Capitalism is a system that encourages private ownership and free market transactions. Although as a person who grew up in China, capitalism does not seem to get a very positive image in my textbooks, I have to admit that it has played a very positive role in promoting the development of social media. In detail, the private ownership of the capitalist system to a large extent encourages the development of various social media, so that they upgrade their functions in order to win more users, make the social platform more functional, and gradually derive social media from its original role of communication to entertainment, news and other aspects. To a large extent, the development of social media has even replaced the function of television sets. It's been a long time since I volunteered to watch TV.

Capitalism is a system that encourages private ownership and free market transactions. Although as a person who grew up in China, capitalism does not seem to get a very positive image in my textbooks, I have to admit that it has played a very positive role in promoting the development of social media. In detail, the private ownership of the capitalist system to a large extent encourages the development of various social media, so that they upgrade their functions in order to win more users, make the social platform more functional, and gradually derive social media from its original role of communication to entertainment, news and other aspects. To a large extent, the development of social media has even replaced the function of television sets. It's been a long time since I volunteered to watch TV.

This happens way too often. Many times a show or series is ruined by a bad sequel or cheap adaptation. It's really easy to spot out cheap cash grab movies that only reel you in with name value rather than movie quality.

Shareholders, who own parts of the business, have decision-making power based on how much money they’ve invested, meaning the rich have the most influence. From my perspective, this system seems unfair to the poor, who have little say despite being impacted by business decisions, like when companies prioritize profits over employee wages or environmental concerns.

In modern society, it is hard to find "pure monopolies" in industries such as social media or technology. This is due to the sharing of design, service, and customer demands through companies. However, some companies seem to function as monopolies by setting the price of their goods which affects that goods and behaviors of other companies. For example, whenever Apple introduces a price hike or new change to their products (removing headphones and charging brick), other competitors follow.

This shows there is conflict beyond the upcoming election. Him dropping out of the election caused concern for Republicans on Biden's current position.

This is a weird way to state the time. They use minutes instead of hours because they want to show how quick it was.

This is the overall consensus. The opinions of Democratic leaders are the same.

They note this because the Obamas are influential in politics. It is important to state who supports Harris but who has not

even those contesting legitimacy reproduce it immigrant populations

cultural capital acquired to move up socially reference to Goffman's performance as apart of upwards mobility

even if God isn't real- religion maintains enough of a cultural stake to argue over

even contrarians to the field legitimate

Conservation- keep dominance Succession - gain dominance from those who don't have it Subversion- not interested in dominating other groups

more complex than an org. a group, or an institution a bunch of goffman's performance groups

fields are battlegrounds of legitimization the right to use symbolic violence

organized around a capital or a configuration of capitals

field- Bourdieu's unit? network if positions

grandpa example

finds master patterns within each class that show up in cognitive, moral, and corporal dimensions of action

this class charateristics of habitus have their signature in vastly different areas of one's life

perception of limitations become reality

outlook informed by habitus

one fashions there aspirations to what is plausible within class family example

we envision similar futures for ourselves? - share a habitus

Durkheim circulation the external informs the internal the informs the external

internalization of ones own class allows it to reproduce itself

The United States is now the center of attention, and the expectations for this new world power is to excel. Churchill claims that the United States should feel worried about failing to meet these new standards.

This is what can gain the attention of the public. Although the communist parties have previously been smaller, they are rapidly growing and looking to gain control in these countries where possible.

Churchill is clarifying that he is not speaking for anyone but himself, in order to potential stop any negative ramifications that might arise as a result from this speech on a global scale.

Churchill is saying that Truman has given him permission to speak his mind in this speech allowing him to say what he truly believes.

Churchill is pointing to the idea that with this great power, there are nearly infinite avenues for innovation and creation.

Churchill emphasizes that being in the position that the United States is in requires absolute attention. The role of being the primary world power is consequential, and entails ultimate responsibility.

He is saying that he is not only speaking to Americans in the audience, but to the entire world.

Churchill is thanking President Truman for traveling all the way to Missouri, just to hear him give a speech in front of the world.

Churchill believes that this is something that is continuing to spread. He infers that if something isn't done about this, the influence will only become greater.

Churchill is referring to himself receiving a personal invitation by US President, Harry Truman, to come and speak at Westminster College.

The "Soviet sphere" is what Churchill describes as anywhere that the Soviets have influenced in Europe.

Churchill is referring to the borough of the City of Westminster in London. The Houses of Parliament where Churchill worked as Prime Minister is located in Westminster, as well as the famous Westminster Abbey, which is located right next door to the Houses of Parliament and Big Ben.

He gave this speech at Westminster College because it was in Harry Truman's home town, and Truman had given him a personal invitation to come and receive an honorary degree.

This is widely regarded as the most famous line in this speech. The "iron curtain" is what Churchill uses to describe the Soviet Union's control over Eastern Europe and their communist influence.

Westminster College is a 4 year private college in Fulton, Missouri. It was originally founded in 1851. The current undergraduate enrollment is only 601.

VO₂max, of maximale zuurstofopname, is een maat voor de maximale hoeveelheid zuurstof die je lichaam kan gebruiken tijdens intensieve inspanning. Het wordt beschouwd als een belangrijke indicator van cardiovasculaire fitheid en aerobe uithoudingsvermogen.

Using this phrase is important to understand the perception of the situation.

This adds to the pressure that Biden was feeling at the time. He was losing support from his own party.

Adding this could be taken as a cause for suspicion or they believe this is factual.

This was also a concern for Democrats and adding this speaks to the tone of the article. It sheds a more positive light on him dropping out if there is someone he supports stepping in.

Her doing this sends a signal that it was for the best. Her showing her support may show the public that this was his decision and she agrees with it.

His age is the main factor for people's change in opinion about him. How serious were these concerns before the debate?

common trend among articles

This scenario happened recently with Instagram's new threads feature. It tried to become the next Twitter by copying most of Twitter's feature. It released at a good time but didn't do a good job of bringing and maintaining it's userbase.

This sort of behavior of privately owned businesses is exactly why government intervention and regulation is necessary in a capitalist society. Though an unregulated economy would allow corporations to (potentially) make the most profit, the harmful actions such as selling our data to misleading/hateful advertisers may show a increase in violence and more negative effects.

"Using behavioral data for tailored advertisements, while profitable, threatens user autonomy. When platforms like Meta use user data without explicit authorization, it seems invasive—I've seen how quickly advertising change depending on a single search or discussion. This type of tracking makes me feel as if my privacy is continually jeopardized, and it underlines the disparity between profit-driven businesses and user control."

"Surveillance capitalism creates severe ethical concerns, particularly regarding the exploitation of 'behavioral surplus.' Reading about Meta's example of permitting harmful targeted ads reminded me of how I've received strangely particular adverts on Instagram, making me wonder how much the platform knows about me. It's distressing to learn how customer data can be abused, and businesses must accept responsibility for protecting against such behaviors."

I find the inclusion of this paragraph to be interesting. I say that because, if they say they have the top experts and high-tech equipment, why hasn't it been solved yet?

This statement's wording makes it seem that it is not very hopeful. These sort of statements have been said for many years, so why does this murder case come and go in waves of importance?

For context: this unsolved murder case has been ongoing since 1996 and since then many articles I have researched from over the years have stated similar headlines. Is this actually the time progress has been made? Hard to believe since this headline is overused in this case.

a period of reflection or thought

walked down in a specified way

crying noisily

having or showing deep religious feeling or commitment

rarely, infrequently

an Asian evergreen climbing plant that when chewed, causes the saliva to go red and the teeth to go black

unmarried young women

depressing, gloomy

to make a shrill howl, like a cat

a quagmire, a band of mud partially submerged

tired, exhausted

A beneficed member of the clergy ; a clergyman (especially in a protestant church)

dull, heavy

kinda sexy

who was not allowed in a brothel

The network effect means that once English became the standard, everyone had to learn it to participate in programming, making it very hard for non-English programming languages to gain traction. Even native speakers of other languages would face challenges, as they would have to relearn terms they already know in English. This makes me reflect on how deeply language and technology are intertwined and how this might create barriers for people who don’t speak English fluently.

How the hell did she get this magic hat? From the sorcerer, obviously, but it just isn't clear exactly how she got her hands on it.

The closest one gets to a Romantic (ie, the artistic movement) sex scene.

Ah the classic romantic trope of a knight entering a ready-made harem castle.

This passage examines the concept of selection in a social media environment, highlighting how various factors influence whether content gets replicated. It explores human motivations like humor, outrage, self-presentation, or inspiration, alongside decisions made by groups or organizations, such as news outlets amplifying posts deemed newsworthy. Additionally, it addresses artificial influences like paid promotions and astroturfing, where bots are used to artificially replicate content.

This passage discusses strategies used by social media users to game recommendation algorithms, such as clickbait, provocative content, or coordinated actions to boost visibility, which are especially impactful for creators relying on social media income. It also mentions YouTuber F.D. Signifier exploring YouTube’s algorithm and interviewing other creators, particularly Black creators, to shed light on these practices. While these strategies may generate short-term engagement, they also carry risks of backlash.

embarrassing a student is not the way to get them to listen it could do more harm than good.

misbehavior is normal for developing students, which is why its important to curate a good response in managing behavior in a classroom so it doesn't become out of hand

Where can we see this in action?

... to answer the oft-asked question: When will we ever use this?

Agreed. There's no time to waste, and no room for fluff.

Makes me think of the constructivist theory of education.

A useful tool!

dopytać o to

overthinking

.

.

Key participant takeaways 1. Ease/convenience 2. Confidence building 3. Vital that employers and other consumers signal to earners that they value the credentials

We don't talk about this enough. Especially for populations that self-select out of seeking opportunities, the impact of confidence building is a key benefit of credentials.

Value propositions! "I'll believe you about these badges and start to care if you convince me that the employers care." On the employer side, this hints at need to get over it with the real/imagined quality concerns and focus on their need to signal to opportunity seekers, "we value your credentials and want to see them."

Curious what the participant concerns were here

When highlighting features, we might under-index this as a key benefit

In addition, this hints at employMENT verification: this could be a light lift sort of Tier 1 entry point for organizations to be both issuing and consuming credentials. Large employers spend a lot of resources responding to requests to verify former workers' employment histories. If part of off-boarding departing workers includes VCs for official employment verification, that could lead to big savings of time and resources (as long as other employers accept the credentials), as well as accelerate hiring processes that sometimes lead to failed hires bc people find another position that starts sooner. For key HR leaders to start with badging from a place of effortlessly improving their efficiency and costs might be a better place to launch than more involved strategies that offer less immediate value propositions.

NEO-Personality Inventory-Revised (NEO-PI-R) and NEO-PI-3. Five-Factor Personality Inventory (FFPI).

Strong interest inventory

Sixteen Personality Factor Questionnaire (16PF): Based on factor analysis, useful for normal and abnormal personality evaluation. California Psychological Inventory (CPI): Derived from the MMPI, measures traits like responsibility and tolerance. Myers-Briggs Type Indicator (MBTI): Based on Carl Jung’s personality type theory, used widely in corporate settings.

No spinning space ships!! .... Please only ones with prudent, wise, and comely (!) admirals.

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Reply to the reviewers

1. Point-by-point description of the revisions

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

The authors present the use of previously identified biosensors in a single-molecule concentration regime to address lipid effector recruitment. Using controlled and careful single-cell based analysis, the study investigates how expression of the commonly used PIP3 sensor based on Akt-PH domain interferes with the native detection of PIP3. Predominantly live-cell fluorescence microscopy coupled to image analysis drives their studies.

Conceptually, this manuscript carefully and quantitatively describes the influence of lipid biosensor overexpression and presents a means to overcome the inherent and long-recognized problems therein. This solution, namely employing low expression of the lipid biosensor, should be generally applicable. The work is of general interest to cell biologists focused on answering questions at membranes and organelles, including especially those interested in lipid-mediated signaling transductions.

Reviewer 1 Major:

#1.1 The terminology "single molecule biosensor" is not really appropriate. A protein is not "single-molecule". An enzyme does not "single molecule". Better is biosensors at single-molecule expression levels. In most cases, this should be changed. Single-molecule vs single-cell vs. bulk measurements are often poorly defined in quantifications and conflating these does not help the case, which is already supported by generally clear data.

We appreciate the reviewer’s thoughtful critique of our grammatically incorrect use of jargon; we saw this as soon as they mentioned it! We have amended the manuscript where appropriate as detailed:

• Title is now changed to “Lipid Biosensors Expressed at Single Molecule Levels Mitigates Inhibition of Endogenous Effector Proteins”
• Last paragraph of the introduction on __ 2__ now reads “As well as alleviating inhibition of PI3K signaling, biosensors expressed at these low levels show improved dynamic range and report more accurate kinetics than their over-expressed counterparts."
• The title of the results section on __ 6__ is now: Mitigating PIP3 competition using biosensors expressed at single molecule levels
• Last paragraph of the results section on 6 now reads: “this showed that when expressed at single molecule levels, the biosensor has substantially better dynamic range”. #1.2 Figure 1D-F, images not as clearly describing quantitation as one would hope. Untransfected cells in 1E should demonstrate more translocated Akt-pS473 than transfected, but it is difficult for this reviewer to find. Consider inset images in addition to the wider field. Consider also moving the "negative" data of Fig 1B-C to Supplement.

We regret not making this figure easier to interpret; we have substantially updated the figure, as comprehensively detailed in our point-by-point response to reviewer 2’s point 2.3. To specifically address this reviewer’s concerns:

The older figure used non-confocal, low-resolution images that were used for quantification. Such an approach was employed to enable fluorescence from the entire cellular volume to be captured, which produces more robust quantification. However, to the reviewer’s point, it is not possible to see the translocation of PH-AKT1 nor translocated AKT-pS473 in these images. Fortunately, we had in parallel captured high resolution confocal images for some experiments. These are now shown in Fig 1D-E, which clearly shows translocated AKT-pS473 and PH-AKT-EGFP

#1.3 The cell line being used is not clearly specified after the initial development of the NG1 followed by CRISPRed NG2 onto Akt. For example, for the Figure 3C experiments, the text states "complete ablation of endogenous AKT1-NG2" but this information is not apparent from the figure legend or figure. Throughout the cell line used and the aspects transfected need to be made explicitly clear.

We are grateful to the reviewer for highlighting this ambiguity. We have now defined the gene-edited cells used throughout as “AKT1-NG2 cells” and expressly used this term when referring to experiments in figures 2-5.

#1.4 Fig. 5 shows single cells. It is therefore unclear if broken promoters have resulted in decreased expression. This point is important because the expression plasmids should be made publicly available, and for their use to be understood properly, this must be clarified. The details of the plasmids are unclear. Perhaps listed in the table? - unclear. This aspect would be important for the field to effectively use the reagents.

Thank you for drawing our attention to the lack of adequate detail here. We have now updated the results text to expressly reference Morita et al., 2022 where the origins of the truncated CMV promoters are detailed. We have also updated the plasmids table 1 to add pertinent details for these constructs: *pCMVd3 plasmids are based on the pEGFP-C1 backbone, with the CMV promoter truncated to remove 18 of the 26 putative transcription factor binding sites in the human Cytomegalovirus Major Intermediate Enhancer/Promoter (pCMV∆3 as described in Morita et al., 2012). The full sequences will be deposited with the plasmids on Addgene.

We did not perform a formal comparison of full vs truncated promoters. Our only observation is that the truncated promoters greatly help in increasing the number of expressing cells presenting single-molecule resolvable expression levels (though the approach can still work with full promoters).

#1.5 This manuscript speculates several times that with more abundant PIs like PI45P2, the observed saturation effect is probably not happening. This should be removed. While the back of envelope calculations may reflect an ideal scenario, the heterogeneity of distribution and multiple key cellular structures involved would seem to corral increased PI45P2 levels in certain regions. These factors amid multivalency and electrostatic mechanisms of lipid effector recruitment (e.g. MARCKS) suggest that speculation may be too strong. Moreover, Maib et al JCB 2024 demonstrated PI4P probe overexpression could directly mask the ability to detect PI4P post-fixation - not fully, but partially. Repeating the titration experiments of this manuscript for multiple PIs is entirely beyond the scope of reasonable, and hence, such experiments are not requested, in favor of adopting more conscientious speculation.

The reviewer’s point is well taken. Whilst we still believe the overall argument for lipids is sounds (for example, PS or cholesterol are far too abundant for any expressed, stoichiometric binding protein to bind the majority of the population) even abundant phosphoinositides like PI4P and PI(4,5)P2 are an edge case. We have therefore undated the first paragraph of the introduction on __p. 1 __to be less explicit: One of the most prominent is the fact that lipid engagement by a biosensor occludes the lipid’s headgroup, blocking its interaction with proteins that mediate biological function. It follows that large fractions of lipid may be effectively outcompeted by the biosensor, inhibiting the associated physiology. We have argued that, in most cases, this is unlikely because the total number of lipid molecules outnumbers expressed biosensors by one to two orders of magnitude (Wills et al., 2018). However, for less abundant lipids, total molecule copy numbers may be in the order of tens to hundreds of thousands, making competition by biosensors a real possibility.

We also removed the explicit discussion of PI(4,5)P2 from the introduction, and focus now solely on the PI3K lipids.

Reviewer 1 Minor:

1.6 Schematics throughout need simplification, enabling their enlargement.

We have now enlarged the size of all schematics

#1.7 Numerous spelling (Fig. 4 schemas) and capitalizations need fixing.

Thank you for drawing our attention to these. We have thoroughly proof-read the figure panels and corrected errors.

#1.8 Pg 1 Famous is not appropriate wording

We respectfully beg to differ with the reviewer here. We believe it is perfectly accurate to state that PIP3 is a second messenger molecule that is known about by many people; we see this as the dictionary definition of the word “famous”.

#1.9 Fig. 1A statistical testing of microscopy quantifications absent (generally, throughout) and should be included.

This was indeed an oversight on our part. We have now added appropriate multiple comparisons tests to the data presented in figures 1F, 3F, 4C, 4F and 5C.

#1.10 Fig.1. In a transient transfection, the protein expression is not uniform. Please explain how you normalized the quantification.

We hope this is now clarified by the expanded “Image Analysis” part of the methods section on pp. 10-11 (relevant sentence is underlined): For immunofluorescence, we identified individual cells by auto thresholding the DAPI channel using the “Huang” method, followed by the Watershed function to segment bunched cells that appeared to touch. We then used the Voronoi function to generate boundary lines for the segmentation of the cells. To identify cytoplasm, auto thresholding of the CellMask channel using the “Huang” function was employed, with the cells segmented by adding the nuclear Voronoi boundaries. The “analyze particles” function was then used to identify individual cellular ROIs that were greater than 10 µm2 and were not touching the image periphery. These ROIs were used to measure the raw 12-bit intensity of the EGFP and AKT-pS473 channels. A cutoff of EGFP > 100 was used to define EGFP-positive cells, since this value was greater than the mean ± 3 standard deviations of the non-transfected cells’ EGFP intensity. Background intensity of AKT-pS473 was estimated from control cells subject to immunofluorescence in the absence of AKT-pS473 antibody; this value was subtracted from the measured values of all other conditions.

#1.11 Fig. 1D. EGFP expression levels increased with EGF stimulation. How is this possible?

There appeared to be a difference due to the presence of 5 strongly expressing cells in the chosen field in the original field for the EGF stimulated, EGFP cells. However, this arose just by chance. The new set of high-resolution images in the new figure 1 were selected to be more representative.

#1.12 Fig. 1D. The images have pS473 whereas the y-axis label on box plots has p473. Can these box plots be labelled separately for consistency?

Thank you. This has now been corrected in the revised Figure 1.

#1.13 Fig.1. T308 phosphorylation is mentioned in Figure 1, but only pS473 data is shown.

Both T308 and S473 phosphorylation are indicative of AKT activation. However, antibodies suitable for immunofluorescence are only available for pS473, hence why our experiments are restricted to this moiety.

#1.14 Fig.1 legend. 'Over-expression of PH-AKT is hypothesised to outcompete the endogenous AKT's PH domain'. Why do you need to state a hypothesis in the legend?

We included this statement for the benefit of the casual reader – i.e. one who looks at the pictures, but doesn’t read the main text!

#1.15 Fig.1E You stated that the PH-AKT R25C-EGFP is stimulated by EGF addition. However, the GFP signal looks the same in both unstimulated and stimulated. Could you please clarify? Are you sure that the stimulation worked?

We have clarified the second paragraph of the results section “Inhibition of AKT activation by PIP3 biosensor”__on __p. 4 as follows: In the non PIP3 binding PH-AKT1R25C-EGFP positive cells, we still observed an increase in pS473 intensity.

The revised figure 1 images also show that PH-AKT1R25C does not translocate to the membrane with EGF stimulation.

#1.16 You mention...that the AKT enzyme is activated by PDK1 and TORC2, which phosphorylate at residues T308 and S473, respectively. Phosphorylation is also known to occur on T450 at c-tail. Does this phosphorylation also contribute to its activation?

Yes and no. Threonine 450 phosphorylation is thought to occur co-translationally and is important for AKT stability (see Truebestein et al as cited in the manuscript). It is not really relevant in the context for T308 and S473, which are phosphorylated acutely to activate the protein.

#1.17 Fig. 1 scale bar in all images equivalent?

We have now added scale bars to panels in both figure 1D and E to clarify.

__#1.18 __Pg. 1 paragraph 1 "we have argued..." vs. paragraph 3"...consider that an..." feels like arguing with themselves.

We believe the re-write we have done in response to major point #1.5 clarifies this point also.

#1.19 Pg. 1 para 3 what is RFC score - must explain

We have now defined this more clearly in third __paragraph of the __introduction on p. 1: PH domain containing PIP3effector proteins can be predicted based on sequence comparison to known PIP3 effectors vs non effectors using a recursive functional classification matrix for each amino acid (Park et al., 2008).

#1.20 Discussion of numbers of PIP3 vs. effectors etc may not be appropriate for the introduction, as the points made by these calculations are already made in the previous paragraphs. May fit better in pg 6 Mitigating PIP3 titration... with an accompanying schematic.

Respectfully, we prefer to keep this discussion of molecular concentrations, as this adds details and specifics to the pathway that is core to the paper.

#1.21 Pg 2 "a neonGreen" not well defined, needs accurate description.

We have clarified this in the sentence in the first paragraph of the results section “Genomic tagging of AKT1…” __on __p. 4, which includes the citation to the full description of the tag: To that end, we used gene editing to incorporate a bright, photostable neonGreen fluorescent protein to the C-terminus of AKT1 via gene editing using a split fluorescent protein approach (Kamiyama et al., 2016).

#1.22 Fig 2C should give a unstimulated trajectory of puncta/100 um2 to compare with the stimulated

Unfortunately, we did not record a full 5.5-minute video-rate time-lapse with unstimulated cells. However, we do not believe this control is essential for this experiment, since this example data is included to illustrate (1) the problem of photobleaching, which is clear in the 30-s pre-stimulus and (2) the variability in the raw molecule counts.

#1.23 Fig 2C and F and G should be systematized for easier comparison. E.g. min vs seconds, 0 timepoint of EGF/rapa addition

We have made the adjustment to figure 2C to be consistent with 2F and G:

#1.24 Pg 5 "...and calibrated them..." unclear what is being calibrated, as the text later states that the histograms are fit to monomer/dimer/multimer model resulting in 98.1% in monomer. Minor point.

We have clarified this point in the second paragraph of the results section “__Genomic tagging of AKT1…” __on __p. 4 __as follows: We analyzed the intensity of these spots and compared them to intensity distributions from a known monomeric protein localized to the plasma membrane (PM) and expressed at single molecule levels

#1.25 Explain why baselines in Fig2CFG are different

We did not comment on figure 2C; it is a single cell measurement, as opposed to the mean of 20 cells reported in F. However, we do now clarify the difference between figure 2F and G as the very end of the “Genomic tagging of AKT1…” results section on p 4: Notably, baseline AKT-NG2 localization increased from ~5 to ~15 per 100 µm2 in iSH2 cells, perhaps because the iSH2 construct does not contain the inhibitory SH2 domains of p85 regulatory subunits, producing higher basal PI3K activity.

#1.26 Fig. 2 has quantification with images; Fig. 3 has it separate. Make consistent.

We sometimes combine images with quantification, and other times separate the panel containing graphs. This is done deliberately, depending on whether the reader is directed to both together, or whether we consider the data separately in the results section.

#1.27 Fig. 3B comes before images? Where are the images? Also, y-axis = Intensity (a.u.). Is intensity just full image field? Or per cell? All very unclear.

We have modified both the graph y-axis label and the figure legend to clarify: (C) TIRF imaging of AKT1-NG2 cells from (B) stimulated with 10 ng/ml EGF

#1.28 Fig. 3C missing images

We believe the reviewer is referring to the mCherry channel for the “0 ng cDNA” condition. These images are missing because they do not exist. Since these cells were transfected with pUC19, there was no mCherry fluorescence to image.

#1.29 Fig 3 C needs brightness/contrast adjusted as images are nearly entirely black (zero values).

We believe the addition of insets addresses this concern. To the reviewer’s specific suggestion, we found that further increases in the brightness and contrast will bring up the camera noise, but this then occludes the signal from single molecules, such as those found after EGF stimulation of the 0 ng condition.

#1.30 Fig 3C needs scale bar systemization

We believe that the incorporation of scaled 6 µm insets addresses this point.

#1.31 Fig 4 needs 4 panels A-D

We have now added these individual panel labels to figure 4.

#1.32 Pg 6 5-OH phosphatases needs reference

We have added a citation to Trésaugues at the very end of the “Sequestration of PIP3 by lipid biosensors” results section on p. 6, which describes the activity of the whole 5-OH phosphatase activity against PIP3, not just the SHIP phosphatases.

#1.33 Fig 5B, make images bigger

Again, we trust that the addition of insets to all single molecule images has addressed this point.

Reviewer 1 Referees cross-commenting**

I have read the other reviews and find them entirely reasonable. My impression is we landed on similar general content that needs work, none of which is out of line. The importance and care taken in the author's work is uniformly lauded.

We agree. At the risk of restoring to alliteration, we have been delighted to receive a trio of clear, concise and consistent comments on the manuscript! We believe it is now much improved.

Reviewer #1 (Significance (Required)):

This manuscript clearly and reasonably demonstrates that the commonly used PIP3 sensor can be titrated to low concentrations, at which it does not interfere with Akt translocation and activation. This work is a good technical reference for the field. Signal transduction and membrane biologists should be especially interested in the data. The reviewer/s have core expertise in phosphoinositides, protein biochemistry, cell biology, and membrane biophysics.

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

The authors characterize the inhibition of lipid second messenger mediated cell signaling through lipid biosensors that outcompete endogenous effector proteins. This is a very important study that as it quantitatively assesses an issue that many people suspected to exit, yet never properly characterized. This paper is therefore as much a service to the community as a research study in its own right and should be published without undue delay. I am glad that the authors decided to carry out this study & really appreciate their work.

I do however, have a number of suggestions that I think will make the manuscript stronger and can be readily implemented, mostly by reformulating and/or re-analysis of exiting datasets. I've structured my comments by the datasets in the respective figures to follow the logic of the paper.

Reviewer 2 Major:

#2.1 Throughout the manuscript, statistical tests are missing, e.g. in figures 1C-F. This must be amended in the revised version. The authors are making a very quantitative point about buffering, data should be treated accordingly.

We have now added appropriate multiple comparisons tests to figures 1F, 3F, 4C, 4F and 5C.

#2.2 I do not think that "PIP3 titration" is the best term to describe the observed effect. "Titration" usually implies the controlled modulation of a concentration, e. g. in analytical chemistry. I think either "competitive binding of PIP3" or "buffering of free PIP3" are more adequate.

This point is well taken. We have now replaced the word “titration” throughout, replacing it with either “competitive binding” or “sequestration”.

#2.3 Specific comments: Figure 1

#2.3a Why are data in 1D-Ff shown as median, with interquartile ranges and 10-90 percentile distance when everything else in the paper is mean +/- se? There might be a good reason for it, but I did not find it mentioned everywhere

For consistency’s sake, we have changed figure 1F to show a bar graph, though as noted in the figure legend: Graphs show medians ± 95% confidence interval of the median from 82-160 cells pooled from three experiments (medians are reported since the data are not normally distributed).

#2.3b The authors should test, whether the difference between the +EGF conditions in 1D (EGFP) and 1F (PH-AktR25C-EGFP) is indeed statistically significant. If this observation holds up, what does it mean? Is the mutant still competing with endogenous Akt despite the much-reduced binding affinity? The authors should discuss.

We have re-analyzed the data in figure 1, with the quantitative data presented in figure 1F combined with statistical analysis. The new data shows no significant effect of the PH-AKT1R25C mutant in either resting or EGF stimulated condition

There results are also described in the__ second paragraph__ of the first results section on pp. 3-4: This analysis showed that the R25C mutant had no substantial effect on pS473 levels, whereas wild-type PH-AKT greatly inhibited pS473 staining in EGF-stimulated cells as well as reducing basal levels in serum starved cells (Fig. 1F).

#2.3c How were biosensor/GFP positive cells chosen? Did the authors choose a defined fluorescence intensity cut-off? I think that a pure manual selection is problematic from a methodological point of view as this may introduce biases. Since the authors use Fiji, they can also simply use the "Analyze particles" function, which allows to automatically segment cells from a thresholded image. By choosing the same threshold for all images, it would be ensured that all images are treated exactly the same way.

We had initially opted for manual outlining of cells since automatic segmentation of irregularly-shaped HEK293a cells is imperfect. However, we agree with André that this opens the possibility of bias. We have therefore re-run the analysis with an automated segmentation and thresholding approach, as suggested. This is detailed in the__ second paragraph__ of the first results section on pp. 3-4: In parallel, we imaged cells with a low resolution 0.75 NA air objective to capture fluorescence from the cells’ entire volume, then quantified these images using an automatically determined threshold for GFP-positive cells (see Materials and Methods). This analysis showed that the R25C mutant had no substantial effect on pS473 levels, whereas wild-type PH-AKT greatly inhibited pS473 staining in EGF-stimulated cells as well as reducing basal levels in serum starved cells (Fig. 1F).

Further detail is provided in the first paragraph of the “Image analysis” subsection of the methods on pp. 10-11: For immunofluorescence, we identified individual cells by auto thresholding the DAPI channel using the “Huang” method, followed by the Watershed function to segment bunched cells that appeared to touch. We then used the Voronoi function to generate boundary lines for the segmentation of the cells’ cytoplasm. To identify cytoplasm, auto thresholding of the CellMask channel using the “Huang” function was employed, with the images segmented by adding the nuclear Voronoi boundaries. The “analyze particles” function was then used to identify individual cellular ROIs that were greater than 10 µm2 and were not touching the image periphery. These ROIs were used to measure the raw 12-bit intensity of the EGFP and AKT-pS473 channels. A cutoff of EGFP > 100 was used to define EGFP-positive cells, since this value was greater than the mean ± 3 standard deviations of the untransfected cells’ EGFP intensity. Background intensity of AKT-pS473 was estimated from control cells subject to immunofluorescence with the AKT-pS473 antibody omitted; this value was subtracted from the measured values of all other conditions.

#2.3d I am missing a statement in the methods section that all images were acquired using the same settings.

This was indeed an important oversight on our part – thanks for spotting the omission of this crucial detail. This is now included at the end of the “Immunofluorescence” section of the Methods on pp. 9-10: Identical laser excitation power, scan speeds and photomultiplier gains were used across experiments to enable direct comparison.

#2.3e I recommend that the authors include a single cell correlation plot of EGFP fluorescence intensity vs AktpS473 intensity in Figure 1 D-F. This should be rather informative & make the concentration dependence clear.

We did not observe a strong correlation between PH-AKT1-EGFP intensity and pS473 staining, likely driven by both the imprecision of the cell segmentation and the fact that very low concentrations of PH domain effectively inhibit endogenous AKT1 (as we show in the later figures with the more precise, live cell AKT-NG2 recruitment experiments: see response to #2.5).

#2.3f I further recommend that the authors look at alterations of baseline Akt activity in the presence of the biosensor. In the images it looks like there might be an effect, but this is then lost in the analysis due to the normalization.

As covered in our response to #2.3b, there is indeed an inhibition of baseline pS473 in PH-AKT1-EGFP expressing cells, now explicitly quantified and documented in results.

#2.3g Please include zoomed image insets in Fig. 1D-F, in the current magnification one needs to zoom in quite a bit to see the effect in the raw data. It is a clear effect, but having a zoomed version would make for much easier reading.

We now include high-resolution confocal images instead of low power, low NA volumes as shown in the last version of the manuscript, which we believe addresses this point and also reviewer #1.2.

2.3h Up to the authors: I wonder whether it is possible to extract an IC50 value for the competitive inhibition of Akt by the respective biosensors. The transient expression gives the authors access to a wide range of expression levels at the single cell level, which could be quantified by counterstaining with a EGFP-nanobody at a different color (since the EGFP fluorophore went through the fixation process, it is likely unsuitable for quantification) and microscope calibration. Activity could be quantified as the ratio of observed and expected Akt-pS473 fluorescence (derived from the mean FI per cell from the EGFP control). This is not strictly necessary, but would be a beautiful quantitative experiment, give an easy-to-understand number & make the paper much stronger.

This is a great suggestion, but does not produce precise enough data to work out, as we detail in response to #2.3e. From our data in new figure 3F and figure 5, it seems we have not explored the appropriate expression range to see intermediate levels of inhibition necessary to estimate IC50. This would be a cool experiment though!

__#2.4 __Specific comments: Figure 2. Overall, compelling data. However, 25 molecules/100 um^2 at maximal recruitment feels low. Assuming a total cell surface area of appr. 2000 um^2 per cell and taking a baseline of 5 molecules/100 um^2 into account, this would mean that only about 400 copies of Akt are recruited in response to a pretty robust stimulus. Is it possible that the association reaction of the split GFP is not complete under these conditions? I think that a direct measurement of intracellular endogenous Akt concentration is required to put these numbers into context.

