP signal in combination with ankyrin-G, or βIV-spectrin antibodies showed significant overlap (see R103). We now included these graphs as supplemental Fig. S2 in the manuscript (pp. 8-9, ll. 173-177).

R1-03 - Does the length and position of the AIS change when Ank-G is tagged with GFP? This seems like important information that is needed to make sure that there are no structural differences in AIS morphology when compared to native Ank-G.

This is a very important point. We used the βIV-spectrin signal to compare the length of AIS with and without GFP modification in acute slices after patch-clamp recordings (N= 3 animals, 27 GFP+ and 48 GFP- AIS). As secondary control, we plotted the measurements of 160 AIS from a Thy1-GFP mouse line (N = 3 animals, 160 AIS). We found no significant difference in the length and position of the βIV-spectrin signal between GFP positive and negative AIS (p=0.3364 unpaired t-test, p=0.6138 non-parametric Mann-Whitney test, respectively). We have now included this analysis as Supplemental Fig. S2A in the manuscript (pp. 8-9, ll. 173-177). 

R1-04 - How was node length measured in Figure 3? Was this done using the endogenous ank-G signal? In this figure, it would be informative to also quantify the number of noRs with a Nav1.6 stain. Perhaps even check if there are correlations between Ank-G-GFP and Nav1.6 levels. In this figure, it appears that comparisons are carried out between Ank-G-GFP +ve and -ve neurons in the same cryosections, from Ank-G-GFP mice crossed with CamKIIa-Cre. I worry that this may not be comparing the same types of axons. What cells do the CamKIIa -ve axons belong to? Also, the labels on the bar graph are confusing - perhaps GFP+ve and GFP-ve would be clearer?

The reviewer raises an important point. We forgot to declare the signal which was used to measure node length in the manuscript. We have corrected this error and clearly state now in the Fig.3C legend that we used the ankyrin-G signal to quantify node length. Furthermore, using CaMKIIa-Cre mediated expression triggers ankyrin-G-GFP only in a genetically defined subset of neurons. Nodes that do not belong to this subgroup might very well have different node properties. Yet, we cannot assign potential differences in node length to the presence or absence of the GFP label, since we do not have an independent labeling technique for the very same subset of neurons. Since node lengths were similar and showed the same spread of lengths in our sample (Fig. 3C), we assume that the GFP length does probably not affect node length to a significant degree. We have now discussed this limitation in the result (p. 7, ll. 159-165) and method section (p. 30, ll. 644-645) and provide Supplementary Fig. S1 for more clarity. As suggested by the reviewer, we have measured mean fluorescence intensities between 91 GFP+ and 141 GFP- nodes using automated image processing in Imaris. The nodes were again defined by the ankyrin-G signal. We found no difference in length and ellipticity between the groups. We repeated this analysis and compared fluorescence intensities of Nav1.6 and ankyrin-G antibodies and again found no statistical differences between both groups. As suggested by the reviewer, we investigated whether ankyrin-G-GFP interferes with the fluorescence intensities of sodium channels (Nav1.6) and ankyrin-G in general. While the GFP signal showed a strong correlation with ankyrin-G, we found no interdependence with the Nav1.6 signal, indicating that the GFP label does not interfere with the general molecular composition of the nodes. We included these new analyses in Supplemental Fig. S1 (p. 7, ll. 159-165).

R1-05 - In Figure 4 it would also be important to show the distribution of AIS molecules along the AIS, compared to the GFP signal, to establish whether this spatial arrangement of AIS-specific molecules remains intact. For example, Nav1.6 has been described as a more distally-located channel. As the authors point out, the example in A appears to show precisely this feature, but there is no quantification. The same applies to Kv1.2. This would also allow the authors to provide some quantification across multiple AISs, rather than just example images.

We agree that quantifying and comparing AIS-associated proteins would be informative. We measured the intensity profiles of Nav1.6 and Kv2.1 in neighboring AIS and found no preferences for either end of the AIS, neither of GFP-positive nor GFP-negative AIS. We want to note that not all neurons exhibit a distal localization of Nav1.6 and hypothesize that our samples (neocortex layer II) also fall into this group. We included this new graph as Supplemental Fig. S2D and E in the manuscript (p. 9, ll. 180-184).

R1-08 - In Figure 4, did the +Cre condition result in all cells showing a GFP-labelled AIS? If not, were the autocorrelations for +Cre-treated neurons done specifically on cells that expressed AnkG-GFP?

We assume the reviewer refers to the autocorrelation in Figure 6. In this in vitro paradigm, we used virus-induced Cre expression which triggered ankyrin-G-GFP in almost all neurons. The orange boxplots describe the autocorrelation of all ankyrin-G, using a C-terminal antibody as in Fig.6C, but in neurons that also express ankyrin-G-GFP. The green samples use the GFP signal of ankyrin-GFP. We clarified this in the graph and legend of Fig. 6C (pages 14-15).

R1-09 - As mentioned above in Figure 3, the comparisons in Figure 5 (GFP +ve and -ve neurons) may not be comparing like-for-like neurons. I imagine that many of the CamKII+ve cells in the cortex and hippocampus will be GABAergic interneurons, whereas presumably all of the CamKII+ve neurons will be pyramidal cells. Have the authors made sure that they are comparing across the same cell types? The fact that the number of axo-axonic synapses is similar across the two populations (Fig. 5B) does suggest that similar neuron types (presumably pyramidal cells) were compared in the hippocampus, but some other way of making sure would be a nice addition.

We agree with the reviewer that the grey and green boxes are not sampled from the same subset of neurons, since only CaMKIIa-positive principal cells will express ankyrin-G-GFP. However, we are confident that the selected AIS belong to pyramidal neurons in both cases. Principal neurons can be well distinguished from interneurons not only by the size, shape, and position of their somas but also by the length and thickness of their AIS. We have performed previous studies on the AIS of interneurons using genetic GAD and parvalbumin markers. Thus, we are confident that the plots in 5A and 5B are sampled from pyramidal neurons, though certainly from genetically different subsets. We now highlight and discuss this limitation in the result section (p. 11, ll. 215-217) and modified the graph in Fig. 5A and 5B for clarity.

R1-10 - In Figure 6, what was the promoter for the DCre and Cre+ lentivirus? Was this also driven by CamKIIa? In culture it is not always easy to be sure of neuronal identity - did the authors try to bias their analysis to specific neuronal types?

