This means passing down the power through the bloodline. Sort of like when a king has a son, the next king in going to be the child.

I was curious about how removing constraints affects the outcome of creativity. I have often heard that ilimitations breed creativity. When would it be most appropirate to use each method?

This is such an interesting approach because it takes the pressure off of and takes the judgement out of generating a "good idea" and lets you think backward toward something that works

If I were a participant, I would feel overwhelmed having to share so much information in such a short time and then present it to my colleagues right after.

The size of the paper also affects our thoughts; when I have a larger paper, the idea I get also becomes larger.

This is a very useful technique. When I think about a new idea, I have a habit of thinking from the goal, so if someone asks me this kind of question, it would increase my creative thoughts.

I love mind mapping, this is my favorite brainstorming idea. Writing down a problem is a good addition to where I would usually use a mind map and start with a goal and map out how to acheive that goal.

This sounds good in theory to me, I feel if everyone is working towards a central goal this should be presented as a group or on a white board so everyone is on the same page.

I question the emphasis on quantity because, if we have a handful of bad ideas, in what ways will they be useful for us? Maybe there us a reason why having a vast number of bad or mediocre ideas is helpful, but it is not clearly articulated in this piece.

I find this strategy very useful because, going back to the previous reading, this helps us eliminate the blinders by confronting the ideas we, deep down, know that will not work. It is, in a way, a goodbye ceremony, for ideas that we could hold dearly to our hearts but know that they don't appeal to a broad range of users and consumers.

As someone who always has a little voice in the back of my head full of criticism, I appreciate this approach. I feel like it is very useful when working in a team and making everyone feel comfortable sharing any and all ideas.

It’s crazy how intense this time was! Elite homes and temples were burned down, showing there was major social unrest. Between 850 and 900, the population dropped by 90%, but most people didn’t die—they just left the cities and moved elsewhere to start new lives.

It’s so interesting how old money systems worked! Even though people usually only minted pennies, the same value ratios lasted for centuries in currencies like the French livre, British pound, Italian lira, and Spanish libra.

It’s so smart how paper and credit payment systems made trade easier! Gold and silver were super heavy, so using paper or trust-based payments meant merchants could carry more goods instead of piles of metal coins.

This is an intriguing scope. I'm interested to know the time frame it take some to think some of their ideas bad.

I can agree with this. I've done a kickstarter for some projects, and it led to me being able to build a community from my supporters.

This is a really cool idea to brainstorm because it helps challenge the status quo and makes you really look into aspects of an organization that are taken for granted

This makes me curious-- how does somebody know if they've questioned every aspect of their business? What if there are some features that are overlooked?

I agree with this idea, sometimes we can get some interesting ideas when we are relaxing.

I saw many brainstorming techniques set 20 to 30 as an ideal number, but what's the difference between 10 and 20 to 30?

I like this idea; questioning is the very first step to changing our perspectives. Without knowing the products deeply, we cannot acquire new ideas and insights.

I am not a huge fan of spending time creating bad ideas, maybe integrate rest breaks instead or something to get people refreshed.

I enjoyed this kind of thinking, every industry is tough to enter, but there usually are cracks within each industry where things can be improved. Looking at tings from this perspective allows you to find niche markets or discover a market that was alot bigger than you may have originally thought.

I love this technique! Not only because it makes you think of an idea, but also because you have to think about how to draw it, and that gives you the opportunity to come up with even more ideas while you're drawing.

This story is a perfect example. When you can't fight the same way, you must change the game. By questioning everything, they found a new way to win.

Yes, I agree that removing mental blinders is key to be able to come up with new ideas that could help ventures and businesses better understand and connect with user and consumer needs and demands.

I am not sure about what "random" or "irrelevant" could mean in this context. I feel like randomness or irrelevancy is in a range, you can be COMPLETELY irrelevant, or you can find random things that could have indirect connections with the main product/concept you are unpacking as part of this activity. Also, these concepts are too subjective. It would be great if the author provided an example of randomness and show us how it works in a couple of sentences.

I like this idea but I wonder if it would bog down people who take longer to generate ideas. Not everyone works at the same pace, even in brainstorming. I know I would hope I was last in the order to make sure I had time to think.

I think this is a technique that I never really thought of. I find it useful for brainstorming with the way my brain works. I am very logical and have a hard time getting to creativity. Framing it as assumptions that I may be making to find ideas makes ideating more attainable for me.

Here we see the sentiment which led to the second war. Its honestly not at all surprising that they would fight again considering what the British were doing.

I like this paper. Its like the other side to Lin Zixu's formally addressed piece. Its always fun to see people outright talking crazy in old language.

Just big and greedy. Fr though the British had such a crazy entitled mindset. Is this what colonialism does to a nation?

I'm not exactly sure what this is referring to. Possibly the many wars between the British and other powers

Such crazy punishments for a war that was fought over literally dumping heroin. I get why the Chinese regard this as the beginning of the century of humiliation.

https://discord.com/channels/639936208734126107/639938269030907914/1302694827682697330

Pelicram's Shellac Rescue Formula aka The Cowboy's Delight.

This will help bring back a deeper black color shellaced panels which have been yellowed and damaged by UV over the years. With enough elbow grease it will remove the old shellac completely but it takes a very long time and you're likely to damage any decals present on the panel. In most cases the procedure described below will be sufficient to restore the appearance to an acceptable level.

The recipe: - 70% Light machine oil. - 30% IPA (Isopropyl Alcohol) or White/Mineral Spirits.

Ideally use an oil that is dissolved into the IPA/Mineral Spirits, if they settle into separate layers make sure you shake the mixture thoroughly before applying.

Mix the oil and solvent in something like a dropper bottle or similar vessel for convenient application.

• Clean part with Fulgentin (Or general purpose cleaner of your choice) and wipe dry.
• Apply oil/ipa mix to part and rub in lightly with clean microfiber cloth or shop towel. Use plenty of the mix, it should not feel dry.
• Wipe with microfiber cloth after 15 minutes to get rid of any excess.
• Do not apply any kind of wax (like Renessaince Wax) afterwards, from my testing it will bring back the haziness.

https://discord.com/channels/639936208734126107/639938983220215828/1131197256691875881

The Marvelous Mrs. Maisel S5, E2

I think the distinction between bilingualism and translanguaging is important. Knowing multiple languages and have a comprehensive, culminating usage of them is different. School environments often encourage the usage of one or the other, but rarely both. Honestly, I didn't even know what translanguaging was until I took Education classes at UCI, and I think that is all the more reason to spread awareness of its benefits and foster it in students.

This passage shows how important trust and relationships are for undocumented students navigating in school. Those placed in advanced tracks had smaller classes and more access to teachers and counselors, which helped them feel safe enough to share their status and ask for help. It wasn’t just about academics, it was about being seen and supported. When students feel like someone genuinely cares, they’re more likely to open up and get the guidance they need. This reminds me how many school structures can either build or block those connections, and how much that matters for students facing extra challenges.

Undocumented students face extra challenges when trying to transfer from high school to college, especially without strong support from their schools. While other marginalized groups also struggle with access, undocumented students deal with even more barriers because they’re excluded from the federal and state aid. This makes it harder for them to apply to college or find scholarships. On top of that, their legal status adds stress that affects their well-being and ability to focus. It’s important for schools to step up and offer real support so all students aren’t left to figure it all out alone. Everyone deserves a fair chance to succeed.

This section highlights how undocumented students are blocked from key support programs like TRIO, things like work-study, even though they often meet the same criteria as students who qualify. These programs are meant to help low-income, first-gen, and minority students, but because they’re federally funded, undocumented students are left out. Which sucks terribly. That means they miss out on both financial help and the chance to build support systems on campus. It’s so frustrating to see how students who need the most help are often the ones with the fewest resources and support. This problem we have to address together.

They began to provide features for creating compound documents to the web - user base at large.

https://typewriterdatabase.com/1926-mercedes-4.19461.typewriter<br /> "Steile Sierschrift" (Steep ornamental font) <br /> RaRo foundry mark 5 circle I<br /> paired with<br /> RaRo foundry mark 57 AR

I think this is probably the root cause of a majority of financial struggles with young adults. It really is crazy just how much student tuition are now and going along with that just how much debt we obtain. I truly believe that student tuitions shouldn't be so expensive. I really don't understand what schools are doing with all of this money and don't think it's necessary how much they take with us.

I don't really understand the logic of this. The story a man told in this article before this said he tried to get a credit card so that he could build his credit score and apply for apartments but when the price is so high to obtain a card and there as so many fees and taxes it makes it really hard for young people to get one. This really makes me realize I need to start building my credit as fast as possible.

RR\ID Summary of Reviews: This preprint retrospectively investigates the relationship between SARS-Cov-2 genomic sequence data, patient clinical characteristics and infection persistence in 69 Californian patients. It is rated as strong/reliable by reviewers. The authors claim that certain populations may be more susceptible to persistent SARS-Cov-2 infection, and that persistent infections may have evolutionary trajectories that are different from circulating lineages. Reviewers highlight the thoroughness of the analysis of existing sequence data and the clear articulation of a framework for mining available public health data to probe the impact of viral mutations on pathogenicity. Reviewers recommend an analysis of the mutations viruses may have acquired before the initial sequencing, increased discussion of the novelty of the findings and of why persistent mutations did not spread further in the population. Further suggestions for improvement focus on the clarity of data representations.

Read directly in RR\ID: https://rrid.mitpress.mit.edu/pub/afgxepb7/release/1

Reviewer #1: Evidentiary Rating: Reliable

Written Review: The data from this manuscript largely support the stated conclusions. There is a thorough evaluation of existing SARS-CoV-2 sequence data to probe interesting questions, such as what conserved mutations (novel and known) were found in persistently infected patients from California, and what factors (age, sex, etc.) were associated with persistent infection versus acute infection.  One of the main strengths of this manuscript was providing a “framework” for mining public health data to ask important questions about viral mutation and association with pathogenicity. The authors do a nice job of describing their approach to this complicated topic and acknowledging limitations and potential improvements to this approach. Overall, the manuscript describes the use of public health data to monitor persistent infection and viral evolution. Some changes, as listed below, could be helpful in improving the manuscript: * Line 166-168, clarify in which direction the statistical significance was found. * The manuscript would benefit from more information on how the findings are different from other related, published manuscripts. * Information on conserved mutations in non-coding regions would be interesting. * Table 3 would benefit from listing references for the different “descriptions.” * Lines 164-166, list the ages for each fatal case * More discussion on how these persistent infections didn’t “spread” throughout the population would be informative.

Reviewer #2: Evidentiary Rating: Strong

Written Review: This is an excellent manuscript.  I have a few suggestions that may make the manuscript more useful for the reader.  * Fig 2.  Please indicate which Omicron lineages the different Nextclade lineages represent (eg, BA.1). * It would be useful if there were a similarly styled graphic below the current figure which shows when the various nextclade clades were in circulation.  If I am not mistaken, some of the patient infections were not detected for the first time until a while after that clade had stopped circulating.  This would help in illustrating it for the reader. * The authors don’t make it easy to look up what the different convergent changes are other than the ones that are 3 or more times.  I would recommend adding all of the mutations to the main table that occurred at a position 2 or more times.   Alternatively, they could just adjust the table to make it so that it can be more easily sorted based on position.  Or they could add another column that lists how many times there were mutations at this position.  Any would work. * The authors only focus on the mutation that occurred between the first and last times it was sequenced.  I think it would be worthwhile to enumerate the consensus changes in the genome that differ from the closest ancestor on the phylogenetic tree.  In other words, what mutations were acquired before the virus was sequenced the first time.  There probably aren’t that many of these.

counters an SAE-only pedagogy,

mixing languages or dialects not as an error, but smart way to use full language toolkit,

diversity, "code-meshing, globalization challenge the premise SAE is the only legitimate academic register,

changing name doesn't solve problem, as long as standard tests use SAE that's what is focused on, meaning SAE is used as the real measure of success

schools openly forcing SAE as the main rule, grading and goals help to confuse speaking or using "correct" English with being smart or good at the subject

accomodations for English proficiency reaffirm SAE as the norm

fear of foreigners helped make SAE the official language in schools,

give SAE power by making it the official rule, makes other styles seem wrong, thus justifying exclusion from situations

policies range from actively attacking to passively doing nothing to help other language styles keeping SAE in charge

eLife Assessment

This study by Roseby and colleagues shows that region-specific mechanosensation - especially anterior-dorsal inputs - controls larval self-righting, and links this to Hox gene function in sensory neurons. The work is important for understanding how body plan cues shape sensorimotor behaviour, and the experimental toolkit will be of use to others. The strength of evidence is solid with respect to the assays developed and the involvement of the anterior region; it is incomplete with respect to dorso-ventral involvement in that region and the role of Hox genes in the process. These findings will be of broad interest to researchers studying neural circuits, developmental genetics, and the evolution of behaviour.

Reviewer #1 (Public review):

Summary:

Roseby and colleagues report on a body region-specific sensory control of the fly larval righting response, a body contortion performed by fly larvae to correct their posture when they find themselves in an inverted (dorsal side down) position. This is an important topic because of the general need for animals to move about in the correct orientation and the clever methodologies used in this paper to uncover the sensory triggers for the behavior. Several innovative methodologies are developed, including a body region-specific optogenetic approach along different axial positions of the larva, region-specific manipulation of surface contacts with the substrate, and a 'water unlocking' technique to initiate righting behaviors, a strength of the manuscript. The authors found that multidendritic neurons, particularly the daIV neurons, are necessary for righting behavior. The contribution of daIV neurons had been shown by the authors in a prior paper (Klann et al, 2021), but that study had used constitutive neuronal silencing. Here, the authors used acute inactivation to confirm this finding. Additionally, the authors describe an important role for anterior sensory neurons and a need for dorsal substrate contact. Conversely, ventral sensory elements inhibit the righting behavior, presumably to ensure that the ventral-side-down position dominates. They move on to test the genetic basis for righting behavior and, consistent with the regional specificity they observe, implicate sensory neuron expression of Hox genes Antennapedia and Abdominal-b in self-righting.

Strengths:

Strengths of this paper include the important question addressed and the elegant and innovative combination of methods, which led to clear insights into the sensory biology of self-righting, and that will be useful for others in the field. This is a substantial contribution to understanding how animals correct their body position. The manuscript is very clearly written and couched in interesting biology.

Limitations:

(1) The interpretation of functional experiments is complicated by the proposed excitatory and inhibitory roles of dorsal and ventral sensory neuron activity, respectively. So, while silencing of an excitatory (dorsal) element might slow righting, silencing of inputs that inhibit righting could speed the behavior. Silencing them together, as is done here, could nullify or mask important D-V-specific roles. Selective manipulation of cells along the D-V axis could help address this caveat.

(2) Prior studies from the authors implicated daIV neurons in the righting response. One of the main advances of the current manuscript is the clever demonstration of region-specific roles of sensory input. However, this is only confirmed with a general md driver, 190(2)80, and not with the subset-specific Gal4, so it is not clear if daIV sensory neurons are also acting in a regionally specific manner along the A-P axis.

(3) The manuscript is narrowly focused on sensory neurons that initiate righting, which limits the advance given the known roles for daIV neurons in righting. With the suite of innovative new tools, there is a missed opportunity to gain a more general understanding of how sensory neurons contribute to the righting response, including promoting and inhibiting righting in different regions of the larva, as well as aspects of proprioceptive sensing that could be necessary for righting and account for some of the observed effects of 109(2)80.

(4) Although the authors observe an influence of Hox genes in righting, the possible mechanisms are not pursued, resulting in an unsatisfying conclusion that these genes are somehow involved in a certain region-specific behavior by their region-specific expression. Are the cells properly maintained upon knockdown? Are axon or dendrite morphologies of the cells disrupted upon knockdown?

(5) There could be many reasons for delays in righting behavior in the various manipulations, including ineffective sensory 'triggering', incoherent muscle contraction patterns, initiation of inappropriate behaviors that interfere with righting sequencing, and deficits in sensing body position. The authors show that delays in righting upon silencing of 109(2)80 are caused by a switch to head casting behavior. Is this also the case for silencing of daIV neurons, Hox RNAi experiments, and silencing of CO neurons? Does daIII silencing reduce head casting to lead to faster righting responses?

(6) 109(2)80 is expressed in a number of central neurons, so at least some of the righting phenotype with this line could be due to silenced neurons in the CNS. This should at least be acknowledged in the manuscript and controlled for, if possible, with other Gal4 lines.

Other points

(7) Interpretation of roles of Hox gene expression and function in righting response should consider previous data on Hox expression and function in multidendritic neurons reported by Parrish et al. Genes and Development, 2007.

(8) The daIII silencing phenotype could conceivably be explained if these neurons act as the ventral inhibitors. Do the authors have evidence for or against such roles?

Reviewer #2 (Public review):

Summary

This work explores the relationship between body structure and behavior by studying self-righting in Drosophila larvae, a conserved behavior that restores proper orientation when turned upside-down. The authors first introduce a novel "water unlocking" approach to induce self-righting behavior in a controlled manner. Then, they develop a method for region-specific inhibition of sensory neurons, revealing that anterior, but not posterior, sensory neurons are essential for proper self-righting. Deep-learning-based behavioral analysis shows that anterior inhibition prolongs self-righting by shifting head movement patterns, indicating a behavioral switch rather than a mere delay. Additional genetic and molecular experiments demonstrate that specific Hox genes are necessary in sensory neurons, underscoring how developmental patterning genes shape region-specific sensory mechanisms that enable adaptive motor behaviors.

Strengths

The work of Roseby et al. does what it says on the tin. The experimental design is elegant, introducing innovative methods that will likely benefit the fly behavior community, and the results are robustly supported, without overstatement.

Weaknesses:

The manuscript is clearly written, flows smoothly, and features well-designed experiments. Nevertheless, there are areas that could be improved. Below is a list of suggestions and questions that, if addressed, would strengthen this work:

(1) Figure 1A illustrates the sequence of self-righting behavior in a first instar larva, while the experiments in the same figure are performed on third instar larvae. It would be helpful to clarify whether the sequence of self-righting movements differs between larval stages. Later on in the manuscript, experiments are conducted on first instar larvae without explanation for the choice of stage. Providing the rationale for using different larval stages would improve clarity.

(2) What was the genotype of the larvae used for the initial behavioral characterization (Figure 1)? It is assumed they were wild type or w1118, but this should be stated explicitly. This also raises the question of whether different wild-type strains exhibit this behavior consistently or if there is variability among them. Has this been tested?

(3) Could the observed slight leftward bias in movement angles of the tail (Figure 1I and S1) be related to the experimental setup, for example, the way water is added during the unlocking procedure? It would be helpful to include some speculation on whether the authors believe this preference to be endogenous or potentially a technical artifact.

(4) The genotype of the larvae used for Figure 2 experiments is missing.

(5) The experiment shown in Figure 2E-G reports the proportion of larvae exhibiting self-righting behavior. Is the self-righting speed comparable to that measured using the setup in Figure 1?

(6) Line 496 states: "However, the effect size was smaller than that for the entire multidendritic population, suggesting neurons other than the daIVs are important for self-righting". Although I agree that this is the more parsimonious hypothesis, an alternative interpretation of the observed phenomenon could be that the effect is not due to the involvement of other neuronal populations, but rather to stronger Gal4 expression in daIVs with the general driver compared to the specific one. Have the authors (or someone else) measured or compared the relative strengths of these two drivers?

(7) Is there a way to quantify or semi-quantify the expression of the Hox genes shown in Figure 6A? Also, was this experiment performed more than once (are there any technical replicates?), or was the amount of RNA material insufficient to allow replication?

(8) Since RNAi constructs can sometimes produce off-target effects, it is generally advisable to use more than one RNAi line per gene, targeting different regions. Given that Hox genes have been extensively studied, the RNAis used in Figure 6B are likely already characterized. If this were the case, it would strengthen the data to mention it explicitly and provide references documenting the specificity and knockdown efficiency of the Hox gene RNAis employed. For example, does Antp RNAi expression in the 109(2)80 domain decrease Antp protein levels in multidendritic anterior neurons in immunofluorescence assays?

(9) In addition to increasing self-righting time, does Antp downregulation also affect head casting behavior or head movement speed? A more detailed behavioral characterization of this genetic manipulation could help clarify how closely it relates to the behavioral phenotypes described in the previous experiments.

(10) Does down-regulation of Antp in the daIV domain also increase self-righting time?

Author response:

We are very pleased to hear the overall positive views and constructive criticisms of eLife Editors and Reviewers on our work. In particular, we appreciate their global assessment that the work is important for understanding how body plan cues shape sensorimotor behavioural patterns, that the strength of evidence is solid, and their views that our experimental toolkit will be useful to others. We also very much appreciate eLife’s assessment that our findings will be of broad interest to researchers studying neural circuits, developmental genetics, and the evolution of behaviour.

Regarding Reviewer 1, we thank them for their positive comments on the value of our study, highlighting that our paper addresses an important question using an elegant and innovative combination of methods, which leads to clear insights into the sensory biology of self-righting, which they consider shall be useful for others in the field. We are also very pleased to hear that they consider that our study makes a substantial contribution to understanding how animals correct their body position and that the manuscript is very clearly written and couched in interesting biology. In a revised version of the manuscript, we will consider some of the interesting points raised by Rev1, including the possibility of conducting new experiments using neuronal subset-specific Gal4s, to establish whether daIV sensory neurons are also acting in a regionally specific manner along the A-P axis.

Turning to the comments by Rev2, we are grateful to them for considering that our experimental design is elegant, and that it introduces innovative methods that will likely benefit the fly behavior community, and the results are robustly supported. In connection to other comments, in a revised manuscript we will consider addressing the question of whether normal levels of expression of the Hox gene Antennapedia within the daIV domain are essential for self-righting. We will also seek to add technical replicates to our Hox expression molecular analysis, amend typos and incorporate several of the constructive corrections mentioned.

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Reply to the reviewers

The authors do not wish to provide a response at this time.

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

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Referee #3

Evidence, reproducibility and clarity

Summary:

In the manuscript "Nucleosome positioning shapes cryptic antisense transcription", Kok and colleagues perform a characterization of nucleosome remodeling factors in S. pombe by assaying the impact of their deletion on antisense transcription and nucleosome organization. They find that deletion of Hrp3 leads to up-regulation of antisense RNA transcripts as well as disruption of phased nucleosomes in gene bodies. The authors then establish a catalogue of antisense transcripts in S. pombe using long read RNA sequencing, which they use to analyze the relationship between nucleosome positioning and antisense transcription. Through this analysis, they associate nucleosome positioning with the initiation of antisense transcription and conclude that nucleosome positioning within gene bodies represses cryptic antisense transcription. They further support this observation by showing that the up-regulated genes in the Hrp3 knock-out are enriched for genes usually expressed in meiosis, which in S. pombe often occur as nested transcripts in reverse orientation. Using growth assays under various stress conditions, the authors narrow down the domain responsible for the phenotype to the C-terminal CHCT domain. To address how Hrp3 gains specificity, they perform an in-silico interaction prediction screen to identify Prf1 as a putative interactor of the CHCT domain. Using recombinant expression in bacteria followed by pulldowns from lysates, they confirm the interaction and introduce point mutants that abolish the interaction. The authors then link the interaction with Prf1 to transcriptional elongation, where they observe a correlation between Hrp3 presence and chromatin marks of transcription elongation, especially H2BK119ub, which is also reduced in the Hrp3 knockout. They further demonstrate that both gene body nucleosome phasing and antisense transcription are similarly affected in the prf1 knockout as well as the hrp1-hrp3-prf1 triple knock-out cells, which indicates that they affect the same pathway.

Major comments:

The manuscript is well-written and the claims are generally supported by the data. The authors demonstrate scientific rigor through comprehensive experiments using single and double knockouts. I have three main comments that can be addressed through additional analysis and limited experimentation:

1. The authors use the terms "Prf1" and "Paf1 complex" interchangeably multiple times in the manuscript (eg. Line 296). However, the experimental data presented only demonstrate a connection between Prf1 and Hrp3. Furthermore, published literature establishes that Prf1 and Paf1 represent distinct entities in S. pombe (Mbogning et al., 2013, PLoS Genetics 9(3): e1004029). The authors should clarify this distinction and use consistent, accurate terminology throughout the text. Reference: Mbogning, J., et al. (2013). The PAF Complex and Prf1/Rtf1 Delineate Distinct Cdk9-Dependent Pathways Regulating Transcription Elongation in Fission Yeast. PLoS Genetics, 9(3), e1004029. https://doi.org/10.1371/journal.pgen.1004029

2. The authors demonstrate that Hrp3 limits antisense promoter usage; however, the analysis lacks characterization of sequence composition, promoter classes (TATA-box versus TATA-less), or identification of enriched transcription factor motifs near these sites. A more thorough bioinformatic analysis would strengthen the paper and potentially reveal interesting biology, as the effect may be specific to certain transcription factors or promoter architectures.

3. The Hrp3-Prf1 interaction is demonstrated solely through recombinant overexpression and pulldown assays, which carries the risk of detecting non-physiological interactions. While the authors use mutations to verify pulldown specificity, in vivo evidence for this interaction is absent. Given that the authors cite a recent preprint demonstrating sophisticated techniques to show S. cerevisiae Chd1-Prf1 interactions, I presume standard approaches such as co-immunoprecipitation followed by mass spectrometry or Western blot were attempted. Even negative results from such experiments should be reported, as readers will likely question the physiological relevance of the interaction. Additionally, establishing the hierarchy between Hrp3, Prf1, and H2BK119Ub is crucial. While the authors show that Hrp3 ChIP-seq signal correlates with gene expression levels, the proposed Prf1-Hrp3 interaction raises questions about recruitment specificity and hierarchy. The authors mention in lines 344-345: "...the CHCT domain of Hrp3 is critical for its association with transcription elongation along the gene body..." which requires support from experimental data. Testing Hrp3 ChIP-seq in Prf1-depleted conditions would clarify how specificity is achieved and substantiate the functional importance of this interaction. As the authors have all the required strains I would estimate around 1.5-2 months for data generation and analysis.

4. [Optional] Based on strucutre predictions the authors suggest that the interaction of of CHD1 and RTF1 is conserved in arabidopsis and mouse. This should be further supported by pulldown assays and also the pre-print (Reference nr. 99) should be cited as they show similar results using yeast-tow-hybrid assays

Minor comments:

1. Figure 1B: Grouping individual panels according to different paralog groups would make the figure more accessible.

2. Figure 1D: The display of antisense transcription is not accessible. Perhaps boxplots, like those in Figures 2B and 5D, would be easier to read.

3. Line 335: The transition is abrupt and would benefit from additional explanation. Why do the authors use Rtf1 instead of Prf1 here? Consistent nomenclature would improve clarity.

4. Line 352: For the phrase "significant loss," please provide a statistical test or omit the word "significant."

5. Figure 7F: The model presented in panel F suggests that there are two parallel routes that lead to nucleosome phasing; however, the authors state in the text (lines 363-364): "further supporting the idea that Hrp3 and Prf1 act together in the same pathway to control antisense transcription." The model and the text should align better.

Significance

• In the study, the authors establish Hrp3, one of the fission yeast CHD1 remodelers, as a crucial regulator of antisense transcription within gene bodies, which they link to both fitness penalties and the regulation of genes typically expressed during meiosis. They further link the recruitment of Hrp3 at gene bodies to transcriptional elongation, which provides an interesting model for how antisense transcription is prevented in actively transcribed regions of the genome.

• The study is overall very well executed and controlled and provides strong evidence for connecting Hrp3 with the repression of antisense transcription using adequate experiments and technologies. This provides novel insights into a widespread phenomenon present in many organisms. A point that needs further improvement is the suggested physical link between Hrp3 and Prf1. Despite potentially being challenging to address using molecular biology techniques, the authors can further improve the study by dissecting the genetic hierarchy of Hrp3 and Prf1 using accessible tools. This study will be of interest to a broad audience in basic research as it addresses the broad question of how antisense transcription is repressed and provides mechanistic insights into this process. Consequently, this study will be relevant for the broader field of transcriptional regulation and could provide entry points for studying the role of CHD remodelers in other organisms.

• Field of expertise: chromatin biology, small RNA mediated heterochromatin formation

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

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Referee #2

Evidence, reproducibility and clarity

Kok et al. report on the role of the chromatin remodelers Hrp1 and Hrp3 in maintaining nucleosome positioning and preventing antisense transcription in Schizosaccharomyces pombe. As commented below, the main criticism of the manuscript is that the first half describes results that are very similar to those already reported by several other laboratories. Therefore, the main novel aspect of the work is the interaction between Hrp3 and the Prf1 subunit of the PAF complex.

Specific points:

1. The articles of Hennig et al. (2012), Pointner et al. (2012) and Shim et al. (2012) are cited in the manuscript (line 119, Refs. 61-63) only as a confirmation of the minor effect of the absence of Hrp1 on nucleosome positioning and antisense expression. However, these three articles reached the same conclusion as Kok et al. that the absence of Hrp3 in S. pombe causes severe, genome-wide loss of nucleosome positioning and overexpression of antisense transcripts, whereas the absence of Hrp1 has a much weaker effect. These results were also discussed in a short review article (Touat-Todeschini et al. EMBO J. 2012. 31: 4371). Although Kok et al. analysed transcription at a higher resolution and mapped transcription initiation using Pro-Seq (Figures 1, 2 and 3), their results do not add much to what was already reported in these previous studies.

2. Several sites in the manuscript state that Hrp3 belongs to the SWI/SNF family of chromatin remodelers (for example, line 92). However, Hrp3 is a member of the CHD family, whose members have a very different structure and function (see, for example, Clapier et al. 2017. Nat Rev Mol Cell Biol 18: 407; Paliwal et al. 2024 TIGs 41:236).

3. The authors should indicate where the nucleosome remodelling activity of some of the proteins in Figure 1A like Irc20, Rrp1, Rrp2 and Mot1) has been reported.

4. The analysis of nucleosome positioning by aggregating thousands of genes, such as those shown in Figure 1B, has low resolution and can only detect gross alterations affecting many genes. Nevertheless, several mutants, such as swr1∆ and rrp1∆, also exhibit altered nucleosomal profiles in Figure 1B. In other cases, the occupancy of the first and second nucleosomes after the TSS is reduced relative to the wild type. Therefore, it cannot be concluded that "nucleosome arrays in wild type and most remodeller mutant cells were highly ordered and regular" (line 105).

5. Although it was previously reported that hrp3∆ mutants overexpress antisense transcripts (see point 1 above), it is unclear how this finding is represented in Figure 1D. Similarly, it not clear either why antisense transcription is undetectable in hrp1∆ relative to WT in Figure 1D, yet significantly higher than in WT in Figures 2B, 3A and 3B. Furthermore, sense transcription in the single and double mutants is comparable to WT in Figure 2A, yet much higher in Figure S3B.

6. Figure S3C claims that antisense transcription is higher in genes with greater nucleosome disruption in the double mutant hrp1∆hrp3∆. However, without a quantitative analysis, it is difficult to discern any significant differences in the degree of disruption across the four quartiles of antisense expression.

7. Figures 3D and S4C show that the TSS of antisense transcription colocalizes with a region resistant to MNase that is at least 300 bp wide. This size does not correspond to that occupied by a nucleosome and contrasts with the expected size of the four nucleosome peaks downstream from it.

8. In relation to the previous point, Figure S4C (bottom) shows that the centre of the region above the TSS is slightly displaced in the three mutants. This displacement corresponds to an increase in the G+C content of approximately 1.5% (Figure S4C top), equivalent to an increase of less than 2.5 Gs and Cs every 150 bp of nucleosomal DNA. Without some cause and effect experiments, it is difficult to attribute a functional significance to such a tiny difference. How repetitive is this difference in biological replicates?

9. The authors should also explain how the position of the dyads was estimated in the double mutant hrp1∆hrp3∆ in Figure S4B. The severe loss of nucleosomal positioning suggests that the dyads occupy different positions in different cells within the same population. While most of the remaining figures show data for the three mutants, this figure shows results for the double hrp1∆hrp3∆ mutant only.

10. Figures 3G and 3H show the analysis of the promoter activity of some regions upstream from antisense transcripts, achieved by replacing the endogenous ura4 gene promoter with these regions. This analysis lacks negative controls showing the level of transcription in the recipient strain following the removal of the endogenous ura4 promoter and its replacement for genomic regions not associated with the initiation of antisense transcription in the mutants. Furthermore, transcription should be measured by quantitative PCR of the ura4 mRNA rather than by the more indirect method of measuring OD600 in 384-well plates (line 708).

11. Figure F4 suggests that Hrp3 may regulate the expression of genes specific to meiosis by showing an anticorrelation between the expression levels of Hrp3 and a selection of genes that are upregulated during meiosis (MUGs) 5 hours after the onset of meiosis. While this is an interesting possibility, it will remain speculative until it is demonstrated that the level of Hrp3 protein is reduced at the same stage of meiosis, and that MUG overexpression is associated with reduced nucleosomal occupancy adjacent to their TSS at that stage.

12. The experiments in Figures 5 and 6, which describe the interaction between the Hpr3-specific CHCT domain and the Prf1 protein, are interesting and represent the main element of novelty of the manuscript. However, this interaction in figure 6D and 6E should be confirmed in vivo.

13. Kok et al. indicate that the triple prf1∆ hrp1∆ hrp3∆ mutant exhibits stronger growth defects than the single prf1∆ mutant. However, Figure S9F shows that no growth is detectable in the single prf1∆ mutant, a phenotype that cannot be exacerbated in the triple mutant. Perhaps the use of a prf1 mutant showing a less severe phenotype migh help.

Significance

As indicated in point 1, the first half of the manuscript describes results that are very similar to those already reported in the literature.

The interaction between Hrp3 and the Prf1 subunit is new and interesting, and could lead to further research and a new manuscript.

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Referee #1

Evidence, reproducibility and clarity

This is an excellent study that leverages the chromatin biology of Schizosaccharomyces pombe to uncover the central role of CHD1-family remodelers in maintaining nucleosome organisation and suppressing cryptic transcription. The work is carefully executed. In short, the authors show that Hrp3 is the primary CHD1-family remodeler responsible for maintaining nucleosome organisation over gene bodies. This represses antisense transcription from cryptic promoters in gene bodies. They provide evidence that Hrp3 is repressed in meiosis to allow the induction of meiotic genes. They further identified that a conserved domain, the CHCT domain of Hrp3, is essential for its interaction with Prf1 (PAF complex subunit), which is critical for the chromatin organisation in gene bodies. This manuscript is of excellent quality and is an important contribution towards understanding how transcription initiation is repressed within gene bodies. I have small comments and suggestions for clarification.

Minor comments:

• The study demonstrates that Hrp3 represses antisense transcription at meiotic genes, showing that Hrp3 is reduced in meiosis, which could facilitate the induction of meiotic genes. Is there a phenotype in the hrp3Δ or the hrp1Δ hrp3Δ mutant in relation to meiosis? E.g. do these strains enter meiosis uncontrolled?

• Figure 3C - ORC4 Locus TSS presentation. The presented data do not show a well-defined TSS on the sense strand. For reference, it would be useful to show that sense TSS is not altered between the different strains.

• The study focuses on antisense cryptic transcription, which is relatively easy to measure by RNA-seq. Often, however, cryptic transcription can also occur in the sense direction in gene bodies. Do the authors also find evidence of cryptic sense transcription in gene bodies (based on TSS-seq data)? This could be useful for completeness to report, as this could lead to aberrant protein-coding isoforms.

• The manuscript alternates between "Prf1" (S. pombe) and "RTF1" (other eukaryotes). This is at times confusing. I recommend consistent use of gene nomenclature.

• The authors show epistatic interaction for nucleosome spacing in Figure 7D for the prf1Δ and hrp1Δ hrp3Δ prf1Δ strains. It would be informative to have the hrp1Δ hrp3Δ data also included in Figure 7D, like in the other figure panels.

Significance

This is an excellent study that leverages the chromatin biology of Schizosaccharomyces pombe to uncover the central role of CHD1-family remodelers in maintaining nucleosome organisation and suppressing cryptic transcription. This manuscript is of excellent quality and is an important contribution towards understanding how transcription initiation is repressed within gene bodies.

I am an expert on transcription regulation and noncoding transcription.

This just ignited rage in me- as a woman with young children and who has been told by peers and family members that staying home with the kids would be better for their 'development'. Societies views towards women's place in the workforce are still sexist and a battle women face every day.

These are interesting questions because as discussed in this article, even with new data collection, and the intention is there to create a diverse data set, if it is people from a majority group collecting the data, often unknowingly a diverse group of people are left out of the collection. This is in reference to the facial recognition example. Moreover, I would ask, 'Who does the work (and who is pushed out) AND how can we actively find ways to figure out the groups being pushed out- because sometimes our own unconscious bias won't be able to come up with that answer ourselves.

These restrictions do persist today; between transportation barriers and computer literacy barriers alone. I have seen first hand several people unable to vote during the last town election because they were unable to get to the fire hall where voting was taking place. And for many, voting online requires one on one help with the technology; something not everyone has.

Yup. Important stuff.

Okay, there's sort of an implied both/and here.

Big Honking Red Flag. Let's Jump right on the Either Or BOAT!!!! TRADITIONAL REFORM I bet one is bad!!!

Can I highlight this in SEVEN COLORS ????

OK, in the context of the 'tough decisions' eh? Good to recognize we need to deal with that instead of pretending everybody will have the resources to do it right.

eLife Assessment

This important study uses single-neuron Patch-seq RNA sequencing to investigate the process by which RNA editing can produce protein diversity and regulate function in various cellular contexts. The computational analyses of the data collected are convincing, and from an analytical standpoint, this paper is a notable advance in seeking to provide a biological context for massive amounts of data in the field. The study would be of interest to biologists looking at the effects of RNA editing in the diversification of cellular behaviour.

Reviewer #1 (Public review):

The importance of RNA editing in producing protein diversity is a widespread process that can regulate how genes function in various cellular contexts. Despite the importance of the process, we still lack a thorough knowledge of the profile of RNA editing targets in known cells. Crane and colleagues take advantage of a recently acquired scRNAseq database for Drosophila type Ib and Is larval motoneurons and identify the RNA editing landscape that differs in those cells. They find both canonical (A --> I) and non-canonical sites and characterize the targets, their frequencies, and determine some of the "rules" that influence RNA editing. They compare their database with existing databases to determine a reliance on the most well-known deaminase enzyme ADAR, determine the activity-dependence of editing profiles, and identify editing sites that are specific to larval Drosophila, differing from adults. The authors also identify non-canonical editing sites, especially in the newly appreciated and identified regulator of synaptic plasticity, Arc1.

The paper represents a strong analysis of recently made RNAseq databases from their lab and takes a notable approach to integrate this with other databases that have been recently produced from other sources. One of the places where this manuscript succeeds is in a thorough approach to analyzing the considerable amount of data that is out there regarding RNAseq in these differing motoneurons, but also in comparing larvae to adults. This is a strong advance. It also enables the authors to begin to determine rules for RNA editing. From an analytical standpoint, this paper is a notable advance in seeking to provide a biological context for massive amounts of data in the field. Further, it addresses some biological aspects in comparing WT and adar mutants to assess one potential deaminase, addresses activity-dependence, and begins to reveal profiles of canonical and non-canonical editing.

Reviewer #2 (Public review):

Summary:

The study uses single-neuron Patch-seq RNA sequencing in two subgroups of Drosophila larval motoneurons (1s and 1b) and identifies 316 high-confidence canonical mRNA edit sites, which primarily (55%) occur in the coding regions of the mRNAs (CDS). Most of the canonical mRNA edits in the CDS regions include neuronal and synaptic proteins such as Complexin, Cac, Para, Shab, Sh, Slo, EndoA, Syx1A, Rim, RBP, Vap33, and Lap, which are involved in neuronal excitability and synaptic transmission. Of the 316 identified canonical edit sites, 60 lead to missense RNAs in a range of proteins (nAChRalpha5, nAChRalpha6, nAChRbeta1, ATPalpha, Cacophony, Para, Bsk, Beag, RNase Z) that are likely to have an impact on the larval motoneurons' development and function. Only 27 sites show editing levels higher than 90% and a similar editing profile is observed between the 1s and 1b motoneurons when looking at the number of edit sites and the fraction of reads edited per cell, with only 26 RNA editing sites showing a significant difference in the editing level. The variability of edited and unedited mRNAs suggests stochastic editing. The two subsets of motoneurons show many noncanonical editing sites, which, however, are not enriched for neuron-specific genes, therefore causing more silent changes compared to canonical editing sites. Comparison of the mRNA editing sites and editing rate of the single neuron Patch-seq RNA sequencing dataset to three other RNAseq datasets, one from same stage larval motoneurons and two from adult heads nuclei, show positive correlations in editing frequencies of CDS edits between the patch-sec larval 1b + 1s MNs and all other three datasets, with stronger correlations for previously annotated edits and weaker correlations for unannotated edits. Several of the identified editing targets are only present in the single neuron Patch-seq RNA sequencing dataset, suggesting cell-type-specific or developmental-specific editing. Editing appears to be resistant to changes in neuronal activity as only a few sites show evidence of being activity-regulated.