This is an excellent point that we had missed. We now specifically address this point in the third paragraph of the “Genomic tagging of AKT…” section on p. 4: __Accumulation of AKT-NG2 was ~25 molecules per 100 µm2, which assuming a surface area of ~1,500 µm2 per cell corresponds to ~375 molecules total. It should be noted that tagging likely only occurred at a single allele in each cell, and the population still exhibited expression of non-edited AKT1 (__Fig. 2B). Given that HEK293 are known to be pseudotriploid (Bylund et al., 2004), the true number of AKT1 molecules would be at least 1,125. However, given an estimated total copy number of 23,000 AKT1 in these cells (Cho et al., 2022), this is still only about 5%. However, we do not interpret these raw numbers due to uncertainties in the efficiency of NG2 complementation under these conditions, as well as potential for reduced expression from the edited allele.

We also removed the specific comment on molecule density from the abstract.

#2.5 Specific comments: Figure 3 I think that the classification by plasmid dose does not make a lot of sense, as the resulting expression levels are rather similar. I suggest to pool all traces and calculate mean curves by actual expression levels using a binning approach (e.g. 0-50 au, 50-100 au and so on in raw intensity from Figure 3b). If there is an effect in the realized concentration regime, this should pick it up.

This is an excellent suggestion, and we have done just that: thank you! The data is now included as a new panel Fig. 3F. The result is described in the results section, “Sequestration of PIP3 by lipid biosensors”, end of the first paragraph on pp. 4-6: To observe the concentration-dependence of AKT1-PH-mCherry inhibition, we pooled the single cell data from these experiments and split transfected cells into cohorts based on raw expression level (excitation and gain were consistent between experiments, allowing direct comparison). This analysis showed profound inhibition of AKT1-NG2 recruitment at all expression levels, with a slightly reduced effect only visible in the lowest expressing cohort (Fig. 2F).

#2.6 Specific comments: Figure 5 These are very interesting data, in particular with regard to the underlying PIP3 dynamics. I agree with the conclusion of the authors that shielding of PIP3 from degradation is the likely culprit. What I would like to see here is actual kinetic fits - and different terms. On- and off-rate imply biosensor binding, but these are likely rather fast and not on the minute-timescale. The detected processes are much more likely to reflect production and degradation of PIP3 and that should be reflected in the terminology. For the fit: I think that a simple rate law for subsequent reactions ([PIP3]=C(e^-k1t-e^k2t)) will give good results and yield effective rate constants for PIP3 generation and degradation. This implies the quasi-steady state assumption for biosensor binding and implies that [PIP3] is proportional to the biosensor bound [PIP3], but these are reasonable assumptions to make.

The is an excellent suggestion, which we have added. Specifically, fits are now present on Figs. 5G and 5I; we describe these in the last paragraph of results on p. 8: Normalizing data from both expression modes to their maximum response (Fig. 5G) and fitting kinetic profiles for cooperative synthesis and degradation reactionsrevealed the rate of synthesis is remarkably similar: 1.09 min–1 (95% C.I. 1.02-1.17) for single molecule expression vs 1.02 min-1 (95% C.I. 0.98-1.06) for over-expression. On the other hand, degradation slowed with over expression from 0.34 min–1 (95% C.I. 0.24-0.58) to 0.13 min–1 (95% C.I. 0.12-0.15). This is expected, since synthesis of PIP3molecules would not be prevented by biosensor. On the other hand, PIP3 degradation could be slowed by the over-expressed biosensor competing with PTEN and 5-OH phosphatases that degrade PIP3. An even more exaggerated result is achieved with the cPHx1 PI(3,4)P2 biosensor; this shows an increase in fold-change over baseline of 600% for single molecule expression levels, compared to only 100% in over-expressed cells (Fig. 5H). Again, the degradation rate of the signal is substantially slowed by the over-expressed sensor, reducing from 0.27 min–1 (95% C.I. 0.22-0.39) to 0.16 min–1 (95% C.I. 0.14-0.19), whereas synthesis remains only minorly impacted, changing from 0.61 min–1 (95% C.I. 0.57-0.64) to 0.54 min–1 (95% C.I. 0.52-0.56) with over-expression (Fig. 5I). Collectively, these data show that single molecule based PI3K biosensors show improved dynamic range and kinetic fidelity compared to the same sensors over-expressed.

Details of the fits are given in a new methods section on p. 11:

Fitting of reaction kinetics

Curve fitting was performed in Graphpad Prism 9 or later. For the data presented in Figs. 5G and 5I, both synthesis and degradation phases displayed clear “s” shaped profiles not well fit by simple first order kinetics. Since activation of the PI3K pathway involves many multiplicative interactions between adapters and allosteric activation of the enzymes themselves, we assumed cooperativity and fit reactions with the two phase reaction as follows:

Where Ft denotes ∆Ft/∆FMAX, nsyn and ndeg are the Hill coefficients of the respective synthesis and degradation reactions, and the rate constants for the reactions are derived from ksyn = 1/τsyn and kdeg = 1/τdeg.

André Nadler

Reviewer #2 (Significance (Required)):

This is an important paper, analyses the effects of over-expressed lipid biosensors on cell signalling in some detail and will be of significant interest to a broad readership.

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

This is essentially a methods paper in which the authors provide a detailed and highly quantitative analysis of the potentially deleterious effects of expressing phosphoinositide-binding domains as biosensors. Specifically, they study the effects on PIP3 signalling, using biosensors that are widely used in the field.

They show that the most-commonly used method of expressing PIP3 biosensors using transient transfection with viral promotors has clear deleterious effects on downstream signalling due to out-competing the endogenous effectors. Importantly, they also describe a new approach to overcome this by developing new plasmids and methodology to express these reporters at low levels.

Reviewer 3 Major comments:

The work in this paper is thorough and very nicely done. I particularly appreciate the efforts to quantitate or estimate actual numbers and densities of molecules, which significantly strengthen their arguments. The data are excellent and strongly support all their conclusions. I would therefore be happy to see this work published in its current form.

Reviewer 3 Minor comments:

I only have some minor and optional suggestions for improvement.

#3.1 In figure 1D-F they show that PH-Atk-EGFP expression can suppress downstream Akt activation by quantifying P-Akt signal my microscopy. In these panels they say tgey selectively measure this in GFP-expressing cells, but it is not clear how they define which cells are expressing GFP - was a threshold used? Also, it would be nice to also measure both PH-Akt-GFP and P-Akt staining by flow cytometry to look for a correlation. Is there a threshold of biosensor expression that blocks downstream signalling, or is there a linear relationship? This might help specifically measure how much biosensor is too much.

This is an important comment, also raised by reviewer 2. We provide a detailed explanation and outline revisions that address this in our response to reviewer #2.3c; essentially, we replaced the analysis with an automated segmentation and quantification, estimating GFP-positive cells from a fraction of non transfected cells. We have not performed a FACS analysis, but as we note in our response to #2.3e __and #2.3h, the correlation between EGFP and pAKT staining is imprecise in these experiments. The new __Fig. 3C does address this point for AKT1-NG2 recruitment, as described in our response to #2.5.

#3.2 Some of their microscopy images (e.g. Fig 1D-F, Fig 5) are very small and would benefit from a zoom box - especially when they are trying to demonstrate single molecule detection.

This is a fair point raised by all of the reviewers in one form or another. We have added zoomed insets to all of the single molecule images in Figs 2-5, and added higher magnification, confocal section images to Fig. 1.

Reviewer #3 (Significance (Required)):

This is both a methods paper and cautionary tale for cell biologists working in this field. Whilst everyone who uses these probes should be aware of the potential risk of biosensors titrating our effectors, this is often not sufficiently acknowledged. This paper is a very nice and clear demonstration of these risks, exemplified with probably the most highly-used biosensor and key downstream signalling pathway.

Whilst the concepts presented are not especially novel, this paper nonetheless makes an important contribution to the community and hopefully will make others more cautious in how they use these biosensors.

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

Learn more at Review Commons

Referee #3

Evidence, reproducibility and clarity

This is essentially a methods paper in which the authors provide a detailed and highly quantitative analysis of the potentially deleterious effects of expressing phosphoinositide-binding domains as biosensors. Specifically, they study the effects on PIP3 signalling, using biosensors that are widely used in the field.

They show that the most-commonly used method of expressing PIP3 biosensors using transient transfection with viral promotors has clear deleterious effects on downstream signalling due to out-competing the endogenous effectors. Importantly, they also describe a new approach to overcome this by developing new plasmids and methodology to express these reporters at low levels.

Major comments:

The work in this paper is thorough and very nicely done. I particularly appreciate the efforts to quantitate or estimate actual numbers and densities of molecules, which significantly strengthen their arguments. The data are excellent and strongly support all their conclusions. I would therefore be happy to see this work published in its current form.

Minor comments:

I only have some minor and optional suggestions for improvement.

In figure 1D-F they show that PH-Atk-EGFP expression can suppress downstream Akt activation by quantifying P-Akt signal my microscopy. In these panels they say tgey selectively measure this in GFP-expressing cells, but it is not clear how they define which cells are expressing GFP - was a threshold used? Also, it would be nice to also measure both PH-Akt-GFP and P-Akt staining by flow cytometry to look for a correlation. Is there a threshold of biosensor expression that blocks downstream signalling, or is there a linear relationship? This might help specifically measure how much biosensor is too much.

Some of their microscopy images (e.g. Fig 1D-F, Fig 5) are very small and would benefit from a zoom box - especially when they are trying to demonstrate single molecule detection.

Significance

This is both a methods paper and cautionary tale for cell biologists working in this field. Whilst everyone who uses these probes should be aware of the potential risk of biosensors titrating our effectors, this is often not sufficiently acknowledged. This paper is a very nice and clear demonstration of these risks, exemplified with probably the most highly-used biosensor and key downstream signalling pathway.

Whilst the concepts presented are not especially novel, this paper nonetheless makes an important contribution to the community and hopefully will make others more cautious in how they use these biosensors.

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

Learn more at Review Commons

Referee #2

Evidence, reproducibility and clarity

The authors characterize the inhibition of lipid second messenger mediated cell signaling through lipid biosensors that outcompete endogenous effector proteins. This is a very important study that as it quantitatively assesses an issue that many people suspected to exit, yet never properly characterized. This paper is therefore as much a service to the community as a research study in its own right and should be published without undue delay. I am glad that the authors decided to carry out this study & really appreciate their work.

I do however, have a number of suggestions that I think will make the manuscript stronger and can be readily implemented, mostly by reformulating and/or re-analysis of exiting datasets. I've structured my comments by the datasets in the respective figures to follow the logic of the paper.

• Throughout the manuscript, statistical tests are missing, e.g. in figures 1C-F. This must be amended in the revised version. The authors are making a very quantitative point about buffering, data should be treated accordingly.
• I do not think that "PIP3 titration" is the best term to describe the observed effect. "Titration" usually implies the controlled modulation of a concentration, e. g. in analytical chemistry. I think either "competitive binding of PIP3" or "buffering of free PIP3" are more adequate.

Specific comments:Figure 1

• Why are data in 1D-Ff shown as median, with interquartile ranges and 10-90 percentile distance when everything else in the paper is mean +/- se? There might be a good reason for it, but I did not find it mentioned everywhere
• The authors should test, whether the difference between the +EGF conditions in 1D (EGFP) and 1F (PH-AktR25C-EGFP) is indeed statistically significant. If this observation holds up, what does it mean? Is the mutant still competing with endogenous Akt despite the much-reduced binding affinity? The authors should discuss.
• How were biosensor/GFP positive cells chosen? Did the authors choose a defined fluorescence intensity cut-off? I think that a pure manual selection is problematic from a methodological point of view as this may introduce biases. Since the authors use Fiji, they can also simply use the "Analyze particles" function, which allows to automatically segment cells from a thresholded image. By choosing the same threshold for all images, it would be ensured that all images are treated exactly the same way.
• I am missing a statement in the methods section that all images were acquired using the same settings.
• I recommend that the authors include a single cell correlation plot of EGFP fluorescence intensity vs AktpS473 intensity in Figure 1 D-F. This should be rather informative & make the concentration dependence clear.
• I further recommend that the authors look at alterations of baseline Akt activity in the presence of the biosensor. In the images it looks like there might be an effect, but this is then lost in the analysis due to the normalization.
• Please include zoomed image insets in Fig. 1D-F, in the current magnification one needs to zoom in quite a bit to see the effect in the raw data. It is a clear effect, but having a zoomed version would make for much easier reading.
• Up to the authors: I wonder whether it is possible to extract an IC50 value for the competitive inhibition of Akt by the respective biosensors. The transient expression gives the authors access to a wide range of expression levels at the single cell level, which could be quantified by counterstaining with a EGFP-nanobody at a different color (since the EGFP fluorophore went through the fixation process, it is likely unsuitable for quantification) and microscope calibration. Activity could be quantified as the ratio of observed and expected Akt-pS473 fluorescence (derived from the mean FI per cell from the EGFP control). This is not strictly necessary, but would be a beautiful quantitative experiment, give an easy-to-understand number & make the paper much stronger.

Specific comments:Figure 2

• Overall, compelling data. However, 25 molecules/100 um^2 at maximal recruitment feels low. Assuming a total cell surface area of appr. 2000 um^2 per cell and taking a baseline of 5 molecules/100 um^2 into account, this would mean that only about 400 copies of Akt are recruited in response to a pretty robust stimulus. Is it possible that the association reaction of the split GFP is not complete under these conditions? I think that a direct measurement of intracellular endogenous Akt concentration is required to put these numbers into context.

Specific comments:Figure 3

• I think that the classification by plasmid dose does not make a lot of sense, as the resulting expression levels are rather similar. I suggest to pool all traces and calculate mean curves by actual expression levels using a binning approach (e.g. 0-50 au, 50-100 au and so on in raw intensity from Figure 3b). If there is an effect in the realized concentration regime, this should pick it up.

Specific comments:Figure 5

• These are very interesting data, in particular with regard to the underlying PIP3 dynamics. I agree with the conclusion of the authors that shielding of PIP3 from degradation is the likely culprit. What I would like to see here is actual kinetic fits - and different terms. On- and off-rate imply biosensor binding, but these are likely rather fast and not on the minute-timescale. The detected processes are much more likely to reflect production and degradation of PIP3 and that should be reflected in the terminology. For the fit: I think that a simple rate law for subsequent reactions ([PIP3]=C(e^-k1t-e^k2t)) will give good results and yield effective rate constants for PIP3 generation and degradation. This implies the quasi-steady state assumption for biosensor binding and implies that [PIP3] is proportional to the biosensor bound [PIP3], but these are reasonable assumptions to make.

André Nadler

Significance

This is an important paper, analyses the effects of over-expressed lipid biosensors on cell signalling in some detail and will be of significant interest to a broad readership.

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

Learn more at Review Commons

Referee #1

Evidence, reproducibility and clarity

The authors present the use of previously identified biosensors in a single-molecule concentration regime to address lipid effector recruitment. Using controlled and careful single-cell based analysis, the study investigates how expression of the commonly used PIP3 sensor based on Akt-PH domain interferes with the native detection of PIP3. Predominantly live-cell fluorescence microscopy coupled to image analysis drives their studies.

Conceptually, this manuscript carefully and quantitatively describes the influence of lipid biosensor overexpression and presents a means to overcome the inherent and long-recognized problems therein. This solution, namely employing low expression of the lipid biosensor, should be generally applicable. The work is of general interest to cell biologists focused on answering questions at membranes and organelles, including especially those interested in lipid-mediated signaling transductions.

Major:

1. The terminology "single molecule biosensor" is not really appropriate. A protein is not "single-molecule". An enzyme does not "single molecule". Better is biosensors at single-molecule expression levels. In most cases, this should be changed. Single-molecule vs single-cell vs. bulk measurements are often poorly defined in quantifications and conflating these does not help the case, which is already supported by generally clear data.
2. Figure 1D-F, images not as clearly describing quantitation as one would hope. Untransfected cells in 1E should demonstrate more translocated Akt-pS473 than transfected, but it is difficult for this reviewer to find. Consider inset images in addition to the wider field. Consider also moving the "negative" data of Fig 1B-C to Supplement.
3. The cell line being used is not clearly specified after the initial development of the NG1 followed by CRISPRed NG2 onto Akt. For example, for the Figure 3C experiments, the text states "complete ablation of endogenous AKT1-NG2" but this information is not apparent from the figure legend or figure. Throughout the cell line used and the aspects transfected need to be made explicitly clear.
4. Fig. 5 shows single cells. It is therefore unclear if broken promoters have resulted in decreased expression. This point is important because the expression plasmids should be made publicly available, and for their use to be understood properly, this must be clarified. The details of the plasmids are unclear. Perhaps listed in the table? - unclear. This aspect would be important for the field to effectively use the reagents.
5. This manuscript speculates several times that with more abundant PIs like PI45P2, the observed saturation effect is probably not happening. This should be removed. While the back of envelope calculations may reflect an ideal scenario, the heterogeneity of distribution and multiple key cellular structures involved would seem to corral increased PI45P2 levels in certain regions. These factors amid multivalency and electrostatic mechanisms of lipid effector recruitment (e.g. MARCKS) suggest that speculation may be too strong. Moreover, Maib et al JCB 2024 demonstrated PI4P probe overexpression could directly mask the ability to detect PI4P post-fixation - not fully, but partially. Repeating the titration experiments of this manuscript for multiple PIs is entirely beyond the scope of reasonable, and hence, such experiments are not requested, in favor of adopting more conscientious speculation.

Minor:

1. Schematics throughout need simplification, enabling their enlargement.
2. Numerous spelling (Fig. 4 schemas) and capitalizations need fixing.
3. Pg 1 Famous is not appropriate wording
4. Fig. 1A statistical testing of microscopy quantifications absent (generally, throughout) and should be included.
5. Fig.1. In a transient transfection, the protein expression is not uniform. Please explain how you normalized the quantification.
6. Fig. 1D. EGFP expression levels increased with EGF stimulation. How is this possible?
7. Fig. 1D. The images have pS473 whereas the y-axis label on box plots has p473. Can these box plots be labelled separately for consistency?
8. Fig.1. T308 phosphorylation is mentioned in Figure 1, but only pS473 data is shown.
9. Fig.1 legend. 'Over-expression of PH-AKT is hypothesised to outcompete the endogenous AKT's PH domain'. Why do you need to state a hypothesis in the legend?
10. Fig.1E You stated that the PH-AKT R25C-EGFP is stimulated by EGF addition. However, the GFP signal looks the same in both unstimulated and stimulated. Could you please clarify? Are you sure that the stimulation worked?
11. You mention...that the AKT enzyme is activated by PDK1 and TORC2, which phosphorylate at residues T308 and S473, respectively. Phosphorylation is also known to occur on T450 at c-tail. Does this phosphorylation also contribute to its activation?
12. Fig. 1 scale bar in all images equivalent?
13. Pg. 1 paragraph 1 "we have argued..." vs. paragraph 3"...consider that an..." feels like arguing with themselves.
14. Pg. 1 para 3 what is RFC score - must explain
15. Discussion of numbers of PIP3 vs. effectors etc may not be appropriate for the introduction, as the points made by these calculations are already made in the previous paragraphs. May fit better in pg 6 Mitigating PIP3 titration... with an accompanying schematic.
16. Pg 2 "a neonGreen" not well defined, needs accurate description.
17. Fig 2C should give a unstimulated trajectory of puncta/100 um2 to compare with the stimulated
18. Fig 2C and F and G should be systematized for easier comparison. E.g. min vs seconds, 0 timepoint of EGF/rapa addition
19. Pg 5 "...and calibrated them..." unclear what is being calibrated, as the text later states that the histograms are fit to monomer/dimer/multimer model resulting in 98.1% in monomer. Minor point.
20. Explain why baselines in Fig2CFG are different
21. Fig. 2 has quantification with images; Fig. 3 has it separate. Make consistent.
22. Fig. 3B comes before images? Where are the images? Also, y-axis = Intensity (a.u.). Is intensity just full image field? Or per cell? All very unclear.
23. Fig. 3C missing images
24. Fig 3 C needs brightness/contrast adjusted as images are nearly entirely black (zero values).
25. Fig 3C needs scale bar systemization
26. Fig 4 needs 4 panels A-D
27. Pg 6 5-OH phosphatases needs reference
28. Fig 5B, make images bigger

Referees cross-commenting

I have read the other reviews and find them entirely reasonable. My impression is we landed on similar general content that needs work, none of which is out of line. The importance and care taken in the author's work is uniformly lauded.

Significance

This manuscript clearly and reasonably demonstrates that the commonly used PIP3 sensor can be titrated to low concentrations, at which it does not interfere with Akt translocation and activation. This work is a good technical reference for the field. Signal transduction and membrane biologists should be especially interested in the data. The reviewer/s have core expertise in phosphoinositides, protein biochemistry, cell biology, and membrane biophysics.

eLife Assessment

This study reports that activation of TFEB promotes lysosomal exocytosis and clearance of cholesterol from lysosomes, the strength of evidence for which is solid and considered valuable in the context of Niemann-Pick Disease Type C. However, beyond this aspect of the study, the reviewers found the strength of the evidence to be incomplete. The manuscript also needs careful editing to improve readability.

Reviewer #1 (Public review):

Summary:

The authors are trying to determine if SFN treatment results in dephosphorylation of TFEB, subsequent activation of autophagy-related genes, exocytosis of lysosomes, and reduction in lysosomal cholesterol levels in models of NPC disease.

Strengths:

(1) Clear evidence that SFN results in translocation of TFEB to the nucleus.

(2) In vivo data demonstrating that SFN can rescue Purkinje neuron number and weight in NPC1-/- animals.

Weaknesses:

(1) Lack of molecular details regarding how SFN results in dephosphorylation of TFEB leading to activation of the aforementioned pathways. Currently, datasets represent correlations.

(2) Based on the manuscript narrative, discussion, and data it is unclear exactly how steady-state cholesterol would change in models of NPC disease following SFN treatment. Yes, there is good evidence that lysosomal flux to (and presumably across) the plasma membrane increases with SFN. However, lysosomal biogenesis genes also seem to be increasing. Given that NPC inhibition, NPC1 knockout, or NPC1 disease mutations are constitutively present and the cell models of NPC disease contain lysosomes (even with SFN) how could a simple increase in lysosomal flux decrease cholesterol levels? It would seem important to quantify the number of lysosomes per cell in each condition to begin to disentangle differences in steady state number of lysosomes, number of new lysosomes, and number of lysosomes being exocytosed.

(3) Lack of evidence supporting the authors' premise that "SFN could be a good therapeutic candidate for neuropathology in NPC disease".

Reviewer #2 (Public review):

Summary:

This study presents a valuable finding that the activation of TFEB by sulforaphane (SFN) could promote lysosomal exocytosis and biogenesis in NPC, suggesting a potential mechanism by SFN for the removal of cholesterol accumulation, which may contribute to the development of new therapeutic approaches for NPC treatment.

Strengths:

The cell-based assays are convincing, utilizing appropriate and validated methodologies to support the conclusion that SFN facilitates the removal of lysosomal cholesterol via TFEB activation.

Weaknesses:

(1) The in vivo experiments demonstrate the therapeutic potential of SFN for NPC. A clear dose-response analysis would further strengthen the proposed therapeutic mechanism of SFN. Additional data supporting the activation of TFEB by SFN for cholesterol clearance in vivo would strengthen the overall impact of the study

(2) In Figure 4, the authors demonstrate increased lysosomal exocytosis and biogenesis by SFN in NPC cells. Including a TFEB-KO/KD in this assay would provide additional validation of whether these effects are TFEB-dependent.

(3) For lysosomal pH measurement, the combination of pHrodo-dex and CF-dex enables ratiometric pH measurement. However, the pKa of pHrodo red-dex (according to Invitrogen) is ~6.8, while lysosomal pH is typically around 4.7. This discrepancy may account for the lack of observed lysosomal pH changes between WT and U18666A-treated cells. Notably, previous studies (PMID: 28742019) have reported an increase in lysosomal pH in U18666A-treated cells.

(4) The authors are also encouraged to perform colocalization studies between CF-dex and a lysosomal marker, as some researchers may be concerned that NPC1 deficiency could reduce or block the trafficking of dextran along endocytosis.

(5) In vivo data supporting the activation of TFEB by SFN for cholesterol clearance would significantly enhance the impact of the study. For example, measuring whole-animal or brain cholesterol levels would provide stronger evidence of SFN's therapeutic potential.

Reviewer #3 (Public review):

Summary:

The authors demonstrate that activation of TFEB facilitates cholesterol clearance in cell models of Niemann-Pick type C (NPC). This is done through a variety of approaches including activation of TFEB by sulforaphane (SFN), a naturally occurring small-molecule TFEB agonist. SFN induces TFEB nuclear translocation and promotes lysosomal exocytosis. In an NPC mouse model, SFN dephosphorylates/activates TFEB in the brain and rescues the loss of Purkinje cells.

Strengths:

NPC is a severe disease and there is little in the way of treatment. The manuscript points towards some treatment options. However, the title, the title "Small-molecule activation of TFEB Alleviates Niemann-Pick Disease..." is far too strong and should be changed.

Weaknesses:

(1) The manuscript is extremely hard to read due to the writing; it needs careful editing for grammar and English.

(2) There are a number of important technical issues that need to be addressed.

(3) The TFEB influence on filipin staining in Figure 1A is somewhat subtle. In the mCherry alone panels there is a transfected cell with no filipin staining and the mCherry-TFEBS211A cells still show some filipin staining.

(4) Figure 1C is impressive for the upregulation of filipin with U18666A treatment. However, SFN is used at 15 microM. This must be hitting multiple pathways. Vauzour et al (PMID: 20166144) use SFN at 10 nM to 1microM. Other manuscripts use it in the low microM range. The authors should repeat at least some key experiments using SFN at a range of concentrations from perhaps 100 nM to 5 microM. The use of 15 microM throughout is an overall concern.

Author Response:

Thank you for your interest in our paper. We would also like to thank the anonymous reviewers for their critical and constructive comments. Although the reviewers found our work interesting, they raised several important concerns about our study. To address these concerns, mostly we will perform new experiments as following.

1. Examine whether antioxidant-NAC can block SFN-induced TFEB-nuclear translocation in NPC cells;

2. Examine whether calcineurin inhibitor (FK506+CsA) or Ca 2+ inhibitor (Bapta-AM) can block SFN-induced TFEB-nuclear translocation in NPC cells.

3. Investigate whether cholesterol was cleared by activation of TFEB by SFN in vivo tissues.

4. Investigate whether SFN-evoked the lysosomal exocytosis is TFEB-dependent by using TFEB-KO cells.

5. Examine the effect of NPC1 deficiency on dextran trafficking by studying the localization of CF- dex and Lamp1.

6. Perform cytotoxicity experiments to examine whether SFN used in this study is cytotoxic in various cell lines

In addition, according to the reviewers’ suggestions, we will make clarifications and corrections wherever appropriate in the manuscript. Below please find our point-by-point responses and plans to the reviewers’ comments.

Reviewer #1 (Public review):

Summary:

The authors are trying to determine if SFN treatment results in dephosphorylation of TFEB, subsequent activation of autophagy-related genes, exocytosis of lysosomes, and reduction in lysosomal cholesterol levels in models of NPC disease.

Strengths:

(1) Clear evidence that SFN results in translocation of TFEB to the nucleus.

(2) In vivo data demonstrating that SFN can rescue Purkinje neuron number and weight in NPC1-/- animals.

Thank you for the support!

Weaknesses:

(1) Lack of molecular details regarding how SFN results in dephosphorylation of TFEB leading to activation of the aforementioned pathways. Currently, datasets represent correlations.

Thank you for this constructive comment. The reviewer is right that in this manuscript the molecular mechanism of SFN-activated TFEB has not been discussed in details. Because previously we have shown that SFN induces TFEB nuclear translocation via a Ca 2+ - dependent but MTOR (mechanistic target of rapamycin kinase)-independent mechanism through a moderate increase in reactive oxygen species (ROS). And calcineurin-mediated TFEB dephosphorylation underlies SFN-induced TFEB activation. These data have been published in 2021 autophagy (Li, Shao et al. 2021) . Therefore, in this study we did not mention this part. We will add the molecular mechanism of TFEB activation by SFN in the discussion part. And to further confirm this mechanism in NPC cells, we will also perform experiments including: 1) examine whether antioxidant-NAC can block SFN-induced TFEB-nuclear translocation in NPC cells; 2) examine whether calcineurin inhibitor (FK506+CsA) can block SFN-induced TFEB-nuclear translocation in NPC cells.

(2) Based on the manuscript narrative, discussion, and data it is unclear exactly how steady-state cholesterol would change in models of NPC disease following SFN treatment. Yes, there is good evidence that lysosomal flux to (and presumably across) the plasma membrane increases with SFN. However, lysosomal biogenesis genes also seem to be increasing. Given that NPC inhibition, NPC1 knockout, or NPC1 disease mutations are constitutively present and the cell models of NPC disease contain lysosomes (even with SFN) how could a simple increase in lysosomal flux decrease cholesterol levels? It would seem important to quantify the number of lysosomes per cell in each condition to begin to disentangle differences in steady state number of lysosomes, number of new lysosomes, and number of lysosomes being exocytosed.

Thank you for the suggestion. It is important to define the three states 1) original number of lysosomes, 2) number of new lysosomes, and 3) number of lysosomes being exocytosis. However, we have checked literature, so far it seems that there is no good method that could clearly differentiate the three states of lysosomes.

(3) Lack of evidence supporting the authors' premise that "SFN could be a good therapeutic candidate for neuropathology in NPC disease".

Suggestion was taken! We will investigate whether cholesterol was reduced by activation of TFEB by SFN in vivo to strength the point that SFN could be a potential therapeutic compound for NPC treatment. And to avoid confusion, we have removed this sentence.

Reviewer #2 (Public review):

Summary:

This study presents a valuable finding that the activation of TFEB by sulforaphane (SFN) could promote lysosomal exocytosis and biogenesis in NPC, suggesting a potential mechanism by SFN for the removal of cholesterol accumulation, which may contribute to the development of new therapeutic approaches for NPC treatment.

Strengths:

The cell-based assays are convincing, utilizing appropriate and validated methodologies to support the conclusion that SFN facilitates the removal of lysosomal cholesterol via TFEB activation.

Weaknesses:

(1) The in vivo experiments demonstrate the therapeutic potential of SFN for NPC. A clear dose-response analysis would further strengthen the proposed therapeutic mechanism of SFN. Additional data supporting the activation of TFEB by SFN for cholesterol clearance in vivo would strengthen the overall impact of the study

We understand the reviewer’s point. We examined two doses of SFN-30 and 50mg/kg. As shown in Fig.6, SFN (50mg/kg), but not 30mg/kg prevents a degree of Purkinje cell loss in the lobule IV/V of cerebellum, suggesting a dose-correlated preventive effect of SFN. In vivo experiments with higher concentrations of SFN and optimized dosage form of SFN were planned in the future study, but will not be included in this study.

We will investigate whether cholesterol was cleared by activation of TFEB by SFN in vivo.

(2) In Figure 4, the authors demonstrate increased lysosomal exocytosis and biogenesis by SFN in NPC cells. Including a TFEB-KO/KD in this assay would provide additional validation of whether these effects are TFEB-dependent.

Thank you for this valuable suggestion. We will investigate whether SFN-evoked the lysosomal exocytosis is TFEB-dependent by using TFEB-KO cells.

(3) For lysosomal pH measurement, the combination of pHrodo-dex and CF-dex enables ratiometric pH measurement. However, the pKa of pHrodo red-dex (according to Invitrogen) is ~6.8, while lysosomal pH is typically around 4.7. This discrepancy may account for the lack of observed lysosomal pH changes between WT and U18666A-treated cells. Notably, previous studies (PMID: 28742019) have reported an increase in lysosomal pH in U18666A-treated cells.

We understand the reviewer’s point. But we used pHrodo™ Green-Dextran (P35368, Invitrogen), but not pHrodo red-dex to measure the lysosomal luminal acidity. According to the product information from Invitrogen, pHrodo Green-dex conjugates are non-fluorescent at neural pH, but fluorescence bright green at acidic pH ranges 4-9, such as those in endosomes and lysosomes. Therefore, pHrodo Green-dex can be used to monitor the acidity of lysosome (Hu, Li et al. 2022) . We also used LysoTracker Red DND-99 (Thermo Scientific, L7528) to measure lysosomal pH (Fig. 4G, H), which is consistent with results of pHrodo Green/CF measurement. Overall, in our hands, we have not detected pH change of lysosomes in U18666A-treated NPC1 cell models.

(4) The authors are also encouraged to perform colocalization studies between CF-dex and a lysosomal marker, as some researchers may be concerned that NPC1 deficiency could reduce or block the trafficking of dextran along endocytosis.

Suggestion was taken! We will examine the effect of NPC1 deficiency on dextran trafficking by studying the localization of CF-dex and Lamp1.

(5) In vivo data supporting the activation of TFEB by SFN for cholesterol clearance would significantly enhance the impact of the study. For example, measuring whole-animal or brain cholesterol levels would provide stronger evidence of SFN's therapeutic potential.

We really appreciate the reviewer’s suggestions. We will investigate whether cholesterol was cleared by activation of TFEB by SFN in vivo.

Reviewer #3 (Public review):

Summary:

The authors demonstrate that activation of TFEB facilitates cholesterol clearance in cell models of Niemann-Pick type C (NPC). This is done through a variety of approaches including activation of TFEB by sulforaphane (SFN), a naturally occurring small-molecule TFEB agonist. SFN induces TFEB nuclear translocation and promotes lysosomal exocytosis. In an NPC mouse model, SFN dephosphorylates/activates TFEB in the brain and rescues the loss of Purkinje cells.

Strengths:

NPC is a severe disease and there is little in the way of treatment. The manuscript points towards some treatment options. However, the title, the title "Small-molecule activation of TFEB Alleviates Niemann-Pick Disease..." is far too strong and should be changed.

Weaknesses:

(1) The manuscript is extremely hard to read due to the writing; it needs careful editing for grammar and English.

We will thoroughly check grammar to improve the manuscript.

(2) There are a number of important technical issues that need to be addressed.

We will address the technical issues mentioned in the following.

(3) The TFEB influence on filipin staining in Figure 1A is somewhat subtle. In the mCherry alone panels there is a transfected cell with no filipin staining and the mCherry-TFEBS211A cells still show some filipin staining.

We understand the reviewer’s point. We will investigate whether cholesterol is cleared by activation of TFEB by SFN in vivo.

(4) Figure 1C is impressive for the upregulation of filipin with U18666A treatment. However, SFN is used at 15 microM. This must be hitting multiple pathways. Vauzour et al (PMID: 20166144) use SFN at 10 nM to 1microM. Other manuscripts use it in the low microM range. The authors should repeat at least some key experiments using SFN at a range of concentrations from perhaps 100 nM to 5 microM. The use of 15 microM throughout is an overall concern.

