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Reply to the reviewers
Reviewer #1
Evidence, reproducibility and clarity
Sanial et al. carefully analyze the use of in-gel fluorescence as an alternative to immunoblotting. The authors show that simple modifications of common protein extraction protocols can preserve (to varying extents) fluorescent proteins in their native, fluorescent states. This can be exploited in different applications for in-gel fluorescence quantification, bypassing immunoblotting. The experimental results are clear, showcasing the ease and linearity of in-gel fluorescence quantification.
In my opinion, the trick of this approach is also potentially its main drawback, the partial denaturation conditions. I think the manuscript could be strengthened with more extensive benchmarking of the approach and further discussion of potential caveats as detailed below.
Major points:
- Protein abundance in the original GFP library (and in other FP-tagged libraries constructed in the meanwhile) have been quantified using fluorescence (flow cytometry, microscopy, colony fluorescence) (Ho et al. 2018 10.1016/j.cels.2017.12.004, Weill et al. 2018 10.1038/s41592-018-0044-9, Meurer et al. 2018 10.1038/s41592-018-0045-8). This provides an opportunity to significantly strengthen the manuscript (where most of the test have been done using two abundant cytosolic proteins Bmh1 and Hxk1) if the authors could apply their approach to a representative fraction of the yeast proteome (sampling from such libraries FP-tagged proteins that differ in abundance, localization, membrane vs cytosolic/nuclear, subunits of large stable complexes vs proteins not part of complexes, etc.) and compare their quantification with previous relative abundance estimates. This information would also help future users in case protein-specific issues are identified.
Indeed, Hxk1 and Bmh1 are quite strongly expressed (41,000 and 65,000 copies/cell, according to SGD, ____www.yeastgenome.org____). In the course of our experiments we were able to detect proteins with a much lower expression level (eg. Reg1, 4000 copies/cell). We have selected a number of proteins based on their expression level as detailed in SGD, ranging from 700 to 75000, and plan to detect the signal by IGF and compare it with published data on absolute protein quantifications for ecah protein. However, this will take a bit of time as each gene must be tagged with EGFP – we cannot use the GFP-S65T from the GFP collection which is poorly amenable to IGF because of its sensitivity to denaturation, as we show in our manuscript.
The authors discuss several drawbacks, including the change in apparent molecular weight compared to denatured proteins; differential recognition of folded vs denatured proteins by antibodies.
Other potentials drawbacks should be discussed. For instance, the need of additional steps post-fluorescence imaging for signal normalization against a loading control; the need of antibodies and immunoblotting to decide on the best denaturing temperature for a specific protein or FP tag; complexity of the native protein extraction protocol compared for example to alkaline lysis followed by TCA precipitation (Knop et al. 1999 PMID: 10407276).
- Regarding the the need of additional steps post-fluorescence imaging for signal normalization against a loading control – this doesn’t take extra time for people using gels with protein stain included in the gel (eg. Stain-Free from BioRad). There are other possibilities of total protein fluorescent labeling that will be discussed. We will provide an example of this application.
- On the need of antibodies and immunoblotting to decide on the best denaturing temperature for a specific protein or FP tag – we believe that the system could be set up for a protein of interest for which antibodies are available (as we did for Bmh1 or Hxk1), and once this is done, there is no need to do these controls anymore. We will mention this in the manuscript.
- On the complexity of the native protein extraction protocol compared for example to alkaline lysis followed by TCA precipitation – indeed the protocol is a bit more time-consuming compared to the mentioned method, and we will mention this in the text. However, please note that people studying mammalian cells, for instance, often use this native protocol for total extracts so this is mostly a yeast-model issue. Yet, we will add this comment. Moreover, although the denatured fraction is FP- and temperature-dependent, even under the milder 30{degree sign}C conditions there is a detectable denatured fraction (Fig.s3b). This would seem to preclude the use of this approach for absolute protein quantification.
True, but it depends a lot on the FP used. For instance, sfGFP is not denatured and could potentially be used for absolute quantification. We will comment in the text.
Finally, any evidence that the denatured fraction would depend on the protein tagged with the FP?
We will use several proteins used for point 1 but fused to the most sensitive FP, GFP-S65T, and do a western blot using anti-GFP antibodies to estimate the variation in native vs. denatured forms of the protein.
Minor points: 1. In the experiments designed to test the linearity and sensitivity of the approach, an alternative approach that would not result in dilution of cell extract is to mix wild type cell extract (no GFP fusion) with extract of the GPF-tagged strain in different ratios.
Yes, this was an alternative but it seemed that dilution was easier to control than mixing two extracts.
Define all acronyms at first appearance. For example, DTT and LDS on page 4.
Thank you, we will address all acronyms in the text.
Fig.4D: the colors chosen to represent EGFP and sfGFP data make them hard to tell apart. The same comment to Fig.S6.
Agreed, we will change the figures accordingly.
As the temperature steps are not uniform in Figures 4 and 5, it would be more informative to indicate the exact temperate above each lane (in addition/instead of the ramp cartoon).
Agreed, we will change the figures accordingly.
Regarding linearity, that HRP-based quantification is not linear is expected. A fairer comparison would be to use fluorescently labeled secondary antibodies. It is also puzzling that detection with signal amplification (HRP) is less sensitive than direct quantification of the fluorescence signal from the FP tag.
We will do a sensitivity tets (dilutions) to compare IGF with HRP-based and fluorescent-based antibody-mediated detection.
I appreciate the workflow Figure 10. But in my opinion it is trying to show too much (protocol, troubleshooting, calls to figure panels). Perhaps it could be made clearer by separating the protocol steps/settings from the optimization/troubleshooting tips.
Thank you, we will work on this to make the workflow clearer.
Some of the discussion of different fluorescent proteins, and expression levels of tagged proteins, could be confounded by the different linkers used in the tagging constructs.
Thank you for this remark. Indeed, there are various linkers on these constructs and we don’t know to which extent they contribute to the effect on protein expression level. We will comment his in the text.
Significance
Could be a generally useful and simple approach for in-gel quantification using fluorescent protein tags.
__ ____Thank you for your comments and overall assesment.__
Reviewer #2
Evidence, reproducibility and clarity
The present manuscript "Direct observation of fluorescent proteins in gels: a rapid cost-efficient, and quantitative alternative to immunoblotting" describes a method how to visualize bands of fluorescent protein fusions onto a common SDS-PAGE without antibody staining. It is based on ability of GFP-like fluorescent proteins (FPs) to retain their fluorescence under conditions of SDS-PAGE if step of extensive heating (boiling) of protein sample is omitted. This property of FPs is not novel; it was known for more than 20 years (for example, see Fig. 2 in Yanushevich et al. FEBS Lett. 2002 Jan 30, 511:11-4; Supporting Fig. 7 in Campbell et al. Proc Natl Acad Sci USA. 2002 Jun 11, 99:7877-82). However, the authors did perform a very accurate and robust study to quantitatively assess the behavior of several FP fusion protein in SDS-PAGE. A thorough analysis of different conditions for a variety of FPs and target proteins was done; detailed protocols were developed. A surprisingly high sensitivity of FP detection (even superior to that of standard Western blotting) was demonstrated. Considering the simplicity of the proposed approach, it appears to be the method of choice for those working with FP fusion proteins.
Thank you for this comment. Indeed we do not claim to discover that FP remain fluorescent in mild denaturing conditions, as presented in the text. We did our best to include original publications showing precedent for this and we missed Yanushevich et al. FEBS Lett. 2002 that we will add. However the Campbell paper is cited, precisely for the Supplementary figure 7 that the reviewer mentions.
I have only minor, discretionary comments:
- It is known that under conditions of SDS-PAGE without heating, FPs retain not only fluorescence but also their oligomeric state. The same can be true for proteins of interest (POIs). If so, even for monomeric FPs, the POI-FP band can potentially migrate much slower than expected because of oligomerization of the POI.
__Thank you for this suggestion. Our data in the manuscript already show that Bmh1 and Bmh2, which are tighlty associated 14-3-3 proteins, no longer intereact in these mild denaturation conditions. In the set of proteins that we will use to answer to Reviewer #1 (point 1), we will include proteins in large complexes to assess whether this can happen. __
It might be useful to briefly discuss a possibility to use other types of fluorescent proteins (namely, Flavin-binding FPs, bacteriophytochrome-based FPs, bilirubin-binding FP UnaG) in the same way as proposed here. In particular, biliverdin-binding near-infrared FPs (IFP, iRFP, etc.) can be detected even after fully denaturing SDS-PAGE by zinc-induced orange fluorescence of proteins carrying covalently attached bilin chromophore (Berkelman TR, Lagarias JC. Visualization of bilin-linked peptides and proteins in polyacrylamide gels. Anal Biochem. 1986, 156, 194-201; Stepanenko OV, Kuznetsova IM, Turoverov KK, Stepanenko OV. Impact of Double Covalent Binding of BV in NIR FPs on Their Spectral and Physicochemical Properties. Int J Mol Sci. 2022, 23, 7347).
__Agreed. ____We will extend the discussion to other fluorescent approaches to visualize proteins in gels and compare them. __
Significance
A simple method of specific visualization of fluorescent protein fusion bands on SDS-PAGE is proposed.
Thank you for your comments and overall assesment.
Reviewer #3
Evidence, reproducibility and clarity
In this paper, Sanial et al present in-gel fluorescence detection (IGF), a method that allows the direct detection of fluorescent proteins from SDS-PAGE gels with minimal adaptation of existing protocols. The authors test a range of fluorescent proteins routinely used, especially when working with yeast, and describe their behavior in IGF. They identify heat-induced denaturation of fluorescent proteins as the main component influencing their assay and systematically test this on a selection of fluorescent proteins. Next, they compare the detection limit and the linearity of the signal between IGF and chemiluminescence, showing that IGF is not only comparable but also superior to chemiluminescence. This is particularly significant given that chemiluminescence can suffer from issues such as a limited dynamic range and limitations in accurately quantifying very low or high-abundance proteins. The authors further demonstrate the utility of IGF in co-immunoprecipitation experiments and test whether the mild denaturing conditions are compatible with proteins from other organisms. Overall, the study is well-presented and is an asset to the scientific community. I have one major and some minor comments that, in my opinion, would improve this already informative paper: Major comment 1. In all cases where there is signal quantification the authors should perform replicates to account for variability of the signal (in Fig 6, S6 and S7).
__Agreed, we will perform triplicates for the indicated experiments. __
Minor comments 1. The study mainly focuses on soluble protein. While the authors have tested one plasma membrane protein, the study would benefit from including more membrane proteins from different environments (e.g., cell wall, nuclear envelope, mitochondrial). This would help determine if incubation at higher temperatures is necessary to properly solubilize these proteins, in which case the experiment would need adaptation.
Thank you for this suggestion. __In the set of proteins that we will use to answer to Reviewer #1 (point 1), we will include proteins from various subcellular locations. __
The authors show that when fluorescent proteins are partially denatured, their migration behavior changes. One cannot exclude that in some cases, the tagged proteins themselves might also be partially resistant to denaturing at the low temperatures used for IGF. This would lead to more than one fluorescent bands. In such cases one should be careful with interpretation, especially in the context of PTMs or isoforms. Could the authors briefly discuss this?
Thank you for this comment. __We will discuss this in the text. __
Based on Fig 4D and 5D, some fluorescent proteins seem to have a higher signal variability between replicates than others. It would be helpful to add this information next to the behavior of the proteins in different temperatures so it would be easier to choose the fluorescent protein for specific experiments.
__Indeed, there are variations between experiments, but it is not clear whether this inherent to the FP considered or the experiment. We will look back at the data and modify the text accordingly if pertinent. __
The sensitivity experiment (Figure 6) is convincing and important for IP conditions, where the total protein concentration of the sample is radically decreased. Could the authors additionally test if very low abundant proteins can be detected (without any dilution of the total protein content), and compare this to chemiluminescence? This could be done either by tagging some very low abundant proteins (for example a few hundred copies per cell) or diluting the lysate in wild-type lysate to artificially reduce their concentration while maintaining the overall protein load the same.
__We have planned an experiment in which low abundant proteins will be tagged in response to reviewer 1 (point 1) which should address this point. __
It would be useful to address the detection of very high molecular weight proteins - or proteins that are problematic in terms of transfer during western blotting.
Again, in the experiment planned in ____response to reviewer 1 (point 1), proteins or various MW as well as membrane proteins will be studied, which should address this point. __ __
Significance
The authors already discuss the strengths and limitations of their approach. The main strength of IGF is that it does not require transfer of the proteins to a membrane and also does not rely on antibody binding and (potential) chemical reactions. In addition to the fact that this is time, cost, equipment, waste and expertise effective, the sensitivity and signal linearity of IGF seems to not only compare but outperforme western blotting. There are two main limitations. First, IGF relies on the resilience to denaturing of the chosen fluorescent protein that depends, according to the authors, at least on the temperature and overall protein concentration and pH. Second, IGF relied on tagging proteins with fluorescent proteins which might affect the stability or even function of the tagged protein. As the authors mention, these factors do not diminish the value of IGF, they highlight the need for appropriate controls.
A potential development of the technique (not at the present study) could be the compatibility of IGF with different self-labelling proteins (Halo, Snap) and fluorescent dyes.
We have conducted experiments in which we show the applicability of IGF in combination to SNAP-tagging, that we could show if needed.
I think IGF will benefit a rather broad range of scientists. As already mentioned by the authors, there are different applications of IGF. From checking of clones when creating strains, to comparison of protein levels in different conditions and coIP experiments.
Thank you for your comments and overall assesment.
Cross reviews. Reviewer 1: I agree with the assessment by Reviewer #2. Considering the comment about potential oligomerization of a protein of interest, I stand by my point about testing the method with more proteins of interest. How extensive this testing should be or whether additional discussion of possible issues would suffice is a matter of opinion. It is clear from the manuscript in it's current form that the method works and that it has caveats.
We believed that the experiments we have planned will clarify these points.
Reviewer 2: In general, I agree with the points raised by Reviewer #1. However, in my opinion, there is already a large body of reliable experimental results in the manuscript that are worth publishing without a new round of extensive experiments.
Reviewer 1: Fair enough, I don't insist on the experiments in my point 1.
We think that this is an important point that will likely be a common question for readers so we will still do our best to provide data for this point.
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Referee #3
Evidence, reproducibility and clarity
In this paper, Sanial et al present in-gel fluorescence detection (IGF), a method that allows the direct detection of fluorescent proteins from SDS-PAGE gels with minimal adaptation of existing protocols. The authors test a range of fluorescent proteins routinely used, especially when working with yeast, and describe their behavior in IGF. They identify heat-induced denaturation of fluorescent proteins as the main component influencing their assay and systematically test this on a selection of fluorescent proteins. Next, they compare the detection limit and the linearity of the signal between IGF and chemiluminescence, showing that IGF is not only comparable but also superior to chemiluminescence. This is particularly significant given that chemiluminescence can suffer from issues such as a limited dynamic range and limitations in accurately quantifying very low or high-abundance proteins. The authors further demonstrate the utility of IGF in co-immunoprecipitation experiments and test whether the mild denaturing conditions are compatible with proteins from other organisms.
Overall, the study is well-presented and is an asset to the scientific community. I have one major and some minor comments that, in my opinion, would improve this already informative paper:
Major comment
- In all cases where there is signal quantification the authors should perform replicates to account for variability of the signal (in Fig 6, S6 and S7). Minor comments
- The study mainly focuses on soluble protein. While the authors have tested one plasma membrane protein, the study would benefit from including more membrane proteins from different environments (e.g., cell wall, nuclear envelope, mitochondrial). This would help determine if incubation at higher temperatures is necessary to properly solubilize these proteins, in which case the experiment would need adaptation.
- The authors show that when fluorescent proteins are partially denatured, their migration behavior changes. One cannot exclude that in some cases, the tagged proteins themselves might also be partially resistant to denaturing at the low temperatures used for IGF. This would lead to more than one fluorescent bands. In such cases one should be careful with interpretation, especially in the context of PTMs or isoforms. Could the authors briefly discuss this?
- Based on Fig 4D and 5D, some fluorescent proteins seem to have a higher signal variability between replicates than others. It would be helpful to add this information next to the behavior of the proteins in different temperatures so it would be easier to choose the fluorescent protein for specific experiments
- The sensitivity experiment (Figure 6) is convincing and important for IP conditions, where the total protein concentration of the sample is radically decreased. Could the authors additionally test if very low abundant proteins can be detected (without any dilution of the total protein content), and compare this to chemiluminescence? This could be done either by tagging some very low abundant proteins (for example a few hundred copies per cell) or diluting the lysate in wild-type lysate to artificially reduce their concentration while maintaining the overall protein load the same.
- It would be useful to address the detection of very high molecular weight proteins - or proteins that are problematic in terms of transfer during western blotting.
Significance
The authors already discuss the strengths and limitations of their approach. The main strength of IGF is that it does not require transfer of the proteins to a membrane and also does not rely on antibody binding and (potential) chemical reactions. In addition to the fact that this is time, cost, equipment, waste and expertise effective, the sensitivity and signal linearity of IGF seems to not only compare but outperforme western blotting. There are two main limitations. First, IGF relies on the resilience to denaturing of the chosen fluorescent protein that depends, according to the authors, at least on the temperature and overall protein concentration and pH. Second, IGF relied on tagging proteins with fluorescent proteins which might affect the stability or even function of the tagged protein. As the authors mention, these factors do not diminish the value of IGF, they highlight the need for appropriate controls.
A potential development of the technique (not at the present study) could be the compatibility of IGF with different self-labelling proteins (Halo, Snap) and fluorescent dyes.
I think IGF will benefit a rather broad range of scientists. As already mentioned by the authors, there are different applications of IGF. From checking of clones when creating strains, to comparison of protein levels in different conditions and coIP experiments.
Keywords/field of expertise: yeast genetics, organelle homeostasis, biochemistry, molecular biology, cell biology, fluorescence microscopy, functional proteomics, gut microbiology
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Referee #2
Evidence, reproducibility and clarity
The present manuscript "Direct observation of fluorescent proteins in gels: a rapid cost-efficient, and quantitative alternative to immunoblotting" describes a method how to visualize bands of fluorescent protein fusions onto a common SDS-PAGE without antibody staining. It is based on ability of GFP-like fluorescent proteins (FPs) to retain their fluorescence under conditions of SDS-PAGE if step of extensive heating (boiling) of protein sample is omitted. This property of FPs is not novel; it was known for more than 20 years (for example, see Fig. 2 in Yanushevich et al. FEBS Lett. 2002 Jan 30, 511:11-4; Supporting Fig. 7 in Campbell et al. Proc Natl Acad Sci USA. 2002 Jun 11, 99:7877-82). However, the authors did perform a very accurate and robust study to quantitatively assess the behavior of several FP fusion protein in SDS-PAGE. A thorough analysis of different conditions for a variety of FPs and target proteins was done; detailed protocols were developed. A surprisingly high sensitivity of FP detection (even superior to that of standard Western blotting) was demonstrated. Considering the simplicity of the proposed approach, it appears to be the method of choice for those working with FP fusion proteins.
I have only minor, discretionary comments:
- It is known that under conditions of SDS-PAGE without heating, FPs retain not only fluorescence but also their oligomeric state. The same can be true for proteins of interest (POIs). If so, even for monomeric FPs, the POI-FP band can potentially migrate much slower than expected because of oligomerization of the POI.
- It might be useful to briefly discuss a possibility to use other types of fluorescent proteins (namely, Flavin-binding FPs, bacteriophytochrome-based FPs, bilirubin-binding FP UnaG) in the same way as proposed here. In particular, biliverdin-binding near-infrared FPs (IFP, iRFP, etc.) can be detected even after fully denaturing SDS-PAGE by zinc-induced orange fluorescence of proteins carrying covalently attached bilin chromophore (Berkelman TR, Lagarias JC. Visualization of bilin-linked peptides and proteins in polyacrylamide gels. Anal Biochem. 1986, 156, 194-201; Stepanenko OV, Kuznetsova IM, Turoverov KK, Stepanenko OV. Impact of Double Covalent Binding of BV in NIR FPs on Their Spectral and Physicochemical Properties. Int J Mol Sci. 2022, 23, 7347).
Referee cross-commenting
In general, I agree with the points raised by Reviewer #1. However, in my opinion, there is already a large body of reliable experimental results in the manuscript that are worth publishing without a new round of extensive experiments.
Significance
A simple method of specific visualization of fluorescent protein fusion bands on SDS-PAGE is proposed.
-
Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.
Learn more at Review Commons
Referee #1
Evidence, reproducibility and clarity
Sanial et al. carefully analyze the use of in-gel fluorescence as an alternative to immunoblotting. The authors show that simple modifications of common protein extraction protocols can preserve (to varying extents) fluorescent proteins in their native, fluorescent states. This can be exploited in different applications for in-gel fluorescence quantification, bypassing immunoblotting. The experimental results are clear, showcasing the ease and linearity of in-gel fluorescence quantification.
In my opinion, the trick of this approach is also potentially its main drawback, the partial denaturation conditions. I think the manuscript could be strengthened with more extensive benchmarking of the approach and further discussion of potential caveats as detailed below.
Major points:
- Protein abundance in the original GFP library (and in other FP-tagged libraries constructed in the meanwhile) have been quantified using fluorescence (flow cytometry, microscopy, colony fluorescence) (Ho et al. 2018 10.1016/j.cels.2017.12.004, Weill et al. 2018 10.1038/s41592-018-0044-9, Meurer et al. 2018 10.1038/s41592-018-0045-8). This provides an opportunity to significantly strengthen the manuscript (where most of the test have been done using two abundant cytosolic proteins Bmh1 and Hxk1) if the authors could apply their approach to a representative fraction of the yeast proteome (sampling from such libraries FP-tagged proteins that differ in abundance, localization, membrane vs cytosolic/nuclear, subunits of large stable complexes vs proteins not part of complexes, etc.) and compare their quantification with previous relative abundance estimates. This information would also help future users in case protein-specific issues are identified.
- The authors discuss several drawbacks, including the change in apparent molecular weight compared to denatured proteins; differential recognition of folded vs denatured proteins by antibodies.
Other potentials drawbacks should be discussed. For instance, the need of additional steps post-fluorescence imaging for signal normalization against a loading control; the need of antibodies and immunoblotting to decide on the best denaturing temperature for a specific protein or FP tag; complexity of the native protein extraction protocol compared for example to alkaline lysis followed by TCA precipitation (Knop et al. 1999 PMID: 10407276).
Moreover, although the denatured fraction is FP- and temperature-dependent, even under the milder 30{degree sign}C conditions there is a detectable denatured fraction (Fig.s3b). This would seem to preclude the use of this approach for absolute protein quantification.
Finally, any evidence that the denatured fraction would depend on the protein tagged with the FP?
Minor points:
- In the experiments designed to test the linearity and sensitivity of the approach, an alternative approach that would not result in dilution of cell extract is to mix wild type cell extract (no GFP fusion) with extract of the GPF-tagged strain in different ratios.
- Define all acronyms at first appearance. For example, DTT and LDS on page 4.
- Fig.4D: the colors chosen to represent EGFP and sfGFP data make them hard to tell apart. The same comment to Fig.S6.
- As the temperature steps are not uniform in Figures 4 and 5, it would be more informative to indicate the exact temperate above each lane (in addition/instead of the ramp cartoon).
- Regarding linearity, that HRP-based quantification is not linear is expected. A fairer comparison would be to use fluorescently labeled secondary antibodies. It is also puzzling that detection with signal amplification (HRP) is less sensitive than direct quantification of the fluorescence signal from the FP tag.
- I appreciate the workflow Figure 10. But in my opinion it is trying to show too much (protocol, troubleshooting, calls to figure panels). Perhaps it could be made clearer by separating the protocol steps/settings from the optimization/troubleshooting tips.
- Some of the discussion of different fluorescent proteins, and expression levels of tagged proteins, could be confounded by the different linkers used in the tagging constructs.
Referee Cross-commenting
This session contains comments from all reviewers.
Reviewer 1: I agree with the assessment by Reviewer #2. Considering the comment about potential oligomerization of a protein of interest, I stand by my point about testing the method with more proteins of interest. How extensive this testing should be or whether additional discussion of possible issues would suffice is a matter of opinion. It is clear from the manuscript in it's current form that the method works and that it has caveats.
Reviewer 2: In general, I agree with the points raised by Reviewer #1. However, in my opinion, there is already a large body of reliable experimental results in the manuscript that are worth publishing without a new round of extensive experiments.
Reviewer 1: Fair enough, I don't insist on the experiments in my point 1.
Significance
Could be a generally useful and simple approach for in-gel quantification using fluorescent protein tags.
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Reply to the reviewers
Thank you very much for your editorial handling of our manuscript entitled 'A conserved fungal Knr4/Smi1 protein is vital for maintaining cell wall integrity and host plant pathogenesis'. We have taken on board the reviewers' comments and thank them for their diligence and time in improving our manuscript.
Please find our responses to each of the comments below.
Reviewer(s)' comments
Reviewer #1
Major comments:
__1.1. As a more critical comment, I find the presentation of the figures somewhat confusing, especially with the mixing of main figures, supplements to the main figures, and actual supplemental data. On top of that, the figures are not called up in the right order (e.g. Figure 4 follows 2D, while 3 comes after 4; Figure 6 comes before 5...), and some are never called up (I think) (e.g. Figure 1B, Figure 2B). __
__Response: __The figure order has been revised according to the reviewer's suggestion, while still following eLife's formatting guidelines for naming supplementals. Thank you.
1.2. I agree that there should be more CWI-related genes in the wheat module linked to the FgKnr4 fungal module, or, vice-versa, CW-manipulating genes in the fungal module. It would at least be good if the authors could comment further on if they find such genes, and if not, how this fits their model.
Response: Thank you for your insightful suggestion regarding the inclusion of more CWI-related genes in the wheat module linked to the FgKnr4 fungal module F16, or vice versa. We did observe a co-regulated response between the wheat module W05 which is correlated to the FgKnr4 module F16. Namely, we observed an enrichment of oxidative stress genes including respiratory burst oxidases and two catalases (lines 304 - 313) in the correlated wheat module (W05). Early expression of these oxidative stress inducing genes likely induces the CWI pathway in the fungus, which is regulated by FgKnr4. Knr4 functions as both a regulatory protein in the CWI pathway and as a scaffolding protein across multiple pathways in S. cerevisiae (Martin-Yken et al., 2016, https://onlinelibrary.wiley.com/doi/10.1111/cmi.12618 ). Scaffolding protein-encoding genes are typically expressed earlier than the genes they regulate to enable pre-assembly with their interacting partners, ensuring that signaling pathways are ready to activate when needed. In this context, the CWI integrity MAPKs Bck1 and Mkk1 are part of module F05, which includes two chitin synthases and a glucan synthase. This module is highly expressed during the late symptomless phase. The MAPK Mgv1, found in module F13, is expressed consistently throughout the infection process, which aligns with the expectation that MAPKs are mainly post-transcriptionally regulated. Thank you for bringing our attention to this, this is now included in the discussion (lines 427 - 443) along with eigengene expression plots of all modules added to the supplementary (Figure 3 - figure supplement 1).
To explore potential shared functions of FgKnr4 with other genes in its module, we re-analyzed the high module membership genes within module F16, which includes FgKnr4, using Knetminer (Hassani-Pak et al., 2021; https://onlinelibrary.wiley.com/doi/10.1111/pbi.13583 ). This analysis revealed that 8 out of 15 of these genes are associated with cell division and ATP binding. Four of the candidate genes are also part of a predicted protein-protein interaction subnetwork of genes within module F16, which relate to cell cycle and ATP binding. In S. cerevisiae, the absence of Knr4 results in cell division dysfunction (Martin-Yken et al., 2016, https://onlinelibrary.wiley.com/doi/10.1111/cmi.12618 ). Accordingly, we tested sensitivity of ΔFgknr4 to microtubule inhibitor benomyl (a compound commonly used to identify mutants with cell division defects; Hoyt et al., 1991 https://www.cell.com/cell/pdf/0092-8674(81)90014-3.pdf). We found that the ΔFgknr4 mutant was more susceptible to benomyl, both when grown on solid agar and in liquid culture. This data has now been added Figure 7, and referred to in lines 338-348.
__Specific issues: __
1.3. In the case of figure 5, I generally find it hard to follow. In the text (line 262/263), the authors state that 5C shows "eye-shaped lesions" caused by ΔFgknr4 and ΔFgtri5, but I can't see neither (5C appears to be a ΔFgknr4 complementation experiment). The figure legend also states nothing in this regard.
__Response: __Thank you for your suggestion. We have amended the manuscript to include an additional panel that shows the dissected spikelet without its outer glumes, making the eye shaped diseased regions more visible in Figure 5.
__1.4. Figure 5D supposedly shows 'visibly reduced fungal burden' in ΔFgknr4-infected plants, but I can't really see the fungal burden in this picture, but the infected section looks a lot thinner and more damaged than the control stem, so in a way more diseased. __
Response: __Thank you for your insight. We have revised our conclusions based on this image to state that while ΔFgknr4 can colonise host tissue, it does so less effectively compared to the wild-type strain as we are unable to quantitatively evaluate fungal burden using image-colour thresholding due to the overlapping colours of the fungal cells and wheat tissues. Decreased host colonisation is evidenced by (i) reduced fungal hyphae proliferation, particularly in the thicker adaxial cell layer, (ii) collapsed air spaces in wheat cells, and (iii) increased polymer deposition at the wheat cell walls, indicating an enhanced defence response. __Figure 5 has been amended to include these observations in the corresponding figure legend and the resin images now include insets with detailed annotation.
__1.5. The authors then go on to state (lines 272-273) that they analyzed the amounts of DON mycotoxin in infected tissues, but don't seem to show any data for this experiment. __
Response: __We have amended this to now include the data in __Figure 5 - figure supplement 2B, thank you.
Reviewer #2
__Major issues: __
2.1 If Knf4 is involved in the CWI pathway, what other genes involved in the CWI pathway are in this fungal module? one of the reasons for developing modules or sub-networks is to assign common function and identify new genes contributing to the function. since FgKnr4 is noted to play a role in the CWI pathways, then genes in that module should have similar functions. If WGCN does not do that, what is the purpose of this exercise?
Response: __Thank you for raising this point regarding the role of FgKnr4 in the CWI pathway and the expectations for genes of shared function within the FgKnr4 module F16. We did observe that the module containing FgKnr4 (F16) was also correlated to a wheat module (W05) which was significantly enriched for oxidative stress genes. This pathogen-host correlated pattern led us to study module F16, which otherwise lacks significant gene ontology term enrichment, unique gene set enrichments, and contains few characterised genes. This is now highlighted in __lines 233-246. This underscores the strength of the WGCNA. By using high-resolution RNA-seq data to map modules to specific infection stages, we identified an important gene that would have otherwise been overlooked. This approach contrasts with other network analyses that often rely on the guilt-by-association principle to identify novel virulence-related genes within modules containing known virulence factors, potentially overlooking significant pathways outside the scope of prior studies. Therefore, our analysis has already benefited from several advantages of WGCNA, including the identification of key genes with high module membership that may be critical for biological processes, as well as generating a high-resolution, stage-specific co-expression map of the F. graminearum infection process in wheat. This point is now emphasised in lines 233-252. As discussed in response to reviewer 1, Knr4 functions as both a regulatory protein in the CWI pathway and as a scaffolding protein across multiple pathways in S. cerevisiae (Martin-Yken et al., 2016, https://onlinelibrary.wiley.com/doi/10.1111/cmi.12618 ) which would explain its clustering separate from the CWI pathway genes. The high module membership genes within module F16 containing FgKnr4 were re-analysed using Knetminer (Hassani-Pak et al., 2021; https://onlinelibrary.wiley.com/doi/10.1111/pbi.13583 ), which found that 8/15 of these genes were related to cell division and ATP binding. Four of the candidate genes are also part of a predicted protein-protein interaction subnetwork of genes within module F16, which relate to cell cycle and ATP binding. In S. cerevisiae, the absence Knr4 leads to dysfunction in cell division. Accordingly, we tested sensitivity of ΔFgknr4 to the microtubule inhibitor benomyl (a compound commonly used to identify mutants with cell division defects; Hoyt et al., 1991 https://www.cell.com/cell/pdf/0092-8674(81)90014-3.pdf). We found that the ΔFgknr4 mutant was more susceptible to benomyl, both when grown on solid agar and in liquid culture. This data has now been added as Figure 7 and referred to in lines 338-348.
2.2. Due to development defects in the Fgknr1 mutant, I would not equate to as virulence factor or an effector gene.
__Response: __We are in complete agreement with the reviewer and are not suggesting that FgKnr4 is an effector or virulence factor, we have been careful with our wording to indicate that FgKnr4 is simply necessary for full virulence and its disruption results in reduced virulence and have outlined how we believe FgKnr4 participates in a fungal signaling pathway required for infection of wheat.
2.3. What new information is provided with WGCN modules compared with other GCN network in Fusarium (examples of GCN in Fusarium is below) ____https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5069591/ https://doi.org/10.1186/s12864-020-6596-y____ DOI: 10.1371/journal.pone.0013021. The GCN networks from Fusarium have already identified modules necessary/involved in pathogenesis.
Response: __The 2016 New Phytologist gene regulatory network (GRN) by Guo et al. is large and comprehensive. However, only three of the eleven datasets are in planta, with just one dataset focusing on F. graminearum infection on wheat spikes. The other two in planta datasets involve barley infection and Fusarium crown rot. By combining numerous in planta and in vitro datasets, the previous GRNs lack the fine resolution needed to identify genetic relationships under specific conditions, such as the various stages of symptomatic and symptomless F. graminearum infection of mature flowering wheat plants. This limitation is highlighted in the 2016 paper itself. This network is expanded in the Guo et al., 2020 BMC genomics paper where it includes one additional in planta and nine in vitro datasets. However, the in planta dataset involves juvenile wheat coleoptile infection, which serves as an artificial model for wheat infection but is not on mature flowering wheat plants reminiscent of Fusarium Head Blight of cereals in the field. This model differs significantly in the mode of action of F. graminearum, notably DON mycotoxin is not essential for virulence in this context (Armer et al. 2024, https://pubmed.ncbi.nlm.nih.gov/38877764/ ). The Guo et al., 2020 paper still faces the same issues in terms of resolution and the inability to draw conclusions specific to the different stages of F. graminearum infection. Additionally, these GRNs use Affymetrix data, which miss over 400 genes (~ 3 % of the genome) from newer gene models. In contrast, our study addresses these limitations by analysing a meticulously sampled, stage- and tissue-specific in planta RNA-seq dataset using the latest reference annotation. Our approach provides higher resolution and insights into host transcriptomic responses during the infection process. The importance of our study in the context of these GRNs is now addressed in the introduction (__lines 85-92).
2.4. Ideally, the WGCN should have been used identify plant targets of Fusarium pathogenicity genes. This would have provided credibility and usefulness of the WGCN. Many bioinformatic tools are available to identify virulence factors and the utility of WGCN in this regard is not viable. However, if the authors had overlapped the known virulence factors in a fungal module to a particular wheat module, the impact of the WGCN would be great. The module W12 has genes from numerous traits represented and WGCN could have been used to show novel links between Fg and wheat. For example, does tri5 mutant affect genes in other traits?
__Response: __Thank you for your suggestions. In this study we have shown the association between the main fungal virulence factor of F. graminearum, DON mycotoxin, with wheat detoxification responses. Through this we have identified a set of tri5 responsive genes and validated this correlation in two genes belonging to the phenylalanine pathway and one transmembrane detoxification gene. Although we could validate more genes in this tri5 responsive wheat module, our paper aimed to investigate previously unstudied aspects of the F. graminearum infection process and how the fungus responded to changing conditions within the host environment. We accomplished this by characterising a gene within a fungal module that had limited annotation enrichment and few characterised genes. Tri5 on the other hand is the most extensively studied gene in F. graminearum and while the network we generated may offer new insights into tri5 responsive genes, this is beyond the scope of our current study. In addition to the tri5 co-regulated response, we have also demonstrated the coordinated response between the fungal module F16, which contains FgKnr4 that is necessary for tolerance to oxidative stress, and the wheat module W05, which is enriched for oxidative stress genes.
While our co-expression network approach can be used to explore and validate other early downstream signaling and defense components in wheat cells, several challenges must be considered: (a) the poor quality of wheat gene calls, (b) genetic redundancy due to both homoeologous genes and large gene families, and (c) the presence of DON, which can inhibit translation and prevent many transcriptional changes from being realised within the host responses. Additionally, most plant host receptors are not transcriptionally upregulated in response to pathogen infection (most R gene studies for the NBS-LRR and exLRR-kinase classes), making their discovery through a transcriptomics approach unlikely. These points will be included in our discussion (lines 408-413), thank you.
Specific issues
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2.5. Since tri5 mutant was used a proof of concept to link wheat/Fg modules, it would have been useful to show that TRI14, which is not involved DON biosynthesis, but involved in virulence ( https://doi.org/10.3390/applmicrobiol4020058____) impact the wheat module genes.
Response: __Our goal was to show that wheat genes respond to the whole TRI cluster, not just individual TRI genes. Therefore, the tri5 mutant serves as a solid proof-of-concept, because TRI5 is essential for DON biosynthesis, the primary function of the TRI gene cluster, thereby representing the function of the cluster as a whole. This is now clarified in __lines 217-219. Additionally, the uncertainties surrounding other TRI mutants would complicate the question we were addressing-namely, whether a wheat module enriched in detoxification genes is responding to DON mycotoxin, as implied by shared co-expression patterns with the TRI cluster. For instance, the referenced TRI14 paper indicates that DON is produced in the same amount in vitro in a single media. Although the difference is not significant, the average DON produced is lower for the two Δtri14 transformants tested. Therefore, we cannot definitively rule out that TRI14 is involved in DON biosynthesis and extrapolate this to DON production in planta. Despite this, the suggestion is interesting, and would make a nice experiment but we believe it does not contribute to the overall aim of this study.
2.6. Moreover, prior RNAseq studies with tri5 mutant strain on wheat would have revealed the expression of PAL and other phenylpropanoid pathway genes?
__Response: __We agree that this would be an interesting comparison to make but unfortunately no dataset comparing in planta expression of the tri5 mutant within wheat spikes exists.
2.7. Table S1 lists 15 candidate genes of the F16 module; however, supplementary File 1 indicates 74 genes in the same module. The basis of exclusion should be explained. The author has indicated genes with high MM was used as representative of the module. The 59 remaining genes of this module did not meet this criteria? Give examples.
Response: __The 15 genes with the highest module membership were selected as initial candidates for further shortlisting from the 74 genes within module F16. In WGCNA, genes with high module membership (MM) (i.e. intramodular connectivity) are predicted to be central to the biological functions of the module (Langfelder and Horvath, 2008; https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-9-559 ) and continues to be a metric to identify biologically significant genes within WGCN analyses (https://bmcplantbiol.biomedcentral.com/articles/10.1186/s12870-024-05366-0 Tominello-Ramirez et al., 2024; https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9151341/ ;Zheng et al., 2022; https://www.nature.com/articles/s41598-020-80945-3 Panahi and Hejazi et al 2021). Following methods by Mateus et al. (2019) (https://academic.oup.com/ismej/article/13/5/1226/7475138 ) key genes were defined as those exhibiting elevated MM within the module, which were also strongly correlated (R > |0.70|) with modules of the partner organism (wheat). We have clarified this point in the manuscript. Thank you for the suggestion. (__Lines 253-263).
2.____8. A list from every module that pass this criteria will be useful resource for functional characterization studies.
__Response: __A supplementary spreadsheet has been generated which includes full lists of the top 15 genes with the highest module membership within the five fungal modules correlated to wheat modules and a summary of shared attributes among them. Thank you for this suggestion.
2.9. Figure 3 indicates TRI genes in the module F12; your PHI base in Supp File S2 lists only TRI14. Why other TRI genes such as TRI5 not present in this File?
Response: For clarity, the TRI genes in module F12 are TRI3, TRI4, TRI11, TRI12, and TRI14 which was stated in Table 1. TRI5 clusters with its neighboring regulatory gene TRI6 in module F11, which exhibits a similar but reduced expression pattern compared to module F12. To improve clarity on this the TRI genes in module F12 are also listed in-text in line 168 and added to Figure 4. The enrichment and correlated relationship of W12 to a cluster's expression still imply a correlated response of the wheat gene to the TRI cluster's biosynthetic product (DON), which is absent in the Δtri5 mutant.
TRI14 and TRI12 are listed in PHI-base. TRI12 was mistakenly excluded due to an unmapped Uniprot ID, which were added separately in the spreadsheet. We will recheck all unmapped ID lists to ensure all PHI-base entries are included in the final output. Thank you for pointing out this error.
2.10. What is purpose of listing the same gene multiple times? Example, osp24 (a single gene in Fg) is listed 13 times in F01 module.
__Response: __This is a consequence of each entry having a separate PHI ID, which represents different interactions including inoculations on different cultivar. Cultivar and various experimental details were omitted from the spreadsheet to reduce information density, however the multiple PHI base ID's will be kept separate to make the data more user friendly when working with the PHI-base database. An explanation for this is now provided in the file's explanatory worksheet, thank you.
Reviewer #3:
3.1. Why only use of high confidence transcripts maize to map the reads and not the full genome like Fusarium graminearum? I have never analyzed plant transcriptome.
__Response: __ In the wheat genome, only high-confidence gene calls are used by the global community (Choulet et al., 2023; https://link.springer.com/chapter/10.1007/978-3-031-38294-9_4 ) until a suitable and stable wheat pan-genome becomes available.
3.2. The regular output of DESeq are TPMs, how did the authors obtain the FPKM used in the analysis?
Response: FPKM was calculated using the GenomicFeatures package and included on GitHub to enhance accessibility for other users. However, the input for WGCNA and this study as a whole was normalised counts rather than FPKM. The FPKM analysis was done to improve interoperability of the data for future users and made available on Github. To complement this, the information regarding FPKM calculation is now included in the methods section of the revised manuscript (line 491).
3.3. Do the authors have a Southern blot to prove the location of the insertion and number of insertions in Zymoseptoria tritici mutant and complemented strains?
__Response: __No, but the phenotype is attributed to the presence or absence of ZtKnr4, as the mutant was successfully complemented in multiple phenotypic aspects. This satisfies Koch's postulates which is the gold standard for reverse genetics experimentation (Falkow 1988; https://www.jstor.org/stable/4454582 ).
__3.4. Boxplots and bar graphs should have the same format. In Figures 5 B and F and supplementary figure 6.3 the authors showed the distribution of samples but it is lacking in figure 3 B and all bar graphs. __
__Response: __Graphs have been modified to display the distribution of all samples, thank you.
3.5. Line 247 FGRAMPH1_0T23707 should be FGRAMPH1_01T23707
__Response: __Thank you this has now been amended.
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Referee #3
Evidence, reproducibility and clarity
The authors of the manuscript entitled "A conserved fungal Knr4/Smi1 protein is vital for maintaining cell wall integrity and host plant pathogenesis" used a weighted gene co-expression network to identify Fusarium graminearum genes highly expressed during early symptomless infection of wheat. Based on its sequence and previous studies, authors selected FgKnr4 from the early symptomless Fusarium modules. The characterization of knockout strains revealed a role in morphogenesis, growth, cell wall stress tolerance, and virulence in F. graminearum and the phylogenetically distant fungus Zymoseptoria tritici.
The methods are properly described and statistical analysis are reasonable so reproducibility is possible. The RNA-seq dataset is already published and the authors provided a repository with the code used to create the co-expression network. However, I have the following questions:
- Why only use of high confidence transcripts maize to map the reads and not the full genome like Fusarium graminearum? I have never analyzed plant transcriptome.
- The regular output of DESeq are TPMs, how did the authors obtain the FPKM used in the analysis?
- Do the authors have a southern blot to prove the location of the insertion and number of insertions in Zymoseptoria tritici mutant and complemented strains?
- Boxplots and bar graphs should have the same format. In Figures 5 B and F and supplementary figure 6.3 the authors showed the distribution of samples but it is lacking in figure 3 B and all bar graphs.
- Line 247 FGRAMPH1_0T23707 should be FGRAMPH1_01T23707
Referees cross-commenting
I agree with reviewer 1, the order in which the figures are called in the text is confusing. Regardless of figures 5C-D I am no expert in the field therefore I can only say they look like they have not been edited.
I agree with reviewer 1, data of DON mycotoxin production in infected issues is need it to support statement in line 272-273.
I agree with Reviewer 2, the criteria to exclude genes from the final selection list should be explained.
Significance
The study showed, once again, that a weighted gene co-expression network is a great method to identify new genes of interest regardless of the organism or condition even if not very popular in the fungal pathogen field yet. The study proved that functions identified in a WGCN module from a pathogen have their opposite in the host module. The authors go beyond the theory and demonstrate the effect of the highest expressed gene during the early symptomless stage of infection in maize and wheat fungal pathogens.
Fungal pathogen, RNA-seq, metabolic models, metabolism, comparative genomics
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Referee #2
Evidence, reproducibility and clarity
Summary: The authors in this manuscript use "dual weighting" to identify clusters or modules of genes from the fungus F. graminearum (Fg) with coordinated expression patterns with genes in wheat modules - potentially uncover key regulators or pathways linking Fg genes with plant traits, including plant pathogenesis. As proof of concept, the authors use one of the fungal genes FgKnr4 identified in a fungal module that has strong link with the wheat module. They were able to show that this gene is likely involved in CWI pathway and affects virulence properties of the fungus
Major comments:
Does the WGCN provide useful framework to link fungal genes affecting plant traits? If Knf4 is involved in the CWI pathway, what other genes involved in the CWI pathway are in this fungal module? This is not forthcoming. Due to development defects in the Fgknr1 mutant, I would not equate to as virulence factor or an effector gene.
Since tri5 mutant was used a proof of concept to link wheat/Fg modules, it would have been useful to show that TRI14, which is not involved DON biosynthesis, but involved in virulence ( https://doi.org/10.3390/applmicrobiol4020058) impact the wheat module genes. Moreover, prior RNAseq studies with tri5 mutant strain on wheat would have revealed the expression of PAL and other phenylpropanoid pathway genes?
Table S1 lists 15 candidate genes of the F16 module; however, supplementary File 1 indicates 74 genes in the same module.
The basis of exclusion should be explained. The author has indicated genes with high MM was used as representative of the module. The 59 remaining genes of this module did not meet this criteria? Give examples. Did similar exclusion criteria used for other modules and if so, how many genes in each module pass the criteria? For example, Did TRI5 in module F12 pass this criteria. A list from every module that pass this criteria will be useful resource for functional characterization studies.
Minor comments:
Figure 3 indicates TRI genes in the module F12; your PHI base in Supp File S2 lists only TRI14. Why other TRI genes such as TRI5 not present in this File? What is purpose of listing the same gene multiple times? Example, osp24 (a single gene in Fg) is listed 13 times in F01 module.
Referees cross-commenting
agree with both reviewers regarding clarification of Figures.
one of the reasons for developing modules or sub-networks is to assign common function and identify new genes contributing to the function. since FgKnr4 is noted to play a role in the CWI pathways, then genes in that module should have similar functions. If WGCN does not do that, what is the purpose of this exercise?
Significance
What new information is provided with WGCN modules compared with other GCN network in Fusarium (examples of GCN in Fusarium is below)
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5069591/ https://doi.org/10.1186/s12864-020-6596-y DOI: 10.1371/journal.pone.0013021
The GCN networks from Fusarium have already identified modules necessary/involved in pathogenesis. Ideally, the WGCN should have been used identify plant targets of Fusarium pathogenicity genes. This would have provided credibility and usefulness of the WGCN.
Many bioinformatic tools are available to Identify virulence factors and the utility of WGCN in this regard is not viable. However, if the authors had overlapped the known virulence factors in a fungal module to a particular wheat module, the impact of the WGCN would be great. The module W12 has genes from numerous traits represented and WGCN could have been used to show novel links between Fg and wheat. For example, does tri5 mutant affect genes in other traits?
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Referee #1
Evidence, reproducibility and clarity
Summary:
A public mRNA-seq dataset from Dilks et al. (2019) for wheat spikelets infected by Fusarium graminearum was used to generate a dual weighted gene co-expression network (WGCN). Since colonization of the spike by F. graminearum progresses from spikelet to spikelet, thereby forming an infection-gradient from early to late stages, quasi spatio-temporal resolution for the transcriptomic dataset can be achieved by cutting the spike into equal pieces along this gradient (in this case cuts were done at rachis internodes 1-2, 3-4, 5-6, and 7-8. The authors created co-expression networks for both, fungal and plant genes, and cross-correlated them. They identify several modules specific for each infection stage. For further analysis, the authors focus on two module pairs. (1) the wheat module 12 (W12), which correlates to Fusarium module 12 (F12), and (2) the Fusarium module 16 (F16) and the correlated wheat modules 1 and 5 (W01/W05). The W12/F12 modules were deemed of interest because they were specific to the transition from symptomless to symptomatic infection stage. Here, the authors find genes related to mycotoxin production to be upregulated in the F12 module, while the W12 is enriched in genes involved in detoxification. F16 and W01/W05 are specific to the earliest stages of infection, and thus most likely involved in fungal virulence. Here, one of the key genes identified is FgKnr4, which the authors show to be important for fungal virulence, as gene knockout leads to a premature stop of disease progression. As the authors show that FgKnr4 is involved in activating cell wall-integrity mechanisms, and may function in oxidative stress-resistance, this reduced virulence may be the result a reduced ability of the fungus to withstand plant defense mechanisms. Interestingly, knocking out an orthologue of FgKnr4 in Zymoseptoria tritici led to similarly reduced virulence of this pathogenic fungus on wheat plant.
Comments:
Overall, I find the WGCN analysis to be very interesting and informative, especially because of the different stages of infection. As the dataset is made public (I believe), I think that this will be a really important resource for the community. The exemplary functional analysis of the F16/W01/W05 modules via FgKnr4 is very interesting and demonstrates that novel genes involved in virulence can be identified via this approach. A similar more detailed analysis of the W12/F12 modules with a focus on detoxification mechanisms in the plant (i.e. the W12 module) would be a very interesting bonus, but as much as I would be interested in reading about it, functional gene analyses in wheat are obviously time-consuming, and it is not essential to this manuscript. As a more critical comment, I find the presentation of the figures somewhat confusing, especially with the mixing of main figures, supplements to the main figures, and actual supplemental data. On top of that, the figures are not called up in the right order (e.g. Figure 4 follows 2D, while 3 comes after 4; Figure 6 comes before 5...), and some are never called up (I think) (e.g. Figure 1B, Figure 2B). In the case of figure 5, I generally find it hard to follow. In the text (line 262/263), the authors state that 5C shows "eye-shaped lesions" caused by ΔFgknr4 and ΔFgtri5, but I can't see neither (5C appears to be a ΔFgknr4 complementation experiment). The figure legend also states nothing in this regard. Figure 5D supposedly shows 'visibly reduced fungal burden' in ΔFgknr4-infected plants, but I can't really see the fungal burden in this picture, but the infected section looks a lot thinner and more damaged than the control stem, so in a way more diseased. The authors then go on to state (lines 272-273) that they analyzed the amounts of DON mycotoxin in infected tissues, but don't seem to show any data for this experiment. In contrast to the sometimes confusing data presentation, I find the table of correlated modules (table 1) very helpful, and obviously am happy to see that all data is available in the first author's GitHub account.
Referees cross-commenting
just to clarify in regards to my comment on Figures 5C-D, and Reviewer #3's comment "Regardless of figures 5C-D I am no expert in the field therefore I can only say they look like they have not been edited." - I didn't want to insinuate that the images have been edited. Based on the images provided, I just can't see what the authors state is shown. So this is not about editing/manipulation - just about image quality/choice. The phenotypic descriptions by the authors are quite detailed ("eye-shaped lesions", 'visibly reduced fungal burden'...), but at least for me, the images aren't good enough to illustrate and underpin their statements. Maybe better images are needed, maybe magnifications of the exact regions showing the phenotypes? But this is simply a matter of presentation, not of editing/manipulation.
Second, I agree that there should be more CWI-related genes in the wheat module linked to the FgKnr4 fungal module, or, vice-versa, CW-manipulating genes in the fungal module. It would at least be good if the authors could comment further on if they find such genes, and if not, how this fits their model.
Significance
In summary, I think that the presented WGCN analysis of mRNA-seq data with quasi-spatio-temporal resolution is a very helpful approach to identify novel fungal virulence and plant immunity genes, and with the created datasets made public, this will be an interesting and valuable resource for the community. The identification and functional analysis of FgKnr4 works as proof-of-principle. If the data presentation is improved, I believe that this will be an interesting publication.
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Reply to the reviewers
Manuscript number:
RC-2024-02569
Corresponding author(s): Mary O'Riordan, Teresa O'Meara
1. General Statements
We thank the reviewers for their positive feedback, highlighting the significance and novelty of our work, especially regarding the novel functions of IRE1a in regulating phagosome biology during infection. We also appreciate some overarching themes that were focused on by multiple reviewers, including the role of XBP1S protein and RIDD activity, which we have addressed here. We have also added additional data, made adjustments to data presentation, and added clarifying language to address concerns from Reviewer 3. We appreciate these constructive suggestions and include our planned experiments to address reviewer concerns here. Our specific responses to the reviewer comments are below.
Specific figures used in the response to reviewers are in the attached file as they cannot be pasted here.
2. Description of the planned revisions
Reviewer 1:
1) The demonstration of protein misfolding independent IRE1 activation should also be demonstrated using molecules such as TUDCA or 4PBA that should be innocuous regarding the splicing of XBP1s. It would also be interesting to evaluate the activation of the other arms of the UPR in particular through the phosphorylation of eIF2a, expression of ATF4 and cleavage of ATF6.
We appreciate the suggestion to strengthen our data regarding protein misfolding-independent activation of IRE1 more robust. We note that canonical UPR transcriptional targets are not induced during C. albicans infection (Fig. 2G,H), suggesting that IRE1 is activated in the absence of a standard unfolded protein response. However, we agree that we can use additional chemical chaperones to assay this. To address this point, we will perform the suggested experiments in the presence or absence of TUDCA with C. albicans, LPS, thapsigargin, and tunicamycin. As 4PBA has been shown to inhibit protein synthesis, rather than promoting protein folding or preventing aggregation (PMC9741500), we will avoid using this compound for these assays.
We will also perform western blots for ATF6 cleavage and eIF2a phosphorylation, although we note that eIF2a can be phosphorylated by multiple kinases and can be triggered by nutrient deprivation or changes in intracellular calcium, both of which occur during C. albicans infection (glucose: PMC6709535; calcium: data within this manuscript).
3) The authors use thioflavin to evaluate the extent of protein misfolding. This type of stain can lead to artefactual results and in general it is rather safer to test several stainers (see for instance the work presented in PMC10720158)
We thank the reviewer for this suggestion. We have previously tried Proteostat staining as an additional method to measure protein misfolding, but we found that it bound strongly to the C. albicans cell wall, which would result in a strong false positive signal that is not indicative of host protein misfolding (see below). Congo Red, an additional dye used in the listed reference, is also known to bind to C. albicans and perturbs cell wall synthesis (PMC266468), therefore we have avoided these dyes.
However, to address this point, we will perform experiments utilizing poly-ubiquitin blotting, as in the suggested reference, as an orthogonal readout of protein misfolding during C. albicans infection or treatment with LPS, depleted zymosan, and thapsigargin.
__Figure legend: Proteostat staining with _C. albicans_ infection. __Macrophages were infected with C. albicans, and subsequently stained with Proteostat to measure protein misfolding. Proteostat bound and displayed strong fluorescence on the C. albicans cell wall.
6) The whole study relies on the use of IRE1deltaR to impair IRE1 signaling. The authors should validate their hypothesis with an orthogonal approach, for instance with IRE1 pharmacological inhibitors (eg MKC8866 or KIRA8).
We consider the use of genetic perturbation of IRE1 to be a strength of this manuscript, as IRE1 inhibitors have been shown to cause off-target effects (KIRA8: PMC9600248). However, to address this point, we will attempt to replicate important phenotypes, including the effect of IRE1 on calcium flux and phagolysosome fusion, using MKC8866 and KIRA8 as representative inhibitors.
3. Description of the revisions that have already been incorporated in the transferred manuscript
__Reviewer 1: __
5) The authors focus on the IRE1/XBP1s signaling arm of the UPR but do not explore RIDD activity which has been linked to several infection mechanisms and lysosomal integrity (in particular by regulating the expression of BLOS1 - see PMC9119680 and PMC6446841). The authors should definitely evaluate how RIDD is activated (or not) in their experimental systems.
We thank the reviewer for this suggestion, as we have considered potential effects of RIDD when analyzing our RNA-seq data, and are aware of the potential links between IRE1, BLOS1 (encoded by Bloc1s1) expression, and lysosome perturbations. We now add additional figures to our supplemental data (Fig. S3C-D; also shown below) showing that established RIDD targets, including Bloc1s1 are not depleted during C. albicans infection, and also not increased in IRE1 null macrophages. We add the following text to describe these findings (lines 322-326): "Additionally, we did not observe depletion of published RIDD targets (14, 65, 66) during C. albicans infection in WT macrophages (Fig. S3C; Table S2), nor increased expression of RIDD targets in IRE1ΔR macrophages, compared to IRE1 WT macrophages (Fig. S3D; Table S1.1), suggesting minimal RIDD activity during C. albicans infection." We also note that experiments with LysoSensor (Fig. 3E) suggested lysosome biogenesis is not impaired in IRE1 null macrophages. Therefore, we expect RIDD activity has negligible effects on our reported phenotypes.
Reviewer #1 (Significance (Required)):
The manuscript is interesting and highlights novel aspects towards the interaction between macrophages and a pathogen, candida albicans, involving the likely selective activation of IRE1. The data are novel and experimentally sound. Several controls are however missing.
The strengths of the study are associated with the novelty of the findings, with the links that could potentially derive from this study to connect ER biology, UPR signaling and phagosome maturation
The main weaknesses are associated i) with the fact that the authors did not evaluate RIDD activity which has already been linked with pathogen infection and with lysosome integrity, ii) with methodological aspects, in particular regarding the demonstration of the IRE1 activation independent on protein misfolding and the sole use of a genetic variant of IRE1 to test their hypotheses
We thank Reviewer 1 for their constructive feedback and for noting the novelty of our findings. We believe that the data we have added regarding RIDD activity and our planned experiments to address additional concerns will add additional evidence to support our findings.
Reviewer 2:
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A point that should be addressed with more detail is the correlation of fungal killing with Ca2+ fluxes and Ire1α activity, given the well-known data regarding the strong ability of the axis dectin/SYK/phospholipase Cγ to induce Ca2+ transients, a response not shared by LPS signaling, and the sequential activation of mitochondrial Ca2+ uniporter (MCU), which is a critical element of fungal killing associated with the citrate-pyruvate shuttle as a NADPH source (Seegren et al., Cell Rep. 33: 108411, 2020). Incidentally, this paper is referred in ref. 46 as a preprint, although it was accessible in Cell Reports in 2020.
This is an excellent suggestion; we have added this topic to our discussion (lines 605-608) and have corrected the citation.
The assay of the expression of V-ATPase complex, mitochondrial calcium uniporter, and mitochondrial uptake 1 and 2 could shed light on the dependence of fungal killing on Ire1α function.
Thank you for this suggestion - below, we plot the transcripts comprising the V-ATPase, as well as Mcu, Micu1, and Micu2. We note that these transcripts are not perturbed in IRE1 null macrophages, suggesting that the basic functions of the V-ATPase complex and mitochondrial calcium uptake are intact in IRE1 null macrophages.
These data are in agreement with our LysoSensor assay (Fig. 3E), which suggested that lysosome biogenesis is not impaired in IRE1 null macrophages.
While we cannot rule out a defect in mitochondrial calcium flux from our RNA-seq data, we have added discussion around this topic to our discussion, as mentioned above.
Expression of V-ATPase subunits and mitochondrial calcium uptake genes in C. albicans-infected IRE1 null macrophages vs C. albicans-infected IRE1 WT macrophages.
Fig. 1A should be explained with more detail to disclose the products of PstI digestion.
Thank you for the suggestion. We have added this information to the Figure 1 legend, "RT-PCR-amplified Xbp1 cDNA was treated with PstI, which recognizes a cleavage site within the 26 base pair intron that is removed by IRE1α activity, resulting in cleavage of the unspliced isoform, specifically."
The anti-XBP1 antibody used to construct the blots in Fig.S1A recognizes epitopes not disclosed by the manufacturers, but they have to pertain to the N-terminal peptide sequence shared by sXBP1 and uXBP1. Showing full lanes encompassing both protein isoforms would allow a better appraisal of protein expression. In connection to point 4, the use of an antibody reactive to the epitopes expressed in sXBP1 in cell lysates or, preferentially in nuclear fractions, could be most valuable to rule out the dependence of the effect of Ire1α on the trans-activating function of sXBP1.
We have un-cropped these westerns and now show spliced and unspliced XBP1 products on a single image in Fig. S1A.
On page 23, the mention to Fig. 5A should be changed to Fig. 5B.
We have fixed this mis-labeling, thank you for calling this to our attention.
Line 209. I understand gene synthesis refers to gene expression.
We have clarified this in the text, thank you for the suggestion.
Line 394. What is the reason to study the cytokine-signature of Candida in LPS-primed cells?
Thank you for the question; we have added the following text (lines 413-414) to clarify that LPS is used for inflammasome priming:
"Therefore, we tested secretion of IL-1β, TNF, and IL-6 from WT and IRE1ΔR macrophages after LPS treatment to transcriptionally prime the NLRP3 inflammasome components, followed by C. albicans infection (Fig. 5D-F)."
Numerous studies have shown that C. albicans can trigger macrophage pyroptosis, resulting in production of pro-inflammatory cytokines like IL-1b, which can also be influenced by phagosome rupture (PMC3910967). However, this requires inflammasome transcriptional priming, and LPS is commonly used to prime macrophages for inflammasome activation in vitro. Therefore, we perform a short pre-treatment with LPS for NLRP3 inflammasome priming to subsequently measure its activation following C. albicans infection, using secreted cytokines as a readout. We also note that macrophages in vivo may not be naive and are often M1-polarized by the microbial or cytokine environment, thus inflammasome priming is likely common during in vivo infection.
Reviewer #2 (Significance (Required)):
This study focuses on an aspect not usually addressed in papers devoted to the UPR.
If more data are shown as suggested, the paper could be of interest for a wider audience
We thank the reviewer for their positive feedback about the novelty of our work and agree that the suggested experiments will bolster our data and story.
Reviewer 3:
Fig. 2:
Panel A-B: same question as for Fig. 1. The variation in TG DMSO-induced splicing is huge. The effects of the treatments with CHX or Act D are smaller than the variation between experiments with TG DMSO alone. As long as that variation is not controlled for, it is impossible to draw any conclusion from the inhibitors. In this regard, it is very difficult to interpret data if they are not done in one and the same experiment.
The variability in thapsigargin fold change over mock likely represents differences in basal Xbp1 expression. We consistently see complete Xbp1 splicing in response to thapsigargin treatment (see Fig. 1A). Additionally, we note that thapsigargin treatment is used only as a positive control, not as a physiologically relevant treatment, as it results in unmitigated ER stress that triggers cell death (PMC6986015).
We have removed the following sentence, "Translation inhibition using cycloheximide was sufficient to alleviate Xbp1 splicing specifically in response to thapsigargin, likely by reducing the nascent protein folding burden (Fig. 2B)," since our data are plotted on separate graphs, matched to their respective controls, for appropriate comparisons.
4. Description of analyses that authors prefer not to carry out
Reviewer 1:
2) Since the IRE1/XBP1 arm of the UPR is also involved in lipid biosynthesis which might be required for phagosome maturation, the authors should perform XBP1s rescues in IRE1 deficient cells to ensure that their observation is XBP1s dependent or IRE1 dependent.
As we do not see XBP1S protein induced in wild-type macrophages at any timepoint during our C. albicans infection scheme (Fig. S1A-B), we interpret our results as being XBP1S-independent. If we were to add back XBP1S with constitutive expression, we would be overexpressing the protein relative to C. albicans infected wild-type macrophages (in which we do not see measurable XBP1S expression). Therefore, we believe these experiments would not address a physiologically-relevant scenario.
4) The authors should evaluate in what compartment IRE1 is activated upon CA infection, does that happen in the ER or in the ER fraction fused to phagosomes?
This is an interesting question for future exploration. In order to answer this question with existing tools, we would need to perform biochemical fractionation of infected cells to isolate an ER-phagosome contact site fraction, followed by phos-tag gel analysis of IRE1 activation in the ER fraction, compared to the ER-phagosome contact site fraction. However, a biochemical fractionation protocol to distinguish the ER fraction from ER-phagosome contact sites has not yet been developed, to our knowledge, and we believe it is outside the scope of this study to develop such a technique.
We have added additional text regarding this intriguing question to our discussion (lines 549-553).
Reviewer 2:
Infection at a MOI 1 of C. albicans is a ratio of infecting agent/susceptible targets not very high for a non-soluble stimulus with limited diffusion in the culture medium. Although I recognize the difficulty of quantitating adhered cell, the mention to 80% confluence makes it more difficult the appraisal of the actual MOI. The delayed time-course of Xbp splicing under these conditions can be explained by the time required for in vitro proliferation, Candida damage, and diffusion of fungal patterns. A study with viable Candida at MOI 5 in human monocyte-derived dendritic cells, which show a robust capacity for non-opsonic phagocytosis associated with C-type lectin receptors only showed initial hypha formation after 2 hours (Rodriguez et al., J. Biol. Chem. 289, P22942-22957, 2014). Consistent with the requirement of a time lag for infecting agent to attain levels of expression consistent with a net response, 16 hours have been considered an appropriate time-course to assay sXBP1 expression following SARS-CoV2 infection (Fernandez et al., Biochim Biophys Acta Mol Basis Dis. 1870(5):167193, 2024). I wonder if a higher MOI could show a similar kinetics.
We use lower MOI in part due to the size and ability of C. albicans to undergo extensive hyphal growth if its numbers greatly exceed the number of host cells. From our microscopy data, we can see that C. albicans spreads well throughout the culture plate (see Fig. 3A, Fig. 4A). We and others have observed considerable death of macrophage cultures after 12 hours with Candida infection, even at low MOI (PMC6709535), therefore we avoid later timepoints in these assays and all other in vitro assays in our manuscript.
As all of our in vitro experiments are performed within an 8 hour window of infection, whether XBP1S is induced at later timepoints by C. albicans or depleted zymosan would not alter the conclusions of the rest of our results.
sXBP1 can be present in nuclear fractions in resting cells, which suggests the involvement of post-translational modifications for the display of transcriptional activity.
As we do not see induction of XBP1S in our lysates after C. albicans infection, it is unlikely that post-translational modification is influencing its function, although we agree post-translational modification is a likely regulatory control over XBP1S during the unfolded protein response.
The independence of sXBP1 transcriptional activity from canonical UPR associated with misfolded protein stress is well known from the seminal paper by Martinon et al., (ref.6). Moreover, the expression of CHOP, the final effector of the PERK route, encoded by DDIT3 gene, has been found to be blunted by Candida (Rodriguez et al., J. Biol. Chem. 289, P22942-22957,2014). This is additional evidence for the recruitment of sXBP1 transcriptional activity in the absence of canonical UPR.
As mentioned, we found that XBP1S protein is not induced during C. albicans infection at any timepoint in our experiments (Fig. S1A-B). Importantly, the work referenced by the reviewer uses RAW267.7 cells, which (as mentioned by the authors) constitutively express CHOP as a result of Abel leukemia virus infection. Based on this specific overexpression, we believe this phenotype is not comparable to our bone marrow-derived macrophages.
Reviewer 3:
Fig. 1:
Panel 1C: please remove outlier in 4h timepoint. This implies that the experiment needs to be redone to reduce variation
We have performed an outlier test on these data, which revealed that this data point is not a statistical outlier, therefore we do not feel that its removal is appropriate (see below).
Panel 1E-H: how is the splicing efficiency determined and normalized? How to explain the big differences in splicing efficiency of Xbp1 upon LPS stimulation (appr. 4 to 6 times in E, G and H versus 30-fold in panel F). Where does this difference come from?
Panel H, outlier needs to be removed.
We do occasionally see differences in magnitude of Xbp1 splicing in different cell lines or experiments, especially with controls, which may be caused by differences in the basal level of Xbp1 expression, especially as Xbp1 levels have been shown to be affected by circadian rhythm in certain cell types (PMCID: PMC11214543; PMCID: PMC6959563).
In panel H, an outlier test reveals that these are not statistical outliers, therefore we feel their removal is inappropriate as we do not wish to mask biological variation. Moreover, this graph includes two cell lines (open and closed circles), showing that our data are robust across multiple independent cell lines and are an appropriate measure of experimental replicates.
Fig. 2:
Panel A-B: same question as for Fig. 1. The variation in TG DMSO-induced splicing is huge. The effects of the treatments with CHX or Act D are smaller than the variation between experiments with TG DMSO alone. As long as that variation is not controlled for, it is impossible to draw any conclusion from the inhibitors. In this regard, it is very difficult to interpret data if they are not done in one and the same experiment.
The variability in thapsigargin fold change over mock likely represents differences in basal Xbp1 expression. We consistently see complete Xbp1 splicing in response to thapsigargin treatment (see Fig. 1A). Additionally, we note that thapsigargin treatment is used only as a positive control, not as a physiologically relevant treatment, as it results in unmitigated ER stress that triggers cell death (PMC6986015).
We have removed the following sentence, "Translation inhibition using cycloheximide was sufficient to alleviate Xbp1 splicing specifically in response to thapsigargin, likely by reducing the nascent protein folding burden (Fig. 2B), since our data are plotted on separate graphs, matched to their respective controls, for appropriate comparisons.
Below, we plot all data together with replicate matching, although our major interpretation of these data is that C. albicans infection can trigger Xbp1 splicing with or without new gene expression, and not about the impact of the inhibitors on the control treatment thapsigargin.
Please provide a scheme of how the experiment was performed, at what time were the inhibitors provided, at what time point the inducers? What are matched mock samples. Which mock samples were chosen since they differ from one experiment to the next? Please plot all the data for one and the same experiment in one graph so that the reader can easily compare the results of DMSO, DMSO + inducer, DMSO + inducer + inhibitor. Indicate whether the points in the graph are technical or experimental repetitions.
-How to explain the increase in XBP1 splicing in combination with ActD? Was this due to differences in Gapdh expression? Where did the authors control for cell death? Please provide the data.
Below is a scheme of the experimental treatments. We have now clarified in the figure legend that inhibitors (ActD and CHX) are added at the same time as experimental treatments (Mock, Ca, TG). All data included in the original submission are biological replicates, as stated in the figure legend. We have now re-written the figure legend to clearly indicate that these are biological replicates.
All data are normalized such that the effects of the drugs are directly compared (for example, the fold change over Mock for Candida is matched to its drug treatment; Mock DMSO vs Ca DMSO and Mock ActD vs Ca ActD, or Mock CHX vs Ca CHX). Actinomycin D does inhibit new transcription, although IRE1 can cleave existing Xbp1 transcript. We now show conditions normalized to DMSO Mock in Supplemental Figure 2, which allows visualization of the effects of ActD and CHX on Xbp1-S abundance in comparison to control DMSO treatment, while also seeing the relative changes in Xbp1 splicing caused by C. albicans or thapsigargin treatment (see below).
-Is RT-qPCR a reliable readout when actinomycinD is used? How can new genes be transcribed.
We interpret RT-qPCR data as a readout of transcript abundance, rather than transcription. Therefore, we are not measuring new gene expression here, but whether the existing Xbp1 transcript can be cleaved by IRE1. Based on the technique, we can still measure changes in Xbp1-S abundance.
Panel D: where is TG at 4h and 6h?
We do not include thapsigargin at later timepoints to avoid autofluorescence from excessive cell death. We include thapsigargin as a positive control at the early 2h timepoint, but note that LPS is sufficient to increase thioflavin T intensity at the 8h timepoint.
Panel G, why was Ddit3 included here as this is not a typical IRE1 dependent gene (rather PERK dependent). What about IRE1 specific genes such as Sec61 or Sec24a?
We have added additional text (lines 235-240; "Finally, we measured induction of UPR-responsive genes by RT-qPCR in response to C. albicans infection, LPS and depleted zymosan treatment, or thapsigargin treatment, to further test whether IRE1α activation occurs without canonical UPR induction (Fig. 2G-H). C. albicans infection and depleted zymosan treatment did not lead to induction of UPR-responsive genes (Ddit3, Grp78, Grp94, and total Xbp1) at 4 or 6 hours.") to clarify that the purpose of this figure is to add evidence that IRE1 activation is independent of the canonical UPR response (indicating that IRE1 is likely specifically activated independently of the other UPR branches) during C. albicans infection. Therefore, the transcripts measured are canonical UPR-responsive transcripts, rather than IRE1/XBP1S targets (although some are overlapping).
Below are RNA-seq data comparing Sec61a1, Sec61a2, and Sec24a in IRE1 null macrophages, compared to IRE1 WT macrophages. While there is less expression of Sec61a1 in IRE1 null macrophages, Sec61a2 and Sec24a are largely unaffected. These data support our finding that XBP1S protein is not induced during C. albicans infection.
Did the authors also check for RIDD activity?
As mentioned above in response to Reviewer 1, we now add additional figures to our supplemental data (Fig. SX; also shown below) showing that established RIDD targets are not depleted during C. albicans infection in WT macrophages, and also not increased in IRE1 null macrophages. Therefore, we expect RIDD activity has negligible effects on our reported phenotypes.
Fig. 3:
Panel C and D look convincing. Lamp1 is a well-known RIDD target gene (see Osorio et al., Nat Imm, 2014). Did the authors check Lamp1 expression in presence and absence of IRE1 and could RIDD explain their phenotype?
As shown above, Lamp1 transcript expression is not strongly perturbed in IRE1 null macrophages. If RIDD activity were depleting Lamp1 transcript abundance, we would expect to see increased Lamp1 expression in IRE1 null macrophages. We also note that our experiments using LysoSensor (Fig. 3E) suggested that lysosome biogenesis is not impaired, but more specifically, lysosome recruitment to the phagosome is impaired in IRE1 null macrophages.
Fig. 4, but especially Fig 5 and Fig 6 suffer from very bad imaging quality. Both Fig 5A and Fig 6A are completely uninterpretable. The SRB staining is all over the cells and it is totally unclear how the authors interpret this as phagosomal leakage or not. Fig. 6A is even worse and appears nothing but vague background. It is difficult to understand how the authors make graphs based on these types of images and dare to draw any conclusions.
In Figure 4, we observe some photobleaching from frequent image acquisition, which is necessary to capture calcium flux dynamics. Image brightness across the timecourse is adjusted in the same way such that we do not attempt to hide the effects of photobleaching. However, our analyses account for photobleaching over time, and the phagosomal calcium flux is clear and quantifiable. `
In Figure 5, the sulforhodamine B pulse-chase assay involves loading of the endosomal system with SRB, thus the cells are expected to ingest a considerable amount of SRB and it will distribute throughout the endosomal network. However, as endosomes fuse, we also observe fusion with the C. albicans-containing phagosome and SRB will surround C. albicans hyphae. Our analysis pipeline first segments C. albicans hyphae (see below) and measures SRB signal in proximity to the phagosome. Thus, we measure loss of phagosome-associated SRB over time, as C. albicans ruptures the phagosome, in hundreds of macrophages. This is a standard assay that has been previously used for this purpose (PMID: 33022213; PMID: 30131363).
For Figure 6, we have added additional wide-field images that we believe will clarify how these images can be readily quantified (Fig. 6A, shown below). The purpose of the previous Fig. 6A (now Fig. 6B) is to demonstrate single cell examples of live and dead C. albicans using the dual-fluorescence assay, although we quantify much wider fields for sufficient numbers. We hope the amended figures provide additional clarity.
Fig. 7 is again an example where differences in expression are mainly due to one or a few complete outliers, and it is hard to understand why the authors did not repeat these experiments to reduce the problems in variation to get proper data sets before submission.
After performing outlier tests, we have found a total of 4 data points that are statistical outliers from all of the panels in Figure 7. These included the highest data point in each genotype in the female IL-1Ra levels (Fig. 7A, second graph), the highest data point among the male IRE1 fl/fl mice IL-1Ra levels (Fig. 7B, second graph), and the highest data point among the male TNF levels in IRE1 fl/fl + LysM-Cre mice (Fig. 7B, third graph). We have removed these data points in our updated graphs and changed the text to only point out differences in serum TNF and IL-6 levels. Moreover, our interpretation includes that serum cytokine levels are not different in male mice. However, no other data points are statistical outliers, therefore we believe their removal is inappropriate.
While the paper started nicely and showed an interesting hypothesis (Fig. 3), the remaining part of the paper was of very poor quality and was not ready for submission.
We thank the reviewer for the constructive feedback and believe that the addition of data and clarifications we have added will demonstrate that our data are of sufficient quality to support our conclusions.
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Referee #3
Evidence, reproducibility and clarity
Reviewer comments for "Non-canonical activation of IRE1a by Candida albicans infection promotes macrophage phagosomal calcium flux to enhance fungal killing"
This paper describes a role for IRE1 in controlling Candida albicans (Ca) infection in macrophages. The authors show that Ca infection slightly induces IRE1 activity as monitored by XBP1 splicing, however this does not result in XBP1 protein expression, nor in IRE1-dependent target gene expression. The authors propose that IRE1 controls phagosomal maturation, as measured by defects in LAMP1 recruitment to Ca containing phagosomes. This would be due to a defect in calcium flux at the phagosomal level leading to an increased propensity to rupture and cytosolic escape of the pathogen. While the data are interesting and the defect in LAMP1 recruitment to the phagosome convincing, the majority of the data are difficult to interpret due to the poor quality. This concerns specifically all imaging experiments but also the ELISAs and qPCRs where differences are due to the effect of outliers rather than to the behavior of a complete population. Therefore, most experiments need to be redone and complemented with additional approaches before any firm conclusions can be drawn. Specific details and examples are provided below.
Fig. 1:
Panel 1C: please remove outlier in 4h timepoint. This implies that the experiment needs to be redone to reduce variation
Panel 1E-H: how is the splicing efficiency determined and normalized? How to explain the big differences in splicing efficiency of Xbp1 upon LPS stimulation (appr. 4 to 6 times in E, G and H versus 30-fold in panel F). Where does this difference come from?
Panel H, outlier needs to be removed.
Fig. 2:
Panel A-B: same question as for Fig. 1. The variation in TG DMSO-induced splicing is huge. The effects of the treatments with CHX or Act D are smaller than the variation between experiments with TG DMSO alone. As long as that variation is not controlled for, it is impossible to draw any conclusion from the inhibitors. In this regard, it is very difficult to interpret data if they are not done in one and the same experiment. Please provide a scheme of how the experiment was performed, at what time were the inhibitors provided, at what time point the inducers? What are matched mock samples. Which mock samples were chosen since they differ from one experiment to the next? Please plot all the data for one and the same experiment in one graph so that the reader can easily compare the results of DMSO, DMSO + inducer, DMSO + inducer + inhibitor. Indicate whether the points in the graph are technical or experimental repetitions.
- How to explain the increase in XBP1 splicing in combination with ActD? Was this due to differences in Gapdh expression? Where did the authors control for cell death? Please provide the data.
- Is RT-qPCR a reliable readout when actinomycinD is used? How can new genes be transcribed.
Panel D: where is TG at 4h and 6h?
Panel G, why was Ddit3 included here as this is not a typical IRE1 dependent gene (rather PERK dependent). What about IRE1 specific genes such as Sec61 or Sec24a?
Did the authors also check for RIDD activity?
Fig. 3:
Panel C and D look convincing. Lamp1 is a well-known RIDD target gene (see Osorio et al., Nat Imm, 2014). Did the authors check Lamp1 expression in presence and absence of IRE1 and could RIDD explain their phenotype?
Fig. 4, but especially Fig 5 and Fig 6 suffer from very bad imaging quality. Both Fig 5A and Fig 6A are completely uninterpretable. The SRB staining is all over the cells and it is totally unclear how the authors interpret this as phagosomal leakage or not. Fig. 6A is even worse and appears nothing but vague background. It is difficult to understand how the authors make graphs based on these types of images and dare to draw any conclusions.
Fig. 7 is again an example where differences in expression are mainly due to one or a few complete outliers, and it is hard to understand why the authors did not repeat these experiments to reduce the problems in variation to get proper data sets before submission.
While the paper started nicely and showed an interesting hypothesis (Fig. 3), the remaining part of the paper was of very poor quality and was not ready for submission.
Significance
The study presents interesting hypothesis but unfortunately the data are not of sufficient quality
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Referee #2
Evidence, reproducibility and clarity
This an interesting report in a widely explored area. This makes it necessary to pigeonhole the new data provided by the study. The paper addresses two issues encompassing a scope distinct from studies focusing on the cytokine-signature and the role of sXbp1. This research singles out fungal killing and Ire1α versus sXbp1 function. More precisely, the reduction of cytokine expression in Ire1fl/fl LysMCre as compared to WT discloses an opposing function of Ire1α and its target sXbp1 in cytokine expression that requires mechanistic explanation.
- A point that should be addressed with more detail is the correlation of fungal killing with Ca2+ fluxes and Ire1α activity, given the well-known data regarding the strong ability of the axis dectin/SYK/phospholipase Cγ to induce Ca2+ transients, a response not shared by LPS signaling, and the sequential activation of mitochondrial Ca2+ uniporter (MCU), which is a critical element of fungal killing associated with the citrate-pyruvate shuttle as a NADPH source (Seegren et al., Cell Rep. 33: 108411, 2020). Incidentally, this paper is referred in ref. 46 as a preprint, although it was accessible in Cell Reports in 2020.
- The assay of the expression of V-ATPase complex, mitochondrial calcium uniporter, and mitochondrial uptake 1 and 2 could shed light on the dependence of fungal killing on Ire1α function.
- Infection at a MOI 1 of C. albicans is a ratio of infecting agent/susceptible targets not very high for a non-soluble stimulus with limited diffusion in the culture medium. Although I recognize the difficulty of quantitating adhered cell, the mention to 80% confluence makes it more difficult the appraisal of the actual MOI. The delayed time-course of Xbp splicing under these conditions can be explained by the time required for in vitro proliferation, Candida damage, and diffusion of fungal patterns. A study with viable Candida at MOI 5 in human monocyte-derived dendritic cells, which show a robust capacity for non-opsonic phagocytosis associated with C-type lectin receptors only showed initial hypha formation after 2 hours (Rodriguez et al., J. Biol. Chem. 289, P22942-22957, 2014). Consistent with the requirement of a time lag for infecting agent to attain levels of expression consistent with a net response, 16 hours have been considered an appropriate time-course to assay sXBP1 expression following SARS-CoV2 infection (Fernandez et al., Biochim Biophys Acta Mol Basis Dis. 1870(5):167193, 2024). I wonder if a higher MOI could show a similar kinetics.
- Fig. 1A should be explained with more detail to disclose the products of PstI digestion.
- sXBP1 can be present in nuclear fractions in resting cells, which suggests the involvement of post-translational modifications for the display of transcriptional activity.
- The independence of sXBP1 transcriptional activity from canonical UPR associated with misfolded protein stress is well known from the seminal paper by Martinon et al., (ref.6). Moreover, the expression of CHOP, the final effector of the PERK route, encoded by DDIT3 gene, has been found to be blunted by Candida (Rodriguez et al., J. Biol. Chem. 289, P22942-22957,2014). This is additional evidence for the recruitment of sXBP1 transcriptional activity in the absence of canonical UPR.
- The anti-XBP1 antibody used to construct the blots in Fig.S1A recognizes epitopes not disclosed by the manufacturers, but they have to pertain to the N-terminal peptide sequence shared by sXBP1 and uXBP1. Showing full lanes encompassing both protein isoforms would allow a better appraisal of protein expression. In connection to point 4, the use of an antibody reactive to the epitopes expressed in sXBP1 in cell lysates or, preferentially in nuclear fractions, could be most valuable to rule out the dependence of the effect of Ire1α on the trans-activating function of sXBP1.
- On page 23, the mention to Fig. 5A should be changed to Fig. 5B.
- Line 209. I understand gene synthesis refers to gene expression.
- Line 394. What is the reason to study the cytokine-signature of Candida in LPS-primed cells?
Significance
This study focuses on an aspect not usually addressed in papers devoted to the UPR. If more data are shown as suggested, the paper could be of interest for a wider audience
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Referee #1
Evidence, reproducibility and clarity
The authors show that in macrophages, IRE1 activation (independent of improperly folded proteins) is essential to promote fungicidal activity towards candida albicans (CA) in vitro and in vivo by ensuring phagosome maturation through the preservation of calcium fluxes
Major comments
- The demonstration of protein misfolding independent IRE1 activation should also be demonstrated using molecules such as TUDCA or 4PBA that should be innocuous regarding the splicing of XBP1s. It would also be interesting to evaluate the activation of the other arms of the UPR in particular through the phosphorylation of eIF2a, expression of ATF4 and cleavage of ATF6.
- Since the IRE1/XBP1 arm of the UPR is also involved in lipid biosynthesis which might be required for phagosome maturation, the authors should perform XBP1s rescues in IRE1 deficient cells to ensure that their observation is XBP1s dependent or IRE1 dependent.
- The authors use thioflavin to evaluate the extend of protein misfolding. This type of stain can lead to artefactual results and in general it is rather safer to test several stainers (see for instance the work presented in PMC10720158)
- The authors should evaluate in what compartment IRE1 is activated upon CA infection, does that happen in the ER or in the ER fraction fused to phagosomes?
- The authors focus on the IRE1/XBP1s signaling arm of the UPR but do not explore RIDD activity which has been linked to several infection mechanisms and lysosomal integrity (in particular by regulating the expression of BLOS1 - see PMC9119680 and PMC6446841). The authors should definitely evaluate how RIDD is activated (or not) in their experimental systems.
- The whole study relies on the use of IRE1deltaR to impair IRE1 signaling. The authors should validate their hypothesis with an orthogonal approach, for instance with IRE1 pharmacological inhibitors (eg MKC8866 or KIRA8).
Significance
The manuscript is interesting and highligths novel aspects towards the interaction between marcrophages and a pathogen, candida albicans, involving the likely selective activation of IRE1. The data are novel and experimentally sound. Several controls are however missing.
The strengths of the study are associated with the novelty of the findings, with the links that could potentially derive from this study to connect ER biology, UPR signaling and phagosome maturation
The main weaknesses are associated i) with the fact that the authors did not evaluate RIDD activity which has already been linked with pathogen infection and with lysosome integrity, ii) with methodological aspects, in particular regarding the demonstration of the IRE1 activation independent on protein misfolding and the sole use of a genetic variant of IRE1 to test their hypotheses
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Reply to the reviewers
Manuscript number: RC-2024-02555
__Corresponding author(s): __Maurizio Molinari
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1. General Statements [optional]
We thank the 3 reviewers for the positive and constructive comments to our manuscript.
Please see below the point-by-point responses to their suggestions.
2. Point-by-point description of the revisions
Reviewer #1 (Evidence, reproducibility and clarity (Required)):
The paper proposes an interesting role for ERp44 in TMX5 retention. The authors identified a list of proposed TMX5 clients which include many Golgi localised proteins but do not discuss the role for TMX for instance in protein folding. In this context it is not absolutely clear whether TMX5 acts as a trafficking chaperone? are clients functionally engaged in the Golgi or ER or both?
This work focuses on the crosstalk between a member of the PDI superfamily lacking a conventional cytosolic ER retention motif (TMX5), and ERp44, a PDI family member previously reported to retrieve in the ER proteins that lack the ER retention motif (ERp44). To support our conclusions on the involvement of ERp44 in control of TMX5’s intracellular distribution, we have added new data obtained by characterization of a cell line lacking ERp44, where more than 50% of TMX5 escapes ER retention (new Fig. 6).
__We agree with the referee that the assessment of the biological role of TMX5 is of interest. We mention in this manuscript that there is a follow-up study (ongoing in the lab) on TMX5 clients and TMX5 function. More specifically, we are monitoring the action of TMX5 on the biogenesis and intracellular trafficking of class I HLA molecules, which are, besides PDI, ERp57 and ERp44, major interactors (clients) of TMX5 (please also refer to the initial and final parts of the new discussion). __
The defining criteria for the client proteins were not included. At last, it might be of interest to evaluate for how long TMX5 clients are retained on the protein, whether it is temporary (as for instance a folding sensor) or more permanent.
The list of interacting proteins is now available (__Data are available via ProteomeXchange with identifier PXD054716."), their selection for presentation in Figure 3B is now explained more clearly (Results, page 6). Also better explained is that we define as “clients of TMX5” those endogenous proteins that associate with TMX5, covalently, via the catalytic Cys220. The mutation of the TMX5 active site cysteine residue does not impact the covalent association of PDI, ERp57 and ERp44 with TMX5. For this reason, we do not consider these PDI family members clients of TMX5. In this submission, we explore the covalent association of ERp44 and its consequences on TMX5 subcellular distribution. Interacting via non-catalytic Cysteine residues 114 and 124 with the catalytic cysteine 29 of ERp44, we identify TMX5 as a client of the latter.__
The preparation of figures could be greatly improved and there is some inconsistency among similar gels.
Please refer to the point-by-point answers below.
The proposed model of ERp44, ER retention vs ER retrieval, is unclear. Overall, there is more room for improved discussion beyond the conclusions from experiments.
__We thank the referee for these comments. We have improved the description of the results, and we separated the Discussion (written de novo) from the Results section. __
The ERp44 interaction is interesting especially since the protein contains an incomplete thioredoxin domain (such as ERp29, PDIA17 and 18), would the interaction between Erp44 and TMX5 be involved in some holdase/competitor role thereby allowing for client selectivity (or kinetics)? In addition, all the experiments were carried out in Hek293T or MEF cells, would the authors anticipate some interactions of TMX5 with PDIA17/18 in cells where those proteins are highly expressed? Testing whether the observation is a general mechanism occurring between TMX5 and PDI family members with incomplete thioredoxin sites would be an asset.
__We thank the referee for this comment that we implemented in the new discussion. __
Major comments Fig 2 - avoid labels on the blots that might obscure information and impede clarity and interpretation. o The % of resistant protein can be otherwise placed.
__This has been modified, thank you. __
- What does the asterix in 2B signify? This should be included in the legend.
We have now specified in the legend of figures that asterisks show cross-reacting polypeptide bands.
- A label for 'deglycosylated' proteins could be included.
__We added a label for de- and for glycosylated proteins in the EndoH essays in Figs. 2A, 2B and 5B. __
- Consider treating with PNGase.
This is now showy in panel 2A, lane 5.
- There is a change in EndoH resistance of about 3-4% among wt, C220A & C114A, is this significant?
We do not consider significant these variations. Our data show that the mutation of TMX5 Cys 114 or of Cys124 to alanine substantially reduce (without abolishing) the co-IP with ERp44. This means that the proteins interact less, or that the interaction is more short living. The EndoH experiment shown in Fig. 2B and the CLSM analyses in Figs. 2D-2O fail to reveal significant differences showing that these reduced or more short living associations with ERp44 are sufficient to control TMX5 distribution.
In the previous submission, the function of ERp44 in retaining TMX5 in the ER was supported by data showing that the co-expression of ERp44 retains TMX5 in the ER, but co-expression of ERp44C29S that cannot bind TMX5 fails to retain TMX5 in the ER. These model is further supported in this new submission by the release of 50% of TMX5 from the ER in cells lacking ERp44, which is substantially inhibited to the levels measured in wild type cells upon back-transfection of ERp44, but not upon the co-transfection of the ERp44C29S mutant (new Figure 6).
- Equivalent inputs (cell lysates) for the IPs should be included.
__ These have now been added in Figs. 4, 5 6, and 7.__
Fig 3A - Indicate the specific bands that were subject to MS. How did the authors correct for non-specific interactors and false positives? Perhaps a more specifically targeted approach could be utilised.
-
How do the authors explain the absence of bands representing the reduced form of interacting TMX5 interactors?
-
What was the inclusion and exclusion criteria used to determine which of the proteins listed were clients?
The endogenous proteins present in the entire region of the gel labeled with the red and blue rectangles have been sequenced (see methods section and this is now also better explained in the results section, page 6). Only the proteins that disappear from the corresponding region of the gel when the samples have been reduced are listed in the table. This is also better explained in the text (page 6). These experiments have been repeated few times with a series of controls (e.g., mock-transfected cells and cells transfected with other members of the TMX family (shown to capture and to impact on the fate of other endogenous polypeptides in previous publications from our lab)). An in parallel analysis of mock, TMX3, TMX4 and TMX5 interactors has been published in (Kucinska et al Nature Comm 2023), where we focused on the biological function of TMX4. The references referring to the TMX1 study (Brambilla et al 2015) and the TMX4 study (Kucinska et al) are given in the text.
The Table in Fig. 3B only lists the interacting polypeptides that have a MW __- It might be useful to perform MS on the C220A mutant and compare those results to the WT.
__To validate few interactions with endogenous proteins detected in MS, and to compare the interactions of TMX5 and TMX5C220A, we have used the specifically targeted approach suggested above by the referee (i.e., co-IP validated by WB, Figs. 3C-3F).
__
Fig 4 - Equivalent inputs (cell lysates) for the IPs should be included.
__This is now shown as panel A in Fig. 4.____
__
Fig 5 - Equivalent inputs (cell lysates) for the IPs should be included.
This is now Figure 7, see new panel 7A
Fig 6 - A loading control should be included.
__This is now Fig. 5. Both panels A and B in Fig. 5 show total cell extracts____
__
- Blot using anti-HA to identify ERp44 should be included to substantiate claims.
The ERp44 and TMX5 components of the ERp44-s-s-TMX5 mixed disulfides are detected upon IP:HA followed by WB:V5 (to show the TMX5 component) and upon IP:V5 followed by WB:HA to show the ERp44 component) in Figs. 4B-4E and 7B-7C.
- How do the authors account for the huge difference in TMX5 associated complexes shown in Fig 6A compared to Fig 3A.
Fig. 6 is now Fig. 5. As specified in the legends of the figures, Fig. 3A shows a gel, where the complexes are stained with silver, Fig. 5A is a WB, where the complexes are stained with an antibody. The intensities of the signals cannot be compared.
- Inappropriate marking on the gel area.
- Inconsistencies in protein standard labeling
This has carefully been checked and corrected where needed. Please note that we used two different MW standards for our figures (200, 117, 97, 66, 45, 31 kDa and 270, 175, 130, 95, 66, 53, 37 kDa)
- It might be useful to demonstrate the colocalisation of ERp44 and ERp44C29S with Giantin and with TMX5 considering that ERp44 is known to cycle between the Golgi and ER.
These data are shown in Fig. 5D-5I.
