Das sagt Will Richardson auch für den Bereich der Schule so. Es muss, in der Schule noch mehr, v.a. pädagogische und didaktische Expertise in digitale Transformationen einfließen. Man läuft sonst Gefahr u.a. Konsumtendenzen nicht kritisch gegenüber treten zu können und unmündiges Verhalten an den Tag zu legen und im schlimmsten Falle zu lehren.

carefully cureated groomed

chaos of link dumps

you start with a door way before entering a maze

wow

!- pearl : tools for thinking, together

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Reply to the reviewers

Reviewer #1 (Evidence, reproducibility and clarity (Required)): **Summary:** Techniques to probe the local environment of membrane proteins are sparse, although the influence of lipids on the membrane protein's function are known since many years. Therefore, the paper by Umebayashi et al. is important. The environment-sensitive dye Nile red (NR) coupled to a membrane protein is an appropriate sensor for monitoring the local membrane fluidity. Linking of Nile red to the receptor via a flexible tether was achieved with the acyl carrier protein (ACP)-tag method. Experiments showed that depending on the ACP site a certain linker length is required to have NR inserted in the membrane and thus be an effective sensor for lipid disorder. This technology could be of general usability to study the environment of membrane proteins in the context of their function. As an example, the technique allowed insulin induced membrane disorder in the close insulin receptor vicinity to be observed. Further, results suggested that tyrosine activity is required for this disorder to happen. The experimental results appear to be complete and controls were made.

**Major comments:** 1) Sometimes technical terms are used without explanation: What is the GP value? What is ACP-IR? The spectrum was measured in number of rois? The reader can find those abbreveations out, but it would be nice to have them defined.

We have made a list of abbreviations.

2) Fig. 1d) is confusing. The ACP-IR labelling is evident in 3 panels, but there is no difference in the color (emission spectra of 1992-ACP-IR vs 2031-ACP-IR should be visible??). The DAPI staining is very different. When doing the latter, how difficult is it to get the staining equal?

The differences in spectra cannot be seen because we used pseudo colors for display of the DAPI and CoA-PEG-NR staining. The reviewer’s comments about the unequal DAPI staining is correct. The reason for this is most likely that the cell membrane is unequally permeabilized by PFA treatment. As the point of this figure is just to show that the plasma membrane is labeled, dependent upon the expression of the ACP-tagged insulin receptor, we don’t think that the variable intensities of the DAPI staining is important. DAPI is simply used to indicate the position of the cells.

3) How can one interpret Fig. 4: a) Control goes over 4 frames, at 240" insulin is added, and 10 frames should show a fluctuation difference?

We showed 4 frames after control treatment that showed no significant change was observed by control treatment. We expected that clear changes would be invoked by insulin treatment in GP images, however these changes, while visible in the GP images, are difficult to see for the untrained observer. This is the reason why we used the ZNCC method in the subsequent figures to better visualize the changes.

1. b) A color shift from blue to green is visible after insulin addition. But it is faint - difficult to assess from the pseudo color scheme. What does 1000 pixel top/1000 pixel bottom mean in c). Is it an attempt to better visualize the fluctuation? It is difficult to recognize a difference before and after adding insulin. d) It seems that the kymograph set should show this. What is the color scale? Why is 3 so untypical, i.e., no change? Box 6 is also peculiar: the left side does not show a strong change upon insulin administration, the right side does. Why? We appreciate the helpful comments for improving our manuscript.

As pointed out, the change of GP value is extremely small before and after insulin addition, so it is difficult to fully visualize the change with normal pseudo-color expression. To deal with this, we adopted the following two methods to visualize minute changes.

1) Visualization of local changes of the statistical GP value showed by ZNCC throughout the time-lapse images (Fig. 6 and Fig. S2B).

2) Visualization of the top/bottom 1000 pixels of the sorting ZNCC value in each image (Fig. 7 and Fig. S2C). The top 1000 pixels are the ones that showed the largest changes. The bottom 1000 pixels are the ones that showed the smallest changes.

Owing to these expressions, we found out that the level of the response against the insulin signal was spatially and temporally heterogeneous in the membrane.

As for the color scale, in order to clarify the meaning of the difference of color, we have added the description about the relationship between the color and the ZNCC value in the results section.

4) How is the kymogram calculated? The legend says 'The horizontal dimension represents the averaged ZNCC inside the rectangular area, and the vertical dimension represents time'. The averaged ZNCC is a single value, so it is not clear why the kymogram shows a variation from left to right. May it be the ZNCC was averaged just vertically?

We apologize that we did not provide information regarding making the kymograph.

In the yellow rectangular area (Fig. 6B), the ZNCC values of the pixels with the same x coordinate value were vertically averaged, which were represented as the horizontal direction of the kymograph. That is, one horizontal line of the kymograph holds the spatial distribution of the ZNCC value along the horizontal direction of the membrane, and the vertical direction shows their time changes. To make it easier to understand, we refined the description about the kymograph in the legend of Fig. 6.

5) When calculating cross-correlation values on images, they need to be aligned. What fraction of the total image does the selected 19x19 box represent? As described, I imagine that a rolling CC over 19x19 pixels is calculated over an image from the time lapse series comparing it with the reference Iave(x,y). Compared to the 3x3 median filtered CP image, the ZNCC image should then be much more blurred??

Below we provide more information regarding the calculation of ZNCC.

Each local window for ZNCC calculation is set to a 19x19 pixels centered on every single pixel excluding the edges of an image. The ZNCC value calculated in that window is set to a center pixel of that area. After that, a new window centered on the adjacent pixel is set and calculate the new ZNCC. That is, the calculation window is slid throughout the image. Also, the calculated ZNCC value is not set to all the pixels of the window, but is set to only the center pixel of the window, so there is no blur effect like median filtering.

The figure below shows a schematic view of our ZNCC calculation.

Schematic view of our ZNCC calculation

**Minor comment:** On page 16 supplementary is not spelled properly.

corrected

Reviewer #1 (Significance (Required)):

The key point of this paper is convincing and the new technology appears to have a lot of potential. It can be applied to study membrane protein function in the context of its environment, the lipid bilayer.

Membrane fluidity measurements have been developed (e.g., using fluorescent probes like laurdan). However, the trick to link a probe like nile red by ACP technology to the insulin receptor and to observe its activity is quite new.

A most recent description of such a technology is in TrAC Trends in Analytical Chemistry Volume 133, December 2020, 116092.

This is an interesting review, but not directly impacting on our work.

**Referees cross-commenting**

All comments are constructive and important. The paper is important but needs to be amended as proposed.

Reviewer #2 (Evidence, reproducibility and clarity (Required)): **Summary:** In this manuscript, authors generated an ACP-attached Nile Red probe in order to specifically label Insulin receptor in the membrane. Owing to this specificity, one can measure the lipid membrane properties around a specific protein in the membrane. **Major comments:**

For the conclusions in the manuscript to be convincing, in my opinion, these additional data need to be added. Some of these are new experiments, and some are detailed analysis of existing data. The new experiments are not for new line of investigation, instead it is to confirm their statements and conclusions. The major point is the reliability of spectral shift. In usual environment sensitive probes, it is certain that they are in the membrane whatever is done to the membrane. However, when the probe is attached to a protein, it is not trivial to have the same confidence that the probe is always inside the membrane, and it is in the same plane of the membrane. 1992-ACP-IR is a good example; authors state that it binds to the protein outside the membrane, but when there is cholesterol addition and -maybe more interestingly- cholesterol removal, the dye still reacts and changes its emission (even PreCT changes its emission quite a bit at the 570 nm region). This is a clear indication of a change in localization of the probe upon some changes in the membrane. This implies that observed spectral shifts may not be due to lipid packing differences, but due to localization of the probes. For this reason, it is crucial to know where any environment sensitive probe localize in the membrane with respect to membrane normal, and this knowledge is more important for this probe. Related to this, the spectral difference upon insulin treatment and activation of insulin receptor could be due to changes in probe's localization in the membrane. Especially because authors show in Fig1e, the spectra can change depending on the probe localization. Relatedly, quantum yield of NR should be significantly different when it is inside vs outside membrane. Authors should show QY for 1992-ACP-NR and 2031-ACP-NR with different PEG lengths and upon insulin treatment.

We understand the logic of the request to measure the QY, since the QY of Nile red is much higher in organic solvents than in aqueous solutions, so it might be predicted that the QY of Nile red is higher in a lipid bilayer than when covalently bound to the protein in an aqueous environment. However, this argument depends upon the mechanism for the increase in quantum yield when going from aqueous to a non-polar solution. One possible explanation is based on the intrinsic properties of the dye under the two conditions. The alternative explanation would be that the dye would aggregate (be insoluble) in aqueous solution and therefore either not fluoresce or self-quench. In this case, we believe that the latter is the explanation because we and others have previously shown the turn-on properties of the probe when binding to proteins (SNAP-tag and others). It is not simple to measure QY in the cell under a microscope, but we have done something similar shown in supplementary figure 4. We labeled the three ACP-receptor complexes with PEG11-Nile red and co-stained with antibody to the Insulin Receptor. We then calculated a relative quantum yield. There were very little differences at all between the relative quantum yields, so we conclude that it is not the environment of the probe, which affects the quantum yield under these conditions, but the fact that it is covalently attached to a protein and incapable of forming aggregates. What distinguishes these constructs is the emission spectrum, not the quantum yield. In supplementary Table 2 we also did QY measurements in vitro and we could reproduce the increase of quantum yield by association with liposomes or in organic solvents. We tested whether non-covalent association with a protein would increase the QY by incubation with the lipid binding protein, BSA, in PBS. This was not the case, strongly pointing to the conclusion that it is the covalent association with the protein that increases the QY, not association with a protein. We believe that our demonstration of changes in fluorescent spectra with changes in cholesterol, large changes in fluorescent spectra with linker length for the 1992 construct and voltage sensitivity using patch-clamp prove that the Nile red is reporting on the membrane environment under the conditions we propose.

**Minor comments:** - Fig 1d requires quantification We do not agree on this. This is simply to show that the labeling is dependent upon expression of the relevant ACP-IR constructs. There is no detectable labeling of the control.

• Voltage sensitivity of different PEG length of 2031-ACP probe should be added. We have added this data in figure 2 panel E.

• Fig 3a graph should show all data points, not only bar graphs. Also, the band in 3a for +CoA-PEG-NR is dimmer than other bands, is it specific to this particular gel since quantification does not show any difference?

There is no significant difference- Fig 4d, colour code is needed.

Done

• Fig 5b and Fig3d are basically the same experiments in terms of control measurement, why is the difference in 3b is 0.04 GP unit while it is 0.007 GP unit?

We explain in the MS, but have improved the title of Y-axis in Fig.5 b graph so that the difference in what is plotted is clear. - Why is inhibitor data so noisy? We should discuss.

We don’t know the exact reason why inhibitor data is noisy, but we speculate that the actin cytoskeleton and phosphoinositide-dependent signaling could affect the membrane stability, and the membrane environment would be fluctuated in the presence of latrunculin B or PI3K inhibitor.

Reviewer #2 (Significance (Required)): Overall, this is a very useful approach, and this line of research will yield very useful tools to shed light on how lipids surrounding proteins can change their function. Major advance of the paper is the new chemical biology tool. There is also biological data on how insulin can change the insulin receptor's membrane environment which is contradictory to some old literature claiming that InsR becomes more "rafty" upon insulin treatment (e.g., PMID: 11751579).

If this type of tagging proves robust and reproducible (limitations and concerns listed above and below), it could be used by other researchers to tag their protein of interest and investigate the lipid environment around those proteins.

The downside of this method is that the probe requires ACP tag, a relatively less used tag than others in biology, therefore researchers interested in using this probe should have their proteins with ACP tag. Moreover, the linker length and ACP-tag position are quite crucial parameters (and probably should be optimized for each protein). Longer PEG lengths cannot report on changes efficiently (Fig3b), while shorter lengths are prone to artefacts as they can go out of membrane (Fig1 and Fig2). This might limit its widespread use.

The reason for using the ACP tag is that neither the SNAP tap nor the HALO tag working. The tethered Nile Red preferred to bind to the tqg rather than inserting into the membrane.

**Referees cross-commenting** I agree with all comments and concerns of other reviewers. I see the usability and potential of this new technology along with its limitations as all three reviewers pointed out.

Reviewer #3 (Evidence, reproducibility and clarity (Required)): See below. No concerns on any of these issues.

Reviewer #3 (Significance (Required)): **Critique:** This MS reports a proof-of-principle for using site-directed environmentally sensitive probe technology to assess the local membrane environment of a receptor tyrosine kinase (IR) upon activation. This technology addresses a major gap in our arsenal of tools to study the mechanisms of membrane signaling as the parameters of interest are biophysical parameters rather than purely biochemical ones. How to do this with spatial and temporal resolution is a major challenge. This study builds on previous work by the Riezman group that develops an extrinsic labeling system to tether Nile Red to specific sites on the ectodomain of a signaling receptor and then probe local membrane environments as a function of receptor activity. This is a carefully done study is well-controlled, is clever in design and is well-described. Although the major issues to which such a general technology could contribute involve intracellular (and not extracellular) event, the advances described will be of general interest -- particularly that local membrane order decreases when IR becomes activated. Specific comments for the authors' consideration follow:

**Specific Comments:** (i) As a general comment, the authors are measuring extracellular plasma membrane leaflet properties that may or may not translate to what is happening in the local inner leaflet environment. A general reader may well miss the significance of this. This point needs to be more explicitly emphasized in the Discussion.

This has been discussed in the revised version.

(ii) Why not treat cells with a PLC inhibitor to block PIP2 hydrolysis and ask if that inhibits membrane disorder. It is PIP2 hydrolysis/resynthesis that regulates the actin cytoskeleton at signaling receptors and this seems an attractive candidate for study.

There is a long list of attractive post-signaling events of the insulin receptor and how this works in different cell types that could be tested. We believe that this is beyond the scope of this study and we encourage others to do this.

(iii) The data acquisition time is at least 4 min which is long enough for activated receptors to be recruited to sites of endocytosis. Can the authors exclude the possibility that what they are measuring isn't reflective of such spatial reorganization? Does a clathrin inhibitor block the observed change in local membrane order for activated IR? We determined localization to AP2 adaptor containing clathrin coated pits at the cell surface and showed that during the time-course of the experiment that there is no significant change in co-localization or evidence for endocytosis (new figure 9). Therefore, we decided not to do the clathrin inhibitor blocking experiment because we believe that it could only lead to indirect effects.

(iv) Receptor activation is accompanied by other transitions such as dimerization, etc. Can the authors exclude the possibility that what they are measuring is related to changes in depth of insertion of the NR probe into the plasma membrane outer leaflet that is a consequence of IR conformational transitions associated with activation? This is highly unlikely given the fact that fluidification of the membrane environment is found with all length linkers. Given the intervals in increases in linker length on the 2031 construct, which is the closest to the membrane, it is very difficult to conceive that any of the ones larger than 5 PEGs restrict significantly the membrane insertion of the dye. **Referees cross-commenting**

I think we have a consensus opinion

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Referee #2

Evidence, reproducibility and clarity

Summary:

In this manuscript, authors generated an ACP-attached Nile Red probe in order to specifically label Insulin receptor in the membrane. Owing to this specificity, one can measure the lipid membrane properties around a specific protein in the membrane.

Major comments:

For the conclusions in the manuscript to be convincing, in my opinion, these additional data need to be added. Some of these are new experiments, and some are detailed analysis of existing data. The new experiments are not for new line of investigation, instead it is to confirm their statements and conclusions. The major point is the reliability of spectral shift. In usual environment sensitive probes, it is certain that they are in the membrane whatever is done to the membrane. However, when the probe is attached to a protein, it is not trivial to have the same confidence that the probe is always inside the membrane, and it is in the same plane of the membrane. 1992-ACP-IR is a good example; authors state that it binds to the protein outside the membrane, but when there is cholesterol addition and -maybe more interestingly- cholesterol removal, the dye still reacts and changes its emission (even PreCT changes its emission quite a bit at the 570 nm region). This is a clear indication of a change in localization of the probe upon some changes in the membrane. This implies that observed spectral shifts may not be due to lipid packing differences, but due to localization of the probes. For this reason, it is crucial to know where any environment sensitive probe localize in the membrane with respect to membrane normal, and this knowledge is more important for this probe. Related to this, the spectral difference upon insulin treatment and activation of insulin receptor could be due to changes in probe's localization in the membrane. Especially because authors show in Fig1e, the spectra can change depending on the probe localization. Relatedly, quantum yield of NR should be significantly different when it is inside vs outside membrane. Authors should show QY for 1992-ACP-NR and 2031-ACP-NR with different PEG lengths and upon insulin treatment.

Minor comments:

• Fig 1d requires quantification
• Voltage sensitivity of different PEG length of 2031-ACP probe should be added.
• Fig 3a graph should show all data points, not only bar graphs. Also, the band in 3a for +CoA-PEG-NR is dimmer than other bands, is it specific to this particular gel since quantification does not show any difference?
• Fig 4d, colour code is needed.
• Fig 5b and Fig3d are basically the same experiments in terms of control measurement, why is the difference in 3b is 0.04 GP unit while it is 0.007 GP unit?
• Why is inhibitor data so noisy?

Significance

Overall, this is a very useful approach, and this line of research will yield very useful tools to shed light on how lipids surrounding proteins can change their function. Major advance of the paper is the new chemical biology tool. There is also biological data on how insulin can change the insulin receptor's membrane environment which is contradictory to some old literature claiming that InsR becomes more "rafty" upon insulin treatment (e.g., PMID: 11751579).

If this type of tagging proves robust and reproducible (limitations and concerns listed above and below), it could be used by other researchers to tag their protein of interest and investigate the lipid environment around those proteins.

The downside of this method is that the probe requires ACP tag, a relatively less used tag than others in biology, therefore researchers interested in using this probe should have their proteins with ACP tag. Moreover, the linker length and ACP-tag position are quite crucial parameters (and probably should be optimized for each protein). Longer PEG lengths cannot report on changes efficiently (Fig3b), while shorter lengths are prone to artefacts as they can go out of membrane (Fig1 and Fig2). This might limit its widespread use.

Referees cross-commenting

I agree with all comments and concerns of other reviewers. I see the usability and potential of this new technology along with its limitations as all three reviewers pointed out.

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---

Referee #1

Evidence, reproducibility and clarity

Summary:

Techniques to probe the local environment of membrane proteins are sparse, although the influence of lipids on the membrane protein's function are known since many years. Therefore, the paper by Umebayashi et al. is important. The environment-sensitive dye Nile red (NR) coupled to a membrane protein is an appropriate sensor for monitoring the local membrane fluidity. Linking of Nile red to the receptor via a flexible tether was achieved with the acyl carrier protein (ACP)-tag method. Experiments showed that depending on the ACP site a certain linker length is required to have NR inserted in the membrane and thus be an effective sensor for lipid disorder. This technology could be of general usability to study the environment of membrane proteins in the context of their function. As an example, the technique allowed insulin induced membrane disorder in the close insulin receptor vicinity to be observed. Further, results suggested that tyrosine activity is required for this disorder to happen. The experimental results appear to be complete and controls were made.

Major comments:

1) Sometimes technical terms are used without explanation: What is the GP value? What is ACP-IR? The spectrum was measured in number of rois? The reader can find those abbreveations out, but it would be nice to have them defined.

2) Fig. 1d) is confusing. The ACP-IR labelling is evident in 3 panels, but there is no difference in the color (emission spectra of 1992-ACP-IR vs 2031-ACP-IR should be visible??). The DAPI staining is very different. When doing the latter, how difficult is it to get the staining equal?

3) How can one interpret Fig. 4: a) Control goes over 4 frames, at 240" insulin is added, and 10 frames should show a fluctuation difference? b) A color shift from blue to green is visible after insulin addition. But it is faint - difficult to assess from the pseudo color scheme. What does 1000 pixel top/1000 pixel bottom mean in c). Is it an attempt to better visualize the fluctuation? It is difficult to recognize a difference before and after adding insulin. d) It seems that the kymograph set should show this. What is the color scale? Why is 3 so untypical, i.e., no change? Box 6 is also peculiar: the left side does not show a strong change upon insulin administration, the right side does. Why?

4) How is the kymogram calculated? The legend says 'The horizontal dimension represents the averaged ZNCC inside the rectangular area, and the vertical dimension represents time'. The averaged ZNCC is a single value, so it is not clear why the kymogram shows a variation from left to right. May it be the ZNCC was averaged just vertically?

5) When calculating cross-correlation values on images, they need to be aligned. What fraction of the total image does the selected 19x19 box represent? As described, I imagine that a rolling CC over 19x19 pixels is calculated over an image from the time lapse series comparing it with the reference Iave(x,y). Compared to the 3x3 median filtered CP image, the ZNCC image should then be much more blurred??

Minor comment:

On page 16 supplementary is not spelled properly.

Significance

The key point of this paper is convincing and the new technology appears to have a lot of potential. It can be applied to study membrane protein function in the context of its environment, the lipid bilayer.

Membrane fluidity measurements have been developed (e.g., using fluorescent probes like laurdan). However, the trick to link a probe like nile red by ACP technology to the insulin receptor and to observe its activity is quite new.

A most recent description of such a technology is in TrAC Trends in Analytical Chemistry Volume 133, December 2020, 116092.

Referees cross-commenting

All comments are constructive and important. The paper is important but needs to be amended as proposed.

Author Response

Reviewer #2 (Public Review):

-Were there any post-translational modifications (phosphorylation etc) or endogenous lipids that need to be quantified to make sense of the data?

A percentage of receptors could be phosphorylated; therefore, our results represent the average behavior of the population. This is a noteworthy point and we have now explicitly discussed this idea in the revised the manuscript.

In the in vivo experiments, heterogeneity in PTMs or local lipid environment of receptors could affect conformational change at the individual receptor level. For our analysis we integrate the intensities over the whole cell membrane, so the results represent the average behavior. Likewise, in the single-molecule FRET experiments many individual receptors are included in the analysis. Additionally, since the receptors are purified in the in vitro experiments, there is no further change in PTMs with application of drugs. We have added a sentence in the discussion to highlight the potential heterogeneity in PTMs and local lipid environment. We have also added a sentence to the methods to clarify how in vivo experiments are analyzed.

Added to line 512 in discussion section: “Potential sources of heterogeneity arising from differences in post-translational modifications or differences in the local lipid environment, may affect receptor conformation. Therefore, our results represent the average of a heterogeneous population of such receptors.”

Changed line 667 to: “ROIs used for analysis included the whole cell membrane for individual cells.”

-mGLUR2 is a dimer. I was expecting that at 15 uM of Glutamate, for example, one might see effects of a single protomer-bound receptor. If I'm not mistaken, some class C receptors don't activate their CRDs until both ligand binding sites in the VFT are bound. Looking at all of the profiles in the VFT, CRD, and 7TM, I don't see any evidence of the 2-site binding of glutamate at the VFT. Presumably, there are Hill slopes for all of these profiles?

Based on our previous work with the wildtype and with the receptor containing one glutamate binding deficient monomer, and available structures, indeed CRD domains do not significantly visit the active state unless both VFT domains are bound to glutamate and in the closed conformation. However, because activations involve progression through 2 intermediate states, we still expect to see FRET change even when both VFT domains are not occupied simultaneously. We have now revised Table 1 to included Hill slope. This data shows that cooperativity is generally observed for the FRET sensors for all the ligands tested.

Reviewer #3 (Public Review):

-The main concerns I had were with respect to labelling stoichiometry of the mixed Cy3/Cy5 compounds or SNAP-tag labels. How was this controlled? Clearly, both label cells, as shown in supplemental data and the single molecule FRET data support that both sites are labelled. Are there any concerns about larger molecular complexes such as oligomers that may confound the simple interpretation of interactions between the dimers?

Among class C GPCRs, only GABA receptors have been shown to be able to potentially form efficient oligomers. Subunit counting experiments have shown that mGluR2 is predominantly dimer (> 90%) on the plasma membrane for the experimental conditions used in this manuscript (Levitz et al., 2016). The same result was obtained from live-cell FRET utilizing a dimer trafficking-control system (Maurel et al., 2008). This work also demonstrated that FRET occurred strictly for dimeric receptors labeled by both donor and acceptor fluorophores and not between neighboring receptors at the plasma membrane. Thus, receptors labeled with donor-only or acceptor-only do not contribute to the relative ΔFRET signal in response to treatment.

-Some additional context might be a discussion of approaches used and results obtained for other types of conformational biosensors for GPCRs in other classes? Can we learn anything by comparison?

We have revised the manuscript to include further discussion of results obtained from the use of other conformational sensors.

Added to line 502: “Recent experiments have shown that GPCRs are dynamic (Nygaard et al., 2013) and undergo transition between multiple conformational states, including multiple intermediate states. For class A GPCRs, studies using conformational biosensors based on nuclear magnetic resonance (NMR) spectroscopy (Huang et al., 2021), double electron-electron resonance (DEER) spectroscopy (Wingler et al., 2019), smFRET (Gregorio et al., 2017), and fluorescent enhancement (Wei et al., 2022) have revealed the importance of conformational dynamics for receptor activation, ligand efficacy, and biased signaling.”

Added to line 536: “Interestingly, the regulation of intermediate state occupancy has recently been shown to be a mechanism of allosteric modulation for other classes of GPCRs as well. NMR studies on the μ-opioid receptor (Kaneko et al., 2022) and cannabinoid receptor 1 (Wang et al., 2021) revealed that PAMs and NAMs regulate receptor function by acting on intermediate conformations in a manner similar to our findings for BINA and MNI-137. Collectively, these results suggest that designing compounds that regulate intermediate state occupancy is a plausible strategy for the development of allosteric modulators for mGluR2 and other families of GPCRs.”

Reviewer #3 (Public Review):

The authors used a combination of site-specific labelling at distinct sites within the mGluR2- the VFT domain in the ligand binding site, ECL2 (newly developed here), and the cysteine-rich domain (CRD) the latter of which is located between the VFT and ECL2. Using live cell FRET based on SNAP-tagged or unnatural amino acids, site-labeled with Cy3 or Cy5 tags, they validate that orthosteric ligands generate FRET changes consistent with their know efficacies and potencies, validating them for use in studying the effects of allosteric modulators. They next use single-molecule FRET to study the effects of the allosteric modulators on the receptor in the presence or absence of the orthosteric ligand, glutamate.

Major strengths include the careful design, conduct, and analysis of the experiments and the validation of the effects of orthosteric ligands alone before proceeding to measurements of allosteric effects. They produce some very interesting results with the allosteric modulators in both experimental formats - the whole cell FRET consistent with known allosteric effects and the single molecule FRET identifying some independent effects of the allosteric modulators - this was quite striking. The approach is scalable to other GPCRs and to other membrane proteins in general.

The main concerns I had were with respect to labelling stoichiometry of the mixed Cy3/Cy5 compounds or SNAP-tag labels. How was this controlled? Clearly, both label cells, as shown in supplemental data and the single molecule FRET data support that both sites are labelled. Are there any concerns about larger molecular complexes such as oligomers that may confound the simple interpretation of interactions between the dimers?

Some additional context might be a discussion of approaches used and results obtained for other types of conformational biosensors for GPCRs in other classes? Can we learn anything by comparison?

Reviewer #3 (Public Review):

In this manuscript Houy and coworkers report new experiments regarding the role of phorbolester-activated Munc13 paralogs, Munc13-1 and ubMunc13-2, on the secretion response of mouse chromaffin cells. They report that expression of either paralog enhanced secretion. Using single knock outs (Figs. 1, 2) or with the expression of either paralog (Figs 3, 4) they found that treatment with the phorbolester PMA was stimulatory when ubMunc13-2 was the predominating paralog, but inhibitory when Munc13-1 dominated. The opposing PMA effects in the presence of either Munc13-1 or ubMunc13-2 were interpreted in the context of a potential competition of both proteins in essential priming reactions (Fig. 5). In simultaneous fluorescence recordings of EGFP tagged Munc13 variants they studied the Ca2+- and PMA-dependent translocation of Munc13 to the plasma membrane (PM). They found that only Munc13-2 (Fig. 3) but not Munc13-1 (Figs. 4, 5) is translocated to the PM in response an intracellular Ca2+-elevation. In this context, they also report that Ca2+ -dependent recruitment of ubMunc13-2 is independent of Synaptotagmin-7 (Fig. 6) and that in the absence of Synaptotagmin-7, ubMunc13-2-dependent secretion is inhibited by PMA (Fig. 7). Based on these results the authors argue that ubMunc13-2, Synaptotagmin-7 and DAG/phorbolester form a stimulatory entity to facilitate dense core vesicle fusion.<br /> Although the manuscript presents interesting observations, some conclusions appear to be compromised by methodological and conceptual concerns.

Major criticism<br /> 1. In order to track Munc13 translocation the authors have chosen EGFP-tagged variants which overlap in the emission with the standard FuraII/Furaptra emission. Consequently, the authors omitted Ca2+-imaging in these experiments and thereby lost crucial information regarding the development of [Ca]I before and after the uncaging flash. These parameters are of central importance for the Ca2+-dependent priming and exocytosis timing, respectively. This is particularly worrisome, because in several experiments with Munc13 expression hardly any RRP component is apparent in the displayed capacitance traces, which may indicate insufficient Ca2+-dependent vesicle priming (Fig. 4). Under proper calcium control, both Ashery et al 2000 (Fig. 2) and Betz et al 2001 (Fig. 6) reported that Munc13-1 overexpression in wt chromaffin cells causes at least a 300% increase in the size of the EB compared to wt cells. Performing the same experiment, but without calcium imaging, the authors in Fig4-Sup1 show hardly any increase in the size of the EB (violet trace Fig4-Sup1) but a rather strong increase in the sustained phase of exocytosis, a phenotype that could be a result of low intracellular pre-flash calcium levels leading to insufficient vesicle priming. I do not understand why the authors have not chosen any other red-shifted protein tag to prevent such uncertainties. Furthermore, the display of the capacitance traces in several figures does not allow the appreciation of changes in the EB size or its components (e.g. RRP).<br /> 2. The authors speculate about the possibility, that PMA treatment PMA-treatment of Unc13b KO cells may lead to spontaneous release, depleting the cells of secretory vesicles. To test this, they determined the integrated CgA-fluorescence over the entire cell (Fig. 1M, N) rather than analyzing submembrane CgA-fluorescence. With the latter strategy, they will be able to focus on a potential subcellular depletion of release-ready vesicles.<br /> 3. After showing a detailed analysis of the exocytotic burst components and their kinetics in Fig. 1 and 2 the authors argue on page 9 Line 275 'Since the measurements above indicated that the main effect of PMA is on secretion amplitude, not kinetics (see also (Nagy et al., 2006)), we only distinguished between burst secretion (first 1s secretion after Ca2+ uncaging, corresponding approximately to RRP and SRP fusion) and sustained secretion (last 4 s of secretion), as well as total secretion (the sum of burst and sustained release). '<br /> I have some concerns with this argumentation because the expression of Munc13 paralogs apparently leads to changes in the burst components and/or it kinetics (e.g. Fig. 4B compare to Fig. 1 or 2). In fact, these differences cannot be directly appreciated, because experiments like in Fig. 3 and 4 lack the littermate wt control without and with PMA.<br /> Moreover, Munc13 expression leads to a disproportionate increase in the sustained phase of release, which is not present with PMA.<br /> I would recommend at least to include detailed analyses of the exocytotic burst components and their kinetics to address these uncertainties.

4. As central hypothesis, the authors propose that they have identified a unique stimulatory triad of ubMunc13-2, Syt7 and DAG/phorbolesters, which is needed for dense core vesicle priming and fusion. For example, in contrast to the behavior of wt cells (e.g. Fig 1A) phorbolester treatment becomes inhibitory in cells lacking Syt7 and expressing ubMunc13-2 (Fig. 7). Nonetheless, previously published data by Sorensen's group, obtained under similar preflash [Ca]I conditions (Tawfik et al., 2021; Fig 6-figure supplement 2 E-H), clearly show that PMA strongly potentiates exocytosis even in the absence of Syt7. Therefore, these previous findings by Tawfik et al. clearly counter the central hypothesis of the manuscript. The authors should clarify these disparate results.

Author Response:

Reviewer #1 (Public Review):

The manuscript by Kanca et al. presents a variety of valuable resources for the use of the Drosophila research community. As an update to the ongoing work of the Drosophila Gene Disruption Project, it includes hundreds of new transgenic fly lines each of which simultaneously knocks out a targeted gene and generates a driver that expresses the Gal4 transcription factor specifically in the pattern of that gene. The "KozakGal4" approach described supplements previous approaches of the GDP, including the powerful "CRIMIC" method, which inserts a synthetic exon containing a T2AGal4 module into an intron of the targeted gene. In the KozakGal4 method, the coding sequence of the native gene is completely replaced by Gal4, which the authors point out will allow them to target genes lacking (suitable) introns. In the KozakGal4 method, gene replacement is accomplished by targeted excision of the native gene using CRISPR-based technology and subsequent incorporation of a Gal4-encoding cassette by homologous recombination. The vectors developed by the authors to effect gene replacement are elegantly optimized to include all components necessary for native gene excision and efficient recombination of Gal4. These components include the guide RNAS (sgRNAs) that cleave flanking regions of the native gene, an sgRNA that liberates the Gal4 cassette from the vector, and short synthetic homology arms that provide effective, site-specific recombination. Importantly, the vectors are designed so that all gene-specific components can be synthesized in a single fragment that can be readily incorporated into the vector backbone followed by insertion of the Gal4 cassette.

Overall, the technical advances described in the manuscript are impressive and the utility of the method is well demonstrated. The one exception is in the validation of Gal4 expression fidelity. As the authors note, fidelity could be compromised if regulatory information is removed along with sequences in and around a targeted gene. In addition, the introduction of new DNA at a particular locus may alter the regulation of gene expression. In any case, establishing the fidelity of expression of KozakGal4 lines is important and the data presented on this point is both confusing and incomplete. Rather than directly comparing the expression of selected KozakGal4 lines against the expression of the endogenous gene (e.g. by immunostaining, in situ hybridization, or by comparing tissue-specific reporter expression against expression in microarray-derived datasets such as Fly Atlas or modEncode), the authors use two indirect methods to demonstrate fidelity. One method uses VNC scRNAseq data together with the expression patterns of T2AGal4 lines that target genes co-expressed (at least in certain cell types) with the KozakGal4 line, while the other method uses phenotypic rescue by driving UAS-cDNA transgenes. The demonstrations are at best suggestive, and the rescue results presented are minimal, with no description of phenotypes, methods used to assay them, or quantification of rescue. There is thus insufficient information to form a judgment about fidelity and a more direct demonstration is needed.

We appreciate that the manuscript can be strengthened by adding supporting evidence about the fidelity of GAL4 expression to the expression pattern of the targeted gene. The direct comparison of the GAL4 expression pattern to the expression pattern of the gene is a complex issue. The seemingly straightforward experiments of comparing the GAL4‐UAS reporter fluorescent protein expression pattern to the antibody staining of the targeted gene product suffers from multiple technical and practical issues: 1) Majority of the genes that we targeted are understudied and do not have a readily available antibody that would work for immunostaining. 2)Even if the antibodies were available, and even if the antibodies were completely specific, the staining pattern would likely be different from the GAL4‐UAS reporter expression pattern due to the subcellular localization of the gene product differing from the subcellular localization of the reporter. 3) GAL4‐UAS system introduces very high level of amplification of the signal compared to the expression of the gene product. We have reported the extent of this difference in the Lee et al. 2018 eLife paper where we used RMCE to convert the same MiMIC lines to EGFP protein trap alleles or T2AGAL4 gene trap alleles. The signals that we could detect in larval or adult brains looked qualitatively different. Comparing the expression pattern of the targeted genes product to the KozakGAL4‐UAS reporter gene signal would suffer from the same issue.<br /> To overcome these issues, we decided to compare GAL4 mRNA expression pattern of KozakGAL4 alleles to the mRNA expression pattern of the targeted gene. We employed smiFISH (single molecule Fluorescent In‐Situ Hybridization) in 3rd instar larval brains for 8 genes. We crossed the KozakGAL4 alleles of these genes to yw flies and performed co‐staining of GAL4 mRNA and targeted genes mRNA. In 7 cases where we could detect the mRNA expression of the gene product reliably, GAL4 mRNA expression pattern was overlapping with the mRNA expression pattern of the targeted gene, suggesting the transcriptional regulation of KozakGAL4 in the locus reflects the transcriptional regulation of the targeted gene. We note that the signal to noise level is quite low for some of the in situ hybridization results. Hence, we attenuated the language about the expression patterns of KozakGAL4 alleles reflecting the expression domain of the targeted genes by adding that there is a caveat that the regulatory elements in the coding regions and UTRs would be removed in these alleles. We include the smiFISH results as a supplementary figure and we add a paragraph describing methodology to the text.

The manuscript could be strengthened in a couple of other spots as well. There is little to no description in either the Introduction or Results/Discussion of similar knock-out/knock-in approaches, although gene-specific knock-ins of Gal4 have been generated in Drosophila using homologous recombination for some time-typically into the site of ATG start codons. CRISPR technology has only facilitated this approach, which has also been used to create gene-specific cre knock-ins in rodents. This is of potential interest since the authors mention that their approach can be generalized for use in other animals. A short overview of existing knock-in approaches and their limitations relative to KozakGal4 would therefore be useful. Also, the authors motivate the need for the KozakGal4 method by asserting that over 50% of Drosophila genes lack "suitable" coding introns for the integration of artificial T2AGal4 exons such as CRIMIC. This seems to unnecessarily overstate the actual need. The authors define a "suitable" gene as one that has an intron common to all its isoforms that is at least 100 nt long. The length requirement is justified based on the need for suitable sgRNA targets within the intron, but it's possible to use sgRNA targets outside the intron (as long as the homology domains replace this sequence). Also, the requirement of a sufficiently long intron common to all isoforms is quite stringent and could be relaxed if multiple T2AGal4 lines were made to target multiple isoforms. Presumably, multiple KozakGal4 lines will, in fact, also be required for genes that have multiple transcription start sites, if the expression patterns of all isoforms are to be reproduced. In general, there's no doubt about the utility of the KozakGal4 approach, but a more balanced presentation of its merits relative to other approaches seems warranted.

We agree with the reviewers that the presence of 100 nt long coding intron in all annotated isoforms is a relatively stringent criterion for deeming a gene to be a suitable target for T2AGAL4 methods. This requirement can indeed be relaxed if the same gene is targeted with multiple T2AGAL4 alleles. Nevertheless, for the GDP project, our aim is to generate genetic reagents for as many conserved genes as possible to make them accessible to the research community. Multiple T2AGAL4 that target individual splice isoforms can be done by the laboratories that work on those genes, using the methodology that we describe in this paper. We attenuate the language about the intron length requirements and included our justification for this requirement for the GDP project in the text.

Reviewer #2 (Public Review):

In this interesting paper, Kanca and coworkers present a set of updated constructs for the replacement of gene coding regions for instance by a Gal4 expression cassette or a GFP protein trap allele, enabling multiple research applications with the generated fly strains. The novel design now allows for the CRISPR-based targeting of almost any gene in Drosophila. The authors apply these novel tools and generate hundreds of fly lines that complement the pool of already existing strains in the Drosophila Gene Disruption Project. The authors report a high success rate for their HDR-mediated gene targeting strategy and show that they can even target genes that previously proved to be difficult to engineer. The authors validate the expression patterns of a set of lines - supported even by single-cell sequencing experiments - and provide strong evidence that the updated toolkit functions as expected.

What may confuse the reader is that there are different targeting strategies that are presented with a strong focus on the validation of the expression cassettes used in combination with a specific targeting strategy (i.e., KozakGal4 or GFP protein trap). This leaves the reader with the impression that the insertion of a particular expression cassette would require a tailored targeting strategy, which is not the case. In fact, the majority of the paper deals with the description and extensive validation of small updates on already published methods for the insertion for the generation of additional KO/Gal4 or eGFP trap lines. However, neither the updated knock-in/knock-out strategies described for the insertion of the KOZAKGal4 cassette at the beginning of the results section nor the experiments to GFP tag proteins at different positions in the open reading frames (Figure 5) are of sufficient novelty and technical advancement.

What really warrants publication is the very elegant and universal method described in Figure 4 that requires only a single vector to be injected into fly embryos. The method is suited to precisely engineer any gene at will in combination with any HDR template. The very smart vector design allows for the directed insertion of custom and commercially synthesized HDR constructs as well as of a specific guide required to target and cut the gene of interest. This makes the method versatile, fast and cheaper with the benefit of being very efficient. This gRNA_int200 targeting strategy will be of broad interest, is straightforward to use and is expected to have a large impact - far beyond the fly community.

We thank the reviewer for the constructive criticism and for seeing the benefits in our methodology. Although the KozakGAL4 and GFP knock‐ins in the genome are not conceptually new, the combination of our vector design makes the application of these concepts straightforward. Additionally, the extent of application and verification of GAL4 knock‐ins was limited compared to what we include in this manuscript which prompted us to include the KozakGAL4 and GFP knock‐in methodology in this manuscript.