Strengths:

The study employs GAL4 driver lines available in the Drosophila model to identify two subtypes of motoneurons with distinct biophysical and morphological features. In combination with single-neuron Patch-seq RNA sequencing, it provides a unique opportunity to identify RNA editing sites and rates specific to specific motoneuron subtypes. The RNA seq data is robustly analysed, and high-confidence mRNA edit sites of both canonical and noncanonical RNA editing are identified.

The mRNA editing sites identified from the single neuron Patch-seq RNA sequencing data are compared to editing sites identified across other RNAseq datasets collected from animals at similar or different developmental stages, allowing for the identification of editing sites that are common to all or specific to a single dataset.

Weaknesses:

Although the analysed motoneurons come from two distinct subtypes, it is unclear from how many Drosophila larvae the motoneurons were collected and from which specific regions along the ventral nerve cord (VNC). Therefore, the study does not consider possible differences in editing rate between samples from different larvae that could be in different active states or neurons located at different regions of the VNC, which would receive inputs from slightly different neuronal networks.

The RNA samples include RNAs located both in the nucleus and the cytoplasm, introducing a potential compartmental mismatch between the RNA and the enzymes mediating the editing, which could influence editing rate. Similarly, the age of the RNAs undergoing editing is unknown, which may influence the measured editing rates.

Reviewer #3 (Public review):

Summary:

The study consists of extensive computational analyses of their previously released Patch-seq data on single MN1-Ib and MNISN-Is neurons. The authors demonstrate the diversity of A>I editing events at single-cell resolution in two different neuronal cell types, identifying numerous A>I editing events that vary in their proportion, including those that cause missense mutations in conserved amino acids. They also consider "noncanonical" edits, such as C>T and G>A, and integrate publicly available data to support these analyses.

In general, the study contains a valuable resource to assess RNA editing in single neurons and opens several questions regarding the diversity and functional implications of RNA editing at single-cell resolution. The conclusions from the study are generally supported by their data; however, the study is currently based on computational predictions and would therefore benefit from experimentation to support their hypotheses and demonstrate the effects of the editing events identified on neuronal function and phenotype.

Strengths:

The study uses samples that are technically difficult to prepare to assess cell-type-specific RNA editing events in a natural model. The study also uses public data from different developmental stages that demonstrate the importance of considering cell type and developmental stage-specific RNA regulation. These critical factors, particularly that of developmental timing, are often overlooked in mechanistic studies.

Extensive computational analysis, using public pipelines, suitable filtering criteria, and accessible custom code, identifies a number of RNA editing events that have the potential to impact conserved amino acids and have subsequent effects on protein function. These observations are supported through the integration of several public data sets to investigate the occurrence of the edits in other data sets, with many identified across multiple data sets. This approach allowed the identification of a number of novel A>I edits, some of which appear to be specific to this study, suggesting cell/developmental specificity, whilst others are present in the public data sets but went unannotated.

The study also considers the role of Adar in the generation of A>I edits, as would be expected, by assessing the effect of Adar expression on editing rates using public data from adar mutant tissue to demonstrate that the edits conserved between experiments are mainly Adar-sensitive. This would be stronger if the authors also performed Patch-seq experiments in adar mutants to increase confidence in the identified edit sites.

Weaknesses:

Whilst the study makes interesting observations using advanced computational approaches, it does not demonstrate the functional implications of the observed editing events. The functional impact of the edits is inferred from either the nature of the change to the coding sequence and the amino acid conservation, or through integration of other data sets. Although these could indeed imply function, further experimentation would be required to confirm such as using their Alphafold models to predict any changes in structure. This limitation is acknowledged by the authors, but the overall strength of the interpretation of the analysis could be softened to represent this.

The study uses public data from more diverse cellular populations to confirm the role of Adar in introducing the A>I edits. Whilst this is convincing, the ideal comparison to support the mechanism behind the identified edits would be to perform patch-seq experiments on 1b or 1s neurons from adar mutants. However, although this should be considered when interpreting the data, these experiments would be a large amount of work and beyond the scope of the paper.

By focusing on the potential impact of editing events that cause missense mutations in the CDS, the study may overlook the importance of edits in noncoding regions, which may impact miRNA or RNA-binding protein target sites. Further, the statement that noncanonical edits and those that induce silent mutations are likely to be less impactful is very broad and should be reconsidered. This is particularly the case when suggesting that silent mutations may not impact the biology. Given the importance of codon usage in translational fidelity, it is possible that silent mutations induced by either A>I or noncanonical editing in the CDS impact translation efficiency. Indeed, this could have a greater impact on protein production and transcript levels than a single amino acid change alone.

Author response:

Reviewer #1:

Indicated the paper provided a strong analysis of RNAseq databases to provide a biological context and resource for the massive amounts of data in the field on RNA editing. The reviewer noted that future studies will be important to define the functional consequences of the individual edits and why the RNA editing rules we identified exist. We address these comments below.

(1) The reviewer wondered about the role of noncanonical editing to neuronal protein expression.

Indeed, the role of noncanonical editing has been poorly studied compared to the more common A-to-I ADAR-dependent editing. Most non-canonical coding edits we found actually caused silent changes at the amino acid level, suggesting evolutionary selection against this mechanism as a pathway for generating protein diversity. As such, we suspect that most of these edits are not altering neuronal function in significant ways. Two potential exceptions to this were non-canonical edits that altered conserved residues in the synaptic proteins Arc1 and Frequenin 1. The C-to-T coding edit in the activity-regulated Arc1 mRNA that encodes a retroviral-like Gag protein involved in synaptic plasticity resulted in a P124L amino acid change (see Author response image 1 panel A below). ~50% of total Arc1 mRNA was edited at this site in both Ib and Is neurons, suggesting a potentially important role if the P124L change alters Arc1 structure or function. Given Arc1 assembles into higher order viral-like capsids, this change could alter capsid formation or structure. Indeed, P124 lies in the hinge region separating the N- and C-terminal capsid assembly regions (panel B) and we hypothesize this change will alter the ability of Arc1 capsids to assemble properly. We plan to experimentally test this by rescuing Arc1 null mutants with edited versus unedited transgenes to see how the previously reported synaptic phenotypes are modified. We also plan to examine the ability of the change to alter Arc1 capsid assembly in a collaboration using CyroEM.

Author response image 1.

A. AlphaFold predictions of Drosophila Arc1 and Frq1 with edit site noted. B. Structure of the Drosophila Arc1 capsid. Monomeric Arc1 conformation within the capsid is shown on the right with the location of the edit site indicated.

The other non-canonical edit (G-to-A) that stood out was in Frequenin 1 (Frq1), a multi-EF hand containing Ca²⁺ binding protein that regulates synaptic transmission, that resulted in a G2E amino acid substitution (location within Frq1shown in panel A above). This glycine residue is conserved in all Frq homologs and is the site of N-myristoylation, a co-translational lipid modification to the glycine after removal of the initiator methionine by an aminopeptidase. Myristoylation tethers Frq proteins to the plasma membrane, with a Ca²⁺-myristoyl switch allowing some family members to cycle on and off membranes when the lipid domain is sequestered in the absence of Ca²⁺. Although the G2E edit is found at lower levels (20% in Ib MNs and 18% in Is MNs), it could create a pool of soluble Frq1 that alters it’s signaling. We plan to functionally assay the significance of this non-canonical edit as well. Compared to edits that alter amino acid sequence, determining how non canonical editing of UTRs might regulate mRNA dynamics is a harder question at this stage and will require more experimental follow-up.

(2) The reviewer noted the last section of the results might be better split into multiple parts as it reads as a long combination of two thoughts.

We agree with the reviewer that the last section is important, but it was disconnected a bit from the main story and was difficult for us to know exactly where to put it. All the data to that point in the paper was collected from our own PatchSeq analysis from individual larval motoneurons. We wanted to compare these results to other large RNAseq datasets obtained from pooled neuronal populations and felt it was best to include this at the end of the results section, as it no longer related to the rules of RNA editing within single neurons. We used these datasets to confirm many of our edits, as well as find evidence for some developmental and neuron-specific cell type edits. We also took advantage of RNAseq from neuronal datasets with altered activity to explore how activity might alter the editing machinery. We felt it better to include that data in this final section given it was not collected from our original PatchSeq approach.

Reviewer #2:

Noted the study provided a unique opportunity to identify RNA editing sites and rates specific to individual motoneuron subtypes, highlighting the RNAseq data was robustly analyzed and high-confidence hits were identified and compared to other RNAseq datasets. The reviewer provided some suggestions for future experiments and requested a few clarifications.

(1) The reviewer asked about Figure 1F and the average editing rate per site described later in the paper.

Indeed, Figure 1F shows the average editing rate for each individual gene for all the Ib and Is cells, so we primarily use that to highlight the variability we find in overall editing rate from around 20% for some sites to 100% for others. The actual editing rate for each site for individual neurons is shown in Figure 4D that plots the rate for every edit site and the overall sum rate for that neuron in particular.

(2) The reviewer also noted that it was unclear where in the VNC the individual motoneurons were located and how that might affect editing.

The precise segment of the larvae for every individual neuron that was sampled by Patch-seq was recorded and that data is accessible in the original Jetti et al 2023 paper if the reader wants to explore any potential anterior to posterior differences in RNA editing. Due to the technical difficulty of the Patch-seq approach, we pooled all the Ib and Is neurons from each segment together to get more statistical power to identify edit sites. We don’t believe segmental identify would be a major regulator of RNA editing, but cannot rule it out.

(3) The reviewer also wondered if including RNAs located both in the nucleus and cytoplasm would influence editing rate.

Given our Patch-seq approach requires us to extract both the cytoplasm and nucleus, we would be sampling both nuclear and cytoplasmic mRNAs. However, as shown in Figure 8 – figure supplement 3 D-F, the vast majority of our edits are found in both polyA mRNA samples and nascent nuclear mRNA samples from other datasets, indicating the editing is occurring co-transcriptionally and within the nucleus. As such, we don't think the inclusion of cytoplasmic mRNA is altering our measured editing rates for most sites. This may not be true for all non-canonical edits, as we did see some differences there, indicating some non-canonical editing may be happening in the cytoplasm as well.

Reviewer #3:

indicated the work provided a valuable resource to access RNA editing in single neurons. The reviewer suggested the value of future experiments to demonstrate the effects of editing events on neuronal function. This will be a major effort for us going forwards, as we indeed have already begun to test the role of editing in mRNAs encoding several presynaptic proteins that regulate synaptic transmission. The reviewer also had several other comments as discussed below.

(1) The reviewer noted that silent mutations could alter codon usage that would result in translational stalling and altered protein production.

This is an excellent point, as silent mutations in the coding region could have a more significant impact if they generate non-preferred rare codons. This is not something we have analyzed, but it certainly is worth considering in future experiments. Our initial efforts are on testing the edits that cause predictive changes in presynaptic proteins based on the amino acid change and their locale in important functional domains, but it is worth considering the silent edits as well as we think about the larger picture of how RNA editing is likely to impact not only protein function but also protein levels.

(2) The reviewer noted future studies could be done using tools like Alphafold to test if the amino acid changes are predicted to alter the structure of proteins with coding edits.

This is an interesting approach, though we don’t have much expertise in protein modeling at that level. We could consider adding this to future studies in collaboration with other modeling labs.

(3) The reviewer wondered if the negative correlation between edits and transcript abundance could indicate edits might be destabilizing the transcripts.

This is an interesting idea, but would need to be experimentally tested. For the few edits we have generated already to begin functionally testing, including our published work with editing in the C-terminus of Complexin, we haven’t seen a change in mRNA levels causes by these edits. However, it would not be surprising to see some edits reducing transcript levels. A set of 5’UTR edits we have generated in Syx1A seem to be reducing protein production and may be acting in such a manner.

(4) The reviewer wondered if the proportion of edits we report in many of the figures is normalized to the length of the transcript, as longer transcripts might have more edits by chance.

The figures referenced by the reviewer (1, 2 and 7) show the number of high-confidence editing sites that fall into the 5’ UTR, 3’ UTR, or CDS categories. Our intention here was to highlight that the majority of the high confidence edits that made it through the stringent filtering process were in the coding region. This would still be true if we normalized to the length of the given gene region. However, it would be interesting to know if these proportions match the expected proportions of edits in these gene regions given a random editing rate per gene region length across the Drosophila genome, although we did not do this analysis.    

(5) The reviewer noted that future studies could expand on the work to examine miRNA or other known RBP binding sites that might be altered by the edits.

This is another avenue we could pursue in the future. We did do this analysis for a few of the important genes encoding presynaptic proteins (these are the most interesting to us given the lab’s interest in the synaptic vesicle fusion machinery), but did not find anything obvious for this smaller subset of targets.

(6) The reviewer suggested sequence context for Adar could also be investigated for the hits we identified.

We haven’t pursued this avenue yet, but it would be of interest to do in the future. In a similar vein, it would be informative to identify intron-exon base pairing that could generate the dsDNA template on which ADAR acts.

(7) The reviewer noted the disconnect between Adar mRNA levels and overall editing levels reported in Figure 4A/B.

Indeed, the lack of correlation between overall editing levels and Adar mRNA abundance has been noted previously in many studies. For the type of single cell Patch-seq approach we took to generate our RNAseq libraries, the absolute amount of less abundant transcripts obtained from a single neuron can be very noisy. As such, the few neurons with no detectable Adar mRNA are likely to represent that single neuron noise in the sampling. Per the reviewer’s question, these figure panels only show A-to-I edits, so they are specific to ADAR.

(8) The reviewer notes the scale in Figure 5D can make it hard to visualize the actual impact of the changes.

The intention of Figure 5D was to address the question of whether sites with high Ib/Is editing differences were simply due to higher Ib or Is mRNA expression levels. If this was the case, then we would expect to see highly edited sites have large Ib/Is TPM differences. Instead, as the figure shows, the vast majority of highly-edited sites were in mRNAs that were NOT significantly different between Ib and Is (red dots in graph) and are therefore clustered together near “0 Difference in TPMs”. TPMs and editing levels for all edit sites can be found in Table 1, and a visualization of these data for selected sites is shown in Figure 5E.

Include a reference & description of the figure to give it context

Museums are filled with material culture.

This is very interesting, dreams mean so much more in other cultures.

The way people are raised can affect how they see the world.

Winking is so similar to blinking, its just using one eye, so its strange that we have to learn how to do it while we are born knowing how to blink.

This is a good way to view culture.

Brushing our teeth is a very unique action that humans take for hygiene because not many, if any, other animals brush their teeth. But we heavily rely on it every day to keep us keep and use it in our routines.

In some way or another, we all share culture. There is a culture for how we act, eat, speak, walk, our manners, what we do doing our day-to-day lives, just about everything. One person is doing the same thing as you are right now somewhere in the world and you are both sharing that culture.

This reminded me of my great grandmother. She tends to joke and be silly often and when she does or even sometimes when shes not, she winks. About every time i see her, she is winking at me for something. I have never known anyone that does it as much as her and i believe it has to do with when she grew up because you dont see many people wink often today but in the 30s and 40s it was much more common

It is interesting how there has been a change over time in the way we live and build out houses. Houses that would last decades used to be built out of clay and stones but now we have houses sometimes out of stone and brick but mostly wood and other various materials that are flimsier. Not to say houses aren't sturdy now but there is a change in the use in resources and materials as we've advanced in time.

Home for some people sometimes isnt physical. It can be an object or a person. A home is supposed to bring us safety and comfort which is not always found inside a literal home.

Efficiency, scale, and impact are nested within the power center of a collaborative effort.

Difficult to assess (and impossible at scale) is proving to be a myth. It's doable, we just have to want to.

Embed, identify, assess. Not enough to have content there; need to validate proficiency. ALSO on this page: partnerships (for application practice of skills) and also the free, statewide learning module...good admissions play on the data.

Where is it? add context

maybe you could potentially talk about other periods. Why is only the Mycenaean Period mentioned

cite this

cite where this piece of pottery is currently residing. Perhaps figure out its context

citation

This is important to note because the investigation had a narrowly tailored and significant purpose that did not extend past the reach of the investigation.

This quote captures the core idea of epistemic injustice in healthcare, the tendency to undervalue patient testimony compared to biomedical evidence. The authors are right: multiple studies confirm that clinicians often interpret patient narratives through diagnostic filters, minimizing subjective experiences, especially in chronic or “invisible” illnesses (e.g., fibromyalgia, long COVID).

eLife Assessment

This study provides useful insights into the ways in which germinal center B cell metabolism, particularly lipid metabolism, affects cellular responses. The authors use sophisticated mouse models to convincingly demonstrate that ether lipids are relevant for B cell homeostasis and efficient humoral responses. The authors then conducted in vivo as well as in vitro experiments, thereby strengthening their conclusions.

Reviewer #1 (Public review):

In this manuscript, Hoon Cho et al. present a novel investigation into the role of PexRAP, an intermediary in ether lipid biosynthesis, in B cell function, particularly during the Germinal Center (GC) reaction. The authors profile lipid composition in activated B cells both in vitro and in vivo, revealing the significance of PexRAP. Using a combination of animal models and imaging mass spectrometry, they demonstrate that PexRAP is specifically required in B cells. They further establish that its activity is critical upon antigen encounter, shaping B cell survival during the GC reaction.

Mechanistically, they show that ether lipid synthesis is necessary to modulate reactive oxygen species (ROS) levels and prevent membrane peroxidation.

Highlights of the Manuscript:

The authors perform exhaustive imaging mass spectrometry (IMS) analyses of B cells, including GC B cells, to explore ether lipid metabolism during the humoral response. This approach is particularly noteworthy given the challenge of limited cell availability in GC reactions, which often hampers metabolomic studies. IMS proves to be a valuable tool in overcoming this limitation, allowing detailed exploration of GC metabolism.

The data presented is highly relevant, especially in light of recent studies suggesting a pivotal role for lipid metabolism in GC B cells. While these studies primarily focus on mitochondrial function, this manuscript uniquely investigates peroxisomes, which are linked to mitochondria and contribute to fatty acid oxidation (FAO). By extending the study of lipid metabolism beyond mitochondria to include peroxisomes, the authors add a critical dimension to our understanding of B cell biology.

Additionally, the metabolic plasticity of B cells poses challenges for studying metabolism, as genetic deletions from the beginning of B cell development often result in compensatory adaptations. To address this, the authors employ an acute loss-of-function approach using two conditional, cell-type-specific gene inactivation mouse models: one targeting B cells after the establishment of a pre-immune B cell population (Dhrs7b^f/f, huCD20-CreERT2) and the other during the GC reaction (Dhrs7b^f/f; S1pr2-CreERT2). This strategy is elegant and well-suited to studying the role of metabolism in B cell activation.

Overall, this manuscript is a significant contribution to the field, providing robust evidence for the fundamental role of lipid metabolism during the GC reaction and unveiling a novel function for peroxisomes in B cells.

Comments on revisions:

There are still some discrepancies in gating strategies. In Fig. 7B legend (lines 1082-1083), they show representative flow plots of GL7+ CD95+ GC B cells among viable B cells, so it is not clear if they are IgDneg, as the rest of the GC B cells aforementioned in the text.

Western blot confirmation: We understand the limitations the authors enumerate. Perhaps an RT-qPCR analysis of the Dhrs7b gene in sorted GC B cells from the S1PR2-CreERT2 model could be feasible, as it requires a smaller number of cells. In any case, we agree with the authors that the results obtained using the huCD20-CreERT2 model are consistent with those from the S1PR2-CreERT2 model, which adds credibility to the findings and supports the conclusion that GC B cells in the S1PR2-CreERT2 model are indeed deficient in PexRAP

Lines 222-226: We believe the correct figure is 4B, whereas the text refers to 4C.

Supplementary Figure 1 (line 1147): The figure title suggests that the data on T-cell numbers are from mice in a steady state. However, the legend indicates that the mice were immunized, which means the data are not from steady-state conditions.

Reviewer #2 (Public review):

Summary:

In this study, Cho et al. investigate the role of ether lipid biosynthesis in B cell biology, particularly focusing on GC B cell, by inducible deletion of PexRAP, an enzyme responsible for the synthesis of ether lipids.

Strengths:

Overall, the data are well-presented, the paper is well-written and provides valuable mechanistic insights into the importance of PexRAP enzyme in GC B cell proliferation.

Weaknesses:

More detailed mechanisms of the impaired GC B cell proliferation by PexRAP deficiency remain to be further investigated. In minor part, there are issues for the interpretation of the data which might cause confusions by readers.

Comments on revisions:

The authors improved the manuscript appropriately according to my comments.

Author response:

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

Reviewer #1 (Public review):

In this manuscript, Hoon Cho et al. present a novel investigation into the role of PexRAP, an intermediary in ether lipid biosynthesis, in B cell function, particularly during the Germinal Center (GC) reaction. The authors profile lipid composition in activated B cells both in vitro and in vivo, revealing the significance of PexRAP. Using a combination of animal models and imaging mass spectrometry, they demonstrate that PexRAP is specifically required in B cells. They further establish that its activity is critical upon antigen encounter, shaping B cell survival during the GC reaction. Mechanistically, they show that ether lipid synthesis is necessary to modulate reactive oxygen species (ROS) levels and prevent membrane peroxidation.

Highlights of the Manuscript:

The authors perform exhaustive imaging mass spectrometry (IMS) analyses of B cells, including GC B cells, to explore ether lipid metabolism during the humoral response. This approach is particularly noteworthy given the challenge of limited cell availability in GC reactions, which often hampers metabolomic studies. IMS proves to be a valuable tool in overcoming this limitation, allowing detailed exploration of GC metabolism.

The data presented is highly relevant, especially in light of recent studies suggesting a pivotal role for lipid metabolism in GC B cells. While these studies primarily focus on mitochondrial function, this manuscript uniquely investigates peroxisomes, which are linked to mitochondria and contribute to fatty acid oxidation (FAO). By extending the study of lipid metabolism beyond mitochondria to include peroxisomes, the authors add a critical dimension to our understanding of B cell biology.

Additionally, the metabolic plasticity of B cells poses challenges for studying metabolism, as genetic deletions from the beginning of B cell development often result in compensatory adaptations. To address this, the authors employ an acute loss-of-function approach using two conditional, cell-type-specific gene inactivation mouse models: one targeting B cells after the establishment of a pre-immune B cell population (Dhrs7b^f/f, huCD20-CreERT2) and the other during the GC reaction (Dhrs7b^f/f; S1pr2-CreERT2). This strategy is elegant and well-suited to studying the role of metabolism in B cell activation.

Overall, this manuscript is a significant contribution to the field, providing robust evidence for the fundamental role of lipid metabolism during the GC reaction and unveiling a novel function for peroxisomes in B cells. 

Comments on revisions:

There are still some discrepancies in gating strategies. In Fig. 7B legend (lines 1082-1083), they show representative flow plots of GL7+ CD95+ GC B cells among viable B cells, so it is not clear if they are IgDneg, as the rest of the GC B cells aforementioned in the text.

We apologize for missing this item in need of correction in the revision and sincerely thank the reviewer for the stamina and care in picking this up. The data shown in Fig. 7B represented cells (events) in the IgD^neg Dump^neg viable lymphoid gate. We will correct this omission/blemish in the final revision that becomes the version of record.

Western blot confirmation: We understand the limitations the authors enumerate. Perhaps an RT-qPCR analysis of the Dhrs7b gene in sorted GC B cells from the S1PR2-CreERT2 model could be feasible, as it requires a smaller number of cells. In any case, we agree with the authors that the results obtained using the huCD20-CreERT2 model are consistent with those from the S1PR2-CreERT2 model, which adds credibility to the findings and supports the conclusion that GC B cells in the S1PR2-CreERT2 model are indeed deficient in PexRAP.

We will make efforts to go back through the manuscript and highlight this limitation to readers, i.e., that we were unable to get genetic evidence to assess what degree of "counter-selection" applied to GC B cells in our experiments.

We agree with the referee that optimally to support the Imaging Mass Spectrometry (IMS) data showing perturbations of various ether lipids within GC after depletion of PexRAP, it would have been best if we could have had a qRT2-PCR that allowed quantitation of the Dhrs7b-encoded mRNA in flow-purified GC B cells, or the extent to which the genomic DNA of these cells was in deleted rather than 'floxed' configuration.

While the short half-life of ether lipid species leads us to infer that the enzymatic function remains reduced/absent, it definitely is unsatisfying that the money for experiments ran out in June and the lab members had to move to new jobs.

Lines 222-226: We believe the correct figure is 4B, whereas the text refers to 4C.

As for the 1st item, we apologize and will correct this error.

Supplementary Figure 1 (line 1147): The figure title suggests that the data on T-cell numbers are from mice in a steady state. However, the legend indicates that the mice were immunized, which means the data are not from steady-state conditions. 

We will change the wording both on line 1147 and 1152.

Reviewer #2 (Public review):

Summary:

In this study, Cho et al. investigate the role of ether lipid biosynthesis in B cell biology, particularly focusing on GC B cell, by inducible deletion of PexRAP, an enzyme responsible for the synthesis of ether lipids.

Strengths:

Overall, the data are well-presented, the paper is well-written and provides valuable mechanistic insights into the importance of PexRAP enzyme in GC B cell proliferation.

Weaknesses:

More detailed mechanisms of the impaired GC B cell proliferation by PexRAP deficiency remain to be further investigated. In minor part, there are issues for the interpretation of the data which might cause confusions by readers.

Comments on revisions:

The authors improved the manuscript appropriately according to my comments.

To re-summarize, we very much appreciate the diligence of the referees and Editors in re-reviewing this work at each cycle and helping via constructive peer review, along with their favorable comments and overall assessments. The final points will be addressed with minor edits since there no longer is any money for further work and the lab people have moved on.

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

Reviewer #1 (Public review):

In this manuscript, Sung Hoon Cho et al. presents a novel investigation into the role of PexRAP, an intermediary in ether lipid biosynthesis, in B cell function, particularly during the Germinal Center (GC) reaction. The authors profile lipid composition in activated B cells both in vitro and in vivo, revealing the significance of PexRAP. Using a combination of animal models and imaging mass spectrometry, they demonstrate that PexRAP is specifically required in B cells. They further establish that its activity is critical upon antigen encounter, shaping B cell survival during the GC reaction. 

Mechanistically, they show that ether lipid synthesis is necessary to modulate reactive oxygen species (ROS) levels and prevent membrane peroxidation.

Highlights of the Manuscript:

The authors perform exhaustive imaging mass spectrometry (IMS) analyses of B cells, including GC B cells, to explore ether lipid metabolism during the humoral response. This approach is particularly noteworthy given the challenge of limited cell availability in GC reactions, which often hampers metabolomic studies. IMS proves to be a valuable tool in overcoming this limitation, allowing detailed exploration of GC metabolism.

The data presented is highly relevant, especially in light of recent studies suggesting a pivotal role for lipid metabolism in GC B cells. While these studies primarily focus on mitochondrial function, this manuscript uniquely investigates peroxisomes, which are linked to mitochondria and contribute to fatty acid oxidation (FAO). By extending the study of lipid metabolism beyond mitochondria to include peroxisomes, the authors add a critical dimension to our understanding of B cell biology.

Additionally, the metabolic plasticity of B cells poses challenges for studying metabolism, as genetic deletions from the beginning of B cell development often result in compensatory adaptations. To address this, the authors employ an acute loss-of-function approach using two conditional, cell-type-specific gene inactivation mouse models: one targeting B cells after the establishment of a pre-immune B cell population (Dhrs7b^f/f, huCD20-CreERT2) and the other during the GC reaction (Dhrs7b^f/f; S1pr2-CreERT2). This strategy is elegant and well-suited to studying the role of metabolism in B cell activation.

Overall, this manuscript is a significant contribution to the field, providing robust evidence for the fundamental role of lipid metabolism during the GC reaction and unveiling a novel function for peroxisomes in B cells.

We appreciate these positive reactions and response, and agree with the overview and summary of the paper's approaches and strengths.

However, several major points need to be addressed:

Major Comments:

Figures 1 and 2

The authors conclude, based on the results from these two figures, that PexRAP promotes the homeostatic maintenance and proliferation of B cells. In this section, the authors first use a tamoxifen-inducible full Dhrs7b knockout (KO) and afterwards Dhrs7bΔ/Δ-B model to specifically characterize the role of this molecule in B cells. They characterize the B and T cell compartments using flow cytometry (FACS) and examine the establishment of the GC reaction using FACS and immunofluorescence. They conclude that B cell numbers are reduced, and the GC reaction is defective upon stimulation, showing a reduction in the total percentage of GC cells, particularly in the light zone (LZ).

The analysis of the steady-state B cell compartment should also be improved. This includes a  more detailed characterization of MZ and B1 populations, given the role of lipid metabolism and lipid peroxidation in these subtypes.

Suggestions for Improvement:

B Cell compartment characterization: A deeper characterization of the B cell compartment in non-immunized mice is needed, including analysis of Marginal Zone (MZ) maturation and a more detailed examination of the B1 compartment. This is especially important given the role of specific lipid metabolism in these cell types. The phenotyping of the B cell compartment should also include an analysis of immunoglobulin levels on the membrane, considering the impact of lipids on membrane composition.

Although the manuscript is focused on post-ontogenic B cell regulation in Ab responses, we believe we will be able to polish a revised manuscript through addition of results of analyses suggested by this point in the review: measurement of surface IgM on and phenotyping of various B cell subsets, including MZB and B1 B cells, to extend the data in Supplemental Fig 1H and I. Depending on the level of support, new immunization experiments to score Tfh and analyze a few of their functional molecules as part of a B cell paper may be feasible.   

Addendum / update of Sept 2025: We added new data with more on MZB and B1 B cells, surface IgM, and on Tfh populations. 

GC Response Analysis Upon Immunization: The GC response characterization should include additional data on the T cell compartment, specifically the presence and function of Tfh cells. In Fig. 1H, the distribution of the LZ appears strikingly different. However, the authors have not addressed this in the text. A more thorough characterization of centroblasts and centrocytes using CXCR4 and CD86 markers is needed.

The gating strategy used to characterize GC cells (GL7+CD95+ in IgD− cells) is suboptimal. A more robust analysis of GC cells should be performed in total B220+CD138− cells.

We first want to apologize the mislabeling of LZ and DZ in Fig 1H. The greenish-yellow colored region (GL7⁺ CD35⁺) indicate the DZ and the cyan-colored region (GL7⁺ CD35⁺) indicates the LZ.    Addendum / update of Sept 2025: We corrected the mistake, and added new experimental data using the CD138 marker to exclude preplasmablasts.  

As a technical note, we experienced high background noise with GL7 staining uniquely with PexRAP deficient (Dhrs7b^f/f; Rosa26-CreER^T2) mice (i.e., not WT control mice). The high background noise of GL7 staining was not observed in B cell specific KO of PexRAP (Dhrs7b^f/f; huCD20-CreER^T2). Two formal possibilities to account for this staining issue would be if either the expression of the GL7 epitope were repressed by PexRAP or the proper positioning of GL7⁺ cells in germinal center region were defective in PexRAPdeficient mice (e.g., due to an effect on positioning cues from cell types other than B cells). In a revised manuscript, we will fix the labeling error and further discuss the GL7 issue, while taking care not to be thought to conclude that there is a positioning problem or derepression of GL7 (an activation antigen on T cells as well as B cells).

While the gating strategy for an overall population of GC B cells is fairly standard even in the current literature, the question about using CD138 staining to exclude early plasmablasts (i.e., analyze B220⁺ CD138^neg vs B220⁺ CD138⁺) is interesting. In addition, some papers like to use GL7⁺ CD38^neg for GC B cells instead of GL7⁺ Fas (CD95)⁺, and we thank the reviewer for suggesting the analysis of centroblasts and centrocytes. For the revision, we will try to secure resources to revisit the immunizations and analyze them for these other facets of GC B cells (including CXCR4/CD86) and for their GL7⁺ CD38^neg. B220⁺ CD138^- and B220⁺ CD138⁺ cell populations. 

We agree that comparison of the Rosa26-CreERT2 results to those with B cell-specific lossof-function raise a tantalizing possibility that Tfh cells also are influenced by PexRAP. Although the manuscript is focused on post-ontogenic B cell regulation in Ab responses, we hope to add a new immunization experiments that scores Tfh and analyzes a few of their functional molecules could be added to this B cell paper, depending on the ability to wheedle enough support / fiscal resources.  

Addendum / update of Sept 2025: Within the tight time until lab closure, and limited $$, we were able to do experiments that further reinforced the GC B cell data - including stains for DZ vs LZ sub-subsetting - and analyzed Tfh cells. We were not able to explore changes in functional antigenic markers on the GC B or Tfh cells. 

The authors claim that Dhrs7b supports the homeostatic maintenance of quiescent B cells in vivo and promotes effective proliferation. This conclusion is primarily based on experiments where CTV-labeled PexRAP-deficient B cells were adoptively transferred into μMT mice (Fig. 2D-F). However, we recommend reviewing the flow plots of CTV in Fig. 2E, as they appear out of scale. More importantly, the low recovery of PexRAP-deficient B cells post-adoptive transfer weakens the robustness of the results and is insufficient to conclusively support the role of PexRAP in B cell proliferation in vivo.

In the revision, we will edit the text and try to adjust the digitized cytometry data to allow more dynamic range to the right side of the upper panels in Fig. 2E, and otherwise to improve the presentation of the in vivo CTV result. However, we feel impelled to push back respectfully on some of the concern raised here. First, it seems to gloss over the presentation of multiple facets of evidence. The conclusion about maintenance derives primarily from Fig. 2C, which shows a rapid, statistically significant decrease in B cell numbers (extending the finding of Fig. 1D, a more substantial decrease after a bit longer a period). As noted in the text, the rate of de novo B cell production does not suffice to explain the magnitude of the decrease. 

In terms of proliferation, we will improve presentation of the Methods but the bottom line is that the recovery efficiency is not bad (comparing to prior published work) inasmuch as transferred B cells do not uniformly home to spleen. In a setting where BAFF is in ample supply in vivo, we transferred equal numbers of cells that were equally labeled with CTV and counted B cells. The CTV result might be affected by lower recovered B cell with PexRAP deficiency, generally, the frequencies of CTV^low divided population are not changed very much. However, it is precisely because of the pitfalls of in vivo analyses that we included complementary data with survival and proliferation in vitro. The proliferation was attenuated in PexRAP-deficient B cells in vitro; this evidence supports the conclusion that proliferation of PexRAP knockout B cells is reduced. It is likely that PexRAP deficient B cells also have defect in viability in vivo as we observed the reduced B cell number in PexRAP-deficient mice. As the reviewer noticed, the presence of a defect in cycling does, in the transfer experiments, limit the ability to interpret a lower yield of B cell population after adoptive transfer into µMT recipient mice as evidence pertaining to death rates. We will edit the text of the revision with these points in mind. 

In vitro stimulation experiments: These experiments need improvement. The authors have used anti-CD40 and BAFF for B cell stimulation; however, it would be beneficial to also include antiIgM in the stimulation cocktail. In Fig. 2G, CTV plots do not show clear defects in proliferation, yet the authors quantify the percentage of cells with more than three divisions. These plots should clearly display the gating strategy. Additionally, details about histogram normalization and potential defects in cell numbers are missing. A more in-depth analysis of apoptosis is also required to determine whether the observed defects are due to impaired proliferation or reduced survival. 

As suggested by reviewer, testing additional forms of B cell activation can help explore the generality (or lack thereof) of findings. We plan to test anti-IgM stimulation together with anti-CD40 + BAFF as well as anti-IgM + TLR7/8, and add the data to a revised and final manuscript. 

Addendum / update of Sept 2025: The revision includes results of new experiments in which anti-IgM was included in the stimulation cocktail, as well as further data on apoptosis and distinguishing impaired cycling / divisions from reduced survival .

With regards to Fig. 2G (and 2H), in the revised manuscript we will refine the presentation (add a demonstration of the gating, and explicate histogram normalization of FlowJo). 

It is an interesting issue in bioscience, but in our presentation 'representative data' really are pretty representative, so a senior author is reminded of a comment Tak Mak made about a reduction (of proliferation, if memory serves) to 0.7 x control. [His point in a comment to referees at a symposium related that to a salary reduction by 30% :) A mathematical alternative is to point out that across four rounds of division for WT cells, a reduction to  0.7x efficiency at each cycle means about 1/4 as many progeny.] 

We will try to edit the revision (Methods, Legends, Results, Discussion] to address better the points of the last two sentences of the comment, and improve the details that could assist in replication or comparisons (e.g., if someone develops a PexRAP inhibitor as potential therapeutic). 

For the present, please note that the cell numbers at the end of the cultures are currently shown in Fig 2, panel I. Analogous culture results are shown in Fig 8, panels I, J, albeit with harvesting at day 5 instead of day 4. So, a difference of ≥ 3x needs to be explained. As noted above, a division efficiency reduced to 0.7x normal might account for such a decrease, but in practice the data of Fig. 2I show that the number of PexRAP-deficient B cells at day 4 is similar to the number plated before activation, and yet there has been a reasonable amount of divisions. So cell numbers in the culture of mutant B cells are constant because cycling is active but decreased and insufficient to allow increased numbers ("proliferation" in the true sense) as programmed death is increased. In line with this evidence, Fig 8G-H document higher death rates [i.e., frequencies of cleaved caspase3⁺ cell and Annexin V⁺ cells] of PexRAP-deficient B cells compared to controls. Thus, the in vitro data lead to the conclusion that both decreased division rates and increased death operate after this form of stimulation. 

An inference is that this is the case in vivo as well - note that recoveries differed by ~3x (Fig. 2D), and the decrease in divisions (presentation of which will be improved) was meaningful but of lesser magnitude (Fig. 2E, F). 

Reviewer #2 (Public review):

Summary:

In this study, Cho et al. investigate the role of ether lipid biosynthesis in B cell biology, particularly focusing on GC B cell, by inducible deletion of PexRAP, an enzyme responsible for the synthesis of ether lipids.

Strengths:

Overall, the data are well-presented, the paper is well-written and provides valuable mechanistic insights into the importance of PexRAP enzyme in GC B cell proliferation.

We appreciate this positive response and agree with the overview and summary of the paper's approaches and strengths. 

Weaknesses:

More detailed mechanisms of the impaired GC B cell proliferation by PexRAP deficiency remain to be further investigated. In the minor part, there are issues with the interpretation of the data which might cause confusion for the readers.

Issues about contributions of cell cycling and divisions on the one hand, and susceptibility to death on the other, were discussed above, amplifying on the current manuscript text. The aggregate data support a model in which both processes are impacted for mature B cells in general, and mechanistically the evidence and work focus on the increased ROS and modes of death. Although the data in Fig. 7 do provide evidence that GC B cells themselves are affected, we agree that resource limitations had militated against developing further evidence about cycling specifically for GC B cells. We will hope to be able to obtain sufficient data from some specific analysis of proliferation in vivo (e.g., Ki67 or BrdU) as well as ROS and death ex vivo when harvesting new samples from mice immunized to analyze GC B cells for CXCR4/CD86, CD38, CD138 as indicated by Reviewer 1. As suggested by Reviewer 2, we will further discuss the possible mechanism(s) by which proliferation of PexRAP-deficient B cells is impaired. We also will edit the text of a revision where to enhance clarity of data interpretation - at a minimum, to be very clear that caution is warranted in assuming that GC B cells will exhibit the same mechanisms as cultures in vitro-stimulated B cells. 

Addendum / update of Sept 2025: We were able to obtain results of intravital BrdU incorporation into GC B cells to measure cell cycling rates. The revised manuscript includes these results as well as other new data on apoptosis / survival, while deleting the data about CD138 populations whose interpretation was reasonably questioned by the referees.  

Reviewer #1 (Recommendations for the authors):

We believe the evidence presented to support the role of PexRAP in protecting B cells from cell death and promoting B cell proliferation is not sufficiently robust and requires further validation in vivo. While the study demonstrates an increase in ether lipid content within the GC compartment, it also highlights a reduction in mature B cells in PexRAP-deficient mice under steady-state conditions. However, the IMS results (Fig. 3A) indicate that there are no significant differences in ether lipid content in the naïve B cell population. This discrepancy raises an intriguing point for discussion: why is PexRAP critical for B cell survival under steady-state conditions?

We thank the referee for all their care and input, and we agree that further intravital analyses could strengthen the work by providing more direct evidence of impairment of GC B cells in vivo. To revise and improve this manuscript before creation of a contribution of record, we performed new experiments to the limit of available funds and have both (i) added these new data and (ii) sharpened the presentation to correct what we believe to be one inaccurate point raised in the review. 

(A) Specifically, we immunized mice with a B cell-specific depletion of PexRAP (Dhrs7b^D/D-B mice) and measured a variety of readouts of the GC B cells' physiology in vivo: proliferation by intravital incorporation of BrdU, ROS in the viable GC B cell gate, and their cell death by annexin V staining directly ex vivo. Consistent with the data with in vitro activated B cells, these analyses showed increased ROS (new - Fig. 7D) and higher frequencies of Annexin V⁺ 7AAD⁺ in GC B cells (GL7⁺ CD38^- B cell-gate) of immunized Dhrs7b^D/D-B mice compared with WT controls (huCD20-CreERT2^+/-, Dhrs7b^+/+)  (new - Fig. 7E). Collectively, these results indicate that PexRAP aids (directly or indirectly) in controlling ROS in GC B cells and reduces B cell death, likely contributing to the substantially decreased overall GC B cell population. These new data are added to the revised manuscript in Figure 7.  