Reviewer #1 (Significance (Required)):
This work provides an additional understanding on how the regulation of Erp44 trafficking might occur (and perhaps additional PDIs), and lead to the characterization of kinetic value that might explain better productive protein folding in the early secretory pathway. This represents a significant advance in the field and may in turn unveil uncharacterized pathophysiological functions in various diseases. This is a serious study well conducted and original by an expert in the field that desserves publication.
Field of expertise: ER homeostasis control
Reviewer #2 (Evidence, reproducibility and clarity (Required)):
The manuscript by Solda et al, investigates TMX5, a poorly understood member of the PDI family that lacks an ER-retrieval motif. They find that it localizes to the ER and the Golgi and that it interacts with ERp44. This interaction requires formation of a mixed disulfide and they identify the cysteine residues in both proteins that mediate this interaction. Overall, this is a well written manuscript that is easy to follow and the story is compact and straight-forward. It provides some new and solid insight into the biology of TMX5 without going into depth of what the cellular role of TMX5 is or might be. I have only very few comments and suggestions:
1- The authors conclude that ERp44 associates only with ER-localized TMX5. I am not sure that this is a valid conclusion based on the data. EndoH sensitivity just means that the protein has not gone to the medial Golgi. The pool of TMX5 could therefore be an ERGIC-based pool, or it could interact with a TMX5 that is recycled directly from the first Golgi cisterna, where complex glycosylation is unlikely to occur. Can this be validated using another type of experiment? Alternatively, the wording could be changed.
We thank the reviewer. We agree with this insightful comment that led us to change the wording used in some part of the text.
2- Is the trafficking of TMX5 dependent on its glycosylation?
This is another insightful comment that we report in the ____new discussion, where we write, page 14 “____It should be noted that in the case of TMX5 the extensive N-glycosylation could engage____ leguminous L-type lectins located in the ER (VIPL), cycling between the ER and the intermediate compartment (ERGIC) (ERGIC-53) or between the ERGIC and the cis-Golgi (VIP36)_33-36_ and have an impact on the subcellular distribution and activity of TMX5.____”
3- Figure 6: The data are not really convincing. Just because the color turns yellow, it does not mean that there is colocalization. The green channel is overexposed in this area of the cell, and anything will produce a yellow color, even if there is no genuine colocalization. Maybe the authors could provide a different example and even better would be a quantification of the colocalization.
__We thank the referee for this comment. We show images of better quality, where the black/white channels clearly show the co-localization (or lack thereof) of TMX5 with the Golgi marker Giantin in cells mock-transfected (co-localization TMX5:Giantin, Fig. 5D), co-transfected with ERp44 (no co-localization TMX5:Giantin, Fig. 5E), or co-transfected with ERp44C29S, co-localization TMX5:Giantin, Fig. 5F). Figs. 5G-5I show the corresponding results for the co-localization or lack thereof between TMX5C220A and Giantin. Importantly, the IF data match the data shown in Fig. 5B, where release from the ER (or arrival in the medial Golgi, see text of the manuscript and comment 1 by the referee) is assessed by monitoring complex glycosylation. __
Reviewer #2 (Significance (Required)):
This is a solid story that will be of interest of scientists working on various aspects of the secretory pathway and protein quality control. The advance is rather incremental, because there are no experiments that provide insight into the cellular roles of TMX5.
Reviewer #3 (Evidence, reproducibility and clarity (Required)):
Solda et al have assembled data on the transmembrane redox enzyme TMX5, on which currently very little information is available. TMX5 does not contain any obvious targeting signal, unlike the other TMX family proteins, which localize to the ER. TMX5 has 5 glycosylation sites, which can be used to determine its intracellular localization biochemically. Indeed, about 20% of TMX5 is found as endoH-resistant, indicating Golgi localization. This is confirmed with beautiful IF imagery and giantin co-localization. The Golgi localization requires two luminal cysteines (C212, 177), which likely form a disulfide bond. TMX5 acts as a natural cysteine-trapping protein, allowing for easy assessment of its interactors. Within its interactome, the authors found multiple members of the thioredoxin family. Many of these interactions occur within the CXXS motif, but notably ERp44 does not require this motif to interact, indicating this and other interactions are not of a catalytic nature. Instead, the authors found this interaction to be essential for ER retention or retrieval and depends on the cysteine within the ERp44 "active" site. The study provides critical first insight about the potential functions and sites of activity of TMX5.
Specific Points: 1. The results are very convincing and of high quality. 2. The cytosolic tail of TMX5 contains an LI motif, which could act as a post-ER localization signal. Since the protein might play a role in ciliogenesis, this motif could be critical. In this context, I am wondering which mutations are known to lead to the disease spectrum.
The position of disease-related TMX5 mutations identified so far are given in Xu H, et al (2024) Mol Genet Genomic Med 12: e2340 _https://www.ncbi.nlm.nih.gov/pubmed/38073519_ and in ____Deng T, Xie Y (2024) Mol Genet Genomic Med 12: e2343 https://www.ncbi.nlm.nih.gov/pubmed/38156946____.
They are all distributed in the luminal part of the protein____.
Mutation of C114 and C124 abrogates interaction with ERp44. Therefore, I would expect these mutations to increase endoH resistance and Golgi staining. This should be investigated by the authors.
__The mutations C114 and C124 reduce (or make short-living), without abrogating the covalent association between TMX5 and ERp44. The EndoH experiment shown in Fig. 2B and the IF in Figs. 2D-2O fail to reveal significant differences showing that these reduced or more short living associations with ERp44 are sufficient to control TMX5 distribution. To strengthen our conclusion that ERp44 is involved in regulation of the intracellular TMX5 distribution, we have now added data in ERp44 cell (50% of TMX5 displays complex glycans as symptom of traffic to the medial Golgi compartment), back-transfection of ERp44 (but not of the ERp44C29S mutant that does not associate with TMX5) restores the complex glycan fraction to the level measured in wild type cells (Fig. 6). __
Minor Points: 1. The position of the % endoH resistance in Figures 1B and 6B is not ideal, as it obstructs a visual inspection of TMX5 resistance to endoH.
This has been modified, thank you.
Reviewer #3 (Significance (Required)):
Given that no information about TMX5 is currently available, the study provides critical first insight that should allow researchers to tackle the disease relevance of TMX5 in the future.
-
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Referee #3
Evidence, reproducibility and clarity
Solda et al have assembled data on the transmembrane redox enzyme TMX5, on which currently very little information is available. TMX5 does not contain any obvious targeting signal, unlike the other TMX family proteins, which localize to the ER. TMX5 has 5 glycosylation sites, which can be used to determine its intracellular localization biochemically. Indeed, about 20% of TMX5 is found as endoH-resistant, indicating Golgi localization. This is confirmed with beautiful IF imagery and giantin co-localization. The Golgi localization requires two luminal cysteines (C212, 177), which likely form a disulfide bond. TMX5 acts as a natural cysteine-trapping protein, allowing for easy assessment of its interactors. Within its interactome, the authors found multiple members of the thioredoxin family. Many of these interactions occur within the CXXS motif, but notably ERp44 does not require this motif to interact, indicating this and other interactions are not of a catalytic nature. Instead, the authors found this interaction to be essential for ER retention or retrieval and depends on the cysteine within the ERp44 "active" site. The study provides critical first insight about the potential functions and sites of activity of TMX5.
Specific Points:
- The results are very convincing and of high quality.
- The cytosolic tail of TMX5 contains an LI motif, which could act as a post-ER localization signal. Since the protein might play a role in ciliogenesis, this motif could be critical. In this context, I am wondering which mutations are known to lead to the disease spectrum.
- Mutation of C114 and C124 abrogates interaction with ERp44. Therefore, I would expect these mutations to increase endoH resistance and Golgi staining. This should be investigated by the authors.
Minor Points:
- The position of the % endoH resistance in Figures 1B and 6B is not ideal, as it obstructs a visual inspection of TMX5 resistance to endoH.
Significance
Given that no information about TMX5 is currently available, the study provides critical first insight that should allow researchers to tackle the disease relevance of TMX5 in the future.
-
Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.
Learn more at Review Commons
Referee #2
Evidence, reproducibility and clarity
The manuscript by Solda et al, investigates TMX5, a poorly understood member of the PDI family that lacks an ER-retrieval motif. They find that it localizes to the ER and the Golgi and that it interacts with ERp44. This interaction requires formation of a mixed disulfide and they identify the cysteine residues in both proteins that mediate this interaction.
Overall, this is a well written manuscript that is easy to follow and the story is compact and straight-forward. It provides some new and solid insight into the biology of TMX5 without going into depth of what the cellular role of TMX5 is or might be. I have only very few comments and suggestions:
- The authors conclude that ERp44 associates only with ER-localized TMX5. I am not sure that this is a valid conclusion based on the data. EndoH sensitivity just means that the protein has not gone to the medial Golgi. The pool of TMX5 could therefore be an ERGIC-based pool, or it could interact with a TMX5 that is recycled directly from the first Golgi cisterna, where complex glycosylation is unlikely to occur. Can this be validated using another type of experiment? Alternatively, the wording could be changed.
- Is the trafficking of TMX5 dependent on its glycosylation?
- Figure 6: The data are not really convincing. Just because the color turns yellow, it does not mean that there is colocalization. The green channel is overexposed in this area of the cell, and anything will produce a yellow color, even if there is no genuine colocalization. Maybe the authors could provide a different example and even better would be a quantification of the colocalization.
Significance
This is a solid story that will be of interest of scientists working on various aspects of the secretory pathway and protein quality control. The advance is rather incremental, because thereare no experiments that provide insight into the cellular roles of TMX5.
-
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Learn more at Review Commons
Referee #1
Evidence, reproducibility and clarity
The paper proposes an interesting role for ERp44 in TMX5 retention. The authors identified a list of proposed TMX5 clients which include many Golgi localised proteins but do not discuss the role for TMX for instance in protein folding. In this context it is not absolutely clear whether TMX5 acts as a trafficking chaperone? are clients functionally engaged in the Golgi or ER or both? The defining criteria for the client proteins were not included. At last, it might be of interest to evaluate for how long TMX5 clients are retained on the protein, whether it is temporary (as for instance a folding sensor) or more permanent.
The preparation of figures could be greatly improved and there is some inconsistency among similar gels. The proposed model of ERp44, ER retention vs ER retrieval, is unclear. Overall, there is more room for improved discussion beyond the conclusions from experiments.
The ERp44 interaction is interesting especially since the protein contains an incomplete thioredoxin domain (such as ERp29, PDIA17 and 18), would the interaction between Erp44 and TMX5 be involved in some holdase/competitor role thereby allowing for client selectivity (or kinetics)? In addition, all the experiments were carried out in Hek293T or MEF cells, would the authors anticipate some interactions of TMX5 with PDIA17/18 in cells where those proteins are highly expressed? Testing whether the observation is a general mechanism occurring between TMX5 and PDI family members with incomplete thioredoxin sites would be an asset.
Major comments
Fig 2
- avoid labels on the blots that might obscure information and impede clarity and interpretation.
- The % of resistant protein can be otherwise placed.
- What does the asterix in 2B signify? This should be included in the legend.
- A label for 'deglycosylated' proteins could be included.
- Consider treating with PNGase to .....
- There is a change in EndoH resistance of about 3-4% among wt, C220A & C114A, is this significant?
- Equivalent inputs (cell lysates) for the IPs should be included.
Fig 3A
- Indicate the specific bands that were subject to MS. How did the authors correct for non-specific interactors and false positives? Perhaps a more specifically targeted approach could be utilised.
- How do the authors explain the absence of bands representing the reduced form of interacting TMX5 interactors?
- What was the inclusion and exclusion criteria used to determine which of the proteins listed were clients?
- It might be useful to perform MS on the C220A mutant and compare those results to the WT.
Fig 4
- Equivalent inputs (cell lysates) for the IPs should be included.
Fig 5
- Equivalent inputs (cell lysates) for the IPs should be included.
Fig 6
- A loading control should be included.
- Blot using anti-HA to identify ERp44 should be included to substantiate claims.
- How do the authors account for the huge difference in TMX5 associated complexes shown in Fig 6A compared to Fig 3A.
- Inappropriate marking on the gel area.
- Inconsistencies in protein standard labeling
- It might be useful to demonstrate the colocalisation of ERp44 and ERp44C29S with Giantin and with TMX5 considering that ERp44 is known to cycle between the Golgi and ER.
Significance
This work provides an additional understanding on how the regulation of Erp44 trafficking might occur (and perhaps additional PDIs), and lead to the characterization of kinetic value that might explain better productive protein folding in the early secretory pathway. This represents a significant advance in the field and may in turn unveil uncharacterized pathophysiological functions in various diseases.
This is a serious study well conducted and original by an expert in the field that desserves publication.
Field of expertise: ER homeostasis control
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www.biorxiv.org www.biorxiv.org
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Reply to the reviewers
Reviewer #1 (Evidence, reproducibility and clarity (Required)): Summary: To explore the relationship between histone post-translational modifications (H3K4me3 and H3K27me3) and enhancer activation with gene expression during early embryonic development, the authors used a monolayer differentiation approach to convert mouse embryonic stem cells (ESCs) into Anterior Definitive Endoderm (ADE). They monitored differentiation stages using a dual reporter mESC line (B6), which has fluorescent reporters inserted at the Gsc (GFP) and Hhex (Redstar) loci. Their analyses indicate that the differentiating cells advanced through stages similar to those in the embryo, successfully converting into endoderm and ADE with high efficiency. This is elegant and well performed stem cell biology.
Their subsequent genome-wide and nascent transcription analyses confirmed that the in vitro gene expression changes correlated with developmental stages and confirmed that transcriptional activation precedes mRNA accumulation. They then focussed on linking active enhancers and histone modifications (H3K4me3 and H3K27me3) were with gene expression dynamics. Finally, the performed PRC2 inhibition and showed that, while it enhanced differentiation efficiency, it also induced ectopic expression of non-lineage specific genes.
Major comments: In terms of mechanistic advances, they propose that transcriptional up-regulation does not require prior loss of H3K27me3, which they show appears to lag behind gene activation, but critically, on a likely mixed population level. I am sceptical of their interpretation of their data because they are looking at heterogenous populations of cells. To explain, one could imagine a particular H3K27me3 coated gene that gets activated during differentiation. In a population of differentiating cells, while the major sub-population of cells could retain H3K27me3 on this particular gene when it is repressed, a minority sub-population of cells could have no H3K27me3 on the gene when it is actively transcribed. The ChIP and RNA-seq results in this mixed cell scenario would give the wrong impression that the gene is active while retaining H3K27me3, when in reality, it's much more likely that the gene is never expressed when its locus in enriched with the repressive H3K27me3 modification. Therefore, to support their claim, they would have to show that a particular gene is active when its locus is coated with H3K27me3. Personally, I don't feel this approach would be worth pursuing.
They also report that inhibition of PRC2 using EZH2 inhibitor (EPZ6438) enhanced endoderm differentiation efficiency but led to ectopic expression of pluripotency and non-lineage genes. However, this is not surprising considering the established role of Polycomb proteins as repressors of lineage genes.
Reviewer #1 (Significance (Required)): I feel that this is a solid and well conducted study in which the authors model early development in vitro. It should be of interest to researchers with an interest in more sophisticated in vitro differentiation systems, perhaps to knockout their gene of interest and study the consequences. However, I don't see any major mechanistic advances in this work.
*>Author Response *
*We agree with the point regarding the delayed loss of H3K27me3 relative to gene activation, and indeed this same point has been raised by reviewer 3 (see below). Our cell-population based data does not allow us to directly test if gene up-regulation in a small population of cells from TSSs lacking H3K27me3, accounts for the observed result. Furthermore, there are currently no robust methods to determine cell- or allele-specific expression simultaneously with ChIP/Cut and Run for chromatin marks. However, we provide the following additional evidence that strongly supports our conclusions. *
- *
Our FACs isolation strategy used to prepare cell populations for ChIP, microarray expression and 4sU-seq analysis is based on expression (or lack thereof) of a fluorescent GSC-GFP reporter. This means that every cell in the G+ populations express the Gsc fluorescent reporter, at least at the protein level, at the point of isolation. This is despite the presence of appreciable and invariant levels of H3K27me3 at the TSS of the Gsc gene in both G+ and G- populations at day 3 of differentiation. Comparable to our meta-analysis of all upregulated genes shown in the original manuscript (Figure 5 and S5), H3K27me3 levels are then subsequently reduced in the G+ relative to the G- populations at day 4. The transcriptional changes which correspond to the GSG-GFP reporter expression and associated ChIP-seq data are shown in the reviewer figure (Fig R1 A shown in revision plan). To further support our observations, we sought to rule out the possibility that the shift in H3K27me3 and transcription were from mutually exclusive gene sets, from nominal transcription levels or from sites with low level H3K27me3. To do this with a gene set of sufficient size to yield a robust result, we selected upregulated TSSs that had a greater than median value for both transcription (4sU-seq) and H3K27me3 (n=49 of 159 genes; Fig R1 B shown in revision plan). Meta-analysis of these genes showed that, as for all upregulated gene TSS (n=159), transcriptional activation occurred in the presence of substantial and invariant levels of H3K27me3 at day 3 followed by a subsequent reduction by day 4 of differentiation (Fig R1 C shown in revision plan). Importantly, many of these genes yielded high absolute 4sU-seq signal, comparable to that of Gsc, arguing against transcriptional activation being limited to a small subpopulation of cells.
- *
Reviewer #2 (Evidence, reproducibility and clarity (Required)): In this paper the authors profile gene expression, including active transcription, and histone modifications (k4 and k27me3) during a complex differentiation protocol from ES cells, which takes advantage of FACS sorting of appropriate fluorescent reporters. The data is of good quality and the experiments are well performed. The main conclusion, that the analyzed histone marks channel differentiation more than they directly allow/block it, is well supported by the data. The paper is interesting and will represent a good addition to an already extensive literature. I have however a few major concerns, described below:
1/ K4me3 may show more changes than they interpret, at least over the +1 nucl. An alternative quantification to aggregate profiles should be used to more directly address the questions regarding the correlations between histone mods and gene expression.
*>Author Response *
*Whilst we state that H3K4me3 levels are somewhat invariant at differentially expressed genes relative to H3K27me3, quantification of individual TSS (+/- 500 bp) did show a direct correlation with gene expression (Figure 5 and S5). To further explore this in response to the reviewer’s comment we will quantify K4me3 signal at the +1 nucleosome to determine if this yields more substantial differences than that observed more broadly across TSSs. *
2/ Related to the previous point, it appears clear in Fig.4 that the promoters of each gene expression cluster do not belong to a single chromatin configuration. I think it would be important to: 1/ cluster the genes based on promoter histone mods and interrogate gene expression and cluster allocation (basically the reverse to what is presented) 2/ order the genes in the heatmaps identically for K4me3 and K27me3 to more easily understand the respective chromatin composition per cluster
>Author Response
We thank the reviewer for these suggestions and will include these analyses in a revised manuscript.
3/ Also, as it is apparent that not all promoters in every cluster are enriched for the studied marks, could the authors separately analyze these genes? What are they? Do they use alternative promoters?
>Author Response
*Indeed, this is the case. Whilst there is significant enrichment of H3K27me3 at the TSS of developmentally regulated genes, not all genes whose expression changes during the differentiation will be polycomb targets. We will further stratify these clusters as suggested and determine what distinguishes the subsets. If informative, this data will be included in a revised manuscript. *
4/ The use of 4SU-seq to identify active enhancers is welcome; however, I have doubts it is working very efficiently: for instance, in the snapshots shown in Fig.2A, the very active Oct4 enhancers in ES cells are not apparent at all... More validation of the efficiency of the approach seems required.
>Author Response
The 4sU-seq data shown in Figure 2A was generated in samples isolated from day 3 and 4 of the ADE differentiation. It is therefore likely that the enhancers have been partly or wholly decommissioned at this point. Indeed, in a separate study we generated 4sU-seq data using the same protocol and conditions as presented here but in ES cells and differentiated NPCs (day 3 to 7) and indeed see transcription at Oct 4 enhancers in ESCs (arrowed in the screenshot shown in revision plan) which are extinguished upon differentiation to neural progenitor cells (NPCs); data from PMID: 31494034).
5/ The effects of the EZH2 inhibitor are quite minor regarding the efficiency of the differentiation as analyzed by FACS, despite significant gene expression changes. To the knowledge of this referee, this is at odds with results obtained with Ezh2 ko ES cells that display defects in mesoderm and endoderm differentiation. I have issues reconciling these results (uncited PMID: 19026780). Either the authors perform more robust assays (inducible KOs) or they more directly explain the limitations of the study and the controversies with published work.
>Author Response
We agree that this result appears to be at odds with the findings in (PMID: 19026780*). This is likely due to the fact that we are acutely reducing H3K27me3 levels for a short period either during or immediately preceding the differentiation rather than removing PRC2 function genetically. This, likely provides a less pronounced defect on the ability to generate endodermal cells. However, we cannot address this without further experimentation which is beyond the scope of this study. We will more fully discuss the results in the context of this and other studies and discuss the limitations of the study in this regard. *
Minor 1/ please add variance captured to PCA plots 2/ Fig1E add color scales to all heatmaps 3/ Fig4C,D are almost impossible to follow, please find a way to identify better the clusters/samples and make easier to correlate all the variables
- *
>Author Response
*We will address all of these points in a revised manuscript. *
Reviewer #2 (Significance (Required)):
The paper is incremental in knowledge, and not by a big margin, as it is known already that histone mods rather channel than drive differentiation. Though, the authors do not clearly address inconsistencies with published work, especially regarding Ezh2 thought to be important to make endoderm. It is however a good addition to current knowledge, provided a better discussion of differences with published work is provided.
>Author Response
*As outlined above, we will address this with a more complete discussion about the distinction between the studies and what can and can’t be concluded from our approach. *
* *
- *
Reviewer #3 (Evidence, reproducibility and clarity (Required)): This study investigates the role of chromatin-based regulation during cell fate specification. The authors use an ESC model of differentiation into anterior primitive streak and subsequently definitive endoderm, which they traced via a dual-reporter system that combines GSC-GFP and HHEX-RedStar. The authors mapped changes in (nascent) gene expression and histone modifications (H3K4me3/H3K27me3) at key timepoints and within different populations over six days of differentiation. Finally, the authors test the functional implications of H3K27me3 landscapes via PRC2 inhibition.
The majority of data chart the descriptive changes in (epi)genomic and transcriptional dynamics coincident with cell differentiation. The use of nascent transcriptomics improves the temporal resolution of expression dynamics, and is an important strategy. By and large the data reinforce established paradigms. For example, that transcription is the dominant mechanism regulating mRNA levels, or that dynamic chromatin states changes occur and largely corelate of gene activity. They also identify putative enhancers with profiling data, albeit these are not validated, and confirm that PRC2 inhibition impacts cell fate processes - in this case promoting endodermal differentiation efficiency. Overall, the study is relatively well-performed and clearly written, with the omics profiling adding more datasets from in vitro cell types that can be difficult to characterise in vivo. Whilst the majority of the study may be considered incremental, the key finding is the authors conclusion that H3K27me3 is subordinate to gene activity rather than an instructive repressor. If borne out, this would mark an important observation with broad implications. However, in my view this conclusion is subject to many confounders and alternative interpretations, and the authors have not ruled out other explanations. Given the centrality of this to the novelty of the study, I would encourage further analysis/stratification of existing data, and potentially further experiments to provide more confidence in this key conclusion.
Primary issue 1.) The authors show that at the earliest timepoint (d3), nascent gene activation of a handful of genes between G+ and G- populations is not associated with a FC loss of H3K27me3. From this the authors extrapolate their key conclusion that H3K27me3 is subordinate. Causality of chromatin modifications in gene regulation is critical to decipher, and therefore this is an important observation to confirm. Below I go through the possible confounders and issues with the conclusion at this point.
(i) Single-cell penetrance. A possible (likely?) possibility is that gene activation initially occurs in a relatively small subset of cells at d3. Because these genes are expressed lowly prior to this, they will register as a significant upregulation in bulk analysis. However, in this scenario H3K27me3 would only be lost from a small fraction of cells, which would not be detectable against a backdrop of most cells retaining the mark. In short, the authors have not ruled out heterogeneity driving the effect. Given the different dynamic range of mRNA and chromatin marks, and that a small gain from nothing (RNA) is easier to detect than a small loss from a pre-marked state (chromatin), investigating this further is critical to draw the conclusions the authors have.
(ii) Initial H3K27me3 levels. The plots in Fig 5 show the intersect FC of H3K27me3 and gene expression. Genes that activate at d3 show no loss of H3K27me3. However, it is important to characterise (and quantitate) whether these genes are significantly marked by H3K27me3 in the first place, which I could not find in the manuscript. Many/several of the genes may not be polycomb marked or may have low levels to begin with. This would obviously confound the analysis, since an absence/low K27 cannot be significantly lost and is unlikely to be functional. Thus, the DEG geneset should be further stratified into H3K27me3+ and K27me3- promoter groups/bins, with significance and conclusions based on the former only (e.g. boxplot in 5F).
(iii) Sample size. The conclusions are based on a relatively small number of genes that upregulate between G+ and G- (n=55 in figure by my count, text mentions n=52). Irrespective of the other confounders above, this is quite a small subset to make the sweeping general conclusion that "loss of the repressive polycomb mark H3K27me3 is delayed relative to transcriptional activation" in the abstract. Indeed, the small number of DEG suggests the cell types being compared are similar and perhaps therefore have specific genomic features (this could be looked at) that drive .
>Author Response
*These are very good points and are also raised by reviewer 1 (see above). We have one example where we can definitively interrogate single cell protein expression, in our current data. Gsc (as monitored by GSC-GFP FACS and the bulk RNA analysis) meets the criteria of being robustly upregulated in all FACs sorted cells in the presence of high levels of H3K27me3 in the D3G+ population. We believe that the additional analysis (Figure R1A shown in revision plan) and the discussion above addresses the reviewer’s concerns about both the levels of expression and magnitude of H3K27me3. With respect to the third point, the numbers are low (although here I present data from the 4SU analysis with approximately three times more data points) however, the point here is not too say this happens in every instance of gene activation but more that it can happen and not just at a small subset of outlier genes. This is important, as the reviewer notes, in our understanding of how polycomb repression is relieved during development. We will also look to see if there are sequence characteristics/ motifs of these genes. In a revised manuscript we would include this data and further analysis as outlined above. The reviewer points out that the numbers vary a little between analyses. This arises due to the annotation of multiple TSSs per genes in some cases. This will be rectified throughout and made clearer in the legends. *
Other comments: 2.) The authors show that promoter H3K4me3 corelates well with gene expression dynamics in their model. They conclude that "transcription itself is required for H3K4me3 deposition", or in other words is subordinate. This may well be the case but from their correlative data this cannot be inferred. Indeed, several recent and past papers have shown that H3K4me3 itself can directly modulate transcription, for example by triggering RNA II pause-release, by preventing epigenetic silencing and/or by recruiting the PIC. The authors could point out or discuss these alternative possibilities to provide a more balanced discourse.
>Author Response
We agree and this will be discussed more thoroughly and both possibilities put forward in the revised manuscript.
3.) The labelling of some figures is unclear. In Fig 4C and 4D (right) it is impossible to tell what sample each of the lines represents. It is also not clear what the blue zone corresponds to in genome view plots (the whole gene?). Moreover, the replicate numbers are not shown in figure legends.
>Author Response
*We agree that the data presented in 4C and D is unclear. We will, as a minimum, collapse profiles into like populations (ESC / G- / G+ / G+H- / G+H+) which makes sense given the similarity of these populations across all analyses (see e.g. PCA analysis in Figure 1). We will also explore alternative ways of presenting the data to better highlight the dynamics and incorporate this with the changes suggested by reviewer 2. The blue shaded area represents the full extent of the key gene being discussed in the screen shot, this is mentioned in the legend but will be made clearer in a revised manuscript. Replication will also be added to the legend throughout (n=2 for ChIP-seq and n=3 for 4sU-seq). *
4.) It would be nice to provide more discussion to reconcile the conclusions that H3K27me3 in endoderm differentiation is subordinate and the final figure showing inhibiting H3K27me3 has a significant effect on differentiation, since the latter is the functional assessment.
>Author Response
*We will build on the points already made that suggests that whilst K27me3 is a passive repressor that serves to act against sub-threshold activating cues, it is nonetheless a critical regulator of developmental fidelity. *
Reviewer #3 (Significance (Required)): Overall, the study's strengths are in that it characterises epigenomic dynamics within a specific and relevant cell fate model. The nascent transcriptomics adds important resolution, and underpins the core conclusions. The weakness is that data is over-interpreted at this point, and other possibilities are not adequately tested. The conclusions should therefore either be scaled back (which reduces novelty) or further analysis and/or experiments should be performed to support the conclusion. If it proves correct, this would be a significant observation for the community,
>Author Response
In a revised manuscript, we will address the reviewer’s concerns with additional data and discussion as indicated above.
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Referee #3
Evidence, reproducibility and clarity
This study investigates the role of chromatin-based regulation during cell fate specification. The authors use an ESC model of differentiation into anterior primitive streak and subsequently definitive endoderm, which they traced via a dual-reporter system that combines GSC-GFP and HHEX-RedStar. The authors mapped changes in (nascent) gene expression and histone modifications (H3K4me3/H3K27me3) at key timepoints and within different populations over six days of differentiation. Finally, the authors test the functional implications of H3K27me3 landscapes via PRC2 inhibition.
The majority of data chart the descriptive changes in (epi)genomic and transcriptional dynamics coincident with cell differentiation. The use of nascent transcriptomics improves the temporal resolution of expression dynamics, and is an important strategy. By and large the data reinforce established paradigms. For example, that transcription is the dominant mechanism regulating mRNA levels, or that dynamic chromatin states changes occur and largely corelate of gene activity. They also identify putative enhancers with profiling data, albeit these are not validated, and confirm that PRC2 inhibition impacts cell fate processes - in this case promoting endodermal differentiation efficiency. Overall, the study is relatively well-performed and clearly written, with the omics profiling adding more datasets from in vitro cell types that can be difficult to characterise in vivo. Whilst the majority of the study may be considered incremental, the key finding is the authors conclusion that H3K27me3 is subordinate to gene activity rather than an instructive repressor. If borne out, this would mark an important observation with broad implications. However, in my view this conclusion is subject to many confounders and alternative interpretations, and the authors have not ruled out other explanations. Given the centrality of this to the novelty of the study, I would encourage further analysis/stratification of existing data, and potentially further experiments to provide more confidence in this key conclusion.
Primary issue
1.) The authors show that at the earliest timepoint (d3), nascent gene activation of a handful of genes between G+ and G- populations is not associated with a FC loss of H3K27me3. From this the authors extrapolate their key conclusion that H3K27me3 is subordinate. Causality of chromatin modifications in gene regulation is critical to decipher, and therefore this is an important observation to confirm. Below I go through the possible confounders and issues with the conclusion at this point.
(i) Single-cell penetrance. A possible (likely?) possibility is that gene activation initially occurs in a relatively small subset of cells at d3. Because these genes are expressed lowly prior to this, they will register as a significant upregulation in bulk analysis. However, in this scenario H3K27me3 would only be lost from a small fraction of cells, which would not be detectable against a backdrop of most cells retaining the mark. In short, the authors have not ruled out heterogeneity driving the effect. Given the different dynamic range of mRNA and chromatin marks, and that a small gain from nothing (RNA) is easier to detect than a small loss from a pre-marked state (chromatin), investigating this further is critical to draw the conclusions the authors have.
(ii) Initial H3K27me3 levels. The plots in Fig 5 show the intersect FC of H3K27me3 and gene expression. Genes that activate at d3 show no loss of H3K27me3. However, it is important to characterise (and quantitate) whether these genes are significantly marked by H3K27me3 in the first place, which I could not find in the manuscript. Many/several of the genes may not be polycomb marked or may have low levels to begin with. This would obviously confound the analysis, since an absence/low K27 cannot be significantly lost and is unlikely to be functional. Thus, the DEG geneset should be further stratified into H3K27me3+ and K27me3- promoter groups/bins, with significance and conclusions based on the former only (e.g. boxplot in 5F).
(iii) Sample size. The conclusions are based on a relatively small number of genes that upregulate between G+ and G- (n=55 in figure by my count, text mentions n=52). Irrespective of the other confounders above, this is quite a small subset to make the sweeping general conclusion that "loss of the repressive polycomb mark H3K27me3 is delayed relative to transcriptional activation" in the abstract. Indeed, the small number of DEG suggests the cell types being compared are similar and perhaps therefore have specific genomic features (this could be looked at) that drive .
Other comments:
2.) The authors show that promoter H3K4me3 corelates well with gene expression dynamics in their model. They conclude that "transcription itself is required for H3K4me3 deposition", or in other words is subordinate. This may well be the case but from their correlative data this cannot be inferred. Indeed, several recent and past papers have shown that H3K4me3 itself can directly modulate transcription, for example by triggering RNA II pause-release, by preventing epigenetic silencing and/or by recruiting the PIC. The authors could point out or discuss these alternative possibilities to provide a more balanced discourse.
3.) The labelling of some figures is unclear. In Fig 4C and 4D (right) it is impossible to tell what sample each of the lines represents. It is also not clear what the blue zone corresponds to in genome view plots (the whole gene?). Moreover, the replicate numbers are not shown in figure legends.
4.) It would be nice to provide more discussion to reconcile the conclusions that H3K27me3 in endoderm differentiation is subordinate and the final figure showing inhibiting H3K27me3 has a significant effect on differentiation, since the latter is the functional assessment.
Significance
Overall, the study's strengths are in that it characterises epigenomic dynamics within a specific and relevant cell fate model. The nascent transcriptomics adds important resolution, and underpins the core conclusions. The weakness is that data is over-interpreted at this point, and other possibilities are not adequately tested. The conclusions should therefore either be scaled back (which reduces novelty) or further analysis and/or experiments should be performed to support the conclusion. If it proves correct, this would be a significant observation for the community,
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Referee #2
Evidence, reproducibility and clarity
In this paper the authors profile gene expression, including active transcription, and histone modifications (k4 and k27me3) during a complex differentiation protocol from ES cells, which takes advantage of FACS sorting of appropriate fluorescent reporters. The data is of good quality and the experiments are well performed. The main conclusion, that the analyzed histone marks channel differentiation more than they directly allow/block it, is well supported by the data. The paper is interesting and will represent a good addition to an already extensive literature. I have however a few major concerns, described below:
- K4me3 may show more changes than they interpret, at least over the +1 nucl. An alternative quantification to aggregate profiles should be used to more directly address the questions regarding the correlations between histone mods and gene expression.
- Related to the previous point, it appears clear in Fig.4 that the promoters of each gene expression cluster do not belong to a single chromatin configuration. I think it would be important to:
- cluster the genes based on promoter histone mods and interrogate gene expression and cluster allocation (basically the reverse to what is presented)
- order the genes in the heatmaps identically for K4me3 and K27me3 to more easily understand the respective chromatin composition per cluster
- Also, as it is apparent that not all promoters in every cluster are enriched for the studied marks, could the authors separately analyze these genes? What are they? Do they use alternative promoters?
- The use of 4SU-seq to identify active enhancers is welcome; however, I have doubts it is working very efficiently: for instance, in the snapshots shown in Fig.2A, the very active Oct4 enhancers in ES cells are not apparent at all... More validation of the efficiency of the approach seems required.
- The effects of the EZH2 inhibitor are quite minor regarding the efficiency of the differentiation as analyzed by FACS, despite significant gene expression changes. To the knowledge of this referee, this is at odds with results obtained with Ezh2 ko ES cells that display defects in mesoderm and endoderm differentiation. I have issues reconciling these results (uncited PMID: 19026780). Either the authors perform more robust assays (inducible KOs) or they more directly explain the limitations of the study and the controversies with published work.
Minor
- please add variance captured to PCA plots
- Fig1E add color scales to all heatmaps
- Fig4C,D are almost impossible to follow, please find a way to identify better the clusters/samples and make easier to correlate all the variables
Significance
The paper is incremental in knowledge, and not by a big margin, as it is known already that histone mods rather channel than drive differentiation. Though, the authors do not clearly address inconsistencies with published work, especially regarding Ezh2 thought to be important to make endoderm. It is however a good addition to current knowledge, provided a better discussion of differences with published work is provided.
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Referee #1
Evidence, reproducibility and clarity
Summary:
To explore the relationship between histone post-translational modifications (H3K4me3 and H3K27me3) and enhancer activation with gene expression during early embryonic development, the authors used a monolayer differentiation approach to convert mouse embryonic stem cells (ESCs) into Anterior Definitive Endoderm (ADE). They monitored differentiation stages using a dual reporter mESC line (B6), which has fluorescent reporters inserted at the Gsc (GFP) and Hhex (Redstar) loci. Their analyses indicate that the differentiating cells advanced through stages similar to those in the embryo, successfully converting into endoderm and ADE with high efficiency. This is elegant and well performed stem cell biology.
Their subsequent genome-wide and nascent transcription analyses confirmed that the in vitro gene expression changes correlated with developmental stages and confirmed that transcriptional activation precedes mRNA accumulation. They then focussed on linking active enhancers and histone modifications (H3K4me3 and H3K27me3) were with gene expression dynamics. Finally, the performed PRC2 inhibition and showed that, while it enhanced differentiation efficiency, it also induced ectopic expression of non-lineage specific genes.
Major comments:
In terms of mechanistic advances, they propose that transcriptional up-regulation does not require prior loss of H3K27me3, which they show appears to lag behind gene activation, but critically, on a likely mixed population level. I am sceptical of their interpretation of their data because they are looking at heterogenous populations of cells. To explain, one could imagine a particular H3K27me3 coated gene that gets activated during differentiation. In a population of differentiating cells, while the major sub-population of cells could retain H3K27me3 on this particular gene when it is repressed, a minority sub-population of cells could have no H3K27me3 on the gene when it is actively transcribed. The ChIP and RNA-seq results in this mixed cell scenario would give the wrong impression that the gene is active while retaining H3K27me3, when in reality, it's much more likely that the gene is never expressed when its locus in enriched with the repressive H3K27me3 modification. Therefore, to support their claim, they would have to show that a particular gene is active when its locus is coated with H3K27me3. Personally, I don't feel this approach would be worth pursuing.
They also report that inhibition of PRC2 using EZH2 inhibitor (EPZ6438) enhanced endoderm differentiation efficiency but led to ectopic expression of pluripotency and non-lineage genes. However, this is not surprising considering the established role of Polycomb proteins as repressors of lineage genes.
Referee cross-commenting
I see that Reviewer #3 has the same concern with over interpretation of data in places - most notably their (in my view not supported) suggestion that transcriptional up-regulation does not require prior loss of H3K27me3.
Significance
I feel that this is a solid and well conducted study in which the authors model early development in vitro. It should be of interest to researchers with an interest in more sophisticated in vitro differentiation systems, perhaps to knockout their gene of interest and study the consequences. However, I don't see any major mechanistic advances in this work.
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Reply to the reviewers
We thank the reviewers for taking the time to read and comprehensively evaluate our manuscript. We are pleased that, overall, they recognize the quality of our data and that it supports our conclusions. We are grateful for their comments, insights and advice and have revised the manuscript accordingly as described in the point-by-point response below. We believe that the revised manuscript is substantially improved by some experimental additions, additional replicates, improved analysis and increased clarity. Some key enhancements are as follows:
Previously we had found increased expression of the WNT pathway following CHRDL2 treatment, using RNA seq. We have now demonstrated this experimentally using the cellular levels and localisation of β-catenin. Previously we had shown that overexpression of CHRDL2 increased resistance to common chemotherapy treatments, as well as irradiation in colorectal cell lines. We have now shown that cells surviving treatment show a further reduction SMAD1/5/8 phosphorylation indicating a selection for CHRLD2 high cells during the treatment. We have also demonstrated a decrease in chemotherapy sensitivity in intestinal organoids treated with secreted forms of CHRDL2.
1. 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. *
Reviewer #1
__Evidence, reproducibility and clarity __
Clarkson and Lewis present data suggesting that overexpression of Chordin like 2 (CHRDL2) can affect colorectal cancer cell responses to chemotherapy agents, possibly by modulating stem-cell like pathways. I have the following comments:
- Fig. 1J-it is standard to show the images of cell migration-this is important here, given the modest effect of CHRDL2 overexpression here.
We have now included 3 replicate control and CHRDL2 overexpressing cell images in this figure panel to support the quantification in the graph.
Fig. 2A-the very small error bars for most of the data on the curves suggests these are n=1 experiments with multiple technical replicates to generate the error bars. Please clarify. The legend says n=3 with ANOVA analysis but no significance detected. Please clarify.
All experiments in this figure were done with 5 technical replicates per experiment, this was replicated at least three times to give n=3 biological replicates. The error bars represent the standard error of the mean of these 3 biological replicates as stated in the legend. Some data points showed very little data variation, hence the small error bars. Raw data is available if requested.
- Fig. 2B-given the overlapping error bars here, how can there be a pWe have removed this representation of the data as it combined many different experiments with variable cell types and chemotherapeutics and it was difficult to carry out meaningful statistics. An overview of the data can be better seen in table form as shown in the revised figure 2B.
Fig. 2G-did the authors try to estimate the concentration of CHRDL2 in the conditioned medium? Which cell line was used to generate this CM?
Conditioned media was harvested from the matching transgenic cell lines with inducible CHRDL2. eg RKO cells were treated with media collected from doxycycline induced transgenic RKO cells whereas CaCO2 cells were treated with media from CaCO2 cells. The concentration of doxycycline was represented by ++ for 10ug/ml, the same notation we have used for directly induced cells treated with 10ug/ml dox.
We did not try to quantify the absolute concentration of CHRDL2 but we have shown the relative amount on a Western blot normalised with a ponceau stain (quantification now included in supplementary figure 1).
We have clarified our description of this experiment, inserting the following statement, "Conditioned media was harvested from corresponding cell lines with the inducible CHRDL2 transgene and the parental control cells. Induction of CHRDL2 to generate conditioned media was carried out using the same concentration and duration of doxycycline treatment as the cells in figure 2A. "
Fig. 5-what is the potential mechanism for gene expression changes in response to CHRDL2 overexpression? Is it all due to BMP inhibition? More mechanistic detail would be welcome here.
We have suggested other pathways involved in these functional effects based on our RNA seq data but at the moment it is not possible to say whether any changes are independent of BMP signaling. CHRDL2 is relatively understudied and as yet there is not much literature supporting BMP independent actions of CHRDL2. However, we have added some discussion and reference to an article suggesting interactions between CHRLD2 and YAP (Wang et al., 2022) including the following statement on page 17: "While the changes in BMP and WNT signaling shown in our GSEA analysis suggest that the effects of CHRDL2 in our system work directly through inhibition of BMP, it is not possible to rule out that some pathways are affected by BMP independent actions of CHRLD2. Indeed, Wang et al, suggest that CHRDL2 can directly alter phosphorylation and activity of YAP in gastric cancer cell lines, which merits further exploration (Wang et al., 2022)"
Significance
Unclear whether genetically engineered inducible overexpression has any real physiological significance but we all use cell models so this is OK.
Reviewer #2
__Evidence, reproducibility and clarity __
Summary: In the manuscript entitled "BMP antagonist CHRDL2 enhances the cancer stem-cell phenotype and increases chemotherapy resistance in Colorectal Cancer" the authors demonstrated that Chordin-like 2 (CHDRL2), a secreted BMP antagonist, promotes a chemo-resistant colorectal cancer stem cell phenotype through the inhibition of BMP signaling. The authors took advantage of both 2D engineered colorectal cancer (CRC) cells and healthy murine 3D organoid systems. Specifically, the authors showed a decreased proliferation rate and reduced clonogenic capability upon overexpression of CHRDL2 in established human colon cancer cell lines. Subsequently, they identified a chemo-resistant phenotype upon standard therapies (5FU, Oxaliplatin and Irinotecan) in CHDRL2 overexpressing cells by performing MTS assay. The authors showed that this chemo-resistant phenotype is associated with ATM and RAD21 activation, supporting an induction of DNA damage signaling pathway. Of note, the authors assessed that the exposure of 3D murine organoid to CHRDL2 resulted in a stem-like phenotype induction accompanied by a reduction of the differentiated counterpart. From RNA-seq data analysis emerged the upregulation of genes associated to stemness and DNA repair pathways in CHRDL2 overexpressing cells.
Major comments: 1. In the first paragraph of the result section authors assessed that "Colorectal adenocarcinoma cell lines were deliberately chosen to encompass a range of CHRDL2 expression levels and genetic mutations", without showing qRT-PCR or WB data on the differential expression levels of CHRDL2 in a panel of immortalized CRC cell lines. Authors should include these data to better support their choice.
*We have now included some qRT-PCR in supplementary figure 1 alongside a table of some of the key driver mutations in each cell line. Western blotting of these cells shows only a very low concentration of CHRDL2 protein. As shown in figure 1B in the control columns, no significant protein expression is observed in any line. *
In Figure 1F, authors described a reduction of cell proliferation in CRC cell lines expressing high levels of CHRDL2 only under low glucose conditions. Why did the authors perform the assay under these conditions? They should better argue this aspect and validated the role of CHRDL2 in metabolism rewiring by performing additional in vitro assays.
We have removed this aspect of the paper as it does not add significantly to our overall conclusions and we can clearly see the effects of CHRDL2 overexpression under standard growth conditions (Figure 1G).
The authors should evaluate the role of CHRDL2 in promoting a stem-like phenotype in human colon cancer stem cells freshly isolated from patients and characterized.
We would very much like to do experiments such as this but it is beyond the scope of this study and will be included in upcoming grant proposals.
In order to confirm the data obtained on 3D murine organoids system obtained from normal Intestinal Stem Cells, authors should investigate the stemness induction, driven by CHRDL2, also in human intestinal organoids.
Experiments using human intestinal organoids are currently planned and ethical approval applications and grant proposals are underway for future experiments of this nature.
The authors should evaluate the oncogenic role of CHRDL2, through the maintenance of stemness, by performing orthotopic or subcutaneous experiments in vivo model.
Similarly, this is not possible for this manuscript but is planned for the future alongside a transgenic mouse model of inducible CHRDL2 overexpression in the intestine.
BMPs proteins are part of a very broad protein family. In the introduction section, authors should indicate the specific BMP protein on which CHRDL2 exerts its inhibitory function. Moreover, they should have assessed BMP protein levels in CACO2, LS180, COLO320 and RKO cell lines.
We have clarified the interactions between CHRDL2 and specific BMPs in the introduction. We have not specifically assessed the BMP protein levels in our cells however we have now included an analysis of expression data from the Cancer Cell Line Encyclopedia in supplementary figure 1 C.
In first panel, the authors should quantify the secreted levels of CHRDL2 in the media of overexpressing CHRDL2 cell lines.
We have done this using the ponceau staining as a loading control and the results are displayed (supplementary figure 1).
In Figure 2D the authors should use the appropriate controls and describe this with more details in results section.
In this figure we have used Hoechst staining followed by FACs analysis to identify the cell cycle profile of our CHRDL2 treated cells. We have improved the description of this in the methods section. Appropriate controls for staining, both negative and positive, are used when setting up the analysis for this experiment. The cell cycle profile is calculated using the Novocyte in house software. We have now included the histogram plots in the main figure to clarify these data in figure 2D.
In Figure 3A, the authors should have performed the assay by choosing IC50.
*We attempted these experiments with the IC50 levels, however the high amount of cell death and frequency of apoptotic cells meant that clear images were difficult to obtain. We therefore reduced the concentrations and still had very measurable effects. *
In Supplementary Fig. 4A-B. the results are unclear. The control cell lines are already chemo resistant.
Again, we used IC25 levels of the drugs so that our cells were damaged but still live throughout the experiment. This has been explained on page 10.
The authors should review and add statistical analysis in both main and supplementary figures.
*We have now added additional details about statistical analysis throughout the figures, legend and main text, showing all significance levels as well as non-significance for each data set. * Minor comments: 1. The quality of immunofluorescence and WB images should be implemented, and in the immunofluorescence panels scale bars should be added.
We have added or improved scale bars on each immunofluorescence image. Western blot images have been improved.
In the graphical abstract authors reported that CHRDL2 overexpression increase WNT and EMT pathways, without performing any specific assay to demonstrate this. Authors should correct and graphically improve the graphical abstract.
*This is a good point and we have now carried out Beta-catenin immunofluorescence as a measure of WNT signaling on both our cancer cell lines - showing an increase in nuclear beta-catenin (figure 1J and K), and our organoids - showing an increase in overall levels and cytoplasmic staining (Figure 4 F). In terms of EMT markers we have carried out immunofluorescence on IQGAP1 (Figure 1I). IQGAP1 is significantly upregulated in CHRDL2 cells, reflecting its role in reduced cell adhesion and increased migration. This correlates with our data showing increased cellular migration as well as the increase in EMT related transcription in our RNAseq data. *
The term "significantly" in the discussion section is inappropriately referred to data showed in the histogram in Figure 1J. Moreover, in Figure 1Jthe authors should delete from the y-axis the term "corrected".
We have changed significantly to substantially
The term "significant" in discussion is inappropriately referred to BMI1 expression level if compared to the histogram in Figure 4G.
We have changed significantly to "a trend to increase"
In Figure 2C the authors should add the unit of measurement (fold over control) in the table.
We have done this
In Figure 4E the authors should add the figure legend reporting OLFM4 protein.
We have done this
The authors should include few sentences summarizing the findings at the end of each paragraph.
We have added short summaries at the start or end of each section to improve the flow of the results section.
Significance
General assessment: Overall, the work is aimed to elucidate the role of CHRDL2 already considered a poor prognosis biomarker involved in the promotion of CRC (PMID: 28009989), in promoting stem-like properties. The authors elucidated new additional insights into the molecular mechanisms regulating stemness phenotype induced by the BMP antagonist CHRDL2 in CRC. The authors include in the study a large amount of data, which only partially support their hypothesis. However, this manuscript lacks organization and coherence, making it challenging to follow and read. Numerous concerns need to be addressed, along with some sentences to rephrase in the result and discussion sections.
Advance: The manuscript reported some functional insights on the role of CHRDL2 in colorectal cancer, but additional data should be added to support authors 'conclusions.
Audience: The manuscript is suggested for basic research scientists.
__Reviewer #3 (Evidence, reproducibility and clarity (Required)):____ __ Summary BMP antagonist CHRDL2 enhances the cancer stem-cell phenotype and increases chemotherapy resistance in Colorectal Cancer Eloise Clarkson et al. The manuscript explored the function of CHRDL2, a BMP antagonist, on colorectal cancer (CRC). The authors found that CHRDL2 overexpression can enhance the survival of CRC cells during chemotherapy and irradiation treatment with elevated levels of stem-cell markers and reduced differentiation. Further RNA-seq analysis revealed that CHRDL2 increased the expression of stem-cell markers, WNT signaling and other well-established cancer-associated pathways. Overall, the manuscript is well-written and presented. I have some suggestions:
Major points:
- The authors assert BMP antagonism was demonstrated by assessing levels of phosphorylated SMAD1/5 (Figure 1G). However, the immunoblotting assay only depicted P-SMAD 1/5 levels and B-ACTIN as internal control. It's suggested to include total-SMAD1/5 immunoblotting as an internal control to further support the claim of BMP antagonism.
The reviewer is correct that this is the best control. Western blotting has now been performed with total SMAD1 protein expression used as an internal control and this is shown in Figure 1D and Supplementary figure 1F
The authors argue that CHRDL2 overexpression reduced the proliferation of CRC cell lines, as evidenced by cell proliferation assays. However, from Figure 1E, the reduction in proliferation appears insignificant. It would be beneficial to perform one-way ANOVA tests on each time point for CHRDL2+ and CHRDL2++ with Control in Figure 1E to ascertain significance.
*We now have repeated this experiment to reduce variability and have also provided two-way ANOVA analysis between Control and CHRDL2+ and Control and CHRDL2++. One-way ANOVA at timepoint 96hr also provided with details in the figure legend. *
The findings indicating that overexpressing CHRDL2 can confer resistance to chemotherapy in CRC cells (Figure 2A-C) are noteworthy. To deepen the understanding of BMP signaling in cancer stemness and the molecular underpinning of CHRDL2 antagonism, additional western blot assays on P-SMAD1/5 with CHRDL2 overexpression and drug treatment are recommended.
*Western blotting of P-SMAD1/5 upon cells treated with IC50 5FU has now been performed in figure 2C (in the same experiment as the revised panels in figure 1D). The data suggest that CHRDL2 overexpressing cells able to survive chemotherapy have higher levels of P-SMAD1/5 reduction compared to that of untreated cells, strongly suggesting that chemotherapy treatment acts to select the cells with the highest CHRDL2 expression. We thank reviewer 3 for this suggested experiment and have included further discussion on this on page 8. *
The assertion that extrinsic CHRDL2 addition diminishes differentiation and enhances stem-cell numbers in an intestinal organoid model is intriguing. As BMP signaling inhibition contributes to intestinal cell stemness, incorporating additional layers for BMP antagonism of CHRDL2 on intestinal organoids through immunoblotting or real-time quantitative PCR for treated organoids would augment the conclusions.
As stated in the response to reviewer 2, we have investigated Beta-catenin in our organoids following CHRDL2 treatment using immunofluorescence and find that the levels are increased with the staining shifting from the membrane to the cytoplasm and nucleus (Figure 4F).
The authors claim CHRDL2 overexpression can decrease BMP signaling based on GSEA analysis (Figure 5E). However, the GSEA results did not demonstrate the downregulation of BMP signaling. Reanalysis of this GSEA analysis is warranted.
*We agree with this point and have changed the description of this result since the gene set covers both positive and negative regulators of the BMP pathway. We cannot conclusively say from this RNAseq data set that BMP signaling is "downregulated", however since SMAD phosphorylation is increased and nuclear beta-catenin is increased, overall we suggest that the changes we see are likely to represent the effects of decreased BMP signaling along with increased WNT signaling. *
Minor Points:
6.Provide the threshold/cutoff values chosen for differential expressed genes (DEGs) in CHRDL2+ and CHRDL2++ RNA-seq compared with control cells. Explain the minimal overlap between CHRDL2 LOW and CHRDL2 HIGH DEGs. Consider presenting all DEGs in CHRDL2 LOW and CHRDL2 HIGH compared with control cells in one gene expression heatmap for better visualization.
We have now provided the cutoff values for the DEGs in the legend for figure 5 (PThe minimal overlap of DEGs in the low and high expressing cells is an interesting point. We hypothesize that this may be related to the different effects of intermediate vs high levels of WNT signaling that occurs in colon cancer cells, frequently discussed in the literature as the "Just right hypothesis" (Lamlum et al. 1999, Albuquerque et al., 2002, Lewis et al., 2010). However, we haven't included this in the discussion as it merits further exploration. However, we have mainly focused on specific genes that are modified in both data sets, which are more likely to be the direct result of CHRDL2 modification. *
After DEGs analysis, perform Gene Ontology (GO) analysis on these DEGs to further investigate possible gene functions rather than selectively discussing some genes, enhancing understanding of CHRDL2 functions in CRC cells.
We have carried out this analysis using a variety of tools and have now included a Gene Ontology Panther analysis as supplementary figure 7. We have included a comment on this in the text on page 14 saying "Gene ontology analysis supports these findings with enrichment in biological processes such as cellular adhesion, apoptosis and differentiation. "
Conduct similar experiments in both 2D culture and organoid systems, if feasible, to provide more comprehensive insights into CHRDL2's oncogenic roles in CRC tumor progression.
*We have now performed chemotherapy treatment on our organoid systems, and have found that organoids with extrinsic CHRDL2 addition have a higher survival rate after chemotherapy compared to a control (Figure 4H and I). *
Label significance (*, **, ***, and n.s.) for every CRC cell line treated with CHRDL2 in Figure 2D, 2F, 2J, 4G, 5D, and 5F.
We have done this
Label the antibodies with different colors used for immunofluorescence in the figure text in Figure 4E.
We have done this
* * Include replicate dots for the Control group in the bar plots in Figure 1F and 2B.
We have done this
* * Add scale bars in Figure 3A and correct similar issues in other figures if applicable.
We have done this
* *13.Correct grammar and punctuation mistakes throughout the manuscript. For example:
We have done this and further proofread our revised manuscript
Page 7: "As seen in Figure 1J, CHRDL2 overexpression significantly increased the number of migrated cells (P *We have now added additional details about statistical analysis throughout the figures, legend and main text, showing all significance levels as well as non-significance for each data set. * Reviewer #3 (Significance (Required)):
The current study presents compelling evidence demonstrating that BMP signaling antagonist CHRDL2 enhances colon stem cell survival in colorectal cancer cell lines and organoid models. Further validation through CRC mouse models could offer invaluable insights into the clinical relevance and therapeutic implications of CHRDL2 in colorectal cancer.
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Referee #3
Evidence, reproducibility and clarity
Summary
BMP antagonist CHRDL2 enhances the cancer stem-cell phenotype and increases chemotherapy resistance in Colorectal Cancer Eloise Clarkson et al. The manuscript explored the function of CHRDL2, a BMP antagonist, on colorectal cancer (CRC). The authors found that CHRDL2 overexpression can enhance the survival of CRC cells during chemotherapy and irradiation treatment with elevated levels of stem-cell markers and reduced differentiation. Further RNA-seq analysis revealed that CHRDL2 increased the expression of stem-cell markers, WNT signaling and other well-established cancer-associated pathways. Overall, the manuscript is well-written and presented. I have some suggestions:
Major points:
- The authors assert BMP antagonism was demonstrated by assessing levels of phosphorylated SMAD1/5 (Figure 1G). However, the immunoblotting assay only depicted P-SMAD 1/5 levels and B-ACTIN as internal control. It's suggested to include total-SMAD1/5 immunoblotting as an internal control to further support the claim of BMP antagonism.
- The authors argue that CHRDL2 overexpression reduced the proliferation of CRC cell lines, as evidenced by cell proliferation assays. However, from Figure 1E, the reduction in proliferation appears insignificant. It would be beneficial to perform one-way ANOVA tests on each time point for CHRDL2+ and CHRDL2++ with Control in Figure 1E to ascertain significance.
- The findings indicating that overexpressing CHRDL2 can confer resistance to chemotherapy in CRC cells (Figure 2A-C) are noteworthy. To deepen the understanding of BMP signaling in cancer stemness and the molecular underpinning of CHRDL2 antagonism, additional western blot assays on P-SMAD1/5 with CHRDL2 overexpression and drug treatment are recommended.
- The assertion that extrinsic CHRDL2 addition diminishes differentiation and enhances stem-cell numbers in an intestinal organoid model is intriguing. As BMP signaling inhibition contributes to intestinal cell stemness, incorporating additional layers for BMP antagonism of CHRDL2 on intestinal organoids through immunoblotting or real-time quantitative PCR for treated organoids would augment the conclusions.
- The authors claim CHRDL2 overexpression can decrease BMP signaling based on GSEA analysis (Figure 5E). However, the GSEA results did not demonstrate the downregulation of BMP signaling. Reanalysis of this GSEA analysis is warranted.
Minor Points:
6.Provide the threshold/cutoff values chosen for differential expressed genes (DEGs) in CHRDL2+ and CHRDL2++ RNA-seq compared with control cells. Explain the minimal overlap between CHRDL2 LOW and CHRDL2 HIGH DEGs. Consider presenting all DEGs in CHRDL2 LOW and CHRDL2 HIGH compared with control cells in one gene expression heatmap for better visualization. 7. After DEGs analysis, perform Gene Ontology (GO) analysis on these DEGs to further investigate possible gene functions rather than selectively discussing some genes, enhancing understanding of CHRDL2 functions in CRC cells. 8. Conduct similar experiments in both 2D culture and organoid systems, if feasible, to provide more comprehensive insights into CHRDL2's oncogenic roles in CRC tumor progression. 9. Label significance (, , **, and n.s.) for every CRC cell line treated with CHRDL2 in Figure 2D, 2F, 2J, 4G, 5D, and 5F. 10. Label the antibodies with different colors used for immunofluorescence in the figure text in Figure 4E. 11. Include replicate dots for the Control group in the bar plots in Figure 1F and 2B. 12. Add scale bars in Figure 3A and correct similar issues in other figures if applicable. 13.Correct grammar and punctuation mistakes throughout the manuscript. For example:
Page 7: "As seen in Figure 1J, CHRDL2 overexpression significantly increased the number of migrated cells (P
Page 7: "As seen in Figure 1J, CHRDL2 overexpression significantly increased the number of migrated cells (P
Page 7: "As seen in Figure 1J, CHRDL2 overexpression significantly increased the number of migrated cells (P < 0.0449)," suggesting increased migratory ability, a hallmark of cancer stem cells."
Page 8: "CHRDL2 overexpression resulted in an approximate twofold increase in IC50 values compared to control cells (P < 0.001)."
Page 10: "As seen in Figure 4B, upon the" should be corrected to "Figure 4B."
- Specify the statistical methods or estimates used for determining statistical significance.
Significance
The current study presents compelling evidence demonstrating that BMP signaling antagonist CHRDL2 enhances colon stem cell survival in colorectal cancer cell lines and organoid models. Further validation through CRC mouse models could offer invaluable insights into the clinical relevance and therapeutic implications of CHRDL2 in colorectal cancer.
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Referee #2
Evidence, reproducibility and clarity
Summary:
In the manuscript entitled "BMP antagonist CHRDL2 enhances the cancer stem-cell phenotype and increases chemotherapy resistance in Colorectal Cancer" the authors demonstrated that Chordin-like 2 (CHDRL2), a secreted BMP antagonist, promotes a chemo-resistant colorectal cancer stem cell phenotype through the inhibition of BMP signaling. The authors took advantage of both 2D engineered colorectal cancer (CRC) cells and healthy murine 3D organoid systems. Specifically, the authors showed a decreased proliferation rate and reduced clonogenic capability upon overexpression of CHRDL2 in established human colon cancer cell lines. Subsequently, they identified a chemo-resistant phenotype upon standard therapies (5FU, Oxaliplatin and Irinotecan) in CHDRL2 overexpressing cells by performing MTS assay. The authors showed that this chemo-resistant phenotype is associated with ATM and RAD21 activation, supporting an induction of DNA damage signaling pathway. Of note, the authors assessed that the exposure of 3D murine organoid to CHRDL2 resulted in a stem-like phenotype induction accompanied by a reduction of the differentiated counterpart. From RNA-seq data analysis emerged the upregulation of genes associated to stemness and DNA repair pathways in CHRDL2 overexpressing cells.
Major comments:
- In the first paragraph of the result section authors assessed that "Colorectal adenocarcinoma cell lines were deliberately chosen to encompass a range of CHRDL2 expression levels and genetic mutations", without showing qRT-PCR or WB data on the differential expression levels of CHRDL2 in a panel of immortalized CRC cell lines. Authors should include these data to better support their choice.
- In Figure 1F, authors described a reduction of cell proliferation in CRC cell lines expressing high levels of CHRDL2 only under low glucose conditions. Why did the authors perform the assay under these conditions? They should better argue this aspect and validated the role of CHRDL2 in metabolism rewiring by performing additional in vitro assays.
- The authors should evaluate the role of CHRDL2 in promoting a stem-like phenotype in human colon cancer stem cells freshly isolated from patients and characterized.
- In order to confirm the data obtained on 3D murine organoids system obtained from normal Intestinal Stem Cells, authors should investigate the stemness induction, driven by CHRDL2, also in human intestinal organoids.
- The authors should evaluate the oncogenic role of CHRDL2, through the maintenance of stemness, by performing orthotopic or subcutaneous experiments in vivo model.
- BMPs proteins are part of a very broad protein family. In the introduction section, authors should indicate the specific BMP protein on which CHRDL2 exerts its inhibitory function. Moreover, they should have assessed BMP protein levels in CACO2, LS180, COLO320 and RKO cell lines.
- In first panel, the authors should quantify the secreted levels of CHRDL2 in the media of overexpressing CHRDL2 cell lines.
- In Figure 2D the authors should use the appropriate controls and describe this with more details in results section.
- In Figure 3A, the authors should have performed the assay by choosing IC50.
- In Supplementary Fig. 4A-B. the results are unclear. The control cell lines are already chemoresistant.
- The authors should review and add statistical analysis in both main and supplementary figures.
Minor comments:
- The quality of immunofluorescence and WB images should be implemented, and in the immunofluorescence panels scale bars should be added.
- In the graphical abstract authors reported that CHRDL2 overexpression increase WNT and EMT pathways, without performing any specific assay to demonstrate this. Authors should correct and graphically improve the graphical abstract.
- The term "significantly" in the discussion section is inappropriately referred to data showed in the histogram in Figure 1J. Moreover, in Figure 1Jthe authors should delete from the y-axis the term "corrected".
- The term "significant" in discussion is inappropriately referred to BMI1 expression level if compared to the histogram in Figure 4G.
- In Figure 2C the authors should add the unit of measurement (fold over control) in the table.
- In Figure 4E the authors should add the figure legend reporting OLFM4 protein.
- The authors should include few sentences summarizing the findings at the end of each paragraph.
Significance
General assessment:
Overall, the work is aimed to elucidate the role of CHRDL2 already considered a poor prognosis biomarker involved in the promotion of CRC (PMID: 28009989), in promoting stem-like properties. The authors elucidated new additional insights into the molecular mechanisms regulating stemness phenotype induced by the BMP antagonist CHRDL2 in CRC. The authors include in the study a large amount of data, which only partially support their hypothesis. However, this manuscript lacks organization and coherence, making it challenging to follow and read. Numerous concerns need to be addressed, along with some sentences to rephrase in the result and discussion sections.
Advance: The manuscript reported some functional insights on the role of CHRDL2 in colorectal cancer, but additional data should be added to support authors 'conclusions.
Audience: The manuscript is suggested for basic research scientists.
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Referee #1
Evidence, reproducibility and clarity
Clarkson and Lewis present data suggesting that overexpression of Chordin like 2 (CHRDL2) can affect colorectal cancer cell responses to chemotherapy agents, possibly by modulating stem-cell like pathways. I have the following comments:
- Fig. 1J-it is standard to show the images of cell migration-this is important here, given the modest effect of CHRDL2 overexpression here.
- Fig. 2A-the very small error bars for most of the data on the curves suggests these are n=1 experiments with multiple technical replicates to generate the error bars. Please clarify. The legend says n=3 with ANOVA analysis but no significance detected. Please clarify.
- Fig. 2B-given the overlapping error bars here, how can there be a p<0.01 between the groups?
- Fig. 2G-did the authors try to estimate the concentration of CHRDL2 in the conditioned medium? Which cell line was used to generate this CM?
- Fig. 5-what is the potential mechanism for gene expression changes in response to CHRDL2 overexpression? Is it all due to BMP inhibition? More mechanistic detail would be welcome here.
Significance
Unclear whether genetically engineered inducible overexpression has any real physiological significance but we all use cell models so this is OK.
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Reply to the reviewers
We would like to thank the reviewers for their overall positive assessment of our manuscript. We have used their constructive feedback to substantially improve our manuscript as described below.
Reviewer #1
Evidence, reproducibility and clarity
This study by Reyes at al is a well conducted analysis of memory B cell dynamics of Plasmodium falciparum (Pf) -specific B cell populations over the course of reducing Pf prevalence in ten Ugandan adults. The data is presented well and the authors provide compelling evidence that 1. There is an overall loss of Ag specific B cells with reduction in exposure and 2. Different antigens (MSP1/AMA-1 vs CIDRa-1) generate different flavors of long lived responses. However, additional clarity to the reader should be provided on certain topics (listed below).
Major comments: 1. While the premise of the study (reduced Pf transmission due to the use of indoor residual spraying (IRS)) is an important one, I think the authors must take into consideration that 9/10 subjects had at least one Pf positive episode between Time Points 1 and 2 (Figure 1). Also, it looks from Fig 1 that some samples were collected at a time of Pf positive test (green squares), while in Table S1 none of the subjects have a positive parasite status at TP1.
We recognize that most individuals had detectable parasitemia before and after time point (TP) 1. In our manuscript, we therefore do not report the time between TP1 and TP2, because we agree that the length of this time interval is not relevant in our study methodology. We only mention the time between the last known P. falciparum infection and collection of blood at the second time point. We use the sample collected at TP1 only as a representative sample obtained during a time with high P. falciparum exposure and do not make any claims based on the time between TP1 and TP2. The occurrence of infections after sample collection at TP1 confirms that parasite transmission was still high at this time. We have added a schematic of the relative levels of parasite transmission to Figure 1 to emphasize this.
With respect to infection status, none of the donors were blood smear positive at TP1. However, as mentioned in Table S1, parasites were detected in three individuals using the more sensitive LAMP assay. These three individuals are therefore marked as parasite positive in Figure 1. Table S1 has been modified to highlight the parasite status of these three individuals.
- Figure S1A: What is trBC? Figure S1B: What is Strep? Are the strep positive cells also CIDR-1 positive and were they gated out? Why is APC used for MZ-1 and one of the MSP1-AMA-1 tetramers? Do these stainings come from multiple panels?
All abbreviations of B cell populations were defined in the figure legend (for example, trBC stands for transitional B cells). To facilitate the interpretation of Figure S1, we have now included the definitions of these abbreviations in the figure.
Strep stands for streptavidin, which has now also been clarified in the figure. In our gating strategy, we used the term “strep” to denote cells that bound to both CIDRa1 and MSP1/AMA1 tetramers, which we interpreted as non-specific binding to streptavidin or other components of the antigen tetramers. Only the “non-strep” cells were used to gate on antigen-specific cells. We have added this clarification to the figure legend.
In panel B, we accidentally used the term MZ (for merozoite) to describe tetramers of the merozoite antigens MSP1 / AMA1. These labels are interchangeable, but to avoid confusion, MZ-1 has been changed to MSP1 / AMA1.
- Figure 3A: how many cells does the umap plot represent? Were there a total of 3555 Ag specific B cells that were non-naive (Figure 3E)?
It is correct that there were a total of 3,555 antigen-specific B cells used for the clustering shown in panel A. This information has been added to Figure 3A.
- Could the authors comment on why in Figure 3, Ig isotype expression was not considered for clustering? This would allow for characterization of DN sub populations/ clusters in addition to the CD21-CD27- ABCs? It looks like IgD expression was low across the clusters (Figure 3D). Was this the case for the cells considered in this analysis, or was it excluded? If it was truly low expressed, how were the assessments in Figure 2 made?
From prior experience, we know that Ig isotype information tends to dominate in the clustering, which would result in major clusters based on IgM, IgD, IgG, and IgA expression, not on expression of other markers. This is illustrated in the example below. The UMAP on the left shows clusters in green and red that consist of IgG+ and IgA+ B cells, respectively. The UMAP on the right shows that switched memory (swM) B cells and DN B cells are found in both IgG and IgA clusters. Because we were mainly interested in identifying different subsets of B cells, irrespective of Ig isotype, we did not include Ig isotype in the clustering. We have clarified in the manuscript that Ig isotypes were excluded from the analysis to prevent these from dominating the clustering:
“Unsupervised clustering was then performed based on expression of all markers, except for Ig isotypes to prevent these from dominating the clustering.”
IgD expression among cell clusters shown in Figure 3 was low because only non-naïve B cells were included in the analyis. The majority of non-naïve cells are class-switched memory B cells and DN B cells, which by definition do not express IgD (see gating strategy in Figure S1A). Figure 2 shows all B cell populations, including naïve B cells and non-naïve B cell populations (unswitched memory, switched memory, and DN), that were gated based on IgD and CD27 expression.
5.Are there differences in these designations / phenotypes of DN populations of atBCs vs CD21-CD27- atBCs?
In the malaria field, atypical B cells are typically defined as CD21-CD27-. The definition of DN2 B cells comes from the autoimmunity field and is stricter: IgD-CD27-CD21-CD11c+ B cells. In our manuscript, we define atypical B cells in a stricter way than typically done in the malaria field, following published guidelines for the identification of B cell subsets (https://doi.org/10.3389/fimmu.2019.02458). Using these guidelines, atypical B cells and DN2 B cells are phenotypically identical. We have added a reference to these published guidelines in the Results section:
“Following published guidelines for the identification of B cell populations (21), total CD19+ B cells were divided into naïve B cells (IgD+CD27-), unswitched memory B cells (IgD+CD27+), switched memory B cells (IgD-CD27+), and double negative B cells (IgD-CD27-).”
- Lines 258-259: In considering only switched MBCs, what clusters from Figure 3a were included? There seem to be 2588 sw MBCs (Table S3, Figure 4). Do the remaining cells (967 cells) come from clusters 2, 5 and 6 (and excludes the atBC clusters)
This analysis did not use the clusters presented in Figure 3, but instead used switched memory B cells gated as shown in Figure S1A. The reason for this is that the clusters in Figure 3 were generated using antigen-specific B cells and cannot be reproduced using non-antigen-specific B cells. Thus, it is not possible to separate all other B cells into the same six clusters. The only way to compare expression of certain markers between antigen-specific and non-antigen specific switched memory B cells is to gate on these populations manually. We have now tried to clarify this in the manuscript as follows:
“we determined the percentages of CD95+ cells and CD11c+ cells among antigen-specific switched memory B cells and the total population of switched memory B cells (gated manually as shown in Figure S1A).”
Minor comments: 1. Line 178- 179: Was there a specific measure of rate of decline made for these cells?
We did not calculate a rate of decline of antigen-specific B cells for several reasons: 1) the time between TP1 and TP2 is not the same for all people in the study, 2) the time between last exposure and TP2 is not the same for all people, and 3) the rate of decline is most likely not linear and cannot accurately be estimated with only two data points. We have changed the wording of this sentence such that we do not use the word “rate”:
“we did not observe a difference in the percentage of B cells with specificity for merozoite antigens or variant surface antigens that were lost.”
In addition, we included the percentage of reduction in size in the paragraph before this section:
“we observed that both populations decreased in size by about 50%, although these differences were not statistically significant.”
Significance
General assessment: Strengths: The authors provide evidence that the dynamics of antigen specific cells in humans can vary with exposure and with the nature of the antigen. They have nicely discussed the potential causes for these differences (Discussion), although they should include the findings of Ambegaonkar et al that ABCs in malaria may be restricted to responding specifically to membrane bound antigens (PMCID: PMC7380957)
As suggested by the reviewer, we have added a paragraph to the Discussion section to discuss the results reported by Ambegaonkar et al. and how the difference between soluble vs. membrane-bound antigens may have an effect on how these antigens are perceived by B cells:
The difference between soluble and membrane-bound antigens may also have a direct effect on how these antigens are perceived by B cells. Atypical B cells have been shown to be restricted to recognition of membrane-bound antigens (41). The interaction of a B cell with membrane-associated antigen allows the formation of an immunological synapse. Inhibitory receptors expressed by atypical B cells are excluded from this synapse, resulting in B cell receptor signaling and differentiation towards antibody-secreting cells (41). This could explain why atypical B cell subset 1 that expresses the highest levels of the inhibitory receptor FcRL5 is enriched for recognition of the CIDRα1 domain of membrane-bound protein PfEMP1. It should however be noted that soluble antigen can also be presented effectively in membrane-context by conventional dendritic cells, follicular dendritic cells, and subcapsular macrophages in secondary lymphoid organs, especially when it is part of an immune complex (reviewed in (42)). This would provide a route for atypical B cells to also respond to soluble merozoite antigens, such as MSP1 and AMA1.
Limitations: 1. Outlined above, and as the authors also mention, a small sample size and homogenous population. 2. The evidence for reduced transmission is not clear, and the negative parasite tests for donors shown in Table S1 do not match with Figure 1 data. 3. Lack of IgD expression across clusters (Figure 3D- the authors will need to clarify this point) would require re-analysis of Figure 2 data
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We have provided clarification in response to the points raised by the reviewer.
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We believe there is clear evidence for reduced transmission, from a median of almost 2 infections per person per year prior to the implementation of IRS to a median parasite-free period of 1.7 years prior to sample collection at TP2. To further emphasize this, we have summarized the number of P. falciparum infections among the ten individuals included in this study (now included in Table S3):
year
Pf infections
comment
2012
20
2013
19
TP1
2014
20
TP1
2015
8
Start IRS
2016
0
TP2
This reduced parasite exposure is reflected in a decrease in immune activation as presented in Figure 2. We have clarified that the data in Table S1 did indeed match those shown in Figure 1.
- We have clarified that IgD expression is low in the clusters presented in Figure 3 because naïve B cells were excluded from this analysis.
Advances: This study highlights the importance of studying antigen specific B cells in humans in the context of natural infection and the use of high-parameter tools such as spectral flow cytometry in assessing a large quantity of data from limited clinical samples. These data are important to inform better vaccine design. Studies in inbred animals can be quite limited or different from human B cell responses.
Audience: This study will be of interest to malariologists and B cell immunologists. Atypical B cells are relevant to many infectious diseases and auto immunity, while the dynamics of memory B cells in malaria all be relevant to those interested in vaccine design against blood stage antigens.
Reviewer #2
Evidence, reproducibility and clarity
Summary: In this study, the authors compared long-lived total and antigen (ag)-specific B-cell levels in a cohort of 10 Ugandan malaria patient samples that were collected before and after local reduction of P. falciparum transmission (pre/post-IRS). The focus is on the novel comparison of the two most common malaria antigens: merozoite antigens (MSP1/AMA1) and variant surface antigens (CIDRα1). Using high-parameter spectral flow cytometry, they also characterized the phenotype of the different populations of cells. Their main findings include 1) a decrease in activated but maintenance of resting ag-specific B-cells in the post-IRS sample and 2) CD95 and CD11c, as the only differentially expressed markers between MSP1/AMA1-specific and CIDRα1-specific long-lived memory B cells. Their further phenotypic characterization suggests functional consequences with MSP1/AMA1-specific B-cells being poised for rapid antibody-secreting cell differentiation while CIDRα1-specific B cells were enriched among a subset of atypical B cells that seem poised for antigen presentation (CD86+CD11chi/ AtBC1). Their findings consolidate and further expand our knowledge of long-lived B-cell levels during P. falciparum malaria and report/compare (for the first time to my knowledge) a differential selection of long-lived B-cell levels between these 2 antigen specificities. Overall, the manuscript is straightforward and well-written and the authors did a good job explaining their methodology, findings, and interpretations. I believe the major gap missing in this study is the reconciliation of long-lived antigen-specific B-cell levels with the serum antigen-specific antibody levels of these patients against the same 2 antigens (MSP1/AMA1 and CIDRα1) in the experiments and the discussion. The antibody data would strengthen their main argument and is the main missing piece for characterizing more completely the long-lived antigen-specific humoral responses. Below are my suggestions that would help improve the manuscript:
Major comments: 1. Serum Anti-Pf antibodies: Do the authors have access to the serum/plasma of these patients? It would be important to correlate the total and ag-specific B-cell populations with levels of serum IgG antibodies against those specific Pf antigens (MSP1/AMA1 and CIDRα1) and total IgG levels to strengthen their point about long-lived humoral responses.
To our understanding, the rationale for such an analysis would be that if IgG levels correlated with the size of a certain B cell population, it would suggest that this B cell population is implicated in the production of IgG against a particular antigen. While a correlation between the percentage of memory B cells and IgG titers has been observed for antigens from several viruses and bacteria (1-4), other studies have reported the absence of such a correlation (4-7). Similarly, for P. falciparum antigens, a moderate correlation between memory B cell abundance and IgG titers has been observed for some merozoite antigens, but not for others (8, 9). The lack of a correlation between the magnitude of the memory B cell and the antibody response fits with the prevailing model that memory B cells and plasma cells are two independently controlled arms of the humoral immune system (10, 11). Given the lack of strong evidence that the levels of IgG titers and memory B cells are interconnected, we do not think this analysis will be informative.
An alternative analysis would be to study the contribution of B cell subsets to the production of IgG after re-exposure, similar to a recent study that identified T-bet+ memory B cells as the main contributors to antibody responses following influenza virus vaccination (12). Unfortunately, we are unable to perform this analysis in this study population, because only four of the individuals included in this study (spanning calendar years 2012 – 2016) were recruited into a follow up cohort (calendar years 2017 – 2019), and none of these four people were infected during this later time frame.
We have however added this future direction to the Discussion section:
To determine the contribution of different memory B cell subsets to the recall response against P. falciparum, it would be interesting to analyze IgG responses upon re-infection. However, none of the individuals included in this study experienced a recorded P. falciparum infection post-IRS, preventing us from performing such an analysis.
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- Nanan et al., Vaccine (2001), https://doi.org/10.1016/s0264-410x(01)00328-0
- Goel et al., Science Immunol (2021), https://doi.org/10.1126/sciimmunol.abi6950
- Rivera-Correa et al., eLife (2019), https://doi.org/10.7554/elife.48309
- Jahnmatz et al., Front Immunol (2021), https://doi.org/10.3389/fimmu.2020.619398
- Weisel et al., Immunity (2016), https://doi.org/10.1016/j.immuni.2015.12.004
- Shinnakasu et al., Nat Immunol (2016), https://doi.org/10.1038/ni.3460
- Nellore et al., Immunity (2023), https://doi.org/10.1016/j.immuni.2023.03.00
- Correlation between populations and initial parasite load: Are the levels between any of the populations at any time point correlated significantly in any way? If the statistical power/N allows it, please perform a correlation array between all populations using all samples both total and ag-specific and initial parasite load.
We agree that this analysis could be very interesting. However, in most recorded infection cases, parasitemia was submicroscopic and parasite load was not reported. Information about parasite density in the blood prior to TP1 is available for only half of the individuals in this study. In these people, the last known parasite density was recorded between three months to two years prior to TP1. Given the small number of individuals for whom these data are available and the large variation in time between parasitemia and sampling, we do not have sufficient data to perform this analysis.
- Figure 2: Why were total and ag-specific plasmablasts/plasma cells not included in this figure? Please include to compare levels in these two time points.
We did not include the levels of total and antigen-specific plasmablasts (PBs) in Figure 2 because the percentages of PBs are relatively low, and very few antigen-specific PBs were detected. We have now included the levels of total PBs in Figure 2A and the percentages of antigen-specific PBs in Supplementary Figure 2. The percentage of PBs among total B cells decreased by about 50% between TP1 and TP2, in line with a decrease in immune activation.
- Healthy baseline: The study is missing "healthy" controls as a reference. I presume this is because each patient is its uninfected control in the post-IRS sample. In methods, they mentioned they used two naïve-USA B-cells as technical controls. It would be important to include and maybe expand (to match age and gender)on that specific data from those controls as supplementary figures to support their findings:
- Show negative Tetramer staining for these samples (to understand the background).
- Levels of all the USA controls total B cell populations and compared to the pre/post-IRS samples to understand "baseline" or "non-endemic" control levels.
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We have included flow cytometry plots of tetramer staining for the non-P. falciparum exposed donors (pooled B cells from two US donors) to show the level of background for these probes. These plots are shown in Figure S1B.
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We have used data from P. falciparum-naive US donors (n = 7) that we generated for a prior study to show the average level of total B cell populations in Figure 2, and the percentage of switched memory B cells that express CD95, CD11c, T-bet, and FcRL5 in Figure 4.
Minor comments: 1. In the gating strategy (S1), please include the percentage of each population of that representative example.
We have added the percentages for all gated populations to Figure S1.
- For Figure 2, since not every panel has the same N, please include the N for each panel in the figure or a supplementary table.
All panels in Figure 2 show data for all 10 individuals. However, since some data points are overlapping, it may appear that some panels show data from fewer individuals. Specifically, no antigen-specific DN1 cells were detected pre- and post-IRS for four individuals. These data points therefore overlap and are not visible. To avoid confusion, we had mentioned this in the legend to Figure 2 (see text in orange). We have tried to further clarify this by emphasizing in the figure legend that data from all 10 individuals are shown (see text in red):
“Figure 2: Abundance of total and antigen-specific B cell subsets in the circulation during high parasite transmission and in the absence of P. falciparum exposure. The percentage of B cell subsets among circulating B cells is shown for total B cells (A), MSP1/AMA1-specific B cells (B), and CIDRα1-specific B cells (C). For MSP1/AMA1-specific B cells and CIDRα1-specific B cells, the total percentage among all circulating B cells is also shown (right most graphs in each panel). All panels show data for all 10 individuals. In panels B and C, no antigen-specific DN1 cells were detected pre- and post-IRS for four individuals. These data points therefore overlap and are not clearly visible. Differences between groups were evaluated using a Wilcoxon matched-pairs signed-rank test. P values
- Please mention the history of past and chronic co-infections of these 10 patients. Particularly if they had any other active or recent infection when the sample was taken.
Four individuals had active or recent infections in the three months prior to sample collection, with upper respiratory tract infections being the most common. This information has been included in Table S3, with a reference to these data in the Methods section. We have also included a link to ClinEpiDB where additional information about the cohort participants, including medical history, can be found.
- Discussion: further discussion with relevant literature on the following points is needed to consolidate cellular and antibody studies: a. Whether the presence of long-lived ag-specific B-cell responses correlates with sustained levels of IgG against Pf antigens. b. The different types of antibodies (protective/pathogenic) that these different B-cell populations have been reported to produce during malaria.
a. We have added the following paragraph to the Discussion section:
To determine how these different long-lived B cell subsets contribute to protection against P. falciparum infection, it would be important to analyze the connection between the cellular repertoire and plasma IgG. For P. falciparum antigens, a moderate correlation between memory B cell abundance and IgG titers has been observed for some merozoite antigens, but not for others (28, 44). This is in line with studies for other pathogens, that showed a correlation between the percentage of memory B cells and IgG titers for antigens from several viruses and bacteria (48-51), while other studies have reported the absence of such a correlation (51-54). The lack of a correlation between the magnitude of the memory B cell and the antibody response fits with the prevailing model that memory B cells and plasma cells are two independently controlled arms of the humoral immune system (55, 56). To determine the contribution of different memory B cell subsets to the recall response against P. falciparum, it would be interesting to analyze IgG responses upon re-infection. However, none of the individuals included in this study experienced a recorded P. falciparum infection post-IRS, preventing us from performing such an analysis.
b. We have added additional discussion about the types of antigens recognized by atypical B cells to the Discussion section:
Prior studies have shown that while atypical B cells harbor reactivity against P. falciparum antigens (9,18), they are also enriched for autoreactivity (43). Specifically, atypical B cells produce antibodies against the membrane lipid phosphatidylserine, which can induce the destruction of uninfected erythrocytes and contribute to anemia (44).
Significance
General assessment:
Strengths: - Novelty in contrasting two different types of P. falciparum antigen responses at the B-cell level. - The use of tetramers is a cutting-edge technique to assess this question. - Analyses were thorough and found contrasting differences in antigen-specific B-cell populations (atypical vs classical) between these 2 antigens for the first time (to my knowledge). - Well-written manuscript with clear data, methodology, and conclusions
Limitations: - Missing serum/plasma antibody data to support their claim about long-lived humoral responses and reconciliation of ag-specific B-cell levels and ag-specific antibody levels in experiments and discussion. - Limited N of 10 patients of the same gender (female), some population analyses had even fewer samples. - Missing baseline levels for non-endemic uninfected control for B-cell populations for comparison.
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We have included a discussion about the correlation between plasma antibody and memory B cell responses in the Discussion section.
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We have clarified that some data points overlap in Figure 2, giving the impression that data from fewer than 10 individuals were shown.
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We have included baseline levels of 1) tetramer reactivity (Figure S1), 2) the size of B cell populations (Figure 2), and 3) expression of select markers (Figure 4).
Advance: The study consolidates antigen-specific responses with the discovery of recently characterized populations (ex. atypical) and finds novel differences between two types of malaria antigen responses at the B-cell level and between specific populations responding differentially to these antigens. The findings are incremental (role of B-cell population in malaria-specific responses), conceptual (contrasting two types of B-cell antigen responses in the same infection), and clinical (finding significant differences in patients).
Audience: This study will attract basic B-cell immunology scientists, infectious disease clinicians/scientists, vaccinologists, and both basic malaria immunology and clinical audiences.
Reviewer expertise: Malaria, immunology, antibodies.
__Reviewer #3 __
Evidence, reproducibility and clarity: The authors analysed the antigen specificity and phenotypes of B cells during high P falciparum transmission and after a period of successful malaria control with IRS in Uganda. The gap between the two sampling time points is close to two years.
They use antigen probes for MSP1/AMA1 and CIDRalpha1, two antigens expressed at different stages of P. falciparum life cycle-merozoites and infected red cells, respectively. While MSP1/AMA1 are involved in the parasite's invasion of red blood cells, CIDRalpha1 is a domain of PFEMP1, a large family of antigenically variant proteins that mediates the sequestration of infected red cells in small blood vessels.
They found that the percentage of activated antigen-specific memory B cells declined with malaria control. However, detectable frequencies of antigen-specific memory B cells were retained after malaria control, which confirms earlier reports.
However, they also demonstrate that the phenotypic characteristics of memory B cells are associated with antigen specificity. The retained MSP1/AMA1-specific B cells were mostly CD95+CD11c+ memory B cells and FcRL5-Tbet- atypical B cells. In contrast, the retained CIDRalpha1-specific B cells were enriched among a subpopulation of atypical B cells.
These findings suggest differences exist in how the MSA1/AMA1 and CIDRalpha1 y are recognised and processed by the human immune system and how the immune response responds to them upon re-infection with P falciparum.
Major issues affecting the conclusion: The findings and conclusions of this study, whilst positively exciting and informative, are based on the analyses of very few cells (at times). Even the authors themselves acknowledge this. I expect the authors to address this issue by toning down their reporting and conclusions (where appropriate). Ultimately, we need to have the confidence that these results are reproducible.
We appreciate the reviewer’s concern about the numbers of antigen-specific cells included in our analyses, which is an inherent limitation of this approach. However, we would like to point out that most analyses included a substantial number of antigen-specific B cells:
Figure 3D: 158 to 2,038 cells per group
Figure 4: an average of 26 to 184 cells per donor
Figure 5B: 55 to 508 cells per group
Figure 5C: 10 to 334 cells per group*
* The group with 10 cells is an outlier here. All other groups contain at least 104 cells. Because this one condition had such a small number of cells, we specifically mentioned this number in the text.
The numbers of cells for analyses shown in Figures 3D and 5B were already included in the figures. All the other numbers were mentioned in Table S3. To further clarify the number of cells included in each analysis, we have added the number of cells to Figures 4 and 5C.
To tone down our reporting, we have rephrased some of our conclusions, and now present our section headers in past tense to make these statements reflect our observation instead of a definitive conclusion. For example:
Conclusion: “The loss of MSP1/AMA1-specific and CIDRα1-specific B cells in the circulation was similar, but the phenotype of long-lived MSP1/AMA1-specific and CIDRα1-specific B cells appeared to differ.”
Section header: “Long-lived MSP1/AMA1-specific and CIDRα1-specific B cells differed in phenotype”
Finally, in the Discussion section, we have added a statement to our paragraph describing the limitations of our study to stress the importance of reproducing our findings:
All in all, it will be important to perform additional studies of the phenotype and functionality of long-lived B cells with specificity for P. falciparum antigens to reproduce and extend our findings.
Minor comments: Figure 2D-I found this figure, and its presentation is unclear. Notably, using contour plots doesn't allow the reader to appreciate the density of the cells being presented.
To facilitate the interpretation of this figure, we have changed the plot type to a contour plot with density color gradient, and added the number of cells shown in each plot. (Please note that this panel has been renumbered to C.)
Figure 4 - label the y-axis.
The y-axis was labeled with “%”, which we have expanded to “% of B cells expressing marker of interest”.
__Significance: __The study design-as outlined-allowed for the analyses of the specificity and phenotypic characteristics of residual P falciparum-specific memory B cells after 1.7 years of little to no P falciparum exposure. The cell phenotyping methods presented are also appropriate. However, antigen-specific cells are rare in blood circulation, and as the authors themselves acknowledge in the discussion, some of the results are based on very few cells. This means we cannot be sure all the results presented are reproducible.
Previous studies demonstrated that P falciparum memory B cells are maintained long after cessation of antigen exposure. However, few (if any) detailed antigen-specific and phenotypic analyses of the characteristics of P falciparum-specific memory B cells following a long period of no exposure exist. Thus, this study presents an incremental advance in our knowledge. In addition, the association of antigen specificity with cell phenotypes is a new concept in malaria immunology. The research presented will greatly interest infectious disease immunologists and vaccinologists.
I am an infectious disease immunologist with substantial experience in malaria immunology.
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Referee #3
Evidence, reproducibility and clarity
The authors analysed the antigen specificity and phenotypes of B cells during high P falciparum transmission and after a period of successful malaria control with IRS in Uganda. The gap between the two sampling time points is close to two years.
They use antigen probes for MSP1/AMA1 and CIDRalpha1, two antigens expressed at different stages of P. falciparum life cycle-merozoites and infected red cells, respectively. While MSP1/AMA1 are involved in the parasite's invasion of red blood cells, CIDRalpha1 is a domain of PFEMP1, a large family of antigenically variant proteins that mediates the sequestration of infected red cells in small blood vessels.
They found that the percentage of activated antigen-specific memory B cells declined with malaria control. However, detectable frequencies of antigen-specific memory B cells were retained after malaria control, which confirms earlier reports.
However, they also demonstrate that the phenotypic characteristics of memory B cells are associated with antigen specificity. The retained MSP1/AMA1-specific B cells were mostly CD95+CD11c+ memory B cells and FcRL5-Tbet- atypical B cells. In contrast, the retained CIDRalpha1-specific B cells were enriched among a subpopulation of atypical B cells.
These findings suggest differences exist in how the MSA1/AMA1 and CIDRalpha1 y are recognised and processed by the human immune system and how the immune response responds to them upon re-infection with P falciparum.
Major issues affecting the conclusion:
The findings and conclusions of this study, whilst positively exciting and informative, are based on the analyses of very few cells (at times). Even the authors themselves acknowledge this. I expect the authors to address this issue by toning down their reporting and conclusions (where appropriate). Ultimately, we need to have the confidence that these results are reproducible.
Minor comments:
Figure 2D-I found this figure, and its presentation is unclear. Notably, using contour plots doesn't allow the reader to appreciate the density of the cells being presented.
Figure 4 - label the y-axis.
Significance
The study design-as outlined-allowed for the analyses of the specificity and phenotypic characteristics of residual P falciparum-specific memory B cells after 1.7 years of little to no P falciparum exposure. The cell phenotyping methods presented are also appropriate. However, antigen-specific cells are rare in blood circulation, and as the authors themselves acknowledge in the discussion, some of the results are based on very few cells. This means we cannot be sure all the results presented are reproducible.
Previous studies demonstrated that P falciparum memory B cells are maintained long after cessation of antigen exposure. However, few (if any) detailed antigen-specific and phenotypic analyses of the characteristics of P falciparum-specific memory B cells following a long period of no exposure exist. Thus, this study presents an incremental advance in our knowledge. In addition, the association of antigen specificity with cell phenotypes is a new concept in malaria immunology. The research presented will greatly interest infectious disease immunologists and vaccinologists.