Reviewer #2 (Public Review):

In this interesting paper, Kanca and coworkers present a set of updated constructs for the replacement of gene coding regions for instance by a Gal4 expression cassette or a GFP protein trap allele, enabling multiple research applications with the generated fly strains. The novel design now allows for the CRISPR-based targeting of almost any gene in Drosophila. The authors apply these novel tools and generate hundreds of fly lines that complement the pool of already existing strains in the Drosophila Gene Disruption Project. The authors report a high success rate for their HDR-mediated gene targeting strategy and show that they can even target genes that previously proved to be difficult to engineer. The authors validate the expression patterns of a set of lines - supported even by single-cell sequencing experiments - and provide strong evidence that the updated toolkit functions as expected.

What may confuse the reader is that there are different targeting strategies that are presented with a strong focus on the validation of the expression cassettes used in combination with a specific targeting strategy (i.e., KozakGal4 or GFP protein trap). This leaves the reader with the impression that the insertion of a particular expression cassette would require a tailored targeting strategy, which is not the case.<br /> In fact, the majority of the paper deals with the description and extensive validation of small updates on already published methods for the insertion for the generation of additional KO/Gal4 or eGFP trap lines. However, neither the updated knock-in/knock-out strategies described for the insertion of the KOZAKGal4 cassette at the beginning of the results section nor the experiments to GFP tag proteins at different positions in the open reading frames (Figure 5) are of sufficient novelty and technical advancement.

What really warrants publication is the very elegant and universal method described in Figure 4 that requires only a single vector to be injected into fly embryos. The method is suited to precisely engineer any gene at will in combination with any HDR template. The very smart vector design allows for the directed insertion of custom and commercially synthesized HDR constructs as well as of a specific guide required to target and cut the gene of interest. This makes the method versatile, fast and cheaper with the benefit of being very efficient. This gRNA_int200 targeting strategy will be of broad interest, is straightforward to use and is expected to have a large impact - far beyond the fly community.

https://hypothes.is/search?q=tag%3A%22ny-stx212-ENG%2FA-18082021_A2a%22

https://hypothes.is/search?q=tag%3A%22ny-stx212-ENG%2FA-18082021_A2c%22

https://hypothes.is/search?q=tag%3A%22ny-stx212-ENG%2FA-18082021_A2b%22

In some cases, the end tag is not required. For example the following is valid html: <br/> <br/> <br/><hr/><img />

What is meta description? The meta description is a snippet of up to about 155 characters – a tag in HTML – which summarizes a page’s content.

What is an HTML tag?

I don't love this, of course, even as I've taped on every Indieweb accoutrement to my own site. It's so prescriptive -- like the converse of the idea that one should slice up one's own posts into neat tag-based filtered feeds for the convenience of The Consumer. Maybe I like that there's some friction in getting from my hypertexting to my more social nattering. To say it must be otherwise feels... real-name-policy-esque.

Author Response

Reviewer #1 (Public Review):

In this manuscript, Abdellatef et al. describe the reconstitution of axonemal bending using polymerized microtubules (MTs), purified outer-arm dyneins, and synthesized DNA origami. Specifically, the authors purified axonemal dyneins from Chlamydomonas flagella and combined the purified motors with MTs polymerized from purified brain tubulin. Using electron microscopy, the authors demonstrate that patches of dynein motors of the same orientation at both MT ends (i.e., with their tails bound to the same MT) result in pairs of MTs of parallel alignment, while groups of dynein motors of opposite orientation at both MT ends (i.e., with the tails of the dynein motors of both groups bound to different MTs) result in pairs of MTs with anti-parallel alignment. The authors then show that the dynein motors can slide MTs apart following photolysis of caged ATP, and using optical tweezers, demonstrate active force generation of up to ~30 pN. Finally, the authors show that pairs of anti-parallel MTs exhibit bidirectional motion on the scale of ~50-100 nm when both MTs are cross-linked using DNA origami. The findings should be of interest for the cytoskeletal cell and biophysics communities.

We thank the reviewer for these comments.

We might be misunderstanding this reviewer’s comment, but the complexes with both parallel and anti-parallel MTs had dynein molecules with their tails bound to two different MTs in most cases, as illustrated in Fig.2 – suppl.1. The two groups of dyneins produce opposing forces in a complex with parallel MTs, and majority of our complexes had parallel arrangement of the MTs. To clarify the point, we have modified the Abstract:

“Electron microscopy (EM) showed pairs of parallel MTs crossbridged by patches of regularly arranged dynein molecules bound in two different orientations depending on which of the MTs their tails bind to. The oppositely oriented dyneins are expected to produce opposing forces when the pair of MTs have the same polarity.”

Reviewer #2 (Public Review):

Motile cilia generate rhythmic beating or rotational motion to drive cells or produce extracellular fluid flow. Cilia is made of nine microtubule doublets forming a spoke-like structure and it is known that dynein motor proteins, which connects adjacent microtubule doublet, are the driving force of ciliary motion. However the molecular mechanism to generate motion is still unclear. The authors proved that a pair of microtubules stably linked by DNA-origami and driven by outer dynein arms (ODA) causes beating motion. They employed in vitro motility assay and negative stain TEM to characterize this complex. They demonstrated stable linking of microtubules and ODAs anchored on the both microtubules are essential for oscillatory motion and bending of the microtubules.

Strength

This is an interesting work, addressing an important question in the motile cilia community: what is the minimum system to generate a beating motion? It is an established fact that dynein power stroke on the microtubule doublet is the driving force of the beating motion. It was also known that the radial spoke and the central pair are essential for ciliary motion under the physiological condition, but cilia without radial spokes and the central pair can beat under some special conditions (Yagi and Kamiya, 2000). Therefore in the mechanistic point of view, they are not prerequisite. It is generally thought that fixed connection between adjacent microtubules by nexin converts sliding motion of dyneins to bending, but it was never experimentally investigated. Here the authors successfully enabled a simple system of nexin-like inter-microtubule linkage using DNA origami technique to generate oscillatory and beating motions. This enables an interesting system where ODAs form groups, anchored on two microtubules, orienting oppositely and therefore cause tag-of-war type force generation. The authors demonstrated this system under constraints by DNA origami generates oscillatory and beating motions.

The authors carefully coordinated the experiments to demonstrate oscillations using optical tweezers and sophisticated data analysis (Fourier analysis and a step-finding algorithm). They also proved, using negative stain EM, that this system contains two groups of ODAs forming arrays with opposite polarity on the parallel microtubules. The manuscript is carefully organized with impressive movies. Geometrical and motility analyses of individual ODAs used for statistics are provided in the supplementary source files. They appropriately cited similar past works from Kamiya and Shingyoji groups (they employed systems closer to the physiological axoneme to reproduce beating) and clarify the differences from this study.

We thank the reviewer for these comments.

Weakness

The authors claim this system mimics two pairs of doublets at the opposite sites from 9+2 cilia structure by having two groups of ODAs between two microtubules facing opposite directions within the pair. It is not exactly the case. In the real axoneme, ODA makes continuous array along the entire length of doublets, which means at any point there are ODAs facing opposite directions. In their system, opposite ODAs cannot exist at the same point (therefore the scheme of Dynein-MT complex of Fig.1B is slightly misleading).

Actually, opposite ODAs can exist at the same point in our system as well, and previous work using much higher concentration of dyneins (e.g, Oda et al., J. Cell biol., 2007) showed two continuous arrays of dynein molecules between a pair of microtubules. To observe the structures of individual dynein molecules we used low concentrations of dynein and searched for the areas where dynein could be observed without superposition, but there were some areas where opposite dyneins existed at the same point.

We realize that we did not clearly explain this issue, so we have revised the text accordingly.

In the 1st paragraph of Results: “In the dynein-MT complexes prepared with high concentrations of dynein, a pair of MTs in bundles are crossbridged by two continuous arrays of dynein, so that superposition of two rows of dynein molecules is observed in EM images (Haimo et al., 1979; Oda et al., 2007). On the other hand, when a low concentration of the dynein preparation (6.25–12.5 µg/ml (corresponding to ~3-6 nM outer-arm dynein)) was mixed with 20-25 µg/ml MTs (200-250 nM tubulin dimers), the MTs were only partially decorated with dynein, so that we were able to observe single layers of crossbridges without superposition in many regions.” Legend of Fig. 1(C): “Note that the geometry of dyneins in the dynein-MT complex shown in (B) mimics that of a combination of the dyneins on two opposite sides of the axoneme (cyan boxes), although the dynein arrays in (B) are not continuous.”

If they want to project their result to the ciliary beating model, more insight/explanation would be necessary. For example, arrays of dyneins at certain positions within the long array along one doublet are activated and generate force, while dyneins at different positions are activated on another doublet at the opposite site of the axoneme. This makes the distribution of dyneins and their orientations similar to the system described in this work. Such a localized activation, shown in physiological cilia by Ishikawa and Nicastro groups, may require other regulatory proteins.

We agree that the distributions of activated dyneins in 3D are extremely important in understanding ciliary beating, and that other regulatory proteins would be required to coordinate activation in different places in an axoneme. However, the main goal of this manuscript is to show the minimal components for oscillatory movements, and we feel that discussing the distributions of activated dyneins along the length of the MTs would be too complicated and beyond the scope of this study.

They attempted to reveal conformational change of ODAs induced by power stroke using negative stain EM images, which is less convincing compared to the past cryo-ET works (Ishikawa, Nicastro, Pigino groups) and negative stain EM of sea urchin outer dyneins (Hirose group), where the tail and head parts were clearly defined from the 3D map or 2D averages of two-dynein ODAs. Probably three heavy chains and associated proteins hinder detailed visualization of the tail structure. Because of this, Fig.2C is not clear enough to prove conformational change of ODA. This reviewer imagines refined subaverage (probably with larger datasets) is necessary.

As the reviewer suggests, one of the reasons for less clear averaged images compared to the past images of sea urchin ODA is the three-headed structure of Chlamydomonas ODA. Another and perhaps the bigger reason is the difficulty of obtaining clear images of dynein molecules bound between 2 MTs by negative stain EM: the stain accumulates between MTs that are ~25 nm in diameter and obscures the features of smaller structures. We used cryo-EM with uranyl acetate staining instead of negative staining for the images of sea urchin ODA-MT complexes we previously published (Ueno et al., 2008) in order to visualize dynein stalks. We agree with the reviewer that future work with larger datasets and by cryo-ET is necessary for revealing structural differences.

That having been said, we did not mean to prove structural changes, but rather intended to show that our observation suggests structural changes and thus this system is useful for analyzing structural changes in future. In the revised manuscript, we have extensively modified the parts of the paper discussing structural changes (Please see our response to the next comment).

It is not clear, from the inset of Fig.2 supplement3, how to define the end of the tail for the length measurement, which is the basis for the authors to claim conformational change (Line263-265). The appearance of the tail would be altered, seen from even slightly different view angles. Comparison with 2D projection from apo- and nucleotide-bound 3-headed ODA structures from EM databank will help.

We agree with the reviewer that difference in the viewing angle affects the apparent length of a dynein molecule, although the 2 MTs crossbridged by dyneins lie on the carbon membrane and thus the variation in the viewing angle is expected to be relatively small. To examine how much the apparent length is affected by the view angle, we calculated 2D-projected images of the cryo-ET structures of Chlamydomonas axoneme (emd_1696 and emd_1697; Movassagh et al., 2010) with different view angles, and measured the apparent length of the dynein molecule using the same method we used for our negative-stain images (Author response image 1). As shown in the plot, the effect of view angles on the apparent lengths is smaller than the difference between the two nucleotide states in the range of 40 degrees measured here. Thus, we think that the length difference shown in Fig.2-suppl.4 reflects a real structural difference between no-ATP and ATP states. In addition, it would be reasonable to think that distributions of the view angles in the negative stain images are similar for both absence and presence of ATP, again supporting the conclusion.

Nevertheless, since we agree with the reviewer that we cannot measure the precise length of the molecule using these 2D images, we have revised the corresponding parts of the manuscript, adding description about the effect of view angles on the measured length in the manuscript.

Author response image 1. Effects of viewing angles on apparent length. (A) and (B) 2D-projected images of cryo-electron tomograms of Chlamydomonas outer arm dynein in an axoneme (Movassagh et al., 2010) viewed from different angles. (C) apparent length of the dynein molecule measured in 2D-projected images.

In this manuscript, we discuss two structural changes: 1) a difference in the dynein length between no-nucleotide and +ATP states (Fig.2-suppl.4), and 2) possible structural differences in the arrangement of the dynein heads (Fig.2-suppl.3). Although we realize that extensive analysis using cryo-ET is necessary for revealing the second structural change, we attempted to compare the structures of oppositely oriented dyneins, hoping that it would lead to future research. In the revised manuscript, we have added 2D projection images of emd_1696 and emd_1697 in Fig.2-suppl.3, so that the readers can compare them with our negative stain images. We had an impression that some of our 2D images in the presence of ATP resembled the cryo-ET structure with ADP.Vi, whereas some others appeared to be closer to the no-nucleotide cryo-ET structure. We have also attempted to calculate cross-correlations, but difficulties in removing the effect of MTs sometimes overlapped with a part of dynein, adjusting the magnifications and contrast of different images prevented us from obtaining reliable results.

To address this and the previous comments, we have extensively modified the section titled ‘Structures of dynein in the dynein-MT-DNA-origami complex’.

In Fig.5B (where the oscillation occurs), the microtubule was once driven >150nm unidirectionally and went back to the original position, before oscillation starts. Is it always the case that relatively long unidirectional motion and return precede oscillation? In Fig.7B, where the authors claim no oscillation happened, only one unidirectional motion was shown. Did oscillation not happen after MT returned to the original position?

Long unidirectional movement of ~150 nm was sometimes observed, but not necessarily before the start of oscillation. For example, in Figure 5 – figure supplement 1A, oscillation started soon after the UV flash, and then unidirectional movement occurred.

With the dynein-MT complex in which dyneins are unidirectionally aligned (Fig.7B, Fig.7-suppl.2), the MTs kept moving and escaped from the trap or just stopped moving probably due to depletion of ATP, so we did not see a MT returning to the original position.

Line284-290: More characterization of bending motion will be necessary (and should be possible). How high frequency is it? Do they confirm that other systems (either without DNA-origami or without ODAs arraying oppositely) cannot generate repetitive beating?

The frequencies of the bending motions measured from the movies in Fig.8 and Fig.8-suppl.1 were 0.6 – 1 Hz, and the motions were rather irregular. Even if there were complexes bending at high frequencies, it would not have been possible to detect them due to the low time resolution of these fluorescence microscopy experiments (~0.1 s). Future studies at a higher time resolution will be necessary for further characterization of bending motions.

To observe bending motions, the dynein-MT complex should be fixed to the glass or a bead at one part of the complex while the other end is free in solution. With the dynein-MT-DNA-origami complexes, we looked for such complexes and found some showing bending motions as in Fig. 8. To answer the reviewer’s question asking if we saw repetitive bending in other systems, we checked the movies of the complexes without DNA-origami or without ODAs arraying oppositely but did not notice any repetitive bending motions. However, future studies using the system with a higher temporal resolution and perhaps with an improved method for attaching the complex would be necessary in these cases as well.

Reviewer #2 (Public Review):

Motile cilia generate rhythmic beating or rotational motion to drive cells or produce extracellular fluid flow. Cilia is made of nine microtubule doublets forming a spoke-like structure and it is known that dynein motor proteins, which connects adjacent microtubule doublet, are the driving force of ciliary motion. However the molecular mechanism to generate motion is still unclear. The authors proved that a pair of microtubules stably linked by DNA-origami and driven by outer dynein arms (ODA) causes beating motion. They employed in vitro motility assay and negative stain TEM to characterize this complex. They demonstrated stable linking of microtubules and ODAs anchored on the both microtubules are essential for oscillatory motion and bending of the microtubules.

Strength<br /> This is an interesting work, addressing an important question in the motile cilia community: what is the minimum system to generate a beating motion? It is an established fact that dynein power stroke on the microtubule doublet is the driving force of the beating motion. It was also known that the radial spoke and the central pair are essential for ciliary motion under the physiological condition, but cilia without radial spokes and the central pair can beat under some special conditions (Yagi and Kamiya, 2000). Therefore in the mechanistic point of view, they are not prerequisite. It is generally thought that fixed connection between adjacent microtubules by nexin converts sliding motion of dyneins to bending, but it was never experimentally investigated. Here the authors successfully enabled a simple system of nexin-like inter-microtubule linkage using DNA origami technique to generate oscillatory and beating motions. This enables an interesting system where ODAs form groups, anchored on two microtubules, orienting oppositely and therefore cause tag-of-war type force generation. The authors demonstrated this system under constraints by DNA origami generates oscillatory and beating motions.<br /> The authors carefully coordinated the experiments to demonstrate oscillations using optical tweezers and sophisticated data analysis (Fourier analysis and a step-finding algorithm). They also proved, using negative stain EM, that this system contains two groups of ODAs forming arrays with opposite polarity on the parallel microtubules.<br /> The manuscript is carefully organized with impressive movies. Geometrical and motility analyses of individual ODAs used for statistics are provided in the supplementary source files. They appropriately cited similar past works from Kamiya and Shingyoji groups (they employed systems closer to the physiological axoneme to reproduce beating) and clarify the differences from this study.

Weakness<br /> The authors claim this system mimics two pairs of doublets at the opposite sites from 9+2 cilia structure by having two groups of ODAs between two microtubules facing opposite directions within the pair. It is not exactly the case. In the real axoneme, ODA makes continuous array along the entire length of doublets, which means at any point there are ODAs facing opposite directions. In their system, opposite ODAs cannot exist at the same point (therefore the scheme of Dynein-MT complex of Fig.1B is slightly misleading). If they want to project their result to the ciliary beating model, more insight/explanation would be necessary. For example, arrays of dyneins at certain positions within the long array along one doublet are activated and generate force, while dyneins at different positions are activated on another doublet at the opposite site of the axoneme. This makes the distribution of dyneins and their orientations similar to the system described in this work. Such a localized activation, shown in physiological cilia by Ishikawa and Nicastro groups, may require other regulatory proteins.<br /> They attempted to reveal conformational change of ODAs induced by power stroke using negative stain EM images, which is less convincing compared to the past cryo-ET works (Ishikawa, Nicastro, Pigino groups) and negative stain EM of sea urchin outer dyneins (Hirose group), where the tail and head parts were clearly defined from the 3D map or 2D averages of two-dynein ODAs. Probably three heavy chains and associated proteins hinder detailed visualization of the tail structure. Because of this, Fig.2C is not clear enough to prove conformational change of ODA. This reviewer imagines refined subaverage (probably with larger datasets) is necessary. It is not clear, from the inset of Fig.2 supplement3, how to define the end of the tail for the length measurement, which is the basis for the authors to claim conformational change (Line263-265). The appearance of the tail would be altered, seen from even slightly different view angles. Comparison with 2D projection from apo- and nucleotide-bound 3-headed ODA structures from EM databank will help.

In Fig.5B (where the oscillation occurs), the microtubule was once driven >150nm unidirectionally and went back to the original position, before oscillation starts. Is it always the case that relatively long unidirectional motion and return precede oscillation? In Fig.7B, where the authors claim no oscillation happened, only one unidirectional motion was shown. Did oscillation not happen after MT returned to the original position?

Line284-290: More characterization of bending motion will be necessary (and should be possible). How high frequency is it? Do they confirm that other systems (either without DNA-origami or without ODAs arraying oppositely) cannot generate repetitive beating?

Author Response

Reviewer #1 (Public Review):

The authors attempt to optimize the FluoroSpot assay to allow for the assessment of cross-reactive antibodies targeting conserved epitopes shared by multi-allelic antigens and those specific to unique antigen variant at the B cells level. This is a critical aspect to consider when identifying targets of a broad range of cross-reactive antibody for vaccine development and the antigen VAR2CSA used in this work is one that will benefit from the method described in the manuscript.

Overall, this is a method manuscript with extensive detail of the assay validation process. The description of the assay performance steps using, first monoclonal antibodies and later hybridoma/immortalized B cells was important to understand conditions that can influence the antigen-antibody interactions in the assay. This multiplex approach can assess the cross-reactivity of antibody to up four allelic variants of an antigen with the possibility to explore the affinity of antibody to a particular variant using the RSV measurements. The validation of the assay with PBMC from malaria exposed donors both men and women (that naturally acquired high titer of antibodies to VAR2CSA during pregnancy) is a strength of this work as this is in the context of polyclonal antibodies with more heterogenous antibody binding specificities.

The ability of the assay to detect cross-reactive antibodies using all four tags appear highly variable even in the context of monoclonal antibody targeting the homologous antigen labelled with all 4 tags.

We understand the concern for variability, but we think that in general the assay was very consistent. Regardless of the configuration used, we detected strikingly comparable number of spots/well, especially when the homologous antigen labelled with four tags was used (Figure 2A). Similar consistency has been previously reported when a similar assay was used to study cross-reactivity in dengue-specific antibodies.

Overall, it appears that the assessed antibody reactivity with TWIN tagged antigens was relatively low and this needs to be explained and discussed as the current multiplex method, as it is, might just be optimized for study of cross-reactive antibodies to 3 antigens.

The LED380 (used to detect and visualize the TWIN tag) indeed gave more background than the other three detection channels. We normally observed a ring of fluorescence at the edge and the middle of the wells, accompanied by lower intensity of the spots. These two characteristics are apparent in the figures and RSV plots presented in the manuscript. In an attempt to reduce these issues, we attempted to substitute the TWIN tag for a BAM tag detected with a peptide-specific antibody (data not presented). However, that approach did not improve the readout and we therefore decided to keep the TWIN-StrepTactin pair for all the experiments. Importantly, even with these issues, routine manual inspection of the wells confirmed the Apex software automatically and efficiently counted “real” spots giving us confidence on the performance of the assay. We acknowledge that exclusion of the LED380 data would lead to higher assay accuracy. However, it would result in reduced ability to assess broad antibody cross-reactivity, which was the primary objective of our study. We have added text briefly discussing this to the revised manuscript (lines 154-160).

As acknowledged by the authors, the validation of this assay on PBMC from only 10 donors (7 women and 3 men) is a caveat to the conclusion and increasing this number of donors (the authors have previously excelled in B cells analyses of PfEMP1 proteins and would have PBMC readily available) will strengthen the validity of this assay.

We thank the reviewer for this comment and agree the number of donors tested is far from sufficient to provide any conclusive evidence regarding frequencies of VAR2CSA-specific and cross-reactive B cells in the context of placental malaria. However, we firmly believe that the validation of the assay – which was the objective of the study – is sufficient, especially because we included human B-cell lines isolated from donors naturally exposed to VAR2CSA-expressing parasites. Futures studies including more donors and full-length VAR2CSA antigens are certainly warranted. As the performance of assay has now been validated (this manuscript) to our satisfaction, we are indeed planning such studies.

Reviewer #2 (Public Review):

The manuscript describes the development of a laboratory-based assay as a tool designed to identify individuals who have developed broadly cross-reactive antibodies with specificity for regions that are common to multiple variants of a given protein (VAR2CSA) of Plasmodium falciparum, the parasite that causes malaria. The assay has potential application in other diseases for which the question ofacquisition of antibody-mediated immunity, either through natural exposure or through vaccination, remains unresolved.

From a purely technical/methodological viewpoint, the work described is of high quality, relying primarily on the availability of custom-designed, in-house-derived protein and antibody reagents that had, for the most part, been validated through use in earlier studies. The authors demonstrate a high degree of rigour in the assay development steps, culminating in a convincing demonstration of the ability to accurately and reproducibly quantify cross-reactive antibody types under controlled conditions using well-characterized monoclonal antibodies.

In a final step, the authors used the assay to assess the content of broadly cross-reactive antibodies in samples from a small number of malaria-exposed African men and women. Given that VAR2CSA is a parasite-derived protein that is exclusively and intimately involved in the manifestation of malaria during pregnancy, with specific localisation to the maternal placental space, the premise is that antibodies -including those with cross-reactive specificities - should be almost exclusively detectable in samples from women, either pregnant at the time of sampling or having been pregnant at least once. The assay functioned technically as expected, identifying antibodies predominantly in women rather than men, but it failed to identify broadly cross-reactive antibodies in the women's samples used, only revealing antibodies with specificity for just one of the different variants used. The latter result could have two mutually non-exclusive explanations. On the one hand, the small number of women's samples (7) screened in the assay could simply be insufficient, demanding the use of a much larger panel. On the other hand, for technical reasons the assay involves the use of only relatively restricted parts of the VAR2CSA protein, and this particular aspect may represent its primary limitation. In earlier work, the authors did identify broadly cross-reactive antibodies in samples from African women, but that work relied on the use of the whole VAR2CSA protein present in its natural state embedded in the membrane of the infected red cell, or as a complete protein produced in the laboratory. The important point being that the whole protein likely interacts with antibodies that recognize protein structures that the isolated smaller parts of the whole protein used in the assay fail to reproduce, and that the cross-reactive antibodies identified recognize these structures that are conserved across different VAR2CSAvariants. The authors recognize these potential weaknesses in their discussion of the results. It is also possible that VAR2CSA variants expressed by parasites from geographically-distinct regions (Africa, Asia, South America) are themselves distinct, and this aspect could also have affected the outcome, since the variant protein sequences used in the assay were derived from parasites originating in these different regions.

The assay could find application in the malaria research field in the specific context of assessments of antibody responses to a range of different parasite proteins that are, or have been, considered candidates for vaccine development but for which their extensive inherent allelic polymorphism has effectively negated such efforts.

We thank the reviewer for the kind evaluation. We fully acknowledge the need for more comprehensive studies to assess the robustness of the pilot data regarding antibody cross-reactivity after natural exposure in the present study, which was aimed to document the performance of the complicated multiplexed assay rather than to provide such evidence. As mentioned above, we are currently planning such a study. We also acknowledge the need to assess the degree of cross-reactivity to full-length antigens rather than domain-specific components of them. This is obviously particularly true for large, multi-domain antigens such as PfEMP1 (including VAR2CSA). Such an exercise is complicated by the need for appropriately tagged antigens. We are intrigued by the apparent discrepancy between the degree of antibody cross-reactivity in depletion experiments using individual DBL domains of VAR2CSA (low cross-reactivity) versus full-length VAR2CSA antigens (very substantial cross-reactivity) reported by Doritchamou et al., and are keen to apply our approach to explore that finding. Therefore, as also mentioned above, we are currently planning a study employing tagged full-length VAR2CSA allelic variants as well.

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

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Reply to the reviewers

We thank the reviewers for their constructive comments and are pleased that all reviewers share our opinion, that the present study “makes an important contribution to the molecular architecture of mitochondria”, is in addition “an important advancement in our understanding of the mechanism by which Cqd1 regulates CoQ distribution” and will “thereby appealing to the broad readership of the journals”. We are convinced that addressing the important points raised by the reviewers will further strengthen the manuscript and result in additional significant insights in the molecular function of Cqd1.

Reviewer #1:

The major concerns affecting the conclusions are: 1) Experimental evidence is lacking on the contribution of contact site formation by Cqd1 to the effects on mitochondrial architecture and respiration-dependent growth. Determining the effects of the overexpression of the kinase-dead mutant on mitochondrial morphology and contact site formation with Por1-Om14 can address that.

We thank reviewer #1 for raising these important points. Indeed, the various functions of Cqd1 might be independent from each other and so far we cannot distinguish between them. As suggested by the reviewer we will analyze the effect of overexpression of CQD1 in the Dups1 deletion mutant and make use of the point mutant in the conserved ATP binding domain which cannot complement the phenotype of the Dups1 Dcqd1 double deletion mutant. We generated a yeast mutant strain expressing Om14-3xHA in the absence of wild type Cqd1. Expression of the cqd1(E330A) mutant in the Om14-3xHA background and subsequent immunoprecipitation will allow us to test whether ATP binding is also essential for contact site formation. Preliminary experiments showed that the overexpression of cqd1(E330A) in the Dcqd1 deletion background results in a growth defect comparable to that caused by overexpression of CQD1 WT. Therefore, we think it might be more promising to analyze the interaction of Om14 and Cqd1 E330A at wild type level in order to avoid pleiotropic effects.

In addition, we will further characterize the cqd1(E330A) mutant by analyzing the effect of its overexpression on mitochondrial morphology, cell growth and assembly of MICOS and F1FO ATP synthase in the Dcqd1 deletion background.

2) Related to point #1, Cqd1 overexpression in deltaUsp1 cells could have addressed whether the role of Cqd1 in contact sites and mitochondrial architecture is independent of its role on CoQ distribution and phospholipid metabolism. Further characterization of the kinase-dead Cqd1 mutant on CoQ distribution, contact sites, mitochondrial archictecture and phsophsolipid metabolism might help discerning how these activities can be separated.

We agree that the related points 1) and 2) raised by reviewer #1 are important and addressed our plans in the response on point 1).

3) It is unclear how both Cqd1 overexpression and deletion induce mitochondrial fragmentation. Performing live cell imaging with a mitochondrial-phoactivatable GFP to measure mitochondrial fusion rates could help discerning the causes for fragmentation. It is a possibility that overexpression induced fragmentation by activating fission without changing fusion, while deletion induced fragmentation by blocking fusion.

We thank reviewer #1 for bringing up this point. Perhaps our explanation in this respect was too short. Fig. 4E shows that deletion of CQD1 does not result in altered mitochondrial morphology, however, deletion of CQD1 in the Dups1 background leads to virtual complete fragmentation of the mitochondrial network. This is likely due to inhibition of mitochondrial fusion through disturbed processing of the fusion protein Mgm1 (see Fig. 4D). In contrast, overexpression of CQD1 does NOT result in formation of small mitochondrial fragments, but in formation of huge mitochondrial clusters which in addition contain a large proportion of ER membranes. So, we don’t think that this phenotype is related to either enhanced fission or reduced fusion. We will clarify this point in text of the revised manuscript.

Minor comment:

1) Figure 4 claims that mitochondrial function is impaired by ups1 deletion, which Cqd1 deletion exacerbates. However, no respiration data is shown in figure 1, only measurements of mitochondrial architecture are shown. Thus, oxygen consumption measurements are needed to claim effects on mitochondrial function.

We did not want to claim that mitochondria lose respiratory competence upon simultaneous deletion of CQD1 and UPS1. Actually, our results indicate that the Dups1 Dcqd1 double deletion mutant grows like wild type on complete medium containing glycerol. Therefore, respiration is not impaired in this mutant. However, mitochondrial function is not restricted to ATP production by oxidative phosphorylation. The reviewer probably refers to Figure 4 where we show that mitochondrial biogenesis and dynamics are impaired in the Dups1 Dcqd1 double deletion mutant – the heading of the legend summarizes this as "mitochondrial function". We will be more precise in the revised version on this point and add a panel showing growth of the mutant strain on non-fermentable carbon source to avoid any further confusion.

2) Some Western blots lack quantifications and statistical analyses of independent experiments.

It is correct that some quantification and the respective statistics were missing in the initially submitted manuscript. We will add the requested information in the revised version of the manuscript.

Reviewer #2:

I have the following concerns for the authors to consider. (1) Although biochemical evidence shows that Cqd1 is likely a factor that forms CS structures in mitochondria, it would make the manuscript stronger if the authors can observe uneven distribution of Cqd1 in the mitochondrial membranes (assessed by fluorescent microscopy or ideally high-resolution microscopy) and the presence of Cqd1 in the region of close apposition of the OM and IM by immunogold labeling for electron microscopy.

Two independent lines of evidence show that Cqd1 is a novel contact site protein: (i) it is found in the contact site fraction in density gradients (Fig. 6A), and (ii) it can be co-immunoprecipitated with outer membrane proteins (Fig. 6G, H, I). Furthermore, the co-IP is supported by cross-links of expected size (Fig. 6F). In sum, we feel that this is solid evidence to support our claim that Cqd1 is present in mitochondrial contact sites. However, it still might be interesting to check an uneven distribution of Cqd1 in mitochondria, as suggested by the reviewer. We will do this by 3D deconvolution fluorescence microscopy.

(2) Since the structural characterization of Cqd1 is important to understand its interactions with the OM proteins and other UbiB protein kinase-like family proteins, Coq8 and Cqd2, take different orientations, the membrane topology of Cqd1 should be experimentally analyzed. The authors state, "two hydrophobic stretches can be identified in the Cqd1 sequence, of which the first one (amino acids 125-142) might be a bona fide transmembrane segment" (lines 97-100); then is Cqd1 a single membrane spanning protein or two-membrane spanning protein?　　

Unfortunately, it was not possible to test the location of the N terminus experimentally because an N-terminally tagged variant of Cqd1 (tag inserted between presequence and mature part) turned out to be unstable. We consider it very unlikely that the second hydrophobic stretch is a transmembrane domain as it is rather short (only 11 amino acids). Furthermore, several Cqd1 homologs in other fungi, including Yarrowia lipolytica, Aspergillus niger and Schizosaccharomyces pombe, are lacking the second hydrophobic stretch. Therefore, we propose that the major part of Cqd1 including the protein kinase-like domain is exposed to the intermembrane space. We will point out this more clearly in the revised manuscript.

(3) The authors state, "conserved GxxxG dimerization motif (amino acids 504‐508)" (Fig. 1A caption), but this description needs a reference. The GxxxG motif was proposed to mediate transmembrane helix-helix association (https://doi.org/10.1006/jmbi.1999.3489), which is not consistent with the membrane topology proposed by the authors.

We thank reviewer #2 for this comment. It is correct that GxxxG motifs are usually present in transmembrane a-helices. However, there is information available indicating that these motifs may also be present in soluble proteins and are stabilizing dimeric interactions for instance in the homodimeric Holliday-junction protein resolvase (Kleiger et al., 2002; doi: 10.1021/bi0200763.). However, as this point is not critical for our conclusions we will remove the discussion of the GxxxG motif from the revised manuscript.

(4) What is the role of the kinase activity of Cqd1 in the CS formation? The effects of overexpression of Cqd1 (Fig. 7) should be tested for its E330A mutant.

We also thank reviewer #2 for raising this important point similar to reviewer #1. Please see our response to point 1) of reviewer #1.

(5) Is there stoichiometric as well as quantitative information on the 400 kD complex consisting of Cqd1, Por1 and Om14? Does the stoichiometry and amount of the complex depend on the growth condition? Does the complex contain other Por1 interacting IM proteins like Mdm31?

We appreciate that reviewer #2 points out this important aspect. It might well be that the amount of the Cqd1 containing complex depends on growth conditions since its presence might be important for phospholipid homeostasis, CoQ distribution and mitochondrial architecture and morphology which for sure strongly depend on growth conditions. Therefore, we will try to analyze the amount of the Cqd1 complex present in mitochondria isolated from yeast cells grown on different media by BN-PAGE. So far we do not have any information on the stoichiometry of this complex and we feel that an analysis would go beyond the scope of this study. We agree with reviewer #2 that Mdm31 is an obvious candidate for an interaction partner of Cqd1. We actually tested this by co-immunoprecipitation using Cqd1-3xHA or Mdm31-3xHA. However, none of these approaches resulted in successful co-isolation of the potential interaction partner. We will mention this result in the revised manuscript.

(6) For Fig. 7E, the authors state, "consistently, we observed dramatically increased mitochondria‐ER interactions Cqd1 overexpression", but this observation could be due to secondary effects because overexpression of Cqd1 itself already caused abnormal morphology of mitochondria.

We thank reviewer #2 for bringing up this important point. To check whether the increased mitochondria‐ER interactions are a secondary effect due to altered mitochondrial morphology we will analyze the mitochondria‐ER interactions in other mitochondrial morphology mutants by fluorescence microscopy. This will reveal whether abnormal mitochondrial morphology generally leads to disturbed ER structure.

(7) Since the antagonistic role of Cqd2 to Cqd1 was proposed, the results of the experiments for Cqd1 can be compared with those for Cqd2. For example, what will become of overexpression of Cqd2 instead of Cqd1 for Fig. 7? What is the lipid composition of the cqd1Dcqd2D double deletion mutant cells (the decreased PA level is recovered?)? Lines 424-425: In summary, overexpression of Cqd1 causes severe phenotypes on growth, formation of mitochondrial structural elements, and mitochondrial architecture and morphology. Is this phenotype affected by overexpression of Cqd2?

This point raised by reviewer #2 is very interesting. Our preliminary experiments and previously published data (Tan et al., 2013) indicate that overexpression of Cqd2 is also toxic and results in the formation of huge mitochondrial clusters. Therefore, we will extend our study and analyze the effect of overexpression of CQD2, either alone or in combination with overexpression of CQD1.

Reviewer #3:

1) The central point of the paper is that Cqd1 is part of a novel contact site between the inner and the outer membrane. Om14 and Por1 were identified as outer membrane components of this contact site by immunoprecipitation. The data look convincing but they were generated from targeted experiments to test the involvement of suspected proteins. Ideally, one would like to see a cross-linking mass spectrometry (XL-MS) experiment that identifies the physical interactions of Cqd1 without bias.

We thank reviewer #3 for acknowledging the presented data as convincing. Considering the significant amount of experiments planned for the revised version of the manuscript, we hope that reviewer #3 agrees that this point is not essential.

2) Could an analogous blot of the MICOS complex be added to Figure 6D?

Of course, we are happy to include BN-PAGE analysis showing the running behavior of MICOS next to the Cqd1 containing complex in Fig. 6D.

3) In the Introduction, a host of contact sites is mentioned, which are partly from older papers. I'm not sure whether this is the accepted view of the field. Also, newer data suggest that the permeability transition pore is derived from complex V rather than ANT, CK, and VDAC. The authors should double check in order to represent the current state of the art

We thank reviewer #3 for this comment. We will update this part according to the more recent literature.

第一原理是马斯克的标签tag,就像多元思维模型是查理芒格的一样!

The addressing system that many digital note taking systems offer is reminiscent of Luhmann's paper system where it served a particular use. Many might ask themselves if they really need this functionality in digital contexts where text search and other affordances can be more directly useful.

Frequently missed by many, perhaps because they're befuddled by the complex branching numbering system which gets more publicity, Luhmann's paper-based system had a highly useful and simple subject heading index (see: https://niklas-luhmann-archiv.de/bestand/zettelkasten/zettel/ZK_2_SW1_001_V, for example) which can be replicated using either #tags or [[wikilinks]] within tools like Obsidian. Of course having an index doesn't preclude the incredible usefulness of directly linking one idea to potentially multiple others in some branching tree-like or network structure.

Note that one highly valuable feature of Luhmann's paper version was that the totality of cards were linked to a minimum of at least one other card by the default that they were placed into the file itself. Those putting notes into Obsidian often place them into their system as singlet, un-linked notes as a default, and this can lead to problems down the road. However this can be mitigated by utilizing topical or subject headings on individual cards which allows for searching on a heading and then cross-linking individual ideas as appropriate.

As an example, because two cards may be tagged with "archaeology" doesn't necessarily mean they're closely related as ideas. This tends to decrease in likelihood if one is an archaeologist and a large proportion of cards might contain that tag, but will simultaneously create more value over time as generic tags increase in number but the specific ideas cross link in small numbers. Similarly as one delves more deeply into archaeology, one will also come up with more granular and useful sub-tags (like Zooarcheology, Paleobotany, Archeopedology, Forensic Archeology, Archeoastronomy, Geoarcheology, etc.) as their knowledge in sub areas increases.

Concretely, one might expect that the subject heading "sociology" would be nearly useless to Luhmann as that was the overarching topic of both of his zettelkästen (I & II), whereas "Autonomie" was much more specific and useful for cross linking a smaller handful of potentially related ideas in the future.

Looking beyond Luhmann can be highly helpful in designing and using one's own system. I'd recommend taking a look at John Locke's work on indexing (1685) (https://publicdomainreview.org/collection/john-lockes-method-for-common-place-books-1685 is an interesting source, though you're obviously applying it to (digital) cards and not a notebook) or Ross Ashby's hybrid notebook/index card system which is also available online (http://www.rossashby.info/journal/index.html) as an example.

Another helpful tip some are sure to appreciate in systems that have an auto-complete function is simply starting to write a wikilink with various related subject heading words that may appear within your system. You'll then be presented with potential options of things to link to serendipitously that you may not have otherwise considered. Within a digital zettelkasten, the popularly used DYAC (Damn You Auto Complete) may turn into Bless You Auto Complete.