Moreover, in each of two independent experiments (each comprising 3 vs 3 immunized mice), BrdU⁺ events among GL7⁺ CD38^- (GC B cell)-gated cells were reduced in the B cell-specific PexRAP knockouts compared with WT controls (new, Fig. 7F and Supplemental Fig 6E). This result on cell cycle rates in vivo is presented with caution in the revised manuscript text because the absolute labeling fractions were somewhat different in Expt 1 vs Expt 2. This situation affords a useful opportunity to comment on the culture of "P values" and statistical methods. It is intriguing to consider how many successful drugs are based on research published back when the standard was to interpret a result of this sort more definitively despite a merged "P value" that was not a full 2 SD different from the mean. In the optimistic spirit of the eLife model, it can be for the attentive reader to decide from the data (new, Fig. 7F and Supplemental Fig 6E) whether to interpret the BrdU results more strongly that what we state in the revised text.  

(B) On the issue of whether or not the loss of PexRAP led to perturbations of the lipidome of B cells prior to activation, we have edited the manuscript to do a better job making this point more clear.  

We point out to readers that in the resting, pre-activation state abnormalities were detected in naive B cells, not just in activated and GC B cells. In brief, the IMS analysis and LC-MS-MS analysis detected statistically significant differences in some, but not all, the ether phospholipids species in PexRAP deficient cells (some of which was in Supplemental Figure 2 of the original version). 

With this appropriate and helpful concern having been raised, we realize that this important point merited inclusion in the main figures. We point specifically to a set of phosphatidyl choline ions shown in Fig. 3 (revised - panels A, B, D) of the revised manuscript (PC O-36:5; PC O-38:5; PC O-40:6 and -40:7). 

For this ancillary record (because a discourse on the limitations of each analysis), we will note issues such as the presence of many non-B cells in each pixel of the IMS analyses (so that some or many "true positives" will fail to achieve a "significant difference") and for the naive B cells, differential rates of synthesis, turnover, and conversion (e.g., addition of another 2-carbon unit or saturation / desaturation of one side-chain). To the extent the concern reflects some surprise and perhaps skepticism that what seem relatively limited differences (many species appear unaffected, etc), we share in the sentiment. But the basic observation is that there are differences, and a reasonable connection between the altered lipid profile and evidence of effects on survival or proliferation (i.e., integration of survival and cell cycling / division). 

Additionally, it would be valuable to evaluate the humoral response in a T-independent setting. This would clarify whether the role of PexRAP is restricted to GC B cells or extends to activated B cells in general. 

We agree that this additional set of experiments would be nice and would extend work incrementally by testing the generality of the findings about Ab responses. The practical problem is that money and time ran out while testing important items that strengthen the evidence about GC B cells. 

Finally, the manuscript would benefit from a thorough revision to improve its readability and clarity. Including more detailed descriptions of technical aspects, such as the specific stimuli and time points used in analyses, would greatly enhance the flow and comprehension of the study. Furthermore, the authors should review figure labeling to ensure consistency throughout the manuscript, and carefully cite the relevant references. For instance, S1PR2 CreERT2 mouse is established by Okada and Kurosaki (Shinnakasu et al ,Nat. Immunol, 2016)

We appreciate this feedback and comment, inasmuch as both the clarity and scholarship matter greatly to us for a final item of record. For the revision, we have given our best shot to editing the text in the hopes of improved clarity, reduction of discrepancies (helpfully noted in the Minor Comments), and further detail-rich descriptions of procedures. We also edited the figure labeling to give a better consistency. While we note that the appropriate citation of Shinnakasu et al (2016) was ref. #69 of the original and remains as a citation, we have rechecked other referencing and try to use citations with the best relevant references.  

Minor Comments: The labeling of plots in Fig. 2 should be standardized. For example, in Fig. 2C, D, and G, the same mouse strain is used, yet the Cre+ mouse is labeled differently in each plot. 

We agree and have tried to tighten up these features in the panels noted as well as more generally (e.g., Fig. 4, 5, 6, 7, 9; consistency of huCD20-CreERT2 / hCD20CreERT2).

According to the text, the results shown in Fig. 1G and H correspond to a full KO  (Dhrs7b^f/f; Rosa26-CreERT2 mice). However, Fig. 1H indicates that the bottom image corresponds to Dhrs7b^f/f, huCD20-CreERT2 mice (Dhrs7bΔ/Δ -B). 

We have corrected Fig. 1H to be labeled as Dhrs7b^Δ/Δ (with the data on Dhrs7b^Δ/Δ-B presented in Supplemental Figure 4A, which is correctly labeled). Thank you for picking up this error that crept in while using copy/paste in preparation of figure panels and failing to edit out the "-B"!  

Similarly, the gating strategy for GC cells in the text mentions IgD− cells, while the figure legend refers to total viable B cells. These discrepancies need clarification.

We believe we located and have corrected this issue in the revised manuscript.   

Figures 3 and 4. The authors claim that B cell expression of PexRAP is required to  achieve normal concentrations of ether phospholipids. 

Suggestions for Improvement: 

Lipid Metabolism Analysis: The analysis in Fig. 3 is generally convincing but could be strengthened by including an additional stimulation condition such as anti-IgM plus antiCD40. In Fig. 4C, the authors display results from the full KO model. It would be helpful to include quantitative graphs summarizing the parameters displayed in the images.

We have performed new experiments (anti-IgM + anti-CD40) and added the data to the revised manuscript (new - Supplemental Fig. 2H and Supplemental Fig 6, D & F). Conclusions based on the effects are not changed from the original. 

As a semantic comment and point of scientific process, any interpretation ("claim") can - by definition - only be taken to apply to the conditions of the experiment. Nonetheless, it is inescapable that at least for some ether P-lipids of naive, resting B cells, and for substantially more in B cells activated under the conditions that we outline, B cell expression of PexRAP is required. 

With regards to the constructive suggestion about a new series of lipidomic analyses, we agree that for activated B cells it would be nice and increase insight into the spectrum of conditions under which the PexRAP-deficient B cells had altered content of ether phospholipids. However, in light of the costs of metabolomic analyses and the lack of funds to support further experiments, and the accuracy of the point as stated, we prioritized the experiments that could fit within the severely limited budget. 

[One can add that our results provide a premise for later work to analyze a time course after activation, and to perform isotopomer (SIRM) analyses with [13] C-labeled acetate or glucose, so as to understand activation-induced increases in the overall   To revise the manuscript, we did however extrapolate from the point about adding BCR cross-linking to anti-CD40 as a variant form of activating the B cells for measurements of ROS, population growth, and rates of division (CTV partitioning). The results of these analyses, which align with and thereby strengthen the conclusions about these functional features from experiments with anti-CD40 but no anti-IgM, are added to Supplemental Fig 2H and Supplemental Fig 6D, F. 

Figures 5, 6, and 7

The authors claim that Dhrs7b in B cells shapes antibody affinity and quantity. They use two mouse models for this analysis: huCD20-CreERT2 and Dhrs7b f/f; S1pr2-CreERT2 mice. 

Suggestions for Improvement:

Adaptive immune response characterization: A more comprehensive characterization of the adaptive immune response is needed, ideally using the Dhrs7b f/f; S1pr2-CreERT2 model. This should include: Analysis of the GC response in B220+CD138− cells. Class switch recombination analysis. A detailed characterization of centroblasts, centrocytes, and Tfh populations. Characterization of effector cells (plasma cells and memory cells).

Within the limits of time and money, we have performed new experiments prompted by this constructive set of suggestions. 

Specifically, we analyzed the suggested read-outs in the huCD20-CreERT2, Dhrs7b^f/f model after immunization, recognizing that it trades greater signal-noise for the fact that effects are due to a mix of the impact on B cells during clonal expansion before GC recruitment and activities within the GC. In brief, the results showed that 

(a) the GC B cell population - defined as CD138^neg GL7⁺ CD38^lo/neg IgD^neg B cells - was about half as large for PexRAP-deficient B cells net of any early- or preplasmablasts (CD138⁺ events) (new - Fig 5G); 

(b) the frequencies of pre- / early plasmablasts (CD138⁺ GL7⁺ CD38^neg) events (see new - Fig. 6H, I; also, new Supplemental Fig 5D) were so low as to make it unlikely that our data with the S1pr2-CreERT2 model (in Fig 7B, C) would be affected meaningfully by analysis of the CD138 levels;

(c) There was a modest decrease in centrocytes (LZ) but not centroblasts (DZ) (new - Fig 5H, I) - consistent with the immunohistochemical data of Supplemental Fig. 5A-C). 

Because of time limitations (the "shelf life" of funds and the lab) and insufficient stock of the S1pr2-CreERT2, Dhrs7b^f/f mice as well as those that would be needed as adoptive transfer recipients because of S1PR2 expression in (GC-)Tfh, the experiments were performed instead with the huCD20-CreERT2, Dhrs7b^f/f model. We would also note that using this Cre transgene better harmonizes the centrocyte/centroblast and Tfh data with the existing data on these points in Supplemental Fig. 4. 

(d) Of note, the analyses of Tfh and GC-Tfh phenotype cells using the huCD20-CreERT2 B cell type-specific inducible Cre system to inactivate Dhrs7b (new - Supplemental Fig 1G-I; which, along with new - Supplemental Fig 5E) provide evidence of an abnormality that must stem from a function or functions of PexRAP in B cells, most likely GC B cells. Specifically, it is known that the GC-Tfh population proliferates and is supported by the GC B cells, and the results of B cell-specific deletion show substantial reductions in Tfh cells (both the GC-Tfh gating and the wider gate for plots of CXCR5/PD-1/ fluorescence of CD4 T cells 

Timepoint Consistency: The NP response (Fig. 5) is analyzed four weeks postimmunization, whereas SRBC (Supp. Fig. 4) and Fig. 7 are analyzed one week or nine days post-immunization. The NP system analysis should be repeated at shorter timepoints to match the peak GC reaction.

This comment may stem from a misunderstanding. As diagrammed in Fig. 5A, the experiments involving the NP system were in fact measured at 7 d after a secondary (booster) immunization. That timing is approximately the peak period and harmonizes with the 7 d used for harvesting SRBC-immunized mice. So in fact the data with each system were obtained at a similar time point. Of course the NP experiments involved a second immunization so that many plasma cell and Ab responses derived from memory B cells generated by the primary immunization. However, the field at present is dominated by the view that the vast majority of the GC B cells after this second immunization (which historically we perform with alum adjuvant) are recruited from the naive rather than the memory B cell pool. For the revised manuscript, we have taken care that the Methods, Legend, and Figure provide the information to readers, and expanded the statement of a rationale. 

It may seem a technicality but under NIH regulations we are legally obligated to try to minimize mouse usage. It also behooves researchers to use funds wisely. In line with those imperatives, we used systems that would simultaneously allow analyses of GC B cells, identification of affinity maturation (which is minimal in our hands at a 7 d time point after primary NP-carrier immunization), and a switched repertoire (also minimal), and where with each immunogen the GC were scored at 7-9 d after immunization (9 d refers to the S1pr2-CreERT2 experiments). Apart from the end of funding, we feel that what little might be learned from performing a series of experiments that involve harvests 7 d after a primary immunization with NP-ovalbumin cannot well be justified. 

In vitro plasma cell differentiation: Quantification is missing for plasma cell differentiation in vitro (Supp. Fig. 4). The stimulus used should also be specified in the figure legend. Given the use of anti-CD40, differentiation towards IgG1 plasma cells could provide additional insights.

As suggested by reviewer, we have added the results of quantifying the in vitro plasma cell differentiation in Supplemental Fig 6B. Also, we edited the Methods and Supplemental Figure Legend to give detailed information of in vitro stimulation. 

Proliferation and apoptosis analysis: The observed defects in the humoral response should be correlated with proliferation and apoptosis analyses, including Ki67 and Caspase markers.

As suggested by the review, we have performed new experiment and analyzed the frequencies of cell death by annexin V staining, and elected to use intravital uptake of BrdU as a more direct measurement of S phase / cell cycling component of net proliferation. The new results are now displayed in Figure 5 and Supplemental Fig. 5. 

Western blot confirmation: While the authors have demonstrated the absence of PexRAP protein in the huCD20-CreERT2 model, this has not been shown in GC B cells from the Dhrs7b f/f; S1pr2-CreERT2 model. This confirmation is necessary to validate the efficiency of Dhrs7b deletion.

We were unable to do this for technical reasons expanded on below. For the revision, we have edited in a bit of text more explicitly to alert readers to the potential impact of counter-selection on interpretation of the findings with GC B cells. Before entering the GC, B cells have undergone many divisions, so if there were major pre-GC counterselection, in all likelihood the GC B cells would PexRAP-sufficient. To recap from the original manuscript and the new data we have added, IMS shows altered lipid profiles in the GC B cells and the literature indicates that the lipids are short-lived, requiring de novo resynthesis. The BrdU, ROS, and annexin V data show that GC B cells are abnormal. Accordingly, abnormal GC B cells represent the parsimonious or straightforward interpretation of the new results with GC-Tfh cell prevalence. 

While we take these findings together to suggest that counterselection (i.e., a Western result showing normal levels of PexRAP in the GC B cells) seems unlikely, it is formally possible and would mean that the in situ defects of GC B cells arose due to environmental influences of the PexRAP-deficient B cells during the developmental history of the WT B cells observed in the GC. 

Having noted all that, we understand that concerns about counter-selection are an issue if a reader accepts the data showing that mutant (PexRAP-deficient) B cells tend to proliferate less and die more readily. Indeed, one can speculate that were we also to perform competition experiments in which the Ighb, Cd45.2 B cells (WT or Dhrs7b D/D) are mixed with equal numbers of Igha, Cd45.1 competitors, the differences would become much greater. With this in mind, Western blotting of flow-purified GC B cells might give a sense of how much counter-selection has occurred. 

That said, the Westerns need at least 2.5 x 10⁶ B cells (those in the manuscript used five million, 5  x 10⁶) and would need replication. Taken together with the observation that ~200,000 GC B cells (on average) were measured in each B cell-specific knockout mouse after immunization (Fig. 1, Fig 5) and taking into account yields from sorting, each Western would require some 20-25 tamoxifen-injected ___-CreERT2, Dhrs7b f/f mice, and about half again that number as controls. The expiry of funds prohibited the time and costs of generating that many mice (>70) and flow-purified GC B cells. 

Figure 8

The authors claim that Dhrs7b contributes to the modulation of ROS, impacting B cell proliferation.

Suggestions for Improvement:

GC ROS Analysis: The in vitro ROS analysis should be complemented by characterizing ROS and lipid peroxidation in the GC response using the Dhrs7b f/f; S1pr2-CreERT2 model. Flow cytometry staining with H2DCFDA, MitoSOX, Caspase-3, and Annexin V would allow assessment of ROS levels and cell death in GC B cells. 

While subject to some of the same practical limits noted above, we have performed new experiments in line with this helpful input of the reviewer, and added the helpful new data to the revised manuscript. Specifically, in addition to the BrdU and phenotyping analyses after immunization of huCD20-CreER^T2, Dhrs7b^f/f mice, DCFDA (ROS), MitoSox, and annexin V signals were measured for GC B cells. Although the mitoSox signals did not significantly differ for PexRAP-deficient GCB, the ROS and annexin V signals were substantially increased. We added the new data to Figure 5 and Supplemental Figure 5. Together with the decreased in vivo BrdU incorporation in GC B cells from Dhrs7b^D/D-B mice, these results are consistent with and support our hypothesis that PexRAP regulates B cell population growth and GC physiology in part by regulating ROS detoxification, survival and proliferation of B cells.  

Quantification is missing in Fig. 8E, and Fig. 8F should use clearer symbols for better readability. 

We added quantification for Fig 8E in Supplemental Fig 6E, and edited the symbols in Fig 8F for better readability.

Figure 9

The authors claim that Dhrs7b in B cells affects oxidative metabolism and ER mass. The  results in this section are well-performed and convincing.

Suggestion for Improvement:

Based on the results, the discussion should elaborate on the potential role of lipids in antigen presentation, considering their impact on mitochondria and ER function.

We very much appreciate the praise of the tantalizing findings about oxidative metabolism and ER mass, and will accept the encouragement that we add (prudently) to the Discussion section to make note of the points mentioned by the Reviewer, particularly now that (with their encouragement) we have the evidence that B cell-specific loss of PexRAP (with the huCD20-CreERT2 deletion prior to immunization) resulted in decreased (GC-)Tfh and somewhat lower GC B cell proliferation.  

Reviewer #2 (Recommendations for the authors):

The authors should investigate whether PexRAP-deficient GC B cells exhibit increased mitochondrial ROS and cell death ex vivo, as observed in in vitro cultured B cells.

We very much appreciate the work of the referee and their input. We addressed this helpful recommendation, in essence aligned with points from Reviewer 1, via new experiments (until the money ran out) and addition of data to the manuscript. To recap briefly, we found increased ROS in GC B cells along with higher fractions of annexin V positive cells; intriguingly, increased mtROS (MitoSox signal) was not detected, which contrasts with the results in activated B cells in vitro in a small way. To keep the text focused and not stray too far outside the foundation supported by data, this point may align with papers that provide evidence of differences between pre-GC and GC B cells (for instance with lack of Tfam or LDHA in B cells).    

It remains unclear whether the impaired proliferation of PexRAP-deficient B cells is primarily due to increased cell death. Although NAC treatment partially rescued the phenotype of reduced PexRAP-deficient B cell number, it did not restore them to control levels. Analysis of the proliferation capacity of PexRAP-deficient B cells following NAC treatment could provide more insight into the cause of impaired proliferation.

To add to the data permitting an assessment of this issue, we performed new experiments in which B cells were activated (BCR and CD40 cross-linking), cultured, and both the change in population and the CTV partitioning were measured in the presence or absence of NAC. The results, added to the revision as Supplemental Fig 6FH, show that although NAC improved cell numbers for PexRAP-deficient cells relative to controls, this compound did not increase divisions at all. We infer that the more powerful effect of this lipid synthesis enzyme is to promote survival rather than division  capacity. 

Primary antibody responses were assessed at only one time point (day 20). It would be valuable to examine the kinetics of antibody response at multiple time points (0, 1w, 2w, 3w, for example) to better understand the temporal impact of PexRAP on antibody production.

We thank the reviewer for this suggestion. While it may be that the kinetic measurement of Ag-specific antibody level across multiple time points would provide an additional mechanistic clue into the of impact PexRAP on antibody production, the end of sponsored funding and imminent lab closure precluded performing such experiments.   

CD138+ cell population includes both GC-experienced and GC-independent plasma cells (Fig. 7). Enumeration of plasmablasts, which likely consists of both PexRAP-deleted and undeleted cells (Fig. 7D and E), may mislead the readers such that PexRAP is dispensable for plasmablast generation. I would suggest removing these data and instead examining the number of plasmablasts in the experimental setting of Fig. 4A (huCD20-CreERT2-mediated deletion) to address whether PexRAP-deficiency affects plasmablast generation. 

We have eliminated the figure panels in question, since it is accurate that in the absence of a time-stamping or marking approach we have a limited ability to distinguish plasma cells that arose prior to inactivation of the Dhrs7b gene in B cells. In addition, we performed new experiments that were used to analyze the "early plasmablast" phenotype and added those data to the revision (Supplemental Fig 5D).

eLife Assessment

The authors quantified intentions and knowledge gaps in scientists' use of sex as a biological variable in their work, and used a workshop intervention to show that while willingness was high, pressure points centered on statistical knowledge and perceived additional monetary costs to research. These important findings demonstrate the difficulty in changing understanding: while interventions can improve knowledge and decrease perceived barriers, the impact was small. The evidence for the findings is solid.

Reviewer #1 (Public review):

Summary:

The authors use the theory of planned behavior to understand whether or not intentions to use sex as a biological variable (SABV), as well as attitude (value), subjective norm (social pressure), and behavioral control (ability to conduct behavior), across scientists at a pharmacological conference. They also used an intervention (workshop) to determine the value of this workshop in changing perceptions and misconceptions. Attempts to understand the knowledge gaps were made.

Strengths:

The use of SABV is limited in terms of researchers using sex in the analysis as a variable of interest in the models (and not a variable to control). To understand how we can improve on the number of researchers examining the data with sex in the analyses, it is vital we understand the pressure points that researchers consider in their work. The authors identify likely culprits in their analyses. The authors also test an intervention (workshop) to address the main bias or impediments for researchers' use of sex in their analyses.

Reviewer #2 (Public review):

Summary:

The investigators tested a workshop intervention to improve knowledge and decrease misconceptions about sex inclusive research.

Strengths:

The investigators included control groups and replicated the study in a second population of scientists. The results appear to be well substantiated. Figures are easy to understand.

Weaknesses: None noted

Comments on revised version:

The authors have responded appropriately to all of my concerns.

Reviewer #3 (Public review):

Summary:

This manuscript aims to determine cultural biases and misconceptions in inclusive sex research and evaluate the efficacy of interventions to improve knowledge and shift perceptions to decrease perceived barriers for including both sexes in basic research.

Overall, this study demonstrates that despite the intention to include both sexes and a general belief in the importance of doing so, relatively few people routinely include both sexes. Further, the perceptions of barriers to doing so are high, including misconceptions surrounding sample size, disaggregation, and variability of females. There was also a substantial number of individuals without the statistical knowledge to appropriately analyze data in studies inclusive of sex. Interventions increased knowledge and decreased perception of barriers.

Strengths:

(1) This manuscript provides evidence for the efficacy of interventions for changing attitudes and perceptions of research.

(2) This manuscript also provides a training manual for expanding this intervention to broader groups of researchers.

Author response:

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

Reviewer #1 (Public review): 

Summary:

The authors use the theory of planned behavior to understand whether or not intentions to use sex as a biological variable (SABV), as well as attitude (value), subjective norm (social pressure), and behavioral control (ability to conduct behavior), across scientists at a pharmacological conference. They also used an intervention (workshop) to determine the value of this workshop in changing perceptions and misconceptions. Attempts to understand the knowledge gaps were made.

Strengths:

The use of SABV is limited in terms of researchers using sex in the analysis as a variable of interest in the models (and not a variable to control). To understand how we can improve on the number of researchers examining the data with sex in the analyses, it is vital we understand the pressure points that researchers consider in their work. The authors identify likely culprits in their analyses. The authors also test an intervention (workshop) to address the main bias or impediments for researchers' use of sex in their analyses. 

Weaknesses:

There are a number of assumptions the authors make that could be revisited: 

(1) that all studies should contain across sex analyses or investigations. It is important to acknowledge that part of the impetus for SABV is to gain more scientific knowledge on females. This will require within sex analyses and dedicated research to uncover how unique characteristics for females can influence physiology and health outcomes. This will only be achieved with the use of female-only studies. The overemphasis on investigations of sex influences limits the work done for women's health, for example, as within-sex analyses are equally important.

The Sex and Gender Equity in Research (SAGER) guidelines (1) provide guidance that “Where the subjects of research comprise organisms capable of differentiation by sex, the research should be designed and conducted in a way that can reveal sex-related differences in the results, even if these were not initially expected.”.  This is a default position of inclusion where the sex can be determined and analysis assessing for sex related variability in response. This position underpins many of the funding bodies new policies on inclusion.   

However, we need to place this in the context of the driver of inclusion. The most common reason for including male and female samples is for those studies that are exploring the effect of a treatment and then the goal of inclusion is to assess the generalisability of the treatment effect (exploratory sex inclusion)(2). The second scenario is where sex is included because sex is one of the variables of interest and this situation will arise because there is a hypothesized sex difference of interest (confirmatory sex inclusion).  

We would argue that the SABV concept was introduced to address the systematic bias of only studying one sex when assessing treatment effect to improve the generalisability of the research.  Therefore, it isn’t directly to gain more scientific knowledge on females.  However, this strategy will highlight when the effect is very different between male and female subjects which will potentially generate sex specific hypotheses.  

Where research has a hypothesis that is specific to a sex (e.g. it is related to oestrogen levels) it would be appropriate to study only the sex of interest, in this case females. The recently published Sex Inclusive Research Framework gives some guidance here and allows an exemption for such a scenario classifying such proposals “Single sex study justified” (3).

We have added an additional paragraph to the introduction to clarify the objectives behind inclusion and how this assists the research process. 

(2) It should be acknowledged that although the variability within each sex is not different on a number of characteristics (as indicated by meta-analyses in rats and mice), this was not done on all variables, and behavioral variables were not included. In addition, across-sex variability may very well be different, which, in turn, would result in statistical sex significance. In addition, on some measures, there are sex differences in variability, as human males have more variability in grey matter volume than females. PMID: 33044802. 

The manuscript was highlighting the common argument used to exclude the use of females, which is that females are inherently more variable as an absolute truth. We agree there might be situations, where the variance is higher in one sex or another depending on the biology.  We have extended the discussion here to reflect this, and we also linked to the Sex Inclusive Research Framework (3) which highlights that in these situations researchers can utlise this argument provided it is supported with data for the biology of interest. 

(3) The authors need to acknowledge that it can be important that the sample size is increased when examining more than one sex. If the sample size is too low for biological research, it will not be possible to determine whether or not a difference exists. Using statistical modelling, researchers have found that depending on the effect size, the sample size does need to increase. It is important to bare this in mind as exploratory analyses with small sample size will be extremely limiting and may also discourage further study in this area (or indeed as seen the literature - an exploratory first study with the use of males and females with limited sample size, only to show there is no "significance" and to justify this as an reason to only use males for the further studies in the work. 

The reviewer raises a common problem: where researchers have frequently argued that if they find no sex differences in a pilot then they can proceed to study only one sex. The SAGER guidelines (1), and now funder guidelines (4, 5), challenge that position. Instead, the expectation is for inclusion as the default in all experiments (exploratory inclusion strategy) to allow generalisable results to be obtained. When the results are very different between the male and female samples, then this can be determined. This perspective shift (2) requires a change in mindset and understanding that the driver behind inclusion is of generalisability not exploration of sex differences. This has been added to the introduction as an additional paragraph exploring the drivers behind inclusion.  

We agree with the reviewer that if the researcher is interested in sex differences in an effect (confirmatory inclusion strategy, aka sex as a primary variable) then the N will need to be higher.  However, in this situation, one, of course, must have male and female samples in the same experiment to allow the simultaneous exploration to assess the dependency on sex. 

Reviewer #2 (Public review): 

Summary:

The investigators tested a workshop intervention to improve knowledge and decrease misconceptions about sex inclusive research. There were important findings that demonstrate the difficulty in changing opinions and knowledge about the importance of studying both males and females. While interventions can improve knowledge and decrease perceived barriers, the impact was small. 

Strengths:

The investigators included control groups and replicated the study in a second population of scientists. The results appear to be well substantiated. These are valuable findings that have practical implications for fields where sex is included as a biological variable to improve rigor and reproducibility. 

Thank you for assessment and highlighting these strengths.  We appreciate your recognition of the value and practical implications of this work. 

Weaknesses:

I found the figures difficult to understand and would have appreciated more explanation of what is depicted, as well as greater space between the bars representing different categories. 

We have improved the figures and figure legends to improve clarity. 

Reviewer #3 (Public review):

Summary:

This manuscript aims to determine cultural biases and misconceptions in inclusive sex research and evaluate the efficacy of interventions to improve knowledge and shift perceptions to decrease perceived barriers for including both sexes in basic research. 

Overall, this study demonstrates that despite the intention to include both sexes and a general belief in the importance of doing so, relatively few people routinely include both sexes. Further, the perceptions of barriers to doing so are high, including misconceptions surrounding sample size, disaggregation, and variability of females. There was also a substantial number of individuals without the statistical knowledge to appropriately analyze data in studies inclusive of sex. Interventions increased knowledge and decreased perception of barriers. 

Strengths:

(1) This manuscript provides evidence for the efficacy of interventions for changing attitudes and perceptions of research.

(2) This manuscript also provides a training manual for expanding this intervention to broader groups of researchers.

Thank you for highlighting these strengths. We appreciate your recognition that the intervention was effect in changing attitudes and perception. We deliberately chose to share the material to provide the resources to allow a wider engagement.  

Weaknesses:

The major weakness here is that the post-workshop assessment is a single time point, soon after the intervention. As this paper shows, intention for these individuals is already high, so does decreasing perception of barriers and increasing knowledge change behavior, and increase the number of studies that include both sexes? Similarly, does the intervention start to shift cultural factors? Do these contribute to a change in behavior? 

Measuring change in behaviour following an intervention is challenging and hence we had implemented an intention score as a proxy for behaviour. We appreciate the benefit of a long-term analysis, but it was beyond the scope of this study and would need a larger dataset size to allow for attrition. We agree that the strategy implemented has weaknesses. We have extended the limitation section in the discussion to include these. 

Reviewer #1 (Recommendations for the authors):  

I would ask them to think about alternative explanations and ask for free-form responses, and to revise with the caveats written above - sample size does need to be increased depending on effect size, and that within sex studies are also important. Not all studies should focus on sex influences.  

The inclusion of the additional paragraph in the introduction to clarify the objective of inclusion and the resulting impact on experimental design should address these recommendations.   

We have also added the free-form responses as an additional supplementary file.  

Reviewer #2 (Recommendations for the authors):  

This is an important set of studies. My only recommendation to improve the data presentation so that it is clear what is depicted and how the analyses were conducted. I know it is in the methods, but reminding the reader would be helpful.  

We have revisited the figures and included more information in the legends to explain the analysis and improve clarity.   

Reviewer #3 (Recommendations for the authors):  

There are parts in the introduction which read as contradictory and as such are confusing - for example, in the 3rd paragraph it states that little progress on sex inclusive research has been made, and in the following sentences it states that the proportion of published studies across sex has improved. The references in these two statements are from the same time range, so has this improved? Or not?  

The introduction does include a summation statement on the position: “Whilst a positive step forward, this proportion still represents a minority of studies, and notably this inclusion was not associated with an increase in the proportion of studies that included data analysed by sex.” We have reworded the text to ensure it is internally consistent with this summary statement and this should increase clarity.

In discussing the results, it is sometimes confusing what the percentages mean. For example, "the researchers reported only conducting sex inclusive research in <=55% of their studies over the past 5 years (55% in study 1 general population and 35% study 2 pre-assessment)." Does that mean 55% of people are conducting sex inclusive research, or does this mean only half of their studies? These two options have very different implications.

We agree that the sentence is confusing and it has been reworded.  

Addressing long-term assessments in attitude and action (ie, performing sex inclusive research) is a crucial addition, with data if possible, but at least substantive discussion.  

We have add this to the limitation section in the discussion

One minor but confusing point is the analogy comparing sex inclusive studies with attending the gym. The point is well taken - knowledge is not enough for behavior change. However, the argument here is that to increase sex inclusive research requires cultural change. To go to the gym, requires motivation.This seems like an oranges-to-lemons comparison (same family, different outcome when you bite into it).

At the core, both scenarios involve the challenge of changing established habits and cultural norms in action based on knowledge (the right thing to do). The exercise scenario is a primary example provided by the original authors to describe how aspects of the theory of planned behaviour (perceived behavioural control, attitude, and social norms) may influence behavioural change. Understanding which of these aspects may drive or influence change is why we used this framework to understand our study population.  We disagree that is an oranges-to-lemons comparison.

References

(1) Heidari S, Babor TF, De Castro P, Tort S, Curno M. Sex and Gender Equity in Research: rationale for the SAGER guidelines and recommended use. Res Integr Peer Rev. 2016;1:2.

(2) Karp NA. Navigating the paradigm shift of sex inclusive preclinical research and lessons learnt. Commun Biol. 2025;8(1):681.

(3) Karp NA, Berdoy M, Gray K, Hunt L, Jennings M, Kerton A, et al. The Sex Inclusive Research Framework to address sex bias in preclinical research proposals. Nat Commun. 2025;16(1):3763.

(4) MRC. Sex in experimental design - Guidance on new requirements https://www.ukri.org/councils/mrc/guidance-for-applicants/policies-and-guidance-forresearchers/sex-in-experimental-design/: UK Research and Innovation; 2022 [

(5) Clayton JA, Collins FS. Policy: NIH to balance sex in cell and animal studies. Nature. 2014;509(7500):282-3.

This approach not always work with queries like:

We should kick off the beta on July 5th, then wrap up testing roughly two weeks after that.

Problem: if it's December of year 2025 already, it's going to still return you July 5th 2025.

You have to additionally instruct it with stuff like:

If a date/time is mentioned without day/weekday/month/year and that date/time has already passed relative to the current date/time, assume it refers to the same date/time in the next day/weekday/month/year.

eLife Assessment

This valuable study reports a critical role of the axonemal protein ANKRD5 in sperm motility and male fertility. Convincing data were presented to support the main conclusion. This work will be of interest to biomedical researchers who study ciliogenesis, sperm biology, and male fertility.

Reviewer #1 (Public review):

Summary:

Asthenospermia, characterized by reduced sperm motility, is one of the major causes of male infertility. The "9 + 2" arranged MTs and over 200 associated proteins constitute the axoneme, the molecular machine for flagellar and ciliary motility. Understanding the physiological functions of axonemal proteins, particularly their links to male infertility, could help uncover the genetic causes of asthenospermia and improve its clinical diagnosis and management. In this study, the authors generated Ankrd5 null mice and found that ANKRD5-/- males exhibited reduced sperm motility and infertility. Using FLAG-tagged ANKRD5 mice, mass spectrometry, and immunoprecipitation (IP) analyses, they confirmed that ANKRD5 is localized within the N-DRC, a critical protein complex for normal flagellar motility. However, transmission electron microscopy (TEM) and cryo-electron tomography (cryo-ET) of sperm from Ankrd5 null mice did not reveal significant structural abnormalities.

Strengths:

The phenotypes observed in ANKRD5-/- mice, including reduced sperm motility and male infertility, are conversing. The authors demonstrated that ANKRD5 is an N-DRC protein that interacts with TCTE1 and DRC4. Most of the experiments are well-designed and executed.

Comments on revised version:

My concerns have been addressed.

Reviewer #2 (Public review):

Summary:

The manuscript investigates the role of ANKRD5 (ANKEF1) as a component of the N-DRC complex in sperm motility and male fertility. Using Ankrd5 knockout mice, the study demonstrates that ANKRD5 is essential for sperm motility and identifies its interaction with N-DRC components through IP-mass spectrometry and cryo-ET. The results provide insights into ANKRD5's function, highlighting its potential involvement in axoneme stability and sperm energy metabolism.

Strengths:

The authors employ a wide range of techniques, including gene knockout models, proteomics, cryo-ET, and immunoprecipitation, to explore ANKRD5's role in sperm biology.

Comments on revised version:

The authors have already addressed the issues I am concerned about.

Author response:

The following is the authors’ response to the previous reviews

Reviewer #1 (Public review):

Summary:

Asthenospermia, characterized by reduced sperm motility, is one of the major causes of male infertility. The "9 + 2" arranged MTs and over 200 associated proteins constitute the axoneme, the molecular machine for flagellar and ciliary motility. Understanding the physiological functions of axonemal proteins, particularly their links to male infertility, could help uncover the genetic causes of asthenospermia and improve its clinical diagnosis and management. In this study, the authors generated Ankrd5 null mice and found that ANKRD5-/- males exhibited reduced sperm motility and infertility. Using FLAG-tagged ANKRD5 mice, mass spectrometry, and immunoprecipitation (IP) analyses, they confirmed that ANKRD5 is localized within the N-DRC, a critical protein complex for normal flagellar motility. However, transmission electron microscopy (TEM) and cryo-electron tomography (cryo-ET) of sperm from Ankrd5 null mice did not reveal significant structural abnormalities.

Strengths:

The phenotypes observed in ANKRD5-/- mice, including reduced sperm motility and male infertility, are conversing. The authors demonstrated that ANKRD5 is an N-DRC protein that interacts with TCTE1 and DRC4. Most of the experiments are well designed and executed.

Weaknesses:

The last section of cryo-ET analysis is not convincing. "ANKRD5 depletion may impair buffering effect between adjacent DMTs in the axoneme".

"In WT sperm, DMTs typically appeared circular, whereas ANKRD5-KO DMTs seemed to be extruded as polygonal. (Fig. S9B,D). ANKRD5-KO DMTs seemed partially open at the junction between the A- and B-tubes (Fig. S9B,D)." In the TEM images of 4E, ANKRD5-KO DMTs look the same as WT. The distortion could result from suboptimal sample preparation, imaging or data processing. Thus, the subsequent analyses and conclusions are not reliable.

Thank you for your valuable advice. To validate the results of cryo-ET, we carefully analyzed the TEM results (previously we only focused on the global "9+2" structure of the axial filament) and found that deletion of ANKRD5 resulted in both normal and deformed DMT morphologies, which was consistent with the results observed by cryo-ET. At the same time, we have added the corresponding text and picture descriptions in the article:

The text description we added is: “Upon re-examining the TEM data in light of the Cryo-ET findings, similar abnormalities were observed in the TEM images (Fig.4E, Fig. S10B). Notably, both intact and deformed DMT structures were consistently observed in both TEM and STA analyses, with the deformation of the B-tube being more obvious (Fig.4E, Fig. S10). ”

This paper still requires significant improvements in writing and language refinement. Here is an example: "While N-DRC is critical for sperm motility, but the existence of additional regulators that coordinate its function remains unclear" - ill-formed sentences.

We appreciate the reviewer’s valuable comment regarding the clarity of our writing. The sentence cited (“While N-DRC is critical for sperm motility, but the existence of additional regulators that coordinate its function remains unclear”) was indeed ill-formed. We have revised it to improve readability and precision. The corrected version now reads:“Although the N-DRC is critical for sperm motility, whether additional regulatory components coordinate its function remains unclear.” We have carefully re-examined the manuscript and refined the language throughout to ensure clarity and conciseness.

Reviewer #2 (Public review):

Summary:

The manuscript investigates the role of ANKRD5 (ANKEF1) as a component of the N-DRC complex in sperm motility and male fertility. Using Ankrd5 knockout mice, the study demonstrates that ANKRD5 is essential for sperm motility and identifies its interaction with N-DRC components through IP-mass spectrometry and cryo-ET. The results provide insights into ANKRD5's function, highlighting its potential involvement in axoneme stability and sperm energy metabolism.

Strengths:

The authors employ a wide range of techniques, including gene knockout models, proteomics, cryo-ET, and immunoprecipitation, to explore ANKRD5's role in sperm biology.

Weaknesses:

“Limited Citations in Introduction: Key references on the role of N-DRC components (e.g.,DRC2, DRC4) in male infertility are missing, which weakens the contextual background.”

We appreciate the reviewer’s valuable suggestion. To address this concern, we have added the following sentence in the Introduction:

“Recent mammalian knockout studies further confirmed that loss of DRC2 or DRC4 results in severe sperm flagellar assembly defects, multiple morphological abnormalities of the sperm flagella (MMAF), and complete male infertility, highlighting their indispensable roles in spermatogenesis and reproduction [31].”

This addition introduces up-to-date evidence on DRC2 and DRC4 functions in male infertility and strengthens the contextual background as recommended.

Reviewer #1 (Recommendations for the authors):

"Male infertility impacts 8%-12% of the global male population, with sperm motility defects contributing to 40%-50% of these cases [2，3]. " Is reference 3 proper? I don't see "sperm motility defects contributing to 40%-50%" of male infertility.

Thank you for identifying this issue. You are correct—reference 3 does not support the statement about sperm motility defects comprising 40–50% of male infertility cases; it actually states:

“Male factor infertility is when an issue with the man’s biology makes him unable to impregnate a woman. It accounts for between 40 to 50 percent of infertility cases and affects around 7 percent of men.”

This was a misunderstanding on my part, and I apologize for the oversight.

To correct this, we have replaced the statement with more accurate references:

PMID: 33968937 confirms:

“Asthenozoospermia accounts for over 80% of primary male infertility cases.”

PMID: 33191078 defines asthenozoospermia (AZS) as reduced or absent sperm motility and notes it as a major cause of male infertility.

We have updated the manuscript accordingly:

In the Significance Statement: “Male infertility affects approximately 8%-12% of men globally, with defects in sperm motility accounting for over 80% of these cases.”

In the Introduction: “Male infertility affects approximately 8% to 12% of the global male population, with defects in sperm motility accounting for over 80% of these cases[2,3].”

Thank you again for your careful review and for giving us the opportunity to improve the accuracy of our manuscript.

"Rather than bypassing the issue with ICSI, infertility from poor sperm motility could potentially be treated or even cured through stimulation of specific signaling pathways or gene therapy." Need references.

We appreciate the reviewer’s insightful comment. In response, we have added three supporting references to the relevant sentence.