I am an infectious disease immunologist with substantial experience in malaria immunology.
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Referee #2
Evidence, reproducibility and clarity
Summary:
In this study, the authors compared long-lived total and antigen (ag)-specific B-cell levels in a cohort of 10 Ugandan malaria patient samples that were collected before and after local reduction of P. falciparum transmission (pre/post-IRS). The focus is on the novel comparison of the two most common malaria antigens: merozoite antigens (MSP1/AMA1) and variant surface antigens (CIDRα1). Using high-parameter spectral flow cytometry, they also characterized the phenotype of the different populations of cells. Their main findings include 1) a decrease in activated but maintenance of resting ag-specific B-cells in the post-IRS sample and 2) CD95 and CD11c, as the only differentially expressed markers between MSP1/AMA1-specific and CIDRα1-specific long-lived memory B cells. Their further phenotypic characterization suggests functional consequences with MSP1/AMA1-specific B-cells being poised for rapid antibody-secreting cell differentiation while CIDRα1-specific B cells were enriched among a subset of atypical B cells that seem poised for antigen presentation (CD86+CD11chi/ AtBC1). Their findings consolidate and further expand our knowledge of long-lived B-cell levels during P. falciparum malaria and report/compare (for the first time to my knowledge) a differential selection of long-lived B-cell levels between these 2 antigen specificities. Overall, the manuscript is straightforward and well-written and the authors did a good job explaining their methodology, findings, and interpretations. I believe the major gap missing in this study is the reconciliation of long-lived antigen-specific B-cell levels with the serum antigen-specific antibody levels of these patients against the same 2 antigens (MSP1/AMA1 and CIDRα1) in the experiments and the discussion. The antibody data would strengthen their main argument and is the main missing piece for characterizing more completely the long-lived antigen-specific humoral responses. Below are my suggestions that would help improve the manuscript:
Major comments:
- Serum Anti-Pf antibodies: Do the authors have access to the serum/plasma of these patients? It would be important to correlate the total and ag-specific B-cell populations with levels of serum IgG antibodies against those specific Pf antigens (MSP1/AMA1 and CIDRα1) and total IgG levels to strengthen their point about long-lived humoral responses.
- Correlation between populations and initial parasite load: Are the levels between any of the populations at any time point correlated significantly in any way? If the statistical power/N allows it, please perform a correlation array between all populations using all samples both total and ag-specific and initial parasite load.
- Figure 2: Why were total and ag-specific plasmablasts/plasma cells not included in this figure? Please include to compare levels in these two time points.
- Healthy baseline: The study is missing "healthy" controls as a reference. I presume this is because each patient is its uninfected control in the post-IRS sample. In methods, they mentioned they used two naïve-USA B-cells as technical controls. It would be important to include and maybe expand (to match age and gender)on that specific data from those controls as supplementary figures to support their findings:
- Show negative Tetramer staining for these samples (to understand the background).
- Levels of all the USA controls total B cell populations and compared to the pre/post-IRS samples to understand "baseline" or "non-endemic" control levels.
Minor comments:
- In the gating strategy (S1), please include the percentage of each population of that representative example.
- For Figure 2, since not every panel has the same N, please include the N for each panel in the figure or a supplementary table.
- Please mention the history of past and chronic co-infections of these 10 patients. Particularly if they had any other active or recent infection when the sample was taken.
- Discussion: further discussion with relevant literature on the following points is needed to consolidate cellular and antibody studies: a. Whether the presence of long-lived ag-specific B-cell responses correlates with sustained levels of IgG against Pf antigens. b. The different types of antibodies (protective/pathogenic) that these different B-cell populations have been reported to produce during malaria.
Significance
General assessment:
Strengths:
- Novelty in contrasting two different types of P. falciparum antigen responses at the B-cell level.
- The use of tetramers is a cutting-edge technique to assess this question.
- Analyses were thorough and found contrasting differences in antigen-specific B-cell populations (atypical vs classical) between these 2 antigens for the first time (to my knowledge).
- Well-written manuscript with clear data, methodology, and conclusions
Limitations:
- Missing serum/plasma antibody data to support their claim about long-lived humoral responses and reconciliation of ag-specific B-cell levels and ag-specific antibody levels in experiments and discussion.
- Limited N of 10 patients of the same gender (female), some population analyses had even fewer samples.
- Missing baseline levels for non-endemic uninfected control for B-cell populations for comparison.
Advance:
The study consolidates antigen-specific responses with the discovery of recently characterized populations (ex. atypical) and finds novel differences between two types of malaria antigen responses at the B-cell level and between specific populations responding differentially to these antigens. The findings are incremental (role of B-cell population in malaria-specific responses), conceptual (contrasting two types of B-cell antigen responses in the same infection), and clinical (finding significant differences in patients).
Audience:
This study will attract basic B-cell immunology scientists, infectious disease clinicians/scientists, vaccinologists, and both basic malaria immunology and clinical audiences.
Reviewer expertise:
Malaria, immunology, antibodies.
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Referee #1
Evidence, reproducibility and clarity
This study by Reyes at al is a well conducted analysis of memory B cell dynamics of Plasmodium falciparum (Pf) -specific B cell populations over the course of reducing Pf prevalence in ten Ugandan adults. The data is presented well and the authors provide compelling evidence that 1. There is an overall loss of Ag specific B cells with reduction in exposure and 2. Different antigens (MSP1/AMA-1 vs CIDRa-1) generate different flavors of long lived responses. However, additional clarity to the reader should be provided on certain topics (listed below).
Major comments:
- While the premise of the study (reduced Pf transmission due to the use of indoor residual spraying (IRS)) is an important one, I think the authors must take into consideration that 9/10 subjects had at least one Pf positive episode between Time Points 1 and 2 (Figure 1). Also, it looks from Fig 1 that some samples were collected at a time of Pf positive test (green squares), while in Table S1 none of the subjects have a positive parasite status at TP1.
- Figure S1A: What is trBC? Figure S1B: What is Strep? Are the strep positive cells also CIDR-1 positive and were they gated out? Why is APC used for MZ-1 and one of the MSP1-AMA-1 tetramers? Do these stainings come from multiple panels?
- Figure 3A: how many cells does the umap plot represent? Were there a total of 3555 Ag specific B cells that were non-naive (Figure 3E)?
- Could the authors comment on why in Figure 3, Ig isotype expression was not considered for clustering? This would allow for characterization of DN sub populations/ clusters in addition to the CD21-CD27- ABCs? It looks like IgD expression was low across the clusters (Figure 3D). Was this the case for the cells considered in this analysis, or was it excluded? If it was truly low expressed, how were the assessments in Figure 2 made?
- Are there differences in these designations / phenotypes of DN populations of atBCs vs CD21-CD27- atBCs?
- Lines 258-259: In considering only switched MBCs, what clusters from Figure 3a were included? There seem to be 2588 sw MBCs (Table S3, Figure 4). Do the remaining cells (967 cells) come from clusters 2, 5 and 6 (and excludes the atBC clusters)
Minor comments:
- Line 178- 179: Was there a specfic measure of rate of decline made for these cells?
Significance
General assessment:
Strengths: The authors provide evidence that the dynamics of antigen specific cells in humans can vary with exposure and with the nature of the antigen. They have nicely discussed the potential causes for these differences (Discussion), although they should include the findings of Ambegaonkar et al that ABCs in malaria may be restricted to responding specifically to membrane bound antigens (PMCID: PMC7380957)
Limitations:
- Outlined above, and as the authors also mention, a small sample size and homogenous population.
- The evidence for reduced transmission is not clear, and the negative parasite tests for donors shown in Table S1 do not match with Figure 1 data.
- Lack of IgD expression across clusters (Figure 3D- the authors will need to clarify this point) would require re-analysis of Figure 2 data
Advances: This study highlights the importance of studying antigen specific B cells in humans in the context of natural infection and the use of high-parameter tools such as spectral flow cytometry in assessing a large quantity of data from limited clinical samples. These data are important to inform better vaccine design. Studies in inbred animals can be quite limited or different from human B cell responses.
Audience: This study will be of interest to malariologists and B cell immunologists. Atypical B cells are relevant to many infectious diseases and auto immunity, while the dynamics of memory B cells in malaria all be relevant to those interested in vaccine design against blood stage antigens.
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- Aug 2024
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www.biorxiv.org www.biorxiv.org
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Reply to the reviewers
The authors do not wish to provide a response at this time.
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Referee #3
Evidence, reproducibility and clarity
Summary
The manuscript presents IGNITE (Inference of Gene Networks using Inverse kinetic Theory and Experiments), an unsupervised machine learning framework for constructing gene regulatory networks from single-cell RNA sequencing (scRNA-seq) data. IGNITE utilizes a kinetic inverse Ising model to infer gene interactions from binarized expression data and can predict genetic perturbation effects, such as those from knockout experiments. Although the application of inverse Ising models to network reconstruction is not entirely novel, IGNITE's specific implementation and its application to single-cell RNA sequencing data represent a new development. The method is tested on the transition from naive to formative states in murine pluripotent stem cells, a system the authors are highly knowledgeable about, and its performance is compared to state-of-the-art alternative methods.
Major concerns
My concern regards the generality of the method, particularly the entire pipeline presented, and the fairness of the performance comparison. These concerns can be easily addressed by the authors by better explaining their choices and their general applicability, and by toning down the conclusions about the comparison with existing inference methods.
The pre-processing steps are extensive, and their rationale is not always clear, though the results heavily depend on this analysis. Several steps appear to involve arbitrary choices optimized for specific outcomes, potentially introducing biases. The authors should better explain the rationale behind their choices to mitigate these concerns.
Specifically, part of the pipeline seems to be built to reproduce a specific expression pattern of 24 genes that some of the authors discovered in a previous paper. Although this prior knowledge could be useful and relevant in this specific system, it could limit the generality of the method. For example, the authors selected approximately 2000 genes based on prior knowledge and used a combination of t-SNE and UMAP for dimensionality reduction (although the two techniques have a similar goal). This specific combination seems to reproduce the pseudotime alignment the authors were expecting to find, but such prior information might not be available in general. Therefore, feature selection and the methods used to project data need more justification, especially if the goal is to create a general tool applicable across different biological systems.
Analogously, the clustering seems manually adjusted to match known expression patterns of 24 relevant genes, rather than being the result of an optimized clustering method. Additionally, the clusters overlap with different time points, raising concerns about potential batch effects. These issues should be addressed to strengthen the validity of the method.
The claims about the comparison with existing methods should be toned down. While the comparisons are useful and interesting, they might be biased due to the method's fine-tuning for the specific system studied. The claim that the model requires only scRNA-seq data is misleading, as strong prior biological knowledge was used to select, for example, the genes analyzed.
Significance
The manuscript is scientifically sound, clearly written, and deserves publication. The proposed method is quantitative, novel, theoretically grounded, and was tested in detail with appropriate null models and statistical methods. Moreover, IGNITE can be applied to various biological systems as the availability of scRNA-seq datasets is continuously growing. The paper will be of interest to a broad community of computational biologists and biology labs interested in gene regulation using scRNA-seq data.
The limitation, in my opinion, is the method's (particularly the pre-processing pipeline) fine-tuning for the specific biological system tested. Testing IGNITE on another biological system without pre-selected pre-processing steps or detailed biological priors would be more convincing and make the paper's conclusions much stronger. The comparison with other methods also may be slightly biased due to this fine-tuning.
My background is in statistical physics, with expertise in biological physics, specifically in mathematical modeling and data analysis in molecular biology.
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Referee #2
Evidence, reproducibility and clarity
Corridori et al introduce IGNITE, a computational framework to infer gene regulatory networks (GRNs) from scRNA-seq data leveraging the kinetic Ising model, which can be used to simulate synthetic gene expression and perform in-silico knockout experiments. Other similar frameworks exist, but none combine these three aspects together. The authors have generated a scRNA-seq of murine ESCs differentiation which they use to compare their method with others. Specifically they show that they can infer known regulatory interactions, that they can generate similar data than the original and that it can potentially predict gene expression changes in transcription factor knock-out perturbations.
Major comments:
- Many of the authors' claims are backed by qualitative results and not properly quantified. In Fig2, authors qualitatively compare intra gene correlations between genes for the original data and their prediction. Instead of just visualizing they should compute and report the Spearman correlation between the original expression and the predicted one. The Fraction of Agreement is not a good metric to compare knockout predictions since it is completely dependent on the class imbalance of signs, for example if the selected genes are 75% positive and 25% negative, a naive predictor that only outputs positive predictions will still have a high score. Instead, the authors should quantify this with Spearman correlation or RMSE and compare across methods. In FigS4a-b the authors qualitatively claim that other methods could not predict the expected cell composition, which they should quantify and report the values across methods. When comparing against the ground truth network, the fraction of correctly inferred interactions is technically the same as precision but is ignoring recall. I suggest the authors compute precision, recall and a combined F1 score to compare the evaluated methods. Authors claim that the method is scalable to a larger number of genes but no data is provided, they should show how their method compares to others when using a different number of cells and number of genes at memory usage and running time.
- The authors need to better describe which tests were performed when talking about significance, which thresholds and which corrections, if any, were employed.
- To reduce the number of dimensions of scRNA-seq data the authors use t-SNE and then from the obtained result UMAP to project the data into a lower dimensional space. This is fundamentally wrong since distances are not well preserved in t-SNE. Instead the authors should first employ PCA and then UMAP. Additionally, the authors use UMAP distances in the Slingshot pseudotime calculation. Similar to t-SNE, UMAP distances have no real meaning and should only be used for visualization purposes. Instead, the authors should provide Slingshot the obtained PCA embeddings.
- Dictys (PMID: 37537351) is a known GRN inference method that also can simulate gene expression but is missing in the benchmark, the authors should add it to the method comparison.
- The current manuscript is not reproducible since it is missing the method's code, the code to reproduce the figures and the generated scRNA-seq data.
- Authors claim that the method is scalable to a larger number of genes but no data is provided to back this claim. They should show how their method compares to others when using a different number of cells and number of genes.
Minor points:
- In the introduction, authors mention multimodal GRN inference methods but do not provide any references.
- In Table 1, CellOracle is annotated as not being able to do multiple KO which is wrong. Additionally, the authors mention that IGNITE uses no prior knowledge which is not really true since it requires pseudotime ordering. The authors should add a column to Table 1 whether methods require pseudotime.
- It is unclear what the dashed arrow of Fig1b means. Moreover, plotting gene expression values on top of UMAPs can be misleading, instead authors should plot the gene expression distributions binned by pseudotime.
- The authors report a p-value of 1.04x10-171 which is below detection limit (see PMID: 30921532). Authors should change it to an interval such as p < 2.2×10-16.
- To make CellOracle results easier to interpret and more comparable, authors should run it at the atlas level instead of at the cell type level, this way generating only one GRN. This can be achieved by assigning the same cluster label to all cells.
- Experimental values in FigS3b seem to have been repeated and do not match the previous ones for IGNITE and SCODE.
- It is unclear what the different circles mean in Fig5b.
Significance
This manuscript is an incremental and methodological work for specialized audiences. Its strengths are that the authors employ kinetic Ising model for GRN inference and that they provide a single framework capable of inferring, simulating and perturbing gene expression. The main limitations are that the claims should be better quantified and that the code and data need to be made accessible.
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Referee #1
Evidence, reproducibility and clarity
Summary
Corridori and colleagues propose IGNITE, a novel method to recover Gene Regulatory Networks (GRN) from single cell RNA-sequencing (scRNA-seq) data. Their method solves the inverse Ising problem generating a cohort of candidate GRN optimising it to minimise the difference to the input expression matrix. Authors report the IGNITE is able to predict wild type data and simulate both single and multiple gene knockouts. Authors benchmark this method on a in-house data set of differentiating pluripotent stem cells (PSC). They focus on a small set of genes known to be involved in PSC differentiation into formative cells. Authors benchmark IGNITE against state of the art tools (SCODE, MaxEnt and CELLORACLE). They evaluate IGNITE ability to predict wild type gene expression by comparing their data with experimental data and with SCODE. They conclude the tool has generative capacity comparable with SCODE. They also evaluate IGNITE ability to recover known interactions with respect to other tools without finding it to significantly outperform them.
Major comments
- Are the key conclusions convincing?
Conclusions appear convincing although model generalizability could be shown in a more thorough manner. For instance, analysing some other publicly available dataset could help demonstrate hyperparameters effects on GRN predictions and their robustness across different experiments. - Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether?
Claims are well supported by data. - Would additional experiments be essential to support the claims of the paper? Request additional experiments only where necessary for the paper as it is, and do not ask authors to open new lines of experimentation.
I think the work would benefit from an additional benchmark on a different cellular system. This experiment would show how hyperparameters generalise across datasets and would provide potential users insights how to tweak them.
Also, how does the model scale with the number of genes? A benchmark on computation time and resources required to infer GRN of growing size would be valuable in the adoption of this tool.
In addition, I think the GRN comparison benchmark presented in section (3.4) would benefit from a quantitative discussion. Authors show inferred GRNs in Figure 4 and S5. For instance, measuring matrix similarity (when appropriate) would help understanding how predicted GRN compare. I understand authors attempt to do so by focusing on validated interactions and computing the fraction of correctly inferred interactions (FCI) but I think a measurement of the overall similarity (eg. Pearson correlation) would add on this.
Another comment regards the dependency between Correlation Matrices Distance (CMD) and FCI, shown in Figure 5. I understand that IGNITE GRN that maximise FCI are not the same that minimise CMD. However, it looks like GRN that maximise FCI have higher value in terms of biological information. I wonder whether optimization for one or the other metric could be left to the end user as a tunable parameter.
Authors should discuss why the expression of some genes does not follow the expected trends (Fig 1C vs Fig S1A). Out of the 24 genes they select for their analysis, at least four do not follow the expected trends: Sox2, according to literature, is a Naive gene, however, in Figure 1C its gene expression pattern is more similar to Formative late genes. Other genes with similar "unexpected" patterns are Zic3, Etv4 and Sall4.
Are the suggested experiments realistic in terms of time and resources? It would help if you could add an estimated cost and time investment for substantial experiments.
I think suggested experiments are doable as long as authors get publicly available data, i.e. the in-house dataset they generated for this study is enough to show applicability. For example datasets analysed in SCODE paper (https://doi.org/10.1093/bioinformatics/btx194) could be used as second benchmark. The point of applying the tool to another dataset is to show how it generalises across different biological systems, experiments and, potentially, sequencing technologies. - Are the data and the methods presented in such a way that they can be reproduced?
The methods section is really clear. To enable reproducibility both raw scRNA-seq data, the IGNITE source code and code written to benchmark it should be released in the public domain in appropriate repositories (eg. ENA, GitHub, Binder etc). - Are the experiments adequately replicated and statistical analysis adequate?
Yes.
Minor comments
- Specific experimental issues that are easily addressable.
Related to the Sox2 expression pattern is the binarization shown in Figure 2D. How is it possible that Sox2 is always marked as active? Could the authors clarify how these outlier behaviours emerge and propose mitigation strategies, if any?
In section 5.11.2 it is unclear if xi are in log scale or not. Since the model starts from binarized, log transformed expression values, should not generated ones be in the same scale as the input? - Are prior studies referenced appropriately?
Yes, referencing is clear. - Are the text and figures clear and accurate?
Yes, figures appear to be clear, readable and well documented both in captions and main text. - Do you have suggestions that would help the authors improve the presentation of their data and conclusions?
Section 3.3 could be improved by better describing experimental datasets. Only in the methods section it is clearly stated that experimental data for single KO experiments were retrieved from the literature.
Check typesetting:
- parenthesis missing in Eq. 1
- Leftover $ in section 3.1
- Parenthesis missing in Section 3.3
- Misplaced comma in section 5.2.1
Significance
- Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field.
The paper presents a method to infer GRN from scRNA-seq data alone. Applications include GRN prediction and their perturbations. This paper represents a technical advance in the field as it is the first application of the inverse Ising problem GRN inference. - Place the work in the context of the existing literature (provide references, where appropriate).
The paper itself presents the landscape of GRN inference tools using scRNA-seq data: SCODE, MaxEnt and CELLORACLE. More tools exist, for instance SCENIC (https://doi.org/10.1038/nmeth.4463) mainly relies on co-expression matrices. Other tools exist but require additional data types e.g. GRaNIE and GRaNPA (https://doi.org/10.15252/msb.202311627) leverage on physical interaction data (ATAC-seq, ChIP-seq). Similarly DeepFlyBrain uses deep neural networks to infer eGRN in Drosophila (https://doi.org/10.1038/s41586-021-04262-z). The value of tools like IGNITE and its competitors is that they do not require additional data types, which, in turn, helps in controlling experimental costs. - State what audience might be interested in and influenced by the reported findings.
The paper might be of interest to biologists interested in regulation of gene expression. The tool might turn out to be useful in planning experimental work by guiding the choice of perturbations to introduce in experimental systems. - Define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate.
I am a computational biologist.
I have no sufficient expertise to evaluate the mathematical details of the method.
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Reply to the reviewers
REVIEWER 1
Reviewer #1 Evidence, reproducibility and clarity: Nornes et al. have generated a cohort of arterial enhancers based on in silico analysis and validation with transgenic lines in both zebrafish and mice. They utilized publicly available datasets for chromatin marks, including ATAC-seq on endothelial cells either from cell culture or isolated from mice, as well as EP300 binding, H3K27Ac, and H3K4Me1. Focusing on eight arterial-expressed genes, they identified a putative enhancer region marked by at least one enhancer feature. After validating the activity of these enhancers in zebrafish and mice, the authors assessed the regulatory pathways upstream of these genes. Using ChIP-seq and Cut&Run for key endothelial transcription factors, they discovered that binding sites for SoXF and ETS factors are shared in arterial enhancers, whereas binding sites for Notch, MEF2, and Fox are present only in the subset of identified enhancers. Together this study provides an arterial enhancer atlas that allows further characterisation of regulatory network behind endothelial cell identity.
__Reviewer #1 Major Comment 1: __The authors have assessed 15 enhancers for arterial-venous specificity, by assessing the expression in DA, ISV, cardinal and ventral veins at 2 dpf. Interestingly there is a clear difference in the expression patterns of these enhancers in the zebrafish axial vasculature, especially seen at the level of ISV. The co-localization of the enhancer expression in the endothelium was done using endothelial marks expressed in both venous and arterial EC (kdrl). To fully distinguish if the expression is venous or arterial endothelial compartment colocalization with Tg expressed in arterial (flt1) or venous (lyve1) EC would be informative.
RESPONSE: We agree with the reviewer that a more detailed description of arterial-venous specificity of each enhancer could be included. In the original manuscript, the expression pattern of each enhancer within the vasculature was primarily assessed at 2 days post fertilization (dpf) in Fig 1-2. This identified arteries using direction of blood flow and available descriptive information, as arterial development in 2pdf zebrafish is very stereotypical and already well characterized.
__REVISION (PLANNED): __The original Figure 3A includes a more detailed assessment of arterial-venous specificity at 3dpf for four arterial enhancers (Cxcr4+135, Cxcr4+151, Gja5-78 and Gja5-7, chosen as enhancers representing the four types of expression patterns seen). We will now extend this more detailed analysis to all arterial enhancer:GFP lines. This analysis uses kdrl-mCherry to mark the entire vasculature, comparative to the expression of the arterial enhancers (GFP). This allows us to clearly identify the intersegmental arteries (as opposed to intersegmental veins) by looking for direct connection to the dorsal aorta, and by assessing the direction of blood flow within these vessels. This analysis is done at 3dpf to give time for the intersegmental arteries to acquire identity and connect definitively with the dorsal aorta, and for the diminishment of any GFP expression originating from the initial sprouting from the dorsal aorta. By extending this analysis to the other arterial enhancer zebrafish lines shown in Figure 2, we will be able to more clearly classify the activity of each enhancer within different vascular beds. This information will also be recorded in a new Table better detailing the timing and specificity of activity of each enhancer.
We chose not to use arterial or venous "marker lines" (e.g. Flt1:reporter or Lyve1:reporter) for the simple reason that these are also enhancer:GFP transgenes, and therefore are not necessarily definitive of the arterial or venous lineage per se (e.g. Flt1:GFP expression is controlled by the transcription factors binding the Flt1 enhancer in the same way that Cxcr4+135 and the others are, with the added caveat that the transcriptional regulation of the Flt1 and Lyve enhancers are not well defined). We felt that morphological determination based on direct connections and blood flow direction was therefore more accurate.
__Reviewer #1 Major Comment 2: __In addition, it is striking that cxc4+135 drives the expression in nearly every ISV as cxcl12+269 only every other. Similarly, not all the enhancers are enriched in the DA to the same level. Is there biological significance to this? could authors discuss these results further? The pattern of expression of the unc5b-identified enhancer is also striking, does this reflect the known roles of unc5b in the vascular formation?
__RESPONSE: __We agree, the diversity of enhancer expression patterns within the arterial compartment is notable, and really very interesting. The variations in enhancer expression pattern must be largely influenced by the transcription factor motifs within each enhancer, as these patterns were seen in both transient and stable transgenic zebrafish and therefore largely independent of chromatin integration location.
__REVISION (PLANNED): __The extension of Figure 3A to all enhancer lines (see previous comment) will permit us to more clearly classify the activity of each arterial enhancer within different beds and at different time points. Currently there were no clear links between a particular transcription factor motif/binding and expression pattern, something that is discussed briefly in the original Results and Discussion sections. However, the expansion of Figure 3A to all enhancers, and the creation of a Table summarizing this more systematically will make the link (or lack of one) between expression patterns within the arterial tree and TF motifs easier to appreciate and discuss.
__Reviewer #1 Major Comment 3: __The final part of the paper focuses on defining the presence of "deeply conserved" transcription factor binding sites (TFSB), defined as TFBS that are as conserved as the enhancer sequence surrounding them. In literature, the term 'deep conservation' refers to evolutionary conservation (genomic sequence preservation) in a wide range of species. Therefore, the additional classification presented by the authors based on the surrounding sequence is not clear. As, the KLF motifs in the Ece1in1, which is conserved between mouse and human, are defined as "deeply conserved". However, the FLK motif in the following enhancer, Flk1in10 (one line below), gets classified as non-deeply conserved, despite also being conserved between mouse and human. Thus, in the current form, there is a contradiction in the way the authors use the term 'deeply conserved' and the accepted meaning of this term. To avoid confusion, it would be important to revise this nomenclature.
RESPONSE: We agree that this nomenclature should be revised. Our aim was to develop a standard approach to transcription factor motif analysis that could be applied to enhancers regardless of conservation levels and size, and easily replicated by others. Because not all functional transcription factor motifs within enhancers are necessarily conserved between species, we were careful to label both conserved and non-conserved motifs for each TF examined. Nonetheless, extra emphasis was placed on motifs with confirmatory TF binding evidence (e.g. ChIP-seq/CUT&RUN), and those conserved at the same depth as the surrounding sequence. This was because our previous work on endothelial enhancers clearly indicates that these motifs are far more likely to play a key role in regulation. However, the reviewer is correct to note that referring to such motifs as "deeply" conserved could be misinterpreted.
REVISION (COMPLETED): We have altered our nomenclature. This is explained in the relevant Results sections: "Because the level of conservation of motifs can often be an indication of their importance to enhancer activity, we classified each motif into three categories: strongly conserved (motif conserved to the same depth of the surrounding sequence), weakly conserved (motif conserved in orthologous human enhancer but not to the same depth as the surrounding sequence) and not conserved (motif is not conserved within the orthologous human sequence)".
Two enhancers (Unc5b-57 and Cdh1-1) were only conserved human-mouse, therefore each TF motif within these enhancers could be annotated as both weakly and strongly conserved. As the reviewer noted, this does create confusion. We have now adjusted Figure 5 to use a distinct shape for motifs for which no distinction between weak and strong motif can be made. This does not cover Ece1in1, which is conserved human-mouse-tenrec but was erroneously originally labelled human-mouse only. This error has been corrected.
__Reviewer #1 Minor Comment 1: __Details on how the corresponding non-coding regions between mice and humans were established are missing, what alignment tool was used?
RESPONSE AND REVISION (COMPLETED): This information has now been included in the relevant Results section: "Orthologous human enhancer sequences were identified for every enhancer using the Vertebrate Multiz Alignment & Conservation Track on the UCSC genome browser"
__Reviewer #1 Minor Comment 2: __Not sufficient details are provided for the re-analysis of siRNA data. E.g., which clustering method was used? How the clusters were assigned to cell identities?
RESPONSE AND REVISION (COMPLETED): The details regarding the re-analysis of scRNA data has been expanded in the Methods sections: "Publicly available E12 and E17.5 scRNA-seq data from EC isolated from BmxCreERT2;RosatdTomato lineage traced murine hearts54 was obtained from GEO (GSE214942) prior to processing FASTQ files with the 10X Genomics CellRanger pipeline (V7.0.0). RNA-seq reads were aligned to the mm10 genome reference downloaded from 10X Genomics with the addition of the TdTomato-WPRE sequence. Exclusion of low quality cells with either a UMI count >100,000, total gene count 10%) was performed using Scater55. Data normalisation was performed using the MultiBatchNormalisation method prior to merging of TdTomato positive and negative datasets from individual timepoints. The top 2000 most highly variable genes (excluding mitochondrial and ribosomal genes) in the merged datasets were identified using the Seurat FindVariableFeatures method and utilised to calculate principal component analysis (PCA). Normalised data was scaled using the ScaleData function. Cell clustering was performed using the standard unsupervised graph-based clustering method implemented within Seurat (V4)56. Clusters were visualised in two dimensions using UMAP based non-linear dimensional reduction following the standard Seurat (V4) workflow49. Identified clusters were assigned identities based on marker genes shown to be differentially expressed between populations previously identified in the original study47. Key markers include Npr3 (endocardial), Fabp4 (coronary vascular endothelial), and Nfatc1 (valvular endothelial). The E12.5 sinus venosus EC cluster was assigned based in Aplnr as previously described54. Arterial and venous EC clusters in the E17.5 datasets were annotated based on their enriched expression of Gja5 and Nr2f2, respectively."
__Reviewer #1 Minor Comment 3: __Details about the first HOMER analysis (in the assessment of transcription factor motifs and binding patterns at arterial enhancers) seem to be missing from the methods section.
RESPONSE AND REVISION (COMPLETED): This has been included in the methods: "Analysis of overrepresented motifs within our validated arterial enhancer cohort was performed with HOMER's findMotifsGenome tool using the full validated region of the arterial enhancers. The analysis used the hg38 masked genome and otherwise default settings for all other parameters including randomly selected background regions".
__Reviewer #1 Minor Comment 4: __Pg 12: "For ETS, 23/23 arterial enhancers contained at least one conserved motif (all "deeply" conserved to the same depth as the surrounding enhancer, see S7)". Is it S8, where conservation is indicated?
__ ____RESPONSE AND REVISION (COMPLETED):__ We have corrected this error in the text - no figure actually needed to be referenced here as the previous sentence contained the full list of relevant figures to this statement (Table 2 and Figures 5 and S9, previously called S8, are the places to see this information).
__Reviewer #1 Minor Comment 5: __Figure 1 and 2 for non-zebrafish readers it would be useful to indicate in Figures 1 and 2 the non EC expression that can be observed in the embryos.
RESPONSE AND REVISION (COMPLETED): In addition to arterial expression, a number of the enhancer:GFP transgenes also showed GFP expression within the neural tube. In addition, some transient transgenic embryos also showed ectopic expression in muscle fibres. These have now been indicated on the images in Figure 1 and 2.
__Reviewer #1 Minor Comment 6: __Table S1: Please, indicate in the legend what the asterisk in the H DNAseI column stands for
RESPONSE AND REVISION (COMPLETED): The asterisk indicates where DNaseI hypersensitivity is also seen in multiple non-EC lines. This explanation has been added to the legend.
__Reviewer #1 Minor Comment 7: __Figure S8: The phrasing "conserved to animal" in Figure S8 is misleading. There is no difference in something being conserved to tenrec or manatee, as both are Afrotherians. Hence, the data show that both Efnb2-141 and Ephb4-2 were present in the common ancestor of Afrotherians and humans, namely the ancestor of all placentals. Instead, it would be good to indicate the phylogenetic group for which the presence of the enhancer can be inferred (in this case, Placentalia).
__RESPONSE: __Whilst I appreciate the point, it is the exact sequence that is important here - obviously tenrec and manatee are similar species but still contain differences in nucleotide sequences. The information about conservation leads the reader to the exact species with which the comparison is being made. We tried to restrict this to just one species per phylogenetic group (e.g. tenrec, opossum, chicken, zebrafish) but occasionally this was not possible.
Reviewer #1 Significance
To date, a systematic approach to identifying the regulatory networks driving endothelial cell identity is missing. This study provides important datasets and validation of enhancers involved in arterial gene expression and the associated transcription factors. Although this is only the tip of the iceberg, this work represents a significant milestone in the systematic understanding of how arterial gene expression is regulated. Overall, this study offers a powerful resource for understanding arterial gene regulation and conducting genome-wide studies of arterial enhancers.
__RESPONSE: __We thank the reviewer for these kind words. Whilst we agree this is only a very small snapshot of all the arterial enhancers involved in gene regulation, we would like to stress that not only is this a massive increase to what has been known previously, but is also deliberately focused on the genes used to define arterial identity during development and in the adult, therefore these enhancers by themselves form an extremely valuable dataset with which to study the key factors driving arterial differentiation and identity.
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REVIEWER 2
__Reviewer #2 Evidence, reproducibility and clarity: __In this work, Nornes and collaborators have described a cohort of arterial enhancers that drive gene expression in arteries and not in veins. The paper is very well written and it is very informative. The authors have used in silico models to identified the potential artery enhancers and then used different developmental in vivo systems, zebrafish and mice, to validate their findings. Finally, the authors have explored what transcription factors may be binding the identified enhancer sequences and thus, drive arterial gene expression. I would like to congratulate the authors for this work that it has been a pleasure to read and review.
Reviewer #2 Major Comment 1: In their identification of enhancers, the authors consider a candidate every enhancer that has a putative mark in both mouse and human. Nevertheless, all the human data comes from in vitro analysis. Considering how much cell culture affects endothelial cell identity, inducing effects like EndoMT, would this have any effect on the enhancer selection? Would it be possible to search any human in vivo data? Would this allow for even stronger and more relevant sequences?
__RESPONSE: __We agree that the use of human endothelial cells in culture raises some potential issues. However, we stress that the mouse EC enhancer marks, which played a key role in defining putative enhancers, come from in vivo analysis (E11 embryos, P6 retina and adult aorta), limiting the potential for significant impact from cell culture-induced issues. Whilst we would have enthusiastically incorporated human in vivo data had it been available, our approach was still indisputably successful at identifying arterial enriched/specific enhancers.
We consider it unlikely that culture/identity-related problems with human cultured ECs led to a significant undercount of enhancers, in part because comparatively few regions with enhancer marks in mouse in vivo ECs were excluded due to the absence of human enhancer marks. In fact, Cxcr4, Cxcl12, and Gja5 were poorly transcribed in the human cell lines studied here and consequently only enhancer marks in mouse were used to define putative enhancers for these three genes (this is clearly stated in the Results section). If a similar rational had applied to the remaining five genes, only an additional six putative enhancers would have been tested (one for Gja4, two for Nrp1 and three for Unc5b). However, we felt it made sense to include analysis of human enhancer marks for these five genes, as all were expressed in the human ECs used (as indicated by H3K1Me3 and DNaseI hypersensitivity at promoter regions) and orthologous human enhancers were identified for all. Additionally, our retrospective analysis of previously described mammalian in vivo-validated EC enhancers (Table S1 in the original manuscription, including eight arterial enhancers) found that all 32 were marked by at least one enhancer mark in human samples (1/32 did not contain mouse enhancer marks). We also tested eleven regions that did not reach our putative enhancer threshold, including five with only mouse marks. None of these directed expression in transgenic analysis.
Reviewer #2 Major Comment 2: The human data comes from vein endothelial or microvasculature endothelial cells. Specially because some of the enhancers identified by the authors drive also vein expression, could the authors discriminate whether this is due to the identification coming from vein cells. Is there available data from HAECs? Would this not be conceptually more correct that using vein endothelial cells data? This should be at least discussed in the paper.
__RESPONSE AND REVISION (COMPLETED): __We have now included a comparison with enhancer marks from HAECs, telo-HAECs and HUAECs as a new Figure S5. The enhancer marks seen in these cells were very similar to those in the HUVEC and microvascular cells already surveyed. Had enhancer marks within HAECs/telo-HAEC/HUAECs been included as a human enhancer mark in our initial survey, it would have been unlikely to have altered our analysis, although we agree it would have made it more conceptually correct. We chose not to go back and engineer this into our original enhancer selection rational however as we felt it would be intellectually dishonest. A paragraph has been added to the Results section about this analysis.
Reviewer #2 Major Comment 3: Although the authors use the mouse embryo to further validate their finding beyond the zebrafish, the expression are a bit different. While on the fish the enhancers label smaller vessels of arterial identity, in the mouse, only bigger arteries are marked. Is this defined by the time of the analysis?
__RESPONSE: __This experiment was conducted to demonstrate that these enhancers were arterial enriched in both zebrafish and mouse transgenesis, and feel this is clearly shown by the current data. Whilst I do not really agree that the expression pattern is different (for example, the Gja5 enhancers are more restricted to the major arteries in both zebrafish and mouse, compared to the more widely expressed Efnb2-333), this is challenging to ascertain at a single time-point in a transient transgenic mouse assay. Whilst it would be potentially interesting to better assess the activity of these enhancers over time in mice, we consider this a lengthy experiment (multiple stable lines would need to be established and characterized for each enhancer) which would not add particular benefit to this paper.
Reviewer #2 Major Comment 4: The analysis of the enhancers is only done during development. Is the activity of these enhancers maintained through live or only important for artery vs vein determination? Is the expression of the different enhancer reporters maintained into adulthood?
RESPONSE AND REVISION (PLANNED): We agree this would be interesting to ascertain. We plan to examine the activity of enhancer:GFP activity in adult fish fins (which are accessible even without crossing into a casper background, which is beyond the timescale of this project) in the fully revised version of this paper. We have already conducted a feasibility study on four arterial enhancers:GFP lines (Gja5-7:GFP, Gja5-78:GFP, Gja4+40:GFP and Efnb2-333:GFP), which found that all four were still active, and arterial-specific, in the adult.
Reviewer #2 Significance
This is a very well done study with potential interest for vascular biologists, in particular to those interested in the determination between artery and veins in a context of development. It advances our knowledge on the field of vascular biology as it not only proposes potential enhancers but also goes on to validation of the enhancers. Nevertheless, it is important to note that some of this enhancers have been identified from in vitro human data. In vitro culture of endothelial cells affects their cellular identity and thus, this study may have underscored many potential enhancers.
REVIEWER 3
__Reviewer #3: Evidence, reproducibility and clarity: __This manuscript by Nornes et al analyzed multiple published databases and identified a group of putative enhancers for 8 selected non-Notch arterial genes in mouse and human ECs. These enhancers were cloned and screened in fish embryos to test their effect in driving GFP reporter expression, which narrowed down a cohort of enhancers for further testing of expression activities in mouse embryonic arteries. The authors then analyzed the sequences of these enhancers, and identified binding motifs of ETS, SOX-F, FOX and MEF2 family TFs and Notch transcription regulator RBPJ commonly present in closed proximity in these arterial enhancers, suggesting interaction between these TFs in determination of arterial identity.
Reviewer #3 Major Comment : This study provides an enormous amount of bioinformatic data analysis and screening results in transgenic fish and mouse models, which led to the discovery of a group of arterial enhancers and TFs binding motifs essential in regulating arterial identity.
Reviewer #3 Other Comments ____1: Choice of arterial genes is slightly biased. Acvrl1/Alk1 is not enriched in arterial ECs. Sema3G, which is highly expressed in arterial ECs, is missing. UNC5B is enriched in arterial ECs but also expressed by sprouting ECs (PMID: 38866944).
__RESPONSE: __When we started this project, scRNA-seq datasets in the developing vasculature were less available. Consequently, we initially based our choice of genes on data from Raftrey et al., Circ Res 2021 (available earlier on bioRxiv), which was focused on mouse coronary arterial ECs at the timepoints that arteries differentiate. This found Acvrl1 to be arterial enriched (not a novel observation, many publications treat Acvrl1 as arterial specific or arterial-enriched) and did not list Sema3g. We also considered a wider dataset from mouse and human mid-gestation embryos when available (Hou et al., Cell Research 2022). However, it is important to note that we did not aim to investigate every arterial-enriched gene, rather to use these datasets to help identify loci associated with gene expression patterns which indicated a high likelihood of containing arterial enhancers active during arterial differentiation.
Sc-RNAseq data from both Raftery et al., and Hou et al., indicated that arterial ECs are subdivided into two groups, reflecting maturity but also potentially slightly different developmental trajectories. The genes studied here were therefore selected to evenly cover both subgroups, with Acvrl1, Cxcl12, Gja5 and Nrp1 primarily restricted to the mature arterial EC subgroup, while Cxcr4, Efnb2, Gja4 and Unc5b were also expressed in the less mature/arterial plexus/pre-arterial EC subgroup. It is notable that genes within the latter subgroup are also associated with angiogenic/sprouting ECs (Dll4 also belongs to this subgroup), which likely indicates biological links between angiogenesis and arterial identity rather than a problem in gene choice and specificity.
__REVISION (COMPLETED): __This is already discussed in the Results section (angiogenic expression of arterial genes is discussed within the MEF2 and RBPJ sections) and in the Discussion (paragraph 2, referring to different expression patterns within arterial ECs). However, we have now edited the relevant Results section to better explain gene selection: "It is therefore clear that a better understanding of the regulatory pathways directing arterial differentiation requires the identification and characterization of a larger number of arterial enhancers directing the expression of key arterial identity genes. To identify a cohort of such enhancers, we looked in the loci of eight non-Notch genes: Acvrl1(ALK1) Cxcr4, Cxcl12, Efnb2, Gja4(CX37), Gja5 (CX40), Nrp1 and Unc5b. Although not a definitive list of arterial identity genes, single cell transcriptomic analysis indicates these genes are all significantly enriched in arterial ECs4,20, and are commonly used to define arterial EC populations in mouse and human scRNAseq analysis4,5,20,54. Additionally, single-cell transcriptomic data indicates that arterial ECs can be divided into two subgroups4,20. The genes selected here are equally split between subgroups (Acvrl1, Cxcl12, Gja5 and Nrp1 from the mature arterial EC subgroup, Cxcr4, Efnb2, Gja4 and Unc5b from the less mature/arterial plexus/pre-arterial EC subgroup)4,20. We did not exclude genes also implicated in angiogenesis/expressed in sprouting ECs, as these genes formed that vast majority of those associated with the less mature EC subgroup".
Reviewer #3 Other Comments ____2: Exclusion of Notch genes. Although the reason for choosing non-notch genes and excluding notch genes for screening is addressed in this paper, it would be interesting to examine how the arterial enhancers identified in this study are present in the Notch genes, especially Dll4 (enriched in arterial and sprouting ECs) and Jag1 (enriched in arterial ECs).
__RESPONSE: __Previous work from our lab and others has already examined arterial enhancers for Notch pathway genes. We already included these enhancers in all our later analysis (Figure 5-6 and relevant supplemental figures), including analysis of TF motifs.
Reviewer #3 Other Comments ____3: SoxF family TFs. Among the 3 members of SoxF TFs, only Sox17 and Sox7 were assessed. Though not specific, Sox18 is highly expressed in the arteries. On the contrary, Sox7 is highly expressed in the vein and shows weak expression in arterial ECs (PMID: 26630461).
__RESPONSE AND REVISION (PLANNED): __We agree. We will include assessment of SOX18 binding in our final revised manuscript. An antibody for this analysis has been identified already.
Reviewer #3 Other Comments ____4: Minor inaccuracy in Intro/paragraph 3: though sox17 is reported as indispensable for arterial specification (PMID: 24153254), losing a single SoxF factor does not seem to completely compromise the arterial program (PMID: 24153254, PMID: 26630461). A combined loss of Sox17/18, or Sox 7/17/18, seems to do the job (PMID: 26630461).
__RESPONSE: __We have altered this section: "The evidence linking SOXF transcription factors to arterial differentiation is more extensive, with loss of either SOX17 (the SOXF factor most specific to arterial ECs) or SOX7 resulting in arterial defects21-24. Whilst losing a single SOXF factor does not entirely compromise the arterial program, arterial differentiation appears absent after compound Sox17;Sox18 and Sox7;Sox17;Sox18 deletion, although this occurs alongside significantly impaired angiogenesis and severe vascular hyperplasia21-24. PMID 24153254 is reference 23, PMID 26630461 is reference 24.
Reviewer #3 Other Comments ____5: Fig.4 e14.5 mouse embryos. If the observation aims to assess the dorsal aorta, it would be better to use mouse embryos at mid-gestation (e9.5-10.5), when the paired DAs are formed with arterial identity but haven't been remodelled and fused as one single aorta. The morphological data in this figure would be better to show the colocalization of LacZ expression and an arterial marker (e.g. Sox17) using immulfluorescence staining instead of purely lacZ.
RESPONSE: This experiment was primarily conducted to demonstrate that our enhancers were arterial enriched in both zebrafish and mouse transgenesis, and feel this is clearly shown with the e14.5 transgenic embryos originally shown. We chose e14.5 because it matched the timepoints used for the single cell transcriptomics first used to select the target arterial identity genes, and feel it is a good match to 2-3 dpf zebrafish in terms of arterial differentiation mechanisms. We agree that E9-10 would have also been an additional useful timepoint, but we do not have the resources to generate this data nor consider it essential for the conclusions of our work here.
__REVISION (PLANNED): __We are unable to perform immunofluorescence in the e14.5 transgenic embryos due to the fixation and staining solutions used for X-gal staining (which was done by an external company and could not be altered), but agree additional information is needed to demonstrate arterial endothelial specificity. We will therefore expand the analysis of sectioned embryos (currently restricted to just the Efnb2-333:LacZ transgene) to all enhancers shown in Figure 4. This analysis has some limitations due to infiltration of the X-gal solution to deeper tissues, but is anticipated it will clearly show enhancer activity in arterial endothelial cells rather than venous ECs or smooth muscle cells.
__Reviewer #3 (Significance (Required)): __This novel work establishes an important foundation for future understanding of how TFs may interact to determine arterial specification.
Other revisions
In addition to changes suggested by the reviewers, we also made one additional adjustment to the paper to include analysis of two additional putative enhancers (Efnb2-159 and Cxcr4+119). These were initially omitted in error yet both regions reach the standard of testable putative enhancers (noted in small changes to Figure S1 and Table S2). When tested in zebrafish transient transgenic embryos, Cxcr4+119 was inactive whilst Efnb2-159 was active in arterial endothelial cells. The relevant tables and figures have been adjusted to reflect these changes, the most significant of which are the inclusion of Efnb2-159 positive zebrafish in Figure 1 (and the necessity to create an additional supplemental Figure (S3) to accommodate the increased number of images), and analysis of Efnb2-159 transcription factor motifs/binding as part of Figure 5 and 6. No conclusions were altered by the inclusion of this additional data.
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Referee #3
Evidence, reproducibility and clarity
This manuscript by Nornes et al analyzed multiple published databases and identified a group of putative enhancers for 8 selected non-Notch arterial genes in mouse and human ECs. These enhancers were cloned and screened in fish embryos to test their effect in driving GFP reporter expression, which narrowed down a cohort of enhancers for further testing of expression activities in mouse embryonic arteries. The authors then analyzed the sequences of these enhancers, and identified binding motifs of ETS, SOX-F, FOX and MEF2 family TFs and Notch transcription regulator RBPJ commonly present in closed proximity in these arterial enhancers, suggesting interaction between these TFs in determination of arterial identity.
Major comments:
This study provides an enormous amount of bioinformatic data analysis and screening results in transgenic fish and mouse models, which led to the discovery of a group of arterial enhancers and TFs binding motifs essential in regulating arterial identity.
Other comments:
- Choice of arterial genes is slightly biased. Acvrl1/Alk1 is not enriched in arterial ECs. Sema3G, which is highly expressed in arterial ECs, is missing. UNC5B is enriched in arterial ECs but also expressed by sprouting ECs (PMID: 38866944).
- Exclusion of Notch genes. Although the reason for choosing non-notch genes and excluding notch genes for screening is addressed in this paper, it would be interesting to examine how the arterial enhancers identified in this study are present in the Notch genes, especially Dll4 (enriched in arterial and sprouting ECs) and Jag1 (enriched in arterial ECs).
- SoxF family TFs. Among the 3 members of SoxF TFs, only Sox17 and Sox7 were assessed. Though not specific, Sox18 is highly expressed in the arteries. On the contrary, Sox7 is highly expressed in the vein and shows weak expression in arterial ECs (PMID: 26630461). Minor inaccuracy in Intro/paragraph 3: though sox17 is reported as indispensable for arterial specification (PMID: 24153254), losing a single SoxF factor does not seem to completely compromise the arterial program (PMID: 24153254, PMID: 26630461). A combined loss of Sox17/18, or Sox 7/17/18, seems to do the job (PMID: 26630461).
- Fig.4 e14.5 mouse embryos. If the observation aims to assess the dorsal aorta, it would be better to use mouse embryos at mid-gestation (e9.5-10.5), when the paired DAs are formed with arterial identity but haven't been remodeled and fused as one single aorta. The morphological data in this figure would be better to show the colocalization of LacZ expression and an arterial marker (e.g. Sox17) using immulfluorescence staining instead of purely lacZ.
Significance
This novel work establishes an important foundation for future understanding of how TFs may interact to determine arterial specification.
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Referee #2
Evidence, reproducibility and clarity
In this work, Nornes and collaborators have described a cohort of arterial enhancers that drive gene expression in arteries and not in veins. The paper is very well written and it is very informative. The authors have used in silico models to identified the potential artery enhancers and then used different developmental in vivo systems, zebrafish and mice, to validate their findings. Finally, the authors have explored what transcription factors may be binding the identified enhancer sequences and thus, drive arterial gene expression. I would like to congratulate the authors for this work that it has been a pleasure to read and review.
Major comments:
- In their identification of enhancers, the authors consider a candidate every enhancer that has a putative mark in both mouse and human. Nevertheless, all the human data comes from in vitro analysis. Considering how much cell culture affects endothelial cell identity, inducing effects like EndoMT, would this have any effect on the enhancer selection? Would it be possible to search any human in vivo data? Would this allow for even stronger and more relevant sequences?
- The human data comes from vein endothelial or microvasculature endothelial cells. Specially because some of the enhancers identified by the authors drive also vein expression, could the authors discriminate whether this is due to the identification coming from vein cells. Is there available data from HAECs? Would this not be conceptually more correct that using vein endothelial cells data? This should be at least discussed in the paper.
- Although the authors use the mouse embryo to further validate their finding beyond the zebrafish, the expression are a bit different. While on the fish the enhancers label smaller vessels of arterial identity, in the mouse, only bigger arteries are marked. Is this defined by the time of the analysis?
- The analysis of the enhancers is only done during development. Is the activity of these enhancers maintained through live or only important for artery vs vein determination? Is the expression of the different enhancer reporters maintained into adulthood?
Significance
This is a very well done study with potential interest for vascular biologists, in particular to those interested in the determination between artery and veins in a context of development. It advances our knowledge on the field of vascular biology as it not only proposes potential enhancers but also goes on to validation of the enhancers. Nevertheless, it is important to note that some of this enhancers have been identified from in vitro human data. In vitro culture of endothelial cells affects their cellular identity and thus, this study may have underscored many potential enhancers.
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Referee #1
Evidence, reproducibility and clarity
Summary:
Nornes et al. have generated a cohort of arterial enhancers based on in silico analysis and validation with transgenic lines in both zebrafish and mice. They utilized publicly available datasets for chromatin marks, including ATAC-seq on endothelial cells either from cell culture or isolated from mice, as well as EP300 binding, H3K27Ac, and H3K4Me1. Focusing on eight arterial-expressed genes, they identified a putative enhancer region marked by at least one enhancer feature. After validating the activity of these enhancers in zebrafish and mice, the authors assessed the regulatory pathways upstream of these genes. Using ChIP-seq and Cut&Run for key endothelial transcription factors, they discovered that binding sites for SoXF and ETS factors are shared in arterial enhancers, whereas binding sites for Notch, MEF2, and Fox are present only in the subset of identified enhancers. Together this study provides an arterial enhancer atlas that allows further characterisation of regulatory network behind endothelial cell identity.
Major comments:
The authors have assessed 15 enhancers for arterial-venous specificity, by assessing the expression in DA, ISV, cardinal and ventral veins at 2 dpf. Interestingly there is a clear difference in the expression patterns of these enhancers in the zebrafish axial vasculature, especially seen at the level of ISV. The co-localization of the enhancer expression in the endothelium was done using endothelial marks expressed in both venous and arterial EC (kdrl). To fully distinguish if the expression is venous or arterial endothelial compartment colocalization with Tg expressed in arterial (flt1) or venous (lyve1) EC would be informative. In addition, it is striking that cxc4+135 drives the expression in nearly every ISV as cxcl12+269 only every other. Similarly, not all the enhancers are enriched in the DA to the same level. Is there biological significance to this? could authors discuss these results further? The pattern of expression of the unc5b-identified enhancer is also striking, does this reflect the known roles of unc5b in the vascular formation? The final part of the paper focuses on defining the presence of "deeply conserved" transcription factor binding sites (TFSB), defined as TFBS that are as conserved as the enhancer sequence surrounding them. In literature, the term 'deep conservation' refers to evolutionary conservation (genomic sequence preservation) in a wide range of species. Therefore, the additional classification presented by the authors based on the surrounding sequence is not clear. As, the KLF motifs in the Ece1in1, which is conserved between mouse and human, are defined as "deeply conserved". However, the FLK motif in the following enhancer, Flk1in10 (one line below), gets classified as non-deeply conserved, despite also being conserved between mouse and human. Thus, in the current form, there is a contradiction in the way the authors use the term 'deeply conserved' and the accepted meaning of this term. To avoid confusion, it would be important to revise this nomenclature.
Minor:
Details on how the corresponding non-coding regions between mice and humans were established are missing, what alignment tool was used?
Not sufficient details are provided for the re-analysis of siRNA data. E.g., which clustering method was used? How the clusters were assigned to cell identities?
Details about the first HOMER analysis (in the assessment of transcription factor motifs and binding patterns at arterial enhancers) seem to be missing from the methods section.
Pg 12: "For ETS, 23/23 arterial enhancers contained at least one conserved motif (all "deeply" conserved to the same depth as the surrounding enhancer, see S7)". Is it S8, where conservation is indicated?
Figure 1 and 2 for non-zebrafish readers it would be useful to indicate in Figures 1 and 2 the non EC expression that can be observed in the embryos.
Table S1: Please, indicate in the legend what the asterisk in the H DNAseI column stands for
Figure S8: The phrasing "conserved to animal" in Figure S8 is misleading. There is no difference in something being conserved to tenrec or manatee, as both are Afrotherians. Hence, the data show that both Efnb2-141 and Ephb4-2 were present in the common ancestor of Afrotherians and humans, namely the ancestor of all placentals. Instead, it would be good to indicate the phylogenetic group for which the presence of the enhancer can be inferred (in this case, Placentalia).
Significance
To date, a systematic approach to identifying the regulatory networks driving endothelial cell identity is missing. This study provides important datasets and validation of enhancers involved in arterial gene expression and the associated transcription factors. Although this is only the tip of the iceberg, this work represents a significant milestone in the systematic understanding of how arterial gene expression is regulated. Overall, this study offers a powerful resource for understanding arterial gene regulation and conducting genome-wide studies of arterial enhancers.
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Reply to the reviewers
REVIEWER 1
Reviewer #1 Evidence, reproducibility and clarity: Nornes et al. have generated a cohort of arterial enhancers based on in silico analysis and validation with transgenic lines in both zebrafish and mice. They utilized publicly available datasets for chromatin marks, including ATAC-seq on endothelial cells either from cell culture or isolated from mice, as well as EP300 binding, H3K27Ac, and H3K4Me1. Focusing on eight arterial-expressed genes, they identified a putative enhancer region marked by at least one enhancer feature. After validating the activity of these enhancers in zebrafish and mice, the authors assessed the regulatory pathways upstream of these genes. Using ChIP-seq and Cut&Run for key endothelial transcription factors, they discovered that binding sites for SoXF and ETS factors are shared in arterial enhancers, whereas binding sites for Notch, MEF2, and Fox are present only in the subset of identified enhancers. Together this study provides an arterial enhancer atlas that allows further characterisation of regulatory network behind endothelial cell identity.
__Reviewer #1 Major Comment 1: __The authors have assessed 15 enhancers for arterial-venous specificity, by assessing the expression in DA, ISV, cardinal and ventral veins at 2 dpf. Interestingly there is a clear difference in the expression patterns of these enhancers in the zebrafish axial vasculature, especially seen at the level of ISV. The co-localization of the enhancer expression in the endothelium was done using endothelial marks expressed in both venous and arterial EC (kdrl). To fully distinguish if the expression is venous or arterial endothelial compartment colocalization with Tg expressed in arterial (flt1) or venous (lyve1) EC would be informative.
RESPONSE: We agree with the reviewer that a more detailed description of arterial-venous specificity of each enhancer could be included. In the original manuscript, the expression pattern of each enhancer within the vasculature was primarily assessed at 2 days post fertilization (dpf) in Fig 1-2. This identified arteries using direction of blood flow and available descriptive information, as arterial development in 2pdf zebrafish is very stereotypical and already well characterized.
__REVISION (PLANNED): __The original Figure 3A includes a more detailed assessment of arterial-venous specificity at 3dpf for four arterial enhancers (Cxcr4+135, Cxcr4+151, Gja5-78 and Gja5-7, chosen as enhancers representing the four types of expression patterns seen). We will now extend this more detailed analysis to all arterial enhancer:GFP lines. This analysis uses kdrl-mCherry to mark the entire vasculature, comparative to the expression of the arterial enhancers (GFP). This allows us to clearly identify the intersegmental arteries (as opposed to intersegmental veins) by looking for direct connection to the dorsal aorta, and by assessing the direction of blood flow within these vessels. This analysis is done at 3dpf to give time for the intersegmental arteries to acquire identity and connect definitively with the dorsal aorta, and for the diminishment of any GFP expression originating from the initial sprouting from the dorsal aorta. By extending this analysis to the other arterial enhancer zebrafish lines shown in Figure 2, we will be able to more clearly classify the activity of each enhancer within different vascular beds. This information will also be recorded in a new Table better detailing the timing and specificity of activity of each enhancer.
We chose not to use arterial or venous “marker lines” (e.g. Flt1:reporter or Lyve1:reporter) for the simple reason that these are also enhancer:GFP transgenes, and therefore are not necessarily definitive of the arterial or venous lineage per se (e.g. Flt1:GFP expression is controlled by the transcription factors binding the Flt1 enhancer in the same way that Cxcr4+135 and the others are, with the added caveat that the transcriptional regulation of the Flt1 and Lyve enhancers are not well defined). We felt that morphological determination based on direct connections and blood flow direction was therefore more accurate.
__Reviewer #1 Major Comment 2: __In addition, it is striking that cxc4+135 drives the expression in nearly every ISV as cxcl12+269 only every other. Similarly, not all the enhancers are enriched in the DA to the same level. Is there biological significance to this? could authors discuss these results further? The pattern of expression of the unc5b-identified enhancer is also striking, does this reflect the known roles of unc5b in the vascular formation?
__RESPONSE: __We agree, the diversity of enhancer expression patterns within the arterial compartment is notable, and really very interesting. The variations in enhancer expression pattern must be largely influenced by the transcription factor motifs within each enhancer, as these patterns were seen in both transient and stable transgenic zebrafish and therefore largely independent of chromatin integration location.
__REVISION (PLANNED): __The extension of Figure 3A to all enhancer lines (see previous comment) will permit us to more clearly classify the activity of each arterial enhancer within different beds and at different time points. Currently there were no clear links between a particular transcription factor motif/binding and expression pattern, something that is discussed briefly in the original Results and Discussion sections. However, the expansion of Figure 3A to all enhancers, and the creation of a Table summarizing this more systematically will make the link (or lack of one) between expression patterns within the arterial tree and TF motifs easier to appreciate and discuss.
__Reviewer #1 Major Comment 3: __The final part of the paper focuses on defining the presence of "deeply conserved" transcription factor binding sites (TFSB), defined as TFBS that are as conserved as the enhancer sequence surrounding them. In literature, the term 'deep conservation' refers to evolutionary conservation (genomic sequence preservation) in a wide range of species. Therefore, the additional classification presented by the authors based on the surrounding sequence is not clear. As, the KLF motifs in the Ece1in1, which is conserved between mouse and human, are defined as "deeply conserved". However, the FLK motif in the following enhancer, Flk1in10 (one line below), gets classified as non-deeply conserved, despite also being conserved between mouse and human. Thus, in the current form, there is a contradiction in the way the authors use the term 'deeply conserved' and the accepted meaning of this term. To avoid confusion, it would be important to revise this nomenclature.
RESPONSE: We agree that this nomenclature should be revised. Our aim was to develop a standard approach to transcription factor motif analysis that could be applied to enhancers regardless of conservation levels and size, and easily replicated by others. Because not all functional transcription factor motifs within enhancers are necessarily conserved between species, we were careful to label both conserved and non-conserved motifs for each TF examined. Nonetheless, extra emphasis was placed on motifs with confirmatory TF binding evidence (e.g. ChIP-seq/CUT&RUN), and those conserved at the same depth as the surrounding sequence. This was because our previous work on endothelial enhancers clearly indicates that these motifs are far more likely to play a key role in regulation. However, the reviewer is correct to note that referring to such motifs as “deeply” conserved could be misinterpreted.
REVISION (COMPLETED): We have altered our nomenclature. This is explained in the relevant Results sections: “Because the level of conservation of motifs can often be an indication of their importance to enhancer activity, we classified each motif into three categories: strongly conserved (motif conserved to the same depth of the surrounding sequence), weakly conserved (motif conserved in orthologous human enhancer but not to the same depth as the surrounding sequence) and not conserved (motif is not conserved within the orthologous human sequence)”.
Two enhancers (Unc5b-57 and Cdh1-1) were only conserved human-mouse, therefore each TF motif within these enhancers could be annotated as both weakly and strongly conserved. As the reviewer noted, this does create confusion. We have now adjusted Figure 5 to use a distinct shape for motifs for which no distinction between weak and strong motif can be made. This does not cover Ece1in1, which is conserved human-mouse-tenrec but was erroneously originally labelled human-mouse only. This error has been corrected.
__Reviewer #1 Minor Comment 1: __Details on how the corresponding non-coding regions between mice and humans were established are missing, what alignment tool was used?
RESPONSE AND REVISION (COMPLETED): This information has now been included in the relevant Results section: “Orthologous human enhancer sequences were identified for every enhancer using the Vertebrate Multiz Alignment & Conservation Track on the UCSC genome browser”
__Reviewer #1 Minor Comment 2: __Not sufficient details are provided for the re-analysis of siRNA data. E.g., which clustering method was used? How the clusters were assigned to cell identities?
RESPONSE AND REVISION (COMPLETED): The details regarding the re-analysis of scRNA data has been expanded in the Methods sections: “Publicly available E12 and E17.5 scRNA-seq data from EC isolated from BmxCreERT2;RosatdTomato lineage traced murine hearts54 was obtained from GEO (GSE214942) prior to processing FASTQ files with the 10X Genomics CellRanger pipeline (V7.0.0). RNA-seq reads were aligned to the mm10 genome reference downloaded from 10X Genomics with the addition of the TdTomato-WPRE sequence. Exclusion of low quality cells with either a UMI count >100,000, total gene count 10%) was performed using Scater55. Data normalisation was performed using the MultiBatchNormalisation method prior to merging of TdTomato positive and negative datasets from individual timepoints. The top 2000 most highly variable genes (excluding mitochondrial and ribosomal genes) in the merged datasets were identified using the Seurat FindVariableFeatures method and utilised to calculate principal component analysis (PCA). Normalised data was scaled using the ScaleData function. Cell clustering was performed using the standard unsupervised graph-based clustering method implemented within Seurat (V4)56. Clusters were visualised in two dimensions using UMAP based non-linear dimensional reduction following the standard Seurat (V4) workflow49. Identified clusters were assigned identities based on marker genes shown to be differentially expressed between populations previously identified in the original study47. Key markers include Npr3 (endocardial), Fabp4 (coronary vascular endothelial), and Nfatc1 (valvular endothelial). The E12.5 sinus venosus EC cluster was assigned based in Aplnr as previously described54. Arterial and venous EC clusters in the E17.5 datasets were annotated based on their enriched expression of Gja5 and Nr2f2, respectively.”
__Reviewer #1 Minor Comment 3: __Details about the first HOMER analysis (in the assessment of transcription factor motifs and binding patterns at arterial enhancers) seem to be missing from the methods section.
RESPONSE AND REVISION (COMPLETED): This has been included in the methods: “Analysis of overrepresented motifs within our validated arterial enhancer cohort was performed with HOMER’s findMotifsGenome tool using the full validated region of the arterial enhancers. The analysis used the hg38 masked genome and otherwise default settings for all other parameters including randomly selected background regions”.
__Reviewer #1 Minor Comment 4: __Pg 12: "For ETS, 23/23 arterial enhancers contained at least one conserved motif (all "deeply" conserved to the same depth as the surrounding enhancer, see S7)". Is it S8, where conservation is indicated?
__ ____RESPONSE AND REVISION (COMPLETED):__ We have corrected this error in the text – no figure actually needed to be referenced here as the previous sentence contained the full list of relevant figures to this statement (Table 2 and Figures 5 and S9, previously called S8, are the places to see this information).
__Reviewer #1 Minor Comment 5: __Figure 1 and 2 for non-zebrafish readers it would be useful to indicate in Figures 1 and 2 the non EC expression that can be observed in the embryos.
RESPONSE AND REVISION (COMPLETED): In addition to arterial expression, a number of the enhancer:GFP transgenes also showed GFP expression within the neural tube. In addition, some transient transgenic embryos also showed ectopic expression in muscle fibres. These have now been indicated on the images in Figure 1 and 2.
__Reviewer #1 Minor Comment 6: __Table S1: Please, indicate in the legend what the asterisk in the H DNAseI column stands for
RESPONSE AND REVISION (COMPLETED): The asterisk indicates where DNaseI hypersensitivity is also seen in multiple non-EC lines. This explanation has been added to the legend.
__Reviewer #1 Minor Comment 7: __Figure S8: The phrasing "conserved to animal" in Figure S8 is misleading. There is no difference in something being conserved to tenrec or manatee, as both are Afrotherians. Hence, the data show that both Efnb2-141 and Ephb4-2 were present in the common ancestor of Afrotherians and humans, namely the ancestor of all placentals. Instead, it would be good to indicate the phylogenetic group for which the presence of the enhancer can be inferred (in this case, Placentalia).
__RESPONSE: __Whilst I appreciate the point, it is the exact sequence that is important here – obviously tenrec and manatee are similar species but still contain differences in nucleotide sequences. The information about conservation leads the reader to the exact species with which the comparison is being made. We tried to restrict this to just one species per phylogenetic group (e.g. tenrec, opossum, chicken, zebrafish) but occasionally this was not possible.
Reviewer #1 Significance
To date, a systematic approach to identifying the regulatory networks driving endothelial cell identity is missing. This study provides important datasets and validation of enhancers involved in arterial gene expression and the associated transcription factors. Although this is only the tip of the iceberg, this work represents a significant milestone in the systematic understanding of how arterial gene expression is regulated. Overall, this study offers a powerful resource for understanding arterial gene regulation and conducting genome-wide studies of arterial enhancers.
__RESPONSE: __We thank the reviewer for these kind words. Whilst we agree this is only a very small snapshot of all the arterial enhancers involved in gene regulation, we would like to stress that not only is this a massive increase to what has been known previously, but is also deliberately focused on the genes used to define arterial identity during development and in the adult, therefore these enhancers by themselves form an extremely valuable dataset with which to study the key factors driving arterial differentiation and identity.
__ __
REVIEWER 2
__Reviewer #2 Evidence, reproducibility and clarity: __In this work, Nornes and collaborators have described a cohort of arterial enhancers that drive gene expression in arteries and not in veins. The paper is very well written and it is very informative. The authors have used in silico models to identified the potential artery enhancers and then used different developmental in vivo systems, zebrafish and mice, to validate their findings. Finally, the authors have explored what transcription factors may be binding the identified enhancer sequences and thus, drive arterial gene expression. I would like to congratulate the authors for this work that it has been a pleasure to read and review.
Reviewer #2 Major Comment 1: In their identification of enhancers, the authors consider a candidate every enhancer that has a putative mark in both mouse and human. Nevertheless, all the human data comes from in vitro analysis. Considering how much cell culture affects endothelial cell identity, inducing effects like EndoMT, would this have any effect on the enhancer selection? Would it be possible to search any human in vivo data? Would this allow for even stronger and more relevant sequences?
__RESPONSE: __We agree that the use of human endothelial cells in culture raises some potential issues. However, we stress that the mouse EC enhancer marks, which played a key role in defining putative enhancers, come from in vivo analysis (E11 embryos, P6 retina and adult aorta), limiting the potential for significant impact from cell culture-induced issues. Whilst we would have enthusiastically incorporated human in vivo data had it been available, our approach was still indisputably successful at identifying arterial enriched/specific enhancers.
We consider it unlikely that culture/identity-related problems with human cultured ECs led to a significant undercount of enhancers, in part because comparatively few regions with enhancer marks in mouse in vivo ECs were excluded due to the absence of human enhancer marks. In fact, Cxcr4, Cxcl12, and Gja5 were poorly transcribed in the human cell lines studied here and consequently only enhancer marks in mouse were used to define putative enhancers for these three genes (this is clearly stated in the Results section). If a similar rational had applied to the remaining five genes, only an additional six putative enhancers would have been tested (one for Gja4, two for Nrp1 and three for Unc5b). However, we felt it made sense to include analysis of human enhancer marks for these five genes, as all were expressed in the human ECs used (as indicated by H3K1Me3 and DNaseI hypersensitivity at promoter regions) and orthologous human enhancers were identified for all. Additionally, our retrospective analysis of previously described mammalian in vivo-validated EC enhancers (Table S1 in the original manuscription, including eight arterial enhancers) found that all 32 were marked by at least one enhancer mark in human samples (1/32 did not contain mouse enhancer marks). We also tested eleven regions that did not reach our putative enhancer threshold, including five with only mouse marks. None of these directed expression in transgenic analysis.
Reviewer #2 Major Comment 2: The human data comes from vein endothelial or microvasculature endothelial cells. Specially because some of the enhancers identified by the authors drive also vein expression, could the authors discriminate whether this is due to the identification coming from vein cells. Is there available data from HAECs? Would this not be conceptually more correct that using vein endothelial cells data? This should be at least discussed in the paper.
__RESPONSE AND REVISION (COMPLETED): __We have now included a comparison with enhancer marks from HAECs, telo-HAECs and HUAECs as a new Figure S5. The enhancer marks seen in these cells were very similar to those in the HUVEC and microvascular cells already surveyed. Had enhancer marks within HAECs/telo-HAEC/HUAECs been included as a human enhancer mark in our initial survey, it would have been unlikely to have altered our analysis, although we agree it would have made it more conceptually correct. We chose not to go back and engineer this into our original enhancer selection rational however as we felt it would be intellectually dishonest. A paragraph has been added to the Results section about this analysis.
Reviewer #2 Major Comment 3: Although the authors use the mouse embryo to further validate their finding beyond the zebrafish, the expression are a bit different. While on the fish the enhancers label smaller vessels of arterial identity, in the mouse, only bigger arteries are marked. Is this defined by the time of the analysis?
__RESPONSE: __This experiment was conducted to demonstrate that these enhancers were arterial enriched in both zebrafish and mouse transgenesis, and feel this is clearly shown by the current data. Whilst I do not really agree that the expression pattern is different (for example, the Gja5 enhancers are more restricted to the major arteries in both zebrafish and mouse, compared to the more widely expressed Efnb2-333), this is challenging to ascertain at a single time-point in a transient transgenic mouse assay. Whilst it would be potentially interesting to better assess the activity of these enhancers over time in mice, we consider this a lengthy experiment (multiple stable lines would need to be established and characterized for each enhancer) which would not add particular benefit to this paper.
Reviewer #2 Major Comment 4: The analysis of the enhancers is only done during development. Is the activity of these enhancers maintained through live or only important for artery vs vein determination? Is the expression of the different enhancer reporters maintained into adulthood?
RESPONSE AND REVISION (PLANNED): We agree this would be interesting to ascertain. We plan to examine the activity of enhancer:GFP activity in adult fish fins (which are accessible even without crossing into a casper background, which is beyond the timescale of this project) in the fully revised version of this paper. We have already conducted a feasibility study on four arterial enhancers:GFP lines (Gja5-7:GFP, Gja5-78:GFP, Gja4+40:GFP and Efnb2-333:GFP), which found that all four were still active, and arterial-specific, in the adult.
Reviewer #2 Significance
This is a very well done study with potential interest for vascular biologists, in particular to those interested in the determination between artery and veins in a context of development. It advances our knowledge on the field of vascular biology as it not only proposes potential enhancers but also goes on to validation of the enhancers. Nevertheless, it is important to note that some of this enhancers have been identified from in vitro human data. In vitro culture of endothelial cells affects their cellular identity and thus, this study may have underscored many potential enhancers.
REVIEWER 3
__Reviewer #3: Evidence, reproducibility and clarity: __This manuscript by Nornes et al analyzed multiple published databases and identified a group of putative enhancers for 8 selected non-Notch arterial genes in mouse and human ECs. These enhancers were cloned and screened in fish embryos to test their effect in driving GFP reporter expression, which narrowed down a cohort of enhancers for further testing of expression activities in mouse embryonic arteries. The authors then analyzed the sequences of these enhancers, and identified binding motifs of ETS, SOX-F, FOX and MEF2 family TFs and Notch transcription regulator RBPJ commonly present in closed proximity in these arterial enhancers, suggesting interaction between these TFs in determination of arterial identity.
Reviewer #3 Major Comment : This study provides an enormous amount of bioinformatic data analysis and screening results in transgenic fish and mouse models, which led to the discovery of a group of arterial enhancers and TFs binding motifs essential in regulating arterial identity.
Reviewer #3 Other Comments ____1: Choice of arterial genes is slightly biased. Acvrl1/Alk1 is not enriched in arterial ECs. Sema3G, which is highly expressed in arterial ECs, is missing. UNC5B is enriched in arterial ECs but also expressed by sprouting ECs (PMID: 38866944).
__RESPONSE: __When we started this project, scRNA-seq datasets in the developing vasculature were less available. Consequently, we initially based our choice of genes on data from Raftrey et al., Circ Res 2021 (available earlier on bioRxiv), which was focused on mouse coronary arterial ECs at the timepoints that arteries differentiate. This found Acvrl1 to be arterial enriched (not a novel observation, many publications treat Acvrl1 as arterial specific or arterial-enriched) and did not list Sema3g. We also considered a wider dataset from mouse and human mid-gestation embryos when available (Hou et al., Cell Research 2022). However, it is important to note that we did not aim to investigate every arterial-enriched gene, rather to use these datasets to help identify loci associated with gene expression patterns which indicated a high likelihood of containing arterial enhancers active during arterial differentiation.
Sc-RNAseq data from both Raftery et al., and Hou et al., indicated that arterial ECs are subdivided into two groups, reflecting maturity but also potentially slightly different developmental trajectories. The genes studied here were therefore selected to evenly cover both subgroups, with Acvrl1, Cxcl12, Gja5 and Nrp1 primarily restricted to the mature arterial EC subgroup, while Cxcr4, Efnb2, Gja4 and Unc5b were also expressed in the less mature/arterial plexus/pre-arterial EC subgroup. It is notable that genes within the latter subgroup are also associated with angiogenic/sprouting ECs (Dll4 also belongs to this subgroup), which likely indicates biological links between angiogenesis and arterial identity rather than a problem in gene choice and specificity.
__REVISION (COMPLETED): __This is already discussed in the Results section (angiogenic expression of arterial genes is discussed within the MEF2 and RBPJ sections) and in the Discussion (paragraph 2, referring to different expression patterns within arterial ECs). However, we have now edited the relevant Results section to better explain gene selection: “It is therefore clear that a better understanding of the regulatory pathways directing arterial differentiation requires the identification and characterization of a larger number of arterial enhancers directing the expression of key arterial identity genes. To identify a cohort of such enhancers, we looked in the loci of eight non-Notch genes: Acvrl1(ALK1) Cxcr4, Cxcl12, Efnb2, Gja4(CX37), Gja5 (CX40), Nrp1 and Unc5b. Although not a definitive list of arterial identity genes, single cell transcriptomic analysis indicates these genes are all significantly enriched in arterial ECs4,20, and are commonly used to define arterial EC populations in mouse and human scRNAseq analysis4,5,20,54. Additionally, single-cell transcriptomic data indicates that arterial ECs can be divided into two subgroups4,20. The genes selected here are equally split between subgroups (Acvrl1, Cxcl12, Gja5 and Nrp1 from the mature arterial EC subgroup, Cxcr4, Efnb2, Gja4 and Unc5b from the less mature/arterial plexus/pre-arterial EC subgroup)4,20. We did not exclude genes also implicated in angiogenesis/expressed in sprouting ECs, as these genes formed that vast majority of those associated with the less mature EC subgroup”.
Reviewer #3 Other Comments ____2: Exclusion of Notch genes. Although the reason for choosing non-notch genes and excluding notch genes for screening is addressed in this paper, it would be interesting to examine how the arterial enhancers identified in this study are present in the Notch genes, especially Dll4 (enriched in arterial and sprouting ECs) and Jag1 (enriched in arterial ECs).
__RESPONSE: __Previous work from our lab and others has already examined arterial enhancers for Notch pathway genes. We already included these enhancers in all our later analysis (Figure 5-6 and relevant supplemental figures), including analysis of TF motifs.
Reviewer #3 Other Comments ____3: SoxF family TFs. Among the 3 members of SoxF TFs, only Sox17 and Sox7 were assessed. Though not specific, Sox18 is highly expressed in the arteries. On the contrary, Sox7 is highly expressed in the vein and shows weak expression in arterial ECs (PMID: 26630461).
__RESPONSE AND REVISION (PLANNED): __We agree. We will include assessment of SOX18 binding in our final revised manuscript. An antibody for this analysis has been identified already.
Reviewer #3 Other Comments ____4: Minor inaccuracy in Intro/paragraph 3: though sox17 is reported as indispensable for arterial specification (PMID: 24153254), losing a single SoxF factor does not seem to completely compromise the arterial program (PMID: 24153254, PMID: 26630461). A combined loss of Sox17/18, or Sox 7/17/18, seems to do the job (PMID: 26630461).
__RESPONSE: __We have altered this section: “The evidence linking SOXF transcription factors to arterial differentiation is more extensive, with loss of either SOX17 (the SOXF factor most specific to arterial ECs) or SOX7 resulting in arterial defects21–24. Whilst losing a single SOXF factor does not entirely compromise the arterial program, arterial differentiation appears absent after compound Sox17;Sox18 and Sox7;Sox17;Sox18 deletion, although this occurs alongside significantly impaired angiogenesis and severe vascular hyperplasia21–24. PMID 24153254 is reference 23, PMID 26630461 is reference 24.
Reviewer #3 Other Comments ____5: Fig.4 e14.5 mouse embryos. If the observation aims to assess the dorsal aorta, it would be better to use mouse embryos at mid-gestation (e9.5-10.5), when the paired DAs are formed with arterial identity but haven't been remodelled and fused as one single aorta. The morphological data in this figure would be better to show the colocalization of LacZ expression and an arterial marker (e.g. Sox17) using immulfluorescence staining instead of purely lacZ.
RESPONSE: This experiment was primarily conducted to demonstrate that our enhancers were arterial enriched in both zebrafish and mouse transgenesis, and feel this is clearly shown with the e14.5 transgenic embryos originally shown. We chose e14.5 because it matched the timepoints used for the single cell transcriptomics first used to select the target arterial identity genes, and feel it is a good match to 2-3 dpf zebrafish in terms of arterial differentiation mechanisms. We agree that E9-10 would have also been an additional useful timepoint, but we do not have the resources to generate this data nor consider it essential for the conclusions of our work here.
__REVISION (PLANNED): __We are unable to perform immunofluorescence in the e14.5 transgenic embryos due to the fixation and staining solutions used for X-gal staining (which was done by an external company and could not be altered), but agree additional information is needed to demonstrate arterial endothelial specificity. We will therefore expand the analysis of sectioned embryos (currently restricted to just the Efnb2-333:LacZ transgene) to all enhancers shown in Figure 4. This analysis has some limitations due to infiltration of the X-gal solution to deeper tissues, but is anticipated it will clearly show enhancer activity in arterial endothelial cells rather than venous ECs or smooth muscle cells.
__Reviewer #3 (Significance (Required)): __This novel work establishes an important foundation for future understanding of how TFs may interact to determine arterial specification.
Other revisions
In addition to changes suggested by the reviewers, we also made one additional adjustment to the paper to include analysis of two additional putative enhancers (Efnb2-159 and Cxcr4+119). These were initially omitted in error yet both regions reach the standard of testable putative enhancers (noted in small changes to Figure S1 and Table S2). When tested in zebrafish transient transgenic embryos, Cxcr4+119 was inactive whilst Efnb2-159 was active in arterial endothelial cells. The relevant tables and figures have been adjusted to reflect these changes, the most significant of which are the inclusion of Efnb2-159 positive zebrafish in Figure 1 (and the necessity to create an additional supplemental Figure (S3) to accommodate the increased number of images), and analysis of Efnb2-159 transcription factor motifs/binding as part of Figure 5 and 6. No conclusions were altered by the inclusion of this additional data.
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Referee #3
Evidence, reproducibility and clarity
This manuscript by Nornes et al analyzed multiple published databases and identified a group of putative enhancers for 8 selected non-Notch arterial genes in mouse and human ECs. These enhancers were cloned and screened in fish embryos to test their effect in driving GFP reporter expression, which narrowed down a cohort of enhancers for further testing of expression activities in mouse embryonic arteries. The authors then analyzed the sequences of these enhancers, and identified binding motifs of ETS, SOX-F, FOX and MEF2 family TFs and Notch transcription regulator RBPJ commonly present in closed proximity in these arterial enhancers, suggesting interaction between these TFs in determination of arterial identity.
Major comments:
This study provides an enormous amount of bioinformatic data analysis and screening results in transgenic fish and mouse models, which led to the discovery of a group of arterial enhancers and TFs binding motifs essential in regulating arterial identity.
Other comments:
- Choice of arterial genes is slightly biased. Acvrl1/Alk1 is not enriched in arterial ECs. Sema3G, which is highly expressed in arterial ECs, is missing. UNC5B is enriched in arterial ECs but also expressed by sprouting ECs (PMID: 38866944).
- Exclusion of Notch genes. Although the reason for choosing non-notch genes and excluding notch genes for screening is addressed in this paper, it would be interesting to examine how the arterial enhancers identified in this study are present in the Notch genes, especially Dll4 (enriched in arterial and sprouting ECs) and Jag1 (enriched in arterial ECs).
- SoxF family TFs. Among the 3 members of SoxF TFs, only Sox17 and Sox7 were assessed. Though not specific, Sox18 is highly expressed in the arteries. On the contrary, Sox7 is highly expressed in the vein and shows weak expression in arterial ECs (PMID: 26630461). Minor inaccuracy in Intro/paragraph 3: though sox17 is reported as indispensable for arterial specification (PMID: 24153254), losing a single SoxF factor does not seem to completely compromise the arterial program (PMID: 24153254, PMID: 26630461). A combined loss of Sox17/18, or Sox 7/17/18, seems to do the job (PMID: 26630461).
- Fig.4 e14.5 mouse embryos. If the observation aims to assess the dorsal aorta, it would be better to use mouse embryos at mid-gestation (e9.5-10.5), when the paired DAs are formed with arterial identity but haven't been remodeled and fused as one single aorta. The morphological data in this figure would be better to show the colocalization of LacZ expression and an arterial marker (e.g. Sox17) using immulfluorescence staining instead of purely lacZ.
Significance
This novel work establishes an important foundation for future understanding of how TFs may interact to determine arterial specification.
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Referee #2
Evidence, reproducibility and clarity
In this work, Nornes and collaborators have described a cohort of arterial enhancers that drive gene expression in arteries and not in veins. The paper is very well written and it is very informative. The authors have used in silico models to identified the potential artery enhancers and then used different developmental in vivo systems, zebrafish and mice, to validate their findings. Finally, the authors have explored what transcription factors may be binding the identified enhancer sequences and thus, drive arterial gene expression. I would like to congratulate the authors for this work that it has been a pleasure to read and review.
Major comments:
- In their identification of enhancers, the authors consider a candidate every enhancer that has a putative mark in both mouse and human. Nevertheless, all the human data comes from in vitro analysis. Considering how much cell culture affects endothelial cell identity, inducing effects like EndoMT, would this have any effect on the enhancer selection? Would it be possible to search any human in vivo data? Would this allow for even stronger and more relevant sequences?
- The human data comes from vein endothelial or microvasculature endothelial cells. Specially because some of the enhancers identified by the authors drive also vein expression, could the authors discriminate whether this is due to the identification coming from vein cells. Is there available data from HAECs? Would this not be conceptually more correct that using vein endothelial cells data? This should be at least discussed in the paper.
- Although the authors use the mouse embryo to further validate their finding beyond the zebrafish, the expression are a bit different. While on the fish the enhancers label smaller vessels of arterial identity, in the mouse, only bigger arteries are marked. Is this defined by the time of the analysis?
- The analysis of the enhancers is only done during development. Is the activity of these enhancers maintained through live or only important for artery vs vein determination? Is the expression of the different enhancer reporters maintained into adulthood?
Significance
This is a very well done study with potential interest for vascular biologists, in particular to those interested in the determination between artery and veins in a context of development. It advances our knowledge on the field of vascular biology as it not only proposes potential enhancers but also goes on to validation of the enhancers. Nevertheless, it is important to note that some of this enhancers have been identified from in vitro human data. In vitro culture of endothelial cells affects their cellular identity and thus, this study may have underscored many potential enhancers.
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Referee #1
Evidence, reproducibility and clarity
Summary:
Nornes et al. have generated a cohort of arterial enhancers based on in silico analysis and validation with transgenic lines in both zebrafish and mice. They utilized publicly available datasets for chromatin marks, including ATAC-seq on endothelial cells either from cell culture or isolated from mice, as well as EP300 binding, H3K27Ac, and H3K4Me1. Focusing on eight arterial-expressed genes, they identified a putative enhancer region marked by at least one enhancer feature. After validating the activity of these enhancers in zebrafish and mice, the authors assessed the regulatory pathways upstream of these genes. Using ChIP-seq and Cut&Run for key endothelial transcription factors, they discovered that binding sites for SoXF and ETS factors are shared in arterial enhancers, whereas binding sites for Notch, MEF2, and Fox are present only in the subset of identified enhancers. Together this study provides an arterial enhancer atlas that allows further characterisation of regulatory network behind endothelial cell identity.
Major comments:
The authors have assessed 15 enhancers for arterial-venous specificity, by assessing the expression in DA, ISV, cardinal and ventral veins at 2 dpf. Interestingly there is a clear difference in the expression patterns of these enhancers in the zebrafish axial vasculature, especially seen at the level of ISV. The co-localization of the enhancer expression in the endothelium was done using endothelial marks expressed in both venous and arterial EC (kdrl). To fully distinguish if the expression is venous or arterial endothelial compartment colocalization with Tg expressed in arterial (flt1) or venous (lyve1) EC would be informative. In addition, it is striking that cxc4+135 drives the expression in nearly every ISV as cxcl12+269 only every other. Similarly, not all the enhancers are enriched in the DA to the same level. Is there biological significance to this? could authors discuss these results further? The pattern of expression of the unc5b-identified enhancer is also striking, does this reflect the known roles of unc5b in the vascular formation? The final part of the paper focuses on defining the presence of "deeply conserved" transcription factor binding sites (TFSB), defined as TFBS that are as conserved as the enhancer sequence surrounding them. In literature, the term 'deep conservation' refers to evolutionary conservation (genomic sequence preservation) in a wide range of species. Therefore, the additional classification presented by the authors based on the surrounding sequence is not clear. As, the KLF motifs in the Ece1in1, which is conserved between mouse and human, are defined as "deeply conserved". However, the FLK motif in the following enhancer, Flk1in10 (one line below), gets classified as non-deeply conserved, despite also being conserved between mouse and human. Thus, in the current form, there is a contradiction in the way the authors use the term 'deeply conserved' and the accepted meaning of this term. To avoid confusion, it would be important to revise this nomenclature.
Minor:
Details on how the corresponding non-coding regions between mice and humans were established are missing, what alignment tool was used?
Not sufficient details are provided for the re-analysis of siRNA data. E.g., which clustering method was used? How the clusters were assigned to cell identities?
Details about the first HOMER analysis (in the assessment of transcription factor motifs and binding patterns at arterial enhancers) seem to be missing from the methods section.
Pg 12: "For ETS, 23/23 arterial enhancers contained at least one conserved motif (all "deeply" conserved to the same depth as the surrounding enhancer, see S7)". Is it S8, where conservation is indicated?
Figure 1 and 2 for non-zebrafish readers it would be useful to indicate in Figures 1 and 2 the non EC expression that can be observed in the embryos.
Table S1: Please, indicate in the legend what the asterisk in the H DNAseI column stands for
Figure S8: The phrasing "conserved to animal" in Figure S8 is misleading. There is no difference in something being conserved to tenrec or manatee, as both are Afrotherians. Hence, the data show that both Efnb2-141 and Ephb4-2 were present in the common ancestor of Afrotherians and humans, namely the ancestor of all placentals. Instead, it would be good to indicate the phylogenetic group for which the presence of the enhancer can be inferred (in this case, Placentalia).
Significance
To date, a systematic approach to identifying the regulatory networks driving endothelial cell identity is missing. This study provides important datasets and validation of enhancers involved in arterial gene expression and the associated transcription factors. Although this is only the tip of the iceberg, this work represents a significant milestone in the systematic understanding of how arterial gene expression is regulated. Overall, this study offers a powerful resource for understanding arterial gene regulation and conducting genome-wide studies of arterial enhancers.
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Reply to the reviewers
We thank the reviewers for going through our manuscript and providing valuable feedback. We are grateful to all 3 reviewers for describing our findings as important and valuable, well-designed and robust, and of value to the Parkinson's and Crohn's disease communities studying LRRK2. Below we detail a point-by-point response to the reviewers.
__Reviewer #1 (Evidence, reproducibility and clarity (Required)): __
The paper by Dikovskaya and collaborators investigated the activitiy and expression of LRRK2 in different subtypes of splenic and intestinal immune cells, taking advantage of a novel GFP-Lrrk2 knockin mouse. Interestingly, they found that T-cell-released IL-4 stimulates Lrrk2 expression in B cells. I have a few comments and suggestions for the authors. 1) Figure 1C. LRRK2 KO cells display residual Rab10 phosphorylation. Do the authors have any idea of which kinase other than LRRK2 could be involved in this phosphorylation?
As far as we are aware no other kinase is known to phosphorylate Rab10 at T73 in vivo. In vitro, recombinant Rab10 can be phosphorylated by MST3 at this site (Knebel A. et al, protocols.io https://dx.doi.org/10.17504/protocols.io.bvjxn4pn), but its relevance in vivo or in cells has not been shown. It is possible that the residual band recognised by anti-pT73 Rab10 ab in splenocytes is unspecific background, as it is mainly seen in LRRK2 KO spleen cells and not in other tissues. But to be certain that our assay assesses LRRK2-dependent Rab10 phosphorylation, we have always compared with the MLi-2 control.
2) Since there are no good antibodies for IF/IHC as pointed by the authors, the GFP-Lrrk2 mouse gives the opportunity to check endogenous LRRK2 localization, i.e. in cells untreated or treated with IL-4 or other cytokines. Also, does endogenous GFP-LRRK2 accumulate into filaments/puncta upon MLi2 inhibition? The relocalization into filaments of inhibited LRRK2 has been observed in overexpression but not under endogenous expression. This analysis would be interesting also in light of the observed side effect of type-I inhibitors.
We thank the reviewer for this suggestion. We will attempt a super-resolution microscopy using Airyscan with isolated B-cells treated with cytokine and/or LRRK2 inhibitor to address this question.
3) Figure 5. The authors need to label more clearly the graphs referring to wt mice versus GFP-Lrrk2 KI mice.
We have now labelled the panels referring to the WT mice only with "WT mice", to distinguish them from the other panels that incorporate data from both EGFP-Lrrk2 mice and their WT littermates used as a background.
They should also replace GFP-LRRK2 with GFP-Lrrk2 since they edited the endogenous murine gene.
Thank you, we have corrected it, and also the other mouse genotypes.
4) In the material and methods MLi-2 administration in mice is indicated at 60 mg/kg for 2 hr whereas in suppl. figure 5 the indicated dose is 30 mg/kg. Please correct with the actual dose used.
Thank you, we have corrected the mistake.
5) The discovery of IL-4 as a Lrrk2 activator in B cells is a very interesting and novel finding. The authors could take advantage of the GFP tag to investigate LRRK2 interactome upon IL-4 stimulation (optional). Also, is the signaling downstream of IL-4 attenuated in Lrrk2 KO cells?
We thank the reviewer for these interesting suggestions. The role of LRRK2 in IL-4 activated B-cells is currently under active research in the lab.
Reviewer #1 (Significance (Required)):
The manuscript is well designed and organized, and the experimental approaches are robust. These results are significant for the field as they add additional layers in the complex regulation and regulatory roles of LRRK2 in immunity, with implication for inflammatory disorders and Parkinson's disease.
We thank the reviewer for their positive comments and for recognising our efforts to provide some clarity to a complex field.
__Reviewer #2 (Evidence, reproducibility and clarity (Required)): __
The authors present a flow cytometry methodology to assess LRRK2 expression and pathway markers in mouse models and explore LRRK2 in splenic and intestinal immune cells. This is a highly valuable study given the emerging understanding that LRRK2 pathway activity in peripheral tissues may be of crucial importance to Parkinson's disease and Crohn's disease. P8 : the authors state that their results indicate 'that the effects of LRRK2-R1441C mutation and inflammation on LRRK2 activity represent two different parallel pathways'. This seems like an overinterpretation as pathway suggests the presence of additional partners in the pathway while R1441C is a LRRK2 intrinsic modification. The results can equally be explained by synergistic effects between both activation mechanisms (mutant and inflammation).