SciScore for 10.1101/2022.06.01.494385: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

<table><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Ethics</td><td style="min-width:100px;border-bottom:1px solid lightgray">Consent: Sera were collected at the U.S. Food and Drug Administration with written consent under an approved Institutional Review Board (IRB) protocol (FDA IRB Study # 2021-CBER-045).<br>IRB: Sera were collected at the U.S. Food and Drug Administration with written consent under an approved Institutional Review Board (IRB) protocol (FDA IRB Study # 2021-CBER-045).</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Sex as a biological variable</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Randomization</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Blinding</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Power Analysis</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Cell Line Authentication</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr></table>

Table 2: Resources

<table><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Antibodies</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Membranes were probed for the V5-tag and γ-actin using V5 epitope tag antibody (Novus Biologicals, Centennial, CO), and mouse gamma actin polyclonal antibody (Thermofisher), respectively.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>V5-tag</div><div>suggested: (Novus Cat# NB100-62264, RRID:AB_965837)</div></div><div style="margin-bottom:8px"><div>V5 epitope tag antibody (Novus Biologicals, Centennial, CO)</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>mouse gamma actin</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Cell Lines</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">ACE2 genes of various species (African green monkey (AGM), Chinese rufous horseshoe bat (Rhinolophus sinicus), ferret, mouse, Chinese hamster, Syrian golden hamster, white-tailed deer, swine, bovine, and pangolin) with a C-terminal V5 tag were synthesized by GenScript as described previously 42. 293T (ATCC, Manassas, VA, USA; Cat no: CRL-11268), 293T.ACE2 (BEI Resources, Manassas, VA, USA; Cat no: NR-52511) 64 and 293T.ACE2.TMPRSS2 cells stably expressing human angiotensin-converting enzyme 2 (ACE2) and transmembrane serine protease 2 (TMPRSS2) (BEI Resources, Manassas, VA, USA; Cat no: NR-55293) 34 were maintained at 37°C in Dulbecco’s modified eagle medium (DMEM) supplemented with high glucose, L-glutamine, minimal essential media (MEM) non-essential amino acids, penicillin/streptomycin, HEPES, and 10% fetal bovine serum (FBS).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>293T.ACE2.TMPRSS2</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Pseudoviruses comprising the spike glycoprotein and a firefly luciferase (FLuc) reporter gene packaged within HIV capsid were produced in 293T cells by co-transfection of 5 μg of pCMVΔR8.2, 5 μg of pHR’CMVLuc and 0.5 μg of pVRC8400 or 4 μg of pcDNA3.1(+) encoding a codon-optimized spike gene.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>293T</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Soluble ACE2 Protein Production: His-tagged soluble human ACE2 was produced in FreeStyle™ 293-F cells by transfecting soluble human ACE2 (1-741 aa) expression vector plasmid DNA using 293fectin (Thermo Fisher) and purified using HiTrap Chelating column charged with nickel (GE healthcare) according to the manufacturer’s instructions.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>293-F</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Recombinant DNA</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Plasmids and Cell Lines: Codon-optimized, full-length open reading frames of the spike genes of B.1 (D614G) and Omicron variants in the study were synthesized into pVRC8400 (B.1, BA.1, BA.2, and BA.3) or pcDNA3.1(+) (BA.1.1) were obtained from the Vaccine Research Center (National Institutes of Health, Bethesda, MD) and GenScript (Piscataway, NJ, USA).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pVRC8400</div><div>suggested: RRID:Addgene_63163)</div></div><div style="margin-bottom:8px"><div>pcDNA3.1</div><div>suggested: RRID:Addgene_79663)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The HIV gag/pol packaging (pCMVΔR8.2) and firefly luciferase encoding transfer vector (pHR’CMV-Luc) plasmids 62,63 were obtained from the Vaccine Research Center (National Institutes of Health, Bethesda, MD, USA).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pHR’CMV-Luc</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Pseudoviruses comprising the spike glycoprotein and a firefly luciferase (FLuc) reporter gene packaged within HIV capsid were produced in 293T cells by co-transfection of 5 μg of pCMVΔR8.2, 5 μg of pHR’CMVLuc and 0.5 μg of pVRC8400 or 4 μg of pcDNA3.1(+) encoding a codon-optimized spike gene.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pCMVΔR8.2</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Software and Algorithms</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Titers were calculated using a nonlinear regression curve fit (GraphPad Prism Software Inc., La Jolla, CA, USA).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>GraphPad</div><div>suggested: (GraphPad Prism, RRID:SCR_002798)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The ACE2 concentration causing a 50% reduction of luciferase activity compared to untreated control was reported as the IC50 using a nonlinear regression curve fit (GraphPad Prism software Inc., La Jolla, CA).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>GraphPad Prism</div><div>suggested: (GraphPad Prism, RRID:SCR_002798)</div></div></td></tr></table>

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Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:

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Our study has several caveats, including the use of pseudoviruses instead of authentic SARS-CoV-2 for conducting experiments. However, our findings using pseudoviruses agree with those reported using authentic SARS-CoV-2. For instance, authentic BA.1 /BA.1.1 VOCs were shown to undergo attenuated replication in TMPRSS2-expressing cells compared to ancestral Wuhan-Hu-1, and Alpha, Beta, and Delta VOCs 6,36. These reports also showed greater sensitivity of BA.1 pseudovirus entry to endosomal inhibitor E64d. While we used pseudovirus entry assays to determine Omicron variant usage of ACE2 receptors of various animal species, it remains unknown whether there may be intrinsic and/or innate host-specific factors that might act to inhibit live Omicron VOCs at an entry or post entry step. Furthermore, although we identified RBM substitutions in Omicron spike that conferred the ability to use mouse or horseshoe bat ACE2, we didn’t confirm ACE2 substitutions that permit or prevent Omicron spike binding. For instance, introducing K35E substitution in horseshoe bat ACE2 should permit Omicron variants’ usage. Finally, analysis of a limited number of serum samples and short follow up after the receipt of three doses of the Pfizer/BNT162b2 mRNA vaccine do not give us insights into the durability of the antibody response. While studies of antibody durability are ongoing, our findings indicate that three dose immunization with the Pfizer/BNT162b2 will likely contribute to protection from sever...

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Results from TrialIdentifier: No clinical trial numbers were referenced.

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Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

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Results from JetFighter: We did not find any issues relating to colormaps.

Results from rtransparent:

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• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• No funding statement was detected.
• No protocol registration statement was detected.

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Results from scite Reference Check: We found no unreliable references.

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About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

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SciScore for 10.1101/2022.06.01.494101: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

<table><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Ethics</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Sex as a biological variable</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Randomization</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Blinding</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Power Analysis</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Cell Line Authentication</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr></table>

Table 2: Resources

<table><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Antibodies</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Protein samples were resolved by SDS polyacrylamide gel electrophoresis and transferred onto a nitrocellulose membrane using Trans-Blot Turbo Transfer System (Bio-Rad, Hercules, CA), followed by blocking for 1 h with 5% nonfat milk in Tris-buffered saline-Tween 20 buffer and probing with antibodies against Strep Tag (SAB2702215, Sigma-Aldrich), γ-catenin (sc-514115, Santa Cruz Biotechnology) and GAPDH (A00084, GenScript) (Supplementary Table 6).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>γ-catenin</div><div>suggested: (Immunological Sciences Cat# AB-90215, RRID:AB_2892157)</div></div><div style="margin-bottom:8px"><div>sc-514115, Santa Cruz Biotechnology</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>GAPDH</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The washed membranes were incubated with secondary antibody StarBright Blue 700 Goat anti-mouse IgG (Bio-rad).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-mouse IgG</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Coverslips were washed three times with PBS before secondary anti-mouse antibodies incubation (1:1000 dilution).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-mouse</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">For staining, cells were resuspended in PBS 0.1% BSA 0.01% NaN3 containing the monoclonal antibody PE mouse anti-human CD54 (BD Pharmingen) or its isotype control at a concentration of 1/500.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-human CD54</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The primary anti-ZO-1 antibody (ref. 61-7300, Invitrogen) was diluted at 1:100 in PBS containing 1% BSA and incubated for 3 h at RT.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-ZO-1</div><div>suggested: (Innovative Research Cat# 61-7300, RRID:AB_138452)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The secondary antibody (anti-rabbit IgG-FITC, ref. 9887, Sigma) was diluted 1:200 in PBS and incubated for 1 h at RT.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-rabbit</div><div>suggested: (Sigma-Aldrich Cat# F9887, RRID:AB_259816)</div></div><div style="margin-bottom:8px"><div>anti-rabbit IgG-FITC</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Cell Lines</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Briefly, VSV-G pseudotyped ORF7a or ORF7b lentivirus was produced by co-transfection of HEK293T cells with the pLVX-ORF7a or pLVX-ORF7b plasmids, pCMV-Gag-Pol and pCMV-VSV-G using Lipofectamine 2000 Reagent (Thermo Fisher Scientific) as per manufacturer instructions.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>HEK293T</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">RNA isolation and sequencing: WT A549, A549-ORF7a and A549-ORF7b cells were seeded (3×10E5) in 6-well plates and lysed using RLT buffer for RNA isolation (RNeasy mini kit, Qiagen).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>A549-ORF7b</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">After 24 h incubation, A549 cells were labeled with calcein-AM.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>A549</div><div>suggested: NCI-DTP Cat# A549, RRID:CVCL_0023)</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Recombinant DNA</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Lentivirus production, cell culture and transduction: ORF7a or ORF7b coding sequences (codon-optimized for mammalian expression) were cloned into pLVX-EF1α-IRES-Puro Cloning and Expression Lentivector (System Biosciences) to generate pseudotyped lentiviral particles encoding the ORF7a or ORF7b accessory proteins of SARS-CoV-2 (Wuhan-Hu-1 isolate) at the CNIC (Centro Nacional de Investigaciones Cardiovasculares) Viral Vector Unit (ViVU).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pLVX-EF1α-IRES-Puro</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Briefly, VSV-G pseudotyped ORF7a or ORF7b lentivirus was produced by co-transfection of HEK293T cells with the pLVX-ORF7a or pLVX-ORF7b plasmids, pCMV-Gag-Pol and pCMV-VSV-G using Lipofectamine 2000 Reagent (Thermo Fisher Scientific) as per manufacturer instructions.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pLVX-ORF7a</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>pLVX-ORF7b</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>pCMV-Gag-Pol</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>pCMV-VSV-G</div><div>suggested: RRID:Addgene_8454)</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Software and Algorithms</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Raw counts were transformed with the vst function in the DESeq2 package (Love et al., 2014) of the R software version 3.6.3 (R Core Team, 2020), and subsequent PCA was performed with the prcomp function.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>DESeq2</div><div>suggested: (DESeq, RRID:SCR_000154)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Finally, the PCA graph was made with Graphad Prism software.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Graphad Prism</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">All sequencing data sets are available in the NCBI BioProject database under accession number PRJNA841835.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>BioProject</div><div>suggested: (NCBI BioProject, RRID:SCR_004801)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Enrichment analyses were carried out by selecting the genomics sources: KEGG Pathway, GO Biological Processes, Reactome Gene Sets, Canonical Pathways, and CORUM.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>GO Biological</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Relative expression results were calculated using GenEx6 Pro software (MultiD-Göteborg, Sweden), based on the Cq values obtained.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>GenEx6 Pro</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">For these experiments, a CytoFLEX flow cytometer (Beckman Coulter) was used and data was analyzed using FlowJo v10 (BD Biosciences).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>FlowJo</div><div>suggested: (FlowJo, RRID:SCR_008520)</div></div></td></tr></table>

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Results from OddPub: Thank you for sharing your data.

Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

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Results from TrialIdentifier: No clinical trial numbers were referenced.

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Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

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Results from JetFighter: We did not find any issues relating to colormaps.

Results from rtransparent:

---

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

---

Results from scite Reference Check: We found no unreliable references.

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About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

</footer>

SciScore for 10.1101/2022.05.27.493767: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

NIH rigor criteria are not applicable to paper type.

Table 2: Resources

<table><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Recombinant DNA</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The pGEX-6P-1-nsp5 (or Mpro) plasmid was a kind gift from Dr. Martin Walsh, Diamond Light Source. pGBWm4046979 (coding for full-length nsp7, NCBI Reference Sequence: YP_009725303.1, codon-optimized, with an initial Met and a cleavable C- terminal TEV 6x-His tag was a gift from Ginkgo Bioworks (Addgene plasmid 145611; http://n2t.net/ addgene:145611; RRID: Addgene_145611).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pGEX-6P-1-nsp5</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>pGBWm4046979</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div></div><div>detected: RRID:Addgene_145611)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">pGBWm4046852 (coding for full- length nsp8, NCBI Reference Sequence: YP_009725304.1, codon-optimized, with an initial Met and a cleavable C-terminal TEV 6x-His tag) was a gift from Ginkgo Bioworks (Addgene plasmid 145584; http://n2t.net/ addgene:145584; RRID: Addgene_145584).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pGBWm4046852</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div></div><div>detected: RRID:Addgene_145584)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The gene encoding SARS-CoV-2 nsp10 was cloned into the pGEX-6P-1 vector to generate an expression construct containing an N-terminal GST tag and an HRV 3C protease cleavage site (GST3CNsp10).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pGEX-6P-1</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Plasmids for codon-optimized pET-28a- His6-nsp7-8 and pET-28a-His6-nsp7-11 (with an HRV 3C protease cleavage site between the 6x- His tag and the coding sequence) were obtained from GenScript (Piscataway, NJ).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pET-28a-</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>pET-28a-His6-nsp7-11</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The pGEX- 6P-1-nsp5 expression plasmid was transformed into E. coli Rosetta gami competent cells and cultured in LB media at 37 °C with 100 μg/mL ampicillin.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pGEX-</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>6P-1-nsp5</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Software and Algorithms</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The gene encoding SARS-CoV-2 nsp10 was cloned into the pGEX-6P-1 vector to generate an expression construct containing an N-terminal GST tag and an HRV 3C protease cleavage site (GST3CNsp10).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>SARS-CoV-2</div><div>suggested: (Active Motif Cat# 91351, RRID:AB_2847848)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The gel band intensity for nsp7-11 was calculated using ImageJ software (https://imagej.nih.gov/ij/index.html) and plotted against the concentration of binders using the GraphPad Prism Version 9.3.1 (GraphPad Software, La Jolla California USA, www.graphpad.com).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>ImageJ</div><div>suggested: (ImageJ, RRID:SCR_003070)</div></div><div style="margin-bottom:8px"><div>GraphPad</div><div>suggested: (GraphPad Prism, RRID:SCR_002798)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Crosslink spectral matches found in Proteome Discoverer were exported and converted to sequence spectrum list format using Excel (Microsoft).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Proteome Discoverer</div><div>suggested: (Proteome Discoverer, RRID:SCR_014477)</div></div><div style="margin-bottom:8px"><div>Excel</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The data was reduced using BioXTAS RAW 2.0.3 (81).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>BioXTAS</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">They are also provided in the SM as PyMOL sessions.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>PyMOL</div><div>suggested: (PyMOL, RRID:SCR_000305)</div></div></td></tr></table>

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Results from OddPub: Thank you for sharing your data.

Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

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Results from TrialIdentifier: No clinical trial numbers were referenced.

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Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

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Results from JetFighter: We did not find any issues relating to colormaps.

Results from rtransparent:

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• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

---

Results from scite Reference Check: We found no unreliable references.

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About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

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Yeah!

SciScore for 10.1101/2022.05.27.493400: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

<table><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Ethics</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Sex as a biological variable</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Randomization</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Blinding</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Power Analysis</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Cell Line Authentication</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr></table>

Table 2: Resources

<table><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Cell Lines</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The mammalian cell line HEK 293/T served as host for recombinant production of the glycoprotein.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>HEK 293/T</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Recombinant DNA</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">, pCAGGS based, NCBI accession number: LT727518) was chosen for the mammalian expression system.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pCAGGS</div><div>suggested: RRID:Addgene_127347)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The amplicon was digested with the respective restriction enzymes (ThermoFisher) and ligated with T4 Ligase (ThermoFisher) into the linearised πα-SHP-H vector to clone πα-SHP-H–Sgene with an N-terminal octahistidin tag.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>πα-SHP-H</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Software and Algorithms</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Raw data (dot mean fluorescence intensity) was processed by GraphPad Prism 9 (GraphPad Software, USA)</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>GraphPad</div><div>suggested: (GraphPad Prism, RRID:SCR_002798)</div></div></td></tr></table>

---

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

---

---

Results from TrialIdentifier: No clinical trial numbers were referenced.

---

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

---

Results from JetFighter: We did not find any issues relating to colormaps.

Results from rtransparent:

---

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

---

Results from scite Reference Check: We found no unreliable references.

---

<footer>

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

</footer>

How to set children, but remember that this function replaces the all children.

Shiny tags are stored in R as a list with three name values: name ( name of tag), attribute (list of key pairs), children (other html components inside), class usually R object class shiny.tag

This tool can be used to check is a custom created tag is valid.

SciScore for 10.1101/2022.05.27.493682: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

<table><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Ethics</td><td style="min-width:100px;border-bottom:1px solid lightgray">Euthanasia Agents: At the end point of the experiment, all remaining animals in the monoclonal antibody-administered group received an overdose of isoflurane and were humanely euthanized.<br>IACUC: Ethics statement: This study was approved by the Experimental Animal Welfare and Ethical Review Board of Wuhan Institute of Biological Products Co.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Sex as a biological variable</td><td style="min-width:100px;border-bottom:1px solid lightgray">Male K18-hACE2 mice (6–8 weeks old, purchased from GemPharmatech Co., Ltd. Company.) were randomly distributed into groups (n = 3–6 mice per group).</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Randomization</td><td style="min-width:100px;border-bottom:1px solid lightgray">Male K18-hACE2 mice (6–8 weeks old, purchased from GemPharmatech Co., Ltd. Company.) were randomly distributed into groups (n = 3–6 mice per group).</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Blinding</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Power Analysis</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Cell Line Authentication</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr></table>

Table 2: Resources

<table><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Antibodies</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Then cells were stained with anti-mouse IgG Taxes red conjugated antibody and anti-human IgG FITC-conjugated antibody (Sigma, USA) for another 30 min then analyzed by FACS Aria II (BD, USA).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-mouse IgG</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>anti-human IgG</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Antibody Binding Kinetics Measured by SPR: The binding kinetics of mAbs to SARS-CoV-2 Delta-RBD or Omicron-RBD monomer were analyzed using SPR (Biacore 8K; GE Healthcare).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>SARS-CoV-2</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>Delta-RBD</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Cell Lines</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Cells, Viruses and Proteins: Cell lines (HEK293T and Vero E6 cells) were initially acquired from the American Type Culture Collection (ATCC; USA).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Vero E6</div><div>suggested: RRID:CVCL_XD71)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">HEK293T-hACE2-cells were generated via the overexpression of the human ACE2 receptor in HEK293T cells and were used in the neutralization assays of pseudoviruses.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>HEK293T-hACE2-cells</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Then mixtures were added to 2.5 × 105 HEK293T cells expressing ACE2 and incubated at 4 °C for another hour.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>HEK293T</div><div>suggested: RRID:CVCL_HA71)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">HEK293-hACE2 cells (2.5 × 104 cells/100μL per well) were then added into the mixture and incubated at 37 °C in a humidified atmosphere with 5% CO2 for 23 h to 25 h.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>HEK293-hACE2</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Organisms/Strains</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Male K18-hACE2 mice (6–8 weeks old, purchased from GemPharmatech Co., Ltd. Company.) were randomly distributed into groups (n = 3–6 mice per group).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>K18-hACE2</div><div>suggested: RRID:IMSR_GPT:T037657)</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Recombinant DNA</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The SARS-CoV-2 Spike ectodomain (1-1208) with a C-terminal Strep tag for purification and a foldon tag for trimerization was inserted into the pFastBac-Dual vector (Invitrogen) and was expressed using Bac-to-Bac baculovirus system (Invitrogen).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pFastBac-Dual</div><div>suggested: RRID:Addgene_137166)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The cDNA encoding SARS-CoV-2 Omicron Spike was synthesized (GenBank ID: ULC25168.1) and cloned into the pCAG vector.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pCAG</div><div>suggested: RRID:Addgene_74288)</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Software and Algorithms</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">All of these data were analyzed using Flow Jo.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Flow Jo</div><div>suggested: (FlowJo, RRID:SCR_008520)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">All statistical analysis was performed using GraphPad Prism 8.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>GraphPad</div><div>suggested: (GraphPad Prism, RRID:SCR_002798)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Coot v.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Coot</div><div>suggested: (Coot, RRID:SCR_014222)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Figures were generated using PyMOL 2.0.779</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>PyMOL</div><div>suggested: (PyMOL, RRID:SCR_000305)</div></div></td></tr></table>

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Results from OddPub: Thank you for sharing your data.

Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

---

---

Results from TrialIdentifier: No clinical trial numbers were referenced.

---

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

---

Results from JetFighter: We did not find any issues relating to colormaps.

Results from rtransparent:

---

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

---

Results from scite Reference Check: We found no unreliable references.

---

<footer>

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

</footer>

SciScore for 10.1101/2022.05.26.493517: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

NIH rigor criteria are not applicable to paper type.

Table 2: Resources

<table><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Antibodies</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Spike proteins were captured through their C-terminal His-tag over an anti-His antibody surface.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-His</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">For each residue within the RBD, the frequency of antibody recognition was calculated as the number of contact antibodies32.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>antibodies32</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The structures of antibody-spike complexes for modeling were also obtained from PDB (7L5B (2-15), 6XDG (REGN10933), and 7KMG (LY-CoV555)).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>REGN10933</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Cell Lines</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Heavy chain variable (VH) and light chain variable (VL) genes for each antibody were synthesized (GenScript), then transfected into Expi293 cells (Thermo Fisher Scientific), and purified from the supernatant by affinity purification using rProtein A Sepharose (GE).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Expi293</div><div>suggested: RRID:CVCL_D615)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">A14527); Vero-E6 cells were obtained from the ATCC (</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Vero-E6</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">CRL-1586); HEK293T cells were obtained from the ATCC (CRL-3216).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>HEK293T</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Software and Algorithms</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Neutralization curves and IC50 values were derived by fitting a nonlinear five-parameter dose–response curve to the data in GraphPad Prism v.9.2.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>GraphPad Prism</div><div>suggested: (GraphPad Prism, RRID:SCR_002798)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">PyMOL v.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>PyMOL</div><div>suggested: (PyMOL, RRID:SCR_000305)</div></div></td></tr></table>

---

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

---

---

Results from TrialIdentifier: No clinical trial numbers were referenced.

---

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

---

Results from JetFighter: We did not find any issues relating to colormaps.

Results from rtransparent:

---

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

---

Results from scite Reference Check: We found no unreliable references.

---

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About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

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gross

SciScore for 10.1101/2022.05.26.493539: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

NIH rigor criteria are not applicable to paper type.

Table 2: Resources

<table><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Antibodies</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">To measure the surface expression level of S protein, effector cells were stained with rabbit anti-SARS-CoV-2 S S1/S2 polyclonal antibody (Thermo Fisher Scientific, Cat# PA5-112048, 1:100)</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-SARS-CoV-2 S</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Normal rabbit IgG (SouthernBiotech, Cat# 0111-01, 1:100) was used as negative controls, and APC-conjugated goat anti-rabbit IgG polyclonal antibody (Jackson ImmunoResearch, Cat# 111-136-144, 1:50) was used as a secondary antibody.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-rabbit IgG</div><div>suggested: (Jackson ImmunoResearch Labs Cat# 111-136-144, RRID:AB_2337987)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The deparaffinized sections were exposed to EnVision FLEX target retrieval solution high pH (Agilent, Cat# K8004) for 20 minutes at 97°C to activate, and mouse anti-SARS-CoV-2 N monoclonal antibody (clone 1035111, R&D systems, Cat# MAB10474-SP, 1:400) was used as a primary antibody.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-SARS-CoV-2 N</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Cell Lines</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Cell culture: HEK293T cells (a human embryonic kidney cell line; ATCC, CRL-3216)</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>HEK293T</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">, HEK293 cells (a human embryonic kidney cell line; ATCC, CRL-1573) and HOS-ACE2/TMPRSS2 cells (HOS cells stably expressing human ACE2 and TMPRSS2) (Ferreira et al., 2021; Ozono et al., 2021) were maintained in DMEM (high glucose) (Sigma-Aldrich, Cat# 6429-500ML) containing 10% fetal bovine serum (FBS, Sigma-Aldrich Cat# 172012-500ML), and 1% penicillin-streptomycin (PS) (Sigma-Aldrich, Cat# P4333-100ML).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>HOS-ACE2/TMPRSS2</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">HEK293-ACE2/TMPRSS2 cells (HEK293 cells stably expressing human ACE2 and TMPRSS2) (Motozono et al., 2021) was maintained in DMEM (high glucose) containing 10% FBS, 1 µg/ml puromycin, 200 ng/ml hygromycin (Nacalai Tesque, Cat# 09287-84) and 1% PS.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>HEK293-ACE2/TMPRSS2</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>HEK293</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">HEK293-C34 cells (IFNAR1 KO HEK293 cells expressing human ACE2 and TMPRSS2 by doxycycline treatment) (Torii et al., 2021) were maintained in DMEM (high glucose) containing 10% FBS, 10 μg/ml blasticidin (InvivoGen, Cat# ant-bl-1) and 1% PS.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>HEK293-C34</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Vero cells [an African green monkey (Chlorocebus sabaeus) kidney cell line; JCRB Cell Bank, JCRB0111] were maintained in Eagle’s minimum essential medium (EMEM) (Sigma-Aldrich, Cat# M4655-500ML) containing 10% FBS and 1% PS.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Vero</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">VeroE6/TMPRSS2 cells (VeroE6 cells stably expressing human TMPRSS2; JCRB Cell Bank, JCRB1819) (Matsuyama et al., 2020) were maintained in DMEM (low glucose) (Wako, Cat# 041-29775) containing 10% FBS, G418 (1 mg/ml; Nacalai Tesque, Cat# G8168-10ML) and 1% PS.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>VeroE6</div><div>suggested: JCRB Cat# JCRB1819, RRID:CVCL_YQ49)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Calu-3 cells (a human lung epithelial cell line; ATCC, HTB-55) were maintained in EMEM (Sigma-Aldrich, Cat# M4655-500ML) containing 20% FBS and 1% PS.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Calu-3</div><div>suggested: ATCC Cat# HTB-55, RRID:CVCL_0609)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Calu-3/DSP1-7 cells (Calu-3 cells stably expressing DSP1-7) (Yamamoto et al., 2020) were maintained in EMEM (Wako, Cat# 056-08385) containing 20% FBS and 1% PS. 293S GnTI(-) cells (HEK293S cells lacking N-acetylglucosaminyltransferase (Kubota et al., 2016) were maintained in DMEM (Nacalai tesque, #08458-16 containing 2% FBS without PS.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>HEK293S</div><div>suggested: RRID:CVCL_A784)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Briefly, the amount of pseudoviruses prepared was quantified by the HiBiT assay using Nano Glo HiBiT lytic detection system (Promega,Cat# N3040) as previously described (Ozono et al., 2021; Ozono et al., 2020), and the same amount of pseudoviruses (normalized to the HiBiT value, which indicates the amount of p24 HIV-1 antigen) was inoculated into HOS-ACE2/TMPRSS2 cells, HEK293-ACE2 cells or HEK293-ACE2/TMPRSS2 and viral infectivity was measured as described above (see “Neutralization assay” section).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>HEK293-ACE2</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">On day 3 (24 hours posttransfection), 16,000 effector cells were detached and reseeded into 96-well black plates (PerkinElmer, Cat# 6005225), and target cells (VeroE6/TMPRSS2 or Calu-3/DSP1-7 cells) were reseeded at a density of 1,000,000 cells/2 ml/well in 6-well plates.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Calu-3/DSP1-7</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">SARS-CoV-2 infection: One day before infection, Vero cells (10,000 cells) and VeroE6/TMPRSS2 cells (10,000 cells) were seeded into a 96-well plate.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>VeroE6/TMPRSS2</div><div>suggested: JCRB Cat# JCRB1819, RRID:CVCL_YQ49)</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Organisms/Strains</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Preparation of mouse sera: BALB/c mice (female, 7 weeks old) were immunized with 1 μg SARS-CoV-2 BA.2 RBD protein in 50% AddaVax (Invivogen, Cat# vac-adx-10) at day 0 and 14.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>BALB/c</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Recombinant DNA</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The resulting PCR fragment was digested with KpnI and NotI and inserted into the corresponding site of the pCAGGS vector (Niwa et al., 1991).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pCAGGS</div><div>suggested: RRID:Addgene_127347)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">2 S RBD (residues 322-536) was cloned into the expression vector pHLsec containing the N-terminal secretion signal sequence and the C-terminal His6-tag sequence (Aricescu et al., 2006).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pHLsec</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">kit (Roche, Cat# KK2601) and assembled in vivo by yeast [Saccharomyces cerevisiae strain EBY100 (ATCC, MYA-4941)] homologous recombination with pJYDC1 plasmid (Addgene, Cat# 162458) as previously described (Dejnirattisai et al., 2022; Kimura et al., 2022a; Kimura et al., 2022b; Motozono et al., 2021; Yamasoba et al., 2022a; Zahradnik et al., 2021a)</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pJYDC1</div><div>suggested: RRID:Addgene_162458)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">To prepare effector cells, HEK293 cells were cotransfected with the S-expression plasmids (500 ng) and pDSP8-11 (500 ng) using PEI Max (Polysciences, Cat# 24765-1).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pDSP8-11</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">To prepare target cells, HEK293 and HEK293-ACE2/TMPRSS2 cells were transfected with pDSP1-7 (500 ng) (Kondo et al., 2011).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pDSP1-7</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Software and Algorithms</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Sequencing reads were trimmed using fastp v0.21.0 (Chen et al., 2018) and subsequently mapped to the viral genome sequences of a lineage A isolate (strain WK-521; GISAID ID: EPI_ISL_408667) (Matsuyama et al., 2020) using BWA-MEM v0.7.17 (Li and Durbin, 2009).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>BWA-MEM</div><div>suggested: (Sniffles, RRID:SCR_017619)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Variant calling, filtering, and annotation were performed using SAMtools v1.9 (Li et al., 2009) and snpEff v5.0e (Cingolani et al., 2012).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>SAMtools</div><div>suggested: (SAMTOOLS, RRID:SCR_002105)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The viral genome sequences were mapped to the reference sequence of Wuhan-Hu-1 (GenBank accession number: NC_045512.2) using Minimap2 v2.17 (Li, 2018) and subsequently converted to a multiple sequence alignment according to the GISAID phylogenetic analysis pipeline (https://github.com/roblanf/sarscov2phylo).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Minimap2</div><div>suggested: (Minimap2, RRID:SCR_018550)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Tree reconstruction was performed by RAxML v8.2.12 (Stamatakis, 2014) under the GTRCAT substitution model.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>RAxML</div><div>suggested: (RAxML, RRID:SCR_006086)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Parameter estimation was performed via the MCMC approach implemented in CmdStan v2.28.1 (https://mc-stan.org) with CmdStanr v0.4.0 (https://mc-stan.org/cmdstanr/).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>CmdStan</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>https://mc-stan.org</div><div>suggested: (Stan, RRID:SCR_018459)</div></div><div style="margin-bottom:8px"><div>CmdStanr</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The assay of each serum was performed in triplicate, and the 50% neutralization titer (NT50) was calculated using Prism 9 software v9.1.1 (GraphPad Software).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>GraphPad</div><div>suggested: (GraphPad Prism, RRID:SCR_002798)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">RBD expression and ACE2 signal were recorded by using a FACS S3e cell sorter device (Bio-Rad), background binding signals were subtracted and data were fitted to a standard noncooperative Hill equation by nonlinear least-squares regression using Python v3.7 (https://www.python.org) as previously described (Kimura et al., 2022a; Kimura et al., 2022b; Motozono et al., 2021; Yamasoba et al., 2022a; Zahradnik et al., 2021b).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Python</div><div>suggested: (IPython, RRID:SCR_001658)</div></div><div style="margin-bottom:8px"><div>https://www.python.org</div><div>suggested: (CVXOPT - Python Software for Convex Optimization, RRID:SCR_002918)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Surface expression level of S proteins (Figures 3C and S2B) was measured using FACS Canto II (BD Biosciences) and the data were analyzed using FlowJo software v10.7.1 (BD Biosciences).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>FlowJo</div><div>suggested: (FlowJo, RRID:SCR_008520)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The size of syncytium (GFP-positive area) was measured using Fiji software v2.2.0 (ImageJ) as previously described (Suzuki et al., 2022; Yamasoba et al., 2022a).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Fiji</div><div>suggested: (Fiji, RRID:SCR_002285)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The stained cells were washed with tap water and dried, and the size of plaques was measured using Fiji software v2.2.0 (ImageJ).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>ImageJ</div><div>suggested: (ImageJ, RRID:SCR_003070)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Images were incorporated as virtual slide by NDP.scan software v3.2.4 (Hamamatsu Photonics).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>NDP.scan</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">These analyses were performed in R v4.1.2 (https://www.r-project.org/).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>https://www.r-project.org/</div><div>suggested: (R Project for Statistical Computing, RRID:SCR_001905)</div></div></td></tr></table>

---

Results from OddPub: Thank you for sharing your code and data.

Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

---

---

Results from TrialIdentifier: No clinical trial numbers were referenced.

---

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

---

Results from JetFighter: We did not find any issues relating to colormaps.

Results from rtransparent:

---

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• No funding statement was detected.
• No protocol registration statement was detected.

---

Results from scite Reference Check: We found no unreliable references.

---

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About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

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

Learn more at Review Commons

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Referee #3

Evidence, reproducibility and clarity

In this manuscript, the authors address the important topic of post-transcriptional gene regulation using the larval nervous system in Drosophila. They utilize a novel approach taking advantage of existing protein trap library, which permits use of the same smFISH probe to detect an array of 200 RNAs and visualize their corresponding protein expression. Furthermore, the authors developed a computational pipeline to visualize and analyze the resulting data, which should enhance the application of this method by other researchers. A major strength of the data comes from the analysis of multiple cell types in distinct compartments of the nervous system, cell types (neuron, glia, neuroblast), and subcellular domains. From the cumulative data, the authors are able to describe several interesting observations relating to cell-specific post-transcriptional regulation, regulation within a central-neuroblast lineage and glial post-transcriptional regulation, among others.

However, in spite of these strengths, there are several concerns related to the organization and interpretation of the manuscript that the authors should address in order to improve the manuscript:

General concerns:

1. The approach relies on gene traps that often fail to be made homozygous, presumably due to deleterious function of the YFP insert. This is an obvious limitation of the study, which the authors address, but do so insufficiently by only analyzing a single case Dlg1. The authors should report how many of the 200 YFP-traps can produce viable homozygous animals, whether phenotypes can be observed, and any other relevant information to assess the functional properties of the tagged genes.
2. The term "discordant" is used for non-congruous RNA/Protein levels in soma and distal processes, and sometimes the two are analyzed in the same figure (e.g Fig 3A). When it is stated that 98% of genes are discordant, this is an over-simplification as what the authors describe as "discordant" is expected to occur frequently in the distal process, but less often in the soma (which is what the authors find when presenting the data for individual compartments - Fig 3B-C). This is confusing because the observation means completely different things in the two compartments, though both are interesting to describe. These analyses, and their interpretation, should be kept separate.
3. There is not enough emphasis placed on the cell-type specific regulation of RNAs. There are very few studies that have investigated how localization of individual RNAs changes in different cell types or regions of the nervous system, and the authors find that this is quite prevalent. Therefore, the rather superficial analysis of these data fails to take advantage of a major strength of the data. For example, for the discordant genes that differ in neuropil localization between different regions of the CNS, what types of molecules do they encode, what is their function in neurons (if known), and why might they be required locally in one region of the CNS but not the other?
4. The authors conclude that mRNA and protein co-localization in glia processes shows that mRNA localization makes a major contribution of the proteome in processes. However, there is not enough evidence for such conclusion since neither translation of these mRNAs nor lack of protein trafficking from the somas was shown.
5. An important caveat of this technique that should be discussed is the lack of knowledge about the translation of these mRNAs, if the mRNA that is being detected is the same as the one that is translated. While the authors emphasize the discordance between mRNA and protein localization, it is not possible to know whether these mRNAs are being translated where they are found, e.g. soma vs neuropil. Moreover, there are many examples (e.g. BDNF) where the isoform influences the subcellular localization of the mRNA. There is no way of studying the isoforms here, and we could be looking for a different mRNA isoform localized to a specific compartment compared to the protein. These points must be discussed.

Minor suggestions:

• The authors should identify GO terms to understand what types of molecules are subjected to RNA regulation. They provide a supplementary table for all genes, but it would be useful to have a chart showing the proportion of different GO terms represented in the overall gene set, genes that show cell-specific regulation, genes that show neuron vs glia specific regulation, etc.
• "However, post-transcriptional regulation can also manifest itself within a cell, so that a protein is localised to a distinct site from the mRNA that encodes it". While subcellular RNA localization may represent a regulatory layer, I do not agree that proteins that function in the cell at a different location than their translation site represents regulation per se. Many such cases exist for proteins that are trafficked!
• "The majority of individual puncta appearing in the dlg1::YFP line (51% in the brain, 64% in larval muscles". Why is the agreement between YFP and endogenous FISH so low? Do many individual RNAs fail to hybridize? This should be discussed.
• "However, one gene, indy, is highly transcribed in neuroblasts and a single ganglion mother cell before it is rapidly shut off (Figure S1A)". This figure does not exist. Where are the data?
• The authors should be consistent about calling perineurial or perineural glia (both correct) in their images and text.
• "We only observe a minority of localised axonal mRNAs that lack the protein they encode at the axon extremities, in contrast to our findings in the mushroom body, optic lobe, and ventral nerve cord neuropils" These results are not contrasted, as in all neuropils the minority of localized mRNAs are those lacking their corresponding proteins. For example, 9% in NMJ vs 7.5% in OL neuropil according to Fig. 1B. What is conflicting with the conclusion?
• "These results suggest that motor axons are more selective than the other neuronal extensions in the mRNAs that are transported over their very long distances from the soma to the neuromuscular synapse" The current literature says that the same mechanism (cis-elements) is used to transport mRNAs to subcellular compartments, which would be inconsistent with the idea of motor axons being "more selective" than other neurons for the same mRNA, but just a result of fewer mRNAs being found in motor neurons: 34.% of the mRNAs are found in motor neurons soma vs 83% in OL soma, 86.5% in VNC soma, and 70.5% in MB soma. To get to this conclusion, the authors should show that mRNAs previously found in the neuronal extensions of other neurons are not found in the axons of motor neurons but are still expressed in thesir somas. They might want to suggest different RBPs involved in the transport or discussing the very long distance they need to travel which can influence their detection in the tips. Figures
• Figure 1. Experimental approach summary
  • Some colors do not show well and should be changed, e.g: grey in Fig. 1A, and Fig. 1B probe sites indicated in light blue and pink within the introns of dlg1.
  • Fig. 1E': There appears to be a large discrepancy in co-detection % for CNS and muscle in the graph judging by the size of circles, yet in the text, it is stated that there is average of 51% and 64% in the two, respectively. I don't see any green circles with over 25% agreement in the graph. Are the colors correct here?
  • Fig. 1D-I: It's difficult to identify where the zoomed panels come from. E has its own square (indicating zoom in E'). Please make this square dashed or a different color in E so it is clear F and G do not come from there.
  • Comparing Fig. 1F vs K: Why does there appear to be so much more dlg1 mRNA in the YFP-tag condition? If this is due to selection of imaging area, please choose a more similar region to image so the RNA levels are comparable. Otherwise it indicates the YFP-tag line has more RNA expression, which is likely not the case.
• Figure 2. Analysis pipeline overview
  • The lines for the first two zoomed panels are switched: The optic lobe is going to VNC and vice-versa.
• Figure 3. Overall summary of results
  • Figure 3A: Soma/Neuropil/muscle should be separate or at least ordered such that they are next to each other to facilitate direct comparison of genes in the same region of the cell in neurons from different CNS areas. Why are glia not included in this summary? A third color should be used to indicate when there is neither mRNA nor protein expression.
  • "Compiling all the information together shows that there are that 196/200 or 98% of the genes show discordance between RNA and protein expression" However, 5 genes shown in Fig. 3A do not show "discordance": CG9650, cup, Lasb, rg, and vsg!!
• Figure 4. Neuroblast lineage analysis
  • Is clustering around the NB sufficient to determine lineage relationship? There seems to be other neurons around the NB.
  • More examples should be shown for the post-transcriptional category, as it is the most interesting category, and there are many different possible outcomes. Are there cases of transcriptional control and post-transcriptional regulation? Are there cases where the youngest neurons (closer to the NB) in the progeny are expressing the protein while the oldest are not? If not, could this be an artifact from a slow translation and the protein being detected only after building up in the cell? Top1 protein (Fig. 4D) seems to be less expressed in the youngest neurons.
  • "The transcription rate of these genes, as indicated by the relative intensity of smFISH nuclear transcription foci, is similar across the neuroblast lineage, however protein signal is only detectable in a minority of the progeny cells (Figure 4E)". Many nuclei lack clear large spots, but have small spots indicative of RNA; how is this interpreted? Do they lack transcription, or is this due failure of the smFISH to capture all transcription sites? Were transcripts actually counted to assess cell-specific differences? This should be possible with smFISH
• Figure 5. RNA synaptic localization
  • A have global analysis comparison of all neuropil areas would be welcome in this figure.
  • "Surprisingly, another 59 transcripts are present at synapses without detectable levels of protein (Figure 5E-H)" This text does not correspond to Fig 5E-H but 5I-L. Where is the text about 5E-H?
  • For Fig. 5J and 5N RNA appears scattered regularly throughout the entire panel area. How sure are the authors that this is not due to poor signal/noise? For example, perhaps too much probe being used for these targets.
  • Fig. 5R is not cited in the text.
• Figure 6. RNA localization in glia
  • For Fig. 6B-G it is hard to tell if there is any overlap of the RNA and Glia. Maybe show multiple zoomed-in merged images and/or highlight the structures with lines that are present in all panels.
  • For Fig. 6L-O: How reproducible is this small amount of RNA puncta in the NMJ glia? Is this possibly biologically important?
  • Why do cartoons labelling subnuclear/perinuclear glia in Fig.6 and Fig.S6 show different localization?
  • The cartoons seem to extrapolate from the data: While in Fig 6B-D, we see neither the big bright spot of transcription in the glial nucleus nor as many transcripts in the neuropil, they are both present in the cartoon. In Fig. 6E-G there is no indication of cortical glia soma nor the transcription spot only in glia nuclei.
  • "To assess glial localisation for the 200 genes of interest, we used a pan-glial gal4 driving a membrane mCherry marker (repo-GAL4>UAS-mcd8-mCherry) to learn the expression pattern of all glial cells, and then classified the pattern in the YFP lines (without the marker) based on knowledge of that expression pattern. We validated this approach by combining the RFP marker" Did the authors use mCherry or RFP for these experiments? Also, the previous sentence is redundant.
• Figure 7. RNA localization at neuromuscular synapse
  • RNA for these genes seems far too spread throughout the muscle to draw any conclusions
  • Also with so many RNAs distributed in the muscle, specific localization of RNA molecule to the precise PSD would have no conceivable benefit
  • I suggest drawing lines around the protein expression to facilitate visualization of the mRNA localization for panels B, F and J. It is especially hard to conclude anything from panels B and F.
  • Light grey with white dots is hard to see in the cartoons
• Figure 8. Role of khc and activity in sgg localization
  • Presumably there is a huge number of developmental problems associated with this mutant that could cause decrease in sgg localization
  • If the authors include this, then they should characterize the mutant NMJs: what is the change in size, synapse number, etc..
  • Is there more sgg accumulated in soma as a result of less transport? Is sgg being expressed at the same level?
  • Fig. 8F-H: Why is Dlg1 accumulated in the entire axon, not just the presume synapse?
  • Fig. 8J: Why is sgg signal occurring in circles disconnected from the main axon? The authors should show a different image

Significance

This is a significant and complex paper that contributes with novel tools to an important issue

indie thinkers

SciScore for 10.1101/2022.05.23.493138: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

<table><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Ethics</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Sex as a biological variable</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Randomization</td><td style="min-width:100px;border-bottom:1px solid lightgray">Polarization Anisotropy: Fluorescent RNA was ordered from IDT as a 10-nt degenerate sequence (random nucleotide at every position) with a 3’-FAM modification.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Blinding</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Power Analysis</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr></table>

Table 2: Resources

<table><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Recombinant DNA</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Briefly, a codon-optimized synthetic DNA (Integrated DNA Technologies, IDT) was inserted into a pET28 expression vector by Gibson assembly, fused to DNA encoding an N-terminal 6xHis-SUMO tag.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pET28</div><div>suggested: RRID:Addgene_21766)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The template for in vitro transcription of 5’-600 RNA was a synthetic DNA (IDT), inserted by Gibson assembly into a pUC18 vector with a 5’ T7 promoter sequence.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pUC18</div><div>suggested: RRID:Addgene_50004)</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Software and Algorithms</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Data from three independent N protein titrations were fit to a one-site binding curve using GraphPad Prism to determine KD.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>GraphPad</div><div>suggested: (GraphPad Prism, RRID:SCR_002798)</div></div></td></tr></table>

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Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

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Results from TrialIdentifier: No clinical trial numbers were referenced.