The first reference (PMID: 39932044) demonstrates that cBiMPs and the PDE-10A inhibitor TAK-063 significantly and sustainably improve motility in human sperm with low activity, including cryopreserved samples, without inducing premature acrosome reaction or DNA damage. The second reference (PMID: 29581387) shows that activation of the PKA/PI3K/Ca²⁺ signaling pathways can reverse reduced sperm motility. The third reference (PMID: 33533741) reports that CRISPR-Cas9-mediated correction of a point mutation in Tex11^PM/Y spermatogonial stem cells (SSCs) restores spermatogenesis in mice and results in the production of fertile offspring.

These references provide mechanistic support and demonstrate the feasibility of treating poor sperm motility through targeted pathway modulation or gene therapy, thus reinforcing the validity of our statement.

"Our findings indicate that ANKRD5 (Ankyrin repeat domain 5; also known as ANK5 or ANKEF1) interacts with N-DRC structure". The full name should be provided the first time ANKRD5 appears. Is ANKRD5 a component of N-DRC or does it interact with N-DRC?

We thank the reviewer for the valuable suggestion. In response, we have moved the full name “Ankyrin repeat domain 5; also known as ANK5 or ANKEF1” to the abstract where ANKRD5 first appears, and have removed the redundant mention from the main text.

Based on our experimental data, we consider ANKRD5 to be a novel component of the N-DRC (nexin-dynein regulatory complex), rather than merely an interacting partner. Therefore, we have revised the sentence in the main text to read:

“Here, we demonstrate that ANKRD5 is a novel N-DRC component essential for maintaining sperm motility.”

Fig 5E, numbers of TEM images should be added.

We thank the reviewer for the suggestion. We would like to clarify that Fig. 5E does not contain TEM images, and it is likely that the reviewer was referring to Fig. 4E instead.

In Fig. 4E, we conducted three independent experiments. In each experiment, 60 TEM cross-sectional images of sperm tails were analyzed for both Ankrd5 knockout and control mice.

The findings were consistent across all replicates.

We have updated the figure legend accordingly, which now reads:

“Transmission electron microscopy (TEM) of sperm tails from control and Ankrd5 KO mice. Cross-sections of the midpiece, principal piece, and end piece were examined. Red dashed boxes highlight regions of interest, and the magnified views of these boxed areas are shown in the upper right corner of each image. In three independent experiments, 20 sperm cross-sections per mouse were analyzed for each group, with consistent results observed.”

There are random "222" in the references. Please check and correct.

I sincerely apologize for the errors caused by the reference management software, which resulted in the insertion of random "222" and similar numbering issues in the reference list. I have carefully reviewed and corrected the following problems:

References 9, 11, 13, 26, 34, 63, and 64 had the number "222" mistakenly placed before the title; these have now been removed. References 15 and 18 had "111" incorrectly inserted before the title; this has also been corrected. Reference 36 had an erroneous "2" before the title and was found to be a duplicate of Reference 32; these have now been merged into a single citation. Additionally, References 22 and 26 were identified as duplicates of the same article and have been consolidated accordingly. 

All these issues have been resolved to ensure the reference list is accurate and properly formatted.

Reviewer #2 (Recommendations for the authors):

The authors have already addressed most of the issues I am concerned about.

In addition, we have also corrected some errors in the revised manuscript:

(1) In Figure 3G, the y-axis label was previously marked as “Sperm count in the oviduct (10⁶)”, which has now been corrected to “Sperm count in the oviduct”.

(2) All p-values have been reformatted to italic lowercase letters to comply with the journal style guidelines.

Figure 6 Legend: A typographical error in the figure legend has been corrected. The text previously read “(A) The differentially expressed proteins of Ankrd5^+/– and Ankrd5^+/- were identified...”. This has now been amended to “(A) The differentially expressed proteins of Ankrd5^+/– and Ankrd5^+/– were identified...” to correctly represent the comparison between heterozygous and homozygous knockout groups.

In the original Figure 4E, we added a zoom-in panel to the image to show the deformed DMT.

eLife Assessment

This manuscript revisits the well-studied KdpFABC potassium transport system from bacteria with a convincing set of new higher resolution structures, a protein expression strategy that permits purification of the active wildtype protein, and insight obtained from mutagenesis and activity assays. The thorough and thoughtful mechanistic analyses make this a valuable contribution to the membrane transport field.

Reviewer #3 (Public review):

Summary:

By expressing protein in a strain that is unable to phosphorylate KdpFABC, the authors achieve structures of the active wildtype protein, capturing a new intermediate state, in which the terminal phosphoryl group of ATP has been transferred to a nearby Asp, and ADP remains covalently bound. The manuscript examines the coupling of potassium transport and ATP hydrolysis by a comprehensive set of mutants. The most interesting proposal revolves around the proposed binding site for K+ as it exits the channel near T75. Nearby mutations to charged residues cause interesting phenotypes, such as constitutive uncoupled ATPase activity, leading to a model in which lysine residues can occupy/compete with K+ for binding sites along the transport pathway.

Strengths:

The high resolution (2.1 Å) of the current structure is impressive, and allows many new densities in the potassium transport pathway to be resolved. The authors are judicious about assigning these as potassium ions or water molecules, and explain their structural interpretations clearly. In addition to the nice structural work, the mechanistic work is thorough. A series of thoughtful experiments involving ATP hydrolysis/transport coupling under various pH and potassium concentrations bolsters the structural interpretations and lends convincing support to the mechanistic proposal. The SSME experiments are rigorous.

Author response:

The following is the authors’ response to the previous reviews

Reviewer #2 (Public review): 

Summary: 

The paper describes the high-resolution structure of KdpFABC, a bacterial pump regulating intracellular potassium concentrations. The pump consists of a subunit with an overall structure similar to that of a canonical potassium channel and a subunit with a structure similar to a canonical ATP-driven ion pump. The ions enter through the channel subunit and then traverse the subunit interface via a long channel that lies parallel to the membrane to enter the pump, followed by their release into the cytoplasm. 

The work builds on the previous structural and mechanistic studies from the authors' and other labs. While the overall architecture and mechanism have already been established, a detailed understanding was lacking. The study provides a 2.1 Å resolution structure of the E1-P state of the transport cycle, which precedes the transition to the E2 state, assumed to be the ratelimiting step. It clearly shows a single K+ ion in the selectivity filter of the channel and in the canonical ion binding site in the pump, resolving how ions bind to these key regions of the transporter. It also resolves the details of water molecules filling the tunnel that connects the subunits, suggesting that K+ ions move through the tunnel transiently without occupying welldefined binding sites. The authors further propose how the ions are released into the cytoplasm in the E2 state. The authors support the structural findings through mutagenesis and measurements of ATPase activity and ion transport by surface-supported membrane (SSM) electrophysiology. 

Reviewer #3 (Public review): 

Summary: 

By expressing protein in a strain that is unable to phosphorylate KdpFABC, the authors achieve structures of the active wildtype protein, capturing a new intermediate state, in which the terminal phosphoryl group of ATP has been transferred to a nearby Asp, and ADP remains covalently bound. The manuscript examines the coupling of potassium transport and ATP hydrolysis by a comprehensive set of mutants. The most interesting proposal revolves around the proposed binding site for K+ as it exits the channel near T75. Nearby mutations to charged residues cause interesting phenotypes, such as constitutive uncoupled ATPase activity, leading to a model in which lysine residues can occupy/compete with K+ for binding sites along the transport pathway. 

Strengths: 

The high resolution (2.1 Å) of the current structure is impressive, and allows many new densities in the potassium transport pathway to be resolved. The authors are judicious about assigning these as potassium ions or water molecules, and explain their structural interpretations clearly. In addition to the nice structural work, the mechanistic work is thorough. A series of thoughtful experiments involving ATP hydrolysis/transport coupling under various pH and potassium concentrations bolsters the structural interpretations and lends convincing support to the mechanistic proposal. The SSME experiments are generally rigorous. 

Weaknesses: 

The present SSME experiments do not support quantitative comparisons of different mutants, as in Figures 4D and 5E. Only qualitative inferences can be drawn among different mutant constructs. 

Thank you to both reviewers for your thorough review of our work. We acknowledge the limitations of SSME experiments in quantitative comparison of mutants and have revised the manuscript to address this point. In addition, we have included new ATPase data from reconstituted vesicles which we believe will help to strengthen our contention that both ATPase and transport are equally affected by Val496 mutations.

Reviewer #2 (Recommendations for the authors): 

I have a minor editorial comment: 

Perhaps I am confused. However, in reference to the text in the Results: "Our WT complex displayed high levels of K+-dependent ATPase activity and generated robust transport currents (Fig. 1 - figure suppl. 1).", I do not see either K+-dependency of ATPase activity nor transport currents in Fig. 1 - figure suppl. 1. Perhaps the text needs to be edited for clarity. 

Thank you for pointing this out. This confusion was caused by our removal of a panel from the revised manuscript, which depicted K+-dependent transport currents. Although this panel is somewhat redundant, given inclusion of raw SSME traces from all the mutants, it has been replaced as Fig. 1 - figure supplement 1F, thus providing a thorough characterization of the preparation used for cryo-EM analysis and supporting the statement quoted by this reviewer.

Reviewer #3 (Recommendations for the authors): 

The authors have provided a detailed description of the SSME data collection, and followed rigorous protocols to ensure that the currents measured on a particular sensor remained stable over time. 

I still have reservations about the direct comparison of transport in the different mutants. Specifically, on page 6, the authors state that "The longer side chain of V496M reduces transport modestly with no effect on ATPase activity. V496R, which introduces positive charge, completely abolishes activity. V496W and V496H reduce both transport and ATPase activity by about half, perhaps due to steric hindrance for the former and partial protonation for the latter." And in figures 4D and 5B, by plotting all of the peak currents on the same graph, the authors are giving the data a quantitative veneer, when these different experiments really aren't directly comparable, especially in the absence of any controls for reconstitution efficiency. 

In terms of overall conclusions, for the more drastic mutant phenotypes, I think it is completely reasonable to conclude that transport is not observed. But a 2-fold difference could easily result from differences in reconstitution or sensor preparation. My suggestion would be to show example traces rather than a numeric plot in 4D/5E, to convey the qualitative nature of the mutant-to-mutant comparisons, and to re-write the text to acknowledge the shortcomings of mutant-to-mutant comparisons with SSME, and avoid commenting on the more subtle phenotypes, such as modest decreases and reductions by about half. 

Figure 4, supplement 1. What is S162D? I don't think it is mentioned in the main text. 

We agree with the reviewer's point that quantitative comparison of different mutants by SSME is compromised by ambiguity in reconstitution. However, we do not think that display of raw SSME currents is an effective way to communicate qualitative effects to the general reader, given the complexity of these data (e.g., distinction between transient binding current seen in V496R and genuine, steady-state transport current seen in WT). So we have taken a compromise approach. To start, we have removed the transport data from the main figure (Fig. 4). Luckily, we had frozen and saved the batch of reconstituted proteoliposomes from Val496 mutants that had been used for transport assays. We therefore measured ATPase activities from these proteoliposomes - after adding a small amount of detergent to prevent buildup of electrochemical gradients (1 mg/ml decylmaltoside which is only slightly more than the critical micelle concentration of 0.87 mg/ml). Differences in ATPase activity from these proteoliposomes were very similar to those measured prior to reconstitution (i.e., data in Fig. 4d) indicating that reconstitution efficiencies were comparable for the various mutants. Furthermore, differences in SSME currents are very similar to these ATPase activities, suggesting that Val496 mutants did not affect energy coupling. These data are shown in the revised Fig. 4 - figure suppl. 1a, along with the SSME raw data and size-exclusion chromatography elution profiles (Fig. 4 - figure suppl. 1b-g). We also altered the text to point out the concern over comparing transport data from different mutants (see below). We hope that this revised presentation adequately supports the conclusion that Val496 mutations - and especially the V496R substitution - influence the passage of K+ through the tunnel without affecting mechanics of the ATP-dependent pump. 

The paragraph in question now reads as follows (pg. 6-7, with additional changes to legends to Fig. 4 and Fig. 4 - figure suppl. 1):

"In order to provide experimental evidence for K+ transport through the tunnel, we made a series of substitutions to Val496 in KdpA. This residue resides near the widest part of the tunnel and is fully exposed to its interior (Fig. 4a). We made substitutions to increase its bulk (V496M and V496W) and to introduce charge (V496E, V496R and V496H). We used the AlphaFold-3 artificial intelligence structure prediction program (Jumper et al., 2021) to generate structures of these mutants and to evaluate their potential impact on tunnel dimensions. This analysis predicts that V496W and V496R reduce the radius to well below the 1.4 Å threshold required for passage of K+ or water (Fig. 4c); V496E and V496M also constrict the tunnel, but to a lesser extent. Measurements of ATPase and transport activity (Fig. 4d) show that negative charge (V496E) has no effect. The or a longer side chain of (V496M) reduces transport modestly with have no apparent effect on ATPase activity. V496R, which introduces positive charge, almost completely abolishes activity. V496W and V496H reduce both transport and ATPase activity by about half, perhaps due to steric hindrance for the former and partial protonation for the latter. Transport activity of these mutants was also measured, but quantitative comparisons are hampered by potential inconsistency in reconstitution of proteoliposomes and in preparation of sensors for SSME. To account for differences in reconstitution, we compared ATPase activity and transport currents taken from the same batch of vesicles (Fig. 4 - figure suppl. 1a).  These data show that differences in ATPase activity of proteoliposomes was consistent with differences measured prior to reconstitution (Fig. 4d). Transport activity, which was derived from multiple sensors, mirrored ATPase activity, indicating that the Val496 mutants did not affect energy coupling, but simply modulated turnover rate of the pump."

S162D was included as a negative control, together with D307A. However, given the inactive mutants discussed in Fig. 5 (Asp582 and Lys586 substitutions), these seem an unnecessary distraction and have been removed from Fig. 4 - figure suppl. 1.

So powerful and instructive.

(of a cancer) spread to other sites in the body

Such a powerful line that holds strong elements of truth.

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

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Reply to the reviewers

General Statements

We would like to thank the referees for their time and effort in giving feedback on our work, and their overall positive attitude towards the manuscript. Most of the referees' points were of clarifying and textual nature. We have identified three points which we think require more attention in the form of additional analyses, simulations or significant textual changes:

Within the manuscript we state that conserved non coding sequences (CNSs) are a proxy for cis regulatory elements (CREs). We proceed to use these terms interchangeably without explaining the underlying assumption, which is inaccurate. To improve on this point we ensured in the new text that we are explicit about when we mean CNS or CRE. Secondly, we added a section to the discussion (‘Limitations of CNSs as CREs’) dedicated to this topic. During stabilising selection (maintaining the target phenotype) DSD can occur fully neutrally, or through the evolution of either mutational or developmental robustness. We describe the evolutionary trajectories of our simulations as neutral once fitness mostly plateaued; however, as reviewer 3 points out, small gains in median fitness still occur, indicating that either development becomes more robust to noisy gene expression and tissue variation, and/or the GRNs become more robust to mutations. To discern between fully neutral evolution where the fitness distribution of the population does not change, and the higher-order emergence of robustness, we performed additional analysis of the given results. Preliminary results showed that many (near-)neutral mutations affect the mutational robustness and developmental robustness, both positively and negatively. To investigate this further we will run an additional set of simulations without developmental stochasticity, which will take about a week. These simulations should allow us to more closely examine the role of stabilising selection (of developmental robustness) in DSD by removing the need to evolve developmental robustness. Additionally, we will set up simulations in which we changed the total number of genes, and the number of genes under selection to investigate how this modelling choice influences DSD. In the section on rewiring (‘Network redundancy creates space for rewiring’) we will analyse the mechanism allowing for rewiring in more depth, especially in the light of gene duplications and redundancy. We will extend this section with an additional analysis aimed to highlight how and when rewiring is facilitated. We will describe the planned and incorporated revisions in detail below; we believe these have led to a greatly improved manuscript.

Kind regards,

Pjotr van der Jagt, Steven Oud and Renske Vroomans

Description of the planned revisions

Referee cross commenting (Reviewer 4)

Reviewer 3's concern about DSD resulting from stabilising selection for robustness is something I missed -- this is important and should be addressed.

We understand this concern, and agree that we should be more thorough in our analysis of DSD by assessing the higher-order effects of stabilising selection on mutational robustness and/or environmental (developmental) robustness (McColgan & DiFrisco 2024).

We will 1) extend our analysis of fitness under DSD by computing the mutational and developmental robustness (similar to Figure 2F) over time for a number of ancestral lineages. By comparing these two measures over evolutionary time we will gain a much more fine grained image of the evolutionary dynamics and should be able to find adaptive trends through gain of either type of robustness. Preliminary results suggest that during the plateaued fitness phase both mutational robustness and developmental robustness undergo weak gains and losses, likely due to the pleiotropic nature of our GPM. Collectively, these weak gains and losses result in the gain observed in Figure S3. So, rather than fully neutral we should discern (near-)neutral regimes in which clear adaptive steps are absent, but in which the sum of them is a net gain. These are interesting findings we initially missed, and give insights into how this high-dimensional fitness landscape is traversed, and will be included in a future revised version of the manuscript.

2) We will run extra simulations without stochasticity to investigate DSD in the absence of adaptation through developmental robustness, and include the comparison between these and our original simulations in a future revised version.

Finally 3) we will address stabilising selection more prominently in the introduction and discussion to accommodate these additional simulations.

Reviewer 3 suggests that the model construction may favor DSD because there are many genes (14) of which only two determine fitness. I agree that some discussion on this point is warranted, though I am not sure enough is known about "the possible difference in constraints between the model and real development" for such a discussion to be on firm biological footing. A genetic architecture commonly found in quantitative genetic studies is that a small number of genes have large effects on the phenotype/fitness, whereas a very large number of genes have effects that are individually small but collectively large (see, e.g. literature surrounding the "omnigenic model" of complex traits). Implementing such an architecture is probably beyond the scope of the study here. More generally, would be natural to assume that the larger the number of genes, and the smaller the number of fitness-determining genes, the more likely DSD / re-wiring is to occur. That being said, I think the authors' choice of a 14-gene network is biologically defensible. It could be argued that the restriction of many modeling studies to small networks (often including just 3 genes) on the ground of convenience artificially ensures that DSD will not occur in these networks.

The choice of 14 genes does indeed stem from a compromise between constraining the number of available genes, but at the same time allowing for sufficient degrees of freedom and redundancy. We have added a ‘modelling choices’ section in the discussion in which we address this point. Additionally, it is important to note that, while the fitness criterion only measures the pattern of 2 genes, throughout the evolutionary lineage additional genes become highly important for the fitness of an individual, because these genes evolved to help generate the target pattern (see for example Figure 4); the other genes indeed reflect reviewer 4’s point that most genes have a small effect. Crucially, we observe that even the genes and interactions that are important for fitness undergo DSD.

Nevertheless, we think it is interesting to investigate this point of the influence of this particular modelling choice on the potential for DSD, and have set up an extra set of simulations with fewer gene types, and one with additional fitness genes.

Furthermore, we discuss the choice of our network architecture more in depth in a discussion section on our modelling choices: ‘Modelling assumptions and choices’.

Reviewer 1

The observation of DSD in the computational models remains rather high-level in the sense that no motifs, mechanisms, subgraphs, mutations or specific dynamics are reported to be associated to it ---with the exception of gene expression domains overlapping. Perhaps the authors feel it is beyond this study, but a Results section with a more in-depth "mechanistic" analysis on what enables DSD would (a) make a better case for the extensive and expensive computational models and (b) would push this paper to a next level. As a starting point, it could be nice to check Ohno's intuition that gene duplications are a creative "force" in evolution. Are they drivers of DSD? Or are TFBS mutations responsible for the majority of cases?

We agree that some mechanistic analysis would strengthen the manuscript, and will therefore extend the section ‘Network redundancy creates space for rewiring’ to address how this redundancy is facilitated. For instance, in the rewiring examples given in Figure 4 we can highlight how this new interaction emerges, if this is through a gene mutation followed by rewiring and loss of a redundant gene, or if the gain, redundancy and loss are all on the level of TFBS mutations. Effectively we will investigate which route of the three in the following schematic is most prominent:

Additionally, we will do analysis on the different effects of the transcription dynamics for each of these routes. (note that this is not an exhaustive schematic, and combinations could be possible).

l171. You discuss an example here, would it be possible to generalize this analysis and quantify the amount of DSD amongst all cloned populations? And related question: of the many conserved interactions in Fig 4A, how many do the two clonal lineages share? None? All?

We agree that this is a good idea. In a new supplementary figure, we will show the number of times a conserved interaction gets lost, and a new interaction is gained as a metric for DSD in every cloned population.

The populations in Fig 4A are cloned at generation 50.000, any interaction starting before then and still present at a point in time is shared. Any interactions starting after 50.000 are unique (or independently gained at least).

- l269. What about phenotypic plasticity due to stochastic gene expression? Does it play a role in DSD in your model? I am thinking about https://pubmed.ncbi.nlm.nih.gov/24884746/ and https://pubmed.ncbi.nlm.nih.gov/21211007/

We agree that this is an interesting point which should be included into the discussion. Following the comments of reviewer 3 we have set up extra simulations to investigate this in more detail, we will make sure to include these citations in the revised discussion when we have the results of those simulations.

Reviewer 3

Issue One: Interpretation of fitness gains under stabilising selection

A central issue concerns how the manuscript defines and interprets developmental systems drift (DSD) in relation to evolution on the fitness landscape. The authors define DSD as the conservation of a trait despite changes in its underlying genetic basis, which is consistent with the literature. However, the manuscript would benefit from clarifying the relationship between DSD, genotype-to-phenotype maps, and fitness landscapes. Very simply, we can say that (i) DSD can operate along neutral paths in the fitness landscape, (ii) DSD can operate along adaptive paths in the fitness landscape. During DSD, these neutral or adaptive paths along the fitness landscape are traversed by mutations that change the gene regulatory network (GRN) and consequent gene expression patterns whilst preserving the developmental outcome, i.e., the phenotype. While this connection between DSD and fitness landscapes is referenced in the introduction, it is not fully elaborated upon. A complete elaboration is critical because, when I read the manuscript, I got the impression that the manuscript claims that DSD is prevalent along neutral paths in the fitness landscape, not just adaptive ones. If I am wrong and this is not what the authors claim, it should be explicitly stated in the results and discussed. Nevertheless, claiming DSD operates along neutral paths is a much more interesting statement than claiming it operates along adaptive paths. However, it requires sufficient evidence, which I have an issue with.

The issue I have is about adaptations under stabilising selection. Stabilising selection occurs when there is selection to preserve the developmental outcome. Stabilising selection is essential to the results because evolutionary change in the GRN under stabilising selection should be due to DSD, not adaptations that change the developmental outcome. To ensure that the populations are under stabilising selection, the authors perform clonal experiments for 100,000 generations for 8 already evolved populations, 5 clones for each population. They remove 10 out of 40 clones because the fitness increase is too large, indicating that the developmental outcome changes over the 100,000 generations. However, the remaining 30 clonal experiments exhibit small but continual fitness increases over 100,000 generations. The authors claim that the remaining 30 are predominantly evolving due to drift, not adaptations (in the main text, line 137: "indicating predominantly neutral evolution", and section M: "too shallow for selection to outweigh drift"). The author's evidence for this claim is a mathematical analysis showing that the fitness gains are too small to be caused by beneficial adaptations, so evolution must be dominated by drift. I found this explanation strange, given that every clone unequivocally increases in fitness throughout the 100,000 generations, which suggests populations are adapting. Upon closer inspection of the mathematical analysis (section M), I believe it will miss many kinds of adaptations possible in their model, as I now describe.

The mathematical analysis treats fitness as a constant, but it's a random variable in the computational model. Fitness is a random variable because gene transcription and protein translation are stochastic (Wiener terms in Eqs. (1)-(5)) and cell positions change for each individual (Methods C). So, for a genotype G, the realised fitness F is picked from a distribution with mean μ_G and higher order moments (e.g., variance) that determine the shape of the distribution. I think these assumptions lead to two problems.

The first problem with the mathematical analysis is that F is replaced by an absolute number f_q, with beneficial mutations occurring in small increments denoted "a", representing an additive fitness advantage. The authors then take a time series of the median population fitness from their simulations and treat its slope as the individual's additive fitness advantage "a". The authors claim that drift dominates evolution because this slope is lower than a drift-selection barrier, which they derive from the mathematical analysis. This analysis ignores that the advantage "a" is a distribution, not a constant, which means that it does not pick up adaptations that change the shape of the distribution. Adaptations that change the shape of the distribution can be adaptations that increase robustness to stochasticity. Since there are multiple sources of noise in this model, I think it is highly likely that robustness to noise is selected for during these 100,000 generations.

The second problem is that the mathematical analysis ignores traits that have higher-order effects on fitness. A trait has higher-order effects when it increases the fitness of the lineage (e.g., offspring) but not the parent. One possible trait that can evolve in this model with higher-order effects is mutational robustness, i.e., traits that lower the expected mutational load of descendants. Since many kinds of mutations occur in this model (Table 2), mutational robustness may be also evolving.

Taken together, the analysis in Section M is set up to detect only immediate, deterministic additive gains in a single draw of fitness. It therefore cannot rule out weak but persistent adaptive evolution of robustness (to developmental noise and/or to mutations), and is thus insufficient evidence that DSD is occurring along neutral paths instead of adaptive paths. The small but monotonic fitness increases observed in all 40 clones are consistent with such adaptation (Fig. S3). The authors also acknowledge the evolution of robustness in lines 129-130 and 290-291, but the possibility of these adaptations driving DSD instead of neutral evolution is not discussed.

To address the issue I have with adaptations during stabilising selection, the authors should, at a minimum, state clearly in their results that DSD is driven by both the evolution of robustness and drift. Moreover, a paragraph in the discussion should be dedicated to why this is the case, and why it is challenging to separate DSD through neutral evolution vs DSD through adaptations such as those that increase robustness.

[OPTIONAL] A more thorough approach would be to make significant changes to the manuscript by giving sufficient evidence that the experimental clones are evolving by drift, or changing the model construction. One possible way to provide sufficient evidence is to improve the mathematical analysis. Another way is to show that the fitness distributions (both without and with mutations, like in Fig. 2F) do not significantly change throughout the 100,000 generations in experimental clones. It seems more likely that the model construction makes it difficult to separate the evolution of robustness from evolution by drift in the stabilising selection regime. Thus, I think the model should be constructed differently so that robustness against mutations and noise is much less likely to evolve after a "fitness plateau" is reached. This could be done by removing sources of noise from the model or reducing the kinds of possible mutations (related to issue two). In fact, I could not find justification in the manuscript for why these noise terms are included in the model, so I assume they are included for biological realism. If this is why noise is included, or if there is a separate reason why it is necessary, please write that in the model overview and/or the methods.

We agree that we should be more precise about whether DSD operates along neutral vs adaptive paths in the fitness landscape, and have expanded our explanation of this distinction in the introduction. We also agree that it is worthwhile to distinguish between neutral evolution that does not change the fitness distribution of the population (either through changes in developmental or mutational robustness), higher-order evolutionary processes that increase developmental robustness, and drift along a neutral path in the fitness landscape towards regions of greater connectivity, resulting in mutational robustness (as described in Huynen et al., 1999). We have performed a preliminary analysis to identify changes in mutational robustness and developmental robustness over evolutionary time in the populations in which the maximum fitness has already plateaued. This analysis shows frequent weak gains and losses, in which clear adaptive steps are absent but a net gain can be seen in robustness, as consistent with higher-order fitness effects.

To investigate the role of stabilising selection more in depth we will run simulations without developmental noise in the form of gene expression noise and tissue connectivity variation, thus removing the effect of the evolution of developmental robustness. We will compare the evolutionary dynamics of the GRNs with our original set of simulations, and include both these types of analyses in a supplementary figure of the revised manuscript.

Furthermore, we now discuss the limitations of the mathematical analysis with regard to adaptation vs neutrality in our simulations, in the supplementary section.

Issue two: The model construction may favour DSD

In this manuscript, fitness is determined by the expression pattern of two types of genes (genes 12 and 13 in Table 1). There are 14 types of genes in total that can all undergo many kinds of mutations, including duplications (Table 2). Thus, gene regulatory networks (GRNs) encoded by genomes in this model tend to contain large numbers of interactions. The results show that most of these interactions have minimal effect on reaching the target pattern in high fitness individuals (e.g. Fig. 2F). A consequence of this is that only a minimal number of GRN interactions are conserved through evolution (e.g. Fig. 2D). From these model constructions and results from evolutionary simulations, we can deduce that there are very few constraints on the GRN. By having very few constraints on the GRN, I think it makes it easy for a new set of pattern-producing traits to evolve and subsequently for an old set of pattern-producing traits to be lost, i.e., DSD. Thus, I believe that the model construction may favour DSD.

I do not have an issue with the model favouring DSD because it reflects real multicellular GRNs, where it is thought that a minority fraction of interactions are critical for fitness and the majority are not. However, it is unknown whether the constraints GRNs face in the model are more or less constrained than real GRNs. Thus, it is not known whether the prevalence of DSD in this model applies generally to real development, where GRN constraints depend on so many factors. At a minimum, the possible difference in constraints between the model and real development should be discussed as a limitation of the model. A more thorough change to the manuscript would be to test the effect of changing the constraints on the GRN. I am sure there are many ways to devise such a test, but I will give my recommendation here.

[OPTIONAL] My recommendation is that the authors should run additional simulations with simplified mutational dynamics by constraining the model to N genes (no duplications and deletions), of which M out of these N genes contribute to fitness via the specific pattern (with M=2 in the current model). The authors should then test the effect of changing N and M independently, and how this affects the prevalence of DSD. If the prevalence of DSD is robust to changes in N and M, it supports the authors argument that DSD is highly prevalent in developmental evolution. If DSD prevalence is highly dependent on M and/or N, then the claims made in the manuscript about the prevalence of DSD must change accordingly. I acknowledge that these simulations may be computationally expensive, and I think it would be great if the authors knew (or devised) a more efficient way to test the effect of GRN constraints on DSD prevalence. Nevertheless, these additional simulations would make for a potentially very interesting manuscript.

We agree that these modelling choices likely influence the potential for DSD. We think that our model setup, where most transcription factors are not under direct selection for a particular pattern, more accurately reflects biological development, where the outcome of the total developmental process (a functional organism) is what is under selection, rather than each individual gene pattern. As also mentioned by the referee, in real multicellular development the majority of interactions is not crucial for fitness, similar to our model. We also observe that, as fitness increases, additional genes experience emergent selection for particular expression patterns or interaction structures in the GRN, resulting in their conservation. Nevertheless, we do agree that the effect of model construction on DSD is an unexplored avenue and this work lends itself to addressing this. We will run additional sets of simulations: one in which we reduce the size of the network (‘N’), and a second set where we double the number of fitness contributing genes (‘M’), and show the effect on the extent of DSD in a future supplementary figure.

Description of the revisions that have already been incorporated in the transferred manuscript

Referee cross commenting (Reviewer 4)

Overall I agree with the comments of Reviewer 1, 2 and 3. I note that reviewers 1, 3, and 4 each pointed out the difficulties with assuming that CNSs = CREs, so this needs to be addressed. Two reviewers (3 and 4) also point out problems with equating bulk RNAseq with a conserved phenotype.

We agree that caution is warranted with the assumption of CNSs = CREs. We have added a section to the discussion in which we discuss this more thoroughly, see ‘Limitations of CNSs as CREs’ in the revised manuscript.

Additionally, we made textual changes to the statement of significance, abstract and results to better reflect when we talk about CNSs or CREs.

I agree with Reviewer 1's hesitancy about the rhetorical framing of the paper potentially generalising too far from a computational model of plant meristem patterning.

We agree that the title should reflect the scope of the manuscript, and our short title reflects that better than ubiquitous, which implies we investigated beyond plant (meristem) development. We have changed the title in the revised version, to ‘System drift in the evolution of plant meristem development’.

Reviewer 1

It is system drift, not systems drift (see True and Haag 2001). No 's' after system.

Thank you for catching this – we corrected this throughout.

- I am afraid I have a problem with the manuscript title. I think "Ubiquitoes" is misplaced, because it strongly suggests you have a long list of case studies across plants and animals, and some quantification of DSD in these two kingdoms. That would have been an interesting result, but it is not what you report. I suggest something along the lines of "System drift in the evolution of plant meristem development", similar to the short title used in the footer.

- Alternatively, the authors may aim to say that DSD happens all over the place in computational models of development? In that case the title should reflect that the claim refers to modeling. (But what then about the data analysis part?)

As remarked in the summary (point 2), we agree with this assessment and have changed the title to ‘System drift in the evolution of plant meristem development’’

Multiple times in the Abstract and Introduction the authors make statements on "cis-regulatory elements" that are actually "conserved non-coding sequences" (CNS). Even if it is not uncommon for CNSs to harbor enhancers etc., I would be very hesitant to use the two as synonyms. As the authors state themselves, sequences, even non-coding, can be conserved for many reasons other than CREs. I would ask the authors to support better their use of "CREs" or adjust language. As roughly stated in their Discussion (lines 310-319), one way forward could be to show for a few CNS that are important in the analysis (of Fig 5), that they have experimentally-verified enhancers. Is that do-able or a bridge too far?

We changed the text such that we use CNS instead of CRE when discussing the bioinformatic analysis. Additionally we added a section in the discussion to clarify the relationship between CNS and CRE.

line 7. evo-devo is jargon

We changed this to ‘…evolution of development (evo-devo) research…’

l9. I would think "using a computational model and data analysis"

Yes, corrected.

l13. Strictly speaking you did not look at CREs, but at conserved non-coding sequences.

Indeed, we changed this to CNS.

l14. "widespread" is exaggerated here, since you show for a single organ in a handful of plant species. You may extrapolate and argue that you do not see why it should not be widespread, but you did not show it. Or tie in all the known cases that can be found in literature.

We understand that ‘widespread’ seems to suggest that we have investigated a broader range of species and organs. To be more accurate we changed the wording to ‘prevalent’.

l16. "simpler" than what?

We added the example of RNA folding.

l27. Again the tension between CREs and non-coding sequence.

Changed to conserved non coding sequence.

l28. I don't understand the use of "necessarily" here.

This is indeed confusing and unnecessary, removed

l34-35. A very general biology statement is backed up by two modeling studies. I would have expected also a few based on comparative analyses (e.g., fossils, transcriptomics, etc).

We added extra citations and a discussion of more experimental work

l36. I was missing the work on "phenogenetic drift" by Weiss; and Pavlicev & Wagner 2012 on compensatory mutations.

Changed the text to:

This phenomenon is called developmental system drift (DSD) (True and Haag, 2001; McColgan and DiFrisco, 2024), or phenogenetic drift (Weiss and Fullerton, 2000), and can occur when multiple genotypes which are separated by few mutational steps encode the same phenotype, forming a neutral (Wagner, 2008a; Crombach et al., 2016); or adaptive path (Johnson and Porter, 2007; Pavlicev and Wagner, 2012) .

l38. Kimura and Wagner never had a developmental process in mind, which is much bigger than a single nucleotide or a single gene, respectively. First paper that I am aware of that explicitly connects DSD to evolution on genotype networks is my own work (Crombach 2016), since the editor of that article (True, of True and Haag 2001) highlighted that point in our communications.

Added citation and moved Kimura to the theoretical examples of protein folding DSD.

l40. While Hunynen and Hogeweg definitely studied the GP map in many of their works, the term goes back to Pere Alberch (1991).

Added citation.

l54-55. I'm missing some motivation here. If one wants to look at multicellular structures that display DSD, vulva development in C. elegans and related worms is an "old" and extremely well-studied example. Also, studies on early fly development by Yogi Jaeger and his co-workers are not multicellular, but at least multi-nuclear. Obviously these are animal-based results, so to me it would make sense to make a contrast animal-plant regarding DSD research and take it from there.

Indeed, DSD has been found in these species and we now reference some of this work; the principle is better known in animals. Nevertheless, within the theoretical literature there is a continuing debate on the importance/extent of DSD.

Changed text:

‘For other GPMs, such as those resulting from multicellular development, it has been suggested that complex phenotypes are sparsely distributed in genotype space, and have low potential for DSD because the number of neutral mutations anti-correlates with phenotypic complexity (Orr, 2000; Hagolani et al., 2021). On the other hand, theoretical and experimental studies in nematodes and fruit flies have shown that DSD is present in a phenotypically complex context (Verster et al., 2014; Crombach et al., 2016; Jaeger, 2018). It therefore remains debated how much DSD actually occurs in species undergoing multicellular development. DSD in plants has received little attention. One multicellular structure which …’

l66-86. It is a bit of a style-choice, but this is a looong summary of what is to come. I would not have done that. Instead, in the Introduction I would have expected a bit more digging into the concept of DSD, mention some of the old animal cases, perhaps summarize where in plants it should be expected. More context, basically.

We extended the paragraph on empirical examples of DSD by adding the animal cases and condensed our summary.

l108. Could you quantify the conserved interactions shared between the populations? Or is each simulation so different that they are pretty much unique?

Each simulation here is independent of the other simulations, so a per interaction comparison would be uninformative. After cloning they do share ancestry, but that is much later in the manuscript and here the quantification of the conserved interactions would be the inverse of the divergence as shown in, for instance Figure 3B.

l169. "DSD driving functional divergence" needs some context, since DSD is supposed to not affect function (of the final phenotype). Or am I misunderstanding?

This is indeed a confusing sentence. We mean to say that DSD allows for divergence to such an extent that the underlying functional pathway is changed. So instead of a mere substitution of the underlying network, in which the topology and relative functions stay conserved, a different network structure is found. We have modified the line to read “Taken together, we found that DSD can drive functional divergence in the underlying GRN resulting in novel spatial expression dynamics of the genes not directly under selection.”

l176. Say which interaction it is. Is it 0->8, as mentioned in the next paragraph?

It is indeed 0->8, we have clarified this in the text.

l197. Bulk RNAseq has the problem of averaging gene expression over the population of cells. How do you think that impacts your test for rewiring? If you would do a similar "bulk RNA" style test on your computational models, would you pick up DSD?

The rewiring is based on the CNSs, whereas the RNAseq is used as phenotype, so it does not impact the test for rewiring.

The averaging of bulk RNAseq does however, mean that we cannot show conservation/divergence of the phenotype within the tissues, only between the different tissues.

The most important implication of doing this in our model would be the definition of the ‘phenotype’ which undergoes DSD. Currently the phenotype is a gene expression pattern on a cellular level, for bulk RNA this phenotype would change to tissue-level gene expression.

This change in what we measure as phenotype implicates how we interpret our results, but would not hinder us in picking up DSD, it just has a different meaning than DSD on a cellular - and single tissue scale.

We added clarification of the roles of the datasets at the start of the paragraph.

‘The Conservatory Project collects conserved non-coding sequences (CNSs) across plant genomes, which we used to investigate the extent of GRN rewiring in flowering plants. Schuster et al. measured gene expression in different homologous tissues of several species via bulk RNAseq, which we used to test for gene expression (phenotype) conservation, and how this relates to the GRN rewiring inferred from the CNSs.’

l202. I do not understand the "within" of a non-coding sequence within an orthogroup. How are non-coding sequences inside an orthogroup of genes?

We clarify this sentence by saying ‘A CNS is defined as a non-coding sequence conserved within the upstream/downstream region of genes within an orthogroup’, to more clearly separate the CNS from the orthogroup of genes. We also updated Figure 5A to reflect this better.

l207-217. This paragraph is difficult to read and would benefit of a rephrasing. Plant-specific jargon, numbers do not add up (line 211), statements are rather implicit (9 deeply conserved CNS are the 3+6? Where do I see them in Fig 5B? And where do I see the lineage-specific losses?).

We added extra annotations to the figure to make the plant jargon (angiosperm, eudicot, Brassicaceae) clear, and show the loss more clearly in the figure. We also clarified the text by splitting up 9 to 3 and 6.

l223. Looking at the shared CNS between SEP1-2, can you find a TF binding site or another property that can be interpreted as regulatory importance?

Reliably showing an active TF binding site would require experimental data, which we don’t have. We do mention in the discussion the need for datasets which could help address this gap.

l225. My intuition says that the continuity of the phenotype may not be necessary if its loss can be compensated for somehow by another part of the organism. I.e., DSD within DSD. It is a poorly elaborated thought, I leave it here for your information. Perhaps a Discussion point?

Although very interesting we think this discussion might be outside of the scope of this work, and would benefit from a standalone discussion – especially since the capacity for such compensation might differ between animals and plants (which are more “modular” organisms). This is our interpretation:

First, let’s take a step back from ‘genotype’ and ‘phenotype’ and redefine DSD more generally: in a system with multiple organisational levels, where a hierarchical mapping between them exists, DSD is changes on one organisational level which do not alter the outcome of the ‘higher’ organisational level. In other words, DSD can exist any many-to-one mapping in which a set of many (which map to the same one) are within a certain distance in space, which we generally define as a single mutational step.

Within this (slightly) more general definition we can extend the definition of DSD to the level of phenotype and function, in which phenotype describes the ‘many’ layer, and multiple phenotypes can fulfill the same function. When we are freed from the constraint of ‘genotype’ and ‘phenotype’, and DSD is defined at the level of this mapping, than it becomes an easy exercise to have multiple mappings (genotype→phenotype→function) and thus ‘DSD within DSD’.

l233. "rarely"? I don't see any high Pearson distances.