Indeed, the nature of the promotor was not stated in the legend or method section, which we now corrected. We used lentiviral FUW-nGFP-Cre and FUW-nGFP-ΔCre constructs to trigger ankyrin-G-GFP expression. Both viruses use the CMV (Cytomegalovirus) promoter, which drives constitutively high levels of gene expression in a wide range of cell types, including neuronal cells. The majority of neurons in dissociated hippocampal cultures are excitatory, especially larger cells with larger AIS, which were preferably used in the analysis. Thus, we cannot claim that AIS nanostructure is intact in cultured interneurons, but this is also true for in vivo conditions in general. Since mice did not show any obvious behavioral phenotypes, we are positive that interneuron functionality is preserved. We also note that the parallel expression of nuclear GFP in the infected neurons was undesired, but did not impact STED imaging due to that technique’s high resolution. 

R1-11 - The ability to visualize the plasticity of the AIS in real-time is an important advance in the field. The loss of proximal Ank-G-GFP signal upon local application of 15 mM KCl is particularly interesting. The fact that neighboring AISs are not affected is surprising - do the authors know how local their KCl application was? Also, although the neighboring AISs are a nice control, the one control lacking here is the local application of normal solution (preferably 15 mM NaCl to account for osmolarity changes) to make sure that this does not affect the properties of the AIS.

We used KCl puffs in previous, unrelated experiments where we observed that only cells directly in front of the pipette are visibly depolarized by an acute KCl puff (measured by patch-clamp). Due to technical limitations, patched and live imaged neurons were generally in the first 2-5 cell layers of the brain slice, which is well perfused by the constant flow of oxygenated ACSF. KCl is thus quickly diluted and carried away. We have visualized the concentration gradients via puff application by puffing the fluorescent marker fluorescein in the same recording condition. The cone of fluorescence was only visible in front of the pipette and vanished in less than a second post-pressure application. To verify that it is indeed KCl and not the mechanical stress that lead to the loss of proximal Ank-G-GFP, one would indeed need an ACSF puff control, which we did for other studies. However, this is not the point we wanted to make. Instead of studying live single-cell AIS plasticity, we want to demonstrate that such investigations are generally possible using the ankyrin-G-GFP line.

Author response image 1.

R1-12 - The ability to be able to image AISs in vivo is another important finding. Were the authors able to image noRs as well?

We believe that this is indeed the case. The panels in Figure 9C contain densely labeled puncta that also remain in position from week 1 to week 2. These are likely nodes of Ranvier, although we do not have the means to verify their presence at this time.

Reviewer #2:

R2-01 - Are there indeed different Ank-G-GFP isoforms expressed in this model and could they correspond to classical neuronal Ank-G isoforms?

This is an important issue that was also raised by reviewer #1. Please consult the respective section R1-01 above for our response.

R2-02 - What is the rationale of doing Ank-G co-labelling in the case of Ank-G-GFP expression, rather than Pan-Nav staining for example? The co-staining with Nav1.6 antibody, when present, is however convincing.

We used the co-labeling to emphasize that the ankyrin-G-GFP construct allows reliable investigation of the whole AIS. This is why we wanted to demonstrate that the ankyrin-G-GFP signal overlaps with other AIS markers, as well as all ankyrin-G in general (including potentially remaining native and unlabeled ankyrin-G). This was also a point raised by Reviewer 1, which is why we provided some additional graphs (see response R1-02). However, we agree that staining with another independent marker, such as Nav1.6 or βIVspectrin was necessary. 

R2-03 - Figure 2D and F: what is the rationale for not using betaIV-Spectrin staining as in the other panels of this figure? Furthermore, could betaIV-Spectrin localization be affected by Ank-GGFP expression, as betaIV-Spectrin is known to depend on Ank-G for its AIS targeting? Are there any other AIS markers, which localization is known to be independent of Ank-G, that could have been used?

We have compiled this figure from a multitude of different experimental setups from different labs to showcase the reliability and robustness of the ankyrin-G-GFP label. This is why the type of staining is not consistent among panels. However, we provide some quantification on the possible impact of ankyrin-G-GFP expression on the βIV-spectrin signal and the composition of the AIS in general. The STED image verifies that the basic subcellular arrangement of the cytoskeleton, including βIV-spectrin, remains intact (Fig. 6). Most AIS markers are at least in some way dependent on ankyrin-G expression, but FGF14 and neurofascin may be the most independent candidates (Fig. 4).

R2-04 - Did the authors measure the mean AIS length and distance from cell soma in Ank-G-GFPexpressing neurons versus non-expressing ones (considering the same neuronal subtypes) to assess whether these were unaffected by Ank-G-GFP expression?

This is an important point that was also raised by Reviewer 1 (see also our comments to R1-03). We have included this analysis now in the manuscript as Supplemental Fig. S2A (pp. 8-9, ll. 173-177).

R2-05 - Figure 5C: the microglial staining and 3D reconstruction could have been clearer.

We have modified the image and 3D rendering to make Figure 5C clearer to the reader. We hope that our changes suffice.

R2-06 - Figure 8: do hippocampal neurons retain their electrophysiological properties after 20 DIV? It could strengthen this part of the work to have access to the electrophysiological data mentioned in the text. 

This is an important issue. We did not perform any electrophysiological recordings in OTCs in the course of this study. Panel E uses acute hippocampal slices like in Fig. 7. We have performed patch-clamp experiments up to DIV 10 for an unrelated study (see graph for action potential firing, Author response image 2). There are not many studies performing electrophysiology in slice cultures due to the formation of a glial scar on top of the slices. However, multielectrode array (MEA) recordings demonstrated that hippocampal organotypic slice cultures remain viable and show electric activity past DIV 20 (though with decreased viability and activity). We kindly refer to the following publications on that matter:

Author response image 2.

Sample traces of action potentials triggered by cuttrent injections

Gong W, Senčar J, Bakkum DJ, Jäckel D, Obien ME, Radivojevic M, Hierlemann AR. Multiple SingleUnit Long-Term Tracking on Organotypic Hippocampal Slices Using High-Density Microelectrode Arrays. Front Neurosci. 2016 Nov 22;10:537. doi: 10.3389/fnins.2016.00537. PMID: 27920665; PMCID: PMC5118563.