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Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

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Results from JetFighter: We did not find any issues relating to colormaps.

Results from rtransparent:

---

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

---

Results from scite Reference Check: We found no unreliable references.

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About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

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SciScore for 10.1101/2022.05.22.492693: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

NIH rigor criteria are not applicable to paper type.

Table 2: Resources

<table><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Antibodies</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">After blocking with 3% albumin (Sigma-Aldrich) and primary antibody incubation RAGE (ab3611, Abcam), ACE2 (XXX), ADAM17 (ab2051, Abcam)), TMPRSS2 (ab109131, Abcam), the membranes were incubated with an anti-rabbit peroxidase-conjugated secondary antibody (GE healthcare).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>ACE2</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>ADAM17</div><div>suggested: (Abcam Cat# ab2051, RRID:AB_302796)</div></div><div style="margin-bottom:8px"><div>TMPRSS2</div><div>suggested: (Abcam Cat# ab109131, RRID:AB_10863728)</div></div><div style="margin-bottom:8px"><div>anti-rabbit peroxidase-conjugated secondary</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">500 μg of protein lysate was incubated with Anti-6X His tag® antibody [HIS.H8] (ab18184, Abcam) overnight at 4°C, anti-Mouse IgG (Invitrogen) was used as isotype control.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Anti-6X</div><div>suggested: (Abcam Cat# ab18184, RRID:AB_444306)</div></div><div style="margin-bottom:8px"><div>anti-Mouse IgG</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">P4417-100TAB-Sigma-Aldrich) plus 1% Bovine Serum Albumin (BSA) (Cat.A9647-500G-Sigma-Aldrich) and 0,02% NP-40 alternative (Cat.492016-100ML) for 1h at room temperature prior to overnight incubation at 4°C with primary antibody 1:100 (6xHisTag clone#HIS.H8 Cat.ab18184-Abcam or SARS-CoV-2 spike polyclonal antibody, GeneTex).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>6xHisTag</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Cells were collected and stained using primary RAGE antibody 1:100 (PA5-24787, Thermo Scientific) for FACS analysis.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>RAGE</div><div>suggested: (Thermo Fisher Scientific Cat# PA5-24787, RRID:AB_2542287)</div></div><div style="margin-bottom:8px"><div>PA5-24787</div><div>suggested: (Thermo Fisher Scientific Cat# PA5-24787, RRID:AB_2542287)</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Cell Lines</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">1 × 105 THP-1 cells were seeded on a 24-well plate in their culture medium.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>THP-1</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">THP1 and Monocytes infection with SARS-CoV-2: THP1 cells were plated at 5×105 cell/ml in 48-well plates in 200 μl of RPMI-1640 supplemented with 1% fetal bovine serum (FBS) (Euroclone).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>THP1</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Cell culture supernatants were collected 24, 48, 72 and 144 h post-infection and stored at – 80°C until the determination of the viral titers by a plaque-forming assay in Vero cells.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Vero</div><div>suggested: CLS Cat# 605372/p622_VERO, RRID:CVCL_0059)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The following day, cells were pretreated or not with 2μM Azeliragon (Cat.S6415-Selleckchem) for 30 minutes before adding 100 ng/mL of Sars-CoV-2 spike protein (RBD, HisTag) (Cat. ZO3483-1-GenScript) or infected using Heat-inactivated SARS-CoV-2 (VR-1986HK, ATCC) at 4 TCID50/mL for 2h at 37°C 5%CO2.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>VR-1986HK</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Software and Algorithms</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Sequencing: Different library types were pooled at different ratios based on their targeted reads per cell and the nanomolarity of the library pools was confirmed using the Agilent Bioanalyzer 2100.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Agilent Bioanalyzer</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Separately for the two selected categories of disease severity (mild vs severe/critical), the pseudo-bulk counts were then fitted with a generalised linear model using the EdgeR package, to identify those genes characterised by a well-defined decreasing or increasing trend of the expression over the sample time-points.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>EdgeR</div><div>suggested: (edgeR, RRID:SCR_012802)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Among these sets, the GO:0050786 genelist was then expanded using the Cytoscape ‘stringApp’ (81) in order to identify among the nearest neighbours with confidence score > 0.7 the ones showing the highest absolute FC values in Myeloid cells.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Cytoscape</div><div>suggested: (Cytoscape, RRID:SCR_003032)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The GSEA has been done with the clusterProfiler library (82, 83), using gene lists ranked by the FDR of the differential analysis and the sign of the logFC.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>GSEA</div><div>suggested: (SeqGSEA, RRID:SCR_005724)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The gating strategy and the relative analysis were performed with FlowJo software.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>FlowJo</div><div>suggested: (FlowJo, RRID:SCR_008520)</div></div></td></tr></table>

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Results from OddPub: Thank you for sharing your code and data.

Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

---

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Results from TrialIdentifier: No clinical trial numbers were referenced.

---

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

---

Results from JetFighter: We did not find any issues relating to colormaps.

Results from rtransparent:

---

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

---

Results from scite Reference Check: We found no unreliable references.

---

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About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

</footer>

SciScore for 10.1101/2022.05.21.492554: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

<table><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Ethics</td><td style="min-width:100px;border-bottom:1px solid lightgray">Consent: A blood sample was taken following consent at least 14 days after symptom onset.<br>IRB: Sera from Beta, Gamma and Delta and BA.1 infected cases: Beta and Delta samples from UK infected cases were collected under the “Innate and adaptive immunity against SARS-CoV-2 in healthcare worker family and household members” protocol affiliated to the Gastro-intestinal illness in Oxford: COVID sub study discussed above and approved by the University of Oxford Central University Research Ethics Committee.<br>Field Sample Permit: The study was approved by the Human Research Ethics Committee of the University of the Witwatersrand (reference number 200313) and conducted in accordance with Good Clinical Practice guidelines.<br>IACUC: Gamma samples were provided by the International Reference Laboratory for Coronavirus at FIOCRUZ (WHO) as part of the national surveillance for coronavirus and had the approval of the FIOCRUZ ethical committee (CEP 4.128.241) to continuously receive and analyse samples of COVID-19 suspected cases for virological surveillance.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Sex as a biological variable</td><td style="min-width:100px;border-bottom:1px solid lightgray">The mean age of vaccinees was 37 years (range 22-66), 21 male and 35 female.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Randomization</td><td style="min-width:100px;border-bottom:1px solid lightgray">AstraZeneca-Oxford vaccine study procedures and sample processing: Full details of the randomized controlled trial of ChAdOx1 nCoV-19 (AZD1222), were previously published (PMID: 33220855/PMID: 32702298).</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Blinding</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Power Analysis</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Cell Line Authentication</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr></table>

Table 2: Resources

<table><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Cell Lines</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">EXPERIMENTAL MODEL AND SUBJECT DETAILS: Bacterial Strains and Cell Culture: Vero (ATCC CCL-81) and VeroE6/TMPRSS2 cells were cultured at 37 °C in Dulbecco’s Modified Eagle medium (DMEM) high glucose (Sigma-Aldrich) supplemented with 10% fetal bovine serum (FBS), 2 mM GlutaMAX (Gibco, 35050061) and 100rnU/ml of penicillin– streptomycin.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Vero</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>VeroE6/TMPRSS2</div><div>suggested: JCRB Cat# JCRB1819, RRID:CVCL_YQ49)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">HEK293T (ATCC CRL- 11268) cells were cultured in DMEM high glucose (Sigma-Aldrich) supplemented with 10% FBS, 1% 100X Mem Neaa (Gibco) and 1% 100X L-Glutamine (Gibco) at 37 °C with 5% CO2.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>HEK293T</div><div>suggested: ATCC Cat# CRL-11268, RRID:CVCL_1926)</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Recombinant DNA</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The resulting S gene-carrying pcDNA3.1 was used for generating pseudoviral particles together with the lentiviral packaging vector and transfer vector encoding luciferase reporter.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pcDNA3.1</div><div>suggested: RRID:Addgene_79663)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The gene fragment was amplified with pNeoRBD333Omi_F (5’- GGTTGCGTAGCTGAAACCGGTCATCACCATCACCATCACACCAATCTGTGCCCTTTCGAC-3’) and pNeoRBD333_R (5’-GTGATGGTGGTGCTTGGTACCTTATTACTTCTTGCCGCACACGGTAGC-3’), and cloned into the pNeo vector (Supasa et al., 2021).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pNeo</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">To generate the BA.4/5 RBD construct containing a BAP- His tag, the gene fragment was amplified with RBD333_F (5’- GCGTAGCTGAAACCGGCACCAATCTGTGCCCTTTCGAC-3’) and RBD333_BAP_R (5’-GTCATTCAGCAAGCTCTTCTTGCCGCACACGGTAGC-3’), and cloned into the pOPINTTGneo-BAP vector (Huo et al., 2020a).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pOPINTTGneo-BAP</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">To express biotinylated RBDs, the RBD-BAP plasmid was co-transfected with pDisplay-BirA-ER (Addgene plasmid 20856; coding for an ER-localized biotin ligase), in the presence of 0.8 mM D-biotin (Sigma-Aldrich).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>RBD-BAP</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>pDisplay-BirA-ER</div><div>suggested: RRID:Addgene_20856)</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Software and Algorithms</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The sensorgrams were plotted using Prism9 (GraphPad).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>GraphPad</div><div>suggested: (GraphPad Prism, RRID:SCR_002798)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The percentage reduction was calculated and IC50 determined using the probit program from the SPSS package.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>SPSS</div><div>suggested: (SPSS, RRID:SCR_002865)</div></div></td></tr></table>

---

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

---

---

Results from TrialIdentifier: We found the following clinical trial numbers in your paper:<br><table><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Identifier</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Status</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Title</td></tr><tr><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">NCT04324606</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Active, not recruiting</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">A Study of a Candidate COVID-19 Vaccine (COV001)</td></tr><tr><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">NCT04400838</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Active, not recruiting</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Investigating a Vaccine Against COVID-19</td></tr></table>

---

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

---

Results from JetFighter: We did not find any issues relating to colormaps.

Results from rtransparent:

---

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

---

Results from scite Reference Check: We found no unreliable references.

---

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About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

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SciScore for 10.1101/2022.05.21.492920: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

<table><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Ethics</td><td style="min-width:100px;border-bottom:1px solid lightgray">IACUC: , Radiation Safety, and Animal Care and Use Committees.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Sex as a biological variable</td><td style="min-width:100px;border-bottom:1px solid lightgray">Male golden Syrian hamsters (7 to 8 weeks of age) were purchased from Envigo (Haslett, MI).</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Randomization</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Blinding</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Power Analysis</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Cell Line Authentication</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr></table>

Table 2: Resources

<table><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Antibodies</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The wells were washed and then incubated with rabbit anti-ERα (1:2000, 1 h, RT) and horseradish-conjugated secondary antibody (1:2000, 1 h, RT) that were provided with the kit.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-ERα</div><div>suggested: (Santa Cruz Biotechnology Cat# sc-542, RRID:AB_631470)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The cells were then incubated at 4°C overnight with 2 μg/ml each of anti-ERα(H222) rat IgG1 monoclonal antibody (mAb) (Santa Cruz Biotech, sc-5349, 1:100) and HA-probe (</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-ERα(H222</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>rat IgG1</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Afterwards, the cells were washed 4 times with PBS + 0.1% Tween-20 (PBS-T) for 5 minutes and incubated at room temperature for 1 hour in the dark with a fluorescent secondary antibody mixture contaning mouse IgGk BP-CFL594 (Santa Cruz Biotech, sc-516178, 1:100) and anti-rat IgG AF488 (ThermoFisher Scientific, cat no. A-11006, 1:500).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-rat IgG</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Then, cells were washed and were incubated with detector anti-BrdU antibody for 1 hour at RT.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-BrdU</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">After the incubation cells were washed and incubated with the horseradish peroxidase conjugated goat anti-mouse antibody for 30 minutes at RT.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-mouse</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Briefly, 50 μl of standard were added to standard wells and 40 μl of sample-to-sample wells and then added 10 μl of anti-TRAP antibody to sample wells and 50 μl of streptavidin-HRP to sample wells and standard wells.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-TRAP</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">After 72 hours, cells were washed, fixed with 4% formaldehyde, permeabilized with 0.1% Triton X-100 in PBS and stained overnight at 4°C with ACE2 protein-specific antibody (Abcam Ab15348).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>ACE2</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Cells were then incubated with anti-rabbit secondary antibody (Alexa Fluor 536 anti-rabbit, Invitrogen Life Technologies) for 1 hour at 37°C.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-rabbit</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Sections were then incubated with cocktails of primary antibodies: rabbit anti-SARS-CoV-2 Spike Protein (1:100, Invitrogen, #MA5-36087) + rat anti-ERα H222 (1:100, Santa Cruz Biotechnology, #sc53492) overnight at 4°C.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-SARS-CoV-2 Spike Protein ( 1:100 , Invitrogen , #MA5-36087 )</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Sections were then incubated with the primary antibodies rat anti-ERα H222(1:100, Santa Cruz Biotechnology, #sc53492), diluted in 1% normal goat serum (NGS), 4% BSA, 0.02% saponin in PB at 4°C overnight.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-ERα H222</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Sections were rinsed and incubated overnight at 4°C in the secondary antibody Nanogold-Fab’ goat anti-rat-IgG (1:100</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-rat-IgG</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">On day one, slides were blocked with a peroxidase blocker (Bio SB Catalog No. BSB 0054), washed with an immunoDNA washer buffer (Bio SB, Catalog No. BSB 0150); then, incubated with 0.2 μg/mL of anti-SARS-CoV-2 spike glycoprotein antibody (abcam, Catalog No. ab272504) for 1 hour.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-SARS-CoV-2 spike glycoprotein</div><div>suggested: (Abcam Cat# ab272504, RRID:AB_2847845)</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Cell Lines</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Briefly, MCF-7 nuclear extracts (5 μg; ab14860, Abcam) were treated with either S (0.01-300 nM; Acro Biosystems)</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>MCF-7</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Proliferation assays: MCF-7 and MDA-MB-23 cells were obtained from ATCC and growth in DMEM without phenol red, supplemented with 10% fetal bovine serum (FBS), penicillin/streptomycin at 37 °C in a 5% CO2 and 95% humidified atmosphere.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>MDA-MB-23</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">TRAP activity by ELISA assay in RAW-OCs: RAW264.7 (murine macrophages ATCC, USA) were cultured as manufacturer’s protocol.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>RAW264.7</div><div>suggested: CLS Cat# 400319/p462_RAW-2647, RRID:CVCL_0493)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">ACE2 expression in Calu-3 cells: Calu-3 cell line was obtained from ATCC and maintained in Eagle’s Minimum Essential Medium(EMEM; Lonza) supplemented with 10% fetal bovine serum (FBS), 1% L-glutamine and 1% penicillin/streptomycin solution at 37°C in a humidified atmosphere of 5% CO2.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Calu-3</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Recombinant DNA</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The next day, cells in each well were transfected with 1.5 μl of ViaFect reagent (Promega, cat no. E498A) and 0.5 μg of empty pcDNA3.1 vector, or an expression vector for the wild-type (WT) SARS-CoV2 S with a C-terminal hemagglutinin (HA) epitope tag (pBOB-CAG-SARS-CoV2-S-HA) or the double mutant (R682S,R685S) SARS-CoV2 S with a C-terminal flag epitope tag (pCAGGS-SARS2-S-FKO).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pcDNA3.1</div><div>suggested: RRID:Addgene_79663)</div></div><div style="margin-bottom:8px"><div>pBOB-CAG-SARS-CoV2-S-HA</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">pBOB-CAG-SARS-CoV2-S-HA was a gift from Gerald Pao (Addgene plasmid # 141347; http://n2t.net/addgene:141347; RRID:Addgene_141347).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div></div><div>detected: RRID:Addgene_141347)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">pCAGGS-SARS2-S-FKO (C-flag) was a gift from Hyeryun Choe & Michael Farzan (Addgene plasmid # 159364; http://n2t.net/addgene:159364; RRID:Addgene_159364).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div></div><div>detected: RRID:Addgene_159364)</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Software and Algorithms</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Data were fitted using the non-linear curve fitting routines in Prism® (Graphpad Software Inc).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Graphpad</div><div>suggested: (GraphPad, RRID:SCR_000306)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The digitized images were also analyzed using ProtoArray Prospector v5.2 and potential hits were identified using the software’s algorithm.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>ProtoArray Prospector</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Protein binding responses were analyzed using BiaEval software.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>BiaEval</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Interactome analysis: The STRING database52, that integrates all known and predicted associations between proteins, including both physical interactions as well as functional associations has been used to analyses functional associations between biomolecules.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>STRING</div><div>suggested: (STRING, RRID:SCR_005223)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Images were prepared for presentation using ImageJ v.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>ImageJ</div><div>suggested: (ImageJ, RRID:SCR_003070)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">After three washes, the Mouse/Rabbit PolyDetector Plus link &HRP label (Bio SB, Catalog No. BSB 0270) were applied.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Mouse/Rabbit PolyDetector Plus</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>PolyDetector</div><div>suggested: None</div></div></td></tr></table>

---

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

---

---

Results from TrialIdentifier: No clinical trial numbers were referenced.

---

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

---

Results from JetFighter: We did not find any issues relating to colormaps.

Results from rtransparent:

---

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

---

Results from scite Reference Check: We found no unreliable references.

---

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About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

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SciScore for 10.1101/2022.05.16.22275163: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

<table><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Ethics</td><td style="min-width:100px;border-bottom:1px solid lightgray">Consent: Individuals provide written, informed consent for collection of demographic and clinical variables as well as blood for biobanking on up to 5 occasions every 6 months</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Sex as a biological variable</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Randomization</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Blinding</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Power Analysis</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr></table>

Table 2: Resources

<table><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Antibodies</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The plate containing the samples and standard curves were then incubated for 30 minutes at room temperature, washed, following which MSD SULFO-TAG-labelled goat anti-human IgG secondary antibody was added at a concentration of 1ug/ml and the plate was further incubated for one hour.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-human IgG</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">These kits comprise 96 well plates, precoated with antigens, proprietary blocker, diluent, wash buffer, detection antibody, read buffer, control sera and reference standard.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>antigens,</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Software and Algorithms</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">In addition, assay performance was compared with the Abbott SARS-CoV-2 IgG assay and the Abbott SARS-CoV-2 IgG II assay, chemiluminescent microparticle immunoassays (CMIA) (Abbott laboratories, IL, USA).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Abbott</div><div>suggested: (Abbott, RRID:SCR_010477)</div></div></td></tr></table>

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Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:

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Although the CEPHR COVID19 Serology Assay has many advantages, it is not without limitations. The assay employs RBD derived from the Wuhan-Hu-1 reference strain of SARS-CoV-2. Although the test has been validated in convalescent plasma from individuals with confirmed SARS-CoV-2, these individuals were infected with the variants circulating in the first wave of infections in early 2020 and the performance of this assay against the different SARS-CoV-2 variants of concern (VOCs) that have emerged since is still under investigation. In addition, the vaccinated population was relatively small, with the majority of individuals less than 3 months from second dose vaccine. Given waning of post vaccine protection22, sensitivity of the assay may alter as time post vaccination increases. Additionally binding assays do not evaluate antibody function, such as neutralising capacity or antibody effector function, although these gold standard assays are time consuming and expensive and do not lend themselves to high throughput. However, correlation of the CEPHR COVID19 Serology Assay with these gold standard functional assays is ongoing. Despite these limitations, the CEPHR SARS-CoV-2 Serology Assay is a robust, customisable, multiplex serologic assay for the detection of several different IgG specific to SARS-CoV-2, with multiple potential real world applications and performance characteristics that support its further development for use in both research and clinical settings.

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Results from TrialIdentifier: No clinical trial numbers were referenced.

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Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

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Results from JetFighter: We did not find any issues relating to colormaps.

Results from rtransparent:

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• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

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Results from scite Reference Check: We found no unreliable references.

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Reply to the reviewers

Reviewer #1 (Evidence, reproducibility and clarity (Required)): Excellent quality of cell biology and biochemistry. the additional supports are needed for the claim of actin elongation using different formin variants.

Reviewer #1 (Significance (Required)): Ingrid Billault-Chaumartin and co-authors described interesting research that provides insights on formin-isoform specific function in fission yeast and a new role of Fus1 FH2 domain in cell-cell fusion event. While three formin isoforms have different localization, research proposed an additional dissection in their functional differences by having different functions in C-terminus, including FH1 FH2 and formin C-terminus. The work also described additional factors that regulate cell fusions from autotrophy effect and formin expression level, in addition to the well-accepted formin biochemical activities. Here are my comments regarding the strengths of the work and improvements that could further strengthen the story.

Major comments 1. Fig.1 shows Cdc12C could recapitulate Fus1 function by ~80% if fused with Fus1C, whereas deletion of the C-terminal tail of Cdc12 following FH2 introduces drastic dysfunction. Together with Fig. 3, these results indicate Cdc12 Cter plays more important roles than Fus1 Cter for there respective functions. Such results suggested a Cter-mediated mechanism that differentiates the functions of three fission yeast formin isoforms. The authors examined contributions from the difference in FH1 (Figs 4,5) and FH2 residues (Fig. 6). Whereas the obvious phenotype of Cter was not further investigated and not much discussed. The Cter of budding yeast formins interacts with nucleation-promoting factors, Bud6 and Aip5. Although S. Pombe does not have orthologs of budding yeast Bud6 and Aip5, I wonder would the author discuss the potential contribution of Cter in differentiating S. Pombe formins.

The reviewer is correct that the C-terminal tail region of Cdc12 beyond the FH1-FH2 domains has a strong influence on the ability of Cdc12C to replace Fus1C. This is one reason why we specifically investigated the possible role of Fus1 C-terminal tail, which is much shorter than that of Cdc12. We found that Fus1 C-terminal tail plays only very minor role in regulating Fus1 function, as described in Figure 3. We note that contrary to what the reviewer states, Bud6 exists in S. pombe and binds the C-terminal tail of the formin For3 (see Martin et al, MBoC 2007), but whether it binds Fus1 is unknown. We have expanded our discussion to include a paragraph on the role of formin C-termini.

Because the manuscript is focused on the function of Fus1 formin, we did not explore further the role of the Cdc12 C-terminal tail. It was previously shown that this region of Cdc12 contains an oligomerization domain that promotes actin bundling (Bohnert et al, Genes and Dev 2013). It is thus likely that this helps Cdc12 FH1-FH2 perform well in replacement of Fus1. In fact, it is likely that oligomerization boosts formin function, as we have discovered that Fus1 N-terminus contains a disordered region that fulfils exactly this function. This is described in a distinct manuscript under review elsewhere and just deposited on BioRxiv (Billault-Chaumartin et al, BioRxiv 2022; DOI: 10.1101/2022.05.05.490810). We have now cited this point in the discussion.

1. Here, the study focuses on the FH1 between Fus1 and Cdc12 to understand their different functions in actin polymerization. FH1 mediated actin elongation through its interaction with profilin via polyP. The transfer rate of G-actin from profilin and profilin sliding depends on the polyP patterns regarding the length of each polyp motif and their distance to FH2 (Naomi Courtemanche and Thomas D. Pollard, JBC, 2012). To better understand the mechanisms by which these engineered FH1 variants on both Fus1 and Cdc12 in Fig. 4, the author may want to list the sequence of these engineered FH1 domains, including the information of the number and length of polyp motifs, and discuss these patterns.

This list and discussion were available in the initial paper that characterized each of the constructs in vitro (Scott et al, MBoC 2011). We have now re-drawn it in a supplemental figure for convenience (as also answered in response to minor point 2), which is already provided in the revised manuscript as Figure S1. (Previous supplementary figures are re-numbered S1>S2, S2>S3 and S3>S4).

1. Figs.4,5 cell biology results do not directly support the point of specific elongation rate unless the LifeAct-labeled actin cable elongation speed could be followed and quantified. The fluorescent tagging of tropomyosin does not show the actin cable pattern, which makes it very difficult to be used to study actin cable dynamics, such as elongation. Therefore, I feel the data in current Fig. 4 and Fig. 5 could not claim the differences in actin elongation without a quantitative comparison of elongation rate. I suggest a CK666 treatment to increase the visibility of the actin cable pattern of LifeAct, used before in both fission and budding yeasts, which would allow the author to quantify the actin cable elongation rate. Another way is to use the TIRF assay used in this study, which would give a better quantitation of formin nucleation and profilin-aided elongation.

We respectfully disagree with the reviewer on this point. All the constructs we use in vivo have been characterized in vitro and their elongation rate carefully measured (Scott et al, MBoC 2011). These values are thus known and can be directly compared to our results in vivo.

Of course, it would be fantastic to be able to directly measure formin elongation rates in vivo, but we are not aware that this has been done in any system. The proxy experiments that the reviewer suggests would be good ones, but each faces technical challenges that make them impossible in our system. First, because the fusion focus is a structure that forms in response to cell-cell pheromonal communication, we cannot add CK-666 or any other drug during this phase, as this perturbs the pheromone signal. Indeed, we had shown that simple buffer wash leads to loss of the fusion focus (see Dudin et al, Genes and Dev 2016). Second, the fusion focus is at the contact site between partner cells, i-e somewhat distant (1-2µm) from the coverslip during imaging. It is thus impossible to use TIRF. Finally, the fusion focus is a tightly packed actin structure. This is the reason why (rather than use of the tropomyosin marker) we cannot image single actin filaments (or even bundles) of which we could follow the dynamics as has been done to measure the retrograde flow of actin cables in yeast.

What we have done is to use a better tropomyosin tag, mNeonGreen-Cdc8, which was just described (Hatano et al, BioRxiv 2022; DOI: 10.1101/2022.05.19.492673) to quantify amounts of linear actin. Although this is not a measure of elongation rate, it would give some sense about amounts of polymer assembled. We have obtained images with mNeonGreen-Cdc8 of all experiments previously conducted with GFP-Cdc8 and have replaced them in Figure 4C, Figure 5E, Figure 6E and Figure S2B. We have also quantified the relevant strains. The relative intensities of mNeonGreen-Cdc8 at the fusion focus at fusion time reflect remarkably well the measured elongation rates of the various formin constructs characterized in vitro. These data are now provided as new panels Figure 4F and Figure 5F.

1. I appreciated the detailed biochemical dissections of multiple aspects of WTFus1 and Fus1R1054E, although the biochemical assays could not identify the mechanism by which R1054E causes the cell fusion. In many cases, the formin functions are diverse in diverse biological processes and sophisticated that cannot be explained well only from its biochemical activities in actin polymerization, such as the bundling, nucleation, and elongation studied in this story regarding fusion. This exciting information allows us to think of more possibilities that might regulate formin function rather than a direct change of formin activities in actin polymerization. I think a discussion of different aspects of functional regulation of formin might inspire society to investigate new possibilities to solve the mysteries. For example, the changes in formin behaviors and functions could be regulated by stress-induced formin turnover by degradation, cell signaling-regulated formin clustering and complex assembly, and their potential relevance to recruit protein constituents for fusion progression.

We have added a paragraph on the role of Fus1 C-terminus. If you feel we should expand more on the diverse modes of regulation of formins, we could, but we have so far kept the discussion centred around the points of investigation in this paper, whose aim was to probe how changes in nucleation and elongation rates, rather than other regulations, affect the in vivo function of Fus1.

Minor comments. 1. There are two types of "C", one includes FH1/FH2 and one following FH2, used in the manuscript, and it is a bit confusing. Better to differentiate them that allows an easy following. Fig. 1 uses Cdc12C-deltaC, Fig. 3 uses Fus1-delta Cter.

We have updated the nomenclature to make this clearer: the C-terminal region beyond the FH1-FH2 domains is now called Cter throughout the manuscript.

1. It's better to specify the amino acid position on the schematic of formins, such as panel A in many figures. It's always more informative to compare formin activities by considering the domain lengths, especially for the C-terminal tail that is variable in lengths and sequences. With similar thoughts, I suggest a supplementary figure that lists the sequence of all FH1 domains variants and Cter domains, such as the FH2 domain in Fig. S1.

We have made a supplementary figure (new Figure S1) listing all constructs with specific aa positions as well as the FH1 domain variants and their sequences (see also answer to point 2 above). We have not added the sequence of the Cter domains in this figure, as these are extremely divergent and not particularly informative at this point.

1. "n" for the statistic needs to be provided for Fig. S3.

We have added the information to the legend of the figure (now Fig S4).

1. The SDS-PAGE staining gel of the purified recombinant proteins for biochemical assays should be provided, particularly for these newly reported mutant variants.

This is now provided as new panel S4C. We show the purified recombinant Cdc122FH1-Fus1FH2 proteins, which are the newly reported ones.

Reviewer #2 (Evidence, reproducibility and clarity (Required)): In this study, Billaut-Chaumartin and colleagues investigate the molecular specialization of the S. pombe formin, Fus1. The authors systematically modulate the actin filament elongation and nucleation activities of Fus1 by expressing chimeric constructs that contain Formin Homology 1 and 2 domains from two other formins with known polymerization activities. By characterizing the architecture of the fusion focus and the efficiency of cell fusion, they find that both the elongation and nucleation properties of Fus1 are specifically tailored for its cellular role. Comparison of formin constructs with similar elongation and nucleation activities also reveals that the Fus1 FH2 domain possesses a specific property that promotes efficient cell fusion. Using sequence alignment and homology modeling, the authors identify R1054 as the residue that confers this novel, fusion-specific activity to Fus1, despite producing no effect on its bundling or polymerization properties in vitro.

Overall, this study is well motivated, and the results support the conclusions that are drawn. I have only minor suggestions, as described below.

Minor comments: (1) The schematic diagrams of the chimeric formin constructs are very helpful. However, it is difficult to distinguish the colors from one another, especially in the case of the Cdc12FH1-Fus1FH2 variant, which requires discernment of the relatively small purple region within the dark blue molecule. Would it be possible to modify the colors to increase their contrast? Similarly, the blue and gray data sets in Figure 3B are very difficult to discern.

We have changed the colours to improve contrasts.

(2) The affinities (Kd) with which the formins bind the barbed ends as described in the second-to-last paragraph on page 8, in Figure Legend 7G, and in the "Analysis of pyrene data" section of the Materials and Methods should be defined as dissociation "constants", rather than dissociation "rates". Also, these affinities are lacking units in the following sentence on page 8.

We have corrected this. The unit is nM.

(3) When comparing the TIRF micrographs in Figure S3A, it looks as though both formins (but especially the R1054E variant) nucleate more filaments in the presence of profilin than in its absence. Is this a reproducible effect? If so, can the authors provide an explanation for this?

There is strong variability in the filament numbers observed by TIRF in replicate experiments, which makes it difficult to use this technique to determine the nucleation efficiency. This may be due for instance to the stickiness of the glass, which may influence the number of observed filaments. We have measured the number of filaments after 130s of polymerization for each condition to test whether there are any significant differences between conditions despite overall variability. The measurements suggest that the addition of profilin increases the number of actin filaments. However, these results should be taken very carefully due to the experimental variations (very large error bars). Additionally, because Fus1-associated filaments are very short in absence of profilin, it is quite likely that this influences their crowding at the glass surface compared to longer filaments (in presence of profilin). Since in TIRF we can only observe the filaments at the glass surface, we may miss a portion of short Fus1-bound actin filaments in absence of profilin.

For these reasons, and because the possible role of profilin in modulating nucleation efficiency by formins is not the object of the work here, would thus prefer not to include this graph in the manuscript.

Reviewer #2 (Significance (Required)): This study contributes a key advancement towards understanding how the polymerization activities of formins are tailored to support diverse and specific cellular functions. The results in this study nicely complement and expand upon similar recent work that dissected the polymerization requirements of the formin Cdc12, which mediates cytokinetic ring assembly in S. pombe, and For2, which drives the assembly of apical networks that are necessary for polarized growth in Physcomitrella patens. As such, this work will likely be of significant interest to scientists who study mechanisms of actin dynamics regulation. The identification of R1054 as a residue that confers a novel regulatory activity to the FH2 domain of Fus1 will also likely be of great interest to biochemists and other scientists who study formins at the molecular level.