True in the given example there are no high Pearson distances, however some of the supplementary figures do so rarely felt like the most honest description. We changed the text to refer to these supplementary figures.

Fig 4. Re-order of panels? I was expecting B at C and vice versa.

Agreed, we swapped the order of the panels

Fig 5B. Red boxes not explained. Mention that it is an UpSetplot?

We added clarification to the figure caption.

Fig 5D. It would be nice to quantify the minor and major diffs between orthologs and paralogs.

We quantify the similarities (and thus differences) in Figure F, but we do indeed not show orthologs vs paralogs explicitly. We have extended Figure F to distinguish which comparisons are between orthologs vs paralogs with different tick marks, which shows their different distributions quite clearly.

- l247. Over-generalization. In a specific organ of plants...

Changed to vascular plant meristem.

- l249. Where exactly is this link between diverse expression patterns and the Schuster dataset made? I suggest the authors to make it more explicit in the Results.

We are slightly overambitious in this sentence. The Schuster dataset confirms the preservation of expression where the CNS dataset shows rewiring. That this facilitates diversification of expression patterns in traits not under selection is solely an outcome of the computational model. We have changed the text to reflect this more clearly.

- l268. Final sentence of the paragraph left me puzzled. Why talk about opposite function?

The goal here was to highlight regulatory rewiring which, in the most extreme case, would achieve an opposite function for a given TF within development. We agree that this was formulated vaguely so we rewrote this to be more to the point.

These examples demonstrate that whilst the function of pathways is conserved, their regulatory wiring often is not.

- l269. What about time scales generated by the system? Looking at Fig 2C and 2D, the elbow pattern is pretty obvious. That means interactions sort themselves into either short-lived or long-lived. Worth mentioning?

Added a sentence to highlight this.

- l291. Evolution in a *constant* fitness landscape increases robustness.

Changed

- l296. My thoughts, for your info: I suspect morphogenesis as single parameters instead of as mechanisms makes for a brittle landscape, resulting in isolated parts of the same phenotype.

We agree, and now include citations to different models in which morphogenesis evolves which seem to display a more connected landscape.

Reviewer 2

Every computational model necessarily makes some simplifying assumptions. It would be nice if the authors could summarise in a paragraph in the Discussion the main assumptions made by their model, and which of those are most worth revisiting in future studies. In the current draft, some assumptions are described in different places in the manuscript, which makes it hard for a non-expert to evaluate the limitations of this model.

We added a section to the discussion: ‘Modelling assumptions and choices’

I did not find any mention of potential energetic constraints or limitations in this model. For example, I would expect high levels of gene expression to incur significant energy costs, resulting in evolutionary trade-offs. Could the authors comment on how taking energy limitations into account might influence their results?

This would put additional constraints on the evolution/fitness landscape. Some paths/regions of the fitness landscape which are currently accessible will not be traversable anymore. On the other hand, an energy constraint might reduce certain high fitness areas to a more even plane and thus make it more traversable. During analysis of our data there were no signs of extremely high gene expression levels.

Figure 3C lists Gene IDs 1, 2, 8, and 11, but the caption refers to genes 1, 2, 4, and 11.

Thank you for catching this.

Reviewer 3

The authors present an analysis correlating conserved non-coding sequence (CNS) composition with gene expression to investigate developmental systems drift. One flaw of this analysis is that it uses deeply conserved sequences as a proxy for the entire cis-regulatory landscape. The authors acknowledge this flaw in the discussion.

Another potential flaw is equating the bulk RNA-seq data with a conserved phenotype. In lines 226-227 of the manuscript, it is written that "In line with our computational model, we compared gene expression patterns to measure changes in phenotype." I am not sure if there is an equivalence between the two. In the computational model, the developmental outcome determining fitness is a spatial pattern, i.e., an emergent product of gene expression and cell interactions. In contrast, the RNA-seq data shows bulk measurements in gene expression for different organs. It is conceivable that, despite having very similar bulk measurements, the developmental outcome in response to gene expression (such as a spatial pattern or morphological shape) changes across species. I think this difference should be explicitly addressed in the discussion. The authors may have intended to discuss this in lines 320-326, although it is unclear to me.

It is correct that the CNS data and RNA-seq data has certain limitations, and the brief discussion of some of these limitations in lines 320-326 is not sufficient. We have been more explicit on this point in the discussion.

The gene expression data used in this study represents bulk expression at the organ level, such as the vegetative meristem (Schuster et al., 2024). This limits our analysis of the phenotypic effects of rewiring to comparisons between organs, which is different to our computational simulations where we look at within organ gene expression. Additionally, the bulk RNA-seq does not allow us to discern whether the developmental outcome of similar gene expression is the same in all these species. More fine-grained approaches, such as single-cell RNA sequencing or spatial transcriptomics, will provide a more detailed understanding of how gene expression is modulated spatially and temporally within complex tissues of different organisms, allowing for a closer alignment between computational predictions and experimental observations.

Can the authors justify using these six species in the discussion or the results? Are there any limitations with choosing four closely related and two distantly related species for this analysis, in contrast to, say, six distantly related species? If so, please elaborate in the discussion.

The use of these six species is mainly limited by the datasets we have available. Nevertheless, the combination of four closely related species, and two more distantly related species gives a better insight into the short vs long term divergence dynamics than six distantly related species would. We have noted this when introducing the datasets:

This set of species contains both closely (A. thaliana, A. lyrata, C. rubella, E. salsugineum) and more distantly related species (M. truncatula, B. distachyon), which should give insight in short and long term divergence.

In Figure S7, some profiles show no conservation across the six species. Can we be sure that a stabilising selection pressure conserves any CNSs? Is it possible that the deeply conserved CNSs mentioned in the main text are conserved by chance, given the large number of total CNSs? A brief comment on these points in the results or discussion would be helpful.

In our simulations, we find that even CREs that were under selection for a long time can disappear; however, in our neutral simulations, CREs were not conserved, suggesting that deep conservation is the result of selection. When it comes to CNSs, the assumption is that they often contain CREs that are under selection.We have added a more elaborate section on CNSs in the discussion. See ‘Limitations of CNSs as CREs’

Line 7-8: I thought this was a bit difficult to read. The connection between (i) evolvability of complex phenotypes, (ii) neutral/beneficial change hindered by deleterious mutations, and (iii) DSD might not be so simple for many readers, so I think it should be rewritten. The abstract was well written, though.

We made the connection to DSD and evolvability clearer and removed the specific mutational outcomes:

*A key open question in evolution of development (evo-devo) is the evolvability of complex phenotypes. Developmental system drift (DSD) may contribute to evolvability by exploring different genotypes with similar phenotypic outcome, but with mutational neighbourhoods that have different, potentially adaptive, phenotypes. We investigated the potential for DSD in plant development using a computational model and data analysis. *

Line 274 vs 276: Is there a difference between regulatory dynamics and regulatory mechanisms?

No, we should use the same terminology. We have changed this to be clearer.

Figure S4: Do you expect the green/blue lines to approach the orange line in the long term? In some clonal experiments, it seems like it will. In others, it seems like it has plateaued. Under continual DSD, I assume they should converge. It would be interesting to see simulations run sufficiently long to see if this occurs.

In principle yes, however this might take a considerable amount of time given that some conserved interactions take >75000 generations to be rewired.

Line 27: Evolutionarily instead of evolutionary?

Changed

Line 67-68: References in brackets?

Changed

Line 144: Capitalise "fig"

Changed

Fig. 3C caption: correct "1, 2, 4, 11" (should be 8)

Changed

Line 192: Reference repeated

Changed

Fig. 5 caption: Capitalise "Supplementary figure"

Changed

Line 277: Correct "A previous model Johnson.."

Changed

Line 290: Brackets around reference

Changed

Line 299: Correct "will be therefore be"

Changed

Line 394: Capitalise "table"

Changed

Line 449: Correct "was build using"

Changed

Fig. 5B: explain the red dashed boxes in the caption

Added explanation to the caption

Some of the Figure panels might benefit from further elaboration in their respective captions, such as 3C and 5F.

Improved the figure captions.

Reviewer 4

Statement of significance. The logical connection between the first two sentences is not clear. What does developmental system drift have to do with neutral/beneficial mutations?

This is indeed an unclear jump. Changed such that the connection between evolvability of complex phenotypes and DSD is more clear:

*A key open question in evolution of development (evo-devo) is the evolvability of complex phenotypes. Developmental system drift (DSD) contributes to evolvability by exploring different genotypes with similar phenotypic outcome, but with mutational neighbourhoods that have different, potentially adaptive, phenotypes..We investigated the potential for DSD in plant development using a computational model and data analysis. *

l 41 - "DSD is found to ... explain the developmental hourglass." Caution is warranted here. Wotton et al 2015 claim that "quantitative system drift" explains the hourglass pattern, but it would be more accurate to say that shifting expression domains and strengths allows compensatory regulatory change to occur with the same set of genes (gap genes). It is far from clear how DSD could explain the developmental hourglass pattern. What does DSD imply about the causes of differential conservation of different developmental stages? It's not clear there is any connection here.

We should indeed be more cautious here. DSD is indeed not in itself an explanation of the hourglass model, but only a mechanism by which the developmental divergence observed in the hourglass model could have emerged. As per Pavlicev and Wagner, 2012, compensatory changes resulting from other shifts would fall under DSD, and can explain how the patterning outcome of the gap gene network is conserved. However, this does not explain why some stages are under stronger selection than others. We changed the text to reflect this.

‘...be a possible evolutionary mechanism involved in the developmental hourglass model (Wotton et al., 2015; Crombach et al., 2016)...’

ll 51-53 - "Others have found that increased complexity introduces more degrees of freedom, allowing for a greater number of genotypes to produce the same phenotype and potentially allowing for more DSD (Schiffman and Ralph, 2022; Greenbury et al., 2022)." Does this refer to increased genomic complexity or increased phenotypic complexity? It is not clear that increased phenotypic complexity allows a greater number of genotypes to produce the same phenotype. Please explain further.

The paragraph discusses complexity in the GPM as a whole, where the first few examples in the paragraph regard phenotypic complexity, and the ones in l51-53 refer to genomic complexity. This is currently not clear so we clarified the text.

‘For other GPMs, such as those resulting from multicellular development, it has been suggested that complex phenotypes are sparsely distributed in genotype space, and have low potential for DSD because the number of neutral mutations anti-correlates with phenotypic complexity (Orr, 2000; Hagolani et al., 2021). Others have found that increased genomic complexity introduces more degrees of freedom, allowing for a greater number of genotypes to produce the same phenotype and potentially allowing for more DSD (Schiffman and Ralph, 2022; Greenbury et al., 2022).’

It was not clear why some gene products in the model have the ability to form dimers. What does this contribute to the simulation results? This feature is introduced early on, but is not revisited. Is it necessary?

*Fitness. The way in which fitness is determined in the model was not completely clear to me. *

Dimers are not necessary, but as they have been found to play a role in actual SAM development we added them to increase the realism of the developmental simulations. In some simulations the patterning mechanism involves the dimer, in others it does not, suggesting that dimerization is not essential for DSD.

We have made changes to the methods to clarify fitness.

Lines 103-104 say: "Each individual is assigned a fitness score based on the protein concentration of two target genes in specific regions of the SAM: one in the central zone (CZ), and one in the organizing center (OC)." How are these regions positionally defined in the simulation?

We have defined bounding boxes to define cells as either CZ, OC or both. We have added these bounds in the figure description and more clearly in the revised methods.

F, one reads (l. 385): "Fitness depends on the correct protein concentration of the two fitness genes in each cell, pcz and poc respectively." This sounds like fitness is determined by the state of all cells rather than the state of the two specific regions of the SAM. Please clarify.

A fitness penalty is given for incorrect expression so it is true that the fitness is determined by the state of all cells. We agree that it is phrased unclearly and have clarified this in the text.

The authors use conserved non-coding sequences as a proxy for cis-regulatory elements. More specification of how CNSs were assigned to an orthogroup seems necessary in this section. Is assignment based on proximity to the coding region? Of course the authors will appreciate that regulatory elements can be located far from the gene they regulate. This data showed extensive gains and losses of CNS. It might be interesting to consider how much of this is down to transposons, in which case rapid rearrangement is not unexpected. A potential problem with the claim that the data supports the simulation results follows from the fact that DSD is genetic divergence despite trait conservation, but conserved traits appear to have only been defined or identified in the case of the SEP genes. It can't be ruled out that divergence in CNSs and in gene expression captured by the datasets is driven by straightforward phenotypic adaptation, thus not by DSD. Further caution on this point is needed.

CNSs are indeed assigned based on proximity up to 50kb, the full methods are described in detail in Hendelman et al., (2021). CREs can be located further than 50kb, but evidence suggests that this is rare for species with smaller genomes.

In the cases where both gene expression and the CNSs diverged it can indeed not be ruled out that there has been phenotypic adaptation. We clarified in the text that the lower Pearson distances are informative for DSD as they highlight conserved phenotypes.

l. 290-291 - "However, evolution has been shown to increase mutational robustness over time, resulting in the possibility for more neutral change." It is doubtful that there is any such unrestricted trend. If mutational robustness only tended to increase, new mutations would not affect the phenotype, and phenotypes would be unable to adapt to novel environments. Consider rethinking this statement.

We have reformulated this statement, since it is indeed not expected that this trend is indefinite. Infinite robustness would indeed result in the absence of evolvability; however, it has been shown for other genotype-phenotype maps that mutational robustness, where a proportion of mutations is neutral, aids the evolution of novel traits. The evolution of mutational robustness also depends on population size and mutation rate. This trend will, most probably, also be stronger in modelling work where the fitness function is fixed, compared to a real life scenario where ‘fitness’ is much less defined and subject to continuous change. We added ‘constant’ to the fitness landscape to highlight this disparity.

ll. 316-317 "experimental work investigating the developmental role of CREs has shown extensive epistasis - where the effect of a mutation depends on the genetic background - supporting DSD." How does extensive epistasis support DSD? One can just as easily imagine scenarios where high interdependence between genes would prevent DSD from occurring. Please explain further.

We should be more clear. Experimental work has shown that the effect of mutating a particular CRE strongly depends on the genetic background, also known as epistasis. Counterintuitively, this indirectly supports the presence of DSD, since it means that different species or strains have slightly different developmental mechanisms, resulting in these different mutational effects. We have shown how epistatic effects shift over evolutionary time.

Overall I found the explanation of the Methods, especially the formal aspects, to be unclear at times and would recommend that the authors go back over the text to improve its clarity.

We rewrote parts of the methods and some of the equations to be more clear and cohesive throughout the text.

C. Tissue Generation. Following on the comment on fitness above, it would be advisable to provide further details on how cell positions are defined. How much do the cells move over the course of the simulation? What is the advantage of modelling the cells as "springs" rather than as a simple grid?

The tissue generation is purely a process to generate a database of tissue templates: the random positions, springs and voronoi method serve the purpose of having similar but different tissues to prevent unrealistic overfitting of our GRNs on a single topology. For each individual’s development however, only one, unchanging template is used. We clarified this in the methods.

E. Development of genotype into phenotype. The diffusion term in the SDE equations is hard to understand as no variable for spatial position (x) is included in the equation. It seems this equation should rather be an SPDE with a position variable and a specified boundary condition (i.e. the parabola shape). In eq. 5 it should be noted that the Wi are independent. Also please justify the choice of how much noise/variance is being stipulated here.

We have rewritten parts of this section for clarity and added citations.

F. Fitness function. I must say I found formula 7 to be unclear. It looks like fi is the fitness of cell(s) but, from Section G, fitness is a property of the individual. It seems formula 7 should define fi as a sum over the cell types or should capture the fitness contribution of the cell types.

Correct. We have rewritten this equation. We’ll define fi as the fitness contribution of a cell, F as the sum of fi, so the fitness of an individual, and use F in function 8.

What is the basis for the middle terms (fractions) in the equation? After plugging in the values for pcz and poc, this yields a number, but how does that number assign a cell to one of the types? If a reviewer closely scrutinizing this section cannot make sense of it, neither will readers. Please explain further.

The cell type is assigned based on the spatial location of the cell, and the correct fitness function for each of these cell types is described in this equation. We have clarified the text and functions.

A minor note: it would be best practice not to re-use variables to refer to different things within the same paper. For example p refers to protein concentration but also probability of mutation.

Corrected

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Referee #4

Evidence, reproducibility and clarity

In "Ubiquitous system drift in the evolution of development," van der Jagt et al. report a large-scale simulation study of the evolution of gene networks controlling a developmental patterning process. The 14-gene simulation shows interesting results: continual rewiring of the network and establishment of essential genes which themselves are replaced on long time scales. The authors suggest that this result is validated by plant genome and expression data from some public datasets. Overall, this study lends support to the idea that developmental system drift may be more pervasive in the evolution of complex gene networks than is currently appreciated.

I have a number of comments, mostly of a clarificatory nature, that the authors can consider in revision.

1. Intro

Statement of significance. The logical connection between the first two sentences is not clear. What does developmental system drift have to do with neutral/beneficial mutations?

l 41 - "DSD is found to ... explain the developmental hourglass." Caution is warranted here. Wotton et al 2015 claim that "quantitative system drift" explains the hourglass pattern, but it would be more accurate to say that shifting expression domains and strengths allows compensatory regulatory change to occur with the same set of genes (gap genes). It is far from clear how DSD could explain the developmental hourglass pattern. What does DSD imply about the causes of differential conservation of different developmental stages? It's not clear there is any connection here.

ll 51-53 - "Others have found that increased complexity introduces more degrees of freedom, allowing for a greater number of genotypes to produce the same phenotype and potentially allowing for more DSD (Schiffman and Ralph, 2022; Greenbury et al., 2022)." Does this refer to increased genomic complexity or increased phenotypic complexity? It is not clear that increased phenotypic complexity allows a greater number of genotypes to produce the same phenotype. Please explain further. 2. Model

It was not clear why some gene products in the model have the ability to form dimers. What does this contribute to the simulation results? This feature is introduced early on, but is not revisited. Is it necessary?

Fitness. The way in which fitness is determined in the model was not completely clear to me. Lines 103-104 say: "Each individual is assigned a fitness score based on the protein concentration of two target genes in specific regions of the SAM: one in the central zone (CZ), and one in the organizing center (OC)." How are these regions positionally defined in the simulation? In Methods section F, one reads (l. 385): "Fitness depends on the correct protein concentration of the two fitness genes in each cell, pcz and poc respectively." This sounds like fitness is determined by the state of all cells rather than the state of the two specific regions of the SAM. Please clarify. 3. Data

The authors use conserved non-coding sequences as a proxy for cis-regulatory elements. More specification of how CNSs were assigned to an orthogroup seems necessary in this section. Is assignment based on proximity to the coding region? Of course the authors will appreciate that regulatory elements can be located far from the gene they regulate. This data showed extensive gains and losses of CNS. It might be interesting to consider how much of this is down to transposons, in which case rapid rearrangement is not unexpected. A potential problem with the claim that the data supports the simulation results follows from the fact that DSD is genetic divergence despite trait conservation, but conserved traits appear to have only been defined or identified in the case of the SEP genes. It can't be ruled out that divergence in CNSs and in gene expression captured by the datasets is driven by straightforward phenotypic adaptation, thus not by DSD. Further caution on this point is needed. 4. Discussion

ll. 290-291 - "However, evolution has been shown to increase mutational robustness over time, resulting in the possibility for more neutral change." It is doubtful that there is any such unrestricted trend. If mutational robustness only tended to increase, new mutations would not affect the phenotype, and phenotypes would be unable to adapt to novel environments. Consider rethinking this statement.

ll. 316-317 "experimental work investigating the developmental role of CREs has shown extensive epistasis - where the effect of a mutation depends on the genetic background - supporting DSD." How does extensive epistasis support DSD? One can just as easily imagine scenarios where high interdependence between genes would prevent DSD from occurring. Please explain further. 5. Methods

Overall I found the explication of the Methods, especially the formal aspects, to be unclear at times and would recommend that the authors go back over the text to improve its clarity.

C. Tissue Generation. Following on the comment on fitness above, it would be advisable to provide further details on how cell positions are defined. How much do the cells move over the course of the simulation? What is the advantage of modelling the cells as "springs" rather than as a simple grid?

E. Development of genotype into phenotype. The diffusion term in the SDE equations is hard to understand as no variable for spatial position (x) is included in the equation. It seems this equation should rather be an SPDE with a position variable and a specified boundary condition (i.e. the parabola shape). In eq. 5 it should be noted that the Wi are independent. Also please justify the choice of how much noise/variance is being stipulated here.

F. Fitness function. I must say I found formula 7 to be unclear. It looks like fi is the fitness of cell(s) but, from Section G, fitness is a property of the individual. It seems formula 7 should define fi as a sum over the cell types or should capture the fitness contribution of the cell types.

What is the basis for the middle terms (fractions) in the equation? After plugging in the values for pcz and poc, this yields a number, but how does that number assign a cell to one of the types? If a reviewer closely scrutinizing this section cannot make sense of it, neither will readers. Please explain further.

A minor note: it would be best practice not to re-use variables to refer to different things within the same paper. For example p refers to protein concentration but also probability of mutation.

Referee cross-commenting

Overall I agree with the comments of Reviewer 1, 2 and 3. I note that reviewers 1, 3, and 4 each pointed out the difficulties with assuming that CNSs = CREs, so this needs to be addressed. Two reviewers (3 and 4) also point out problems with equating bulk RNAseq with a conserved phenotype.

I agree with Reviewer 1's hesitancy about the rhetorical framing of the paper potentially generalising too far from a computational model of plant meristem patterning.

Reviewer 3's concern about DSD resulting from stabilising selection for robustness is something I missed -- this is important and should be addressed.

Reviewer 3 suggests that the model construction may favor DSD because there are many genes (14) of which only two determine fitness. I agree that some discussion on this point is warranted, though I am not sure enough is known about "the possible difference in constraints between the model and real development" for such a discussion to be on firm biological footing. A genetic architecture commonly found in quantitative genetic studies is that a small number of genes have large effects on the phenotype/fitness, whereas a very large number of genes have effects that are individually small but collectively large (see, e.g. literature surrounding the "omnigenic model" of complex traits). Implementing such an architecture is probably beyond the scope of the study here. More generally, would be natural to assume that the larger the number of genes, and the smaller the number of fitness-determining genes, the more likely DSD / re-wiring is to occur. That being said, I think the authors' choice of a 14-gene network is biologically defensible. It could be argued that the restriction of many modeling studies to small networks (often including just 3 genes) on the ground of convenience artificially ensures that DSD will not occur in these networks.

I agree with the other reviewers on the overall positive assessment of the significance of the manuscript. There are many points to address and revise, but the core setup and result of this study is sound and should be published.

Significance

In "Ubiquitous system drift in the evolution of development," van der Jagt et al. report a large-scale simulation study of the evolution of gene networks controlling a developmental patterning process. The 14-gene simulation shows interesting results: continual rewiring of the network and establishment of essential genes which themselves are replaced on long time scales. The authors suggest that this result is validated by plant genome and expression data from some public datasets. Overall, this study lends support to the idea that developmental system drift may be more pervasive in the evolution of complex gene networks than is currently appreciated.

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Referee #3

Evidence, reproducibility and clarity

Summary:

This manuscript uses an Evo-Devo model of the plant apical meristem to explore the potential for developmental systems drift (DSD). DSD occurs when the genetic underpinnings of development change through evolution while reaching the same developmental outcome. The mechanisms underlying DSD are theoretically intriguing and highly relevant for our understanding of how multicellular species evolve. The manuscript shows that DSD occurs extensively and continuously in their evolutionary simulations whilst populations evolve under stabilising selection. The authors examine regulatory rewiring across plant angiosperms to link their theoretical model with real data. The authors claim that, despite the conservation of genetic wiring in angiosperm species over shorter evolutionary timescales, this genetic wiring changes over long evolutionary timescales due to DSD, which is consistent with their theoretical model.

Major comments:

I enjoyed reading the author's approach to understanding DSD and the link to empirical data. I think it is a very important line of investigation that deserves more theoretical and experimental attention. All the data and methods are clearly presented, and the software for the research is publicly available. Sufficient information is given to reproduce all results. However, I have two major issues relating to the theoretical part of the research.

Issue One: Interpretation of fitness gains under stabilising selection

A central issue concerns how the manuscript defines and interprets developmental systems drift (DSD) in relation to evolution on the fitness landscape. The authors define DSD as the conservation of a trait despite changes in its underlying genetic basis, which is consistent with the literature. However, the manuscript would benefit from clarifying the relationship between DSD, genotype-to-phenotype maps, and fitness landscapes. Very simply, we can say that (i) DSD can operate along neutral paths in the fitness landscape, (ii) DSD can operate along adaptive paths in the fitness landscape. During DSD, these neutral or adaptive paths along the fitness landscape are traversed by mutations that change the gene regulatory network (GRN) and consequent gene expression patterns whilst preserving the developmental outcome, i.e., the phenotype. While this connection between DSD and fitness landscapes is referenced in the introduction, it is not fully elaborated upon. A complete elaboration is critical because, when I read the manuscript, I got the impression that the manuscript claims that DSD is prevalent along neutral paths in the fitness landscape, not just adaptive ones. If I am wrong and this is not what the authors claim, it should be explicitly stated in the results and discussed. Nevertheless, claiming DSD operates along neutral paths is a much more interesting statement than claiming it operates along adaptive paths. However, it requires sufficient evidence, which I have an issue with. The issue I have is about adaptations under stabilising selection. Stabilising selection occurs when there is selection to preserve the developmental outcome. Stabilising selection is essential to the results because evolutionary change in the GRN under stabilising selection should be due to DSD, not adaptations that change the developmental outcome. To ensure that the populations are under stabilising selection, the authors perform clonal experiments for 100,000 generations for 8 already evolved populations, 5 clones for each population. They remove 10 out of 40 clones because the fitness increase is too large, indicating that the developmental outcome changes over the 100,000 generations. However, the remaining 30 clonal experiments exhibit small but continual fitness increases over 100,000 generations. The authors claim that the remaining 30 are predominantly evolving due to drift, not adaptations (in the main text, line 137: "indicating predominantly neutral evolution", and section M: "too shallow for selection to outweigh drift"). The author's evidence for this claim is a mathematical analysis showing that the fitness gains are too small to be caused by beneficial adaptations, so evolution must be dominated by drift. I found this explanation strange, given that every clone unequivocally increases in fitness throughout the 100,000 generations, which suggests populations are adapting. Upon closer inspection of the mathematical analysis (section M), I believe it will miss many kinds of adaptations possible in their model, as I now describe. The mathematical analysis treats fitness as a constant, but it's a random variable in the computational model. Fitness is a random variable because gene transcription and protein translation are stochastic (Wiener terms in Eqs. (1)-(5)) and cell positions change for each individual (Methods C). So, for a genotype G, the realised fitness F is picked from a distribution with mean μ_G and higher order moments (e.g., variance) that determine the shape of the distribution. I think these assumptions lead to two problems. The first problem with the mathematical analysis is that F is replaced by an absolute number f_q, with beneficial mutations occurring in small increments denoted "a", representing an additive fitness advantage. The authors then take a time series of the median population fitness from their simulations and treat its slope as the individual's additive fitness advantage "a". The authors claim that drift dominates evolution because this slope is lower than a drift-selection barrier, which they derive from the mathematical analysis. This analysis ignores that the advantage "a" is a distribution, not a constant, which means that it does not pick up adaptations that change the shape of the distribution. Adaptations that change the shape of the distribution can be adaptations that increase robustness to stochasticity. Since there are multiple sources of noise in this model, I think it is highly likely that robustness to noise is selected for during these 100,000 generations. The second problem is that the mathematical analysis ignores traits that have higher-order effects on fitness. A trait has higher-order effects when it increases the fitness of the lineage (e.g., offspring) but not the parent. One possible trait that can evolve in this model with higher-order effects is mutational robustness, i.e., traits that lower the expected mutational load of descendants. Since many kinds of mutations occur in this model (Table 2), mutational robustness may be also evolving. Taken together, the analysis in Section M is set up to detect only immediate, deterministic additive gains in a single draw of fitness. It therefore cannot rule out weak but persistent adaptive evolution of robustness (to developmental noise and/or to mutations), and is thus insufficient evidence that DSD is occurring along neutral paths instead of adaptive paths. The small but monotonic fitness increases observed in all 40 clones are consistent with such adaptation (Fig. S3). The authors also acknowledge the evolution of robustness in lines 129-130 and 290-291, but the possibility of these adaptations driving DSD instead of neutral evolution is not discussed. To address the issue I have with adaptations during stabilising selection, the authors should, at a minimum, state clearly in their results that DSD is driven by both the evolution of robustness and drift. Moreover, a paragraph in the discussion should be dedicated to why this is the case, and why it is challenging to separate DSD through neutral evolution vs DSD through adaptations such as those that increase robustness. [OPTIONAL] A more thorough approach would be to make significant changes to the manuscript by giving sufficient evidence that the experimental clones are evolving by drift, or changing the model construction. One possible way to provide sufficient evidence is to improve the mathematical analysis. Another way is to show that the fitness distributions (both without and with mutations, like in Fig. 2F) do not significantly change throughout the 100,000 generations in experimental clones. It seems more likely that the model construction makes it difficult to separate the evolution of robustness from evolution by drift in the stabilising selection regime. Thus, I think the model should be constructed differently so that robustness against mutations and noise is much less likely to evolve after a "fitness plateau" is reached. This could be done by removing sources of noise from the model or reducing the kinds of possible mutations (related to issue two). In fact, I could not find justification in the manuscript for why these noise terms are included in the model, so I assume they are included for biological realism. If this is why noise is included, or if there is a separate reason why it is necessary, please write that in the model overview and/or the methods.

Issue two: The model construction may favour DSD

In this manuscript, fitness is determined by the expression pattern of two types of genes (genes 12 and 13 in Table 1). There are 14 types of genes in total that can all undergo many kinds of mutations, including duplications (Table 2). Thus, gene regulatory networks (GRNs) encoded by genomes in this model tend to contain large numbers of interactions. The results show that most of these interactions have minimal effect on reaching the target pattern in high fitness individuals (e.g. Fig. 2F). A consequence of this is that only a minimal number of GRN interactions are conserved through evolution (e.g. Fig. 2D). From these model constructions and results from evolutionary simulations, we can deduce that there are very few constraints on the GRN. By having very few constraints on the GRN, I think it makes it easy for a new set of pattern-producing traits to evolve and subsequently for an old set of pattern-producing traits to be lost, i.e., DSD. Thus, I believe that the model construction may favour DSD. I do not have an issue with the model favouring DSD because it reflects real multicellular GRNs, where it is thought that a minority fraction of interactions are critical for fitness and the majority are not. However, it is unknown whether the constraints GRNs face in the model are more or less constrained than real GRNs. Thus, it is not known whether the prevalence of DSD in this model applies generally to real development, where GRN constraints depend on so many factors. At a minimum, the possible difference in constraints between the model and real development should be discussed as a limitation of the model. A more thorough change to the manuscript would be to test the effect of changing the constraints on the GRN. I am sure there are many ways to devise such a test, but I will give my recommendation here. [OPTIONAL] My recommendation is that the authors should run additional simulations with simplified mutational dynamics by constraining the model to N genes (no duplications and deletions), of which M out of these N genes contribute to fitness via the specific pattern (with M=2 in the current model). The authors should then test the effect of changing N and M independently, and how this affects the prevalence of DSD. If the prevalence of DSD is robust to changes in N and M, it supports the authors argument that DSD is highly prevalent in developmental evolution. If DSD prevalence is highly dependent on M and/or N, then the claims made in the manuscript about the prevalence of DSD must change accordingly. I acknowledge that these simulations may be computationally expensive, and I think it would be great if the authors knew (or devised) a more efficient way to test the effect of GRN constraints on DSD prevalence. Nevertheless, these additional simulations would make for a potentially very interesting manuscript.

Minor comments:

1. The authors present an analysis correlating conserved non-coding sequence (CNS) composition with gene expression to investigate developmental systems drift. One flaw of this analysis is that it uses deeply conserved sequences as a proxy for the entire cis-regulatory landscape. The authors acknowledge this flaw in the discussion. Another potential flaw is equating the bulk RNA-seq data with a conserved phenotype. In lines 226-227 of the manuscript, it is written that "In line with our computational model, we compared gene expression patterns to measure changes in phenotype." I am not sure if there is an equivalence between the two. In the computational model, the developmental outcome determining fitness is a spatial pattern, i.e., an emergent product of gene expression and cell interactions. In contrast, the RNA-seq data shows bulk measurements in gene expression for different organs. It is conceivable that, despite having very similar bulk measurements, the developmental outcome in response to gene expression (such as a spatial pattern or morphological shape) changes across species. I think this difference should be explicitly addressed in the discussion. The authors may have intended to discuss this in lines 320-326, although it is unclear to me.
2. Can the authors justify using these six species in the discussion or the results? Are there any limitations with choosing four closely related and two distantly related species for this analysis, in contrast to, say, six distantly related species? If so, please elaborate in the discussion.
3. In Figure S7, some profiles show no conservation across the six species. Can we be sure that a stabilising selection pressure conserves any CNSs? Is it possible that the deeply conserved CNSs mentioned in the main text are conserved by chance, given the large number of total CNSs? A brief comment on these points in the results or discussion would be helpful.
4. Line 7-8: I thought this was a bit difficult to read. The connection between (i) evolvability of complex phenotypes, (ii) neutral/beneficial change hindered by deleterious mutations, and (iii) DSD might not be so simple for many readers, so I think it should be rewritten. The abstract was well written, though.
5. Line 274 vs 276: Is there a difference between regulatory dynamics and regulatory mechanisms?
6. Figure S4: Do you expect the green/blue lines to approach the orange line in the long term? In some clonal experiments, it seems like it will. In others, it seems like it has plateaued. Under continual DSD, I assume they should converge. It would be interesting to see simulations run sufficiently long to see if this occurs.
7. Line 27: Evolutionarily instead of evolutionary?
8. Line 67-68: References in brackets?
9. Line 144: Capitalise "fig"
10. Fig. 3C caption: correct "1, 2, 4, 11" (should be 8)
11. Line 192: Reference repeated
12. Fig. 5 caption: Capitalise "Supplementary figure"
13. Line 277: Correct "A previous model Johnson.."
14. Line 290: Brackets around reference
15. Line 299: Correct "will be therefore be"
16. Line 394: Capitalise "table"
17. Line 449: Correct "was build using"
18. Fig. 5B: explain the red dashed boxes in the caption
19. Some of the Figure panels might benefit from further elaboration in their respective captions, such as 3C and 5F.

Significance

General Assessment:

This manuscript tackles a fundamental evolutionary problem of developmental systems drift (DSD). Its primary strength lies in its integrative approach, combining a multiscale evo-devo model with a comparative genomic analysis in angiosperms. This integrative approach provides a new way of investigating how developmental mechanisms can evolve even while the resulting phenotype is conserved. The details of the theoretical model are well defined and succinctly combined across scales. The manuscript employs several techniques to analyse the conservation and divergence of the theoretical model's gene regulatory networks (GRNs), which are rigorous yet easy to grasp. This study provides a strong platform for further integrative approaches to tackle DSD and multicellular evolution.

The study's main limitations are due to the theoretical model construction and the interpretation of the results. The central claim that DSD occurs extensively through predominantly neutral evolution is not sufficiently supported, as the analysis does not rule out an alternative: DSD is caused by adaptive evolution for increased robustness to developmental or mutational noise. Furthermore, constructing the model with a high-dimensional GRN space and a low-dimensional phenotypic target may create particularly permissive conditions for DSD, raising questions about the generality of the theoretical conclusions. However, these limitations could be resolved by changes to the model and further simulations, although these require extensive research. The genomic analysis uses cis-regulatory elements as a proxy for the entire regulatory landscape, a limitation the authors are aware of and discuss. The genomic analysis uses bulk RNA-seq as a proxy for the developmental outcome, which may not accurately reflect differences in plant phenotypes.

Advance:

The concept of DSD is well-established, but mechanistic explorations of its dynamics in complex multicellular models are still relatively rare. This study represents a mechanistic advance by providing a concrete example of how DSD can operate continuously under stabilising selection. I found the evolutionary simulations and subsequent analysis of mechanisms underlying DSD in the theoretical model interesting, and these simulations and analyses open new pathways for studying DSD in theoretical models. To my knowledge, the attempt to directly link the dynamics from such a complex evo-devo model to patterns of regulatory element conservation across a real phylogeny (angiosperms) is novel. However, I think that the manuscript does not have sufficient evidence to show a high prevalence of DSD through neutral evolution in their theoretical model, which would be a highly significant conceptual result. The manuscript does have sufficient evidence to show a high prevalence of DSD through adaptive evolution under stabilising selection, which is a conceptually interesting, albeit somewhat expected, result.

Audience:

This work will be of moderate interest to a specialised audience in the fields of evolutionary developmental biology (evo-devo), systems biology, and theoretical/computational biology. Researchers in these areas will be interested in the model and the dynamics of GRN conservation and divergence. The results may interest a broader audience across the fields of evolutionary biology and molecular evolution.

Expertise:

My expertise is primarily in theoretical and computational models of biology and biophysics. While I have sufficient background knowledge in bioinformatics to assess the logic of the authors' genomic analysis and its connection to their theoretical model, I do not have sufficient expertise to critically evaluate the technicalities of the bioinformatic methods used for the identification of conserved non-coding sequences (CNSs) or analysis of RNA-seq data. A reviewer with expertise in plant comparative genomics would be better suited to judge the soundness of these specific methods.

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Referee #2

Evidence, reproducibility and clarity

Summary:

In this manuscript, van der Jagt and co-workers present a computational model of the evolution of gene regulatory networks that underpin the development of shoot apical meristems in plants. They find evidence for conservation of a subset of regulatory interactions over many thousands of generations. They also show that after reaching a fitness plateau, the topology of regulatory interactions continues to evolve, giving rise to substantial differences in regulatory networks among cloned populations. Their model suggests that cis-regulatory rewiring is key for developmental evolution, and they reach a similar conclusion after analysing two empirical datasets covering six land plant species. Overall, I find that this study is excellently executed, its methodology sufficiently described, and that its claims are well-supported by the data presented.

Major comments:

• Every computational model necessarily makes some simplifying assumptions. It would be nice if the authors could summarise in a paragraph in the Discussion the main assumptions made by their model, and which of those are most worth revisiting in future studies. In the current draft, some assumptions are described in different places in the manuscript, which makes it hard for a non-expert to evaluate the limitations of this model.
• I did not find any mention of potential energetic constraints or limitations in this model. For example, I would expect high levels of gene expression to incur significant energy costs, resulting in evolutionary trade-offs. Could the authors comment on how taking energy limitations into account might influence their results?

Minor comments:

• Figure 3C lists Gene IDs 1, 2, 8, and 11, but the caption refers to genes 1, 2, 4, and 11.

Significance

I have to note that my expertise is not in developmental systems drift, but I am generally interested in the evolution of complex phenotypes in response to various environmental pressures. Thus, I do not feel qualified to evaluate the novelty of this work, which I hope other reviewers have done. Nevertheless, I found this study very interesting and the manuscript generally easy to understand. I believe that this study will be of strong interest primarily (but not only) to evolutionary and systems biologists, regardless of the taxonomic group of their research focus.

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Referee #1

Evidence, reproducibility and clarity

# Summary

On the basis of computational modelling and bioinformatic data analysis, the authors report evidence for Developmental System Drift in the plant apical meristem (a plant stem cell tissue from which other tissues and organs grow, like shoots and roots). The modelling focuses on a general (shoot) apical meristem, the data analysis on the floral meristem. As a non-plant computational biologist, I was lacking some basic plant biology to immediately understand all the technical terms. It hindered a bit, but was not a show-stopper. That said, I interpret their study as follows.

In the computational modelling part, the authors take into account gene expression, protein complex formation, stochasticity (expression noise), tissue shape, etc. to do evolutionary simulations to obtain a "standard" gene expression pattern known from the shoot apical meristem. Next, they analyze the gene regulatory networks in terms of conserved regulatory interactions. They find two timescales, either interactions quickly turn-over or they are slowly replaced (because under selection). The slowly replaced interactions are important for the realization of the phenotype and their turnover (further explored in a separate set of "neutral evolution" simulations) is called DSD by the authors. The authors state that at the basis of DSD is overlap in gene expression domains, such that genes can take over from each other. Next, the authors analyze two public data sets to show that DSD-associated phenomena such as turn-over of (conserved) noncoding sequences and differences in gene expression patterns occur in plants.

Considering my limited amount of time and energy, I apologize in advance for stupidities and/or un-elegantly formulated sentences. I'll be happy to discuss with the authors about this work, it was a pleasant summer read!