We understand the reviewer’s point. See RESPONSE #1, previously we have shown that SFN (10–15 μM, 2–9 h) induces robust TFEB nuclear translocation in a dose- and time-dependent manner in HeLa GFP-TFEB stable cells as well as in other human cell lines without cytotoxicity (Li, Shao et al. 2021) . According to previous results, in this study, we chose SFN (15 μM) to examine its effect on cholesterol clearance. We will add the information in the discussion part. In this study, we will perform dose-response TFEB nuclear translocation in NPC model cells as well as cytotoxicity experiments to examine whether the concentrations of SFN used in various cell lines are toxic.

References:

Hu, M. Q., P. Li, C. Wang, X. H. Feng, Q. Geng, W. Chen, M. Marthi, W. L. Zhang, C. L. Gao, W. Reid, J. Swanson, W. L. Du, R. Hume and H. X. Xu (2022). "Parkinson's disease-risk protein TMEM175 is a proton-activated proton channel in lysosomes.” Cell 185(13): 2292-+.

Li, D., R. Shao, N. Wang, N. Zhou, K. Du, J. Shi, Y. Wang, Z. Zhao, X. Ye, X. Zhang and H. Xu (2021). “Sulforaphane Activates a lysosome-dependent transcriptional program to mitigate oxidative stress.” Autophagy 17(4): 872-887.

A giant... and a dwarf... are brothers.

A bitch-slap so potent that even the rude giant sees the light.

What an interesting metaphor: the wounded giant as a heckled artist.

The vibes are impeccable.

????

Study this section

england

ορισμος για resilience & adaptation/ can be used in my concepts framework

the eu commision report, is focusing on the negative effects of cc for the european continent, stressing that the southern countries and in some cases the Balkans, are the ones that wil mainly be influenced by cc. Agriculture as we have seen in many previous readings today, will decline bc of drought and extreme weathwer events in countries such as greece italy and spain. The bad thing is, that since southern economies are very connected to agriculture, this deterioration, will have a negative effect in the whole country bc the infrasrructure is not good, and it cannot easily repair potential damages caused by CC.

:from:

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I started reading this paper with great interest, which flagged over time. As someone with extensive experience both publishing peer-reviewed research articles and working with publication data (Web of Science, Scopus, PubMed, PubMedCentral) I understand there are vagaries in the data because of how and when it was collected, and when certain policies and processes were implemented. For example, as an author starting in the late 1980s, we were instructed by the journal “guide to authors” to use only initials. My early papers were all only using initials. This changed in the mid-late 1990s. Another example, when working with NIH publications data, one knows dates like 1946 (how far back MedLine data go), 1996 (when PubMed was launched), and 2000 (when PubMedCentral was launched) and 2008 (when NIH Open Access policy enacted). There are also intermediate dates for changes in curation policy…. that underlie a transition from initials to full name in the biomedical literature.

I realize that the study covers all research disciplines, but still I am surprised that the authors of this paper don’t start with an examination of the policies underlying publications data, and only get to this at the end of a fairly torturous study.

As a reader, this reviewer felt pulled all over the place in this article and increasingly frustrated that this is a paper that explores the Dimensions database vagaries only and not really the core overall challenges of bibliometric data, irrespective of data source. Dimensions ingests data from multiple sources — so any analysis of its contents needs to examine those sources first.

A few specific comments:

• The “history of science” portion of the paper focuses on English learned societies in the 17th century. There were many other learned societies across Europe, and also “papers” (books, treatises) from long before the 17th century in Middle-eastern and Asian countries (e.g, see history of mathematics, engineering, governance and policy, etc.). These other histories were not acknowledged by the authors. Research didn’t just spring full-formed out of Zeus’ head.

• It is unclear throughout if the authors are referring to science, research, which disciplines are or are not included. The first chart on discipinary coverage is Fig 13 and goes back to 1940ish. Also, which languages are included in the analysis? For example, Figure 2 says “academic output” but from which academies? What countries? What languages? Disciplines? Also, in Figure 2, this reviewer would have like to see discussion about the variability in the noisiness of the data over time.

• The inclusion of gender in the paper misses the mark for this reviewer. When dealing with initials, how can one identify gender? And when working in times/societies where women had to hide their identity to be published…. how can a name-based analysis of gender be applied? If this paper remains a study of the “initial era”, this reviewer recommends removing the gender analysis.

• Reference needed for “It is just as important to see ourselves reflected in the outputs of the research careers…” (section B).

• Reference needed for “This period marked the emergence of “Big Science” (Section B). How do we know this is Big Science? What is the relationship with the nature of science careers? Here it would be useful perhaps to mention that postdocs were virtually unheard of before Sputnik.

• Fig 3. This would be more effective as a % total papers than absolute #.

• Gradual Evolution of the Scholarly Record. This reviewer would like to see proportion of papers without authors. A lot of history of science research is available for this period, and a few references here would be welcome, as well as a by-country analysis (or acknowledgement that the data are largely from Europe and/or English-speaking countries).

• Accelerated Changes in Recent Times. Again, this reviewer would like to see reference to scholarship on the history of science. One of the things happening in the post WW2 timeframe is the increase in government spending (in the US particularly) on R&D and academic research. So, is the academy changing or is it responding to “market forces”.

• Reflective richness of data. “Evolution of the research community” is not described in the text, not is collaborative networks.

• In the following paragraph, one could argue that evaluation was a driver of change, not a response to it. This reviewer would like to see references here.

• II. Methodology. (i) 2nd sentence missing “to” “… and full form to refer to an author name…”. (ii) 2nd para the authors talk about epochs, but the data could be (are) discontinuous because of (a) curation policy, (b) curation technology, (c) data sources (e.g., Medline rolled out in the 1960s and back-populated to 1946). (iii) 4th para referes to Figs 3 and 4 showing a marked change between 1940 and 1950, but Fig 3 goes back only to 1960, and Fig 4 is so compressed it is hard to see anything in that time range. (iv) Para 7. “the active publishing community is a reasonable proxy for the global research population”. We need a reference here and more analysis. Is this Europe? English language? Which disciplines? All academia? Dimensions data? (v) Para 12 “In exploring the issue of gender…” see comments above. Gender is an important consideration but is out of scope, in this reviewer’s opinion, for this paper focused on use of initials vs. full name.

• Listing 1. Is there a resolvable URL/DOI for this query?

• Figs 9-11, 14, 15. This reviewer would like to see a more fulsome examination / discussion of data discontinuities. Particularly around ~1985-2000.

Discussion

• The country-level discussion suggests the data (publications included) are only those that have been translated into English. Please clarify. Also, please add references in this section. There are a lot of bold statements, such as “A characteristic of these countries was the establishment of strong national academies.” Is this different from other places in the world? How? In the para before this statement, there is a phrase “picking out Slavonic stages” that is not clear to this reviewer.

• The authors seem to get ahead of themselves talking about “formal” and “informal” in relation to whether initials or full names are used. And then discuss the “Power Distance” and end up arguing that it isn’t formal/informal … but rather publisher policies and curation practices driving the initial era and its end.

• And then the authors come full circle on research articles being a technology, akin to a contract. Which is neat and useful. But all the intermediate data analysis is focused on the Dimensions data base and this reviewer would argue should be a part of the database documentation rather than a scholarly article.

• This reviewer would prefer this paper be focused much more tightly on how publishing technology can and has driven the sociology of science. Dig more into the E. Journal Analysis and F. Technological analysis. Stick with what you have deep data for, and provide us readers with a practical and useful paper that maybe, just maybe, publishers will read and be incentivized to up their game with respect to adoption of “new” technologies like ORCID, DOIs for data, etc. Because these papers are not just expositions on a disciplinary discourse, they are also a window into how science (research) works and is done.

The presented preprint is a well-researched study on a relevant topic that could be of interest to a broad audience. The study's strengths include a well-structured and clearly presented methodology. The code and data used in the research are openly available on Figshare, in line with best practices for transparency. Furthermore, the findings are presented in a clear and organized manner, with visualization that aid understanding.

At the same time, I would like to draw your attention to a few points that could potentially improve the work.

1. I think it would be beneficial to expand the annotation to approximately 250 words.

2. The introduction starts with a very broad context, but the connection between this context and the object of the research is not immediately clear. There are few references in this section, making it difficult to determine whether the authors are citing others or their own findings.

3. The transition to the main topic of the study is not well-defined, and there is no description of the gap in the literature regarding the object of study. Additionally, "bibliometric archaeology" appears at the end of the introduction but is only mentioned again later in the discussion, which may cause confusion for the reader.

4. It would be helpful to clearly state the purpose and objectives of the study both in the Introduction and in the abstract as well.

5. Besides, it is important to elaborate on the contribution of this study in the introduction section.

6. The same applies to the background - a very broad context, but the connection with the object of the research is not entirely clear.

7. Page 4 - as far as I understand, these are conclusions from a literature review, while point 3 (Reflective Richness of Data) does not follow from the previous analysis.

8. The overall impression of the introduction and background is that it is an interesting text, but it is not well related to the objectives of the study. I would recommend shortening these sections by making the introduction and literature review more pragmatic and structured. At the same time, this text could be published as a standalone contribution.

9. As I mentioned above, the methodology refers to the strengths of the study. However, in this section, it would be helpful to introduce and justify the structure of presenting the results.

10. In the methodology section, the authors could also provide a footnote with a link to the code and dataset (currently, it is only given at the end).

11. With regard to the discussion, I would like to encourage the authors to place their results more clearly in the academic context. Ideally, references from the introduction and/or literature review would reappear in this section to help clarify the research contribution.

12. Although Discussion C is an interesting read, it seems more related to the introduction than the results. Again, the text itself is rather interesting, but it would benefit from a more thorough justification.

Remarks on the images:

1. At least the data source for the images should be specified in the background, because it is not obvious to the reader before describing the methodology.

2. The color distinction between China and Russia in Figure 8 is not very clear.

3. The gray lines in Figures 9-11 make the figures difficult to read. Additionally, the meaning of these lines is not clearly indicated in the legends of Figures 10 and 11. These issues should be addressed. 

All comments and suggestions are intended to improve the article. Overall, I have a very positive impression of the work.

Sincere, 

Dmitry Kochetkov

Overview

This manuscript provides an in-depth examination of the use of initials versus full names in academic publications over time, identifying what the authors term the "Initial Era" (1945-1980) as a period during which initials were predominantly used. The authors contextualize this within broader technological, cultural, and societal changes, leveraging a large dataset from the Dimensions database. This study contributes to the understanding of how bibliographic metadata reflects shifts in research culture.

Strengths

+ Novel concept and historical depth

The paper introduces a unique angle on the evolution of scholarly communication by focusing on the use of initials in author names. The concept of the "Initial Era" is original and well- defined, adding a historical dimension to the study of metadata that is often overlooked. The manuscript provides a compelling narrative that connects technological changes with shifts in academic culture.

+ Comprehensive dataset

The use of the Dimensions database, which includes over 144 million publications, lends significant weight to the findings. The authors effectively utilize this resource to provide both anecdotal and statistical analyses, giving the paper a broad scope. The differentiation between the anecdotal and statistical epochs helps clarify the limitations of the dataset and strengthens the authors' conclusions.

+ Cross-disciplinary relevance

The study's insights into the sociology of research, particularly the implications of name usage for gender and cultural representation, are highly relevant across multiple disciplines. The paper touches on issues of diversity, bias, and the visibility of researchers from different backgrounds, making it an important contribution to ongoing discussions about equity in academia.

+ Technological impact

The authors successfully connect the decline of the "Initial Era" to the rise of digital publishing technologies, such as Crossref, PubMed, and ORCID. This link between technological infrastructure and shifts in scholarly norms is a critical insight, showing how the adoption of new tools has real-world implications for academic practices.

Weaknesses

-  Lack of clarity and readability

While the manuscript is rich in data and analysis, it can be dense and challenging to follow for readers not familiar with the technical details of bibliometric studies. The text occasionally delves into highly specific discussions that may be difficult for a broader audience to grasp while other concepts are introduced in cursory. Consider condensing the introduction section, removing unrelated historical accounts, and leading the audience to the key objectives of this research much earlier.

-  Missing empirical case studies

The manuscript remains largely theoretical, relying heavily on data analysis without providing concrete case studies or empirical examples of how the "Initial Era" affected individual disciplines or researchers. A more detailed exploration of specific instances where the use of initials had significant consequences would make the findings more tangible. Incorporating case studies or anecdotes from the history of science that illustrate the real-world impacts of the trends identified in the data would enrich the paper. These examples could help ground the analysis in practical outcomes and demonstrate the relevance of the "Initial Era" to contemporary debates.

-  Half-baked comparative analysis

Although the paper presents interesting data about different countries and disciplines, the comparative analysis between these groups could be further developed. For example, the reasons behind the differences in initial use between countries with different writing systems or academic cultures are not fully explored. A more in-depth comparative analysis that explains the cultural, linguistic, or institutional factors driving the observed differences in initial use would add nuance to the findings. This could involve a more detailed discussion of how non-Roman writing systems influence name formatting or how specific national academic policies shape author metadata.

-  Limited discussion of alternative explanations

While the authors link the decline of the "Initial Era" to technological advancements, other potential explanations, such as changing editorial policies (“technological harmonisation”), shifts in academic prestige, or the influence of global collaboration, are not fully explored. The paper could benefit from a broader discussion of these factors. Expanding the discussion to include alternative explanations for the decline of initial use, and how these might interact with technological changes, would provide a more comprehensive view. Engaging with literature on academic publishing practices, editorial decisions, and global research trends could help contextualize the findings within a wider framework.

Conclusion

This manuscript offers a novel and insightful analysis of the evolution of name usage in academic publications, providing valuable contributions to the fields of bibliometrics, science studies, and research culture. With improvements in clarity, comparative analysis, and the incorporation of case studies, this paper has the potential to make a significant impact on our understanding of how metadata reflects broader societal and technological changes in academia. The authors are encouraged to refine their discussion and expand on the implications of their findings to make the manuscript more accessible and applicable to a wider audience.

Aug 14, 2024

Nov 20, 2024

Nov 20, 2024

Authors:

• Simon Porter (Digital Science) s.porter@digital-science.com
• Daniel Hook (Digital Science) d.hook@digital-science.com

2

10.48550/arXiv.2404.06500

The Rise and Fall of the Initial Era

this paragrapgh is about solutions for cc impacts but since its mostly recommendations, im not sure it will be effective for my Lit Review/ Maybe ill wiat until my fieldwork?

resilience mesures, potentia solutions for what could be done to withstand cc schocks

In "Evolution of Peer Review in Scientific Communication", Kochetkov provides a point-of-view discussion of the current state of play of peer review for scientific literature, focussing on the major models in contemporary use and recent innovations in reform. In particular, they present a typology of three main forms of peer review: traditional pre-publication review; registered reports; and post-publication review, their preferred model. The main contribution it could make would be to help consolidate typologies and terminologies, to consolidate major lines of argument and to present some useful visualisations of these. On the other hand, the overall discussion is not strongly original in character.

The major strength of this article is that the discussion is well-informed by contemporary developments in peer-review reform. The typology presented is modest and, for that, readily comprehensible and intuitive. This is to some extent a weakness as well as a strength; a typology that is too straightforward may not be useful enough. As suggested at the end it might be worth considering how to complexify the typology at least at subordinate levels without sacrificing this strength. The diagrams of workflows are particularly clear.

The primary weakness of this article is that it presents itself as an 'analysis' from which they 'conclude' certain results such as their typology, when this appears clearly to be an opinion piece. In my view, this results in a false claim of objectivity which detracts from what would otherwise be an interesting and informative, albeit subjective, discussion, and thus fails to discuss the limitations of this approach. A secondary weakness is that the discussion is not well structured and there are some imprecisions of expression that have the potential to confuse, at least at first.

This primary weakness is manifested in several ways. The evidence and reasoning for claims made is patchy or absent. One instance of the former is the discussion of bias in peer review. There are a multitude of studies of such bias and indeed quite a few meta-analyses of these studies. A systematic search could have been done here but there is no attempt to discuss the totality of this literature. Instead, only a few specific studies are cited. Why are these ones chosen? We have no idea. To this extent I am not convinced that the references used here are the most appropriate. Instances of the latter are the claim that "The most well-known initiatives at the moment are ResearchEquals and Octopus" for which no evidence is provided, the claim that "we believe that journal-independent peer review is a special case of Model 3" for which no further argument is provided, and the claim that "the function of being the "supreme judge" in deciding what is "good" and "bad" science is taken on by peer review" for which neither is provided.

A particular example of this weakness, which is perhaps of marginal importance to the overall paper but of strong interest to this reviewer is the rather odd engagement with history within the paper. It is titled "Evolution of Peer Review" but is really focussed on the contemporary state-of-play. Section 2 starts with a short history of peer review in scientific publishing, but that seems intended only to establish what is described as the 'traditional' model of peer review. Given that that short history had just shown how peer review had been continually changing in character over centuries - and indeed Kochetkov goes on to describe further changes - it is a little difficult to work out what 'traditional' might mean here; what was 'traditional' in 2010 was not the same as what was 'traditional' in 1970. It is not clear how seriously this history is being taken. Kochetkov has earlier written that "as early as the beginning of the 21st century, it was argued that the system of peer review is 'broken'" but of course criticisms - including fundamental criticisms - of peer review are much older than this. Overall, this use of history seems designed to privilege the experience of a particular moment in time, that coincides with the start of the metascience reform movement.

Section 2 also demonstrates some of the second weakness described, a rather loose structure. Having moved from a discussion of the history of peer review to detail the first model, 'traditional' peer review, it then also goes on to describe the problems of this model. This part of the paper is one of the best - and best -evidenced. Given the importance of it to the main thrust of the discussion it should probably have been given more space as a Section all on its own.

Another example is Section 4 on Modular Publishing, in which Kochetkov notes "Strictly speaking, modular publishing is primarily an innovative approach for the publishing workflow in general rather than specifically for peer review." Kochetkov says "This is why we have placed this innovation in a separate category" but if it is not an innovation in peer review, the bigger question is 'Why was it included in this article at all?'.

One example of the imprecisions of language is as follows. The author also shifts between the terms 'scientific communication' and 'science communication' but, at least in many contexts familiar to this reviewer, these are not the same things, the former denoting science-internal dissemination of results through publication (which the author considers), conferences and the like (which the author specifically excludes) while the latter denotes the science-external public dissemination of scientific findings to non-technical audiences, which is entirely out of scope for this article.

A final note is that Section 3, while an interesting discussion, seems largely derivative from a typology of Waltman, with the addition of a consideration of whether a reform is 'radical' or 'incremental', based on how 'disruptive' the reform is. Given that this is inherently a subjective decision, I wonder if it might not have been more informative to consider 'disruptiveness' on a scale and plot it accordingly. This would allow for some range to be imagined for each reform as well; surely reforms might be more or less disruptive depending on how they are implemented. Given that each reform is considered against each model, it is somewhat surprising that this is not presented in a tabular or graphical form.

Beyond the specific suggestions in the preceding paragraphs, my suggestions to improve this article are as follows:

1. Reconceptualize this as an opinion piece. Where systematic evidence can be drawn upon to make points, use that, but don't be afraid to just present a discussion from what is clearly a well-informed author.

2. Reconsider the focus on history and 'evolution' if the point is about the current state of play and evaluation of reforms (much as I would always want to see more studies on the history and evolution of peer review).

3. Consider ways in which the typology might be expanded, even if at subordinate level.

I have no competing interests in the compilation of this review, although I do have specific interests as noted above.

The work ‘Evolution of Peer Review in Scientific Communication’ provides a concise and readable summary of the historical role of peer review in modern science. The paper categorises the peer review practices into three models: (1) traditional pre-publication peer review; (2) registered reports; (3) post-publication peer review. The author compares the three models and draws the conclusion that the “third model offers the best way to implement the main function of scientific communication”.

I would contest this conclusion. In my eyes the three models serve different aims - with more or less drawbacks. For example, although Model 3 is less chance to insert bias to the readers, it also weakens the filtering function of the review system. Let’s just think about the dangers of machine-generated articles, paper-mills, p-hacked research reports and so on. Although the editors do some pre-screening for the submissions, in a world with only Model 3 peer review the literature could easily get loaded with even more ‘garbage’ than in a model where additional peers help the screening.

Compared to registered reports other aspects can come to focus that Model 3 cannot cover. It’s the efficiency of researchers’ work. In the care of registered reports, Stage 1 review can still help researchers to modify or improve their research design or data collection method. Empirical work can be costly and time-consuming and post-publication review can only say that “you should have done it differently then it would make sense”.

Finally, the author puts openness as a strength of Model 3. In my eyes, openness is a separate question. All models can work very openly and transparently in the right circumstances. This dimension is not an inherent part of the models.

In conclusion, I would not make verdict over the models, instead emphasise the different functions they can play in scientific communication.

A minor comment: I found that a number of statements lack references in the Introduction. I would have found them useful for statements such as “There is a point of view that peer review is included in the implicit contract of the researcher.”

In this manuscript, the author provides a historical review of the place of peer review in the scientific ecosystem, including a discussion of the so-called current crisis and a presentation of three important peer review models. I believe this is a non-comprehensive yet useful overview. My main contention is that the structure of the paper could be improved. More specifically, the author could expand on the different goals of peer review and discuss these goals earlier in the paper. This would allow readers to better interpret the different issues plaguing peer review and helps put the costs and benefits of the three models into context. Other than that, I found some claims made in the paper a little too strong. Presenting some empirical evidence or downplaying these claims would improve the manuscript in my opinion. Below, you can find my comments:

1. In my view, the biggest issue with the current peer review system is the low quality of reviews, but the manuscript only mentions this fleetingly. The current system facilitates publication bias, confirmation bias, and is generally very inconsistent. I think this is partly due to reviewers’ lack of accountability in such a closed peer review system, but I would be curious to hear the author’s ideas about this, more elaborately than they provide them as part of issue 2.

2. I’m missing a section in the introduction on what the goals of peer review are or should be. You mention issues with peer review, and these are mostly fair, but their importance is only made salient if you link them to the goals of peer review. The author does mention some functions of peer review later in the paper, but I think it would be good to expand that discussion and move it to a place earlier in the manuscript.

3. Table 1 is intuitive but some background on how the author arrived at these categorizations would be welcome. When is something incremental and when is something radical? Why are some innovations included but not others (e.g., collaborative peer review, see https://content.prereview.org/how-collaborative-peer-review-can-transform-scientific-research/)?

4. “Training of reviewers through seminars and online courses is part of the strategies of many publishers. At the same time, we have not been able to find statistical data or research to assess the effectiveness of such training.” (p. 5)  There is some literature on this, although not recent. See work by Sara Schroter for example, Schroter et al., 2004; Schroter et al., 2008)

5. “It should be noted that most initiatives aimed at improving the quality of peer review simultaneously increase the costs.” (p. 7)  This claim needs some support. Please explicate why this typically is the case and how it should impact our evaluations of these initiatives.

6. I would rephrase “Idea of the study” in Figure 2 since the other models start with a tangible output (the manuscript). This is the same for registered reports where they submit a tangible report including hypotheses, study design, and analysis plan. In the same vein, I think study design in the rest of the figure might also not be the best phrasing.  Maybe the author could use the terminology used by COS (Stage 1 manuscript, and Stage 2 manuscript, see Details & Workflow tab of https://www.cos.io/initiatives/registered-reports). Relatedly, “Author submits the first version of the manuscript” in the first box after the ‘Manuscript (report)’ node maybe a confusing phrase because I think many researchers see the first version of the manuscript as the stage 1 report sent out for stage 1 review.

7. One pathway that is not included in Figure 2 is that authors can decide to not conduct the study when improvements are required. Relatedly, in the publish-review-curate model, is revising the manuscripts based on the reviews not optional as well? Especially in the case of 3a, authors can hardly be forced to make changes even though the reviews are posted on the platform.

8. I think the author should discuss the importance of ‘open identities’ more. This factor is now not explicitly included in any of the models, while it has been found to be one of the main characteristics of peer review systems (Ross-Hellauer, 2017). More generally, I was wondering why the author chose these three models and not others. What were the inclusion criteria for inclusion in the manuscript? Some information on the underlying process would be welcome, especially when claims like “However, we believe that journal-independent peer review is a special case of Model 3 (“Publish-Review-Curate”).” are made without substantiation.

9. Maybe it helps to outline the goals of the paper a bit more clearly in the introduction. This helps the reader to know what to expect.

10. The Modular Publishing section is not inherently related to peer review models, as you mention in the first sentence of that paragraph. As such, I think it would be best to omit this section entirely to maintain the flow of the paper. Alternatively, you could shortly discuss it in the discussion section but a separate paragraph seems too much from my point of view.

11. Labeling model 3 as post-publication review might be confusing to some readers. I believe many researchers see post-publication review as researchers making comments on preprints, or submitting commentaries to journals. Those activities are substantially different from the publish-review-curate model so I think it is important to distinguish between these types.

12. I do not think the conclusions drawn below Table 3 logically follow from the earlier text. For example, why are “all functions of scientific communication implemented most quickly and transparently in Model 3”? It could be that the entire process takes longer in Model 3 (e.g. because reviewers need more time), so that Model 1 and Model 2 lead to outputs quicker. The same holds for the following claim: “The additional costs arising from the independent assessment of information based on open reviews are more than compensated by the emerging opportunities for scientific pluralism.” What is the empirical evidence for this? While I personally do think that Model 3 improves on Model 1, emphatic statements like this require empirical evidence. Maybe the author could provide some suggestions on how we can attain this evidence. Model 2 does have some empirical evidence underpinning its validity (see Scheel, Schijen, Lakens, 2021; Soderberg et al., 2021; Sarafoglou et al. 2022) but more meta-research inquiries into the effectiveness and cost-benefits ratio of registered reports would still be welcome in general.

13. What is the underlaying source for the claim that openness requires three conditions?

14. “If we do not change our approach, science will either stagnate or transition into other forms of communication.” (p. 2)  I don’t think this claim is supported sufficiently strongly. While I agree there are important problems in peer review, I think would need to be a more in-depth and evidence-based analysis before claims like this can be made.

15. On some occasions, the author uses “we” while the study is single authored.

16. Figure 1: The top-left arrow from revision to (re-)submission is hidden

17. “The low level of peer review also contributes to the crisis of reproducibility in scientific research (Stoddart, 2016).” (p. 4)  I assume the author means the low quality of peer review.

18. “Although this crisis is due to a multitude of factors, the peer review system bears a significant responsibility for it.” (p. 4)  This is also a big claim that is not substantiated

19. “Software for automatic evaluation of scientific papers based on artificial intelligence (AI) has emerged relatively recently” (p. 5)  The author could add RegCheck (https://regcheck.app/) here, even though it is still in development. This tool is especially salient in light of the finding that preregistration-paper checks are rarely done as part of reviews (see Syed, 2023)

20. There is a typo in last box of Figure 1 (“decicion” instead of “decision”). I also found typos in the second box of Figure 2, where “screns” should be “screens”, and the author decision box where “desicion” should be “decision”

21. Maybe it would be good to mention results blinded review in the first paragraph of 3.2. This is a form of peer review where the study is already carried out but reviewers are blinded to the results. See work by Locascio (2017), Grand et al. (2018), and Woznyj et al. (2018).

22. Is “Not considered for peer review” in figure 3b not the same as rejected? I feel that it is rejected in the sense that neither the manuscript not the reviews will be posted on the platform.

23. “In addition to the projects mentioned, there are other platforms, for example, PREreview12, which departs even more radically from the traditional review format due to the decentralized structure of work.” (p. 11)  For completeness, I think it would be helpful to add some more information here, for example why exactly decentralization is a radical departure from the traditional model.

24. “However, anonymity is very conditional - there are still many “keys” left in the manuscript, by which one can determine, if not the identity of the author, then his country, research group, or affiliated organization.” (p.11)  I would opt for the neutral “their” here instead of “his”, especially given that this is a paragraph about equity and inclusion.

25. “Thus, “closeness” is not a good way to address biases.” (p. 11)  This might be a straw man argument because I don’t believe researchers have argued that it is a good method to combat biases. If they did, it would be good to cite them here. Alternatively, the sentence could be omitted entirely.

26. I would start the Modular Publishing section with the definition as that allows readers to interpret the other statements better.

27. It would be helpful if the Models were labeled (instead of using Model 1, Model 2, and Model 3) so that readers don’t have to think back what each model involved.

28. Table 2: “Decision making” for the editor’s role is quite broad, I recommend to specify and include what kind of decisions need to be made.

29. Table 2: “Aim of review” – I believe the aim of peer review differs also within these models (see the “schools of thought” the author mentions earlier), so maybe a statement on what the review entails would be a better way to phrase this.

30. Table 2: One could argue that the object of the review’ in Registered Reports is also the manuscript as a whole, just in different stages. As such, I would phrase this differently.

Good luck with any revision!

Olmo van den Akker (ovdakker@gmail.com)

References

Grand, J. A., Rogelberg, S. G., Banks, G. C., Landis, R. S., & Tonidandel, S. (2018). From outcome to process focus: Fostering a more robust psychological science through registered reports and results-blind reviewing. Perspectives on Psychological Science, 13(4), 448-456.

Ross-Hellauer, T. (2017). What is open peer review? A systematic review. F1000Research, 6.

Sarafoglou, A., Kovacs, M., Bakos, B., Wagenmakers, E. J., & Aczel, B. (2022). A survey on how preregistration affects the research workflow: Better science but more work. Royal Society Open Science, 9(7), 211997.

Scheel, A. M., Schijen, M. R., & Lakens, D. (2021). An excess of positive results: Comparing the standard psychology literature with registered reports. Advances in Methods and Practices in Psychological Science, 4(2), 25152459211007467.

Schroter, S., Black, N., Evans, S., Carpenter, J., Godlee, F., & Smith, R. (2004). Effects of training on quality of peer review: randomised controlled trial. Bmj, 328(7441), 673.

Schroter, S., Black, N., Evans, S., Godlee, F., Osorio, L., & Smith, R. (2008). What errors do peer reviewers detect, and does training improve their ability to detect them?. Journal of the Royal Society of Medicine, 101(10), 507-514.

Soderberg, C. K., Errington, T. M., Schiavone, S. R., Bottesini, J., Thorn, F. S., Vazire, S., ... & Nosek, B. A. (2021). Initial evidence of research quality of registered reports compared with the standard publishing model. Nature Human Behaviour, 5(8), 990-997.

Syed, M. (2023). Some data indicating that editors and reviewers do not check preregistrations during the review process. PsyArXiv Preprints.

Locascio, J. J. (2017). Results blind science publishing. Basic and applied social psychology, 39(5), 239-246.

Woznyj, H. M., Grenier, K., Ross, R., Banks, G. C., & Rogelberg, S. G. (2018). Results-blind review: A masked crusader for science. European Journal of Work and Organizational Psychology, 27(5), 561-576.

Overall thoughts: This is an interesting history piece regarding peer review and the development of review over time. Given the author’s conflict of interest and association with the Centre developing MetaROR, I think that this paper might be a better fit for an information page or introduction to the journal and rationale for the creation of MetaROR, rather than being billed as an independent article. Alternatively, more thorough information about advantages to pre-publication review or more downsides/challenges to post-publication review might make the article seem less affiliated. I appreciate seeing the history and current efforts to change peer review, though I am not comfortable broadly encouraging use of these new approaches based on this article alone.

Page 3: It’s hard to get a feel for the timeline given the dates that are described. We have peer review becoming standard after WWII (after 1945), definitively established by the second half of the century, an example of obligatory peer review starting in 1976, and in crisis by the end of the 20th century. I would consider adding examples that better support this timeline – did it become more common in specific journals before 1976? Was the crisis by the end of the 20th century something that happened over time or something that was already intrinsic to the institution? It doesn’t seem like enough time to get established and then enter crisis, but more details/examples could help make the timeline clear. 

Consider discussing the benefits of the traditional model of peer review.

Table 1 – Most of these are self-explanatory to me as a reader, but not all. I don’t know what a registered report refers to, and it stands to reason that not all of these innovations are familiar to all readers. You do go through each of these sections, but that’s not clear when I initially look at the table. Consider having a more informative caption. Additionally, the left column is “Course of changes” here but “Directions” in text. I’d pick one and go with it for consistency.

3.2: Considering mentioning your conflict of interest here where MetaROR is mentioned.

With some of these methods, there’s the ability to also submit to a regular journal. Going to a regular journal presumably would instigate a whole new round of review, which may or may not contradict the previous round of post-publication review and would increase the length of time to publication by going through both types. If someone has a goal to publish in a journal, what benefit would they get by going through the post-publication review first, given this extra time?

There’s a section talking about institutional change (page 14). It mentions that openness requires three conditions – people taking responsibility for scientific communication, authors and reviewers, and infrastructure. I would consider adding some discussion of readers and evaluators. Readers have to be willing to accept these papers as reliable, trustworthy, and respectable to read and use the information in them. Evaluators such as tenure committees and potential employers would need to consider papers submitted through these approaches as evidence of scientific scholarship for the effort to be worthwhile for scientists.

Based on this overview, which seems somewhat skewed towards the merits of these methods (conflict of interest, limited perspective on downsides to new methods/upsides to old methods), I am not quite ready to accept this effort as equivalent of a regular journal and pre-publication peer review process. I look forward to learning more about the approach and seeing this review method in action and as it develops.

Kochetkov, D. (2024, March 21). Evolution of Peer Review in Scientific Communication. https://doi.org/10.31235/osf.io/b2ra3

Jul 26, 2024

Nov 20, 2024

Nov 20, 2024

Authors:

• Dmitry Kochetkov (Leiden University ) d.kochetkov@cwts.leidenuniv.nl

5

10.31235/osf.io/b2ra3

Evolution of Peer Review in Scientific Communication

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

Learn more at Review Commons

Reply to the reviewers

Manuscript number: RC-2024-02546

Corresponding author: Woo Jae, Kim

1. General Statements

This is the second version of revision.