My expertise is in the field of formins and actin polymerization.

```css @namespace svg "http://www.w3.org/2000/svg";

a { / These rules would apply to any a elements. / text-decoration: underline; color: purple; } svg|a { / These rules would apply to SVG a elements, but not HTML links. / stroke: purple; } svg| { / These rules apply to all SVG-namespaced elements, but not HTML elements. */ mix-blend-mode: multiply; } ```

Reviewer #2 (Public Review):

In this manuscript by Ma et al., the authors develop a mass cytometry that includes 5 heavy metal conjugated lectins. After some validation of this panel, the authors use the panel to analyze human PBMCs, tonsils and endometrial CD4 T cells before and after infection with an HIV virus with HSA reporter tag. They found that HIV infection was associated with higher levels of staining with 4 out of 5 lectins (sialic acid and fucose binders). Using the PP-SLIDE algorithm they previously developed, and they predicted that HIV preferentially infected higher cells with higher lectin binding and led to an increase in staining after infection. To validate this hypothesis, sorting of high vs. low lectin staining cells was performed to show that cells with higher lectin staining also had higher rates of HIV infection. They also used sialidase to reduce sialic acid levels and showed that it reduced HIV infection in PBMCs from two different donors. In addition to the development, validation and demonstration of mass cytometry lectin staining, the finding that glycosylation can influence HIV infectivity is novel and could open up new avenues for investigation. I think this work will be generally useful to the mass cytometry and HIV communities.

GWG, Some random thoughts:

Your challenge question is tough, not just for the mere discovery portion, but for the multiple other functions involved, particularly a "submit/reply" portion and a separate "I want to subscribe to something for future updates".

I can't think of any sites that do both of these functionalities at the same time. They're almost always a two step process, and quite often, after the submission part, few people ever revisit the original challenge to see further updates and follow along. The lack of an easy subscribe function is the downfall of the second part. A system that allowed one to do both a cross-site submit/subscribe simultaneously would be ideal UI, but that seems a harder problem, especially as subscribe isn't well implemented in IndieWeb spaces with a one click and done set up.

Silo based spaces where you're subscribed to the people who might also participate might drip feed you some responses, but I don't think that even micro.blog has something that you could use to follow the daily photo challenges by does it?

Other examples: https://daily.ds106.us/ is a good example of a sort of /planet that does regular challenges and has a back end that aggregates responses (usually from Twitter). I imagine that people are subscribed to the main feed of the daily challenges, but I don't imagine that many are subscribed to the comments feed (is there even one?)

Maxwell's Sith Lord Challenge is one of the few I've seen in the personal site space that has aggregated responses at https://www.maxwelljoslyn.com/sithlordchallenge. I don't think it has an easy way to subscribe to the responses though an h-feed of responses on the page might work in a reader? Maybe he's got some thoughts about how this worked out.

Ongoing challenges, like a 30 day photography challenge for example, are even harder because they're an ongoing one that either requires a central repository to collect, curate, and display them (indieweb.xyz, or a similar planet) or require something that can collect one or more of a variety of submitted feeds and then display them or allow a feed(s) of them. I've seen something like this before with http://connectedcourses.net/ in the education space using RSS, but it took some time to not only set it up but to get people's sites to work with it. (It was manual and it definitely hurt as I recall.)

I don't think of it as a challenge, but I often submit to the IndieWeb sub on indieweb.xyz and I'm also subscribed to its output as well. In this case it works as an example since this is one of its primary functions. It's not framed as a challenge, though it certainly could be. Here one could suggest that participants tag their posts with a particular hashtag for tracking, but in IndieWeb space they'd be "tagging" their posts with the planet's particular post URL and either manually or automatically pinging the Webmention endpoint.

Another option that could help implement some fun in the system is to salmention all the prior submissions on each submission as an update mechanism, but one would need to have a way to unsubscribe to this as it could be(come) a spam vector.

JSX的底层实现逻辑实际上是调用了React.createElement函数

Reviewer #1 (Public Review):

In this manuscript, authors found Halo tag become resistant to lysosome degradation upon ligand binding, using this unique property, they developed a highly sensitive assay to monitor the autophagy flux. Measuring autophagy flux is one of the most important assays for studying autophagy, there are a few widely used assays to monitor the autophagy flux, such as p62 degradation, and LC3 processing, however, each of them has its own limitation, which is well known in the field. In this regard, this assay provides a simple, straight forward and sensitive assay for measuring autophagy flux, which I personally think is very likely it will be widely used by the autophagy community. This is a well-controlled, rigorous study and the manuscript is clearly written.

Reviewer #2 (Public Review):

Yim et al have utilised the HaloTag system to generate tools and assays to measure autophagy flux. The assays are highly accessible and straight forward to conduct. The study does not have any major weaknesses, with all key conclusions strongly supported by clear data. A major strength of the study is the robustness of the assay and its ease of use across SDS-PAGE and imaging techniques that I expect will help with its uptake by the research community. The assay utilises the HaLo tag and its inherent stability within lysosomes once pulsed with a HaLo ligand. This enables analysis of autophagy flux over a set period of time. The approach is highly complementary to the recently published study by Rudinskiy et al (2022) MBoC, but also includes additional tools to measure different types of selective autophagy and bulk autophagy. The inclusion of limitations of their approach within the discussion will be very useful for researchers planning to use the assay in their work. Overall, this is an excellent study that has generated very valuable tools for the study of autophagy.

Reviewer #3 (Public Review):

Monitoring autophagy induction and flux in mammalian cells is challenging and depends largely on the mammalian ATG8 proteins (LC3 and GABARAP), typically tagged at the N-terminus with a small tag (HA, flag, myc) or a range of fluorescent tags. When autophagy is induced these ATG8 proteins get captured into autophagosomes and delivered to lysosomes for degradation. Monitoring flux by western blots relies on a molecular weight shift caused by lipidation, and quantification of loss of signal from degradation (analysis of initiation), or accumulation by the addition of inhibition of lysosomal inhibitors (analyses of flux). Fluorescent tags provide similar results but the measurements rely on counting degradation sensitive or resistant fluorescent signals. Image-based analysis is more challenging than western blot but both require significant optimization. In this manuscript these existing assays are modified by the use of a probe (Halo tag) again appended to the N-terminus of ATG8s which becomes resistant to lysosomal degradation after binding a ligand (TMR). The ligand can be pulsed-in to allow detection of acute induction of autophagy eliminating the background from basal accumulation. Generation of the Halo-TMR is then monitored by western blot or using an in gel-fluorescent assay. The authors present data which shows the adaptability of the system for imaging analysis, and for both quantitative analysis using western blot and imaging of selective autophagy or bulk, non-selective autophagy. The authors have developed a robust, useful alternative to existing assay and present the results in a careful, well described brief manuscript. These modifications are important for the field and for those who require quantitative results. The drawbacks are similar to existing assays and will usually require the generation of stable cell lines because over-expressed ATG8s can aggregate and confound the measurements.

SciScore for 10.1101/2022.05.13.491770: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

<table><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Ethics</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Sex as a biological variable</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Randomization</td><td style="min-width:100px;border-bottom:1px solid lightgray">4 colonies from each transformed plate were randomly picked and the insert was checked by performing colony PCR using nested PCR primers.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Blinding</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Power Analysis</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Cell Line Authentication</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr></table>

Table 2: Resources

<table><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Antibodies</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Cells were incubated with an anti-SARS-CoV spike primary antibody directly conjugated with alexaflour-647 (CR3022-AF647) for up to 4 hours at room temperature.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-SARS-CoV spike</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Ten million PBMCs of select COVID-19 recovered donors were stained with RBD-Alexa Fluor 488 for 1 hour at 4°C, followed by washing with PBS containing 2% FBS (FACS buffer) and incubation with efluor780 Fixable Viability (Live Dead) dye (Life Technologies, #65-0865-14) and anti-human CD3, CD19, CD20, CD27, CD38 and IgD antibodies (BD Biosciences) for 30 minutes.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-human CD3</div><div>suggested: (RayBiotech Cat# CS-11-0105, RRID:AB_1227994)</div></div><div style="margin-bottom:8px"><div>CD19</div><div>suggested: (Agilent Cat# TC67401, RRID:AB_579635)</div></div><div style="margin-bottom:8px"><div>CD20</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>CD27 , CD38</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>IgD</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">mAb antibody binding was then detected with 50 μl/well of MSD SULFO-TAG anti-human IgG antibody (diluted 1:200) incubated for one hour at room temperature with shaking at 700rpm.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-human IgG</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Cell Lines</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Briefly, 100 pfu of SARS-CoV-2 (2019-nCoV/USA_WA1/2020), Alpha, Beta, Gamma, Delta and Omicron (BA.1 and BA.2) were used on Vero TMPRSS2 cells.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Vero TMPRSS2</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Software and Algorithms</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">IC50 titers were calculated by non-linear regression analysis using the 4PL sigmoidal dose curve equation on Prism 9 (Graphpad Software).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Graphpad</div><div>suggested: (GraphPad, RRID:SCR_000306)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Data were analyzed using FlowJo software 10</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>FlowJo</div><div>suggested: (FlowJo, RRID:SCR_008520)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">CryoEM data analysis and model building: CryoEM movies were motion-corrected either in Motioncorr2 in Relion3.0 (30) or using Patch motion correction implemented in Cryosparc v3.3.1 (31).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Cryosparc</div><div>suggested: (cryoSPARC, RRID:SCR_016501)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The combined Focused Map tool in Phenix was used to integrate high resolution locally refined maps into an overall map.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Phenix</div><div>suggested: (Phenix, RRID:SCR_014224)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Glycans with visible density were modelled in Coot (36).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Coot</div><div>suggested: (Coot, RRID:SCR_014222)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Model validation was performed using Molprobity (37).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Molprobity</div><div>suggested: (MolProbity, RRID:SCR_014226)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Figures were prepared in ChimeraX(34) and PyMOL (39).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>PyMOL</div><div>suggested: (PyMOL, RRID:SCR_000305)</div></div></td></tr></table>

---

Results from OddPub: Thank you for sharing your data.

Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

---

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Results from TrialIdentifier: No clinical trial numbers were referenced.

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Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

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Results from JetFighter: We did not find any issues relating to colormaps.

Results from rtransparent:

---

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

---

Results from scite Reference Check: We found no unreliable references.

---

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About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

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Flems does something like this:

To allow you to use Flems with only a single file to be required the javascript and the html for the iframe runtime has been merged into a single file disguised as flems.html. It works by having the javascript code contained in html comments and the html code contained in javascript comments. In that way if loaded like javascript the html is ignored and when loaded as html the javascript part is ignored.

https://github.com/porsager/flems#html-script-tag

SciScore for 10.1101/2022.05.10.491349: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

<table><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Ethics</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Sex as a biological variable</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Randomization</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Blinding</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Power Analysis</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr></table>

Table 2: Resources

<table><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Recombinant DNA</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Protein expression of Nsp15 variants: WT Nsp15 was previously synthesized by Genscript (Piscataway, NJ), and contains an N-terminal His tag with thrombin and TEV cleavage sites in pET14b [28].</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pET14b</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Software and Algorithms</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The sequences of Nsp15 isolates were then aligned to that of the original Wuhan isolate (GenBank NC_045512.2) using the nucmer command from MUMmer 4.0 package [53] with default parameters.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>MUMmer</div><div>suggested: (MUMmer, RRID:SCR_018171)</div></div></td></tr></table>

---

Results from OddPub: Thank you for sharing your data.

Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

---

---

Results from TrialIdentifier: No clinical trial numbers were referenced.

---

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

---

Results from JetFighter: We did not find any issues relating to colormaps.

Results from rtransparent:

---

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

---

Results from scite Reference Check: We found no unreliable references.

---

<footer>

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

</footer>

SciScore for 10.1101/2022.05.10.491295: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

<table><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Ethics</td><td style="min-width:100px;border-bottom:1px solid lightgray">Field Sample Permit: The study was carried out following a protocol approved by the ANSES/EnvA/UPEC Ethics Committee (CE2A-16) and authorized by the French ministry of Research under the number APAFIS#25384-2020041515287655 v6 in accordance with the French and European regulations.<br>IRB: The study was carried out following a protocol approved by the ANSES/EnvA/UPEC Ethics Committee (CE2A-16) and authorized by the French ministry of Research under the number APAFIS#25384-2020041515287655 v6 in accordance with the French and European regulations.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Sex as a biological variable</td><td style="min-width:100px;border-bottom:1px solid lightgray">Golden Syrian hamster infections and assessment of αREPs antiviral activity: Hamster infections: Thirty-two specific-pathogen-free (SPF) 8 weeks-old Golden Syrian hamsters (Mesocricetus auratus, males, provided by Janvier-Labs, Le Genest-Saint-Isle, France) housed under BSL-III conditions were kept according to the standards of French law for animal experimentation.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Randomization</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Blinding</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Power Analysis</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Cell Line Authentication</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr></table>

Table 2: Resources

<table><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Antibodies</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The sections were then incubated overnight in PBS with 0.2% BSA and 0.05% Tween-20 with primary antibodies directed against SARS-CoV-2 Nucleocapsid Protein (1:500; mouse monoclonal, # ZMS1075, Merck);</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>SARS-CoV-2 Nucleocapsid Protein ( 1:500</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Fluorescence staining was performed using goat anti-rabbit Alexa-Fluor-488 (1:800; Molecular Probes – A32731; Invitrogen, Cergy Pontoise, France) and donkey anti-mouse Alexa-Fluor 555 (1:800; Molecular Probes – A32773; Invitrogen, Cergy Pontoise, France) secondary antibodies.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-rabbit</div><div>suggested: (Thermo Fisher Scientific Cat# A32731, RRID:AB_2633280)</div></div><div style="margin-bottom:8px"><div>anti-mouse</div><div>suggested: (Thermo Fisher Scientific Cat# A32773, RRID:AB_2762848)</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Cell Lines</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Briefly, HEK-293TT cells (106 cells per P6 well) were transfected with plasmids encoding GAG-POL, F-LUC and SARS-CoV-2 spikes.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>HEK-293TT</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Viral stocks were prepared by propagation in Vero E6 cells in Dulbecco’s modified Eagle’s medium (DMEM) supplemented with 2% (v/v</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Vero E6</div><div>suggested: RRID:CVCL_XD71)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">This mixture was added to Vero-E6 cells (CRL-1586, ATCC) seeded in a 96-well plate one day before.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Vero-E6</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">To prepare the virus working stocks, a 25cm2 culture flask of confluent VeroE6 TMPRSS2 cells growing with MEM medium with 2.5% FCS was inoculated at a multiplicity of infection (MOI) of 0.001.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>VeroE6 TMPRSS2</div><div>suggested: JCRB Cat# JCRB1819, RRID:CVCL_YQ49)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">EC50 and EC90 determination: One day prior to infection, 5×104 VeroE6/TMPRSS2 cells per well were seeded in 100 µL assay medium (containing 2.5% FCS) in 96 well culture plates.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>VeroE6/TMPRSS2</div><div>suggested: JCRB Cat# JCRB1819, RRID:CVCL_YQ49)</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Organisms/Strains</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">SARS CoV-2 Beta (SA lineage B 1.351) was isolated in France in 2021, The strain is available through EVA GLOBAL: UVE/SARS-CoV-2/2021/FR/1299-ex SA (lineage B 1.351) at https://www.european-virus-archive.com/virus/sars-cov-2-uvesars-cov-22021fr1299-ex-sa-lineage-b-1351. Sars-Cov-2 Gamma (SARS-CoV-2/2021/JP/TY7-503 lineage P.1, ex Brazil) was isolated in Japan in January 2021.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Sars-Cov-2 Gamma</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Recombinant DNA</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Production of the receptor binding domain (RBD) of the SARS-CoV-2 spike: The RBD (223 amino acids starting at position 319 of the spike sequence) coding sequence was cloned in frame behind a sequence encoding a signal peptide and in front of a His-tag coding sequence in the eukaryotic pYD11 expression plasmid.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pYD11</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">αReps expression and purification: The αRep genes encoding the RBD binders were subcloned in the bacterial expression vector pQE81 and resulting plasmids used for transforming Rosetta cells.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pQE81</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Software and Algorithms</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The sections were then incubated overnight in PBS with 0.2% BSA and 0.05% Tween-20 with primary antibodies directed against SARS-CoV-2 Nucleocapsid Protein (1:500; mouse monoclonal, # ZMS1075, Merck);</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Merck)</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Images were quantified using ImageJ (Rasband, W.S., ImageJ, U. S. National Institutes of Health, Bethesda, Maryland, USA, http://imagej.nih.gov/ij/, 1997–2012) to threshold specific staining of SARS-CoV-2 in the dorso-medial area of the nasal cavity.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>ImageJ</div><div>suggested: (ImageJ, RRID:SCR_003070)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">All data obtained were analyzed using GraphPad Prism 8 software (Graphpad software).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>GraphPad Prism</div><div>suggested: (GraphPad Prism, RRID:SCR_002798)</div></div><div style="margin-bottom:8px"><div>Graphpad</div><div>suggested: (GraphPad, RRID:SCR_000306)</div></div></td></tr></table>

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Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

---

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Results from TrialIdentifier: No clinical trial numbers were referenced.

---

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

---

Results from JetFighter: We did not find any issues relating to colormaps.

Results from rtransparent:

---

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

---

Results from scite Reference Check: We found no unreliable references.

---

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About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

</footer>

SciScore for 10.1101/2022.05.09.491254: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

<table><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Ethics</td><td style="min-width:100px;border-bottom:1px solid lightgray">IRB: EXPERIMENTAL MODELS AND SUBJECT DETAILS: MATERIALS AND METHODS: Human subjects: This study was approved by the Institutional Review Board of the University of Hong Kong/Hospital Authority Hong Kong West Cluster (Ref No. UW 21-452).<br>Consent: Written informed consent and questionnaire of vaccination and infection were obtained from these subjects.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Sex as a biological variable</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Randomization</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Blinding</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Power Analysis</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Cell Line Authentication</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr></table>

Table 2: Resources

<table><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Antibodies</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Antigen-specific B cells: To characterize the SARS-CoV-2 Spike-specific B cells, PBMCs from each vaccinee were first stained with an antibody cocktail contained dead cell dye (Zombie aquae), CD3-Pacific Blue, CD14-Pacific Blue, CD56-Pacific Blue, CD19-BV785, IgD-BV605, IgG-PE, CD27-BV711, CD21-PE/Cy7, CD38-Percp/Cy5.5, CD11C-APC/Fire750 and His-tag Spike protein.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>IgD-BV605, IgG-PE</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>CD38-Percp/Cy5.5</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>CD11C-APC/Fire750</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>His-tag Spike protein.</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Cells were then washed with FACS buffer (PBS with 2% FBS) and further stained with the secondary antibodies including APC anti-His and DyLight 488 anti-his antibodies.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-His</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Cell Lines</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">In brief, different SARS-CoV-2 pseudotyped viruses were generated through co-transfection of 293T cells with 2 plasmids, pSARS-CoV-2 S and pNL4-3Luc_Env_Vpr, carrying the optimized SARS-CoV-2 S gene and a human immunodeficiency virus type 1 backbone, respectively.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>293T</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The plasma-virus mixtures were then added into pre-seeded HEK293T-hACE2 cells.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>HEK293T-hACE2</div><div>suggested: RRID:CVCL_A7UK)</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Recombinant DNA</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">In brief, different SARS-CoV-2 pseudotyped viruses were generated through co-transfection of 293T cells with 2 plasmids, pSARS-CoV-2 S and pNL4-3Luc_Env_Vpr, carrying the optimized SARS-CoV-2 S gene and a human immunodeficiency virus type 1 backbone, respectively.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pNL4-3Luc_Env_Vpr</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Software and Algorithms</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">For intracellular staining, cells were fixed and permeabilized with BD Cytofix/Cytoperm (BD Biosciences) prior to staining with the mAbs against IFN-γ-PE, TNF-α-AF488 and IL-2-PE-Cy7.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>BD Cytofix/Cytoperm</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Stained cells were acquired by FACSAriaIII Flow Cytometer (BD Biosciences) and analyzed with FlowJo software (v10.6) (BD Bioscience).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>FlowJo</div><div>suggested: (FlowJo, RRID:SCR_008520)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Correlation plots and heatmap visualizations: Correlograms plotting the Spearman rank correlation coefficient (r), between all parameter pairs were generated with the corrplot package (v0.84) (Wei and Sikmo, 2017) running under R (v3.6.1) in Rstudio (1.1.456).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Rstudio</div><div>suggested: (RStudio, RRID:SCR_000432)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Spearman rank two-tailed P values were calculated using corr.test (psych v1.8.12) and graphed (ggplot2 v3.1.1) based on *p<0.05, **p<0.01, ***p<0.001.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>ggplot2</div><div>suggested: (ggplot2, RRID:SCR_014601)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Statistical analysis: Statistical analysis was performed using PRISM 8.0.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>PRISM</div><div>suggested: (PRISM, RRID:SCR_005375)</div></div></td></tr></table>

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Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:

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Limitations of the study: There are some limitations in our study. First, we were unable to obtain blood samples from our subjects after they became infected and quarantined. We, therefore, could not determine the B and T cell activation post BA.2 infection. Nevertheless, vaccine breakthrough infections often recall rapid NAbs and T responses against various VOCs, including Omicron (Collier et al., 2022; Suntronwong et al., 2022; Zhou et al., 2022). Second, most of our infected vaccinees were confirmed infection by self-RAT, thus the effect of different vaccine regimens on controlling viral loads was not determined. It remains necessary to compare the dynamics and magnitudes of recalled immune responses among vaccinees with BA.2 breakthrough infection patients in the future. In summary, we report that 3×BNT and 3×CorV provided better protection against SARS-COV-2 BA.2 than 2×BNT and 2×CorV. High frequencies of S-specific activated memory B cells and cross-reactive T cell responses induced by the third vaccination are critical for protection and illness reduction during the Omicron BA.2 breakthrough infection.

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Results from TrialIdentifier: No clinical trial numbers were referenced.

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Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

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Results from JetFighter: We did not find any issues relating to colormaps.

Results from rtransparent:

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• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

---

Results from scite Reference Check: We found no unreliable references.

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About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

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SciScore for 10.1101/2022.05.10.491301: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

<table><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Ethics</td><td style="min-width:100px;border-bottom:1px solid lightgray">Field Sample Permit: All experiments with mice, hamsters, and macaques were carried out in accordance with the Regulations in the Guide for the Care and Use of Laboratory Animals of the Ministry of Science and Technology of the People’s Republic of China.<br>IACUC: All animal procedures were approved by the Institutional Animal Care and Use Committee of the Institute of Medical Biology, Chinese Academy of Medical Science.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Sex as a biological variable</td><td style="min-width:100px;border-bottom:1px solid lightgray">The 6- to 8-year-old male or female rhesus macaque experiments were performed in the animal biosafety level 4 (ABSL-4) facility at Wuhan Institute of Virology (WIV), Hubei, China.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Randomization</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Blinding</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Power Analysis</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Cell Line Authentication</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr></table>

Table 2: Resources

<table><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Antibodies</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The following primary antibodies were used in this study: anti-SARS-CoV-2 (2019-nCoV) Spike Antibody (40589-T62, Sino Biological), and anti-GAPDH Antibody (60004, Proteintech).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-SARS-CoV-2</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>anti-GAPDH</div><div>suggested: (Proteintech Cat# 60004-1-Ig, RRID:AB_2107436)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The secondary antibodies used were Peroxidase AffiniPure Goat Anti-Rabbit IgG (H+L) (111-035-003, Jackson ImmunoResearch), Peroxidase</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Anti-Rabbit IgG</div><div>suggested: (Jackson ImmunoResearch Labs Cat# 111-035-003, RRID:AB_2313567)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">For determination of S-specific antibody response, plates were incubated with goat anti-mouse IgG HRP (for mouse sera, Proteintech Cat: SA00001-1) or goat anti-Syrian hamster IgG HRP (for hamster sera, abcam Cat: ab6892) or goat anti-monkey IgG HRP (for NHP sera, Invitrogen Cat: PA1-84631) at 37°C for 1 hour and then substrate tetramethylbenzidine (TMB) solution (Invitrogen) was used to develop.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-mouse IgG</div><div>suggested: (Proteintech Cat# SA00001-1, RRID:AB_2722565)</div></div><div style="margin-bottom:8px"><div>anti-Syrian hamster IgG</div><div>suggested: (Abcam Cat# ab6892, RRID:AB_955427)</div></div><div style="margin-bottom:8px"><div>anti-monkey IgG</div><div>suggested: (Thermo Fisher Scientific Cat# PA1-84631, RRID:AB_933605)</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Cell Lines</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Cell lines and antibodies: HEK 293F cells were grown in FreeStyle Media (Gibco-Thermo Fisher Scientific) and transiently transfected using polyethylenimine (PEI) (Polysciences, Inc.) in an 8% CO2 environment at 37°C.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>HEK 293F</div><div>suggested: RRID:CVCL_6642)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">HEK 293A and Vero E6 cells were maintained in high glucose DMEM(GIBCO) supplemented with 10% FBS(GIBCO) and 1% penicillin/streptomycin (P/S) (GIBCO) in a 5% CO2 environment at 37°C.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>HEK 293A</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Virus titration: Virus titrations were performed by endpoint titration in Vero E6 cells.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Vero E6</div><div>suggested: RRID:CVCL_XD71)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">After 1 hour of incubation, 100 μL mixtures were inoculated onto monolayer Vero cells in a 24- well plate for 1 hour with shaking every 15 minutes.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Vero</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Organisms/Strains</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Animal vaccination and serum collection: Mice: For mouse vaccination, groups of 6- to 8-week-old female BALB/c mice or female K18-hACE2 Transgenic Mice were intramuscularly immunized with LNP vaccine candidates or a placebo in 50 μL, and 3 weeks later, a second dose was administered to boost the immune responses.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>BALB/c</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Recombinant DNA</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Cloning, expression, and preparation of the RQ3013 encoded Spike proteins: The gene encoding the RQ3013 was fused with a C-terminal twin Strep-tag (LEVLFQGPSGS WSHPQFEK GGGSGGGSGGSA WSHPQFEK) and cloned into a mammalian cell expression vector pcDNA3.1.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pcDNA3.1</div><div>suggested: RRID:Addgene_79663)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The resulting plasmid, pcDNA3.1-RQ3013-Twinstrep, was transformed into HEK 293F cells using polyethylenimine (PEI) in FreeStyle Media (Gibco-Thermo Fisher Scientific).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pcDNA3.1-RQ3013-Twinstrep</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Software and Algorithms</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Other procedures of cryo-EM data processing were performed within RELION v3.1 or CryoSPARC v3 using the dose-weighted micrographs23, 24.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>RELION</div><div>suggested: (RELION, RRID:SCR_016274)</div></div><div style="margin-bottom:8px"><div>CryoSPARC</div><div>suggested: (cryoSPARC, RRID:SCR_016501)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Briefly, the initial templates were fit into the map using Chimera and Coot28, followed by a ten-cycle rigid body refinement using Phenix.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Phenix</div><div>suggested: (Phenix, RRID:SCR_014224)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Then, a combined manual refinement and real-space refinement were carried out for both prefusion state and postfusion state S structures in Coot and Phenix29.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Coot</div><div>suggested: (Coot, RRID:SCR_014222)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The dose-response curves were plotted from viral RNA copies versus the drug concentrations using GraphPad Prism 8.0 software.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>GraphPad Prism</div><div>suggested: (GraphPad Prism, RRID:SCR_002798)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Statistical Analysis: All statistics data were performed and graphed using GraphPad Prism8.0.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>GraphPad</div><div>suggested: (GraphPad Prism, RRID:SCR_002798)</div></div></td></tr></table>

---

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

---

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Results from TrialIdentifier: No clinical trial numbers were referenced.

---

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

---

Results from JetFighter: We did not find any issues relating to colormaps.

Results from rtransparent:

---

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

---

Results from scite Reference Check: We found no unreliable references.

---

<footer>

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

</footer>

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

Learn more at Review Commons

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Reply to the reviewers

Manuscript number: RC-2021-01219

Corresponding author(s): Rajan, Akhila

1) General Statements [optional]

This section is optional. Insert here any general statements you wish to make about the goal of the study or about the reviews.

The goal of this study is to:

• Define how prolonged exposure to a high-sugar diet (HSD) regime alters both the lipid landscape and feeding behavior.
• Determine how changes in lipid classes within the adipose tissue regulates feeding behavior. Key findings:

In this study, by taking an unbiased systems level and genetic approach, we reveal that phospholipid status of the fat tissue controls global satiety sensing.

Impact of Key findings:

By uncovering a critical role for adipose tissue phospholipid balance as a key regulator of organismal feeding, our work raises the possibility that the rate-limiting enzymes in phospholipid synthesis, including Pect, are potential targets for therapeutic interventions for obesity and feeding disorders.

Peer review comments:

This study has immensely benefited from the thoughtful peer-review of three reviewers. As per their recommendations, we have performed a major revision by performing additional experiments (see summary table below in next section) and strived to address the major concerns raised. Based on our reading, there were two major concerns that overlapped between all three reviewers raised. They are as follows:

• Does the genetic disruption of Pect in fly fat body alter phospholipid levels? Two reviewers (#2 and #3) recommended that we perform lipidomic analyses on adult flies with adipose tissue specific knockdown of For the revised version, we have completed this lipidomic experiment, and present results as a new main Figure 6, Supplemental S7 and S9.
• Is the dampened HSD induced hunger-driven feeding (HDF) behavior because of increased baseline feeding (#1 and #3)? In addition, reviewer #1, asked us whether HSD flies experience an energy-deficit? In other words, we were asked to uncouple whether what we observed was HSD-driven allostasis or indeed, as we had interpreted, that HSD dampened hunger-driven feeding response.

Hence, they recommended that we:

1. Re-analyze our hunger-driven feeding datasets and present non-normalized data (also requested by Reviewer #3) and show baseline feeding behavior on HSD. To address this, we have completed this analysis and present our results in Figure 1B-D and S1.
2. Determine whether the HSD fed flies display an energy deficit on starvation. To this end, we performed an assayed starvation-induced fat mobilization on HSD, results for this are now presented on Figure 1E-G and S2. Conclusions after the revision:

First, it is important to note here that the additional experiments have not caused a significant revision of the major conclusions of the original version of our study. In fact, we hope that the revised version provides clarity and further substantiation to our original arguments.

• The lipidomics experiments on Pect fat-specific knock-down flies show that reducing Pect in fat-body causes a significant reduction in certain PE lipid species (PE 36.2 specifically- Figure 6B). This is consistent with a prior report on lipidomics of the Pect null allele by Tom Clandinin’s group (PMID: 30737130). Furthermore, we note that when Pect is knocked down in the fat body, there is a significant increase in two other classes of phospholipids LPC and LPE (Figure 6A). Together, this suggests that an imbalance in phospholipid composition in the absence of Pect activity in fat.
• The starvation-induced fat mobilization experiments show that despite being fed a prolonged HSD, adult flies sense starvation and effectively mobilize fat stores, at a level comparable to Normal food (NF) fed adult flies, suggesting that even despite HSD exposure, adult flies experience an energy deficit on starvation.
• In our non-normalized data, we find that the baseline feeding events are not significantly altered between HSD and NF-fed flies (Figure 1D). This suggests that the effects we observe are not due to an increase in the “denominator”, but a dampening of hunger-driven feeding on HSD. With regard to our original version, all three peer-reviewers found that the study was interesting, significant, important, and novel – Reviewer #1: “The work is potentially novel and interesting”; #2 : “I find the study to be potentially very important - the authors combine a longitudinal study that would be difficult in any other model with the powerful genetic tools available in the fly. The conclusions are mostly convincing”; #3: “This manuscript demonstrates how fat body Pect levels affect HSD induced changes in hunger-driven feeding response. I agree with all the reviewers points; potentially very interesting”. But had requested that we provide further substantiation and clarification.

We sincerely hope that the peer-reviewers find that our revised version with additional new experimental datasets, improved data visualization, and the presentation of non-normalized raw data points, makes this study clear, compelling, and well-substantiated.

• Point-by-point description of the revisions This section is mandatory. *Please insert a point-by-point reply describing the revisions that were already carried out and included in the transferred manuscript. *

Below we summarize in Part A, the key experiments that were performed to address the major concerns. In Part B, we provide a point-point response to each reviewer with embedded datasets.

Part a:

We performed several new experiments, including:

• To address the primary concern of Reviewer #1 regarding whether the HSD flies have a similar energy deficit to Normal food (NF) fed flies, we performed analysis of stored neutral fat Triacylglycerol (TAG) reserves and how HSD fed flies mobilized fat stores on starvation. We present these results in Figure 1E-G, S2. These results show that HSD-flies despite accumulating more TAG (S2), breakdown a similar amount of fat reserves as NF-fed flies on starvation at any time-point (Figure 1E-G). This suggests that HSD-fed flies do sense and respond to energy deficit.
• To address concerns of reviewer #2 and #3 on whether Pect genetic manipulation affects specific phospholipid classes, we performed lipidomic analyses. The table below summarizes the new 3 new figures and 4 supplemental figures (blue text are all new figure numbers and figure panels) and three new Supplementary files as per reviewer’s request.

Figure #

Main point

New datasets in revision

Companion Supplement

1

HSD alters feeding behavior, but flies still breakdown TAG on starvation.

TAG storage and breakdown over longitudinal HSD shows that HSD and NF fed flies show similar levels of TAG breakdown on starvation, despite consistently elevated TAG on HSD. This supports the idea that flies do sense starvation even on HSD, but there is a uncoupling of the feeding behavior after Day 14. Revised the data representation of Figure 1 to show non-normalized data over time. S1 and S2 companions are new in the revision. Panels 1D to 1E are new for the revision.

S1- Raw data of feeding events plotted.

S2 Elevated TAG at all time points.

2

HSD causes insulin resistance

S3A added to show that insulin transcript levels remain the same in response to reviewer #3’s concerns.

S3

3

Phospholipid concentration raw data from lipidomic on Day 7 and Day 14 HSD suggest that PC, PE levels are increased on Day 14 HSD.

Figure 3 revamped to show new data visualization and non-normalized raw data to address Reviewer #2’s major concerns. S4A and S4B added. In addition Supplementary File 1 and 2 provided with raw lipidomics data as per reviewer #2’s request.

S4.

S4A- non normalized raw data of all other lipid classes on HSD.

S4B- fatty acid species data on Day 14 added as per request of rev.#2.

4

HSD regulate Apo-I levels in the IPCs and phenocopies Pect KD.

Added Figure 4A to show that HSD phenocopies Pect-KD in terms of delivery to brain

S5 showing the validation of the Apo-I antibody.

S6 validation of Pect KD and over-expression and Pect mRNA levels dysregulation on HSD.

5

Pect RNAi is insulin resistant

N/A

N/A

6

Pect knockdown shows significant increase in LPC and LPE, and a non-significant reduction in PC, PE levels. Specifically, the PE lipid class PE36.2 is downregulated.

Fig 6, S7, S9 are completely new based on reviewer #2 and #3 requests. In addition Supplementary File 3 provided with raw lipidomics data as per reviewer #2’s request

S7, S8, S9#.

S7- new Pect KD other classes

S8- new PE classes for day 14 and Pect associated classes.

S9- Pect OE lipidomics

7

Pisd and Pect activity in adipocytes are required for hunger-driven feeding behavior in normal diets

Pisd RNAi data was moved from supplement to main figure.

N/A

Note on revised text: We have revised text not only in the results section, but also as per reviewer #2’s recommendation, we have revamped our introduction and discussion as well. Since the manuscript has been significantly revised to include a main figure 6, fully altered Figure 1 and 3, multiple new supplemental figures, the changes in text are extensive. Hence, they are unmarked in the main text. Nonetheless, we hope that the reviewers will be able to evaluate these changes, as we have provided the specific locations in text and embed key figures in the point-point response below.

__Part B: __Point-Point responses to reviewer comments.

Reviewer #1 comments in Blue, author response in black.

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

In this manuscript, Kelly et al. show that the difference between the feeding behavior of fed and starved flies (hunger-driven feeding; HDF) is absent in animals fed a high-sugar diet (HSD) for two weeks or more. The disappearance of HDF with HSD coincides with changes in phospholipid profiles caused by HSD. Furthermore, RNAi-mediated downregulation of Pect in the fat body-a key enzyme in the PE biosynthesis pathway-phenocopies physiological effects of HSD. Moreover, downregulation or overexpression in the fat body abolishes or induces HDF, respectively, abolishes or induces HDF, respectively, independent of HSD treatment.

Overall, the manuscript is well-written and the phenotypes are clear. However, I have major concerns regarding the authors' interpretation of the data and their conclusion. Most importantly, while it is clear that the authors' high-sugar dietary treatment affects feeding behavior and physiology, I am not convinced that the changes can be considered "hunger-driven"-which is central to the main point of the manuscript. Therefore, it is my recommendation that the authors substantially revise the manuscript by either showing additional/re-analyzed data that rule out alternative hypotheses, or rewriting the manuscript keeping alternative interpretations in mind.

We are thankful to this reviewer for their thoughtful critique, and constructive and specific suggestions on how we can redress these concerns. We have taken on board the concerns of this reviewer regarding our interpretation of whether the changes in feeding behavior can be considered hunger-driven or not. Based on their advice, we have made significant changes by addressing: i) does HSD increased baseline feeding- we now show non-normalized raw data and data supports conclusion that baseline feeding is not higher; ii) whether HSD- fed flies can sense an energy deficit at levels similar to NF fed flies- we show that HSD flies sense energy deficit. We have provided detailed response below, and we hope the reviewer finds the additional datasets and re-analyzed data are consistent with the interpretation that prolonged HSD dampens starvation induced feeding. In addition to this key concern this reviewer has made a many other salient points that we have addressed with additional data or by clarifying the text.

Major comments: 1) The data do not sufficiently show that the long-term HSD regime disrupts "hunger-sensing." The manuscript should address alternative hypotheses by showing raw instead of normalized data, rewriting the manuscript with a new central conclusion, or running additional experiments that actually show a defect in hunger-driven response. a. The main results that the authors rely on for the argument is that the ratio of feeding events that the starved and non-starved flies eat is different between the groups fed normal or HSD. However, because the authors only show normalized data (normalized to non-starved flies; Fig. 1), it is difficult to tell whether the change is due to a chronically increased feeding in non-starved HSD flies-maybe in perpetual hunger-like allostasis-or dampened starvation response. Indeed, the data shown in Fig S1 show that flies fed HSD for as short as 5 days show more frequent feeding events compared to age-matched controls fed normal food. It is possible that because the HSD-fed flies eat more than NF-fed flies, even without being starved, the ratio of starved/non-starved feeding is lower in the HSD-fed group-due to changes in the denominator, rather than the numerator.

We have taken onboard this concern regarding presenting only normalized data, and that clouded the interpretation and left open other possibilities. In the completely revised figure 1 and S1. We now show non-normalized data, as a function of time. First we note that HSD-fed flies, do not show higher baseline feeding that NF fed flies, except on Day 10 of HSD, when there is a modest but significant elevation (Figure 1D).

Nonetheless, on Day 10 HSD, flies still display increased hunger-driven feeding HDF (Figure 1C), it is only after Day 14 HSD that HSD dampens the starvation induced feeding.

1. It is also possible that the HSD-fed flies are simply not in as big an energy deficit physiologically, due to the increased fat deposits they've accumulated (as the authors show later in the manuscript). It may take longer for the fat HSD flies to reach substantial energy deficiency than the NF flies, but they still may eventually be able to appropriately respond to hunger, just like NF flies. In such case, it would be a misnomer to call this behavioral change a 'defect in hunger-driven feeding behavior.' Maybe an experiment with a dose-response curve of "hunger driven feeding response" as a function of duration of starvation would help? Prompted by this reviewers question, we asked whether HSD fed flies, that have a higher baseline neutral fat store (Triacylglycerol-TAG) level, and if HSD-fed flies can sense energy deficit. For this, we revisited the longitudinal assays for neutral fat triacylglycerol (TAG) storage that our lab had generated, along with the HSD-HDF studies. We now present this evidence as Figure 1E-1G and Figure S2. Overall, our experiments point to the idea that adult flies fed HSD, are able to sense and mobilize TAG stores effectively throughout the 28-day time point that we analysed.

First as shown in Figure S2, flies fed HSD display an increase in TAG levels. But it is to be noted that while TAG stores increase, the increase is not linear with time. This suggests that adult flies exposed to HSD store excess energy as TAG, but the increased TAG stores stay within a certain range despite the length of HSD exposure. This suggests that adult flies on HSD still display TAG homeostasis.

Next, to directly address the reviewers point about HSD fed flies not sensing an energy deficit, we subject HSD-fed flies to an overnight starvation, same regime as used in the overnight feeding experiments, and asked whether they mobilize TAG. We noted that flies exposed to HSD breakdown TAG throughout the 28-day exposure at statistically significant levels for Day 3- Day 28, except on 14 and 21 days (Figure 1F). While there is TAG mobilization on Day 14 and 21, the difference is not statistically significant. Nonetheless, we note the same levels TAG breakdown for normal lab food (NF) fed flies on Day 14 and 21 (Figure 1E). Overall, HSD fed flies sense and display energy deficit, as measured by TAG store mobilization, throughout the 28 days of HSD exposure, at levels comparable to NF-fed flies (Figure 1G).