We agree with the reviewer, and have added this into the text. The sentence now reads "suggesting that the LRRK2-R1441C mutation and inflammation have different impacts on LRRK2 activity, either in parallel or in synergy."
Methods and experiment descriptions in results : the authors appear to use the terms anti-CD3 stimulation and CD3 stimulation interchangeably, although it is not always clear in the text that these are synonymous. This should be clarified.
We thank reviewer for pointing out this error on our part. We have made the necessary changes to always refer to the stimulation as anti-CD3.
One major observation in this paper is that LRRK2 is not detected in gut epithelial cells as previously has been reported. It would be useful to comment on any differences between the presented protocol and the previous reports, in particular relating to the antigen retrieval step. In order to reinforce the finding, it would be useful to include in situ hybridization data that could further strengthen the observations of which cellular subtypes express LRRK2 and which do not. Indeed, while the KO control shows that there is an unacceptable high non-specific staining, it does not prove absence of expression. Also, can any conclusions be made about expression of LRRK2 in neural cells of the gut? This important information on LRRK2 detection in gut should be mentioned in the abstract and highlighted in the discussion.
We thank the reviewer for pointing this out. In fact, we think the observation that LRRK2 is not detected in epithelial cells is so important that we have a separate manuscript exploring this point. Please see 1. Tasegian, A. et al.https://doi.org/10.1101/2024.03.07.582590 (2024). In this manuscript we have explored the expression of LRRK2 in human and murine intestinal epithelial cells using qPCR. Although we do not have in situ hybridization data, we believe that using both the EGFP-LRRK2 and the pRab10 flow cytometry, as well as qPCR and proteomics on selected cell types, corroborates our findings on the cell types that express LRRK2. We did not analyse LRRK2 expression in the neural cells of the gut, as the focus was on the immune cells, however we hope that others will use the tools developed here to explore this further.
The authors mention in the discussion that they 'show for the first time that eosinophils also express active LRRK2 at levels comparable to B-cells and DCs.' The relevance of this finding should be further developed. Why is this important?
We thank the reviewer for this point. We don't know how LRRK2 is important in these cells. However, as the role of LRRK2 in eosinophils and neutrophils has not yet been explored and both cell types play important roles in IBD, we think it is important to point out. We have now added a sentence to the discussion highlighting the importance of eosinophils in IBD. "Since eosinophils have recently been implicated as key player in intestinal defense and colitis(Gurtner et al, 2022), it will be interesting to evaluate LRRK2 functions in these cells."
In the isolation of lamina propria cells, what efforts were made to characterize the degree of purification of the lamina propria cells compared to cells of other gut wall layers such as epithelium, muscularis mucosa, or deeper layers? Please specify.
Isolation of lamina propria cells is a very well-established process (LeFrancois and Lycke, 'Isolation of Mouse Small Intestinal Intraepithelial Lymphocytes, Peyer's Patch, and Lamina Propria Cells.' Curr. Protocols in Immunology 2001), where we extensively wash off the epithelial layer before digesting the tissue for the LP. After the digestion the muscle and wall of the gut are still intact, so we do not get any contamination with other deeper layers. The subsets of cells we find in the LP are in line with isolations from other labs.
Minor comments Figure 5G, for the graphs indicating LRRK2 activity and LRRK2 phosphorylation, the specific measures should be specified in the graph titles to avoid any ambiguity (pT73-Rab10, pS935-LRRK2).
We have added the specifications to the new version of the figure.
Suppl figure 1 : please specify the figure label and abbreviation AF568 in the legend. Suppl figure 2 : please specify the figure label and abbreviation anti-rb in the legend
Thank you, we added the abbreviations to the legends. The Figure labels for both figures have been already included at the top of figure legends.
Reviewer #2 (Significance (Required)):
The authors present a flow cytometry methodology to assess LRRK2 expression and pathway markers in mouse models and explore LRRK2 in splenic and intestinal immune cells. This is a highly valuable study given the emerging understanding that LRRK2 pathway activity in peripheral tissues may be of crucial importance to Parkinson's disease and Crohn's disease.
We thank the reviewer for recognising the value of this study.
Reviewer #3
Evidence, reproducibility and clarity
The paper describes a set of experiments to analyse LRRK2 activity in tissues and despite it has very important findings and technical developments is largely descriptive. It does look like a collection of experiments more than a defined hypothesis and experiments to address that.
We thank the reviewer for recognising the importance of our findings and the technical developments. We agree that the paper's focus is to describe where LRRK2 is expressed in immune cells, and in which cells is it active or activated after inflammation in a hypothesis-free unbiased manner. We believe this is important data to share as a resource for the wider LRRK2 community and we will submit the manuscript as a Resource.
The flow cytometry assay of the first part is a great technical challenge and represents the establishment of a potentially very useful tool for the field. It would have been important to test other organs, either as controls or for example because of their relevance e.g. lungs. This first part is disconnected from the second part below.
We thank the reviewer for pointing out that the pRab10 assay would be useful to apply to other organs too. Since we are interested in the role of LRRK2 in IBD, we had focused on applying the pRab10 assay on intestinal tissue, with spleens also analysed as major lymphoid organ and a source of immune cells that can translocate to the gut in inflammation. We hope that the publication of this method would allow other researchers to analyse other tissues in the future.
The authors generated a new mouse KI mouse expressing EGFP-LRRK2 and show data the levels of LRRK2 expression are reduced in tissues at different degrees and established a flow cytometry assay to measure LRRK2 expression by monitoring the GFP signal. Interestingly they found that expression does not correlate with activity (as measured by phospho-Rabs). I suggest taking this part out as it breaks the flow of the paper. If data using this mouse is included, then microscopy should be included to complement the flow cytometry data. I understand the mice were used later with the anti-CD3 treatment, but it is very confusing that some experiments are done with EGFP-LRRK2 mice and others not. It does look in general like the mice do not behave as wild types and this is an important caveat. Without microscopy of the tissues or even cells (Figure 4) is hard to conclude much about these experiments.
We thank the reviewer for this point and would like to explain. It is true that in Suppl Figure 5, we show reduction of LRRK2 signal in the EGFP-Lrrk2-KI mice. However, based on immunoblotting, a significant reduction in EGFP-LRRK2 expression levels was seen only in the brain, but not in the tissues we analysed, that is the spleen and the intestine. Further, we have shown clearly using proteomics (Fig. 3D and 5E), that the GFP signal in immune cells correlates very well with the WT LRRK2 expression. Therefore, we think that the GFP signal in these mice reflects WT LRRK2 expression pattern. Further, despite the limitations of reduced kinase activity that we thoroughly describe, we think this model is very useful since no antibodies work to stain for LRRK2 in mice. We therefore respectfully disagree with this reviewer that the EGFP-LRRK2 data should be taken out, as it has proven to be an invaluable tool to measure and track changes in endogenous LRRK2 expression. Moreover, we think the fact that LRRK2 expression does not correlate with levels of activity, that is, LRRK2 is more active in some immune cells than in others, is a very important finding that evidences the cell-specific regulation of LRRK2 activity beyond its expression level.
We tried but failed to visualize the EGFP-LRRK2 signal using fluorescence microscopy in the tissue. This is most likely due to the low expression of LRRK2 (proteomics data suggests that even neutrophils express less than 9000 copies), confounded further by the high background autofluorescence in tissues, especially in the gut. We now explain the lack of tissue images from the EGFP-LRRK2 mice in the text. However, we can visualize the EGFP-LRRK2 in B cells, and we will provide these images in a revised version of the manuscript.
We have also added the following paragraph to the discussion:
"We complemented the pRab10 assay with the development of the EGFP-Lrrk2-KI reporter mouse. Although the reporter was initially designed as a fluorescent tracker for imaging LRRK2 localisation in cells and tissues, the low expression of LRRK2, combined with high and variable autofluorescence in tissues precluded its use for microscopy. Even in neutrophils, which express highest level of LRRK2 among immune cells, there are less than 9000 copies of LRRK2 per cell (Sollberger et al, 2024), making it difficult to identify localization. However, the EGFP signal was sufficient for flow cytometry-based measurements, where background autofluorescence of each cell type was taken into account and subtracted."
Then the authors show that LRRK2 expression and activity is different in different cell types and depends on inflammation. The anti-CD3 strategy to induce inflammation is very different from physiological inflammation such as sepsis and LPS stimulation, so experiments with other stimuli could be important here to contribute to the message of inflammatory trigger of LRRK2 activation and decoupling of cell type.
We thank the reviewer for this suggestion. We used the anti-CD3 model as it also causes intestinal inflammation, and mimics T-cell cytokine storms that happens in many diseases. However, for the revisions we will also test another model of inflammation as suggested, such as LPS stimulation, to measure how inflammation affects LRRK2 expression and activity.
The IL-4 data is intriguing but too preliminary. The lack of strong effect of IFN-gamma is expected as the promoter of LRRK2 in mice and humans is different and human cells responds much better with regards to LRRK2 expression after IFN-gamma stimulation.
We are confused by what the reviewer means by saying the IL-4 data is preliminary. We have shown by flow cytometry, immunoblotting, qPCR and proteomics that IL-4 induced LRRK2 expression in B-cells. So we are uncertain as to how else this can be shown. As to the effect of IFNγ on LRRK2 expression, it may indeed be that human cells respond better than murine cells. Importantly, the IL-4 ability to induce LRRK2 in B-cells is a novel and important finding, regardless of the effects of IFNγ.
Reviewer #3 (Significance (Required))
The paper describes a set of experiments to analyse LRRK2 activity in tissues and despite it has very important findings and technical developments is largely descriptive. It does look like a collection of experiments more than a defined hypothesis and experiments to address that.
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Referee #3
Evidence, reproducibility and clarity
The authors present a flow cytometry methodology to asses LRRK2 epxression and pathway markers in mouse models and explore LRRK2 in splenic and intestinal immune cells. This is a highly valuable study given the emerging understanding that LRRK2 pathway activity in peripheral tissues may be of crucial importance to Parkinson's disease and Crohn's disease.
P8 : the authors state that their results indicate 'that the effects of LRRK2-R1441C mutation and inflammation on LRRK2 activity represent two different parallel pathways'. This seems like an overinterpretation as pathway suggests the presence of additional partners in the pathway while R1441C is a LRRK2 intrinsic modification. The results can equally be explained by synergistic effects between both activation mechanisms (mutant and inflammation).
Methods and experiment descriptions in results : the authors appear to use the terms anti-CD3 stimulation and CD3 stimulation interchangeably, although it is not always clear in the text that these are synonymous. This should be clarified.
One major observation in this paper is that LRRK2 is not detected in gut epithelial cells as previously has been reported. It would be useful to comment on any differences between the presented protocol and the previous reports, in particular relating to the antigen retrieval step. In order to reinforce the finding, it would be useful to include in situ hybridization data that could further strengthen the observations of which cellular subtypes express LRRK2 and which do not. Indeed, while the KO control shows that there is an unacceptable high non-specific staining, it does not prove absence of expression. Also, can any conclusions be made about expression of LRRK2 in neural cells of the gut? This important information on LRRK2 detection in gut should be mentioned in the abstract and highlighted in the discussion. The authors mention in the discussion that they 'show for the first time that eosinophils also express active LRRK2 at levels comparable to B-cells and DCs.' The relevance of this finding should be further developed. Why is this important ?
In the isolation of lamina propria cells, what efforts were made to characterize the degree of purification of the lamina propria cells compared to cells of other gut wall layers such as epithelium, muscularis mucosa, or deeper layers? Please specify.
Minor comments
Figure 5G, for the graphs indicating LRRK2 activity and LRRK2 phosphorylation, the specific measures should be specified in the graph titles to avoid any ambiguity (pT73-Rab10, pS935-LRRK2).
Suppl figure 1 : please specify the figure label and abbreviation AF568 in the legend.
Suppl figure 2 : please specify the figure label and abbreviation anti-rb in the legend
Significance
The authors present a flow cytometry methodology to asses LRRK2 epxression and pathway markers in mouse models and explore LRRK2 in splenic and intestinal immune cells. This is a highly valuable study given the emerging understanding that LRRK2 pathway activity in peripheral tissues may be of crucial importance to Parkinson's disease and Crohn's disease.
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Referee #2
Evidence, reproducibility and clarity
The paper by Dikovskaya and collaborators investigated the activitiy and expression of LRRK2 in different subtypes of splenic and intestinal immune cells, taking advantage of a novel GFP-Lrrk2 knockin mouse. Interestingly, they found that T-cell-released IL-4 stimulates Lrrk2 expression in B cells.
I have a few comments and suggestions for the authors.
- Figure 1C. LRRK2 KO cells display residual Rab10 phosphorylation. Do the authors have any idea of which kinase other than LRRK2 could be involved in this phosphorylation?
- Since there are no good antibodies for IF/IHC as pointed by the authors, the GFP-Lrrk2 mouse gives the opportunity to check endogenous LRRK2 localization, i.e. in cells untreated or treated with IL-4 or other cytokines. Also, does endogenous GFP-LRRK2 accumulate into filaments/puncta upon MLi2 inhibition? The relocalizaiton into filaments of inhibited LRRK2 has been observed in overexpression but not under endogenous expression. This analysis would be interesting also in light of the observed side effect of type-I inhibitors.
- Figure 5. The authors need to label more clearly the graphs referring to wt mice versus GFP-Lrrk2 KI mice. They should also replace GFP-LRRK2 with GFP-Lrrk2 since they edited the endogenous murine gene.
- In the material and methods MLi-2 administration in mice is indicated at 60 mg/kg for 2 hr whereas in suppl. figure 5 the indicated dose is 30 mg/kg. Please correct with the actual dose used.
- The discovery of IL-4 as a Lrrk2 activator in B cells is a very interesting and novel finding. The authors could take advantage of the GFP tag to investigate LRRK2 interactome upon IL-4 stimulation (optional). Also, is the signaling downstream of IL-4 attenuated in Lrrk2 KO cells?
Significance
The manuscript is well designed and organized, and the experimental approaches are robust. These results are significant for the field as they add additional layers in the complex regulation and regulatory roles of LRRK2 in immunity, with implication for inflammatory disorders and Parkinson's disease.
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Referee #1
Evidence, reproducibility and clarity
The paper describes a set of experiments to analyse LRRK2 activity in tissues and despite it has very important findings and technical developments is largely descriptive. It does look like a collection of experiments more than a defined hypothesis and experiments to address that.
The flow cytometry assay of the first part is a great technical challenge and represents the establishment of a potentially very useful tool for the field. It would have been important to test other organs, either as controls or for example because of their relevance e.g. lungs. This first part is disconnected from the second part below.
The authors generated a new mouse KI mouse expressing EGFP-LRRK2 and show data the levels of LRRK2 expression are reduced in tissues at different degrees and established a flow cytometry assay to measure LRRK2 expression by monitoring the GFP sugnal. Interestengly they found that expression does not correlate with activity (as measured by phospho-Rabs). I suggest taking this part out as it breaks the flow of the paper. If data using this mouse is included, then microscopy should be included to complement the flow cytometry data. I understand the mice were used later with the anti-CD3 treatment, but it is very confusing that some experiments are done with EGFP-LRRK2 mice and others not. It does look in general like the mice do not behave as wild types and this is an important caveat. Without microscopy of the tissues or even cells (Figure 4) is hard to conclude much about these experiments.
Then the authors show that LRRK2 expression and activity is different in different cell types and depends on inflammation. The anti-CD3 strategy to induce inflammation is very different from physiological inflammation such as sepsis and LPS stimulation, so experiments with other stimuli could be important here to contribute to the message of inflammatory trigger of LRRK2 activation and decoupling of cell type.
The IL-4 data is intriguing but too preliminary. The lack of strong effect of IFN-gamma is expected as the promoter of LRRK2 in mice and humans is different and human cells responds much better with regards to LRRK2 expression after IFN-gamma stimulation.
Significance
The paper describes a set of experiments to analyse LRRK2 activity in tissues and despite it has very important findings and technical developments is largely descriptive. It does look like a collection of experiments more than a defined hypothesis and experiments to address that.
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Reply to the reviewers
Reviewer #1
Evidence, reproducibility and clarity:
In this work, Anandi et al. propose an ex vivo model that can be used to recapitulate the in vivo structure of the tumor microenvironment, which allows the observation of morphological and functional changes in tumor cells in a 3D context. Due to the ability of cancer cells to induce hypoxic condition within the TME, authors propose this model to tackle the study of metastasis initiation in vitro. The proposed system successfully displays an ischemic gradient with cells accessing nutrients at different rates, similarly to what happens in solid tumors in vivo. Moreover, in line with the literature, tumor cell migration and invasiveness were promoted by hypoxic conditions. Authors also show that the system could be used to study cell-cell interaction, as co-cultures of macrophages and cancer cells were successfully cultured in the system and studied in the context of tumor hypoxia.
The study proposed is interesting and timely, as cancer cell invasion remains an important area of tumor biology that needs further exploration. The methodology is well explained and proposed in a linear flow. However, the work could benefit from some improvement and changes, as well as from additional experiments. On an important note, authors do not properly refer to the current literature, as several studies on 3D culture systems/chambers have already been studied and developed to investigate the tumor microenvironment, but they are not cited nor referred to in the manuscript. Authors should refer to such literature and explain how this system is different and adds to it.
Major comments:
- Authors propose this method to study the TME in 3D. When culturing cells with different ECM (Collagen vs. matrigel+collagen I) authors should take into consideration the effect of these materials on different cell types. It is known how collagen and matrigel can differently influence the polarization and phenotype of stromal cells (particularly in regards of fibroblasts - major components of solid cancers - e.g., PMID 21029367), therefore these points should be addressed at least in the discussion.
We completely agree with the reviewer so we added this point (and reference) to our manuscript's introduction (lines 45-46) and discussion (lines 442-445).
- In addition to the previous comment, matrigel and collagen are also known to alter cancer cell phenotype (e.g., PMID 21029367) and this point should be taken into account.
We completely agree with the reviewer so we added this point (and reference) to our discussion in the main text (lines 442-445).
- The need for novel 3D systems to study different aspects of the TME in vitro/ex vivo are certainly needed, however they are not inexistent. Authors should address this in the text, as the current literature already started to propose 3D models (including models involving matrigel/collagen in combination with other materials). 3D chambers (of different materials, and with different aims) are being used and designed and can be found in the literature. These works are not cited in the current study at all. For instance, Anguiano 2017; Cavo et al. 2018; Anguiano et al., 2020; Sodek et al. 2008, etc.
We agree so we have now added those references to the main text (line 56-57).
- Even though the focus is on hypoxia and the achievement of an ischemic gradient in the chamber to allow resemblance of an in vivo tumor, the authors write in line 123 (and also in other parts of the text) that: "these results show that consumer cells in the 3MIC form ischemic gradients that can influence the local metabolic microenvironment experienced by neighboring tumor spheroids". The addition of the use of the PMDS membrane partly supports the claim, however it would be interesting to check whether this is indeed true, by measuring for example the levels of certain metabolites (e.g., glucose, glutamine, glutamate, lactate, aspartate) reached with the system, or pH levels, etc., in presence or absence of the hypoxic gradient/consumer cells.
This is an insightful question and defining the exact composition of this complex ischemic microenvironment is a major ambition of our lab, so we completely agree with the reviewer's comment. However, as the 3MIC was designed specifically for microscopy, measuring specific metabolites it is unfortunately outside its capabilities.
Having said that, and following the spirit of the reviewer's comments, we used microscopy to measure additional signs of metabolic stress. Specifically, we used fluorescent probes to detect changes in intracellular pH (pHrodo, Molecular Probes) and in Redox status (CellROX, Molecular Probes) and glucose (2-NBDG - a fluorescent D-glucose analog). As we explain below, we found exciting results from our pH measurements which led us to additional functional experiments. We are very excited about these new results, and we thank the reviewer for encouraging these experiments. These new results also provide evidence that other parameters in ischemia - and not just hypoxia - change along the 3MIC and can have an impact on tumor cells.
- When looking at the references presented in the manuscript, authors quote too many review articles, rather than scientific articles. Given the extremely wide literature on cancer metastasis, more of these works should be quoted in this context. For example: in the introduction - text lines 27-38 - only 4 references are research articles, out of 14 references presented in that paragraph.
The reviewer is correct in pointing this out. Our intention was to use reviews on topics that are well established where citing primary research could be unfair to other contributions. But again, we agree with the reviewer, so we replaced reviews with primary research articles in multiple locations along the manuscript.
- As authors showed successfully how macrophages and cancer cells can interact in the chamber, recapitulating cell interactions in an in vivo context, it would be very interesting to see whether different consumer cells would induce similar or different changes to the spheroids and the ischemic gradient (for instance using stromal cells or non-tumor cell lines as consumers, instead of cancer cells only), as we know how tumors are a multitude of cell subsets, each contributing to nutrient production, oxygen consumption, etc.
This is a great point. We thought about that very same point and conducted several experiments to test the combinatorial effects of different consumer cells. In broad terms, we did not observe major differences when using different consumer cells. However, we agree that this system may provide compelling opportunities to test the effect of different cell types on each other. Still, for consistency and ease, we conducted most of our experiments using the same cells in both consumers and in spheroids.
In the resubmitted version, we added an experiment where we looked at the sprouting of SVEC endothelial cells using the same cells or Lung KPs as consumers (Fig. S6A).
Minor comments:
- Studying the early metastatic development/seeding remains a timely quest, however authors should refer to several new studies in which various mouse models are used to study metastasis from different points of view (e.g., PMID 25822788; PMID 36991128; PMID 25171411; PMID 25633981; PMID 34632412; PMID 35921474; etc). Or line 41, three reviews are quoted (refs 27-29), whilst there are several works that could be quoted on metabolism in solid tumors also in the context of metastasis (e.g., PMID 36522548; PMID: 26719539, PMID 34303764). This comment applies to the rest of the text.
We thank the reviewer for their help in processing this vast literature. We were aware of most of those works but some were new to us so thanks again! We have now added these references.
- The order of the references is not properly presented. In the introduction, the first reference is n. 4 (text line 22), instead of it being reference 1. Moreover, the subsequent literature ref. is number 12 and not number 2. Please revise the order of the references, and position them within the bibliography from first cited to last cited in the text.
We apologize for this confusion. We have now revised all the references and we hope they are correctly formatted and numbered. The origin of this confusion may have been that we had references in the abstract thus their numbering started there rather than from the introduction. To avoid further confusions, we removed all references from the abstract.
- Lines 98-104. It would be helpful to the reader to define here what these consumer cells are. Even though it is explained in the methods that the consumer cells are cancer cells, it is important to make it clear in the text, as it could be misleading at times.
We agree with the reviewer although we did not mean to be misleading. As mentioned above, we chose to use the same cells for both: consumers and spheroids and we have now added a new figure to illustrate this point (Fig S6A). Following the advice, we are also including additional text to make the message clearer (lines 107-109).
- The English grammar and spelling should be revised in some parts, as well as typos and missing words throughout the text (e.g., Line 38, the word "interraction" is misspelled and should be corrected with "interaction". Line 49, the first sentence seems incomplete. Lines 68-69 should be revised as the sentences do not flow well together, probably due to a missing word. In line 77 it should be "presents". Line 341 should be "cannot be explained").
We apologize for these typos and mistakes. We have tried our best to avoid these type of errors in the new manuscript version.
Referees cross-commenting
I find the comments from the other reviewers to be in line with one another as well as with my general assessment. The major and comments of all reviewers should be addressed. The minor comments should be taken into account as well, as they would render the text and the figures more precise. I suggest that 3-6 months to complete the revision process is an appropriate time frame for the authors.
Finally, I strongly encourage the authors to add in the discussion the points and questions raised by all reviewers, as well as to improve the bibliography in terms of organisation, linearity, and state of the art.
Significance:
General assessment:
The work by Anandi et al. offers an additional tool to tackle the issue of studying the tumor microenvironment, in a 3D culture system.
The authors show a model that can be used to study tumor hypoxia in 3D, offering the possibility to study the TME in a more in vivo-like manner without turning to mice models. The development of new tools to study the TME avoiding the excessive use of animals is definitely a timely quest. In addition, the system has the potential to be applied to tackle different biological questions, as the methodology is well explained and could be suitable to many other fields of cancer biology (e.g., drug resistance or uptake). The work is overall presented in a clear way and the methodology is explained thoroughly and it has the potential to be a useful tool for the study of cancer hypoxia.
However, authors should address how their method could differently impact other cells when applied to other systems. As one major claim is the potential use of this methodology to study the TME, it should be taken into consideration how stromal cells are strongly affected by the ECM, and how certain settings or features of the system may impact such cell populations. In addition, the work does not properly refer to the current state of the art. As other studies started to propose 3D systems for the study of TME and cell-cell interactions - besides organoids - the authors should cite these works and frame their own study in a more appropriate context, pointing out differences with the current 3D chambers available, the advantages of one vs the other, and so on.
Advance: the study adds to the current literature as the study of tumor hypoxia in 3D remains a complicated issue. The interesting co-culture settings with macrophages suggests potential uses of this model to study cell-cell interactions.
Audience: the study is very methodological and offers a tool that could be used by cancer biologists - and maybe by other biology fields.
Reviewer #2
Evidence, reproducibility and clarity:
Summary
Anandi and colleagues present a manuscript describing a nice assay for exploring the progressive effect of metabolic depletion of the nutrients and oxygen on the invasion of cancer cells. This builds upon and extends a device that they previously described - MEMIC - and now enables 3D analysis of small numbers of cells. The key to their method is the inclusion of a layer of consumer cells that deplete oxygen and nutrients. Using this tool, they demonstrate that depleted environments promote invasive behavior and lower cell-cell adhesion. This is related to the nutrient-deprived and hypoxic environments found in the center of many tumors. Cellular Potts Modelling is used to explore ideas around the cooperation between reduced cell-cell adhesion and increase ECM adhesion in promoting invasion. Overall, this is a well-constructed manuscript that will be of interest to cell biologists and cancer biologists.
Major comments
I realize this work is submitted to review commons and this complicates the recommendation regarding publication. My view is that the 'more prestigious' journals would require greater mechanistic insight, but that the work could find a suitable place in other members of the review commons stable. My comments are divided into those essential for any journal and those that might be journal dependent.
We hope that the mechanistic experiments added to our new manuscript version will appeal the reviewer and merit publication in any of the review commons journals.
Essential regardless of journal
- Many of the figures lack information about the number of spheroids analyzed and from how many biological repeats they are derived.
We have now added this information to all our experiments. This information can be found in the figures and on the figure legends.
- The authors need to provide citations for their assertion that only gases can cross the PDMS, but not other small metabolites. They should also comment on whether the build-up of CO2 might be relevant.
We have now added the original reference where they describe PDMS's properties (Cox and Dunn, 1986).
The point raised about CO2 is very interesting, but we do not expect a buildup of this gas. When using PDMS, CO2 would not accumulate as PDMS membranes are permeable to gases - including CO2. When using glass covers, the lack of oxygen should minimize CO2 production as hypoxic cells will not be able to conduct oxidative phosphorylation and produce lactic acid instead.
- The data on the directionality of migration when consumers are present are not significant and doesn't warrant the speculation in lines 186-189.
Following the reviewer's advice we have removed this speculation.
- The ECM degradation in Figure 3 should be quantified.
We agree. We added additional quantifications for the gelatin degradation assay. We also highlight the quantification we already had of the ECM degradation assessed via DQ collagen. Those data can be found in the new figures 4 and S4, respectively.
- Do the authors have evidence that the hypoxia-exposed cells are more adhesive to ECM. This is central to their Potts model and I could not locate the supporting experimental data. If not, then the Potts model should include matrix proteolysis, which they do have data about.
Again, this is a very insightful observation, and we completely understand this confusion. We think that this may part of the inherent challenge of trying to condense biological problems into analogies or "metaphors" when using physical/mathematical models.
The algorithm in a Cellular Potts model (CPM) tries to minimize the energy of the system (the entire group of cells/ECM that we are modelling). This global energy reduction is achieved by minimizing local energies in the cell-cell and cell-ECM interactions. The way the algorithm executes this minimization, is by always (probability p=1) accepting a configuration that decrease the energy while restricting the configurations that lead to higher energies (with a probability of p = e-DHT) where DH is the difference between the current and previous energy.
So, the only thing the model is really doing is to increase the likelihood that cells are in a more "comfortable" environment - i.e. that the energy from the interactions with their neighboring cells and ECM is as low as possible. For example, if cell 1 and cell 2 adhere strongly but not to cell 3, in a CPM this is modelled as a low DH between cell 1 and 2 and a higher DH with cell 3. Conversely, when people model cells better at "invading" into a new "territory" they choose a lower energy between that cell type and that type of substratum.
In other words, our CPM does not "care" whether ischemic cells invade the ECM because they create space through increased proteolysis or because they are more adherent to the ECM. These two scenarios are the same in a CPM and it is consistent with previous CPM models of similar scenarios (e.g.: PMID: 18835895, 33933478, 26436883, 23596570).
We have now reworded the description of the model on the main text, and we added an illustration hoping to make this aspect of the model clearer (Fig. S4F).
- Is the down-regulation of E-cadherin transcriptional - i.e. is the mRNA level reduced?
This is a great question. After the reviewer posed this question, we looked at out data and we concluded E-cad's downregulation is transcriptional. Assessing local mRNA levels in the 3MIC is challenging. However, our E-Cad reporter (pHAGE-E-cadherin-RFP, addgene #79603) is a red fluorescent protein driven by the CDH1 (E-Cad) reporter. RFP levels decrease with ischemia indicating that this regulation occurs at the promoter/transcriptional level. We now added this point to the revised manuscript (lines 259-261). We thank the reviewer for this insight!
- The title of figure 6 is misleading. The authors do not demonstrate chemoresistance in terms of cell survival or cell proliferation, which is how the term is normally used. The authors should measure cell number, proliferation, and cell viability. The data presented in the Supplementary Figure are inadequate with no quantification. The FUCCI reporter cells would be a good tool for this. Also, why use 150nM paclitaxel when the IC50 is 817nM? This seems bizarre. Lastly, there is a typo in the figure that suggest 150mM drug was used.
We apologize if these experiments caused confusion. Our intention was to look at the anti-migratory effects of Taxol-related drugs. As such, we first determined the concentrations at which the drug was lethal to our cells (this is the LD50 of ~800nM). Then, we tested if lower concentrations - which we knew where not lethal - would affect cell migration, protrusions, etc. Hence the 30-150nM range we used in our experiments.
We have now completely rewritten this section hoping that our approach is now clearer. We have also changed the title of the section and the figure legend to clarify that we are studying the effects of Taxol as anti-motility drug rather than its effects on cell survival and proliferation (now Fig. 7). Finally, we have now fixed the 150mM/150nM typo in the figure legend.
Journal dependent
- The authors have not excluded that either changes in nutrients, or even a pro-invasive factor, produced by consumer cells are necessary for the increased invasion. They have only shown that they are not sufficient. The authors should perform a series of experiments comparing hypoxic conditions with normal media and normoxic conditions with nutrient depleted/condition media by prior culturing of KP cancer cells.
This is a great point. We actually do not want or propose to exclude this possibility. So, we have now added text to clarify this issue (lines 431-435).
In fact, we would be thrilled if there is a pro-invasive factor. If that would be the case, our results indicate this factor is only effective under ischemia. Because the same consumer cells do not have an effect on the same type of tumor spheroids under well-nurtured environments. In addition, our new pH measurements and perturbations experiments agree with this reviewer's intuition about additional factors being key in the increased invasion (see new Figure 2). We are very excited about these new results, and we hope this reviewer will be excited too.
- What is the oxygen sensor for increased invasion? PHD1-3 would be a good place to start looking. Is the PHD2-HIF axis important? Do VHL mutant cells still show responses to the consumer cells?
Following the reviewer's feedback, we generated isogenic HIF1A KO cell lines to study whether HIF1A was directly needed in the invasion of tumor spheroids within the 3MIC. We complemented these loss-of-function experiments with For HIF1A gain-of-function using pharmacological interventions that stabilize HIF1A under normal oxygen levels (CoCl2 and DMOG).
As shown in the new figure 2, these experiments mirrored our hypoxia experiments: HIF1A activity was not sufficient but it was required to drive the invasion of ischemic spheroids. We think that these new results are particularly interesting when taken together with our new pH-perturbation experiments. Briefly, our new experiments results show that in addition to the requirement of hypoxia/HIF1A, media acidity also has a strong effect on spheroid invasion. More excitingly, a drop in pH is sufficient to dramatically increase invasion - even in control well-nurtured spheroids. We think that the effects of pH and hypoxia are linked. HIF1A activation and hypoxia the increase glycolysis and thus lactic acid secretion. We speculate that this glycolytic switch is where hypoxia is important, but it is not sufficient because under well-perfused conditions (e.g. healthy tissue or large culture media volume) lactic acid levels may not buildup enough to significantly lower the extracellular pH. In contrast, under poor perfused conditions (3MIC and solid tumors) or if we flood cell cultures with lactic acid, the media's pH drops dramatically (Fig. 2).
- If they include both spheroids of endothelial cells and cancer cells, will the resulting protrusions in hypoxia grow towards each other? Would macrophages enhance this process?
We agree with the reviewer this is an interesting question and we have anecdotally observed this effect. In the manuscript, we used these chimeric endothelial/tumor spheroids rather than separate ones (Fig. 6E). We do not find strong evidence that their protrusions grew towards each other, but this is something that we would like to explore in the future with more detail.
Significance:
The main advance is technical, as many previous studies have related hypoxia to increased cancer cell invasion, which the authors correctly acknowledge and cite. It is scholarly study, which will be of interest to many readers, and the method reported is likely to be adopted by several groups.
Reviewer #3
Evidence, reproducibility and clarity:
In this work, Anandi et al., developed a cell culture system to live image the initial transformation of cells in deprivation of oxygen and nutrients in a 3D context. Using this system, 3MIC, they were able to create oxygen and nutrient gradients to simulate ischemic conditions that arise deep within tumors and that typically precede metastasis. With the 3MIC system they validated that ischemia triggers cell migration and invasion of tumor cells. In addition, 3MIC also allowed them to study the interaction of tumor spheroids with stromal cells such as macrophages and endothelial cells. Interestingly, the authors showed that co-culturing tumor spheroids with stromal cells increased the pro-metastatic features induced by ischemia conditions. Lastly, using 3MIC allowed the authors to discern that a poor paclitaxel response in ischemic-like cells is driven by intrinsic cellular resistance rather than due to lower drug concentration.
Overall, the work is very well-written, and the results are consisting, convincing and support the conclusions. The methods are clear and complete and allow the reproducibility of the experiments. The experiments are adequately replicated and statistical analyses are well described. However, I have few suggestions to improve the impact of the manuscript:
- The authors conclude that 3MIC results in the accumulation of lactic acid and nutrient deprivation in an increasing manner when moving far from the opening site. Is there a way to actually show this? So far, the authors employ a hypoxia sensor only. A sensor for internal pH or other method for nutrient deprivation would help to support the conclusion and further validate the model.
This is an excellent point. Following the reviewer's feedback, we tested additional sensors including for extra- and intra-cellular pH. As mentioned above, we observed dramatic changes in extracellular pH levels. We followed up these observations with a series of experiments that showed a key functional role for media acidification in driving invasion (Figure 2).
- According to figure S3E, the main cell line used by the authors is already quite mesenchymal. It would be good to know if the results showed here are consistent in cells with a more basal epithelial phenotype. Do epithelial cells need stronger ischemic conditions to undergo phenotypic changes?
This is a great catch. To explore this further, we run a Western Blot analysis to compare epithelial and mesenchymal markers expressed by the main cells we used here (Lung KPs) and to compare them to levels in a stereotypical epithelial (MCF-7) and a mesenchymal (MDA-MB-231) cell line (new Fig. S4D). As the reviewer correctly points out, we do see that E-Cad and Vimentin are co-expressed in KP cells.
So far, our observations in a range of cell lines are a consistent decrease in E-Cad levels with no significant effects in vimentin levels - regardless of the basal levels of this protein.
Interestingly, a recent study[1] demonstrated in triple-negative breast cancer models, that an EMT hybrid phenotype - including the presence of Vimentin - is required for metastasis. A compelling hypothesis then is that ischemia in the tumor microenvironment may favor these hybrid phenotypes. We briefly discuss this topic in the revised version of this manuscript.
- The number of replicates should be included in each figure legend and not only in the methods section. From data presented it is not clearly stated what do points mean in boxplots (e.g, Fig1H, 2B,G...). How many cells/spheroids did the authors count in each experiment?
We have now added this information to all our experiments. This information can be found in the figures and on the figure legends.
- Figure 3B is not mentioned in the main text.
We apologize for this error, and we thank the reviewer for catching this issue, which have now corrected.
- Line 295: "In the absence of macrophages, clusters of endothelial cells remained mostly rounded, even in the presence of consumer cells and regardless of their location along the ischemic gradient (Fig. 5A; Video S6)." However, in Video S6, both images show endothelial cells co-cultured with macrophages. I consider that Video S6 should be not referenced here.
The reviewer is correct so have removed that reference.
- References style should be homogeneous (e.g, in Ref 13 appears "Nature Reviews Cancer" whereas in Ref 14 "Nat Rev Cancer"). Also, in Ref 25, the journal is missing.
We apologize for this oversight, and we have not tried to be more consistent in our references.
- In plots where distance to open chamber site is not especify (e.g. 6B), at what distance were the data recorded? Please, indicate in the figure legend.
We have now added this information to our figures.
- In the experiment showed in Fig 4, the sorting strategy would include stromal cells such as fibroblasts and endothelial cells in the GFP- population (as only CD45+ cells are removed). These cells will likely also grow in the 3MIC system and have an effect in migration. Can the authors rule out this confounding effect?
The reviewer is correct. We still think that the possibility of fibroblast contamination is low. First, the fluorescence of HRE-GFP cells under normoxic, is still higher than the autofluorescence of cells not expressing this constructs (such as fibroblasts). This is quite normal as most sensors/reporter have some leakage and thus there is a small amount of transcription. Second, intradermal and subcutaneous tumors are quite poor in fibroblasts. In fact, to study the role of fibroblasts in these tumors, they are usually co-injected with tumor cells (PMID: 20138012). Third, in the process of tumor dissociation and in vitroestablishment, non-transformed cells tend to die more. Since these are more technical points, we moved the cell sorting details to the material and methods section.
- In Fig 5C the panel of proximal + macrophages is missing
We apologize for this mistake, and we have corrected in the new version of the manuscript.
- In Fig. 5, Linifanib is used to study the effect of blocking VEGF. Linifanib can also interact with RTKs and PDGF. This fact should be acknowledged.
We agree with this point. Following the reviewer's advice, we now acknowledged the potential off-target effects of these inhibitors (lines 354-355).
Significance
This is a very interesting work with the development of a simple and cost-effective system that allows to continuously monitor biological processes in 3D cultures under nutrient-modified conditions. In general, these data would be broadly interesting to cancer community in general, as 3MIC is a very versatile system, where several aspects can be studied and precisely discerned.
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Referee #3
Evidence, reproducibility and clarity
In this work, Anandi et al., developed a cell culture system to live image the initial transformation of cells in deprivation of oxygen and nutrients in a 3D context. Using this system, 3MIC, they were able to create oxygen and nutrient gradients to simulate ischemic conditions that arise deep within tumors and that typically precede metastasis. With the 3MIC system they validated that ischemia triggers cell migration and invasion of tumor cells. In addition, 3MIC also allowed them to study the interaction of tumor spheroids with stromal cells such as macrophages and endothelial cells. Interestingly, the authors showed that co-culturing tumor spheroids with stromal cells increased the pro-metastatic features induced by ischemia conditions. Lastly, using 3MIC allowed the authors to discern that a poor paclitaxel response in ischemic-like cells is driven by intrinsic cellular resistance rather than due to lower drug concentration.
Overall, the work is very well-written, and the results are consisting, convincing and support the conclusions. The methods are clear and complete and allow the reproducibility of the experiments. The experiments are adequately replicated and statistical analyses are well described. However, I have few suggestions to improve the impact of the manuscript:
- The authors conclude that 3MIC results in the accumulation of lactic acid and nutrient deprivation in an increasing manner when moving far from the opening site. Is there a way to actually show this? So far, the authors employ a hypoxia sensor only. A sensor for internal pH or other method for nutrient deprivation would help to support the conclusion and further validate the model.
- According to figure S3E, the main cell line used by the authors is already quite mesenchymal. It would be good to know if the results showed here are consistent in cells with a more basal epithelial phenotype. Do epithelial cells need stronger ischemic conditions to undergo phenotypic changes?
- The number of replicates should be included in each figure legend and not only in the methods section. From data presented it is not clearly stated what do points mean in boxplots (e.g, Fig1H, 2B,G...). How many cells/spheroids did the authors count in each experiment?
- Figure 3B is not mentioned in the main text.
- Line 295: "In the absence of macrophages, clusters of endothelial cells remained mostly rounded, even in the presence of consumer cells and regardless of their location along the ischemic gradient (Fig. 5A; Video S6)." However, in Video S6, both images show endothelial cells co-cultured with macrophages. I consider that Video S6 should be not referenced here.
- References style should be homogeneous (e.g, in Ref 13 appears "Nature Reviews Cancer" whereas in Ref 14 "Nat Rev Cancer"). Also, in Ref 25, the journal is missing.
- In plots where distance to open chamber site is not especify (e.g. 6B), at what distance were the data recorded? Please, indicate in the figure legend.
- In the experiment showed in Fig 4, the sorting strategy would include stromal cells such as fibroblasts and endothelial cells in the GFP- population (as only CD45+ cells are removed). These cells will likely also grow in the 3MIC system and have an effect in migration. Can the authors rule out this confounding effect?
- In Fig 5C the panel of proximal + macrophages is missing
- In Fig. 5, Linifanib is used to study the effect of blocking VEGF. Linifanib can also interact with RTKs and PDGF. This fact should be acknowledged.
Significance
This is a very interesting work with the development of a simple and cost-effective system that allows to continuously monitor biological processes in 3D cultures under nutrient-modified conditions. In general, these data would be broadly interesting to cancer community in general, as 3MIC is a very versatile system, where several aspects can be studied and precisely discerned.
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Referee #2
Evidence, reproducibility and clarity
Summary
Anandi and colleagues present a manuscript describing a nice assay for exploring the progressive effect of metabolic depletion of the nutrients and oxygen on the invasion of cancer cells. This builds upon and extends a device that they previously described - MEMIC - and now enables 3D analysis of small numbers of cells. The key to their method is the inclusion of a layer of consumer cells that deplete oxygen and nutrients. Using this tool, they demonstrate that depleted environments promote invasive behavior and lower cell-cell adhesion. This is related to the nutrient-deprived and hypoxic environments found in the center of many tumors. Cellular Potts Modelling is used to explore ideas around the cooperation between reduced cell-cell adhesion and increase ECM adhesion in promoting invasion. Overall, this is a well-constructed manuscript that will be of interest to cell biologists and cancer biologists.
Major comments
I realize this work is submitted to review commons and this complicates the recommendation regarding publication. My view is that the 'more prestigious' journals would require greater mechanistic insight, but that the work could find a suitable place in other members of the review commons stable. My comments are divided into those essential for any journal and those that might be journal dependent. Essential regardless of journal
- Many of the figures lack information about the number of spheroids analyzed and from how many biological repeats they are derived.
- The authors need to provide citations for their assertion that only gases can cross the PDMS, but not other small metabolites. They should also comment on whether the build-up of CO2 might be relevant.
- The data on the directionality of migration when consumers are present are not significant and doesn't warrant the speculation in lines 186-189.
- The ECM degradation in Figure 3 should be quantified.
- Do the authors have evidence that the hypoxia-exposed cells are more adhesive to ECM. This is central to their Potts model and I could not locate the supporting experimental data. If not, then the Potts model should include matrix proteolysis, which they do have data about.
- Is the down-regulation of E-cadherin transcriptional - i.e. is the mRNA level reduced?
- The title of figure 6 is misleading. The authors do not demonstrate chemoresistance in terms of cell survival or cell proliferation, which is how the term is normally used. The authors should measure cell number, proliferation, and cell viability. The data presented in the Supplementary Figure are inadequate with no quantification. The FUCCI reporter cells would be a good tool for this. Also, why use 150nM paclitaxel when the IC50 is 817nM? This seems bizarre. Lastly, there is a typo in the figure that suggest 150mM drug was used.
Journal dependent
- The authors have not excluded that either changes in nutrients, or even a pro-invasive factor, produced by consumer cells are necessary for the increased invasion. They have only shown that they are not sufficient. The authors should perform a series of experiments comparing hypoxic conditions with normal media and normoxic conditions with nutrient depleted/condition media by prior culturing of KP cancer cells.
- What is the oxygen sensor for increased invasion? PHD1-3 would be a good place to start looking. Is the PHD2-HIF axis important? Do VHL mutant cells still show responses to the consumer cells?
- If they include both spheroids of endothelial cells and cancer cells, will the resulting protrusions in hypoxia grow towards each other? Would macrophages enhance this process?
Significance
The main advance is technical, as many previous studies have related hypoxia to increased cancer cell invasion, which the authors correctly acknowledge and cite. It is scholarly study, which will be of interest to many readers, and the method reported is likely to be adopted by several groups.
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Referee #1
Evidence, reproducibility and clarity
Summary
In this work, Anandi et al. propose an ex vivo model that can be used to recapitulate the in vivo structure of the tumor microenvironment, which allows the observation of morphological and functional changes in tumor cells in a 3D context. Due to the ability of cancer cells to induce hypoxic condition within the TME, authors propose this model to tackle the study of metastasis initiation in vitro. The proposed system successfully displays an ischemic gradient with cells accessing nutrients at different rates, similarly to what happens in solid tumors in vivo. Moreover, in line with the literature, tumor cell migration and invasiveness were promoted by hypoxic conditions. Authors also show that the system could be used to study cell-cell interaction, as co-cultures of macrophages and cancer cells were successfully cultured in the system and studied in the context of tumor hypoxia. The study proposed is interesting and timely, as cancer cell invasion remains an important area of tumor biology that needs further exploration. The methodology is well explained and proposed in a linear flow. However, the work could benefit from some improvement and changes, as well as from additional experiments. On an important note, authors do not properly refer to the current literature, as several studies on 3D culture systems/chambers have already been studied and developed to investigate the tumor microenvironment, but they are not cited nor referred to in the manuscript. Authors should refer to such literature and explain how this system is different and adds to it.
Major comments:
- Authors propose this method to study the TME in 3D. When culturing cells with different ECM (Collagen vs. matrigel+collagenI) authors should take into consideration the effect of these materials on different cell types. It is known how collagen and matrigel can differently influence the polarization and phenotype of stromal cells (particularly in regards of fibroblasts - major components of solid cancers - e.g., PMID 21029367), therefore these points should be addressed at least in the discussion.
- In addition to the previous comment, matrigel and collagen are also known to alter cancer cell phenotype (e.g., PMID 21029367) and this point should be taken into account.
- The need for novel 3D systems to study different aspects of the TME in vitro/ex vivo are certainly needed, however they are not inexistent. Authors should address this in the text, as the current literature already started to propose 3D models (including models involving matrigel/collagen in combination with other materials). 3D chambers (of different materials, and with different aims) are being used and designed and can be found in the literature. These works are not cited in the current study at all. For instance, Anguiano 2017; Cavo et al. 2018; Anguiano et al., 2020; Sodek et al. 2008, etc.
- Even though the focus is on hypoxia and the achievement of an ischemic gradient in the chamber to allow resemblance of an in vivo tumor, the authors write in line 123 (and also in other parts of the text) that: "these results show that consumer cells in the 3MIC form ischemic gradients that can influence the local metabolic microenvironment experienced by neighboring tumor spheroids". The addition of the use of the PMDS membrane partly supports the claim, however it would be interesting to check whether this is indeed true, by measuring for example the levels of certain metabolites (e.g., glucose, glutamine, glutamate, lactate, aspartate) reached with the system, or pH levels, etc., in presence or absence of the hypoxic gradient/consumer cells.
- When looking at the references presented in the manuscript, authors quote too many review articles, rather than scientific articles. Given the extremely wide literature on cancer metastasis, more of these works should be quoted in this context. For example: in the introduction - text lines 27-38 - only 4 references are research articles, out of 14 references presented in that paragraph.
- As authors showed successfully how macrophages and cancer cells can interact in the chamber, recapitulating cell interactions in an in vivo context, it would be very interesting to see whether different consumer cells would induce similar or different changes to the spheroids and the ischemic gradient (for instance using stromal cells or non-tumor cell lines as consumers, instead of cancer cells only), as we know how tumors are a multitude of cell subsets, each contributing to nutrient production, oxygen consumption, etc.
Minor comments:
- Studying the early metastatic development/seeding remains a timely quest, however authors should refer to several new studies in which various mouse models are used to study metastasis from different points of view (e.g., PMID 25822788; PMID 36991128; PMID 25171411; PMID 25633981; PMID 34632412; PMID 35921474; etc). Or line 41, three reviews are quoted (refs 27-29), whilst there are several works that could be quoted on metabolism in solid tumors also in the context of metastasis (e.g., PMID 36522548; PMID: 26719539, PMID 34303764). This comment applies to the rest of the text.
- The order of the references is not properly presented. In the introduction, the first reference is n. 4 (text line 22), instead of it being reference 1. Moreover, the subsequent literature ref. is number 12 and not number 2. Please revise the order of the references, and position them within the bibliography from first cited to last cited in the text.
- Lines 98-104. It would be helpful to the reader to define here what these consumer cells are. Even though it is explained in the methods that the consumer cells are cancer cells, it is important to make it clear in the text, as it could be misleading at times.
- The English grammar and spelling should be revised in some parts, as well as typos and missing words throughout the text (e.g., Line 38, the word "interraction" is misspelled and should be corrected with "interaction". Line 49, the first sentence seems incomplete. Lines 68-69 should be revised as the sentences do not flow well together, probably due to a missing word. In line 77 it should be "presents". Line 341 should be "cannot be explained").
Referees cross-commenting
I find the comments from the other reviewers to be in line with one another as well as with my general assessment. The major and comments of all reviewers should be addressed. The minor comments should be taken into account as well, as they would render the text and the figures more precise. I suggest that 3-6 months to complete the revision process is an appropriate time frame for the authors. Finally, I strongly encourage the authors to add in the discussion the points and questions raised by all reviewers, as well as to improve the bibliography in terms of organisation, linearity, and state of the art.
Significance
General assessment:
The work by Anandi et al. offers an additional tool to tackle the issue of studying the tumor microenvironment, in a 3D culture system. The authors show a model that can be used to study tumor hypoxia in 3D, offering the possibility to study the TME in a more in vivo-like manner without turning to mice models. The development of new tools to study the TME avoiding the excessive use of animals is definitely a timely quest. In addition, the system has the potential to be applied to tackle different biological questions, as the methodology is well explained and could be suitable to many other fields of cancer biology (e.g., drug resistance or uptake). The work is overall presented in a clear way and the methodology is explained thoroughly and it has the potential to be a useful tool for the study of cancer hypoxia.
However, authors should address how their method could differently impact other cells when applied to other systems. As one major claim is the potential use of this methodology to study the TME, it should be taken into consideration how stromal cells are strongly affected by the ECM, and how certain settings or features of the system may impact such cell populations. In addition, the work does not properly refer to the current state of the art. As other studies started to propose 3D systems for the study of TME and cell-cell interactions - besides organoids - the authors should cite these works and frame their own study in a more appropriate context, pointing out differences with the current 3D chambers available, the advantages of one vs the other, and so on.
Advance: the study adds to the current literature as the study of tumor hypoxia in 3D remains a complicated issue. The interesting co-culture settings with macrophages suggests potential uses of this model to study cell-cell interactions.
Audience: the study is very methodological and offers a tool that could be used by cancer biologists - and maybe by other biology fields.
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Reply to the reviewers
We would first like to thank the reviewers for their careful reading and thoughtful feedback.
We have substantially revised the manuscript and included additional experimental evidence on O-GlcNAc and OGT/OGA protein levels in the placenta of embryos bearing the OGT-Y851A hypomorphic mutation.
Overall, we believe our improved manuscript provides compelling evidence that the glycosyltransferase activity of OGT, and thus the O-GlcNAc modification itself, plays a sexually dimorphic function in placental development and the developmental repression of retrotransposons in the developing embryo.
We have addressed each of the reviewers' comments below. The original comments (C) are in italic, our responses (R) in Roman font.
Reviewer #1
Evidence, reproducibility and clarity
C1: Formichetti at el. developed mice with OGT catalytic dead mutations and then studied their function during early embryogenesis. Not surprisingly, dramatic reduction in OGT activity failed to produce embryos; however, mild reduction in OGT did produce animals. The authors then use the T931 animals that have a mild reduction in activity to further characterize the function in the early embryo. Not surprisingly, male mice showed changes in gene expression, implantation sub-lethality, and an uptick in loss of retrotransposon silencing. The authors also show that an even milder reduction in OGT activity (Y851A) effects male placenta function and chromatin remodeling. Finally, the authors make a less stable OGT transgene within the mouse and again found embryogenesis issues in the males and alterations in numerous gene families including mTOR signaling and p53 function. All in all, this is an interesting study that track functions of OGT in early embryonic development. The studies are well-controlled and rigorous.
R1: We thank the reviewer for their clear understanding and their appreciation of the rigor and impact of this work.
Significance
C2: This is a good study and novel. Not only is it of interest to reproductive biologist, but it echos themes found in O-GlcNAc biology.
R1: We are pleased that the reviewer underlined the novelty of the study and its impact across fields.
Reviewer #2
Evidence, reproducibility and clarity
Comments to authors
C3: To investigate the function of OGT at specific developmental stages, the authors perturbed OGT's function in vivo by creating a murine allelic series featuring four single amino acid substitutions that variably reduced OGT's catalytic activity. The goal was to identify the direct effect of O-GlcNAcylation, using a sophisticated collection of genetic mutants to evaluate in vivo the role of this modification at early stages of development. Overall, the severity of embryonic lethality correlated with the extent of catalytic impairment of OGT, demonstrating that the O-GlcNAc modification is essential for early development.The study represents a substantial advance in our understanding of OGT and O-GlcNAcylation in mammalian development. The creation of novel murine models and inducible systems is an important contribution, providing powerful tools for future research in this field. The insights into the role of OGT's catalytic activity and its involvement in epigenetic regulation during embryonic development are noteworthy, opening new avenues for research.
R3: We thank the reviewer for their insightful comments. We are grateful for the supporting statements. Please find below detailed response to all your comments.
However, there are a few considerations and concerns:
Major:
C4: 1. An assumption of the study is that different mutations cause different levels of O-GlcNAcylation rather than alterations in substrate specificity. It might be important to test, at least in cultured cells, that the different mutations do not change the preference of OGT to modify certain proteins rather than others, which can provide alternative explanations for their findings.
R4: Thanks for asking this question, it helped us to better explain the rationale behind the choice of the Ogt amino-acid substitutions.
This is a critical point that we carefully considered in the design of the single amino-acid substitutions. Two lines of evidence support that the precise mutations created impact the catalytic rate without modifying the substrate specificity:
First, as explained in the text, the choice of the single amino-acid substitutions was driven by previous structural and enzymology knowledge. The impact of the four point mutations selected on OGT protein stability and on the Michaelis-Menten kinetic values had previously been determined experimentally (Fig. 1A legend and Martinez-Fleites, C. et al. Nature Structure Molecular Biology 2008; https://doi.org/10.1038/nsmb.1443).
There is a second important rationale that we added in the revised manuscript: the four point mutations selected are all located in the catalytic domain (specifically, H568A in the N-Cat domain and Y851A, T931A and Q849A in the C-Cat domain), while the substrate recognition is operated via two other domains namely the intervening domain (Int-D) https://doi.org/10.1038/s41589-023-01422-2) and the tetratricopeptide Repeat (TPR) superhelix (10.1021/jacs.7b13546; https://doi.org/10.1073/pnas.2303690120). Therefore, for both these reasons, it is extremely unlikely that these mutations could influence the substrate specificity.
C5.1: 2. In Fig 1D and 1H, the thresholds to define a gene or TE as differentially expressed are not strong. According to the figure legends, "any" change in terms of log2Fc was considered as DE and colored. I think the figures should illustrate better that the changes are subtle, by for example adding a dotted line (at least) in the value 0.5 of the y-axis. These subtle transcriptional changes should be reflected better in certain paragraphs where the expression of TEs are presented/and discussed as a hallmark of the absence of O-GlcNAcylation in the OGT-mutants. The same happens with Suppl Fig 3C (changes are very minor). {. Applying a stronger threshold, among the upregulated genes, only Xist will be significantly overexpressed. If a gentle threshold needs to be applied to this data, authors should at least justify the reasons behind doing so. Same for Fig2D.
R5.1: The reviewer means Figure 2D for MA plot of gene expression and Figure 2H for retrotransposons expression. These figures now include a dash line to indicate Log2FC = 0.5 (as all MA plots).
The text is explicit on the subtle changes in transcription, it reads "with 2/3 of the genes downregulated and 90% of the significant changes below 1 log__2__FC"; "most of the Ogt__T931del/Y embryos showed a low magnitude upregulation of retrotransposons".
The revised text states "Notably, most of the OgtT931__del/Y embryos showed a low magnitude (log2FC < 1) upregulation of retrotransposons".
We expand on this topic in the next response (R5.2) noting that changes in gene expression upon O-GlcNAc perturbation in different systems were previously characterized as subtle and widespread. We suggest that this phenotype may arise from the scarcely understood pleiotropic function of O-GlcNAc in fine-tuning gene expression; this phenotype could have a biological significance.
C5.2: If a gentle threshold needs to be applied to this data, authors should at least justify the reasons behind doing so. Same for Fig2D.
R5.2: Previous studies in different systems reported that O-GlcNAc perturbation causes a widespread change in gene expression of low magnitude (https://doi.org/10.1101/2024.01.22.576677, https://www.pnas.org/doi/10.1073/pnas.2218332120). We use the same thresholds as a recent functional Ogt study in ES cells to call differentially expressed genes, specifically: p<0.05 (Wald test), any FC (Li et al. PNAS 2023, https://www.pnas.org/doi/10.1073/pnas.2218332120). The p value threshold is standard; the absence of FC threshold is dictated by the insufficient knowledge of the significance of the low magnitude changes observed across many transcripts.
C6: 3. In Figure 2B, the T931del allele was recovered in the blastocyst population with a very high frequency, even higher than the male WT group (T931del: 10; WT: 3). This observation suggests that the T931del allele did not significantly affect blastocyst survival. Further clarification or additional experiments might be necessary to understand the implications of this finding on early developmental stages.
R6: This is only a hint as the numbers of blastocysts recovered were too small to perform statistics on Mendelian distribution. Thus, more experiments are needed to perform these statistical tests. These experiments are onerous because the low frequency of germline transmission is incompatible with maintaining this mutation by breeding heterozygous animals. Because of this, a new mouse line needs to be created by CRISPR-HDR targeting in the zygote in order to compute statistics on Mandelian ratios. Importantly, this question - does T931del affect blastocyst survival? - is peripheral, and the results of these experiments would not affect our conclusions in any way.
C7: 4. Similarly, in Figure 2G, there is an apparent higher expression of TE expression in the T931A/Y embryos group than in the T931del/Y group, which combined with the higher frequency of blastocyst generated in this latest group it may indicate a deeper molecular consequence after the deletion of the T931. A comparison of the transcriptome between these two cell lines help to address this possibility. Also, the authors should compare the O-GlcNAc levels of WT, T931A, and T931del mutant blastocysts by immunostaining, similar to what was done in Figure S5F.
R7: We agree that a direct comparison between the two mutations of the T931 residue would be interesting; however, this comment is very difficult to address experimentally for the reasons outlined below:
Firstly, it is not possible to perform a statistical comparison of the transcriptome T931A/Y VS. T931del/Y with the data generated because the number of hemizygous T931A/Y (n=2) is too small. Hence, it cannot be ruled out that the seemingly milder retrotransposon reactivation in one of the T931A/Y embryos could have occurred by chance.
Secondly, considering the low magnitude effect on gene expression changes upon O-GlcNAc genetic perturbation, to statistically assess the penetrance of the molecular phenotype and perform the differential expression analysis, numerous (>>3) hemizygous blastocysts of each genotype would be needed. Because females heterozygous for the T931 mutations transmit the mutant allele at very low frequency, these experiments require numerous de novo CRISPR injection sessions.
Thirdly, for the immunostaining of O-GlcNAc to be semi-quantitative, a large number of hemizygous blastocysts for each genotype would be required (note that in Figure S5F, 29 morulae per condition were imaged), thus requiring numerous CRISPR injection experiments as discussed above. Moreover, O-GlcNAc changes could be subtler than what expected based on the strong reduction of OGT activity, since as a compensatory mechanism Ogt expression is upregulated in the Ogt__T931A/del blastocysts (Fig. S2D), making a quantification even more challenging despite a high number of stained embryos.
In sum, these in vivo experiments are difficult and require sacrificing many animals (about 20 females per CRISPR injection experiment). Because the results would bring refinement to the study but would not change our conclusions, we suggest that the cost/benefit is too high.
C8: 5. In Boulard et al. 2019 O-GlcNAcylation was shown to be sufficient to modulate expression of DNA methylation-dependent TEs. It would be interesting to know (or at least discuss) if the changes in TE expression observed in OGT-mutant embryos in this study involve changes in DNA methylation. Ideally, some DNA methylation measurement optimized for low input numbers of cells would be useful.
R8: Thank you for making the link with our previous study. In the PNAS paper, we report that targeted removal of O-GlcNAc at proteins bound to specific TEs (e.g. IAPez) causes their full-blown reactivation without detectable changes in DNA methylation, thus suggesting a role of the O-GlcNAc modification for the silencing of methylated TEs downstream or independent of DNA methylation. We agree that it would be informative to quantify DNA methylation in the T931-mutant blastocysts to test if the in vitro result is the same in vivo, but this would require performing onerous microinjection sessions as explained above.
C9: 6. The data related with the OGT-degron system in MEs seem disconnected with the rest of the manuscript. While the developmental models (blastocyst, etc) elegantly assess the contribution of O-GlcNAcylation to the control of cell survival and gene expression through the use of different OGT mutants, the degron system is a system of graded depletion that unfortunately was only possible to be used in MEFs (instead of embryos). Thus, the results obtained with the degron system in MEFs are difficult to intersect with the data from the use of OGT-mutants in embryos. Even though there are obvious interesting questions that one may want to know about this OGT degron MEF system, none of them would demonstrate a direct role for O-GlcNAcylation in cellular function, the major point addressed in the developmental system. Using the degron system in embryonic stem cells might have provided a more parallel comparison. The authors should discuss this point in more detail and either use ESC instead of MEFs or provide a stronger justification for the use of MEFs over ESC.
R9: We thank the reviewer for their clear understanding of the system. The choice of primary MEF as an in vitro model was imposed by technical limitations we encountered during the study. We fully agree that ES cells is the model of choice for preimplantation embryos; thus we initially derived ES cells and obtained only one male clone bearing the AID degron system. Upon auxin addition to the culture media, OGT's level remained unchanged in ES cells. Thus, the ES cells model was not usable. To test the AID degron in a different cell type, we then derived MEFs and showed its effectiveness (Figures 4C and S4C-E), which also allowed to collect functional data on OGT's cellular function (Figures 4D-F). We took the comment on board and clarified the rationale of studying MEFs in the revised manuscript. We agree that it remains to be verified that the OGT-dependent pathways uncovered in MEFs are relevant in the preimplantation embryo. Despite this caveat, we feel the mouse model for endogenous OGT-degron, as well as the negative results in vivo and conclusions in MEFs should be shared with the community, which could take advantage of our results to refine the system.
Minor:C10: 7. In Fig 2C the color and shape codes are confusing to understand - there are some colors/shapes that are not represented in the PCA plot. The same in Fig 3H, where in the PCA plot there are pink triangles that do not match with the code legends.
R10: We apologize for the confusion with the legends of Figures 2C and 3H, that we have made unambiguous in the revised version (as well as Figures S2B,C and S3C).
C11: 8. In the figure legends of Figures 2D, 2E, 2F, and 2H, the notation should be corrected from "OgtT931A/Y" to "OgtT931del/Y".
R11: This has been corrected; many thanks for bringing it to our attention.
Significance
C12: To investigate the function of OGT at specific developmental stages, the authors perturbed OGT's function in vivo by creating a murine allelic series featuring four single amino acid substitutions that variably reduced OGT's catalytic activity. The goal was to identify the direct effect of O-GlcNAcylation, using a sophisticated collection of genetic mutants to evaluate in vivo the role of this modification at early stages of development. Overall, the severity of embryonic lethality correlated with the extent of catalytic impairment of OGT, demonstrating that the O-GlcNAc modification is essential for early development.
R12: We thank the reviewer for their clear understanding of our work and their appreciation of the biological importance of the findings.
Reviewer #3
Evidence, reproducibility and clarity
C13: This is a conceptually interesting paper that attempts to leverage the knowledge of OGT catalysis to begin to dissect OGT function. The evidence is presented I a straightforward fashion and is in general well documented. The breeding strategies are well informed and the paper draws heavily on previous work carried out in the mouse.
R13: We greatly appreciate the overall supporting review. However, we fail to understand what they mean with "the paper draws heavily on previous work carried out in the mouse". This comment may stem from a misunderstanding because this work is not based on any previously published study. Specifically, neither the seven murine alleles presented and analyzed nor the single embryo-transcriptomic data sets on which our conclusions are based have been published elsewhere.
To put this work into context, before our study there were two seminal studies published two decades ago that reported the essential role of Ogt for mouse development, but no molecular profiling was performed (10.1073/pnas.100471497, 10.1128/mcb.24.4.1680-1690.2004). The two Ogt loss-of-function alleles studied in these papers were deemed as not suitable for interrogating molecular phenotypes because they caused cell death that confounds molecular profiling and embryonic lethality at implantation, thus preventing study of the sexually-dimorphic role of Ogt placenta. To overcome this long-standing problem, we created new seven murine alleles, which allowed us to tease apart molecular phenotypes at key stages of mouse embryonic development, focusing on the blastocyst and the placenta.
Significance
C14: The paper describes tools which will help dissect the many potential roles of O-GlcNAc addition in early development. As it stands, this is a descriptive manuscript that will lead to hypothesis generation and testing and this should not be undervalued. The biological reagents produced and characterized will be of general interest to the field. Most of the findings presented represented a verification of existing ideas in the field but this is not meant as a criticism since part of the motivation for the approach was to generate a reproducible system for analyzing the biological phenomena.
R14: We thank the reviewer for their appreciation of the importance of experimentally testing ideas shared in the field without direct evidence.
However, we must respectfully disagree with the qualification of "descriptive manuscript". This qualification may stem from the particularly difficult challenge to accessing the molecular details on how the O-GlcNAc modification exerts the biological functions we report. We are fully cognizant of the limitations of the study that we discussed in the discussion section and in R20.2. However, we feel that the adjective "descriptive" is not a fair qualification because we provide numerous novel functional evidence. Specifically, we introduce two novel orthogonal in vivo perturbations for endogenous Ogt that allowed us to interrogate for the first time its function in the developing mouse embryo. These perturbations allow us to draw causative conclusions (not descriptive) on the essential role of the O-GlcNAc modification itself for preimplantation development, its sexually-dimorphic role in the placenta and its requirement in vivo for the stable repression of retrotransposons.
C15: There are perhaps some bioinformatic shortcuts taken that may need to be corrected upon thorough review. These do not lessen the overall impact of the contribution.
R15: All the code written for the bioinformatic analyses performed in this study is publicly available: https://github.com/boulardlab/Ogt_mouse_models_Formichetti2024. The reviewer needs to specify which bioinformatic analysis they suggest could be improved.
Reviewer #4 (Evidence, reproducibility and clarity (Required)):
Summary
C16: O-GlcNAcylation is the fundamental post-translational modification of numerous nuclear and cytosolic proteins. OGT is the sole enzyme catalyzing O-GlcNAc addition onto the proteins. The essentiality of OGT for early development and cellular viability has been established by using OGT-KO mice and cell lines. However, it remains to be elucidated whether the catalytic activity of OGT is required for the early development, and if the catalytic activity of OGT is required what are the functions of OGT or O-GlcNAcylation in early development due to a lack of appropriate mouse models. To overcome the technical difficulty of manipulating the levels of O-GlcNAcylation in early embryos, Formichetti et al. created the series of four mouse models (OgtY851A, OgtT931A, OgtQ849N, and OgtH568A) with different OGT activity by introducing single amino acid substitution in the catalytic domain. By analyzing the inheritance of the hypomorphic OGT alleles and the lethality of mouse embryos, they discovered OGT activity is a critical factor for early development. Subsequently, RNA-seq analyses with two mouse models showing the maternal inheritance of the hypomorphic OGT alleles indicated that sever hypo-OGT activity altered transcription and silencing of retrotransposon in preimplantation development while mild reduction of OGT's activity affected placental development in a sexually dimorphic manner rather than preimplantation development. Furthermore, to study the function of OGT at specific developmental stages, they developed a mouse model bearing endogenously AID-tagged OGT for acute degradation of OGT. Although the degron system wasn't efficient in preimplantation embryos, they discovered quick transcriptional changes upon OGT deletion in MEFs. The quality of the manuscript is good because the question to be solved was appropriately set, the approach was well designed, and their findings were interesting, although their writing was sometimes hard to understand as I raised in my following comments. Nevertheless, there are several points to be fixed before being published.
R16: We thank the reviewer for their clear understanding of our work and their appreciation of the biological importance of the findings. Your comprehensive review of the manuscript and the questions you raised were extremely helpful in improving the manuscript and fully addressing its limitations. Below, we respond to comments in full, have revised the manuscript to improve clarity and have included novel results.
Major Comments
C17: 1. Although the authors showed in vitro activity of each mutant of OGT used in this manuscript by referencing the previous literature, they never showed the levels of global O-GlcNAcylation (and OGT itself) in their established mouse embryos. Although it could be impossible to determine O-GlcNAc levels in OgtQ849N and OgtH568A embryos because of the lack of germline transmission and founder line, respectively, they could do that in OgtY851A and OgtT931A embryos. Given that Y851A and T931A mutants had similar VMAX/KM with different VMAX, it is possible that their activity is comparable or Y851A has even lower activity in vivo depending on the concentration of UDP-GlcNAc in embryos. Therefore, it is critical to assess whether in vivo OGT activity is correlated with that in vitro as expected to conclude that severity of sub-Mendelian inheritance is proportional to the reduction of activity of OGT in vivo. Moreover, since the authors developed the elegant system to deplete OGT, the activity of Q849N and H568A mutant OGT can be examined at least in cells by expressing them in MEFs with OGT-degron system. Thus, I propose determination of global O-GlcNAc levels compensated by OGT levels by western blotting in OgtY851A, and OgtT931A embryos or MEFs with the OGT degron system re-expressing the individual four mutant OGTs. If the protein amount is insufficient for western blotting in the embryos because of the sizes of the earlier stages of embryos, I believe the author could address this by utilizing immunofluorescence as shown in Figure S5.
R17: We fully agree that this is an important point that requires revision. The only mutation for which the level of O-GlcNAc and OGT can be assessed by western blot in vivo is Y851A, the other mutations resulting in embryonic lethality before the blastocyst stage.
We have included in the revised manuscript western blot analyses of protein expression for OGT, OGA and O-GlcNAc levels in the placenta of the OgtY851A mutants (new Figures 3C,D). The new data show that OGT is upregulated at the protein level in homozygous females, in good agreement with our transcriptomic analysis. Furthermore, O-GlcNAc levels were slightly reduced in homozygous and hemizygous placentae thus showing the impact of the point mutation on global O-GlcNAc levels in the placentae. Moreover, the analysis of OGA protein level unexpectedly revealed the enrichment of a previously uncharacterized OGA fast migrating isoform in hemizygous and homozygous placentae.
We agree that it would be informative to compare O-GlcNAc levels in OgtT931A versus OgtY851A embryos. A comparison implies performing the experiment at the same developmental stage, which has to be the blastocyst stage or prior because T931A/Y embryos die around implantation. The blastocyst being made of approximately 140 cells, it would require to pool many single blastocysts to obtain the necessary protein input for western blot. We are not aware of another study performing western blot with pooled blastocysts. An additional great challenge for this experiment is the necessity to genotype and sex the blastocysts before pooling. Thus, the feasibility of this experiment is uncertain.
As an alternative, the reviewer suggests measuring O-GlcNAc levels in the degron MEFs after introduction of OGT transgenes bearing the mutation studied. This experiment would not be conclusive because of residual O-GlcNAc after OGT degradation (Figure S4E). Furthermore, the O-GlcNAc proteome is dynamic during development (as shown in the developing brain by Liu et al. https://doi.org/10.1371/journal.pone.0043724), therefore the MEFs results would have limited value to explain our results in the early embryo.
In sum, available technologies to quantify O-GlcNAc (e.g. western bot, mass spectrometry) are inadequate for low input samples as the early embryo. However, our series of hypomorphic alleles backed up with in vitro enzymology measurements brings indirect evidence to this question. Specifically, the qualitative correlation between the measured OGT activity in vitro and the developmental phenotype indicates that the resulting relative levels of O-GlcNAc are consistent with in vitro measurements.
C18.1 : 2. I didn't understand why the authors couldn't find any founder lines of the OgtH568A mutant. Was that because mosaic mice with OgtH568A mutation are lethal?
R18.1: To answer to this question, it is important to recall two key features of the biological system:
1) The mutation H568A was reported to disrupt the glycosyltransferase activity completely (10.1038/nsmb.1443). Hence, OGT-H588A is catalytic dead.
2) We performed the CRISPR-HDR targeting in the 1-cell embryo.
Based on these premises, the absence of F0 with the OgtH568A mutation (0/31) suggests that introducing this mutation causes embryonic lethality in both males and females. This hypothesis is consistent with the previously reported lethality around implementation of Ogt-null alleles (10.1128/mcb.24.4.1680-1690.2004). It is possible that the sgRNA is very efficient and results in homozygous mutations in all female zygotes injected (as we have not obtained heterozygous females bearing these mutations). High efficiency of the targeted mutagenesis in the zygote results in mutants where all or the majority of cells bear the mutation (no or low mosaicism). The high number of microinjections performed (416 embryos over the 3 injection sessions) allows us to make these claims.
C18.2 : Also, I believe there was no explanation why the OgtQ849N allele showed no maternal inheritance. Was that because Q849N possesses enough activity for sustaining mosaic embryos, but not oocytes? The authors should better explain these points in the manuscript text.
R18.2: Thanks for this comment, we agree that this maternal effect phenotype demands further explanation.
The phenotype observed suggests two possibilities: either that the oocyte cannot maturate or that the cleavage-stage embryo cannot develop with the resulting lower levels of O-GlcNAc. The cleavage-stage embryo does not transcribe a catalytically active OGT before the 8-cell stage and thus relies on the OGT protein inherited from the oocyte until this stage (https://doi.org/10.1101/2024.01.22.576677).
Thank you for this comment, we added this interpretation of the result in the text:<br /> "The lack of maternal transmission of the Q849N allele from seemingly mosaic founder females is likely explained by the reliance of the cleavage stage embryo onto the oocyte payload of OGT and O-GlcNAc modified proteins. Specifically, Ogt's exons encoding for the catalytic domains are not detectable before the 8-cell stage, while OGT full-length protein is present and thus maternally inherited (Formichetti et al, 2024)."
C19: 3. The authors serendipitously found a T931del-allele in the "WT" allele of the OgtT931A line, and suggested that T931del had milder activity loss, although the lethality of embryos was greatly mitigated. Nevertheless, transcriptome analyses in male blastocysts revealed that 120 genes' expression was changed in T931del/Y males. This raised the question about which mutant OGT has higher activity, Y851A or T931del. I think comparing the activity of Y851A and T931del mutants in MEFs with OGT-degron system is important to confirm the proportional relationship between activity and phenotypic severity.
R19: We agree that it is a limitation that the effect of the T931del mutation on OGT activity has not been biochemically characterized. However, the important point here is that our assessment of phenotypic severity based on maternal inheritance of the mutant allele and embryonic lethality is based on the point mutations for which the catalytic activity has been determined, namely Y851A, T931A, Q849N and H568A, but not T931del.
We studied the serendipitously discovered T931del mutation to obtain transcriptional insights in the blastocyst. Because the deleted residue T931 is key for the binding to the donor substrate, we can reasonably assume that this mutation affects the catalytic activity, albeit to an undetermined level.
Hence, our conclusions regarding the requirement of O-GlcNAcylation for development are unaffected by the lack of biochemical knowledge on T931del.
C20.1: 4. Regarding transcriptomes of T931del/Y, the authors found the upregulation of proteasomal activity and stress granules along with the downregulation of amino acid metabolism, mitochondrial respiration, and so on. To validate the results, the authors should perform qPCR on several up- or down-regulated genes.
R20.1 : We agree that, in principle, qPCR validation is suitable. However, this validation experiment is particularly expensive in this case because of the requirement of numerous CRISPR zygote pronuclear injection sessions.
The conclusions of the RNA-seq analysis are strongly supported by a high number of biological replicates (n=10). This high number of biological replicates was essential to obtain sufficient statistical power to quantify with a high level of confidence transcriptional changes of low magnitudes (below 2-fold change, see R5.1 and R5.2).
Therefore, the qPCR validation experiment would require to repeat the CRISPR zygote pronuclear injection sessions with the same high number of animals. This represents a major investment in experimental work and the sacrificing of about 40 animals. Importantly, the RNA-seq results presented are authoritative because of a high number of biological replicates and high number of sequencing reads per sample. Thus, we argue that qPCR validation is not essential and thus the high cost of this experiment is difficult to justify.
C20.2: In addition, according to Figure S2E, the authors pointed out that at least for genes upregulated in OgtT931A embryos, the changes were not explained by a developmentally delayed transcriptome, suggesting that upregulation of these genes was the cause of developmental delay. Therefore, I strongly encourage them to discuss in the manuscript text how up-regulated genes could contribute to developmental delay.
R20.2: Throughout the manuscript, we have been cautious to avoid establishing causal relationships between the differentially expressed genes uncovered and the developmental phenotypes (e.g. delayed development). There are two main obstacles which we believe prevent us from establishing causality with the data available. Firstly, it is not possible to disentangle differentially expressed genes and developmental delay (in other words, we have no way to tell which is the cause and which is the consequence). Secondly, O-GlcNAc modifies over 5000 proteins and the developing embryo is a particularly dynamic system; thus we cannot know whether the differentially expressed promoters are direct targets of O-GlcNAc modified proteins (or alternatively secondary effect of another molecular alteration, for example of the proteome). We discuss this limitation of the study in the discussion section.
C21: 5. Regarding the transcriptome in OgtY851A mice, Y851A/Y male mice had huge transcriptomic differences, while Y851A/Y851A female mice barely had any. Although it seems to agree with the number of Ogt alleles, I wonder whether other X-linked genes expressed higher in female placenta as shown in Figure 3C could attenuate the effects of decreased OGT activity. I don't think this possibility can be excluded, unless the authors further decrease OGT activity in Y851A/Y851A female placenta and obtain the similar results as for male placenta. Or if they compared the levels of global O-GlcNAcylation between Y851A/Y and Y851A/Y851A mouse placentas and discovered they had similar levels of O-GlcNAcylation, then the authors could conclude that the number of Ogt alleles was not the reason of sexual-dimorphism. The authors should determine the levels of O-GlcNAcylation in Y851A/Y and Y851A/Y851A mouse placentas and/or at least discuss the above possibilities in the manuscript text.
R21: Thank you for the thoughtful feedback. We agree that the most likely explanation for the higher sensitivity of males placenta as compared to females to OGT reduced activity is the difference in Ogt copy number, especially because Ogt escapes X-chromosome inactivation in the placenta (new Figure S3A).
Western blot quantification of global O-GlcNAc levels was now performed (new Figures 3C,D). We measured similar level of O-GlcNAc in Y851A/Y and Y851A/Y851A placentas (lowered than WT males in both cases), but we cannot exclude that the WB does not have the dynamic range required to detect a subtle difference. In fact, female homozygous were expected to have an intermediate level between WT males and hemizygous males, and the difference between the two male genotypes (also considering sample-to-sample variability) is already small when quantified from the blot (new Figure 3D). It is possible that a X-linked modifier attenuates the impact of hypo-O_GlcNAcylation in female mutant placenta in the case of identical O-GlcNAc levels in homozygous females and hemizygous males. Thank you for the idea that we included in the revised manuscript:
"Of note, the lower sensitivity of the homozygous females' transcriptome to Ogt disruption (Fig. 3F,I and S3B) seems difficult to reconcile with their lower O-GlcNAc level comparable (lower) O-GlcNAc level to the hemizygous males (Fig. 3C). It is possible that the western blot technique is not sensitive enough to detect subtle differences in O-GlcNAcylation. An alternative hypothesis, if O-GlcNAc levels were truly identical between Y851A/Y and Y851A/Y851A, could be the existence of a modifier in female that could be a XCI-escapee."
C22: 6. In terms of the transcriptome in OgtY851A mice, similar to comment 4, the authors should confirm their transcriptomics data shown as Figure 3D by qPCR. In addition, the authors should describe the potential mechanisms by which the differentiation of precursor cells of LaTPs and JZPs were disrupted. Were master regulators of the differentiation known to be O-GlcNAcylated and loss of O-GlcNAcylation perturbed the function?
R22: As for the whole embryo discussed in R20.2, we also interpret cautiously the gene expression phenotype observed in the placenta. Specifically, we state in the manuscript that it could either be caused by an impact of lower O-GlcNAcylation on placental differentiation or by a general delay in placentation or in the development of the embryo as a whole. The hypothesis of a general delay (of the whole embryo and/or of placental formation specifically) is supported by the downregulation of essentially all markers of more differentiated cell types and the upregulation of the precursor marker. We favor this hypothesis because it is consistent with what observed with the T931 mutants and also with the enzymatic removal of O-GlcNAc in the zygote (Formichetti et al., 2024 BioRxiv). Because of the thousands of O-GlcNAcylated proteins present in the cell, it is impossible to know which is the responsible molecular mechanism, which could even start at much earlier stages.
Minor Comments
C23: 1. Regarding DFP461-463 mutant, I couldn't understand the point of this figure because the results had no difference, and the meaning of the mutation was quite different from the others. Thus, the figure was awkward and a little confusing to me. If the authors still want to include the figures, I would suggest that they should reorganize the position of the figure (maybe after figure 3 is better to show you had tried to investigate the effects of nuclear localization of OGT on the changes of transcriptomes) and add some results. Since WT OGT seems to be localized mainly in the cytosol at steady state (Figure S1B and S1C), the effect of mutation on its nuclear localization should not be obvious. Therefore, it is difficult to conclude the mutation had no effect on the nuclear localization unless the ratio of nuclear and cytosol localization is quantified. Also, I wonder whether the O-GlcNAc levels of nuclear and cytosolic proteins in the mutant cells were comparable to those in WT cells. If this is the case, the results would also support the authors' conclusion.
R23: We took the comments on board and made it clearer that the rationale for the DFP461-463 mutant was an attempt to separate OGT's nuclear and cytosolic functions. We fully agree that these results are peripheral, and thus we presented these results in Supplementary Figure 1 (not in the main figure).
The biochemical evidence presented in Fig S1C shows that the genetic substitution of DFP to AAA on endogenous OGT has no detectable impact on its nuclear localization in primary MEFs. This result is far more authoritative than the evidence provided by Seo et al. 2016 (doi: 10.1038/srep34614), which is based on the overexpression of OGT transgenes in HeLa cells. Importantly, Seo et al. 2016 did not assess the impact of their mutations on endogenous OGT.
We believe that the negative results we obtained with the DFP461-463 mouse model shall be extremely valuable for the field. Firstly, science can move forward only if both negative and positive results are shared. In this specific case, we found that mutation of endogenous OGT in MEFs yielded to a different result than previously reported overexpression of the same mutant construct in HeLa cells. Secondly, we want to make the Ogt-NLS- mouse model available for further investigations.
C24: 2. Since OGT or O-GlcNAcylation regulates chromatin status, the authors analyzed the gene expression profiles of retrotransposons in T931del/Y or T931A/Y mice. Is it possible to investigate if the release of gene silencing is also seen in non-retrotransposon genes? I assumed retrotransposons might be a well-established system to analyze gene silencing status, however, if the authors could find similar effects on genes other than retrotransposons, that would be highly valuable.
R24: This is an interesting idea. This notion refers to the activation of promoters that are normally epigenetically repressed (e.g. silent despite the presence of all trans-active factors required for their expression). Epigenetically repressed promoters include retrotransposons, imprinted genes and germline specific genes that are normally expressed in germ cells and maintained in a repressed state in somatic cells (10.1038/s41580-019-0159-6). Testing of mono-allelic expression of imprinted genes required F1-hybrid. Thus, we assessed whether well-studied germline specific genes could be realized from silencing in T931del/Y or T931A/Y blastocyst and found no evidence for it (see dot plot below). The unbiased transcriptomic analysis presented in the manuscript shows that the product of upregulated genes are enriched in mRNA processing (Figure 2E), but these genes are not normally epigenetically repressed. Thus, contrary to retrotransposons, the role of O-GlcNAc at cellular gene promoters appears not to be linked to epigenetic silencing. This could be explained by the many different protein substrates for O-GlcNAc.
C25: 3. OgtY851A mice with milder OGT activity loss didn't exhibit impaired preimplantation development, but did display postimplantation development such as placental development, suggesting that O-GlcNAcylation of proteins required for preimplantation and postimplantation development relies on different degrees of OGT activity. I wonder whether global O-GlcNAc levels in embryos in preimplantation and postimplantation developmental stages are different or not. This might include both the pattern of blotting and intensities. The results would give the authors an explanation why the dependency on OGT activity was different in two developmental stages. Can the authors provide data? If not, then the authors should at least describe hypotheses in the manuscript to address these questions.
R25: We recently reported that the subcellular patterns of O-GlcNAc are highly dynamic during preimplantation development (Formichetti et al. 2024, BioRxiv). The most striking O-GlcNAc remodeling we observed is the enrichment of nuclear O-GlcNAc as compared to cytoplasmic O-GlcNAc that is concomitant to embryonic genome activation (Formichetti et al. 2024, BioRxiv). We quantified the ratio of the nuclear/cytoplasmic signal by immunofluorescence, but absolute quantification is not possible with this method. Due to the limited number of cells of the preimplantation embryo, this analysis cannot be performed by western blot. Hence, there is no appropriate method to quantitatively compare O-GlcNAc levels between preimplantation and postimplantation embryos.
C26: 4. The authors' AID-degron system elegantly worked in MEFs but was inefficient in preimplantation embryos. I wonder if this was because of the high expression of the shorter isoform of OGT detected as OGTp78 in the author's western blot. Is it possible to examine this possibility in the embryos? Either way, the authors should describe a potential explanation for why the efficiency in the embryos was low. In addition, the authors should describe why they inserted the AID tag only into the longest OGT isoform.
R26: This is a good point. The smallest isoform OGTp78 bears the catalytic domain and thus can partially compensate for the degradation of OGTp110. Note that the level of OGTp78 is low and does not increase upon OGTp110 degradation; thus a compensation can only be partial (Figures S4A and S4D). Alternative hypotheses for the ineffectiveness of the degron system in ex vivo grown embryos include: i) the expression level of OsTIR that may be too low in the early embryo (Rosa26 promoter not being activated at EGA), ii) a possible steric hindrance of the N-ter AID tag in these cells, iii) the lower concentration of Auxin imposed by toxicity on the embryo is likely suboptimal. Testing these possibilities is very difficult in preimplantation embryos.
It is unclear how the OGTp78 isoform is produced; it was hypothesized to originate from an alternative transcription start site (https://doi.org/10.1007/s00335-001-2108-9). We initially attempted to target both isoforms by inserting the AID tag at the C-terminus, but we were unsuccessful in producing this mouse model. It is possible that the C-terminus that is near the catalytic site cannot tolerate the AID knock-in.
C27: 5. In Figure S1C, is the band detected right below OGTp78 in nuclei fractions non-specific or do both bands correspond to OGTp78 ?
R27: To answer this question, a knockout control would be needed. OGTp78 being not targeted by our AID-degron, we cannot test the specificity of these bands using our perturbation tool kit.
C28: 6. Figure 1D top row third column: hemizgous -> hemizygous
R28: Many thanks; the embarrassing typo has been corrected.
C29: 7. Figure 1D second row third column: hemyzygous -> hemizygous
R29: Thanks for bringing this other typo to our attention, it is now corrected.
Reviewer #4 (Significance (Required)):
General assessment: strengths and limitations
C30: Strength: This manuscript elegantly revealed the requirement of OGT in mammalian development by taking advantage knock-in mouse models with different OGT activity. In addition, the manuscript provided the interesting and important transcriptomics data in both pre- and post-implantation embryos of OGT mutant mice. These data sets could explain detailed mechanisms how OGT or O-GlcNAcylation regulates mammalian development in the future. Furthermore, development of AID-tagged OGT system would be a useful tool for other researchers studying OGT function.
Limitation: Although they found interesting changes in terms transcriptomes in developing mice with different OGT activity, they lack the data showing how these changes caused the observed phenotypes. In other words, there are less mechanistic insights behind the developmental problems seen in mice with different OGT activity.
In addition, although I agree the question about whether OGT activity itself is crucial for the early development of mammals has not been completely solved for a long time, I assume people thought OGT activity is actually important for the mammalian development thorough the observation of OGT-linked congenital disorders of glycosylation.
Therefore, I would say the novelty of the manuscript is a little less impactful. Furthermore, although AID-tagged OGT system revealed fundamental questions regarding the transcriptional changes upon acute depletion of OGT in cellular levels, the system was inefficient in mouse embryos. So, they showed nothing about developmental-stage specific requirements of OGT.
Advance: The manuscript can fill a current gap regarding requirement of OGT in mammalian development. Also, the manuscript developed a series of mutant mice with different OGT activity and an AID-tagged OGT mouse line. These mice provide technical advances.
Audience: The manuscript will be interested in researchers in specific fields such as glycobiology, developmental biology, and clinical fields.
Describe your expertise: Biochemistry, Glycobiology, Cell biology
R30: We are thankful for the constructive and supportive review.
We fully agree with the limitations of the study and discussed them in the manuscript. Our in vivo approach revealed the most phenotypically relevant transcriptional phenotypes resulting from OGT catalytic impairment during embryonic development. We make the mouse models created for this study available to the community to facilitate follow-up studies aiming at exploring the underlying molecular details.
As pointed out in the comments, the requirement of OGT glycosyltransferase activity for mammalian development was widely assumed by the field, but this belief was without direct experimental evidence. This study provides the first in vivo evidence for this important conclusion.
Conclusion: The reviewers' comments were tremendously useful to improving the clarity of the manuscript and adding important new in vivo evidence. We note that none of the reviewers provided any reason to doubt our important conclusions:
- The demonstration that the enzymatic activity of Ogt, thus the O-GlcNAc modification itself, is essential for preimplantation development.
- The finding that a mild reduction of OGT's activity is sufficient to perturb the silencing of multiple families of retrotransposons in the growing embryo.
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The indication, from transcriptomes of hypo-O-GlcNAcylated embryos, of a developmental retardation upon a mild O-GlcNAc perturbation.
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The discovery that OGT's rapid depletion in vitro downregulates basal cellular function, including translation. This result provides mechanistic support to the embryonic growth delay resulting from decreasing O-GlcNAc in vivo.
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Referee #4
Evidence, reproducibility and clarity
Summary
O-GlcNAcylation is the fundamental post-translational modification of numerous nuclear and cytosolic proteins. OGT is the sole enzyme catalyzing O-GlcNAc addition onto the proteins. The essentiality of OGT for early development and cellular viability has been established by using OGT-KO mice and cell lines. However, it remains to be elucidated whether the catalytic activity of OGT is required for the early development, and if the catalytic activity of OGT is required what are the functions of OGT or O-GlcNAcylation in early development due to a lack of appropriate mouse models. To overcome the technical difficulty of manipulating the levels of O-GlcNAcylation in early embryos, Formichetti et al. created the series of four mouse models (OgtY851A, OgtT931A, OgtQ849N, and OgtH568A) with different OGT activity by introducing single amino acid substitution in the catalytic domain. By analyzing the inheritance of the hypomorphic OGT alleles and the lethality of mouse embryos, they discovered OGT activity is a critical factor for early development. Subsequently, RNA-seq analyses with two mouse models showing the maternal inheritance of the hypomorphic OGT alleles indicated that sever hypo-OGT activity altered transcription and silencing of retrotransposon in preimplantation development while mild reduction of OGT's activity affected placental development in a sexually dimorphic manner rather than preimplantation development. Furthermore, to study the function of OGT at specific developmental stages, they developed a mouse model bearing endogenously AID-tagged OGT for acute degradation of OGT. Although the degron system wasn't efficient in preimplantation embryos, they discovered quick transcriptional changes upon OGT deletion in MEFs. The quality of the manuscript is good because the question to be solved was appropriately set, the approach was well designed, and their findings were interesting, although their writing was sometimes hard to understand as I raised in my following comments. Nevertheless, there are several points to be fixed before being published.
Major Comments
- Although the authors showed in vitro activity of each mutant of OGT used in this manuscript by referencing the previous literature, they never showed the levels of global O-GlcNAcylation (and OGT itself) in their established mouse embryos. Although it could be impossible to determine O-GlcNAc levels in OgtQ849N and OgtH568A embryos because of the lack of germline transmission and founder line, respectively, they could do that in OgtY851A and OgtT931A embryos. Given that Y851A and T931A mutants had similar VMAX/KM with different VMAX, it is possible that their activity is comparable or Y851A has even lower activity in vivo depending on the concentration of UDP-GlcNAc in embryos. Therefore, it is critical to assess whether in vivo OGT activity is correlated with that in vitro as expected to conclude that severity of sub-Mendelian inheritance is proportional to the reduction of activity of OGT in vivo. Moreover, since the authors developed the elegant system to deplete OGT, the activity of Q849N and H568A mutant OGT can be examined at least in cells by expressing them in MEFs with OGT-degron system. Thus, I propose determination of global O-GlcNAc levels compensated by OGT levels by western blotting in OgtY851A, and OgtT931A embryos or MEFs with the OGT degron system re-expressing the individual four mutant OGTs. If the protein amount is insufficient for western blotting in the embryos because of the sizes of the earlier stages of embryos, I believe the author could address this by utilizing immunofluorescence as shown in Figure S5.
- I didn't understand why the authors couldn't find any founder lines of the OgtH568A mutant. Was that because mosaic mice with OgtH568A mutation are lethal? Also, I believe there was no explanation why the OgtQ849N allele showed no maternal inheritance. Was that because Q849N possesses enough activity for sustaining mosaic embryos, but not oocytes? The authors should better explain these points in the manuscript text.