After thoroughly reviewing the comments provided by the EMBO Journal reviewers, we found their feedback to be highly constructive and valuable for enhancing our manuscript without the need for additional experiments. For example, Reviewer 1 acknowledged that our "data are intriguing and some of the experiments are quite convincing," but suggested that the manuscript contained excessive data that required simplification. This sentiment was echoed by Reviewer 2. In response, we have completely reformatted our manuscript to eliminate unnecessary imaging quantification data and CrzR-related screening data. The reviewers noted the density of our experimental data, which has led us to focus on the SIFa to Crz-CrzR circuit mechanisms related to heart function and interval timing in future projects.

Reviewer 2's comments were generally more moderate, and we successfully addressed all five of their points with detailed explanations and modifications to our manuscript. They positively remarked that "Overall, this highly interesting study advances our knowledge about the behavioral roles of SIFamide and contributes to an understanding of how motivated behavior such as mating is orchestrated by modulatory peptides." Additionally, Reviewer 3 accepted our manuscript without any further comments.

In summary, we believe we have effectively addressed all concerns raised by Reviewers 1 and 2, resulting in a clearer manuscript that is more accessible to a broader audience.

2. Point-by-point description of the revisions

Reviewer #1

General Comments: In this revision of their manuscript, Zhang et al have attempted to address most of the points raised by the reviewers, however, they have not assuaged my most important concerns. The manuscript contains a ton of information, but at times this is to the detriment of the narrative flow. I had a lot of trouble following the rationale of each experiment, and the throughline from one experiment to the next is not always obvious. The data are intriguing, and some of the experiments are quite convincing, but other experiments are either superfluous or have methodological issues. I will summarize the most acute issues below.

• *Answer: Thank you for your thoughtful feedback and for acknowledging our efforts to address your previous comments. We appreciate your recognition of the intriguing nature of our data and the convincing aspects of our experiments. In this second revision, we have taken your concerns regarding the narrative flow and data overload to heart. We have completely reshaped our manuscript, significantly reducing unnecessary data, including the NP5270 data and overlapping quantification results that did not contribute meaningfully to the storytelling. Our goal was to streamline the presentation of our findings to enhance clarity and coherence, ensuring that each experiment clearly supports the overarching narrative. We believe these revisions will not only improve the readability of our manuscript but also allow readers to follow the rationale behind each experiment more easily. We are confident that this refined approach will make our contributions clearer and more impactful. Thank you once again for your constructive insights, which have been invaluable in guiding us toward a more focused and compelling presentation of our work.

Comment 1. *The authors argue that genetic controls are unnecessary because they have been conducted in previously published papers. I am concerned with this argument, as it is good practice to repeat controls with each experiment. However, I am overall convinced by the basic phenotype indicating that panneuronal SIFaR knockdown eliminates the changes in mating duration associated with previous experience. As for the more restricted 24F06-GAL4, the phenotype is odd-the flies do actually change their mating duration, just in the opposite direction of controls. Doesn't this imply that these flies are still capable of "interval timing", and of changing their mating strategy following exposure to rivals or following sexual experience? *

• *

__ Answer:__ We appreciate the reviewer's critical comments regarding genetic control and the intriguing phenotypes we observed in specific genetic combinations. We fully agree with the reviewer and have repeated all genetic control experiments for this revision, confirming that our genetic controls consistently demonstrate intact LMD and SMD behaviors, as previously reported. These genetic control experiments have been included in Supplementary Information 1-2. We are grateful to the reviewer for the opportunity to reaffirm that LMD and SMD represent stable behavioral phenotypes suitable for genetically studying interval timing, supported by reproducible data.

• *

We acknowledge the reviewer's insightful comments about the exciting phenotype observed when SIFaR is knockdown which shows both singly reared and sexually experienced male show lengthened mating duration in contrast to normal LMD and SMD behaviors. Actually, we have observed such phenotype when specific neural circuits are disrupted such as when sNPF peptidergic signaling is disrupted in restricted neuronal population [4]. We are now investigating such phenotype as hypothesis as disinhibition. We explained this phenotype and about disinhibition in main text as below.

In the spatial, the targeted reduction of SIFaR expression in the GAL424F06 neuronal subset resulted in a notable alteration of mating behavior. Both singly reared and sexually experienced flies exhibited an extended mating duration relative to naïve flies, contrary to the expected reduction. This observation indicates a deficit in the neural mechanism responsible for modulating mating duration, suggesting a disinhibition-like effect within the neural circuitry governing mating behavior. We have also previously observed a similar phenotype when sNPF peptidergic signaling is inhibited in specific neuronal circuits [62]. Disinhibition, characterized by the alleviation of inhibitory constraints, permits the activation of neural circuits that are ordinarily repressed. This process is instrumental in sculpting behavioral patterns and facilitating the sequential progression of behaviors. Through the orchestrated promotion of select neuronal activation and concurrent inhibition of competing neural routes, disinhibition empowers the brain with the ability to dynamically ascertain and preserve the requisite behavioral state, concurrently smoothing the transition to ensuing behavioral phases [63]. It is known that Drosophila neural circuits also exhibit disinhibition phenotypes in light preference and ethanol sensitization [64,65]. Further investigation is needed to uncover the underlying mechanisms of this disinhibition-like phenotype observed in LMD and SMD behaviors.

This reversed phenotype strongly suggests a disruption in interval timing, as one would expect that if interval timing were normal and intact, male flies would decrease their mating duration in response to appropriate environmental changes. For instance, research has shown that patients with Parkinson's disease exhibit heterogeneity in temporal processing, leading to disrupted interval timing phenotypes [5]. Therefore, if male flies subjected to social isolation or sexual experience do not show a reduction in mating duration compared to control conditions, it indicates a potential disruption in their interval timing mechanisms. We appreciate the reviewer's encouragement to further explore this intriguing disinhibition-like phenotype, and we plan to investigate this aspect in our future projects.

Comment 2. *I am glad the see the addition of data assessing the extent of SIFaR and CrzR RNAi knockdown; however, this has not completely addressed my concerns about interpretation of behavioral phenotypes. In both cases, the knockdown was assessed by qPCR using the very strong tub-GAL4 driver. mRNA levels are decreased but not nearly eliminated. Thus, when in line 177-178 the authors assert: "Consequently, we infer that the knockdown of SIFaR using the HMS00299 line nearly completely diminishes the levels of the SIFaR protein," the statement is not supported by the data. The qPCR results showed a knockdown at the mRNA level of ~50%. No assays were conducted to measure protein levels. The conclusions should be tempered to align with the data. Furthermore, it is not clear that knockdown is as successful with other drivers, which means that negative behavioral data must be interpreted with caution. For example, the lack of phenotype with repo-GAL4 driving SIFaR RNAi or elav-GAL4 driving CrzR RNAi could be due to a lack of efficient knockdown. This should be acknowledged. *

   __Answer:__ We appreciate the reviewer's critical observation regarding the efficiency of SIFaR knockdown. We fully agree that it is essential to confirm both for ourselves and our readers that the SIFaR knockdown phenotype is robust and convincing. At the outset of this project, we tested all available SIFaR-RNAi strains following established protocols within the fly community to ensure consistency in our findings. When we employed strong drivers such as tub-GAL4 and nSyb-GAL4 for SIFaR-RNAi knockdown, we observed that the flies failed to eclose and exhibited a lethal phenotype during the larval stage, which closely resembles the homozygous lethal phenotype seen in SIFaR mutants. This suggests that, in most cases, the effects of SIFaR knockdown can effectively mimic those of SIFaR mutations. To share our methodology and reinforce our findings, we have added clarifying statements in the main text as follows:

"Employment of broad drivers, including the tub-GAL4 and the strong neuronal driver nSyb-GAL4, with HMS00299 line consistently results in 100% embryonic lethality (data not shown). This phenotype mirrors the homozygous lethality observed in the SIFaRB322 mutant."

• *

Due to the significant lethality phenotype observed, we conducted PCR analyses using a combination of tub-GAL80ts and SIFaR-RNAi. As detailed in Fig. 1E, we reared the flies at 22{degree sign}C to suppress RNAi expression and then shifted the temperature to 29{degree sign}C for just three days prior to performing PCR. While our PCR results indicate a 50% reduction in SIFaR levels, we believe that experiments conducted without the tub-GAL80ts system would likely demonstrate an even greater reduction in SIFaR expression. To clarify this point and provide additional context, we have included the following description in the main text:

"The silencing of SIFaR mRNA was achieved at approximately 50% using the HMS00299 knockdown line in combination with tub-GAL80ts, with RNAi induction lasting for three days (bottom diagram in Fig. 1E). Notably, the same tub-GAL4 driver, when used without the tub-GAL80ts combination, resulted in embryonic lethality while still reducing SIFaR mRNA levels by 50% after three days of RNAi induction. This finding suggests that SIFaR knockdown using the HMS00299 line with GAL4 drivers is likely sufficient to elicit the observed LMD and SMD behaviors. This rationale underscores the effectiveness of our experimental approach and its potential implications for understanding the role of SIFaR in mating behaviors."

We also concur with the reviewer that the absence of a behavioral phenotype associated with CrzR-RNAi may be due to inefficient RNAi knockdown. Consequently, we have included a description of this issue in the main text as follows:

• *

"It is important to consider that the 50% knockdown of SIFaR and CrzR may be sufficient to disrupt LMD and/or SMD behavior. However, the lack of phenotype with repo-GAL4 or elav-GAL4 could be due to a less efficient knockdown. This possibility highlights the need for cautious interpretation of negative behavioral data."

Comment 3. *Regarding the issue of outcrossing, I am confused by the authors' statement: "To reduce the variation from genetic background, all flies were backcrossed for at least 3 generations to CS strain. For the generation of outcrosses, all GAL4, UAS, and RNAi lines employed as the virgin female stock were backcrossed to the CS genetic background for a minimum of ten generations. Notably, the majority of these lines, which were utilized for LMD assays, have been maintained in a CS backcrossed state for long-term generations subsequent to the initial outcrossing process, exceeding ten backcrosses." It's not clear what this means. Perhaps the authors could definitively state how many times each line was outcrossed. The genetic background is important because of 1) the lack of all controls, and 2) the variability of the behavioral phenotype. Often, the presence or absence of LMD or SMD appears to depend on the behavior of the control flies. When these flies show low mating duration, there is typically not a reduction following sexual experience or group raising. Could these differences derive from genetic background or transgenic insertion effects? *

Answer: We appreciate the reviewer's concern regarding the potential for confusion stemming from our descriptions of the genetic background. As the reviewer noted, we have published multiple papers on LMD and SMD behaviors, and we have conducted our experiments with careful attention to controlling the genetic background [1-3,6-8]. In response to the reviewer's comments about the importance of genetic control and background, we have completed all necessary genetic control experiments and confirmed that all our flies have been backcrossed for more than ten generations to the Canton-S (CS) strain. We believe that we have adequately addressed the reviewer's concerns regarding potential differences arising from genetic background or transgenic insertion effects. To provide readers with more detailed information about our genetic background, we have added a paragraph in the MATERIALS AND METHODS section as follows:

"The CS background was selected as the experimental background due to its well-characterized and consistent LMD and SMD behaviors. To ensure that genetic variation did not confound our results, all GAL4, UAS, and RNAi lines employed in our assays were rigorously backcrossed into the CS strain, often exceeding ten generations of backcrossing. This approach was undertaken to isolate the effects of our genetic manipulations from those of genetic background. We assert that the extensive backcrossing to the CS background, in concert with the internal control in LMD and SMD, provides a stable platform for the accurate interpretation of the LMD and SMD phenotypes observed in our experiments."

Comment 4. *I continue to have substantial concerns about the thresholding method used across many experiments to quantify overlap, and then to claim that this indicates that synaptic connections are being made between different neuronal populations. The degree of overlap will depend on factors including the settings during imaging (was care taken to prevent pixel saturation?). It is also not clear to me from the methods whether analysis was done on single confocal images or on projections. The images shown in the figures look like maximum projections of a confocal stack. Overlap would have to be assessed on individual confocal sections-it is possible that this is what was done for analysis but not clear from the description in the methods. Furthermore, a lot of figure space is dedicated to superfluous information. For example, in Figure 1F-J, there is a massive amount of space dedicated to assessing the agree of overlap between red stinger and CD4GFP, each driven from the same SIFaR2A driver, and further assessing what percentage of the CD4GFP signal overlaps with nc82, with the apparent goal of showing that a lot of the SIFaR signal is at active zones. This information does little to drive the narrative forward, and is quite confusing to read. Finally, the confocal images are generally too small to actually assess. *

   __Answer:__ We appreciate the reviewer's concerns regarding our imaging quantification methods. We recognize the importance of providing a clear and transparent methodology for both readers and the broader scientific community. Instead of using maximum projection of confocal images, we employed a projection method that incorporates the standard deviation function available in ImageJ. Based on our experience, this approach yields more reliable quantification results, allowing for a more accurate assessment of our data. To ensure clarity and reproducibility, we have detailed our methods in the MATERIALS AND METHODS section as follows:

• *

"The quantification of the overlap was performed using confocal images with projection by standard deviation function provided by ImageJ to ensure precise measurements and avoid pixel saturation artifacts."

We appreciate the reviewer's suggestion regarding the inclusion of image quantification data for overlapping regions, which may not be essential to the logical flow of our narrative and could lead to confusion for readers. In response, we have removed nearly all of the quantification data related to overlapping regions, retaining only those that we consider critical for the paper. Currently, only Fig. S3B-E remains, as it is important for illustrating how SIFa neuronal arborization interacts with SIFaR neurons in the central nervous system.

Additionally, we fully agree with the reviewer that the overall size of the confocal images was too small for effective assessment. To address this concern, we have enlarged all confocal images and increased the spacing in the figures. We believe these improvements will enhance the clarity of our manuscript and facilitate a better understanding of our findings.

• *

Comment 5. *In general, the figures are still very cluttered, with panels too close together, and the labels are hard to read. *

Answer: We thank the reviewer for their valuable feedback regarding the clarity of our figures. In response to their concern, we have enlarged the figures to enhance readability and ensure that the panels are more distinct. We believe these adjustments will significantly improve the viewer's ability to interpret the data. We appreciate the reviewer's attention to detail, which has helped us to refine the presentation of our findings.

Comment 6. *There are no methodological details on how the VFB was used. The authors have not addressed my concern that they are showing only the neuronal skeleton (rather than the actual site of synapses). They are simply identifying all locations where the neuronal skeleton overlaps an entire brain region, and suggesting that these represent synapses. Many papers use the VFB to denote the actual location of synapses, which should be done in Figures 3B and S4A. *

Answer: We appreciate the reviewer's constructive comments regarding the methodological details of using VFB data. We fully agree that we cannot draw definitive conclusions about SIFa projections to specific regions based solely on neuronal skeleton data, which do not indicate the actual locations of synapses. To address this concern, we have made it clear to readers that the VFB skeleton data serves only as a preliminary indication of potential SIFa projections to GA, FB, and AL.

To confirm the presence of actual synapses from SIFa neurons, we conducted a thorough analysis using FlyWire data, which validated our findings from VFB. By integrating insights from VFB with the detailed synaptic mapping provided by FlyWire, we can confidently assert the functional relevance of these connections within the context of SIFa neuronal activity. This comprehensive approach not only bolsters our conclusions but also enhances our understanding of how SIFa neurons interact within the broader neural circuitry. We believe this rationale highlights the significance of our work in elucidating the complex relationships among these neuronal populations. We have detailed our findings in the main text as follows:

"We utilized the "Virtual Fly Brain (VFB)" platform, an interactive tool designed for exploring neuronal connectivity, to gain insights into the connectivity of SIFa neurons with four other neurons, specifically GA, FB, and AL (Fig. 3B and Fig. S4B) [74]. While VFB provides valuable information, it does not offer precise locations of synapses originating from SIFa neurons. To address this limitation, we incorporated data from the FlyWire connectome, which allowed us to confirm that SIFa projections indeed form actual synapses with GA, AL, FB, and SMP (Fig. S3F and S3G) [75]. This multi-faceted approach enhances the robustness of our findings by integrating different data sources to validate neuronal connections."

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Comment 7. *The changes in GRASP and CaLexA with experience are very interesting, and suggest a substantial rearrangement of synaptic connectivity associated with changes in mating duration following group rearing or female exposure. I am still concerned, however, that the nsyb and tGRASP images look so different. I wouldn't expect them to be identical, but it is puzzling that the nsyb-GRASP data show connections in a few discrete brain areas, while the tGRASP data show connections in a much larger overall brain area, but curiously not in the major regions seen with nsyb-GRASP (ie PI, FB and GA). Shouldn't the tGRASP signal appear in all the places that the nsyb-GRASP does? For CaLexA and GRASP data, the methods should indicate the timing of the dissections and staining relative to the group/sexual experience. *

Answer: We appreciate the reviewer's constructive comments regarding our GRASP data, which indeed reveal an intriguing neural plasticity phenotype, as the reviewer noted. In our previous response, we suggested that the observed differences may be attributed to the distinct SIFa-GAL4 strains utilized, as described in another manuscript focused on SIFa inputs [9]. In that manuscript, we classified the four SIFa neurons into two groups: SIFaDA (dorsal-lateral) and SIFaVP (ventral-posterior). The SIFa2A-GAL4 specifically labels only the SIFaVP neurons, while the SIFa-PT driver labels all four neurons. We acknowledge that we did not clearly communicate this distinction to the reviewer or our readers, and we apologize for any confusion this may have caused. To rectify this oversight, we have added a detailed explanation of these differences in the main text as follows:

"The subtle differences in GRASP signals observed in Fig. 3A may stem from the distinct expression patterns of the SIFa2A-lexA and GAL4SIFa.PT drivers. We would like to emphasize that the SIFa2A driver labels only a subset of SIFa neurons in other regions (Kim 2024)."

We recognize that a clear and transparent methodology is essential for generating reproducible data. In response to the reviewer's suggestion, we have revised our MATERIALS AND METHODS section to include more detailed descriptions of the dissection conditions. This enhancement aims to provide readers with the necessary information to replicate our experiments effectively.

"To ascertain calcium levels and synaptic intensity from microscopic images, we dissected and imaged five-day-old flies of various social conditions and genotypes under uniform conditions. For group reared (naïve) flies, the flies were reared in group condition and dissect right after 5 days of rearing without any further action. For single reared flies, the flies were reared in single condition and dissect at the same time as group reared flies right after 5 days of rearing without any further action. For sexual experienced flies, the flies were reared in group condition after 4 days of rearing and will be given virgins to give them sexual experience for one day, those flies will also be dissected at the same time as group and single reared flies after one day."

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Comment 8. *The calcium imaging data are odd. In most cases, the experimental flies don't actually show an increase in calcium levels but rather a lack of a decrease that is present in the ATR- controls. Also, in the cases where they argue for an excitatory affect of SIF neuron stimulation, the baseline signal intensity appears higher in ATR- controls compared to ATR+ experimental flies (eg Fig 5L, 6O), while it is significantly higher in ATR+ flies compared to ATR- controls when the activation results in decreased calcium signals. Perhaps more details on how these experiments were conducted and whether data were normalized in some way would help to clarify this. *

Answer: Thank you for your valuable feedback. We appreciate your careful analysis of our calcium imaging data and have addressed your concerns below:

In our experiments, we observed that ATR+ flies maintained relatively stable calcium levels, whereas ATR- controls exhibited a gradual decrease. Under confocal imaging, GFP signals typically decrease over time, which we observed in ATR- controls. However, ATR+ flies did not exhibit this decline. To better convey this observation, we have refined the language in the manuscript. Specifically, we now describe this as a tendency to sustain the activity of Crz neurons in the OL and AG regions (Fig. 6K-M, Fig. S6G-I). This is supported by the sustained intracellular calcium activity in ATR+ flies compared to the gradual decline to baseline levels observed in ATR- controls (Fig. 6K-M).

Baseline signal intensity differences: You correctly noted that in some cases, the baseline signal intensity appears higher in ATR- controls compared to ATR+ flies. These differences are likely due to technical factors, such as variations in the distance between the imaged brain and the objective lens. Even minor positional shifts in the brain (forward or backward) can affect the observed signal intensity.

Our analyses focus on relative changes in fluorescence intensity within the same sample, which we present as line graphs to highlight trends rather than absolute values. However, we acknowledge that showing the magnitude of relative values instead of absolute values may have caused some confusion. We have revised the images to better align with our conclusions, ensuring that the adjustments do not affect the observed relative changes.

Normalization and experimental details: The calcium imaging data were normalized to ΔF/F to account for differences in baseline fluorescence intensity. However, we recognize that further clarification of the normalization process and experimental setup is essential. We have expanded the methods section to include detailed descriptions of data acquisition, normalization steps, and statistical analyses.

As the reviewer correctly noted, calcium signals in ATR+ flies are generally higher than those in ATR- flies. However, it appears that the calcium levels exhibit a maintained response rather than a dramatic increase compared to the control ATR- condition, particularly in the case shown in Fig. 6K, which illustrates SIFa-to-Crz signaling. We believe this observation may reflect the actual physiological conditions under which SIFa influences SIFaR neurons to sustain activity during activation. We have included our interpretation of these findings in the main text as follows:

"Upon optogenetic stimulation of SIFa neurons, we observed a tendency to maintain the activity of Crz neurons in OL and AG regions (Fig. 6K-M, Fig. S6H-J), evidenced by a sustained activity in intracellular Ca2+ levels that persisted in a high level compared to control ATR- condition which shows gradual declining to baseline levels (Fig. 6K-M). In contrast to the OL and AG regions, the cells in the upper region of the SIP consistently show a decrease in Ca2+ levels following stimulation of the SIFa neurons (Fig. 6N-P)."

To enhance readers' understanding of our calcium imaging results, we have reformatted our GCaMP data for improved clarity and included additional details in the MATERIALS AND METHODS section regarding the quantification of GCaMP imaging methods. Furthermore, as the reviewer correctly noted, discrepancies in baseline activity were due to our error in presenting the baseline data. We have now corrected this oversight accordingly.

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Comment 9. *The models in Fig 4 J and T show data from Song et al, though I could not find a citation for this. I would omit this part of the model since these data are not discussed at all in the manuscript. *

Answer: We appreciate the reviewer for correctly identifying our oversight in failing to properly cite Song et al.'s paper. This error occurred partly because the preprint was not available at the time we submitted our manuscript. We now have a preprint for Song et al.'s paper, which discusses the contributions of SIFa neurons to various energy balance behaviors, and we plan to submit this paper back-to-back with our current submission to PLOS Biology. We have briefly cited Song et al.'s work in the manuscript; however, we have removed references to it from Fig. 4J and T to avoid any potential confusion for readers.

Comment 10. *The graphs for the SCOPE data (eg Figure 8I-L) are still too small to make sense of. *

Answer: We enlarged the tSNE plot generated from the SCOPE data.

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Comment 11. The rationale behind including the data in Figure 9 is not well explained. I would omit this data to help streamline and focus the manuscript.

Answer: We fully understand and agree with the reviewer's concerns, and we have removed all previous versions of Figure 9 from the manuscript to prevent any confusion regarding the storyline.

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Comment 12. *The single control group is still being duplicated in two different graphs but with different names in each graph. The authors updated figure caption hints at this but does not make it explicit. At the very least, these should be given the same name across all graphs, as is done, for example, in the CaLexA experiments in Figure 4B-C. *

Answer: We concur with the reviewer and have changed the label for all "group" conditions to "naïve" in all figures.

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Comment 13. *Lines 640-641: Moreover, the pacemaker function is essential for the generation of interval timing capabilities (Meck et al, 2012; Matell, 2014; Buhusi & Meck, 2005), with the heart being recognized as the primary pacemaker organ within the animal body". This is an intriguing idea, however, I attempted to look at the cited references and don't see any claim about the heart being involved in interval timing. I could not find a paper matching the citation of Matell 2014. Meck et al 2012 is an introduction to a Frontiers in Integrative Neuroscience Research Topic and does not mention the heart, nor does the Buhusi and Meck 2005 paper. Perhaps there is a more suitable reference to make the assertion that the fly's interval timer would be affected by changes in heart rate. My suggestion would be to simplify the manuscript, focusing on the most robust findings-the behavioral effect of SIFaR knockdown, the GRASP and CaLexA data showing differences following group rearing or female exposure, and the effect of Crz knockdown in SIFaR neurons. Other details could be included but would have to be verified with more rigorous experiments. *

__ Answer:__ We appreciate the reviewer's interest in our exploration of the role of heart function in interval timing. While we found that knocking down CrzR in the heart specifically disrupts LMD behavior, we agree that our manuscript needs to be streamlined for clarity. As a result, we have eliminated all CrzR-RNAi knockdown data except for the oenocyte, neuronal and glial knockdown data presented in Fig. S8C-H. This decision was made to ensure a more focused comparison with the SIFaR knockdown experiments shown in Fig. 1. We are dedicated to further investigating the role of Crz-CrzR in heart function and its influence on interval timing in a future project. This approach allows us to maintain clarity in our current manuscript while laying the groundwork for more comprehensive studies ahead.

In line with the reviewer's suggestions, we have simplified our manuscript by eliminating unnecessary data, such as overlapping image quantification and CrzR-RNAi screening, allowing us to focus on SIFaR knockdown and GRASP, as well as CaLexA with GCaMP imaging. We are grateful to the reviewer for providing us with the opportunity to delineate the role of CrzR in heart function related to LMD as a significant future project. We believe that our manuscript has been greatly improved by the reviewer's constructive feedback.

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Reviewer #2

General Comments:* The authors investigate mating behavior in male fruit flies, Drosophila melanogaster, and test for a role of the SIFamide receptor (SIFaR) in this type of behavior, in particular mating duration in dependence of social isolation and prior mating experience. The anatomy of SIFamide-releasing neurons in comparison with SIFamide receptor-expressing neurons is characterized in a detail-rich manner. Isolating males or exposing them to mating experience modifies the anatomical organization of SIFamidergic axon termini projecting onto SIFamide receptor-expressing neurons. This structural synaptic plasticity is accompanied by changes in calcium influx. Lastly, it is reported that corazonin-releasing neurons are modulated by SIFamide releasing neurons and impact the duration of mating behavior.

Overall, this highly interesting study advances our knowledge about the behavioral roles of SIFamide, and contributes to an understanding how motivated behavior such as mating is orchestrated by modulatory peptides. The manuscript has some points that are less convincing.*

__ Answer:__ We appreciate the reviewer's positive feedback regarding our investigation into the role of the SIFamide receptor (SIFaR) in mating behavior in male Drosophila melanogaster. We are pleased that the detailed characterization of SIFamide-releasing neurons and their anatomical changes in response to social isolation and mating experience has been recognized as a valuable contribution to the understanding of synaptic plasticity and its impact on behavior. We are also grateful that the reviewer described our manuscript as a "highly interesting study" that advances knowledge about the behavioral roles of SIFamide and contributes to the understanding of how motivated behaviors, such as mating, are orchestrated by modulatory peptides. We sincerely thank the reviewer for these encouraging comments about our work.

We acknowledge the reviewer's concerns about certain aspects of our manuscript that may be less convincing. We are committed to addressing these points thoroughly to strengthen our arguments and enhance the clarity of our findings. In response to the feedback, we have made several revisions throughout the manuscript, including clarifying our methodology, enhancing the presentation of our data, and providing additional context where needed. We believe these changes will improve the overall quality of the manuscript and make our conclusions more compelling. Thank you for your thoughtful review, and we look forward to your further insights.

Comment 1. *It remains unclear why the authors link the differentially motivated duration of mating behavior with the psychological concept of interval timing. This distracts from the actually interesting neurobiology and is not necessary to make the study interesting. The study deals with the modulation of mating behavior by SIFamide. The abstraction that SIFamide plays a role in the neuronal calculation of time intervals for the perception of time sequenc es is not convincing in itself. *

• Answer: We appreciate the reviewer's thoughtful comments regarding our conclusion that links SIFamide to interval timing in mating behavior. We recognize that our data primarily indicate that SIFamide is essential for normal mating duration and influences the motivation-dependent aspects of this behavior. We also acknowledge the need for more robust evidence to establish a clearer connection between these findings and interval timing. Recent research by Crickmore et al. has provided valuable insights into how mating duration in Drosophila *serves as an effective model for examining changes in motivation over time as behavioral goals are achieved. For example, around six minutes into mating, sperm transfer occurs, resulting in a significant shift in the male's nervous system, where he no longer prioritizes continuing the mating at the expense of his own survival. This pivotal change is mediated by four male-specific neurons that release the neuropeptide Corazonin (Crz). When these Crz neurons are inhibited, sperm transfer does not take place, and as a result, the male fails to reduce his motivation, leading to matings that can extend for hours instead of the typical duration of approximately 23 minutes [10].

Recent research conducted by Crickmore et al. has secured NIH R01 funding (Mechanisms of Interval Timing, 1R01GM134222-01) to investigate mating duration and sperm transfer timing in Drosophila as a genetic model for understanding interval timing. Their study emphasizes how fluctuations in motivation over time can affect mating behavior, particularly noting that significant behavioral changes occur during mating. For instance, around six minutes into the mating process, sperm transfer takes place, which corresponds with a notable decrease in the male's motivation to continue mating [10]. These findings indicate that mating duration serves not only as an endpoint for behavior but may also reflect fundamental mechanisms associated with interval timing.

   We believe that by leveraging the robustness and experimental tractability of these findings, along with our own work on SIFamide's role in mating behavior, we can gain deeper insights into the molecular and circuit mechanisms underlying interval timing. We will revise our manuscript to clarify this relationship and emphasize how SIFamide may interact with other neuropeptides and neuronal circuits involved in motivation and timing.

   In addition to the efforts of Crickmore's group to connect mating duration with a straightforward genetic model for interval timing, we have previously published several papers demonstrating that LMD and SMD can serve as effective genetic models for interval timing within the fly research community. For instance, we have successfully connected SMD to an interval timing model in a recently published paper [3], as detailed below:

"We hypothesize that SMD can serve as a straightforward genetic model system through which we can investigate "interval timing," the capacity of animals to distinguish between periods ranging from minutes to hours in duration.....

In summary, we report a novel sensory pathway that controls mating investment related to sexual experiences in Drosophila. Since both LMD and SMD behaviors are involved in controlling male investment by varying the interval of mating, these two behavioral paradigms will provide a new avenue to study how the brain computes the 'interval timing' that allows an animal to subjectively experience the passage of physical time [11-16]."

   Lee, S. G., Sun, D., Miao, H., Wu, Z., Kang, C., Saad, B., ... & Kim, W. J. (2023). Taste and pheromonal inputs govern the regulation of time investment for mating by sexual experience in male Drosophila melanogaster. *PLoS Genetics*, *19*(5), e1010753.

   We have also successfully linked LMD behavior to an interval timing model and have published several papers on this topic recently [6-8].

   Sun, Y., Zhang, X., Wu, Z., Li, W., & Kim, W. J. (2024). Genetic Screening Reveals Cone Cell-Specific Factors as Common Genetic Targets Modulating Rival-Induced Prolonged Mating in male Drosophila melanogaster. *G3: Genes, Genomes, Genetics*, jkae255.

   Zhang, T., Zhang, X., Sun, D., & Kim, W. J. (2024). Exploring the Asymmetric Body's Influence on Interval Timing Behaviors of Drosophila melanogaster. *Behavior Genetics*, *54*(5), 416-425.

   Huang, Y., Kwan, A., & Kim, W. J. (2024). Y chromosome genes interplay with interval timing in regulating mating duration of male Drosophila melanogaster. *Gene Reports*, *36*, 101999.

   Finally, in this context, we have outlined in our INTRODUCTION section below how our LMD and SMD models are related to interval timing, aiming to persuade readers of their relevance. We hope that the reviewer and readers are convinced that mating duration and its associated motivational changes such as LMD and SMD provide a compelling model for studying the genetic basis of interval timing in *Drosophila*.

"The dimension of time is the fundamental basis for an animal's survival. Being able to estimate and control the time between events is crucial for all everyday activities [25]. The perception of time in the seconds-to-hours range, referred to as 'interval timing', is involved in foraging, decision making, and learning via activation of cortico-striatal circuits in mammals [26]. Interval timing requires entirely different neural mechanisms from millisecond or circadian timing [27-29]. There is abundant psychological research on time perception because it is a universal cognitive dimension of experience and behavioral plasticity. Despite decades of research, the genetic and neural substrates of temporal information processing have not been well established except for the molecular bases of circadian timing [30,31]. Thus, a simple genetic model system to study interval timing is required. Considering that the mating duration in fruit flies, which averages approximately 20 minutes, is well within the range addressed by interval timing mechanisms, this behavioral parameter provides a relevant context for examining the neural circuits that modulate the Drosophila's perception of time intervals. Such an investigation necessitates an understanding of the extensive neural and behavioral plasticity underlying interval timing [32-37]."

We would like to highlight that many researchers are currently working to bridge the gap between interval timing as a purely psychological concept and its neurobiological underpinnings, as illustrated in the following articles [15,17-20]. We appreciate the reviewer's concerns regarding the relationship between mating duration and interval timing. However, we believe that our LMD and SMD model can effectively bridge the gap between psychological concepts and neurobiological mechanisms using a straightforward genetic model organism. By employing Drosophila as our model, we aim to elucidate the underlying neural circuits that govern these behaviors, thereby contributing to a deeper understanding of how interval timing is represented in both psychological and biological contexts.

Matell, M. S. Neurobiology of Interval Timing. Adv. Exp. Med. Biol. 209-234 (2014) doi:10.1007/978-1-4939-1782-2_12.

Matell, M. S. & Meck, W. H. Cortico-striatal circuits and interval timing: coincidence detection of oscillatory processes. Cogn. Brain Res. 21, 139-170 (2004).

Merchant, H. & Lafuente, V. de. Introduction to the neurobiology of interval timing. Adv Exp Med Biol 829, 1-13 (2014).

Golombek, D. A., Bussi, I. L. & Agostino, P. V. Minutes, days and years: molecular interactions among different scales of biological timing. Philosophical Transactions Royal Soc B Biological Sci 369, 20120465 (2014).

Balcı, F. & Toda, K. Editorial: Psychological and neurobiological mechanisms of time perception and temporal information processing: insight from novel technical approaches. Front. Behav. Neurosci. 17, 1208794 (2023).

Comment 2. *For all behavioral experiments, genetic controls should always be conducted. That is, both the heterozygous Gal4-line as well as the heterozygous UAS-line should be used as controls. This is laborious, but important and common standard. The authors often report data only for offspring from genetc crosses in which UAS-lines and Gal4-lines are combined (e.g. figure S1). This is not sufficient. *

• *Answer: We are grateful for the reviewer's constructive suggestions regarding the genetic control experiments. In response to similar concerns raised by another reviewer, we have conducted all necessary genetic control experiments and included the results in Supplementary Information 1-2. We hope that this thorough effort will demonstrate to both the reviewer and readers that the LMD and SMD behaviors represent stable and reproducible phenotypes for investigating the genetic components of interval timing.