Mohajerani MH, Cherubini E. Spontaneous recurrent network activity in organotypic rat hippocampal slices. Eur J Neurosci. 2005 Jul;22(1):107-18. doi: 10.1111/j.1460-9568.2005.04198.x. PMID: 16029200.

Retrieval Practice

eLife Assessment

This manuscript addresses infections of the parasite Taenia solium, which causes neurocysticercosis (NCC). NCC is a common parasitic infection that leads to severe neurological problems. It is a major cause of epilepsy, but little is known about how the infection causes epilepsy. The authors used neuronal recordings, imaging of calcium transients in neurons, and glutamate-sensing fluorescent reporters. A strength of the paper is the use of both rodent and human preparations. The results provide convincing evidence that the larvae secrete glutamate and this depolarizes neurons. Although it is still uncertain exactly how epilepsy is triggered, the results suggest that glutamate release contributes. Therefore, the paper is a fundamental step towards understanding how Taenia solium infection leads to epilepsy.

Reviewer #1 (Public review):

In the manuscript, the authors explore the mechanism by which Taenia solium larvae may contribute to human epilepsy. This is extremely important question to address because T. solium is a significant cause of epilepsy and is extremely understudied. Advances in determining how T. solium may contribute to epilepsy could have significant impact on this form of epilepsy. Excitingly, the authors convincingly show that Taenia larvae contain and release glutamate sufficient to depolarize neurons and induce recurrent excitation reminiscent of seizures. They use a combination of cutting-edge tools including electrophysiology, calcium and glutamate imaging, and biochemical approaches to demonstrate this important advance. They also show that this occurs in neurons from both mice and humans. This is relevant for pathophysiology of chronic epilepsy development. This study does not rule out other aspects of T. solium that may also contribute to epilepsy, including immunological aspects, but demonstrates a clear potential role for glutamate.

Strengths:

- The authors examine not only T. solium homogenate, but also excretory/secretory products which suggests glutamate may play a role in multiple aspects of disease progression.<br /> - The authors confirm that the human relevant pathogen also causes neuronal depolarization in human brain tissue<br /> - There is very high clinical relevance. Preventing epileptogenesis/seizures possibly with Glu-R antagonists or by more actively removing glutamate as a second possible treatment approach in addition to/replacing post-infection immune response.<br /> - Effects are consistent across multiple species (rat, mouse, human) and methodological assays (GluSnFR AND current clamp recordings AND Ca imaging)<br /> - High K content (comparable levels to high-K seizure models) of larvae could have also caused depolarization. Adequate experiments to exclude K and other suspected larvae contents (i.e. Substance P).

Weaknesses:

- Acute study is limited to studying depolarization in slices and it is unclear what is necessary/sufficient for in vivo seizure generation or epileptogenesis for chronic epilepsy.<br /> - There is likely a significant role of the immune system that is not explored here. This issue is adequately addressed in the discussion, however, and the glutamate data is considered in this context.<br /> Discuss impact:<br /> - Interfering with peri-larval glutamate signaling may hold promise to prevent ictogenesis and chronic epileptogenesis as this is a very understudied cause of epilepsy with unknown mechanistic etiology.<br /> Additional context for interpreting significance:<br /> - High medical need as most common adult onset epilepsy in many parts of the world

Reviewer #2 (Public review):

Since neurocysticercosis is associated with epilepsy, the authors wish to establish how cestode larvae affect neurons. The underlying hypothesis is that the larvae may directly excite neurons and thus favor seizure genesis.

To test this hypothesis, the authors collected biological materials from larvae (from either homogenates or excretory/secretory products), and applied them to hippocampal neurons (rats and mice) and human cortical neurons.

This constitutes a major strength of the paper, providing a direct reading of larvae's biological effects. Another strength is the combination of methods, including patch clamp, Ca, and glutamate imaging.

Comments on revised version:

The concerns have been addressed.

Reviewer #3 (Public review):

This paper has high significance because it addresses a prevalent parasitic infection of the nervous system, Neurocysticercosis (NCC). The infection is caused by larvae of the parasitic cestode Taenia solium It is a leading cause of epilepsy in adults worldwide

To address the effects of cestode larvae, homogenates and excretory/secretory products of larvae were added to organotypic brain slice cultures of rodents or layer 2/3 of human cortical brain slices from patients with refractory epilepsy.

A self-made pressure ejection system was used to puff larvae homogenate (20 ms puff) onto the soma of patched neurons. The mechanical force could have caused depolarizaton so a vehicle control is critical. On line 150 they appear to have used saline in this regard, and clarification would be good. Were the controls here (and aCSF elsewhere) done with the low Mg2+o aCSF like the larvae homogenates?

They found that neurons depolarized after larvae homogenate exposure and the effect was mediated by glutamate but not nicotinic receptors for acetylcholine (nAChRs), acid-sensing channels or substance P.

They also showed the elevated K+ in the homogenate (~11 mM) could not account for the depolarization. They also confirmed that only small molecules led to the depolarization after filtering out very large molecules. That supports the conclusion that glutamate - which is quite small - could be responsible.

They suggest the effects could underlie seizure generation in NCC.

Using Glutamate-sensing fluorescent reporters they found the larvae contain glutamate and can release it, a strength of the paper.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

In the manuscript, the authors explore the mechanism by which Taenia solium larvae may contribute to human epilepsy. This is extremely important question to address because T. solium is a significant cause of epilepsy and is extremely understudied. Advances in determining how T. solium may contribute to epilepsy could have significant impact on this form of epilepsy. Excitingly, the authors convincingly show that Taenia larvae contain and release glutamate sufficient to depolarize neurons and induce recurrent excitation reminiscent of seizures. They use a combination of cutting-edge tools including electrophysiology, calcium and glutamate imaging, and biochemical approaches to demonstrate this important advance. They also show that this occurs in neurons from both mice and humans. This is relevant for pathophysiology of chronic epilepsy development. This study does not rule out other aspects of T. solium that may also contribute to epilepsy, including immunological aspects, but demonstrates a clear potential role for glutamate.

Strengths:

- The authors examine not only T. solium homogenate, but also excretory/secretory products which suggests glutamate may play a role in multiple aspects of disease progression.