- The authors serendipitously found a T931del-allele in the "WT" allele of the OgtT931A line, and suggested that T931del had milder activity loss, although the lethality of embryos was greatly mitigated. Nevertheless, transcriptome analyses in male blastocysts revealed that 120 genes' expression was changed in T931del/Y males. This raised the question about which mutant OGT has higher activity, Y851A or T931del. I think comparing the activity of Y851A and T931del mutants in MEFs with OGT-degron system is important to confirm the proportional relationship between activity and phenotypic severity.
- Regarding transcriptomes of T931del/Y, the authors found the upregulation of proteasomal activity and stress granules along with the downregulation of amino acid metabolism, mitochondrial respiration, and so on. To validate the results, the authors should perform qPCR on several up- or down-regulated genes. In addition, according to Figure S2E, the authors pointed out that at least for genes upregulated in OgtT931A embryos, the changes were not explained by a developmentally delayed transcriptome, suggesting that upregulation of these genes was the cause of developmental delay. Therefore, I strongly encourage them to discuss in the manuscript text how up-regulated genes could contribute to developmental delay.
- Regarding the transcriptome in OgtY851A mice, Y851A/Y male mice had huge transcriptomic differences, while Y851A/Y851A female mice barely had any. Although it seems to agree with the number of Ogt alleles, I wonder whether other X-linked genes expressed higher in female placenta as shown in Figure 3C could attenuate the effects of decreased OGT activity. I don't think this possibility can be excluded, unless the authors further decrease OGT activity in Y851A/Y851A female placenta and obtain the similar results as for male placenta. Or if they compared the levels of global O-GlcNAcylation between Y851A/Y and Y851A/Y851A mouse placentas and discovered they had similar levels of O-GlcNAcylation, then the authors could conclude that the number of Ogt alleles was not the reason of sexual-dimorphism. The authors should determine the levels of O-GlcNAcylation in Y851A/Y and Y851A/Y851A mouse placentas and/or at least discuss the above possibilities in the manuscript text.
- In terms of the transcriptome in OgtY851A mice, similar to comment 4, the authors should confirm their transcriptomics data shown as Figure 3D by qPCR. In addition, the authors should describe the potential mechanisms by which the differentiation of precursor cells of LaTPs and JZPs were disrupted. Were master regulators of the differentiation known to be O-GlcNAcylated and loss of O-GlcNAcylation perturbed the function?
Minor Comments
- Regarding DFP461-463 mutant, I couldn't understand the point of this figure because the results had no difference, and the meaning of the mutation was quite different from the others. Thus, the figure was awkward and a little confusing to me. If the authors still want to include the figures, I would suggest that they should reorganize the position of the figure (maybe after figure 3 is better to show you had tried to investigate the effects of nuclear localization of OGT on the changes of transcriptomes) and add some results. Since WT OGT seems to be localized mainly in the cytosol at steady state (Figure S1B and S1C), the effect of mutation on its nuclear localization should not be obvious. Therefore, it is difficult to conclude the mutation had no effect on the nuclear localization unless the ratio of nuclear and cytosol localization is quantified. Also, I wonder whether the O-GlcNAc levels of nuclear and cytosolic proteins in the mutant cells were comparable to those in WT cells. If this is the case, the results would also support the authors' conclusion.
- Since OGT or O-GlcNAcylation regulates chromatin status, the authors analyzed the gene expression profiles of retrotransposons in T931del/Y or T931A/Y mice. Is it possible to investigate if the release of gene silencing is also seen in non-retrotransposon genes? I assumed retrotransposons might be a well-established system to analyze gene silencing status, however, if the authors could find similar effects on genes other than retrotransposons, that would be highly valuable.
- OgtY851A mice with milder OGT activity loss didn't exhibit impaired preimplantation development, but did display postimplantation development such as placental development, suggesting that O-GlcNAcylation of proteins required for preimplantation and postimplantation development relies on different degrees of OGT activity. I wonder whether global O-GlcNAc levels in embryos in preimplantation and postimplantation developmental stages are different or not. This might include both the pattern of blotting and intensities. The results would give the authors an explanation why the dependency on OGT activity was different in two developmental stages. Can the authors provide data? If not, then the authors should at least describe hypotheses in the manuscript to address these questions.
- The authors' AID-degron system elegantly worked in MEFs but was inefficient in preimplantation embryos. I wonder if this was because of the high expression of the shorter isoform of OGT detected as OGTp78 in the author's western blot. Is it possible to examine this possibility in the embryos? Either way, the authors should describe a potential explanation for why the efficiency in the embryos was low. In addition, the authors should describe why they inserted the AID tag only into the longest OGT isoform.
- In Figure S1C, is the band detected right below OGTp78 in nuclei fractions non-specific or do both bands correspond to OGTp78 ?
- Figure 1D top row third column: hemizgous -> hemizygous
- Figure 1D second row third column: hemyzygous -> hemizygous
Significance
General assessment: strengths and limitations
Strength: This manuscript elegantly revealed the requirement of OGT in mammalian development by taking advantage knock-in mouse models with different OGT activity. In addition, the manuscript provided the interesting and important transcriptomics data in both pre- and post-implantation embryos of OGT mutant mice. These data sets could explain detailed mechanisms how OGT or O-GlcNAcylation regulates mammalian development in the future. Furthermore, development of AID-tagged OGT system would be a useful tool for other researchers studying OGT function.
Limitation: Although they found interesting changes in terms transcriptomes in developing mice with different OGT activity, they lack the data showing how these changes caused the observed phenotypes. In other words, there are less mechanistic insights behind the developmental problems seen in mice with different OGT activity. In addition, although I agree the question about whether OGT activity itself is crucial for the early development of mammals has not been completely solved for a long time, I assume people thought OGT activity is actually important for the mammalian development thorough the observation of OGT-linked congenital disorders of glycosylation. Therefore, I would say the novelty of the manuscript is a little less impactful. Furthermore, although AID-tagged OGT system revealed fundamental questions regarding the transcriptional changes upon acute depletion of OGT in cellular levels, the system was inefficient in mouse embryos. So, they showed nothing about developmental-stage specific requirements of OGT.
Advance: The manuscript can fill a current gap regarding requirement of OGT in mammalian development. Also, the manuscript developed a series of mutant mice with different OGT activity and an AID-tagged OGT mouse line. These mice provide technical advances.
Audience: The manuscript will be interested in researchers in specific fields such as glycobiology, developmental biology, and clinical fields.
Describe your expertise: Biochemistry, Glycobiology, Cell biology
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Referee #3
Evidence, reproducibility and clarity
This is a conceptually interesting paper that attempts to leverage the knowledge of OGT catalysis to begin to dissect OGT function. The evidence is presented I a straightforward fashion and is in general well documented. The breeding strategies are well informed and the paper draws heavily on previous work carried out in the mouse.
Significance
The paper describes tools which will help dissect the many potential roles of O-GlcNAc addition in early development. As it stands, this is a descriptive manuscript that will lead to hypothesis generation and testing and this should not be undervalued. The biological reagents produced and characterized will be of general interest to the field. Most of the findings presented represented a verification of existing ideas in the field but this is not meant as a criticism since part of the motivation for the approach was to generate a reproducible system for analyzing the biological phenomena.
There are perhaps some bioinformatic shortcuts taken that may need to be corrected upon thorough review. These do not lessen the overall impact of the contribution.
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Referee #2
Evidence, reproducibility and clarity
Comments to authors
To investigate the function of OGT at specific developmental stages, the authors perturbed OGT's function in vivo by creating a murine allelic series featuring four single amino acid substitutions that variably reduced OGT's catalytic activity. The goal was to identify the direct effect of O-GlcNAcylation, using a sophisticated collection of genetic mutants to evaluate in vivo the role of this modification at early stages of development. Overall, the severity of embryonic lethality correlated with the extent of catalytic impairment of OGT, demonstrating that the O-GlcNAc modification is essential for early development.<br /> The study represents a substantial advance in our understanding of OGT and O-GlcNAcylation in mammalian development. The creation of novel murine models and inducible systems is an important contribution, providing powerful tools for future research in this field. The insights into the role of OGT's catalytic activity and its involvement in epigenetic regulation during embryonic development are noteworthy, opening new avenues for research. However, there are a few considerations and concerns:
Major:
- An assumption of the study is that different mutations cause different levels of O-GlcNAcylation rather than alterations in substrate specificity. It might be important to test, at least in cultured cells, that the different mutations do not change the preference of OGT to modify certain proteins rather than others, which can provide alternative explanations for their findings.
- In Fig 1D and 1H, the thresholds to define a gene or TE as differentially expressed are not strong. According to the figure legends, "any" change in terms of log2Fc was considered as DE and colored. I think the figures should illustrate better that the changes are subtle, by for example adding a dotted line (at least) in the value 0.5 of the y-axis. These subtle transcriptional changes should be reflected better in certain paragraphs where the expression of TEs are presented/and discussed as a hallmark of the absence of O-GlcNAcylation in the OGT-mutants. The same happens with Suppl Fig 3C (changes are very minor). Similarly, in Fig2C, the changes in gene expression are lower than log2FC 1 (which represent the double in absolute expression). Applying a stronger threshold, among the upregulated genes, only Xist will be significantly overexpressed. If a gentle threshold needs to be applied to this data, authors should at least justify the reasons behind doing so. Same for Fig2D.
- In Figure 2B, the T931del allele was recovered in the blastocyst population with a very high frequency, even higher than the male WT group (T931del: 10; WT: 3). This observation suggests that the T931del allele did not significantly affect blastocyst survival. Further clarification or additional experiments might be necessary to understand the implications of this finding on early developmental stages.
- Similarly, in Figure 2G, there is an apparent higher expression of TE expression in the T931A/Y embryos group than in the T931del/Y group, which combined with the higher frequency of blastocyst generated in this latest group it may indicate a deeper molecular consequence after the deletion of the T931. A comparison of the transcriptome between these two cell lines help to address this possibility. Also, the authors should compare the O-GlcNAc levels of WT, T931A, and T931del mutant blastocysts by immunostaining, similar to what was done in Figure S5F.
- In Boulard et al. 2019 O-GlcNAcylation was shown to be sufficient to modulate expression of DNA methylation-dependent TEs. It would be interesting to know (or at least discuss) if the changes in TE expression observed in OGT-mutant embryos in this study involve changes in DNA methylation. Ideally, some DNA methylation measurement optimized for low input numbers of cells would be useful.
- The data related with the OGT-degron system in MEs seem disconnected with the rest of the manuscript. While the developmental models (blastocyst, etc) elegantly assess the contribution of O-GlcNAcylation to the control of cell survival and gene expression through the use of different OGT mutants, the degron system is a system of graded depletion that unfortunately was only possible to be used in MEFs (instead of embryos). Thus, the results obtained with the degron system in MEFs are difficult to intersect with the data from the use of OGT-mutants in embryos. Even though there are obvious interesting questions that one may want to know about this OGT degron MEF system, none of them would demonstrate a direct role for O-GlcNAcylation in cellular function, the major point addressed in the developmental system. Using the degron system in embryonic stem cells might have provided a more parallel comparison. The authors should discuss this point in more detail and either use ESC instead of MEFs or provide a stronger justification for the use of MEFs over ESC.
Minor:
- In Fig 2C the color and shape codes are confusing to understand - there are some colors/shapes that are not represented in the PCA plot. The same in Fig 3H, where in the PCA plot there are pink triangles that do not match with the code legends.
- In the figure legends of Figures 2D, 2E, 2F, and 2H, the notation should be corrected from "OgtT931A/Y" to "OgtT931del/Y".
Significance
To investigate the function of OGT at specific developmental stages, the authors perturbed OGT's function in vivo by creating a murine allelic series featuring four single amino acid substitutions that variably reduced OGT's catalytic activity. The goal was to identify the direct effect of O-GlcNAcylation, using a sophisticated collection of genetic mutants to evaluate in vivo the role of this modification at early stages of development. Overall, the severity of embryonic lethality correlated with the extent of catalytic impairment of OGT, demonstrating that the O-GlcNAc modification is essential for early development.
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Referee #1
Evidence, reproducibility and clarity
Formichetti at el. developed mice with OGT catalytic dead mutations and then studied their function during early embryogenesis. Not surprisingly, dramatic reduction in OGT activity failed to produce embryos; however, mild reduction in OGT did produce animals. The authors then use the T931 animals that have a mild reduction in activity to further characterize the function in the early embryo. Not surprisingly, male mice showed changes in gene expression, implantation sub-lethality, and an uptick in loss of retrotransposon silencing. The authors also show that an even milder reduction in OGT activity (Y851A) effects male placenta function and chromatin remodeling. Finally, the authors make a less stable OGT transgene within the mouse and again found embryogenesis issues in the males and alterations in numerous gene families including mTOR signaling and p53 function. All in all, this is an interesting study that track functions of OGT in early embryonic development. The studies are well-controlled and rigorous.
Significance
This is a good study and novel. Not only is it of interest to reproductive biologist, but it echos themes found in O-GlcNAc biology.
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Reply to the reviewers
Point-to-point answer to reviewers comments
Reviewer #1
Evidence, reproducibility and clarity
*Summary: *
*The study by Cottignies-Calamarte et al. describes that AMP-activated protein kinase (AMPK) regulates cell energy balance by suppressing energy-consuming pathways like lipid and protein synthesis and promoting nutrient availability through autophagy. These pathways contribute to SARS-CoV-2 infection by hijacking autophagy and accumulating lipid droplets for viral replication. The antiviral activity of MK-8722, a direct pan-AMPK allosteric activator, was evaluated in vitro. MK-8722 effectively inhibited Alpha and Omicron SARS-CoV-2 variants in Vero76 and human bronchial epithelial Calu-3 cells at micromolar concentrations. This inhibition restored autophagic flux, degrading newly synthesized viral proteins, and reduced lipid metabolism, affecting viral factories. Additionally, MK-8722 treatment increased the type I interferon (IFN-I) response. Post-infection treatment with MK-8722 efficiently suppressed viral replication and restored the IFN-I response without altering the SARS-CoV-2-specific CD8+ T cell response elicited by Spike vaccination. The authors concluded that, MK-8722 acts as an effective antiviral against SARS-CoV-2 infection, even when applied post-exposure, suggesting potential for preclinical tests to inhibit viral replication and alleviate patient symptoms. *
__Major comments: __
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- Are the key conclusions convincing?** Partially. See comments below! *
- Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether?
From my perspective, the title "Direct pharmacological AMPK activation inhibits mucosal SARS-CoV-2 infection by reducing lipid metabolism, restoring autophagy flux and the type I IFN response" is a clear overstatement. In no way can the authors make statements about the autophagic flux, as it simply was not measured. The study would greatly benefit from conducting an autophagy/autophagic flux assay. See specific comments below!
Answer: As suggested by the author, we have now investigated the autophagic flux by staining the cells for LC3b expression and colocalization with the lysosomal marker LAMP1. Results are shown in the new Fig4.D and E and detailed in the results section to read lines 468-475 page 23-24:
“To assess directly the impact of MK-8722 on the autophagic flux, Calu3 cells were infected by SARS-CoV-2 without and with MK-8722 (5uM), double labelled with LC3b and LAMP1, and co-localisation of the two marker quantified (Fig.4D). MK-8722 treatment, compared with no treatment, increased LC3b colocalization in the LAMP1 compartment as shown by the increase in MOCs in treated versus non treated infected cells (LAMP1 signal in LC3b signal 0.078±0.014 vs 0.01±0.006, Mann-Whitney pand lines 480-481 page 24:
“This result indicates that MK-8722 restores the autophagic flux to address viral components to the lysosome, where they are degraded.”
- Would additional experiments be essential to support the claims of the paper? Request additional experiments only where necessary for the paper as it is, and do not ask authors to open new lines of experimentation.
See below specific comments regarding cell line consistency and autophagy measurements.
- Are the suggested experiments realistic in terms of time and resources? It would help if you could add an estimated cost and time investment for substantial experiments.
This question depends on various factors such as access to relevant biosafety labs, availability of required reagents, etc. In my estimation, experiments involving WT viruses and autophagy measurements could be conducted within 3-4 months. The proposed experiments with the delta-N-SARS-CoV-2 mutants, of course, depend on access to such viruses. Overall, I believe all experiments could be completed within 6 months. The costs of those assays are not very high.
- Are the data and the methods presented in such a way that they can be reproduced?
In the present study, a total of 3 cell lines and human PBMCs were utilized for various experiments. Please indicate why each cellular model was chosen and highlight the differences between these models considering what is known for SARS-CoV-2 infection and autophagy! Furthermore, the study would greatly benefit if the key findings were consistently demonstrated in a single cell line.
Answer: We agree with the reviewer that three cell lines and PBMC from patients were used but it was necessary for the experimental design of our experiments as detailed below.
In Figures 1 and 2, experiments use both Vero76 and Calu3 cells for the following reasons. We first used the simian Vero76 cells in order to validate the inhibitory effect of MK-8722 in widely a used cell line in virology, but which lacks the interferon response. This lack of the IFN response renders Vero cells a poor model for the pathophysiology of SARS-CoV-2. We therefore then used the IFN-competent human lung Calu-3 cell line as a more relevant cell model.
In Figures 3 both cells were again and western blots show a similar pattern of activation downstream AMPK after activation by MK-8722 and similar antiviral activity of MK-8722 in Vero76 and Calu3 cells. In Figure.4, we then mostly focused on deciphering the antiviral mechanism of MK-8722 and therefore focused on Calu3, which is more relevant from a pathophysiological point of view.
In Figure 5, we then investigated the T-cell response and therefore used primary human, namely PBMC/purified CD8 T-cells from healthy donors. As the reviewer knows, T-cell activation is restricted by HLA-TCR interaction, or in other words, only matched HLA cells can activate CD8 T-cells. As HLA-A2 is the main HLA expressed in human and only a HLA-A2 antibody is available on the market, for these experiments we used only CD8 T cells from HLA-A2 patients and had to find an additional HLA-A2-expressing epithelial cell susceptible to SARS-CoV-2 infection. It is the case of Caco2 cells, which are HLA-A2+ susceptible to infection and competent for IFN responses. We could not use simian Vero cells that are IFN-deficient and do not express human MHC, nor Calu3 cells being HLA-A2 negative. Altogether, in experiments in Figure 5 addressing HLA-A2 restricted antigen presentation, the use of Caco-2 cells was appropriate in contrast to that of Vero or Calu3.
Furthermore, for sake of clarification, we have now added the following p. 19 lines 390-392:
“The trends in modification of all markers by MK-8722 treatment by being conserved between cell lines indicates a common antiviral mechanism. We therefore focused our study on Calu-3 cells since they are more relevant to SARS-CoV-2 infection.”
The authors conclude that selective activation of AMPK has a pro-autophagic effect which in turn leads to a reduced SARS-CoV-2 replication. I would generally agree with this statement, but throughout the entire manuscript, no real autophagy assays are shown. This should definitely be rectified. It is important to demonstrate that (1) MK-8722 is capable of increasing autophagy and particularly autophagic flux in the cell models used, (2) that in the cell models employed, SARS-CoV-2 infection leads to modulation of autophagy, and (3) that SARS-CoV-2 infected cells, when co-treated with MK-8722, lead to a re-established autophagy. The autophagy assays should be performed according to the expert-curated guidelines by Klionsky et al. This is extremely important so that the results can be compared with the now vast number of existing autophagy-SARS-CoV-2 studies.
We fully agree with the reviewer that it was important for our study to formally address the impact of MK-8722 on the autophagic flux. Following reviewer recommendation and reading of the guidelines for autophagy study, we therefore have evaluated whether MK-8722 affected localization of LC3b, which is essential for autophagosome biogenesis/maturation and also functions as an adaptor protein for selective autophagy, in lysosomal compartment (labelled, by LAMP1). Therefore we labelled cells infected in the presence or absence of MK-8722 for LC3b expression and colocalization with the lysosomal marker LAMP1. Results are shown in the new Fig4.D, E. Please see our above answer (p1) for results description. These results indicate that MK-8722 restored the autophagic flux that had been interrupted by SARS-CoV-2 infection in Calu3 cells.
- Are the experiments adequately replicated and statistical analysis adequate?
Minor comments:
- Specific experimental issues that are easily addressable.
Typo: Line 288: It should be "MK-8722" instead of "MK-7288".
In general, a space between value and unit is not consistently used.
Please indicate always the phosphosite of substrate proteins when phosphorylation is described. E.g. line 288 and throughout the manuscript: regarding ACC phosphorylation.
Answer: We apologize for these typos, which have now been corrected in the revised MS.
- Are prior studies referenced appropriately?
See comment below. Fundmental work that describes the virus-autophagy relationship, such as the work by the Beth Levine lab would be important to add. Also the work bei Konstantin Sparrer and colleagues is important and leads to the current work presented here.
Answer: Konstantin Sparrer’s work is already cited as by ref 17. However, as suggested by the reviewer, the work by the Beth Livine lab has now been added in the revised MS in p29-30 lines 610-613, to read:
.“Convergence of Beclin-Atg14 and P62 activation could stimulated the selective clearance of viral components, in a process called virophagy64–66. We thus propose that AMPK pharmacological activation induce virophagy and is responsible, at least partially, for its antiviral effect.”
- Are the text and figures clear and accurate?
Introduction: In general, for some of the statements claimed in the introduction, which is in sum nicely written, informative and well structured, citations are required. For example - line 59 "...autophagy is sequentially activated and inhibited."
Answer: Citation has been added, namely: Koepke 2021 Autophagy ref 17
Lines 59-65. In the introduction, the interaction between autophagy and SARS-CoV-2 is primarily described in a one-sided manner. There are now several studies demonstrating that both inhibition of autophagy and also induction of autophagy in context of coronaviral infection. Both aspects should be illuminated and introduced here.
Answer: The reviewer points towards a crucial point of autophagy during ß-coronaviruses infection. Indeed, we fully agree that the autophagy is both inhibited and activated, at different levels, by different proteins as exemplified by, Sparrer’s group (ref 17). As we have mentioned in the initial version of our MS: “Throughout the SARS-CoV-2 viral cycle, autophagy is sequentially activated and inhibited 17–20”. As it may have not been clear enough, we have reformulated this paragraph as follows to read line 68-75 p5:
“Throughout the SARS-CoV-2 viral cycle, autophagy is sequentially activated and inhibited 17–20. Indeed, autophagy is initiated by the early expressed nsp6, resulting in the formation of autophagosomes that are essential for the establishment of viral factories 17,21 and subsequent viral proteins expression 17,21. In turn, viral proteins OFR3a and ORF7a expressed at a latter time post-infection, prevent the fusion between autophagosomes and lysosomes, thereby blocking completion of autophagy, as evidenced by increased LC3-B expression, activation of the ULK1 kinase and increase in the autophagy cargo receptor sequestosome-1/p62. Overall, this disruption of autophagy protects newly formed virus from degradation in the LAMP1+ lysosome 19,22–25”
Discussion: The selectivity of the compound should be discussed.
Answer: Manuscript have been revised as follows to discuss both specificity in regards of AMPK and tissue accessibility:
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lines 557-560, p27: “We show here that the blockade of AMPK activation upon infection can be reversed by MK-8722, the pharmacological allosteric pan-activator of AMPK, which blocks infection at a µM concentration, in agreement with the predicted role of AMPK activity on SARS-CoV-2 infection48 and with MK-8722 action on infection by other viruses49”
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Line 576-581, p28: “In contrast, MK-8722, as systemic drug, may reach these tissues with minimal side effects, as a daily treatment in diabetic Non-human primates (NHPs) with MK-8722 (10 mg/kg) for a month induced only a limited and reversible cardiac hypertrophy 36”
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Do you have suggestions that would help the authors improve the presentation of their data and conclusions?
The presentation of the data is understandable. For reader with a less mechanistic background it would be helpful to present a schematic figure like a graphical abstract.
Answer: As suggested by the reviewer, we have now introduced a graphical abstract in the revised MS.
SECTION B – Significance
======================== - Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field.
- Even though there is now a plethora of studies on autophagy and SARS-CoV-2, this study is important and of great interest to a broad readership. Not only virologists, immunologists, and autophagy researchers will eagerly anticipate this study, but especially researchers focusing on pharmacology around AMPK and autophagy will recognize the importance of the data presented here.*
Answer: We thank the reviewer for this positive appreciation of our work.
- Place the work in the context of the existing literature (provide references, where appropriate).
The work builds upon a variety of studies on the interplay between coronaviruses and the mechanism of autophagy. Foundational contributions to this research stem from groundbreaking preliminary work conducted in the laboratories of Gassen and Müller (Gassen et al. 2019 and Gassen et al. 2021), as well as general virus-autophagy studies from the laboratory of Beth Levine. Additionally, recent work by Konstantin Sparrer and colleagues would be important to cite, as it underscores the insights gained in this manuscript.
Answer: As suggested and previously mentioned, these references have now been added to the discussion p27 lines 557-560: “We show here that the blockade of AMPK activation upon infection can be reversed by MK-8722, the pharmacological allosteric pan-activator of AMPK, which blocks infection at a µM concentration, in agreement with the predicted role of AMPK activity on SARS-CoV-2 infection48 and with MK-8722 action on infection by other viruses49”.
In addition, Gassen et al. 2019 is already cited as ref 18
- State what audience might be interested in and influenced by the reported findings. See comment above!__ __
Reviewer #2
Evidence, reproducibility and clarity
In the current manuscript, Cottignes-Calamarte et al. have shown tha pharmacological activation of AMPK can be a strategy for overcoming SARS-CoV-2 infection induced reprogramming of host degradation pathways and innate immune response, without hindering the efficacy of spike expression from vaccine agents. Though the suggestion of selective activity of MK-8722 in degrading viral proteins in infection but not the ectopic spike peptides expression is interesting, the evaluation of the mechanism and providing therapeutic index for the drug will overall improve the study.
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Majorly, I have these suggestions;*
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- The manuscript did not clarify the mechanism clearly but correlated the reduction in viral proteins and their association to LAMP1 as the mechanisim of activity of MK-8722. In this way, the authors did not separate whether the reduction in SARS-CoV-2 infection could lead to the potentiation of these effects. There is mentioning of potential mechanism of the drug by inhibiting the activity of SARS-CoV-2 proteins that reduce the autophagic flux without directly showing this. SARS-CoV-2 Orf3a is a known inhibitor of autophagosome maturation and hence the authors should directly probe the activity of MK-8722 in overcoming the suppression of autophagic flux in cells by ectopically expressing Orf3a.* Answer: We fully agree with the reviewer that our work correlated the reduction in viral proteins and their association to LAMP1 to explain the mechanism of MK-8722 activity and did not focus on particular viral protein(s) that could induced autophagic flux.
Indeed, our approach has been to describe the MK-8722 antiviral activity against full primary SARS-CoV-2 viruses (not ectopically expressed viral proteins or recombinant viruses) in a pathologically relevant cell model including primary CD8+ T cells to mimic at best the initial steps of SARS-CoV-2 infection. Although the ectopical expression of Orf3a is recognized as a potent tool to study the principle of autophagy in the guidelines of autophagy measurement methods, when applied to infection, this ectopical expression of Orf3a will modify the endogenous level of Orf3a (as compared to infection level) and probably affect by itself the autophagy kinetics. To address the question of autophagic flux as suggested by the reviewer, we therefore preferred to use another recommended technique from the guidelines of autophagy measurement methods directly applicable to infected cells. We now evaluated the colocalization of LC3b with LAMP1 during treatment and infection of Calu-3 cells by primary viruses as now described in figure 4.D and E and discussed lines 468-475 pages 23-24 :
“To assess directly the impact of MK-8722 on the autophagic flux, Calu3 cells were infected by SARS-CoV-2 without and with MK-8722 (5uM), double labelled with LC3b and LAMP1, and co-localisation of the two marker quantified (Fig.4D, E). MK-8722 treatment, compared with no treatment, increased LC3b colocalization in the LAMP1 compartment as shown by the increase in MOCs in treated versus non treated infected cells (LAMP1 signal in LC3b signal 0.078±0.014 vs 0.01±0.006, Mann-Whitney p * As the authors propose MK-8722 as a preclinical candidate, they should present therapeutic measures and indexes. All across the data presented, there was no mentioning or measurement of drug toxicity for extended uses up to 36h pi.*
Answer: Our study aimed to describe the antiviral activity of MK-8722 in a cellular model mimicking only the initial steps of infection up to 3 dpi. Complementary experiments were conducted as required and show that treatment with MK-8722 up to 10mM did not result in an increase in cell death at any time post treatment as shown in the new Fig S2I and corresponding description in line 328-332 p17, which read__: __
__“__Furthermore, we also investigated MK-8722 toxicity at 4h, 24h and 96h (Fig. S2I) and found that the drug was not toxic up to 10mM. The 50% toxicity dose was calculated to be 57mM at 96h in Calu3 cells. This allows us to determine a therapeutic index of 76 against the SARS-CoV-2 Alpha variant and 36 against the Omicron variant, thus, placing MK-8722 as an attractive antiviral candidate.”
- Also, it was not clarified if the drug reduced the replication or exclusively worked by restoration of autophagic flux. For the earlier, the authors can consider two assays, i.e., a direct assay of looking into the replicon of SARS-CoV-2 (Bigotti et al 2024; PMID 38387750), or looking at earlier time points of infection (up to 8h pi; Twu et al. 2021; PMID 34788596) with intracellular sgRNA specific RNA probes.In this regards, 24h or 32h (Fig 1F-G) is too long to only measure single-round of infection.*
Answer: We thank the reviewer for this interesting question. The drug likely acts on both steps. Concerning replication, as shown in figure 1 GH at 24 and 32hrs, there is a block in replication but not in virus entry into the cell, since there is no difference in viral N cellular content after 1hr chase. Concerning the autophagic flux, the new Fig4 D and E shows that MK-8722 restores the autophagic flux in infected cells, which had been interrupted by the infection in the absence of the drug.
The viral cycle of ß-coronaviruses is highly dependent on hijacking cellular autophagy and lipid biosynthesis, which are both necessary for the assembly of DMV, essential for viral replication. Since MK-8722 treatment reduces cellular lipid content and increases autophagic flux, the use of viral replicons deficient in structural proteins or early post-infection timepoints will most likely show a decrease in viral genome replication under the effect of MK-8722 due to the inability of the replicons to establish such favourable environments (indicated in the corresponding Fig3A, B and C). However, the effect of MK-8722 on the replicon system will not distinguish between whether MK-8722 affects the viral replication complex and whether it is unable to induce viral factory formation.
- Are the viral components degraded by restored lysosomal activity include replicase or replication organelle components? This needs to be shown with intracellular nsp3/nsp4 levels in infection or ectopic expression.*
Answer: The reviewer raises a question highly relevant to the biology of coronaviruses, which we have addressed in Figure 4. Indeed, the Replication-Transcription Complex (RTC) is a multifactorial complex, in which N protein bound to the viral RNA is believed to help recruiting RdRp (Cong 2020 J Virol 10.1128/jvi.01925-19, Scherer 2022 Sci Adv DOI: 10.1126/sciadv.abl4895). The increased colocalisation of in N staining in LAMP-1+ compartments observed after MK-8722 treatment indicates that MK-8722 addresses RTC to the lysosomes and therefore our results indicate that replication-transcription organelles could be degraded in lysosomes.
- Previous studies suggested the decrease in AMPK phospharhorylation in SARS-CoV-2 infection What is inconsistent to previous studies should be commented by the authors. Eg, why is AMPK suppression not see in SARS-CoV- 2 inefction as reported before (Parthasarathy et al 2023; PMID 36417940)*
Answer: We agree with the reviewer and had already mentioned in the discussion that SARS-CoV-2 infection can have different outcome on AMPK activation as reported in ref 45 and 46 in the original version. Indeed, Gassen and colleagues reports that activation of AMPK and resulting phosphorylation are decreased in cells infected by SARS-CoV-2, as shown in (Ref 18), whereas Parthasarathy reports an increase in AMPK phosphorylation which is clear only at 96 h p.i. (Ref 47). Interestingly in this later study at earlier time point (24hr p.i.), authors report a tendency of AMPK phosphorylation to decrease although not in a statistically significant manner but for only n=3. In our present study, no statistically significant differences were found in AMPK phosphorylation although a small increase tendency is observed. Furthermore, cells used in all these studies differ: Gassen et al. used Vero-FM cells whereas Parthasarathy et al. used intestinal Caco2 cells, and in our study Vero 76 and lung Calu3 cells. Thus the differences reported on the effect of SARS-CoV-2 infection on AMPK activation are likely due to a question of kinetics and/or cellular background. Of note, the level of AMPK phosphorylation observed after SARS-CoV-2 infection is not to compare with the AMPK phosphoryalton induced by drug activation such as MK-8722.
This heterogenity in AMPK activation during SARS-CoV-2 infection might contribute to the various effect of AMPK activator as antiviral reported in the literature. Indeed, as mentioned p27 lines 560-563 “metformin, a drug approved by FDA since 1994, and the adenosine analogue AICAR that activates AMPK indirectly can inhibit replication of SARS-CoV-2 as well as Flaviviruses, but at a concentrations of 10 and 1 mM, respectively 35,47 ”. The reported discrepancy on the antiviral effect of AMPK activation might rely then on an activation threshold, as 10mM metformin and 1mM AICAR blocks infection, while 25__m__M AICAR does not (ref 18, 47). This later concentration was not evaluated in Gassen et al. on AMPK activation. Furthermore; the time frame evaluated in each study is different and could also be a confusing factor. Finally, as shown by Myers and colleagues (Myers 2017, Science ref 36), MK-8722 activates AMPK more efficiently than AICAR, most probably due to its direct activation of AMPK. We believe that direct AMPK activation by MK-8722 and the higher activation level of AMPK is responsible for the antiviral effect reported in our study.
As suggested by the reviewer, we have now clarified this section in the revised MS to read p27-28, lines 557-568:
“We show here that the blockade of AMPK activation upon infection can be reversed by MK-8722, the pharmacological allosteric pan-activator of AMPK, which, at a µM concentration, blocks infection, in agreement with the predicted role of AMPK activity on SARS-CoV-2 infection48 and with MK-8722 action on infection by other viruses49” .In line, metformin, a drug approved by FDA since 1994, and the adenosine analogue AICAR that activates AMPK indirectly can inhibit replication of SARS-CoV-2 as well as Flaviviruses, but at concentrations of 10 and 1 mM, respectively 35,47. These AMPK-sensitive viruses replicate all in viral factories, disturbing lipid synthesis and escaping autophagy 50,51. AMPK activation by metformin at high concentration (10mM) inhibits SARS-CoV-2 replication in vitro 47. However, AMPK activation with 5-amino-imidazolecarboxamide riboside (AICAR), a non-metabolised analogue of AMP able to activate AMPK, used at 25mM is unable to inhibit SARS-CoV-2 infection 18 while at 1mM has been proven to reduce by 10-fold viral production 47, suggesting that AMPK needs to reach an activation threshold to inhibit viral replication. “
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Although AMPK activation is shown to be antiviral for SARS-CoV-2 before, e.g., with Metformin (Parthasarathy et al 2023; PMID 36417940), the role of AMPK-related kinases are shown to be pro-viral (e.g., NUAK2; Prasad et al. 2023; PMID 37421942). The authors should discuss these points to provide the reader a context in this sub-field.*
Answer: As suggested by the reviewer, we have now included in the discussion the following p28 lines 568-571:
“Conversely, NUAK2, an AMPK-related kinase, was reported to stimulate viral replication in A549 and Calu3 cells50. Altogether our results, in agreement with the literature, indicate that AMPK-dependent antiviral activity is restricted to AMPK-members only, confirming their distance with AMPK-related kinases such as NUAK2 53”
Minor
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- Was there an increase of lipid droplets in SARS-CoV-2 infected cells to non-infected condition? It is not mentioned here.* Answer: Yes indeed, lipid droplets content was increased in Infected non treated cells as indicated in Figure Suppl. 4B (p21 lines 409-411) and as stated in the Result section as follows:
” Infection increased the overall Nile Red staining by 40±10% compared to non-infected cells (Fig. S4B, ANOVA: p * There are several typos and grammatical errors.*
Answer: We have now carefully checked the text for typographical and grammatical errors, which have been corrected.
There is no Fig S2I but is mentioend in the legend.
Answer: We apologize for this mistake in labelling figure S2 panel and have corrected their labelling as follows:
“____G-H: __ACE2 expression and viability were evaluated in Vero76 cells after 24h or in Calu-3 cells after 4days of MK-8722 continuous treatment (1 µM or 5 µM respectively) by flow cytometry. ACE2 expression is expressed as MFI (__G) and viability as frequency of cells non-stained by the amine-reactive dye Viobility (H). n=3 independent experiments. “
- Fig 4A - degradation is not shown, only association.*
Answer: We agree with the reviewer and have modified the text accordingly to read p37 lines 873-874:
Figure 4: MK-8722 treatment increases the ____autophagic flux directing viral components ____to ____lysosomes and restores type I interferon response.”
- Line 333 - there is no 3d pi post exposure treatment with MK-8722 in Fig 1l.*
Answer: We meant that treatment initiated at 1dpi lasted until harvest at 3dpi. This is clarified now to read, p18 line 351
“Post-exposure treatment at 1dpi and until harvest at 3dpi, …”
Significance
General assessment*: *
Strengths and limitations: The study provides evidence supporting previous results that the activation AMPK can be harnessed as a strategy to combat SARS-CoV2- infection. Whereas the results suggesting that this is targeted in infection by restoration of autopahgic flux and hence is selective is interesting, but the authors did not directly investigated the SARS-CoV-2 protein that is implicated in this effect. The study also does not discuss the context of AMPK and related kinases in discussion.*
*
Advance -* The study makes a pre-clinical advance, and has potential to make it fundamentally or conceptually sound. *
Audience - Virologists and Clinicians.
Expertise - SARS-CoV-2 infection biology, Cell biology an Molecular virology__ __
__Reviewer #3 __
Evidence, reproducibility and clarity (Required):
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Summary.** The authors address a topic of great importance to the development of antivirals, particularly in light of the possibility of future pandemics due to hitherto understudied viruses: the evaluation of interfering with host pathways (host-directed antivirals), potentially leading to compounds that can be used against a broad spectrum of viruses which require the same host cell functions for their life cycle. The authors studied the effects of activating AMPK with a small molecule (MK-8722) on various aspects of infectivity of SARS-CoV-2. They find that the compound indeed markedly reduced viral infectivity in two cell lines (Vero and Calu3), which correlated with the ability of the compound to activate signaling downstream of AMPK and was associated with increased lysosomal genesis/function and reduced density of "lipid viral factories". *
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General assessment. The study provides important proof of concept in reductionist cellular models, which may lead to pharmacologically more conclusive in vivo studies later on. It is limited by the use of cell lines, instead of primary human cells, for viral infectivity. The caption "MK-8722 restores the IFN I pathway" is an overstatement, as the observed increase in ISG expression is quite modest.
Answer: We agree with the reviewer that we did not use primary cells in our study, which was designed to evaluate the antiviral activity of MK-8722 in a simple epithelial cell model still relevant for the pathophysiology. However, we believe that MK-8722 antiviral effect we observed in the present study will be conserved using primary cells given the breadth of cell lines we used such as Vero, pulmonary Calu-3 and intestinal Caco2 cells. Furthermore, concerning the magnitude of the IFN I response we report, we remind the reviewer that Calu-3 cells are infected with an MOI of 0.05, which does not result in 100% cells infected after 24h. Consequently, the IFN-I response as limited to the sole infected cells in a limited amount in this setup and thus, bulk RT-qPCR of Type I IFN and targeted ISG mRNA is expected to remain modest.
In future studies, as suggested by the reviewer, we plan to include few ALI culture treated primary cells. We have clarified this limitation of our study in the Discussion p. 31 line 636-638 to read:
“The antiviral effect of AMPK activation in primary lung reconstruction and preclinical models such as hamster remains to be tested.”
__ Significance (Required): __
Taken as an early proof-of-concept study, the findings are quite important to investigators aiming to develop host-directed antivirals. However, the overall interest and impact of the manuscript would greatly benefit from verifying key findings in a primary cell model. Also, checking at least one other viral species (the authors mention flaviviruses) would be helpful to test how broadly applicable the current compound (or others to be developed in the future) would be as host-directed antivirals.
Answer: We thank the reviewer for the positive evaluation of our work. We fully agree with the reviewer in that preparedness is the key to fighting future pandemics. However to our knowledge, the literature about Flaviviruses is already stating that AMPK activation by metformin is an antiviral strategy with regards to its lipid metabolism normalization and autophagy activation (Farfan-Morales 2021 Sci Rep doi: 10.1038/s41598-021-87707-9. Ref 50). Furthermore in a previous review, we discussed the role of AMPK activation on viral infection that could be either pro- or antiviral depending on the viral family and even within a viral family such as HSV-1 which first inhibits AMPK in early infection while activating AMPK in the latter stage (Moreira, D. et al. Curr Drug Targets 17, 942–953 (2016), ref 34). Hence, adding more viruses to our study will not add novelty to the study, even though the molecule is much stronger compared to metformin.
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Referee #3
Evidence, reproducibility and clarity
Summary. The authors address a topic of great importance to the development of antivirals, particularly in light of the possibility of future pandemics due to hitherto understudied viruses: the evaluation of interfering with host pathways (host-directed antivirals), potentially leading to compounds that can be used against a broad spectrum of viruses which require the same host cell functions for their life cycle. The authors studied the effects of activating AMPK with a small molecule (MK-8722) on various aspects of infectivity of SARS-CoV-2. They find that the compound indeed markedly reduced viral infectivity in two cell lines (Vero and Calu3), which correlated with the ability of the compound to activate signaling downstream of AMPK and was associated with increased lysosomal genesis/function and reduced density of "lipid viral factories".
General assessment. The study provides important proof of concept in reductionist cellular models, which may lead to pharmacologically more conclusive in vivo studies later on. It is limited by the use of cell lines, instead of primary human cells, for viral infectivity. The caption "MK-8722 restores the IFN I pathway" is an overstatement, as the observed increase in ISG expression is quite modest.
Significance
Taken as an early proof-of-concept study, the findings are quite important to investigators aiming to develop host-directed antivirals. However, the overall interest and impact of the manuscript would greatly benefit from verifying key findings in a primary cell model. Also, checking at least one other viral species (the authors mention flaviviruses) would be helpful to test how broadly applicable the current compound (or others to be developed in the future) would be as host-directed antivirals.
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Referee #2
Evidence, reproducibility and clarity
In the current manuscript, Cottignes-Calamarte et al. have shown tha pharmacological activation of AMPK can be a strategy for overcoming SARS-CoV-2 infection induced reprogramming of host degradation pathways and innate immune response, without hindering the efficacy of spike expression from vaccine agents. Though the suggestion of selective activity of MK-8722 in degrading viral proteins in infection but not the ectopic spike peptides expression is interesting, the evaluation of the mechanism and providing therapeutic index for the drug will overall improve the study.
Majorly, I have these suggestions;
- The manuscript did not clarify the mechanism clearly but correlated the reduction in viral proteins and their association to LAMP1 as the mechanisim of activity of MK-8722. In this way, the authors did not separate whether the reduction in SARS-CoV-2 infection could lead to the potentiation of these effects. There is mentioning of potential mechanism of the drug by inhibiting the activity of SARS-CoV-2 proteins that reduce the autophagic flux without directly showing this. SARS-CoV-2 Orf3a is a known inhibitor of autophagosome maturation and hence the authors should directly probe the activity of MK-8722 in overcoming the suppression of autophagic flux in cells by ectopically expressing Orf3a.
- As the authors propose MK-8722 as a preclinical candidate, they should present therapeutic measures and indexes. All across the data presented, there was no mentioning or measurement of drug toxicity for extended uses up to 36h pi.
- Also, it was not clarified if the drug reduced the replication or exclusively worked by restoration of autophagic flux. For the earlier, the authors can consider two assays, i.e., a direct assay of looking into the replicon of SARS-CoV-2 (Bigotti et al 2024; PMID 38387750), or looking at earlier time points of infection (up to 8h pi; Twu et al. 2021; PMID 34788596) with intracellular sgRNA specific RNA probes.In this regards, 24h or 32h (Fig 1F-G) is too long to only measure single-round of infection.
- Are the viral components degraded by restored lysosomal activity include replicase or replication organelle components? This needs to be shown with intracellular nsp3/nsp4 levels in infection or ectopic expression.
- Previous studies suggested the decrease in AMPK phospharhorylation in SARS-CoV-2 infection What is inconsistent to previous studies should be commented by the authors. Eg, why is AMPK suppression not see in SARS-CoV- 2 inefction as reported before (Parthasarathy et al 2023; PMID 36417940)
- Although AMPK activation is shown to be antiviral for SARS-CoV-2 before, e.g., with Metformin (Parthasarathy et al 2023; PMID 36417940), the role of AMPK-related kinases are shown to be pro-viral (e.g., NUAK2; Prasad et al. 2023; PMID 37421942). The authors should discuss these points to provide the reader a context in this sub-field.
Minor
- Was there an increase of lipid droplets in SARS-CoV-2 infected cells to non-infected condition? It is not mentioned here.
- There are several typos and grammatical errors.
- There is no Fig S2I but is mentioend in the legend.
- Fig 4A - degradation is not shown, only association.
- Line 333 - there is no 3d pi post exposure treatment with MK-8722 in Fig 1l.
Significance
General assessment
Strengths and limitations: The study provides evidence supporting previous results that the activation AMPK can be harnessed as a strategy to combat SARS-CoV2- infection. Whereas the results suggesting that this is targeted in infection by restoration of autopahgic flux and hence is selective is interesting, but the authors did not directly investigated the SARS-CoV-2 protein that is implicated in this effect. The study also does not discuss the context of AMPK and related kinases in discussion.
Advance - The study makes a pre-clinical advance, and has potential to make it fundamentally or conceptually sound.
Audience - Virologists and Clinicians.
Expertise - SARS-CoV-2 infection biology, Cell biology an Molecular virology
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Referee #1
Evidence, reproducibility and clarity
Summary:
Provide a short summary of the findings and key conclusions (including methodology and model system(s) where appropriate).
The study by Cottignies-Calamarte et al. describes that AMP-activated protein kinase (AMPK) regulates cell energy balance by suppressing energy-consuming pathways like lipid and protein synthesis and promoting nutrient availability through autophagy. These pathways contribute to SARS-CoV-2 infection by hijacking autophagy and accumulating lipid droplets for viral replication. The antiviral activity of MK-8722, a direct pan-AMPK allosteric activator, was evaluated in vitro. MK-8722 effectively inhibited Alpha and Omicron SARS-CoV-2 variants in Vero76 and human bronchial epithelial Calu-3 cells at micromolar concentrations. This inhibition restored autophagic flux, degrading newly synthesized viral proteins, and reduced lipid metabolism, affecting viral factories. Additionally, MK-8722 treatment increased the type I interferon (IFN-I) response. Post-infection treatment with MK-8722 efficiently suppressed viral replication and restored the IFN-I response without altering the SARS-CoV-2-specific CD8+ T cell response elicited by Spike vaccination. The authors concluded that, MK-8722 acts as an effective antiviral against SARS-CoV-2 infection, even when applied post-exposure, suggesting potential for preclinical tests to inhibit viral replication and alleviate patient symptoms.
Major comments:
- Are the key conclusions convincing?
Partially. See comments below! - Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether?
From my perspective, the title "Direct pharmacological AMPK activation inhibits mucosal SARS-CoV-2 infection by reducing lipid metabolism, restoring autophagy flux and the type I IFN response" is a clear overstatement. In no way can the authors make statements about the autophagic flux, as it simply was not measured. The study would greatly benefit from conducting an autophagy/autophagic flux assay. See specific comments below! - Would additional experiments be essential to support the claims of the paper? Request additional experiments only where necessary for the paper as it is, and do not ask authors to open new lines of experimentation.
See below specific comments regarding cell line consistency and autophagy measurements. - Are the suggested experiments realistic in terms of time and resources? It would help if you could add an estimated cost and time investment for substantial experiments.
This question depends on various factors such as access to relevant biosafety labs, availability of required reagents, etc. In my estimation, experiments involving WT viruses and autophagy measurements could be conducted within 3-4 months. The proposed experiments with the delta-N-SARS-CoV-2 mutants, of course, depend on access to such viruses. Overall, I believe all experiments could be completed within 6 months. The costs of those assays are not very high. - Are the data and the methods presented in such a way that they can be reproduced?
In the present study, a total of 3 cell lines and human PBMCs were utilized for various experiments. Please indicate why each cellular model was chosen and highlight the differences between these models considering what is known for SARS-CoV-2 infection and autophagy! Furthermore, the study would greatly benefit if the key findings were consistently demonstrated in a single cell line.
The authors conclude that selective activation of AMPK has a pro-autophagic effect which in turn leads to a reduced SARS-CoV-2 replication. I would generally agree with this statement, but throughout the entire manuscript, no real autophagy assays are shown. This should definitely be rectified. It is important to demonstrate that (1) MK-8722 is capable of increasing autophagy and particularly autophagic flux in the cell models used, (2) that in the cell models employed, SARS-CoV-2 infection leads to modulation of autophagy, and (3) that SARS-CoV-2 infected cells, when co-treated with MK-8722, lead to a re-established autophagy. The autophagy assays should be performed according to the expert-curated guidelines by Klionsky et al. This is extremely important so that the results can be compared with the now vast number of existing autophagy-SARS-CoV-2 studies. - Are the experiments adequately replicated and statistical analysis adequate?
Minor comments: - Specific experimental issues that are easily addressable.
Typo: Line 288: It should be "MK-8722" instead of "MK-7288". In general, a space between value and unit is not consistently used. Please indicate always the phosphosite of substrate proteins when phosphorylation is described. E.g. line 288 and throughout the manuscript: regarding ACC phosphorylation. - Are prior studies referenced appropriately?
See comment below. Fundmental work that describes the virus-autophagy relationship, such as the work by the Beth Levine lab would be important to add. Also the work bei Konstantin Sparrer and colleagues is important and leads to the current work presented here. - Are the text and figures clear and accurate?
Introduction:
In general, for some of the statements claimed in the introduction, which is in sum nicely written, informative and well structured, citations are required. For example - line 59 "...autophagy is sequentially activated and inhibited." Lines 59-65. In the introduction, the interaction between autophagy and SARS-CoV-2 is primarily described in a one-sided manner. There are now several studies demonstrating that both inhibition of autophagy and also induction of autophagy in context of coronaviral infection. Both aspects should be illuminated and introduced here.
Discussion: The selectivity of the compound should be discussed. - Do you have suggestions that would help the authors improve the presentation of their data and conclusions?
The presentation of the data is understandable. For reader with a less mechanistic background it would be helpful to present a schematic figure like a graphical abstract.
Significance
- Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field.
Even though there is now a plethora of studies on autophagy and SARS-CoV-2, this study is important and of great interest to a broad readership. Not only virologists, immunologists, and autophagy researchers will eagerly anticipate this study, but especially researchers focusing on pharmacology around AMPK and autophagy will recognize the importance of the data presented here. - Place the work in the context of the existing literature (provide references, where appropriate).
The work builds upon a variety of studies on the interplay between coronaviruses and the mechanism of autophagy. Foundational contributions to this research stem from groundbreaking preliminary work conducted in the laboratories of Gassen and Müller (Gassen et al. 2019 and Gassen et al. 2021), as well as general virus-autophagy studies from the laboratory of Beth Levine. Additionally, recent work by Konstantin Sparrer and colleagues would be important to cite, as it underscores the insights gained in this manuscript. - State what audience might be interested in and influenced by the reported findings.
See comment above! - Define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate.
My expertise is limited to the mechanistic aspects of autophagy, metabolism, and signaling cascades related to autophagy and endosomal-exosomal mechanisms. In some studies, I have been able to investigate the interplay between CoV and autophagy in collaborations with coronavirus experts. I can only provide a superficial assessment of the purely virological (methodological) aspects of the present study.
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Reply to the reviewers
Answers to reviewers
Reviewer #1
Sagia et al. present a manuscript using A. nidulans as model to study different transport routes of membrane proteins from the ER to the plasma membrane. They showed in earlier work that apparently at least two different transport routes exist, one involving the classical ER-ERES-ERGIC-Golgi route, one bypassing the Golgi. Unpolarized membrane proteins use the former, apically sorted membrane proteins the latter route. The study here confirms their earlier findings, uses a better model (co-expression of representatives for both routes in the same cell) and provides additional mechanistic insights about the roles of rabs, SNARES and other important proteins of the secretory pathway. The study is thoroughly done, figures are of high quality, data and methods well described and adequately replicated.
Thank you for your positive comments
I do have, however, a number of comments that could help to improve the manuscript.
-I suggest using the term polarized or apical rather than polar. Polar alone to me refers more to physico-chemical properties like water-solubility.
Amended in most parts of the revised text.
-introduction and discussion: I don’t think the literature about unconventional secretion bypassing the Golgi is complete, for example studies about TMED10 like Zhang, M. et al. Cell 181, 637-652 e615 (2020) or Zhang et al. Elife 4 (2015) are missing, there might be others. Is UapA a leader-less cargo that could be inserted via TMED10 translocation?
Thank you for letting us know, we have missed these articles. More references on UPS are now added, including the Zhang et all publications. UapA, as all transporters, is a multispan transmembrane protein with no leader peptide. In fact, we have checked the role of p24 family proteins (homologous to TMED10) in UapA trafficking. The knock-out of key p24 proteins does not affect UapA sorting to the PM (please consider this as confidential unpublished results)
-Fig. 1C. Can these intracellular structures be characterized in more detail?
As explained briefly to the handling editor above, and following the reviewer’s suggestion, we performed new experiments to better characterize the identity of the cargo-labeled fluorescent puncta. To do so, we used co-expression of a standard ERES marker, Sec16, in cells expressing either UapA or SynA, tagged with different fluorescent tags. More specifically, we constructed and analyzed strains co-expressing UapA-GFP/Sec16-mCherry or GFP-SynA/mCherry-Sec16 in the sec31ts genetic background, which allows synchronization and better analysis of ER exit, as described in our text. The new findings appear as Figure 5C __in the revised manuscript. Notice that sec16-mCherry introduced in the native sec16 locus by standard knock-in reverse genetics of A. nidulans (see Materials and methods) does not affect Aspergillus growth or secretion. Experiments depicted in __5C show that both cargoes, UapA and SynA, co-localize significantly (PCC ≈ 0.6), with Sec16, suggesting that most of these puncta are indeed ERES structures. Given that the puncta marked with UapA or SynA are clearly distinct (see Figures 1C,2A, 3A, 5B), this new experiment strongly suggests that there are indeed two distinct ERES, one populated mostly by UapA and the other by SynA. Notice, as we already outline in our response to the editor above, a three-colored approach using Sec16-BFP (or Sec13-BFP) for showing directly the existence of these two populations of cargo-specific ERES in the same cell failed as the BFP signal was problematic for colocalization studies.
Where is the Golgi localized in A. nidulans, is it decentralized like in yeast?
Yes, as in S. cerevisiae, A. nidulans Golgi cisternae are individually scattered throughout the cytoplasm, also similarly to other filamentous fungi. Notice that in A. nidulans Golgi structures are moderately polarized (Pantazopoulou and Penalva 2009).
Is the UapA at the time points shown in Fig. 1C in some sub-PM structures? To me the distribution at or near the PM is more punctate than in the steady state image shown in 1B
The punctuate appearance of PM transporters at the periphery of fungal cells is a common theme when these do not reach high, steady-state, levels of accumulation. In fact, several transporters mark specific subdomains of the PM, more evident before achieving their steady-state levels. For example, in yeast several amino acid and nucleobase transporters mark punctuate structures that colocalize with eisosomes markers (caveolin-like PM subdomains), while the proton pump ATPase Pma1 marks distinct punctuate domains. Similarly, UapA and other solute transporters mark punctuate structures before reaching their state-state accumulation in the PM. Figure 1C shows the de novo synthesis of cargoes after 100 min of transcription, while Figure 1B depicts the steady-state localization of UapA and SynA after 4h. In the latter case, the PM is ‘saturated’ with UapA molecules and thus the fluorescent signal of distinct puncta ‘fuses’, creating continuous fluorescent labeling. Notice also that in several cases, in our work, we have also performed UapA transport assays, which provide a direct tool to test and confirm the presence of UapA in the PM (see Figures 4D or 6C).
-Fig. 3A. To me it looks like there is actually a lot of colocalization of UapA and SynA, especially at or near the PM, where there is quite some white, punctate staining. The green fluorescence is just much stronger, overlaying the violet. Can you show separate channels and explain?
We think the reviewer means Figure 2A, which compares UapA and SynA (Figure 3A compares UapA with Golgi markers). If so, we have quantitatively estimated and performed statistical analysis (PCC) which indicates that this, visually apparent colocalization, is not significant (right panel in Figure 2A). Notice also that we cannot totally exclude very minimal colocalization of UapA and SynA signals as both cargoes mark very proximal early secretory domains (i.e., ERES or ERGIC), especially in fungal cells. Anyhow, in the revised Figure 2 we also added a panel depicting separate channels, as the reviewer asks.
-Fig. 3: In my opinion the statement that UapA "is probably sorted from an early secretory compartment, ultimately bypassing the need for Golgi maturation" is too strong at that point. You say for both UapA and SynA you don’t get significant colocalization with early Golgi/ERGIC marker, then you cannot conclude that one takes the conventional route via early-late Golgi and the other does not. What you can say is that UapA is apparently not going through late Golgi.
The reviewer is in principle correct. However, significant colocalization with the late Golgi marker, as SynA shows, strongly suggests that this cargo has passed via the early Golgi compartment. The fact we failed to detect significant colocalization of any cargo tested with early Golgi/ERGIC markers (e.g., SedV) is very probably due to very rapid passage of cargoes from these compartments, which conventional widefield or confocal microscopy cannot detect. To achieve this, ultra-fast fluorescent microcopy, as Lattice Light Sheet Microscopy (LLSM), should be used. In fact, we are currently initiating these studies, which will appear in the near future elsewhere.
-Fig. 4C: UapA does not seem to accumulate in the ER in the Sec24 and 13 mutants but in punctate structures. This for me is unexpected, any explanations? Can you characterize that punctate staining?
This is an interesting observation. Notice that UapA is a large homodimeric protein (e.g., 28 transmembrane domains) that oligomerizes further upon translocation into the ER membrane. Repression of Sec24, and to a less extent of Sec13, leads to inability to exit the ER properly. Consequently, this will lead to UapA overaccumulation in the ER, which might in turn lead to ER stress and turnover, reflected in UapA aggregates. In line with this, we have previously shown that specific mutants of UapA unable to exit the ER are indeed degraded by selective autophagy (Evangelinos et al., 2016). In contrast to UapA, SynA partitions in the entire ER without forming aggregates when sec24 or sec13 are repressed. This might be due to the fact that is a single-pass, much smaller, membrane protein compared to UapA and one that is not known to form oligomers. Thus, its overaccumulation in the ER might not lead to aggregation, allowing it to diffuse laterally in the membrane of the ER. A note on this is included in the Figure legend of the revised manuscript.
-Fig. 6D: You state that BFA "has only a very modest effect on UapA translocation to the PM". To me the PM (or very near PM) staining of UapA looks very different in the PFA treated cells, more uneven/punctate. Is there an explanation for that?
Our explanation is the following. When BFA is added, conventional secretion is blocked and Golgi collapses. We believe that this might have a moderate indirect effect also on cargoes bypassing the late Golgi/TGN, as UapA (i.e., lower levels of UapA present in the PM). This is based on the fact that UapA, in addition to conventional cargoes, requires the Q-SNARE complex SsoA/Sec9 to translocate to the PM. SsoA, being a membrane protein cargo itself, also needs to traffic to the PM. Interestingly, we have previously obtained evidence suggesting that SsoA traffics to the PM by both conventional and a Golgi-bypass routes (Dimou et al 2020). Thus, UapA translocation to the PM might indeed be partially impeded or delayed due to repression of proteins, such as SsoA (and probably Sec9), needed for its final integration into the PM bilayer. Importantly, in line with an indirect effect of BFA on the levels of UapA localized in the PM, notice that, unlike SynA, UapA was never trapped in brefeldin bodies (i.e., Golgi aggregates).
Reviewer #1 (Significance):
One strength of the study is the use of a model organism, A. nidulans, not cell cultures. Also, the use of both reporters, UapA and SynA, in the same cell is an advantage over previous studies using different lines and different promotors. Limitation of the study might be that it remains unclear to what extend the basic mechanism (UapA and SynA are transported to PM in different carrier and via different routes) can be generalized to other polarized (apically?) membrane proteins versus non-polarized membrane proteins in A. nidulans and whether a similar mechanism exists in other organisms. Some of the basic findings of the study are not new but were published by the same group. However, as the authors point out, the current study uses improved assays and extends their previous studies, advancing our understanding of the mechanistics of transport in the conventional secretory pathway and novel alternative routes. The study will be of interest for basic researchers in the trafficking field. My own expertise is transport through the secretory pathway in mammalian cells, many years ago more post-Golgi, now mostly ER-Golgi and ER itself.
We thank the reviewer for his positive comments.
__Reviewer #2 __
__ __The idea that transmembrane proteins of the plasma membrane move from the ER to the Golgi and then to the cell surface is firmly entrenched, and the mechanisms and components of this secretory pathway have been extensively characterized. Secretory vesicles are often delivered from the Golgi to sites of polarized growth. This paper builds on previous work by the same group to provide evidence that in Aspergillus nidulans, some non-polarly localized plasma membrane proteins follow a very different pathway, which bypasses components of the conventional secretory machinery such as SNAREs that have been implicated in secretion as well as the exocyst. In particular, they systematically compare the trafficking of the SNARE SynA, which follows the conventional secretory pathway, with that of the purine transporter UapA, which apparently does not. The two proteins were co-expressed in the same cells using the same promoter. A variety of genetic and microscopy methods are used to support the conclusion that UapA reaches the plasma membrane by a route distinct from that followed by SynA.
In my view, the authors present a convincing case. The individual experimental results are sometimes ambiguous, but the combined results favor the conclusion that UapA follows a novel pathway to the plasma membrane. I have only a few relatively minor comments.
Thank you for your positive comments
- In the Introduction and elsewhere: to my knowledge, there is no clear evidence that AP-1-containing clathrin-coated vesicles carry cargoes from the Golgi to the plasma membrane. On the contrary, as recently reported by Robinson (https://pubmed.ncbi.nlm.nih.gov/38578286/), AP-1-containing vesicles likely mediate retrograde traffic in the late secretory pathway.
Thank you for this comment and the relative reference. We are aware that AP-1 is likely to also mediate retrograde traffic in the late secretory pathway or/and intra-Golgi recycling, as also reported by the group of Benjamin Glick. Thus, in the revised version we added a short comment on this plus relative references. Along this line, our previous work has shown that transcriptional repression of AP-1 arrests the polar localization of several apical markers in A. nidulans and we reported that this might be due to an effect on both anterograde and retrograde trafficking. Please see “Secretory Vesicle Polar Sorting, Endosome Recycling and Cytoskeleton Organization Require the AP-1 Complex in Aspergillus nidulans”. Martzoukou O, Diallinas G, Amillis S. Genetics. 2018 Aug;209(4):1121-1138. Overall, the fact that AP-1 was found absolutely dispensable for UapA trafficking, further strengthens our conclusion that UapA bypasses the Golgi.
- In Figure 2, is there any known significance to the presence of UapA in "cytoplasmic oscillating thread structures decorated by pearl-like foci as well as a very faint vesicular/tubular network"?
At present we cannot answer this question. In order to understand what these structures represent and answer what is their role, we will need to employ super-resolution and ultra-fast microscopy and additional markers, which we envision to do. We suspect that they might be tubular networks, but this extends beyond the present work.
- SynA is related to S. cerevisiae Snc1/2, which are known to be present in late Golgi compartments due to repeated rounds of endocytosis to the Golgi and exocytosis to the plasma membrane. The SynA shown here to colocalize with PHosbp is probably present in a similar recycling loop rather than being en route to the plasma membrane for the first time. Therefore, the differential colocalization of UapA and SynA with PHosbp does not by itself provide "strong evidence that the two cargoes studied traffic via different routes" as stated in the text but might instead indicate that only SynA undergoes frequent endocytosis. The text should be amended accordingly.
The reviewer is in principle correct. However, given that colocalization of SynA and PHosbp occurred all over the cytoplasm of hyphae and not only at the apical region, and because we record colocalization of cargoes before their steady-state accumulation to the PM, thus at a stage where recycling must be minimal, the recorded colocalization should reflect anterograde transport rather than recycling. We added this reasoning it the revised text.
- A missing piece of the story is a test of whether the puncta visualized for the two cargoes in Figure 5B are indeed distinct populations of COPII-containing ER exit sites. The relevant experiment would involve co-labeling of the cargoes together with a COPII marker. Three-color labeling would presumably be needed.
This point was also raised by reviewer 1 (and review 3) and thus performed new experiments to better characterize the identity of the cargo-labeled fluorescent puncta. To do so, we used co-expression of a standard ERES marker, Sec16, in cells expressing either UapA or SynA, tagged with different fluorescent tags. More specifically, we constructed and analyzed strains co-expressing UapA-GFP/Sec16-mCherry or GFP-SynA/Sec16-mCherry in the sec31ts genetic background, which allows synchronization and better analysis of ER exit, as described in our text. The new findings appear as Figure 5C __in the revised manuscript. Notice that sec16-mCherry introduced in the native sec16 locus by standard knock-in reverse genetics of A. nidulans (see Materials and methods) does not affect Aspergillus growth or secretion. Experiments depicted in __5C show that both cargoes, UapA and SynA, co-localize significantly (PCC ≈ 0.6), with Sec16, suggesting that most of these puncta are indeed ERES structures. Given that the puncta marked with UapA or SynA are clearly distinct (see Figures 1C,2A, 3A, 5B), this new experiment strongly suggests that there are indeed two distinct ERES, one populated mostly by UapA and the other by SynA. Notice, as we already outline in our response to the editor above, a three-colored approach using Sec16-BFP (or Sec13-BFP) for showing directly the existence of these two populations of cargo-specific ERES in the same cell failed as the BFP signal was problematic for colocalization studies.
Reviewer #2 (Significance):
This study provides compelling evidence that in the fungus Aspergillus nidulans, some transmembrane transporter proteins reach the plasma membrane by a pathway that bypasses much of the conventional machinery associated with the Golgi apparatus and secretory vesicles. Although previous publications pointed toward a similar conclusion, the present work tackles the problem in a more rigorous and systematic way. These findings are important for cell biologists who study membrane traffic, it remains to be determined how prevalent this type of non-canonical secretion might be in other organisms.
We thank the reviewer for his positive comments
Reviewer #3
The manuscript by Sagia et al compares the trafficking of a polarized (SynA) with a non-polarized (UapA) transmembrane protein. In agreement with previous work of the same lab, they find that UapA reaches the plasma membrane through a Golgi-bypass route, which they characterize to some extent. Overall, the data are of good quality and the story is interesting and timely. Understanding trafficking routes that bypass the Golgi is highly interesting. Nevertheless, there are several points of criticism that I have and below is a list where I combine major and minor points together:
Thank you for your positive comments
Major Comments:
1- Is it possible that the polarized phenotype of SynA is caused by selective removal, i.e. SynA is delivered to the entire plasma membrane, but endocytosed rapidly from all areas except the tip of the hyphae. This would also result in a polarized distribution.
This is in principle possible, but here this is not the case. SynA is polarized due to rapid local endocytosis and immediate recycling at the subapical region, known as the subapical collar. Please see:
Taheri-Talesh N, Horio T, Araujo-Bazán L, Dou X, Espeso EA, Peñalva MA, Osmani SA, Oakley BR. The tip growth apparatus of Aspergillus nidulans. Mol Biol Cell. 2008 Apr;19(4):1439-49. doi: 10.1091/mbc.e07-05-0464.
Hernández-González M, Bravo-Plaza I, Pinar M, de Los Ríos V, Arst HN Jr, Peñalva MA. Endocytic recycling via the TGN underlies the polarized hyphal mode of life. PLoS Genet. 2018;14(4):e1007291. Published 2018 Apr 2. doi:10.1371/journal.pgen.1007291
This applies to all apical markers; they remain polarized by continuous local recycling after the diffuse laterally to the subapical collar.
2- The authors describe the distribution of SynA and UapA in cells deficient of various COPII/ERES proteins. However, these data are not shown, and it is not clear how they were quantified. It would be important to add quantitative data here.
Quantitative data are included in Figure 4C, displaying the percentages of cells with UapA either retained in the ER or reaching the PM for each background deficient in a COPII protein. Repression of SarA and Sec31 resulted in UapA retention in the ER in all analyzed cells (100%). However, repression of Sec12, Sec24, or Sec13 had a differential effect across the cell population, with UapA reaching the PM in some cells, while remaining trapped in the ER in others. To quantify these data and determine which cargo localization pattern prevails, we measured the number of cells in each category and represented them as percentages. A similar approach was used to examine the role of Golgi proteins in the trafficking of UapA and SynA (Figure 6).
3- on page 8, the authors discuss the discrepancy regarding the role of Sec13. They offer as an explanation that the previous studies have been performed in strains that separately expressed the two cargoes. However, I am unable to see why and how this would be a valid explanation.
Given that Sec13 has a variable/partial effect on UapA, we have previously been biased towards images that showed an effect on localization, as expected, and considered that the lack of an effect might have been due to inefficient repression in a fraction of cells. In our new system, we were able to directly compare UapA to SynA and find out that while SynA was always affected under our conditions, the effect of UapA was still variable. Thus, the partial effect of Sec13 on UapA is physiologically valid and not a matter of insufficient repression in a fraction of cells. This shows the importance of our new improved system where we follow the synchronous expression of two cargoes in the same cells.
4- Why is the effect of Sec24 depletion so much stronger than of Sec12 depletion? Sec12 is the GEF for SarA, without which Sec24 should not be recruited to ERES. The explanation that low amounts of Sec12 are still present and sufficient to carry out the role of this protein. What is the evidence for that?
Sec24 is the principal receptor of cargoes responsible for their recruitment to ERES. Sec12 is the catalytic effector for SarA required for the initiation of COPII vesicle formation. The question of the reviewer is thus logical.
However, Sec12 is indeed present at extremely very low levels when expressed from its native promoter under the condition of our experiment (minimal media). This is supported by our recent proteomic analysis, performed under similar conditions, which failed to detect the Sec12 protein, unlike all other COPII components (see Dimou et al., 2021, doi; 10.3390/jof7070560), but also by cellular studies of the group of M.A. Peñalva, who failed to detect Sec12 tagged with GFP (Bravo-Plaza et al., 2019, doi: 10.1016/j.bbamcr.2019.118551). Additionally, in yeast, immune detection of Sec12 has been possible only in cells harboring sec12 on a multicopy plasmid, suggesting its low abundance in wild-type cells (Nakano et al., 1988, doi:10.1083/jcb.107.3.851).
Given that repression of sec12 transcription via the thiAp promoter still allows 68% of cells to secrete normally both SynA and UapA, while 32% of cells are blocked in the trafficking of both cargoes, suggests that in most cells either SarA can catalyze the exchange of GDP for GTP without Sec12, maybe through a cryptic guanine nucleotide exchange factor (GEF), or that very small amounts of Sec12 remaining after repression are sufficient for significant SarA activation. Whichever scenario is true, Sec12, similarly to SarA, is not critical for distinguishing Golgi-dependent from Golgi-independent routes, as both cargoes are affected similarly. In the revised text we added a not on this issue.
5- In Figure 5, it would help readers who are not so familiar with Aspergillus organelle morphology to explain the figure a bit better. This might appear trivial for experts, but anyone from outside this field is slightly lost.
In the revised manuscript we added a figure panel depicting a schematic representation of A. nidulans key secretory compartments.
6- The authors write that not seeing UapA in Golgi membranes is evidence that it does not pass through this organelle. However, when they write that SynA is never seen in cis-Golgi elements, they do not conclude that SynA bypasses the cis-Golgi.
The fact that SynA, unlike UapA, colocalized significantly with late-Golgi/TGN and follows conventional secretion in general, strongly suggests that SynA also passes from the early-Golgi. Cargo traffic through the Golgi is mediated by cisternal maturation, where an individual cisterna gradually changes its nature from an earlier to a later one, while the cargo remains inside. UapA, unlike SynA, never colocalized with any Golgi marker used and was not affected by BFA. We agree with the reviewer that we did not have direct proof for passage of UapA or SynA from the early-Golgi in the wt background, which allows for the alternative, but rather unlikely hypothesis, that none of the two cargos is sorted to the early Golgi and that SynA traffics directly to late-Golgi/TGN. Our inability to detect sorting of any cargo to the early-Golgi is seemingly due to ultra-fast passage of cargoes from very early secretory compartments, such as ERGIC/early-Golgi. In fact, we have obtained evidence of this using Lattice Light Sheet microscopy (results in progress, to appear elsewhere).
7- Figure 5C: the authors claim that the CopA and ArfA affects trafficking of UapA and SynA from ER to plasma membrane and assign CopA and ArfA as regulators for anterograde trafficking. I think this interpretation is not justified by the data. Depletion of CopA and ArfA will affect the Golgi apparatus in structure and function. The more straight-forward interpretation is that repression of the COPI machinery results in a defect in Golgi exit and therefore retention in pre-Golgi compartments (including the ER and maybe the ERGIC should it exist in Aspergillus). The same is true for BFA treatment where there are also negative effects on ER export, which are rather indirect consequences of alterations of Golgi function and integrity. Likewise, the interpretation of the papers by Weigel et al and Shomron et al is not correct. It is more likely that COPI is recruited to the growing ERES-derived tubule (or ERGIC) to recycle proteins back to the ER. This is not necessarily a proof that COPI regulates anterograde trafficking
This is a highly debatable issue which our work cannot address. However, we amended the text accordingly.
8- Figure 6: The images look like in Figure 5, yet here you don't call them ER-associated.
The two images are not alike. In Figure 5 upon activation of Sec31 (permissive temperature) we detect mostly punctual structures resembling ERES, whereas at the nonpermissive temperature we detect a membranous network typical of the ER. Upon repression of CopA we also detect punctual structures similar to ERES. In Figure 6, we mostly detect an effect on SynA. Repression of early secretory steps (SedV, GeaA) lead to collapse of SynA in the entire ER network. Repression at later stages of Golgi maturation and post-Golgi secretion (RabO, HypB, RabE, AP-1) lead to the appearance of punctual structures, most probably Golgi aggregates.
9- Figure 6D: How long was the BFA treatment. I am surprised that the pool of SynA preexisting at the plasma membrane seems to also be sensitive to BFA.
Cells were grown overnight under repressed conditions for both UapA and SynA. After 12-14h cells were shifted to derepressed conditions using fructose as carbon source. BFA was added after 90min of cargo derepression, while both cargoes were still in cytoplasmic structures so there was not preexisting SynA or UapA at the PM (see also Figure 1C). Subcellular localization of both cargoes was studied for 60min after BFA treatment.
10- This might be beyond the scope of this study, but as far as I know UapA is not N-glycosylated. Would the introduction of an N-glycosylation site shift it towards the Golgi-based route?
Thank you for this suggestion. We have performed this experiment, adding a glycosylation site on UapA, based on the glycosylation sites found in tis mammalians homologues. We did not detect any effect on UapA trafficking route or its activity. As the reviewer recognizes this goes beyond the scope of this study and thus, we did not include it the manuscript. Differential cargo glycosylation is however an important issue to be studied systemically in respect to different trafficking routes, and we envision to investigate it systematically.
Minor Comments
1- This might be just a personal preference, but I think that the term polar is misleading, because it implies something about the polarity of the amino acids. I think "polarized" might be the more common term. Anyway, this is just a minor point and just a suggestion from my side.
Amended in the revised text.
2- The paper by the Saraste lab should be mentioned and discussed (PMID: 16421253), which I think is very relevant to the current story.
We thank the reviewer for pointing out this important publication. In that case, the Rab1 GTPase defined a pathway connecting a pre-Golgi intermediate compartment with the PM in mammalians nerve cells. Thus, the Saraste lab publication is indeed along the lines of findings supporting that Golgi-independent unconventional cargo trafficking routes initiate at very early secretory compartments. Notice, however, that RabO, the A. nidulans homologue of Rab1, which in their case was essential for direct cargo sorting from the ERES/ERGIC to the PM, in or system, was dispensable for Golgi bypass. The Saraste lab article is now mentioned and discussed.
3- Having worked with ERES for over two decades, I find it strange to see it written ERes. I see no reason why ER exit sites in Aspergillus should be abbreviated differently from all other types of cells (yeast, drosophila, worms, mammals). I think that the entire acronym should be capitalized.
Amended in the text
4- When discussing the data about the partial effect of Sec13, it would be good to refer to a previous paper by the Stephens lab that showed that silencing Sec13/31 results in a defect in trafficking of collagen, but not of VSVG (PMID: 18713835).
We thank the reviewer for also pointing out the publication of the Stephens lab, now mentioned in the revised text. Noticeably, in that case silencing of both Sec13 and Sec31 has no effect on the trafficking of specific cargoes, whereas in our case Sec31 is still absolutely needed for both conventional and Golgi-independent secretion of SynA and UapA, respectively.
Reviewer #3 (Significance):
Overall, the data are of good quality and the story is interesting and timely. Understanding trafficking routes that bypass the Golgi is highly interesting. The main weakness is the lack of mechanistic understanding of the Golgi-bypass pathway. In addition, the study is limited to two proteins as representatives of polarized vs. non-polarized proteins. The main target audience for this paper are scientists working in the area of secretion and trafficking in the secretory pathway.
We thank the reviewer for his positive comments.
We are aware that the mechanistic details of Golgi bypass are missing and this is our next goal, dissecting those via various approaches genetic and biochemical approaches and employment of super resolution and ultra-fast microscopy.
__Reviewer #4 __
In this study, Sagia et al investigate the trafficking of different secretory cargo in Aspergillus nidulans under conditions that repress expression of transport factors or block stages in membrane trafficking. The primary approach is to conduct dual live-cell imaging of GFP-tagged UapA (plasma membrane localized purine transporter) and SynA (plasma membrane R-SNARE) after their simultaneous derepression to monitor trafficking routes. In germlings, both secretory proteins are detected in non-overlapping intracellular compartments and puncta after 60-90 min of derepression. After 4-6 hrs, SynA localizes to hyphal tips whereas UapA localizes to non-polar regions of the PM. Colocalization studies do not show UapA overlap with Golgi markers (SedV, PH-OSBP) during its biogenesis whereas SynA displays significant co-localization. Repression of COPII and COPI components generally block transport of both cargos to the PM and cause accumulation in ER compartments, although there are some differential effects on UapA and SynA localization. Finally, repression of other transport factors (ER-Golgi SNAREs, Golgi transport factors, and exocytic machinery) had differential effects on UapA and SynA localization over time with UapA reaching the plasma membrane in many instances and SynA accumulating in intracellular compartments.
Based on these observations, the authors conclude that UapA and SynA follow distinct trafficking routes to the plasma membrane where SynA uses a canonical SNARE-dependent secretory pathway route and UapA follows a non-canonical route that may bypass Golgi compartments. The study is extensive and supports the model that biogenesis of SynA and UapA follow distinct processes. However, there are some complexities that may limit interpretation. First, the cargo studied are targeted to the ER differently. UapA is a multispanning transmembrane protein that is likely dependent on the Sec61 translocon for co-translational membrane insertion and will involve ER chaperones and quality control machinery for its biogenesis. SynA will depend on the tail-anchored machinery (GET/TRC pathway) for insertion into the ER and is processed by cytosolic factors/chaperones. Therefore, the sites of ER insertion and the rates of biogenesis of these cargoes will be different. In addition, the repression of trafficking machinery used in this study appears to be variable and may exert partial blocks on intracellular transport stages. Regardless, the study clearly documents that SynA and UapA follow distinct biogenesis and transport processes when co-expressed in cells under experimentally controlled conditions.
Thank you for your positive comments.
To our knowledge there is no evidence suggesting that SynA translocates via a tail-anchored machinery (GET/TRC pathway) and not through the translocase. Despite this, we agree with the reviewer that translocation to the ER, as well as exit from it, might be cargo-dependent, especially when it concerns proteins with very different size, structures and oligomerization. Thus, the rate of biogenesis of UapA and SynA is probably quite different. However, this still does not dismiss our basic conclusion that the two cargoes follow distinct routes to traffic to the PM. The ‘problem’ of variable transcriptional repression of some trafficking-related proteins is solved by comparing the relative effect on the two cargoes in the same cells, and this is in fact the advantage of our new system. Importantly, notice that we took care to use conditions of repression where SynA trafficking by the conventional path was totally abolished and compared it to UapA.
- It was not clear if the translation, ER insertion and folding of UapA and SynA are fully synchronous. Is it possible that the rate of UapA synthesis and transport to the plasma membrane is substantially faster than for SynA? The imposition of transport blocks could trap SynA and not UapA if this cargo was at later transport stages.
As already discussed above translation, ER insertion and folding of UapA and SynA might indeed by different. This might somehow affect the trafficking path followed, but this issue is beyond the scope of this work. Notice, however, that the transcription of both cargoes is kept fully repressed during establishment of repression of secretion. Only when repression and blocking of secretion is established (12-14 h germination), as verified by Western blot analysis, we derepress the transcription of UapA and SynA, expressed from the same promoter, and follow their dynamic subcellular localization. Hence, this system ensures that both cargoes start from the earliest transport stage, the ER, upon imposition of transport blocks.
- In repressing transport factors (e.g., SarA, Sec12, Sec24, Sec13, SedV, RabE), it is clear that under thiamine repressing conditions these cells do not grow or have greatly reduced growth rates. However, it was not clear if proteins are depleted to the same extent in cells after repression for 12-14 hr or 16-22 hr. as mentioned in the methods. Indeed, in some cases depleted cells display different cargo localization patterns, for example 67% of cells show normal localization of UapA and SynA after sec12 repression and 33% show ER accumulation of both cargoes. There is differential localization of UapA and SynA in many cases where transport factors are repressed, but this could be due to partial inhibition and not complete blocks. It would be helpful to clearly indicate the time points and conditions in each of the figure legends as in points 3-5 below.
In the revised manuscript we did our best to clearly indicate the time points and conditions in each of the figure legends. Differential localization of UapA and SynA in many cases where trafficking factors are repressed is indeed an interesting outcome. Inefficient repression was dismissed based on the lack of colony growth (see relative growth tests of SarA, Sec24, Sec13, Sec31, SedV, GeaA, RabO, RabE, Ykt6, Sft1, SsoA and Sec9), but also by western blots (e.g., Sec24, Sec13, Sec31 or Sec9 shown in the present manuscript, or other trafficking proteins studied previously. Martzoukou et al., 2018; Dimou et al., 2020). Repression of Sec12 and HypB, and to lower degree AP-1, allowed formation of small and/or compact colonies, but even in these cases relative protein levels could not be detected in western blots, guaranteeing efficient repression.
- In Fig 4A immunoblot, HA-tagged proteins are not detected after thiamine repression. Please state the time of thiamine repression used before protein extraction and blot. Is this for the same length of time as for cells shown in panel 4C? It would also be helpful to state the time of cargo derepression before capturing images in 4C. The methods section mentions 12-14 hr or 16-22 hr of growth, presumably with thiamine in the culture, and then 1-8 hr or 60 min to 4 hr of cargo derepression before imaging. Please specify.
The time of thiamine repression before protein extraction was 16-18h. The same repression time was used for experiments shown in Figures 4C and 6C (ER/COPII and Golgi/post-Golgi repression respectively). More specifically, for microscopy experiments cells were grown in the presence of glucose and thiamine for 12-14h (repressed UapA/SynA and thiAp expressed gene). After this time, cells were shifted to fructose and thiamine for 4h (derepression of UapA/SynA and repression of thiAp expressed gene). In both cases (protein extraction and microscopy experiments) the total time of thiamine repression was 16-18h.
- For the thiA-copA and thiA-arfA repression experiments (Fig 5C), the methods section states that thiamine was not added ab initio in the culture, but after an 8 h time window without thiamine at the start of spore incubation. This is interpreted to mean that repression was for a shorter period to time than the 12-14 hr overnight growth. However, the figure legend states that De novo synthesis of cargos takes place after full repression of CopA and ArfA is achieved (>16 hr). Please clarify.
We think that the review was confused with repression of cargo synthesis (via alcAp+glucose) versus repression of trafficking proteins (via thiAp+thiamine). Please see Materials and methods. We clarify our protocol also here:
For the thiAp-copA and thiAp-arfA repression experiments addition of thiamine ab initio in the culture leads to total arrest of spore germination and germling formation. Thus, we added an 8-hour time window without thiamine to allow conidiospores to germinate until the stage of young germlings, under conditions where cargo expression via the alcAp was repressed by glucose. Subsequently, thiamine was added in the media (16-18 h) to repress CopA and ArfA, while cargo expression remained glucose-repressed. The transcriptional repression of the cargoes UapA and SynA was maintained for a longer period (24-26 h) compared to other repression experiments, but longer times of repression of cargoes do not make any difference, as full repression is achieved already at 12 h. De novo cargo trafficking was followed next day by eliciting depression, via a shift to fructose media, while still maintaining thiamine to repress CopA or ArfA.
- In Fig 6D, BFA treatment is shown to trap SynA in Golgi aggregates while UapA still reaches the plasma membrane. Please state the time of BFA treatment before collecting these images. Do longer treatments with BFA before cargo derepression cause accumulation of UapA in intracellular compartments?
As mentioned above (response to Reviewer’s #3 comment 9) cells were grown overnight under repressed conditions for both UapA and SynA. After 12-14h cells were shifted to derepressed conditions using fructose as carbon source. BFA was added after 90min of cargo derepression, while both cargoes were still in cytoplasmic structures so there was not preexisting SynA or UapA at the PM (see also Figure 1C). We have not noticed any different effect on UapA trafficking after a max of 1h of BFA treatment.
- A minor point, but on page 21 the methods state that "cells were shifted down to the permissive temperature (25 C), to restore the secretory block...". Suggest changing to "to reverse the secretory block..."
Modified accordingly
Reviewer #4 (Significance):
This manuscript nicely builds on a developing line of investigation in the Aspergillus nidulans model that specific plasma membrane proteins are efficiently delivered to the cell surface in a pathway that is distinct from the canonical secretory pathway. Previous work from this lab has suggested that a subpopulation of COPII carriers can bypass the Golgi for delivery of specific cargo to the plasma membrane. The current study uses dual expression of UapA-GFP and mCherry-SynA to provide further support for this model. Molecular definition of a direct ER to PM transport pathway for secretory cargo would be a significant advance to a broad audience. This study provides additional depth and support that such a pathway exists but does not define how COPII vesicles or related intermediates are transported to the PM.
Again, thank you for your positive comments.
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Referee #4
Evidence, reproducibility and clarity
In this study, Sagia et al investigate the trafficking of different secretory cargo in Aspergillus nidulans under conditions that repress expression of transport factors or block stages in membrane trafficking. The primary approach is to conduct dual live-cell imaging of GFP-tagged UapA (plasma membrane localized purine transporter) and SynA (plasma membrane R-SNARE) after their simultaneous derepression to monitor trafficking routes. In germlings, both secretory proteins are detected in non-overlapping intracellular compartments and puncta after 60-90 min of derepression. After 4-6 hrs, SynA localizes to hyphal tips whereas UapA localizes to non-polar regions of the PM. Colocalization studies do not show UapA overlap with Golgi markers (SedV, PH-OSBP) during its biogenesis whereas SynA displays significant co-localization. Repression of COPII and COPI components generally block transport of both cargos to the PM and cause accumulation in ER compartments, although there are some differential effects on UapA and SynA localization. Finally, repression of other transport factors (ER-Golgi SNAREs, Golgi transport factors, and exocytic machinery) had differential effects on UapA and SynA localization over time with UapA reaching the plasma membrane in many instances and SynA accumulating in intracellular compartments.
Based on these observations, the authors conclude that UapA and SynA follow distinct trafficking routes to the plasma membrane where SynA uses a canonical SNARE-dependent secretory pathway route and UapA follows a non-canonical route that may bypass Golgi compartments. The study is extensive and supports the model that biogenesis of SynA and UapA follow distinct processes. However, there are some complexities that may limit interpretation. First, the cargo studied are targeted to the ER differently. UapA is a multispanning transmembrane protein that is likely dependent on the Sec61 translocon for co-translational membrane insertion and will involve ER chaperones and quality control machinery for its biogenesis. SynA will depend on the tail-anchored machinery (GET/TRC pathway) for insertion into the ER and is processed by cytosolic factors/chaperones. Therefore, the sites of ER insertion and the rates of biogenesis of these cargo will be different. In addition, the repression of trafficking machinery used in this study appear to be variable and may exert partial blocks on intracellular transport stages. Regardless, the study clearly documents that SynA and UapA follow distinct biogenesis and transport processes when co-expressed in cells under experimentally controlled conditions.
- It was not clear if the translation, ER insertion and folding of UapA and SynA are fully synchronous. Is it possible that the rate of UapA synthesis and transport to the plasma membrane is substantially faster than for SynA? The imposition of transport blocks could trap SynA and not UapA if this cargo was at later transport stages.
- In repressing transport factors (e.g. sarA, sec12, sec24, sec13, sedV, rabE), it is clear that under thiamine repressing conditions these cells do not grow or have greatly reduced growth rates. However, it was not clear if proteins are depleted to the same extent in cells after repression for 12-14 hr or 16-22 hr as mentioned in the methods. Indeed, in some cases depleted cells display different cargo localization patterns, for example 67% of cells show normal localization of UapA and SynA after sec12 repression and 33% show ER accumulation of both cargo. There is differential localization of UapA and SynA in many cases where transport factors are repressed, but this could be due to partial inhibition and not complete blocks. It would be helpful to clearly indicate the time points and conditions in each of the figure legends as in points 3-5 below.
- In Fig 4A immunoblot, HA-tagged proteins are not detected after thiamine repression. Please state the time of thiamine repression used before protein extraction and blot. Is this for the same length of time as for cells shown in panel 4C? It would also be helpful to state the time of cargo derepression before capturing images in 4C. The methods section mentions 12-14 hr or 16-22 hr of growth, presumably with thiamine in the culture, and then 1-8 hr or 60 min to 4 hr of cargo derepression before imaging. Please specify.
- For the thiA-copA and thiA-arfA repression experiments (Fig 5C), the methods section states that thiamine was not added ab initio in the culture, but after an 8 h time window without thiamine at the start of spore incubation. This is interpreted to mean that repression was for a shorter period to time than the 12-14 hr overnight growth. However, the figure legend states that De novo synthesis of cargos takes place after full repression of CopA and ArfA is achieved (>16 hr). Please clarify.
- In Fig 6D, BFA treatment is shown to trap SynA in Golgi aggregates while UapA still reaches the plasma membrane. Please state the time of BFA treatment before collecting these images. Do longer treatments with BFA before cargo derepression cause accumulation of UapA in intracellular compartments?
- A minor point, but on page 21 the methods state that "cells were shifted down to the permissive temperature (25 C), to restore the secretory block...". Suggest changing to "to reverse the secretory block..."
Significance
This manuscript nicely builds on a developing line of investigation in the Aspergillus nidulans model that specific plasma membrane proteins are efficiently delivered to the cell surface in a pathway that is distinct from the canonical secretory pathway. Previous work from this lab has suggested that a subpopulation of COPII carriers can bypass the Golgi for delivery of specific cargo to the plasma membrane. The current study uses dual expression of UapA-GFP and mCherry-SynA to provide further support for this model.
Molecular definition of a direct ER to PM transport pathway for secretory cargo would be a significant advance to a broad audience. This study provides additional depth and support that such a pathway exists but does not define how COPII vesicles or related intermediates are transported to the PM.
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Referee #3
Evidence, reproducibility and clarity
The manuscript by Sagia et al compares the trafficking of a polarized (SynA) with a non-polarized (UapA) transmembrane protein. In agreement with previous work of the same lab, they find that UapA reaches the plasma membrane through a Golgi-bypass route, which they characterize to some extent. Overall, the data are of good quality and the story is interesting and timely. Understanding trafficking routes that bypass the Golgi is highly interesting. Nevertheless, there are several points of criticism that I have and below is a list where I combine major and minor points together:
Major Comments:
- Is it possible that the polarized phenotype of SynA is caused by selective removal, i.e. SynA is delivered to the entire plasma membrane, but endocytosed rapidly from all areas except the tip of the hyphae. This would also result in a polarized distribution.
- The authors describe the distribution of SynA and UapA in cells deficient of various COPII/ERES proteins. However, these data are not shown, and it is not clear how they were quantified. It would be important to add quantitative data here.
- on page 8, the authors discuss the discrepancy regarding the role of Sec13. They offer as an explanation that the previous studies have been performed in strains that separately expressed the two cargoes. However, I am unable to see why and how this would be a valid explanation.
- Why is the effect of Sec24 depletion so much stronger than of Sec12 depletion? Sec12 is the GEF for SarA, without which Sec24 should not be recruited to ERES. The explanation that low amounts of Sec12 are still present and sufficient to carry out the role of this protein. What is the evidence for that?
- In Figure 5, it would help readers who are not so familiar with Aspergillus organelle morphology to explain the figure a bit better. This might appear trivial for experts, but anyone from outside this field is slightly lost.
- The authors write that not seeing UapA in Golgi membranes is evidence that it does not pass through this organelle. However, when they write that SynA is never seen in cis-Golgi elements, they do not conclude that SynA bypasses the cis-Golgi.
- Figure 5C: the authors claim that the CopA and ArfA affects trafficking of UapA and SynA from ER to plasma membrane and assign copA and ArfA as regulators fo anterograde trafficking. I think this interpretation is not justified by the data. Depletion of CopA and ArfA will affect the Golgi apparatus in structure and function. The more straight-forward interpretation is that repression of the COPI machinery results in a defect in Golgi exit and therefore retention in pre-Golgi compartments (including the ER and maybe the ERGIC should it exist in Aspergillus). The same is true for BFA treatment where there are also negative effects on ER export, which are rather indirect consequences of alterations of Golgi function and integrity. Likewise, the interpretation of the papers by Weigel et al and Shomron et al is not correct. It is more likely that COPI is recruited to the growing ERES-derived tubule (or ERGIC) to recycle proteins back to the ER. This is not necessarily a proof that COPI regulates anterograde trafficking
- Figure 6: The images look like in Figure 5, yet here you don't call them ER-associated.
- Figure 6D: How long was the BFA treatment. I am surprised that the pool of SynA preexisting at the plasma membrane seems to also be sensitive to BFA.
- This might be beyond the scope of this study, but as far as I know UapA is not N-glycosylated. Would the introduction of an N-glycosylation site shift it towards the Golgi-based route?
Minor Comments
- This might be just a personal preference, but I think that the term polar is misleading, because it implies something about the polarity of the amino acids. I think "polarized" might be the more common term. Anyway, this is just a minor point and just a suggestion from my side.