Comment 3. *There are quite a lot of citations of preprints, including preprints from the authors's own lab. It seems inappropriate to cite non-peer reviewed preprints in order to present the basic principles of the study (interval timing in flies) as recognized knowledge. In general, it is unclear whether the information presented in these multiple preprints will turn out to be credible and acceptable. *

• *Answer: We concur with the reviewer and have removed most of the preprint material, retaining only one preprint that discusses SIFa function, which has been co-submitted with this manuscript.

Comment 4. *Anatomical images are often very small and not informative. For example, figure S1 O, R, S and U shows small images of fly brains and ventral nerve chords that do not convincingly describe the expression of fluorescent proteins. The choice of a threshold to quantify fluorescence seems arbitrary. It is also not clear what the quantification "83% of brain and 71% of VNC SIFaR+ neurons" actually tells us. This quantification does not rely on counting neurons (such as 83% of neurons), but only shows how fluorescence in these neurons overlaps with an immunostaining of an ubiquitous active zone protein. The same is true for figure S2 or S3: overlapping brain areas do not inform you about numbers of cells, as stated in the text. *

Answer: We appreciate the reviewer's concerns regarding our imaging quantification methods. In response to similar questions raised by another reviewer, we have thoroughly reformatted our methods section and eliminated much of the overlapping data that appeared unnecessary for this paper. We recognize the importance of providing a clear and transparent methodology for both readers and the broader scientific community. Instead of using maximum projection of confocal images, we employed a projection method that incorporates the standard deviation function available in ImageJ. Based on our experience, this approach yields more reliable quantification results, allowing for a more accurate assessment of our data. To ensure clarity and reproducibility, we have detailed our methods in the MATERIALS AND METHODS section as follows:

• *

"The quantification of the overlap was performed using confocal images with projection by standard deviation function provided by ImageJ to ensure precise measurements and avoid pixel saturation artifacts."

We appreciate the reviewer's suggestion regarding the inclusion of image quantification data for overlapping regions, which may not be essential to the logical flow of our narrative and could lead to confusion for readers. In response, we have removed nearly all of the quantification data related to overlapping regions, retaining only those that we consider critical for the paper. Currently, only Fig. S3B-E remains, as it is important for illustrating how SIFa neuronal arborization interacts with SIFaR neurons in the central nervous system.

Additionally, we fully agree with the reviewer that the overall size of the confocal images was too small for effective assessment. To address this concern, we have enlarged all confocal images and increased the spacing in the figures. We believe these improvements will enhance the clarity of our manuscript and facilitate a better understanding of our findings.

Comment 5. *The authors have consistently confused the extensive overlap of neuronal processes (dendrites and presynaptic regions) across large brain areas with synaptic connections. One cannot infer functional synaptic connectivity from the overlap of these fluorescent signals. *

Answer: We appreciate the reviewer's feedback and, in light of similar comments from another reviewer, we have removed most of the DenMark and syt.eGFP data, retaining only Fig. 3A. We are grateful for the constructive suggestions, which have significantly enhanced our manuscript. We believe that these revisions have clarified the narrative for readers, allowing for a more focused exploration of SIFaR's role in synaptic plasticity and neuronal orchestration.

Reviewer #3

General Comments: In this revised manuscript, the authors have fully and satisfactorily addressed my comments on the previous version. I recommend publication of this manuscript.

__ Answer:__ We would like to extend our heartfelt thanks for the careful consideration and positive assessment of our revised manuscript. Your insightful feedback has been instrumental in shaping the final version of our work, and we are delighted to hear that our revisions have met your expectations.

Your dedication to ensuring the quality and rigor of the scientific literature is truly commendable, and we are immensely grateful for the time and effort you have devoted to reviewing our paper. Your support for publication is a significant encouragement to us and validates the hard work we have put into addressing the issues you raised.

Please accept our sincere appreciation for your professional and constructive approach throughout the review process. We look forward to the possibility of contributing to the scientific community through the dissemination of our research.

REFERENCES

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2. Kim WJ, Jan LY, Jan YN. A PDF/NPF Neuropeptide Signaling Circuitry of Male Drosophila melanogaster Controls Rival-Induced Prolonged Mating. Neuron. 2013;80: 1190-1205. doi:10.1016/j.neuron.2013.09.034
3. Lee SG, Sun D, Miao H, Wu Z, Kang C, Saad B, et al. Taste and pheromonal inputs govern the regulation of time investment for mating by sexual experience in male Drosophila melanogaster. PLOS Genet. 2023;19: e1010753. doi:10.1371/journal.pgen.1010753
4. Zhang X, Miao H, Kang D, Sun D, Kim WJ. Male-specific sNPF peptidergic circuits control energy balance for mating duration through neuron-glia interactions. bioRxiv. 2024; 2024.10.17.618859. doi:10.1101/2024.10.17.618859
5. Merchant H, Luciana M, Hooper C, Majestic S, Tuite P. Interval timing and Parkinson's disease: heterogeneity in temporal performance. Exp Brain Res. 2008;184: 233-248. doi:10.1007/s00221-007-1097-7
6. Sun Y, Zhang X, Wu Z, Li W, Kim WJ. Genetic Screening Reveals Cone Cell-Specific Factors as Common Genetic Targets Modulating Rival-Induced Prolonged Mating in male Drosophila melanogaster. G3: Genes, Genomes, Genet. 2024; jkae255. doi:10.1093/g3journal/jkae255
7. Zhang T, Zhang X, Sun D, Kim WJ. Exploring the Asymmetric Body's Influence on Interval Timing Behaviors of Drosophila melanogaster. Behav Genet. 2024; 1-10. doi:10.1007/s10519-024-10193-y
8. Huang Y, Kwan A, Kim WJ. Y chromosome genes interplay with interval timing in regulating mating duration of male Drosophila melanogaster. Gene Rep. 2024; 101999. doi:10.1016/j.genrep.2024.101999
9. Kim WJ, Song Y, Zhang T, Zhang X, Ryu TH, Wong KC, et al. Peptidergic neurons with extensive branching orchestrate the internal states and energy balance of male Drosophila melanogaster. bioRxiv. 2024; 2024.06.04.597277. doi:10.1101/2024.06.04.597277
10. Thornquist SC, Langer K, Zhang SX, Rogulja D, Crickmore MA. CaMKII Measures the Passage of Time to Coordinate Behavior and Motivational State. Neuron. 2020;105: 334-345.e9. doi:10.1016/j.neuron.2019.10.018
11. Buhusi CV, Meck WH. What makes us tick? Functional and neural mechanisms of interval timing. Nat Rev Neurosci. 2005;6: 755-765. doi:10.1038/nrn1764
12. Merchant H, Harrington DL, Meck WH. Neural Basis of the Perception and Estimation of Time. Annu Rev Neurosci. 2012;36: 313-336. doi:10.1146/annurev-neuro-062012-170349
13. Allman MJ, Teki S, Griffiths TD, Meck WH. Properties of the Internal Clock: First- and Second-Order Principles of Subjective Time. Annu Rev Psychol. 2013;65: 743-771. doi:10.1146/annurev-psych-010213-115117
14. Rammsayer TH, Troche SJ. Neurobiology of Interval Timing. Adv Exp Med Biol. 2014; 33-47. doi:10.1007/978-1-4939-1782-2_3
15. Golombek DA, Bussi IL, Agostino PV. Minutes, days and years: molecular interactions among different scales of biological timing. Philosophical Transactions Royal Soc B Biological Sci. 2014;369: 20120465. doi:10.1098/rstb.2012.0465
16. Jazayeri M, Shadlen MN. A Neural Mechanism for Sensing and Reproducing a Time Interval. Curr Biol. 2015;25: 2599-2609. doi:10.1016/j.cub.2015.08.038
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20. Matell MS. Neurobiology of Interval Timing. Adv Exp Med Biol. 2014; 209-234. doi:10.1007/978-1-4939-1782-2_12

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Referee #3

Evidence, reproducibility and clarity

Summary

The article investigates the role of the neuropeptide SIFa and its receptor SIFaR in regulating two distinct mating duration behaviors in male Drosophila melanogaster, Longer-Mating-Duration (LMD) and Shorter-Mating-Duration (SMD). The study reveals that SIFaR expression in specific neurons is required for both behaviors. It shows that social context and sexual experience lead to synaptic reorganization between SIFa and SIFaR neurons, altering internal brain states. The SIFa-SIFaR/Crz-CrzR neuropeptide relay pathway is essential for generating these behaviors, with Crz neurons responding to SIFa neuron activity. Furthermore, CrzR expression in non-neuronal cells is critical for regulating LMD and SMD behaviors. The study utilizes neuropeptide RNAi screening, chemoconnectome (CCT) knock-in, and genetic intersectional methods to elucidate these findings.

Major Comments

• Are the key conclusions convincing? The key conclusions are intriguing but require more robust data to be fully convincing. While the study presents compelling evidence for the involvement of SIFa and SIFaR in mating behaviors, additional experiments are needed to firmly establish the proposed mechanisms.
• Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether? The authors should qualify certain claims as preliminary or speculative. Specifically, the proposed SIFa-SIFaR/Crz-CrzR neuropeptide relay pathway is only investigated via imaging approach. More experiments using behavioral tests are needed to confirm that Crz relays the SIFa signaling pathway. For example, Crz-Gal4>UAS-SIFaR RNAi should be done to show that SIFaR+ Crz+ cells are necessary for LMD and SMD.
• Would additional experiments be essential to support the claims of the paper? Yes, additional experiments are essential. Detailed molecular and imaging studies are needed to support claims about synaptic reorganization. For example:
  • More controls are needed for RNAi and Gal80ts experiments, such as Gal4-only control, RNAi-only control, etc.
  • Using synaptic markers and high-resolution imaging to observe synaptic changes directly.
  • Electrophysiological recordings from neurons expressing SIFa and SIFaR to analyze their functional connectivity and activity patterns.
• Are the suggested experiments realistic in terms of time and resources? The suggested experiments are realistic but will require considerable time and resources. Detailed molecular interaction studies, imaging synaptic plasticity, and electrophysiological recordings could take several months to over a year, depending on the complexity and availability of necessary equipment and expertise. The cost would be moderate to high, involving expenses for reagents, imaging equipment, and animal husbandry for maintaining Drosophila stocks.
• Are the data and the methods presented in such a way that they can be reproduced? The methods are generally described in detail, allowing for potential reproducibility. However, more precise documentation of certain experimental conditions, such as the timing and conditions of RNAi induction and temperature controls, is necessary. The methods about imaging analysis are too detailed. The exact steps about how to use ImageJ should be removed.
• Are the experiments adequately replicated and statistical analysis adequate? Most figures in the manuscript need to be re-plotted. The right y-axis "Difference between means" is not necessary, if not confusing. The image panels are too small to see, while the quantification of overlapping cells are unnecessarily large. The figures are too crowded with labels and texts, which makes it extremely difficult to comprehend the data.

Minor Comments

• Specific experimental issues that are easily addressable. Clarify the timing of RNAi induction and provide more detailed figure legends for better understanding and reproducibility.
• Are prior studies referenced appropriately? Yes.
• Are the text and figures clear and accurate? The text is generally clear, but the figures need re-work. See comment above.
• Suggestions to improve the presentation of data and conclusions. Use smaller fonts in the bar plots and make the plots smaller. Enlarge the imaging panels and let the pictures tell the story.

Significance

Nature and Significance of the Advance

This study aims to advance understanding of how neuropeptides modulate context-dependent behaviors in Drosophila. It provides novel insights into the role of SIFa and SIFaR in interval timing behaviors, contributing to the broader field of neuropeptide research and behavioral neuroscience. However, the significance of the findings is limited by the preliminary nature of some claims and the need for additional supporting data.

Context in Existing Literature

The work builds on previous studies that identified various roles of neuropeptides in behavior modulation but lacked detailed mechanistic insights. By elucidating the SIFa-SIFaR/Crz-CrzR pathway, this study attempts to fill a gap in the literature, but more robust evidence is required to solidify its contributions.

Interested Audience

The findings will interest neuroscientists, behavioral biologists, and researchers studying neuropeptides and their roles in behavior and neural circuitry. Additionally, this research may have implications for understanding neuropeptidergic systems in other organisms, making it relevant to a broader audience in the fields of neurobiology and physiology.

Field of Expertise

Keywords: Neuropeptides, Drosophila melanogaster, Behavioral Neuroscience. Areas without sufficient expertise: courtship behavior.

Recommendation

I recommend a major revision of this manuscript. The study presents intriguing findings, but several key claims are preliminary and require additional experiments for support. The data is poorly presented and the figures can be significantly improved. Detailed molecular and imaging studies, as well as more rigorous statistical analyses, are necessary to strengthen the conclusions. Addressing these concerns will significantly improve the robustness and impact of the paper.

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Referee #2

Evidence, reproducibility and clarity

Zhang et al., "Long-range neuropeptide relay as a central-peripheral communication mechanism for the context-dependent modulation of interval timing behaviors".

The authors investigate mating behavior in male fruit flies, Drosophila melanogaster, and test for a role of the SIFamide receptor (SIFaR) in this type of behavior, in particular mating duration in dependence of social isolation and prior mating experience. The anatomy of SIFamide-releasing neurons in comparison with SIFamide receptor-expressing neurons is characterized in a detail-rich manner. Isolating males or exposing them to mating experience modifies the anatomical organization of SIFamidergic axon termini projecting onto SIFamide receptor-expressing neurons. This structural synaptic plasticity is accompanied by changes in calcium influx. Lastly, it is shown that corazonin-releasing neurons are modulated by SIFamide releasing neurons and impact the duration of mating behavior.

Overall, this highly interesting study advances our knowledge about the behavioral roles of SIFamide, and contributes to an understanding how motivated behavior such as mating is orchestrated by modulatory peptides. The approach to take the entire organism, including peripheral tissue, into consideration, is very good and a rather unique point. The manuscript has only some points that are less convincing, and these should be addressed.

Major concerns:

• It is highly interesting that the duration of mating behavior is dependent on external and motivational factors. In fact, that provides an elegant way to study which neuronal mechanisms orchestrate these factors. However, it remains elusive why the authors link the differentially motivated durations of mating behavior to the psychological concept of interval timing. This distracts from the actually interesting neurobiology, and is not necessary to make the study interesting.
• In figure 4 A and 4K, fluorescence microscopy images of brains and ventral nerve chords are shown, one illustrating GRASP experiments, and one showing CaLexA experiments. The extreme difference between the differentially treated flies (bright fluorescence versus almost no fluorescence) is - in its drastic form- surprising. Online access to the original confocal microscopy images (raw data) might help to convince the reader that these illustrations do not reflect the most drastic "representative" examples out of a series of brain stainings.
• In particular for behavioral experiments, genetic controls should always be conducted. That is, both the heterozygous Gal4-line as well as the heterozygous UAS-line should be used as controls. This is laborious, but important.

Minor comments:

• Line 75: word missing ("...including FEEDING-RELATED BEHAVIOR, courtship, ...").
• Line 120: word missing ("SIFaR expression in adult neurons BUT not glia...").
• I find the figures often to be quite overloaded, and anatomical details often very small (e.g., figure 7A).

Significance

Overall, this highly interesting study advances our knowledge about the behavioral roles of SIFamide, and contributes to an understanding how motivated behavior is orchestrated by modulatory peptides. The approach to take the entire organism, including peripheral tissue, into consideration, is very good and a rather unique point.

Since decades it has been investigated how sensory stimuli are processed and encoded by the brain, and how behavioral actions are executed. Likewise, principles underlying learning and memory, sleep, orentation, circadian rhythms, etc. are subject to intense investigation. However, how motivational factors (sleep pressure, hunger, sexual drive) are actually "encoded", signaled and finally used to orchstrate behavior and guide decision-making is, to a very large degree, unknown - in any species. The model use here (Drosophila and its peptidergic system wit SIFamide as a central hub) represents actually a ideal entry point to study just this question. In this sense, the manuscript is at the forefront of modern, state-of-the-art neurobiology.

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Referee #1

Evidence, reproducibility and clarity

This manuscript from Zhang et al. primarily investigates the contribution of the SIFa neuropeptide receptor (SIFaR) to mating duration in male fruit flies. Through RNAi-mediated downregulation, they show that SIFaR receptor is necessary for previous experience to alter mating duration. Using cell-specific knockdown and rescue of the SIFaR receptor, they identify a population of ~400 neurons that could underlie this effect. This is still a large number of cells but is narrowed from the ~1,200 total SIFaR-expressing neurons. They then use the GRASP synaptic labeling technique to show that SIFa+ neurons form synapses onto the relevant SIFaR-expressing population, and that the area of synaptic contact is systematically altered depending on the fly's past mating history. Finally, they provide evidence to argue that SIFa neurons act through SIFaR neurons that release the neuropeptide corazonin to regulate mating duration. Overall, the authors have used an impressive array of techniques in their attempt to define the neural circuits and molecules involved in changing internal state to modify the duration of mating.

Major Comments:

1. The authors are to be commended for the sheer quantity of data they have generated, but I was often overwhelmed by the figures, which try to pack too much into the space provided. As a result, it is often unclear what components belong to each panel. Providing more space between each panel would really help.
2. The use of three independent RNAi lines to knock down SIFaR expression is experimentally solid, as the common phenotype observed with all 3 lines supports the conclusion that the SIFaR is important for mating duration choice. However, the authors have not tested whether these lines effectively reduce SIFaR expression, nor whether the GAL80 constructs used to delimit knockdown are able to effectively do so. This makes it hard to make definitive conclusions with these manipulations, especially in the face of negative results. A lack of complete knockdown is suggested by the fact that the F24F06 driver rescues lethality when used to express SIFaR in the B322 mutant background, but does not itself produce lethality when used to express SIFaR RNAi. The authors should either conduct experiments to determine knockdown efficiency or explicitly acknowledge this limitation in drawing conclusions from their experiments. A similar concern relates to the CrzR knockdown experiments (eg Figure 7).
3. Most of the behavioral experiments lack traditional controls, for example flies that contain either the GAL4 or UAS elements alone. The authors should explain their decision to omit these control experiments and provide an argument for why they are not necessary to correctly interpret the data. In this vein, the authors have stated in the methods that stocks were outcrossed at least 3x to Canton-S background, but 3 outcrosses is insufficient to fully control for genetic background.
4. Throughout the manuscript, the authors appear to use a single control condition (sexually naïve flies raised in groups) to compare to both males raised singly and males with previous sexual experience. These control conditions are duplicated in two separate graphs, one for long mating duration and one for short mating duration, but they are given different names (group vs naïve) depending on the graph. If these are actually the same flies, then this should be made clear, and they should be given a consistent name across the different "experiments".
5. The authors have consistently conflated overlap of neuronal processes with synaptic connections. Claims of synaptic connectivity deriving solely from overlap of processes should be tempered and qualified.
   • For example, they say (Lines 201-202) "These findings suggest that SIFa neurons and GAL424F06-positive neurons form more synapses in the VNC than in the brain." This is misleading. Overlap of24F06-LexA>CD8GFP and SIFa-GAL4>CD8RFP tells us nothing about synapse number, or even whether actual synapses are being formed.
   • Lines 210-211: "The overlap of DenMark and syt.EGFP signals was highly enriched in both SOG and ProNm regions, indicating that these regions are where GAL424F06 neurons form interconnected networks". This is misleading. Overlap of DenMark and syt.EGFP does not indicate synapses (especially since these molecules can be expressed outside the expected neuronal compartment if driven at high enough levels).
   • Lines 320-322: "Neurons expressing Crz exhibit robust synaptic connections with SIFaR24F06 neurons located in the PRW region of the SOG in the brain (panels of Brain and SOG in Fig. 5A)". This is again misleading. They are not actually measuring synapses here, but instead looking at area of overlap between neuronal processes of Crz and SIFaR cells.
   • In Figs 3B and S4A, they are claiming that all neuronal processes within a given delineated brain area are synapses. The virtual fly brain and hemibrain resource have a way to actually identify synapses. This should be used in addition to the neuron skeleton. Otherwise, it is misleading to label these as synapses.
   • Furthermore, measuring the area of GRASP signal is not the same as quantifying synapses. We don't know if synapse number changes (eg in lines 240-242).
6. In general, the first part of the manuscript (implicating SIFaR in mating duration) is much stronger than the second part, which attempts to demonstrate that SIFa acts through Crz-expressing neurons to induce its effects. The proof that SIFa acts through Crz-expressing neurons to modify mating duration is tenuous. The most direct evidence of this, achieved via knockdown on Crz in SIFaR-expressing cells, is relegated to supplemental figures. The calcium response of the Crz neurons to SIFa neuron activation (Fig. 6) is more of a lack of a decrease that is observed in controls. Also, this is only done in the VNC. Why not look in the brain, because the authors previously stated a hypothesis that the "transmission of signals through SIFaR in Crz-expressing neurons is limited to the brain" (lines 381-382)?

Furthermore, the authors suggest that Crz acts on cells in the heart to regulate mating duration. It would be useful to add a discussion/speculation as to possible mechanisms for heart cells to regulate mating decisions. Is there evidence of CrzR in the heart? The SCope data presented in Fig. 7I-L and S7G-H is hard to read. 7. In several cases, the effects of being raised single are opposite the effects of sexual experience. For example, in Fig. 4T, calcium activity is increased in the AG following sexual experience, but decreased in flies raised singly. Likewise, Crz-neurons in the OL have increased CaLexA signal in singly-raised flies but reduced signals in flies with previous sexual experience. In some cases, manipulations selectively affect LMD or SMD. It would be useful to discuss these differences and consider the mechanistic implications of these differential changes, when they all result in decreased mating duration. This could help to clarify the big picture of the manuscript.

Minor Comments:

1. For CaLexA experiments (eg Fig 7A-D), signal intensity should be quantified in addition to area covered. Increased intensity would indicate greater calcium activity within a particular set of neurons.
2. In Figure 5K: quantification of cell overlap is missing. In the text they state that there are ~100 neurons that co-express SIFaR24F06 and Crz. How was this determined? Is there a graph or numerical summary of this assertion?
3. In lines 709-711: "Our experience suggests that the relative mating duration differences between naïve and experienced condition and singly reared are always consistent; however, both absolute values and the magnitude of the difference in each strain can vary. So, we always include internal controls for each treatment as suggested by previous studies." I had trouble understanding this section of methods. What is done with the data from the internal controls?
4. Could the authors comment on why the brain GRASP signal is so different in Figures 3A and 4A? I realize that different versions of GRASP were used in these experiments, but I would expect broad agreement between the different approaches.

Significance

This study will be most relevant to researchers interested in understanding neuronal control of behavior. The manuscript offers a conceptual advance in identifying cell types and molecules that influence mating duration decisions. The strength of the manuscript is the number of different assays used; however, there is a sense that this has occurred at the cost of providing a cohesive narrative. The first part of the manuscript (detailing the role of SIFaR in LMD and SMD) is relatively stronger and more conclusive.

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Reply to the reviewers

We want to thank both reviewers for their thorough and constructive review of our manuscript. Below, we have re-iterated their comments followed by an explanation of how we have revised the manuscript to address this.

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

This manuscript presented by Segeren et al. applied an interesting HRASG12V inducible cell model to study the mechanism of cellular resistance to replication stress inducing agents. They also employed a novel reversible fixation technique which allows them to FAC sort cells according to their replication stress levels before applying single cell sequencing analysis to the same cell populations. By comparing cells with low levels of replication stress to cells with high levels of replication stress, they found that reduction in gene expression of FOXM1 target genes potentially protects cells against replication stress induced by CHK1i plus gemcitabine combination. Overall, this is a very interesting study. However, the following points should be addressed prior to publication:

Major: 1. Figure 3E and 3F showed two lists of differentially expressed genes in γH2Ax low cells. However, instead of arbitrarily extracting the FOXM1 target genes and TP53 targeted genes, it would be appreciated if the author could perform an unbiased and unsupervised gene set enrichment analysis such as Enrichr.

As recommended, we performed an enrichment analysis using Enrichr to identify transcriptional programs associated with the we used the genes that were downregulated in the γH2AX-low cells. FOXM1 appeared as a prominent hit in different databases (both experimental and computational). We have included the lists of differentially expressed genes as an additional supplemental table (Table S1) and have included the Enrichr results as Table S3 (i.e. CHEA and ENCODE). We have described our results in lines 198-200 of the revised manuscript.

1. At the experiment design stage, the authors also included HRASG12V status as a test condition because they previously found that HRASG12V mutation induces basal level replication stress and they would like to include this condition to study the adaptation to replication stress (line 110). However, the difference in HRASG12V negative and HRASG12V positive cells was not followed up in the later part of the paper. Can they show lists of differentially expressed genes identified under HRASG12V negative conditions as well (in the same format of Figure 3E and 3F) and comment on the differences as well?

In the original manuscript, we included heatmaps of differentially expressed genes in the control cells in Figure S2. For improved clarity, we have modified this figure so that the heatmaps are labeled "Control cells". In the revised manuscript, we have also included Table S2, which lists the differentially expressed genes between yH2AX low and yH2AX high control cells, and Table S3, which lists the Enrichr results obtained based on these gene lists.

We observed FOXM1 target genes in both the control and HRASG12V cells. Thus, the mechanism we identify does not appear to be specific to oncogenic Ras expression. We discuss this in lines 221-225. Because there were no other notable differences between the gene sets, we do not focus on this in the manuscript.

1. In line 194 and in Figure S2B, the authors claimed that ANLN, HMGB2, CENPE, MKI67, and UBE2C demonstrated co-expression, but other genes displaying similar correlation scores were not commented (such as F3, CYR61, CTGF, etc). To avoid being biased at the analysis stage, the authors should define clearly what the cut-off of correlation score is and why only co-expression of ANLN, HMGB2, CENPE, MKI67, and UBE2C were mentioned.

As suggested, we explain now in the revised manuscript that we focused on gene clusters consisting of at least 3 genes, that had a correlation coefficient greater than or equal to 0.4 with at least one other gene within the clusters. This cutoff is typically defined as representing a "moderate to good" correlation in biological data (Overholser, Sowinski, 2008). To make clear which clusters correlating gene sets passed these criteria, we have also highlighted these genes in Figure S3B. This returned the cluster we had already identified as FOXM1 targets, and as well spotted by the reviewer, a larger cluster which included F3, CYR61, CTGF, SERPINE1, ANKRD1, KRTAP2-3, UGCG, and AMOTL. Our Enrichr analysis did not identify any putative transcription factors linking the genes in this larger cluster. We are still interested to identify the putative transcription regulation mechanism linking these genes in future studies, but this is beyond the scope of the current manuscript. We have described these observations in lines 211-218.

1. In line 215, instead of validating CENPE, UBE2C, HMGB2, ANLN, and MKI67 individually, the authors decided to validate FOXM1 instead, because they believe all the aforementioned genes are targets of FOXM1, therefore, validating FOXM1 alone would suffice. Again, this makes the validation process also biased. CENPE, UBE2C, HMGB2, ANLN, and MKI67 should be validated individually because they might sensitize cells to replication stress via different mechanisms. Besides, if all these genes were identified together because they are FOXM1 target genes, why did the authors not identify FOXM1 itself as a differentially expressed gene from the single cell sequencing? The sequencing only analyzed the S/G2/M cells, expression of FOXM1 should be detected easily.

We agree with the reviewer that the omission of individual FOXM1 target genes in the validation process makes a biased impression. Therefore we ordered siRNAs against CENPE, UBE2C, HMGB2, ANLN, and MKI67. Similar to the other DE genes in the original mini-screen we first knocked down these genes using the siRNA Smartpools (pools of 4 individual siRNAs against each genes). Here, we observed a decrease in γH2AX signal compared to drug-treated cells transfected with all 5 Smartpools compared to drug-treated cells transfected with control siRNA. We next moved on to the deconvolution step of the screen, where we transfected cells with 4 individual siRNA against each gene. Here, we observed inconsistent effects of ANLN, CENPE, and HMGB2 when comparing the individual siRNAs, which all produce efficient knockdown of their target genes. But interestingly, for both MKI67 and UBE2C, each of the 4 individual siRNAs similar decreased yH2AX signal, though it was not as strong as the decrease observed when FOXM1 is knocked out. Understanding the exact mechanism of how MKI67 and UBE2C reduce replication stress is beyond the scope of this paper, but we hypothesize that, as with FOXM1, it is likely linked to their role in promoting progression through the cell cycle. These results are shown in Figures S5, and we mention these remarkable findings in the revised abstract and discuss these in the light of the recent literature in the Discussion section (lines 275-286).

Then, we also addressed the comment about FOXM1 not being changed in the single cell RNA-seq analysis. We could indeed readily detect FOXM1 expression our single-cell RNA sequencing data. The difference in expression did not change significantly in cells sorted according to γH2AX level (Figure 4C). Because FOXM1 is highly regulated post-translationally, we hypothesized that an increase in the (active) protein is correlated to increased replication stress rather than transcript levels. This was indeed the case and we further explain our experiment to test this hypothesis in response to Point #6 (results are displayed in Figure 4D and described in lines 201-209).

1. As pointed out by the author in the Discussion, single cell sequencing is not good at differentiating the causes from the consequences. The author tried to validate many of the differentially expressed genes in γH2Ax low cells. However, the fact that only FOXM1 knockdown passed the validation and deconvolution pointed out that the great majority of the identified genes are not the cause of the sensitivity change to replication stress inducing agents but likely the consequences. Therefore, in Figure S2C and S2D, it would be better that the authors could just name the genes as 'downregulated genes' in Figure S2C and 'upregulated genes' in Figure S2D. Taking into consideration that the expression change in the great majority of these genes are just consequences of sensitivity change to replication stress, defining them as 'potentially sensitizing' genes and 'potentially conferring resistance' genes is rather misleading.

We agree that the way we originally labeled these plots may have been misleading. We have renamed then to "Downregulated in yH2AXlow" and "Upregulated in yH2AXlow", as recommended by the reviewer.

1. To better prove that FOXM1 is the leading cause of the sensitivity to CHK1i+Gemcitabine induced replication stress, can the authors show the FOXM1 expression status in the tolerant cell population identified in Figure 1B (lowest panel)? Alternatively, can they plot FOXM1 expression level in the same tSNE plots shown in Figure 3B to 3D to see whether some of the γH2Ax low populations also show reduced FOXM1 expression?

FOXM1 expression levels were not increased with gH2AXhigh versus gH2AXlow HRASG12V cells in the single cell RNA-sequencing data (Figure 4C in revised manuscript). However, as mentioned in our answer to point #4 we performed an additional experiment, which showed a strong positive correlation between phospho-FOXM1 and γH2AX (as measured by flow cytometry) in S-phase cells (Figure 4D). This indicates that the active form of the FOXM1 indeed increases as yH2AX levels increase, consistent with the observed increase in FOXM1 target genes. These results are described in lines 201-209.

1. Clonogenic survival assay in Figure 4D was not quantified properly in Figure 4E. To rule out the siFOXM1 mediated growth/survival defects and to only focus on the siFOXM1 mediated resistance to CHK1i+Gemcitabine, the survival rate (intensity percent in this case) of CHK1i+Gemcitabine treated condition should be normalized against the survival rate of the Vehicle condition. E.g., the intensity percent of the siSCRAMBLE after treatment should be divided by the intensity percent of the untreated siSCRAMBLE; the intensity percent of the si#1 after treatment should be divided by the intensity percent of the untreated si#1, and so on. If the authors would like to show siFOXM1 induced growth/survival defects, they can still present the left part of the Figure 4E (the Vehicle group).

Originally, we chose to show the absolute IntensityPercent for all groups, without normalizing to the untreated group, because we wanted to also highlight the FOXM1-mediated changes in growth. We agree that normalizing the IntensityPercent of the drug-treated group to the vehicle group better highlights the siFOXM1-mediated resistance. We have therefore re-analyzed the data and presented it this way in Figure 5E (described in lines 293-295). We moved our original Figure 4E to a new supplemental figure (Figure S4B) to still point out the effects of siFOXM1 on cell growth in untreated cells.

Minor:

1. In line 176, the author claimed that 'Interestingly, rare cells treated with CHK1i + gemcitabine are located within the untreated cell cluster (Fig. 3C)'. However, it is not as obvious where these cells are in the plot, especially to people who are new to tSNE plots. It would be appreciated if the authors could label these cells by circling them with red lines and make the point stronger.

Rather than circling these points (we thought this would make the plot too "busy"), we have created an inset that zooms in on the region where we see the untreated cells within the untreated cell cluster. Within the inset, we use arrows to point out the cells we are referring to. This can be seen in our updated Figure 3C.

1. In Figure S2B, it will be ideal to label clearly which genes are upregulated genes and which are downregulate.

On the x-axis of the heatmap, we have drawn lines to separate the downregulated and upregulated genes.

1. In line 50, the word 'multifaced' needs to be corrected to 'multifaceted'.

Thank you for catching this, we have fixed it.

1. It is unclear what 'underly drug resistance' means in line 150.

We have reworded this sentence so that is more clear. It is now written as follows: "we aimed to identify gene-expression programs that mediate the low level of RS in a subset of cells, which could potentially mediate drug resistance". This change is in lines 155.

1. It is advised that the phrase 'cell cycle position' could be changed to 'cell cycle phase' or 'cell cycle stage'.

We purposefully used the phrase "cell cycle position" because we wanted to emphasis gradient-like progress through the cell cycle rather than a discrete distinction from one-phase to the next. We have reworded the text slightly to now say "position within S-phase" (lines 163, 187, 191, 208), since all the cells we are interested in are in S phase, but some are further through S phase than others.

1. In line 185, the word 'in' after 'within' can be removed.

Thank you for catching this, we have fixed it.

1. In line 194, 'Among genes downregulated in γH2AXlow cells, the expression of ANLN, HMGB2, CENPE, MKI67 and UBE2C correlated' is missing an 'are' in front of the word 'correlated'.

Thank you for catching this, we have fixed it.

1. In line 239, Fig.SC3 should be Fig. S3C.

Thank you for catching this, we have fixed it.