Taken together, these results suggest that while HSD-fed flies experience an energy deficit on starvation, at levels comparable to NF-fed flies, throughout the 28-day time point assayed. But, their starvation driven feeding-response is dampened by Day 14 and by Day 28, the HSD-fed flies display more feeding events than HSD starved flies. These results are consistent with the interpretation that in HSD-fed flies the starvation-induced feeding behavior becomes desynchronized from the starvation induced TAG-mobilization, suggesting that there is an absence of hunger-driven feeding.

2) How can you be sure that lower Dilp5 immunofluorescence is indicative of increased Dilp5 secretion? Wouldn't decreased production of dilp5 also have the same results?

It has been shown previously in HSD fed larvae are hyperinsulinemic, i.e., they have 55% increase in circulating Dilp2 ( PMID: 22567167). Additionally, we have shown that ectopic activation of the insulin-producing neurons by expressing TRPA1, an ion channel that activates neurons, reduces Dilp5 accumulation without a change in Dilp5 mRNA levels (PMID: 32976758), suggesting that reduced Dilp5 accumulation, without alterations to mRNA levels is a proxy for increased secretion. Now, in response to this concern, in the revised manuscript, we have added qPCR data of Dilp2 and 5 (Figure S3A), which show no difference in expression levels after 14 days on HSD. Therefore, there is no dip in Dilp5 mRNA production. Given that Dilp2 and Dilp5 mRNA levels remain the same, but we see reduced Dilp5 accumulation, we interpret this to mean that Dilp5 secretion is increased.

1. Also, the authors should state in the main text that it is Dilp5, not just any Dilp. Thanks for this suggestion and we have fixed this and referred to Dilp5 specifically throughout the text in the results section.

3) Data presentation: a. Sometimes the data are normalized to NF (Fig 4B-C), sometimes not (ex. Fig 4A, S4C). Unless there is a specific rationale for the data transformation, it would be more appropriate to show untransformed data (ex. Fig 4A, S4C), especially as the authors use two-way ANOVA to determine significance. Only showing the differences implies comparison against a hypothetical mean (i.e. μ0=0), not between two group means.

We thank the reviewers for bringing this issue to our attention. We updated all the figures to show untransformed data in the revised manuscript.

1. Some figures show both individual data points and summary statistics (mean, SD, ... ex. Fig 2A)-which I believe is ideal-but some show only one or the other (ex. Fig 2B, no summary statistics; Fig. 3, no data points. The manuscript would read more convincing if data visualization is consistent across figures. We thank the reviewers for their feedback. We have made changes to all the figures in the revised manuscript to improve visual consistency.

Minor comments: 1) High sugar diet: what is the actual sugar concentration in the NF v. HSD diets? The authors write that the HSD diet contains "30% more sugar" than the NF, but providing the final sugar concentrations-sucrose or others-would be informative for other scientists studying the effect of high sugar diets.

We thank the reviewer for their suggestion and now we have updated the methods to include this sentence. “After 7 days, flies were either maintained on normal diet or moved to a high sugar diet (HSD), composed of the same composition as normal diet but with an additional 300g of sucrose per liter”.

1. Additionally, the definition of HSD is inconsistent. Main text (Page 5, line 17) states that their HSD is "60% more sugar than normal media," whereas the figure legend (Fig 1) and the Methods state that the HSD contains "30% more sugar." We apologize for this egregious typo in the figure legend! We have now fixed this to say 30% HSD. Only 30% HSD was used throughout this study.

2) Starvation medium: please provide justification for why the authors used 1% sucrose/agar for starvation medium, instead of plain agar/water that most labs use. At least clarify and provide a reference for the claim that the 1% sucrose/agar "is a minimal food media to elicit a starvation response."

We are very grateful for this reviewer identifying this this methods description error and bring it to our attention. We used 0% sucrose agar for overnight starvation in this study as most labs do. The error occurred because we were using another manuscript from the lab to help draft the methods section (PMID: 29017032). In that study, where we assayed the effect of chronic starvation our lab used: “1% sucrose agar for 5 days at 25C”. However, in this current study, because we are testing acute effects of overnight starvation, we are using 0% sucrose agar.

3) Pect mRNA level is higher with HSD. This is surprising because not only, as authors mention, is increased PC32.2 with HSD suggests lower Pect activity, but also because Pect RNAi phenocopies long-term HSD in HDF behavior, lipid morphology, FOXO accumulation in fat body. The authors speculate that the data "likely shown an upregulation in an attempt to mediate the Pect dysregulation occurring at the protein level." If that were true, a western blot may be informative. Zhao and Wang (2020, PLoS Genetics) generated a Pect antibody that seems compatible with western blot applications. That being said, I don't think such data is critical for the manuscript. I mention this simply as a suggestion for the authors. a. page 8, line 22-23, did you mean to write "Given how PC32.2 is elevated after 14 days of exposure to HSD, we assumed that Pect levels would be low for flies under HSD," not "high?" Otherwise the subsequent 2 sentences don't make sense.

We agree that the most confusing aspect of the study was that Pect mRNA levels being very high on Day 14 HSD, but nonetheless the effects of Pect-KD phenocopied HSD. To resolve this, we have now performed lipidomic analyses on whole adult flies, when Pect is knocked-down (KD) by RNAi in the fat tissue. We now present a new dataset in Figure 6. Two striking changes occur. They are:

1. Pect-KD shows increase in the phospholipid classes LPC and LPE (Figure 6A). In contrast, LPE is significantly downregulated on HSD Day 14 (Figure 3).
2. Pect-KD shows a significant reduction in specific class of PE 36.2 (Figure 6B). Our data regarding increase in PE 36.2 agree with a previous lipidomic analyses of Pect mutant retina (PMID: 30737130). In contrast, PE 36.2 trends upwards on 14 day HSD (Figure S7C) though not significantly. On 14-day HSD consistent with extreme upregulation of Pect mRNA fed flies (Figure S6A; Pect mRNA 200-250 fold), PE trends upwards on 14-day HSD (Figure 3) and PE 36.2 trends higher (Figure S7C). We note that on the surface of it PE and LPE per se are contrasting between 14-day HSD lipidome and fat-specifc Pect-KD. But there is a significant commonality that under both states there is an imbalance of phospholipids classes PE and LPE. Hence, we propose that maintaining the compositional balance of phospholipid classes PE and LPE is critical to hunger-driven feeding and insulin sensitivity. Hence, either increase or decrease, of these key phospholipid species, may lead to abnormal hunger-driven feeding.

We agree that a western blot would be informative as well, but we were unable to obtain the reagent from Dr. Wang’s group, precluding us from performing this request. See email snapshot.

To ensure that we appropriately discuss and clarify this issue, we have now included a section in the discussion - Page 14 Lines 26-34- under the subtitle “The implications of relationship between Pect levels and HSD”. We have pasted an excerpt from that subsection below for this reviewers assessment.

“Also, we note that over-expression of Pect cDNA in the fat-body does not alter phospholipid balance (Figure S9) and indeed improves HDF on HSD (Figure 7B). While this may appear inconsistent, it is critical to note that over-expression of Pect cDNA using UAS/Gal4 only increases Pect mRNA expression by 7-fold (Figure S6A), whereas HSD causes its upregulation by 250-fold (Figure S6B). Hence, we speculate that an increased ‘basal’ level of Pect such as by that provided by a cDNA over-expression in fat, may be protective to the negative effects of HSD (Figure 7B) without affecting overall phospholipid levels (Figure S9) , but extreme upregulation Pect on HSD affects the PE and LPE balance (Figure 3).”

Reviewer #1 (Significance (Required)):

The work is potentially novel and interesting, but at this stage it's difficult to interpret what the phenotype signifies. Although the manuscript could be revised simply by modifying the text, experimentally addressing the concerns would significantly improve the work.

In sum, we hope we have addressed the key concern for Reviewer #1 as to whether the behavior we report here is indeed a dampening of starvation-induced feeding, or an effect of increase in baseline feeding. We hope that by reviewing our non-normalized data, they can appreciate that it is the former. Also, we hope that Reviewer #1 appreciates that we have strived to address the concerns by additional experiments, to clarify our findings and improve the impact of the work.

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

This intriguing manuscript by Kelly and colleagues uses the fruit fly Drosophila melanogaster as a model to understand how diet-induced obesity alters the feeding response over time. In particular, the authors findings indicate that chronic exposure to a high-sugar diet significantly alters the starvation-induced feeding response. These behavioral studies are complemented by a lipidomics approach that reveals how a chronic high sugar affects many lipid species, including phospholipids. The authors then pursue mechanistic studies that indicate phospholipid metabolism within the fat body appears to remotely affect insulin secretion from the insulin producing cells. Moreover, the changes in phospholipid abundance are associated with changes in insulin-signaling, including increased insulin secretion from the IPCs and elevated levels of FOXO within the nucleus.

I find the study to be potentially very important - the authors combine a longitudinal study that would be difficult in any other model with the powerful genetic tools available in the fly. The conclusions are mostly convincing, but a few follow-up experiments are required:

We are grateful for the reviewers constructive, detail-oriented, and balanced feedback, and their recognition of the value of this study. Now, we have performed additional experiments to address the key concerns raised by all reviewers. We hope that on reading the revised version of our study, that the reviewer continues to feel positive about the message of this study and its potential impact.

1. The key conclusions from the manuscript assume that manipulation of Pect expression levels alters phosphatidylethanolamine (PE) levels. However, the authors make no attempt to verify that the genetic experiments described herein actually affect PE levels. At a minimum, changes in PE levels should be verified for the Pect knockdown and overexpression lines. Similarly, there is no evidence that manipulation of either EAS or Pcyt2 induces the expected metabolic effects. I'm not asking that the longitudinal feeding experiments be repeated, simply that the authors measure the relevant lipid species, preferably with a targeted LC-MS approach.

Prompted by this reviewer, we performed targeted LC-MS on whole adult flies, on normal diet, to assess lipid levels for fat-specific Pect-KD and overexpression. We decided to focus on Pect, as its knock-down even on normal diet causes a dampened hunger-driven feeding behavior (Figure 7A) and phenocopied a 14-day HSD feeding phenotype.

We now present a new dataset in Figure 6. Two striking changes occur:

They are:

Pect-KD shows a significant reduction in specific class of PE 36.2 (Figure 6B). Our data regarding decrease in PE 36.2 agree with a previous lipidomic analyses of Pect mutant retina (PMID: 30737130). It is to be noted that though overall levels of all PE species trend downwards, like the Clandinin lab study on Pect (PMID: 30737130), we did not find a significant change in the overall PC and PE levels.

• Pect-KD shows increase in the phospholipid classes LPC and LPE (Figure 6A). In contrast, LPE is significantly downregulated on HSD Day 14 (Figure 3). On 14-day HSD consistent with extreme upregulation of Pect mRNA fed flies (Figure S6A; Pect mRNA 200-250 fold), PE trends upwards on 14-day HSD (Figure 3) and PE 36.2 trends higher (Figure S7C). We note that on the surface of it PE and LPE per se are contrasting between 14-day HSD lipidome and fat-specifc Pect-KD. But there is a significant commonality that under both states there is an imbalance of phospholipids classes PE and LPE. Hence, we propose that maintaining the compositional balance of phospholipid classes PE and LPE is critical to hunger-driven feeding and insulin sensitivity. Hence, either increase or decrease, of these key phospholipid species, may lead to abnormal hunger-driven feeding.

Finally, fat-specific Pect-OE did not cause significant changes to lipid species (Figure S9). This could either be due to the fact that in fat-specific Pect-OE flies under normal food and that we were assaying whole body lipid levels and not fat-specific lipid changes. But to counter that, even a 60% reduction in Pect mRNA levels (Figure S6A), was sufficient to produce an effect on whole body phospholipid balance (Figure 6). Hence, we speculate that by maintaining a basally higher (7-fold higher Pect mRNA level Figure S6A), might allow 14-day HSD-fed flies to buffer the negative effects of HSD and we predict that it might take longer to disrupt the phospholipid balance and HDF response.

We have now included a section in the discussion - Page 14 Lines 26-34- under the subtitle “The implications of relationship between Pect levels and HSD”. We have pasted an excerpt from that subsection below for this reviewers assessment.

“Also, we note that over-expression of Pect cDNA in the fat-body does not alter phospholipid balance (Figure S9) and indeed improves HDF on HSD (Figure 7B). While this may appear inconsistent, it is critical to note that over-expression of Pect cDNA using UAS/Gal4 only increases Pect mRNA expression by 7-fold (Figure S6A), whereas HSD causes its upregulation by 250-fold (Figure S6B). Hence, we speculate that an increased ‘basal’ level of Pect such as by that provided by a cDNA over-expression in fat, may be protective to the negative effects of HSD (Figure 7B) without affecting overall phospholipid levels (Figure S9), but extreme upregulation Pect on HSD affects the PE and LPE balance (Figure 3).”

A central hypothesis in the study is that the HSD over a period of 14 days results in insulin resistant and that these changes are leading to changes in hunger dependent feeding. I would encourage the authors to determine if Foxo mutants are resistant to these HSD-induced effects on HFD.

We thank the reviewers for this suggestion. However, given that dFOXO nuclear localization rather than expression levels regulate insulin sensitivity, we feel that disrupting dFOXO levels via mutation or knockdown will produce a plethora of indirect effects including developmental abnormalities (PMID: 24778227, PMID: 16179433, PMID: 29180716, PMID: 12893776). Our data suggest that chronic HSD treatment and Pect affect insulin sensitivity in fat tissue. However, we feel that investigating whether insulin sensitivity/FOXO signaling in fat tissue regulates feeding behavior is outside the scope of our work.

1. In lines 25-30, the authors draw the conclusion that an increase in unsaturated fatty acid species is associated with the HSD and that these changes results in a more fluid lipid environment. While I agree with the model, the manuscript contains no evidence to support such a model. Either test the hypothesis or move the last line of the section to the discussion.

We thank the reviewer for this important and insightful comment. We agree that the data we presented and discussed in the original version is at the moment speculative. Addressing the hypothesis that increase in unsaturated fatty acid species result in a more fluid lipid environment will require us to build tools and expertise. Hence, this hypothesis is better suited for exploration in a future study. Given this, we have moved this out of the results section into the Discussion section titled “HSD and fat-specific PECT-KD causes changes to phospholipid profile” (See excerpt below from page 13, lines 24-35).

“In addition to changes in phospholipid classes, we found that HSD caused an increase in the concentration of PE and PC species with double bonds (Figure S4C and S4D). Double bonds create kinks in the lipid bilayer, leading to increased lipid membrane fluidity which impacts vesicle budding, endocytosis, and molecular transport14,92. Hence it is possible that a mechanism by which HSD induces changes to signaling is by altering the membrane biophysical properties, such as by increased fluidity, which would have a significant impact on numerous biological processes including synaptic firing and inter-organ vesicle transport.”

Also, as per the reviewer’s guidance, given that we are speculating here, we have also shifted this dataset from Main figure 4 to supplement S4C and S4D.

In addition, lines 25-30 state that FFAs are increased after 14 days of a HSD. Figure 3A shows the exact opposite - FFAs are significantly decreased in 14 day fed animals despite being elevated in the 7 day fed animals. This is an interesting result that warrants discussion. Moreover, I would encourage to examine the lipidomic data more carefully to ensure that the text accurately portrays the lipid profiles.

We apologize for misstating that FFAs are decreased on 14-day HSD in the lines 25-30. It was an error and we have corrected this. We agree with the reviewer that the reduction of FFA on Day 14-HSD is an intriguing and unexpected observation that needs to be emphasized and further discussed. To this end, we have added figure S4B, wherein we have provided the difference in FFA concentration (by species) after days 7 and 14.

Furthermore, we have discussed what the potential meaning of reduced FFA at Day 14 implies in page 12, lines 19-27 of the Discussion section titled “HSD and fat-specific PECT-KD causes changes to phospholipid profile”. We have stated the following-

“We speculate that this reduction in FFA maybe due to their involvement in TAG biogenesis (PMID: 13843753). We were interested to see if the decrease in FFA correlated to a particular lipid species, as PE and PC are made from DAGs with specific fatty acid chains. However, further analysis of FFAs at the species level did not reveal any distinct patterns. The majority of FFA chains decreased in HSD, including 12.0, 16.0, 16.1, 18.0, 18.1, and 18.2 (Figure S4B). This data was more suggestive of a global decrease in FFA, likely being converted to TAG and DAG, rather than a specific fatty acid chain being depleted.”

The processed lipidomics data should also be included as supplementary data table so that they can be independently analyzed by the reader.

We thank the reviewer for this suggestion. As per the reviewers request, we have included the raw data as an attachment in our supplementary material (Supplementary Files 1-3.), so that interested readers can use the datasets generated in this study for future work and further analysis.

Beyond these experimental suggestions, the manuscript needs significant editing for clarity. While I won't provide a comprehensive list, the authors need to provide accurate descriptions and annotation of genotypes (including w[1118], which is written as W1118), typos, and formatting. I've listed a few examples below:

1. Page 3, Line 1 and 2: "...have been shown to impact feeding behavior and metabolism that leads to..." This is an awkward and grammatically incorrect sentence.
2. Page 3, Lines 7-32 is one very large paragraph but contains concepts that should be broken down over at least three paragraphs.
3. Page 3, Line 25: A description of the reaction catalyzed by Pect would be helpful for a manuscript focused on Pecte activity.
4. Page 4, Line 10: "previously characterized method of eliciting diet induced feeding behavior." As stated in the text, the method is previously described yet the manuscript characterizing the method isn't cited.
5. Figure legend 3 contains a random assortment of capitalized lipid species. Also, the names of lipid species are inappropriately broken into multiple names. Please use correct nomenclature throughout the manuscript.

The list above is nowhere near comprehensive. The manuscript requires significant editing.

We are grateful to the reviewer for drawing our attention to these errors. We have made significant edits to the revised manuscript to address the above-mentioned concerns, as well as made additional textual changes throughout and copyedited it. We hope that the reviewer will find the manuscript reads better and the clarity and preciseness is significantly improved.

Reviewer #2 (Significance (Required)):

I find the study to be potentially very important - the authors combine a longitudinal study that would be difficult in any other model with the powerful genetic tools available in the fly. The findings will significantly advance our understanding of how lipid metabolism links dietary nutrition with feeding behavior.

Once again, we are grateful for this reviewer’s thoughtful critique and encouraging words regarding our work and its potential impact.

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

Summary: This manuscript uses Drosophila to investigate how diet-induced obesity and the changes in the lipid metabolism of the fat boy modulate hunger-driven feeding (HDF) response. The authors first demonstrate that chronic exposure (14 days) of high sugar diet (HSD) suppresses HDF response. Through lipidome analysis, the authors identify a specific class of lipids to be elevated upon chronic HSD feeding. This coincided with the changes in expression of Pect, an enzyme that regulates the biosynthesis of these lipids. Modulating the expression of Pect specifically in the fat body affected HDF response.

We thank this reviewer for their rigorous and thoughtful critique and for identifying a key issue with our original study pertaining to a gap in how Pect mRNA levels on 14-day HSD are elevated but the Pect-KD phenocopies the HDF. Now by performing whole-body adult fly lipidomic on fat-specific Pect-KD we have resolved this issue and provided clarity on role of Pect in maintaining phospholipid homeostasis and thus subsequently impacts hunger-driven feeding. We hope the reviewer finds that the revised manuscript provides further clarity to the functional link between Pect’s role in fat-body and hunger-driven feeding.

Major comments: The author claim that the HDF response in HSD is distinct between early (5d, 7d) and chronic (day 14) HSD feeding. However, the data seem to indicate that HDF response is significantly decreased at all time points in HSD. For example, at day 5 HDF response was increased only 3-fold in HSD (Figure 1C) compared to around 50-fold increase in NF (Figure 1B). The scale of the Y-axis in Figure 1B and 1C is an order of magnitude different. Including the starved data (NFstv and HSDstv) in Figure S1, normalized to NF fed group, would better visualize the overall trends. Related to this, having the source data for the actual number of feeding events would be useful (e.g., to see the baseline changes in feeding in different time points in Figure 1 and the effect of genetic manipulations in Figure 7).

As per the reviewers request, we now have modified our graphs to show source data (Figure S1) and show the raw feeding events.

Then in the non-normalized graphs we plot, over a longitudinal time course, baseline and hunger-driven feeding events (Figure 1B-D). We also show that HSD fed flies do not display increased baseline feeding (Figure 1D) suggesting that the effect we see on HDF are no clouded by increased baseline feeding.

Yes, the reviewer makes an important point that HDF response on HSD fed flies is of a lower magnitude than NF fed flies. We think that is a biologically meaningful observation, as it suggests that flies have a remarkably fine-tuned ability to coordinate food-intake with nutrient store levels.

­­Now we have included a paragraph in the Discussion, Page 11 Lines 23-27, that say the following to ensure the readers appreciate this salient point raised by this reviewer.

*It is to be noted that the HDF response of HSD-fed flies (Figure 1C, Days 3-10) is of lower order of magnitude than the NF-fed flies. This suggests that that in addition to sensing an energy deficit and mobilizing fat stores (Figure 1F, 1G, S1), HSD fed flies calibrate their starvation-induced feeding to compensate only for the lost amount of fat. Overall, this suggests that flies have a remarkably fine-tuned ability to coordinate food-intake with nutrient store levels. *

The association between fat body Pect level and phospholipid levels is not clear. Day 14 of HSD feeding shows high expression of Pect in the fat body and elevated levels of PC32.0 and PC32.2. The authors assume the high expression of Pect in the fat body is due to the compensatory response, but there are no data indicating downregulation of Pect levels at the earlier time points of HSD feeding. A previous study demonstrated that Pect mutant flies have lower levels of PC32.0 but higher PC32.2 (PMID: 30737130).

We agree that one puzzling aspect of the original version of this study was that Pect mRNA levels being very high on Day 14 HSD, but nonetheless the effects of Pect-KD phenocopied HSD. To resolve this, prompted by Reviewer #2 and #3 concerns, for this revised version we have now performed lipidomic analyses on whole adult flies, when Pect is knocked down (KD) by RNAi in the fat tissue. We now present a new dataset in Figure 6. Two striking changes occu. They are:

1. Pect-KD shows increase in the phospholipid classes LPC and LPE (Figure 6A). In contrast, LPE is significantly downregulated on HSD Day 14 (Figure 3).
2. Pect-KD shows a significant reduction in specific class of PE 36.2 (Figure 6B). Our data regarding increase in PE 36.2 agree with a previous lipidomic analyses of Pect mutant retina (PMID: 30737130). In contrast, PE 36.2 trends upwards on 14 day HSD (Figure S7C) though not significantly. On 14-day HSD consistent with extreme upregulation of Pect mRNA fed flies (Figure S6A; Pect mRNA 200-250 fold), PE trends upwards on 14-day HSD (Figure 3) and PE 36.2 trends higher (Figure S7C). We note that on the surface of it PE and LPE per se are contrasting between 14-day HSD lipidome and fat-specifc Pect-KD. But there is a significant commonality that under both states there is an imbalance of phospholipids classes PE and LPE. Hence, we propose that maintaining the compositional balance of phospholipid classes PE and LPE is critical to hunger-driven feeding and insulin sensitivity. Hence, either increase or decrease, of these key phospholipid species, may lead to abnormal hunger-driven feeding.

On day 14, HDF response was increased 70-fold in w1118 flies in NF (Figure 1B; w1118), but only 2.5-fold in lpp>LucRNAi control flies in NF (Figure 7A). This suggests that lpp-gal4 driver lines have a significant effect on HDF response. Using a different fat-body specific Gal4 line would be necessary to validate conclusions.

Regards reduced HDF magnitude, in our experience using UAS-Gal4 reduces HDF response magnitude consistently and cannot be compared to w1118 which is more robust. To account for background differences, we use Uas-Gal4 with control RNAi. It clearly shows differences in HDF response on starvation, but Pect and Pisd RNAi does not (Figure 7A). Hence, given that this experiment internally controls for any changes in HDF response for UAS-Gal4>RNAi, we conclude that HDF response in disrupted in Pect and PISD KD (Figure 7).

We only presented the Lpp-driver in our study, as this driver is the only fat-specific driver that has no leaky expression in other tissues, and is specific to fat as apolpp promoter used to generate this Gal4 line is only expressed in fat tissue (Eaton and colleagues, PMID: 22844248). Other widely used fat-specific drivers, including the pumpless-Gal4 (ppl-Gal4) driver has leaky expression in gut or other tissues (See Table 2 of this detailed study by Dr. Drummond- Barbosa https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7642949/). If the reviewer is aware of a fat-specific Gal4 line, other than Lpp-Gal4, which has a highly specific expression in the fat tissue without leaky expression in other tissues, then we are happy to take onboard the reviewer’s suggestion and try that fat-specific Gal4 that they suggest.

HSD feeding promotes Pect expression (Figure S3C) and global changes in phospholipid levels (Figure 3, 4). Therefore, shouldn't Pect overexpression (not Pect RNAi) in a normal diet mimic HSD feeding state and promote loss of HDF response? Conversely shouldn't knockdown of Pect in HSD rescue loss of HDF response?

We agree that a puzzling aspect is that Pect mRNA levels are significantly elevated in HSD Day-14, but Pect-KD showed displays the inappropriate HDF response. As we have described in our response to this reviewer on Page 19, we believe that Pect-KD and HSD disrupt PE and LPE balance overall but in different ways. Whereas Pect-OE using cDNA expression in fat body does not cause a significant change to any lipid class (Figure S9), and our results suggest that basally higher level of PECT is likely to be protective on HSD with respect to HDF(Figure 7B).

To ensure that we appropriately discuss and clarify this issue, we have now included a section in the discussion - Page 14 Lines 26-33- under the subtitle “The implications of relationship between Pect levels and HSD”. We have pasted an excerpt from that subsection below for this reviewers assessment.

“Also, we note that over-expression of Pect cDNA in the fat-body does not alter phospholipid balance (Figure S9) and indeed improves HDF on HSD (Figure 7B). While this may appear inconsistent, it is critical to note that over-expression of Pect cDNA using UAS/Gal4 only increases Pect mRNA expression by 7-fold (Figure S6A), whereas HSD causes its upregulation by 250-fold (Figure S6B). Hence, we speculate that an increased ‘basal’ level of Pect such as by that provided by a cDNA over-expression in fat, may be protective to the negative effects of HSD (Figure 7B) without affecting overall phospholipid levels (Figure S9) , but extreme upregulation Pect on HSD affects the PE and LPE balance (Figure 3).”

We would have liked to test Pect protein expression on HSD, but since we were unable to access antibodies for Pect published in a prior study (PMID: 33064773) from Dr. Wang’s lab (see Page 10-11, of response to Reviewer #1). Hence, we were unable to test how the proteins levels of Pect correlate with the 250-fold increase mRNA expression.

In conclusion, we hope the reviewer appreciates that our results regarding Pect function are consistent with the main conclusion that achieving the right phospholipid balance between PE and LPE, is critical for an organism to display an appropriate HDF response.

Minor comments: All graphs should plot individual data points and showed as box and whisker plot as much as possible.

Thanks for this suggestion, we have added individual data points to the vast majority of figures in the paper. We have made exceptions to graphs such as seen in figure 1 and FigureS4B-D where we find individual data points add an unnecessary layer of complexity. We hope these changes provide additional clarity and strength to the claims made in this manuscript.

Data for day 14 missing in Figure S4A and S4B.

We have provided Day 14 for the PC composition and PE composition, due to changes in Figures, they are now S7A and S7B.

Reviewer #3 (Significance (Required)):

The interactions between diet-induced obesity, peripheral tissue homeostasis and feeding behavior is an interesting topic that can be addressed using Drosophila. This manuscript demonstrates how fat body Pect levels affect HSD induced changes in hunger-driven feeding response. However, at this point, the functional association between fat body Pect level, global phospholipid level, and loss of hunger-driven feeding response in chronic HSD feeding is not clear.

We hope the revised data, and discussion of the paper, provides well-substantiated functional association on the importance of maintaining phospholipid balance, driven by Pect enzyme, as a critical regulator of hunger-driven feeding behavior. As stated in the revised discussion, the key take home message of our manuscript is that on prolonged HSD exposure PC, PE and LPE levels are dysregulated, the loss of phospholipid homeostasis coincided with a loss of hunger-driven feeding. Following this lead on phospholipid imbalance, we then uncovered a critical requirement for the activity of the rate-limiting PE enzyme PECT within the fat tissue in controlling hunger-driven feeding.

Reviewer #1 (Public Review):

The work, mostly performed in yeast S. cerevisiae, shows that the knockout of DIP2 leads to accumulation in cells of some DAG subspecies (36:0 and 36:1), and also a deficit of similar TAG subspecies (something which mostly occurs, as they showed, in early to mid log growth phase). Accordingly, over-expression of DIP2 leads to the opposite outcome (lower DAG and higher TAG subspecies levels). ∆DIP2 cells showed increased ER stress and UPR, which can be counterbalanced by incubating cells with oleic acid. Moreover, the authors show that the absence of DIP2 causes vacuole fusion defects, which they ascribe to a localization of the protein in the vacuole and possibly to the fact that enhanced levels of DAG in the vacuole membrane can promote vacuole fusion. Although it is true that neither of these claims are fully supported by the experimental results, the data that the authors show serves as a starting point for future, more robust studies to test those claims. Finally, the authors show that the DBD1 domain is not necessary and that the two FLD domains are key for the observed lipid metabolism induced by DIP2 expression. Altogether this manuscript presents interesting new data on an uncharacterized protein that seems to be regulating the metabolism of relatively low abundant DAG/TAG subspecies in cells, and by doing so possibly control cell homeostasis.

Reviewer #3 (Public Review):

This study examines a family of poorly defined enzymes that contain fatty acyl-AMP ligase like domains (FAALs). The study reveals that these DISCO-interacting protein 2 (DIP2) enzymes are required to maintain a specific pool of diacylglycerol (DAG) lipids containing primarily C36 acyl chain lengths in budding yeast. Using primarily yeast, the study shows that deletion of ScDIP2 significantly increases C36 DAG pools while leaving the more abundant C32 and C34 DAG pools generally unaltered. Triglyceride (TAG) is also reduced in this deletion. Conversely, ScDIP2 over-expression promotes C36 inclusion in TAG. The ScDIP2 KO yeast manifests ER stress that can be relieved by the addition of oleic acid, but not other fatty acids. In the last section of the study, ScDIP2 is proposed to localize to the vacuole and mitochondria, where it maintains a specific DAG pool to enable proper vacuole morphology and fusion, as well as proper osmoregulation of the vacuole.

This is a well executed study that begins to characterize a conserved and generally poorly understood family of enzymes. However, questions still remain about some of the conclusions of the study. There are two general issues with the study. The first is the specificity of the effect of loss of ScDIP2. The study beautifully shows that loss of ScDIP2 (or its over-expression) affects a specific sub-pool of DAG (mainly the C36 species). TAG levels are also somewhat lower. However, how ScDIP2 impacts other lipid precursors to DAG synthesis such as PA and lyso-PA is under-examined, and should be looked at as they can also affect ER stress. Whether the change in DAG/TAG is primarily driven by decreased synthesis versus increased lipolysis also required additional analysis.

The second issue relates to how ScDIP2 relates to the yeast vacuole. It is proposed that some of the ScDIP2 enzyme is vacuole localized, and influences vacuole morphology. The evidence presented here does not strongly support that model. From imaging at least, it appears that ScDIP2 is primarily mitochondria localized. It is therefore possible that it influences vacuole lipid composition and morphology distally from the mitochondria. Resolving ScDIP2's native subcellular localization would strengthen the manuscript.

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SciScore for 10.1101/2022.05.07.491022: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

<table><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Ethics</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Sex as a biological variable</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Randomization</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Blinding</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Power Analysis</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Cell Line Authentication</td><td style="min-width:100px;border-bottom:1px solid lightgray">Contamination: Reagents: Cell lines: All cells were maintained in Dulbecco’s modified Eagle medium (DMEM) supplemented with 10% fetal calf serum (FCS), 100 U ml−1 penicillin and 100 mg ml−1 streptomycin and regularly tested and found to be mycoplasma free.</td></tr></table>

Table 2: Resources

<table><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Antibodies</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Anti-rabbit IgG, HRP-linked Antibody (7074); Cyclin D3 Mouse mAb (DCS22, 2936); from Cell Signaling.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Anti-rabbit IgG</div><div>suggested: (Cell Signaling Technology Cat# 7074, RRID:AB_2099233)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Goat anti-Mouse IgG (H+L) Cross-Adsorbed Secondary Antibody: Alexa 488 (A-11001), Alexa 594 (A-11032), Alexa 647 (A-21236); Goat anti-Rabbit IgG (H+L) Cross-Adsorbed Secondary Antibody: Alexa 488 (A-11034), Alexa 405 (A-48254); Rabbit polyclonal SARS-CoV-2 Spike (PA1-41165); Rabbit monoclonal SARS-CoV-2 Nucleocapsid (MA5-29982) from Thermo Fisher Scientific.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-Mouse IgG</div><div>suggested: (Thermo Fisher Scientific Cat# A-11001, RRID:AB_2534069)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Rabbit Polyclonal Cyclin A2 antibody (GTX103042); Rabbit Polyclonal Cyclin D1 antibody (N1C3, GTX108824); Rabbit Polyclonal Cyclin E1 antibody (GTX103045); Rabbit Polyclonal Cyclin B1 antibody (GTX100911); monoclonal SARS-CoV-2 Spike (GTX632604) from GeneTex.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Cyclin A2</div><div>suggested: (GeneTex Cat# GTX103042, RRID:AB_1949884)</div></div><div style="margin-bottom:8px"><div>Cyclin D1</div><div>suggested: (GeneTex Cat# GTX108824, RRID:AB_10618686)</div></div><div style="margin-bottom:8px"><div>Cyclin E1</div><div>suggested: (GeneTex Cat# GTX103045, RRID:AB_10731259)</div></div><div style="margin-bottom:8px"><div>Cyclin B1</div><div>suggested: (GeneTex Cat# GTX100911, RRID:AB_1949886)</div></div><div style="margin-bottom:8px"><div>GTX632604</div><div>suggested: (GeneTex Cat# GTX632604, RRID:AB_2864418)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">pre-cleared cell lysates were incubated with a-HA magnetic beads, MagStrep beads (IBA-Lifescience, Gottingen, Germany) or anti-cyclin D3 monoclonal antibody (sc-xx) bound Protein G Dynabeads for 1h at 4°C.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-cyclin D3</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Cell Lines</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Following cells were a gift from: A549 ACE2/TMPRSS2 40 Massimo Palmerini, Vero E6 ACE2/TMPRSS2 from Emma Thomson, HeLa-ACE2 from James Voss, 293T (a human embryonic kidney cell line, ATCC CRL-3216).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>A549</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>Vero E6</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">293T GFP11 cells and Vero-GFP10 cells for Split GFP assay were a gift from Leo James41.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>293T GFP11</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>Vero-GFP10</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">293Tv cells were transfected with pEXN-MNCX-Fucci, CMVi and pMD2.G.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>293Tv</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Recombinant DNA</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Plasmids: pBOB-EF1-FastFUCCI-Puro was a gift from Kevin Brindle & Duncan Jodrell (Addgene plasmid # 86849 ; http://n2t.net/addgene:86849 ; RRID:Addgene_86849) 29. pCMV5 cyclin D3 HA was obtained from MRC-PPU Reagents and Services. Rc/CMV cyclin D1 HA was a gift from Philip Hinds (Addgene plasmid # 8948 ; http://n2t.net/addgene:8948 ; RRID:Addgene_8948) 44. pLVX-EF1alpha-SARS-CoV-2-E-2xStrep-IRES-Puro (Addgene plasmid # 141385 ; http://n2t.net/addgene:141385 ; RRID:Addgene_141385); pLVX-EF1alpha-SARS-CoV-2-M-2xStrep-IRES-Puro (Addgene plasmid # 141386 ; http://n2t.net/addgene:141386 ; RRID:Addgene_141386).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div></div><div>detected: RRID:Addgene_86849)</div></div><div style="margin-bottom:8px"><div>pCMV5</div><div>suggested: RRID:Addgene_15002)</div></div><div style="margin-bottom:8px"><div></div><div>detected: RRID:Addgene_8948)</div></div><div style="margin-bottom:8px"><div></div><div>detected: RRID:Addgene_141385)</div></div><div style="margin-bottom:8px"><div></div><div>detected: RRID:Addgene_141386)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">pLVX-EF1alpha-SARS-CoV-2-nsp9-2xStrep-IRES-Puro (Addgene plasmid # 141375 ; http://n2t.net/addgene:141375 ; RRID:Addgene_141375); pLVX-EF1alpha-SARS-CoV-2-N-2xStrep-IRES-Puro (Addgene plasmid # 141391 ; http://n2t.net/addgene:141391 ; RRID:Addgene_141391) were a gift from Nevan Krogan 34. pEXN-MNCX, MLV vector encoding N-terminal double HA tag 45.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div></div><div>detected: RRID:Addgene_141375)</div></div><div style="margin-bottom:8px"><div></div><div>detected: RRID:Addgene_141391)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">pCAGGS_SARS-CoV-2_Spike was obtained from NIBS.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pCAGGS_SARS-CoV-2_Spike</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Cell cycle analysis using fluorescence ubiquitination cell cycle indicator (Fucci): Fucci cassete was cloned from pBOB-EF1-FastFucci-Puro vector to pEXN-MNCX using BamHI/NotI restriction sites.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pEXN-MNCX</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">293Tv cells were transfected with pEXN-MNCX-Fucci, CMVi and pMD2.G.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pEXN-MNCX-Fucci</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>pMD2.G</div><div>suggested: RRID:Addgene_12259)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Cell to cell fusion assay: 293T GFP11 cells were transfected with WT full length Spike, and/or with WT Envelope, Membrane, cyclin D3, and empty vector (pCDNA, to ensure equal amount of transfected DNA).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pCDNA</div><div>suggested: RRID:Addgene_66792)</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Software and Algorithms</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Harmony (PerkinElmer, Waltham, MA, USA) and ImageJ software were used to measure MFI for each protein in each region.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>ImageJ</div><div>suggested: (ImageJ, RRID:SCR_003070)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Cell populations positive or negative for SARS-CoV-2 nucleocapsid staining were gated and Cdt1-RFP positive (G1 phase), Geminin-GFP positive (S/G2/M phase), and Cdt1-RFP/ Geminin-GFP positive (early S phase) populations were identified using flow cytometry using LSRFortessa X-20 (BD Biosciences, UK) and FlowJo software (Tree Star, OR, USA).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>FlowJo</div><div>suggested: (FlowJo, RRID:SCR_008520)</div></div></td></tr></table>

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Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

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Results from TrialIdentifier: No clinical trial numbers were referenced.

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Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

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Results from rtransparent:

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Reply to the reviewers

1. General Statements [optional]

We would like to thank the reviewers for their helpful and constructive comments.

2. Point-by-point description of the revisions

Reviewer #1

This reviewer thought our findings would be of interest to a broad range of scientists from both the centrosome and mitosis fields, but noted some important aspects for improvements.

Additional Experiments (we number these points for ease of discussion).

1. • Figure 3. The reviewer points out that because our analysis of Ana2-∆CC and Ana2-∆STAN mutant proteins was conducted in the presence of endogenous WT protein, we should be more cautious in our interpretation.* We agree and apologise for overstating these findings. We have now rewritten the title and text of this section to be more cautious (p11, para.2)

2. Figure 5A. The reviewer wonders whether the reduced recruitment of Sas-6 in the presence of Ana2(12A) is due to reduced binding, and they request we test this biochemically. This is our favoured interpretation, but we have been unable to test this biochemically for two reasons. First, although we have successfully purified several recombinant Sas-6 and/or Ana2 fragments (Cottee et al., eLife, 2015), the full-length proteins are poorly behaved (tending to precipitate, likely due to their inherent ability to self-oligomerise). Thus, we have been unable to reconstitute their interaction in vitro*. Second, as we show here, the proteins are normally expressed in embryos at surprisingly low concentrations (~5-20nM), and we can detect no interaction between them in coimmunoprecipitation experiments from embryo extracts (not shown). Indeed, this concentration is so low that Sas-6 does not even appear to form a homo-dimer in the embryo, even though Sas-6 clearly functions as a homo-dimer in centriole assembly (new Figure S4A). We now explain these points, and state that our favoured hypothesis that Ana2(12A) has reduced affinity for Sas-6 (or other core duplication proteins) remains to be tested (p22, para.2).

3. The Reviewer wonders if all 12 of the potential Cdk1 phosphorylation sites that we mutate in Ana2(12A) are important in vivo, and whether we have tested whether mutating fewer sites (e.g. the two sites [S284/T301] that we show are phosphorylated by Cdk1/Cyclin B in vitro) might be sufficient to recapitulate the Ana2(12A) phenotype. *We have now tested this by mutating just the S284/T301 sites to Alanine [Ana2(2A)], but the results were not very informative (Reviewer Figure 1 [RF1]). Whereas Ana2(12A) is recruited to centrioles for a longer period and to higher levels than WT Ana2 (Figure 4A), Ana2(2A) is recruited to centrioles for a normal period but to lower levels (RF1A,B). The interpretation of this result is complicated because western blots show that Ana2(2A) is also present at lower-levels than normal (RF1B). Thus, it is clear that Ana2(2A) does not recapitulate well the behaviour of Ana2(12A). We have decided not to present this data as it is difficult to interpret and it does not change any of our conclusions.

4. Figure 6. The reviewer asks whether the 12A mutations impair the interaction with Plk4, influence Plk4’s kinase activity or the ability of Plk4 to phosphorylate Ana2. These are excellent questions but, for the same reasons described in point 2 above, we cannot address them biochemically as we cannot purify well-behaved recombinant full-length Ana2 or active Plk4 in vitro, and both proteins are present at such low levels in the embryo that we cannot detect any interaction between them in embryo extracts. We are working hard to reconstitute in vitro* systems to probe these important points, but it may be sometime before we are able to do so.

5. Figure 7. The reviewer suggests that the 12D/E phosphomimetic substitutions introduce more negative charge than the putative phosphorylation of Ser/Thr residues and they ask if the Ana2(2D/E) [stated as Ana2(3D/E)] is, like the Ana2(12D/E) mutant, not efficiently recruited to centrioles.* This is a fair comment, but we have not analysed an Ana2(2D/E) mutant because, as described in point 3 above, the Ana2(2A) mutant did not recapitulate well the Ana2(12A) phenotype.

Minor comments

1. • Figure S1. The reviewer requests that we show that the mNG tag on its own is not recruited to centrioles.* We do not show this (as it would create a lot of white space in this Figure), but now state that mNG and dNG do not detectably localise to centrioles (p7, para.1).
2. • Figure S4C.* We have included the missing error bars (now Figure S4B).
3. • Figure S5A. The reviewer asks about the expression levels of the Ana2(12A) mutant, which are not shown in this Figure. They also state that the expression levels of the transgenes shown in Figure 5A are not similar.* The expression level of Ana2(12A) is shown in Figure S9, as this data was analysed independently of the other mutant proteins shown in Figure S5. We agree that it was overly simplifying the situation to state that the expression levels of WT Ana2-mNG, eAna2(∆CC)-mNG and eAna2(∆STAN)-mNG were “similar” (Figure S5), and we now specifically mention the differences between them (p11, para.3). Reviewer #2

This reviewer found this a rigorous study that advances our understanding of the regulation of centriole duplication, but raised some minor points.

Minor Points

The reviewer requests that we mention the literature describing how Ana2/STIL can influence the abundance and centriolar localisation of Plk4. We apologise for this omission, and have amended our description of this literature in the Introduction to include this point (p3, para.2).

The reviewer notes that we interpret the ability of the Ana2(12A) mutant to keep incorporating into the centrioles for a longer period as being consistent with our idea that rising levels of Cdk activity during S-phase normally reduce the ability of WT Ana2 to bind to the centriole. They ask us to show how Cdk activity increases over this time-course, and to test whether dampening Cdk has the same effect on Ana2 recruitment (i.e. allows Ana2 to be recruited for a longer period). The time-course of Cdk activation in these embryos has been reported previously (Deneke et al., Dev. Cell, 2016; we present the relevant data from this paper in RF#2A [black line]). This reveals how Cdk activity rises throughout S-phase, which is crucial for our model. To assess the effect of dampening Cdk activity in these embryos we have now analysed the effect of halving the genetic dose of Cyclin B (RF#2B). This perturbation extends S-phase length, but has a complicated effect on the recruitment dynamics of Ana2 (RF#2B). As we would predict, Ana2 is recruited to centrioles for a longer period in these embryos, but it is also recruited more slowly (so it accumulates to lower levels). This is consistent with our hypothesis that Cdk1 activity might first stimulate and then ultimately inhibit the centriolar recruitment of Ana2. The interpretation of this experiment is not straightforward, however, as dampening Cdk1 activity alters Ana2 recruitment dynamics (and many other processes in the embryo) in complicated ways, so we have decided not to include it in the manuscript.

The reviewer suggests that it would be valuable to show that all 12 of the potential Cdk1 phosphorylation sites in Ana2 can be phosphorylated by Cdk1 in vitro. We think this would not be particularly informative as our hypothesis does not rely on all 12 sites being phosphorylated to generate the Ana2(12A) phenotype. We simply mutate all 12 sites because we don’t know which, if any, are relevant. Thus, showing that some/all of the 12 sites can/cannot be phosphorylated in vitro does not test any hypothesis and would not change any of our conclusions. We now explain our thinking on this in more detail (p12, para.2)

Other points

Figure 3. We have corrected the amino-acid numbering mistakes.

Figure 5Aii. We have changed the x-axis (time) labelling in this and all other Figures.

Figure Legends. We have tried to eliminate the typos from the Figure legends, and apologise that these errors made it through to the final submitted version of our manuscript.

Reviewer #3

This reviewer thought our manuscript would be of great interest to not only the centrosome field but also to cell biologists more generally. Although they had no major concerns, they made a number of suggestions for improvements.

1. As the reviewer suggests, we now explicitly state that although the Ana2(12A) mutant appears to be largely functional, the overall conformation of the protein may be altered, changing its function in ways we do not appreciate (p21, para.2).

2. The reviewer suggests we include a multiple sequence alignment of Ana2/STIL proteins to provide more context about the distribution and conservation of the 12 S/T-P sites mutated in Ana2(12A).* This is an excellent idea, and we now include this in a new Figure S6, where we also provide more information about which of these sites have been shown to be phosphorylated in embryo or S2-cell extracts

3. The reviewer is confused as to why the 12A and 12D/E mutants rescue the ana2-/- mutant flies so well, which suggests that the mechanism we propose here cannot be essential for centriole duplication. We understand this confusion and we now make this point more clearly and explain why we think this occurs in more detail (e.g. p22, para.1). We propose that Cdk normally phosphorylates Ana2 to inhibit its ability to promote centriole duplication, but this phosphorylation does not entirely block this function. So, if all other elements of the system are functional, Ana2(12A) is recruited to centrioles for longer than normal, but this does not dramatically perturb centriole duplication because the many other factors that regulate centriole duplication (such as the pulse of Plk4 recruitment to centrioles [Aydogan et al., Cell, 2020]) still occur normally and are sufficient to ensure that centrioles still duplicate normally. When Ana2 phosphorylation is mimicked [Ana2(12D/E)], the ability of Ana2 to promote centriole duplication is perturbed (but not abolished). This perturbation is lethal in the early embryo—where the centrioles must duplicate in just a few minutes to keep pace with the rapid nuclear divisions. In somatic cells S-phase is much longer, so these cells can still duplicate their centrioles (as we observe) even though Ana2(12D/E) does not function efficiently. As we now explain, this phenotype (being lethal in the early embryo, but not in somatic cells) is a common feature of mutations that influence the efficiency* of centriole and centrosome assembly (p17, para.2).