- The paper by the Saraste lab should be mentioned and discussed (PMID: 16421253), which I think is very relevant to the current story.
- Having worked with ERES for over two decades, I find it strange to see it written ERes. I see no reason why ER exit sites in Aspergillus should be abbreviated differently from all other types of cells (yeast, drosophila, worms, mammals). I think that the entire acronym should be capitalized.
- When discussing the data about the partial effect of Sec13, it would be good to refer to a previous paper by the Stephens lab that showed that silencing Sec13/31 results in a defect in trafficking of collagen, but not of VSVG (PMID: 18713835)
Significance
Overall, the data are of good quality and the story is interesting and timely. Understanding trafficking routes that bypass the Golgi is highly interesting. The main weakness is the lack of mechanistic understanding of the Golgi-bypass pathway. In addition, the study is limited to two proteins as representatives of polarized vs. non-polarized proteins. The main target audience for this paper are scientists working in the area of secretion and trafficking in the secretory pathway.
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Referee #2
Evidence, reproducibility and clarity
The idea that transmembrane proteins of the plasma membrane move from the ER to the Golgi and then to the cell surface is firmly entrenched, and the mechanisms and components of this secretory pathway have been extensively characterized. Secretory vesicles are often delivered from the Golgi to sites of polarized growth. This paper builds on previous work by the same group to provide evidence that in Aspergillus nidulans, some non-polarly localized plasma membrane proteins follow a very different pathway, which bypasses components of the conventional secretory machinery such as SNAREs that have been implicated in secretion as well as the exocyst. In particular, they systematically compare the trafficking of the SNARE SynA, which follows the conventional secretory pathway, with that of the purine transporter UapA, which apparently does not. The two proteins were co-expressed in the same cells using the same promoter. A variety of genetic and microscopy methods are used to support the conclusion that UapA reaches the plasma membrane by a route distinct from that followed by SynA.
In my view, the authors present a convincing case. The individual experimental results are sometimes ambiguous, but the combined results favor the conclusion that UapA follows a novel pathway to the plasma membrane. I have only a few relatively minor comments.
- In the Introduction and elsewhere: to my knowledge, there is no clear evidence that AP-1-containing clathrin-coated vesicles carry cargoes from the Golgi to the plasma membrane. On the contrary, as recently reported by Robinson (https://pubmed.ncbi.nlm.nih.gov/38578286/), AP-1-containing vesicles likely mediate retrograde traffic in the late secretory pathway.
- In Figure 2, is there any known significance to the presence of UapA in "cytoplasmic oscillating thread structures decorated by pearl-like foci as well as a very faint vesicular/tubular network"?
- SynA is related to S. cerevisiae Snc1/2, which are known to be present in late Golgi compartments due to repeated rounds of endocytosis to the Golgi and exocytosis to the plasma membrane. The SynA shown here to colocalize with PH-osbp is probably present in a similar recycling loop rather than being en route to the plasma membrane for the first time. Therefore, the differential colocalization of UapA and SynA with PH-osbp does not by itself provide "strong evidence that the two cargoes studied traffic via different routes" as stated in the text, but might instead indicate that only SynA undergoes frequent endocytosis. The text should be amended accordingly.
- A missing piece of the story is a test of whether the puncta visualized for the two cargoes in Figure 5B are indeed distinct populations of COPII-containing ER exit sites. The relevant experiment would involve co-labeling of the cargoes together with a COPII marker. Three-color labeling would presumably be needed.
Significance
This study provides compelling evidence that in the fungus Aspergillus nidulans, some transmembrane transporter proteins reach the plasma membrane by a pathway that bypasses much of the conventional machinery associated with the Golgi apparatus and secretory vesicles. Although previous publications pointed toward a similar conclusion, the present work tackles the problem in a more rigorous and systematic way. These findings are important for cell biologists who study membrane traffic, although it remains to be determined how prevalent this type of non-canonical secretion might be in other organisms.
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Referee #1
Evidence, reproducibility and clarity
Sagia et al. present a manuscript using A. nidulans as model to study different transport routes of membrane proteins from the ER to the plasma membrane. They showed in earlier work that apparently at least two different transport routes exist, one involving the classical ER-ERES-ERGIC-Golgi route, one bypassing the Golgi. Unpolarized membrane proteins use the former, apically sorted membrane proteins the latter route. The study here confirms their earlier findings, uses a better model (co-expression of representatives for both routes in the same cell) and provides additional mechanistic insights about the roles of rabs, SNARES and other important proteins of the secretory pathway. The study is thoroughly done, figures are of high quality, data and methods well described and adequately replicated.
I do have, however, a number of comments that could help to improve the manuscript.
- I suggest to use the term polarized or apical rather than polar. Polar alone to me refers more to physico-chemical properties like water-solubility.
- introduction and discussion: I don´t think the literature about unconventional secretion bypassing the Golgi is complete, for example studies about TMED10 like Zhang, M. et al. Cell 181, 637-652 e615 (2020) or Zhang et al. Elife 4 (2015) are missing, there might be others. Is UapA a leader-less cargo that could be inserted via TMED10 translocation?
- Fig. 1C. Can these intracellular structures be characterized in more detail? Where is the Golgi localized in A. nidulans, is it decentralized like in yeast? Is the UapA at the time points shown in Fig. 1C in some sub-PM structures? To me the distribution at or near the PM is more punctate than in the steady state image shown in 1B.
- Fig. 3A. To me it looks like there is actually a lot of colocalization of UapA and SynA, especially at or near the PM, where there is quite some white, punctate staining. The green fluorescence is just much stronger, overlaying the violet. Can you show separate channels and explain?
- Fig. 3: In my opinion the statement that UapA "is probably sorted from an early secretory compartment, ultimately bypassing the need for Golgi maturation" is too strong at that point. You say for both UapA and SynA you don´t get significant colocalization with early Golgi/ERGIC marker, then you cannot conclude that one takes the conventional route via early-late Golgi and the other does not. What you can say is that UapA is apparently not going through late Golgi.
- Fig. 4C: UapA does not seem to accumulate in the ER in the Sec24 and 13 mutants but in punctate structures. This for me is unexpected, any explanations? Can you characterize that punctate staining?
- Fig. 6D: You state that BFA "has only a very modest effect on UaPA translocation to the PM". To me the PM (or very near PM) staining of UaPA looks very different in the PFA treated cells, more uneven/punctate. Is there an explanation for that?
Significance
One strength of the study is the use of a model organism, A. nidulans, not cell cultures. Also the use of both reporters, UapA and SynA, in the same cell is an advantage over previous studies using different lines and different promotors. Limitation of the study might be that it remains unclear to what extend the basic mechanism (UapA and SynA are transported to PM in different carrier and via different routes) can be generalized to other polarized (apically?) membrane proteins versus non-polarized membrane proteins in A. nidulans and whether a similar mechanism exists in other organisms.
Some of the basic findings of the study are not new but were published by the same group. However, as the authors point out, the current study uses improved assays and extends their previous studies, advancing our understanding of the mechanistics of transport in the conventional secretory pathway and novel alternative routes. The study will be of interest for basic researchers in the trafficking field.
My own expertise is transport through the secretory pathway in mammalian cells, many years ago more post-Golgi, now mostly ER-Golgi and ER itself.
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Reply to the reviewers
Reviewer #1 (Evidence, reproducibility and clarity (Required)):
Summary: MIC26 is a subunit of the 'mitochondrial contact site and cristae organizing system' (MICOS) complex required for crista junction (CJ) formation and was functionally linked to diabetes and modulation of lipid metabolism. In order to understand the role of MIC26 in metabolism, the authors generated MIC26-KO HepG2 cells and investigated the pathways regulated by MIC26 under normo- and hyper-glycemic culture conditions. They employed a multi-omics approach that include transcriptomics, proteomics, targeted metabolomics, and functional assays to document the changes in mRNAs, proteins, and metabolites as a result of MIC26 deletion. Through bioinformatic analyses, they showed that the function of MIC26 is critical in various pathways regulating fatty acid synthesis, oxidation, cholesterol metabolism, and glycolysis. Interestingly, they found an entirely antagonistic effect of lipogenesis in MIC26-KO cells compared to WT cells depending on the glucose concentration of the culture media. In addition, they showed that MIC26 deletion led to a major metabolic rewiring of glutamine utilization as well as oxidative phosphorylation.
Major comments: 1) This is basically a descriptive study that document the transcriptomic, proteomic, and metabolic consequences of lacking MIC26 in normal or high glucose environment. It is data rich but insight poor. The connections between MIC26 as a subunit of MICOS complex and all those metabolic pathways are so tenuous that it is hard to see what to follow up after.
__Response: __We respectfully differ from the reviewer’s opinion that the manuscript is data rich and insight poor. Our study provides significant insights by demonstrating how MIC26, strategically residing in the mitochondrial inner membrane (IM), regulates major cellular pathways.
MIC26 operates in a dual manner:
A) Depending on the nutritional status, normoglycemia or hyperglycemia, MIC26 regulates the glycolysis, lipid, cholesterol metabolism and TCA cycle intermediates in an antagonistic manner. B) Independent of the nutritional status, it regulates glutamine and OXPHOS metabolism.
In addition, based on the suggestions by Reviewer #2, we have tested whether other proteins of MICOS (MIC27 and MIC19) present in two different sub-complexes regulate important metabolic pathways. Using the experimental results achieved (See reply to comments from Reviewer #2), we conclude that MIC26 plays a unique role as metabolic regulator in the IM and this study is therefore important to the general field of metabolism.
2) In the Results section (page 5, line 114-123), the description of the Western blot (WB) analysis appears inconsistent with several blot images of Fig. 1A, which makes the result unconvincing. The authors should select appropriate representative WB images, assuming they have them, to support their claim.
Response: Thank you for the suggestion. Firstly, we have performed additional experiments in this regard, and included the relevant quantification. Secondly, we have replaced some WBs which depict the appropriate quantification.
Further, we also modified the relevant lines in the manuscript stating that ‘Mitochondrial apolipoproteins, MIC26, MIC27, and MIC25 are increased in cells exposed to hyperglycemia’ and not only MIC26 as stated before.
Minor comments:
3) As the functional role of MIC26 in metabolism is the primary focus, the authors should present the results in the figures in the order of WT-N, MIC26KO-N, WT-H, MIC26KO-H for easier comparison.
Response: We understand the reasoning to interchange the two conditions. However, such an endeavour will involve cropping images of WBs, BN-PAGE and Clear native PAGE to better represent the corresponding quantification. This will also involve modifying all figures (data-sets and functional assays) in the whole manuscript. Overall, considering that the benefits of interchanging the order, of the WT-Hyperglycemia and MIC26 KO-Normoglycemia, are relatively minor, we have decided to stick to the original representation of the figures.
Reviewer #1 (Significance (Required)):
Mitochondria play important roles in metabolism and metabolic disorders. This study generated large amount of data relating to the role of a mitochondrial protein MIC26 in metabolism. Mutations in MIC26 have been associated with mitochondrial myopathy, lactic acidosis, and cognition defects (Beninca et al. 2021) and a lethal progeria-like condition (Peifer-Weib et al. 2023). There is also a connection between MIC26 and metabolic disorders. The results of this study will be of interest to researchers in the fields of mitochondrial diseases and metabolic disorders. My field of expertise is mitochondrial disease, proteomics, lipidomics, phospholipid biochemistry.
Reviewer #2 (Evidence, reproducibility and clarity (Required)):
Summary
This study determines the role of MIC26, a mitochondrial component of the MICOS complex, in influencing cellular metabolic status. Using a variety of multiomic profiling techniques, as well as functional assays such as Seahorse-based respirometry, the authors propose that MIC26 regulates a variety of metabolic processes, including lipid and cholesterol homeostasis, glycolysis, fatty acid oxidation, fatty acid synthesis, TCA cycle homeostasis, glutamine metabolism, and general mitochondrial bioenergetics via OxPhos activity and supercomplex formation. The data were generated in MIC26 knockout HepG2 hepatocellular carcinoma cells grown in two nutrient conditions: high glucose (which they term "hyperglycemia") and low glucose (which they term "normoglycemia") DMEM. While the data support the authors' conclusion that loss of MIC26 causes braod metabolic changes across these conditions, the authors do not distinguish whether MIC26 knockout affects metabolism due to its canonical role in MICOS, or whether it acts as "a metabolic rheostat" to directly regulate central cellular fuel pathways, as is claimed in the title of this manuscript. Given the breadth of metabolic alterations seen in the MIC26 KO cells, it seems likely that at least a subset of these changes are indirect, rather than that MIC26 plays a direct regulatory role in the eight distinct metabolic pathways outlined above. Thus, while the data generally support the conclusion that expression of MIC26 is important for metabolic homeostasis, the mechanism(s) by which MIC26 influences cellular metabolism remains unclear and should be further addressed.
Major Comments
1) The authors infer that MIC26 influences cellular metabolism by referencing a handful of papers on MIC26 transcript differences in select metabolic models, metabolic alterations seen in a MIC26 transgenic/overexpression mouse model, or metabolic effects seen due to MIC26 tissue specific KO models. They thus hypothesize that "MIC26 has an unidentified regulatory role under nutrient-enriched conditions" (line 88). They support this observation by showing that MIC26 is increased in high glucose DMEM relative to low glucose DMEM in Figure 1. However, the authors claim, in both the figure legend title as well as the results section header, that "MIC26 is selectively increased in cells exposed to hyperglycemia" (lines 103-4), when their data demonstrate that this is not true. Figure 1B shows that MIC27, MIC26, and MIC25 increase in high glucose relative to low glucose conditions, indicating that MIC26 is not selectively increased, but rather that multiple subunits of MICOS are increased under nutrient-enriched conditions.
Response: We agree to the reviewer’s comment and have now replaced the title in the results section in the manuscript accordingly - ‘Mitochondrial apolipoproteins, MIC26, MIC27, and MIC25 are increased in cells exposed to hyperglycemia’ and not only MIC26 as stated before. Further, in order to strengthen the WB data from Fig 1A & B, we also increased the number of experiments and updated the Fig 1B and also some WBs in Fig 1A to better represent the quantification.
2) This does not suggest that MIC26 does not have an important role in maintaining metabolism under such nutrient conditions, but rather supports a model in which MICOS itself dynamically responds to altered nutrient conditions in cell culture. Interestingly, other MICOS subunits do not change in abundance (e.g., MIC19 and MIC60), which have previously been associated with a separate MICOS subcomplex than MIC26 in yeast (PMID: 33053165). These data may suggest that the nutrient responsive behavior of MIC26 may be due to its assembly within this specific MICOS subcomplex, rather than an independent "unidentified regulatory role under nutrient-enriched conditions".
To test this, the authors should repeat a subset of functional assays (perhaps the Seahorse metabolic assays) in other MICOS deletion cell lines, including one that dynamically changes in expression in a similar manner to MIC26 (e.g., MIC27 KO or MIC25 KO) and one that does not dynamically change in expression in high glucose conditions (e.g., MIC60 or MIC13). In these sets of experiments the authors will be able to distinguish three possibilities:
Model 1: if MICOS in general affects metabolic pathways, similar results will be seen for all MICOS subunit KOS.
Model 2: if the MIC26-specific subcomplex is dynamically regulated to influence cellular metabolism, KO of MIC26 and other subunits of this subcomplex should show similar results, but KO of subunits of the non-dynamic subcomplex (e.g., MIC60) would not show similar phenotypes.
Model 3: If only MIC26 KO, but not other MICOS subunits, show metabolic phenotypes, this would support a MICOS-independent role for MIC26 in influencing cellular metabolism.
Importantly, any of these models are interesting, and testing these models does not invalidate any of the phenotypes presented in the manuscript. Rather, these experiments would assist the reader in understanding the underlying mechanism by which MIC26 loss causes cellular metabolic defects. Furthermore, it is worth stating that performing all experiments with multiple other MICOS cell lines is beyond the scope of the manuscript, but testing effects in select (preferably functional) assays, such as the glycolysis stress test (Fig 3E), FAO Seahorse (Fig 3H-J) glutamine oxidation (Fig 6B), and general stress test (Fig 7E) would be appropriate. Other more defined and easily achievable experiments could also be used to support these claims (e.g., western blots probing for levels of key metabolic regulators).
__Response: __We appreciate the balanced comments of the reviewer who has carefully read and appreciated our manuscript. We also appreciate the constructive criticism of the reviewer who suggested various possible models considering the MIC26 role.
In this endeavour, we have performed the following extensive experiments:
- Generated MIC19 KOs in HepG2 cells. We had already generated MIC27 KOs during the course of a previous publication (Lubeck et al, 2023) and used them for this publication (Fig S4). WB analyses was performed using the MIC27 KO and MIC19 KO cells.
- Measured glycolysis function using the glycolysis stress test in MIC27 and MIC19 KOs (Fig S5).
- Analysed lipid metabolism by imaging and extensively quantifying LD number and BODIPY fluorescence intensities in MIC27 and MIC19 KO cells (Fig S6).
- Measured glutamine oxidation using Mito Flex fuel tests in MIC27 and MIC19 KOs (Fig S10F).
- Analysed general mitochondrial respiration by using mito stress kit in MIC27 and MIC19 KOs (Fig S11-E-H). We provide a summary of the above results:
#
Metabolic Pathway
Experiment
MIC26____ KO
MIC27____ KO
MIC19____ KO
1
Glycolysis
Glycolysis stress kit
Glycolytic reserve increased
Glycolytic reserve unchanged
Glycolytic reserve unchanged
2A
Lipid Metabolism
LD number
General increase
No consistent increase in treatment with and without palmitate in normoglycemia and hyperglycemia
No consistent increase in treatment with and without palmitate in normoglycemia and hyperglycemia
2B
Lipid Metabolism
LD (BODIPY) intensities
Presence of antagonistic regulation of LD content
Presence of antagonistic regulation of LD content
Absence of antagonistic regulation of LD content
3
Glutamine oxidation
Mito flex fuel test
No dependency
Dependent
Dependent
4
Steady-state respiration
Mito stress test
Basal respiration increased
Basal respiration unchanged
Basal respiration unchanged
5
Steady-state respiration
Mito stress test
SRC decreased in normoglycemia
SRC unchanged
SRC unchanged
Taking the above summary, we investigated three possibilities considering the role of MIC26:
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General role of MICOS – whether deletion of any MICOS protein leads to similar phenotype as MIC26 deletion
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Specific role of MIC26/27/10 subcomplex – whether deletion of any other protein in the MIC26-subcomplex like MIC27 leads to similar results, accompanied by dissimilar results in KOs of any protein belonging to the other MICOS subcomplex (MI19/MIC25/MIC60) and whose protein levels were not changed upon hyperglycemia treatment (MIC19 or MIC60 KOs).
- MICOS-independent role of MIC26. Considering the various metabolic pathways analysed, we conclude that MIC26 has a MICOS-independent role in regulating major cellular pathways.
Minor Comments 1. The multiomics data as presented in Figure 2 is difficult to interpret. This is mainly driven by the fact that there are mulitpe comparisons that should be communicated (KO v WT, normoglycemia v hyperglycemia, upregulated v. downregulated), but only select enrichment values are shown (e.g., normoglycemia upregulated in 2C and hyperglycemia downregulated in 2D). It took me a long time as a reader to understand what I was looking at because only select analyses are presented. What pathways are upregulated in hyperglycemia in MIC26 KO v. WT?
__Response: __Thank you for pointing this out. We have now represented all the four conditions regarding enrichment analysis as suggested. The antagonistic metabolic regulation is only observed in MIC26 KO cells cultured in normoglycemia (upregulated) when compared to MIC26 KOs cells cultured in hyperglycemia (downregulated). When MIC26 KOs cultured in hyperglycemia (upregulated) were compared with MIC26 KOs cultured in normoglycemia (downregulated), there were also few pathways which showed an antagonistic regulation, but not directly involved with metabolism, relating to apoptosis etc. Hence, we did not focus on these pathways in the current manuscript.
The Treemaps have been shifted to Fig S1. All four Treemaps have been represented instead of the two shown before. In the process, the previous proteomics Fig S1B and S1C depicting antagonism in different pathways have been excluded.
2. Figure 3 would be stronger if expression from all glycolytic proteins were shown instead of only a subset. If the authors are making the claim that MIC26 KO increases glycolytic flux via protein-level upregulation of glycolysis, this could be substantiated at a pathway level. These data could be included in supplemental data if they are difficult to fit into the figure.
__Response: __Thank you. We have now included the data of proteins regulating glycolysis as a new figure (Fig S3C). MIC26 KO cells cultured in normoglycemia had increased levels of aldolase (ALDOA & ALDOC), phosphoglycerate kinase (PGK1) and pyruvate kinase (PKM & PKLR) when compared to control cells. We have included this data in the manuscript.
3. In Figure 4H-J - the raw data for the Seahorse traces should be shown, and OCR should be reported in pmol/min rather than relative percentages so as to help the reader more critically evaluate the data.
__Response: __For the FAO assays, we treat the cells with palmitate or mock (BSA serving as control). The histograms (Fig 4H-J) are represented as such because we normalised the oxygen consumption after palmitate treatment with oxygen consumption of mock-treated cells. We understand the reviewer’s concern and have now included the absolute values of oxygen consumption of FAO assay in an excel sheet (Supplementary Table S5). In addition, we have also included the absolute values for mito stress test and glycolysis assays where the oxygen consumption has been normalised to WT-normoglycemia condition (Supplementary Table S5). The original oxygen consumption curves for glycolysis stress kit (Fig 3E, S5A) and mito stress kits (Fig 7E, S11E) are shown as figures.
4. The majority of the plots are shown with 4 comparisons, but statistical comparisons are often only provided for a subset of comparisons. It is unclear whether statistics were compared across all comparisons and the non-annotated comparisons are not significant, or whether those calculations were not performed. Defining this in the figure, or, better, annotating all relevant comparisons on each graph with "ns" for not significant, would assist the reader with interpreting the data.
__Response: __Thanks for pointing this out. After comparing all meaningful conditions (except WT-N to MIC26 KO-H and WT-H to MIC26 KO-N), only those that were significant were represented using asterisks. We have now mentioned this information in the respective figure legends. We avoided using ‘non-significant (ns)’ in the figure as it would make some figures very crowded as seen from some of our trials.
Reviewer #2 (Significance (Required)):
This study broadly profiles the metabolic defects associated with loss of the MICOS subunit MIC26 in hepatocellular carcinoma cells in variable nutrient conditions (e.g., high glucose and low glucose). As a reviewer with expertise in multiomic profiling of metabolic models, I found the breadth of pathways studied in this manuscript to be impressive. Furthermore, the authors use a variety of techniques, including multiomic profiling, isotopic flux analysis, and functional Seahorse assays to support their conclusions. The study provides a comprehensive analysis of metabolic changes associated with MIC26, and is thus an important advance in profiling how loss of MIC26 (or MICOS in general; see below) affects cellular metabolism in the context of dynamic nutrient changes. However, the claim that "MIC26 is a metabolic rheostat regulating central cellular fuel pathways", as is proposed in the title of the manuscript, is unsubstantiated, as the authors do not test whether loss of MIC26 specifically influences cellular metabolism independent of its role in MICOS. This paper would be significantly strengthened if a subset of functional assays across metabolic pathways were repeated with other MICOS KO cell lines to delineate whether these metabolic effects are direct or indirect.
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Referee #2
Evidence, reproducibility and clarity
Summary
This study determines the role of MIC26, a mitochondrial component of the MICOS complex, in influencing cellular metabolic status. Using a variety of multiomic profiling techniques, as well as functional assays such as Seahorse-based respirometry, the authors propose that MIC26 regulates a variety of metabolic processes, including lipid and cholesterol homeostasis, glycolysis, fatty acid oxidation, fatty acid synthesis, TCA cycle homeostasis, glutamine metabolism, and general mitochondrial bioenergetics via OxPhos activity and supercomplex formation. The data were generated in MIC26 knockout HepG2 hepatocellular carcinoma cells grown in two nutrient conditions: high glucose (which they term "hyperglycemia") and low glucose (which they term "normoglycemia") DMEM. While the data support the authors' conclusion that loss of MIC26 causes braod metabolic changes across these conditions, the authors do not distinguish whether MIC26 knockout affects metabolism due to its canonical role in MICOS, or whether it acts as "a metabolic rheostat" to directly regulate central cellular fuel pathways, as is claimed in the title of this manuscript. Given the breadth of metabolic alterations seen in the MIC26 KO cells, it seems likely that at least a subset of these changes are indirect, rather than that MIC26 plays a direct regulatory role in the eight distinct metabolic pathways outlined above. Thus, while the data generally support the conclusion that expression of MIC26 is important for metabolic homeostasis, the mechanism(s) by which MIC26 influences cellular metabolism remains unclear and should be further addressed.
Major Comments
- The authors infer that MIC26 influences cellular metabolism by referencing a handful of papers on MIC26 transcript differences in select metabolic models, metabolic alterations seen in a MIC26 transgenic/overexpression mouse model, or metabolic effects seen due to MIC26 tissue specific KO models. They thus hypothesize that "MIC26 has an unidentified regulatory role under nutrient-enriched conditions" (line 88). They support this observation by showing that MIC26 is increased in high glucose DMEM relative to low glucose DMEM in Figure 1. However, the authors claim, in both the figure legend title as well as the results section header, that "MIC26 is selectively increased in cells exposed to hyperglycemia" (lines 103-4), when their data demonstrate that this is not true. Figure 1B shows that MIC27, MIC26, and MIC25 increase in high glucose relative to low glucose conditions, indicating that MIC26 is not selectively increased, but rather that multiple subunits of MICOS are increased under nutrient-enriched conditions. This does not suggest that MIC26 does not have an important role in maintaining metabolism under such nutrient conditions, but rather supports a model in which MICOS itself dynamically responds to altered nutrient conditions in cell culture. Interestingly, other MICOS subunits do not change in abundance (e.g., MIC19 and MIC60), which have previously been associated with a separate MICOS subcomplex than MIC26 in yeast (PMID: 33053165). These data may suggest that the nutrient responsive behavior of MIC26 may be due to its assembly within this specific MICOS subcomplex, rather than an independent "unidentified regulatory role under nutrient-enriched conditions".
To test this, the authors should repeat a subset of functional assays (perhaps the Seahorse metabolic assays) in other MICOS deletion cell lines, including one that dynamically changes in expression in a similar manner to MIC26 (e.g., MIC27 KO or MIC25 KO) and one that does not dynamically change in expression in high glucose conditions (e.g., MIC60 or MIC13). In these sets of experiments the authors will be able to distinguish three possibilities:
Model 1: if MICOS in general affects metabolic pathways, similar results will be seen for all MICOS subunit KOS.
Model 2: if the MIC26-specific subcomplex is dynamically regulated to influence cellular metabolism, KO of MIC26 and other subunits of this subcomplex should show similar results, but KO of subunits of the non-dynamic subcomplex (e.g., MIC60) would not show similar phenotypes.
Model 3: If only MIC26 KO, but not other MICOS subunits, show metabolic phenotypes, this would support a MICOS-independent role for MIC26 in influencing cellular metabolism.
Importantly, any of these models are interesting, and testing these models does not invalidate any of the phenotypes presented in the manuscript. Rather, these experiments would assist the reader in understanding the underlying mechanism by which MIC26 loss causes cellular metabolic defects. Furthermore, it is worth stating that performing all experiments with multiple other MICOS cell lines is beyond the scope of the manuscript, but testing effects in select (preferably functional) assays, such as the glycolysis stress test (Fig 3E), FAO Seahorse (Fig 3H-J) glutamine oxidation (Fig 6B), and general stress test (Fig 7E) would be appropriate. Other more defined and easily achievable experiments could also be used to support these claims (e.g., western blots probing for levels of key metabolic regulators).
Minor Comments
- The multiomics data as presented in Figure 2 is difficult to interpret. This is mainly driven by the fact that there are mulitpe comparisons that should be communicated (KO v WT, normoglycemia v hyperglycemia, upregulated v. downregulated), but only select enrichment values are shown (e.g., normoglycemia upregulated in 2C and hyperglycemia downregulated in 2D). It took me a long time as a reader to understand what I was looking at because only select analyses are presented. What pathways are upregulated in hyperglycemia in MIC26 KO v. WT?
- Figure 3 would be stronger if expression from all glycolytic proteins were shown instead of only a subset. If the authors are making the claim that MIC26 KO increases glycolytic flux via protein-level upregulation of glycolysis, this could be substantiated at a pathway level. These data could be included in supplemental data if they are difficult to fit into the figure.
- In Figure 4H-J - the raw data for the Seahorse traces should be shown, and OCR should be reported in pmol/min rather than relative percentages so as to help the reader more critically evaluate the data.
- The majority of the plots are shown with 4 comparisons, but statistical comparisons are often only provided for a subset of comparisons. It is unclear whether statistics were compared across all comparisons and the non-annotated comparisons are not significant, or whether those calculations were not performed. Defining this in the figure, or, better, annotating all relevant comparisons on each graph with "ns" for not significant, would assist the reader with interpreting the data.
Significance
This study broadly profiles the metabolic defects associated with loss of the MICOS subunit MIC26 in hepatocellular carcinoma cells in variable nutrient conditions (e.g., high glucose and low glucose). As a reviewer with expertise in multiomic profiling of metabolic models, I found the breadth of pathways studied in this manuscript to be impressive. Furthermore, the authors use a variety of techniques, including multiomic profiling, isotopic flux analysis, and functional Seahorse assays to support their conclusions. The study provides a comprehensive analysis of metabolic changes associated with MIC26, and is thus an important advance in profiling how loss of MIC26 (or MICOS in general; see below) affects cellular metabolism in the context of dynamic nutrient changes. However, the claim that "MIC26 is a metabolic rheostat regulating central cellular fuel pathways", as is proposed in the title of the manuscript, is unsubstantiated, as the authors do not test whether loss of MIC26 specifically influences cellular metabolism independent of its role in MICOS. This paper would be significantly strengthened if a subset of functional assays across metabolic pathways were repeated with other MICOS KO cell lines to delineate whether these metabolic effects are direct or indirect.
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Referee #1
Evidence, reproducibility and clarity
Summary:
MIC26 is a subunit of the 'mitochondrial contact site and cristae organizing system' (MICOS) complex required for crista junction (CJ) formation and was functionally linked to diabetes and modulation of lipid metabolism. In order to understand the role of MIC26 in metabolism, the authors generated MIC26-KO HepG2 cells and investigated the pathways regulated by MIC26 under normo- and hyper-glycemic culture conditions. They employed a multi-omics approach that include transcriptomics, proteomics, targeted metabolomics, and functional assays to document the changes in mRNAs, proteins, and metabolites as a result of MIC26 deletion. Through bioinformatic analyses, they showed that the function of MIC26 is critical in various pathways regulating fatty acid synthesis, oxidation, cholesterol metabolism, and glycolysis. Interestingly, they found an entirely antagonistic effect of lipogenesis in MIC26-KO cells compared to WT cells depending on the glucose concentration of the culture media. In addition, they showed that MIC26 deletion led to a major metabolic rewiring of glutamine utilization as well as oxidative phosphorylation.
Major comments:
This is basically a descriptive study that document the transcriptomic, proteomic, and metabolic consequences of lacking MIC26 in normal or high glucose environment. It is data rich but insight poor. The connections between MIC26 as a subunit of MICOS complex and all those metabolic pathways are so tenuous that it is hard to see what to follow up after.
In the Results section (page 5, line 114-123), the description of the Western blot (WB) analysis appears inconsistent with several blot images of Fig. 1A, which makes the result unconvincing. The authors should select appropriate representative WB images, assuming they have them, to support their claim.
Minor comments:
As the functional role of MIC26 in metabolism is the primary focus, the authors should present the results in the figures in the order of WT-N, MIC26KO-N, WT-H, MIC26KO-H for easier comparison.
Significance
Mitochondria play important roles in metabolism and metabolic disorders. This study generated large amount of data relating to the role of a mitochondrial protein MIC26 in metabolism. Mutations in MIC26 have been associated with mitochondrial myopathy, lactic acidosis, and cognition defects (Beninca et al. 2021) and a lethal progeria-like condition (Peifer-Weib et al. 2023). There is also a connection between MIC26 and metabolic disorders. The results of this study will be of interest to researchers in the fields of mitochodrial diseases and metabolic disorders.
My field of expertise is mitochondrial disease, proteomics, lipidomics, phospholipid biochemistry.
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Reply to the reviewers
Rebuttal_ Preprint- #RC-2023-02144
First of all we would like to thank the three reviewers for their constructive and positive comments and suggestions, and the time spent in reviewing our manuscript. Their suggestions and comments had contributed to improve our manuscript. We feel the manuscript is much strengthened by this revision.
Reviewer #1 (Evidence, reproducibility and clarity (Required)):
__Summary:____ __The manuscript by Dabsan et al builds on earlier work of the Igbaria lab, who showed that ER-luminal chaperones can be refluxed into the cytosol (ERCYS) during ER stress, which constitutes a pro-survival pathway potentially used by cancer cells. In the current work, they extent these observations and a role for DNAJB12&14 in ERCYS. The work is interesting and the topic is novel and of great relevance for the proteostasis community. I have a number of technical comments:
We thank the reviewer for his/her positive comments on our manuscript.
__Major and minor comments: __
1- In the description of Figure 2, statistics is only show to compare untreated condition with those treated with Tg or Tm, but no comparison between condition and different proteins. As such, the statement made by the authors "...DNAJB14-silenced cells were only affected in AGR2 but not in DNAJB11 or HYOU1 cytosolic accumulation" cannot be made.
Answer: We totally agree with the reviewer#1. The aim of this figure is to show that during ER stress, a subset of ER proteins are refluxed to the cytosol. This is happening in cells expressing DNAJB12 and DNAJB14. We are not comparing the identity of the expelled proteins between DNAJB12-KD cells and DNAJB14-KD cells, This is not the scoop of this paper as such the statement was removed.
2- Figure S2C: D11 seems to increase in the cytosolic fraction after Tm and Tg treatment. However, this is not reflected in the text. The membrane fraction also increases in the DKO. Is the increase of D11 in both cytosol and membrane and indication for a transcriptional induction of this protein by Tm/Tg? Again, the authors are not reflecting on this in their text.
Answer: We performed qPCR experiments in control, DNAJB12-KD, DNAJB14-KD and in the DNAJB12/DNAJB14 double knock down cells (in both A549 and PC3 cells) to follow the mRNA levels of DNAJB11. As shown in (Figure S2F-S2N), there is no increase in the mRNA levels of DNAJB11, AGR2 or HYOU1 in the different cells in normal (unstressed conditions). Upon ER stress with tunicamycin or thapsigargin there is a little increase in the mRNA levels of HYOU1 and AGR2 but not in DNAJB11 mRNA levels. On the other hand, we also performed western blot analysis and we did not detect any difference between the different knockdown cells when we analyzed the levels of DNAJB11 compared to GAPDH. Those data are now added as (Figure S2F-S2N).
We must note that although AGR2 and HYOU1 are induced at the mRNA as a result of ER stress, the data with the overexpression of DNAJB12 and DNAJB14 are important as control experiments because when DNAJB12 is overexpressed it doesn’t inducing the ER stress (Figure S3C-S3D). In those conditions there is an increase of the cytosolic accumulation of AGR2, HYOU1 and DNAJB11 despite that there was no induction of AGR2, HYOU1 or DNAJB11 (Figure 3C and Figure 3E, Figure S3, Figure 4, and Figure S4) . Those results argue against the idea that the reflux is a result of protein induction and an increase in the total proteins levels.
3- Figure 2D: Only p21 is quantified. phospho-p53 and p53 levels are not quantified.
Answer: We added the quantification of phospho-p53 and the p53 levels to (Figure 2E-G). Additional blots of the P21, phosphor-p53 and p53 now added to FigureS2O.
4- Figure 2D: There appears to be a labelling error
Answer: Yes, the labelling error was corrected.
5- Are there conditions where DNAJB12 would be higher?
Answer: In some cancer types there is a higher DNAJB12, DNAJB14 and SGTA expression levels that are associated with poor prognosis and reduced survival (New Figure S6E-M). The following were added to the manuscript: “Finally, we tested the effect of DNAJB12, DNAJB14, and SGTA expression levels on the survival of cancer patients. A high copy number of DNAJB12 is an unfavorable marker in colorectal cancer and in head and neck cancer because it is associated with poor prognosis in those patients (Figure S6E). A high copy number of DNAJB12, DNAJB14, and SGTA is associated with poor prognosis in many other cancer types, including colon adenocarcinoma (COAD), acute myeloid leukemia (LAML), adrenocortical carcinoma (ACC), mesothelioma (MESO), and Pheochromocytoma and paraganglioma (PCPG) (Figure S6F-M). In uveal melanoma (UVM), a high copy number of the three tested genes, DNAJB12, DNAJB14, and SGTA, are associated with poor prognosis and poor survival (Figure S6I, S6J, and S6M). The high copy number of DNAJB12, DNAJB14, and SGTA is also associated with poor prognosis in many other cancer types but with low significant scores. More data is needed to make significant differences (TCGA database). We suggest that the high expression of DNAJB12/14 and SGTA in those cancer types may account for the poor prognosis by inducing ERCYS and inhibiting pro-apoptotic signaling, increasing cancer cells' fitness.
6- What do the authors mean by "just by mass action"?
Answer: Mass action means increasing the amount of the protein (overexpression). We corrected this in the main text to overexpression.
7- Figure 3C: Should be labelled to indicate membrane and cytosolic fraction. The AGR2 blot in the left part is not publication quality and should be replaced.
Answer: We added the labelling to indicate cytosolic and membrane fractions to Figure 3C. We re-blotted the AGR2, new blot of AGR2 was added.
8- What could be the reason for the fact that DNAJB12 is necessary and sufficient for ERCYS, while DNAJB14 is only necessary?
Answer: Because of their very high homology, we speculate that the two proteins have partial redundancy. Partial because we believe that some of the roles of DNAJB12 cannot be carried by DNAJB14 in its absence. Although they are highly homologous, we expect that they probably have different affinities in recruiting other factors that are necessary for the reflux of proteins.
We further developed around this point in the discussion and the main text.
9- Figure 5A: Is the interaction between SGTA and JB12 UPR-independent?HCS70 seems to show only background binding. The interaction of JB12 with SGTA is not convincing. A better blot is needed.
Answer: In the conditions of Figure 5A, we did not observe any induction of the UPR (Figure S3C-D). Thus, we concluded that in those condition of overexpression, DNAJB12 interacts with SGTA in UPR independent manner.
We repeated this experiment another 3 times with very high number of cells (2X15cm2 culture dishes for each condition) and instead of coimmunoprecipitating with DNAJB12 antibodies we IP-ed with FLAG-beads, the results are very clear as shown in the new Figure 5A compared to Figure S5A.
10- Figure 5B: the expression of DNAJB14 was induced by Tg50, but not by Tg25 or Tm. However, the authors have not commented on this. This should be mentioned in the text and discussed.
Answer: In most of the experiments we did not see an increase in DNAJB14 upon ER stress except in this replicate. To be sure we looked at the DNAJB14 levels upon ER stress by protein and qPCR experiment as shown in new (in the Input of Figure 5 and Figure S5) and (Figure S5H-I). We also added new IP experiments in Figure 5 and Figure S5.
11- Figure 6A: Why is a double knockdown important at all? DNAJB14 does not seem to do much at all (neither in overexpression nor with single knockdown).
Answer: the data shows that DNAJB12 can compensate for the lack of DNAJB14 while DNAJB14 can only partially compensate for some of the DNAJB12 functions. DNAJB12 could have higher affinity to recruit other factor needed for the reflux process and thus the impact of DNAJB12 is higher. In summary, neither DNAJB12 or DNAJB14 is essential in the single knockdown which means that they compensate for each other. In the overexpression experiment, it is enough to have the endogenous DNAJB14 for the DNAJB12 activity. When DNAJB14 is overexpressed at very high levels, we believe that it binds to some factors that are needed for proper DNAJB12 activity (Figure 4 showing that the WT-DNAJB14 inhibits ER-stress induced ER protein reflux when overexpressed). We believe that DNAJB14 is important because only when we knock both DNAJB12 and DNAJB14 we see an effect on the ER-protein reflux. DNAJB14 is part of a complex of DNAJB12/HSC70 and DSGTA.
(DNAJB12 is sufficient while DNAJB14 is not- please refer to point #8 above).
**Referees cross-commenting**
I agree with the comments raised by reviewer 1 about the manuscript. I also agree with the points written in this consultation session. In my opinion, the comments of reviewer 2 are phrased in a harsh tone and thus the reviewer reaches the conclusion that there are "serious" problems with this manuscript. However, I think that the authors could address many of the points of this reviewer in a matter of 3 months easily. For instance, it is easy to control for the expression levels of exogenous wild type and mutant D12 and compare it to the endogenous one (point 3). This is a very good point of this reviewer and I agree with this experiment. Likewise, it is easy to provide data about the levels of AGR2 to address the concern whether its synthesis is affected by D12 and D14 overexpression. Again, an excellent suggestion, but no reason for rejecting the story. As for not citing the literature, I think this can also easily be addressed and I am sure that this is just an oversight and no ill intention by the authors. __Overall, I am unable to see why the reviewer reaches such a negative verdict about this work. With proper revisions that might take 3 months, I think the points of all reviewers can be addressed. __
Reviewer #1 (Significance (Required)):
Significance: The strength of the work is that it provides further mechanistic insight into a novel cellular phenomenon (ERCYS). The functions for DNAJB12&14 are unprecedented and therefore of great interest for the proteostasis community. Potentially, the work is also of interest for cancer researchers, who might capitalize of the ERCYS to establish DNAJB12/14 as novel therapeutic targets. The major weaknesses are as follows: (i) the work is limited to a single cell line. To better probe the cancer relevance, the work should have used at least a panel of cell lines from one (or more) cancer entity. Ideally even data from patient derived samples would have been nice. Having said this, I also appreciate that the work is primarily in the field of cell biology and the cancer-centric work could be done by others. Certainly, the current work could inspire cancer specialists to explore the relevance of ERCYS. (ii) No physiological or pathological condition is shown where DNAJB12 is induced or depleted.
Answer: We previously showed that ERCYS is conserved in many different cell lines including A549, MCF7, GL-261, U87, HEK293T, MRC5 and others and is also conserved in murine models of GBM (GL-261 and U87 derived tumors) and human patients with GBM (Sicari et al. 2021). Here, we tested the reflux process and the IP experiments in many different cell lines including A549, MCF-7, PC3 and Trex-293 cells. We also added new fractionation experiment in DNAJB12 and DNAJB14 -depleted MCF-7, PC3 and A549 cells. We added all those data to the revised version.
We also added survival curves from the TCGA database showing that high copy number of DNAB12, DNAJB14 and SGTA are associated with poor prognosis compared to conditions where DNAJB12, DNAJB14, and SGTA are at low copy number (Figure S6E-M). Finally, we included immunofluorescent experiment to show that the interaction between the refluxed AGR2 and the cytosolic SGTA occurs in tumors collected from patients with colorectal cancer patients (Figure S5F-G) compared to non-cancerous tissue.
This study is highly significant and is relevant not only to cancer but for other pathways that may behave in similar manner. For instance, DNAJB12 and DNAJB14 are part of the mechanism that is used by non-envelope viruses to escape the ER to the cytosol. Thus, the role of those DNAJB proteins seems to be mainly in the reflux of functional (not misfolded) proteins from the ER to the cytosol. We reported earlier that the UDP-Glucose-Glucosyl Transferase 1 (UGGT1) is also expelled during ER stress. UGGT1 is important because it is redeploy to the cytosol during enterovirus A71 (EA71) infection to help viral RNA synthesis (Huang et al, 2017). This redeployment of EAA71 is similar to what happens during the reflux process because on one hand, UGGT1 exit the ER by an ER stress mediated process (Sicari et al. 2021) and it is also a functional in the cytosol as a proteins which help viral RNA synthesis ((Huang et al, 2017). All those data showing that there is more of DNAJB12, DNAJB14, DNAJC14, DNAJC30 and DNAJC18 that still needs to be explored in addition to what is published. Thus, we suggest that viruses hijacked this evolutionary conserved machinery and succeeded to use it in order to escape the ER to the cytosol in a manner that depends on all the component needed for ER protein reflux.
Reviewer #2 (Evidence, reproducibility and clarity (Required)):
The authors present a study in which they ascribe a role for a complex containing DNAJB12/14-Hsc70-SGTA in facilitating reflux of a AGR2 from the ER to cytosol during ER-stress. This function is proposed to inhibit wt-P53 during ER-stress.
Concerns: 1. The way the manuscript is written gives the impression that this is the first study about mammalian homologs of yeast HLJ1, while there are instead multiple published papers on mammalian orthologs of HLJ1. Section 1 and Figure 1 of the results section is redundant with a collection of previously published manuscripts and reviews. The lack of proper citation and discussion of previous literature prevents the reader from evaluating the results presented here, compared to those in the literature.
Answer: We highly appreciate the reviewer’s comments. This paper is not to show that DNAJB12 and DNAJB14 are the orthologues of HLJ-1 but rather to show that DNAJB12 and DNAJB14 are part of a mechanism that we recently discovered and called ERCYS that cause proteins to be refluxed out of the ER. A mechanism that is regulated in by HLJ-1 in yeast. ERCYS is an adaptive and pro-survival mechanism that results in increased chemoresistance and survival in cancer cells. The papers that reviewer #2 refer to are the ones that report DNAJB12 can replace some of the ER-Associated Degradation (ERAD) functions of HLJ-1 in degradation of membranal proteins such as CFTR. These two mechanism are totally different and the role of the yeast HLJ-1 in degradation of CFTR is not needed for ERCYS. This is because we previously showed that the role of the yeast HLJ-1 and probably its orthologues in ERCYS is independent of their activity in ERAD(Igbaria et al. 2019). Surprisingly, the role of HLJ-1 in refluxing the ER proteins is not only independent of the reported ERAD-functions of HLJ1 and the mammalian DNAJBs but rather proceeds more rigorously when the ERAD is crippled (Igbaria et al. 2019). This role of DNAJBs is unique in cancer cells and is responsible in regulating the activity of p53 during the treatment of DNA damage agents.
In our current manuscript we show by similarity, functionality, and topological orientation, that DNAJB12 and DNJB14 may be part of a well conserved mechanism to reflux proteins from the ER to the cytosol. A mechanism that is independent of DNAJB12/14’s reported activity in ERAD(Grove et al. 2011; Yamamoto et al. 2010; Youker et al. 2004). In addition, DNAJB12 and DNAJB14 facilitate the escape of non-envelope viruses from the ER to the cytosol in similar way to the reflux process(Goodwin et al. 2011; Igbaria et al. 2019; Sicari et al. 2021). All those data show that HLJ-1 reported function may be only the beginning of our understanding on the role that those orthologues carry and that are different from what is known about their ERAD function.
Action: We added the references to the main text and discussed the differences between the reported DNAJB12 and HLJ-1 functions to the function of DNAJB12, DNAJB14 and the other DNAJ proteins in the reflux process. We also developed around this in the discussion.
The conditions used to study DNAJB12 and DNAJ14 function in AGR2 reflux from the ER do not appear to be of physiological relevance. As seen below they involve two transfections and treatment with two cytotoxic drugs over a period of 42 hours. The assay for ERCY is accumulation of lumenal ER proteins in a cytosolic fraction. Yet, there is no data or controls that describe the path taken by AGR2 from the ER to cytosol. It seems like pleotropic damage to the ER due the experimental conditions and accompanying cell death could account for the reported results?
Transfection of cells with siRNA for DNAJB12 or DNAJB14 with a subsequent 24-hour growth period.
Transfection of cells with a p53-lucifease reporter.
Treatment of cells with etoposide for 2-hours to inhibit DNA synthesis and induce p53. D. Treatment of cells for 16 hours with tunicamycin to inhibit addition of N-linked glycans to secretory proteins and cause ER-stress.
Subcellular fractionation to determine the localization of AGR2, DNAJB11, and HYOU1
KD of DNAJB12 or DNAJB14 have modest if any impact on AGR2 accumulation in the cytosol. There is an effect of the double KD of DNAJB12 or DNAJB14 on AGR2 accumulation in the cytosol. Yet there are no western blots showing AGR2 levels in the different cells, so it is possible that AGR2 is not synthesized in cells lacking DNAJB12 and DNAKB14. The lack of controls showing the impact of single and double KD or DNAJB12 and DNAJB14 on cell viability and ER-homeostasis make it difficult to interpret the result presented. How many control versus siRNA KD cells survive the protocol used in these assays?
Answer: Despite the long protocol we see differences between the control cells and the DNAJB-silenced cells in terms of the quantity of the refluxed proteins to the cytosol. The luciferase construct was used to assess the activity of p53 so the step of the second transfection was used only in experiments were we assayed the p53-luciferase activity. The rest of the experiments especially those where we tested the levels of p53 and P21 levels, were performed with one transfection. Moreover, all the experiments with the subcellular protein fractionation were performed after one transfection without the second transfection of the p53-Luciferase reporter. Finally, the protocol of the subcellular protein fractionation requires first to trypsinize the cells to lift them up from the plates, at the time of the experiment the cells were almost at 70-80% confluency and in the right morphology under the microscope.
Here, we performed XTT assay and Caspase-3 assay to asses cell death at the end of the experiment and before the fractionation assay. We did not observe any differences at this stage between the different cell lines (Figure-RV1 for reviewers Only). This can be explained by the fact that we use low concentrations of Tm and Tg for short time of 16 hour after the pulse of etoposide.
Finally, the claim that and ER-membrane damage result in a mix between the ER and cytosolic components is not true for the following reasons: (1) In case of mixing we would expect that GAPDH levels in the membrane fraction will be increased and that we do not see, and (2) we used our previously described transmembrane-eroGFP (TM-eroGFP) that harbors a transmembrane domain and is attached to the ER membrane facing the ER lumen. The TM-eroGFP was found to be oxidized in all conditions tested. Those data argue against a rupture of the ER membrane which can results in a mix of the highly reducing cytosolic environment with the highly oxidizing ER environment by the passage of the tripeptide GSH from the cytosol to the ER. All those data argue against (1) cell death, and (2) rupture of the ER membrane. Figure RV1 Reviewers Only.
Moreover, as it is shown in Figure S2, AGR2 is found in the membrane fraction in all the four different knock downs, thus it is synthesized in all of them. Moreover, we assayed the mRNA levels of AGR2 in all the knockdowns and we so that they are at the same levels in all the 4 different conditions and still AGR2 mRNA levels increase upon ER stress in all of the 4 knockdown cells in different backgrounds (Figure S2F-N).
In Figure 3 the authors overexpress WT-D12 and H139Q-D12 and examine induction of the p53-reporter. There are no western blots showing the expression levels of WT-D12 and H139Q-D12 relative to endogenous DNAJB12. HLJ1 stands for high-copy lethal DnaJ1 as overexpression of HLJ1 kills yeast. The authors present no controls showing that WT-D12 and H139-D12 are not expressed at toxic levels, so the data presented is difficult to evaluate.
Answer: The expression levels of the overexpression of DNAJB12 and DNAJB14 were present in the initial submission of the manuscript as Figure S3A and S3B. The data showing the relationship between the expression degree and the viability were also included in the initial submission as Figure S3C (Now S3H).
There is no mechanistic data used to help explain the putative role DNAJB12 and DNAJB14 in ERCY? In Figure 4, why does H139Q JB12 prevent accumulation of AGR2 in the cytosol? There are no westerns showing the level to which DNAJB12 and DNAJB14 are overexpressed.
Answer: The data showing the levels of DNAJB12 compared to the endogenous were present in the initial submission as Figure S3A and S3B.
We suggest a mechanism by which DNAJB12 and DNAJB14 interact (Figure 5 and Figure S5) and oligomerize to expel those proteins in similar way to expelling non-envelope viruses to the cytosol. Thus, when expressing the mutant DNAJB12 H139Q may indicate that the J-domain dead-mutant can still be part of the complex but affects the J-domain activity in this oligomer and thus inhibit ER-protein reflux. In other words, we showed that the H139Q exhibits a dominant negative effect when overexpressed. Moreover, here we added another IP experiment in the D12/D14-DKD cells to show that in the absence of DNAJB12 and DNAJB14, SGTA cannot bind the ER-lumenal proteins because they are not refluxed (Figure 5 and Figure S5). Those data indicate that in order for SGTA bind the refluxed proteins they have to go through the DNAJB12 and DNAJB14 and their absence this interaction does not occur. This explanation was also present in the discussion of the initial submission.
Mechanistically, we show that AGR2 interacts with DNAJB12/14 which are necessary for its reflux. This mechanism involves the functionality of cytosolic HSP70 chaperones and their cochaperones (SGTA) proteins that are recruited by DNAJB12 and 14. This mechanism is conserved from yeast to mammals. Moreover, by using the alpha-fold prediction tools, we found that AGR2 is predicted to interact with SGTA in the cytosol by the interaction between the cysteines of SGTA and AGR2 in a redox-dependent manner.
**Referees cross-commenting**
__ __ I appreciate the comments of the other reviewers. I agree that the authors could revise the manuscript. Yet, based on my concerns about the physiological significance of the process under study and lack of scholarship in the original draft, I would not agree to review a revised version of the paper.
Answer: Regards the physiological relevance, we showed in our previous study (Sicari et al. 2021) how relevant is ERCYS in human patients of GBM and murine model of GBM. ERCYS is conserved from yeast to human and is constitutively active in GL-261 GBM model, U87 GBM model and human patients with GBM (Sicari et al. 2021). Here, extended that to other tumors and showed that DNAJB12, DNAJB14 and SGTA high levels are associated with poor prognosis in many cancer types (Figure S6). We also show some data from to show the relevance and added data showing the interaction of SGTA with AGR2 in CRC samples obtained from human patients compared to healthy tissue (Figure S5). This study is highly significant and is relevant not only to cancer but for other pathways that may behave in similar manner. For instance, DNAJB12 and DNAJB14 are part of the mechanism that is used by non-envelope viruses to escape the ER to the cytosol. Thus, the role of those DNAJB proteins seems to be mainly in the reflux of functional (not misfolded) proteins from the ER to the cytosol. We reported earlier that the UDP-Glucose-Glucosyl Transferase 1 (UGGT1) is also expelled during ER stress. UGGT1 is important because it is redeploy to the cytosol during enterovirus A71 (EA71) infection to help viral RNA synthesis (Huang et al, 2017). This redeployment of EAA71 is similar to what happens during the reflux process because on one hand, UGGT1 exit the ER by an ER stress mediated process (Sicari et al. 2021) and it is also a functional in the cytosol as a proteins which help viral RNA synthesis ((Huang et al, 2017). All those data showing that there is more of DNAJB12, DNAJB14, DNAJC14, DNAJC30 and DNAJC18 that still needs to be explored in addition to what is published. We suggest that viruses hijacked this evolutionary conserved machinery and succeeded to use it in order to escape.
We appreciate the time spent to review our paper and we are sorry that the reviewer reached such verdict that is also not understood by the other reviewers. Most of the points raised by reviewer 2 were already addressed and explained in the initial submission, anyways we appreciate the time and the comments of reviewer #2 on our manuscript.
Reviewer #2 (Significance (Required)):
Overall, there are serious concerns about the writing of this paper as it gives the impression that it is the first study on higher eukaryotic and mammalian homologs of yeast HLJ1. The reader is not given the ability to compare the presented data to related published work. There are also serious concerns about the quality of the data presented and the physiological significance of the process under study. In its present form, this work does not appear suitable for publication.
Answer: Again we thank reviewer #2 for giving us the opportunity to explain how significant is this manuscript especially for people who are less expert in this field. The significance of this paper (1) showing a the unique role of DNAJB12 and DNAJB14 in the molecular mechanism of the reflux process in mammalian cells (not their role in ERAD), (2) showing the implication of other cytosolic chaperones in the process including HSC70 and SGTA (3), our alpha-fold prediction show that this process may be redox dependent that implicate the cysteines of SGTA in extracting the ER proteins, (4) overexpression of the WT DNAJB12 is sufficient to drive this process, (5) mutation in the HPD motif prevent the reflux process probably by preventing the binding to the cytosolic chaperones, and (6) we need both DNAJB12 and DNAJB14 in order to make the interaction between the refluxed ER-proteins and the cytosolic chaperones occur.
In Summary, this study is highly significant in terms of physiology, we previously reported that ERCYS is conserved in mammalian cells and is constitutively active in human and murine tumors (Sicari et al. 2021). Moreover, DNAJB12 and DNAJB14 are part of the mechanism that is used by non-envelope viruses to escape the ER to the cytosol in a mechanism that is similar to reflux process (Goodwin et al. 2011; Goodwin et al. 2014). Thus, the role of those DNAJB proteins seems to be mainly in the reflux of functional proteins from the ER to the cytosol, viruses used this evolutionary conserved machinery and succeeded to use in order to escape. This paper does not deal with the functional orthologues of the HLJ-1 in ERAD but rather suggesting a mechanism by which soluble proteins exit the ER to the cytosol.
__Reviewer #3 (Evidence, reproducibility and clarity (Required)):____ __
Summary: Reflux of ER based proteins to the cytosol during ER stress inhibits wt-p53. This is a pro-survival mechanism during ER stress, but as ER stress is high in many cancers, it also promotes survival of cancer cells. Using A549 cells, Dabsan et al. demonstrate that this mechanism is conserved from yeast to mammalian cells, and identify DNAJB12 and DNAJB14 as putative mammalian orthologues of yeast HLJ1.
This paper shows that DNAJB12 and 14 are likely orthologues of HLJ1 based on their sequences, and their behaviour. The paper develops the pathway of ER-stress > protein reflux > cytosolic interactions > inhibition of p53. The authors demonstrate this nicely using knock downs of DNAJB12 and/or 14 that partially blocks protein reflux and p53 inhibition. Overexpression of WT DNAJB12, but not the J-domain inactive mutant, blocks etoposide-induced p53 activation (this is not replicated with DNAJB14) and ER-resident protein reflux. The authors then show that DNAJB12/14 interact with refluxed ER-resident proteins and cytosolic SGTA, which importantly, they show interacts with the ER-resident proteins AGR2, PRDX4 and DNAJB11. Finally, the authors show that inducing ER stress in cancer cell lines can increase proliferation (lost by etoposide treatment), and that this is partially dependent on DNAJB12/14.
This is a very interesting paper that describes a nice mechanism linking ER-stress to inhibition of p53 and thus survival in the face of ER-stress, which is a double edged sword regarding normal v cancerous cells. The data is normally good, but the conclusions drawn oversimplify the data that can be quite complex. The paper opens a lot of questions that the authors may want to develop in more detail (non-experimentally) to work on these areas in the future, or alternatively to develop experimentally and develop the observations further. There are only a few experimental comments that I make that I think should be done to publish this paper, to increase robustness of the work already here, the rest are optional for developing the paper further.
We thank the reviewer for his/her positive comments His/her comments contributed to make our manuscript stronger.
__Major comments:____ __
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Number of experimental repeats must be mentioned in the figure legends. Figures and annotations need to be aligned properly
__Answer____: __All experiments were repeated at least 3 times. We added the number of repeats on each figure in the figures legends
Results section 2:
No intro to the proteins you've looked at for relocalization. Would be useful to have some info on why you chose AGR2. Apart from them being ER-localized, do they all share another common characteristic? Does ability to inhibit p53 vary in potency?
Answer: We previously showed that AGR2 is refluxed from the ER to the cytosol to bind and inhibit wt-p53 (Sicari et al. 2021). Here, we used AGR2 because, (1) we know that AGR2 is refluxed from the ER to the cytosol, and (2) we know which novel functions it gains in the cytosol so we are able to measure and provide a physiological significance of those novel functions when the levels of DNAJB12 and DNAJB14 are altered. Moreover, we used DNAJB11 (41 kDa) and HYOU1 (150 kDa) proteins to show that alteration in DNAJB12 or DNAJB14 prevent the reflux small, medium and large sized proteins. We added a sentence in the discussion stating that DNAJB12/14 are responsible for the reflux of ER-resident proteins independently of their size. We also added in the result section that we are looking at proteins of different sizes and activities.
What are the roles of DNAJB12/14 if overexpression can induce reflux? Does it allow increased binding of an already cytosolic protein, causing an overall increase in an interaction that then causes inhibition of p53? What are your suggested mechanisms?
Answer: Previously it was reported that over-expression of DNAJB12 and DNAJB14 tend to form membranous structures within cell nuclei, which was designate as DJANGOS for DNAJ-associated nuclear globular structures(Goodwin et al. 2014). Because those structures which contain both DNAJB12 and DNAJB14 also form on the ER membrane (Goodwin et al. 2014), we speculate that during stress DNAJB12/14 overexpression may facilitate ERCYS. Interestingly, those structures contain Hsc70 and markers of the ER lumen, the nuclear and ER and nuclear membranes (Goodwin et al. 2014).
The discussion was edited accordingly to further strengthen and clarify this point
Fig3: A+B show overexpression of individual DNAJs but not combined. As you go on to discuss the effect of the combination on AGR2 reflux, it would be useful to include this experimentally here.
Answer: This is a great idea, we tried to do it for long time. Unfortunately when we used cells overexpress DNAJB12 under the doxycycline promoter and transfect with DNAJB14 plasmid expressing DNAJB14 under the CMV promoter, most of the cells float within 24 hours compared to cells transfected with the empty vector alone or with DNAJB14-H136Q. We also did overexpression of DNAJB14 in cells with DNAJB12 conditional expression and also were lethal in Trex293T cells and A549-cells.
Fig 3C: Subfractionation of cells shows AGR2 in the cytosol of A549 cells. The quality of the data is good but the bands are very high on the blot. For publication is it possible to show this band more centralized so that we are sure that we are not missing bands cut off in the empty and H139Q lanes?
Also, you have some nice immunofluorescence in the 2021 EMBO reports paper, is it possible to show this by IF too? It is not essential for the story, but it would enrich the figure and support the biochemistry nicely. Also it is notable that the membrane fraction of the refluxed proteins doesn't appear to have a decrease in parallel (especially for AGR2). Is this because the % of the refluxed protein is very small? Is there a transcriptional increase of any of them (the treatments are 12+24 h so it would be enough time)? This could be a nice opportunity to discuss the amount of protein that is refluxed, whether this response is a huge emptying of the ER or more like a gentle release, and also the potency of the gain of function and effect on p53 vs the amount of protein refluxed. This latter part isn't essential but it would be a nice element to expand upon.
Answer: We re-blotted the AGR2 again, new blot of AGR2 was added. More blots also are added in Figure S2, the text is edited accordingly.
In new Figure S5 we added immunofluorescence experiment from tumors and non-tumors tissues obtained from Colorectal cancer (CRC) patients showing that the interaction between SGTA and the refluxed AGR2 also occurs in more physiological settings. It is also to emphasize that the suggested mechanism that implicates SGTA is also valid in CRC tumors.
We performed qPCR experiments in control, DNAJB12-KD, DNAJB14-KD and in the DNAJB12/DNAJB14 double knock down cells (in both A549 and PC3 cells) to follow the mRNA levels of DNAJB11. As shown in the Figure S2F-N, there is no increase in the mRNA levels of DNAJB11, AGR2 or HYOU1 in the different cells in normal (unstressed conditions). Upon ER stress with tunicamycin or thapsigargin there is a little increase in the mRNA levels of HYOU1 and AGR2 but in DNAJB11 mRNA levels. On the other hand, we also performed western blot analysis and we did not detect any difference between the different knockdown cells when we analyzed the levels of DNAJB11 compared to GAPDH. Those data are now added to Figure S2F-N. We must note that in AGR2 and HYOU1 are induced at the mRNA as a result of ER stress. The data with the overexpression of DNAJB12 and DNAJB14 are important control experiment where we show a reflux when DNAJB12 is overexpressed without inducing the ER stress (Figure 3, Figure 4, and Figure S3). In those conditions no induction of AGR2, HYOU1 or DNAJB11 were observed. Those results argue against the reflux as a result of protein induction and the increase in the proteins levels.
The overall protein levels in steady state are function of how much proteins are made, degraded and probably secreted outside the cell. We do see in Figure S2 under ER stress there are some differences in the levels of the mRNA, moreover, from our work in yeast we showed that the expelled proteins have very long half-life in the cytosol (Igbaria et al. 2019). Because it is difficult to assay how many of the mRNA is translated and how much of it is stable/degraded and the stability of the cytosolic fraction vs the ER, it is hard to interpret on the stability and the levels of the proteins.
Those data are now added to the manuscript, the text is edited accordingly.
You still mention DNAJB12 and 14 as orthologues, even though DNAJB14 has no effect on p53 activity when overexpressed. Do you think that this piece of data diminishes this statement?
Answer: The fact that DNAJB12 and DNAJB14 are highly homologous and that only the double knockdown has a great effect on the reflux process may indicate that they are redundant. Moreover, because only DNAJB12 is sufficient may indicate that some of DNAJB12 function cannot be carried by DNAJB14. In one hand they share common activities as shown in the double knock down and on the other hand DNAJB12 has a unique function that may not be compensated by DNAJB14 when overexpressed.
__ __ Fig 3D/F: Overexpression of DNAJB14 induces reflux of DNAJB11 at 24h, what does this suggest? Does this indicate having the same role as DNAJB12 but less potently? What's your hypothesis?
Answer: ERCYS is new and interesting phenomenon and the redistribution of proteins to the cytosol has been documented lately by many groups. Despite that we still do not know what is the specificity of DNAJB12 and DNAJB14 to the refluxed proteins. DNAJB11 is glycosylated protein and now we are testing whether other glycosylated proteins prefer the DNAJB14 pathway or not. This data is beyond the scope of this paper
"This suggests that the two proteins may have different functions when overexpressed, despite their overlapping and redundant functions" What does it suggest about their dependence on each other? If overexpression of WT DNAJB12 inhibits Tg induced reflux, is it also blocking the ability of DNAJB14 to permit flux?
Answer: We hypothesize that it is all about the stichometry and the ratios between proteins. When we overexpress DNAJB14 (the one that is not sufficient to cause reflux it may hijack common components and factor by non-specifically binding to them. Those factors may be needed for DNAJB12 to function properly (Like the dominant negative effect of the DNAJB12-HPD mutant for instance). On the other hand, DNAJB12 may have higher affinity for some cytosolic partner and thus can do the job when overexpressed. Here, we deal with the DNAJB12/DNAJB14 as essential components of the reflux process, yet we need to identify the interactome of each of the proteins during stress and the role of the other DNAJ proteins that also share some of the topological and structural similarity to DNAJB12, DNAJB14 and HLJ-1 (DNAJC30, DNAJC14, and DNAJC18). We edited the text accordingly and integrated this in the discussion.
__ __ Fig 4: PDI shown in blots but not commented on in text. Then included in the schematics. Please comment in the text.
Answer: We commented PDI in the text.
Fig 4F: Although the quantifications of the blots look fine, the blot shown does not convincingly demonstrate this data for AGR2. The other proteins look fine, but again it could be useful to see the individual means for each experiment, or the full gels for all replicates in a supplementary figure.
Answer: the other two repeats are in Figure S4
__ __Results section 3
Fig 5A, As there is obviously a difference between DNAJB12/14 it would be useful to do the pulldown with DNAJB14 too. Re. HSC70 binding to DNAJB12 and 14, the abstract states that DNAJB12/14 bind HSC70 and SGTA through their cytosolic J domains. Fig 5 shows pulldowns of DNAJB12 with an increased binding of SGTA in FLAG-DNAJB12 induced conditions, but the HSC70 band does not seem to be enriched in any of the conditions, including after DNAJB12 induction. This doesn't support the statement that DNAJB12 binds HSC70. In fact, in the absence of a good negative control, this would suggest that the HSC70 band seen is not specific. There is also no data to show that DNAJB14 binds HSC70. I recommend including a negative condition (ie beads only) and the data for DNAJB14 pulldown.
Answer: In Figure 5A we used the Flp-In T-REx-293 cells as it is easier to control and to tune up and down the expression levels of DNAJB12 and DNAJB14. According to new Figure S5A, DNAJB12 binds at the basal levels to HSC70 all the time. It was also surprising for us not to see the differences in the overexpression and we relate that to the fact that all the HSC70 are saturated with DNAJB12. In order to better assay that we repeated the IP in Figure 5A but instead of the IP with DNAJB12, we IP-ed with FLAG antibodies to selectively IP the transfected DNAJB12. As shown in the new Fig 5A, the increase of DNAJB12-FLAG is accompanied with an increase in the binding of HSC70.
We further tested the interaction between DNAJB12, DNAJB14 and HSC70 during ER stress in cancer cells. In those cells we found that DNAJB12 and DNAJB14 bind to HSC70 and they recruit SGTA upon stress. We also tested the binding between DNAJB12 and DNAJB14, in unstressed conditions, there was a basal binding between both, this interaction was stronger during ER stress. Those data are now added to Figure 5 and Figure S5 and the discussion was edited accordingly.
The binding of DNAJB12 to SGTA under stress conditions in Fig5B looks much more convincing than SGTA to DNAJB12 in Fig 5A. Bands in all blots need to be quantified from 3 independent experiments, and repeated if not already n=3. If this is solely a technical difference, please explain in the text.
The conclusions drawn from this interaction data are important and shold be elaborated upon to support th claims made in the paper. The authors may also chose to expand the pulldowns to demonstrate their claims made on olidomerisation of DNAJB12 and 14 here. It is also clear that the interaction data of the SGTA with ER-resident proteins AGR2, PRDX4 and DNAJB11 is strong. The authors may want to draw on this in their hypotheses of the mechanism. I would imagine a complex such as DNAJB14/DNAJB12 - SGTA - AGR2/PRDX4/DNAJB11 would be logical. Have any experiments been performed to prove if complexes like this would form?
Answer: In Figure 5A we used the Flp-In T-REx-293 cells as it is easier to control and to tune up and down the expression levels of DNAJB12 and DNAJB14. T-REx-293 are highly sensitive to ER stress, they do not die (as we did not observe apoptosis markers to be elevated) but they float and can regrow after the stress is gone. In Figure 5B we are using ER stress without the need to express DNAJB12 in A549 cell line. In order to further verify those data, we repeated the IP in another cell line as well to confirm the data in 5B. We also repeated the IP in 5A with anti-FLAG antibody to improve the IP and to specifically map he interaction with the overexpressed FLAG-DNAJB12 (discussed above). All experiments were done in triplicates and added to Figure 5 and Figure S5.
We agree with the reviewer on the complex between the refluxed proteins and SGTA. We believed that SGTA may form a complex with other refluxed ER-proteins but we were unable to see an interaction between AGR2-DNAJB11 in the cytosolic fraction or between AGR2-PRDX4 in the conditions tested in the cytosolic fraction. We could not do this in the whole cell lysate because those proteins bind each other in the ER. Finally, our structural prediction using Alpha-fold suggests that the interaction between SGTA and the refluxed AGR2 (and probably others) is redox depending and that it requires disulfide bridge between cysteine 81 on AGR2 and cysteine 153 on SGTA. Thus, we hypothesize that SGTA binds one refluxed protein at the time.
We repeated the figure with improvement: (1) using more cells in order to increase the amount of IP-ed proteins and to overcome the problem of the faint bands, (2) performing the IP with the FLAG antibodies instead of the DNAJB12 endogenous antibodies.
Fig 5B: It is clear that DNAJB12 interacts with SGTA. The authors state that DNAJB14 also interacts with SGTA under normal and stress conditions, but the band in 25/50 Tg is very feint. Why would there be stronger binding at the 2 extremes than during low stress induction? In the input, there is a much higher expression of DNAJB14 in 50 Tg. What does this say about the interaction? Is there an effect of ER stress on DNAJB14 expression? A negative control should be included to show any background binding, such as a "beads only" control
__Answer: __DNAJB14 does not change with ER stress as shown in the Ips (Input) and in the qPCR experiment in Figure S5I. We added beads only control, we also added new Ips to assess the binding between DNAJB14 and DNAJB12, and between DNAJB14-SGTA. All the new Ips and controls now added as Figure 5 and Figure S5.
Fig 5C data is sound, although a negative control should be included.
Answer: Negative control was added in Figure S5.
__Results section 4____ __
Fig 6A-B: Given that there is the complexity of overexpression v KD of DNAJB12 v 14 causing similar effects on p53 actvity (Fig 2 v 3), it would be interesting to see whether the effect of overexpression mirrors the results in Fig 6A. Is it known what SGTA overexpression does (optional)?
Answer: In the overexpression system, cells overexpressing DNAJB12 start to die between 24-48 hours as shown in Figure S3C. Thus, it is difficult to assay the proliferation of these cells in those conditions. On the other hand, overexpression of Myc-tagged SGTA in A549 cells, MCF7 or T-ReX293 did not show any reflux of ER-proteins to the cytosol and it didn’t show any significant changes in the proliferation index (Figure Reviewers only RV2).
Fig 6D: resolution very low
Answer: Figure 6D was changed
__ __ Fig 6C-D: There is an interesting difference though between the proposed cytosolic actions of the refluxed proteins. You show that AGR2, PRDX4 and DNAJB11 all bind to SGTA in stress conditions, but in the schematics you show: DNAJB11 binding to HSC70 through SGTA (not shown in the paper), then also PDIA1, PDIA3 binding to SGTA and AGR2 binding to SGTA. What role does SGTA have in these varied reactions? Sometimes it is depicted as an intermediate, sometimes a lone binder, what is its role as a binder? It should be clarified which interactions are demonstrated in the paper (or before) and which are hypothesized in a graphical way (eg. for hypotheses dotted outlines or no solid fill etc). The schematics also suggest that DNAJB14 binding to HSC70 and SGTA is inducible in stress conditions, as is PDIA3, which is not shown in the paper. Discussion "In cancer cells, DNAJB12 and DNAJB14 oligomerize and recruit cytosolic chaperones and cochaperones (HSC70 and SGTA) to reflux AGR2 and other ER-resident proteins and to inhibit wt-p53 and probably different proapoptotic signaling pathways (Figure 5, and Figure 6C-6D)." You havent shown oligomerisation between DNAJB12/14. Modify the text to make it clear that it is a hypothesis.
Answer: We removed “oligomerize” from the text and added that it as a hypothesis. Figure (C-D) also were changed to be compatible with the text.
Minor comments:
__ __ It would be useful to have page or line numbers to help with document navigation, please include them. Typos and inconsistency in how some proteins are named throughout the manuscript
Answer: Page numbers and line numbers are added. Typos are corrected
Title: Include reference to reflux. Suggest: "chaperone complexes (?proteins) reflux from the ER to cytosol..." I presume it would be more likely that the proteins go separately rather than in complex. Do you have any ideas on the size range of proteins that can undergo this process?
Answer: this is true, proteins may cross the ER membrane separately and then be in a complex with cytosolic chaperones. The title is changed accordingly. As discussed earlier, the protein we chose were of different sizes to show that they are refluxed independently of their size. Moreover, our previous work showed that the proteins that were refluxed are of different sizes. Most importantly UGGT1 (around 180 Kda) which is reported to deploy to the cytosol upon viral infection (Huang et al. 2017; Sicari et al. 2020). In this study we used AGR2 (around 19 Kda) and HYOU1 (150Kda).
ERCY in abstract, ERCYS in intro. There are typos throughout, could be a formatting problem, please check
Answer: Checked and corrected
What about the selection of refluxed proteins? Is this only a certain category of proteins? Could it be anything? Have you looked at other cargo / ER resident proteins?
__ ____Answer: __in our previous study by (Sicari, Pineau et al. 2020) we looked at many other proteins especially glycoproteins from the ER. In (Sicari, Pineau et al. 2020) we used mass spectrometry in order to identify new refluxed proteins and we found 26 new glycoprotein that are refluxed from cells treated with ER stressor and from human tissues obtained from GBM patients (Sicari, Pineau et al. 2020).
We previously showed that AGR2 is refluxed from the ER to the cytosol to bind and inhibit p53 (Sicari, Pineau et al. 2020). Here, we selected AGR2 because we know that (1) it is refluxed, and (2) we know which novel functions it acquires in the cytosol so we are able to measure and provide a physiological significance of those novel functions when the levels of DNAJB12 and DNAJB14 are altered. Moreover, we selected DNAJB11 (41 kDa) and HYOU1 (150 kDa) proteins to show that alteration in DNAJB12 or DNAJB14 prevent the reflux small, medium and large protein (independently of their size). We also showed earlier by mass spectrometry analysis that the refluxed proteins range from small to very large proteins such as UGGT1, thus we believe that soluble ER-proteins can be substrates of ERCYS independently of their size. In the discussion, we added a note that the reflux by the cytosolic and ER chaperones operates on different proteins independently of their size.
"Their role in ERCYS and cells' fate determination depends..." Suggest change to "Their role in ERCYS and determination of cell fate..."
Answer: changed and corrected
I think that the final sentence of the intro could be made stronger and more concise. There's a repeat of ER and cytosol. Instead could you comment on the reflux permitting new interactions between proteins otherwise spatially separated, then the effect on wt-p53 etc.
Answer: The sentence was rephrased as suggested to “ In this study, we found that HLJ1 is conserved through evolution and that mammalian cells have five putative functionality orthologs of the yeast HLJ1. Those five DNAJ- proteins (DNAJB12, DNAJB14, DNAJC14, DNAJC18, and DNAJC30) reside within the ER membrane with a J-domain facing the cytosol (Piette et al. 2021; Malinverni et al. 2023). Among those, we found that DNAJB12 and DNAJB14, which are strongly related to the yeast HLJ1 (Grove et al. 2011; Yamamoto et al. 2010), are essential and sufficient for determining cells' fate during ER stress by regulating ERCYS. Their role in ERCYS and determining cells' fate depends on their HPD motif in the J-domain. Downregulation of DNAJB12 and DNAJB14 increases cell toxicity and wt-p53 activity during etoposide treatment. Mechanistically, DNAJB12 and DNAJB14 interact and recruit cytosolic chaperones (HSC70/SGTA) to promote ERCYS. This later interaction is conserved in human tumors including colorectal cancer.
In summary, we propose a novel mechanism by which ER-soluble proteins are refluxed from the ER to the cytosol, permitting new inhibitory interactions between spatially separated proteins. This mechanism depends on cytosolic and ER chaperones and cochaperones, namely DNAJB12, DNAJB14, SGTA, and HSC70. As a result, the refluxed proteins gain new functions to inhibit the activity of wt-p53 in cancer cells. “
__Figure legends: __
In some cases the authors state the number of replicates, but this should be stated for all experiments. If experiments don't already include 3 independent repeats, this should be done. Check text for typos, correct letter capitalisation, spaces and random bold text (some of this could be from incompatability when saving as PDF)
Answer: all experiments were repeated at least three times. The number of repeats is now indicated in the figure legends of each experiment. Typos and capitalization is corrected as well.
Fig2E: scrambled not scramble siRNA
Answer: corrected
Fig 3: "to expel" is a term not used in the rest of the paper for reflux. Useful to remain consistent with terminology where possible
Answer: Rephrased and corrected
Results section 1:
"Protein alignment of the yeast HLJ1p showed high amino acids similarity to the mammalian..."
Answer: Rephrased to “ Comparing the amino acid sequences revealed significant similarity between the yeast protein HLJ1p and the mammalian proteins DNAJB12 and DNAJB14”
__ __ Fig 1C: state in legend which organism this is from (presumably human)
Answer: in Figure 1C legends it is stated that: “ the HPD motif within the J-domain is conserved in HLJ-1 and its putative human orthologs DNAJB12, DNAJB14, DNAJC14, DNAJC18, and DNAJC30.”
Results Section 2
"Test the two strongest hits DNAJB12/14" Add reference to previous paper showing this
Answer: the references were added.
__ __ "In the WT and J-protein-silenced A549 cells, there were no differences in the cytosolic enrichment of the three ER resident proteins AGR2, DNAJB11, and HYOU1 in normal and unstressed conditions (Figure 2A-C and Figure S2C)." I think that this is an oversimplification, and in your following discussion, you show this it's more subtle than this.
Answer: We expanded on this both in the discussion and the results section.
__ __ The text here isn't so clear: normal and unstressed conditions? Do you mean stressed? Please be careful in your phrases: "DNAJB12-silenced cells are slightly affected in AGR2 and DNAJB11 cytosolic accumulation but not HYOU1." This is the wrong way around. DNAJB12 silencing effects AGR2, not that AGR2 effects the cells (which is how you have written it). This also occurs agan in the next para:
Answer: Normal cells are non-cancer cells. Unstressed conditions= without ER stress. The sentence was rephrased to: In the absence of ER stress, the cytosolic levels of the three ER-resident proteins (AGR2, DNAJB11, and HYOU1) were similar in wild-type and J-protein-silenced A549 cells.
"During stress, DNAJB12/DNAJB14 double knockdown was highly affected in the cytosolic..." I think you mean it highly affected the cytosolic accumulation, not that it was affected by the cytosolic accumulation. Please change in the text
Answer: the sentence is now rephrased to” During stress, double knockdown of DNAJB12 and DNAJB14 highly affected the cytosolic accumulation of all three tested proteins”
__ __ "DNAJB12 and DNAJB14 are strong hits of the yeast HLJ1" Not clear, I presume you mean they are likely orthologues? Top candidates for being closest orthologues?
Answer: this is correct, the sentence is rephrased and corrected
__ __ Fig 2D: typos in WB labelling? I think Tm should be - - +, not - + +as it is now (if it's not a typo, you need more controls, eto alone.
Answer: the labeling is now corrected
Fig 2D-E-F typos for DKD? D12/D12 or D12/14?
Answer: This is correct, thank you for pointing this out. The labeling in corrected
__ __ "We assayed the phosphorylation state of wt- p53 and p21 protein expression levels (a downstream target of p53 signaling) during etoposide treatment." What are the results of this? Explain what Fig 2D-E shows, then build on this with the +Tm results. Results should be explained didactically to be clear.
Answer: The paragraph was edited and we explained the results: In these conditions, we saw an increase in the phosphorylation of wt-p53 in the control cells and in cells knocked-down with DNAJB12, DNAJB14 or both. This phosphorylation increased the protein levels of p21 as well (Figure 2D-G). Tm addition to cells treated with etoposide resulted in a reduction in wt-p53 phosphorylation, and as a consequence, the p21 protein levels were also decreased (Figure 2D-G and Figure S2O). Cells lacking DNAJB12 or DNAJB14 have partial protection in wt-p53 phosphorylation and p21 protein levels. Silencing both proteins in A549 and MCF7 cells rescued wt-p53 phosphorylation and p21 levels (Figure 2D-G and Figure S2D). Moreover, similar results were obtained when we assayed the transcriptional activity of wt-p53 in cells transfected with a luciferase reporter under the p53-DNA binding site (Figure 2H). These data confirm that DNAJB12 and DNAJB14 are involved in ER protein reflux and the inhibition of wt-p53 activity during ER stress.
"(Figure 2D- E). Cells lacking DNAJB12 and or DNAJB14 have partial protection in wt-p53 phosphorylation and p21 protein levels."
Answer: This sentence is now removed
You comment on p53 phosphorylation, but you haven't quantified this. This should be done, normalized to p53 levels, if you want to draw these conclusions, especially as total p53 varies between condition. Does Eto increase p53 txn? Does Tm alone increase p53 activity/phospho-p53? These are shown in the Sicari EMBO reports paper in 2021, you should briefly reference those.
Answer: The blots are now quantified and new blot is added to Figure S2D. The Paragraph was edited and referenced to our previous paper (Sicari et al. 2021). “We then wanted to examine whether the gain of function of AGR2 and the inhibition of wt-p53 depends on the activity of DNAJB12 and DNJAB14. We assayed the phosphorylation state of wt-p53 and p21 protein expression levels (a downstream target of wt-p53 signaling) during etoposide treatment. In these conditions, there was an increase in the phosphorylation of wt-p53 in the control cells and in cells knocked down with DNAJB12, DNAJB14, or both. This phosphorylation also increases protein levels of p21 (Figure 2D-G and Figure S2O). Tm addition to cells treated with etoposide resulted in a reduction in wt-p53 phosphorylation, and as a consequence, the p21 protein levels were also decreased (Figure 2D-G and Figure S2O). Silencing DNAJB12 and DNAJB14 in A549 and MCF-7 cells rescued wt-p53 phosphorylation and p21 levels (Figure 2D-G and Figure S2O). Moreover, similar results were obtained when we assayed the transcriptional activity of wt-p53 in cells transfected with a luciferase reporter under the p53-DNA binding site (Figure 2H). In the latter experiment, etoposide treatment increased the luciferase activity in all the cells tested. Adding ER stress to those cells decreased the luciferase activity except in cells silenced with DNAJB12 and DNAJB14.
These data confirm that DNAJB12 and DNAJB14 are involved in the reflux of ER proteins in general and AGR2 in particular. Inhibition of DNAJB12 and DNAJB14 prevented the inhibitory interaction between AGR2 and wt-p53 and thus rescued wt-p53 phosphorylation and its transcriptional activity as a consequence. “
Fig3A: overexpression of DNAJB12 decreases Eto induced p53 but not at steady state. Is this because at steady state the activity is already basal? Or is there another reason?
Answer: yes, at steady state the activity is already basal
Switch Figs S3D and S3C as they are not referred to in order. Also Fig S3C: vary colour (or add pattern) on bars more between conditions
Answer: The Figures now are called by their order in the new version. Colors are now added to Figure S3C.
Need to define HLJ1 at first mention
Answer: defined as” HLJ1 - High copy Lethal J-protein -an ER-resident tail-anchored HSP40 cochaperone.
Results section 3
HSC70 cochaperone (SGTA) defined twice
Answer: the second one was removed
"These data are important because SGTA and the ER-resident proteins (PRDX4, AGR2, and DNAJB11) are known to be expressed in different compartments, and the interaction occurs only when those ER-resident proteins localize to the cytosol." Is there a reference for this?
Answer: Peroxireoxin 4 is the only peroxerodin that is expressed in the ER. AGR2 and DNAJB11 are also ER luminal proteins that are known to be solely expressed in the ER. SGTA is part of the cytosolic quality control system and is expressed in the cytosol. The references are added in the main text.
Results section 4
"by almost two folds"
Answer: corrected
Fig 6A: It seems strange that the difference between purple and blue bars in scrambled, and D14-KD are very significant but D12-KD is only significant. Why is this? The error bars don't look that different. It would be interesting to see the individual means for each different replicate.
Answer: Thank you for pointing this, the two asterixis were aligned in the middle as one during figure alignments. In D14 the purple one has a lower error bar thus this changes the significance when compared to the blue while in D12-KD, the error bars in the eto treatment and the eto-Tm both are slightly higher. Graphs of the three different replicates are now added in Figure S6. Each one of the three biological replicates was repeated in three different technical repeats (averaged in the graphs).
Figures: Fig 6A: Scale bars not well placed. Annotation on final set should be D12/D14 DKD?
Answer: both were Corrected
__Discussion __47. The authors mention that they want to use DNAJB12/4-HSC70/SGTA axis to impair cancer cell fitness: What effect would this have though in a non cancer model? Would this be a viable approach Although it is obviously early days, which approach would the authors see as potentially favorable?
Answer: In our previous study we used an approach to target AGR2 in the cytosol because the reflux of AGR2 occurs only in cancer cells and not in normal cells. In that study we targeted AGR2 with scFv that targets AGR2 and is expressed in the cytosol, in this case it will target AGR2 in the cytosol which only occurs in cancer. Here, we suggest to target the interaction between the refluxed proteins and their new partners in the cytosol or to target the mechanism that causes their reflx to the cytosol by inhibiting for instance the interaction between SGTA and DNAJB proteins.
__ __ Second para: Should be "Here we present evidences"
Answer: we replaced with “Here we present evidences”
"DNAJB12 overexpression was also sufficient to promote ERCYS by refluxing AGR2 and inhibit wt-p53 signaling in cells treated with etoposide" Suggest:
Answer: DNAJB12 overexpression is also sufficient to promote ERCYS by refluxing AGR2 and inhibit wt-p53 signaling in cancer cells treated with etoposide (Figure 3). This suggests that it is enough to increase the levels of DNAJB12 without inducing the unfolded protein response in order to activate ERCYS. Moreover, the downregulation of DNAJB12 and DNAJB14 rescued the inhibition of wt-p53 during ER stress (Figure 2). Thus, wt-p53 inhibition is independent of the UPR activation but depends on the inhibitory interaction of AGR2 with wt-p53 in the cytosol.
.
DNAJB12 overexpression was also sufficient to promote ERCYS by increasing reflux of AGR2 and inhibition of wt-p53 signaling in cells treated with etoposide
Answer: This sentence is repeated twice and was removed
"Moreover, DNAJB12 was sufficient to promote this phenomenon and cause ER protein reflux by mass action without causing ER stress (Figure 3, Figure 4, and Figure S3)." You dont look at induction of ER stress here, please change the text or explain in more depth with refs if suitable
Answer: In the initial submission and in the revised version we assayed the activation of the UPR by looking at the levels of spliced Xbp1 and Bip in the different conditions when DNAJB12 and DNAJB14 are overexpressed (Figure S3C and S3D). Our data show that although DNAJB12 overexpression induces ERCYS, there was no UPR activation.
The mention of viruses is sparse in this paper. If it is a main theory, put it more centrally to the concept, and explain in more detail. As it is, its appearance in the final sentence is out of context.
Answer: DNAJB12 and DNAJB14 were reported to facilitate the escape of non-envelope viruses from the endoplasmic reticulum to the cytosol. The mechanism of non-envelope penetration is highly similar to the reflux of proteins from the ER to the cytosol. Interestingly, this mechanism takes place when the DNAJB12 and DNAJB14 form a complex with chaperones from both the ER and the cytosol including HSC70, SGTA and BiP (Walczak et al. 2014; Goodwin et al. 2011; Goodwin et al. 2014)..
Moreover, the UGGT1 that was independently found in our previous mass spectrometry analysis of the digitonin fraction obtained from HEK293T cells treated with the ER stressor thapsigargin and from isolated human GBM tumors (Sicari et al. 2020), is known to deploy to the cytosol upon viral infection (Huang et al. 2017; Sicari et al. 2020). We therefore hypothesized that the same machinary that is known to allow viruses to escape the ER to penetrate the cytosol may play an important role in the reflux of ER proteins to the cytosol.
Because ER protein reflux and the penetration of viruses from the ER to the cytosol behave similarly, we speculate that viruses hijacked an evolutionary conserved machinery -ER protein reflux- to penetrate to the cytosol. This is key because it was also reported that during the process of nonenveloped viruses penetration, large, intact and glycosylated viral particles are able to penetrate the ER membrane on their way to the cytosol (Inoue and Tsai 2011).
Action: we developed the discussion around this point and clarified it better because we believe it central to show that viruses hijacked this conserved mechanism.
**Referees cross-commenting**
I agree with the comments from Reviewer 1.
Reviewer 2 also is correct in many ways, but I think that they have somewhat overlooked the relevance of the ER-stress element and treatments. The authors do need to reference past papers more to give a full story, as this includes the groups own papers, I don't think that it is an ethical problem but rather an oversight in the writing. Regarding reviewer 2's concerns about overexpression levels and cell death, the authors do use an inducible cell line and show the levels of DNAJB12 induced (could CRISPR also be considered?). This could be used to further address reviewer 2's concerns. It would also be useful to see data on cell death in the conditions used in the paper. Re concerns about ER integrity, this could be addressed by using IF (or EM) to show a secondary ER marker that remains ER-localised, and this would also be of interest regarding my comment on which categories of proteins can undergo reflux. If everything is relocalised, then reviewer 2's point would be validated.
Reviewer #3 (Significance (Required)):
Significance
General assessment: This paper robustly shows that the yeast system of ER to cytosol reflux of ER-resident proteins is conserved in mammalian cells, and it describes clearly the link between ER stress, protein reflux and inhibition of p53 in mammalian cells. The authors have the tools to delve deeper into this mechanism and robustly explore this pathway, however the mechanistic elements - where not instantly clear from the results - have been over interpreted somewhat The results have been oversimplified in their explanations and some points and complexities of the study need to be addressed further to make the most of them - these are often some of the more interesting concepts of the paper, for example the differences in DNAJB12/14 and how the proteins orchestrate in the cytosol to play their cytosol-specific effects. I think that many points can be addressed in the text, by the authors being clear and concise with their reporting, while other experiments would turn this paper from an observational one, into a very interesting mechanistic one.
Advance: This paper is based on previous nice papers from the group. It is a nice progressions from yeast, to basic mechanism, to physiological model. But as mentioned, without a strong mechanistic improvement, the paper would remain observatory.
Audience: This paper is interesting to cell biologists (homeostasis, quality control and trafficking) as well as cancer cell biologists (fitness of cancer cells and homeostasis) and it is a very interesting demonstration of a process that is a double edged sword, depending on the environment of the cells.
My expertise: cell biology, trafficking, ER homeostasis
Answer: We would like to thank the reviewer for his/her positive feedback on our manuscript. All the comments of the three reviewers are now addressed and the manuscript has been strengthen. We put more emphasis on the mechanistic aspect with more Ips and knockdowns. We also added data to show that it is physiologically relevant. We hope that after that the revised version addressed all the concerns raised by the reviewers.
Goodwin, E. C., A. Lipovsky, T. Inoue, T. G. Magaldi, A. P. Edwards, K. E. Van Goor, A. W. Paton, J. C. Paton, W. J. Atwood, B. Tsai, and D. DiMaio. 2011. 'BiP and multiple DNAJ molecular chaperones in the endoplasmic reticulum are required for efficient simian virus 40 infection', MBio, 2: e00101-11.
Goodwin, E. C., N. Motamedi, A. Lipovsky, R. Fernandez-Busnadiego, and D. DiMaio. 2014. 'Expression of DNAJB12 or DNAJB14 causes coordinate invasion of the nucleus by membranes associated with a novel nuclear pore structure', PLoS One, 9: e94322.
Grove, D. E., C. Y. Fan, H. Y. Ren, and D. M. Cyr. 2011. 'The endoplasmic reticulum-associated Hsp40 DNAJB12 and Hsc70 cooperate to facilitate RMA1 E3-dependent degradation of nascent CFTRDeltaF508', Mol Biol Cell, 22: 301-14.
Huang, P. N., J. R. Jheng, J. J. Arnold, J. R. Wang, C. E. Cameron, and S. R. Shih. 2017. 'UGGT1 enhances enterovirus 71 pathogenicity by promoting viral RNA synthesis and viral replication', PLoS Pathog, 13: e1006375.
Igbaria, A., P. I. Merksamer, A. Trusina, F. Tilahun, J. R. Johnson, O. Brandman, N. J. Krogan, J. S. Weissman, and F. R. Papa. 2019. 'Chaperone-mediated reflux of secretory proteins to the cytosol during endoplasmic reticulum stress', Proc Natl Acad Sci U S A, 116: 11291-98.
Inoue, T., and B. Tsai. 2011. 'A large and intact viral particle penetrates the endoplasmic reticulum membrane to reach the cytosol', PLoS Pathog, 7: e1002037.