1. FOXM1 is known as a crucial gene for G2/M transition. Therefore, FOXM1 knockdown cells are expected to be mostly arrested at the G2/M interface. Therefore, in line 244, it is incorrect to say stronger FOXM1 knockdown induced a 'lower proportion of cells in G2 phase'. In fact, as shown in Figure 4C, cells are accumulating in G2 phase (peaking around 11M on the DAPI axis) and depleted from G1 phase (peaking around 7M).

We have reworded this to say that there is "a higher proportion of cells in S-phase and a less distinct G2 peak" (lines 270-271). The DAPI profiles of the scrambled, siFOXM1 #1, and siFOXM1 #2 conditions all show an S-phase "valley" between a G1 and G2 peak (the valley sits at about 8M-9M). In the siFOXM1 #3 and siFOXM1 #4 conditions, we no longer see this valley, therefore we interpret this as cells still in S-phase. If they had progressed from S-phase into G2 phase, we expect that we would again see this "valley" to the left of a clear G2 peak. In the figure below, we overlayed DNA content histograms of the different FOXM1 targeting siRNAs with the scrambled siRNA to demonstrate this point more clearly.

Reviewer #1 (Significance (Required)):

Advance: The study reported a novel reversible fixation technique which can lead to potentially good citations. However, the findings from the single cell sequencing alone fell short in novelty to reach high impact because FOXM1 has been reported to impact on cellular sensitivity to CHK1 inhibition mediated replication stress (PMC7970065). Moreover, the study did not provide mechanistic explanation to the observed phenotype but only validated the finding from the sequencing, and the gene of focus (FOXM1) was not originally identified from the sequencing, slightly undermining the paper's foundation. To make it a better paper. the authors need to be less biased when it comes to data analysis and interpretation.

Audience: People who are interested in basic research in cell cycle, DNA damage, cancer, chemotherapy would be interested.

My expertise: Cancer, DNA damage, cell cycle

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

Summary:

Replication stress activates ATR and CHEK1 kinases as part of the inter S phase DNA damage response. CHEK1 kinase inhibitors (CHK1i) have been shown to induce an accumulation of unresolved replication stress and widespread DNA damage and cell death caused by replication catastrophe, and are therefore under clinical evaluation. At the same time, CHEK1 inhibition results in the activation of CDK1 and FOXM1 and premature expression of G2/M genes (Saldivar et al., 2018 Science). FOXM1-drivent premature mitosis has been shown to be required for the replication catastrophe and CHK1i sensitivity (Branigan et al., 2021 Cell Rep.). In this study, Segeren and colleagues set out to investigate the mechanisms of replication stress tolerance. They used CHK1i inhibitors in combination with the DNA-damaging chemotherapeutic agent Gemcitabine and oncogenic HRASG12V expression to increase replication stress. The authors utilized an intriguing setup of combined immunofluorescence staining followed by single cell RNA-seq analysis to overcome limitations of bulk cell analyses. In particular, the authors sought to identify genes that are differentially regulated in replication stress-tolerant cells compared to sensitive cells. However, even single cell analyses can be confounded by differences in cell cycle distribution. To mitigate this, the authors selected mid S-phase cells for their analysis. While this may not have completely eliminated minor differences in cell cycle progression, the authors identified FOXM1-regulated G2/M cell cycle genes, among others, that were down-regulated in the tolerant cells. When the authors followed up on the effect of these genes on replication stress tolerance, they identified FOXM1 knockdown as the only robust mediator of replication stress tolerance.

Major comments:

The authors observed that cell cycle distribution could be a major confounding factor in their single cell analysis and attempted to reduce this variation by selecting mid S-phase cells based on the DAPI signal. The authors then chose to compare gH2AXlow and gH2AXhigh subpopulations of RPE-HRASG12V cells because their "DAPI signal was comparable" (line 181-184). However, their data show that these subpopulations also show differences in their DAPI signal distribution, with gH2AXlow cells tending to have lower DAPI signals than gH2AXhigh cells (Supplementary Figure 2A). Thus, the major confounding factor that the authors sought to remove seems to have prevailed and it remains possible that the difference in cell cycle gene expression is merely due to differences in cell cycle progression of the individual cells. Given that DAPI information seem to be readily available for the individual cells, the authors should normalize their analysis to the DAPI signal to remove this potential confounding effect or clearly state this potential limitation.

We agree that indeed it is very challenging to fully disentangle the influence of cell cycle distribution on our analysis. And indeed, the γH2AXlow HRASG12V cells have slightly reduced median DNA content compared to γH2AXmid and γH2AXhigh. However, this was not the case in the RPE control cells, and we still found that FOXM1 target genes were strongly enriched in the γH2AXhigh cells (Fig S2C and Table S4). Therefore, it is highly unlikely that bias in S-phase position distributions does not explain our results. Nevertheless, to be transparent about this write in the Results on lines 192-193 the following: "The other groups all showed similar DAPI intensities, although gH2AXlow RPE-HRASG12V cells showed a slight but statistically significant reduction compared to their gH2AXhigh counterparts (Fig. S2A)".

In our subsequent experiments to assess the relationship between phospho-FOXM1 (representing the transcriptionally active protein) and γH2AX, we observed that though there was a strong correlation between pFOXM1 and γH2AX, there was no correlation between phospho-FOXM1 and DAPI (Figure 4D-E). We therefore would like to point out that although our readout for replication stress inevitably increases as cells progress through DNA replication, heterogeneity in phospho-FOXM1 levels cannot be explained by position in S-phase. These results are described in lines 203-209.

Finally, we do not think it would be statistically appropriate to use the DAPI signal (generated by fluorescence intensity as measured by the flow cytometer) as a normalization factor for our gene expression data.

Minor comments:

The findings of Saldivar et al., 2018 Science and Branigan et al., 2021 Cell Rep. should be mentioned in the introduction.

As recommended, we mentioned both these papers in the introduction. In line 62, we cite the Branigan paper as showing that modulation of cell cycle regulators is a strategy used by cancer cells to resist replication stress. In lines 63-65, we reference them as follows: "The RS response is tightly linked with cell cycle progression, as multiple intra S-phase checkpoint kinases play a role in curtailing proteins involved in the S-G2 transition (Branigan et al., 2021, Saldivar et al., 2018)."

The authors conclude that "cell cycle position can be a major confounding factor when evaluating the transcriptomic response to RS." It should be noted that stochastic differences in the cell cycle distribution of bulk cells are perhaps the best-known confounder in single cell analyses (see, for example, Buettner et al., 2015 Nat. Biotechnol.).

We chose to reference the Buettner paper to justify our decision to select only cycling cells in our scRNA seq approach. Our reference to the paper, and to the fact that cell cycle distribution is a major confounder in single cell analysis, is in lines 138-140.

Supplementary Figure 2A: The median should be added to the violin plots.

As suggested, we have added medians to the violin plots. In addition, we added details on statistical analysis.

The statement "Differential expression analysis revealed 19 genes that were significantly downregulated in gH2AXlow RPE-HRASG12V cells, suggesting that elevated levels of these genes are correlated with sensitivity to RS-inducing drugs" refers to Figure 3E and Table S1. However, Table S1 lists the "key resources" and does not seem to be related to this statement. A table showing log2fold-changes and FDR values should be added and referenced here.

We have generated tables with the fold change values of differentially expressed genes between the yH2AX low and yH2AX high cells. These are found in Table S1 (for HRAS G12V cells) and Table S2 (for Control cells) in the supplementary file of the revised manuscript. The "key resources" has been moved to Table S5.

The statement "Remarkably, Braningan and co-workers observed no effect of full FOXM1 deletion on cell cycle progression" seems somewhat inconsistent with what has been stated and assessed in that study. The authors may want to replace "progression" with "distribution". A reduction in proliferation is commonly observed when FOXM1 levels are reduced.

In addition, the authors may want to consider that their addition of HRASG12V and Gemcitabine may contribute to a more substantial S phase checkpoint response.

We agree with the reviewer that a reduction in proliferation is commonly observed when FOXM1 levels are reduced (Barger et al., 2021, Cheng et al., 2022, Yang et al., 2015, Wu et al., 2010), but in Branigan et al., they see no decrease in proliferation with knockout of FOXM1. They state "There were no apparent differences in the growth rate of the LIN54 and FOXM1 KO versus EV cells over 10 days (Figure 1G)". Though they do not elaborate on why they see this unexpected response, we suspect a permanent full knockout of FOXM1 could cause compensatory adaptation in their cell lines. In our experiments, we perform transient knockdowns, so cells may not have the time to adapt to the loss of FOXM1 and obtain compensatory mechanisms that would allow them to continue cycling as rapidly as control cells treated with non-targeting siRNA.

However, we decided to remove this from the Discussion section, as it seemed to interrupt the discussion about the potential mechanisms underlying protection against DNA damage by FOXM1 depletion.

The statement that "the mechanism by which high FOXM1 activity is a prerequisite to accumulate DNA damage in S-phase during CHK1 inhibition remains to be uncovered" seems to neglect that premature mitosis has been suggested as a mechanistic cause (Branigan et al., 2021 Cell Rep.). It would be helpful if the authors could elaborate on this.

In our discussion, we do already emphasize the described role of FOXM1 in promoting premature mitosis (lines 330-337), but we argue that in our experimental conditions we are observing another - previously undescribed- role for FOXM1 in promoting replication stress during S phase. We previously observed with live cell imaging that CHK1i + gemcitabine does not cause premature mitosis in RPE-HRASG12V cells (published in Segeren et al. Oncogene 2022, Figure 5). Instead, these cells typically showed a cell cycle exit from G2. This makes it highly unlikely that premature mitosis is the reason why these cells would accumulate excessive DNA damage. We realize now that it was an important omission not to elaborate on this and have added this clarification to the Discussion (lines 341-345 in revised manuscript). In addition, we have removed a few lines of less important text (about the lack of direct effect of FOXM1 KO in the Branigan paper; see answer to previous point) to improve clarity and readability.

Reviewer #2 (Significance (Required)):

General assessment: The strength of the study is the intriguing methodology of combined immunofluorescence followed by single cell RNA-seq. The limitations are that this methodology does not seem to fully solve the stated problems. In addition, the study is essentially limited to confirming previous findings.

Advance: The study strengthens current knowledge but provides essentially no advance. The authors confirm existing knowledge with an additional approach. While this is not an advance in itself, it is important to the community.

Audience: I felt that the study would appeal to a basic science audience. In particular, the CHK1i and intra S-phase checkpoint areas, with limited interest beyond that.

My relevant expertise lies in transcriptomics, gene regulation and the cell cycle.

Reference list

Barger, C.J., Chee, L., Albahrani, M., Munoz-Trujillo, C., Boghean, L., Branick, C., Odunsi, K., Drapkin, R., Zou, L. & Karpf, A.R. 2021, "Co-regulation and function of FOXM1/RHNO1 bidirectional genes in cancer", eLife, vol. 10, pp. 10.7554/eLife.55070.

Branigan, T.B., Kozono, D., Schade, A.E., Deraska, P., Rivas, H.G., Sambel, L., Reavis, H.D., Shapiro, G.I., D'Andrea, A.D. & DeCaprio, J.A. 2021, "MMB-FOXM1-driven premature mitosis is required for CHK1 inhibitor sensitivity", Cell reports, vol. 34, no. 9, pp. 108808.

Cheng, Y., Sun, F., Thornton, K., Jing, X., Dong, J., Yun, G., Pisano, M., Zhan, F., Kim, S.H., Katzenellenbogen, J.A., Katzenellenbogen, B.S., Hari, P. & Janz, S. 2022, "FOXM1 regulates glycolysis and energy production in multiple myeloma", Oncogene, vol. 41, no. 32, pp. 3899-3911.

Overholser, B.R. & Sowinski, K.M. 2008, "Biostatistics primer: part 2", Nutrition in clinical practice : official publication of the American Society for Parenteral and Enteral Nutrition, vol. 23, no. 1, pp. 76-84.

Saldivar, J.C., Hamperl, S., Bocek, M.J., Chung, M., Bass, T.E., Cisneros-Soberanis, F., Samejima, K., Xie, L., Paulson, J.R., Earnshaw, W.C., Cortez, D., Meyer, T. & Cimprich, K.A. 2018, "An intrinsic S/G(2) checkpoint enforced by ATR", Science (New York, N.Y.), vol. 361, no. 6404, pp. 806-810.

Segeren, H.A., van Liere, E.A., Riemers, F.M., de Bruin, A. & Westendorp, B. 2022, "Oncogenic RAS sensitizes cells to drug-induced replication stress via transcriptional silencing of P53", Oncogene, vol. 41, no. 19, pp. 2719-2733.

Wu, Q., Liu, C., Tai, M., Liu, D., Lei, L., Wang, R., Tian, M. & Lu, Y. 2010, "Knockdown of FoxM1 by siRNA interference decreases cell proliferation, induces cell cycle arrest and inhibits cell invasion in MHCC-97H cells in vitro", Acta Pharmacologica Sinica, vol. 31, no. 3, pp. 361-366.

Yang, K., Jiang, L., Hu, Y., Yu, J., Chen, H., Yao, Y. & Zhu, X. 2015, "Short hairpin RNA- mediated gene knockdown of FOXM1 inhibits the proliferation and metastasis of human colon cancer cells through reversal of epithelial-to-mesenchymal transformation", Journal of experimental & clinical cancer research : CR, vol. 34, no. 1, pp. 40-1.

We want to thank both reviewers for their thorough and constructive review of our manuscript. Below, we have re-iterated their comments followed by an explanation of how we have revised the manuscript to address this.

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Referee #2

Evidence, reproducibility and clarity

Summary:

Replication stress activates ATR and CHEK1 kinases as part of the inter S phase DNA damage response. CHEK1 kinase inhibitors (CHK1i) have been shown to induce an accumulation of unresolved replication stress and widespread DNA damage and cell death caused by replication catastrophe, and are therefore under clinical evaluation. At the same time, CHEK1 inhibition results in the activation of CDK1 and FOXM1 and premature expression of G2/M genes (Saldivar et al., 2018 Science). FOXM1-drivent premature mitosis has been shown to be required for the replication catastrophe and CHK1i sensitivity (Branigan et al., 2021 Cell Rep.). In this study, Segeren and colleagues set out to investigate the mechanisms of replication stress tolerance. They used CHK1i inhibitors in combination with the DNA-damaging chemotherapeutic agent Gemcitabine and oncogenic HRASG12V expression to increase replication stress. The authors utilized an intriguing setup of combined immunofluorescence staining followed by single cell RNA-seq analysis to overcome limitations of bulk cell analyses. In particular, the authors sought to identify genes that are differentially regulated in replication replication stress-tolerant cells compared to sensitive cells. However, even single cell analyses can be confounded by differences in cell cycle distribution. To mitigate this, the authors selected mid S-phase cells for their analysis. While this may not have completely eliminated minor differences in cell cycle progression, the authors identified FOXM1-regulated G2/M cell cycle genes, among others, that were down-regulated in the tolerant cells. When the authors followed up on the effect of these genes on replication stress tolerance, they identified FOXM1 knockdown as the only robust mediator of replication stress tolerance.

Major comments:

The authors observed that cell cycle distribution could be a major confounding factor in their single cell analysis and attempted to reduce this variation by selecting mid S-phase cells based on the DAPI signal. The authors then chose to compare gH2AXlow and gH2AXhigh subpopulations of RPE-HRASG12V cells because their "DAPI signal was comparable" (line 181-184). However, their data show that these subpopulations also show differences in their DAPI signal distribution, with gH2AXlow cells tending to have lower DAPI signals than gH2AXhigh cells (Supplementary Figure 2A). Thus, the major confounding factor that the authors sought to remove seems to have prevailed and it remains possible that the difference in cell cycle gene expression is merely due to differences in cell cycle progression of the individual cells. Given that DAPI information seem to be readily available for the individual cells, the authors should normalize their analysis to the DAPI signal to remove this potential confounding effect or clearly state this potential limitation.

Minor comments:

The findings of Saldivar et al., 2018 Science and Branigan et al., 2021 Cell Rep. should be mentioned in the introduction.

The authors conclude that "cell cycle position can be a major confounding factor when evaluating the transcriptomic response to RS." It should be noted that stochastic differences in the cell cycle distribution of bulk cells are perhaps the best-known confounder in single cell analyses (see, for example, Buettner et al., 2015 Nat. Biotechnol.).

Supplementary Figure 2A: The median should be added to the violin plots.

The statement "Differential expression analysis revealed 19 genes that were significantly downregulated in gH2AXlow RPE-HRASG12V cells, suggesting that elevated levels of these genes are correlated with sensitivity to RS-inducing drugs" refers to Figure 3E and Table S1. However, Table S1 lists the "key resources" and does not seem to be related to this statement. A table showing log2fold-changes and FDR values should be added and referenced here.

The statement "Remarkably, Braningan and co-workers observed no effect of full FOXM1 deletion on cell cycle progression" seems somewhat inconsistent with what has been stated and assessed in that study. The authors may want to replace "progression" with "distribution". A reduction in proliferation is commonly observed when FOXM1 levels are reduced. In addition, the authors may want to consider that their addition of HRASG12V and Gemcitabine may contribute to a more substantial S phase checkpoint response.

The statement that "the mechanism by which high FOXM1 activity is a prerequisite to accumulate DNA damage in S-phase during CHK1 inhibition remains to be uncovered" seems to neglect that premature mitosis has been suggested as a mechanistic cause (Branigan et al., 2021 Cell Rep.). It would be helpful if the authors could elaborate on this.

Significance

General assessment: The strength of the study is the intriguing methodology of combined immunofluorescence followed by single cell RNA-seq. The limitations are that this methodology does not seem to fully solve the stated problems. In addition, the study is essentially limited to confirming previous findings.

Advance: The study strengthens current knowledge but provides essentially no advance. The authors confirm existing knowledge with an additional approach. While this is not an advance in itself, it is important to the community.

Audience: I felt that the study would appeal to a basic science audience. In particular, the CHK1i and intra S-phase checkpoint areas, with limited interest beyond that.

My relevant expertise lies in transcriptomics, gene regulation and the cell cycle.

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

Learn more at Review Commons

Referee #1

Evidence, reproducibility and clarity

This manuscript presented by Segeren et al. applied an interesting HRASG12V inducible cell model to study the mechanism of cellular resistance to replication stress inducing agents. They also employed a novel reversible fixation technique which allows them to FAC sort cells according to their replication stress levels before applying single cell sequencing analysis to the same cell populations. By comparing cells with low levels of replication stress to cells with high levels of replication stress, they found that reduction in gene expression of FOXM1 target genes potentially protects cells against replication stress induced by CHK1i plus gemcitabine combination.

Overall, this is a very interesting study. However, the following points should be addressed prior to publication:

Major:

1. Figure 3E and 3F showed two lists of differentially expressed genes in γH2Ax low cells. However, instead of arbitrarily extracting the FOXM1 target genes and TP53 targeted genes, it would be appreciated if the author could perform an unbiased and unsupervised gene set enrichment analysis such as Enrichr.
2. At the experiment design stage, the authors also included HRASG12V status as a test condition because they previously found that HRASG12V mutation induces basal level replication stress and they would like to include this condition to study the adaptation to replication stress (line 110). However, the difference in HRASG12V negative and HRASG12V positive cells was not followed up in the later part of the paper. Can they show lists of differentially expressed genes identified under HRASG12V negative conditions as well (in the same format of Figure 3E and 3F) and comment on the differences as well?
3. In line 194 and in Figure S2B, the authors claimed that ANLN, HMGB2, CENPE, MKI67, and UBE2C demonstrated co-expression, but other genes displaying similar correlation scores were not commented (such as F3, CYR61, CTGF, etc). To avoid being biased at the analysis stage, the authors should define clearly what the cut-off of correlation score is and why only co-expression of ANLN, HMGB2, CENPE, MKI67, and UBE2C were mentioned.
4. In line 215, instead of validating CENPE, UBE2C, HMGB2, ANLN, and MKI67 individually, the authors decided to validate FOXM1 instead, because they believe all the aforementioned genes are targets of FOXM1, therefore, validating FOXM1 alone would suffice. Again, this makes the validation process also biased. CENPE, UBE2C, HMGB2, ANLN, and MKI67 should be validated individually because they might sensitize cells to replication stress via different mechanisms. Besides, if all these genes were identified together because they are FOXM1 target genes, why did the authors not identify FOXM1 itself as a differentially expressed gene from the single cell sequencing? The sequencing only analyzed the S/G2/M cells, expression of FOXM1 should be detected easily.
5. As pointed out by the author in the Discussion, single cell sequencing is not good at differentiating the causes from the consequences. The author tried to validate many of the differentially expressed genes in γH2Ax low cells. However, the fact that only FOXM1 knockdown passed the validation and deconvolution pointed out that the great majority of the identified genes are not the cause of the sensitivity change to replication stress inducing agents but likely the consequences. Therefore, in Figure S2C and S2D, it would be better that the authors could just name the genes as 'downregulated genes' in Figure S2C and 'upregulated genes' in Figure S2D. Taking into consideration that the expression change in the great majority of these genes are just consequences of sensitivity change to replication stress, defining them as 'potentially sensitizing' genes and 'potentially conferring resistance' genes is rather misleading.
6. To better prove that FOXM1 is the leading cause of the sensitivity to CHK1i+Gemcitabine induced replication stress, can the authors show the FOXM1 expression status in the tolerant cell population identified in Figure 1B (lowest panel)? Alternatively, can they plot FOXM1 expression level in the same tSNE plots shown in Figure 3B to 3D to see whether some of the γH2Ax low populations also show reduced FOXM1 expression?
7. clonogenic survival assay in Figure 4D was not quantified properly in Figure 4E. To rule out the siFOXM1 mediated growth/survival defects and to only focus on the siFOXM1 mediated resistance to CHK1i+Gemcitabine, the survival rate (intensity percent in this case) of CHK1i+Gemcitabine treated condition should be normalized against the survival rate of the Vehicle condition. E.g., the intensity percent of the siSCRAMBLE after treatment should be divided by the intensity percent of the untreated siSCRAMBLE; the intensity percent of the si#1 after treatment should be divided by the intensity percent of the untreated si#1, and so on. If the authors would like to show siFOXM1 induced growth/survival defects, they can still present the left part of the Figure 4E (the Vehicle group).

Minor:

1. In line 176, the author claimed that 'Interestingly, rare cells treated with CHK1i + gemcitabine are located within the untreated cell cluster (Fig. 3C)'. However, it is not as obvious where these cells are in the plot, especially to people who are new to tSNE plots. It would be appreciated if the authors could label these cells by circling them with red lines and make the point stronger.
2. In Figure S2B, it will be ideal to label clearly which genes are upregulated genes and which are downregulate.
3. In line 50, the word 'multifaced' needs to be corrected to 'multifaceted'.
4. It is unclear what 'underly drug resistance' means in line 150.
5. It is advised that the phrase 'cell cycle position' could be changed to 'cell cycle phase' or 'cell cycle stage'.
6. In line 185, the word 'in' after 'within' can be removed.
7. In line 194, 'Among genes downregulated in γH2AXlow cells, the expression of ANLN, HMGB2, CENPE, MKI67 and UBE2C correlated' is missing an 'are' in front of the word 'correlated'.
8. In line 239, Fig.SC3 should be Fig. S3C.
9. FOXM1 is known as a crucial gene for G2/M transition. Therefore, FOXM1 knockdown cells are expected to be mostly arrested at the G2/M interface. Therefore, in line 244, it is incorrect to say stronger FOXM1 knockdown induced a 'lower proportion of cells in G2 phase'. In fact, as shown in Figure 4C, cells are accumulating in G2 phase (peaking around 11M on the DAPI axis) and depleted from G1 phase (peaking around 7M).

Significance

Advance:

The study reported a novel reversible fixation technique which can lead to potentially good citations. However, the findings from the single cell sequencing alone fell short in novelty to reach high impact because FOXM1 has been reported to impact on cellular sensitivity to CHK1 inhibition mediated replication stress (PMC7970065). Moreover, the study did not provide mechanistic explanation to the observed phenotype but only validated the finding from the sequencing, and the gene of focus (FOXM1) was not originally identified from the sequencing, slightly undermining the paper's foundation. To make it a better paper. the authors need to be less biased when it comes to data analysis and interpretation.

Audience:

People who are interested in basic research in cell cycle, DNA damage, cancer, chemotherapy would be interested.

My expertise:

Cancer, DNA damage, cell cycle

In "Researchers Are Willing to Trade Their Results for Journal Prestige: Results from a Discrete Choice Experiment", the authors investigate researchers’ publication preferences using a discrete choice experiment in a cross-sectional survey of international health and medical researchers. The study investigates publishing decisions in relation to negotiation of trade-offs amongst various factors like journal impact factor, review helpfulness, formatting requirements, and usefulness for promotion in their decisions on where to publish. The research is timely; as the authors point out, reform of research assessment is currently a very active topic. The design and methods of the study are suitable and robust. The use of focus groups and interviews in developing the attributes for study shows care in the design. The survey instrument itself is generally very well-designed, with important tests of survey fatigue, understanding (dominant choice task) and respondent choice consistency (repeat choice task) included. Respondent performance was good or excellent across all these checks. Analysis methods (pMMNL and latent class analysis) are well-suited to the task. Pre-registration and sharing of data and code show commitment to transparency. Limitations are generally well-described.

In the below, I give suggestions for clarification/improvement. Except for some clarifications on limitations and one narrower point (reporting of qualitative data analysis methods), my suggestions are only that – the preprint could otherwise stand, as is, as a very robust and interesting piece of scientific work.

1. Respondents come from a broad range of countries (63), with 47 of those countries represented by fewer than 10 respondents. Institutional cultures of evaluation can differ greatly across nations. And we can expect variability in exposure to the messages of DORA (seen, for example, in level of permeation of DORA as measured by signatories in each country, https://sfdora.org/signers/)..%3B!!NVzLfOphnbDXSw!HdeyeHHei6yWQHFjhN3deSSfp82ur9i9JNOLEVOYZN0BvyslUO2S8DlvjBbautmafJEvlUsxQZbT0JLQX7lO8EcOYtZsJkA%24&data=05%7C02%7Ca.l.brasil.varandas.pinto%40cwts.leidenuniv.nl%7C9f47a111adec49d04bb608dd0614ae94%7Cca2a7f76dbd74ec091086b3d524fb7c8%7C0%7C0%7C638673408085242099%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=by5mhPfSM0MFFG9LE2iiYjdtSs5IhvpuukqVv%2FLak2s%3D&reserved=0 "https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Furldefense.com%2Fv3%2F__https%3A%2F%2Fsfdora.org%2Fsigners%2F).%3B!!NVzLfOphnbDXSw!HdeyeHHei6yWQHFjhN3deSSfp82ur9i9JNOLEVOYZN0BvyslUO2S8DlvjBbautmafJEvlUsxQZbT0JLQX7lO8EcOYtZsJkA%24&data=05%7C02%7Ca.l.brasil.varandas.pinto%40cwts.leidenuniv.nl%7C9f47a111adec49d04bb608dd0614ae94%7Cca2a7f76dbd74ec091086b3d524fb7c8%7C0%7C0%7C638673408085242099%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=by5mhPfSM0MFFG9LE2iiYjdtSs5IhvpuukqVv%2FLak2s%3D&reserved=0") In addition, some contexts may mandate or incentivise publication in some venues using measures including IF, but also requiring journals to be in certain databases like WoS or Scopus, or having preferred journal lists). I would suggest the authors should include in the Sampling section a rationale for taking this international approach, including any potentially confounding factors it may introduce, and then adding the latter also in the limitations.

2. Reporting of qualitative results: In the introduction and methods, the role of the focus groups and interviews seems to have been just to inform the design of the experiment. But then, results from that qualitative work then appear as direct quotes within the discussion to contextualise or explain results. In this sense though, the qualitative results are being used as new data. Given this, I feel that the methods section should include description of the methods and tools used for qualitative data analysis (currently it does not). But in addition, to my understanding (and this may be a question of disciplinary norms – I’m not a health/medicine researcher), generally new data should not be introduced in the discussion section of a research paper. Rather the discussion is meant to interpret, analyse, and provide context for the results that have already been presented. I personally hence feel that the paper would benefit from the qualitative results being reported separately within the results section.

3. Impact factors – Discussion section: While there is interesting new information on the relative trade-offs amongst other factors, the most emphasised finding, that impact factors still play a prominent role in publication venue decisions, is hardly surprising. More could perhaps be done to compare how the levels of importance reported here differ with previous results from other disciplines or over time (I know a like-for-like comparison is difficult but other studies have investigated these themes, e.g., https://doi.org/10.1177/01655515209585). In addition, beyond the question of whether impact factors are important, a more interesting question in my view is why they still persist. What are they used for and why are they still such important “driver[s] of researchers’ behaviour”? This was not the authors’ question, and they do provide some contextualisation by quoting their participants, but still I think they could do more to contextualise what is known from the literature on that to draw out the implications here. The attribute label in the methods for IF is “ranking”, but ranking according of what and for what? Not just average per-article citations in a journal over a given time frame. Rather, impact factors are used as a proxy indicators of less-tangible desirable qualities – certainly prestige (as the title of this article suggests), but also quality, trust (as reported by one quoted focus group member “I would never select a journal without an impact factor as I always publish in journals that I know and can trust that are not predatory”, p.6), journal visibility, importance to the field, or improved chances of downstream citations or uptake in news media/policy/industry etc. Picking apart the interactions of these various factors in researchers’ choices to make use of IFs (which is not in all cases bogus or unjustified) could add valuable context. I’d especially recommend engaging at least briefly with more work from Science and Technology Studies - especially Müller and de Rijcke’s excellent Thinking with Indicators study (doi: 10.1093/reseval/rvx023), but also those authors other work, as well as work from Ulrike Felt, Alex Rushforth (esp https://doi.org/10.1007/s11024-015-9274-5), Björn Hammerfelt and others.

4. Disciplinary coverage: (1) A lot of the STS work I talk about above emphasises epistemic diversity and the ways cultures of indicator use differ across disciplinary traditions. For this reason, I think it should be pointed out in the limitations that this is research in Health/Med only, with questions on generalisability to other fields. (2) Also, although the abstract and body of the article do make clear the disciplinary focus, the title does not. Hence, I believe the title should be slightly amended (e.g., “Health and Medical Researchers Are Willing to Trade …”)

This manuscript reports the results of an interesting discrete choice experiment designed to probe the values and interests that inform researchers’ decisions on where to publish their work.

Although I am not an expert in the design of discrete choice experiments, the methodology is well explained and the design of the study comes across as well considered, having been developed in a staged way to identify the most appropriate pairings of journal attributes to include.

The principal findings to my mind, well described in the abstract, include the observations that (1) researchers’ strongest preference was for journal impact factor and (2) that they were prepared to remove results from their papers if that would allow publication in a higher impact factor journal. The first of these is hardly surprising – and is consistent with a wide array of literature (and ongoing activism, e.g. through DORA, CoARA). The second is much more striking – and concerning for the research community (and its funders). This is the first time I have seen evidence for such a trade-off. 

Overall, the manuscript is very clearly written. I have no major issues with the methods or results. However, I think but some minor revisions would enhance the clarity and utility of the paper.

First, although it is made clear in Table 1 that the researchers included in the study are all from the medical and clinical sciences, this is not apparent from the title or the abstract. I think both should be modified to reflect the nature of the sample. In my experience researchers in these fields are among those who feel most intensely the pressure to publish in high IF journals. The authors may want also to reflect in a revised manuscript how well their findings may transfer to other disciplines.

Second, in several places I felt the discussion of the results could be enriched by reference to papers in the recent literature that are missing from the bibliography. These include (1) Muller and De Rijcke’s 2017 paper on Thinking with Indicators, which discusses how the pressure of metrics impacts the conduct of research (https://doi.org/10.1093/reseval/rvx023); (2) Bjorn Brembs’ analysis of the reliability of research published in prestige science journals (https://www.frontiersin.org/journals/human-neuroscience/articles/10.3389/fnhum.2018.00376/full; and (3) McKiernan’s et al.’s examination of the use of the Journal Impact Factor in academic review, promotion, and tenure evaluations (https://pubmed.ncbi.nlm.nih.gov/31364991/). 

Third, although the text and figures are nicely laid out, I would recommend using a smaller or different font for the figure legends to more easily distinguish them from body text.

Bohorquez, N. G., Weerasuriya, S., Brain, D., Senanayake, S., Kularatna, S., & Barnett, A. (2024, July 31). Researchers are willing to trade their results for journal prestige: results from a discrete choice experiment. https://doi.org/10.31219/osf.io/uwt3b

Aug 03, 2024

Nov 20, 2024

Authors:

• Natalia Gonzalez Bohorquez (Queensland University of Technology) natalia.gonzalezbohorquez@hdr.qut.edu.au
• Sucharitha Weerasuriya (Queensland University of Technology) sucharitha.weerasuriya@qut.edu.au
• David Brain (Queensland University of Technology) david.brain@qut.edu.au
• Sameera Senanayake (Duke-NUS Medical School) sameera.senanayake@duke-nus.edu.sg
• Sanjeewa Kularatna (Duke-NUS Medical School) sanjeewa.kularatna@duke-nus.edu.sg
• Adrian Barnett a.barnett@qut.edu.au

https://doi.org/10.31219/osf.io/uwt3b

10.31219/osf.io/uwt3b

Researchers are willing to trade their results for journal prestige: results from a discrete choice experiment

https://www.liberation.fr/international/amerique/pour-assurer-la-domination-energetique-des-etats-unis-sur-le-monde-trump-sappuie-sur-lindustrie-des-fossiles-20241203_DYBODLTQ4BD3XMQUEHNPLS3LIM/

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The work from Petazzi et al. aimed at identifying novel factors supporting the differentiation of human hematopoietic progenitors from induced pluripotent stem cells (iPSCs). The authors developed an inducible CRISPR-mediated activation strategy (iCRISPRa) to test the impact of newly identified candidate factors on the generation of hematopoietic progenitors in vitro. They first compared previously published transcriptomic data of iPSCderived hemato-endothelial populations with cells isolated ex vivo from the aorta-gonadmesonephros (AGM) region of the human embryo and they identified 9 transcription factors expressed in the aortic hemogenic endothelium that were poorly expressed in the in vitro differentiated cells. They then tested the activation of these candidate factors in an iPSCbased culture system supporting the differentiation of hematopoietic progenitors in vitro. They found that the IGF binding protein 2 (IGFBP2) was the most upregulated gene in arterial endothelium after activation and they demonstrated that IGFBP2 promotes the generation of functional hematopoietic progenitors in vitro.