Anton Crombach

Major comments

• It is system drift, not systems drift (see True and Haag 2001). No 's' after system.
• I am afraid I have a problem with the manuscript title. I think "Ubiquitoes" is misplaced, because it strongly suggests you have a long list of case studies across plants and animals, and some quantification of DSD in these two kingdoms. That would have been an interesting result, but it is not what you report. I suggest something along the lines of "System drift in the evolution of plant meristem development", similar to the short title used in the footer.
• Alternatively, the authors may aim to say that DSD happens all over the place in computational models of development? In that case the title should reflect that the claim refers to modeling. (But what then about the data analysis part?)
• The observation of DSD in the computational models remains rather high-level in the sense that no motifs, mechanisms, subgraphs, mutations or specific dynamics are reported to be associated to it ---with the exception of gene expression domains overlapping. Perhaps the authors feel it is beyond this study, but a Results section with a more in-depth "mechanistic" analysis on what enables DSD would (a) make a better case for the extensive and expensive computational models and (b) would push this paper to a next level. As a starting point, it could be nice to check Ohno's intuition that gene duplications are a creative "force" in evolution. Are they drivers of DSD? Or are TFBS mutations responsible for the majority of cases?
• Multiple times in the Abstract and Introduction the authors make statements on "cis-regulatory elements" that are actually "conserved non-coding sequences" (CNS). Even if it is not uncommon for CNSs to harbor enhancers etc., I would be very hesitant to use the two as synonyms. As the authors state themselves, sequences, even non-coding, can be conserved for many reasons other than CREs. I would ask the authors to support better their use of "CREs" or adjust language. As roughly stated in their Discussion (lines 310-319), one way forward could be to show for a few CNS that are important in the analysis (of Fig 5), that they have experimentally-verified enhancers. Is that do-able or a bridge too far?

Minor comments

Statement of significance:

• line 7. evo-devo is jargon
• l9. I would think "using a computational model and data analysis"
• l13. Strictly speaking you did not look at CREs, but at conserved non-coding sequences.
• l14. "widespread" is exaggerated here, since you show for a single organ in a handful of plant species. You may extrapolate and argue that you do not see why it should not be widespread, but you did not show it. Or tie in all the known cases that can be found in literature..

Abstract:

• l16. "simpler" than what?
• l27. Again the tension between CREs and non-coding sequence.
• l28. I don't understand the use of "necessarily" here.

Introduction:

• l34-35. A very general biology statement is backed up by two modeling studies. I would have expected also a few based on comparative analyses (e.g., fossils, transcriptomics, etc).
• l36. I was missing the work on "phenogenetic drift" by Weiss; and Pavlicev & Wagner 2012 on compensatory mutations.
• l38. Kimura and Wagner never had a developmental process in mind, which is much bigger than a single nucleotide or a single gene, respectively. First paper that I am aware of that explicitly connects DSD to evolution on genotype networks is my own work (Crombach 2016), since the editor of that article (True, of True and Haag 2001) highlighted that point in our communications.
• l40. While Hunynen and Hogeweg definitely studied the GP map in many of their works, the term goes back to Pere Alberch (1991).
• l54-55. I'm missing some motivation here. If one wants to look at multicellular structures that display DSD, vulva development in C. elegans and related worms is an "old" and extremely well-studied example. Also, studies on early fly development by Yogi Jaeger and his co-workers are not multicellular, but at least multi-nuclear.
• Obviously these are animal-based results, so to me it would make sense to make a contrast animal-plant regarding DSD research and take it from there.
• l66-86. It is a bit of a style-choice, but this is a looong summary of what is to come. I would not have done that. Instead, in the Introduction I would have expected a bit more digging into the concept of DSD, mention some of the old animal cases, perhaps summarize where in plants it should be expected. More context, basically.

Results:

• l108. Could you quantify the conserved interactions shared between the populations? Or is each simulation so different that they are pretty much unique?
• l169. "DSD driving functional divergence" needs some context, since DSD is supposed to not affect function (of the final phenotype). Or am I misunderstanding?
• l171. You discuss an example here, would it be possible to generalize this analysis and quantify the amount of DSD amongst all cloned populations? And related question: of the many conserved interactions in Fig 4A, how many do the two clonal lineages share? None? All?
• l176. Say which interaction it is. Is it 0->8, as mentioned in the next paragraph?
• l190. In the section on DSD in plant gene regulation, the repeated explanation of where the data comes from is a bit tedious to read. You intro it clearly at the start, that is enough.
• l197. Bulk RNAseq has the problem of averaging gene expression over the population of cells. How do you think that impacts your test for rewiring? If you would do a similar "bulk RNA" style test on your computational models, would you pick up DSD?
• l202. I do not understand the "within" of a non-coding sequence within an orthogroup. How are non-coding sequences inside an orthogroup of genes?
• l207-217. This paragraph is difficult to read and would benefit of a rephrasing. Plant-specific jargon, numbers do not add up (line 211), statements are rather implicit (9 deeply conserved CNS are the 3+6? Where do I see them in Fig 5B? And where do I see the lineage-specific losses?).
• l223. Looking at the shared CNS between SEP1-2, can you find a TF binding site or another property that can be interpreted as regulatory importance?
• l225. My intuition says that the continuity of the phenotype may not be necessary if its loss can be compensated for somehow by another part of the organism. I.e., DSD within DSD. It is a poorly elaborated thought, I leave it here for your information. Perhaps a Discussion point?

• l233. "rarely"? I don't see any high Pearson distances.

• Fig 4. Re-order of panels? I was expecting B at C and vice versa.

• Fig 5B. Red boxes not explained. Mention that it is an UpSetplot?
• Fig 5D. It would be nice to quantify the minor and major diffs between orthologs and paralogs.

Discussion: - l247. Over-generalization. In a specific organ of plants...<br /> - l249. Where exactly is this link between diverse expression patterns and the Schuster dataset made? I suggest the authors to make it more explicit in the Results. - l268. Final sentence of the paragraph left me puzzled. Why talk about opposite function?<br /> - l269. What about phenotypic plasticity due to stochastic gene expression? Does it play a role in DSD in your model? I am thinking about https://pubmed.ncbi.nlm.nih.gov/24884746/ and https://pubmed.ncbi.nlm.nih.gov/21211007/ - l269. What about time scales generated by the system? Looking at Fig 2C and 2D, the elbow pattern is pretty obvious. That means interactions sort themselves into either short-lived or long-lived. Worth mentioning? - l291. Evolution in a constant fitness landscape increases robustness. - l296. My thoughts, for your info: I suspect morphogenesis as single parameters instead of as mechanisms makes for a brittle landscape, resulting in isolated parts of the same phenotype.

Methods: I have diagonally read through the Methods section, I did not have time to dig in. I hope another reviewer can compensate for me.

Significance

Nature and significance of advance

I find this study a strong contribution to the concept of DSD. It was good to see that colleagues have done the effort of making a convincing case for the presence of DSD in plants. This will be appreciated by the evo-devo community in general. On top of that, the computational modelling work is excellent and sets new standards that will be appreciated by computational colleagues. And I anticipate that the evolutionary biology community welcomes the extension of DSD to the plant kingdom; so far it has been dominated by animal studies.

I see two limitations: (1) almost no mechanistic explanation of what drives DSD in the simulations. (2) the Abstract, Introduction, etc. need some polishing to be better in line with the results reported.

Context of existing literature

Literature is very modeling focused, it could use some empirical support. Also, some literature on DSD is missing: Weiss 2005, Pavlicev 2012, "Older" C. elegans work by the group of Marie-Anne Felix. Probably some more recent empirical case studies have established DSD as well... I may not be aware, as I did not keep track of it.

What audience?

In no particular order: plant evolution, plant development, evo-devo, computational biology.

My field of expertise

My expertise: gene regulatory networks, evolution, development (in animals), computational modelling, bioinformatic data analysis (single cell omics).

Phylogenetic tree building is surely not my strength.

Even with Medicare, health insurance and healthcare costs do not disappear in retirement.

Most European earn less, during their working lifetimes and in retirement, but are not burdened by healthcare costs and the high cost of living in the US--which is the third highest worldwide, following Israel and Iceland.

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eLife Assessment

In flies defective for axonal transport of mitochondria, the authors report the upregulation of one subunit, the beta subunit, of the heterotrimeric eIF2 complex via mass spectroscopy proteomics. Neuronal overexpression of eIF2β phenocopied aspects of neuronal dysfunction observed when axonal transport of mitochondria was compromised. Conversely, lowering eIF2β expression suppressed aspects of neuronal dysfunction. While these are intriguing and useful observations, technical weaknesses limit the interpretation. On balance, the evidence supporting the current claims is suggestive but incomplete, especially concerning the characterization of the eIF2 heterotrimer and the data regarding translational regulation.

Reviewer #1 (Public review):

The study presents significant findings on the role of mitochondrial depletion in axons and its impact on neuronal proteostasis. It effectively demonstrates how the loss of axonal mitochondria and elevated levels of eIF2β contribute to autophagy collapse and neuronal dysfunction. The use of Drosophila as a model organism and comprehensive proteome analysis adds robustness to the findings.

In this revision, the authors have responded thoughtfully to previous concerns. In particular, they have addressed the need for a quantitative analysis of age-dependent changes in eIF2β and eIF2α. By adding western blot data from multiple time points (7 to 63 days), they show that eIF2β levels gradually increase until middle age, then decline. In milton knockdown flies, this pattern appears shifted, supporting the idea that mitochondrial defects may accelerate aging-related molecular changes. These additions clarify the temporal dynamics of eIF2β and improve the overall interpretation.

Other updates include appropriate corrections to figures and quantification methods. The authors have also revised some of their earlier mechanistic claims, presenting a more cautious interpretation of their findings.

Overall, this work provides new insights into how mitochondrial transport defects may influence aging-related proteostasis through eIF2β. The manuscript is now more convincing, and the revisions address the main points raised earlier. I find the updated version much improved.

Reviewer #2 (Public review):

In the manuscript, the authors aimed to elucidate the molecular mechanism that explains neurodegeneration caused by the depletion of axonal mitochondria. In Drosophila, starting with siRNA depletion of milton and Miro, the authors attempted to demonstrate that the depletion of axonal mitochondria induces the defect in autophagy. From proteome analyses, the authors hypothesized that autophagy is impacted by the abundance of eIF2β and the phosphorylation of eIF2α. The authors followed up the proteome analyses by testing the effects of eIF2β overexpression and depletion on autophagy. With the results from those experiments, the authors proposed a novel role of eIF2β in proteostasis that underlies neurodegeneration derived from the depletion of axonal mitochondria, which they suggest accelerates age-dependent changes rather than increasing their magnitude.

Strong caution is necessary regarding the interpretation of translational regulation resulting from the milton KD. The effect of milton KD on translation appears subtle, if present at all, in the puromycin incorporation experiments in both the initial and revised versions. Additionally, the polysome profiling data in the revised manuscript lack the clear resolution for ribosomal subunits, monosomes, and polysomes that is typically expected in publications.

Author response:

The following is the authors’ response to the previous reviews

Reviewer #1 (Public review):

The study presents significant findings on the role of mitochondrial depletion in axons and its impact on neuronal proteostasis. It effectively demonstrates how the loss of axonal mitochondria and elevated levels of eIF2β contribute to autophagy collapse and neuronal dysfunction. The use of Drosophila as a model organism and comprehensive proteome analysis adds robustness to the findings.

In this revision, the authors have responded thoughtfully to previous concerns. In particular, they have addressed the need for a quantitative analysis of age-dependent changes in eIF2β and eIF2α. By adding western blot data from multiple time points (7 to 63 days), they show that eIF2β levels gradually increase until middle age, then decline. In milton knockdown flies, this pattern appears shifted, supporting the idea that mitochondrial defects may accelerate aging-related molecular changes. These additions clarify the temporal dynamics of eIF2β and improve the overall interpretation.

Other updates include appropriate corrections to figures and quantification methods. The authors have also revised some of their earlier mechanistic claims, presenting a more cautious interpretation of their findings.

Overall, this work provides new insights into how mitochondrial transport defects may influence aging-related proteostasis through eIF2β. The manuscript is now more convincing, and the revisions address the main points raised earlier. I find the updated version much improved.

Thank you so much for the review, insightful comments and encouragement. We appreciate it.  

Reviewer #2 (Public review):

In the manuscript, the authors aimed to elucidate the molecular mechanism that explains neurodegeneration caused by the depletion of axonal mitochondria. In Drosophila, starting with siRNA depletion of milton and Miro, the authors attempted to demonstrate that the depletion of axonal mitochondria induces the defect in autophagy. From proteome analyses, the authors hypothesized that autophagy is impacted by the abundance of eIF2β and the phosphorylation of eIF2α. The authors followed up the proteome analyses by testing the effects of eIF2β overexpression and depletion on autophagy. With the results from those experiments, the authors proposed a novel role of eIF2β in proteostasis that underlies neurodegeneration derived from the depletion of axonal mitochondria, which they suggest accelerates age-dependent changes rather than increasing their magnitude.

Strong caution is necessary regarding the interpretation of translational regulation resulting from the milton KD. The effect of milton KD on translation appears subtle, if present at all, in the puromycin incorporation experiments in both the initial and revised versions. Additionally, the polysome profiling data in the revised manuscript lack the clear resolution for ribosomal subunits, monosomes, and polysomes that is typically expected in publications.

Thank you so much for the review and insightful comments. We appreciate it.  

Reviewer #2 (Recommendations for the authors):

The revised manuscript demonstrates many improvements. The authors have provided a more comprehensive data set and a more detailed description of their results. Furthermore, their explanation of the Integrated Stress Response (ISR) has been corrected, and this correction is reflected in the data interpretation.

As in the public review, I maintained my emphasis on the weakness of the claim on suppressed global translation, since the data are the same in the initial and the revised versions.

Thank you for your review. We understand that further studies will be needed to elucidate the roles on mitochondrial distribution in global translation profile. We will keep working on it. 

A few suggestions for minor corrections.

(1) The order of figures in the revised version is disorganized.

Thank you for pointing it out. We corrected the order. 

(2) In Figure 1A, mitochondria is bound by milton, and kinesin is bound by Miro. Their roles should be opposite.

Thank you for pointing it out, and we are sorry for the oversight. We corrected it.

eLife Assessment

Xenacoelomorpha is an enigmatic phylum, displaying various presumably simple or ancestral bilaterian features. This valuable study characterises the reproductive life history of Hofstenia miamia, a member of class Acoela in this phylum. The authors describe the morphology and development of the reproductive system, its changes upon degrowth and regeneration, and the animals' egg-laying behaviour. The evidence is convincing, with fluorescent microscopy and quantitative measurements as a considerable improvement to historical reports based mostly on histology and qualitative observations.

Reviewer #1 (Public review):

The aim of this study was a better understanding of the reproductive life history of acoels. The acoel Hofstenia miamia, an emerging model organism, is investigated; the authors nevertheless acknowledge and address the high variability in reproductive morphology and strategies within Acoela.

The morphology of male and female reproductive organs in these hermaphroditic worms is characterised through stereo microscopy, immunohistochemistry, histology, and fluorescent in situ hybridization. The findings confirm and better detail historical descriptions. A novelty in the field is the in situ hybridization experiments, which link already published single-cell sequencing data to the worms' morphology. An interesting finding, though not further discussed by the authors, is that the known germline markers cgnl1-2 and Piwi-1 are only localized in the ovaries and not in the testes.

The work also clarifies the timing and order of appearance of reproductive organs during development and regeneration, as well as the changes upon de-growth. It shows an association of reproductive organ growth to whole body size, which will be surely taken into account and further explored in future acoel studies. This is also the first instance of non-anecdotal degrowth upon starvation in H. miamia (and to my knowledge in acoels, except recorded weight upon starvation in Convolutriloba retrogemma [1]).

Egg laying through the mouth is described in H. miamia for the first time as well as the worms' behavior in egg laying, i.e. choosing the tanks' walls rather than its floor, laying eggs in clutches, and delaying egg-laying during food deprivation. Self-fertilization is also reported for the first time.

The main strength of this study is that it expands previous knowledge on the reproductive life history traits in H. miamia and it lays the foundation for future studies on how these traits are affected by various factors, as well as for comparative studies within acoels. As highlighted above, many phenomena are addressed in a rigorous and/or quantitative way for the first time. This can be considered the start of a novel approach to reproductive studies in acoels, as the authors suggest in the conclusion. It can be also interpreted as a testimony of how an established model system can benefit the study of an understudied animal group.

The main weakness of the work is the lack of convincing explanations on the dynamics of self-fertilization, sperm storage, and movement of oocytes from the ovaries to the central cavity and subsequently to the pharynx. These questions are also raised by the authors themselves in the discussion. Another weakness (or rather missing potential strength) is the limited focus on genes. Given the presence of the single-cell sequencing atlas and established methods for in situ hybridization and even transgenesis in H. miamia, this model provides a unique opportunity to investigate germline genes in acoels and their role in development, regeneration, and degrowth. It should also be noted that employing Transmission Electron Microscopy would have enabled a more detailed comparison with other acoels, since ultrastructural studies of reproductive organs have been published for other species (cfr e.g. [2],[3],[4]). This is especially true for a better understanding of the relation between sperm axoneme and flagellum (mentioned in the Results section), as well as of sexual conflict (mentioned in the Discussion).

(1) Shannon, Thomas. 2007. 'Photosmoregulation: Evidence of Host Behavioral Photoregulation of an Algal Endosymbiont by the Acoel Convolutriloba Retrogemma as a Means of Non-Metabolic Osmoregulation'. Athens, Georgia: University of Georgia [Dissertation].

(2) Zabotin, Ya. I., and A. I. Golubev. 2014. 'Ultrastructure of Oocytes and Female Copulatory Organs of Acoela'. Biology Bulletin 41 (9): 722-35.

(3) Achatz, Johannes Georg, Matthew Hooge, Andreas Wallberg, Ulf Jondelius, and Seth Tyler. 2010. 'Systematic Revision of Acoels with 9+0 Sperm Ultrastructure (Convolutida) and the Influence of Sexual Conflict on Morphology'.

(4) Petrov, Anatoly, Matthew Hooge, and Seth Tyler. 2006. 'Comparative Morphology of the Bursal Nozzles in Acoels (Acoela, Acoelomorpha)'. Journal of Morphology 267 (5): 634-48.

Reviewer #2 (Public review):

Summary:

While the phylogenetic position of Acoels (and Xenacoelomorpha) remains still debated, investigations of various representative species are critical to understanding their overall biology.

Hofstenia is an Acoels species that can be maintained in laboratory conditions and for which several critical techniques are available. The current manuscript provides a comprehensive and widely descriptive investigation of the productive system of Hofstenia miamia.

Strengths:

(1) Xenacoelomorpha is a wide group of animals comprising three major clades and several hundred species, yet they are widely understudied. A comprehensive state-of-the-art analysis on the reprodutive system of Hofstenia as representative is thus highly relevant.

(2) The investigations are overall very thorough, well documented, and nicely visualised in an array of figures. In some way, I particularly enjoyed seeing data displayed in a visually appealing quantitative or semi-quantitative fashion.

(3) The data provided is diverse and rich. For instance, the behavioral investigations open up new avenues for further in-depth projects.

Weaknesses:

While the analyses are extensive, they appear in some way a little uni-dimensional. For instance the two markers used were characterized in a recent scRNAseq data-set of the Srivastava lab. One might have expected slightly deeper molecular analyses. Along the same line, particularly the modes of spermatogenesis or oogenesis have not been further analysed, nor the proposed mode of sperm-storage.

[Editors' note: In their response, the authors have suitably addressed these concerns or have satisfactorily explained the challenges in addressing them.]

Author response:

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

Reviewer #1 (Recommendations for the authors): 

I will address here just some minor changes that would improve understanding, reproducibility, or cohesion with the literature.

(1) It would be good to mention that the prostatic vesicle of this study is named vesicula granulorum in (Steniböck, 1966) and granule vesicle in (Hooge et al, 2007).

We have now included this (line 90 of our revised manuscript).  

(2) A slightly more detailed discussion of the germline genes would be interesting. For example, a potential function of pa1b3-2 and cgnl1-2 based on the similarity to known genes or on the conserved domains.

Pa1b3-2 appears to encode an acetylhydrolase; cgnl1-2 is likely a cingulin family protein involved in cell junctions. However, given the evolutionary distance between acoels and model organisms in whom these genes have been studied, we believe it is premature to speculate on their function without substantial additional work. We believe this work would be more appropriate in a future publication focused on the molecular genetic underpinnings of Hofstenia’s reproductive systems and their development.  

(3) It is mentioned that the animals can store sperm while lacking a seminal bursa "given that H. miamia can lay eggs for months after a single mating" (line 635) - this could also be self-fertilization, according to the authors' other findings.

We agree that it is possible this is self-fertilization, and we believe we have represented this uncertainty accurately in the text. However, we do not think this is likely, because self-fertilization manifests as a single burst of egg laying (Fig. 6D). We discuss this in the Results (line 540). 

(4) A source should be given for the tree in Figure 7B. 

We have now included this source (line 736), and we apologize for the oversight.  

(5) Either in the Methods or in the Results section, it would be good to give more details on why actin and FMRFamide and tropomyosin are chosen for the immunohistochemistry studies.

We have now included more detail in the Methods (line 823). Briefly, these are previously-validated antibodies that we knew would label relevant morphology.

(6) In the Methods "a standard protocol hematoxylin eosin" is mentioned. Even if this is a fairly common technique, more details or a reference should be provided.

We have now included more detail, and a reference (lines 766-774).  

(7) Given the historical placement of Acoela within Platyhelminthes and the fact that the readers might not be very familiar with this group of animals, two passages can be confusing: line 499 and lines 674-678.

We have edited these sentences to clarify when we mean platyhelminthes, which addresses this confusion.  

(8) A small addition to Table S1: Amphiscolops langerhansi also presents asexual reproduction through fission ([1], cited in [2]]).

Thanks. We have included this in Table S1.

(a) Hanson, E. D. 1960. 'Asexual Reproduction in Acoelous Turbellaria'. The Yale Journal of Biology and Medicine 33 (2): 107-11.

(b) Hendelberg, Jan, and Bertil Åkesson. 1991. 'Studies of the Budding Process in Convolutriloba Retrogemma (Acoela, Platyhelminthes)'. In Turbellarian Biology: Proceedings of the Sixth International Symposium on the Biology of the Turbellaria, Held at Hirosaki, Japan, 7-12 August 1990, 11-17. Springer. 

Reviewer #2 (Recommendations for the authors): 

I do not have any major comments on the manuscript. By default, I feel descriptive studies are a critical part of the advancement of science, particularly if the data are of great quality - as is the case here. The manuscript addresses various topics and describes these adequately. My minor point would be that in some sections, it feels like one could have gone a bit deeper. I highlighted three examples in the weakness section above (deeper analysis of markers for germline; modes of oogenesis/spermatogenesis; or proposed model for sperm storage). For instance, ultrastructural data might have been informative. But as said, I don't see this as a major problem, more a "would have been nice to see".

We have responded to these points in detail above.

eLife Assessment

This is a valuable manuscript that reframes Gaucher's disease pathology through the analysis of renal health, using a Drosophila model mutant for glucocerebrosidase (GBA1). The authors provide physiological and cellular data showing that renal dysfunction may be a critical disease-modifying feature. This work broadens the field's focus beyond the nervous system to include systemic ionic regulation as a potential contributor to disease initiation and progression. The genetic and experimental approaches are solid and offer a rationale for investigating analogous dysfunction in human tissues; however, several claims extend beyond the presented evidence and would benefit from additional experimental support to fully support the conclusions.

Reviewer #1 (Public review):

This study investigates the contribution of renal dysfunction to systemic and neuronal decline in Drosophila models of Gaucher disease (Gba1b mutants) and Parkinson's disease (Parkin mutants). While lysosomal and mitochondrial pathways are known drivers in these disorders, the role of kidney-like tissues in disease progression has not been well explored.

The authors use Drosophila melanogaster to model renal dysfunction, focusing on Malpighian tubules (analogous to renal tubules) and nephrocytes (analogous to podocytes). They employ genetic mutants, tissue-specific rescues, imaging of renal architecture, redox probes, functional assays, nephrocyte dextran uptake, and lifespan analyses. They also test genetic antioxidant interventions and pharmacological treatment.

The main findings show that renal pathology is progressive in Gba1b mutants, marked by Malpighian tubule disorganization, stellate cell loss, lipid accumulation, impaired water and ion regulation, and reduced nephrocyte filtration. A central theme is redox dyshomeostasis, reflected in whole-fly GSH reduction, paradoxical mitochondrial versus cytosolic redox shifts, reduced ROS signals, increased lipid peroxidation, and peroxisomal impairment. Antioxidant manipulations (Nrf2, Sod1/2, CatA, and ascorbic acid) consistently worsen outcomes, suggesting a fragile redox balance rather than classical oxidative stress. Parkin mutants also develop renal degeneration, with impaired mitophagy and complete nephrocyte dysfunction by 28 days, but their mechanism diverges from that of Gba1b. Rapamycin treatment rescues several renal phenotypes in Gba1b but not in Parkin, highlighting distinct disease pathways.

The authors propose that renal dysfunction is a central disease-modifying feature of Gaucher and Parkinson's disease models, driven by redox imbalance and differential engagement of lysosomal (Gba1b) vs. mitochondrial (Parkin) mechanisms. They suggest that maintaining renal health and redox balance may represent therapeutic opportunities and biomarkers in neurodegenerative disease. This is a significant manuscript that reframes GD/PD pathology through the lens of renal health. The data are extensive. However, several claims are ahead of the evidence and should be supported with additional experiments.

Major Comments:

(1) The abstract frames progressive renal dysfunction as a "central, disease-modifying feature" in both Gba1b and Parkin models, with systemic consequences including water retention, ionic hypersensitivity, and worsened neuro phenotypes. While the data demonstrates renal degeneration and associated physiological stress, the causal contribution of renal defects versus broader organismal frailty is not fully disentangled. Please consider adding causal experiments (e.g., temporally restricted renal rescue/knockdown) to directly establish kidney-specific contributions.

(2) The manuscript shows multiple redox abnormalities in Gba1b mutants (reduced whole fly GSH, paradoxical mitochondrial reduction with cytosolic oxidation, decreased DHE, increased lipid peroxidation, and reduced peroxisome density/Sod1 mislocalization). These findings support a state of redox imbalance, but the driving mechanism remains broad in the current form. It is unclear if the dominant driver is impaired glutathione handling or peroxisomal antioxidant/β-oxidation deficits or lipid peroxidation-driven toxicity, or reduced metabolic flux/ETC activity. I suggest adding targeted readouts to narrow the mechanism.

(3) The observation that broad antioxidant manipulations (Nrf2 overexpression in tubules, Sod1/Sod2/CatA overexpression, and ascorbic acid supplementation) consistently shorten lifespan or exacerbate phenotypes in Gba1b mutants is striking and supports the idea of redox fragility. However, these interventions are broad. Nrf2 influences proteostasis and metabolism beyond redox regulation, and Sod1/Sod2/CatA may affect multiple cellular compartments. In the absence of dose-response testing or controls for potential off-target effects, the interpretation that these outcomes specifically reflect redox dyshomeostasis feels ahead of the data. I suggest incorporating narrower interpretations (e.g., targeting lipid peroxidation directly) to clarify which redox axis is driving the vulnerability.

(4) This manuscript concludes that nephrocyte dysfunction does not exacerbate brain pathology. This inference currently rests on a limited set of readouts: dextran uptake and hemolymph protein as renal markers, lifespan as a systemic measure, and two brain endpoints (LysoTracker staining and FK2 polyubiquitin accumulation). While these data suggest that nephrocyte loss alone does not amplify lysosomal or ubiquitin stress, they may not fully capture neuronal function and vulnerability. To strengthen this conclusion, the authors could consider adding functional or behavioral assays (e.g., locomotor performance)

(5) The manuscript does a strong job of contrasting Parkin and Gba1b mutants, showing impaired mitophagy in Malpighian tubules, complete nephrocyte dysfunction by day 28, FRUMS clearance defects, and partial rescue with tubule-specific Parkin re-expression. These findings clearly separate mitochondrial quality control defects from the lysosomal axis of Gba1b. However, the mechanistic contrast remains incomplete. Many of the redox and peroxisomal assays are only presented for Gba1b. Including matched readouts across both models (e.g., lipid peroxidation, peroxisome density/function, Grx1-roGFP2 compartmental redox status) would make the comparison more balanced and strengthen the conclusion that these represent distinct pathogenic routes.

(6) Rapamycin treatment is shown to rescue several renal phenotypes in Gba1b mutants (water retention, RSC proliferation, FRUMS clearance, lipid peroxidation) but not in Parkin, and mitophagy is not restored in Gba1b. This provides strong evidence that the two models engage distinct pathogenic pathways. However, the therapeutic interpretation feels somewhat overstated. Human relevance should be framed more cautiously, and the conclusions would be stronger with mechanistic markers of autophagy (e.g., Atg8a, Ref(2)p flux in Malpighian tubules) or with experiments varying dose, timing, and duration (short-course vs chronic rapamycin).

(7) Several systemic readouts used to support renal dysfunction (FRUMS clearance, salt stress survival) could also be influenced by general organismal frailty. To ensure these phenotypes are kidney-intrinsic, it would be helpful to include controls such as tissue-specific genetic rescue in Malpighian tubules or nephrocytes, or timing rescue interventions before overt systemic decline. This would strengthen the causal link between renal impairment and the observed systemic phenotypes.

Reviewer #2 (Public review):

Summary:

In the present study, the authors tested renal function in Gba1b-/- flies and its possible effect on neurodegeneration. They showed that these flies exhibit progressive degeneration of the renal system, loss of water homeostasis, and ionic hypersensitivity. They documented reduced glomerular filtration capacity in their pericardial nephrocytes, together with cellular degeneration in microtubules, redox imbalance, and lipid accumulation. They also compared the Gba1b mutant flies to Parkin mutants and evaluated the effect of treatment with the mTOR inhibitor rapamycin. Restoration of renal structure and function was observed only in the Gba1b mutant flies, leading the authors to conclude that the mutants present different phenotypes due to lysosomal stress in Gba1b mutants versus mitochondrial stress in Parkin mutant flies.

Comments:

(1) The authors claim that: "renal system dysfunction negatively impacts both organismal and neuronal health in Gba1b-/- flies, including autophagic-lysosomal status in the brain." This statement implies that renal impairments drive neurodegeneration. However, there is no direct evidence provided linking renal defects to neurodegeneration in this model. It is worth noting that Gba1b-/- flies are a model for neuronopathic Gaucher disease (GD): they accumulate lipids in their brains and present with neurodegeneration and decreased survival, as shown by Kinghorn et al. (The Journal of Neuroscience, 2016, 36, 11654-11670) and by others, which the authors failed to mention (Davis et al., PLoS Genet. 2016, 12: e1005944; Cabasso et al., J Clin Med. 2019, 8:1420; Kawasaki et al., Gene, 2017, 614:49-55).

(2) The authors tested brain pathology in two experiments:

(a) To determine the consequences of abnormal nephrocyte function on brain health, they measured lysosomal area in the brain of Gba1b-/-, Klf15LOF, or stained for polyubiquitin. Klf15 is expressed in nephrocytes and is required for their differentiation. There was no additive effect on the increased lysosomal volume (Figure 3D) or polyubiquitin accumulation (Figure 3E) seen in Gba1b-/- fly brains, implying that loss of nephrocyte viability itself does not exacerbate brain pathology.

(b) The authors tested the consequences of overexpression of the antioxidant regulator Nrf2 in principal cells of the kidney on neuronal health in Gba1b-/- flies, using the c42-GAL4 driver. They claim that "This intervention led to a significant increase in lysosomal puncta number, as assessed by LysoTrackerTM staining (Figure 5D), and exacerbated protein dyshomeostasis, as indicated by polyubiquitin accumulation and increased levels of the ubiquitin-autophagosome trafficker Ref(2)p/p62 in Gba1b-/- fly brains (Figure 5E). Interestingly, Nrf2 overexpression had no significant effect on lysosomal area or ubiquitin puncta in control brains, demonstrating that the antioxidant response specifically in Gba1b-/- flies negatively impacts disease states in the brain and renal system."<br /> Notably, c42-GAL4 is a leaky driver, expressed in salivary glands, Malpighian tubules, and pericardial cells (Beyenbach et al., Am. J. Cell Physiol. 318: C1107-C1122, 2020). Expression in pericardial cells may affect heart function, which could explain deterioration in brain function.

Taken together, the contribution of renal dysfunction to brain health remains debatable.

Based on the above, I believe the title should be changed to: Redox Dyshomeostasis Links Renal and Neuronal Dysfunction in Drosophila Models of Gaucher disease. Such a title will reflect the results presented in the manuscript.

(3) The authors mention that Gba1b is not expressed in the renal system, which means that no renal phenotype can be attributed directly to any known GD pathology. They suggest that systemic factors such as circulating glycosphingolipids or loss of extracellular vesicle-mediated delivery of GCase may mediate renal toxicity. This raises a question about the validity of this model to test pathology in the fly kidney. According to Flybase, there is expression of Gba1b in renal structures of the fly.

(4) It is worth mentioning that renal defects are not commonly observed in patients with Gaucher disease. Relevant literature: Becker-Cohen et al., A Comprehensive Assessment of Renal Function in Patients With Gaucher Disease, J. Kidney Diseases, 2005, 46:837-844.

(5) In the discussion, the authors state: "Together, these findings establish renal degeneration as a driver of systemic decline in Drosophila models of GD and PD..." and go on to discuss a brain-kidney axis in PD. However, since this study investigates a GD model rather than a PD model, I recommend omitting this paragraph, as the connection to PD is speculative and not supported by the presented data.

(6) The claim: "If confirmed, our findings could inform new biomarker strategies and therapeutic targets for GBA1 mutation carriers and other at-risk groups. Maintaining renal health may represent a modifiable axis of intervention in neurodegenerative disease," extends beyond the scope of the experimental evidence. The authors should consider tempering this statement or providing supporting data.

(7) The conclusion, "we uncover a critical and previously overlooked role for the renal system in GD and PD pathogenesis," is too strong given the data presented. As no mechanistic link between renal dysfunction and neurodegeneration has been established, this claim should be moderated.

(8) The relevance of Parkin mutant flies is questionable, and this section could be removed from the manuscript.

Reviewer #3 (Public review):

Summary:

Hull et al examine Drosophila mutants for the Gaucher's disease locus GBA1/Gba1b, a locus that, when heterozygous, is a risk factor for Parkinson's. Focusing on the Malpighian tubules and their function, they identify a breakdown of cell junctions, loss of haemolymph filtration, sensitivity to ionic imbalance, water retention, and loss of endocytic function in nephrocytes. There is also an imbalance in ROS levels between the cytoplasm and mitochondria, with reduced glutathione levels, rescue of which could not improve longevity. They observe some of the same phenotypes in mutants of Parkin, but treatment by upregulation of autophagy via rapamycin feeding could only rescue the Gba1b mutant and not the Parkin mutant.

Strengths:

The paper uses a range of cellular, genetic, and physiological analyses and manipulations to fully describe the renal dysfunction in the GBa1b animals. The picture developed has depth and detail; the data appears sound and thorough.

Weaknesses:

The paper relies mostly on the biallelic Gba1b mutant, which may reflect dysfunction in Gaucher's patients, though this has yet to be fully explored. The claims for the heterozygous allele and a role in Parkinson's is a little more tenuous, making assumptions that heterozygosity is a similar but milder phenotype than the full loss-of-function.

Author response:

Reviewer #1 (Public review):

Major Comments:

(1) The abstract frames progressive renal dysfunction as a "central, disease-modifying feature" in both Gba1b and Parkin models, with systemic consequences including water retention, ionic hypersensitivity, and worsened neuro phenotypes. While the data demonstrates renal degeneration and associated physiological stress, the causal contribution of renal defects versus broader organismal frailty is not fully disentangled. Please consider adding causal experiments (e.g., temporally restricted renal rescue/knockdown) to directly establish kidney-specific contributions.

We concur that this would help strengthen our conclusions. However, manipulating Gba1b in a tissue-specific manner remains challenging due to its propensity for secretion via extracellular vesicles (ECVs). Leo Pallanck and Marie Davis have elegantly shown that ectopic Gba1b expression in neurons and muscles (tissues with low predicted endogenous expression) is sufficient to rescue major organismal phenotypes. Consistent with this, we have been unable to generate clear tissue-specific phenotypes using Gba1b RNAi.

We will pursue more detailed time-course experiments of the progression of renal pathology, (water weight, renal stem cell proliferation, redox defects, etc.) with the goal of identifying earlier-onset phenotypes that potentially drive dysfunction.

(2) The manuscript shows multiple redox abnormalities in Gba1b mutants (reduced whole fly GSH, paradoxical mitochondrial reduction with cytosolic oxidation, decreased DHE, increased lipid peroxidation, and reduced peroxisome density/Sod1 mislocalization). These findings support a state of redox imbalance, but the driving mechanism remains broad in the current form. It is unclear if the dominant driver is impaired glutathione handling or peroxisomal antioxidant/β-oxidation deficits or lipid peroxidation-driven toxicity, or reduced metabolic flux/ETC activity. I suggest adding targeted readouts to narrow the mechanism.

We agree that we have not yet established a core driver of redox imbalance. Identifying one is likely to be challenging, especially as our RNA-sequencing data from aged Gba1b^⁻/⁻ fly heads (Atilano et al., 2023) indicate that several glutathione S-transferases (GstD2, GstD5, GstD8, and GstD9) are upregulated. We can attempt overexpression of GSTs, which has been elegantly shown by Leo Pallanck to ameliorate pathology in Pink1/Parkin mutant fly brains. However, mechanisms that specifically suppress lipid peroxidation or its associated toxicity, independently of other forms of redox damage, remain poorly understood in Drosophila. Our position is there probably will not be one dominant driver of redox imbalance. Notably, CytB5 overexpression has been shown to reduce lipid peroxidation (Chen et al., 2017), and GstS1 has been reported to conjugate glutathione to the toxic lipid peroxidation product 4-HNE (Singh et al., 2001). Additionally, work from the Bellen lab demonstrated that overexpression of lipases, bmm or lip4, suppresses lipid peroxidation-mediated neurodegeneration (Liu et al., 2015). We will therefore test the effects of over-expressing CytB5, bmm and lip4 in Gba1b^⁻/⁻ flies to help further define the mechanism.

(3) The observation that broad antioxidant manipulations (Nrf2 overexpression in tubules, Sod1/Sod2/CatA overexpression, and ascorbic acid supplementation) consistently shorten lifespan or exacerbate phenotypes in Gba1b mutants is striking and supports the idea of redox fragility. However, these interventions are broad. Nrf2 influences proteostasis and metabolism beyond redox regulation, and Sod1/Sod2/CatA may affect multiple cellular compartments. In the absence of dose-response testing or controls for potential off-target effects, the interpretation that these outcomes specifically reflect redox dyshomeostasis feels ahead of the data. I suggest incorporating narrower interpretations (e.g., targeting lipid peroxidation directly) to clarify which redox axis is driving the vulnerability.

We are in agreement that Drosophila Cnc exhibits functional conservation with both Nrf1 and Nrf2, which have well-established roles in proteostasis and lysosomal biology that may exacerbate pre-existing lysosomal defects in Gba1b mutants. In our manuscript, Nrf2 manipulation forms part of a broader framework of evidence, including dietary antioxidant ascorbic acid and established antioxidant effectors CatA, Sod1, and Sod2. Together, these data indicate that Gba1b mutant flies display a deleterious response to antioxidant treatments or manipulations. To further characterise the redox state, we will quantify lipid peroxidation using Bodipy 581/591 and assess superoxide levels via DHE staining under our redox-altering experimental conditions.

As noted above, we will attempt to modulate lipid peroxidation directly through CytB5 and GstS1 overexpression, acknowledging the caveat that this approach may not fully dissociate lipid peroxidation from other aspects of redox stress. We have also observed detrimental effects of PGC1α on the lifespan of Gba1b^⁻/⁻ flies and will further investigate its impact on redox status in the renal tubules.

(4) This manuscript concludes that nephrocyte dysfunction does not exacerbate brain pathology. This inference currently rests on a limited set of readouts: dextran uptake and hemolymph protein as renal markers, lifespan as a systemic measure, and two brain endpoints (LysoTracker staining and FK2 polyubiquitin accumulation). While these data suggest that nephrocyte loss alone does not amplify lysosomal or ubiquitin stress, they may not fully capture neuronal function and vulnerability. To strengthen this conclusion, the authors could consider adding functional or behavioral assays (e.g., locomotor performance)

We will address this suggestion by performing DAM activity assays and climbing assays in the Klf15; Gba1b^⁻/⁻ double mutants.

(5) The manuscript does a strong job of contrasting Parkin and Gba1b mutants, showing impaired mitophagy in Malpighian tubules, complete nephrocyte dysfunction by day 28, FRUMS clearance defects, and partial rescue with tubule-specific Parkin re-expression. These findings clearly separate mitochondrial quality control defects from the lysosomal axis of Gba1b. However, the mechanistic contrast remains incomplete. Many of the redox and peroxisomal assays are only presented for Gba1b. Including matched readouts across both models (e.g., lipid peroxidation, peroxisome density/function, Grx1-roGFP2 compartmental redox status) would make the comparison more balanced and strengthen the conclusion that these represent distinct pathogenic routes.