Malinverni, D., S. Zamuner, M. E. Rebeaud, A. Barducci, N. B. Nillegoda, and P. De Los Rios. 2023. 'Data-driven large-scale genomic analysis reveals an intricate phylogenetic and functional landscape in J-domain proteins', Proc Natl Acad Sci U S A, 120: e2218217120.
Piette, B. L., N. Alerasool, Z. Y. Lin, J. Lacoste, M. H. Y. Lam, W. W. Qian, S. Tran, B. Larsen, E. Campos, J. Peng, A. C. Gingras, and M. Taipale. 2021. 'Comprehensive interactome profiling of the human Hsp70 network highlights functional differentiation of J domains', Mol Cell, 81: 2549-65 e8.
Sicari, D., F. G. Centonze, R. Pineau, P. J. Le Reste, L. Negroni, S. Chat, M. A. Mohtar, D. Thomas, R. Gillet, T. Hupp, E. Chevet, and A. Igbaria. 2021. 'Reflux of Endoplasmic Reticulum proteins to the cytosol inactivates tumor suppressors', EMBO Rep: e51412.
Sicari, Daria, Raphael Pineau, Pierre-Jean Le Reste, Luc Negroni, Sophie Chat, Aiman Mohtar, Daniel Thomas, Reynald Gillet, Ted Hupp, Eric Chevet, and Aeid Igbaria. 2020. 'Reflux of Endoplasmic Reticulum proteins to the cytosol yields inactivation of tumor suppressors', bioRxiv.
Walczak, C. P., M. S. Ravindran, T. Inoue, and B. Tsai. 2014. 'A cytosolic chaperone complexes with dynamic membrane J-proteins and mobilizes a nonenveloped virus out of the endoplasmic reticulum', PLoS Pathog, 10: e1004007.
Yamamoto, Y. H., T. Kimura, S. Momohara, M. Takeuchi, T. Tani, Y. Kimata, H. Kadokura, and K. Kohno. 2010. 'A novel ER J-protein DNAJB12 accelerates ER-associated degradation of membrane proteins including CFTR', Cell Struct Funct, 35: 107-16.
Youker, R. T., P. Walsh, T. Beilharz, T. Lithgow, and J. L. Brodsky. 2004. 'Distinct roles for the Hsp40 and Hsp90 molecular chaperones during cystic fibrosis transmembrane conductance regulator degradation in yeast', Mol Biol Cell, 15: 4787-97.
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Referee #3
Evidence, reproducibility and clarity
Summary:
Reflux of ER based proteins to the cytosol during ER stress inhibits wt-p53. This is a pro-survival mechanism during ER stress, but as ER stress is high in many cancers, it also promotes survival of cancer cells. Using A549 cells, Dabsan et al. demonstrate that this mechanism is conserved from yeast to mammalian cells, and identify DNAJB12 and DNAJB14 as putative mammalian orthologues of yeast HLJ1.
This paper shows that DNAJB12 and 14 are likely orthologues of HLJ1 based on their sequences, and their behaviour. The paper develops the pathway of ER-stress > protein reflux > cytosolic interactions > inhibition of p53. The authors demonstrate this nicely using knock downs of DNAJB12 and/or 14 that partially blocks protein reflux and p53 inhibition. Overexpression of WT DNAJB12, but not the J-domain inactive mutant, blocks etoposide-induced p53 activation (this is not replicated with DNAJB14) and ER-resident protein reflux. The authors then show that DNAJB12/14 interact with refluxed ER-resident proteins and cytosolic SGTA, which importantly, they show interacts with the ER-resident proteins AGR2, PRDX4 and DNAJB11. Finally, the authors show that inducing ER stress in cancer cell lines can increase proliferation (lost by etoposide treatment), and that this is partially dependent on DNAJB12/14.
This is a very interesting paper that describes a nice mechanism linking ER-stress to inhibition of p53 and thus survival in the face of ER-stress, which is a double edged sword regarding normal v cancerous cells. The data is normally good, but the conclusions drawn oversimplify the data that can be quite complex. The paper opens a lot of questions that the authors may want to develop in more detail (non-experimentally) to work on these areas in the future, or alternatively to develop experimentally and develop the observations further. There are only a few experimental comments that I make that I think should be done to publish this paper, to increase robustness of the work already here, the rest are optional for developing the paper further.
Major comments:
- Number of experimental repeats must be mentioned in the figure legends. Figures and annotations need to be aligned properly
Results section 2: 2. No intro to the proteins you've looked at for relocalisation. Would be useful to have some info on why you chose AGR2. Apart from them being ER-localised, do they all share another common characteristic? Does ability to inhibit p53 vary in potency? 3. What are the roles of DNAJB12/14 if overexpression can induce reflux? Does it allow increased binding of an already cytosolic protein, causing an overall increase in an interaction that then causes inhibition of p53? What are your suggested mechanisms? 4. Fig3: A+B show overexpression of individual DNAJs but not combined. As you go on to discuss the effect of the combination on AGR2 reflux, it would be useful to include this experimentally here. 5. Fig 3C: Subfractionation of cells shows AGR2 in the cytosol of A549 cells. The quality of the data is good but the bands are very high on the blot. For publication is it possible to show this band more centralized so that we are sure that we are not missing bands cut off in the empty and H139Q lanes? Also, you have some nice immunofluorescence in the 2021 EMBO reports paper, is it possible to show this by IF too? It is not essential for the story, but it would enrich the figure and support the biochemistry nicely. Also it is notable that the membrane fraction of the refluxed proteins doesn't appear to have a decrease in parallel (especially for AGR2). Is this because the % of the refluxed protein is very small? Is there a transcriptional increase of any of them (the treatments are 12+24 h so it would be enough time)? This could be a nice opportunity to discuss the amount of protein that is refluxed, whether this response is a huge emptying of the ER or more like a gentle release, and also the potency of the gain of function and effect on p53 vs the amount of protein refluxed. This latter part isn't essential but it would be a nice element to expand upon. 6. You still mention DNAJB12 and 14 as orthologues, even though DNAJB14 has no effect on p53 activity when overexpressed. Do you think that this piece of data diminishes this statement? 7. Fig 3D/F: Overexpression of DNAJB14 induces reflux of DNAJB11 at 24h, what does this suggest? Does this indicate having the same role as DNAJB12 but less potently? What's your hypothesis? 8. "This suggests that the two proteins may have different functions when overexpressed, despite their overlapping and redundant functions" What does it suggest about their dependence on each other? If overexpression of WT DNAJB12 inhibits Tg induced reflux, is it also blocking the ability of DNAJB14 to permit flux? 9. Fig 4: PDI shown in blots but not commented on in text. Then included in the schematics. Please comment in the text. 10. Fig 4F: Although the quantifications of the blots look fine, the blot shown does not convincingly demonstrate this data for AGR2. The other proteins look fine, but again it could be useful to see the individual means for each experiment, or the full gels for all replicates in a supplementary figure. Results section 3 11. Fig 5A, As there is obviously a difference between DNAJB12/14 it would be useful to do the pulldown with DNAJB14 too. Re. HSC70 binding to DNAJB12 and 14, the abstract states that DNAJB12/14 bind HSC70 and SGTA through their cytosolic J domains. Fig 5 shows pulldowns of DNAJB12 with an increased binding of SGTA in FLAG-DNAJB12 induced conditions, but the HSC70 band does not seem to be enriched in any of the conditions, including after DNAJB12 induction. This doesn't support the statement that DNAJB12 binds HSC70. In fact, in the absence of a good negative control, this would suggest that the HSC70 band seen is not specific. There is also no data to show that DNAJB14 binds HSC70. I recommend including a negative condition (ie beads only) and the data for DNAJB14 pulldown. 12. The binding of DNAJB12 to SGTA under stress conditions in Fig5B looks much more convincing than SGTA to DNAJB12 in Fig 5A. Bands in all blots need to be quantified from 3 independent experiments, and repeated if not already n=3. If this is solely a technical difference, please explain in the text. The conclusions drawn from this interaction data are important and shold be elaborated upon to support th claims made in the paper. The authors may also chose to expand the pulldowns to demonstrate their claims made on olidomerisation of DNAJB12 and 14 here. It is also clear that the interaction data of the SGTA with ER-resident proteins AGR2, PRDX4 and DNAJB11 is strong. The authors may want to draw on this in their hypotheses of the mechanism. I would imagine a complex such as DNAJB14/DNAJB12 - SGTA - AGR2/PRDX4/DNAJB11 would be logical. Have any experiments been performed to prove if complexes like this would form? 13. Fig 5B: It is clear that DNAJB12 interacts with SGTA. The authors state that DNAJB14 also interacts with SGTA under normal and stress conditions, but the band in 25/50 Tg is very feint. Why would there be stronger binding at the 2 extremes than during low stress induction? In the input, there is a much higher expression of DNAJB14 in 50 Tg. What does this say about the interaction? Is there an effect of ER stress on DNAJB14 expression? A negative control should be included to show any background binding, such as a "beads only" control. 14. Fig 5C data is sound, although a negative control should be included. Results section 4 15. Fig 6A-B: Given that there is the complexity of overexpression v KD of DNAJB12 v 14 causing similar effects on p53 actvity (Fig 2 v 3), it would be interesting to see whether the effect of overexpression mirrors the results in Fig 6A. Is it known what SGTA overexpression does (optional)? 16. Fig 6D: resolution very low 17. Fig 6C-D: There is an interesting difference though between the proposed cytosolic actions of the refluxed proteins. You show that AGR2, PRDX4 and DNAJB11 all bind to SGTA in stress conditions, but in the schematics you show: DNAJB11 binding to HSC70 through SGTA (not shown in the paper), then also PDIA1, PDIA3 binding to SGTA and AGR2 binding to SGTA. What role does SGTA have in these varied reactions? Sometimes it is depicted as an intermediate, sometimes a lone binder, what is its role as a binder? It should be clarified which interactions are demonstrated in the paper (or before) and which are hypothesized in a graphical way (eg. for hypotheses dotted outlines or no solid fill etc). The schematics also suggest that DNAJB14 binding to HSC70 and SGTA is inducible in stress conditions, as is PDIA3, which is not shown in the paper. Discussion "In cancer cells, DNAJB12 and DNAJB14 oligomerize and recruit cytosolic chaperones and cochaperones (HSC70 and SGTA) to reflux AGR2 and other ER-resident proteins and to inhibit wt-p53 and probably different proapoptotic signaling pathways (Figure 5, and Figure 6C-6D)." You havent shown oligomerisation between DNAJB12/14. Modify the text to make it clear that it is a hypothesis. Minor comments: 18. It would be useful to have page or line numbers to help with document navigation, please include them. Typos and inconsistency in how some proteins are named throughout the manuscript 19. Title: Include reference to reflux. Suggest: "chaperone complexes (?proteins) reflux from the ER to cytosol..." I presume it would be more likely that the proteins go separately rather than in complex. Do you have any ideas on the size range of proteins that can undergo this process? 20. ERCY in abstract, ERCYS in intro. There are typos throughout, could be a formatting problem, please check 21. What about the selection of refluxed proteins? Is this only a certain category of proteins? Could it be anything? Have you looked at other cargo / ER resident proteins? 22. "Their role in ERCYS and cells' fate determination depends..." Suggest change to "Their role in ERCYS and determination of cell fate..." 23. I think that the final sentence of the intro could be made stronger and more concise. There's a repeat of ER and cytosol. Instead could you comment on the reflux permitting new interactions between proteins otherwise spatially separated, then the effect on wt-p53 etc.
Figure legends:
- In some cases the authors state the number of replicates, but this should be stated for all experiments. If experiments don't already include 3 independent repeats, this should be done. Check text for typos, correct letter capitalisation, spaces and random bold text (some of this could be from incompatability when saving as PDF)
- Fig2E: scrambled not scramble siRNA
- Fig 3: "to expel" is a term not used in the rest of the paper for reflux. Useful to remain consistent with terminology where possible
Results section 1:
- "Protein alignment of the yeast HLJ1p showed high amino acids similarity to the mammalian..."
- Fig 1C: state in legend which organism this is from (presumably human) Results Section 2
- "Test the two strongest hits DNAJB12/14" Add reference to previous paper showing this
- "In the WT and J-protein-silenced A549 cells, there were no differences in the cytosolic enrichment of the three ER resident proteins AGR2, DNAJB11, and HYOU1 in normal and unstressed conditions (Figure 2A-C and Figure S2C)." I think that this is an oversimplification, and in your following discussion, you show this it's more subtle than this.
- The text here isn't so clear: normal and unstressed conditions? Do you mean stressed? Please be careful in your phrases: "DNAJB12-silenced cells are slightly affected in AGR2 and DNAJB11 cytosolic accumulation but not HYOU1." This is the wrong way around. DNAJB12 silencing effects AGR2, not that AGR2 effects the cells (which is how you have written it). This also occurs agan in the next para:
- "During stress, DNAJB12/DNAJB14 double knockdown was highly affected in the cytosolic..." I think you mean it highly affected the cytosolic accumulation, not that it was affected by the cytosolic accumulation. Please change in the text
- "DNAJB12 and DNAJB14 are strong hits of the yeast HLJ1" Not clear, I presume you mean they are likely orthologues? Top candidates for being closest orthologues?
- Fig 2D: typos in WB labelling? I think Tm should be - - +, not - + +as it is now (if it's not a typo, you need more controls, eto alone.
- Fig 2D-E-F typos for DKD? D12/D12 or D12/14?
- "We assayed the phosphorylation state of wt- p53 and p21 protein expression levels (a downstream target of p53 signaling) during etoposide treatment." What are the results of this? Explain what Fig 2D-E shows, then build on this with the +Tm results. Results should be explained didactically to be clear.
- "(Figure 2D- E). Cells lacking DNAJB12 and or DNAJB14 have partial protection in wt-p53 phosphorylation and p21 protein levels."
- You comment on p53 phosphorylation, but you haven't quantified this. This should be done, normalized to p53 levels, if you want to draw these conclusions, especially as total p53 varies between condition. Does Eto increase p53 txn? Does Tm alone increase p53 activity/phospho-p53? These are shown in the Sicari EMBO reports paper in 2021, you should briefly reference those.
- Fig3A: overexpression of DNAJB12 decreases Eto induced p53 but not at steady state. Is this because at steady state the activity is already basal? Or is there another reason?
- Switch Figs S3D and S3C as they are not referred to in order. Also Fig S3C: vary colour (or add pattern) on bars more between conditions
- Need to define HLJ1 at first mention Results section 3
- HSC70 cochaperone (SGTA) defined twice
- "These data are important because SGTA and the ER-resident proteins (PRDX4, AGR2, and DNAJB11) are known to be expressed in different compartments, and the interaction occurs only when those ER-resident proteins localize to the cytosol." Is there a reference for this? Results section 4
- "by almost two folds"
- Fig 6A: It seems strange that the difference between purple and blue bars in scrambled, and D14-KD are very significant but D12-KD is only significant. Why is this? The error bars don't look that different. It would be interesting to see the individual means for each different replicate.
- Figures: Fig 6A: Scale bars not well placed. Annotation on final set should be D12/D14 DKD? Discussion
- The authors mention that they want to use DNAJB12/4-HSC70/SGTA axis to impair cancer cell fitness: What effect would this have though in a non cancer model? Would this be a viable approach? Although it is obviously early days, which approach would the authors see as potentially favourable?
- Second para: Should be "Here we present evidences"
- "DNAJB12 overexpression was also sufficient to promote ERCYS by refluxing AGR2 and inhibit wt-p53 signaling in cells treated with etoposide" Suggest:
- DNAJB12 overexpression was also sufficient to promote ERCYS by increasing reflux of AGR2 and inhibition of wt-p53 signaling in cells treated with etoposide
- "Moreover, DNAJB12 was sufficient to promote this phenomenon and cause ER protein reflux by mass action without causing ER stress (Figure 3, Figure 4, and Figure S3)." You dont look at induction of ER stress here, please change the text or explain in more depth with refs if suitable
- The mention of viruses is sparse in this paper. If it is a main theory, put it more centrally to the concept, and explain in more detail. As it is, its appearance in the final sentence is out of context.
Referees cross-commenting
I agree with the comments from Reviewer 1. Reviewer 2 also is correct in many ways, but I think that they have somewhat overlooked the relevance of the ER-stress element and treatments. The authors do need to reference past papers more to give a full story, as this includes the groups own papers, I don't think that it is an ethical problem but rather an oversight in the writing. Regarding reviewer 2's concerns about overexpression levels and cell death, the authors do use an inducible cell line and show the levels of DNAJB12 induced (could CRISPR also be considered?). This could be used to further address reviewer 2's concerns. It would also be useful to see data on cell death in the conditions used in the paper. Re concerns about ER integrity, this could be addressed by using IF (or EM) to show a secondary ER marker that remains ER-localised, and this would also be of interest regarding my comment on which categories of proteins can undergo reflux. If everything is relocalised, then reviewer 2's point would be validated.
Significance
General assessment: This paper robustly shows that the yeast system of ER to cytosol reflux of ER-resident proteins is conserved in mammalian cells, and it describes clearly the link between ER stress, protein reflux and inhibition of p53 in mammalian cells. The authors have the tools to delve deeper into this mechanism and robustly explore this pathway, however the mechanistic elements - where not instantly clear from the results - have been over interpreted somewhat. The results have been oversimplified in their explanations and some points and complexities of the study need to be addressed further to make the most of them - these are often some of the more interesting concepts of the paper, for example the differences in DNAJB12/14 and how the proteins orchestrate in the cytosol to play their cytosol-specific effects. I think that many points can be addressed in the text, by the authors being clear and concise with their reporting, while other experiments would turn this paper from an observational one, into a very interesting mechanistic one.
Advance: This paper is based on previous nice papers from the group. It is a nice progressions from yeast, to basic mechanism, to physiological model. But as mentioned, without a strong mechanistic improvement, the paper would remain observatory.
Audience: This paper is interesting to cell biologists (homeostasis, quality control and trafficking) as well as cancer cell biologists (fitness of cancer cells and homeostasis) and it is a very interesting demonstration of a process that is a double edged sword, depending on the environment of the cells.
My expertise: cell biology, trafficking, ER homeostasis
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Referee #2
Evidence, reproducibility and clarity
The authors present a study in which they ascribe a role for a complex containing DNAJB12/14-Hsc70-SGTA in facilitating reflux of a AGR2 from the ER to cytosol during ER-stress. This function is proposed to inhibit wt-P53 during ER-stress.
Concerns:
- The way the manuscript is written gives the impression that this is the first study about mammalian homologs of yeast HLJ1, while there are instead multiple published papers on mammalian orthologs of HLJ1. Section 1 and Figure 1 of the results section is redundant with a collection of previously published manuscripts and reviews. The lack of proper citation and discussion of previous literature prevents the reader from evaluating the results presented here, compared to those in the literature.
- The conditions used to study DNAJB12 and DNAJ14 function in AGR2 reflux from the ER do not appear to be of physiological relevance. As seen below they involve two transfections and treatment with two cytotoxic drugs over a period of 42 hours. The assay for ERCY is accumulation of lumenal ER proteins in a cytosolic fraction. Yet, there is no data or controls that describe the path taken by AGR2 from the ER to cytosol. It seems like pleotropic damage to the ER due the experimental conditions and accompanying cell death could account for the reported results?
A. Transfection of cells with siRNA for DNAJB12 or DNAJB14 with a subsequent 24-hour growth period.
B. Transfection of cells with a p53-lucifease reporter.
C. Treatment of cells with etoposide for 2-hours to inhibit DNA synthesis and induce p53.
D. Treatment of cells for 16 hours with tunicamycin to inhibit addition of N-linked glycans to secretory proteins and cause ER-stress.
E. Subcellular fractionation to determine the localization of AGR2, DNAJB11, and HYOU1
KD of DNAJB12 or DNAJB14 have modest if any impact on AGR2 accumulation in the cytosol. There is an effect of the double KD of DNAJB12 or DNAJB14 on AGR2 accumulation in the cytosol. Yet there are no western blots showing AGR2 levels in the different cells, so it is possible that AGR2 is not synthesized in cells lacking DNAJB12 and DNAKB14. The lack of controls showing the impact of single and double KD or DNAJB12 and DNAJB14 on cell viability and ER-homeostasis make it difficult to interpret the result presented. How many control versus siRNA KD cells survive the protocol used in these assays? 3. In Figure 3 the authors overexpress WT-D12 and H139Q-D12 and examine induction of the p53-reporter. There are no western blots showing the expression levels of WT-D12 and H139Q-D12 relative to endogenous DNAJB12. HLJ1 stands for high-copy lethal DnaJ1 as overexpression of HLJ1 kills yeast. The authors present no controls showing that WT-D12 and H139-D12 are not expressed at toxic levels, so the data presented is difficult to evaluate. 4. There is no mechanistic data used to help explain the putative role DNAJB12 and DNAJB14 in ERCY? In Figure 4, why does H139Q JB12 prevent accumulation of AGR2 in the cytosol? There are no westerns showing the level to which DNAJB12 and DNAJB14 are overexpressed.
Referees cross-commenting
I appreciate the comments of the other reviewers. I agree that the authors could revise the manuscript. Yet, based on my concerns about the physiological significance of the process under study and lack of scholarship in the original draft, I would not agree to review a revised version of the paper.
Significance
Overall, there are serious concerns about the writing of this paper as it gives the impression that it is the first study on higher eukaryotic and mammalian homologs of yeast HLJ1. The reader is not given the ability to compare the presented data to related published work. There are also serious concerns about the quality of the data presented and the physiological significance of the process under study. In its present form, this work does not appear suitable for publication
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Referee #1
Evidence, reproducibility and clarity
Summary:
The manuscript by Dabsan et al builds on earlier work of the Igbaria lab, who showed that ER-luminal chaperones can be refluxed into the cytosol (ERCYS) during ER stress, which constitutes a pro-survival pathway potentially used by cancer cells. In the current work, they extent these observations and a role for DNAJB12&14 in ERCYS. The work is interesting and the topic is novel and of great relevance for the proteostasis community. I have a number of technical comments:
Major and minor comments:
- In the description of Figure 2, statistics is only show to compare untreated condition with those treated with Tg or Tm, but no comparison between condition and different proteins. As such, the statement made by the authors "...DNAJB14-silenced cells were only affected in AGR2 but not in DNAJB11 or HYOU1 cytosolic accumulation" cannot be made.
- Figure S2C: D11 seems to increase in the cytosolic fraction after Tm and Tg treatment. However, this is not reflected in the text. The membrane fraction also increases in the DKO. Is the increase of D11 in both cytosol and membrane and indication for a transcriptional induction of this protein by Tm/Tg? Again, the authors are not reflecting on this in their text.
- Figure 2D: Only p21 is quantified. phospho-p53 and p53 levels are not quantified.
- Figure 2D: There appears to be a labelling error
- Are there conditions where DNAJB12 would be higher?
- What do the authors mean by "just by mass action"?
- Figure 3C: Should be labelled to indicate membrane and cytosolic fraction. The AGR2 blot in the left part is not publication quality and should be replaced.
- What could be the reason for the fact that DNAJB12 is necessary and sufficient for ERCYS, while DNAJB14 is only necessary?
- Figure 5A: Is the interaction between SGTA and JB12 UPR-independent?HCS70 seems to show only background binding. The interaction of JB12 with SGTA is not convincing. A better blot is needed.
- Figure 5B: the expression of DNAJB14 was induced by Tg50, but not by Tg25 or Tm. However, the authors have not commented on this. This should be mentioned in the text and discussed.
- Figure 6A: Why is a double knockdown important at all? DNAJB14 does not seem to do much at all (neither in overexpression nor with single knockdown).
Referees cross-commenting
I agree with the comments raised by reviewer 1 about the manuscript. I also agree with the points written in this consultation session. In my opinion, the comments of reviewer 2 are phrased in a harsh tone and thus the reviewer reaches the conclusion that there are "serious" problems with this manuscript. However, I think that the authors could address many of the points of this reviewer in a matter of 3 months easily. For instance, it is easy to control for the expression levels of exogenous wild type and mutant D12 and compare it to the endogenous one (point 3). This is a very good point of this reviewer and I agree with this experiment. Likewise, it is easy to provide data about the levels of AGR2 to address the concern whether its synthesis is affected by D12 and D14 overexpression. Again, an excellent suggestion, but no reason for rejecting the story. As for not citing the literature, I think this can also easily be addressed and I am sure that this is just an oversight and no ill intention by the authors. Overall, I am unable to see why the reviewer reaches such a negative verdict about this work. With proper revisions that might take 3 months, I think the points of all reviewers can be addressed.
Significance
The strength of the work is that it provides further mechanistic insight into a novel cellular phenomenon (ERCYS). The functions for DNAJB12&14 are unprecedented and therefore of great interest for the proteostasis community. Potentially, the work is also of interest for cancer researchers, who might capitalize of the ERCYS to establish DNAJB12/14 as novel therapeutic targets.
The major weaknesses are as follows:
- (i) the work is limited to a single cell line. To better probe the cancer relevance, the work should have used at least a panel of cell lines from one (or more) cancer entity. Ideally even data from patient derived samples would have been nice. Having said this, I also appreciate that the work is primarily in the field of cell biology and the cancer-centric work could be done by others. Certainly, the current work could inspire cancer specialists to explore the relevance of ERCYS.
- (ii) No physiological or pathological condition is shown where DNAJB12 is induced or depleted.
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Reply to the reviewers
Manuscript number: RC-2024-02491
Corresponding author(s): Gilbert, Vassart
1. General Statements [optional]
We thank referees 1 and 2 for their in-depth analysis of our manuscript. They see interest in our study, with questions to be answered. Referee 3 is essentially negative, considering that there is nothing new ("novel finding is missing"). We respectfully disagree with him/her, comforted by the opinion of referee 2 that "the authors developed a protocol to reproducibly generate fetal-like spheroids from adult tissue is an important advancement in the field and ... the manuscript should attract a significant amount of attention in the intestinal field" and we provide evidence in our answers that he/she did not read the manuscript with the same attention as referees 1 and 2 (see in particular answer to his/her question 5).
Here is a summary of the main reason why we consider that our study represents valuable new information in the field of intestinal regeneration.
It is based on the serendipitous observation that dissociation of adult intestinal tissue by collagenase generates stably replatable spheroids upon culture in matrigel. Surprisingly and contrary to canonical EDTA-generated intestinal organoids and fetal spheroids, these spheroids are not traced in Rosa26Tomato mice harboring a VilCre transgene, despite expressing robustly endogenous Villin. Our interpretation is that adult intestinal spheroids originate from a cell lineage, distinct from the main developmental intestinal lineage, in which the VilCre transgene is unexpectedly not expressed, probaly due to the absence of cis regulatory sequences required for expression in this lineage.
Adult spheroid transcriptome shares a gene signature with the YAP/TAZ signature commonly expressed in models of intestinal regeneration. This led us to look for VilCre negative crypts in the regenerating intestine of Lgr5/DTR mice in which Lgr5-positive stem cells have been ablated by diphtheria toxin. Numerous VilCre negative clones were observed, identifying a novel lineage of stem cells implicated in intestinal regeneration.
FACS purification and scRNAseq analysis of the rare VilCre negative cells present at homeostasis identified a population of cells with characteristics of quiescent stem cells.
In sum, we believe that our study demonstrates the existence of a hitherto undescribed stem cell lineage involved in intestinal regeneration. It points to the existence of a hierarchical model of intestinal regeneration in addition to the well-accepted plasticity model.
2. Description of the planned revisions
See section 3 below.
3. Description of the revisions that have already been incorporated in the transferred manuscript
Here is a point-by-point reply to the queries of the three referees, with indication of the revisions introduced in the manuscript.
Reviewer #1 (Evidence, reproducibility and clarity (Required)):
- *In this manuscript, Marefati et al report an Lgr5-independent lineage in the regenerating intestine using in vitro organoids and in vivo injury-coupled lineage tracing model. In organoids, collagenase/dispase dissociated resulted in "immortal spheroids" that maintain a cystic and undifferentiated phenotype in the absence of standard growth factors (Rspondin/Noggin/EGF). Bulk RNAseq of spheroids demonstrates downregulation of classical CBC signatures and upregulation of fetal spheroid, mesenchymal, inflammation and regenerative signatures. In mice, Villin-Cre lineage tracing revealed some Villin- negative progenies that lack reporter tracing throughout crypt-villus ribbons after injury.
*The authors proposed that there is Lgr5-independent population support the regenerative response upon CBC depletion. A major caveat of this study is the identification of this population is based on absence of VilCre expression. *
We respectfully disagree. It is precisely this characteristic that makes the interest of our study. Whereas mosaicism of transgene expression is widespread and usually of little significance, our study shows that the rare VilCre-negative cells in the intestinal epithelium are not randomly showing this phenotype: they give specifically birth to what we call adult spheroids and regenerating crypts, which cannot be due to chance. The absence of VilCre expression allows tracing these cells from the zygote stage of the various VilCre/Ros26 reporter mice. We have modified our text to emphasize this point.
*It is surprising that there is no characterisation of Lgr5 expression throughout the manuscript whilst claiming of a Lgr5- independent lineage. *
We understand the perplexity of the referee not to see direct Lgr5 expression data in our manuscript, given our title. However, our point is that it is the cells at the origin of adult spheroids and the regenerating crypts we have identified that are Lgr5-negative, not the spheroids or the regenerated crypts themselves. Those are downstream offspring that may, and indeed have, gained some Lgr5 expression (e.g. figure 3F). We believe that our data showing that VilCre-negative spheroids are not traced in Lgr5-CreERT2/Rosa reporter mice convincingly demonstrate absence of Lgr5 expression in the cells at the origin of adult spheroids (figure 4G). We think that this experiment is better evidence than attempts to show absence of two markers (Tom and Lgr5) in the rare "white" cells present in the epithelium. Regarding the Lgr5 status of cells at the origin of the regenerating "white" crypts that we have identified, the early appearance of these crypts following ablation of CBC (i.e. Lgr5+ve) cells is a strong argument that they originate from Lgr5-negative cells. Regarding the scRNAseq experiment, Lgr5 transcripts are notoriously low and difficult to measure reliably in CBCs (Haber et al 2017). However, blowing up the pertinent regions of the merged UMAP allows showing some Lgr5 transcripts in clusters 5,6 and none in cluster 1 of figure 8GH. Given the very low level of detection, we had chosen not to include these data in the manuscript, but we hope they may help answer the point of the referee (see portion of UMAP below, with Olfm4 as a control, together with the corresponding violin plot). Several markers that gave significant signals in the CBC cluster (Smoc2, Axin2, Slc12a2) were virtually undetectable in the Olfm4-low /Tom-negative cluster of our scRNAseq data (figure 8I) supporting our conclusion.
Although the research question is potentially interesting, the concept of epithelial reprogramming upon injury is well documented in the field. The data generated in this manuscript also seem to be preliminary and lack of detailed characterisation. Below are specific comments.
We do not question the existence of epithelial reprogramming upon injury. We believe our data show, in addition to this well demonstrated phenomenon, the existence of rare cells traced by absence of VilCre expression that are at the origin of a developmental cell lineage distinct from Lgr5+ stem cells and also implicated in regeneration.
- Expression of Lgr5 should be properly characterised throughout the manuscript in both organoid models and injury-induced regeneration in vivo.
- *
See above for a detailed answer to this point.
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An important question is the origin of these "Lgr5-independent" adult spheroids. They look and appear like fetal organoids, which could be induced by injury (e.g. upon collagenase/dispase dissociation). Have the authors tried to culture fetal spheroids in BCM over extensive period of time? Do they behave the same? This would be a great way to directly compare the collagenase/dispase-derived organoids with fetal origin. * *Fetal spheroids require ENR for survival and die in BCM. We have chosen to illustrate this point in Fig2A by showing that, contrary to adult spheroid, they die even when only Rspondin is missing.
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Fig 1C, Why is the replating spheroid culture time different between mesenchymal cells and conditioned medium? We took the earliest time showing convincingly the return to the organoid phenotype. This timing difference does not modify the conclusion that EDTA organoids becoming spheroid-like when exposed to factors originating from mesenchymal cells revert to the organoid phenotype when returned to ENR medium without mesenchymal influence.
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*It is unclear how the bulk RNA-seq data in Fig. 3 were compared. How long were the adult organoids and spheroids cultured for (how many passages)? Were they culture in the same condition of were they in ENR vs BCM? * Both EDTA organoids and spheroids displaying a stable phenotype were used in this experiment. Organoids were collected at passage 4, day 5; spheroids were collected at passage passage 9 day 3.
As stated in the legend to the figure: "...to allow pertinent comparison spheroids and organoids were cultured in the same ENR-containing medium...".
These are important information to consider when interpreting the results. For instance, are Ptgs1 & Ptgs2 expression in adult spheroids the same in ENR vs BCM? Are the gene signatures (regenerative, fetal and YAP) changed in adult spheroids culturing in ENR vs BCM?
We did compare bulk RNAseq of EDTA organoids to ENR-cultured spheroids, short term (passage 6, day 6) BCM-cultured spheroids and long term BCM-cultured (passage 26, day 6) spheroids. To avoid overloading the manuscript these data were not shown in the original manuscript. In summary the BCM-cultured spheroids display a similar phenotype as those cultured in ENR, but with further de-differentiation. See in revision plan folder the results for PTGS, some differentiation markers and fetal regenerative markers including YAP induced genes.
We have included a brief description of these data in the new version of the manuscript and added an additional supplementary file (Suppl table 2) presenting the whole data set.
- It is stated: "In agreement with their aptitude to grow indefinitely, adult spheroids express a set of upregulated genes overlapping significantly with an "adult tissue stem cell module" [159/721 genes; q value 2.11 e-94) (Fig.S2F)].". What is the definition of "indefinitely"? Are they referring to the Fig 1B where spheroid were passaged to P10? The authors should avoid the term "indefinitely" but use a more specific time scale, e.g. passages, months etc.
We agree that the term indefinitely should be avoided, as it is vague. We have introduced the maximum number of passages during which we have maintained the stable spheroid phenotype (26 passages). Also worth noting, the spheroids could be frozen and cultured repeatedly over many months.
SuppFig 3D: Row Z-Score is missing the "e" in Score.
Corrected
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Fig 4E: Figure legend says QNRQ instead of CNRQ. Corrected
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Fig 4G: The brightfield image of adult spheroids 5 days after 3x TAM injections doesn't look like a spheroid. It seems to be differentiating. True, the choice was not the best as the spheroids started to darken. When further replated, however, the offspring of these spheroids showing a clear phenotype remain negative 30 days after tamoxifen administration as shown on the figure. We are sorry, but for reasons explained in section 4 below, we cannot redo the experiment to get a better picture.
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Fig 4: Most mouse model data are missing the number of mice & their respective age used for organoid isolation. We have introduced these data in the legend.
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*Fig 4A-D, H-G: How was fluorescent signal of organoids quantified? *
The settings of fluo imaging or time of LacZ staining were the same for organoids and spheroid pictures. This has been added to the material and methods of the figure and an example is shown below for Rosa26Tomato.
*How many images? * 2 per animal per condition.
*Were there equal numbers of organoids? *
No, see number of total elements counted added to the figure
This all needs to be included in methods/figure legends.
We have introduced additional pertinent information in the material and methods section.
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Figure 4B-D, G-H: Which culturing conditions were used for adult spheroids? Original method or sandwich method? These data were obtained with the original protocol
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Fig 6D-E: Please add the timepoint after DT administration these samples are from. It is not listed in text or figure legend. These samples were those obtained from mice sacrificed at the end of the 5 day period as indicated in panel A. This has been emphasized in the legend of the figure.
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SuppFig 6D: again timepoint is missing. In this experiment all samples were untreated as indicated. This has been emphasized in the legend of the figure.
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SuppFig 6: How were the crypts of these mice (DT WT & DT HE) isolated? Was this via EDTA? This was RNA extracted from total uncultured EDTA-released material (crypts). This has been emphasized in the legend of the figure.
Also, what is the timepoint for isolation for these samples? Even if untreated, the timepoint adds context to the data. Please add more context to describing these different experiments, either in the figure legends or methods section.
All these experiments were from 2 month old animals. We have indicated this in the legend of the figure.
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SuppFig 6E: The quality of the heatmap resolution is too poor to read gene names. We have improved the resolution of the figure and hope the name of the genes are readable now.
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5-7, are the regenerating crypt-villus units fully differentiated or are they maintained in the developmental state? Immunostaining of markers for stem cells (Lgr5), differentiated lineages (Alpi, Muc2, Lyz, ChgA etc.) and fetal state (Sca1, Trop2 etc) should be analysed in those "white" unrecombined crypt-villus units. The differentiation phenotype is shown by the clear presence of morphologically-identified Paneth and Goblet cells. We agree that specific immunostainings could have been performed to further explore this point. Regarding the fetal state, Clu expression was shown during the regeneration period (see figure 7D,E).
Unfortunately, for reasons explained in section 4 below, we are not in a position to perform these additional experiments.
- The following text needs clarification: "The kinetics of appearance of newly formed un-recombined ("white") crypts was studied after a single pulse of DT (Fig.7A). This demonstrated an increase at 48 hours, with further increase at day 10 and stable maintenance at day 30. The presence of newly formed white crypts one month after toxin administration indicates that the VilCre-negative lineage is developmentally stable and does not turn on the transgene during differentiation of the various epithelial lineages occurring after regeneration (Fig.7B).
*Comment: The "newly formed" is an overstatement, the data doesn't conclude that those are "new" crypts. *
Except if we do not understand the point, we think we can write that a fraction of "white" crypts must be "newly formed", since they are in excess of those present in untreated animals at the same time point.
*The end of the sentence states that these "white" crypts form developmentally stable lineages, thus these white crypts at day 30 could originate from the initial injury. *
As stated above, we consider that crypts found in excess of those present in untreated animals result from the initial injury.
*There was no characterisation of the various epitheial lineages. Are they fully differentiated? *
See above the point related to Paneth cells and Goblet cells.
Is Lgr5 expressed one month after toxin administration? Can the VilCre neg lineage give rise to CBCs?
We have tried hard to show presence or absence of Lgr5 in white crypts at the various times following DT administration. We tried double RFP / Lgr5-RNA scope labeling and double GFP/RFP immunolabeling. Unfortunately, we could not get these methods to produce convincing specific labeling of CBCs in homeostatic crypts, which explains why we could not reach a conclusion regarding the white crypts.
However, there is an indirect indication that "chronic" white crypts (i.e. those caused by DTR expression in CBC, plus those observed 30 days after DT administration) do not express Lgr5. Indeed, acute regeneration indicated by Clu expression at day 5 in Fig.7C is lower in white crypts than in red ones strongly suggesting that white crypts preexisting DT administration (the "chronic ones) do not express Lgr5DTR.
The relationship between white crypt generation and appearance of Clu-positive revival cells (Ayyaz et al., 2019) was then explored. In agreement with others and similar to what happens in the irradiation model, (Ayyaz et al., 2019; Yuan et al., 2023) Clu-positive cells were rare in crypts of untreated mice and their number transiently increased forty-eight hours after a single pulse of DT, and more so after three pulses of DT (Fig.7C,D).
Comment: Comparing 1 pulse at day 2 vs 3 pulses at day 5 makes the data hard to interpret. How is the Clu ISH level for 1 pulse at day 5? Are they equivalent?
After a single pulse of of DT, Clu is only transiently increased. As shown by Ayyaz et al it is back to the starting point at day 5 (supplementary figure 4 of Ayyaz et al).
Clu-positive cells were less frequently observed in white crypts (see "Total" versus "White" in Fig.7C). This fits with the hypothesis that Clu expression marks acutely regenerating crypts and that a proportion of the white crypts are chronically regenerating due to DTR expression in CBCs."
*Comment: I believe the authors suggested that the discrepancy of less Clu expression in white crypts is due to the ectopic expression of DTR in CBCs causing low grade injury without DT administration. This means that some white crypts could have been formed before the administration of DT, and thus are on a different regenerative timeline compared to the white crypts formed from DT administration. *
Yes, this is our interpretation. We have clarified it in the text.
Is there any proof of the chronic regeneration? Immunostaining of chronic regenerative markers such as Sca1, Anxa1 or Yap1 nuclear localization would support the claim. It'd be important to show only the white crypts, but not the RFP+ ones, show regenerative markers.
We think that the steady state higher number of white crypts in untreated Lgr5-DTR animals, compared to wild type siblings indicates chronical low-grade regeneration, which is supported by the RNAseq data (Suppl fig6). It must be noted, however, that this phenotype is mild compared to the well described fetal-like regeneration phenotype described in most injury models. Since these white crypts were made at undetermined earlier stages, the great majority of them are not expected to show markers of acute regeneration like Clu, Sca1....
Fig 7D-E: What are the timepoints of harvest for HE-WT-HE 1 pulse DT mice and HE- HE-HE PBS injected mice?
We have added this information in the figure.
- *Fig 8-9: Regarding the CBC-like Olfm4 low population, what is the status of Lgr5? This should be shown in the figure since the argument is that this is an Lgr5-independent lineage. * See response to the second point.
And what about the regenerative, Yap, mesenchymal and inflammatory signatures? Are they enriched in the white crypts similar to the in vitro spheroids?
In a portion of white crypts, those we believe are newly formed after CBC ablation (see above), there is a transient increase in Clu, which may be considered a marker of Yap activation. In the CBC-like Olfm4 low cells, as seen by scRNAseq, there is nothing like an actively regenerating phenotype. This is expected, since these cells are coming from homeostatic untreated VilCre/Rosa26Tom animals and are supposed to be quiescent "awaiting to be activated".
Reviewer #1 (Significance (Required)):
Strengths: The study employed a range of in vitro and in vivo models to test the hypothesis.
- *
*Limitations: Unfortunately, the models chosen did not provide sufficient evidence to draw the conclusions. Injury induced reprogramming, both in vivo and in vitro, has been well documented in the field. The new message here is to show that such reprogrammed state is continuous rather than transient; instead of regenerating Lgr5+ stem cells, it can continue to differentiate to all cell lineages in Lgr5-independent manner.
*
We respectfully disagree with this analysis of our results. What we show is not "that such reprogrammed state is continuous rather than transient; instead of regenerating Lgr5+ stem cells, it can continue to differentiate to all cell lineages in Lgr5-independent manner", but that a quiescent stem cell line, not previously identified, is activated to regenerate a portion of crypts following CBC ablation. These cells are not reprogrammed, they correspond to a developmental lineage waiting to be activated and keep their VilCre-negative state at least of 30 days. We believe that their "by default tracing" (VilCre negative from the zygote stage) is as strong an evidence for the existence of such a lineage as positive lineage tracing would be. The increase in crypts originating from this lineage after CBC ablation indicates that it is implicated in regeneration. We do not question the well-demonstrated plasticity-associated reprogramming taking place during regeneration; we simply suggest that this would coexist with the involvement of the quiescent VilCre-negative lineage we have identified.
*However, through the manuscript, there was no immunostaining of Lgr5 and other differentiation markers. The conclusion is an overstatement without solid proof. * We have provided the best answer we could to this point in our answer to the second question of the referee hereabove.
Reviewer #2 (Evidence, reproducibility and clarity (Required)):
In this manuscript, the Marefati et al. developed a novel approach to generate spheroids from adult intestinal epithelium using a collagenase/dispase based protocol. Adult spheroids were found to be distinct from classic budding-type organoids normally generated from EDTA based release of the crypt epithelium. Transcriptional profiling indicated that adult spheroids were undifferentiated and similar to regenerating crypts or fetal spheroids. To identify the cell of origin that generates adult spheroids, the authors labelled epithelial cells with VilCreERT-LSL-Tom, VilCre-LSL-GFP and Lgr5CreERT- LSLTom mice. From these experiments the authors conclude that that spheroids are only generated from Vil-Cre negative and Lgr5 negative cells. Next the authors deleted the anti- apoptotic gene Mcl1 using Vil-CreERT mice. This led to a strong apoptotic response throughout the crypt epithelium and tissues processed from knockout mice readily generated spheroids, and in vivo, replenishment of the gut epithelium was mediated by unrecombined cells. In a second model, CBCs were ablated using Lgr5DTR mice and VilCre negative cells were found again to contribute to regeneration of the crypt epithelium. Finally based on the absence of Vil-Cre reporter activity, the authors were able to sort out and perform scRNAseq to profile VilCre negative cells. These cells were found to be quiescent, express the stem cell marker Olfm4 and were also abundant in ribosomal gene expression.
- *
The fact that the authors developed a protocol to reproducibly generate fetal-like spheroids from adult tissue is an important advancement in the field. Previous reports have shown that treatment with various small molecule inhibitors can revert budding organoids into a spheroid morphology, but this manuscript demonstrates that spheroids can also be generated from otherwise untreated cells. This new methodology will provide new tools to dissect the molecular determinants of fetal/regenerative cells in the gut. Based on this, the manuscript should attract a significant amount of attention in the intestinal field.
- *
As pointed out by the authors themselves the study has important limitations that diminish enthusiasm. The primary issue relates to the inability of the team to identify markers of VilCre neg cells other than the fact that these cells are Olfm4+ and quiescent. Nonetheless, for the reasons stated above the manuscript should reach the target audience within the research community, if the authors can address the specific points below related to issues with methodology as well as defining more precisely the characteristics and growth requirements of adult spheroid cultures.
Thank you for this positive analysis of our study.
Major comments
The main conclusion of the study is that Vil-Cre neg cells are rare quiescent Olfm4+ crypt cells. If this is the case, then standard EDTA treatment should release these cells as well. Consequently, spheroids should also emerge from isolated crypts grown in the absence of ENR. If this is not the case how do the authors explain this?
We have tried hard to generate spheroids by culturing EDTA organoids in medium lacking ENR and by treating EDTA organoids with collagenase/dispase, without success. Therefore, we are left with the conclusion that spheroid-generating cells must be more tightly attached to the matrix than those released by EDTA, and that it is their release from this attachment by collagenase that triggers a regeneration-like phenotype. This hypothesis is supported by several models of regeneration in other tissues as indicated in our references (Gilbert et al., 2010; Machado et al., 2021; Montarras et al., 2005).
From the text the authors appear to suggest that growth of adult spheroids is dependent initially on "material" released by collagenase/dispase treatment. An obvious candidate would be mesenchymal cells, which are known to secrete factors such as Wnts and PGE2 that drive spheroid morphology. To test this, the authors should treat spheroid cultures with Porcupine and/or PGE2 inhibitors.
We followed similar reasoning, considering that spheroids express strongly Ptgs1 ,2 (Figure 3A). We thought their phenotype might be maintained by autocrine prostaglandin action. We tested aspirin, a Ptgs inhibitor, which was without effect on the spheroid phenotype. Besides, we explored a wide variety of conditions to test whether they would affect the spheroid phenotype [Aspirin-see above, cAMP agonists/antagonists, YapTaz inhibitors (verteporfin and CA3), valproic acid, Notch inhibitors (DAPT, DBZ, LY511455), all-trans retinoic acid, NFkB inhibitors (TCPA, BMS), TGFbeta inhibitor (SB431542)]. As these results were negative, we did not include them in the manuscript.
- If these inhibitors block growth then this would suggest that either stromal cells or autocrine signalling involving these pathways is important. Overall, more in-depth analysis of the growth requirements of adult spheroids is required.*
Figure 1d indicates that adult spheroids can be propagated for at least 10 passages. The abstract mentions they are "immortal". The text itself does not address this issue. More precise information as to how long spheroids can be propagated is required. If these cultures can be propagated for 10 passages or more it becomes important to determine what nutrients/mitogens in the basal media are driving growth? Alternatively, what is the evidence that spheroid cultures are completely devoid of mesenchymal cells. The text only mentions that "Upon replating, these spheroids could be stably cultured free of mesenchymal cells (Fig.1B)". No validation is shown to support this.
We agree that "immortal" is not a good way to characterize our spheroids, as also pointed out by referee nr 1. We have changed that in the text, indicating the maximal number of replating we tested was 26 and replacing immortal by stably replatable. Of note, the spheroids could frozen/thawed and recultured many times.
Related to the question whether mesenchymal cells could still contaminate the spheroid cultures, we can provide the following answers:
- No fibroblasts could be seen in replated cultures and multiple spheroids could be repeatedly propagated from a single starting spheroid.
- The bulk RNAseq experiment comparing organoids to ENR or BCM cultured spheroids show, despite expression of several mesenchymal markers (see matrisome in Fig3), absence of significant expression of Pdgfra (see in revision plan folder for CP20Millions results from the raw data of new suppl table 2, with Clu, Tacstd2 and Alpi shown as controls).
- Regarding the nutrients/mitogens in the medium driving spheroid growth, we did not explore the point further than showing that they grow in basal medium (i.e. advanced DMEM), given that the presence of Matrigel makes it difficult to pinpoint what is really needed. In Figure 2, the authors describe the growth requirements for adult spheroids and indicate that spheroids grown in ENR or EN became dark and shrink. The representative images showing this are clear, but this analysis should be quantified.
Added to the manuscript.
In SF3, the gene expression profile of organoids from the sandwich method only partially overlaps with that of organoids from the old protocol. What are the gene expression differences between the 2 culture systems? Secondly, the sandwich method appears to sustain growth of Tom+ spheroids based on RNAseq and the IF images. This suggest that Vil-Cre negative cells are not necessarily the only source of adult spheroids and thus this experiment seems to indicate that any cell may be converted to grow as a spheroid under the right conditions. These points should be addressed.
Looking back to our data in order to answer the point raised by the referee, we realized that we had inadvertently-compared organoids to ENR-cultured spheroids generated by the first protocol to BCM-cultured spheroids generated by the sandwich method. We have corrected this error in a new version of suppl fig3. This shows increased correspondence between genes up- or downregulated in the spheroids obtained in the two protocols (from 49/48% to 57/57% (Venn diagram on the new figure). We agree that, even after this correction, the spheroids obtained with the two protocols present sizeable differences in their transcriptome. However, considering the very different way these spheroids were obtained and cultured initially, we do not believe this to be unexpected. The important point in our opinion is that the core of the up- and down-regulated genes typical of the de-differentiation phenotype of adult spheroids is very similar, as shown in the heatmap (which was made with the correct samples!). Also, a key observation is that that both kind of spheroids survive and can be replated in basal medium. As already stated, this characteristic is only seen rare cases [spheroids obtained from rare FACS-purified cells (Smith et al 2018) or helminth-infected intestinal tissue (Nusse et al.2018)]. Together with the observation that the majority of them is not traced by VilCre constitutes what we consider the halmark of the spheroids described in our study. As shown in figure 4E (old protocol) and Suppl Fig.3 (sandwich protocol) both red and white spheroids were extremely low in VilCre expression. As stated in the text, the fact that some spheroids are nevertheless red is most probably related to the extreme sensitivity of the Rosa26Tom marker to recombination (Liu et al., 2013), but this does not mean that there are two phenotypically different kind of spheroids. It means that the arbitrary threshold of Rosa26Tom recombination introduces an artificial subdivision of spheroids with no phenotypical significance.
Regarding the point made by the referee that "that any cell may be converted to grow as a spheroid under the right conditions", we agree and have shown with others that organoids acquire indeed a spheroid phenotype when cultured for instance in fibroblasts-conditioned medium (see suppl fig1B and (Lahar et al., 2011; Roulis et al., 2020) quoted in the manuscript). However, these spheroids cannot be propagated in basal medium, and revert to an organoid phenotype when put back in ENR (Suppl fig1B).
*In Figure 4, the authors conclude that spheroids do not originate from Lgr5 cell derived clones even after 30days post Tam induction. Does this suggest that in vivo and under homeostatic conditions VilCre neg cells are derived from a distinct stem cell pool or are themselves a quiescent stem cell. Given the rarity of VilCre neg cells, the latter seems unlikely.
*
Despite their rarity, we believe VilCre-negative cells observed under homeostatic conditions are themselves quiescent stem cells. Actually, if they were derived from a larger stem cell pool, this pool should also be VilCre-negative. And we do not see such larger number of VilCre-neg cells under homeostatic conditions.
The problem with the original assertion is that Lgr5-CreERT mice are mosaic and therefore not all Lgr5+ cells are labelled in this model. "White" spheroids may thus derive from cells that in turn derive from these unlabelled Lgr5 cells.
We had considered the possibility that mosaicism [very low for VilCre (Madison et al., 2002); in the 40-50% range for Lgr5CreERT2 (Barker & Clevers. Curr Protoc Stem Cell Biol. 2010 Chapter 5)] could explain our data. We think, however that we can exclude this possibility on the basis that spheroids do not conform to the expected ratio of unrecombined cells, given the observed level of mosaicism. Indeed, for VilCre, a few percent, at most, of unrecombined cells in the epithelium translates into almost 100% unrecombined spheroids. For Lgr5CreERT2 mice, the mosaicism level is in the range of 40%, which is what we observe for EDTA organoids (Figure 4G), while spheroids were in their vast majority unrecombined.
We have included a discussion about the possible role of mosaicism in the new version.
ATACseq experiments were briefly mentioned in the manuscript but unfortunately little information was extracted from this experiment. What does this experiment reveal about the chromatin landscape of adult spheroids relative to normal organoids?
We only performed this experiment to search for an explanation to the paradoxical absence of expression of the VilCre transgene in spheroids, despite robust expression of endogenous villin (Suppl Fig.4). We chose to show the chromatin landscape of a gene equally expressed in both organoids and spheroids (Krt19), a gene specifically expressed in spheroids (Tacstd2) and the endogenous Villin gene also expressed in both. We believe that the observation of a clear difference in pattern of the chromatin accessibility around the endogenous villin gene in organoids and spheroids provides an explanation to the observed results. The cis regulatory sequences needed for expression of the endogenous villin gene seem to be different in organoids and spheroids, which may explain why the regulatory sequences present in the transgene (only 12.4kb) might not allow expression of the transgene in spheroids. We have added a sentence in the manuscript clarifying this point. Missing is obviously the chromatin landscape around the VilCre transgene, but this is beyond reach in such kind of experiments.
Reviewer #2 (Significance (Required)):
The fact that the authors developed a protocol to reproducibly generate fetal-like spheroids from adult tissue is an important advancement in the field. Previous reports have shown that treatment with various small molecule inhibitors can revert budding organoids into a spheroid morphology, but this manuscript demonstrates that spheroids can also be generated from otherwise untreated cells. This new methodology will provide new tools to dissect the molecular determinants of fetal/regenerative cells in the gut. Based on this, the manuscript should attract a significant amount of attention in the intestinal field.
Reviewer #3 (Evidence, reproducibility and clarity (Required)): CR-2024-02491
An Lgr5-independent developmental lineage is involved in mouse intestinal regeneration
Marefati et al.
Homeostatic maintenance of the intestinal epithelium has long been thought to rely upon Wnt signaling responsive Lgr5-expressing stem cells that reside at the crypt base.
However, myriad reported mechanisms or populations have been reported to underlie epithelial regeneration after injury. Many groups have reported that reacquisition of a fetal- link intestinal phenotype is an import part of the regenerative response, however the originating cell type has not been definitively identified. Herein, the authors demonstrate that cells from adult homeostatic intestine can generate immortal spheroids that resemble fetal spheroids and are derived independent of Lgr5+ intestinal stem cells (ISCs). The authors then draw the conclusion that this indicates that a hierarchical stem cell model applies to regeneration of the intestinal epithelium, in addition to the plasticity model.
- *
Comments:
- Please indicate what species is used for studies in Fig 1.
All experiments were performed in Mus musculus.
Please clarify if Figure 2 studies utilize Matrigel or not.
Yes
RNA-seq analyses of adult intestinal generated spheroids lack the granularity of single cell analyses and thus it is unclear if this is a homogeneous population or if the population has diversity across it (i.e., enteroids/organoids have a high level of diversity). Many of the conclusions from the RNA-seq study are broad and generalized-for example Fig 3F indicates that markers of the +4 ISC populations (Bmi1, tert, lrig1, hopx) were all expressed similarly in adult spheroids as compared to adult organoids. However, while this may be true in the bulk-RNA-seq analyses, clearly scRNA-seq would provide a better foundation to make this statement, as enteroids/organoids are comprised of heterogeneous subpopulations. . .and it might indicate that these +4 markers have only very low expression in the spheroids. Based upon these concerns, misconclusions are likely to be drawn.
We agree and it would be certainly worthwhile to perform scRNAseq of adult spheroid populations. This would certainly be worth doing in future studies to explore the possible heterogeneity of adult spheroids. We nevertheless believe that our scRNAseq performed on homeostatic intestinal tissue from VilCre/Rosa26Tom mice identify Olfm4-low VilCre-neg cells that are likely at the origin of adult spheroids and display a quite homogenous phenotype.
*The language around Figure 4 results is confusing. Please define "white" and "red". It might be simpler to designate recombined versus not recombined lineage.
*
We have clarified this in the figure.
The hypothesis that collagenase/dispase solution acts as a proxy for injury is not demonstrated and backed by data. Thus, it is difficult to make the conclusion that this approach could represent a "stable avatar" of intestinal regenerating cells. It is clear that subpopulations of crypt-based cells generate spheroids in culture without collagenase/dispase (see the cited reference Smith et al, 2018).
* *Smith et al demonstrate clearly the possibility to obtain spheroids with properties probably similar to ours from EDTA derived intestinal crypt cells. However they need to prepurify them by FACS. Besides, Nusse et al describe spheroids similar to ours after infection of the intestine by helminths (Nusse et al. 2018). In our case, and for most labs preparing enteroids with the EDTA protocol, the result is close to 100% organoids. Even if we treat EDTA organoids with collagenase, we do not obtain spheroids. This brought us to the conclusion that spheroid-generating cells must be more tightly attached to the matrix than CBCs and that it is their release from the matrix that activates the spheroid regeneration-like phenotype. This hypothesis is supported by several models of regeneration in other tissues as indicated in our references (Gilbert et al., 2010; Machado et al., 2021; Montarras et al., 2005)
A study based on the absence of recombination in a VilCre lineage tracing scenario is not well-established to be strong experimental approach, as there are many reasons why recombination may not cells may not be lineage marked. In order to use this system as the authors intend, they first need to demonstrate that villin is not expressed in the discrete cell population that they are targeting. For the presented observational studies, this would be difficult to do. While they do demonstrate differences in chromatin accessibility between cells from organoids versus spheroids (fig s4), some of these differences could merely be due to the bulk analytical nature of the study and the lack of comparing stem cell populations from spheroids to stem cell populations from organoids-since the spheroids are likely homogenous versus the organoids that only have a small fraction of stem cells-and thus represent a mix of stem cell and differentiated cell populations. The authors do not demonstrate that villin protein expression varies in these cells.
If it were found that villin is not expressed in their "novel" population, then one would expect that the downstream use of villin-based recombination would demonstrate the same recombination potential (i.e., Mcl1 would not be recombined). Both recombination studies in Fig 6 are difficult to interpret, and thus it is not clear if these studies support the stated conclusions. Quantification of number of crypts that are negative should be reported as a percentage of recombined crypts.
We are sorry but there seems to be a complete misunderstanding of our data regarding the point raised by the referee. The important point of our initial observation is that despite robust expression of villin in spheroids, the VilCre transgene is not expressed (see figure 4E). This in our opinion makes absence of VilCre expression (or of Rosa marker recombination) a trustful marker of a new developmental lineage. All the data in figure 4 constitute an answer.
*The reasoning about heterogeneity of cell type in organoids versus probable homogeneity of spheroids is well taken. However, as the endogenous villin gene is expressed in all cells of both organoids and spheroids, it is highly significant that only spheroids do not express the transgene. *
We performed the ATACseq experiment to search for an explanation to the paradoxical absence of expression of the VilCre transgene in spheroids, despite robust expression of endogenous villin (Suppl Fig.4). We chose to show the chromatin landscape of a gene equally expressed in both organoids and spheroids (Krt19), a gene specifically expressed in spheroids (Tacstd2) and the endogenous Villin gene also expressed in both. We believe that the observation of a clear difference in pattern of the chromatin accessibility around the endogenous villin gene in organoids and spheroids provides an explanation to the observed results. The cis regulatory sequences needed for expression of the endogenous villin gene seem to be different in organoids and spheroids, which may explain why the regulatory sequences present in the transgene (only 12.4kb) might not allow expression of the transgene in spheroids. We have added a sentence in the manuscript clarifying this point. Missing is obviously the chromatin landscape around the VilCre transgene, but this is beyond reach in such kind of experiments.
*Figure 8 indicates that the cell population identified by scRNA-seq may be quiescent. Companion IF or IHC should be conducted to confirm this finding, as well as other conclusions from the informatics conducted.
*
We agree that additional experiments could be performed to support this point. We are unfortunately not in a position to perform these experiments (see section 4 below).
Clearly the data is intriguing, however, the conclusion is strong and is an over interpretation of the presented data. There are a number of validation or extension data that would enhance the overall interpretation of the study: 1. validation of scRNA-seq or bulk RNA-seq concepts by protein staining of intestinal tissues in the damage model will serve as a secondary observation. 2. identification of the ISC that they are defining is critical and important. There is already the notion that this cell type exists and it has been shown with various different markers. 3. expand the analyses of the fetal-like expression profiling to injured intestines to demonstrate that the lineage negative cells indeed express fetal-like proteins. 4. expand the discussion of the Clu+ cell type. Is this cell the previously described revival cell? If so, how does this body of work provide unique aspects to the field?
We agree that all these suggested experiments could be performed and would be of interest. However, we consider that they would not modify the main message of our study and would only constitute an expansion of the present work. As already stated, we are not in the position to perform them (see section 4).
*There is some level of conflicting data, with the stem population being proliferative in culture stimulated by the stromal cells, but quiescent in vivo and also based upon scRNA- seq data in Fig 9.
*
We do not see any conflict in our observation regarding this point. The observation that cells that are quiescent in vivo become proliferative when subjected to culture (with or without addition of stromal cells) is routinely made in a multitude of cell culture systems. In particular, it has been shown that intestinal tissue dissociation activates the Yap/Taz pathway, resulting in proliferation (Yu et al. Hippo Pathway Regulation of Gastrointestinal Tissues. Annual Review of Physiology, 2015 Volume 77, 201-227).
Many of the findings have been previously reported: Population that grows as spheroids (Figure 2), Population that is Wnt independent (Figure 2), Lgr5 independent regenerative growth of the intestine (figure 3F, Figure 4), Clu+ ISCs drive regeneration (Figure 7).
Whereas these individual findings have indeed been reported, it was in a different context. We strongly disagree with the underlying suggestion that our study would not bring new information. We have identified here a developmental lineage involved in intestinal regeneration that has not been described up to now.
Minor comments:
-
- The statement that spheroids must originate from collagenase/dispase digested material might be an overstatement. As spheroids generation from EDTA treated intestines have been previously reported (Smith et al, 2018). * See answer to point 4 above. *Overall while the study includes an extensive amount of work and different approaches, a foundationally supported novel finding is missing. Many of the statements have already been demonstrated by others in the fields. In addition, one of the most intriguing aspects of the study is that the stromal population impacts this stem cell population, however, interactions and factors stimulating the crosstalk are not addressed.
*
Reviewer #3 (Significance (Required)):
Overal while the study includes an extensive amount of work and different approaches, a foundationally supported novel finding is missing. Many of the statements have already been demonstrated by others in the fields. In addition, one of the most intriguing aspects of the study is that the stromal population impacts this stem cell population, however, interactions and factors stimulating the crosstalk are not addressed.
We can only disagree.
4. Description of analyses that authors prefer not to carry out
- *
We have answered most questions raised by the referees by explaining our view, by clarifying individual points and, in several cases, by providing additional information that was not included in the original manuscript.
In a limited number of cases when additional experiments were suggested, we were unfortunately obliged to write that we are not in a position to perform them. This is because my lab is closing after more than fifty years of uninterrupted activity. There will unfortunately be nobody to perform additional experiments.
Nevertheless, as written by referees 1 and 2, we believe that the revised manuscript, as it stands, contains data that will be of interest to the people in the field and may be the bases for future developments. We hope editors will find interest in publishing it.
-
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Referee #3
Evidence, reproducibility and clarity
RC-2024-02491
An Lgr5-independent developmental lineage is involved in mouse intestinal regeneration Marefati et al.
Homeostatic maintenance of the intestinal epithelium has long been thought to rely upon Wnt signaling responsive Lgr5-expressing stem cells that reside at the crypt base. However, myriad reported mechanisms or populations have been reported to underlie epithelial regeneration after injury. Many groups have reported that reacquisition of a fetal-link intestinal phenotype is an import part of the regenerative response, however the originating cell type has not been definitively identified. Herein, the authors demonstrate that cells from adult homeostatic intestine can generate immortal spheroids that resemble fetal spheroids and are derived independent of Lgr5+ intestinal stem cells (ISCs). The authors then draw the conclusion that this indicates that a hierarchical stem cell model applies to regeneration of the intestinal epithelium, in addition to the plasticity model.
Comments:
- Please indicate what species is used for studies in Fig 1.
- Please clarify if Figure 2 studies utilize Matrigel or not.
- RNA-seq analyses of adult intestinal generated spheroids lack the granularity of single cell analyses and thus it is unclear if this is a homogeneous population or if the population has diversity across it (i.e., enteroids/organoids have a high level of diversity). Many of the conclusions from the RNA-seq study are broad and generalized-for example Fig 3F indicates that markers of the +4 ISC populations (Bmi1, tert, lrig1, hopx) were all expressed similarly in adult spheroids as compared to adult organoids. However, while this may be true in the bulk-RNA-seq analyses, clearly scRNA-seq would provide a better foundation to make this statement, as enteroids/organoids are comprised of heterogeneous subpopulations. . .and it might indicate that these +4 markers have only very low expression in the spheroids. Based upon these concerns, misconclusions are likely to be drawn.
- The language around Figure 4 results is confusing. Please define "white" and "red". It might be simpler to designate recombined versus not recombined lineage.
- The hypothesis that collagenase/dispase solution acts as a proxy for injury is not demonstrated and backed by data. Thus, it is difficult to make the conclusion that this approach could represent a "stable avatar" of intestinal regenerating cells. It is clear that subpopulations of crypt-based cells generate spheroids in culture without collagenase/dispase (see the cited reference Smith et al, 2018).
- A study based on the absence of recombination in a VilCre lineage tracing scenario is not well-established to be strong experimental approach, as there are many reasons why recombination may not cells may not be lineage marked. In order to use this system as the authors intend, they first need to demonstrate that villin is not expressed in the discrete cell population that they are targeting. For the presented observational studies, this would be difficult to do. While they do demonstrate differences in chromatin accessibility between cells from organoids versus spheroids (fig s4), some of these differences could merely be due to the bulk analytical nature of the study and the lack of comparing stem cell populations from spheroids to stem cell populations from organoids-since the spheroids are likely homogenous versus the organoids that only have a small fraction of stem cells-and thus represent a mix of stem cell and differentiated cell populations. The authors do not demonstrate that villin protein expression varies in these cells. If it were found that villin is not expressed in their "novel" population, then one would expect that the downstream use of villin-based recombination would demonstrate the same recombination potential (i.e., Mcl1 would not be recombined). Both recombination studies in Fig 6 are difficult to interpret, and thus it is not clear if these studies support the stated conclusions. Quantification of number of crypts that are negative should be reported as a percentage of recombined crypts.
- Figure 8 indicates that the cell population identified by scRNA-seq may be quiescent. Companion IF or IHC should be conducted to confirm this finding, as well as other conclusions from the informatics conducted.
- Clearly the data is intriguing, however, the conclusion is strong and is an over interpretation of the presented data. There are a number of validation or extension data that would enhance the overall interpretation of the study:
- a. validation of scRNA-seq or bulk RNA-seq concepts by protein staining of intestinal tissues in the damage model will serve as a secondary observation.
- b. identification of the ISC that they are defining is critical and important. There is already the notion that this cell type exists and it has been shown with various different markers.
- c. expand the analyses of the fetal-like expression profiling to injured intestines to demonstrate that the lineage negative cells indeed express fetal-like proteins.
- d. expand the discussion of the Clu+ cell type. Is this cell the previously described revival cell? If so, how does this body of work provide unique aspects to the field?
- There is some level of conflicting data, with the stem population being proliferative in culture stimulated by the stromal cells, but quiescent in vivo and also based upon scRNA-seq data in Fig 9.
- Many of the findings have been previously reported: Population that grows as spheroids (Figure 2), Population that is Wnt independent (Figure 2), Lgr5 independent regenerative growth of the intestine (figure 3F, Figure 4), Clu+ ISCs drive regeneration (Figure 7).
Minor comments:
- The statement that spheroids must originate from collagenase/dispase digested material might be an overstatement. As spheroids generation from EDTA treated intestines have been previously reported (Smith et al, 2018).
Overall while the study includes an extensive amount of work and different approaches, a foundationally supported novel finding is missing. Many of the statements have already been demonstrated by others in the fields. In addition, one of the most intriguing aspects of the study is that the stromal population impacts this stem cell population, however, interactions and factors stimulating the crosstalk are not addressed.
Significance
Overall while the study includes an extensive amount of work and different approaches, a foundationally supported novel finding is missing. Many of the statements have already been demonstrated by others in the fields. In addition, one of the most intriguing aspects of the study is that the stromal population impacts this stem cell population, however, interactions and factors stimulating the crosstalk are not addressed.
-
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Referee #2
Evidence, reproducibility and clarity
In this manuscript, the Marefati et al. developed a novel approach to generate spheroids from adult intestinal epithelium using a collagenase/dispase based protocol. Adult spheroids were found to be distinct from classic budding-type organoids normally generated from EDTA based release of the crypt epithelium. Transcriptional profiling indicated that adult spheroids were undifferentiated and similar to regenerating crypts or fetal spheroids. To identify the cell of origin that generates adult spheroids, the authors labelled epithelial cells with VilCreERT-LSL-Tom, VilCre-LSL-GFP and Lgr5CreERT-LSLTom mice. From these experiments the authors conclude that that spheroids are only generated from Vil-Cre negative and Lgr5 negative cells. Next the authors deleted the anti-apoptotic gene Mcl1 using Vil-CreERT mice. This led to a strong apoptotic response throughout the crypt epithelium and tissues processed from knockout mice readily generated spheroids, and in vivo, replenishment of the gut epithelium was mediated by unrecombined cells. In a second model, CBCs were ablated using Lgr5DTR mice and VilCre negative cells were found again to contribute to regeneration of the crypt epithelium. Finally based on the absence of Vil-Cre reporter activity, the authors were able to sort out and perform scRNAseq to profile VilCre negative cells. These cells were found to be quiescent, express the stem cell marker Olfm4 and were also abundant in ribosomal gene expression.
The fact that the authors developed a protocol to reproducibly generate fetal-like spheroids from adult tissue is an important advancement in the field. Previous reports have shown that treatment with various small molecule inhibitors can revert budding organoids into a spheroid morphology, but this manuscript demonstrates that spheroids can also be generated from otherwise untreated cells. This new methodology will provide new tools to dissect the molecular determinants of fetal/regenerative cells in the gut. Based on this, the manuscript should attract a significant amount of attention in the intestinal field.
As pointed out by the authors themselves the study has important limitations that diminish enthusiasm. The primary issue relates to the inability of the team to identify markers of VilCre neg cells other than the fact that these cells are Olfm4+ and quiescent. Nonetheless, for the reasons stated above the manuscript should reach the target audience within the research community, if the authors can address the specific points below related to issues with methodology as well as defining more precisely the characteristics and growth requirements of adult spheroid cultures.
Major comments
The main conclusion of the study is that Vil-Cre neg cells are rare quiescent Olfm4+ crypt cells. If this is the case, then standard EDTA treatment should release these cells as well. Consequently, spheroids should also emerge from isolated crypts grown in the absence of ENR. If this is not the case how do the authors explain this?
From the text the authors appear to suggest that growth of adult spheroids is dependent initially on "material" released by collagenase/dispase treatment. An obvious candidate would be mesenchymal cells, which are known to secrete factors such as Wnts and PGE2 that drive spheroid morphology. To test this, the authors should treat spheroid cultures with Porcupine and/or PGE2 inhibitors. If these inhibitors block growth then this would suggest that either stromal cells or autocrine signalling involving these pathways is important. Overall, more in-depth analysis of the growth requirements of adult spheroids is required.
Figure 1d indicates that adult spheroids can be propagated for at least 10 passages. The abstract mentions they are "immortal". The text itself does not address this issue. More precise information as to how long spheroids can be propagated is required. If these cultures can be propagated for 10 passages or more it becomes important to determine what nutrients/mitogens in the basal media are driving growth? Alternatively, what is the evidence that spheroid cultures are completely devoid of mesenchymal cells. The text only mentions that "Upon replating, these spheroids could be stably cultured free of mesenchymal cells (Fig.1B)". No validation is shown to support this.
In Figure 2, the authors describe the growth requirements for adult spheroids and indicate that spheroids grown in ENR or EN became dark and shrink. The representative images showing this are clear, but this analysis should be quantified.
In SF3, the gene expression profile of organoids from the sandwich method only partially overlaps with that of organoids from the old protocol. What are the gene expression differences between the 2 culture systems? Secondly, the sandwich method appears to sustain growth of Tom+ spheroids based on RNAseq and the IF images. This suggest that Vil-Cre negative cells are not necessarily the only source of adult spheroids and thus this experiment seems to indicate that any cell may be converted to grow as a spheroid under the right conditions. These points should be addressed.
In Figure 4, the authors conclude that spheroids do not originate from Lgr5 cell derived clones even after 30days post Tam induction. Does this suggest that in vivo and under homeostatic conditions VilCre neg cells are derived from a distinct stem cell pool or are themselves a quiescent stem cell. Given the rarity of VilCre neg cells, the latter seems unlikely. The problem with the original assertion is that Lgr5-CreERT mice are mosaic and therefore not all Lgr5+ cells are labelled in this model. "White" spheroids may thus derive from cells that in turn derive from these unlabelled Lgr5 cells.
ATACseq experiments were briefly mentioned in the manuscript but unfortunately little information was extracted from this experiment. What does this experiment reveal about the chromatin landscape of adult spheroids relative to normal organoids?
Significance
The fact that the authors developed a protocol to reproducibly generate fetal-like spheroids from adult tissue is an important advancement in the field. Previous reports have shown that treatment with various small molecule inhibitors can revert budding organoids into a spheroid morphology, but this manuscript demonstrates that spheroids can also be generated from otherwise untreated cells. This new methodology will provide new tools to dissect the molecular determinants of fetal/regenerative cells in the gut. Based on this, the manuscript should attract a significant amount of attention in the intestinal field.
-
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Referee #1
Evidence, reproducibility and clarity
In this manuscript, Marefati et al report an Lgr5-independent lineage in the regenerating intestine using in vitro organoids and in vivo injury-coupled lineage tracing model. In organoids, collagenase/dispase dissociated resulted in "immortal spheroids" that maintain a cystic and undifferentiated phenotype in the absence of standard growth factors (Rspondin/Noggin/EGF). Bulk RNAseq of spheroids demonstrates downregulation of classical CBC signatures and upregulation of fetal spheroid, mesenchymal, inflammation and regenerative signatures. In mice, Villin-Cre lineage tracing revealed some Villin-negative progenies that lack reporter tracing throughout crypt-villus ribbons after injury. The authors proposed that there is Lgr5-independent population support the regenerative response upon CBC depletion. A major caveat of this study is the identification of this population is based on absence of VilCre expression. It is surprising that there is no characterisation of Lgr5 expression throughout the manuscript whilst claiming of a Lgr5-independent lineage. Although the research question is potentially interesting, the concept of epithelial reprogramming upon injury is well documented in the field. The data generated in this manuscript also seem to be preliminary and lack of detailed characterisation. Below are specific comments.
- Expression of Lgr5 should be properly characterised throughout the manuscript in both organoid models and injury-induced regeneration in vivo.
- An important question is the origin of these "Lgr5-independent" adult spheroids. They look and appear like fetal organoids, which could be induced by injury (e.g. upon collagenase/dispase dissociation). Have the authors tried to culture fetal spheroids in BCM over extensive period of time? Do they behave the same? This would be a great way to directly compare the collagenase/dispase-derived organoids with fetal origin.
- Fig 1C, Why is the replating spheroid culture time different between mesenchymal cells and conditioned medium?
- It is unclear how the bulk RNA-seq data in Fig. 3 were compared. How long were the adult organoids and spheroids cultured for (how many passages)? Were they culture in the same condition of were they in ENR vs BCM? These are important information to consider when interpreting the results. For instance, are Ptgs1 & Ptgs2 expression in adult spheroids the same in ENR vs BCM? Are the gene signatures (regenerative, fetal and YAP) changed in adult spheroids culturing in ENR vs BCM?
- It is stated: "In agreement with their aptitude to grow indefinitely, adult spheroids express a set of upregulated genes overlapping significantly with an "adult tissue stem cell module" [159/721 genes; q value 2.11 e-94) (Fig.S2F)].". What is the definition of "indefinitely"? Are they referring to the Fig 1B where spheroid were passaged to P10? The authors should avoid the term "indefinitely" but use a more specific time scale, e.g. passages, months etc.
- SuppFig 3D: Row Z-Score is missing the "e" in Score.
- Fig 4E: Figure legend says QNRQ instead of CNRQ.
- Fig 4G: The brightfield image of adult spheroids 5 days after 3x TAM injections doesn't look like a spheroid. It seems to be differentiating.
- Fig 4: Most mouse model data are missing the number of mice & their respective age used for organoid isolation.
- Fig 4A-D, H-G: How was fluorescent signal of organoids quantified? How many images? Were there equal numbers of organoids? This all needs to be included in methods/figure legends.
- Figure 4B-D, G-H: Which culturing conditions were used for adult spheroids? Original method or sandwich method?
- Fig 6D-E: Please add the timepoint after DT administration these samples are from. It is not listed in text or figure legend.
- SuppFig 6D: again timepoint is missing.
- SuppFig 6: How were the crypts of these mice (DT WT & DT HE) isolated? Was this via EDTA? Also, what is the timepoint for isolation for these samples? Even if untreated, the timepoint adds context to the data. Please add more context to describing these different experiments, either in the figure legends or methods section.
- SuppFig 6E: The quality of the heatmap resolution is too poor to read gene names.
- Fig.5-7, are the regenerating crypt-villus units fully differentiated or are they maintained in the developmental state? Immunostaining of markers for stem cells (Lgr5), differentiated lineages (Alpi, Muc2, Lyz, ChgA etc.) and fetal state (Sca1, Trop2 etc) should be analysed in those "white" unrecombined crypt-villus units.
- The following text needs clarification:
"The kinetics of appearance of newly formed un-recombined ("white") crypts was studied after a single pulse of DT (Fig.7A). This demonstrated an increase at 48 hours, with further increase at day 10 and stable maintenance at day 30. The presence of newly formed white crypts one month after toxin administration indicates that the VilCre-negative lineage is developmentally stable and does not turn on the transgene during differentiation of the various epithelial lineages occurring after regeneration (Fig.7B). Comment: The "newly formed" is an overstatement, the data doesn't conclude that those are "new" crypts. The end of the sentence states that these "white" crypts form developmentally stable lineages, thus these white crypts at day 30 could originate from the initial injury. There was no characterisation of the various epitheial lineages. Are they fully differentiated? Is Lgr5 expressed one month after toxin administration? Can the VilCre neg lineage give rise to CBCs?
The relationship between white crypt generation and appearance of Clu-positive revival cells (Ayyaz et al., 2019) was then explored. In agreement with others and similar to what happens in the irradiation model, (Ayyaz et al., 2019; Yuan et al., 2023) Clu-positive cells were rare in crypts of untreated mice and their number transiently increased forty-eight hours after a single pulse of DT, and more so after three pulses of DT (Fig.7C,D). Comment: Comparing 1 pulse at day 2 vs 3 pulses at day 5 makes the data hard to interpret. How is the Clu ISH level for 1 pulse at day 5? Are they equivalent?
Clu-positive cells were less frequently observed in white crypts (see "Total" versus "White" in Fig.7C). This fits with the hypothesis that Clu expression marks acutely regenerating crypts and that a proportion of the white crypts are chronically regenerating due to DTR expression in CBCs." Comment: I believe the authors suggested that the discrepancy of less Clu expression in white crypts is due to the ectopic expression of DTR in CBCs causing low grade injury without DT administration. This means that some white crypts could have been formed before the administration of DT, and thus are on a different regenerative timeline compared to the white crypts formed from DT administration. Is there any proof of the chronic regeneration? Immunostaining of chronic regenerative markers such as Sca1, Anxa1 or Yap1 nuclear localization would support the claim. It'd be important to show only the white crypts, but not the RFP+ ones, show regenerative markers. - Fig 7D-E: What are the timepoints of harvest for HE-WT-HE 1 pulse DT mice and HE-HE-HE PBS injected mice? - Fig 8-9: Regarding the CBC-like Olfm4 low population, what is the status of Lgr5? This should be shown in the figure since the argument is that this is an Lgr5-independent lineage. And what about the regenerative, Yap, mesenchymal and inflammatory signatures? Are they enriched in the white crypts similar to the in vitro spheroids?
Significance
Strengths: The study employed a range of in vitro and in vivo models to test the hypothesis.
Limitations: Unfortunately, the models chosen did not provide sufficient evidence to draw the conclusions. Injury induced reprogramming, both in vivo and in vitro, has been well documented in the field. The new message here is to show that such reprogrammed state is continuous rather than transient; instead of regenerating Lgr5+ stem cells, it can continue to differentiate to all cell lineages in Lgr5-independent manner. However, through the manuscript, there was no immunostaining of Lgr5 and other differentiation markers. The conclusion is an overstatement without solid proof.
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Manuscript number: RC-2023-02306
Corresponding author(s): John, Yates
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1. General Statements [optional]
We greatly appreciate the reviewers taking time from their busy scientific careers to evaluate our manuscript. We were elated to read all the positive comments, such as “the conclusions are well-supported and convincing”, “should contribute to a more nuanced understanding of SCZ pathogenesis”; “The potential implications for drug development underscore the broader significance of the study in advancing our knowledge of neurobiology and its relevance to neurological disorders like schizophrenia”, and “The study is informative, and has great potential to enrich the specific literature of this field”. We also found the constructive criticism very helpful for improving our manuscript. We performed additional experiments and bioinformatic analyses, as requested. We modified the manuscript to answer the reviewers’ questions. Due to its complexity, it is difficult to describe the different and sometimes conflicting hypotheses of SCZ pathogenesis in a single manuscript. This complexity is reflected in the conflicting requests from the reviewers. One reviewer requested we investigate and highlight the role of non-neuronal cells in SCZ while another reviewer suggested we did not focus enough on synaptic proteins. We believe we have achieved a balance to represent the intricacy of SCZ biology and the different opinions of the reviewers.
Thanks again.
2. 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. *
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Reviewer #1 (Evidence, reproducibility and clarity (Required)):
Summary: Provide a short summary of the findings and key conclusions (including methodology and model system(s) where appropriate). In this manuscript, McClatchy and colleagues used a conventional approach combining immunoprecipitation (IP) of endogenous target proteins (baits) followed by liquid chromatography mass spectrometry (MS) analysis of the co-immunoprecipitating proteins to map protein-protein interaction (PPI). This interaction network is centered around baits that had been annotated as susceptibility factors for schizophrenia (SCZ). A variety of previous studies have identified thousands of such SCZ susceptibility factors. Mostly based on the availability of antibodies, 8 bait proteins were selected in this study. The authors reasoned that immunoprecipitating endogenous proteins from tissues using specific antibodies was a more accurate view of physiological conditions than epitope tagging followed by affinity purification (AP) from cells in culture. The model system from which proteins were extracted was the hippocampus dissected from mice that had been treated or not by phencyclidine (PCP), a drug that has been shown to induce SCZ symptoms in humans and animals. By comparing the proteins identified and quantified from the PCP-treated samples against control IPs and/or saline-injected mouse controls, a large number of PPI were deemed statistically significant. Most of these potential interactors were not present in PPI databases (BioGRID), most likely because such databases are populated with large-scale APMS datasets from cell cultures, with very few studies using brain tissue. Strikingly, many of the co-immunoprecipitated proteins were also known as SCZ susceptibility factors, which lend weight to the hypothesis that these factors form a large protein interaction network, localized at the synapses.
Major comments: - Are the key conclusions convincing? Overall, the conclusions drawn from the experimental design, data analysis, and corroboration with existing literature are well-supported and convincing. When selecting the SCZ susceptibility factors, the authors clearly state their goal, the databases used for gene selection, and the rationale for choosing proteins with synaptic localization. The inclusion of evidence from genetic studies and previous publications strengthens the credibility of the selected genes. The methodology used to establish the novel SCZ PPI network is mostly well-described (see minor comments below). The use of an 15N internal standard also adds rigor to the quantitation of PPI. The GO enrichment analysis provides valuable insights into the biological functions and cellular components associated with the SCZ PPI network. The annotation of identified proteins using the SynGo synaptic database and the distribution of annotated synaptic proteins among different baits further support the biological relevance of this PPI network. The cross-referencing of the PPI network with published genetic studies on SCZ susceptibility genes adds robustness to the findings. Specifically, the observation that 68% of protein interactors have evidence of being potential SCZ risk factors is a strong corroboration of the prevailing hypothesis in the field. Finally, the significant changes induced by PCP that were identified for all baits except Syt1, along with the comparison of altered proteins with SAINT-identified PPI, add depth to the understanding of PCP modulation.
- Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether? No, but note that APMS/IPMS has been around for more than a decade (Introduction page 3).
We agree and did not mean to imply that IP-MS is new technology. We tried to convey that IP-MS is not new technology, but the number of IP-MS studies employed to study the PPI of endogenous proteins in brain tissue is a small percentage of all the published PPI MS studies.
We added the following to the Conclusions to clarify this point: “Although IP-LC-MS technology has been employed for more than a decade, quantitation of proteins using this strategy in mammalian tissue is scarce in the literature.””
- Would additional experiments be essential to support the claims of the paper? Request additional experiments only where necessary for the paper as it is, and do not ask authors to open new lines of experimentation. One piece of data that is missing are Western blots using the 8 selected antibodies against the proteins extracted from their experimental samples to validate the antibodies recognize 1 protein of the expected size from these tissue extracts.
We took your suggestion and performed immunoblots with our 8 IP antibodies using the starting material (i.e. rat brain hippocampus). All antibodies recognized a single band of the approximate molecular weight of the target except for the Gsk3b, which produced a doublet instead of a single band. This image is similar to what has been observed with the phosphorylation of Gsk3b(Krishnankutty, Kimura et al. 2017, Vainio, Taponen et al. 2021). To provide evidence that the additional band observed for Gsk3b is the phosphorylated target protein, we searched our Gsk3b IP dataset for a differential phosphorylation (i.e. 79.9663) on S,T, or Y. Even though we did not perform phosphorylation enrichment, we identified S389 as abundantly phosphorylated in all Sal and PCP samples consistent with our immunoblot. Images of these immunoblots are now Supplementary Figure 1.
- Are the suggested experiments realistic in terms of time and resources? It would help if you could add an estimated cost and time investment for substantial experiments. Running SDS-PAGE and Western blotting should be straightforward and cheap.
- Are the data and the methods presented in such a way that they can be reproduced? Yes
- Are the experiments adequately replicated and statistical analysis adequate? Yes
Minor comments: - Specific experimental issues that are easily addressable. The rationale for the short duration between PCP injection and animal sacrifice is only explained in the discussion section (page 17). The fact that this short treatment of less than 30 min should prevent any change in transcription or translation should be introduced earlier (in the experimental procedures).
We agree this is an important aspect of the study and that it suggests that the effect of PCP is independent of changes in transcription and translation as stated in the Discussion.
We added the following to the Introduction:
“PCP was administered for less than 30min., which precluded any changes in transcription or translation and allowed us to focus on PPI.*” *
Note that the duration is written as 26 min on page 4 and 25 min on page 9. Please reconcile these numbers*. *
We have corrected this typo. It was 26min.<br /> Is there any biological significance for this SCZ study that the mice were maintained on a reverse day-night cycle?
Rats are nocturnal animals, i.e. active at night and sleep during the day. In this study, rats were housed on a reverse day-night cycle so that assessment of the response to PCP could be evaluated during their active phase. This is not specific SCZ research and is the routine protocol for behavioral testing in the Powell laboratory. It is not clear from reading Experimental Procedures/Bioinformatic Analysis section (page 6) if normalized N14/N15 protein ratios measured in the bait-IPs and control-IPs were used for the SAINT analysis? Or did the authors used label-free quantitation with spectral counts?
We apologize for not making the methods clearer. In the results, it is stated that the N14 identifications are used in the SAINT analysis, and we state in the Discussion that SAINT uses spectral counts. We modified the Experimental Procedures/Bioinformatic Analysis section (page 6) to state: The input for SAINT was only the 14N identifications.
*- Are prior studies referenced appropriately? Yes
- Are the text and figures clear and accurate? *Fig1C: The workflow is a little too simple, the authors might want to add more details.
We revised Fig1C with more details as suggested.
FigS1C: Please add x-axis title (spectral counts) directly to the figure.
“Spectral counts” was added to the x-axis. FigS1C is now FigS2C ,with the addition of the immunoblots you suggested. Fig2B-D: The color scale bar should have number values to denote lower and upper limits in % (as opposed to "lowest" and "highest"). Numerical values were added to replace the upper and lower limits. - Do you have suggestions that would help the authors improve the presentation of their data and conclusions? No * *
Reviewer #1 (Significance (Required)):
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Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field. In this study, the authors have drastically expanded the protein interaction landscape around 8 known SCZ susceptibility factors by using a conventional IPMS approach. Performing the IPs on protein extracted from hippocampus dissected from mice treated with phencyclidine to model SCZ increases the biological significance of such lists of proteins. Furthermore, the co-immunoprecipitation of many other SCZ susceptibility factors along with the 8 selected baits supports the hypothesis that these proteins of varied functions are part of large interaction networks. Overall, the integration of experimental data with in silico networks, along with the quantification of PPI changes in response to PCP, should contribute to a more nuanced understanding of SCZ pathogenesis. The potential implications for drug development underscore the broader significance of the study in advancing our knowledge of neurobiology and its relevance to neurological disorders like schizophrenia.
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Place the work in the context of the existing literature (provide references, where appropriate). Overall, this study contributes to the existing literature by providing experimental data on in vivo PPI networks related to SCZ risk factors. Not only do the authors validate 124 known interactions but also they identify many novel PPI, due to a gap in the existing literature regarding the comprehensive mapping of PPI directly from tissue extracts, especially brain tissue. The authors advocate for more IPMS studies in mammalian tissues to generate robust tissue-specific in silico networks, which agrees with the growing understanding of the importance of tissue-specific networks for identifying disease mechanisms and potential drug targets. Furthermore, the SCZ PPI network reported here is enriched in proteins previously associated with SCZ, which aligns with the existing literature emphasizing the involvement of certain proteins and pathways in the pathogenesis of SCZ [References: 78-85]. The authors also investigate the response of the SCZ network to PCP treatment, hence providing insights into the potential effects of post-translational modifications, protein trafficking, and PPI alterations in a model of schizophrenia, which adds to existing knowledge about the impact of PCP on the molecular processes associated with SCZ [References: 88, 89, 92].
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State what audience might be interested in and influenced by the reported findings. Overall, the findings reported in this manuscript have implications for both basic research in molecular biology and potential translational applications in the development of targeted therapies for neurological disorders, particularly schizophrenia. The study delves into in vivo protein-protein interaction (PPI) networks related to genes implicated in schizophrenia (SCZ) risk factors. Researchers in neuroscience, molecular biology, and psychiatry would find the information valuable for understanding the molecular basis of SCZ. The study highlights the potential for identifying disease "hubs" that could be drug targets. Pharmacologists and drug developers interested in targeting protein complexes for drug development, especially in the context of neurological disorders, may find the study relevant.
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Define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate. Technical Expertise | biochemistry, liquid chromatography mass spectrometry, proteomics, computational biology, protein engineering, protein interaction networks, post-translational modifications, protein crosslinking, proximity labeling, limited proteolysis, thermal shift assay, label-free and isotope-labeled quantitation. Biological Applications | human transcriptional complexes, apicomplexan parasites, viruses, nuclear envelope, ubiquitin ligases, non-model organisms.
Reviewer #2 (Evidence, reproducibility and clarity (Required)):
Summary: McClatchy, Powell and Yates aimed at identifying a protein interactome associated to schizophrenia. For that, they treated rats (N14 and N15) with PCP, which disturbs gutamatergic transmission, as a model for the disease and co-immunoprecipitated hippocampi proteins, which were further analyzed by standard LC-MS.
The study is new, considering not much has been done in this direction in the field of schizophrenia. This justifies its publication. On the other hand, a major flaw of the is the lack of information on the level of interaction of the so called protein interactome. Meaning, we cannot distinguish, as the study was performed, which proteins are directly interacting with the targets of interest from proteins which are interacting with targets´ interactors. The different shells of interaction are crucial information in protein interactomics.
Major: most of I am pointing below must be at least discussed or better presented in the paper, as It may not be solvable considering how the study has been conducted.
1) The study fails in defining the level of interaction of the protein interactome with the considered targets. This has been shortly mentioned in the discussion, but must be more explicit to readers, for instance, in the abstract, introduction and in the methods sections. We agree this is crucial information that is absent from our dataset. As we explained in the Discussion, we cannot distinguish between PPI that are direct interactors with the target protein and PPI that reside in a multi-protein complex that includes the protein (i.e. indirect). This is an inherent problem with any IP-MS study. We amended the Introduction to highlight the ambiguity of the interaction data produced by the IP-MS approach, as you suggested.
Text added to the Introduction:
“Regardless of whether Ab or tagged proteins are employed to identify PPI from a biological sample, it cannot be determined if the identified interactor binds directly to the target protein or reside in a complex of proteins that includes the target protein (i.e. indirect).”
Since this important information is routinely missing from IP-MS studies, we decided to try to determine the level of interaction by using the artificial intelligence algorithm AlphaFold3(AF3). We believe it is not yet optimized for PPI, but AF3 is a big leap forward in the field of structural biology. For example, we observed AF3 did not predict high confident structures for our large membrane target proteins and was unable to validate known direct PPI of these targets. In addition, analyzing data with AF3 is currently not automated or streamlined so with ~1600 PPI identified in our dataset, we chose to look at one target protein, Ppp1ca. AF3 identified many known direct binding proteins in our Ppp1ca PPI dataset, which gives high confidence to the novel PPI predicted to be direct interactors. The AF3 data is encompassed in an additional Figure 6.