Strengths:

The authors developed an extremely useful doxycycline-inducible system to activate the expression of specific candidate genes in human iPSC. This approach allows us to simultaneously test the impact of 9 different transcription factors on in vitro differentiation of hematopoietic cells, and the system appears to be very versatile and applicable to a broad variety of studies.

The system was extensively validated for the expression of 1 transcription factor (RUNX1) in both HeLa cells and human iPSC, and a detailed characterization of this test experiment was provided.

The authors exhaustively demonstrated the role of IGFBP2 in promoting the generation of functional hematopoietic progenitors in vitro from iPSCs. Even though the use of IGFBP2interacting proteins IGF1 and IGF2 have been previously reported in human iPSC-derived hematopoietic differentiation in vitro (Ditadi and Sturgeon, Methods 2016; Ng et al., Nature Biotechnology 2016), and IGFBP-2 itself has been shown to promote adult HSC expansion ex vivo (Zhang et al., Blood 2008), its role on supporting in vitro hematopoiesis was demonstrated here for the first time.

Weaknesses:

Although the authors performed a very thorough characterization of the system in proof-ofprinciple experiments activating a single transcription factor, the data provided when 9 independent factors were used is not sufficient to fully validate the experimental strategy. Indeed, in the current version of the manuscript, it is not clear whether the results presented in both the scRNAseq analysis and the functional assays are the consequence of the simultaneous activation of all 9 TF or just a subset of them. This is essential to establish whether all the proposed factors play a role during embryonic hematopoiesis, and a more complete analysis of the scRNAseq dataset could help clarify this aspect.

Similarly, the data presented in the manuscript are not sufficient to clarify at what stage of the endothelial-to-hematopoietic transition (EHT) the TF activation has an impact. Indeed, even though the overall increase of functional hematopoietic progenitors is fully demonstrated, the assays proposed in the manuscript do not clarify whether this is due to a specific effect at the endothelial level or to an increased proliferation rate of the generated hematopoietic progenitors. Similar conclusions can be applied to the functional validation of IGFBP2 in vitro.

The overall conclusions are sometimes vague and not always supported by the data. For instance, the authors state that the CRISPR activation strategy resulted in transcriptional remodeling and a steer in cell identity, but they do not specify which cell types are involved and at what level of the EHT process this is happening. In the discussion, the authors also claim that they provided evidence to support that RUNX1T1 could regulate IGFBP2 expression. However, this is exclusively based on the enrichment of RUNX1T1 gRNA in cells expressing higher levels of IGFBP2 and it does not demonstrate any direct or indirect association of the two factors.

We thank the reviewer for the positive comments about the importance of our work and have now addressed the points raised as weaknesses by performing additional analysis and experiments, adding a new schematic of the mechanism, and rewording our claims.

We have clarified the different effects mediated by the activation and the IGFBP2 addition in a summary section at the end of the results and added Figure 6, showing this in visual form. We have also clearly stated the limitations related to the correlation between RUNX1T1 and IGFBP2 in the discussion and toned down our claims regarding this throughout the entire paper. We have also reworded the text to clarify the specific cell types identified in the sequencing data that we refer to.

Reviewer #2 (Public Review):

To enable robust production of hematopoietic progenitors in-vitro, Petazzi et al examined the role of transcription factors in the arterial hemogenic endothelium. They use IGFBP2 as a candidate gene to increase the directed differentiation of iPSCs into hematopoietic progenitors. They have established a novel induced-CRISPR mediated activation strategy to drive the expression of multiple endogenous transcription factors and show enhanced production of hematopoietic progenitors through expansion of the arterial endothelial cells. Further, upregulation of IGFBP2 in the arterial cells facilitates the metabolic switch from glycolysis to oxidative phosphorylation, inducing hematopoietic differentiation. While the overall study and resources generated are good, assertions in the manuscript are not entirely supported by the experimental data and some claims need further experimental validation.

We thank the reviewer for the positive comments, and we have provided new data and analysis to make sure that all our assertations are clearly supported and also reworded those where limitations were identified by the reviewers.

Recommendations for the authors:

Reviewing Editor (Recommendations For The Authors):

The assessment could change from "incomplete" to "solid" if the authors: i) improve data analysis (for both scRNAseq and functional assays) by providing additional information that could strengthen their conclusions, as suggested in the specific comments by both reviewers; ii) either provide new functional evidence supporting their mechanistic conclusion or alternatively tone down the claims that are not fully supported by data and acknowledge the limitations raised by reviewers in the discussion; (iii) the issue of paracrine signaling to expand only hematopoietic progenitors needs to be addressed.

We have now improved the data analysis and provided additional functional tests to strengthen our conclusions and toned down those that were identified by the reviewers as not supported enough and included a discussion on these limitations. We have also reworded the section about the paracrine signaling throughout the paper.

Reviewer #1 (Recommendations For The Authors):

Figure 1 contains exclusively published data. It might be more appropriate to use it as a supplementary figure or as part of a more exhaustive figure (maybe combining Figures 1 and 2 together?).

Figure 1 contained novel bioinformatic analyses that represent the base of our research and it has a different content and focus to figure 2, which is already a large figure. We therefore believe it is better to keep it as a separate figure, containing a new panel now too. 

It seems there is an issue with Figure S3 labelling:

• In line 112, Figure S2A-B does not display genomic PCR and sequencing results;

• In line 123, Figure S3D-E does not show viability and proliferation data;

• In line 127, Figure S3G does not show mCherry expression in response to DOX;

We apologies for the confusion with the numbers, we have now correctly labelled the figures.

It would be more informative to include gates and frequency on flow cytometry plots in Figure S3, to be able to evaluate the extent of the reduction in mCherry expression.

We have now included the gating and frequency of mCherry-expressing cells in Supplementary Figure 3D.

It is not clear from the text and figures whether the SB treatment was maintained throughout the hematopoietic differentiation protocol (line 122):

• If so, it would be important to confirm that HDAC treatment does not affect EHT cultures

• If not, can the authors provide some evidence that transgene silencing is not occurring during hematopoietic differentiation?

We have clarified that we decided to treat the cells with SB exclusively in maintenance condihons because HDACs have been shown to be essenhal for the EHT (lines 138-142). We have now also included addihonal data showing the high expression of the mCherry tag reporhng the iSAM expression on day 8 (Supplementary Figure 4F).

Can the authors provide a simple diagram summarizing the experimental strategy for each differentiation experiment in the respective supplementary figure? For instance, at what stage of the protocol was DOX added in Figure 3? Or at what stage IGFBP2 was added in Figure 5? It would be a very useful addition to the interpretation of the results.

We have now included three schemahcs for all the experiments in the manuscript in supplementary figure 4 A-C.

In Figure 3, the authors should provide more detailed information about the data filtering of the scRNAseq experiment, and more specifically:

• How many cells were included in the analysis for each library after QC and filtering?

• How "cells in which the gRNAs expression was detected" were selected? Do they include only cells showing expression of gRNAs for all 9 TF?

This informahon is now included in the method sechon lines 773-781; the detailed code is available on the GitHub link provided in the same sechon. We have filtered the cells expressing one gRNA for the non-targehng gRNA (iSAM_NT) control and more than one for the iSAM_AGM sample. 

In Figure 3A, it is not clear whether the expression of the 9 factors is consistently detected in all cells or just a subset of them, and the heatmap in Figure 3A does not provide this information. It would be more accurate to provide expression on a per-cell basis, for instance, as a violin plot displaying single dots representing each cell. 

We have now included this violin plot in Supplementary Figure 4G as requested. However, this visualisation is difficult to interpret because some of the target genes’ expression seems variable in both experimental and control conditions. We had envisaged that this could have been the case and so this is why we had included the three different controls.  For this reason we chose to show the normalised expression which takes all the different variables into account (Figure 3A). 

In Figure 3B-C, it seems that clusters EHT1 and EHT2 do not express endothelial markers anymore. Are these fully differentiated hematopoietic cells rather than cells undergoing EHT? In general, it would be quite important to provide evidence of expressed marker genes characterizing each cluster (eg. heatmap summarizing top DEG in the supplementary figure?). 

We have now provided a spreadsheet containing the clusters’ markers that we used in

Supplementary Table 1) a heatmap in Figure 3E. Furthermor,e we have now edited Figure 3C to include Pan Endothelial markers (PECAM1 and CDH5). These data show that the EHT1 and EHT2 cluster both express endothelial markers but are progressively downregulated as expected during endothelial to hematopoietic transition. We have also included and discussed this in the manuscript lines 192-195 and a schematic for the mechanism in Figure 6.

In Figure 3E, displaying the proportion of clusters within each sample/library would be a more accurate way of comparing the cell types present in each library (removing potential bias introduced by loading different numbers of cells in each sample).

We have now included the requested data in Supplementary Figure 4I and it confirms again the expansion of arterial cells in the activated cells.    

In Figure 3G, by plating 20,000 total CD34+, the assay does not account for potential differences in sample composition. It is then hard to discriminate between the increased number of progenitors in the input or an enhanced ability of HE to undergo EHT. This is an important aspect to consider to precisely identify at what level the activation of the 9 factors is acting. A proper quantification of flow cytometry data summarizing the % of progenitors, arterial cells, etc. would be useful to interpret these results.

Lines 204-205 reworded. We are very much aware of the fact that the CD34+ cell population consists of a range of cells across the EHT process and this is precisely why we carried out this single cell sequencing analyses.  We purposely tested the effect of the observed changes in composition by colony assays

In Figure 3G, it seems that NT cells w/o DOX have very little CFU potential (if any). Can the authors provide an explanation for this?

We think that the limited CFU potential is due to the extensive genetic manipulation and selection that the cells underwent for the derivation of all the iSAM lines but this did not impede us from observing an effect of gene activation on CFU numbers. This is one of the primary reasons that we then validated our overall findings using the parental iPSC line in control condition and with the addition of IGFBP2. We show that the parental iPSC line gives rise to hematopoietic progenitor, both immunophenotypically (Figure 4D) and functionally, at expected levels (Figure 4B left column).

Figure 4A shows an upregulation of IGFBP2 in arterial cells as a result of TF activation. However, from the data presented here, it is not possible to evaluate whether this is specific to the arterial cluster, or it is a common effect shared by all cell types regardless of their identity. 

Data has now been included in Supplementary Figure 4H, which shows that all the cells show an increase in IGFBP2, but arterial cells show the highest increase. We have now edited the text to reflect this, in lines 228-230.

In Figure 5A-B only a minority of arterial cells express RUNX1 in response to IGFBP2 treatment. Is this sufficient to explain the very significant increase in the generation of functional hematopoietic progenitors described in Figure 4? Quantification and statistical analysis of RUNX1 upregulation would strengthen this conclusion.

We have now provided the statistical analysis showing significant upregulation of RUNX1 upon IGFBP2 addition. The p values are now provided in the figure 5 legend.

In Figure 5 the authors conclude that IGFBP2 remodels the metabolic profile of endothelial cells. However, it is not clear which cell types and clusters were included in the analysis of Figure 5C-G. Is the switch from Glycolysis to Oxidative Phosphorylation specific to endothelial cells? Or it is a more general effect on the entire culture, including hematopoietic cells? 

We based this conclusion on the fact that the single-cell RNAseq allows to verify that the metabolic differences are obtained in the endothelial cells. Given that we sorted the adherent cells, the majority of these are endothelial cells as shown in Figure 5A. The Seahorse pipeline includes a number of washing steps resulting in the analyses being performed on the adherent compartment which we know consists primarily of endothelial cells. We cannot exclude some contamination from non-endothelial cells but we highlight to this reviewer that the initial observation of the metabolic changes was identified in endothelial cells in the single cell sequencing data. Taken together, we believe that this implies that metabolic changes are specific to this population. We have clarified this in the line 317.

In the discussion, the authors conclude that they "provide evidence to support the hypothesis that RUNX1T1 could regulate IGFBP2 expression". To further support this conclusion, the authors could provide a correlation analysis of the expression of the two genes in the cell type of interest. 

Following the observation of the IGFBP2 high expression across clusters, we have now reworded this sentence in lines 382-385  We have tried to perform the correlation analysis but we believe this not to be appropriate due to the detection level of the gRNA, we have now included this as a limitation point in the discussion lines 416-427, and also toned down the conclusion we did draw about RUNX1T1 throughout the whole manuscript.

As mentioned by the authors, IGFBP2 binds IGF1 and IGF2 modulating their function. Both IGF1 (http://dx.doi.org/10.1016/j.ymeth.2015.10.001) and IGF2 (doi:10.1038/nbt.3702) have been used in iPSC differentiation into definitive hematopoietic cells. It would be relevant to discuss/reference this in the discussion.

We have now included the suggested reference in the section where we discuss the role of IGFBP2 in binding IGF1 and IGF2.

Reviewer #2 (Recommendations For The Authors):

(1) Figure 1 compares the transcriptome of human AGM and in-vitro derived hemogenic endothelial cells (HECs). It is not clear why only the genes downregulated in the latter were chosen. Are there any significantly upregulated genes, knockdown/knockout which could also serve a similar purpose? Single-cell transcriptome database analysis is very preliminary. A detailed panel with differences in cluster properties of HECs between the two systems should be provided. A heatmap of all differentially expressed genes between the two samples must be generated, along with a logical explanation for choosing the given set of genes. 

We have now included another panel in figure 1 to better clarify the logic behind the strategy used to identify our target genes (Figure 1A).

(2) Figure 2 - a panel describing the workflow of gRNA design and targeting for the 9 candidate genes, along with lentiviral packaging and transduction would make it easier to follow. 

We have now included three schematics for all the experiments in the manuscript in supplementary figure 4 A-C. 

(3) Figure 3- to assess the effect of arterial cell expansion on the emergence of hematopoietic progenitors, CD34+ Dll4+ cells should be sorted for OP9 co-culture assay.

Using only CD34+ cells does not answer the question raised. Also, the CFU assay performed does not fully support the claim of enhanced hematopoietic differentiation since only CFU-E and CFU-GM colonies are increased in Dox-treated samples, with no effect on other colony types. OP9 co-culture assay with these cells would be required to strengthen this claim. 

We wanted to clarify that the effect on the methylcellulose coming from the activated cells was not limited to CFU-E, as the reviewer reported; instead, it also affected CFU-GM and CFU-M. 

We have now performed additional experiments where we sorted the CD34+ compartment into DLL4- and DLL4+ in Supplementary Figure 5D-E, which we discussed in lines 250-258. 

(4) In Figure 3F, there appears to be a lot of variation in the DLL4% fold change values for

DOX treated iSAM_AGM sample, which weakens the claim of increased arterial expansion.

Can the authors explain the probable reason? It is suggested that the two other controls (iSAM_+DOX and iSAM_-DOX) should be included in this analysis. It is imperative to also show % populations rather than just fold change to gain confidence.

We agree that there is a lot of variability. That is because differentiation happens in 3D in embryoid bodies, which contain many different cell types that differentiate in different proportions across independent experiments. We have now included the raw data in Supplementary Figure 4 D, with additional statistical analysis to show the expansion of arterial cells including also the suggested additional controls.

(5) How does activation of these target genes cause increased arterialization? Is the emergence of non-HE populations suppressed? Or is it specific to the HE? The data on this should be clarified and also discussed. ANTO/Lesley text

We have provided additional data clarifying the connection between increased arterialisation and hemogenic potential. We showed that the activation induces increased arterialisation and that IGFBP2 acts by supporting the acquisition of hemogenic potential. We have discussed this in lines 326-348 and provided a new figure to explain this in detail (figure 6)

(6) Considering that IGFBP2 was chosen from the activated target gene(s) cluster, can the authors explain why the reduced CFU-M phenomenon observed in Figure 3G does not appear in the MethoCult assay for IGFBP2 treated cells (Figure 4B)?

The difference could be explained by the fact that in Figure 3G, the cells underwent activation of multiple genes, while in Figure 4B, they were only exposed to IGFBP2. Our results show that IGFBP2 could at least partially explain the phenotype that we see with the activation, but we believe that during the activation experiments, there might be other signals available that might not be induced by IGFBP2 alone. We have also added a summary section and a figure to clarify the different mechanisms of action of the gene activation and IGFBP2.

(7) Figure 4- while the experiments conducted support the role of IGFBP2 in increasing hematopoietic output, there is no experimental evidence to prove its function through paracrine signalling in HECs. The authors need to provide some evidence of how IGFBP2 supplementation specifically expands only the hematopoietic progenitors. Experimental strategies involving specifically targeting IGFBP2 in hemogenic/arterial endothelial cells are required to prove its cell type specific function. Additionally, assessing the in vivo functional potential of the hematopoietic cells generated in the presence of IGFBP2, by bone-marrow transplantation of CD34+ CD43+ cells, is essential. 

The role of IGFBP2 in the context of HSC production and expansion was not the topic of our research, and we have not claimed that IGFBP2  affects the long-term repopulating capacity of HSPCs. Therefore, we believe that the requested experiments are not required to support the specific claims that we do make. We have now provided more experiments and bioinformatic analysis that support the role of IGFBP2 in inducing the progression of EHT from arterial cells to hemogenic endothelium, and to avoid misunderstandings, we have toned down our claims by editing the text regarding its paracrine effect s. 

(8) Figure 4C-D -It is recommended to plot % populations along with fold change value. As this is a key finding, it is important to perform flow cytometry for additional hematopoietic markers- CD144, CD235a and CD41a to demonstrate whether this strategy can also expand erythroid-megakaryocyte progenitors. Telma

Figure 4C already shows the percentage values; we have now added the percentage for Figure 4D in SF5C. We have also performed additional analysis as requested and added the data obtained to Supplementary Figure 5D.

(9) In Figure 5, analysis showing the frequency of cells constituting different clusters, between untreated and IGFBP2-treated samples in the single-cell transcriptome analysis is essential. Additional experiments are required to validate the function of IGFBP2 through modulation of metabolic activity. Inhibition of oxidative phosphorylation in the IGFBP2treated cells should reduce the hematopoietic output. Authors should consider doing these experiments to provide a stronger mechanistic insight into IGFBP2-mediated regulation of hematopoietic emergence.

We have now included the requested cluster composition in Supplementary Figure 5F. We decided not to include further tests on the metabolic profile of IGFBP2 as we already discussed in other papers that showed, using selective inhibitors, that the EHT coincides with a glycol to OxPhos switch. 

(10) It is very striking to see that IGFBP2 supplementation changes the transcriptional profile of developing hematopoietic cells by increasing transcription of OXPHOS-related genes with concomitant reduction of glycolytic signatures, particularly at Day 13. However, the mitochondrial ATP rate measurements do not seem convincing. The bioenergetic profiles show that when mitochondrial inhibitors are added, both groups exhibit decreased OCR values and, on the other hand, higher ECAR. This indicates that both groups have the capability to utilize OXPHOS or glycolysis and may only differ in their basal respiration rates.

Differences in proliferation rate can cause basal respiration to change. There is no information on how the bioenergetic profile was normalized (cell no./protein amount). Given that IGFBP2 has been shown to increase proliferation, it is very likely that the cells treated with IGFBP2 proliferated faster and therefore have higher OCR. The data needs to be normalized appropriately to negate this possibility.

We have previously tested whether IGFBP2 causes an increase in proliferation by analysing the cell cycle of cells treated with it, as we initially thought this could be a mechanism of action. We have now provided the quantification of the cell cycle in the cells treated with IGFBP2, showing no effect was observed in cell cycle Supplementary Figure 4E. Following this analysis, we decided to plate the same number of cells and test their density under the microscope before running the experiment; each experiment was done in triplicate for each condition. We have now added this info to the method sections lines 806-813.  We did not comment on the basal difference, which we agree might be due to several factors, but we only compared the difference in response to the inhibitors, which isn’t affected by the basal level but exclusively by their D values. We have also included the formulas used to calculate the ATP production rate.

Overall, it appears that IGFBP2 does not seem to primarily cause metabolic changes, but simply accelerates the metabolic dependency on OXPHOS. Hence, the term 'metabolic remodelling' must be avoided unless IGFBP2 depletion/loss of function analysis is shown.

We thank the reviewer for suggesting how to interpret the data about the dependency on OXPHOS. We have now changed the conclusions and claims about the effect of IGFBP2. We have also included a cell cycle analysis of the hematopoietic cells derived upon IGFBP2 addition to show that they don’t show differences in proliferation that could cause the increase in colony formation we observed. Regarding the assay, we have plated the same number of cells for each group to make sure we were comparing the same number of cells, which we also assessed in the microscope before the test, and we eliminated the suspension cells during the washes that preceded the measurement. The review is correct in indicating that there is a basal difference in the value of OCR and ECAR where the IGFBP2 is lower at the start and not higher, which would not conceal higher proliferation. Finally, the ATP production rate is calculated on the variation of OCR and ECAR upon the addition of inhibitors, which normalizes for the basal differences.

eLife Assessment

This study presents useful findings to inform and improve the in vitro differentiation of hematopoietic progenitor cells from human induced pluripotent stem cells. Relying on a well-characterised technical approach, the data analysis is overall solid and reasonably supports the main conclusions.

Reviewer #1 (Public Review):

Summary:

The work from Petazzi et al. aimed at identifying novel factors supporting the differentiation of human hematopoietic progenitors from induced-pluripotent stem cells (iPSCs). The authors developed an inducible CRISPR-mediated activation strategy (iCRISPRa) to test the impact of newly identified candidate factors on the generation of hematopoietic progenitors in vitro. They first compared previously published transcriptomic data of iPSC-derived hemato-endothelial populations with cells isolated ex vivo from the aorta-gonad-mesonephros (AGM) region of the human embryo and they identified 9 transcription factors expressed in the aortic hemogenic endothelium that were poorly expressed in the in vitro differentiated cells. They then tested the activation of these candidate factors in an iPSC-based culture system supporting the differentiation of hematopoietic progenitors in vitro. They found that the IGF binding protein 2 (IGFBP2) was the most upregulated gene in arterial endothelium after activation and they demonstrated that IGFBP2 promotes the generation of functional hematopoietic progenitors in vitro.

Strengths:

The authors developed a very useful doxycycline-inducible system to activate the expression of specific candidate genes in human iPSC. This approach allows us to simultaneously test the impact of 9 different transcription factors on in vitro differentiation of hematopoietic cells, and the system appears to be very versatile and applicable to a broad variety of studies. Using this approach, the authors exhaustively demonstrated the role of IGFBP2 in promoting the generation of functional hematopoietic progenitors in vitro from iPSCs.

Weaknesses:

The authors performed a very thorough characterization of the system in proof-of-principle experiments activating a single transcription factor. However, when 9 independent factors were used, it is not always clear whether the observed results were the consequence of the simultaneous activation of all 9 TF or just a subset of them.

μετρα προσαρμογης απο τους αγροτεσ για τους ελαιωνες και την αυξημενη παραγωγικοτητα τους σε περιοδους ξηρασιας

ουσιαστικα εδω αναφερονται μετρα προσαρμογης της ελληνικης γεωργιασ στην κλιματικη αλλαγη με βαση διεθνησ δεικτεσ, και κινησεις που εχουν γινει πο ΗΕ και ΕΕ. (ειναι προτασεις η πραγματικότητα)? θα μπορουσε να μπει στο κομματι του ρεσιλιενς

concept of resilience: how to minimize the impacts of CC for greek agriculture theory of adaptive capacity framework: what is the framework in which people and institutions are working in, in order to resist cc?

εδω μπορω να βαλω το αρθρο που μου εστειλε ο Ανδρονικοσ για ενα παρασιτο που επηρεαζει την ελαιοπαραγωγη. How does climate change affect agriculture? answers back to the central question.

implications of climate change in greek agriculture

social influence is not just something people do to affect others, but something that can be bought, sold, or traded like a product.

Incentivize means to motivate or encourage someone to take a specific action by offering a reward or benefit.

we as viewers don't always see what infleuncers do to us and when they are sponsered etc, there should be guidlines for this, like #ad. even if an influencer is not sponsered, they should think about what their content can do to the viewer

The phrase implies that brands and marketers should move beyond mere operational efficiency and focus on creativity that aligns with their core values.

Author response:

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

Reviewer #1 (Recommendations For The Authors):

Summary:

In this manuscript, the molecular mechanism of interaction of daptomycin (DAP) with bacterial membrane phospholipids has been explored by fluorescence and CD spectroscopy, mass spectrometry, and RP-HPLC. The mechanism of binding was found to be a two-step process. A fast reversible step of binding to the surface and a slow irreversible step of membrane insertion. Fluorescence-based titrations were performed and analysed to infer that daptomycin bound simultaneously two molecules of PG with nanomolar affinity in the presence of calcium. Conformational change but not membrane insertion was observed for DAP in the presence of cardiolipin and calcium.

Strengths:

The strength of the study is skillful execution of biophysical experiments, especially stoppedflow kinetics that capture the first surface binding event, and careful delineation of the stoichiometry.

Weaknesses:

The weakness of the study is that it does not add substantially to the previously known information and fails to provide additional molecular details. The current study provides incremental information on DAP-PG-calcium association but fails to capture the complex in mass spectrometry. The ITC and NMR studies with G3P are inconclusive. There are no structural models presented. Another aspect missing from the study is the reconciliation between PG in the monomer, micellar, and membrane forms.

Besides the two-stage process, another important finding in the current work is the stable complex that plays a critical role in the drug uptake both in vitro and in B. subtilis. This complex has been shown to be a stable species in HPLC and its binding stoichiometry and affinity have been quantitatively characterized. The complex may not be stable enough in gas phase to be detected in the MS analysis, which was designed to detect the phospholipid and Dap components, not the complex itself. The structural model of this complex is clearly proposed and presented in Figure 6. 

The NMR and ITC studies have a very clear conclusion that Dap has a weak interaction with the PG headgroup alone, which is unable to account for the Dap-PG interaction observed in the fluorescence studies. Thus, the whole PG molecule has to be involved in the interaction, leading to the discovery of the stable complex.  

Reviewer #2 (Recommendations For The Authors):

(1) I appreciate and agree with the comment that there are stages of daptomycin insertion, and these might involve the formation of different complexes with different binding partners (e.g. pre-insertion vs quaternary vs bactericidal). However, it seems like lipid II is an apparent participant in daptomycin membrane dynamics (Grein et al. Nature Communications 2020). It's not clear why this was excluded from analysis by the authors, or what basis there is for the discussion statement that the quaternary complex can shift into the bactericidal complex by exchanging 1 PG for lipid II. 

We agree that lipid II and other isoprenyl lipids may be involved in the uptake and insertion of daptomycin into membrane according to the results of the Nat. Comm. paper. However, these isoprenyl lipids are very small components of the membrane in comparison to PG and their contribution to the drug uptake is thus expected to be much less significant. Nonetheless, we included farnesyl pyrophosphate (FPP) as an analog of bactoprenol pyrophosphate (C55PP), which was reported to have the same promoting effect as lipid II in the previous study, in our study but found no promoting effect in the fluorescence assay (Fig. 2B). In addition, no complex was formed when FPP replaced PG in our preparation and analysis of the drug-lipid complex. In consideration of these negative results and the expected small contribution, other isoprenyl lipids or their analogs were not included in the study.

The statement of forming the proposed bactericidal complex from the identified complex is a speculation that is possible only when lipid II has a higher affinity for Dap than a PG ligand. To avoid confusion, we deleted the sentence’ in the revision. 

(2) The detailed examination of daptomycin dynamics, particularly on the millisecond scale, in this paper is ideal for characterizing the effect of lipid II on daptomycin insertion. It would be helpful to either include lipid II in some analyses (micelle binding, fluorescence shifts, CD) or at least address why it was excluded from the scope of this work.

As mentioned in the response to the first comment, we did not exclude isoprenyl lipids in our study but used some of their analogs in the fluorescence assay. Besides FPP mentioned above, we also tested geranyl pyrophosphate and geranyl monophosphate but obtained the same negative results. Lipid II was not directly used because it is one of the three isoprenyl lipids reported to have the same promoting effects in the Nat. Comm. paper and also because its preparation is not easy. Even if lipid II were different from other isoprenyl lipids in promoting membrane binding, its contribution is likely negligible at the reversible stage compared to the phospholipids because of its minuscule content in bacterial membrane. This is the main reason we did not use the isoprenyl lipids in the fast kinetic study (this stage only involves reversible binding, not insertion). 

(3) Grein et al. 2020 saw that PG did not have a strong effect on daptomycin interaction with membranes. I believe this discrepancy is more likely due to the complex physical parameters of supported bilayers versus micelles/vesicles or some other methodological variable, but if the authors have more insight on this, it would be valuable commentary in the discussion.

We totally agree that the discrepancy is likely due to the different conditions in the assays. It is hard to tell exactly what causes the difference. Thus, we did not attempt to comment on the cause of this difference in the discussion.

(4) Isolation of the daptomycin complex from B. subtilis cells clearly had different traces from the in vitro complex; is it possible that lipid II is present in the B. subtilis complex? If not, a time-course extraction could be useful to support the model that different complexes have different activities. Isolates from early-stage incubation with daptomycin may lack lipid II but isolates from longer incubations may have lipid II present as the complex shifts from insertion to bactericidal.

From the day we isolated the complex from B. subtilis, we have been looking for evidence for the previously proposed lipid complexes containing lipid II or other isoprenyl lipids but have not been successful. We did not see any sign of lipid II or other isoprenyl lipids in the MALDI or ESI mass spectroscopic data. The minute peaks in the HPLC traces are not the expected complexes in separate LC-MS analysis. However, this does not mean that such complexes are not present in the isolated PG-containing complex because: (1) the amount of such complexes may be too small to be detected due to the low content of the isoprenyl lipids; (2) the isoprenyl lipids, particularly lipid II, are not easily ionizable due to their size and unique structure for detection in mass spectrometry. 

We don’t think the drug treatment time is the reason for the failure in detecting lipid II or other isoprenyl lipids. In our reported experiment, the cells were treated with a very high dose of Dap for 2 hours before extraction. In a separate experiment done recently, we treated B. subtilis at 1/3 of the used dose under the same condition and found all treated cells were dead after 1 hour in a titration assay, consistent with the results from reported time-killing assays in the literature. From this result, the proposed bactericidal lipid-containing complex should have been formed in the treated cells used in our extraction and isolated along with the PG-containing complex. It was not detected likely due to the reasons discussed above. To avoid the interference of the PG-containing complex, a large amount of bacterial cells might have to be treated at a low dose to isolate enough amount of the lipid II-containing complex for identification. However, isolation or identification of the lipid II-containing complex is outside the scope of the current investigation and is therefore not pursued. 

(5) Part of the daptomycin mechanism of interacting with bacterial membranes involves the flipping of daptomycin from one leaflet to another. There was some mentioned work on the consistency of results between micelles and vesicles, but the dynamics or existence of a flipping complex in the bilayer system wasn't addressed at all in this paper.

The current investigation makes no attempt to solve all problems in the daptomycin mode of action and is limited to the uptake of the drug, up to the point when Dap is inserted into the membrane. Within this scope, flipping of the complex is not yet involved and is thus irrelevant to the study. How the complex is flipped and used to kill the bacteria is what should be investigated next.  

(6) The authors mention data with phosphatidylethanolamine in the text, but I could not find the data in the main or supplemental figures. I recommend including it in at least one of the figures.

It is much appreciated that this error is identified. The POPE data was lost when the graphic (Fig. 2B) was assembled in Adobe to create Figure 2. We re-draw the graphic and reassemble the figure to solve this problem. Fig. 2B has also been modified to use micromolar for the concentration of the lipids.

(7) Readability point: I'd suggest some consistency in the concentrations mentioned. Making the concentrations either all molar-based or all percentage-based would make comparison across figures easier.

As suggested, we have changed the % into micromolar concentrations in Fig. 2B and also in Fig. 3A. 

(8) The model figure is quite difficult to interpret, particularly the final stage of the tail unfolding. I recommend the authors use a zoomed-in inset for this stage, or at least simplify the diagram by removing the non-participating lipid structures. The figure legend for the model figure should also have a brief description of the events and what the arrows mean, particularly the POPS PG arrow in the final panel of the figure. I am assuming here the authors are implying that daptomycin can transiently interact with one lipid species and move to another, but the arrow here suggests that daptomycin is moving through the lipid headgroup space.

We really appreciate the suggestions. As suggested, we put an inset to show the preinsertion complex more clearly. In addition, we have removed the green arrows originally intended to show the re-organization/movement of the phospholipids. Moreover, the legend is changed to ‘Proposed mechanism for the two-phased uptake of Dap into bacterial membrane. In the first phase, Dap reversibly binds to negative phospholipids with a hidden tail in the headgroup region, where it combines with two PG molecules to form a pre-insertion complex. In the second phase, the hidden tail unfolds and irreversibly inserts into the membrane. The inset shows the headgroup of the pre-insertion complex with the broad arrow showing the direction for the unfolding of the hidden tail. The red dots denote Ca2+.’  

(9) The authors listed the Kd for daptomycin and 2 PG as 7.2 x 10-15 M2. Is this correct? This is an affinity in the femtomolar range.

Please note that this Kd is for the simultaneous binding of two PG molecules, not for the binding of a single ligand that we usually refer to. Assuming that each PG contributes equally to this interaction, the binding affinity for each ligand is then the squared root of 7.2 x 10-15 M2, which equals to 8.5 x 10-8 M. This is equivalent to a nanomolar affinity for PG and is a reasonably high affinity.

Reviewer #3 (Recommendations For The Authors):

(1) The authors reported an increase in daptomycin intensity with the increasing amount of negatively charged DMPG. A similar observation has been reported for GUVs, however, the authors did not refer to this paper in their manuscript: E. Krok, M. Stephan, R. Dimova, L. Piatkowski, Tunable biomimetic bacterial membranes from binary and ternary lipid mixtures and their application in antimicrobial testing, Biochim. Biophys. Acta - Biomembr. 1865 (2023) [1]. This paper is also consistent with the authors' observation that there is negligible fluorescence detected for the membranes composed of PC lipids upon exposure to the Dap treatment.

As suggested, this paper is cited as ref. 29 in the revision by adding the following sentence at the end of the section ‘Dependence of Dap uptake on phosphatidylglycerol.’: ‘PG-dependent increase of the steady-state fluorescence was also observed in giant unilamellar vesicles (GUVs).29’. The numbering is changed accordingly for the remaining references.  