- The authors confirm that the human relevant pathogen also causes neuronal depolarization in human brain tissue

- There is very high clinical relevance. Preventing epileptogenesis/seizures possibly with Glu-R antagonists or by more actively removing glutamate as a second possible treatment approach in addition to/replacing post-infection immune response.

- Effects are consistent across multiple species (rat, mouse, human) and methodological assays (GluSnFR AND current clamp recordings AND Ca imaging)

- High K content (comparable levels to high-K seizure models) of larvae could have also caused depolarization. Adequate experiments to exclude K and other suspected larvae contents (i.e. Substance P).

Weaknesses:

- Acute study is limited to studying depolarization in slices and it is unclear what is necessary/sufficient for in vivo seizure generation or epileptogenesis for chronic epilepsy. - There is likely a significant role of the immune system that is not explored here. This issue is adequately addressed in the discussion, however, and the glutamate data is considered in this context.

Discuss impact:

- Interfering with peri-larval glutamate signaling may hold promise to prevent ictogenesis and chronic epileptogenesis as this is a very understudied cause of epilepsy with unknown mechanistic etiology.

Additional context for interpreting significance:

- High medical need as most common adult onset epilepsy in many parts of the world

We thank Reviewer 1 for their positive and thorough assessment of our manuscript. We have elected to respond to and address the following aspects from their “Recommendations For The Authors” below:

Reviewer #1 (Recommendations For The Authors):

Additional experiments/analysis:

-   Fig 4a-c: Larva on a slice and not next to it? Negative results maybe because its E/S products are just washed away (assuming submerged recording chamber/conditions)? Experiments and negative results described here do not seem conclusive. Should be discussed at least?

We agree with the reviewer and have added the following sentence to the relevant section of the Results: ‘Our submerged recording setup might have led to swift diffusion or washout of released glutamate, possibly explaining the lack of observable changes.’

Writing & presentation:

- Data is not always reported consistently in text and figures, examples:

- Results in text are reported varyingly without explanation:

- Mean and/or median? SEM or SD and/or IQR? Stat info included in text or not? i.e. lines 130/131 vs. 160/161

Results and data are now presented in a more uniform fashion. We report medians and IQRs, sample size, statistical test result, statistical test used in that order.

- Larval release data interrupts reading flow, lines 246-252 double up results presented in Fig 5F.

This section has now been significantly abbreviated and reads as follows: ‘T. crassiceps larvae released a relatively constant median daily amount of glutamate, ranging from 41.59 – 60.15 ug/20 larvae, which showed no statistically significant difference across days one to six. Similarly, T. crassiceps larvae released a relatively constant median daily amount of aspartate, ranging from 9.431 – 14.18 ug/20 larvae, which showed no statistically significant difference across days one to six.’

- Results in figures are reported in different styles:

Results have now been made uniform, reporting medians and IQRs and: sample size, p test result, statistical test used, figure # reported in that order.

- Fig 6: E/S glu concentration seems to be significantly higher in solium vs crassiceps (about 6fold higher in solium). Should be discussed at least.

Given the small sample size from T. solium (see response below), we do not draw attention to this difference and instead simply make the point that T. solium larvae contain and release glutamate.

- In this context - N=1 may be sufficient for proof of principle (release) but seems too small of a cohort to describe non-constant release of glu over days (Fig 6D). Is initial release on day 1, no release and recovery in the following days reproducible? Is very high glu content of E/S content (15-fold higher in comparison to solium homogenate AND 6-fold higher in comparison to crassiceps homogenate and E/S content). Not sure if Fig 6D is adding relevant information, especially since it is based on n = 1

We agree that a N=1 is only sufficient for proof of principle. However it is worth noting that the measurements still reflect the cumulative release from 20 larvae. Nonetheless, the statement in text has been simplified to say: ‘These results demonstrate that T. solium larvae continually release glutamate and aspartate into their immediate surroundings.’ As this focusses on the point that the larvae release glutamate and aspartate continuously and that we can’t draw conclusions about the variability over days.

Methods:

- Human slices, mention cortex - what part, patient data would be interesting. I.e. etiology of epilepsy, epilepsy duration 

In the Materials and Methods section “Brain slice preparation” we have now added a table with the requested information.

- For Taenia solium: How were they acquired and used in these experiments?

In the Materials and Methods section “Taenia maintenance and preparation of whole cyst homogenates and E/S products” we describe how Taenia solium larvae were acquired and used.

- Was access resistance monitored? Add exclusion criteria for patch experiments

Figure supplement tables containing the basic properties for each cell recording have been added for each figure and the following statements were added to the electrophysiology section of the Methods: ‘Basic properties of each cell were recorded (supplementary files 1, 2, 3, 4, 6).’ and ‘Cells were excluded from analyses if the Ra was greater than 80 Ω or if the resting membrane potential was above –40 mV.’  

- Cannot see any reference to mouse slices in methods? Also, mouse organotypic cultures (for AAV?)? Or only acute slices from mice and organotypic hip cultures from rats? Seems to have been mouse and rat organotypic cultures? But not clear with further clarification in methods.

We have now added the following clarification to the methods: ‘For experiments using calcium and glutamate imaging mouse hippocampal organotypic brain slices were used. For all other experiments rat hippocampal organotypic brain slices were used. A subset of experiments used acute human cortical brain slices and are specified.’

- How long after the wash-in phase was the wash-out phase data collected?

For wash-in recordings drugs were washed in for 8 mins before recordings were made. Drugs were washed out for at least 8 mins before wash-out recordings were made. This information has been added to the Materials and Methods section.

- In general, the M&M section seems to have been written hastily - author's internal remarks "supplier?" are still present.

The M&M section has been thoroughly proofread for errors and internal remarks removed or corrected.

- A little more information on the clinical subjects would be appreciated. I.e. duration of epilepsy? Localization? What cortex? Usual temporal lobe or other regions?

We have now added a table with this information to the Materials and Methods section “Brain slice preparation”.

Minor corrections text/figures:

- i.e. 3D,F,H,J show individual data points, thats great, but maybe add mean/median marker (as results are reported like this in text)  like in fig 4G,I and others

Figures 3D,F,H & J have been revised to include median and IQR.

- Only one patient mentioned in acknowledgements, but 2 in methods and text

We apologize for this oversight and now acknowledge both patients in the acknowledgements.