4A. The reviewer asks us to comment in more detail on why centrioles do not seem to be elongated in the Ana2(12A) mutant wing disc cells (now Figure S8C), even though we show that Ana2(12A) (Figure 4A), and also Sas-6 (Figure 5), are recruited to centrioles for an abnormally long period. This is an excellent question and, although we do not know the answer, we now discuss this interesting point in more detail (p16, para.1). We think this is likely due to the “homeostatic” nature of centriole growth: in our hands, almost any perturbation that makes centrioles grow for a longer/shorter period, also makes them grow more slowly/quickly, so that they tend to grow to a similar size (Aydogan et al., JCB, 2018; Cell, 2020). This is fascinating, but poorly understood. When we perturb the system by expressing Ana2(12A), both Ana2(12A) and Sas-6 incorporate into centrioles for a longer period, as we predict (Figure 4A and 5A). Unexpectedly, however, Sas-6 is also recruited to centrioles much more slowly. Thus, as so often happens, when we perturb the system so the centrioles grow for a longer time, the centrioles “adapt” by growing more slowly. We do not currently understand why this occurs (although we speculate that Ana2 may also be regulated by Cdk/Cyclins to help recruit Sas-6 to centrioles in early S-phase). In the embryo, where S-phase is very short, this homeostatic compensation is not perfect, and the centrioles appear to actually be shorter than normal. In somatic wing-disc cells, where S-phase is much longer, we suspect that there is more scope for homeostatic compensation and so the centrioles grow to the correct size.

4B. In this point (also labelled [4] by the reviewer, so we have retained this numbering but labelled the points A and B) the reviewer asks why levels of Ana2(12A) eventually decline at centrioles once the embryos actually enter mitosis. The reviewer notes our rheostat theory, but suggests a discussion of other mechanisms might be interesting. This is a good point, and we agree that the observation that Ana2(12A) levels ultimately still decline at centrioles during mitosis is likely to be important in explaining why centriole duplication is not more dramatically perturbed by Ana2(12A). We now expand our discussion of this point, highlighting that other mechanisms must help to ensure that Ana2 is not recruited to centrioles during M-phase, and discussing the possibility that the receptors that recruit Ana2 to centrioles are themselves inactivated during mitosis by high levels of Cdk activity (p15, para.1). In such a model, the rapid drop in WT Ana2 centriolar levels is due to a combination of switching off Ana2’s ability to bind to centrioles (as we propose here) and switching off the ability of the centrioles to recruit Ana2. For Ana2(12A), only the latter mechanism would operate, so Ana2(12A) levels would start to drop later in the cycle (as the inflexion point at which Ana2 recruitment and loss balances out would be moved to later in the cycle), and these levels would drop more slowly—as we observe.

• The reviewer is confused to how the Ana2(12D/E) mutant can rescue the mutant phenotype when it is recruited to centrioles so poorly. Ana2(12D/E) is indeed recruited very poorly to centrioles in the experiment shown in Figure 7. However, this experiment had to be conducted in the presence of WT untagged Ana2—as the embryos do not develop in the presence of only Ana2(12D/E). We would predict that WT Ana2 would bind more efficiently to centrioles than Ana2(12D/E) (which appears to behave as if it has been phosphorylated by Cdk/Cyclins, and so cannot be recruited to centrioles efficiently). Thus, in the experiment we show in Figure 7, the Ana2(12D/E) protein is probably being “outcompeted” for binding to the centriole by the WT protein. In somatic cells expressing only* Ana2(12D/E) presumably sufficient mutant protein can be recruited to centrioles to support normal centriole duplication (as it no longer has to compete with the WT protein). We now explain our thinking on this point (p18, para.1).

• The reviewer wonders whether Ana2(12D/E) may be unable to homo-oligomerize, and this may explain why the protein is not recruited to centrioles efficiently even in the presence of WT protein. This is indeed a possibility, but we think it unlikely as it is widely believed that Ana2/STIL proteins must multimerize to be functional (Arquint et al., eLife, 2015; Cottee et al., eLife, 2015; Rogala et al., eLife, 2015; David et al., Sci. Rep., 2016). As Ana2(12D/E) strongly restores centriole duplication in ana2-/-* mutant somatic cells, it seems unlikely that it cannot multimerize. Nevertheless, we now specifically highlight that the 12D/E (and 12A) mutations might alter the ability of Ana2 to multimerise (p21, para.2).

We thank the reviewers again for their thoughtful and constructive comments. We hope they will agree that the revised manuscript is now improved and would be appropriate for publication in The Journal of Cell Biology.

With best wishes,

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Referee #1

Evidence, reproducibility and clarity

Centriole duplication is a conserved pathway that need to be tightly regulated. The key enzyme of centriole assembly is Plk4 which is recruited to the centrioles and undergoes dynamic re-localization from a ring-like pattern around a centriole to a dot-like morphology at the daughter centriole assembly site. This event is central for inducing centriole biogenesis. Plk4 then phosphorylates Ana2/STIL which allows recruitment of Sas-6 to form the cartwheel structure for centriole assembly.

In the present study, Steinacker, Wong et al. monitor how cytoplasmic concentrations of the key proteins in centriole assembly, Plk4, Asl/Cep152, Ana2/STIL, Sas-6 and Sas-4/CPAP change during the centriole assembly process in the Drosophila embryo by using fluorescence correlation spectroscopy (FCS) and Peak Counting Spectroscopy (PeCoS). They find that their concentrations remain constant with exception of Ana2/STIL of which cytoplasmic diffusion rate increased at the end of S-phase and is dependent on phosphorylation by Cdk1/CyclinB. Phosphorylated Ana2/STIL blocks centriole duplication thus preventing premature initiation of centriole duplication in mitosis.

Major comments

The manuscript is interesting and very well written. Most of the experiments are carefully performed. However, there are some important aspects for improvements that are listed below

Additional experiments:

• Figure 3: the transgenic flies that were generated here, CC and STAN, still contain wild-type Ana2. So, the authors therefore need remove or dampen their claim that the change in Ana2's cytoplasmic diffusion does not depend on its interaction with Sas-6 (page 11).
• Figure 5A: is the observed reduced recruitment of Sas-6 by Ana2(12A) due to a decrease in binding affinity? This should also be shown by analyzing protein-protein interactions between Ana2(12A) and Sas-6 biochemically.
• The authors use an Ana2(12A) mutant which comprises putative Cdk1 phosphorylation sites that have been identified in Mc Lamarrah et al. JCB 2018. However, only three of them were phosphorylated by Cdk1/cyclin B in vitro (Fig. S6). Are all these 12 putative Cdk1 phosphorylation sites important in vivo? Did the authors generate the Ana2(3A) or the S284A/T301A mutants to see whether it can rescue the ana2-/- mutant phenotype similar to the 12A mutant? These might be sufficient to observe the phenotype.
• Figure 6: is the interaction between Plk4 and Ana2(12A) impaired? Similarly, Plk4 activity and phosphorylation of Ana2(12A) by Plk4
• Figure 7: Phosphomimetics, in this case 12 amino acid changes, have the disadvantage of introducing more negative charge than the phosphorylated residue. The Ana2/(12D/E)-mNG is not efficiently recruited to centrioles. Is effect also observed for the Ana2/(3D/E) mutant?

Minor comments

Figure S1: only mNG-tagged centriolar proteins are shown. An empty mNGtag or an mNG-tagged non-centriolar protein should be shown to exclude that the tag by itself shows centriolar localization or somehow affects the localization

S4C: Sas6-mNG CPM error bars are missing for the 10min time point

S5A: What are the expression levels of the Ana2(12A) mutant? The expression levels shown in this Figure are not similar.

Significance

Centriole duplication normally begins at the G1/S phase transition. An important question in the field is how premature centriole duplication in mitosis is prevented. The authors used fluorescence correlation spectroscopy (FCS) and Peak Counting Spectroscopy (PeCoS) to study the major conserved proteins in the centriole assembly pathwayq and found that only Ana2/STIL's cytoplasmic diffusion increases at the end of S-phase. It is known from the literature that Cdk1 prevent Plk4-STIL complex assembly in centriole biogenesis by directly competing with Plk4 for the CC domain of Ana2/STIL (Zitouni et al. Curr Biol 26, 1127-1137 (2016). However, Ana2/STIL can also bind to Plk4 via its conserved C-terminal region of STIL (Ohta et al., Cell Reports 11, 2018; McLamarrah et al., J Cell Biol 2018, 217, 1217-1231). The work by Steinacker, Wong et al. suggest that at least in fly embryos, growth of the daughter centriole is regulated though phosphorylation of Ana2 by Cdk1/CyclinB rather than binding. The findings described in this manuscript are interesting for a broad range of scientists from both the centrosome and mitosis fields

Expertise of the reviewer: centriole biogenesis, structural and numerical centrosomal aberrations in disease

Your task:

What is the topic and the main idea of this article, based on the abstract? TAG the sentences with "topic" and "main idea" - make your annotations public, please.

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Referee #1

Evidence, reproducibility and clarity

In this manuscript, Dantas and colleagues report that confinement is sufficient to restore G2/M transition in cells than can't adhere to their matrix. Exploring further the mechanisms involved, they show that confinement (dynamic cell compression) stimulates nuclear import of cyclin B1 and nuclear envelope permeability using cells in 2D culture. The authors observed that actomyosin contractility increases NE tension in cells preparing for prophase, leading to an increase in nuclear translocation of cyclin B1. However, a few inconsistencies between the data and the conclusion make the current report too preliminary for publication. It may require significant additional work to consolidate the authors' model.

• The specific contribution of Nuclear Envelope tension. The authors conclude that confinement acts through increasing NE tension, although confinement may affect cytoplasmic signaling, which could contribute to G2/M transition. The authors should test whether compressing the nucleus versus compressing the cytoplasm have distinct effects on cyclin B1 nuclear translocation and G2/M, as it has been done by others when addressing nuclear mechanosensitive mechanisms (Elosegui-Artola et al. or Lomakin et al.). To consolidate their model, the authors should also test whether decreasing NE tension (independently of actomyosin tension) has opposite effect on G2/M (for example using LBR overexpression). Increase in nuclear membrane tension has been shown to trigger cPLA2 recruitment to the NE (Enyeidi et al, 2013; Lomakin et al. 2020), although the authors show here that confinement does not induce cPLA2 recruitment (but still increases NE tension figure 4G) in the absence of Rock activity or when the LINC complex is disrupted. This is surprising considering that confinement should increase NE tension independently of actomyosin contractility and should increase cPLA2 recruitment at the NE, unless in this case cPLA2 recruitment is not mediated by an increase in NE tension.
• NPC transport versus NE permeability. The authors suggest that confinement increases cyclin B1 transport via NPC-mediated transport and rule out that confinement may affect NE permeability based on the absence of NE rupture using the INM marker lap2. However, the sample size for this observation is missing and NE permeability could be altered even in the absence of major INM rupture observed by confocal. The authors should use a reporter of nuclear permeability (fluorescent cytoplasmic marker or nuclear marker as previously used by Denais et al or, 2016 or Raab et al., 2016) to make sure that NE permeability is not affected by confinement. In addition, NPC function should be tested in parallel with other fluorescent reporter (such as NLS-GFP constructs) to test whether global NPC-mediated transport is changed during prophase (with or without confinement).
• Effect of confinement on cyclin B transport (NEP) in adherent cells. In figure 1D, we can see that confinement enhances cyclin B1 nuclear translocation in cells adhering on fibronectin. Although it is unclear whether confinement has a significant effect in other figures, for example in figure 2F: DMSO is not significantly different from confiner+CDKi (same thing in 3i and 3j with Rock inhibitor and Kash construct). In these figures the untreated+confiner (or control in 3j) is missing, and the absence of difference between treated+confiner and control is puzzling. Either there is no difference between confiner and CDKi+confiner and it means there is no difference between control and confiner (surprising considering figure 1D); or there is a difference between CDKi+confiner and confiner, indicating that CDK inhibition affects confinement-induced cyclin B import. Both possibilities suggest that the authors should significantly revisit their model. In any case, all control (untreated, treated +/- confiner should be in all figures to avoid any misunderstanding).
• Consequences of cPLA2 recruitment at the NE. The authors state that "Active cPLA2 then stimulates actomyosin contractility creating a positive feedback loop" But the NE is already unfolded and distance between NPR is increased before cPLA2 recruitment. Does PLA2 inhibition affect nuclear irregularity (or distance between NPC)? Or does cPLA2 impact cyclin B1 transport via a distinct mechanism? Did the author analyze CDK1 phosphorylation in presence of PLA2 inhibitor?
• Robustness of the main observation. On page 4, the authors report that cells enter mitosis after 140 sec (+/- 80 sec) of confinement, although in the example showed in figure 1b, the cell enters at least 420 sec min after confinement, as we can see that the cell is already confined -420 sec (compressed shape) and NEP occurs at 0. Did the author showed a cell that was not included in their statistics? This would be very surprising considering the very low sample size used for this experiment (n=6 and 10). In addition, many observations have been made on small sample size (n=6 for figure 1) or/and not from independent experiments. The authors should increase their sample size and compare results from independent experiments to consolidate their model.
• 2h shows nuclear signal (cyclin in grayscale), while 2e does not, why?
• starting point to quantify cyclin entry is the lowest intensity, which may depend on many factors (and could be affected by experimental design). It would be necessary to have synchronized cells to homogenize the starting point of these experiments.
• DN-KASH have been transiently transfected for single cell experiments, how does the authors unsure that cell observed are transfected? Does it have a fluorescent tag, if so which one?
• "requires contact with external stimuli" or "that mechanical confinement is sufficient to overcome the lack of external stimuli." (page 4): external stimuli is vague here and it could be better to replace it with a more specific description

Significance

While the physiological relevance of these findings remain to be determined, the authors report an interesting observation that could have a significant impact in the field. The authors do not comment the potential overlap of their findings with other reports involving the LINC complex (Booth et al., ELife) or CDK-mediated actin remodeling (Ramanathan et al., NCB 2015) during prophase.

Reviewer #3 (Public Review):

Four decades after the seminal work of the Schekman's lab on the genetic identification of the core eukaryotic secretory machinery the molecular roles of the individual components have been largely characterized. Yet our understanding of how these components are organized to define processes is wanting, with notable controversies still hovering over at several levels of the secretory pathway, including the events that take place in the ER/Golgi interface, the transit across the Golgi, the biogenesis of secretory vesicles and the delivery, tethering and docking of these vesicles to the membrane. This manuscript mostly addresses the latest steps of this chain of events and makes some incursions into the biogenesis of vesicles at the TGN. It represents a serious and honest attempt to define the timeline of events that, driven by key components such as the Sec4 ras-in-brain (Rab) GTPase, its effectors myosin-5, Sro7 and the exocyst, its GEF, Sec2 and the prototypic Sec/Munc protein Sec1, a regulator of trans-SNARE complex formation, ultimately result in the tethering, docking and fusion of vesicles with the membrane of the polarized bud of the ascomycete yeast Saccharomyces cerevisiae. Tethering, as defined by light microscopy appears to be a robust process reproducibly lasting for five seconds, before fusion, as defined by the loss of vesicle components, takes place. Important evidence is provided that the exocyst is incorporated as an holo-complex to secretory vesicles. Overall, even though this work will likely suffer modifications and amendments as knowledge and technology progress, it will nevertheless become the reference blueprint around which any future work in the field will pivot.

This work represents a very substantial advance in the field of exocytosis. Besides reporting with unmatched time resolution the tethering of vesicles with the membrane, it describes a herculean effort to gain mechanistic understanding of the process by using a score of genetic perturbations and fluorescent reporters. I feel that evidence that Sec3 travels with the exocyst rather than contributing a milestone for exocyst landing will be disputed, but this referee finds it as convincing as appealing. Nearly as important is the timing of Sec1 action in the fusion step. However, it is the delineation of a timeline that will make this paper a reference in the field.

Understanding the technology for image acquisition is critical to appreciating the strengths of this MS (333 ms/Z-stack time point may be considered super-resolution - in the time dimension. Therefore, its description requires clarification in places. The experimental work is almost exclusively based on live microscopy using fluorescent proteins tagged by allelic replacement. The microscopy routine for single fluorophore analysis provides time series with a resolution of 3-5 fps that enables authors to resolve, using robust statistical tests, events separated by seconds. In this context, it is notable that dual-channel imaging appears to be made by sequential, not simultaneous, acquisition, which deserves a currently missing comment. Moreover, given the weight that image acquisition plays in this project, it might be described and justified better. The Materials and methods lack detail, for example, the laser lines & power used for excitation. This referee could not fully understand the routine of image acquisition, specifically, the continuous movement of the stage in the Z-axis as images are streamed (to the RAM or to the disk? the latter takes time, line 177); does it mean that Z-stepping is solely governed by the exposure time? The CCD camera penalizes pixel size (16 µm) at the expense of achieving outstanding quantum efficiency. The optical path includes a 100x objective and a 2x magnification lens to compensate for the large camera pixel size, thereby achieving 0.085 µm/pixel, but these lenses 'waste' part of the fluorescent signal. One wonders if the CMOS camera (6.5 µm pixel size) coupled with a 63x objective wouldn't be appropriate? A brief discussion on this choice would be helpful for readers.

There is an elephant in the room of in vivo microscopy that no one dares to comment on: reporter proteins are mutant versions carrying a heavy and potentially oligomerising rucksack - the fluorescent protein tag. The authors take the honest approach of acknowledging that some of the tagged proteins such as Sec4 are disfunctional and that certain reporters are incompatible with each other as they give rise to synthetic negative effects. In the end, they conclude that using diploids carrying the GFP-tagged allele in heterozygosis with the wt represents the most physiological approach to track proteins until less intrusive fluorescent tags are developed.

It is remarkable that Sec2 and Sec4 are recruited to membranes even before a vesicle is formed (Fig 6I). I find somewhat weak the evidence that RAB11s 'mark' the TGN, and disturbing the fact that RAB11 reaches the PM (does GFP tagging prevent GAP accession?). I should like to recommend strongly that the authors integrate into the introduction/discussion information on the late steps of exocytosis available for Aspergillus nidulans, another ascomycete that is particularly well suited for studying this process. Here RAB11 is not a late Golgi resident but is transiently (20 s) recruited to TGN cisternae in the late stages of their 120 s maturation cycle to drive the transition between Golgi and post-Golgi (Pantazopoulou MBoC, 2014). Recruitment of RAB11 to the TGN is preceded by the arrival of its TRAPPII GEF (Pinar, PNAS 2015; Pinar PLOS Gen 2019), a huge complex that is incorporated en bloc to the TGN (Pinar JoCS, 2020). Upon RAB11 acquisition RAB11 membranes engage molecular motors (Penalva, MBoC 2017) to undertake a several-micron journey that transports them to a vesicle supply center located underneath the apex (review, Pinar & Penalva, 2021). Here is where Sec4 is located, strongly indicating that there is a division of work between two Rabs each mediating one of the two stages between the TGN and the membrane (Pantazopoulou, 2014, MBoC).

SciScore for 10.1101/2022.05.03.22274395: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

<table><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Ethics</td><td style="min-width:100px;border-bottom:1px solid lightgray">IRB: PCR confirmed and clinically suspected severe COVID-19 cases admitted to hospital were recruited into the DISCOVER study at North Bristol NHS Trust for which HRA Approval was granted by the South Yorkshire Research Ethics Committee (20/YH/0121).<br>Consent: All samples were used in accordance with the Human Tissue Act (2004) with appropriate consent and ethical approvals in place.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Sex as a biological variable</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Randomization</td><td style="min-width:100px;border-bottom:1px solid lightgray">Blinding of validation set: The validation set of samples (n=807) were split into multiple aliquots (n=5) for randomisation and blinding by assigning a new barcode ID for each aliquot.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Blinding</td><td style="min-width:100px;border-bottom:1px solid lightgray">Blinding of validation set: The validation set of samples (n=807) were split into multiple aliquots (n=5) for randomisation and blinding by assigning a new barcode ID for each aliquot.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Power Analysis</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Cell Line Authentication</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr></table>

Table 2: Resources

<table><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Antibodies</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Purified proteins were analysed by SDS-PAGE and by Western-blots assays using an anti-His tag antibody (Sigma).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-His tag</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">After washing, HRP-conjugated anti-human Pan-Immunoglobulin (Pan) (Sigma), IgG (Southern Biotech), IgA (Sigma) or IgM (Sigma) secondary antibody, in the same dilution buffer as the samples, was added (50 µl per well) and incubated for 1 hour.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>IgG ( Southern Biotech)</div><div>suggested: (SouthernBiotech Cat# 1050-01, RRID:AB_2737431)</div></div><div style="margin-bottom:8px"><div>IgA ( Sigma )</div><div>suggested: (Sigma-Aldrich Cat# I1010, RRID:AB_1163625)</div></div><div style="margin-bottom:8px"><div>IgM ( Sigma ) secondary antibody</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>IgM</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Roche SARS-CoV-2 anti-nucleocapsid antibody assay: Serum samples from PCR-confirmed cases were analysed using the commercial Elecsys® Anti-SARS-CoV-2 (Roche) in the Department of Microbiology, Infection Sciences, Southmead Hospital, North Bristol NHS Trust, Southmead Road, BS10 5NB, UK following manufacturer’s instructions.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-nucleocapsid</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>Anti-SARS-CoV-2</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Transfected 293T cells were then infected with VSV*G-FLuc particles for 2 hours, washed with PBS, then incubated with fresh DMEM, supplemented with 10% FBS and 1:2000 (v/v) I1 (anti-VSV-G) antibody (absolute antibody Ab01401-10.3).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>I1</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>anti-VSV-G</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Cell Lines</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">VSV-G-harbouring BHK21 cells were infected with VSV*ΔG- FLuc particles to generate complemented VSV*G-FLuc particles as previously described (35).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>BHK21</div><div>suggested: ATCC Cat# CRL-6281, RRID:CVCL_1914)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">, 293T cells were seeded and transiently transfected with a plasmid corresponding to the original Wuhan strain Spike protein (pCAGGS-S2-spike) using Turbofect transfection reagent (ThermoFisher R0532) for 16 hours following the manufacturer’s instructions.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>293T</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Optimal pseudotype cell entry was achieved using VeroE6 cells stably expressing the human angiotensin-converting enzyme 2 (ACE2) receptor and the cell surface protease TMPRSS2 (Vero ACE2 TMPRSS2 (VAT) cells, which were a kind gift from Dr Suzannah Rihn, MRC-University of Glasgow Centre for Virus Research (36)</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>VeroE6</div><div>suggested: JCRB Cat# JCRB1819, RRID:CVCL_YQ49)</div></div><div style="margin-bottom:8px"><div>Vero ACE2</div><div>suggested: RRID:CVCL_A7UJ)</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Recombinant DNA</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The sequence was synthesized with an NdeI restriction site at the 5’ end and the BamHI site at the 3’ end and cloned into pET28a expression vector.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pET28a</div><div>suggested: RRID:Addgene_139598)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The recombinant plasmids (pET28a-NP-FL) were transformed into E. coli strain BL21 (DE3)</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pET28a-NP-FL</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">, 293T cells were seeded and transiently transfected with a plasmid corresponding to the original Wuhan strain Spike protein (pCAGGS-S2-spike) using Turbofect transfection reagent (ThermoFisher R0532) for 16 hours following the manufacturer’s instructions.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pCAGGS-S2-spike</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Software and Algorithms</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Images were acquired on the ImageXpress Pico Automated Cell Imaging System (Molecular Devices) using a 10X objective and infected cells detected and quantified using Cell ReporterXpress software (Molecular Devices).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Cell ReporterXpress</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Statistical Analysis: Data analyses were performed using either R software with R Studio, and GraphPad Prism (version 9) as detailed below.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>GraphPad Prism</div><div>suggested: (GraphPad Prism, RRID:SCR_002798)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">However, sample readouts using other methods including interpolated unit values (from a 4- parameter logistic regression model fit (on Prism or within BMG software) to the 7- point standard pool dilution series) and AUC from sample dilution series were used in the development stage.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Prism</div><div>suggested: (PRISM, RRID:SCR_005375)</div></div></td></tr></table>

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Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:

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Strengths of this study include rigorous development of high performance, low blood volume, cost-effective tests which can be easily deployed in a variety of settings, but our approach also has several limitations. Firstly, whilst samples from pre-pandemic children were included, samples from children with COVID-19 were not available to us and as such, assay performance for detecting recent paediatric infections cannot be reported. However, since widespread vaccination of children is not currently common in many countries while asymptomatic/mild paediatric infections are, antibody assays offer a useful tool for monitoring infection in this age group. The antigens used in the in-house assays were generated using the genetic sequence from the parent Wuhan strain of SARS-CoV-2 first described in 2020 (7) from which several new variants of concern (VOC) have evolved and have caused significant waves of infection globally. Some of these variants, especially Omicron, include multiple mutations in these target antigens and as such, may lead to antibody responses with differential binding to the target antigens. Indeed, antibodies responses raised to antigens from one SARS-CoV-2 variant genetic sequence lead to differential ability to neutralise VOC strains. However, whilst others have shown reduced binding to antigens from sequences of VOCs, rates of seropositivity when using different antigens, and/or from people who were infected with non-Wuhan variants, appear to be relatively un...

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Results from TrialIdentifier: No clinical trial numbers were referenced.

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Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

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Results from JetFighter: We did not find any issues relating to colormaps.

Results from rtransparent:

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• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

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Results from scite Reference Check: We found no unreliable references.

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About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

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Here's where things get spicy

See also https://hypothes.is/search?q=tag%3A%27%22algorithm%22+vs+%22heuristic%22%27

SciScore for 10.1101/2022.05.04.490614: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

<table><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Ethics</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Sex as a biological variable</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Randomization</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Blinding</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Power Analysis</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr></table>

Table 2: Resources

<table><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Antibodies</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Staining antibodies are as follows (Hu Fc Block Pure Fc1.3216 (BD, Cat# 564220), APC anti-HLA-ABC (Thermofisher, Cat# 17- 9983-42), APC/Cy7 anti-HLA-DR (BioLegend, Cat# 307618), PE anti- DYKDDDDK Tag (BioLegend, Cat# 637309), AF488 anti-SARS-CoV-2 Spike S1 Subunit (R&D Systems,Cat# FAB105403G), FITC anti-Influenza A NP (Thermofisher, Cat# MA1-7322), PE anti-mouse CD45 (BioLegend, Cat# 109808), BV421 anti-mouse CD31 (BioLegend, Cat# 102423), APC anti-mouse EpCAM (BioLegend, Cat# 118213), PerCP/Cy5.5 anti-H-2Kb/H-2Db (BioLegend,Cat# 114620)).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-HLA-ABC</div><div>suggested: (Thermo Fisher Scientific Cat# 17-9983-41, RRID:AB_10753773)</div></div><div style="margin-bottom:8px"><div>anti-HLA-DR</div><div>suggested: (BioLegend Cat# 307618, RRID:AB_493586)</div></div><div style="margin-bottom:8px"><div>anti-SARS-CoV-2</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>anti-Influenza</div><div>suggested: (Thermo Fisher Scientific Cat# MA1-7322, RRID:AB_1017747)</div></div><div style="margin-bottom:8px"><div>anti-H-2Kb/H-2Db</div><div>suggested: (BioLegend Cat# 114620, RRID:AB_2750200)</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Recombinant DNA</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Plasmids: pDONR207-SARS-CoV-2 E (#141273), pDONR207-SARS-CoV-2 M (#141274), pDONR207-SARS-CoV-2 ORF7a (#141276), pDONR223-SARS-CoV-2 ORF7b (#141277), pDONR223-SARS-CoV-2 ORF8 (#141278) were purchased from addgene (Kim et al., 2020) and used as templates for construction of plasmids expressing SARS-CoV-2 viral proteins.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pDONR207-SARS-CoV-2</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>pDONR223-SARS-CoV-2</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">For HIV Nef expressing plasmid construction, NL4-3-dE-EGFP (kindly provided by Dr. Ya-Chi Ho) was used as a template.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>NL4-3-dE-EGFP</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">For construction of plasmids expressing SARS-CoV viral proteins, oligonucleotides corresponding to both strands of SARS-CoV Tor2 (GenBank accession: NC_004718.3) ORF8a and ORF8b containing XhoI and BamHI sites at the 5’ and 3’ ends were synthesized (IDT) and cloned into XhoI-BamHI site of c-Flag pcDNA3 vector.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pcDNA3</div><div>suggested: RRID:Addgene_15475)</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Software and Algorithms</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">To investigate the prevalence of amino acid mutations, we downloaded up to 965 sequences of each lineage and aligned the ORF8 nucleotide sequences using Jalview software (http://www.jalview.org/) (Waterhouse et al. Bioinformatics. 2009) by MUSCLE algorithm (Edgar RC.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Jalview</div><div>suggested: (Jalview, RRID:SCR_006459)</div></div><div style="margin-bottom:8px"><div>MUSCLE</div><div>suggested: (MUSCLE, RRID:SCR_011812)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">FlowJo software (Tree Star) was used for the data analysis.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>FlowJo</div><div>suggested: (FlowJo, RRID:SCR_008520)</div></div></td></tr></table>

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Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

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Results from TrialIdentifier: No clinical trial numbers were referenced.

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Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

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Results from JetFighter: We did not find any issues relating to colormaps.

Results from rtransparent:

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• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

---

Results from scite Reference Check: We found no unreliable references.

---

<footer>

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

</footer>

SciScore for 10.1101/2022.05.03.490428: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

<table><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Ethics</td><td style="min-width:100px;border-bottom:1px solid lightgray">IRB: Human convalescent serum samples: Human convalescent serum samples from recovered COVID-19 patients were obtained from Public Health Clinical Center of Chengdu in Chengdu, China, under approved guidelines by the Institutional Review Board (IRB), and all patients had provided written informed consent before serum sample were collected.<br>Consent: Human convalescent serum samples: Human convalescent serum samples from recovered COVID-19 patients were obtained from Public Health Clinical Center of Chengdu in Chengdu, China, under approved guidelines by the Institutional Review Board (IRB), and all patients had provided written informed consent before serum sample were collected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Sex as a biological variable</td><td style="min-width:100px;border-bottom:1px solid lightgray">Animal studies, facilities and ethics statements: Specific pathogen-free (SPF) BALB/c female mice (6-8 weeks old) for immunogenicity studies were purchased from Charles River Experimental Animals Co.,</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Randomization</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Blinding</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Power Analysis</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Cell Line Authentication</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr></table>

Table 2: Resources

<table><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Antibodies</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Serum was collected on D35 (2 weeks PD2), D56 (Day of 3rd dose boost), D85 (1 month post dose 3), D113 (2 months post dose3) and D141 (3 months post dose 3) for pseudovirus neutralizing antibody test.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>D56</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>D85</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>D113</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>D141</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Cell Lines</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Pseudovirions were produced by co-transfection HEK 293T cells with psPAX2, pLVX-AcGFP-N1-Fluc, and plasmids encoding various S genes by using Lipofectamine 3000 (Invitrogen, L3000-015).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>HEK 293T</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Pseudoviruses stock were titrated by infecting 293T-ACE2 cells and luciferase activity was determined following a 44-48 h incubation period at 37°C and 5% CO2 by addition Bright-Glo Luciferase Assay System (Promega, E2650) using a microplate reader (TECAN, Spark).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>293T-ACE2</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Organisms/Strains</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">For three dose boost study, Balb/c mice, female (n=10/group) prime and boost with SCB-2019 3 μg adjuvanted with 75 μg alum plus 150 μg CpG 1018 twice on Day 0 and Day 21, then boosted with 3 μg SCB-2019, or SCB-2022B or Bivalent adjuvanted with 75 μg alum plus 150 μg CpG 1018 on Day 57 via intramuscular injection.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Balb/c</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Recombinant DNA</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The cDNA was subcloned into pTRIMER expression vector (GenHunter Corporation) at Hind III and Bgl II sites to allow in-frame fusion of the soluble S protein to Trimer-Tag (amino acid residue 1156-1406 from human Type I(α) collagen).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pTRIMER</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Pseudovirus construction and production: The variants of concern of SARS-CoV-2 spike protein genes were optimized using mammalian codon and synthesized by Genscript, then cloned into pcDNA3.1(+) eukaryotic expression vector.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pcDNA3.1</div><div>suggested: RRID:Addgene_79663)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Pseudovirions were produced by co-transfection HEK 293T cells with psPAX2, pLVX-AcGFP-N1-Fluc, and plasmids encoding various S genes by using Lipofectamine 3000 (Invitrogen, L3000-015).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>psPAX2</div><div>suggested: RRID:Addgene_12260)</div></div><div style="margin-bottom:8px"><div>pLVX-AcGFP-N1-Fluc</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Software and Algorithms</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Statistical analysis: Data arrangement was performed by Excel and statistical analyses were performed using the Prism 9.2.0 (GraphPad Software).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Excel</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>GraphPad</div><div>suggested: (GraphPad Prism, RRID:SCR_002798)</div></div></td></tr></table>

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Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

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Results from TrialIdentifier: We found the following clinical trial numbers in your paper:<br><table><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Identifier</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Status</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Title</td></tr><tr><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">NCT04405908</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Completed</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">SCB-2019 as COVID-19 Vaccine</td></tr><tr><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">NCT04672395</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Recruiting</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">A Controlled Phase 2/3 Study of Adjuvanted Recombinant SARS-…</td></tr></table>

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Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

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Results from JetFighter: We did not find any issues relating to colormaps.

Results from rtransparent:

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• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

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Results from scite Reference Check: We found no unreliable references.

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About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

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I argue that PHP is not reasonable here. The only appropriate thing for this use case is (unminified) JS—or some other program text encoded as a document resource permitting introspection and that the user agent just happens to be able to execute/simulate.*

• Just like the advocates of "a little jQuery", author here doesn't seem to realize that the use of PHP was the first step towards what is widely acknowledged to be messed up about the "modern" Web. People can pine for the days of simple server-side rendering, but there's no use denying that today's Web is the natural result of an outgrowth that began with abuses of the fundamental mechanisms underpinning the Web—abuses that first took root with PHP.

* Refer to the fourth and sixth laws of "Sane Personal Computing, esp. re "reveals purpose"

Controversial opinion: this is actually closer to doing the Web the way that it should be done, taking the intent of its design into account. NB: this is not exculpatory of minified JS bundles (where "megabyte" is the appropriate unit order of magnitude for measuring their weight) or anything about "modern" SPAs that thumb their nose at graceful degradation.

Most people born since the advent of the filing cabinet and the computer have spent a lifetime using a hierarchical folder-based mental model for their knowledge. For greater value and efficiency one needs to get away from this model and move toward linking individual ideas together in ways that they can more easily be re-used.

To accomplish this many people use an index-based method that uses topical or subject headings which can be useful. However after even a few years of utilizing a generic tag (science for example) it may become overwhelmed and generally useless in a broad search. Even switching to narrower sub-headings (physics, biology, chemistry) may show the same effect. As a result one will increasingly need to spend time and effort to maintain and work at this sort of taxonomical system.

The better option is to directly link related ideas to each other. Each atomic idea will have a much more limited set of links to other ideas which will create a much more valuable set of interlinks for later use. Limiting your links at this level will be incredibly more useful over time.

One of the biggest benefits of the physical system used by Niklas Luhmann was that each card was required to be placed next to at least one card in a branching tree of knowledge (or a whole new branch had to be created.) Though he often noted links to other atomic ideas there was at least a minimum link of one on every idea in the system.

For those who have difficulty deciding where to place a new idea within their system, it can certainly be helpful to add a few broad keywords of the type one might put into an index. This may help you in linking your individual ideas as you can do a search of one or more of your keywords to narrow down the existing ones within your collection. This may help you link your new idea to one or more of those already in your system. This method may be even more useful and helpful for those who are starting out and have fewer than 500-1000 notes in their system and have even less to link their new atomic ideas to.

For those who have graphical systems, it may be helpful to look for one or two individual "tags" in a graph structure to visually see the number of first degree notes that link to them as a means of creating links between atomic ideas.

To have a better idea of a hierarchy of value within these ideas, it may help to have some names and delineate this hierarchy of potential links. Perhaps we might borrow some well ideas from library and information science to guide us? There's a system in library science that uses a hierarchical set up using the phrases: "broader terms", "narrower terms", "related terms", and "used for" (think alias or also known as) for cataloging books and related materials.

We might try using tags or index-like links in each of these levels to become more specific, but let's append "connected atomic ideas" to the bottom of the list.

Here's an example:

• broader terms (BT): [[physics]]
• narrower terms (NT): [[mechanics]], [[dynamics]]
• related terms (RT): [[acceleration]], [[velocity]]
• used for (UF) or aliases:
• connected atomic ideas: [[force = mass * acceleration]], [[$$v^2=v_0^2​+2aΔx$$]]

Chances are that within a particular text, one's notes may connect and interrelate to each other quite easily, but it's important to also link those ideas to other ideas that are already in your pre-existing body of knowledge.

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See also: Thesaurus for Graphic Materials I: Subject Terms (TGM I) https://www.loc.gov/rr/print/tgm1/ic.html

Author Response

*Reviewer #2 (Public Review):

This manuscript describes studies on the structural determinants of activation for the adhesion GPCR (aGPCR) GPR116 both in vitro and in vivo. The authors define key residues for activation on the receptors' N-terminus (the "tethered agonist") and the extracellular loops. Thus, the studies provide novel insights into the structural determinants of GPR116 activation. However, some interpretational issues (detailed below) complicate some of the authors' conclusions. Specific comments are as follows:

1. Results section, first paragraph, last sentence: The authors write, "These results taken together indicate that the H991A mutant is capable of proper trafficking to the membrane, is able to response to exogenous peptide, but is unable to be cleaved and activated by endogenous ligands in vivo." The last part of this sentence represents an over-interpretation, as the data shown in Figure 1 do NOT show that the non-cleavable receptor is unable to be activated by endogenous ligands in vivo. It is entirely conceivable that a non-cleavable aGPCR could still be activated by endogenous adhesive ligands if those ligands were to change the position of the tethered agonist in manner that alters receptor signaling activity.

Thank you for highlighting this misleading wording. We rephrased the sentence to read as follows: Taken together, these results demonstrate that the H991 residue within the GAIN domain is critical for cleavage of GPR116 into NTF and CTF fragments but dispensable for trafficking of the receptor to the plasma membrane and response to exogenous peptide activation in vitro.

1. The data shown in Fig. 1B (surface expression of non-cleavable H991A mutant) need to be quantified in some way in order to be interpretable.

As the H991A construct does not contain a cell surface epitope tag, it is difficult to directly quantitate surface expression of this protein. The data in transiently transfected HEK293 cells (Figure 1, panels C and D) and in primary alveolar epithelial cells (Figure 2, panels C&D) clearly demonstrate that the H991A mutant is activated to comparable levels as the wild-type receptor in response to exogenous peptide stimulation. In light of these functional data, we are confident that the surface expression of H991A is comparable to that of the WT receptor in vitro and in vivo.

1. Results section, second paragraph, penultimate sentence: The authors write, "These data demonstrate that while the non-cleavable receptor is fully activated in vitro by exogenous peptides corresponding to the tethered agonist sequence, cleavage of the receptor and unmasking of the tethered agonist sequence is critical for GPR116 activation in vivo." However, the non-cleavable GPR116 mutant actually has two key differences from WT: i) lack of full liberation of the tethered agonist sequence, and ii) lack of liberation of a free NTF, which might dissociate from the CTF and have important in vivo physiological actions on its own. Isn't it conceivable that the lack of a freely mobile NTF contributes to the similarity in lung phenotype between the non-cleavable knock-in mutant and the GPR116 knockout? Based on the data shown in Figure 2, how can the authors claim these data demonstrate that unmasking of the tethered agonist is critical for GPR116 activation? The data could equally be interpreted as showing that liberation of a free NTF is critical for the physiological effects of GPR116 in vivo.

We thank the reviewer for this comment and, in retrospect, agree that we may have overstated the interpretation of our results for the H991A transgenic mouse. While it is possible that the free NTF may be responsible for the physiological effects of GPR116 in vivo, in light of recently published data by Mitgau et al. (BioRxiv https://doi.org/10.1101/2021.09.13.460127), we believe this not to be the case for the following reasons. First, the H991A and WT receptors are activated to an identical level by exogenous peptide stimulation in a transformed cell line (HEK293) and in primary alveolar type 2 epithelial cells (Figures 1 and 2), irrespective of if the NTF is free floating in solution in the context of the WT receptor. These data would argue against a role of the free NTF in receptor activity. Second, in a recent publication by Mitgau et al., the authors clearly demonstrate that activation of GPR126, an adhesion GPCR that is also cleaved at the GPS and activated by exogenous peptides corresponding to the tethered agonist, by antibodies that bind and crosslink the NTF is completely dependent on cleavage at the GPS. They further demonstrate that antibody-mediated activation does not lead to liberation of the NTF from the CTF. Rather, they postulate that proper GPS processing, as occurs for the WT receptor, leads to a favorable protein confirmation of the tethered agonist, which is indispensable for GPR126 activity. Given these results, we postulate that cleavage at the GPS of WT GPR116 results in a conformation that is critical for the tethered agonist sequence to reach and bind the ECLs, resulting in activation of the receptor, similar to that observed with GPR126. We have edited our interpretation of these data in the revised manuscript.

1. Figure 3: If the authors' hypothesis is that the tethered agonist must be liberated in order to allow activation of GPR116, then why do ANY of the Flag-tagged mutant constructs exhibit constitutive signaling activity? Doesn't the N-terminal Flag tag prevent the tethered agonist from being exposed? How can these data be reconciled with the authors' model?

It is unlikely that the 27 amino acid N-terminal FLAG epitope tag envelopes the tethered agonistic peptide to the same extent as the tertiary structure of the carboxy terminus of the NTF (based on published structures for other aGPCRs). Additionally, we provided data demonstrating that an untagged version of the CTF protein is activated to a similar extent at FLAG-tagged CTF in response to activating peptides (Supplemental Figure 2A). Based on our data from mutagenesis experiments and modeling of GPR116 with the agonist, we do not believe the tethered agonist dives deeply within the binding pocket but rather interacts with critical amino acids at the surface of ECL2 to induce conformational changes to the receptor and downstream activation.