We agree that park^⁻/⁻ mutants have been characterised in greater detail than park^⁻/⁻. The primary aim of our study was not to provide an exhaustive characterisation of park¹/¹, but rather to compare key shared and distinct mechanisms underlying renal dysfunction. We have included several relevant readouts for park^⁻/⁻ tubules (e.g., Figure 7D and 8H: mito-Grx1-roGFP2; Figure 8J: lipid peroxidation using BODIPY 581/591). To expand our characterisation of park¹/¹ flies, we will express the cytosolic Grx1 reporter and the peroxisomal marker YFP::Pts.

(6) Rapamycin treatment is shown to rescue several renal phenotypes in Gba1b mutants (water retention, RSC proliferation, FRUMS clearance, lipid peroxidation) but not in Parkin, and mitophagy is not restored in Gba1b. This provides strong evidence that the two models engage distinct pathogenic pathways. However, the therapeutic interpretation feels somewhat overstated. Human relevance should be framed more cautiously, and the conclusions would be stronger with mechanistic markers of autophagy (e.g., Atg8a, Ref(2)p flux in Malpighian tubules) or with experiments varying dose, timing, and duration (short-course vs chronic rapamycin).

We will measure Atg8a, polyubiquitin, and Ref(2)P levels in Gba1b^⁻/⁻ and park^¹/¹ tubules following rapamycin treatment. In our previous study focusing on the gut (Atilano et al., 2023), we showed that rapamycin treatment increased lysosomal area, as assessed using LysoTracker^TM. We will extend this analysis to the renal tubules following rapamycin exposure. Another reviewer requested that we adopt more cautious language regarding the clinical translatability of this work, and we will amend this in Version 2.

(7) Several systemic readouts used to support renal dysfunction (FRUMS clearance, salt stress survival) could also be influenced by general organismal frailty. To ensure these phenotypes are kidney-intrinsic, it would be helpful to include controls such as tissue-specific genetic rescue in Malpighian tubules or nephrocytes, or timing rescue interventions before overt systemic decline. This would strengthen the causal link between renal impairment and the observed systemic phenotypes.

As noted in our response to point 1, we currently lack reliable approaches to manipulate Gba1b in a tissue-specific manner. However, we agree that it is important to distinguish kidney-intrinsic dysfunction from generalised organismal frailty. In the park model, we have already performed renal cell-autonomous rescue: re-expression of Park specifically in Malpighian tubule principal cells (C42-Gal4) throughout adulthood partially normalises water retention, whereas brain-restricted Park expression has no effect on renal phenotypes. Because rescuing Park only in the renal tubules is sufficient to correct a systemic fluid-handling phenotype in otherwise mutant animals, these findings indicate that the systemic defects are driven, at least in part, by renal dysfunction rather than nonspecific organismal frailty.

To strengthen this causal link, we will now extend this same tubule-specific Park rescue (C42-Gal4 and the high-fidelity Malpighian tubule driver CG31272-Gal4) to additional systemic readouts raised by the reviewer. Specifically, we will assay FRUMS clearance and salt stress survival in rescued versus non-rescued park mutants to determine whether renal rescue also mitigates these systemic phenotypes.

Reviewer #2 (Public review):

(1) The authors claim that: "renal system dysfunction negatively impacts both organismal and neuronal health in Gba1b-/- flies, including autophagic-lysosomal status in the brain." This statement implies that renal impairments drive neurodegeneration. However, there is no direct evidence provided linking renal defects to neurodegeneration in this model. It is worth noting that Gba1b-/- flies are a model for neuronopathic Gaucher disease (GD): they accumulate lipids in their brains and present with neurodegeneration and decreased survival, as shown by Kinghorn et al. (The Journal of Neuroscience, 2016, 36, 11654-11670) and by others, which the authors failed to mention (Davis et al., PLoS Genet. 2016, 12: e1005944; Cabasso et al., J Clin Med. 2019, 8:1420; Kawasaki et al., Gene, 2017, 614:49-55).

With the caveats noted in the responses below, we show that driving Nrf2 expression using the renal tubular driver C42 results in decreased survival, more extensive renal defects, and increased brain pathology in Gba1b^⁻/⁻ flies, but not in healthy controls. This suggests that a healthy brain can tolerate renal dysfunction without severe pathological consequences. Our findings therefore indicate that in Gba1b^⁻/⁻ flies, there may be an interaction between renal defects and brain pathology. We do not explicitly claim that renal impairments drive neurodegeneration; rather, we propose that manipulations exacerbating renal dysfunction can have organism-wide effects, ultimately impacting the brain.

The reviewer is correct that our Gba1b^⁻/⁻ fly model represents a neuronopathic GD model with age-related pathology. Indeed, we reproduce the autophagic-lysosomal defects previously reported (Kinghorn et al., 2016) in Figure 5. We agree that the papers cited by the reviewer merit inclusion, and in Version 2 we will incorporate them into the following pre-existing sentence in the Results:

“The gut and brain of Gba1b^⁻/⁻ flies, similar to macrophages in GD patients, are characterised by enlarged lysosomes (Kinghorn et al., 2016; Atilano et al., 2023).”

(2) The authors tested brain pathology in two experiments:

(a) To determine the consequences of abnormal nephrocyte function on brain health, they measured lysosomal area in the brain of Gba1b-/-, Klf15LOF, or stained for polyubiquitin. Klf15 is expressed in nephrocytes and is required for their differentiation. There was no additive effect on the increased lysosomal volume (Figure 3D) or polyubiquitin accumulation (Figure 3E) seen in Gba1b-/- fly brains, implying that loss of nephrocyte viability itself does not exacerbate brain pathology.

(b) The authors tested the consequences of overexpression of the antioxidant regulator Nrf2 in principal cells of the kidney on neuronal health in Gba1b-/- flies, using the c42-GAL4 driver. They claim that "This intervention led to a significant increase in lysosomal puncta number, as assessed by LysoTrackerTM staining (Figure 5D), and exacerbated protein dyshomeostasis, as indicated by polyubiquitin accumulation and increased levels of the ubiquitin-autophagosome trafficker Ref(2)p/p62 in Gba1b-/- fly brains (Figure 5E). Interestingly, Nrf2 overexpression had no significant effect on lysosomal area or ubiquitin puncta in control brains, demonstrating that the antioxidant response specifically in Gba1b-/- flies negatively impacts disease states in the brain and renal system."Notably, c42-GAL4 is a leaky driver, expressed in salivary glands, Malpighian tubules, and pericardial cells (Beyenbach et al., Am. J. Cell Physiol. 318: C1107-C1122, 2020). Expression in pericardial cells may affect heart function, which could explain deterioration in brain function.

Taken together, the contribution of renal dysfunction to brain health remains debatable.

Based on the above, I believe the title should be changed to: Redox Dyshomeostasis Links Renal and Neuronal Dysfunction in Drosophila Models of Gaucher disease. Such a title will reflect the results presented in the manuscript

We agree that C42-Gal4 is a leaky driver; unfortunately, this was true for all commonly used Malpighian tubule drivers available when we began the study. A colleague has recommended CG31272-Gal4 from the Perrimon lab’s recent publication (Xu et al., 2024) as a high-fidelity Malpighian tubule driver. If it proves to maintain principal-cell specificity throughout ageing in our hands, we will repeat key experiments using this driver.

(3) The authors mention that Gba1b is not expressed in the renal system, which means that no renal phenotype can be attributed directly to any known GD pathology. They suggest that systemic factors such as circulating glycosphingolipids or loss of extracellular vesicle-mediated delivery of GCase may mediate renal toxicity. This raises a question about the validity of this model to test pathology in the fly kidney. According to Flybase, there is expression of Gba1b in renal structures of the fly.

Our evidence suggesting that Gba1b is not substantially expressed in renal tissue is based on use of the Gba1b-CRIMIC-Gal4 line, which fails to drive expression of fluorescently tagged proteins in the Malpighian tubules and we have previously shown there is no expression within the nephrocytes with this driver line (Atilano et al., 2023). This does not exclude the possibility that Gba1b functions within the tubules. Notably, Leo Pallanck has provided compelling evidence that Gba1b is present in extracellular vesicles (ECVs) and given the role of the Malpighian tubules in haemolymph filtration, these cells are likely exposed to circulating ECVs. The lysosomal defects observed in Gba1b^⁻/⁻ tubules therefore suggest a potential role for Gba1b in this tissue.  

John Vaughan and Thomas Clandinin have developed mCherry- and Lamp1.V5-tagged Gba1b constructs. We intend to express these in tissues shown by the Pallanck lab to release ECVs (e.g., neurons and muscle) and examine whether the protein can be detected in the tubules.

(4) It is worth mentioning that renal defects are not commonly observed in patients with Gaucher disease. Relevant literature: Becker-Cohen et al., A Comprehensive Assessment of Renal Function in Patients With Gaucher Disease, J. Kidney Diseases, 2005, 46:837-844.

We have identified five references indicating that renal involvement, while rare, does occur in association with GD. We agree that this is a valid citation and will include it in the revised introductory sentence:

“However, renal dysfunction remains a rare symptom in GD patients (Smith et al., 1978; Chander et al., 1979; Siegel et al., 1981; Halevi et al., 1993).”

(5) In the discussion, the authors state: "Together, these findings establish renal degeneration as a driver of systemic decline in Drosophila models of GD and PD..." and go on to discuss a brain-kidney axis in PD. However, since this study investigates a GD model rather than a PD model, I recommend omitting this paragraph, as the connection to PD is speculative and not supported by the presented data.

Our position is that Gba1b^⁻/⁻ represents a neuronopathic Gaucher disease model with mechanistic relevance to PD. The severity of GBA1 mutations correlates with the extent of GBA1/GCase loss of function and, consequently, with increased PD risk. Likewise, biallelic park^⁻/⁻ mutants cause a severe and heritable form of PD, and the Drosophila park^⁻/⁻ model is a well-established and widely recognised system that has been instrumental in elucidating how Parkin and Pink1 mutations drive PD pathogenesis.

We therefore see no reason to omit this paragraph. While some aspects are inherently speculative, such discussion is appropriate and valuable when addressing mechanisms underlying a complex and incompletely understood disease, provided interpretations remain measured. At no point do we claim that our work demonstrates a direct brain-renal axis. Rather, our data indicate that renal dysfunction is a disease-modifying feature in these models, aligning with emerging epidemiological evidence linking PD and renal impairment.

(6) The claim: "If confirmed, our findings could inform new biomarker strategies and therapeutic targets for GBA1 mutation carriers and other at-risk groups. Maintaining renal health may represent a modifiable axis of intervention in neurodegenerative disease," extends beyond the scope of the experimental evidence. The authors should consider tempering this statement or providing supporting data.

(7) The conclusion, "we uncover a critical and previously overlooked role for the renal system in GD and PD pathogenesis," is too strong given the data presented. As no mechanistic link between renal dysfunction and neurodegeneration has been established, this claim should be moderated.

We agree that these sections may currently overstate our findings. In Version 2, we will revise them to ensure our claims remain balanced, while retaining the key points that arise from our data and clearly indicating where conclusions require confirmation (“if confirmed”) or additional study (“warrants further investigation”).

“If confirmed, our findings could inform new biomarker strategies and therapeutic targets for patients with GD and PD. Maintaining renal health may represent a modifiable axis of intervention in these diseases.”

“We uncover a notable and previously underappreciated role for the renal system in GD and PD, which now warrants further investigation.”

(8) The relevance of Parkin mutant flies is questionable, and this section could be removed from the manuscript.

We intend to include the data for the Parkin loss-of-function mutants, as these provide essential support for the PD-related findings discussed in our manuscript. To our knowledge, this represents the first demonstration that Parkin mutants display defects in Malpighian tubule function and water homeostasis. We therefore see no reason to remove these findings. Furthermore, as Reviewer 1 specifically requested additional experiments using the Park fly model, we plan to incorporate these analyses in the revised manuscript.

Minor comments:

(1)  Figure 1G: The FRUMS assay is not shown for Gba1b-/- flies.

The images in Figure 1G illustrate representative stages of dye clearance. We have quantified the clearance time course for both genotypes. During this process, the tubules of Gba1b^⁻/⁻ flies, similar to controls, sequentially resemble each of the three example images. As the Gba1b^⁻/⁻ tubules appear morphologically identical to controls, differing only in population-level clearance dynamics, we do not feel that including additional example images would provide further informative value.

(2) In panels D and F of Figure 2, survival of control and Gba1b-/- flies in the presence of 4% NaCl is presented. However, longevity is different (up to 10 days in D and ~3 days in F for control). The authors should explain this.

We agree. In our experience, feeding-based stress survival assays show considerable variability between experiments, and we therefore interpret results only within individual experimental replicates. We have observed similar variability in oxidative stress, starvation, and xenobiotic survival assays, which may reflect batch-specific or environmental effects.

(3) In Figure 7F, the representative image does not correspond to the quantification; the percentage of endosome-negative nephrocytes seems to be higher for the control than for the park1/1 flies. Please check this.

The example images are correctly oriented. Typically, an endosome-negative nephrocyte shows no dextran uptake, whereas an endosome-positive nephrocyte displays a ring of puncta around the cell periphery. In park¹/¹ mutants, dysfunctional nephrocytes exhibit diffuse dextran staining throughout the cell, accompanied by diffuse DAPI signal, indicating a complete loss of membrane integrity and likely cell death. We have 63× images from the preparations shown in Figure 7F demonstrating this. In Version 2, we will include apical and medial z-slices of the nephrocytes to illustrate these findings (to be added as supplementary   data).

(4) In Figure 7H, the significance between control and park1/1 flies in the FRUMS assay is missing.

We observe significant dye clearance from the haemolymph; however, the difference in complete clearance from the tubules does not reach statistical significance. This may speculatively reflect alterations in specific aspects of tubule function, where absorption and transcellular flux are affected, but subsequent clearance from the tubule lumen remains intact. We do not feel that our current data provide sufficient resolution to draw detailed conclusions about tubule physiology at this level.

Reviewer #3 (Public review):

Weaknesses:

The paper relies mostly on the biallelic Gba1b mutant, which may reflect dysfunction in Gaucher's patients, though this has yet to be fully explored. The claims for the heterozygous allele and a role in Parkinson's is a little more tenuous, making assumptions that heterozygosity is a similar but milder phenotype than the full loss-of-function.

We agree with the reviewer that studying heterozygotes may provide valuable insight into GBA1-associated PD. We will therefore assess whether subtle renal defects are detectable in Gba1b^⁻/⁻ heterozygotes. We clearly state that GBA1 mutations act as a risk factor for PD rather than a Mendelian inherited cause. Consistent with findings from Gba heterozygous mice, Gba1b^⁻/⁻ flies display minimal phenotypes (Kinghorn et al. 2016), and any observable effects are expected to be very mild and age dependent.

(1) Figure 1c, the loss of stellate cells. What age are the MTs shown? Is this progressive or developmental?

These experiments were conducted on flies that were three weeks of age, as were all manipulations unless otherwise stated. We will ensure that this information is clearly indicated in the figure legends in Version 2. We did not observe changes in stellate cell number at three days of age, and this result will be included in the supplementary material in Version 2. Our data therefore suggest that this is a progressive phenotype.

(2) I might have missed this, but for Figure 3, do the mutant flies start with a similar average weight, or are they bloated?

We will perform an age-related time course of water weight in response to Reviewer 1’s comments. For all experiments, fly eggs are age-matched and seeded below saturation density to ensure standardised conditions. Gba1b mutant flies do not exhibit any defects in body size or timing of eclosion.

(3) On 2F, add to the graph that 4% NaCl (or if it is KCL) is present for all conditions, just to make the image self-sufficient to read.

Many thanks for the suggestion. We agree that this will increase clarity and will make this amendment in Version 2 of the manuscript

(4) P13 - rephrase, 'target to either the mitochondria or the cytosol' (as it is phrased, it sounds as though you are doing both at the same time).

We agree and we plan to revise the sentence as follows:

Original:

“To further evaluate the glutathione redox potential (E_GSH) in MTs, we utilised the redox-sensitive green, fluorescent biosensor Grx1-roGFP2, targeted to both the mitochondria and cytosol (Albrecht et al., 2011).”

Revised:

“To further evaluate the glutathione redox potential (E_GSH) in MTs, we utilised the redox-sensitive fluorescent biosensor Grx1-roGFP2, targeted specifically to either the mitochondria or the cytosol using mito- or cyto-tags, respectively (Albrecht et al., 2011).”

(5) In 6F - the staining appears more intense in the Park mutant - perhaps add asterisks or arrowheads to indicate the nephrocytes so that the reader can compare the correct parts of the image?

Reviewer 2 reached the same interpretation. Typically, an endosome-negative nephrocyte shows no dextran uptake, whereas an endosome-positive nephrocyte displays a ring of puncta around the cell periphery. In park¹/¹ mutants, dysfunctional nephrocytes exhibit diffuse dextran staining throughout the cell, accompanied by diffuse DAPI signal, indicative of a complete loss of membrane integrity and likely cell death. We have 63× images from the preparations shown in Figure 7F demonstrating this, and in Version 2 we will include apical and medial z-slices of the nephrocytes to illustrate these findings (to be added as supplementary data).

(6) In the main results text - need some description/explanation of the SOD1 v SOD2 distribution (as it is currently understood) in the cell - SOD2 being predominantly mitochondrial. This helps arguments later on.

Thank you for this suggestion. We plan to amend the text as follows:

“Given that Nrf2 overexpression shortens lifespan in Gba1b^⁻/⁻ flies, we investigated the effects of overexpressing its downstream antioxidant targets, Sod1, Sod2, and CatA, both ubiquitously using the tub-Gal4 driver and with c42-Gal4, which expresses in PCs.”

to:

“Given that Nrf2 overexpression shortens lifespan in Gba1b^⁻/⁻ flies, we investigated the effects of overexpressing its downstream antioxidant targets, Sod1, Sod2, and CatA, both ubiquitously using the tub-Gal4 driver and with c42-Gal4, which expresses in PCs. Sod1 and CatA function primarily in the cytosol and peroxisomes, whereas Sod2 is localised to the mitochondria. Sod1 and Sod2 catalyse the dismutation of superoxide radicals to hydrogen peroxide, while CatA subsequently degrades hydrogen peroxide to water and oxygen.”

(7) Figure 1G, what age are the flies? Same for 3D and E, 4C,D,E, 5B - please check the ages of flies for all of the imaging figures; this information appears to have been missed out.

As stated above, all experiments were conducted on three-week-old flies unless otherwise specified. In Version 2 of the manuscript, we will ensure this information is included consistently in the figure legends to prevent any potential confusion.

The reference preview shows, and links to the equation that belongs to definition 5.48, while the actual 5.17 is just an exercise

eLife Assessment

This work uses enhanced sampling molecular dynamics methods to generate potentially useful information about a conformational change (the DFG flip) that plays a key role in regulating kinase function and inhibitor binding. The focus of the work is on the mechanism of conformational change and how mutations affect the transition. The evidence supporting the conclusions is incomplete.

Reviewer #1 (Public review):

Summary:

The authors used weighted ensemble enhanced sampling molecular dynamics (MD) to test the hypothesis that a double mutant of Abl favors the DFG-in state relative to the WT and therefore causes the drug resistance to imatinib.

Strengths:

The authors employed three novel progress coordinates to sample the DFG flip of ABl. The hypothesis regarding the double mutant's drug resistance is novel.

Weaknesses:

The study contains many uncertain aspects. As such, major conclusions do not appear to be supported.

Comments on revisions:

The authors have addressed some of my concerns, but these concerns remain to be addressed:

(1) Definition of the DFG conformation (in vs out). The authors specified their definition in the revised manuscript, but it has not been validated for a large number of kinases to distinguish between the two states. Thus, I recommend that the authors calculate the FES using another definition (see Tsai et al, JACS 2019, 141, 15092−15101) to confirm their findings. This FES can be included in the SI.

(2) There is no comparison to previous computational work. I would like to see a comparison between the authors' finding of the DFG-in to DFG-out transition and that described in Tsai et al, JACS 2019, 141, 15092−15101.

(3) My previous comment: "The study is not very rigorous. The major conclusions do not appear to be supported. The claim that it is the first unbiased simulation to observe DFG flip is not true. For example, Hanson, Chodera et al (Cell Chem Biol 2019), Paul, Roux et al (JCTC 2020), and Tsai, Shen et al (JACS 2019) have also observed the DFG flip." has not been adequately addressed.

The newly added paragraph clearly does not address my original comment.

"Through our work, we have simulated an ensemble of DFG flip pathways in a wild-type kinase and its variants with atomistic resolution and without the use of biasing forces, also reporting the effects of inhibitor-resistant mutations in the broader context of kinase inactivation likelihood with such level of detail. "

(4) My previous comment, "Setting the DFG-Asp to the protonated state is not justified, because in the DFG-in state, the DFG-Asp is clearly deprotonated." has not been addressed.

In the authors's response stated:

According to previous publications, DFG-Asp is frequently protonated in the DFG-in state of Abl1 kinase. For instance, as quoted from Hanson, Chodera, et al., Cell Chem Bio (2019), "Consistent with previous simulations on the DFG-Asp-out/in interconversion of Abl kinase we only observe the DFG flip with protonated Asp747 ( Shan et al., 2009 ). We showed previously that the pKa for the DFG-Asp in Abl is elevated at 6.5."

Since the pKa of DFG-Asp is 6.5, it should be deprotonated at the physiological pH 7.5. Thus, the fact that the authors used protonated DFG-Asp contradicts this. I am not requesting the authors to redo the entire simulations, but they need to acknowledge this discrepancy and add a brief discussion. See a constant pH study that demonstrates the protonation state population shift for DFG-Asp as the DFG transitions from in to out state (see Tsai et al, JACS 2019, 141, 15092−15101).

Reviewer #2 (Public review):

Summary:

This is a well-written manuscript on the mechanism of the DFG flip in kinases. This conformational change is important for the toggling of kinases between active (DFG-in) and inactive (DFG-out) states. The relative probabilities of these two states are also an important determinant of the affinity of inhibitors for a kinase. However, it is an extremely slow/rare conformational change, making it difficult to capture in simulations. The authors show that weighted ensemble simulations can capture the DFG flip and then delve into the mechanism of this conformational change and the effects of mutations.

Strengths:

The DFG flip is very hard to capture in simulations. Showing that this can be done with relatively little simulation by using enhanced sampling is a valuable contribution. The manuscript gives a nice description of the background for non-experts.

Weaknesses:

The anecdotal approach to presenting the results is disappointing. Molecular processes are stochastic and the authors have expertise in describing such processes. However, they chose to put most statistical analysis in the SI. The main text instead describes the order of events in single "representative" trajectories. The main text makes it sound like these were most selected as they were continuous trajectories from the weighted ensemble simulations. It is preferable to have a description of the highest probability pathway(s) with some quantification of how probable they are. That would give the reader a clear sense of how representative the events described are.

Author response:

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

Reviewer #1:

Specifically, the authors need to define the DFG conformation using criteria accepted in the field, for example, see https://klifs.net/index.php.

We thank the reviewer for this suggestion. In the manuscript, we use pseudodihedral and bond angle-based DFG definitions that have been previously established by literature cited in the study (re-iterated below) to unambiguously define the side-chain conformational states of the DFG motif. As we are interested in the specific mechanics of DFG flips under different conditions, we’ve found that the descriptors defined below are sufficient to distinguish between DFG states and allow a more direct comparison with previously-reported results in the literature using different methods.

We amended the text to be more clear as to those definitions and their choice:

DFG angle definitions:

Phe382/Cg, Asp381/OD2, Lys378/O

Source: Structural Characterization of the Aurora Kinase B "DFG-flip" Using Metadynamics. Lakkaniga NR, Balasubramaniam M, Zhang S, Frett B, Li HY. AAPS J. 2019 Dec 18;22(1):14. doi: 10.1208/s12248-019-0399-6. PMID: 31853739; PMCID: PMC7905835.

“Finally, we chose the angle formed by Phe382's gamma carbon, Asp381's protonated side chain oxygen (OD2), and Lys378's backbone oxygen as PC3 based on observations from a study that used a similar PC to sample the DFG flip in Aurora Kinase B using metadynamics \cite{Lakkaniga2019}. This angular PC3 should increase or decrease (based on the pathway) during the DFG flip, with peak differences at intermediate DFG configurations, and then revert to its initial state when the flip concludes.”

DFG pseudodihedral definitions:

Ala380/Cb, Ala380/Ca, Asp381/Ca, Asp381/Cg

Ala380/Cb, Ala380/CA, Phe382/CA, Phe382Cg

Source: Computational Study of the “DFG-Flip” Conformational Transition in c-Abl and c-Src Tyrosine Kinases. Yilin Meng, Yen-lin Lin, and Benoît Roux The Journal of Physical Chemistry B 2015 119 (4), 1443-1456 DOI: 10.1021/jp511792a

“For downstream analysis, we used two pseudodihedrals previously defined in the existing Abl1 DFG flip simulation literature \cite{Meng2015} to identify and discriminate between DFG states. The first (dihedral 1) tracks the flip state of Asp381, and is formed by the beta carbon of Ala380, the alpha carbon of Ala380, the alpha carbon of Asp381, and the gamma carbon of Asp381. The second (dihedral 2) tracks the flip state of Phe382, and is formed by the beta carbon of Ala380, the alpha carbon of Ala380, the alpha carbon of Phe381, and the gamma carbon of Phe381. These pseudodihedrals, when plotted in relation to each other, clearly distinguish between the initial DFG-in state, the target DFG-out state, and potential intermediate states in which either Asp381 or Phe381 has flipped.”

Convergence needs to be demonstrated for estimating the population difference between different conformational states.

We agree that demonstrating convergence is important for accurate estimations of population differences between conformational states. However, as the DFG flip is a complex and concerted conformational change with an energy barrier of 30 kcal/mol [1], and considering the traditional limitations of methods like weighted ensemble molecular dynamics (WEMD), it would take an unrealistic amount of GPU time (months) to observe convergence in our simulations. As discussed in the text (see examples below), we caveat our energy estimations by explicitly mentioning that the state populations we report are not converged and are indicative of a much larger energy barrier in the mutant.

“These relative probabilities qualitatively agree with the large expected free energy barrier for the DFG-in to DFG-out transition (~32 kcal/mol), and with our observation of a putative metastable DFG-inter state that is missed by NMR experiments due to its low occupancy.”

“As an important caveat, it is unlikely that the DFG flip free energy barriers of over 70 kcal/mol estimated for the Abl1 drug-resistant variants quantitatively match the expected free energy barrier for their inactivation. Rather, our approximate free energy barriers are a symptom of the markedly increased simulation time required to sample the DFG flip in the variants relative to the wild-type, which is a strong indicator of the drastically reduced propensity of the variants to complete the DFG flip. Although longer WE simulations could allow us to access the timescales necessary for more accurately sampling the free energy barriers associated with the DFG flip in Abl1's drug-resistant compound mutants, the computational expense of running WE for 200 iterations is already large (three weeks with 8 NVIDIA RTX3900 GPUs for one replicate); this poses a logistical barrier to attempting to sample sufficient events to be able to fully characterize how the reaction path and free energy barrier change for the flip associated with the mutations. Regardless, the results of our WE simulations resoundingly show that the Glu255Lys/Val and Thr315Ile compound mutations drastically reduce the probability for DFG flip events in Abl1.”

(1) Conformational states dynamically populated by a kinase determine its function. Tao Xie et al., Science 370, eabc2754 (2020). DOI:10.1126/science.abc2754

The DFG flip needs to be sampled several times to establish free energy difference.

Our simulations have captured thousands of correlated and dozens of uncorrelated DFG flip events. The per-replicate free energy differences are computed based on the correlated transitions. Please consult the WEMD literature (referenced below and in the manuscript, references 34 and 36) for more information on how WEMD allows the sampling of multiple such events and subsequent estimation of probabilities:

Zuckermann et al (2017) 10.1146/annurev-biophys-070816-033834

Chong et al (2021) 10.1021/acs.jctc.1c01154

The free energy plots do not appear to show an intermediate state as claimed.

Both the free energy plots and the representative/anecdotal trajectories analyzed in the study show a saddle point when Asp381 has flipped but Phe382 has not (which defines the DFG-inter state), we observe a distinct change in probability when going to the pseudodihedral values associated with DFG-inter to DFG-up or DFG-out. We removed references to the putative state S1 as we we agree with the reviewer that its presence is unlikely given the data we show.

The trajectory length of 7 ns in both Figure 2 and Figure 4 needs to be verified, as it is extremely short for a DFG flip that has a high free energy barrier.

We appreciate this point. To clarify, the 7 ns segments corresponds to a collated trajectory extracted from the tens of thousands of walkers that compose the WEMD ensemble, and represent just the specific moment at which the dihedral flips occur rather than the entire flip process. On average, our WEMD simulations sample over 3 us of aggregate simulation time before the first DFG flip event is observed, in line with a high energy barrier. This is made clear in the manuscript excerpt below: “Over an aggregate simulation time of over 20 $\mu$s, we have collected dozens of uncorrelated and unbiased inactivation events, starting from the lowest energy conformation of the Abl1 kinase core (PDB 6XR6) \cite{Xie2020}.”

The free energy scale (100 kT) appears to be one order of magnitude too large.

As discussed in the text and quoted in response to comment 2, the exponential splitting nature of WEMD simulations (where the probability of individual walkers are split upon crossing each bin threshold) often leads to unrealistically high energy barriers for rare events. This is not unexpected, and as discussed in the text, we consider that value to be a qualitative measurement of the decreased probability of a DFG flip in Abl1 mutants, and not a direct measurement of energy barriers.

Setting the DFG-Asp to the protonated state is not justified, because in the DFG-in state, the DFG-Asp is clearly deprotonated.

According to previous publications, DFG-Asp is frequently protonated in the DFG-in state of Abl1 kinase. For instance, as quoted from Hanson, Chodera, et al., Cell Chem Bio (2019), “C onsistent with previous simulations on the DFG-Asp-out/in interconversion of Abl kinase we only observe the DFG flip with protonated Asp747 ( Shan et al., 2009 ). We showed previously that the pKa for the DFG-Asp in Abl is elevated at 6.5.”

Finally, the authors should discuss their work in the context of the enormous progress made in theoretical studies and mechanistic understanding of the conformational landscape of protein kinases in the last two decades, particularly with regard to the DFG flip. and The study is not very rigorous. The major conclusions do not appear to be supported. The claim that it is the first unbiased simulation to observe DFG flip is not true. For example, Hanson, Chodera et al (Cell Chem Biol 2019), Paul, Roux et al (JCTC 2020), and Tsai, Shen et al (JACS 2019) have also observed the DFG flip.

We thank the reviewer for pointing out these issues. We have revised the manuscript to better contextualize our claims within the limitations of the method and to acknowledge previous work by Hanson, Chodera et al., Paul, Roux et al., and Tsai, Shen et al.

The updated excerpt is described below

“Through our work, we have simulated an ensemble of DFG flip pathways in a wild-type kinase and its variants with atomistic resolution and without the use of biasing forces, also reporting the effects of inhibitor-resistant mutations in the broader context of kinase inactivation likelihood with such level of detail. “

Reviewer #2:

I appreciated the discussion of the strengths/weaknesses of weighted ensemble simulations. Am I correct that this method doesn't do anything to explicitly enhance sampling along orthogonal degrees of freedom? Maybe a point worth mentioning if so.

Yes, this is correct. We added a sentence to WEMD summary section of Results and Discussion discussing it.

“As a supervised enhanced sampling method, WE employs progress coordinates (PCs) to track the time-dependent evolution of a system from one or more basis states towards a target state. Although weighted ensemble simulations are unbiased in the sense that no biasing forces are added over the course of the simulations, the selection of progress coordinates and the bin definitions can potentially bias the results towards specific pathways \cite{Zuckerman2017}. Additionally, traditional WEMD simulations do not explicitly enhance sampling along orthogonal degrees of freedom (those not captured by the progress coordinates). In practice, this means that insufficient PC definitions can lead to poor sampling.”

I don't understand Figure 3C. Could the authors instead show structures corresponding to each of the states in 3B, and maybe also a representative structure for pathways 1 and 2?

We have remade Figure 3. We removed 3B and accompanying discussion as upon review we were not confident on the significance of the LPATH results where it pertains to the probability of intermediate states. We replaced 3B with a summary of the pathways 1 and 2 in regards to the Phe382 flip (which is the most contrasting difference).

Why introduce S1 and DFG-inter? And why suppose that DFG-inter is what corresponds to the excited state seen by NMR?

As a consequence of dropping the LPATH analysis, we also removed mentions to S1 as it further analysis made it hard to distinguish from DFG-in, For DFG-inter, we mention that conformation because (a) it is shared by both flipping mechanisms that we have found, and (b) it seems relevant for pharmacology, as it has been observed in other kinases such as Aurora B (PDB 2WTV), as Asp381 flipping before Phe382 creates space in the orthosteric kinase pocket which could be potentially targeted by an inhibitor.

It would be nice to have error bars on the populations reported in Figure 3.

Agreed, upon review we decided do drop the populations as we were not confident on the significance of the LPATH results where it pertains to the probability of intermediate states.

I'm confused by the attempt to relate the relative probabilities of states to the 32 kca/mol barrier previously reported between the states. The barrier height should be related to the probability of a transition. The DFG-out state could be equiprobable with the DFG-in state and still have a 32 kcal/mol barrier separating them.

Thanks for the correction, we agree with the reviewer and have amended the discussion to reflect this. Since we are starting our simulations in the DFG-in state, the probability of walkers arriving in DFG-out in our steady state WEMD simulations should (assuming proper sampling) represent the probability of the transition. We incorrectly associated the probability of the DFG-out state itself with the probability of the transition.

How do the relative probabilities of the DFG-in/out states compare to experiments, like NMR?

Previous NMR work has found the population of apo DFG in (PDB 6XR6) in solution to be around 88% for wild-type ABL1, and 6% for DFG out (PDB 6XR7). The remaining 6% represents post-DFG-out state (PDB 6XRG) where the activation loop has folded in near the hinge, which we did not simulate due to the computational cost associated with it. The same study reports the barrier height from DFG-in to DFG-out to be estimated at around 30 kcal/mol.

(1) Conformational states dynamically populated by a kinase determine its function. Tao Xie et al., Science 370, eabc2754 (2020). DOI:10.1126/science.abc2754

(we already have that in the text, just need to quote here)

“Do the staggered and concerted DFG flip pathways mentioned correspond to pathways 1 and 2 in Figure 3B, or is that a concept from previous literature?”

Yes, we have amended Figure 3B to be clearer. In previous literature both pathways have been observed [1], although not specifically defined.

Source: Computational Study of the “DFG-Flip” Conformational Transition in c-Abl and c-Src Tyrosine Kinases. Yilin Meng, Yen-lin Lin, and Benoît Roux The Journal of Physical Chemistry B 2015 119 (4), 1443-1456 DOI: 10.1021/jp511792a

eLife Assessment

This work uses enhanced sampling molecular dynamics methods to generate potentially useful information about a conformational change (the DFG flip) that plays a key role in regulating kinase function and inhibitor binding. The focus of the work is on the mechanism of conformational change and how mutations affect the transition. The evidence supporting the conclusions is incomplete.

Reviewer #1 (Public review):

Summary:

The authors used weighted ensemble enhanced sampling molecular dynamics (MD) to test the hypothesis that a double mutant of Abl favors the DFG-in state relative to the WT and therefore causes the drug resistance to imatinib.

Strengths:

The authors employed the state-of-the-art weighted ensemble MD simulations with three novel progress coordinates to explore the conformational changes the DFG motif of Abl kinase. The hypothesis regarding the double mutant's drug resistance is novel.

Weaknesses:

The study contains many uncertain aspects. A major revision is needed to strengthen the support for the conclusions.

(1) Specifically, the authors need to define the DFG conformation using criteria accepted in the field, for example, see https://klifs.net/index.php.

(2) Convergence needs to be demonstrated for estimating the population difference between different conformational states.

(3) The DFG flip needs to be sampled several times to establish free energy difference.

(4) The free energy plots do not appear to show an intermediate state as claimed.

(5) The trajectory length of 7 ns in both Figure 2 and Figure 4 needs to be verified, as it is extremely short for a DFG flip that has a high free energy barrier.

(6) The free energy scale (100 kT) appears to be one order of magnitude too large.

(7) Setting the DFG-Asp to the protonated state is not justified, because in the DFG-in state, the DFG-Asp is clearly deprotonated.

(8) Finally, the authors should discuss their work in the context of the enormous progress made in theoretical studies and mechanistic understanding of the conformational landscape of protein kinases in the last two decades, particularly with regard to the DFG flip.

Reviewer #2 (Public review):

Summary:

This is a well-written manuscript on the mechanism of the DFG flip in kinases. This conformational change is important for the toggling of kinases between active (DFG-in) and inactive (DFG-out) states. The relative probabilities of these two states are also an important determinant of the affinity of inhibitors for a kinase. However, it is an extremely slow/rare conformational change, making it difficult to capture in simulations. The authors show that weighted ensemble simulations can capture the DFG flip and then delve into the mechanism of this conformational change and the effects of mutations.

Strengths:

The DFG flip is very hard to capture in simulations. Showing that this can be done with relatively little simulation by using enhanced sampling is a valuable contribution. The manuscript gives a nice description of the background for non-experts.

Weaknesses:

I was disappointed by the anecdotal approach to presenting the results. Molecular processes are stochastic and the authors have expertise in describing such processes. However, they chose to put most statistical analysis in the SI. The main text instead describes the order of events in single "representative" trajectories. The main text makes it sound like these were most selected as they were continuous trajectories from the weighted ensemble simulations. I would much rather hear a description of the highest probability pathway(s) with some quantification of how probable they are. That would give the reader a clear sense of how representative the events described are.

I appreciated the discussion of the strengths/weaknesses of weighted ensemble simulations. Am I correct that this method doesn't do anything to explicitly enhance sampling along orthogonal degrees of freedom? Maybe a point worth mentioning if so.

I don't understand Figure 3C. Could the authors instead show structures corresponding to each of the states in 3B, and maybe also a representative structure for pathways 1 and 2?

Why introduce S1 and DFG-inter? And why suppose that DFG-inter is what corresponds to the excited state seen by NMR?

It would be nice to have error bars on the populations reported in Figure 3.

I'm confused by the attempt to relate the relative probabilities of states to the 32 kca/mol barrier previously reported between the states. The barrier height should be related to the probability of a transition. The DFG-out state could be equiprobable with the DFG-in state and still have a 32 kcal/mol barrier separating them.

How do the relative probabilities of the DFG-in/out states compare to experiments, like NMR?

Do the staggered and concerted DFG flip pathways mentioned correspond to pathways 1 and 2 in Figure 3B, or is that a concept from previous literature?

This is another fascinating model from Zappos. I had no idea they were so creative in their business models and the way they operate. I think this makes them stand out but I am just now learning this so apparently it was before things could go viral. I think if this was happening right now with tik tok and other social media people would be all over a customer service representative for Zappos that can help you with shoes and also tell you what to watch on Netflix.

This is really interesting and I am surprised I have never heard of this before. Honestly I feel like this is a dangerous model because I don’t know how many people feel that “loyal” to a low level retail company like Zappos. This would be different if it was a company like Nike where employees are so committed that you could offer thousands and I don’t think they would take it and leave.

Introns are absent in mRNAs - alas, only the length of the spliced exons can be seen, but not exon intron structure

Lack of selective pressure from HIV in african populations? Perhaps you meant smallpox or bubonic plague

Not on brightspace, but just down below

eLife Assessment

In this valuable study, the authors present traces of bone modification on ~1.8 million-year-old proboscidean remains from Tanzania, which they infer to be the earliest evidence for stone-tool-assisted megafaunal consumption by hominins. Challenging published claims, the authors argue that persistent megafaunal exploitation roughly coincided with the earliest Achulean tools. Notwithstanding the rich descriptive and spatial data, the behavioral inferences about hominin agency rely on traces (such as bone fracture patterns and spatial overlap) that are not unequivocal; the evidence presented to support the inferences thus remains incomplete. Given the implications of the timing and extent of hominin consumption of nutritious and energy-dense food resources, as well as of bone toolmaking, the findings of this study will be of interest to paleoanthropologists and other evolutionary biologists.

Reviewer #1 (Public review):

Domínguez-Rodrigo and colleagues make a moderately convincing case for habitual elephant butchery by Early Pleistocene hominins at Olduvai Gorge (Tanzania), ca. 1.8-1.7 million years ago. They present this at the site scale (the EAK locality, which they excavated), as well as across the penecontemporaneous landscape, analyzing a series of findspots that contain stone tools and large-mammal bones. The latter are primarily elephants, but giraffids and bovids were also butchered in a few localities. The authors claim that this is the earliest well-documented evidence for elephant butchery; doing so requires debunking other purported cases of elephant butchery in the literature, or in one case, reinterpreting elephant bone manipulation as being nutritional (fracturing to obtain marrow) rather than technological (to make bone tools). The authors' critical discussion of these cases may not be consensual, but it surely advances the scientific discourse. The authors conclude by suggesting that an evolutionary threshold was achieved at ca. 1.8 ma, whereby regular elephant consumption rich in fats and perhaps food surplus, more advanced extractive technology (the Acheulian toolkit), and larger human group size had coincided.