- Fig 1 B-F individual puffs are described as increasing - consistent with cellular effects (1st puff depolarizes, 2nd puff elicits 1 AP, 3rd puff elicits AP burst)  However, dilution ratio of homogenate or puff concentrations are not mentioned (or potentially longer than 20 ms puffs for 2nd and 3rd stimulus?) in text or figures. Seems to be enough space to indicate in figure as well (i.e. multiple or thicker arrows for subsequent puffs or label with homogenate dilution/concentration in figure).

We state in the results section associated with Fig. 1 that increasing the amount of homogenate delivered was achieved by increasing the pressure applied to the ejection system. We now include this information in the figure legend.

- Figure legend describes 30 ms puff for Ca imaging whereas ephys data (from text) is 20 ms puff. Was Ca imaging performed in acute mouse hippocampal slices (as figure text suggests) or were those organotypic hippocampal cultures from mice?

Ca2+  imaging was performed in mouse hippocampal organotypic brain slice cultures. The figure text for Fig. 1 E) states “widefield fluorescence image of neurons in the dentate gyrus of a mouse hippocampal organotypic brain slice culture expressing the genetically encoded Ca2+ reporter GCAMP6s...”

- 11.4 mM K is reported for homogenate in text only. How variable is that? How many n? No SD reported in text and no individual data points reported since this experiment is not represented as a figure.

This has been clarified in the text by adding (N = 1, homogenate prepared from >100 larvae).

- Same results (effect of 11.4 mM K on Vm) described twice in one paragraph, compare lines 126-131 with 131-136.

The repetition has been removed.

- Line 182 - example for consistency: decide IQR or SD/SEM

To improve consistency, we have changed to median and IQR throughout.

- Neuronal recordings are reported as hippocampal pyramidal neurons (i.e. line 222) but some recordings were made from dentate granule cells - please clarify which neurons were recorded in ephys, ca imaging, GluSnFr imaging

For each experiment we describe which type of neurons were recorded from. For rodent recordings these were hippocampal pyramidal neurons except in the case of the Ca2+ imaging example where the widefield recording was over the dentate gyrus subfield.

- Line 309: "should" seems to be an extra word

We have removed the word ‘should’ and made the sentence shorter and clearer. It now reads: ‘Given our finding that cestode larvae contain and release significant quantities of glutamate, it is possible that homeostatic mechanisms for taking up and metabolizing glutamate fail to compensate for larvalderived glutamate in the extracellular space. Therefore, similar glutamate-dependent excitotoxic and epileptogenic processes that occur in stroke, traumatic brain injury and CNS tumors are likely to also occur in NCC.’

Reviewer #2 (Public Review):

Since neurocysticercosis is associated with epilepsy, the authors wish to establish how cestode larvae affect neurons. The underlying hypothesis is that the larvae may directly excite neurons and thus favor seizure genesis.

To test this hypothesis, the authors collected biological materials from larvae (from either homogenates or excretory/secretory products), and applied them to hippocampal neurons (rats and mice) and human cortical neurons.

This constitutes a major strength of the paper, providing a direct reading of larvae's biological effects. Another strength is the combination of methods, including patch clamp, Ca, and glutamate imaging.

We thank the Reviewer 2 for their review of the strength and weaknesses of our manuscript. We respond to the identified weaknesses below.

There are some weaknesses:

(1) The main one relates to the statement: "Together, these results indicate that T. crassiceps larvae homogenate results not just in a transient depolarization of cells in the immediate vicinity of application, but can also trigger a wave of excitation that propagates through the brain slice in both space and time. This demonstrates that T. crassiceps homogenate can initiate seizurelike activity under suitable conditions."

The only "evidence" of propagation is an image at two time points. It is one experiment, and there is no quantification. Either increase n's and perform a quantification, or remove such a statement.

We acknowledge that the data is from one experiment, with the intention of demonstrating that it is plausible for intense depolarization of a subset of neurons to result in the initiation and propagation of seizure-like activity to nearby neurons under suitable conditions. However, we agree that it is prudent to remove this statement and have done so.

Likewise, there is no evidence of seizure genesis. A single cell recording is shown. The presence of a seizure-like event should be evaluated with field recordings.

In this experiment the Ca2+ imaging demonstrates activity spreading from the site of the restricted homogenate puff to all surrounding neurons. Furthermore, the whole-cell recoding is typical of a slice wide seizure-like event.  

(2) Control puff experiments are lacking for Fig 1. Would puffing ACSF also produce a depolarization, and even firing, as suggested in Fig. 2D? This is needed for at least one species.

We agree and have added this data for the rat and mouse neuron in a new Figure 1-figure supplement 1.

(3) What is the rationale to use a Cs-based solution? Even in the presence of TTX and with blocking K channels, the depolarization may be sufficient to activate Ca channels (LVGs), which would further contribute to the depolarization. Why not perform voltage clamp recordings to directly the current?

The intention of the Cs-based solution was to block K+ channels and reduce the effect of moderately raised K+ in the homogenate to isolate the contribution of other causative agents of depolarization (i.e. glutamate / aspartate). We agree that performing voltage clamp recordings would have been useful for directly recording the currents responsible for depolarization. 

(4) Why did you use organotypic slices? Since you wish to model adult epilepsy, it would have been more relevant to use fresh slices from adult rats/mice. At least, discuss the caveat of using a network still in development in vitro.

Recordings were performed 6–14 days post culture, which is equivalent to postnatal Days (P) 12 to 22. Previous work has shown that neurons in the organotypic hippocampal brain slice are relatively mature (Gähwiler et al., 1997). For example they possess mature Cl- homeostasis mechanisms at this point, as evidenced by their hyperpolarizing EGABA (Raimondo et al., 2012).  

(5) Please include both the number of slices and number of cells recorded in each condition. This is the standard (the number of cells is not enough).

This has now been added to all relevant sections of the results text.  

(6) Please provide a table with the basic properties of cells (Rin, Rs, etc.). This is standard to assess the quality of the recordings.

Tables containing the basic properties for each cell recording have been created for each figure (as Figure supplements) and the following statement was added to the electrophysiology section of the Methods: ‘Basic properties of each cell were recorded (see Figure supplements).’

(7) Please provide a table on patient's profile. This is standard when using human material. Were these TLE cases (and "control" cortex) or epileptogenic cortex?