1. The data shown in Fig. 3D are lacking statistical comparisons, so it is not possible to tell whether any of the differences between the mutants are statistically significant.

Statistical analyses for data in this panel have been added

1. The data shown in Fig. 4D (surface expression of the ECL mutants) need to be quantified in some way.

We have added additional data to this figure (Fig4 F-G-H) using the V5-tagged mFL construct as control. As the tag is C-terminal, we quantified by flow cytometry the total expression using an anti-V5 antibody, to complement to immunocytochemistry data showing membrane expression.

1. In interpreting the results of the ECL mutations on GPR116 signaling activity, it is unclear why the authors so explicitly propose that these data demonstrate that the tethered agonist must be interacting with ECL2. Isn't it possible that ECL2 mutants with impaired receptor signaling activity simply lock the receptor in an inactive state? In this way, the effects of the ECL2 mutations could be explained without invoking a physical interaction between the putative tethered agonist and ECL2.

Yes, this interpretation is also possible. We have rephrased the Results and Discussion sections accordingly to reflect this possibility.

SciScore for 10.1101/2022.04.28.489772: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

<table><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Ethics</td><td style="min-width:100px;border-bottom:1px solid lightgray">Euthanasia Agents: 2 variant at 100 TCID50/mouse under isoflurane anesthesia.<br>IACUC: All procedures were performed according to the animal study protocols approved by the FDA White Oak Animal Program Animal Care and Use Committee.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Sex as a biological variable</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Randomization</td><td style="min-width:100px;border-bottom:1px solid lightgray">In the ABSL-3 lab, K18-hACE2 mice were randomly grouped and were inoculated intranasally with NY (G614), Delta, Omicron BA.1 or Omicron BA.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Blinding</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Power Analysis</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Cell Line Authentication</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr></table>

Table 2: Resources

<table><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Antibodies</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Western blot: Western blot was performed using an anti-SARS-COV-2 S antibody following a protocol described previously (58).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-SARS-COV-2 S</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Alkaline phosphatase conjugated anti-Rabbit IgG (1:5000) (Sigma-Aldrich, St. Louis, MO) was used as a secondary antibody.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-Rabbit IgG</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Control sensors with no ACE2 or antibody were also dipped in the S protein solutions and the running buffer as references.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>ACE2</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">For antibody staining, an Alexa Fluor 647 conjugated donkey anti-human IgG Fc F(ab’)2 fragment (Jackson ImmunoResearch, West Grove, PA) was used as secondary antibody at 5 μg/ml concentration.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-human IgG</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">After washing, plates were probed with 1 μg/ml of inhouse developed rabbit polyclonal antibody specific for SARS-CoV-2 membrane/nucleocapsid (33) at 4°C overnight followed by peroxidase-conjugated goat anti-rabbit secondary antibody (SeraCare #5220-0336, 1:2000) for 2h at room temperature.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-rabbit</div><div>suggested: (SeraCare KPL Cat# 5220-0336, RRID:AB_2857917)</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Cell Lines</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Briefly, Expi293F cells transfected with monomeric ACE2 or dimeric ACE2 expression construct and the supernatant of the cell culture was collected.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Expi293F</div><div>suggested: RRID:CVCL_D615)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Murine Leukemia Virus (MLV) particles (plasmids of the MLV components kindly provided by Dr. Gary Whittaker at Cornell University and Drs. Catherine Chen and Wei Zheng at National Center for Advancing Translational Sciences, National Institutes of Health), pseudotyped with various SARS-CoV-2 S protein constructs, were generated in HEK 293T cells, following a protocol described previously for SARS-CoV (59, 60).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>HEK 293T</div><div>suggested: KCB Cat# KCB 200744YJ, RRID:CVCL_0063)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">To prepare for infection, 7.5×103 of HEK 293 cells, stably transfected with a full-length human ACE2 expression construct, in 15 μl culture medium were plated into a 384-well white-clear plate coated with poly-D-Lysine to enhance the cell attachment.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>HEK 293</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Pseudotyped virus particles were produced in 293T/17 cells (ATCC) by co-transfection of plasmids encoding codon-optimized SARS-CoV-2 full-length S constructs, packaging plasmid pCMV DR8.2, and luciferase reporter plasmid pHR’ CMV-Luc.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>293T/17</div><div>suggested: ATCC Cat# CRL-11268, RRID:CVCL_1926)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The 293T cell line stably overexpressing the human ACE2 cell surface receptor protein was kindly provided by Drs.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>293T</div><div>suggested: KCB Cat# KCB 200744YJ, RRID:CVCL_0063)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Seed viruses were amplified in Vero E6 (ATCC CRL-1586) or Vero E6 with TMPRSS2 overexpression (BPS Bioscience #78081)</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Vero E6</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">In vitro virus replication and focus-forming assay: Vero-E6 cells were pre-seeded in 12-well tissue culture plates overnight and were infected with authentic viruses (G614, Delta, Omicron BA.1 or BA.2) at MOI of 0.01 in Gibco™ high glucose DMEM containing 3% FBS.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Vero-E6</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Briefly, 10-fold serially diluted postinfection were added at 100 μl/well to Vero E6-TMPRSS2 cells pre-seeded in 96-well tissue culture plates.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Vero E6-TMPRSS2</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Organisms/Strains</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Mouse study: Hemizygous B6.Cg-Tg(K18-ACE2)2Prlmn/J (K18-hACE2</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>B6.Cg-Tg(K18-ACE2)2Prlmn/J</div><div>suggested: RRID:IMSR_JAX:034860)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">In the ABSL-3 lab, K18-hACE2 mice were randomly grouped and were inoculated intranasally with NY (G614), Delta, Omicron BA.1 or Omicron BA.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>K18-hACE2</div><div>suggested: RRID:IMSR_GPT:T037657)</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Recombinant DNA</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The S gene was fused with a C-terminal twin Strep tag (SGGGSAWSHPQFEKGGGSGGGSGGSSAWSHPQFEK) and cloned into a mammalian cell expression vector pCMV-IRES-puro (Codex BioSolutions, Inc, Gaithersburg,</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pCMV-IRES-puro</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Pseudotyped virus particles were produced in 293T/17 cells (ATCC) by co-transfection of plasmids encoding codon-optimized SARS-CoV-2 full-length S constructs, packaging plasmid pCMV DR8.2, and luciferase reporter plasmid pHR’ CMV-Luc.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pCMV DR8.2 , and luciferase reporter</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>pHR’</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Serially diluted pCMV6-AC-ACE2-GFP plasmid or pCC1-CoV2-F7 plasmid expressing SARS-CoV-2 N (62) was used to construct a standard curve.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pCMV6-AC-ACE2-GFP</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>pCC1-CoV2-F7</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Software and Algorithms</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The KD was obtained by fitting Req value and its corresponding concentration to the model: “one site-specific” using GraphPad Prism 8.0.2 according to H.J. Motulsky, Prism 5 Statistics Guide, 2007, GraphPad Software Inc.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>GraphPad Prism</div><div>suggested: (GraphPad Prism, RRID:SCR_002798)</div></div><div style="margin-bottom:8px"><div>GraphPad</div><div>suggested: (GraphPad Prism, RRID:SCR_002798)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Automated data collection was carried out using SerialEM version 3.8.6 (63) at a nominal magnification of 105,000× and the K3 detector in counting mode (calibrated pixel size, 0.83 Å) at an exposure rate of 13.761 electrons per pixel per second.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>SerialEM</div><div>suggested: (SerialEM, RRID:SCR_017293)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Local resolution was also determined using cryoSPARC.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>cryoSPARC</div><div>suggested: (cryoSPARC, RRID:SCR_016501)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Several rounds of manual building were performed in Coot.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Coot</div><div>suggested: (Coot, RRID:SCR_014222)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Iteratively, refinement was performed in both Phenix (real space refinement) and ISOLDE (66), and the Phenix refinement strategy included minimization_global, local_grid_search, and adp, with rotamer, Ramachandran, and reference-model restraints, using 7KRQ and 7KRR as the reference models.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Phenix</div><div>suggested: (Phenix, RRID:SCR_014224)</div></div></td></tr></table>

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Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

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Results from TrialIdentifier: No clinical trial numbers were referenced.

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Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

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Results from JetFighter: We did not find any issues relating to colormaps.

Results from rtransparent:

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• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

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Results from scite Reference Check: We found no unreliable references.

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About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

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https://github.com/dariusk/rss-to-activitypub/releases/tag/v2.0.0

RSS to ActivityPub

have https://gaming.amazon.com/home?tag=isthcom0a-20

have code (not redeemed yet)

https://gaming.amazon.com/loot/ageofempires3?ingress=amzn&ref_=SM_AOE01_P01_CRWN&tag=isthcom0a-20

SciScore for 10.1101/2022.04.22.22274032: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

<table><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Ethics</td><td style="min-width:100px;border-bottom:1px solid lightgray">IRB: Subjects or households with suspected or confirmed SARS-CoV-2 infection were recruited from the Greater New Orleans community under Tulane Biomedical Institutional Review Board (federalwide assurance number FWA00002055, under study number 2020-585).</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Sex as a biological variable</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Randomization</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Blinding</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Power Analysis</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Cell Line Authentication</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr></table>

Table 2: Resources

<table><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Antibodies</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Determination of antigen-specific antibody reactivity by multiplexed Luminex analysis: Recombinant SARS-CoV-2 antigens (full-length spike, RBD, and N) and the recombinant spike protein from OC43, HKU1, 229E, and NL63 (Frederick National Laboratories) were coupled with MagPlex beads (Luminex) via sulfo-NHS coupling chemistry.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>antigen-specific</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>HKU1</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The Spike protein ELISA for IgG antibodies has been validated by testing a standard set of positive and negative samples provided by NCI SeroNet staff.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>IgG</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">NK92 cells in complete alphaMEM culture medium were added at 5 × 104 cells/well in the presence of 4 µg/ml brefeldin A (Biolegend Cat# 420601), 5 µg/ml GolgiStop (BD Biosciences Cat# 554724) and 0.15µg of anti-CD107a antibody (Clone H4A3 PE-Cy7, Biolegend Cat# 328618) for 5 hours at 37°C.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-CD107a</div><div>suggested: (BioLegend Cat# 328618, RRID:AB_11147955)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Antibody-dependent neutrophil phagocytosis (ADNP): Protocol was adapted from [72]</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Antibody-dependent neutrophil phagocytosis</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Beads were washed with PBS containing 15 mM EDTA and stained with an FITC-conjugated anti-guinea pig C3 antibody (MP Biomedicals).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-guinea pig C3</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Cell Lines</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Neutralization of SARS CoV-2 in Pseudovirus Assay: CHO cells were generated and stably expressed ACE2 by transfecting CHO cells with an ACE2 expression plasmid containing the blasticidin resistance gene.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>CHO</div><div>suggested: CLS Cat# 603479/p746_CHO, RRID:CVCL_0213)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">CHO-ACE2 cells were similar in SARS CoV-2 susceptibility to the 293T/ACE2 cell line developed by Dr.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>CHO-ACE2</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Virus neutralization was measured in CHO/ACE2 cells.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>CHO/ACE2</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Pseudoviruses were produced by co-transfection of the four plasmids into 293T cells grown in T75 flasks with Fugene 6 as transfection reagent.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>293T</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Unbound antibodies were removed by centrifugation before adding THP-1 cells at 2.5×104 cells/well.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>THP-1</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Recombinant DNA</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">RBD (aa321-535) was similarly expressed in the phCMV plasmid and purified on Streptactin X affinity columns.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>phCMV</div><div>suggested: RRID:Addgene_15802)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">A DNA fragment encoding SARS CoV-2 N protein, including its natural leader sequence was generated by PCR of full-length N protein gene from a lentiviral N Protein expression vector (pLVX-EF1alpha-SARS-CoV-2-N-2xStrep-IRES-Puro, which was a gift from Nevan Krogan (Addgene plasmid # 141391 ; http://n2t.net/addgene:141391; RRID:Addgene_141391, [68]).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div></div><div>detected: RRID:Addgene_141391)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">These included an expression plasmid for full-length spike protein of the Wuhan-1 strain containing the D614G amino acid chain (VRC7480.G614) [70], a pCMV ΔR8.2 lentivirus backbone plasmid (VRC5602) [71], the VRC5601 plasmid pHR’ CMV Luc containing the firefly luciferase reporter gene [71], and VRC9260 for TMPRSS2 expression.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pCMV ΔR8.2 lentivirus</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>VRC5601</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>pHR’</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Software and Algorithms</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Neutralization titers were defined as the serum dilution (ID50) at which relative luminescence units (RLU) were reduced by 50% compared to virus control wells after subtraction of background RLUs (determined by GraphPad Prism, version 9 for macOS, GraphPad Software, San Diego, California USA).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>GraphPad Prism</div><div>suggested: (GraphPad Prism, RRID:SCR_002798)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Spike Glycoprotein (stabilized) from SARS-Related Coronavirus 2, Wuhan-Hu-1 with C-Terminal Histidine Tag, Recombinant from Baculovirus), and SARS-CoV-2 specific mega pools at 0.2 μg/well including PepTivator SARS-CoV-2 Prot_S (Miltinyi - 130-126-700), SARS-CoV-2 Prot_M (130-126-702), SARS-CoV-2 Prot_N (130-126-699) in 96-well U bottom tissue culture plate (CytoOne CC7672-7596) in 200 μl RPMI-1640 with 10% FBS.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>PepTivator</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">) GraphPad Prism (version 9.0.0, GraphPad Software, San Diego, CA), JMP (version 16.2.0, SAS Institute, Inc.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>GraphPad</div><div>suggested: (GraphPad Prism, RRID:SCR_002798)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">, Cary, NC), and SAS (version 9.4, SAS Institute, Inc.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>SAS Institute</div><div>suggested: (Statistical Analysis System, RRID:SCR_008567)</div></div></td></tr></table>

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Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:

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Study limitations primarily involved using SARS-CoV-2 infection to differentiate subjects rather than pre-pandemic samples. In addition, the assays were limited to peripheral blood samples and not tissue-specific responses, which included only effector functions to spike protein and cytokine secretion instead of T-cell subset analyses. Detection of secreted cytokines allowed a greater number of cytokines to be evaluated but prevented confirmation of cells producing cytokines as would be observed intracellular stained cytokines for specific T-cell populations. However, cytokines between spike or peptide pools were highly correlated (Figure S5), indicating T-cell production. Also, high expression of IL-2 has been routinely observed from CD4+ T-cell and not CD8+ T-cells after SARS-CoV-2 infection [28, 38]. In this study, IL-17A secretion was closely correlated to IL-2 and Th1 cytokine release after stimulation with protein or peptide pools, suggesting that IL-17A may be serving as a proxy for a Th1/Th17 subset, as identified in other post-vaccination studies [61] which should be more closely examined. Finally, while the critical role for age in SARS-CoV-2 immunity was validated, it remains an ongoing question of why children exhibit less severity with infection and how differences in qualitative features of immunity depend on patient age. Our study used samples collected from subjects only shortly after the pandemic which will be difficult to perform as COVID subsides and vaccin...

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Results from TrialIdentifier: No clinical trial numbers were referenced.

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Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

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Results from JetFighter: We did not find any issues relating to colormaps.

Results from rtransparent:

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• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

---

Results from scite Reference Check: We found no unreliable references.

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About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

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SciScore for 10.1101/2022.04.21.489021: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

<table><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Ethics</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Sex as a biological variable</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Randomization</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Blinding</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Power Analysis</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Cell Line Authentication</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr></table>

Table 2: Resources

<table><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Antibodies</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The cells were incubated with 100μM of the respective peptide and 1:200 dilution of rabbit anti-HA tag antibody (Sigma; catalogue no. Cat # H6908) at 4°C for 1hr.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-HA</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Cells were incubated with 1:200 dilution of goat anti-rabbit Alexa fluor-647 antibody (Invitrogen) and 1:100 dilution of neutravidin fluorescein conjugate (Invitrogen; FITC, catalogue no. A2662, used for the first FACS) at 4°C for 30min.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-rabbit</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>antibody (Invitrogen)</div><div>suggested: (Rockland Cat# 00-8844-25, RRID:AB_2610705)</div></div><div style="margin-bottom:8px"><div>30min</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Cells were incubated with 1:500 dilution of mouse anti-FLAG monoclonal antibody (Merck, catalogue no. F3165) overnight at 4°C.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-FLAG</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The blots were probed with 1:5000 anti Flag antibody (Sigma, F3165) overnight at 4□C followed by 1:10000 anti-mouse secondary at room temperature for 1hr.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti Flag</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>anti-mouse</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The surface of the well was washed twice with blocking buffer and incubated with HRP-conjugated rabbit anti-6xHis tag antibody (Abcam; catalogue no. AB1187), 1:10,000 dilution at 4°C overnight.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-6xHis tag</div><div>suggested: (Abcam Cat# ab1187, RRID:AB_298652)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">eGFP-ACE2/HEK293T immunostained with FLAG antibody was imaged at 60x oil objective of FV3000 confocal microscope (Figure 2B) and 100x oil objective of H-TIRF microscope (Supplementary Figure 2A) using 405nm, 488nm and 647nm laser lines for DAPI, eGFP, and Alexa fluor-647 fluorophores.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>eGFP</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Cell Lines</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Cell culture experiments: Wild type mammalian HEK293T cells and LentiX-293T cells (Takara Bio, catalogue no. 632180) were used in this study for pseudotyped spike virus production and viral transduction assay.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>HEK293T</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>LentiX-293T</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Caco2 cells were lysed for total RNA purification using the Trizol method.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Caco2</div><div>suggested: CLS Cat# 300137/p1665_CaCo-2, RRID:CVCL_0025)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Pseudoviral transduction assay: The GFP/HEK293T cells or eGFP-ACE2/HEK293T cells were grown up to 60-70% confluency in complete media before viral transduction.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>GFP/HEK293T</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>eGFP-ACE2/HEK293T</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">In this assay, the viral titre was used in a concentration such that to obtain more than 70% transduction efficiency in the eGFP-ACE2/HEK293T or eGFP/HEK293T cell line.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>eGFP/HEK293T</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Recombinant DNA</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Cloning and protein purification: The nanobody gene was amplified from isolated yeast colonies and cloned between HindIII and XhoI sites in a pET-22b(+) plasmid containing a C-terminal 6x histidine tag.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pET-22b(+)</div><div>suggested: RRID:Addgene_12651)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">For viral particle production, 5 µg pHR lentiviral vector cloned with mCherry fluorescent protein, 3.75 µg packaging plasmid psPAX2 (Addgene; #12260), and 2.5 µg envelope plasmid for the expression of Spike glycoprotein (obtained as a kind gift from Prof. Nevan Krogan, UCSF, USA) of SARS-CoV-2 were mixed in 500 µl OptiMEM media and 20 µl PLUS reagent (Invitrogen; LTX transfection reagent, catalogue no. L15338100) and kept for incubation at room temperature for 5min.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pHR</div><div>suggested: RRID:Addgene_16514)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">We also generated control lentiviral particles by replacing Spike plasmid with VSV-G envelope protein, pmDG2 (Addgene; #12259) plasmid.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pmDG2</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Omicron pseudotyped virus production: Omicron pseudotyped viruses were produced similarly as described above for spike pseudoviruses but instead used omicron envelope plasmid along with packaging plasmid (psPAX2) and lentiviral plasmid (pHR mCherry) in the following ratio: psPAX2 (1.3pmol), pHR mCherry: 1.64pmol, SARS-CoV-2 Omicron Strain S gene (Genscript, Cat # MC_0101274): 0.72pmol.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>psPAX2</div><div>suggested: RRID:Addgene_12260)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">This construct contains amino-terminus EGFP followed by self-cleaving 2A peptide sequence followed by ACE2 and carboxy-termini SNAP-tag and FLAG tags (eGFP-ACE2/HEK293T) Generation of stable HEK293T cell line for over-expression of ACE2: The lentiviral pTRIP vector cloned with eGFP-ACE2/HEK293T under CMV enhancer and chicken β-actin promoter (CAG promoter) flanked with 5′and 3′ long terminal repeat (LTR) sequences (39), were used to produce lentiviral particles as per the method described before (35).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pTRIP</div><div>suggested: RRID:Addgene_127663)</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Software and Algorithms</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The following peptide sequences from the receptor-binding domain (RBD) of the spike were synthesized from LifeTein with a biotin tag: Peptide-1: [FNCYFPLQS]S-K-Biotin Peptide-2: Biotin-[GFQPTNGVGY] Sequence Alignment for Covid Variants The hCoV19 spike (Wuhan/WIV04/2019), GISAID (EPI_ISL_402124) construct is a kind gift from Prof. Nevan Krogan, UCSF, USA (38).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>LifeTein</div><div>suggested: (LifeTein, RRID:SCR_012626)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">For viral particle production, 5 µg pHR lentiviral vector cloned with mCherry fluorescent protein, 3.75 µg packaging plasmid psPAX2 (Addgene; #12260), and 2.5 µg envelope plasmid for the expression of Spike glycoprotein (obtained as a kind gift from Prof. Nevan Krogan, UCSF, USA) of SARS-CoV-2 were mixed in 500 µl OptiMEM media and 20 µl PLUS reagent (Invitrogen; LTX transfection reagent, catalogue no. L15338100) and kept for incubation at room temperature for 5min.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Addgene</div><div>suggested: (Addgene, RRID:SCR_002037)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Images were analysed on Fiji software to calculate mean fluorescence intensity (MFI) for eGFP (ACE2 expression) and mCherry (viral transduction) channel from the z-projected stacks.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Fiji</div><div>suggested: (Fiji, RRID:SCR_002285)</div></div></td></tr></table>

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Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

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Results from TrialIdentifier: No clinical trial numbers were referenced.

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Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

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Results from JetFighter: We did not find any issues relating to colormaps.

Results from rtransparent:

---

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

---

Results from scite Reference Check: We found no unreliable references.

---

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About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

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Author Response:

Reviewer #2:

The authors investigated changes in the unstressed and stressed oligomeric states of the mammalian endoplasmic reticulum (ER) stress sensor, IRE1a. Previous biochemical and microscopy studies in mammalian cells and studies of the related protein Ire1 in yeast, describe an increase in oligomerization of the stress sensor upon treatment of cells with chemical agents that impair the ER protein folding environment. The general view has been that IRE1 in unstressed cells is a monomer and varying degrees of misfolded protein stress stimulate dimerization, activation, and higher order oligomerization. Distinguishing between monomers and dimers, as well as tetramers or other small oligomers is technically challenging, especially for integral membrane proteins. To address this challenge, the authors turned to single particle tracking fluorescence microscopy of Halo-tagged endogenous IRE1. Using a clever combination of random labeling with two fluorescent dyes and oblique angle illumination to visualize single molecules, as well as dimers, the authors surprisingly find that their endogenous IRE1 reporter appears to be dimeric in homeostatic cells. This observation challenges the predominant model in which IRE1 is monomeric in unstressed cells and that even dimerization represents a switch into an active state. The authors claim to detect evidence for higher order oligomers following treatment with stressors. The authors then use a series of IRE1 mutants to identify how oligomerization is regulated and present a new model to reconcile the different models of IRE1 activation in the literature.

The authors have extensively characterized their novel experimental system in terms of protein expression levels, functionality, and ability to distinguish monomers and dimers. The data are well presented and the authors are clearly familiar with the arguments that have surrounded the IRE1 oligomer question. That the authors observe the characteristic XBP1 mRNA splicing activity in the absence of visible large IRE1 clusters may suggest that the large clusters reported by others may have distinct roles, perhaps in more permissive mRNA cleavage.

The present study is undermined by two major weaknesses. First, while the authors persuasively demonstrate that they can detect IRE1a dimers, a major claim of the manuscript rests upon detection of tetramers and possibly higher order oligomers. Unfortunately, the authors provide no independent controls to show what tetramer or higher order oligomer data would look like. Thus, the authors can only infer that higher order oligomers are detected, based on modest shifts in the percent of correlated particle trajectories observed in some cells. More robust evidence is needed to make claims of oligomerization. Tools have been developed by others that can induce reversible oligomerization of proteins. Application of these tools would provide powerful controls for tetramers or even higher order oligomers in this study.

The second, deeper concern, is the discrepancy between the Halo Tag clustering results in this study and studies by this lab and several other labs that report a distinct stress phenotype. In mammalian cells and yeast, IRE1 and Ire1, tagged with different fluorescent proteins or even a small HA peptide epitope tag, undergo quantitative visible formation of puncta or clusters upon treatment with stressors. The small number of bright clusters that form effectively deplete the rest of the ER of IRE1 signal. In the present study, the authors observe no visible change in IRE1-Halo localization in stress cells. The authors do not investigate the cause of this difference. While one might argue that the presence of stress-inducible IRE1 activity is sufficient to argue that the reporter in this study is functional, IRE1 reporters (that do cluster) described in previous studies by the Walter lab and other groups are also demonstrably functional. Does IRE1 normally cluster? Is it cell-type dependent? Tag-dependent? Notably, the Pincus et al. PLoS Biology paper from the Walter lab used two different fluorescent protein tags that do not heterozygously dimerize. Robust colocalization and FRET signals were detected upon treatment of cells with stressors and clustering was subsequently observed. A 2007 Journal of Cell Biology study from Kimata et al. reported clustering in yeast with an Ire1 tagged with an HA epitope peptide. The HA peptide seems unlikely to be prone to any oligomerization propensities that GFP tagged reporters might experience. Importantly, a 2020 PNAS paper from the Walter lab (Belyy et al.) studied clustering of a robustly monomeric mNeonGreen-tagged IRE1 in U2-OS cells and mouse embryonic fibroblasts and this construct readily clustered following stress induction.

When evaluated against the backdrop of the extensive literature describing the visual behavior of IRE1a in live cells, the absence of stress-induced clustering is both puzzling and disconcerting. Given the focus of this study is to use visual techniques to study IRE1a interactions, the burden of proof is on the authors to resolve this significant discrepancy with the rest of the IRE1a literature. One can easily imagine that incorporation of the majority of the pool of IRE1a into 10-100 clusters could produce very different correlated trajectory behavior. Until the authors can determine why their reporters behave differently from other IRE1a reporters and establish which version accurately reflects physiologic IRE1a behavior, the potential impact of the findings of this manuscript are of unknown value.

We thank the reviewer for this detailed assessment of our work. We agree that the question of apparent discrepancy in the formation of observable IRE1 clusters between this manuscript and earlier work is important. We have now addressed this issue both in the revised version of the manuscript and in specific point-by-point responses to reviewers’ comments. As a brief summary, we addressed the reviewer’s first concern (lack of controls larger than dimers) by cloning and validating a tetrameric HaloTag construct, the measurements from which were entirely consistent with the model we presented in the original version of the manuscript. To address the reviewer’s second concern, we present several lines of evidence showing that the discrepancy between the formation of microscopically visible IRE1 clusters in earlier studies and the absence of such clusters in the present work almost certainly results from differences in expression levels. First, our IRE1-HaloTag construct is perfectly capable of forming stress- induced clusters, as we show in the new Figure 1 – Figure Supplement 3. Second, we point to a parallel study by Gómez-Puerta et al., who demonstrate that a more “conventional” IRE1-GFP construct does not form visible stress-dependent puncta when it is expressed at a low level comparable to that of untagged IRE1 in HeLa cells, despite being fully active. Third, our earlier work in the 2020 PNAS paper referenced by the reviewer actually showed that even in the overexpression context, IRE1-mNeonGreen only forms visible puncta in just over half of all cells, despite the fact that XBP1 processing is nearly 100% effective in bulk assays. Furthermore, in the same paper we show that, rather than all IRE1 molecules being sequestered in clusters, only a small fraction (~5%) of IRE1-mNeonGreen assembles into large puncta while the remaining 95% of IRE1 stays uniformly distributed throughout the ER. Taken together, we believe that IRE1 does have the propensity to assemble into larger clusters when its expression levels are high (regardless of the tag used), but that these clusters are not strictly required for its activation. We have made significant changes to the discussion section of the manuscript to clarify the above points and directly address the apparent discrepancy between the present work and earlier studies.

Reviewer #3:

In this paper, the authors' aim was to test how IRE1's oligomerization state relates to its activation status without relying on ectopic overexpression. The principle underlying the work is a rather simple one, which is that, if the population of IRE1 can be labeled stochastically with either of two different fluorescent probes, then if the protein dimerizes, presuming single molecules can be visualized, correlated migration of a spot of each fluorophore should be observed for some of those dimers. Any correlated migration, maintained for long enough, will by necessity by some sort of dimer or multimer. In principle, if my math is right, the correlation should be 50% of spots of each color, assuming all the molecules are in a dimer, all molecules are labeled with one fluorophore or the other, and the koff of the fluorophores is very low. In practice, the correlation appears closer to 10%, which the authors establish using a control molecule that should not dimerize except by chance, and another for which pseudo-dimerization is enforced due to the two HALO domains used to bind the fluorophores being conjugated to the same molecule in cis. Much of the paper is devoted to establishing the fundamentals of the system. For these experiments, the authors replaced endogenous IRE1 with the HALO-tagged version to generate near-normal expression and show that the IRE1-HALO behaves similarly to endogenous. They also show that correlated migration is observed in the dimer control to a much greater extent than in the monomer.

Using these findings, they demonstrate, in my mind quite conclusively, that IRE1 exists as a dimer even in the unstimulated state. During ER stress, the authors observe a state that is more highly ordered. Mathematical modeling suggests a transition from predominantly dimers to a mix of dimers and something more highly ordered, with tetramers being the simplest explanation. Satisfyingly, a mutation that breaks the known dimer interface causes the protein to exist solely in monomers, as does deletion of the IRE1 lumenal domain, while disrupting the oligomerization interface keeps the protein as dimers. Mutation or deletion of the kinase and RNase domains does not affect higher order status, suggesting that activation of these domains is not a prerequisite for assembly. It is clear from this that the central claims of the paper, which is that IRE1 exists in a dimer in the basal state and transitions to a higher ordered structure in the activated state, are supported. Moreover, the general approach is likely to be appealing to the study of other molecules activated by multimerization.

We thank the reviewer for this thoughtful and helpful analysis of our work.

The principal advance of the paper is the technological approach for tracking IRE1 (and, presumably, other molecules whose activity is regulated by dimerization). The approach is quite elegant for that purpose. Its impact in terms of conclusions about IRE1 is perhaps less clear. The authors rationalize their endogenous-replacement approach by describing how their previous efforts and those of others relied on ectopic overexpression of GFP-tagged IRE1. The authors take great pains to claim that the observed multimerization status of the IRE1-HALO constructs is not a function of expression level, which would imply then that expression level alone is not responsible for the previously observed IRE1 oligomeric puncta. It is not clear why exactly the authors' results differ from this group's previous studies on the topic nor where the truth lies, including whether something inherent to the GFP-tagged overexpression approach favors non-physiologic structures, whether the difference is fundamentally one of cell type, or whether multimerization and activation are correlated but not causally related, with multimer-breaking mutations killing IRE1 by some other mechanism.

The question of reconciling our present data with earlier work (including work from our group) is clearly and understandably a central question for all three reviewers. As we detailed above in our responses to reviewers 1 and 2, we are convinced that the formation of large IRE1 clusters is largely dependent on expression level rather than the differences between fluorescent protein tags and the HaloTag. We added new supplementary figures and substantially revised the text of the manuscript to address this question directly.

Interpreting the data is also complicated by the fact that, while the authors point out that the percent of correlated trajectories (i.e., the measurement of multimerization state) does not itself correlate with expression level (using trajectories-per-movie as a proxy), the proper conclusion from that lack of correlation is not that variance in expression level does not account for the changes in apparent multimerization status, but instead that it cannot be the only factor. In some sense, the authors are attempting to play the argument both ways, by arguing that expression level matters for IRE1 activation (from previous studies) and that it doesn't (from this study). I think to address this the authors will need to better account, one way or another, for why the findings presented here differ from their previous findings and why these are the more salient (if in fact they are).

This is a very important point, and we thank the reviewer for raising it. We are not arguing that expression levels do not matter for the formation of oligomers; quite the contrary, as detailed above and in the revised version of the text, we believe that the formation of massive IRE1 oligomers observed in previous studies and in the new Figure 1 – Figure Supplement 3 is mainly a function of elevated concentration. What we do claim is that our approach can reliably pick out oligomeric differences within the relatively narrow range of concentrations used for single-particle tracking experiments in this paper. We are using the very weak truncated CMVd3 promoter in all transient transfection experiments, and we are only analyzing data from cells that have a comparable density of single-molecule spots to the density we observe in endogenously tagged IRE1-HaloTag cells. In fact, the metric of “trajectories per movie” used as a proxy for expression levels in Figure 5 – Figure Supplement 1 is an overestimation of the true variability of expression levels, since each movie only covers a small fraction of each cell’s area and the number of observed molecules varies depending on cell morphology. Practically speaking, all cells that we image have expression levels that are clustered together rather narrowly, roughly within differences of no more than a factor of 3. These levels, in turn, are significantly lower than the expression levels used in earlier papers by our group and others.

The other somewhat substantial issue is that there is no control for what higher order structures look like. The authors give no sense for the dynamic range of the multimerization assay. I would presume that tetramers would show a higher percentage of correlated trajectories than dimers, and octamers higher still, and that the mathematical model accounts for this theoretical possibility in calculating an average protomer number of 2.7 in the stress condition, but it would be better to see that in practice; at first glance it would seem that engineering a tetrameric and/or higher order control and validating it would be straightforward.

This is another great point raised by all reviewers. In the revised version of the manuscript, we engineered a new tetrameric control construct (See Figure 2 – Figure Supplement 1), the results from which agree remarkably well with the mathematical model we developed in the original version of the manuscript (see Figure 2 – Figure Supplement 3)

Lastly, the data analysis lacks statistical justification for its conclusions. I presume given the high number of readings that the observed changes are all statistically significant, but that should be indicated, as in most cases the 95% confidence intervals shown are overlapping.

This is another excellent point. The reviewer is correct that all relevant conclusions are statistically supported by the data, and our analysis code immediately calculates pairwise p- values for every plot using one of several relevant tests. Our preferred test is the permutation test, since it makes no assumptions about the underlying distributions being compared. To avoid cluttering the main plots, we have included tables of pairwise p-values for each plot in the revised version of the manuscript.

Reviewer #1 (Public Review): 

In this manuscript, the authors sought to define the early events associated with activation of the ER stress-responsive membrane protein IRE1. Towards that aim, they used CRISPR to integrate a HALO tag into the genomic locus of IRE1 at the C-terminus of the protein. The authors then adapted a single molecule fluorescence microscopy approach where the HALO tag is liganded with two different fluorophores to define the oligomeric state of membrane proteins in cellular models. They validated this approach using ER membrane proteins containing defined number of HALO tags (single or double) and imaged with oblique angle illumination microscopy to confirm their ability to detect effect monomer and dimers of these tags. Using this approach with IRE1, they showed that in the absence of stress, there is a high fraction of apparent IRE1 dimers in the membrane. In response to ER stress, this oligomer size (calculated by correlated trajectories) increased, suggesting that ER stress promotes IRE1 oligomerization, eventually returning to dimers at longer treatment times. Intriguingly, using the ER stressor thapsigargin, the authors indicate that oligomerization precedes auto-phosphorylation of IRE1, suggesting that oligomerization is a key step in the activation of this enzyme. Extending this, the authors then transition to an overexpression model where they incorporate IRE1 constructs containing mutant that disrupt specific parts of the protein or prevent dimeric or oligomeric interactions to probe their importance in this early oligomerization observed in response to ER stress. This demonstrated that the oligomerization was primarily dictated by the ER luminal domain and involved two distinct interfaces specifically required for IRE1 dimer formation (in the absence of stress) and oligomer formation (following ER stress). Ultimately, with these results, the authors propose a model whereby IRE1 exists primarily as a autophosphorylation-deficient, back-to-back dimer that upon ER stress oligomerizes to a phosphorylation competent oligomer that allow autophosphorylations and IRE1 activation. 

Overall this is an interesting approach and study to define early stages of IRE1 activation. Notably, it reveals a different model of these early stages of IRE1 activation than those previously reported by this group and others using GFP-tagged IRE1 overexpression constructs (something that was enabled by the integration of HALO tags into the genomic locus). The experiments are well performed and the data appear to all be interpreted correctly, although there are a few remaining questions that should still be addressed.

Reviewer #2 (Public Review): 

The authors investigated changes in the unstressed and stressed oligomeric states of the mammalian endoplasmic reticulum (ER) stress sensor, IRE1a. Previous biochemical and microscopy studies in mammalian cells and studies of the related protein Ire1 in yeast, describe an increase in oligomerization of the stress sensor upon treatment of cells with chemical agents that impair the ER protein folding environment. The general view has been that IRE1 in unstressed cells is a monomer and varying degrees of misfolded protein stress stimulate dimerization, activation, and higher order oligomerization. Distinguishing between monomers and dimers, as well as tetramers or other small oligomers is technically challenging, especially for integral membrane proteins. To address this challenge, the authors turned to single particle tracking fluorescence microscopy of Halo-tagged endogenous IRE1. Using a clever combination of random labeling with two fluorescent dyes and oblique angle illumination to visualize single molecules, as well as dimers, the authors surprisingly find that their endogenous IRE1 reporter appears to be dimeric in homeostatic cells. This observation challenges the predominant model in which IRE1 is monomeric in unstressed cells and that even dimerization represents a switch into an active state. The authors claim to detect evidence for higher order oligomers following treatment with stressors. The authors then use a series of IRE1 mutants to identify how oligomerization is regulated and present a new model to reconcile the different models of IRE1 activation in the literature. 

The authors have extensively characterized their novel experimental system in terms of protein expression levels, functionality, and ability to distinguish monomers and dimers. The data are well presented and the authors are clearly familiar with the arguments that have surrounded the IRE1 oligomer question. That the authors observe the characteristic XBP1 mRNA splicing activity in the absence of visible large IRE1 clusters may suggest that the large clusters reported by others may have distinct roles, perhaps in more permissive mRNA cleavage. 

The present study is undermined by two major weaknesses. First, while the authors persuasively demonstrate that they can detect IRE1a dimers, a major claim of the manuscript rests upon detection of tetramers and possibly higher order oligomers. Unfortunately, the authors provide no independent controls to show what tetramer or higher order oligomer data would look like. Thus, the authors can only infer that higher order oligomers are detected, based on modest shifts in the percent of correlated particle trajectories observed in some cells. More robust evidence is needed to make claims of oligomerization. Tools have been developed by others that can induce reversible oligomerization of proteins. Application of these tools would provide powerful controls for tetramers or even higher order oligomers in this study. 

The second, deeper concern, is the discrepancy between the Halo Tag clustering results in this study and studies by this lab and several other labs that report a distinct stress phenotype. In mammalian cells and yeast, IRE1 and Ire1, tagged with different fluorescent proteins or even a small HA peptide epitope tag, undergo quantitative visible formation of puncta or clusters upon treatment with stressors. The small number of bright clusters that form effectively deplete the rest of the ER of IRE1 signal. In the present study, the authors observe no visible change in IRE1-Halo localization in stress cells. The authors do not investigate the cause of this difference. While one might argue that the presence of stress-inducible IRE1 activity is sufficient to argue that the reporter in this study is functional, IRE1 reporters (that do cluster) described in previous studies by the Walter lab and other groups are also demonstrably functional. Does IRE1 normally cluster? Is it cell-type dependent? Tag-dependent? Notably, the Pincus et al. PLoS Biology paper from the Walter lab used two different fluorescent protein tags that do not heterozygously dimerize. Robust colocalization and FRET signals were detected upon treatment of cells with stressors and clustering was subsequently observed. A 2007 Journal of Cell Biology study from Kimata et al. reported clustering in yeast with an Ire1 tagged with an HA epitope peptide. The HA peptide seems unlikely to be prone to any oligomerization propensities that GFP tagged reporters might experience. Importantly, a 2020 PNAS paper from the Walter lab (Belyy et al.) studied clustering of a robustly monomeric mNeonGreen-tagged IRE1 in U2-OS cells and mouse embryonic fibroblasts and this construct readily clustered following stress induction. 

When evaluated against the backdrop of the extensive literature describing the visual behavior of IRE1a in live cells, the absence of stress-induced clustering is both puzzling and disconcerting. Given the focus of this study is to use visual techniques to study IRE1a interactions, the burden of proof is on the authors to resolve this significant discrepancy with the rest of the IRE1a literature. One can easily imagine that incorporation of the majority of the pool of IRE1a into 10-100 clusters could produce very different correlated trajectory behavior. Until the authors can determine why their reporters behave differently from other IRE1a reporters and establish which version accurately reflects physiologic IRE1a behavior, the potential impact of the findings of this manuscript are of unknown value.