(2) Please include the plot of the steady-state Kyn fluorescence vs the content of POPA (Figure 2C shows traces for DMPG, CL, and POPS). Both POPA and POPS lipids are negatively charged, however, POPS seems to interact with Dap, while POPA does not. In my opinion, this observation is really interesting and might deserve a more thorough discussion. The authors might want to describe what could be the mechanism behind this lipid-specific mode of binding.

As suggested, a plot is now added for POPA in Fig. 2C, which is basically a flat line without significant increase for the Kyn fluorescence. Indeed, the different effect of the negative phospholipids is very interesting, indicating that the reversible binding of Dap to the lipid surface is dependent not only on the Ca2+-mediated ionic interaction but also the structure of the headgroup. In other words, Dap recognizes the phospholipids at the surface binding stage. Considering this headgroup specificity, the last sentence in the second paragraph in “Discussion’ is changed from ‘In addition, due to the low lipid specificity, this reversible binding likely involves Ca2+-mediated ionic interaction between Dap and the phosphoryl moiety of the headgroups.’ to ‘In addition, due to the specificity for negative phospholipids (Fig. 2B and 2C), this reversible binding of Dap likely involves both a nonspecific Ca2+-mediated ionic interaction and a specific interaction with the remaining part of the headgroups.’

(3) The authors write that they propose a novel mechanism for the Ca2+-dependent insertion of Dap to the bacterial membrane, however, they rather ignored the already published findings and hypotheses regarding this process. In fact the role of Ca2+, as well as the proposed conformational changes of Dap, which allow its deeper insertion into the membrane are well known:

The role of Ca2+ ions in the mechanism of binding is actually three-fold: (i) neutralization of daptomycin charge [2], (iii) creating the connection between lipids and daptomycin and (iii) inducing two daptomycin conformational changes. It should be noted that the interactions between calcium ions and daptomycin are 2-3 orders of magnitude stronger than between daptomycin and PG lipids [3,4]. Thus, upon the addition of CaCl2 to the solution, the divalent cations of calcium bind preferentially to the daptomycin, rather than to the negatively charged PG lipids, which results in the decrease of daptomycin net negative charge but also leads to its first conformational change [4]. Upon binding between calcium ions and two aspartate residues, the area of the hydrophobic surface increases, which allows the daptomycin to interact with the negatively charged membrane. In the next step, Ca2+ acts as a bridge connecting daptomycin with the anionic lipids. This event leads to the second conformational change, which enables deeper insertion of daptomycin into the lipid membrane and enables its fluorescence [4]. The overall mechanism has a sequential character, where the binding of daptomycin-Ca2+ complex to the negatively charged PG (or CA) occurs at the end.

The authors should focus on emphasizing the novelty of their manuscript, keeping in mind the already published paper.

We agree with the comments on the three general roles of calcium ion in the Dap interaction with membrane. The current investigation does not ignore the previous findings, which involve many more works than mentioned above, but takes these findings as common knowledge. Actually, the role of calcium ion is not the focus of current work. Instead, the current work focuses on how the drug is taken up and inserted into the membrane in the presence of the ion and how its structure changes in this process. With the known roles of calcium ion in mind, we propose an uptake mechanism (Fig. 6) that shows no conflict with the common knowledge.

We would like to point out that the ‘deeper insertion into the membrane’ in the comment is different from the membrane insertion referred to in our manuscript. This ‘deeper insertion’ still remains in the reversible stage of binding to the membrane surface because all negative phospholipids can do this (causing a conformational change and fluorescence increase, as quantified in Fig.2C) but now we know that only PG can enable irreversible membrane insertion because of our work. In addition, the comment that calcium binding to daptomycin causes first conformational change is not supported by our finding that no conformational change is found for Dap in the presence of calcium in a lipid-free environment (Fig. 3B). One important aspect of novelty and contribution of our work is to clear up some of these ambiguities in the literature. Another contribution of our work is to demonstrate the formation of a stable complex between Dap and PG with a defined stoichiometry and its crucial role in the drug uptake. 

(4) One paragraph in the section "Ca2+- dependent interaction between Dap and DMPG" is devoted to a discussion of the formation of precipitate upon extraction of DMPG-containing micelles, exposed to Dap in the calcium-rich environment. Contrary, in the absence of Dap, no precipitate was detected. The authors did not provide any visual proof for their statement. Please include proper photographs in the supplementary information.

The precipitate formed upon extraction of the DMPG-containing micelles was too little to be visually identifiable but could be collected by centrifugation and detected by fluorescence or HPLC after dissolving in DMSO. For visualization, we show below the precipitate formed using higher amount of Dap and DMPG. The Dap-DMPG-Ca2+ complex (left tube) was formed by mixing 1 mM Dap, 2 mM DMPG and 1 mM Ca2+ and the control (right tube) was a mixture of 2 mM DMPG and 1 mM Ca2+. This is now added as Fig. S7 in the supplementary information (the index is modified accordingly) and cited in the main text.

(5) The authors wrote that it is not clear how many calcium ions are bound to Dap-2PG complex (page 11, Discussion section). There are already reports discussing this issue. I recommend citing the paper discussing that exactly two Ca2+ ions bind to a single Dap molecule: R. Taylor, K. Butt, B. Scott, T. Zhang, J.K. Muraih, E. Mintzer, S. Taylor, M. Palmer, Two successive calcium-dependent transitions mediate membrane binding and oligomerization of daptomycin and the related antibiotic A54145, Biochim. Biophys. Acta - Biomembr. 1858, (2016) 1999-2005 [5]

We were aware of the cited work that shows binding of two Ca2+ but also noted that there are more works showing one Ca2+ in the binding, such as the paper in [Ho, S. W., Jung, D., Calhoun, J. R., Lear, J. D., Okon, M., Scott, W. R. P., Hancock, R. E. W., & Straus, S. K. (2008), Effect of divalent cations on the structure of the antibiotic daptomycin. European Biophysics Journal, 37(4), 421–433.]. That was the reason we said ‘it is not clear how many calcium ions are bound to Dap-2PG complex’. Now, both papers are cited (as Ref. #33, 34) to support this statement.

(6) The authors wrote two contradictory statements:

-  PG cannot be found in mammalian cell membranes:

"Moreover, the complete dependence of the membrane insertion on PG also explains why Dap selectively attacks Gram-positive bacteria without affecting mammalian cells, because PG is present only in bacterial membrane but not in mammalian membrane. " (Page 10, Discussion section, last sentence of the first paragraph)

"However, Dap absorbed on bacterial surface is continuously inserted into the acyl layer via formation of complex with PG in a time scale of minutes, whereas no irreversible insertion of Dap occurs on mammalian membrane due to the absence of PG while the bound Dap is continuously released to the circulation as the drug is depleted by the bacteria." (Page 13, Discussion section)

-  PG in trace amounts is present in mammalian membranes:

"The proposed requirement of the pre-insertion quaternary complex increases the threshold of PG content for the membrane insertion to happen and thus makes it impossible on the surface of mammalian cells even if their plasma membrane contains a trace amount of PG." (Page 13, Discussion section).

In fact, phosphatidylglycerol comprises 1-2 mol% of the mammalian cell membranes. Please, correct this information, which in this form is misleading to the readers.

We appreciate the comments about the PG content in mammalian cells. Changes are made as listed below:

(1) p10, the sentence is changed to ‘Moreover, the complete dependence of the membrane insertion on PG also explains why Dap selectively attacks Gram-positive bacteria without affecting mammalian cells, because PG is a major phospholipid in bacterial membrane but is a minor component in mammalian membrane.’ 

(2) p13, the sentence is changed to ‘However, Dap absorbed on bacterial surface is continuously inserted into the acyl layer via formation of complex with PG in a time scale of minutes, whereas little irreversible insertion of Dap occurs on mammalian membrane due to the low content of PG while the bound Dap is continuously released to the circulation as the drug is depleted by the bacteria.’

(3) p13, another sentence is modified to ‘The proposed requirement of the pre-insertion quaternary complex increases the threshold of PG content for the membrane insertion to happen and thus makes it less likely on the surface of mammalian cells that contain PG at a low level in the membrane.’ 

(7) Please include information that Dap is effective only against Gram-positive bacteria and does not show antimicrobial properties against Gram-negative strains. The authors focused on emphasizing that Dap does not affect mammalian membranes, most likely due to the low PG content, however even membranes of Gram-negative bacteria are not susceptible to the Dap, despite the relatively high content of negatively charged PG in the inner membrane (e.g. inner cell membrane of E. coli has ~20% PG).

The requested information is already included in ‘Introduction’. In this part, Dap is introduced to be only active against Gram-positive bacteria, implicating that it is not active against Gram-negative bacteria. The reason Dap is inactive against E. coli or other Gramnegative bacteria is because the outer membrane prevents the antibiotic from accessing the PG in the inner membrane to cause any harm. When the outer membrane is removed, Dap will also attack the plasma membrane of Gram-negative bacteria. 

Literature cited in the comments:

(1) E. Krok, M. Stephan, R. Dimova, L. Piatkowski, Tunable biomimetic bacterial membranes from binary and ternary lipid mixtures and their application in antimicrobial testing, Biochim. Biophys. Acta - Biomembr. 1865 (2023). https://doi.org/10.1101/2023.02.12.528174.

(2) S.W. Ho, D. Jung, J.R. Calhoun, J.D. Lear, M. Okon, W.R.P. Scott, R.E.W. Hancock, S.K. Straus, Effect of divalent cations on the structure of the antibiotic daptomycin, Eur. Biophys. J. 37 (2008) 421-433. https://doi.org/10.1007/S00249-007-0227-2/METRICS.

(3) A. Pokorny, P.F. Almeida, The Antibiotic Peptide Daptomycin Functions by Reorganizing the Membrane, J. Membr. Biol. 254 (2021) 97-108. https://doi.org/10.1007/s00232-02100175-0.

(4) L. Robbel, M.A. Marahiel, Daptomycin, a bacterial lipopeptide synthesized by a nonribosomal machinery, J. Biol. Chem. 285 (2010) 2750127508. https://doi.org/10.1074/JBC.R110.128181.

(5) R. Taylor, K. Butt, B. Scott, T. Zhang, J.K. Muraih, E. Mintzer, S. Taylor, M. Palmer, Two successive calcium-dependent transitions mediate membrane binding and oligomerization of daptomycin and the related antibiotic A54145, Biochim. Biophys. Acta - Biomembr. 1858 (2016) 1999-2005. https://doi.org/10.1016/J.BBAMEM.2016.05.020.

eLife Assessment

This valuable study describes the molecular mechanism of daptomycin insertion into bacterial membranes. The authors provide solid in vitro evidence for the early events of daptomycin interaction with phospholipid headgroups and stronger, specific interaction with phosphatidylglycerol. This work will be of interest to bacterial membrane biologists and biochemists working in the antimicrobial resistance field.

Reviewer #3 (Public review):

Summary:

Machhua et al. in their work focused on unravelling the molecular mechanism of daptomycin binding and interaction with bacterial cell membranes. Daptomycin (Dap) is an acidic, cyclic lipopeptide composed of 13 amino acids, known for preferential binding to anionic lipids, particularly phosphatidylglycerol (PG), which are prevalent components in the membranes of Gram-positive bacteria. The process of binding and antimicrobial efficacy of Dap are significantly influenced by the ionic composition of the surrounding environment, especially the presence of Ca2+ ions. The authors underscore the presence of significant knowledge gaps in our understanding of daptomycin's mode of action. Several critical questions remain unanswered, including the basis for selective recognition and accumulation in membranes of Gram-positive strains, the specific role of Ca2+ ions in this process, and the mechanisms by which daptomycin binds to and inserts into the cell membrane.

Dap is intrinsically fluorescent due to its kynurenine residue (Kyn-13) and this property allows direct imaging of Dap binding to model cell membranes without the need of additional labeling. Taking advantage of this Dap autofluorescence, authors monitored the emission intensity of micelles, composed of varying DMPG content upon their exposure to Dap and compared it with the kinetics of fluorescence observed for zwitterionic DMPC and other negatively charged lipids such as cardiolipin (CA), POPA and POPS. The authors noted that the linear relationship between DMPG content and Dap fluorescence is strongly lipid-specific, as it was not observed for other anionic lipids. The manuscript sheds light on the specificity of Dap's interaction with CA and DMPG lipids. Through Ca2+ sequestration with EGTA, the authors demonstrated that the binding of Dap with CA is reversible, while its interaction with DMPG results in the irreversible insertion of Dap into the lipid membrane structure, caused by the significant conformational change of this lipopeptide. The formation of a stable DMPG-Dap complex was also verified in bacterial cells isolated from Gram-positive bacteria B. subtilis, where Dap exhibited a permanent binding to PG lipids.

Altogether, the authors endeavored to illuminate novel insights into the molecular basis of Dap binding, interaction, and the mechanism of insertion into bacterial cell membranes. Such understanding holds promise for the development of innovative strategies in combating drug resistance and the emerging of the so-called superbugs.

Strengths:

- The manuscript by Machhua et al. provides a comprehensive analysis of the Dap mechanism of binding and interaction with the membrane. It discusses various aspects of this, only apparently trivial interaction such as the importance of PG presence in the membrane, the impact of Ca2+ ions, and different mechanisms of Dap binding with other negatively charged lipids.<br /> - The authors focused not only on model membranes (micelles) but also extended their research to bacterial cell membranes obtained from B. subtilis<br /> - The research is not only a report of the experimental findings but tries to give potential hypotheses explaining the molecular mechanisms behind the observed results

Weaknesses:

- The authors overestimate their findings, stating that they propose a novel mechanism of Dap interaction with bacterial cell membranes. This research is the extension of the hypotheses that have already been reported.<br /> - The literature study and overall discussion about the mechanism of action of Ca2+ ions or conformational changes of daptomycin could be improved.

This manuscript examines preprint review services and their role in the scholarly communications ecosystem.  It seems quite thorough to me. In Table 1 they list many peer-review services that I was unaware of e.g. SciRate and Sinai Immunology Review Project.

To help elicit critical & confirmatory responses for this peer review report I am trialling Elsevier’s suggested “structured peer review” core questions, and treating this manuscript as a research article.

Introduction

1. Is the background and literature section up to date and appropriate for the topic?

   Yes.

2. Are the primary (and secondary) objectives clearly stated at the end of the introduction?

   No. Instead the authors have chosen to put the two research questions on page 6 in the methods section. I wonder if they ought to be moved into the introduction – the research questions are not methods in themselves. Might it be better to state the research questions first and then detail the methods one uses to address those questions afterwards? [as Elsevier’s structured template seems implicitly to prefer.

Methods

1. Are the study methods (including theory/applicability/modelling) reported in sufficient detail to allow for their replicability or reproducibility?

   I note with approval that the version number of the software they used (ATLAS.ti) was given.

   I note with approval that the underlying data is publicly archived under CC BY at figshare.

   The Atlas.ti report data spreadsheet could do with some small improvement – the column headers are little cryptic e.g. “Nº  ST “ and “ST” which I eventually deduced was Number of Schools of Thought and Schools of Thought (?)   

   Is there a rawer form of the data that could be deposited with which to evidence the work done? The Atlas.ti report spreadsheet seemed like it was downstream output data from Atlas.ti. What was the rawer input data entered into Atlas.ti? Can this be archived somewhere in case researchers want to reanalyse it using other tools and methods.

   I note with disapproval that Atlas.ti is proprietary software which may hinder the reproducibility of this work. Nonetheless I acknowledge that Atlas.ti usage is somewhat ‘accepted’ in social sciences despite this issue.

   I think the qualitative text analysis is a little vague and/or under-described: “Using ATLAS.ti Windows (version 23.0.8.0), we carried out a qualitative analysis of text from the relevant sites, assigning codes covering what they do and why they have chosen to do it that way.” That’s not enough detail. Perhaps an example or two could be given? Was inter-rater reliability performed when ‘assigning codes’ ? How do we know the ‘codes’ were assigned accurately?

2. Are statistical analyses, controls, sampling mechanism, and statistical reporting (e.g., P-values, CIs, effect sizes) appropriate and well described?

   This is a descriptive study (and that’s fine) so there aren’t really any statistics on show here other than simple ‘counts’ (of Schools of Thought) in this manuscript. There are probably some statistical processes going on within the proprietary qualitative analysis of text done in ATLAS.ti but it is under described and so hard for me to evaluate. 

Results

1. Is the results presentation, including the number of tables and figures, appropriate to best present the study findings?

   Yes. However, I think a canonical URL to each service should be given.  A URL is very useful for disambiguation, to confirm e.g. that the authors mean this Hypothesis (www.hypothes.is) and NOT this Hypothesis (www.hyp.io). I know exactly which Hypothesis is the one the authors are referring to but we cannot assume all readers are experts 😊

   Optional suggestion: I wonder if the authors couldn’t present the table data in a slightly more visual and/or compact way? It’s not very visually appealing in its current state. Purely as an optional suggestion, to make the table more compact one could recode the answers given in one or more of the columns 2, 3 and 4 in the table e.g. "all disciplines = ⬤ , biomedical and life sciences = ▲, social sciences =  ‡  , engineering and technology = † ". I note this would give more space in the table to print the URLs for each service that both reviewers have requested.

   ———————————————————————————————

   | Service name | Developed by | Scientific disciplines | Types of outputs |

   | Episciences | Other | ⬤ | blah blah blah. |

   | Faculty Opinions | Individual researcher | ▲ | blah blah blah. |

   | Red Team Market | Individual researcher | ‡ | blah blah blah. |

   ———————————————————————————————

   The "Types of outputs" column might even lend themselves to mini-colour-pictograms (?) which could be more concise and more visually appealing? A table just of text, might be scientifically 'correct' but it is incredibly dull for readers, in my opinion.

2. Are additional sub-analyses or statistical measures needed (e.g., reporting of CIs, effect sizes, sensitivity analyses)?

   No / Not applicable. 

Discussion

1. Is the interpretation of results and study conclusions supported by the data and the study design?

   Yes.

2. Have the authors clearly emphasized the limitations of their study/theory/methods/argument?

   No. Perhaps a discussion of the linguistic/comprehension bias of the authors might be appropriate for this manuscript. What if there are ‘local’ or regional Chinese, Japanese, Indonesian or Arabic language preprint review services out there? Would this authorship team really be able to find them?

Additional points:

• Perhaps the points made in this manuscript about financial sustainability (p24) are a little too pessimistic. I get it, there is merit to this argument, but there is also some significant investment going on there if you know where to look. Perhaps it might be worth citing some recent investments e.g. Gates -> PREreview (2024) https://content.prereview.org/prereview-welcomes-funding/  and Arcadia’s $4 million USD to COAR for the Notify Project which supports a range of preprint review communities including Peer Community In, Episciences, PREreview and Harvard Library.  (source: https://coar-repositories.org/news-updates/coar-welcomes-significant-funding-for-the-notify-project/ ) 

• Although I note they are mentioned, I think more needs to be written about the similarity and overlap between ‘overlay journals’ and preprint review services. Are these arguably not just two different terms for kinda the same thing? If you have Peer Community In which has it’s overlay component in the form of the Peer Community Journal, why not mention other overlay journals like Discrete Analysis and The Open Journal of Astrophysics.   I think Peer Community In (& it’s PCJ) is the go-to example of the thin-ness of the line the separates (or doesn’t!) overlay journals and preprint review services. Some more exposition on this would be useful.

Thank you very much for the opportunity to review the preprint titled “Preprint review services: Disrupting the scholarly communication landscape?” (https://doi.org/10.31235/osf.io/8c6xm) The authors review services that facilitate peer review of preprints, primarily in the STEM (science, technology, engineering, and math) disciplines. They examine how these services operate and their role within the scholarly publishing ecosystem. Additionally, the authors discuss the potential benefits of these preprint peer review services, placing them in the context of tensions in the broader peer review reform movement. The discussions are organized according to four “schools of thought” in peer review reform, as outlined by Waltman et al. (2023), which provides a useful framework for analyzing the services. In terms of methodology, I believe the authors were thorough in their search for preprint review services, especially given that a systematic search might be impractical.

As I see it, the adoption of preprints and reforming peer review are key components of the move towards improving scholarly communication and open research. This article is a useful step along that journey, taking stock of current progress, with a discussion that illuminates possible paths forward. It is also well-structured and easy for me to follow. I believe it is a valuable contribution to the metaresearch literature.

On a high level, I believe the authors have made a reasonable case that preprint review services might make peer review more transparent and rewarding for all involved. Looking forward, I would like to see metaresearch which gathers further evidence that these benefits are truly being realised.

In this review, I will present some general points which merit further discussion or clarification to aid an uninitiated reader. Additionally, I raise one issue regarding how the authors framed the article and categorised preprint review services and the disciplines they serve. In my view, this problem does not fundamentally undermine the robust search, analyses, and discussion in this paper, but it risks putting off some researchers and constrains how broadly one should derive conclusions.

General comments

Some metaresearchers may be aware of preprints, but not all readers will be familiar with them. I suggest briefly defining what they are, how they work, and which types of research have benefited from preprints, similar to how “preprint review service” is clearly defined in the introduction.

Regarding Waltman et al.’s (2023) “Equity & Inclusion” school of thought, does it specifically aim for “balanced” representation by different groups as stated in this article? There is an important difference between “balanced” versus “equitable” representation, and I would like to see it addressed in this text.

Another analysis I would like to see is whether any of the 23 services reviewed present any evidence that their approach has improved research quality. For instance, the discussion on peer review efficiency and incentives states that there is currently “no hard evidence” that journals want to utilise reviews by Rapid Reviews: COVID-19, and that “not all journals are receptive” to partnerships. Are journals skeptical of whether preprint review services could improve research quality? Or might another dynamic be at work?

The authors cite Nguyen et al. (2015) and Okuzaki et al. (2019), stating that peer review is often “overloaded”. I would like to see a clearer explanation by what “overloaded” means in this context so that a reader does not have to read the two cited papers.

To the best of my understanding, one of the major sticking points in peer review reform is whether to anonymise reviewers and/or authors. Consequently, I appreciate the comprehensive discussion about this issue by the authors.

However, I am only partially convinced by the statement that double anonymity is “essentially incompatible” with preprint review. For example, there may be, as yet not fully explored, ways to publish anonymous preprints with (a) a notice that it has been submitted to, or is undergoing, peer review; and (b) that the authors will be revealed once peer review has been performed (e.g. at least one review has been published). This would avoid the issue of publishing only after review is concluded as is the case for Hypothesis and Peer Community In.

Additionally, the authors describe 13 services which aim to “balance transparency and protect reviewers’ interests”. This is a laudable goal, but I am concerned that framing this as a “balance” implies a binary choice, and that to have more of one, we must lose an equal amount of the other. Thinking only in terms of “balance” prevents creative, win-win solutions. Could a case be made for non-anonymity to be complemented by a reputation system for authors and reviewers? For example, major misconduct (e.g. retribution against a critical review) would be recorded in that system and dissuade bad actors. Something similar can already be seen in the reviewer evaluation system of CrowdPeer, which could plausibly be extended or modified to highlight misconduct.

I also note that misconduct and abusive behaviour already occur even in fully or partially anonymised peer review, and they are not limited to the review or preprints. While I am not aware of existing literature on this topic, academics’ fears seem reasonable. For example, there is at least anecdotal testimonies that a reviewer would deliberately reject a paper to retard the progress of a rival research group, while taking the ideas of that paper and beating their competitors to winning a grant. Or, a junior researcher might refrain from giving a negative review out of fear that the senior researcher whose work they are reviewing might retaliate. These fears, real or not, seem to play a part in the debates about if and how peer review should (or should not) be anonymised. I would like to see an exploration of whether de-anonimisation will improve or worsen this behaviour and in what contexts. And if such studies exist, it would be good to discuss them in this paper.

I found it interesting that almost all preprint review services claim to be complementary to, and not compete with, traditional journal-based peer review. The methodology described in this article cannot definitely explain what is going on, but I suspect there may be a connection between this aversion to compete with traditional journals, and (a) the skepticism of journals towards partnering with preprint review services and (b) the dearth of publisher-run options. I hypothesise that there is a power dynamic at play, where traditional publishers have a vested interest in maintaining the power they hold over scholarly communication, and that preprint review services stress their complementarity (instead of competitiveness) as a survival mechanism. This may be an avenue for further metaresearch.

To understand preprints from which fields of research are actually present on the services categorised under “all disciplines,” I used the Random Integer Set Generator by the Random.org true random number service (https://www.random.org/integer-sets/) to select five services for closer examination: Hypothesis, Peeriodicals, PubPeer, Qeios, and Researchers One. Of those, I observed that Hypothesis is an open source web annotation service that allows commenting on and discussion of any web page on the Internet regardless of whether it is research or preprints. Hypothesis has a sub-project named TRiP (Transparent Review in Preprints), which is their preprint review service in collaboration with Cold Spring Harbor Laboratory. It is unclear to me why the authors listed Hypothesis as the service name in Table 1 (and elsewhere) instead of TRiP (or other similar sub-projects). In addition, Hypothesis seems to be framed as a generic web annotation service that is used by some as a preprint review tool. This seems fundamentally different from others who are explicitly set up as preprint review services. This difference seems noteworthy to me.

To aid readers, I also suggest including hyperlinks to the 23 services reviewed in this paper. My comments on disciplinary representation in these services are elaborated further below.

One minor point of curiosity is that several services use an “automated tool” to select reviewers. It would be helpful to describe in this paper exactly what those tools are and how they work, or report situations where services do not explain it.

Lastly, what did the authors mean by “software heritage” in section 6? Are they referring to the organisation named Software Heritage (https://www.softwareheritage.org/) or something else? It is not clear to me how preprint reviews would be deposited in this context.

Respecting disciplinary and epistemic diversity

In the abstract and elsewhere in the article, the authors acknowledge that preprints are gaining momentum “in some fields” as a way to share “scientific” findings. After reading this article, I agree that preprint review services may disrupt publishing for research communities where preprints are in the process of being adopted or already normalised. However, I am less convinced that such disruption is occurring, or could occur, for scholarly publishing more generally.

I am particularly concerned about the casual conflation of “research” and “scientific research” in this article. Right from the start, it mentions how preprints allow sharing “new scientific findings” in the abstract, stating they “make scientific work available rapidly.” It also notes that preprints enable “scientific work to be accessed in a timely way not only by scientists, but also…” This framing implies that all “scholarly communication,” as mentioned in the title, is synonymous with “scientific communication.” Such language excludes researchers who do not typically identify their work as “scientific” research. Another example of this conflation appears in the caption for Figure 1, which outlines potential benefits of preprint review services. Here, “users” are defined as “scientists, policymakers, journalists, and citizens in general.” But what about researchers and scholars who do not see themselves as “scientists”?

Similarly, the authors describe the 23 preprint review services using six categories, one of which is “scientific discipline”. One of those disciplines is called “humanities” in the text, and Table 1 lists it as a discipline for Science Open Reviewed. Do the authors consider “humanities” to be a “scientific” discipline? If so, I think that needs to be justified with very strong evidence.

Additionally, Waltman et al.’s four schools of thought for peer review reform works well with the 23 services analysed. However, at least three out of the four are explicitly described as improving “scientific” research.

Related to the above are how the five “scientific disciplines” are described as the “usual organisation” of the scholarly communication landscape. On what basis should they be considered “usual”? In this formulation, research in literature, history, music, philosophy, and many other subjects would all be lumped together into the “humanities”, which sit at the same hierarchical level as “biomedical and life sciences”, arguably a much more specific discipline. My point is not to argue for a specific organisation of research disciplines, but to highlight a key epistemic assumption underlying the whole paper that comes across as very STEM-centric (science, technology, engineering, and math).

How might this part of the methodology affect the categories presented in Table 1? “Biomedical and life sciences” appear to be overrepresented compared to other “disciplines”. I’d like to see a discussion that examines this pattern, and considers why preprint review services (or maybe even preprints more generally) appear to cover mostly the biomedical or physical sciences.

In addition, there are 12 services described as serving “all disciplines”. I believe this paper can be improved by at least a qualitative assessment of the diversity of disciplines actually represented on those services. Because it is reported that many of these service stress improving the “reproducibility” of research, I suspect most of them serve disciplines which rely on experimental science.

I randomly selected five services for closer examination, as mentioned above. Of those, only Qeios has demonstrated an attempt to at least split “arts and humanities” into subfields. The others either don’t have such categories altogether, or have a clear focus on a few disciplines (e.g. life sciences for Hypothesis/TRiP). In all cases I studied, there is a heavy focus on STEM subjects, especially biology or medical research. However, they are all categorised by the authors as serving “all disciplines”.

If preprint review services originate from, or mostly serve, a narrow range of STEM disciplines (especially experiment-based ones), it would be worth examining why that is the case, and whether preprints and reviews of them could (or could not) serve other disciplines and epistemologies.

It is postulated that preprint review services might “disrupt the scholarly communication landscape in a more radical way”. Considering the problematic language I observed, what about fields of research where peer-reviewed journal publications are not the primary form of communication? Would preprint review services disrupt their scholarly communications?

To be clear, my concern is not just the conflation of language in a linguistic sense but rather inequitable epistemic power. I worry that this conflation would (a) exclude, minoritise, and alienate researchers of diverse disciplines from engaging with metaresearch; and (b) blind us from a clear pattern in these 23 services, that is their strong focus on the life sciences and medical research and a discussion of why that might be the case. Critically, what message are we sending to, for example, a researcher of 18th century French poetry with the language and framing of this paper? I believe the way “disciplines” are currently presented here poses a real risk of devaluing and minoritising certain subject areas and ways of knowing. In its current form, I believe that while this paper is a very valuable contribution, one should not derive from it any conclusions which apply to scholarly publishing as a whole.

The authors have demonstrated inclusive language elsewhere. For example, they have consciously avoided “peer” when discussing preprint review services, clearly contrasting them to “journal-based peer review”. Therefore, I respectfully suggest that similar sensitivity be adopted to avoid treating “scientific research” and “research” as the same thing. A discussion, or reference to existing works, on the disciplinary skew of preprints (and reviews of them) would also add to the intellectual rigour of this already excellent piece.

Overall, I believe this paper is a valuable reflection on the state of preprints and services which review them. Addressing the points I raised, especially the use of more inclusive language with regards to disciplinary diversity, would further elevate its usefulness in the metaresearch discourse. Thank you again for the chance to review.

Signed:

Dr Pen-Yuan Hsing (ORCID ID: 0000-0002-5394-879X)

University of Bristol, United Kingdom

Data availability

I have checked the associated dataset, but still suggest including hyperlinks to the 23 services analysed in the main text of this paper.

Competing interests

No competing interests are declared by me as reviewer.

Henriques, S. O., Rzayeva, N., Pinfield, S., & Waltman, L. (2023, October 13). Preprint review services: Disrupting the scholarly communication landscape?. https://doi.org/10.31235/osf.io/8c6xm

Aug 11, 2024

Nov 20, 2024

Nov 20, 2024

Authors:

• Susana Henriques (Research on Research Institute (RoRI) Centre for Science and Technology Studies (CWTS), Leiden University, Leiden, the Netherlands Scientific Research Department, Azerbaijan University of Architecture and Construction, Baku, Azerbaijan) s.oliveira@cwts.leidenuniv.nl
• Narmin Rzayeva (Research on Research Institute (RoRI) Information School, University of Sheffield, Sheffield, UK) n.rzayeva@cwts.leidenuniv.nl
• Stephen Pinfield (Research on Research Institute (RoRI) Centre for Science and Technology Studies (CWTS), Leiden University, Leiden, the Netherlands) s.pinfield@sheffield.ac.uk
• Ludo Waltman waltmanlr@cwts.leidenuniv.nl

7

10.31235/osf.io/8c6xm

Preprint review services: Disrupting the scholarly communication landscape

Explanation:

The annotated text, which is a URL (https://errors.edgesuite.net/18.aa2d3e17.1733239827.ea4e98df), appears to be a reference link that leads to an error page. This indicates that the link provided in the original text is either broken or incorrect.

Given the user question, which asks for an annotation of a Spanish bill with key provisions and related bills, the broken link is significant for several reasons:

1. Access to Information: The broken link prevents access to the actual content of the Spanish bill that the user is interested in. This is crucial because without access to the original document, it is impossible to analyze or annotate the key provisions or related bills.

2. Reliability of Source: The presence of a broken link raises questions about the reliability and accuracy of the source. It suggests that the provided reference might not have been verified, which is important in legal and academic contexts where accuracy is paramount.

3. User Guidance: Highlighting the broken link is essential to inform the user that the provided reference is not functional. This helps in setting the correct expectations and guides the user to seek an alternative source or correct the link if possible.

4. Implications for Research: For anyone conducting research or needing detailed information about the bill, the broken link is a significant hindrance. It implies that further steps need to be taken to locate the correct document, such as contacting the source, searching for the document through official legislative databases, or using other references that might be available.

In summary, the annotated text underscores the importance of having a functional and accurate reference link when dealing with legal documents. It highlights the need for the user to obtain the correct URL to access the Spanish bill in question, which is crucial for providing a comprehensive annotation of its key provisions and related bills.

Explanation:

The annotated text "Reference #18.aa2d3e17.1733239827.ea4e98df" appears to be a reference code or link to an external resource, likely pertaining to the Spanish bill mentioned in the user question. The significance of this annotation lies in its function as a placeholder or identifier for additional information that is not directly included in the provided text. This reference could potentially lead to a detailed document, database, or error page that contains the key provisions of the bill or outlines its relationship to other bills.

The implications of this annotation are multifaceted:

1. Identification and Retrieval: The reference code is crucial for locating the specific document or webpage that contains the relevant details about the Spanish bill. This ensures that users can access comprehensive information that may not be immediately visible in the main text.

2. Contextual Linking: By referencing an external source, the annotation suggests that the full understanding of the bill's provisions and related legislation requires consulting the linked material. This highlights the interconnected nature of legal documents and the importance of cross-referencing for thorough legal analysis.

3. Potential Error: The URL provided in the reference indicates an "errors.edgesuite.net" domain, which might imply that the link is broken or leads to an error page. This could signify issues with accessing the necessary information, suggesting the need for alternative methods to obtain the details about the bill.

In summary, the annotated text serves as a critical reference point for accessing detailed information regarding the Spanish bill. Its significance is rooted in its role as a connector to external resources, which are essential for a full understanding of the bill's key provisions and its relationship to other legislation.

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