The fieldwork and spatial statistics methods are presented in detail and are solid and helpful, especially the excellent description (all too rare in zooarchaeology papers) of bone conservation and preservation procedures. However, the methods of the zooarchaeological and taphonomic analysis - the core of the study - are peculiarly missing. Some of these are explained along the manuscript, but not in a standard Methods paragraph with suitable references and an explicit account of how the authors recorded bone-surface modifications and the mode of bone fragmentation. This seems more of a technical omission that can be easily fixed than a true shortcoming of the study. The results are detailed and clearly presented.

By and large, the authors achieved their aims, showcasing recurring elephant butchery in 1.8-1.7 million-year-old archaeological contexts. Nevertheless, some ambiguity surrounds the evolutionary significance part. The authors emphasize the temporal and spatial correlation of (1) elephant butchery, (2) Acheulian toolkits, and (3) larger sites, but do not actually discuss how these elements may be causally related. Is it not possible that larger group size or the adoption of Acheulian technology have nothing to do with megafaunal exploitation? Alternative hypotheses exist, and at least, the authors should try to defend the causation, not just put forward the correlation. The only exception is briefly mentioning food surplus as a "significant advantage", but how exactly, in the absence of food-preservation technologies? Moreover, in a landscape full of aggressive scavengers, such excess carcass parts may become a death trap for hominins, not an advantage. I do think that demonstrating habitual butchery bears very significant implications for human evolution, but more effort should be invested in explaining how this might have worked.

Overall, this is an interesting manuscript of broad interest that presents original data and interpretations from the Early Pleistocene archaeology of Olduvai Gorge. These observations and the authors' critical review of previously published evidence are an important contribution that will form the basis for building models of Early Pleistocene hominin adaptation.

Reviewer #2 (Public review):

The authors argue that the Emiliano Aguirre Korongo (EAK) assemblage from the base of Bed II at Olduvai Gorge shows systematic exploitation of elephants by hominins about 1.78 million years ago. They describe it as the earliest clear case of proboscidean butchery at Olduvai and link it to a larger behavioral shift from the Oldowan to the Acheulean.

The paper includes detailed faunal and spatial data. The excavation and mapping methods appear to be careful, and the figures and tables effectively document the assemblage. The data presentation is strong, but the behavioral interpretation is not supported by the evidence.

The claim for butchery is based mainly on the presence of green-bone fractures and the proximity of bones and stone artifacts. These observations do not prove human activity. Fractures of this kind can form naturally when bones break while still fresh, and spatial overlap can result from post-depositional processes. The studies cited to support these points, including work by Haynes and colleagues, explain that such traces alone are not diagnostic of butchery, but this paper presents them as if they were.

The spatial analyses are technically correct, but their interpretation extends beyond what they can demonstrate. Clustering indicates proximity, not behavior. The claim that statistical results demonstrate a functional link between bones and artifacts is not justified. Other studies that use these methods combine them with direct modification evidence, which is lacking in this case.

The discussion treats different bodies of evidence unevenly. Well-documented cut-marked specimens from Nyayanga and other sites are described as uncertain, while less direct evidence at EAK is treated as decisive. This selective approach weakens the argument and creates inconsistency in how evidence is judged.

The broader evolutionary conclusions are not supported by the data. The paper presents EAK as marking the start of systematic megafaunal exploitation, but the evidence does not show this. The assemblage is described well, but the behavioral and evolutionary interpretations extend far beyond what can be demonstrated.

Physicians should not blindly listen to AI, as they went to school for a reason. It should be an addition to your present intellect and knowledge on the situation, not your replacement.

Over time, the AI has hurt the efficiency and work being done in the hospital. This could be due to the change in habit, but most likely due to the addition of AI specifically.

With a plan like this, they can maintain efficiency along with accuracy of solutions as AI can be used for the more mentally taxing or demanding tasks, which allows for more doctors to focus on other tasks.

AI is not always necessarily a fix for a problem in the workplace, especially where people's lives are on the line.

Even some patients would rather have AI helping the doctor, just showing how far we have gone in trusting AI.

Collaboration allows for growth and advancement for both the worker and the AI.

AI is incredibly powerful and intelligent when applied properly, able to find potential solutions to diseases without cures, which could be really useful, but also really concerning that it can do something like that so easily.

Despite AI having complete access to all of the internet, it is still limited in its capabilities and access, which is where humans come in to work with AI rather than one or the other.

eLife Assessment

This study presents a valuable finding on mutations in ZNF217, ZNF703, and ZNF750 through 23 breast cancer samples alongside matched normal tissues in Kenyan breast cancer patients. The evidence supporting the claims of the authors is solid, yet the analysis of the manuscript lacks methodological transparency, statistical detail, and sufficient comparison with existing large-scale datasets. The work will be of interest to medical biologists and scientists working in the field of breast cancer.

Reviewer #1 (Public review):

Summary:

This manuscript investigates mutations and expression patterns of zinc finger proteins in Kenyan breast cancer patients.

Strengths:

Whole-exome sequencing and RNA-seq were performed on 23 breast cancer samples alongside matched normal tissues in Kenyan breast cancer patients. The authors identified mutations in ZNF217, ZNF703, and ZNF750.

Weaknesses:

(1) Research scope:

The results primarily focus on mutations in ZNF217, ZNF703, and ZNF750, with limited correlation analyses between mutations and gene expression. The rationale for focusing only on these genes is unclear. Given the availability of large breast cancer cohorts such as TCGA and METABRIC, the authors should compare their mutation profiles with these datasets. Beyond European and U.S. cohorts, sequencing data from multiple countries, including a recent Nigerian breast cancer study (doi: 10.1038/s41467-021-27079-w), should also be considered. Since whole-exome sequencing was performed, it is unclear why only four genes were highlighted and why comparisons to previous literature were not included.

(2) Language and Style Issues:

Several statements read somewhat 'unnaturally', and I strongly recommend proofreading.

(3) Methods and Data Analysis Details:

The methods section is vague, with general descriptions rather than specific details of data processing and analysis. The authors should provide:

(a) Parameters used for trimming, mapping, and variant calling (rather than referencing another paper such as Tang et al. 2023).

(b) Statistical methods for somatic mutation/SNP detection.

(c) Details of RNA purification and RNA-seq library preparation.

Without these details, the reproducibility of the study is limited.

(4) Data Reporting:

This study has the potential to provide a valuable resource for the field. However, data-sharing plans are unclear. The authors should:

(a) deposit sequencing data in a public repository.

(b) provide supplementary tables listing all detected mutations and all differentially expressed genes (DEGs).

(c) clarify whether raw or adjusted p-values were used for DEG analysis.

(d) perform DEG analyses stratified by breast cancer subtypes, since differential expression was observed by HER2 status, and some zinc finger proteins are known to be enriched in luminal subtypes.

(5) Mutation Analysis:

Visualizations of mutation distribution across protein domains would greatly strengthen interpretation. Comparing mutation distribution and frequency with published datasets would also contextualize the findings.

Reviewer #2 (Public review):

Summary:

This work integrated the mutational landscape and expression profile of ZNF molecules in 23 Kenyan women with breast cancer.

Strengths:

The mutation landscape of ZNF217, ZNF703, and ZNF750 was comprehensively studied and correlated with tumor stage and HER2 status to highlight the clinical significance.

Weaknesses:

The current study design is relatively simple, and there is a limited cohort size, which is relatively small to reach significant findings. Thus, sample size enrichment, along with more analytic work, is needed.

Targeted exploration of the ZNF family without emphasizing the reason or clinical significance hinders the overall significance of the entire work.

Reviewer #3 (Public review):

Summary:

The authors aimed to define the somatic mutational landscape and transcriptomic expression of the ZNF217, ZNF703, and ZNF750 genes in breast cancers from Kenyan women and to investigate associations with clinicopathological features like HER2 status and cancer stage. They employed whole-exome and RNA-sequencing on 23 paired tumor-normal samples to achieve this.

Strengths:

(1) A major strength is the focus on a Kenyan cohort, addressing a critical gap in genomic studies of breast cancer, which are predominantly based on European or Asian populations.

(2) The integration of DNA- and RNA-level data from the same patients provides a comprehensive view, linking genetic alterations to expression changes.

Weaknesses:

(1) The small cohort size (n=23) significantly limits the statistical power to detect associations between genetic features and clinical subgroups (e.g., HER2 status, stage), rendering the negative findings inconclusive.

(2) The study is primarily descriptive. While it effectively catalogs mutations and expression changes, it does not include functional experiments to validate the biological impact of the identified alterations.

eLife Assessment

Clonal hematopoiesis of indeterminate potential (CHIP) is a known risk factor for coronary artery disease, though its precise role in disease progression continues to emerge. This study leverages valuable single-cell RNA data from patients with CHIP mutations and controls to predict key interactions between endothelial cells and monocytes. Using an AI prediction model, the authors identify druggable targets that mediate immune cell interactions in CHIP and provide solid evidence to support their findings.

Reviewer #1 (Public review):

Summary:

Using single-cell RNA sequencing and bioinformatics approaches, the authors aimed to discover if and how cells carrying mutations common to clonal haematopoiesis were more adherent to endothelial cells.

Strengths:

(1) The authors used matched blood and adipose tissue samples from the same patients (with the exception of the control people) to conduct their analysis.

(2) The use of bioinformatics and in-silico approaches helped to fast-track their aims to test specific inhibitors in their model cell adhesion system.

Weaknesses:

(1) The analysis was done on pooled cells; it would have been interesting to know if the same adhesion gene signatures were observed across the donors.

(2) The adhesion assays were conducted under static conditions; shear flow adhesion experiments would have been better. Mixed cultures using cell trackers would have been even better.

(3) In the intervention studies, the authors should have directly targeted the monocytes (not the endothelial cells) and should have also included DNMT3A mutant/KO cells to show specificity to TET2 CHIP.

Reviewer #2 (Public review):

Summary:

The authors describe potential mechanisms underlying the changes in endothelial-monocyte interactions in patients with clonal hematopoiesis of indeterminate potential (CHIP), including reduced velocity and increased ligand interactions of CHIP-mutated monocytes. They use a combination of transcriptomics (some for the first time in these tissues in patients with CHIP), in silico analyses, and ex vivo approaches to outline the changes that occur in blood monocytes derived from patients with CHIP. These findings advance the current field, which has previously mostly used mice and/or has been focused on cancer outcomes. The authors identify distinct alterations in signaling downstream of DNTM3A or TET2 mutations, which further distinguish two major mutations that contribute to CHIP.

Strengths:

(1) Combinatorial transcriptomics was used to identify potential therapeutic targets, which is an important proof-of-concept for multiple fields.

(2) The authors identify distinct ligand interactions downstream of TET2 and DNMT3A mutations.

Weaknesses:

(1) The authors extrapolate findings in adipose tissue in diabetic patients to vascular disease (ostensibly in the carotid or cardiac arteries), citing the difficulty of using tissue-matched samples. Broad-reaching conclusions need to be backed up in the relevant systems, considering how different endothelial cells in various vascular beds react. Considering these data were obtained with n=3 patients being sufficient to identify these changes, it seems that this can be performed (perhaps in silico) in the correct tissue.

(2) The selection/exclusion criteria for the diabetes samples are not noted, and therefore, the relevant conclusions cannot be fully evaluated, nor is the source of adipose tissue stated.

Appraisal:

While authors describe how to as well as the technical feasibility of integrating a number of transcriptomic techniques, they do not seem to do so to produce highly compelling data or targets within this manuscript. The potential is there to drill down to mechanisms; however, the data gathered herein do not highlight novel targets. For example, CXCL2 and 3 are already shown to be differentially expressed in TET2 loss combined with LDL treatment in the macrophages of mice. Furthermore, these authors then show that in humans, the prototypical CXC chemokine, IL8 (which mice lack), is significantly higher in TET2-mutated patients (DOI: 10.1056/NEJMoa1701719). The authors should demonstrate the utility of their transcriptomics by identifying and testing novel targets and focusing on the proper disease states. This could easily be a deep dive into CHIP in adipose tissue in diabetic patients.

eLife Assessment

This important study presents a thoughtful design and characterization of chimeric influenza hemagglutinin (HA) head domains combining elements of distinct receptor-binding sites. The results provide convincing evidence that polyclonal cross-group responses to influenza A virus can be elicited by a single immunization. While the mechanistic basis of heterotrimer formation and immunodominance differences remains unclear, the authors provide new insights for protein design, vaccinology, and computational vaccine design.

Reviewer #1 (Public review):

Summary:

The study by Castro et al. presents an interesting blueprint for designing influenza immunogens that can induce cross-group influenza-specific antibodies. The authors used a structure-based design to transplant receptor binding site (RBS) residues from H5 and H3 into an H1 scaffold. In addition, they assembled the transplanted structures as heterotrimers. They characterized the constructs structurally and used them to immunize mice to define ELISA binding and neutralizing antibodies (Abs) to different influenza strains.

Strengths and Weaknesses:

The authors succeeded in generating the different, correctly folded immunogens. The heterotrimers would benefit from more characterization: it remains unclear whether they are even formed or whether the sample is a mix of homotrimers and whether some combinations are more likely than others. While some of these questions are complex to answer, authors should at least confirm the presence of heterotrimers.

While all constructs were able to elicit H1-specific Abs, different immunogens displayed differential ability to induce a response to the transplanted epitope. While H3-transplant resulted in H3-specific Abs, this was not the case for H5 or the heterotrimers. The importance of the finding is that authors are able to elicit polyclonal Abs neutralizing group 1 and group 2 influenza viruses with a single immunogen. A more in-depth discussion on why the H3-transplant but not the H5-transplant resulted in those specific Abs could be beneficial.

Overall, the work is a proof of concept that H1-H3 chimeric proteins can be produced and an important first step towards computational vaccines, inducing Abs to multiple groups.

Reviewer #2 (Public review):

Summary:

The manuscript from Castro et al describes the engineering of influenza hemagglutinin H1-based head domains that display receptor-binding-site residues from H5 and H3 HAs. The initial head-only chimeras were able to bind to FluA20, which recognizes the trimer interface, but did not bind well to H5 or H3-specific antibodies. Furthermore, these constructs were not particularly stable in solution as assessed by low melting temperatures. Crystal structures of each chimeric head in complex with FluA20 were obtained, demonstrating that the constructs could adopt the intended conformation upon stabilization with FluA20. The authors next placed the chimeric heads onto an H1 stalk to create homotrimeric HA ectodomains, as well as a heterotrimeric HA ectodomain. The homotrimeric chimeric HAs were better behaved in solution, and H3- and H5-specific antibodies bound to these trimers with affinities that were only about 10-fold weaker compared to their respective wildtype HAs. The heterotrimeric chimeric HA showed transient stability in solution and could bind more weakly to the H3- and H5-specific antibodies. Mice immunized with these trimers elicited cross-reactive binding antibodies, although the cross-neutralizing titers were less robust. The most positive result was that the H1H3 trimer was able to elicit sera that neutralized both H1 and H3 viruses.

Strengths:

The manuscript is very well-written with clear figures. The biophysical and structural characterizations of the antigen were performed to a high standard. The engineering approach is novel, and the results should provide a basis for further iteration and improvement of RBS transplantation.

Weaknesses:

The main limitation of the study is that there are no statistical tests performed for the immunogenicity results shown in Figures 4 and 5. It is therefore unknown whether the differences observed are statistically significant. Additionally, fits of the BLI data in Figure 3 to the binding model used to determine the binding constants should be shown.

eLife Assessment

This fundamental work reveals that the accessibility of the unstructured C-terminal tails of α- and β-tubulins differs with the state of the microtubule lattice. Their accessibility increases with the expansion of the lattice induced by GTP and certain MAPs, which can then dictate the subsequent interactions between MAPs and microtubules, and post-translational modifications of tubulin tails. The evidence supporting the conclusion is compelling, although the characterisation of the probes does not answer whether they directly affect the lattice or expose the C-terminal tails of tubulin. This work will be of great interest to the cytoskeleton field.

Reviewer #1 (Public review):

Summary:

This is a careful and comprehensive study demonstrating that effector-dependent conformational switching of the MT lattice from compacted to expanded deploys the alpha tubulin C-terminal tails so as to enhance their ability to bind interactors.

Strengths:

The authors use 3 different sensors for the exposure of the alpha CTTs. They show that all 3 sensors report exposure of the alpha CTTs when the lattice is expanded by GMPCPP, or KIF1C, or a hydrolysis-deficient tubulin. They demonstrate that expansion-dependent exposure of the alpha CTTs works in tissue culture cells as well as in vitro.

Weaknesses:

There is no information on the status of the beta tubulin CTTs. The study is done with mixed isotype microtubules, both in cells and in vitro. It remains unclear whether all the alpha tubulins in a mixed isotype microtubule lattice behave equivalently, or whether the effect is tubulin isotype-dependent. It remains unclear whether local binding of effectors can locally expand the lattice and locally expose the alpha CTTs.

Appraisal:

The authors have gone to considerable lengths to test their hypothesis that microtubule expansion favours deployment of the alpha tubulin C-terminal tail, allowing its interactors, including detyrosinase enzymes, to bind. There is a real prospect that this will change thinking in the field. One very interesting possibility, touched on by the authors, is that the requirement for MAP7 to engage kinesin with the MT might include a direct effect of MAP7 on lattice expansion.

Impact:

The possibility that the interactions of MAPS and motors with a particular MT or region feed forward to determine its future interaction patterns is made much more real. Genuinely exciting.

Reviewer #2 (Public review):

The unstructured α- and β-tubulin C-terminal tails (CTTs), which differ between tubulin isoforms, extend from the surface of the microtubule, are post-translationally modified, and help regulate the function of MAPs and motors. Their dynamics and extent of interactions with the microtubule lattice are not well understood. Hotta et al. explore this using a set of three distinct probes that bind to the CTTs of tyrosinated (native) α-tubulin. Under normal cellular conditions, these probes associate with microtubules only to a limited extent, but this binding can be enhanced by various manipulations thought to alter the tubulin lattice conformation (expanded or compact). These include small-molecule treatment (Taxol), changes in nucleotide state, and the binding of microtubule-associated proteins and motors. Overall, the authors conclude that microtubule lattice "expanders" promote probe binding, suggesting that the CTT is generally more accessible under these conditions. Consistent with this, detyrosination is enhanced. Mechanistically, molecular dynamics simulations indicate that the CTT may interact with the microtubule lattice at several sites, and that these interactions are affected by the tubulin nucleotide state.

Strengths:

Key strengths of the work include the use of three distinct probes that yield broadly consistent findings, and a wide variety of experimental manipulations (drugs, motors, MAPs) that collectively support the authors' conclusions, alongside a careful quantitative approach.

Weaknesses:

The challenges of studying the dynamics of a short, intrinsically disordered protein region within the complex environment of the cellular microtubule lattice, amid numerous other binders and regulators, should not be understated. While it is very plausible that the probes report on CTT accessibility as proposed, the possibility of confounding factors (e.g., effects on MAP or motor binding) cannot be ruled out. Sensitivity to the expression level clearly introduces additional complications. Likewise, for each individual "expander" or "compactor" manipulation, one must consider indirect consequences (e.g., masking of binding sites) in addition to direct effects on the lattice; however, this risk is mitigated by the collective observations all pointing in the same direction.

The discussion does a good job of placing the findings in context and acknowledging relevant caveats and limitations. Overall, this study introduces an interesting and provocative concept, well supported by experimental data, and provides a strong foundation for future work. This will be a valuable contribution to the field.

Reviewer #3 (Public review):

Summary:

In this study, the authors investigate how the structural state of the microtubule lattice influences the accessibility of the α-tubulin C-terminal tail (CTT). By developing and applying new biosensors, they reveal that the tyrosinated CTT is largely inaccessible under normal conditions but becomes more accessible upon changes to the tubulin conformational state induced by taxol treatment, MAP expression, or GTP-hydrolysis-deficient tubulin. The combination of live imaging, biochemical assays, and simulations suggests that the lattice conformation regulates the exposure of the CTT, providing a potential mechanism for modulating interactions with microtubule-associated proteins. The work addresses a highly topical question in the microtubule field and proposes a new conceptual link between lattice spacing and tail accessibility for tubulin post-translational modification.

Strengths:

(1) The study targets a highly relevant and emerging topic-the structural plasticity of the microtubule lattice and its regulatory implications.

(2) The biosensor design represents a methodological advance, enabling direct visualization of CTT accessibility in living cells.

(3) Integration of imaging, biochemical assays, and simulations provides a multi-scale perspective on lattice regulation.

(4) The conceptual framework proposed lattice conformation as a determinant of post-translational modification accessibility is novel and potentially impactful for understanding microtubule regulation.

Weaknesses:

There are a number of weaknesses in the paper, many of which can be addressed textually. Some of the supporting evidence is preliminary and would benefit from additional experimental validation and clearer presentation before the conclusions can be considered fully supported.

In particular, the authors should directly test in vitro whether Taxol addition can induce lattice exchange (see comments below).

insert glossary entry: tshe dpag med/ amitāyus/ AD/ Amitāyus/ 'Infinite Life,' the name of the buddha in the realm of Sukhāvatī. Also known as Amitābha ('Infinite Light').

please make this link to the glossary entry for chos smra ba/ dharmabhāṇaka with these alternative entries:

chos smra ba'i dge slong/ dharmabhāṇakabhikṣu/ AD/ Dharma-teaching monk

This work has been peer reviewed in GigaScience (see https://doi.org/10.1093/gigascience/giaf138), which carries out open, named peer-review. These reviews are published under a CC-BY 4.0 license and were as follows:

Reviewer 3: Julia Markowski

In the presented Technical Note "TinkerHap - A Novel Read-Based Phasing Algorithm with Integrated Multi-Method Support for Enhanced Accuracy" by Hartmann et al., the authors introduce TinkerHap, a new hybrid phasing tool that primarily relies on read-based phasing for both short- and long-read sequencing data, but can additionally incorporate externally phased haplotypes, enabling it to build upon phase information derived from existing statistical or pedigree-based phasing approaches. This hybrid approach addresses an important and timely challenge in the field: integrating the complementary strengths of different phasing strategies to improve the accuracy and span of haplotype blocks, particularly for rare variants, or in variant-sparse genomic regions. The authors clearly articulate the limitations of existing approaches and present their solution in a manner that is both elegant and accessible. Design features such as multiple output formats and compatibility with third-party tools demonstrate a practical awareness of user needs. The authors evaluate TinkerHap using both short-read and long-read state-of-the-art benchmarking datasets, and compare its performance against commonly used phasing tools, demonstrating improvements in both phasing accuracy and haplotype block lengths. Overall, this is a well-conceived and thoughtfully implemented contribution to the phasing community.

While the manuscript is overall well written, there are a few areas where additional clarification or extension would improve its impact. I recommend the following revisions to help clarify key aspects of the method, enhance the generalizability of the evaluation, and align the manuscript more closely with journal guidelines.

Major Comments * (1) Limited scope of benchmarking The evaluation on the highly polymorphic MHC class II region is appropriate for highlighting TinkerHap's strengths in phasing rare variants in variable regions. However, the current evaluation on short -read based phasing is based on a ∼700 kb region selected for its high variant density, which limits the generalizability of the findings. Since the manuscript emphasizes improved performance in regions with sparse genetic variation, it would strengthen the work to include chromosome-wide or genome-wide benchmarks, particularly on short-read data. This would also provide a more balanced comparison with tools like SHAPEIT5, which predictably underperform in the MHC class II region due to their reliance on population allele frequencies and linkage disequilibrium patterns that are less effective for rare or private variants. * (2) Coverage and scalability The manuscript describes TinkerHap as scalable, but since the algorithm relies on overlapping reads, it is unclear how its performance varies with sequencing depth. Including a figure or supplementary analysis showing phasing accuracy, runtime, and memory usage at different coverage levels (particularly for short-read data) would help support this claim and guide users on appropriate coverage requirements. * (3) Clarify algorithmic novelty It would be helpful to elaborate on how TinkerHap's read-based phasing algorithm differs from existing approaches such as the weighted Minimum Error Correction (wMEC) framework implemented in WhatsHap. For example, what specifically enables TinkerHap's read-based mode to produce longer haplotype blocks than other read-based tools? * (4) Data description A brief characterization of the input datasets, such as the sequencing depth, as well as the number and average genomic distance of heterozygous variants in the MHC class II region and the GIAB trio data would provide important context for interpreting the reported phasing accuracy and haplotype block lengths. * (5) Manuscript structure Since the algorithm itself is the core novel contribution, it should be part of the results section, as well as the description of the evaluation currently in placed in the discussion. According to GigaScience's Technical Note guidelines, the method section should be reserved for "any additional methods used in the manuscript, that are not part of the new work being described in the manuscript."

Minor Comments * (a) Novelty of hybrid approach While TinkerHap's ability to integrate externally phased haplotypes is valuable, similar functionality exists in other tools, for example, SHAPEIT can accept pre-phased scaffolds (including those generated from read-based phasing), and WhatsHap supports trio-based phasing. Consider refining the language to more precisely describe what is uniquely implemented in TinkerHap's hybrid strategy. It would be interesting to see how the presented results of using SHAPEIT's phasing output as input for TinkerHap compare to an approach of feeding TinkerHap's read-based phasing results into SHAPEIT. * (b) Reference bias claim The introduction states that read-based phasing is "independent of reference bias." While this approach is generally less susceptible to reference bias than statistical phasing, bias can still arise during the read alignment stage, potentially affecting downstream phasing. This point should be clarified. * (c) GIAB datasets The abstract mentions only the GIAB Ashkenazi trio, but later the Chinese trio is included in the analysis as well. Please clarify whether results are averaged across the two datasets. * (d) Tool version citation Please clarify in the text that the comparison was made using SHAPEIT5, not an earlier version.

Recommendation: Minor Revision With additional clarification on generalizability and coverage sensitivity, this manuscript will make a valuable contribution to the field.

This work has been peer reviewed in GigaScience (see https://doi.org/10.1093/gigascience/giaf138), which carries out open, named peer-review. These reviews are published under a CC-BY 4.0 license and were as follows:

Reviewer 2: Yilei Fu

TinkerHap is a read-based phasing algorithm designed to accurately assign alleles to parental haplotypes using sequencing reads. General comments: 1. The manuscript would greatly benefit from the inclusion of a flowchart or schematic overview of the TinkerHap algorithm. Given that the method incorporates multiple components—including read-based phasing, pairwise distance-based unsupervised classification, and optional integration with statistical phasing tools like ShapeIT—a visual diagram would help readers grasp the workflow more intuitively. Major comments: 1. The authors are missing experiments for long-read based phasing. How does TinkerHap performs with ShapeIT on PacBio long-reads? I would suggest the authors using the same phasing method class as their short-read analysis: TinkerHap+ShapeIT; TinkerHap; WhatsHap; HapCUT2; ShapeIT. Also I believe ShapeIT is capable to take long-read SNV/INDEL calls as vcf. 2. Following up on the point 1, the experimental design of this study is quite skewed. WhatsHap is not suitable for short-read sequencing data. It does not make sense to apply WhatsHap on short-read data. 3. I would caution the authors to read and potentially compare with SAPPHIRE (https://doi.org/10.1371/journal.pgen.1011092). This is a method that developed by the ShapeIT team for incorporating long-read sequencing data and ShapeIT. 4. To better justify the hybrid strategy, I recommend adding an analysis of sites where TinkerHap and ShapeIT disagree. Are these differences due to reference bias, read coverage, variant type, or true ambiguity? Such an evaluation would help users understand when to rely on the read-based output vs. ShapeIT, and enhance confidence in the merging strategy. Minor comments: 1. I could see the versions of the software in the supplementary github, but I think it is also important to include those in the manuscript. For example, shapeIT 2-5 are having quite different functions. The citation for ShapeIT in the manuscript is for ShapeIT 2, but the program that has been used is for ShapeIT 5. 2. Need to mention the benchmarking hardware information for runtime comparison. 3. "...a novel and unique phasing algorithm..." -> "...a novel phasing algorithm..."

This work has been peer reviewed in GigaScience (see https://doi.org/10.1093/gigascience/giaf138), which carries out open, named peer-review. These reviews are published under a CC-BY 4.0 license and were as follows:

Reviewer 1: Arang Rhie

The authors present TinkerHap, a tool that accepts a variant call set and read alignment, and assigns heterozygous variants and reads to a particular haplotype based on a greedy pairwise distance-based classification. It accepts a pre-phased VCF as an option to further extend phased blocks. The results sound neat with statistics making it look the greatest compared to current state-of-the-art read alignment based phasing methods such as HapCut2, WhatsHap, and ShapeIT which uses statistical inference from reference panel data. However, there are several aspects the authors need to address to make their results more compelling. 1. The benchmarking was only performed on MHC Class II, which is a relatively small and easy to phase region based on the high level of heterozygosity. How does the statistics look when applied to the whole genome? After generating the phased read set, what % of reads can be accurately assigned to the original haplotype in the whole genome scale? To benchmark the latter, I would recommend doing it on HG002 phased variants and reads by using the HG002Q100 genome (https://github.com/marbl/hg002) - i.e. map the classified reads and calculate the coverage and accuracy based on where the reads align to. I would be curious to see how the MHC Class II phased read alignment looks like on the HG002Q100 truth assembly, on each haplotype. 2. When showing benchmarking results, key features are missing - 1) number of heterozygous variant sites are used for phasing, in addition to the Phased % (what's the denominator here?), 2) number of phase blocks, phase block NG50 and total length and 3) Show the NGx length distribution by plotting the cumulative covered genome length as a function of the longest to shortest phase block. 3. After phasing the variants (and reads), are the authors accurately able to type the HLA Class II genes? The goal of MHC phasing is to accurately genotype the HLA-genes. It is unclear to me why the authors applied their phasing on the 1,040 parent-offspring trios. I agree that it is 'phasable', however, it is unclear what the motivation here is - the MHC Class II is particularly known to have linked HLA types (e.g., HLA-DRB3 and HLA-DRB5 are inherited together depending on the HLA-DRB1 type, while in some haplotypes HLA-DRB3 is entirely missing), and depending on the HLA types and because the reference is incompletely representing this locus, there are multiple tools developed for genotyping this locus. I would be more convinced if the authors could show the HLA genotyping accuracy together based on their phasing method. 4. Is it possible to use additional data types to further extend the phase blocks, by using datasets such as low coverage PacBio data in addition to the short-read WGS? How about phasing with linked-reads or Hi-C? Both Whatshap and HapCut2 are specifically designed to combine such short and long-range datasets, giving the advantage of using such tools. 5. The authors claim their method is free from reference bias, which I strongly disagree. Using a bam file aligned to a reference inherently has the issue of mapping biases, so any such tools are limited by the reads that aligns incorrectly. Repeats, especially copy number variable region with collapses in the reference are very difficult to accurately phase. Any large structural variant not properly represented in the reference will cause problems due to unmapped reads. 6. In Methods, 2nd section - I would suggest to use allele 1 and allele 2 instead of 'reference' and 'alternative' in the equation and the code. This will increase the number of heterozygous 'phasable' variants that does not carry any reference allele.

eLife Assessment

This valuable study presents EM structures of new conformational states of the LONP1 AAA+ protease in conjunction with the mitochondrial protein substrates (StAR, TFAM), along with biochemical functional assays. The EM structures revealed new conformational states in a closed configuration. The structures and associated functional results are solid. However, a notable weakness is the absence of substrates found threaded through the ATPase pores.

Reviewer #1 (Public review):

The remodeling of macromolecular substrates by AAA+ proteins is an essential aspect of life at the molecular scale, and understanding conserved and divergent features of substrate recognition across the AAA+ protein family remains an ongoing area of research. AAA+ proteins are highly modular and typically combine N-terminal recognition domain(s) with ATPase domain(s) to recognize and unfold some macromolecular target, such as dsDNA or protein substrates. This can be coupled to activity by additional C-terminal domains that further modify the substrate, such as a protease domain that hydrolyzes the extended, unstructured protein chain that emerges from the ATPase domain during substrate processing.

This work focuses on one such AAA+ protease, LONP1. LONP1 is an essential AAA+ protein involved in mitochondrial proteostasis, and disruption of its function in vivo has serious developmental consequences. This work explores the processing of two new mitochondrial protein substrates (StAR, TFAM) by LONP1 and presents new conformational states of LONP1 with closed configurations and no substrate threaded through the ATPase pores. The quality of the reconstructions and models is very good. Critically, one of these states (LONP1C3) has a completely occluded ATPase pore from the N-terminal side of the ATPase ring, where three of the six NTDs/CCDs interact tightly to form a C3-symmetric substructure preventing substrate ingress. The authors note several key interactions between amino acids forming these substructures, and perform ATPase assays on mutant LONP1 proteins to determine hydrolysis rates in the absence or presence of substrate. These patterns are recapitulated in casein disassembly assays as well. Based on these results, the authors note that the mutants have differential effects depending on the "foldedness" of the substrate, and surmise that disruption of the C3-symmetric substructure from the EM experiments is responsible for these effects - an intriguing idea. In addition to the C3 state, the authors observe additional intermediates which they place on the same conformational coordinate. One such structure is the LONP1C2 state with two splits, hinting at a conformational transition from LONP1C3 to the closed/active state.

Taken together, these results form the basis of an interesting story. However, I feel that more experimentation and analysis are needed to address several key points, or that the conclusions should be toned down. First and foremost, I note that while the hypothesis that the LONP1C3 state is a critical step in recognizing substrate "foldedness" is an interesting one, the claim is made solely on the basis of biochemical experiments with mutant LONP1, and that there is no substrate density associated with LONP1C3. In the absence of substrate density and/or structural data for the mutants, this seems like a very strong claim. More generally, the manuscript invokes the conformational landscape of LONP1C3 in multiple instances, but no such landscape is presented to show how LONP1C3 and the other states are quantitatively linked. Finally, I note the prevalence of ADP-only active sites in these intermediates, and am concerned that this might be related to the depletion of ATP under the on-grid reaction conditions. The inclusion of an ATP regeneration system may be a useful way to ensure that ATP/ADP concentrations are more physiological and that excessive ADP will not bias the conformations of the ring systems.

In summary, I believe this manuscript is exciting but would benefit from a paring back of claims, or the inclusion of some additional data to fill in some of the conceptual gaps outlined above.

Reviewer #2 (Public review):

This paper by Mindrebo et al. reveals multiple novel conformations of the human LONP1 protease. AAA+ proteases, like LONP1, are needed for maintaining proteostasis in cells and organelles. While structures of fully active (closed) and fully inactive (open) conformations of LONP1 are now established, the dynamics between these states and how changes in conformations may contribute to or be triggered by substrates and nucleotides are unclear. In this work, the authors characterize a novel C3-symmetric state of LONP1 bound to TFAM (a native substrate), suggesting that this C3-state is an intermediate in the open to closed cycle, and make mutations to test this model biochemically. Deeper inspection of their TFAM-bound LONP1 dataset reveals additional conformations, including a C2-symmetric and two asymmetric intermediates. All these conformations are synthesized by the authors to propose a model for how LONP1 transitions from an inactive OFF state to an active ENZ state. There are clear, interesting structural aspects to this work, revealing alternate conformations to shed light on the dynamics of LONP1. However, some of the conclusions interpret well beyond the scope of the experiments shown, and this is discussed below.

Overall, there are two major comments with the work as written that, if addressed, would make the results more compelling. First, the order of events and existence of intermediate states is primarily from static structural snapshots and fitting these structures to a possible mechanism. It would be ideal to have some biochemical or kinetic data supporting these steps and the existence of these intermediates. For example, the model is that the C3-state is an ADP-bound intermediate that blocks access and acts as a checkpoint for progression to the ENZ state of LONP1. The major evidence for this comes from a mutation (D449A) that fails to degrade TFAM as well as StAR or casein, which is taken as evidence that failure to form the C3 state reduces the ability to degrade more 'folded' substrates. A prediction of this model would be that destabilizing TFAM through mutation should improve D449A degradation. Ideally, other measures of conformational changes, such as FRET or HDX-MS, could be used to visualize this C3-state in unmutated LONP1 during the process of substrate engagement and degradation. At a minimum, using ATP hydrolysis as a proxy for forming the ENZ state and the assumption that different substrates will differentially promote formation of the C3-state means that measuring ATP hydrolysis of wt LONP1 with different substrates will be informative.

The second major comment is that the primary evidence for the importance of the C3 state is a mutation (D449A) that, based on the cryoEM structure, is incompatible with this conformation but should not affect any other state. A concern that arises is whether this mutation is doing more than simply destabilizing the C3 state and affecting substrate recognition/enzymatic activity in some other manner. To address this point, the authors could perform cryoEM characterization of the D449A mutant, which should show reduced or no presence of the C3-state, but still an intact ability to form the closed ENZ state.

Reviewer #3 (Public review):

Summary:

The AAA+ protease LON1P is a central component of mitochondrial protein quality control and has crucial functions in diverse processes. Cryo-EM structures of LON1P defined inactive and substrate-processing active states. Here, the authors determined multiple new LON1P structural states by cryo-EM in the presence of diverse substrates. The structures are defined as on-pathway intermediates to LON1P activation. A C3-symmetry state is suggested to function as a checkpoint to scan for LON1P substrates and link correct substrate selection to LON1P activation.

Strengths:

The determination of multiple structures provides relevant information on substrate-triggered activation of LON1P. The authors support structural data by biochemical analysis of structure-based mutants.

Weaknesses:

How substrate selection is achieved remains elusive, also because substrates are not detectable in the diverse structures. It also remains in parts unclear whether mutant phenotypes can be specifically linked to a single structural state (C3). Some mutant phenotypes appear complex and do not seem to be in line with the model proposed.

This work has been peer reviewed in GigaScience (see https://doi.org/10.1093/gigascience/giaf135), which carries out open, named peer-review. These reviews are published under a CC-BY 4.0 license and were as follows:

Reviewer 2: Ameet Pinto

The manuscript provides long-read mock community datasets from GridION and PromethION sequencing platforms along with draft genomes of mock community organisms sequenced on the Illumina Platform. The entire dataset is available for reuse by the research community and this is an extremely valuable resource that the authors have made available. While there are some analyses of the data included in the current manuscript, it is largely limited to summary statistics (which seems appropriate for a Data Note type manuscript) and some analyses of interest to the field (e.g., de novo metagenome assembly). It would have been helpful to have a more detailed evaluation of the de novo assembly and parameter optimization, but this may have been outside the scope of a Data Note type manuscript. I have some minor comments below to improve clarity of the manuscript.

Minor comments: 1. Line 28-29: Would suggest that the authors provide the citation (15) without the statement in parenthesis or revised version of statement in parenthesis.

"DNA extraction protocol" section 2. The last few lines were a little bit unclear. For instance: "45 ul (Even) and 225ul (Log) of the supernatant retained earlier…" It was a bit confusing. Possibly because the line "The standard was spun…before removing the supernatant and retaining." seems incomplete. I would suggest that the authors consider posting the entire protocol on protocols.io - as is quite possible that other groups may want to reproduce the sequencing step for these mock community standards. This would be particularly helpful as the authors suggest that the protocol was modified to increase fragment length.

"Illumina sequencing" section: 3. Suggest that the authors improve clarity in this section by re-structuring this paragraph. For instance, early in paragraph it is stated that the pooled library was sequenced on four lanes on Illumina HiSeq 1500, but later stated that the even community was sequenced on a MiSeq.

"Nanopore sequencing metrics" in results: 4. Table 2, Figure 3a. - please fix this to Figure 1a. 5. Figure 1B: The x-axis is "accuracy" while in this section Figure 1b is referred to as providing "quality scores". Please replace "quality scores" with "accuracy" for consistency. 6. Figure 1C: Please provide a legend mapping colors to "even" and "log". I realize this information is in Figure 1B, but would be helpful for the reader. Finally, there is no significant trend in sequencing speed over time. Considering this, would be easier to remove the Time component and just have a single panel with the GridION and PromethION sequencing speed for both even and log community in the same panel. It would make it easier to compare the different in sequencing speeds visually.

"Illumina sequencing metrics" in results: 7. Table 5 is mentioned before Tables 3 and 4. Please correct this.

"Nanopore mapping statistics" in results: 8. For Figure 2, consider also providing figure for the even community. 9. Further, it would be helpful to get clarity on where the data for Figure 2 is coming from. Is this from mapping of long-reads to mock community draft (I think so) or from the kraken analyses.

"Nanopore metagenome assemblies" in results: 1. It is unclear how the genome completeness was estimated. 2. The consensus accuracy data is provided for all assemblies combined. Would be helpful if there was some discussion on accuracy of assemblies as a function of wtdgb2 parameters tested. There is some discussion of this in the "Discussion section", but would be helpful if this was laid out clearly in the results, with an additional appropriate figure/table.