We have now added a basic table on the patient’s profiles to the Materials and Methods section.

Globally, the authors achieved their aims. They show convincingly that larvae material can depolarize neurons, with glutamate (and aspartate) as the most likely candidates.

This is important not only because it provides mechanistic insight but also potential therapeutic targets. The result is impactful, as the authors use quasi-naturalistic conditions, to assess what might happen in the human brain. The experimental design is appropriate to address the question. It can be replicated by any interested person.

We thank the Reviewer 2 for their enthusiastic and constructive assessment of our manuscript. We have elected to respond to and address the following aspects from their “Recommendations For The Authors” below:

Reviewer #2 (Recommendations For The Authors):

lines 132 and following are a repetition of those above

These have been removed.

line 151 Fig "2" missing

This has been added.

187, 190 should be E, F not C, D

This has been changed in the text.  

481, 482 supplier?

This has been corrected and the correct suppliers described.

Reviewer #3 (Public Review):

This paper has high significance because it addresses a prevalent parasitic infection of the nervous system, Neurocysticercosis (NCC). The infection is caused by larvae of the parasitic cestode Taenia solium It is a leading cause of epilepsy in adults worldwide

To address the effects of cestode larvae, homogenates and excretory/secretory products of larvae were added to organotypic brain slice cultures of rodents or layer 2/3 of human cortical brain slices from patients with refractory epilepsy.

We thank Reviewer 3 for their helpful comments and suggestions for improvement which we address below.

A self-made pressure ejection system was used to puff larvae homogenate (20 ms puff) onto the soma of patched neurons. The mechanical force could have caused depolarizaton so a vehicle control is critical. On line 150 they appear to have used saline in this regard, and clarification would be good. Were the controls here (and aCSF elsewhere) done with the low Mg2+o aCSF like the larvae homogenates?

We agree and have added examples where aCSF alone was pressure ejected onto the same rat and mouse neurons in a new Figure 1-figure supplement 1. In Figure 1, the same aCSF as that was used to bathe the slices was used. In Figure 2D-G, either PBS (which larval homogenates were prepared in) or growth medium (which contain larval E/S products) were used as comparative controls.

They found that neurons depolarized after larvae homogenate exposure and the effect was mediated by glutamate but not nicotinic receptors for acetylcholine (nAChRs), acid-sensing channels or substance P. To address nAChRs, they used 10uM mecamyline, and for ASICs 2mM amiloride which seems like a high concentration. Could the concentrations be confirmed for their selectivity? 

We did not independently verify the selectivity of the antagonist concentrations used in our study. However, the persistence of depolarizations despite the use of high concentrations of mecamylamine (10 μM) and amiloride (2 mM) provides strong evidence that neither nAChRs nor ASICs are primarily responsible for mediating these responses. The high concentrations used, while potentially raising concerns about specificity, actually strengthen our conclusion that these receptor types are not involved in the observed effect.

Glutamate receptor antagonists, used in combination, were 10uM CNQX, 50uM DAP5, and 2mM kynurenic acid. These concentrations are twice what most use. Please discuss. 

We intentionally used higher-than-typical concentrations of glutamate receptor antagonists in our experimental design. Our rationale for this approach was to ensure maximal blockade of glutamate receptors, thereby minimizing the possibility of residual receptor activity confounding our results.

Also, it would be very interesting to know if the glutamate receptor is AMPA, Kainic acid, or NMDA. Were metabotropic antagonists ever tested? That would be logical because CNQX/DAPR/Kynurenic acid did not block all of the depolarization.

We appreciate the reviewer's interest in the specific glutamate receptor subtypes involved in our study. Our research primarily focused on ionotropic glutamate receptors as a group, without differentiating the individual contributions of AMPA, Kainate, and NMDA receptors. This approach, while broad, allowed us to establish the involvement of glutamatergic signalling in the observed effects. We acknowledge that we did not investigate metabotropic glutamate receptors in this study. Importantly, we demonstrate later in our manuscript that the larval products contain both glutamate and aspartate. Therefore the precise nature of the glutamate-dependent depolarization observed using a particular experimental preparation would depend on the specific types of neurons exposed to the homogenate and the expression profile of different glutamate receptor subtypes on these neurons.

They also showed the elevated K+ in the homogenate (~11 mM) could not account for the depolarization. However, the experiment with K+ was not done in a low Mg2+o buffer (Or was it -please clarify). 

The experiment where 11.39 mM K+ as well as the experiment with T. crass. Homogenate with a cesium internal and added TTX were all done in standard 2 mM Mg2+ containing aCSF.

They also confirmed that only small molecules led to the depolarization after filtering out very large molecules. That supports the conclusion that glutamate - which is quite small - could be responsible. It is logical to test substance P because the Intro points out prior work links the larvae and seizures by inflammation and implicates substance P. However, why focus on nAChRs and ASIC?

These were chosen as they are ionotropic receptors which mediate depolarization and hence could conceivably be responsible for the homogenate-induced depolarization we observed.

The depolarizations caused seizure-like events in slices. The slices were exposed to a proconvulant buffer though- low Mg2+o. This buffer can cause spontaneous seizure-like events so it is important to know what the buffer did alone.

We agree that a low M2+ buffer solution can elicit seizure-like events in organotypic slices alone. However, the timing of the onset of the seizure-like event in the example presented in Figure 1 strongly suggests that it was triggered by the T. crass homogenate puff. Nonetheless, on the suggestion of the other reviewers we have reduced emphasis on our experimental evidence for the ability of T. crass. homogenate to illicit seizure-like events.  

They suggest the effects could underlie seizure generation in NCC. However, there is only one event that is seizure-like in the paper and it is just an inset. Were others similar? How frequency were they? How long?

Please see the response above as well as our response to Reviewer 1 who raised a similar concern.

Using Glutamate-sensing fluorescent reporters they found the larvae contain glutamate and can release it, a strength of the paper.

Fig. 4. Could an inset be added to show the effects are very fast? That would support an effect of glutamate.

We have not added an inset. However, given the scale bar (500 ms) for the trace provided, the response is very fast.  

Why is aspartate relatively weak and glutamate relatively effective as an agonist?

Glutamate generally has a higher affinity for glutamate receptors compared to aspartate. This is particularly true for AMPA and kainate receptors, where glutamate is the primary endogenous agonist. Similarly iGluSnFR has a higher sensitivity for glutamate over aspartate (Marvin et al., 2013).

Could some of the variability in Fig 4G be due to choice of different cell types? That would be consistent with Fig 5B where only a fraction of cells in the culture showed a response to the larvae nearby. 

Whilst differences in cell types could contribute to the variability in Fig 4G, all the responses were recorded from hippocampal pyramidal neurons and hence it is more likely that the variability is a function of other sources of variation including differences in iGluSnFR expression, depth of the cell imaged, the proximity of the puffer pipette etc. In Fig. 5B we think the lack of response may be due to the fact that any released glutamate by the live larvae was not able reach the iGluSnFR neurons at sufficient concentrations due to the nature of our submerged recording setup. We have added the following sentence to the results. ‘Our submerged recording setup might have led to swift diffusion or washout of released glutamate, possibly explaining the lack of observable changes.’

On what basis was the ROI drawn in Fig. 5B.

The ROI drawn in Fig. 5B was selected to include all iGluSnFR expressing neurons in the brain slice. which were captured in the field of view.

Also in 5B, I don't see anything in the transmitted image. What should be seen exactly?

We agree that it is difficult to resolve much in the transmitted image. However, both the brain slice on the left as well as a T. crass. larva on the right is visible and outlined with a green or orange dashed line respectively.

Human brain slices were from temporal cortex of patients with refractory epilepsy. Was the temporal cortex devoid of pathology and EEG abnormalities? This area may be quite involved in the epilepsy because refractory epilepsy that goes to surgery is often temporal lobe epilepsy. Please discuss the limitations of studying the temporal cortex of humans with epilepsy since it may be more susceptible to depolarizations of many kinds, not just larvae.

We acknowledge the important limitations of using temporal cortex tissue from patients with refractory epilepsy. While we aimed to use visually normal tissue, we recognize that the tissue may have underlying pathology or functional abnormalities not visible to the naked eye. It may also be more susceptible to induced depolarizations due to epilepsy-related changes in neuronal excitability. Despite these limitations, we believe our human tissue data still provides valuable data that the larval homogenates can induce depolarization in human as well as rodent neurons.  

Please discuss the limitations of the cultures - they are from very young animals and cultured for 6-14 days.

We acknowledge the potential limitations of our experimental model using organotypic hippocampal slice cultures from young animals. The use of relatively immature tissue may not fully represent the adult nervous system due to developmental differences in receptor expression, synaptic connections, and network properties. The 6-14 day culture period, while allowing some maturation, may induce changes that differ from the in vivo environment, including alterations in cellular physiology and network reorganization. Despite these limitations, this model provides a valuable balance between preserved local circuitry and experimental accessibility. Future studies comparing results with acute adult slices and in vivo models would be beneficial to validate and extend our findings.

References:

Gähwiler, B.H. et al. (1997) ‘Organotypic slice cultures: a technique has come of age.’, Trends in neurosciences, 20(10), pp. 471–7.

Marvin, J.S. et al. (2013) ‘An optimized fluorescent probe for visualizing glutamate neurotransmission.’, Nature methods, 10(2), pp. 162–70. Available at: https://doi.org/10.1038/nmeth.2333.

Raimondo, J.V. et al. (2012) ‘Optogenetic silencing strategies differ in their effects on inhibitory synaptic transmission.’, Nat. Neurosci., 15(8), pp. 1102–4. Available at: https://doi.org/10.1038/nn.3143.

eLife Assessment

This study presents a valuable new method for probing the DNA and proteins associated with targeted genomic elements in cells. The authors present solid evidence that the method can map DNA-DNA interactions for individual loci and can detect enriched proteins at repetitive DNA loci such as telomeres, but benchmarks of the method's resolution and specificity remain incomplete. The methodological details of this study will be of particular interest and utility to chromatin biologists.

Reviewer #1 (Public review):

Summary:

The authors describe a method to probe both the proteins associated with genomic elements in cells, as well as 3D contacts between sites in chromatin. The approach is interesting and promising, and it is great to see a proximity labeling method like this that can make both proteins and 3D contacts. It utilizes DNA oligomers, which will likely make it a widely adopted method. However, the manuscript over-interprets its successes, which are likely due to the limited appropriate controls, and of any validation experiments. I think the study requires better proteomic controls, and some validation experiments of the "new" proteins and 3D contacts described. In addition, toning down the claims made in the paper would assist those looking to implement one of the various available proximity labeling methods and would make this manuscript more reliable to non-experts.

Strengths:

(1) The mapping of 3D contacts for 20 kb regions using proximity labeling is beautiful.

(2) The use of in situ hybridization will probably improve background and specificity.

(3) The use of fixed cells should prove enabling and is a strong alternative to similar, living cell methods.

Weaknesses:

(1) A major drawback to the experimental approach of this study is the "multiplexed comparisons". Using the mtDNA as a comparator is not a great comparison - there is no reason to think the telomeres/centrosomes would look like mtDNA as a whole. The mito proteome is much less complex. It is going to provide a large number of false positives. The centromere/telomere comparison is ok, if one is interested in what's different between those two repetitive elements. But the more realistic use case of this method would be "what is at a specific genomic element"? A purely nuclear-localized control would be needed for that. Or a genomic element that has nothing interesting at it (I do not know of one). You can see this in the label-free work: non-specific, nuclear GO terms are enriched likely due to the random plus non-random labeling in the nucleus. What would a Telo vs general nucleus GSEA look like? (GSEA should be used for quantitative data, no GO). That would provide some specificity. Figures 2G and S4A are encouraging, but a) these proteins are largely sequestered in their respective locations, and b) no validation by an orthogonal method like ChIP or Cut and Run/Tag is used.

You can also see this in the enormous number of "enriched" proteins in the supplemental volcano plots. The hypothesis-supporting ones are labeled, but do the authors really believe all of those proteins are specific to the loci being looked at? Maybe compared to mitochondria, but it's hard to believe there are not a lot of false positives in those blue clouds. I believe the authors are more seeing mito vs nucleus + Telo than the stated comparison. For example, if you have no labeling in the nucleus in the control (Figures 1C and 2C) you cannot separate background labeling from specific labeling. Same with mito vs. nuc+Telo. It is not the proper control to say what is specifically at the Telo.

I would like to see a Telo vs nuclear control and a Centromere vs nuc control. One could then subtract the background from both experiments, then contrast Telo vs Cent for a proper, rigorous comparison. However, I realize that is a lot of work, so rewriting the manuscript to better and more accurately reflect what was accomplished here, and its limitations, would suffice.

(2) A second major drawback is the lack of validation experiments. References to literature are helpful but do not make up for the lack of validation of a new method claiming new protein-DNA or DNA-DNA interactions. At least a handful of newly described proximal proteins need to be validated by an orthogonal method, like ChIP qPCR, other genomic methods, or gel shifts if they are likely to directly bind DNA. It is ok to have false positives in a challenging assay like this. But it needs to be well and clearly estimated and communicated.

(3) The mapping of 3D contacts for 20 kb regions is beautiful. Some added discussion on this method's benefits over HiC-variants would be welcomed.

(4) The study claims this method circumvents the need for transfectable cells. However, the authors go on to describe how they needed tons of cells, now in solution, to get it to work. The intro should be more in line with what was actually accomplished.

(5) Comments like "Compared to other repetitive elements in the human genome...." appear to circumvent the fact that this method is still (apparently) largely limited to repetitive elements. Other than Glopro, which did analyze non-repetitive promoter elements, most comparable methods looked at telomeres. So, this isn't quite the advancement you are implying. Plus, the overlap with telomeric proteins and other studies should be addressed. However, that will be challenging due to the controls used here, discussed above.

Reviewer #2 (Public review):

Summary

Liu and MacGann et al. introduce the method DNA O-MAP that uses oligo-based ISH probes to recruit horseradish peroxidase for targeted proximity biotinylation at specific DNA loci. The method's specificity was tested by profiling the proteomic composition at repetitive DNA loci such as telomeres and pericentromeric alpha satellite repeats. In addition, the authors provide proof-of-principle for the capture and mapping of contact frequencies between individual DNA loop anchors.

Strengths

Identifying locus-specific proteomes still represents a major technical challenge and remains an outstanding issue (1). Theoretically, this method could benefit from the specificity of ISH probes and be applied to identify proteomes at non-repetitive DNA loci. This method also requires significantly fewer cells than other ISH- or dCas9-based locus-enrichment methods. Another potential advantage to be tested is the lack of cell line engineering that allows its application to primary cell lines or tissue.

Weaknesses

The authors indicate that DNA O-MAP is superior to other methods for identifying locus-specific proteomes. Still, no proof exists that this method could uncover proteomes at non-repetitive DNA loci. Also, there is very little validation of novel factors to confirm the superiority of the technique regarding specificity.<br /> The authors first tested their method's specificity at repetitive telomeric regions, and like other approaches, expected low-abundant telomere-specific proteins were absent (for example, all subunits of the telomerase holoenzyme complex). Detecting known proteins while identifying noncanonical and unexpected protein factors with high confidence could indicate that DNA O-MAP does not fully capture biologically crucial proteins due to insufficient enrichment of locus-specific factors. The newly identified proteins in Figure 1E might still be relevant, but independent validation is missing entirely. In my opinion, the current data cannot be interpreted as successfully describing local protein composition.

Finally, the authors could have discussed the limitations of DNA O-MAP and made a fair comparison to other existing methods (2-5). Unlike targeted proximity biotinylation methods, DNA O-MAP requires paraformaldehyde crosslinking, which has several disadvantages. For instance, transient protein-protein interactions may not be efficiently retained on crosslinked chromatin. Similarly, some proteins may not be crosslinked by formaldehyde and thus will be lost during preparation (6).

(1) Gauchier M, van Mierlo G, Vermeulen M, Dejardin J. Purification and enrichment of specific chromatin loci. Nat Methods. 2020;17(4):380-9.<br /> (2) Dejardin J, Kingston RE. Purification of proteins associated with specific genomic Loci. Cell. 2009;136(1):175-86.<br /> (3) Liu X, Zhang Y, Chen Y, Li M, Zhou F, Li K, et al. In Situ Capture of Chromatin Interactions by Biotinylated dCas9. Cell. 2017;170(5):1028-43 e19.<br /> (4) Villasenor R, Pfaendler R, Ambrosi C, Butz S, Giuliani S, Bryan E, et al. ChromID identifies the protein interactome at chromatin marks. Nat Biotechnol. 2020;38(6):728-36.<br /> (5) Santos-Barriopedro I, van Mierlo G, Vermeulen M. Off-the-shelf proximity biotinylation for interaction proteomics. Nat Commun. 2021;12(1):5015.<br /> (6) Schmiedeberg L, Skene P, Deaton A, Bird A. A temporal threshold for formaldehyde crosslinking and fixation. PLoS One. 2009;4(2):e4636.

Reviewer #3 (Public review):

Significance of the Findings:

The study by Liu et al. presents a novel method, DNA-O-MAP, which combines locus-specific hybridisation with proximity biotinylation to isolate specific genomic regions and their associated proteins. The potential significance of this approach lies in its purported ability to target genomic loci with heightened specificity by enabling extensive washing prior to the biotinylation reaction, theoretically improving the signal-to-noise ratio when compared with other methods such as dCas9-based techniques. Should the method prove successful, it could represent a notable advancement in the field of chromatin biology, particularly in establishing the proteomes of individual chromatin regions - an extremely challenging objective that has not yet been comprehensively addressed by existing methodologies.

Strength of the Evidence:

The evidence presented by the authors is somewhat mixed, and the robustness of the findings appears to be preliminary at this stage. While certain data indicate that DNA-O-MAP may function effectively for repetitive DNA regions, a number of the claims made in the manuscript are either unsupported or require further substantiation. There are significant concerns about the resolution of the method, with substantial biotinylation signals extending well beyond the intended target regions (megabases around the target), suggesting a lack of specificity and poor resolution, particularly for smaller loci. Furthermore, comparisons with previous techniques are unfounded since the authors have not provided direct comparisons with the same mass spectrometry (MS) equipment and protocols. Additionally, although the authors assert an advantage in multiplexing, this claim appears overstated, as previous methods could achieve similar outcomes through TMT multiplexing. Therefore, while the method has potential, the evidence requires more rigorous support, comprehensive benchmarking, and further experimental validation to demonstrate the claimed improvements in specificity and practical applicability.