The following was added to the Results Section:
“A disadvantage of IP-MS studies is that it cannot distinguish between a PPI that binds directly to the target protein, and a PPI in which the interactor and target protein reside the same multiprotein complex (i.e. indirect). We sought to predict which PPI may be directly interacting with its target protein by using the artificial intelligence algorithm AlphaFold3(AF3) (Abramson, Adler et al. 2024). First, we analyzed the predicted AF3 structure of the targets using the pTM score and the fraction of each structure calculated to be disordered (Figure 6A and Supplementary Table7). Our reasoning was that if targets have a poorly resolved structures, it will be difficult to screen them for direct PPI. A pTM score >0.5 suggests that the structure may be correct (the highest confidence score is 1). Undefined or disordered regions hinder the accuracy of the prediction. All targets possessed a pTM score > 0.5 except Syt1. The disordered fraction negatively correlated with the pTM score, as expected. Gsk3b, Ppp1ca, and Map2k1 had the highest pTM scores and were also the smallest of our target proteins (Figure 6B). Ppp1ca had the most confident structure (i.e. pTM 0.9) and the smallest disordered fraction (i.e. 0.07). Next, we determined the AF3 prediction of previously reported direct interactions of the targets. We used the iPTM score to determine interaction confidence. An iPTM score >0.8 is considered a highly confident direct interaction, whereas 0.8. These eight PPI have all previously been reported to form a direct interaction with Ppp1ca, except Phactr3 (Zhang, Zhang et al. 1998, Terrak, Kerff et al. 2004, Hurley, Yang et al. 2007, Marsh, Dancheck et al. 2010, Ragusa, Dancheck et al. 2010, Ferrar, Chamousset et al. 2012, Choy, Srivastava et al. 2024, Xu, Sadleir et al. 2024)*. Phactr3 is structurally similar to, but less studied than, the reported direct interactor Phactr1. These interactors are all inhibitors of PP1 except Ppp1r9b which targets Ppp1ca to specific subcellular compartments. Nine PPI were assigned a score The following has been added to the Discussion:
“Our SCZ PPI network consists of two types of PPI: direct physical interactions and “co-complex” or indirect interactions. Typically, the nature of the interaction can be distinguished in IP-MS studies. We decided to employ the new AF3 algorithm to screen the PPI of Ppp1ca to provide evidence for direct interactors. We chose to examine the PPI assigned to Ppp1ca, because its structure was the most confident among our target proteins and AF3 correctly predicted a known direct interactor with high confidence. Ppp1ca is a catalytic subunit of the phosphatase PP1, which is required to associate with regulatory subunits to create holoenzymes (Li, Wilmanns et al. 2013). Eighteen PPI were predicted to be directly interacting with Ppp1ca using a 0.6 or higher iPTM filter. This filter may be too conservative and generate false negatives, because another study employed a 0.3 filter followed by additional interrogation to screen for direct PPI (Weeratunga, Gormal et al. 2024). Forty-four percent of these predictions were confirmed by previous publications. Most of the validated direct interactions are inhibitors of the phosphatase, but one, Ppp1r9b (aka spinophilin), is known to target Ppp1ca to dendrite spines to enhance its activity to specific substrates (Allen, Ouimet et al. 1997, Salek, Claeboe et al. 2023). This high correlation with the literature provides substantial confidence in the novel PPI predicted to be direct Ppp1ca interactors. The AF3 screen predicted that NDRG2 directly interacts with Ppp1ca. This protein is known to regulate many phosphorylation dependent signaling pathways by directly interacting with other phosphatases including Pp1ma and PP2A (Feng, Zhou et al. 2022, Lee, Lim et al. 2022). Actin binding protein Capza1 was also predicted to directly interact with Ppp1ca and Ppp1ca interacts with actin and its binding proteins to maintain optimal localization for efficient activity to specific substrates (Foley, Ward et al. 2023). Hsp1e is a heat shock protein predicted to directly interact with Ppp1ca. Although there is no direct connection to Ppp1ca, other heat shock proteins have been reported to regulate Ppp1ca (Mivechi, Trainor et al. 1993, Flores-Delgado, Liu et al. 2007, Qian, Vafiadaki et al. 2011). We also observed that many of these direct PPI were altered with PCP treatment. One direct interactor, Ppp1r1b (aka DARPP-32), is phosphorylated at Thr34 by PKA in the brain upon PCP treatment. This phosphorylation event converts Ppp1rb to a potent inhibitor of Ppp1ca(Svenningsson, Tzavara et al. 2003). Importantly, manipulation of Thr34 attenuated the behavioral effects of PCP. Consistent with this report, Ppp1r1b-Ppp1ca interaction was only observed with PCP in our study. Further investigation is needed to determine if our novel direct interactors regulate the PCP phenotype. We conclude that AF3 can provide important structural insights into the nature of PPI obtained from large scale IP-MS studies.”
2) Considering the protein extraction protocol, it is fair to mention that only the most soluble proteins are being considered here. I am bringing this up since the importance of membrane receptors is clear in the studied context. This is an interesting point. It has been predicted that transmembrane proteins constitute 25-30% of the proteome(Dobson, Remenyi et al. 2015). Thus, we would predict our dataset will have more soluble proteins than membrane proteins. Half of our target proteins were transmembrane proteins, so in designing the protocol for this study we ensured that these membrane proteins could be significantly enriched compared to the control IPs (Supplementary Figure 2C). In addition, compared to soluble proteins, membrane proteins are notoriously difficult to identify by bottom-up proteomics (Savas, Stein et al. 2011). We decided to investigate how many of our protein interactors were transmembrane proteins. Using Uniprot, 199 (20%) of our protein interactors were determined to have a transmembrane domain. Therefore, this data does not support the statement that only the most soluble proteins are being considered in our study. We added this percentage of transmembrane proteins in our network to the text of the Results section.
3) It is not clear from the methods description if antibodies from all 8 targets were all together in one Co-IP or have been incubated separately in 8 different hippocampi samples. It seems the first, given how results have been presented. If so, this maximizes the major issue raised above (in 1). We apologize for not clearly describing our experimental design. All the targets were immunoprecipitated separately and analyzed separately on the mass spectrometer. With all the biological replicates and two conditions (i.e. Saline and PCP), we performed 48 individual, separate IPs. There were an additional 48 individual, separate IPs run in parallel that were the control IPs.
We modified the schematic of our experimental design in Figure 1C to clarify that the 8 targets IPs were analyzed separately. In addition, we modified the Results to read:
“In total, 96 (48 bait and 48 control) IPs were performed, and each was analyzed separately by LC-MS analysis.”
4) Definitely, results here are not representing a "SCZ PPI network". PCP-treated animals, as any other animal model, are rather limited models to schizophrenia. As a complex multifactorial disease, synaptic deficits, which is the focus of this study, can no longer be considered "the pivot" of the disease. Synaptic dysfunction is only one among many other factors associated to schizophrenia.
We do agree that synaptic dysfunction is only one factor associated with SCZ and we will discuss this more in our response to your next comment.
We understand the limitations of PCP as an animal model of SCZ. It is quite difficult to model a specific human complex multifactorial neurological disease in rodents and we would contend that there is no single universal SCZ model that everyone agrees with. We addressed this by adding the following to the Introduction:
“Since many SCZ symptoms are uniquely human, this is no single animal model that truly replicates all the complex human SCZ phenotypes(Winship, Dursun et al. 2019). In this respect, all SCZ animal models can be considered limited.* “ *
We respectfully disagree, however, with the term SCZ PPI network. This study is focused on SCZ by choosing proteins implicated in SCZ, quantitating how the PPI changes in a SCZ model, and discussing how our findings are relevant to SCZ pathogenesis. So, it seems logical to call our dataset a SCZ PPI network. We do concede that without further experimentation we do not know if these PPI play a causal role in SCZ. Furthermore, our novel PPI may involve biological pathways unrelated to SCZ and that have relevance to other biological conditions.
We added the following statement to the Discussion to address this comment:
“Even though our network was constructed in the context of SCZ, our dataset has relevance to other neurological diseases where our targets have been implicated in the pathogenesis.”
5) Authors should look for protein interactions that might be happening also in glial cells. They are not the majority in hippocampus, but are present in the type of tissue analyzed here. Thus, some of the interactions observed might be more abundantly present in those cells. Maybe enriching using bioinformatics tools the PPI network to different cell types.
As mentioned above, we agree that synaptic dysfunction is just one of the hypotheses of SCZ pathogenesis and emerging evidence suggests that dysfunction in astrocytes and microglia are factors. Since these non-neuronal cells can regulate synapses, these hypotheses are not mutually exclusively and suggests that at the cellular level SCZ etiology involves multiple cell types.
We addressed your query by comparing our PPI network to an RNA-seq analysis of different cell types in the rodent brain(Zhang, Chen et al. 2014). First, we analyzed our target proteins, and found that they were expressed in all cell types to varying degrees except Syngap which was not in the RNA-seq database. This data is now represented in Figure 3E. We then determined the RNA abundance distribution of all the protein interactors, which is represented in Figure 3D as a heatmap. From a bird’s eye view, it suggests that some PPI exist in non-neuronal cells. Next, we determine how many of our protein interactors were enriched in one cell type, which is shown in Figure 3F. We defined an enriched protein as having >50% of the RNA signal in one cell type. We identified 175 proteins that were enriched in one cell type compared to the entire RNA-seq dataset which had 4008 enriched proteins. In the entire RNA-seq dataset, 24% of the enriched proteins were in neurons whereas 47% of our protein interactors were enriched in neurons. This is consistent with the enrichment of synaptic proteins in our network. There was also an increased percentage of astrocytes (19%) and oligodendrocytes (6%) in our network compared to the entire database (i.e. astrocytes-11% and oligodendrocytes-4%). In other cell types, such as microglia, there was less protein enrichment in our network compared to the database. We have amended this cell type analysis to our manuscript and concluded that a portion of our PPI network may occur in non-neuronal cells. We also created a supplementary table of our network with its associated RNA-seq data.
Text added to the Results:
“Non-synaptic proteins represented 59% of our network suggesting that some PPI may occur in non-neuronal cells. To investigate this possibility, we annotated our network with a transcriptome rodent brain database of eight cell types(Zhang, Chen et al. 2014). All the targets were detected in all cell types but there was obvious enrichment in specific cell types for some targets (Figure 3E). Syngap1 was not in the database. We also observed a large variation of cellular distributions for the interactors (Figure 3D). Next, we sought to determine how many interactors are enriched in a particular cell type by defining cell enrichment as a protein having >50% RNA signal in one cell type. We identified 175 protein interactors enriched in one cell type, whereas the entire database had 4008 proteins enriched (Figure 3F). Consistent with our synaptic enrichment, 47% of the enriched protein interactors were in neurons whereas only 24% of the enriched protein in the entire database were in neurons. We also observed an increase in protein interactors enriched in astrocytes compared to the database. Overall, this analysis provides evidence that our identified PPI may occur in non-neuronal cells.”
Text added to the Discussion:
“The exact etiology of SCZ, however, remains unclear and synaptic dysfunction is only one hypothesis (Misir and Akay 2023). There is evidence for the involvement of non-neuronal cell types, including endothelial cells, astrocytes, and microglia(Tarasov, Svistunov et al. 2019, Rodrigues-Neves, Ambrosio et al. 2022, Stanca, Rossetti et al. 2024). Although we observed an enrichment of synaptic proteins in our SCZ network, we provided evidence that a portion of our network may occur in non-neuronal cells. Since non-neuronal cells can regulate synapses(Vilalta and Brown 2018, Bauminger and Gaisler-Salomon 2022), synaptic dysfunction and perturbations in non-neuron cells in SCZ etiology are not mutually exclusive. Our data corresponds with emerging evidence that pathogenesis is multifaceted, involving dysfunction in multiple cell types. “
Minor: 1) in the abstract, it is not clear if 90% of the PPI are novel to brain tissue in general or specifically schizophrenia. We apologize for the confusing sentence. 90% are novel meaning the PPI have not been reported in any study. We changed the abstract to read:
“Over 90% of the PPI have not been previously reported.”
2) authors refer to LC-MS-based proteomics as "MS" all across the text. Who am I to say this to Yates et al, but I think it is rather simplified use "Mass Spectrometry Analysis", when this is a typical LC-MS type of analysis We agree with you. We have replaced MS analysis with LC-MS analysis in the manuscript.
3) Several references used to construct the hypothesis of the paper are rather outdated: several from 10-15 years ago. It would be interesting to provide to the reader up to date references, given the rapid pace science has been progressing. We agree many of the references are 10-15 years old. Many of the hypotheses and biological mechanisms we discussed can be supported by too many studies to cite them all, due to space. If we could, we would. We also agree that there are many more recent studies that have confirmed and added more details to the original discovery or hypothesis cited. We cite the first study to support our conclusions because it deserves the most credit.
4) "UniProt rat database". Please, state the version and if reviewed or unreviewed.
This information was added to the Methods section. UniProt reviewed rat database with isoforms 03-25-2014.
Reviewer #2 (Significance (Required)):
The study is informative, and has great potential to enrich the specific literature of this field. But should tone down some arguments, given the experimental limitations of the PPI network (as described above) and should state PCP-treated rats as a limited model to schizophrenia.
Reviewer #3 (Evidence, reproducibility and clarity (Required)):
Summary
It is now widely accepted that schizophrenia is polygenic disorder in which a large fraction of the genetic risk is in variants affecting the expression of synaptic proteins. Moreover, it is known that these synaptic proteins are found in multiprotein complexes and that many proteins encoded by schizophrenia risk genes interact directly or indirectly in these complexes. It is also known that some drugs including phencyclidine, which binds to NMDA receptors and to Dopamine D2 receptors (not mentioned by the authors) can induce schizophreniform psychosis. The authors have set out to advance on this position by performing proteomic mass spectrometry studies on proteins identified as encoded by schizophrenia risk genes. They target 8 proteins for immunoprecipitation from rat brain and identify coisolated proteins and perform various network analyses. In the most interesting part of the paper they ask if PCP-treatment altered protein interactions and report various changes.
Major comments:
- Choice of target proteins. It was not until the first paragraph of the results section that the authors first name the 8 synaptic proteins that have chosen to study. This information should be in the abstract.
This information was added to the abstract as requested.
The authors then use figure 1A and 1B as evidence that these 8 "baits" are schizophrenia-relevant proteins. Figure 1A does not provide any evidence at all and Figure 1B is about as weak a line of evidence imaginable - a histogram of the number of papers that have the search term "schizophrenia" and the protein name. I tried this search for Grin2B and almost immediately found papers that reported no association between Grin2B and schizophrenia (e.g. PMID: 33237434). Figure 1B should be scrapped.
The purpose of Figure 1A was not to demonstrate that there is evidence that our proteins are involved in SCZ. The purpose of this figure is to show that these proteins are diverse in function and structure (blue = membrane proteins; yellow = soluble proteins), and that there are published studies reporting physical and functional interactions between these 8 proteins. This suggests that a more extensive network may exist.
We agree that Figure 1B does not specifically describe how each protein is related to SCZ but demonstrates how many papers investigating their connection to SCZ have been published. We understand how by itself, this can be considered weak. We still think it is important to show that multiple laboratories have published papers connecting these proteins to SCZ. Instead of scrapping this figure, we have moved it to the Supplementary Figure 2A.
We read PMID: 33237434 and interpret their findings quite differently than you. This report examined whether one single nucleotide mutation (SNV) in Grin2b is associated with the cognitive dysfunction in SCZ but did not examine if this mutation is associated with the other major SCZ phenotypes (i.e. psychotic and emotional). Specifically, the study selected 117 “patients in whom cognitive dysfunctions are present despite effective antipsychotic treatment of other schizophrenia symptoms.” The study concluded that Grin2B SNV was not associated with this subset of patients but concluded that they need to search for other NMDAR variants and study their association with SCZ. We would argue that the only reason this group performed these experiments was the well-known association between Grin2b and SCZ. Many studies have found SNVs in Grin2B that are associated with SCZ, but there are conflicting reports. It is unclear if the discrepancies are connected to different cohorts, complexity of SCZ phenotype, or small sample sizes. Regardless of Grin2B mutations significantly associated with SCZ, there are several lines of evidence that Grin2B is involved in SCZ. Most importantly, Grin2b is a component of the NMDAR, which is a key player to the SCZ hypo-glutamate hypothesis and the receptor that binds PCP. By immunoprecipitating Grin2b, we are analyzing the PPI network of NMDAR, which is arguably the most studied complex in SCZ research.
The remaining part of paragraph 1 of the results does not provide an adequate, let alone systematic, justification for the use of the 8 baits. It would be appropriate to construct a table with the 8 proteins and cite relevant papers and identify the basis for why they are implicated in schizophrenia (is it a direct mutation or some other evidence?). What makes these 8 proteins better than many others that are cited as synaptic schizophrenia relevant proteins?
We apologize for not clearly and thoroughly describing the reasons for choosing our baits. As stated in the first paragraph of the Results, we chose the proteins that had evidence of being a SCZ risk factor in SCZ databases that included a plethora of human genomic studies. This criterion by itself results in ~5000 genes. To further narrow our candidates, we chose targets that were synaptic and were observed to have phosphorylation changes in response to PCP in an SCZ animal model. Since protein-protein interactions (PPI) are often dependent on phosphorylation, we believe this is an important criterion for quantitation of PPI in response to PCP. These requirements still resulted in a list of hundreds of proteins. So, what makes these better than any other SCZ relevant protein? As stated in the manuscript, the major limiting criterion was identifying commercial antibodies that can efficiently immunoprecipitate their target in brain tissue. Since there are many reports associating our targets with SCZ, we directed the reader to SCZ databases that compile large genomic association studies. We understand, however, the request for more specific information regarding the biological connection between these proteins and SCZ. We took your suggestion and constructed a table with our 8 targets, and it is now Figure 1A. In this table, we selected references to indicate if the target has reported changes in expression and/or activity in SCZ samples (i.e. human and animal model) or genetic association with SCZ in human studies.
The methods of protein extraction are particularly concerning. The postsynaptic density of excitatory synapses (which contains several of the target proteins in this study) has been notoriously difficult to solubilise unless one uses high pH (9) and harsh detergent extraction (1% deoxycholate). The authors use pH 7 and weak detergent conditions, which are likely to be inefficient for solubilising at least several of the target proteins. Nowhere do the authors report how much of the total of their target protein is being solubilised. Indeed, there are no figures showing biochemical conditions at all. What if only a small percentage of the target protein is being immunoprecipitated - what does this mean for the interaction data? How do we know if the fraction being immunoprecipitated is from the synapse? (why did they not use synaptosomes).
How do we know if the fraction being immunoprecipitated is from the synapse? (why did they not use synaptosomes). The absence of this kind of data undermines the reader's confidence in the findings.
We apologize for not clearly explaining our experimental design We were not interested in identifying the PPI of the PSD. All these proteins have been localized to the synapse, but they are also localized to other neuronal compartments and non-neuronal cell types. Synaptic dysfunction is one hypothesis of SCZ pathogenesis, but there is evidence of other cell types, including astrocytes, microglia, and oligodendrocytes(Kerns, Vong et al. 2010, Ma, Abazyan et al. 2013, Goudriaan, de Leeuw et al. 2014, Park, Noh et al. 2020). For these reasons, we chose an unbiased approach to identifying PPI.
The Results have been amended to read: “All the targets are localized to the synapse, but also localized to non-synaptic compartments and expressed in non-neuronal cells. Thus, since there is also evidence for non-synaptic perturbations contributing to SCZ pathogenesis, we chose to perform an unbiased analysis in unfractionated brain tissue (Tarasov, Svistunov et al. 2019, Rodrigues-Neves, Ambrosio et al. 2022, Stanca, Rossetti et al. 2024). “
Why do we choose a specific solubilization strategy? Harsh detergents can disrupt PPI and prevent efficient enrichment of the target by disrupting the target-antibody interaction(Pankow, Bamberger et al. 2015). To identify protein interactions, mild detergent conditions are typically employed in PPI studies. We used a combination of “weak” detergents (i.e. 0.5% NP-40, 0.5% Triton, and 0.01% Deoxycholate) to help prevent non-specific PPI, but still allowing efficient enrichment of the target proteins. We do agree that with our conditions the targets were not completely solubilized. It is a balancing act to find the correct conditions for IP-MS analysis. Since we are unable to immunoprecipitate all the target protein, we did not identify all the PPI for each target, and we did not make this claim. Importantly, we did identify known interactions for all our targets. Our mild detergent protocol is similar to other PPI studies and our results validates results reported in previous studies. It is more important to significantly enrich the target protein over control than to achieve complete solubilization (Supplementary Figure 2D). This allows us to use control IPs to successfully employ the SAINT algorithm to determine which proteins are confident PPI using a 5% FDR.
How do we know protein are being immunoprecipitated from the synapse? As we show in Figures 2B and 3A, multiple proteins are annotated to the synapse with different databases, Gene ontology and SynGO. Well-known synaptic PPI were also observed, such as Grin2B-Dlg4(i.e. PSD-95), providing further evidence for proteins being immunoprecipitated for the synapses. Besides validating over a hundred published PPI interactions, we also identified many reciprocal interactions between the target datasets demonstrating the reproducibility of our protocol. Thus, we respectfully disagree with you and assert that our PPI network is very confident.
The immunoprecipitation protocol is unusual in that the homogenates were incubated overnight (twice), which is a very long period compared to most published protocols. This is a concern because spurious protein interactions could form during this long incubation.
There are many different immunoprecipitation protocols in the literature. The IP conditions depend upon the target protein and the antibody employed. Specifically, the abundance of the target and the affinity of the antibody to the target will dictate the IP conditions. We routinely perform overnight incubation for our IP-MS studies(Pankow, Bamberger et al. 2016, McClatchy, Yu et al. 2018). In our experience with brain tissue, this results in the highest enrichment of the target protein and the best reproducibility between biological replicates compared to IP protocols with shorter incubation times. Many other laboratories use overnight incubations(Lin and Lai 2017, Iqbal, Akins et al. 2018, Lagundzin, Krieger et al. 2022), so we do not consider our protocol unusual. We do find that IPs with tagged proteins in cell culture are more amenable to short incubation times. We have no evidence that overnight incubation causes spurious protein interactions nor could find any in the literature. Non-specific interactions are a concern with IP-MS experiments regardless of the incubation time. We took multiple steps to reduce the non-specific PPI from affecting our dataset. The first overnight incubation was incubating the brain lysate with agarose beads linked to IgGs to preclear the lysate from “sticky” non-specific interactors binding to IgGs and the beads. In addition, control IPs with IgG crosslinked to beads were incubated with brain lysate in parallel to each target IP. We computationally compared the non-specific control IPs with the target IPs using the SAINT algorithm to generate a confident list of PPI with a stringent 5% FDR. Therefore, our pipeline is specifically designed to prevent spurious PPI.
In the section "Biological interpretation of scz PPI network". Surprisingly the authors found that synaptic proteins that are exclusively postsynaptic (Grin2B, SynGAP) or exclusively presynaptic (Syt1) show very high percentages of their interacting proteins are from the synaptic compartments where the target protein is not expressed. The authors offer no explanation for this paradox. One explanation for this could be that spurious PPIs have formed in the protein extraction/immunoprecipitation protocol. These findings need validation by biochemical fractionation of synapses into pre and post synaptic fractions and immunohistochemistry to demonstrate the subsynaptic localisation of the proteins. Grin2b is traditionally described as exclusively post-synaptic, but there is evidence for other localizations, including presynaptic(Berretta and Jones 1996, Sjostrom, Turrigiano et al. 2003, Bouvier, Larsen et al. 2018) and expression in astrocytes(Serrano, Robitaille et al. 2008, Lee, Ting et al. 2010, Lalo, Koh et al. 2021, Kim, Choi et al. 2024). Syngap has been localized to non-synaptic sites and glia expression in addition to its heavily studied role at the post synapse(Moon, Sakagami et al. 2008, Araki, Zeng et al. 2015, Birtele, Del Dosso et al. 2023). Syt1 is commonly used as a presynaptic marker, but along with other proteins previously reported to be exclusively presynaptic (such as SNAP-25), it has been localized to the postsynapse (Selak, Paternain et al. 2009, Tomasoni, Repetto et al. 2013, Hussain, Egbenya et al. 2017, Madrigal, Portales et al. 2019, Sumi and Harada 2023). Similarly, SynGo database assigns both post-synaptic and pre-synaptic localizations to Grin2b as stated in the manuscript. Thus, our data is not paradoxical, but supports the emerging evidence against the canonical exclusivity of the pre- and post-synaptic compartments. Determining subsynaptic localization of a protein is a huge undertaking and requires expertise we do not possess. This is why we relied on synaptic databases and the literature for our interpretation of our data, as other publications have done.
We added the following to the Discussion to address this issue:
“Using the SynGo database, 418 proteins (i.e. 41% of our network) were identified as synaptic proteins consistent with the targets having a synaptic localization. Defining the synaptic proteome is inherently difficult because the synapse is an “open organelle”, and many synaptic proteins also have non-synaptic localizations and are expressed in non-neuronal cells. We further attempted to define our synaptic PPI by differentiating between pre- and post- synaptic compartments via SynGo. Half of our targets were annotated to both compartments and all targets had PPI that were annotated to both. This data supports the emerging evidence against the canonical localization exclusivity of the pre and post synapse(Bouvier, Larsen et al. 2018, Madrigal, Portales et al. 2019).”
My concerns about spurious interactions are raised again because the authors say that 92% of their interactions are novel (I note that they authors have not compared their interaction data of the NMDA receptor with published datasets from Dr Seth Grant's laboratory). BioGrid itself is good but not enough for comparison, maybe at this point it worth taking String, which accumulates several sources of PPIs, just select the direct PPIs.
Since the MS-IP experiments in our study have never been performed before, we are not surprised by the extent of novel data we produced. As described above, we took many steps to prevent spurious PPI from entering our final dataset, including the use of detergents, preclearing and stringent bioinformatic filtering. Our entire dataset is very large, so the 8% of PPI that we replicated from other studies represents 124 interactions. We believe this to be an impressive number which correlates to the confidence of our data. Providing more confidence, we identified many reciprocal PPI where shared protein interactors between target proteins were identified in both target protein datasets.
The PPI described for our targets in BioGrid encompassed 713 publications. Two of the BioGrid datasets that were compared to our Grin2b PPI data were from the laboratory of Seth Grant. Arbuckle et al (2010) is a low-throughout paper that describes a Grin2b and DLG4 PPI (that we also identified) and Husi et al (__2000__) is a seminal paper using high-throughput LC-MS to identify PPI in the PSD of mouse brain. There were many differences between Husi et al and our pipeline. Husi et al employed the C-terminal Grin2b peptide to pull down interactors from the PSD fraction whereas we employed Grin2b antibody to enrich Grin2b and its interactors from unfractionated brain tissue. Despite these differences, our studies found 8 proteins in common.We took your suggestion and compared our data to String which includes direct PPI and functional PPI. Our input was the high confidence PPI identified by SAINT with 5% FDR as with the BioGrid comparison. The PPI network for each target protein had a more significant enrichment (p We think the problem you suggest with SynGO is more of an inherent problem with characterizing the synaptic proteome. The synaptic proteome is difficult to define since it is an “open organelle” with proteins transporting in and out. In addition, most synaptic proteins, such as mitochondrial and translational proteins, also have non-synaptic localizations. It is not possible to isolate a contaminant-free “pure” synaptic preparation by biochemical fractionation. Recently, SynGO was used in a meta-analysis of previously published PSD datasets(Kaizuka, Hirouchi et al. 2024). Kaizuka et al. found 123 proteins identified in 20 PSD datasets. SynGo annotated proteins with post-synaptic localization from this list. To a lesser extent they also identified presynaptic localizations, but it is unclear if the presynaptic proteins are novel localizations. Kaizuka et al. continued the investigation and identified a novel PSD protein, thus demonstrating that our knowledge of pre- and post- synaptic proteomes is incomplete.
Minor comments
- A number of papers have reported protein interactions of native NMDA receptor complexes and their associated proteins isolated from rodent brain and are neither referenced in this paper. It would be relevant to compare these published datasets with the Grin2B IP datasets.
We employed BioGrid as a reference of reported PPI for each of our target proteins. For Grin2B, the PPI came from 142 different publications. For eight target proteins, we decided *BioGrid * was the best resource for determining the novelty of our PPI because it is routinely used for large-scale unbiased PPI analysis. To determine the novelty of our network, we compared our PPI network to 713 publications via BioGrid. We are unsure whether the papers you are referring to are included in the BioGrid database. To make it easier for readers with similar queries, we added an additional supplementary table (TableS4) including all the publications (i.e. PMID numbers) included in BioGrid comparison for each target protein.
We amended the Results with the following sentence, so the readers realized the extensiveness of the Biogrid comparison analysis:
“There were 713 publications in BioGrid that describe at least one interaction with one of our targets (Supplementary Table4).”
The use of the term "bait" in purification experiments typically refers to a protein and not an antibody. I suggest removing the word bait to avoid ambiguity and simply use the word target. We took your suggestion and used “target” instead of “bait” to avoid ambiguity.
26 mins of treatment gives completely different set of PPIs between PCP and saline which is very interesting, so both networks should be included in Supplementary. Also, it would be useful to have a list of modulated (phosphorylated in their case, but also ubiquitinated etc) proteins, which is not presented. Table S1 lists the PPI for each target, and we designated whether the interactors were for Sal, PCP, or both. Phosphorylated and ubiquitinated proteins are very hard to reproducibly identify without an additional enrichment step. Since we did not perform this enrichment step, we did not search for these modifications and do not have any modified proteins to report.
As they say their final network is composed of "direct physical and "co-complex" interactors and they cannot distinguish between them. This is particularly bad for the postsynapse, where all the PSD components can be co-IP-ed in different combinations. It can explain the Figure 5C, where most of the proteins have FDR = 1, which means they do not reproduce. Figure 5C represents the intersection of 15N quantification and SAINT analysis. The x-axis is the FDR reported for SAINT analysis, and the y-axis is the significant proteins from the N15 analysis. This figure demonstrates that some proteins that were significantly different with PCP via N15 quantification also were annotated as PPI by SAINT (i.e. 5%. As stated in the Discussion, we concluded that the SAINT analysis and N15 quantitation are complementary in identifying PPI and that the quantification of a biological perturbation may aid the identification of PPI. Figure 5C is not related to whether our PPI are direct physical or "co-complex" interactors. Distinguishing between direct physical and co-complex interactors is an inherent problem for all IP studies. Since another reviewer also highlighted this deficit in our manuscript, we decided to analyze our PPI dataset with the artificial intelligence algorithm AlphaFold 3(AF3). The AF3 data is encompassed in Figure 6.
The following AF3 data was added to the Results Section:
“A disadvantage of IP-MS studies is that it cannot distinguish between a PPI that binds directly to the target protein, and a PPI in which the interactor and target protein reside in the same multiprotein complex (i.e. indirect). We sought to predict which PPI may be directly interacting with its target protein by using the artificial intelligence algorithm AlphaFold3(AF3) (Abramson, Adler et al. 2024). First, we analyzed the predicted AF3 structure of the targets using the pTM score, and determined the fraction of each structure that was calculated to be disordered (Figure 6A and Supplementary Table7). Our reasoning was that if our targets have a poorly resolved structures then it will be difficult to screen for direct PPI. A pTM score >0.5 suggests that the structure may be correct, with the highest confidence equaling 1. Undefined or disordered regions hinder the accuracy of the prediction, and all our targets possessed a pTM score > 0.5 except Syt1. The fraction of disordered negatively correlated with the pTM score, as expected. Gsk3b, Ppp1ca, and Map2k1 were the target proteins with the highest pTM scores and were also the smallest of our targets (Figure 6B). Ppp1ca had the most confident structure (i.e. pTM 0.9) and the least fraction disordered (i.e. 0.07). Next, we determined the AF3 prediction of previously reported direct interactions of the targets. We used the iPTM score to determine an interaction confidence. An iPTM score >0.8 is a highly confident direct interaction, whereas 0.8. These eight PPI have all previously been reported to form a direct interaction with Ppp1ca, except Phactr3 (Zhang, Zhang et al. 1998, Terrak, Kerff et al. 2004, Hurley, Yang et al. 2007, Marsh, Dancheck et al. 2010, Ragusa, Dancheck et al. 2010, Ferrar, Chamousset et al. 2012, Choy, Srivastava et al. 2024, Xu, Sadleir et al. 2024)*. Phactr3 is structurally similar to, but less studied than, the reported direct interactor, Phactr1. These interactors are all inhibitors of PP1 except for Ppp1r9b which targets Ppp1ca to specific subcellular compartments. Nine PPI were assigned a score The following AF3 interpretation was added to the Discussion:
“Our SCZ PPI network consists of two types of PPI: direct physical interactions and “co-complex” or indirect interactions. Typically, the nature of the interaction cannot be distinguished in IP-MS studies. We decided to employ the new AF3 algorithm to screen the PPI of Ppp1ca to provide evidence for direct interactors. We chose to examine the PPI assigned to Ppp1ca, because its structure was the most confident among our target proteins and AF3 correctly predicted a known direct interactor with high confidence. Ppp1ca is a catalytic subunit of the phosphatase PP1, which is required to associate with regulatory subunits to create holoenzymes (Li, Wilmanns et al. 2013). Eighteen PPI were predicted to be directly interacting with Ppp1ca using a 0.6 or higher iPTM filter. This filter may be too conservative and may generate false negatives, because another study employed a 0.3 filter followed by additional interrogation to screen for direct PPI (Weeratunga, Gormal et al. 2024). Forty-four percent of these predictions were confirmed by previous publications. Most of these validated direct interactions are inhibitors of the phosphatase, but one, Ppp1r9b (aka spinophilin), is known to target Ppp1ca to dendritic spines (Allen, Ouimet et al. 1997, Salek, Claeboe et al. 2023). This high correlation with the literature provides substantial confidence to the novel PPI predicted to be direct Ppp1ca interactors. The AF3 screen predicted that NDRG2 directly interacts with Ppp1ca. This protein is known to regulate many phosphorylation dependent signaling pathways by directly interacting with other phosphatases including Pp1ma and PP2A (Feng, Zhou et al. 2022, Lee, Lim et al. 2022). Actin binding protein Capza1 was also predicted to directly interact with Ppp1ca and Ppp1ca interacts with actin and its binding proteins to maintain optimal localization for efficient activity to specific substrates (Foley, Ward et al. 2023). Hsp1e is a heat shock protein predicted to directly interact with Ppp1ca. Although there is no direct connection to Ppp1ca, other heat shock proteins have been reported to regulate Ppp1ca (Mivechi, Trainor et al. 1993, Flores-Delgado, Liu et al. 2007, Qian, Vafiadaki et al. 2011). We also observed that many of the direct PPI were altered with PCP treatment. One direct interactor, Ppp1r1b (aka DARPP-32), is phosphorylated at Thr34 by PKA in the brain upon PCP treatment. This phosphorylation event converts Ppp1rb to a potent inhibitor of Ppp1ca(Svenningsson, Tzavara et al. 2003). Importantly, the manipulation of Thr34 attenuated the behavioral effects of PCP. Consistent with this report, Ppp1r1b-Ppp1ca interaction was only observed with PCP in our study. Further investigation is needed to determine if our novel direct interactors regulate the PCP phenotype. We conclude that AF3 can provide important structural insights into the nature of PPI obtained from large scale IP-MS studies.”
The way PPI data is reported can be improved so that I does not have to be extracted from Table 1 and 2. It would be good if they provide just two columns PPI list, with names or IDs, plus PSP/saline/both conditions in third column, for ease of comparison with other sources and building the graph. They can add it as another spreadsheet to Table 2. We generated this table (TableS2) as you requested.
Is Figure 2 built for Sal or PCP conditions? as they have only 23% interactions in common (Figure 4A) the Figure 2 should be pretty different for two conditions. Are the 1007 interactors combined from SAL and PCP?
Figure 2 contains ALL the unique PPI for each target regardless of Sal or PCP conditions. The 1007 protein interactors shown in Figure 2Awhere Sal and PCP were combined to generate a non-redundant list of proteins for each target.
We amended the Results to make this clearer:
“When the PCP and SAL datasets were combined, there were 1007 unique proteins.”
This sentence was added to Figure 2A:
“For this comparison, Sal and PCP PPI were combined into a unique PPI list for each target.”
Figure 1F is mentioned but no figure is shown. We apologize for this oversight, and we have corrected the manuscript. 8. Overall the paper could be edited and made more concise, especially the introduction and discussion. We extensively edited the manuscript to be more concise.
Reviewer #3 (Significance (Required)):
General assessment
Proteomic mass spectrometry of immunoprecipitated complexes from synapses has been extensively studied since Husi et al (2000) first study of NMDA receptor and AMPA receptor complexes. Since then, a wide variety of methods have been employed to purify synaptic protein complexes including peptide affinity, tandem-affinity purification of endogenous proteins tagged with FLAG and Histine-affinity tags amongst other methods. Purification of protein complexes and the postsynaptic density from the postsynaptic terminal of mammalian excitatory synapses have been crucial for establishing that schizophrenia is a polygenic disorder affecting synapses (e.g. Fernandez et al, 2009; Kirov et al, 2012; Purcell et al, 2014, Fromer et al, 2014 etc). Network analyses of the postsynaptic proteome have described networks of schizophrenia interacting proteins (e.g. Pocklington et al, 2006; Fernandez et al, 2009) and other neuropsychiatric disorders.
Hundreds of synaptic protein complexes have been identified (Frank et al, 2016), but very few have been characterised using proteomic mass spectrometry. This paper has chosen 8 protein targets for such analysis and identified many proteins that a putative interactors of the target protein. At this level the current manuscript does not represent a conceptual advance and the value of the data lies in its utility as a resource that may be used in future studies.
The findings from the 8 target proteins from normal adult rat brain were used for a secondary study that describes the effects that PCP has on the interaction networks. Interestingly, this work shows that 26 minutes of drug treatment leads to considerable changes in the interactomes of the target proteins. These descriptive data could be used in future studies to understand the cell biological mechanisms that mediate these rapid changes in the proteome. PCP and drugs that interact with NMDA receptors are known to induce changes in synaptic proteome phosphorylation including modifications in protein-protein interaction sites, which may explain the PCP effects.
The study would benefit from validation of experimental protocols for solubilisation and immunoprecipitation and validation of described interactions using orthogonal biochemical or localisation experiments.
Audience Specialists in synapse proteins and mechanisms of schizophrenia.
Expertise
The reviewers' expertise is in molecular biology of synapses including synapse proteomics, protein interaction and network analysis, and genetics of schizophrenia and other brain disorders.
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Referee #3
Evidence, reproducibility and clarity
Summary
It is now widely accepted that schizophrenia is polygenic disorder in which a large fraction of the genetic risk is in variants affecting the expression of synaptic proteins. Moreover, it is known that these synaptic proteins are found in multiprotein complexes and that many proteins encoded by schizophrenia risk genes interact directly or indirectly in these complexes. It is also known that some drugs including phencyclidine, which binds to NMDA receptors and to Dopamine D2 receptors (not mentioned by the authors) can induce schizophreniform psychosis. The authors have set out to advance on this position by performing proteomic mass spectrometry studies on proteins identified as encoded by schizophrenia risk genes. They target 8 proteins for immunoprecipitation from rat brain and identify coisolated proteins and perform various network analyses. In the most interesting part of the paper they ask if PCP-treatment altered protein interactions and report various changes.
Major comments:
- Choice of target proteins. It was not until the first paragraph of the results section that the authors first name the 8 synaptic proteins that have chosen to study. This information should be in the abstract. The authors then use figure 1A and 1B as evidence that these 8 "baits" are schizophrenia-relevant proteins. Figure 1A does not provide any evidence at all and Figure 1B is about as weak a line of evidence imaginable - a histogram of the number of papers that have the search term "schizophrenia" and the protein name. I tried this search for Grin2B and almost immediately found papers that reported no association between Grin2B and schizophrenia (e.g. PMID: 33237434). Figure 1B should be scrapped. The remaining part of paragraph 1 of the results does not provide an adequate, let alone systematic, justification for the use of the 8 baits. It would be appropriate to construct a table with the 8 proteins and cite relevant papers and identify the basis for why they are implicated in schizophrenia (is it a direct mutation or some other evidence?). What makes these 8 proteins better than many others that are cited as synaptic schizophrenia relevant proteins?
- The methods of protein extraction are particularly concerning. The postsynaptic density of excitatory synapses (which contains several of the target proteins in this study) has been notoriously difficult to solubilise unless one uses high pH (9) and harsh detergent extraction (1% deoxycholate). The authors use pH 7 and weak detergent conditions, which are likely to be inefficient for solubilising at least several of the target proteins. Nowhere do the authors report how much of the total of their target protein is being solubilised. Indeed, there are no figures showing biochemical conditions at all. What if only a small percentage of the target protein is being immunoprecipitated - what does this mean for the interaction data? How do we know if the fraction being immunoprecipitated is from the synapse? (why did they not use synaptosomes). The absence of this kind of data undermines the reader's confidence in the findings.
- The immunoprecipitation protocol is unusual in that the homogenates were incubated overnight (twice), which is a very long period compared to most published protocols. This is a concern because spurious protein interactions could form during this long incubation.
- In the section "Biological interpretation of scz PPI network". Surprisingly the authors found that synaptic proteins that are exclusively postsynaptic (Grin2B, SynGAP) or exclusively presynaptic (Syt1) show very high percentages of their interacting proteins are from the synaptic compartments where the target protein is not expressed. The authors offer no explanation for this paradox. One explanation for this could be that spurious PPIs have formed in the protein extraction/immunoprecipitation protocol. These findings need validation by biochemical fractionation of synapses into pre and post synaptic fractions and immunohistochemistry to demonstrate the subsynaptic localisation of the proteins.
- My concerns about spurious interactions are raised again because the authors say that 92% of their interactions are novel (I note that they authors have not compared their interaction data of the NMDA receptor with published datasets from Dr Seth Grant's laboratory). BioGrid itself is good but not enough for comparison, maybe at this point it worth taking String, which accumulates several sources of PPIs, just select the direct PPIs.
- A major concern is that they use SynGO as a reference database, and even test the enrichment against it. SynGO is about ~ 2000 genes in size and was built around the presynaptic datasets, so it is biased and incomplete in terms of the whole synapse. This may be one of the reasons there is the strangely high percentage of presynaptic proteins interacting with postsynaptic proteins as noted above.
Minor comments
- A number of papers have reported protein interactions of native NMDA receptor complexes and their associated proteins isolated from rodent brain and are neither referenced in this paper. It would be relevant to compare these published datasets with the Grin2B IP datasets.
- The use of the term "bait" in purification experiments typically refers to a protein and not an antibody. I suggest removing the word bait to avoid ambiguity and simply use the word target.
- 26 mins of treatment gives completely different set of PPIs between PCP and saline which is very interesting, so both networks should be included in Supplementary. Also, it would be useful to have a list of modulated (phosphorylated in their case, but also ubiquitinated etc) proteins, which is not presented.
- As they say their final network is composed of "direct physical and "co-complex" interactors and they cannot distinguish between them. This is particularly bad for the postsynapse, where all the PSD components can be co-IP-ed in different combinations. It can explain the Figure 5C, where most of the proteins have FDR = 1, which means they do not reproduce.
- The way PPI data is reported can be improved so that I does not have to be extracted from Table 1 and 2. It would be good if they provide just two columns PPI list, with names or IDs, plus PSP/saline/both conditions in third column, for ease of comparison with other sources and building the graph. They can add it as another spreadsheet to Table 2.
- Is Figure 2 built for Sal or PCP conditions? as they have only 23% interactions in common (Figure 4A) the Figure 2 should be pretty different for two conditions. Are the 1007 interactors combined from SAL and PCP?
- Figure 1F is mentioned but no figure is shown.
- Overall the paper could be edited and made more concise, especially the introduction and discussion.
Significance
General assessment
Proteomic mass spectrometry of immunoprecipitated complexes from synapses has been extensively studied since Husi et al (2000) first study of NMDA receptor and AMPA receptor complexes. Since then, a wide variety of methods have been employed to purify synaptic protein complexes including peptide affinity, tandem-affinity purification of endogenous proteins tagged with FLAG and Histine-affinity tags amongst other methods. Purification of protein complexes and the postsynaptic density from the postsynaptic terminal of mammalian excitatory synapses have been crucial for establishing that schizophrenia is a polygenic disorder affecting synapses (e.g. Fernandez et al, 2009; Kirov et al, 2012; Purcell et al, 2014, Fromer et al, 2014 etc). Network analyses of the postsynaptic proteome have described networks of schizophrenia interacting proteins (e.g. Pocklington et al, 2006; Fernandez et al, 2009) and other neuropsychiatric disorders.
Hundreds of synaptic protein complexes have been identified (Frank et al, 2016), but very few have been characterised using proteomic mass spectrometry. This paper has chosen 8 protein targets for such analysis and identified many proteins that a putative interactors of the target protein. At this level the current manuscript does not represent a conceptual advance and the value of the data lies in its utility as a resource that may be used in future studies.
The findings from the 8 target proteins from normal adult rat brain were used for a secondary study that describes the effects that PCP has on the interaction networks. Interestingly, this work shows that 26 minutes of drug treatment leads to considerable changes in the interactomes of the target proteins. These descriptive data could be used in future studies to understand the cell biological mechanisms that mediate these rapid changes in the proteome. PCP and drugs that interact with NMDA receptors are known to induce changes in synaptic proteome phosphorylation including modifications in protein-protein interaction sites, which may explain the PCP effects.
The study would benefit from validation of experimental protocols for solubilisation and immunoprecipitation and validation of described interactions using orthogonal biochemical or localisation experiments.
Audience
Specialists in synapse proteins and mechanisms of schizophrenia.
Expertise
The reviewers' expertise is in molecular biology of synapses including synapse proteomics, protein interaction and network analysis, and genetics of schizophrenia and other brain disorders.
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Referee #2
Evidence, reproducibility and clarity
Summary: McClatchy, Powell and Yates aimed at identifying a protein interactome associated to schizophrenia. For that, they treated rats (N14 and N15) with PCP, which disturbs gutamatergic transmission, as a model for the disease and co-immunoprecipitated hippocampi proteins, which were further analyzed by standard LC-MS.
The study is new, considering not much has been done in this direction in the field of schizophrenia. This justifies its publication. On the other hand, a major flaw of the is the lack of information on the level of interaction of the so called protein interactome. Meaning, we cannot distinguish, as the study was performed, which proteins are directly interacting with the targets of interest from proteins which are interacting with targets´ interactors. The different shells of interaction are crucial information in protein interactomics.
Major: most of I am pointing below must be at least discussed or better presented in the paper, as It may not be solvable considering how the study has been conducted.
- The study fails in defining the level of interaction of the protein interactome with the considered targets. This has been shortly mentioned in the discussion, but must be more explicit to readers, for instance, in the abstract, introduction and in the methods sections.
- Considering the protein extraction protocol, it is fair to mention that only the most soluble proteins are being considered here. I am bringing this up since the importance of membrane receptors is clear in the studied context.
- It is not clear from the methods description if antibodies from all 8 targets were all together in one Co-IP or have been incubated separately in 8 different hippocampi samples. It seems the first, given how results have been presented. If so, this maximizes the major issue raised above (in 1).
- Definitely, results here are not representing a "SCZ PPI network". PCP-treated animals, as any other animal model, are rather limited models to schizophrenia. As a complex multifactorial disease, synaptic deficits, which is the focus of this study, can no longer be considered "the pivot" of the disease. Synaptic dysfunction is only one among many other factors associated to schizophrenia.
- Authors should look for protein interactions that might be happening also in glial cells. They are not the majority in hippocampus, but are present in the type of tissue analyzed here. Thus, some of the interactions observed might be more abundantly present in those cells. Maybe enriching using bioinformatics tools the PPI network to different cell types.
Minor:
- in the abstract, it is not clear if 90% of the PPI are novel to brain tissue in general or specifically schizophrenia.
- authors refer to LC-MS-based proteomics as "MS" all across the text. Who am I to say this to Yates et al, but I think it is rather simplified use "Mass Spectrometry Analysis", when this is a typical LC-MS type of analysis
- Several references used to construct the hypothesis of the paper are rather outdated: several from 10-15 years ago. It would be interesting to provide to the reader up to date references, given the rapid pace science has been progressing.
- "UniProt rat database". Please, state the version and if reviewed or unreviewed.
Significance
The study is informative, and has great potential to enrich the specific literature of this field. But should tone down some arguments, given the experimental limitations of the PPI network (as described above) and should state PCP-treated rats as a limited model to schizophrenia.
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Referee #1
Evidence, reproducibility and clarity
Summary:
Provide a short summary of the findings and key conclusions (including methodology and model system(s) where appropriate).
In this manuscript, McClatchy and colleagues used a conventional approach combining immunoprecipitation (IP) of endogenous target proteins (baits) followed by liquid chromatography mass spectrometry (MS) analysis of the co-immunoprecipitating proteins to map protein-protein interaction (PPI). This interaction network is centered around baits that had been annotated as susceptibility factors for schizophrenia (SCZ). A variety of previous studies have identified thousands of such SCZ susceptibility factors. Mostly based on the availability of antibodies, 8 bait proteins were selected in this study. The authors reasoned that immunoprecipitating endogenous proteins from tissues using specific antibodies was a more accurate view of physiological conditions than epitope tagging followed by affinity purification (AP) from cells in culture. The model system from which proteins were extracted was the hippocampus dissected from mice that had been treated or not by phencyclidine (PCP), a drug that has been shown to induce SCZ symptoms in humans and animals. By comparing the proteins identified and quantified from the PCP-treated samples against control IPs and/or saline-injected mouse controls, a large number of PPI were deemed statistically significant. Most of these potential interactors were not present in PPI databases (BioGRID), most likely because such databases are populated with large-scale APMS datasets from cell cultures, with very few studies using brain tissue. Strikingly, many of the co-immunoprecipitated proteins were also known as SCZ susceptibility factors, which lend weight to the hypothesis that these factors form a large protein interaction network, localized at the synapses.
Major comments:
- Are the key conclusions convincing?
Overall, the conclusions drawn from the experimental design, data analysis, and corroboration with existing literature are well-supported and convincing. When selecting the SCZ susceptibility factors, the authors clearly state their goal, the databases used for gene selection, and the rationale for choosing proteins with synaptic localization. The inclusion of evidence from genetic studies and previous publications strengthens the credibility of the selected genes. The methodology used to establish the novel SCZ PPI network is mostly well-described (see minor comments below). The use of an 15N internal standard also adds rigor to the quantitation of PPI. The GO enrichment analysis provides valuable insights into the biological functions and cellular components associated with the SCZ PPI network. The annotation of identified proteins using the SynGo synaptic database and the distribution of annotated synaptic proteins among different baits further support the biological relevance of this PPI network. The cross-referencing of the PPI network with published genetic studies on SCZ susceptibility genes adds robustness to the findings. Specifically, the observation that 68% of protein interactors have evidence of being potential SCZ risk factors is a strong corroboration of the prevailing hypothesis in the field. Finally, the significant changes induced by PCP that were identified for all baits except Syt1, along with the comparison of altered proteins with SAINT-identified PPI, add depth to the understanding of PCP modulation. - Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether?
No, but note that APMS/IPMS has been around for more than a decade (Introduction page 3). - Would additional experiments be essential to support the claims of the paper? Request additional experiments only where necessary for the paper as it is, and do not ask authors to open new lines of experimentation.
One piece of data that is missing are Western blots using the 8 selected antibodies against the proteins extracted from their experimental samples to validate the antibodies recognize 1 protein of the expected size from these tissue extracts. - Are the suggested experiments realistic in terms of time and resources? It would help if you could add an estimated cost and time investment for substantial experiments.
Running SDS-PAGE and Western blotting should be straightforward and cheap. - Are the data and the methods presented in such a way that they can be reproduced?
Yes - Are the experiments adequately replicated and statistical analysis adequate?
Yes
Minor comments:
- Specific experimental issues that are easily addressable.
The rationale for the short duration between PCP injection and animal sacrifice is only explained in the discussion section (page 17). The fact that this short treatment of less than 30 min should prevent any change in transcription or translation should be introduced earlier (in the experimental procedures). Note that the duration is written as 26 min on page 4 and 25 min on page 9. Please reconcile these numbers. Is there any biological significance for this SCZ study that the mice were maintained on a reverse day-night cycle? It is not clear from reading Experimental Procedures/Bioinformatic Analysis section (page 6) if normalized N14/N15 protein ratios measured in the bait-IPs and control-IPs were used for the SAINT analysis? Or did the authors used label-free quantitation with spectral counts? - Are prior studies referenced appropriately?
Yes - Are the text and figures clear and accurate?
Fig1C: The workflow is a little too simple, the authors might want to add more details. FigS1C: Please add x-axis title (spectral counts) directly to the figure. Fig2B-D: The color scale bar should have number values to denote lower and upper limits in % (as opposed to "lowest" and "highest"). - Do you have suggestions that would help the authors improve the presentation of their data and conclusions?
No
Significance
- Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field.
In this study, the authors have drastically expanded the protein interaction landscape around 8 known SCZ susceptibility factors by using a conventional IPMS approach. Performing the IPs on protein extracted from hippocampus dissected from mice treated with phencyclidine to model SCZ increases the biological significance of such lists of proteins. Furthermore, the co-immunoprecipitation of many other SCZ susceptibility factors along with the 8 selected baits supports the hypothesis that these proteins of varied functions are part of large interaction networks. Overall, the integration of experimental data with in silico networks, along with the quantification of PPI changes in response to PCP, should contribute to a more nuanced understanding of SCZ pathogenesis. The potential implications for drug development underscore the broader significance of the study in advancing our knowledge of neurobiology and its relevance to neurological disorders like schizophrenia. - Place the work in the context of the existing literature (provide references, where appropriate).
Overall, this study contributes to the existing literature by providing experimental data on in vivo PPI networks related to SCZ risk factors. Not only do the authors validate 124 known interactions but also they identify many novel PPI, due to a gap in the existing literature regarding the comprehensive mapping of PPI directly from tissue extracts, especially brain tissue. The authors advocate for more IPMS studies in mammalian tissues to generate robust tissue-specific in silico networks, which agrees with the growing understanding of the importance of tissue-specific networks for identifying disease mechanisms and potential drug targets.
Furthermore, the SCZ PPI network reported here is enriched in proteins previously associated with SCZ, which aligns with the existing literature emphasizing the involvement of certain proteins and pathways in the pathogenesis of SCZ [References: 78-85]. The authors also investigate the response of the SCZ network to PCP treatment, hence providing insights into the potential effects of post-translational modifications, protein trafficking, and PPI alterations in a model of schizophrenia, which adds to existing knowledge about the impact of PCP on the molecular processes associated with SCZ [References: 88, 89, 92]. - State what audience might be interested in and influenced by the reported findings.
Overall, the findings reported in this manuscript have implications for both basic research in molecular biology and potential translational applications in the development of targeted therapies for neurological disorders, particularly schizophrenia. The study delves into in vivo protein-protein interaction (PPI) networks related to genes implicated in schizophrenia (SCZ) risk factors. Researchers in neuroscience, molecular biology, and psychiatry would find the information valuable for understanding the molecular basis of SCZ. The study highlights the potential for identifying disease "hubs" that could be drug targets. Pharmacologists and drug developers interested in targeting protein complexes for drug development, especially in the context of neurological disorders, may find the study relevant. - Define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate.
Technical Expertise | biochemistry, liquid chromatography mass spectrometry, proteomics, computational biology, protein engineering, protein interaction networks, post-translational modifications, protein crosslinking, proximity labeling, limited proteolysis, thermal shift assay, label-free and isotope-labeled quantitation. Biological Applications | human transcriptional complexes, apicomplexan parasites, viruses, nuclear envelope, ubiquitin ligases, non-model organisms.
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The authors do not wish to provide a response at this time.
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Referee #3
Evidence, reproducibility and clarity
The manuscript by Balachandra and Amodeo presents Bellymount-Pulsed Tracking as a technique for continuous long-term imaging of Drosophila oogenesis. This approach modifies the existing Bellymount technique by exposing restrained female flies to pulses of CO2 anesthesia in combination with image acquisition. Flies that survived the restraint were kept alive for many hours by addition of a liquid diet in the restraint apparatus. This allowed for imaging and tracking of egg chamber development over longer time periods than capable with ex vivo culturing methods. However, the authors did report a 40% mortality rate and decreased fecundity compared to unrestrained flies. Using this method the authors were able to image and measure the growth rate of developing egg chambers in living flies, and capture events like vitellogenesis which relies on the interactions of multiple organ systems.
This technique is a notable contribution to the fly community, as it could be useful for studying processes that require interactions between multiple tissues and organs, as well as for long-term imaging of other internal structures in the adult fly. The significance is somewhat reduced due to the relatively high mortality rate and the decreased fecundity and egg chamber growth rate reported. However, the authors should be commended for their diligence in documenting the limitations of the procedure, as this now provides a strong jumping off point to improve the technique if it becomes widely adopted by the fly community. Overall, the experiments appear to have been carefully performed and the manuscript is clearly written. However, there are several issues that should be addressed prior to publication.
Major concerns
- The movies of egg chamber development are challenging to interpret. They could be improved by the addition of timestamps and other annotations. Having multiple example movies of the same process would also be valuable. It could be helpful to potential users of this technique to show the process the authors used for identifying the same egg chamber between such long time points.
- Figure 4 - Given that the Bellymount PT technique slows oogenesis and reduces egg chamber growth in vitellogenic stages (Figure 3E), it is possible that Bellymount PT slows yolk protein uptake. It would be important to establish a baseline for how much to expect yolk protein levels to change across stages to compare to measurements obtained with Bellymount PT. It would be a relatively simple experiment to show the change in yolk protein uptake across stages in fixed samples. This could also be performed for His2Av dynamics during nurse cell dumping.
- Movie 11 - The authors propose that Bellymount-PT can be used to visualize the process of border cell migration. However, there is no obvious movement of the cluster relative to the nurse cell nuclei over the course of the 3 hour long movie. The authors should either show a better movie of border cell migration, or remove this claim from the manuscript.
- Movie 13 - The authors claim that they see egg chamber rotation continue in stage 9 and 10 egg chambers. This movie is not convincing. There is also very strong evidence in the literature that egg chamber rotation ends at stage 8. Chen et al., Cell Reports, 2017 showed using a method that tracks follicle cell migration in vivo that rotational migration ends during stage 8. The only movement of follicle cells after stage 8 is due to the epithelial reorganization that occurs during the posterior movement of the follicle cells as the stretch cells flatten. Additionally, after stage 8 follicle cells lose their circumferentially oriented actin protrusions that drive rotation. This claim should be removed from the manuscript.
Minor comments
- Line 104 - The authors mention that CO2 affects fertility in flies. They should also reference Sustar et al., Genetics, 2023 and Zimmerman and Berg, PLoS One, 2024 for wider ranging effects of CO2 on oogenesis.
- Line 244 - Although it is true that the original paper describing egg chamber rotation reported that it starts at 5, subsequent studies from multiple labs have confirmed that it begins much earlier. First shown by Cetera et al., Nature Communications, 2014 but later confirmed by Bilder, Dahmann, and Mirouse labs. Chen et al., Cell Reports, 2016 has even published a movie of an egg chamber initiating rotation as it buds from the germarium.
- Figures of egg chambers are generally oriented anterior on the left and posterior on the right. Reorienting all the figures would be challenging, so the recommendation is to be clear in the figure legends the orientation of the images. This is important given they are shown in different orientations in Figure 1 than throughout the rest of the paper, and also will be helpful for readers who may not be familiar with the structure of the ovary/egg chambers.
- Figure 1B and Methods line 334 - Should "Rely" be "Relay"?
- Figure 1E - Oocyte nuclei are missing from the diagrams of stage 7, 13 and 14 egg chambers. Also, "G" looks like a figure panel label, could just say Germarium
- Figure 3F-H - "Stagee" should be "Stage"
- Figure 4B - Why is the fluorescence for egg chamber #6 so much higher than the others? It makes the slopes of the other samples hard to see.
- Figure 4D,E,G - For clarity, the labeled boxes should be the same color as the lines on the associated graphs. In line 790 "Note the steady increase of H2Av in all three regions as it exits the nurse cell nuclei" - this is not actually shown without the nurse cell nuclei average intensity being on the graph as well.
- Line 787 - "Note the flow of H2Av" - "flow" is not actually shown in these static images. Consider a more precise description.
Referee Cross-commenting
The other reviewers make several excellent points. We personally feel that it is beyond the scope of this initial report to ask the authors to show that they can see all aspects of oogenesis with this technique. If the method becomes widely adopted by the oogenesis community, individual researchers can optimize it to suit the exact process they want to study. If the authors want to claim they can see a particular process, it needs to be well documented and convincing. For example, we agree that the movies that claim to show egg chamber rotation (both during established stages and later) and border cell migration need to be improved or the claims need to be removed. However, we feel that the authors have documented enough other interesting processes to make the study worthy of publication. Likewise, asking the authors to determine the minimal time window that can be used for imaging could take months of open-ended work and is something that could be better tackled by subsequent users depending on the requirements of the biological process they want to study. It seems better to get the work out into the public sooner rather than later so that improvements can be crowd sourced.
Finally, although Flp-out clones were used for cell tracking in the original Belly mount paper, this technique will be less effective during the first half of oogenesis when the egg chamber is rotating, as the clone is likely to rotate into and out of sight between imaging time points.
Significance
This technique is a notable contribution to the fly community, as it could be useful for studying processes that require interactions between multiple tissues and organs, as well as for long-term imaging of other internal structures in the adult fly. The significance is somewhat reduced due to the relatively high mortality rate and the decreased fecundity and egg chamber growth rate reported. However, the authors should be commended for their diligence in documenting the limitations of the procedure, as this now provides a strong jumping off point to improve the technique if it becomes widely adopted by the fly community. Overall, the experiments appear to have been carefully performed and the manuscript is clearly written. However, there are several issues that should be addressed prior to publication.
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Referee #2
Evidence, reproducibility and clarity
Summary:
The authors describe an improvement of the Bellymount imaging method for internal tissues of the fly's abdomen. They are able to increase the total duration of the imaging by introducing pulsed anesthesia. This allows the immobilized flies to take up food in between the imaging; this increases survival rate and allows for longer total imaging times. The authors illustrate the technique by tracking the development of egg chambers.
Major Points
- The Bellymount PT method results in decreased fecundity, which might affect the processes (oogenesis) the authors looked at. Indeed, the authors conclude that "oogenesis is not completely stalled under the Bellymount-PT protocol" (line 140). The authors do provide some data indicating that egg chambers develop (Fig. 2G,H; Fig. 3F,H), in particular a stage 10 egg chamber proceeding to a stage where dorsal appendages seem to form. However, for early stage egg chambers this is less convincing. The egg chambers show an increase in (cross-sectional) area, however, what is the evidence that they also mature? For example, during egg chamber maturation, the ratio of oocyte/nurse cell volume changes, follicle cells re-arrange, etc. The authors should test whether any of these characteristics can be observed in egg chambers imaged using Bellymount PT. This may include the imaging of egg chambers in which both nuclei and plasma membranes are visualized.
- A potential advantage of the Bellymount PT method is the ability to follow the dynamics of processes. A current drawback, however, is the rather low temporal resolution as the fly needs to wake up between single images. The authors should provide an estimate for the minimal possible cycle time and should test whether flies imaged at 10 minutes interval show lower survival/fecundity than flies imaged at 2 hours interval.
- The authors claim that they can track on a cellular level (based on nuclei), but it is unclear how accurate the tracking is. Especially cell tracking over very long times might be challenging here, as the time delay between two time points is big. The authors should test the accuracy of their tracking, potentially by creating Flip-out clones and using them as a control.
- The authors show that they can visualize cell membranes (Moesin-GFP, Fig. 2C). Tracking cells over time based on their membranes would greatly widen the applicability of the method as it would enable to analyze the complex cellular dynamics during egg chamber maturation. The authors should test whether cells can be tracked over time (e.g. using Moesin-GFP) using their technique.
- Movie 11. The authors claim that they can capture border cell migration. However, it is unclear whether the border cells actually migrate towards posterior. The authors should track and quantitatively analyze the migration path of the border cells in their movies.
- Movie 12. The authors claim that they can observe egg chamber rotation. However, it is unclear whether the egg chambers actually rotate. The authors should track cells and quantify the angular velocity of movement.
Minor Points
- Please move the labels of the scale bars to the legends.
- The figures (especially 2 and 3) would benefit from a clearer structuring. Moving part of them to supplementary figures would also help.
- "stage" typo in figure 3
Significance
The authors describe here an improvement of an existing technique. The advantage of the improved technique is the longer imaging time, which potentially allows users to track cells/organelles/proteins over time. However, tracking requires the user to connect single time points with each other, which is somewhat unclear at this time. Moreover, the potential applicability (and significance) of the technique would be widened if visualization and tracking of cell membranes/organelles/vesicles would be possible. With these further optimizations, the technique would add a useful tool to the Drosophila community.
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Referee #1
Evidence, reproducibility and clarity
Summary
The Drosophila ovary is an established model system for many aspects of development and cell biology. In vitro culture of live ovaries has provided valuable insight, yet these methods do not accurately mimic oogenesis in vivo for some stages. Here the authors develop a new method that allows for sustained imaging of ovaries in intact flies, maintaining normal physiology.
The method provides a valuable addition to the field. Processes such as growth, cell migration, egg chamber rotation, yolk uptake and nurse cell dumping can be observed in the intact fly. Time lapse and 3D reconstruction provide valuable tools. While the detail/resolution of the images is not as good as ex vivo or fixed samples, the ability to maintain normal development and homeostasis provides a novel advantage. The figures and movies are well-presented and sufficient detail is provided in the methods.
Major comments
- Why do the authors think that growth is slowed? The imaging process or the trapping/anesthesia of the fly? For example, if the frequency of imaging was varied, it could reveal whether it was the actual imaging that affected development. Did the length of time the fly had been in the trap make a difference? The sentence on lines 190-191 is not clear.
- In Movie 6, the nurse cell nuclear shape does not look normal - more ovoid than round. Perhaps some settings are off in the 3D reconstruction.
- Movie 11 - why do the border cells seem stalled?
- There is no discussion of the earliest stages of oogenesis. Is it possible to see egg chambers forming from the germarium?
Minor comments
- It would be helpful to mention if the egg chambers stay in similar locations or move around - is it challenging to locate the same egg chamber after 2 hours?
- Are any egg chambers degenerating? This could indicate stress in the fly.
- In Figure 4D, release of HisAV into the cytoplasm is described. Similar release of nuclear proteins was described by Cooley et al. 1992 so this paper could be cited.
- At 321 minutes in Figure 4D, a large nucleus is apparent in the oocyte. Is this an oocyte nucleus or evidence for nurse cell translocation to the oocyte as described in Ali-Murthy et al. 2021?
Significance
The technique provides a significant advance to the field, extending the time period currently possible to image ovaries through the Belly Mount method. It will immediately benefit researchers working on the ovary but could be extended to many other tissues in the fly abdomen such as the gut and tumor models.
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Reply to the reviewers
Reviewer #1
__Evidence, reproducibility and clarity __
The work by Przanowska et al., sought to understand the role of ORC2 in murine development and further wanted to discover its role in liver endo-reduplication. The overall methods used is sufficient enough to address its role but is not very conclusive based on their overall results and data provided as elaborated in below comments.
Major Comments:
- The major issue of the paper is how well is ORC2 depleted in perinatal liver (Fig. 2C) and is not very clear from the data as all the western blots are at very low exposure levels and bands are very weak (still weak bands seen). There are good antibodies of ORC2 which can be used for IHC staining and can be used to address the extent of ORC2 depletion.
We have now shown that ORC2 protein is significantly decreased in the hepatocytes of the Orc2 KO and DKO livers (New Fig. 2C and 6D). The decrease is consistent, with 4-5 mice examined, and all showing the depletion. We have been unable to do immunohistochemistry on tissue sections of the mouse livers with the anti-ORC antibodies we have tried, and this could be a reflection of the low level of the proteins. On hepatocytes in culture we have obtained faint signal with the anti-ORC2 antibody in WT cells, and this is clearly absent in 100% of the hepatocytes. See Fig. R1 below.
__Reviewer Fig R1: __
A) Immunofluorescence of hepatocytes in culture from livers of WT and two DKO mice.
B) Quantitation of A) from counting 70-100 cells from each specimen.
However, the calculations in the methods and the discussion are very compelling that at least the last 6-9 cell divisions in normal development start with 2n nuclei in the livers at baseline (Fig. 3B-G and 6I).
Why in Fig 2C, the M2 mice is showing an equivalent level of ORC2 protein compared to mice M1 with NO CRE expression (compare lane1 and lane5). So, the results are based on one mouse which I do not think is significant enough to come to the conclusion. The authors need to add more data from different mice for statistical significance. Please use IHC to show the depletion of ORC2 protein in the liver sections.
We had used total liver and had pointed out that residual ORC2 protein will be seen from stromal cells (endothelia, blood vessels and blood cells). We have therefore removed the figure which measured ORC2 levels in total liver and have now shown that when hepatocytes are isolated from five animals there was a massive depletion of ORC2 in all five animals (new Fig. 3C).
As nicely demonstrated in the previous paper by Okano-Uchida et al., 2018 that ORC1 depletion in the liver shows an DNA ploidy effect from 6-week onwards. The authors need to demonstrate in this paper also when the 16N phenotype is observed starting from week1 to 12 months.
Based on the results from our previous paper (Okano-Uchida et al., 2018) we decided to measure 16N phenotype at 6 weeks of age. The endoreduplication occurs at a stage when ORC2 protein is undetectable during normal development or during regeneration.
In the double knockout experiments (ORC1 and ORC2) the authors are not even bothered to demonstrate that how much are both the proteins are actually depleted from the cells, so on the results obtained from these mice experiments are not conclusive or explanatory.
We have performed immunoblotting of isolated hepatocytes and immunohistochemistry of livers for ORC1 and ORC2. Our data shows that both proteins are depleted in all four mice tested (New Fig. 6D).
Minor points:
- Why are scale bars missing in right panel of Fig. 2G, Fig. 6D Supp Fig. 2B KO studies. The authors need to confirm that that all the large nuclei have NO or less significant ORC2 protein through IHC H&E staining.
The scale bars are missing from the right panels to avoid redundancy. We have added “Both panels are at the same scale.” in the figure legend, according to https://doi.org/10.1371/journal.pbio.3001161.
- Please explain why is EYFP in Fig. 5G is cytoplasmic compared to Fig 4C (nuclear). We consistently see this variability and it was there in our previous results (Okano-Uchida et al., 2018), where EYFP was cytoplasmic in tissues, but was nuclear (and some cytoplasmic) in hepatocytes in culture.
We do not know the reason for this difference but consistently see this difference. We now say in the text: “We did not explore why the EYFP protein is mostly nuclear in hepatocytes in culture (Fig. 4C) and mostly cytoplasmic in hepatocytes in the liver tissue (Fig. 5G, 7G), but speculate that differences in signaling pathways or fixation techniques between the two conditions contribute to this difference.”
Are authors using the same genotype of Alb-Cre mice as shown by Okano-Uchida et al., 2018 as I do not find the reference of Schuler et. al., 2004 (PMID:15282742).
We have been using two independent Alb-Cre animals. This is now described in the Methods.
Significance
The article is exactly based on their previous published paper but instead of ORC1, they were interested in dissecting the role of ORC2. Although they have discussed that CDC6 may be involved in replacing ORC1 KO mice to rescue the extensive DNA replication in endoreduplication, but instead of going to hunt the role of CDC6 in endoreduplication they checked the effect of ORC2 which actually lower the overall impact of the paper.
We studied ORC2 conditional KO mice in a similar manner to the previously published ORC1 conditional KO in order to ensure (1) that the lack of effect in the Orc1 KO was not because ORC1 can theoretically be substituted for by CDC6 and (2) to establish the double KO of Orc1 and Orc2. To the best of our knowledge this is the first description of removal of two subunits of ORC complex at once in a mouse model. Moreover, in the light of rising recognition of sex as biological variable, we report sex-dependent effects which are very intriguing.
We have not attempted knocking out CDC6 to uncover novel mechanisms of DNA replication, because we first needed to make sure that the mice can truly endo-reduplicate without two of the six subunits of ORC. Note that our published results in cancer cell lines (Shibata, 2016) show that CDC6 is still essential in the ORC KO cell lines, so a future experiment will likely reveal that CDC6 is still essential for endoreduplication in the ORC KO mice in vivo.
Reviewer #2
__Evidence, reproducibility and clarity __
It has been reported that in the absence of ORC1, liver cells can still endoreduplicate and it has been speculated that this might occur if CDC6 can replace, at least partially, the function of ORC1. Here, authors evaluate if this is also true in the absence of ORC2 and found that ORC2 is required for cell proliferation in mouse hepatocytes but not for endoreduplication. This is also the case after combining the conditional mutations of ORC1 and ORC2. They propose that a mechanism must exist to load sufficient MCM2-7 to support DNA replication in the absence of these two ORC subunits. Some of the conclusions need further experimental support. The rationale for testing the requirement of ORC2, with or without ORC1, for endoreduplication is valid. However, a key point is that the endoreduplication level seems to be higher in the absence of ORC2 or both ORC1 and ORC2, and this is not properly addressed. Also, mechanistic details on how this could be triggered are absent from this study. As indicated below almost every figure in this manuscript contains weak points (see below).
We now discuss the following: “One possible explanation of the greater endoreduplication in both our papers is that mitosis may be arrested earlier in development by G2 DNA damage checkpoints activated by incomplete licensing and replication of the genome in the absence of ORC. As a result, endoreduplication cycles could begin earlier in development resulting in greater endoreduplication.”
Major 1. Fig 1G, needs a detailed comment and justification.
We have added the following to the text: “The proliferation rate of the MEF were measured by MTT assays. Even in the Orc2+/+ MEF, the infection with adeno-Cre decreased proliferation a little (the orange line compared to the blue line in Fig. 1G). However, for Orc2f/f MEF infection with adeno-Cre impairs proliferation even further (yellow line compared to black line in Fig. 1G)..
Note that Adeno-Cre has been reported to be toxic for cell proliferation (citations 1, 2, 3), and so we included Adeno-Cre expression in ORC2+/+ (WT) as a background control.
Citation:
- Pfeifer A, Brandon EP, Kootstra N, Gage FH, Verma IM: Delivery of the Cre recombinase by a self deleting lentiviral vector: Efficient gene targeting in vivo. Proc Natl Acad Sci USA. 2001, 98: 11450-11455. 10.1073/pnas.201415498.
- Loonstra A, Vooijs M, Beverloo HB, Allak BA, Drunen EV, Kanaar R, Berns A, Jonkers J: Growth inhibition and DNA damage induced by Cre recombinase in mammalian cells. Proc Natl Acad Sci USA. 2001, 98: 9209-9214. 10.1073/pnas.161269798.
- Schmidt EE, Taylor DS, Prigge JR, Barnet S, Capecchi R: Illegitimate Cre-dependent chromosome rearrangements in transgenic mouse spermatids. Proc Natl Acad Sci USA. 2000, 97: 13702-13707. 10.1073/pnas.240471297.
- Fig 2D-F. Is this conclusion applicable to other endoreplicating tissues? Have authors consider to analyze body weight and liver weight measurements after normalization with similar data from a non-affected organ? The conditional KO was performed specifically in the liver. ORC is intact in other tissues in these animals. As a future direction our lab plans to study cardiac-specific conditional KO of ORC subunits to test whether other endo-reduplicating tissues can also synthesize DNA in the absence of ORC subunits.
Fig 3 shows inconsistent results or results that lack proper justification in the text. The 2C peak is missing in Fig 3E (yellow line, positive control). However, 2n nuclei appear in Fig 3F-H. Also, the blue and yellow peaks do not coincide in the flow cytometry profiles, in particular for 8C and 16C.
There was an error in the plotting of the former Fig. 3E. The information is better presented in the former Fig. 3F-H (now Fig. 3E-G) and so have removed the former Fig. 3E from the paper.
Fig 4. Shorter EdU pulses could be more informative of the actual amount of S-phase cells. Thus, the use of a 2h EdU pulse needs a clear justification.
The half-life of EDU incorporation differs slightly between in vivo and in vitro conditions. In vivo, slower cell proliferation requires a longer time, approximately 4 hours. However, in vitro, liver cells grow faster, and a 2-hour EDU pulse with 20 µM is sufficient for detection compared to a 3-hour pulse with 10 µM BrdU (Okano-Uchida et al., 2018). Several publications also use a 2-hour EDU incubation time (https://doi.org/10.1098/rsob.150172).
Fig 5. EYFP is cytoplasmic, in contrast with results shown in Fig 4C
We consistently see this variability and it was there in our previous results (Okano-Uchida et al., 2018), where EYFP was cytoplasmic in tissues, but was nuclear (and some cytoplasmic) in hepatocytes in culture.
We do not know the reason for this difference but consistently see this difference. We now say in the text: “We did not explore why the EYFP protein is mostly nuclear in hepatocytes in culture (Fig. 4C) and mostly cytoplasmic in hepatocytes in the liver tissue (Fig. 5G, 7G), but speculate that differences in signaling pathways or fixation techniques between the two conditions contribute to this difference.”
Fig 6. Results obtained with the double mutant are poorly described.
We have split the figure into two figures (New Fig. 6 and 7) edited the results section to ensure that they are easily comprehended by the readers. We have also included Westerns from hepatocyte cell lysates of four DKO mice to show that ORC1 and ORC2 proteins are reproducible decreased (New Fig. 6D).
What are the level of other pre-RC components in the mutants used in this study. This could be easily evaluated by Western blotting
Despite the technical difficulty of not having antibodies that recognize all the mouse initiation proteins, we have now measured mouse ORC1, ORC2, ORC3, ORC5, ORC6, CDC6 and the MCM2 and MCM3 subunits of MCM2-7. The results do not show a consistent decrease or increase of any of these proteins in individual mice of the two genotypes, Orc2-/- or DKO (New Fig. 2D and 6E)
How do authors justify their claim that a very limited amount of ORC are sufficient to load a vast excess of MCM2-7 hexamers?
The rationale is stated in the introduction from data from cancer cell lines: “Given that WT cells have about 150,000 molecules of ORC2, even if this truncated protein is functional ORC2, ~150 molecules of the protein would be expected to load MCM2-7 double hexamers on at least 50,000 origins of replication. Experimentally, we show in Shibata, 2020 (Fig. 7C), that although ORC subunits are undetectable on Westerns, MCM2-7 association with the chromatin is unchanged. By the way, we do not say “vast excess” of MCM2-7, just sufficient MCM2-7 to fire 50,000 origins.
Minor 1. The titles of the Results section could be more informative of the main conclusion rather than simply descriptive
We updated our Results titles to be more informative.
The Discussion is too long
We have shortened the discussion by removing our calculations to the Results section and abbreviating some of the discussion on endoreduplication. However we had to insert new items brough forth by the reviewers. Due to the controversy of this topic in our field, we had to include extensive discussion of current literature and put our results in their proper context.
Significance
The topic is relevant and the hypothesis tested is reasonable, although the conceptual advance is limited (see also below). The major limitation is the absence of mechanistic details addressing the occurrence of extra endoreduplication cycles (compared to controls) in the ORC1 and ORC2 mutants.
Reviewer #3
__Evidence, reproducibility and clarity: __
The origin recognition complex (ORC) is an essential loading factor for the replicative Mcm2-7 helicase complex. Despite ORC's critical role in DNA replication, there have been instances where the loss of specific ORC subunits has still seemingly supported DNA replication in cancer cells, endocycling hepatocytes, and Drosophila polyploid cells. Critically, all tested ORC subunits are essential for development and proliferation in normal cells. This presents a challenge, as conditional knockouts need to be generated, and a skeptic can always claim that there were limiting but sufficient ORC levels for helicase loading and replication in polyploid or transformed cells. That being said, the authors have consistently pushed the system to demonstrate replication in the absence or extreme depletion of ORC subunits.
Here, the authors generate conditional ORC2 mutants to counter a potential argument with prior conditional ORC1 mutants that Cdc6 may substitute for ORC1 function based on homology. They also generate a double ORC1 and ORC2 mutant, which is still capable of DNA replication in polyploid hepatocytes. While this manuscript provides significantly more support for the ability of select cells to replicate in the absence or near absence of select ORC subunits, it does not shed light on a potential mechanism. While a mechanistic understanding of how these cells proliferate in the absence or extreme depletion of ORC subunits is outside the scope of the current manuscript, it would have been beneficial to see more functional analyses to help guide the field. For example, is there a delay or impairment in Mcm2-7 loading in G1 (FACs-based loading assay from the Cook Lab (Matson et al., eLife. 2017)) in primary hepatocytes with the ORC2 conditional deletion? Is copy number maintained as cells increase polyploidy in the absence of ORC subunits, or are some regions of the genome more sensitive to ORC depletion (CGH arrays or sequencing of the flow-sorted polyploid cells)?
We thank the reviewer for recognizing the main point of these experiments: to dispel the argument that CDC6 can substitute for ORC1 in the six-subunit ORC (although no one has demonstrated this, the argument is made on the basis of close sequence homology between CDC6 and ORC1). The second point, also appreciated by the reviewer is to show that it is possible to find cells that replicate in the absence or near absence of two ORC subunits.
The mechanistic questions raised are important, and we will address them here:
Is there a delay or impairment of MCM2-7 loading in G1? The hepatocytes in culture are fragile and not immortalized and thus, this issue can be much more easily addressed in the cancer cell lines we have made that are missing several ORC subunits and will do that in a later paper. Note however, the surprising lack of change in MCM2-7 association in cell lines where both ORC2 and ORC5 are deleted (Shibata, 2020, Fig. 7C).
Are some regions of the genome more sensitive to ORC deletion during the polyploidization? We could not find any paper where people have investigated whether the whole genome is uniformly polyploidized in livers. In other words, the baseline conditions in WT livers have not been established. We therefore have postponed experiments to answer this question for a later paper. Note that in unpublished data from mapping SNS-seq origins in WT and ORC deletion cell lines there does not appear to be selective firing of certain origins over others in the deletion cell lines.
Additional points: I didn't understand how the numbers were derived in Table 2. Was there really a 20-fold decrease in nuclear density for female ORC1 and ORC2 double-deletion hepatocytes? The differences in Figure S2 are dramatic, but not 20-fold dramatic.
We measure the relative nuclear density by counting the number of plump nuclei (hepatocytes) per field as described for Fig. 5F and 7F now in the Methods section. The reviewer is correct in that we overestimated the decrease of nuclear density in the female DKO mice by two-fold. The revised calculations suggest that 6 cell divisions occur in the female DKO mice after the ORC proteins have decreased to at least __Significance: __
The strengths of this manuscript are the mouse genetics and the generation of conditional alleles of Orc2 and the rigorous assessment of phenotypes resulting from limiting amounts of specific ORC subunits. It also builds on prior work with ORC1 to rule out Cdc6 complementing the loss of ORC1. The weakness is that it is a very hard task to resolve the fundamental question of how much ORC is enough for replication in cancer cells or hepatocytes. Clearly, there is a marked reduction in specific ORC subunits that is sufficient to impact replication during development and in fibroblasts, but the devil's advocate can always claim limiting levels of ORC remaining in these specialized cells. The significance of the work is that the authors keep improving their conditional alleles (and combining them), thus making it harder and harder (but not impossible) to invoke limiting but sufficient levels of ORC. At this point, the investigators and the field are well-positioned to attempt future functional CRISPR screens to identify other factors that may modulate the response to the loss of ORC subunits. This work will be of interest to the DNA replication, polyploidy, and genome stability communities.
We thank the reviewer for getting the important point of this paper: “making it harder and harder (but not impossible) to invoke limiting but sufficient levels of ORC….” In other words, either ORC is completely dispensable for loading MCM2-7 in certain cancer cell lines and hepatocytes or it is highly catalytic and one molecule of ORC can load a few hundred MCM2-7 doublets so that most origins in the genome are licensed and capable of firing. We are trying the CRISPR screens in cancer cell lines that the reviewer envisages
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Referee #3
Evidence, reproducibility and clarity
The origin recognition complex (ORC) is an essential loading factor for the replicative Mcm2-7 helicase complex. Despite ORC's critical role in DNA replication, there have been instances where the loss of specific ORC subunits has still seemingly supported DNA replication in cancer cells, endocycling hepatocytes, and Drosophila polyploid cells. Critically, all tested ORC subunits are essential for development and proliferation in normal cells. This presents a challenge, as conditional knockouts need to be generated, and a skeptic can always claim that there were limiting but sufficient ORC levels for helicase loading and replication in polyploid or transformed cells. That being said, the authors have consistently pushed the system to demonstrate replication in the absence or extreme depletion of ORC subunits.
Here, the authors generate conditional ORC2 mutants to counter a potential argument with prior conditional ORC1 mutants that Cdc6 may substitute for ORC1 function based on homology. They also generate a double ORC1 and ORC2 mutant, which is still capable of DNA replication in polyploid hepatocytes. While this manuscript provides significantly more support for the ability of select cells to replicate in the absence or near absence of select ORC subunits, it does not shed light on a potential mechanism. While a mechanistic understanding of how these cells proliferate in the absence or extreme depletion of ORC subunits is outside the scope of the current manuscript, it would have been beneficial to see more functional analyses to help guide the field. For example, is there a delay or impairment in Mcm2-7 loading in G1 (FACs-based loading assay from the Cook Lab (Matson et al., eLife. 2017)) in primary hepatocytes with the ORC2 conditional deletion? Is copy number maintained as cells increase polyploidy in the absence of ORC subunits, or are some regions of the genome more sensitive to ORC depletion (CGH arrays or sequencing of the flow-sorted polyploid cells)?
Additional points: I didn't understand how the numbers were derived in Table 2. Was there really a 20-fold decrease in nuclear density for female ORC1 and ORC2 double-deletion hepatocytes? The differences in Figure S2 are dramatic, but not 20-fold dramatic.
Significance
The strengths of this manuscript are the mouse genetics and the generation of conditional alleles of Orc2 and the rigorous assessment of phenotypes resulting from limiting amounts of specific ORC subunits. It also builds on prior work with ORC1 to rule out Cdc6 complementing the loss of ORC1. The weakness is that it is a very hard task to resolve the fundamental question of how much ORC is enough for replication in cancer cells or hepatocytes. Clearly, there is a marked reduction in specific ORC subunits that is sufficient to impact replication during development and in fibroblasts, but the devil's advocate can always claim limiting levels of ORC remaining in these specialized cells. The significance of the work is that the authors keep improving their conditional alleles (and combining them), thus making it harder and harder (but not impossible) to invoke limiting but sufficient levels of ORC. At this point, the investigators and the field are well-positioned to attempt future functional CRISPR screens to identify other factors that may modulate the response to the loss of ORC subunits. This work will be of interest to the DNA replication, polyploidy, and genome stability communities.
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Referee #2
Evidence, reproducibility and clarity
It has been reported that in the absence of ORC1, liver cells can still endoreduplicate and it has been speculated that this might occur if CDC6 can replace, at least partially, the function of ORC1. Here, authors evaluate if this is also true in the absence of ORC2 and found that ORC2 is required for cell proliferation in mouse hepatocytes but not for endoreduplication. This is also the case after combining the conditional mutations of ORC1 and ORC2. They propose that a mechanism must exist to load sufficient MCM2-7 to support DNA replication in the absence of these two ORC subunits.
Some of the conclusions need further experimental support. The rationale for testing the requirement of ORC2, with or without ORC1, for endoreduplication is valid. However, a key point is that the endoreduplication level seems to be higher in the absence of ORC2 or both ORC1 and ORC2, and this is not properly addressed. Also, mechanistic details on how this could be triggered are absent from this study. As indicated below almost every figure in this manuscript contains weak points (see below).
Major
- Fig 1G, needs a detailed comment and justification.
- Fig 2D-F. Is this conclusion applicable to other endoreplicating tissues? Have authors consider to analyze body weight and liver weight measurements after normalization with similar data from a non-affected organ?
- Fig 3 shows inconsistent results or results that lack proper justification in the text. The 2C peak is missing in Fig 3E (yellow line, positive control). However, 2n nuclei appear in Fig 3F-H. Also, the blue and yellow peaks do not coincide in the flow cytometry profiles, in particular for 8C and 16C.
- Fig 4. Shorter EdU pulses could be more informative of the actual amount of S-phase cells. Thus, the use of a 2h EdU pulse needs a clear justification.
- Fig 5. EYFP is cytoplasmic, in contrast with results shown in Fig 4C
- Fig 6. Results obtained with the double mutant are poorly described.
- What are the level of other pre-RC components in the mutants used in this study. This could be easily evaluated by Western blotting
- How do authors justify their claim that a very limited amount of ORC are sufficient to load a vast excess of MCM2-7 hexamers?
Minor
- The titles of the Results section could be more informative of the main conclusion rather than simply descriptive
- The Discussion is too long
Significance
The topic is relevant and the hypothesis tested is reasonable, although the conceptual advance is limited (see also below). The major limitation is the absence of mechanistic details addressing the occurrence of extra endoreduplication cycles (compared to controls) in the ORC1 and ORC2 mutants
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Referee #1
Evidence, reproducibility and clarity
The work by Przanowska et al., sought to understand the role of ORC2 in murine development and further wanted to discover its role in liver endo-reduplication. The overall methods used is sufficient enough to address its role but is not very conclusive based on their overall results and data provided as elaborated in below comments.
Major Comments:
- The major issue of the paper is how well is ORC2 depleted in perinatal liver (Fig. 2C) and is not very clear from the data as all the western blots are at very low exposure levels and bands are very weak (still weak bands seen). There are good antibodies of ORC2 which can be used for IHC staining and can be used to address the extent of ORC2 depletion.
- Why in Fig 2C, the M2 mice is showing an equivalent level of ORC2 protein compared to mice M1 with NO CRE expression (compare lane1 and lane5). So, the results are based on one mouse which I do not think is significant enough to come to the conclusion. The authors need to add more data from different mice for statistical significance. Please use IHC to show the depletion of ORC2 protein in the liver sections.
- As nicely demonstrated in the previous paper by Okano-Uchida et al., 2018 that ORC1 depletion in the liver shows an DNA ploidy effect from 6-week onwards. The authors need to demonstrate in this paper also when the 16N phenotype is observed starting from week1 to 12 months.
- In the double knockout experiments (ORC1 and ORC2) the authors are not even bothered to demonstrate that how much are both the proteins are actually depleted from the cells, so on the results obtained from these mice experiments are not conclusive or explanatory.
Minor points:
- Why are scale bars missing in right panel of Fig. 2G, Fig. 6D Supp Fig. 2B KO studies. The authors need to confirm that that all the large nuclei have NO or less significant ORC2 protein through IHC H&E staining.
- Please explain why is EYFP in Fig. 5G is cytoplasmic compared to Fig 4C (nuclear).
- Are authors using the same genotype of Alb-Cre mice as shown by Okano-Uchida et al., 2018 as I do not find the reference of Schuler et. al., 2004 (PMID:15282742).
Significance
The article is exactly based on their previous published paper but instead of ORC1, they were interested in dissecting the role of ORC2. Although they have discussed that CDC6 may be involved in replacing ORC1 KO mice to rescue the extensive DNA replication in endoreduplication, but instead of going to hunt the role of CDC6 in endoreduplication they checked the effect of ORC2 which actually lower the overall impact of the paper.
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Reply to the reviewers
Reviewer 1
R1 Cell profiling is an emerging field with many applications in academia and industry. Finding better representations for heterogeneous cell populations is important and timely. However, unless convinced otherwise after a rebuttal/revision, the contribution of this paper, in our opinion, is mostly conceptual, but in its current form - not yet practical. This manuscript combined two concepts that were previously reported in the context of cell profiling, weakly supervised representations. Our expertise is in computational biology, and specifically applications of machine learning in microscopy.
In our revised manuscript, we have aimed to better clarify the practical contributions of our work by demonstrating the effectiveness of the proposed concepts on real-world datasets. We hope that these revisions and our detailed responses address your concerns and highlight the potential impact of our approach.
R1.1a. CytoSummaryNet is evaluated in comparison to aggregate-average profiling, although previous work has already reported representations that capture heterogeneity and self-supervision independently. To argue that both components of contrastive learning and sets representations are contributing to MoA prediction we believe that a separate evaluation for each component is required. Specifically, the authors can benchmark their previous work to directly evaluate a simpler population representation (PMID: 31064985, ref #13) - we are aware that the authors report a 20% improvement, but this was reported on a separate dataset. The authors can also compare to contrastive learning-based representations that rely on the aggregate (average) profile to assess and quantify the contribution of the sets representation.
We agree that evaluating the individual contributions of the contrastive learning framework and single-cell data usage is important for understanding CytoSummaryNet's performance gains.
To assess the impact of the contrastive formulation independently, we applied CytoSummaryNet to averaged profiles from the cpg0004 dataset. This isolated the effect of contrastive learning by eliminating single-cell heterogeneity. The experiment yielded a 32% relative improvement in mechanism of action retrieval, compared to the 68% gain achieved with single-cell data. These findings suggest that while the contrastive formulation contributes significantly to CytoSummaryNet's performance, leveraging single-cell information is crucial for maximizing its effectiveness. We have added a discussion of this experiment to the Results section:
“We conducted an experiment to determine whether the improvements in mechanism of action retrieval were due solely to CytoSummaryNet's contrastive formulation or also influenced by the incorporation of single-cell data. We applied the CytoSummaryNet framework to pre-processed average profiles from the 10 μM dose point data of Batch 1 (cpg0004 dataset). This approach isolated the effect of the contrastive architecture by eliminating single-cell data variability. We adjusted the experimental setup by reducing the learning rate by a factor of 100, acknowledging the reduced task complexity. All other parameters remained as described in earlier experiments.
This method yielded a less pronounced but still substantial improvement in mechanism of action retrieval, with an increase of 0.010 (32% enhancement - Table 1). However, this improvement was not as high as when the model processed single-cell level data (68% as noted above). These findings suggest that while CytoSummaryNet's contrastive formulation contributes to performance improvements, the integration of single-cell data plays a critical role in maximizing the efficacy of mechanism of action retrieval.”
We don't believe comparing with PMID: 31064985 is useful: while the study showcased the usefulness of modeling heterogeneity using second-order statistics, its methodology is limited in scalability due to the computational burden of computing pairwise similarities for all perturbations, particularly in large datasets. Additionally, the study's reliance on similarity network fusion, while expedient, introduces complexity and inefficiency. We contend that this comparison does not align with our objective of testing the effectiveness of heterogeneity in isolation, as it primarily focuses on capturing second and first-order information. Thus, we do not consider this study a suitable baseline for comparison.
R1.1b. The evaluation metric of mAP improvement in percentage is misleading, because a tiny improvement for a MoA prediction can lead to huge improvement in percentage, while a much larger improvement in MoA prediction can lead to a small improvement in percentage. For example, in Fig. 4, MEK inhibitor mAP improvement of ~0.35 is measured as ~50% improvement, while a much smaller mAP improvement can have the same effect near the origins (i.e., very poor MoA prediction).
We agree that relying solely on percentage improvements can be misleading, especially when small absolute changes result in large percentage differences.
However, we would like to clarify two key points regarding our reporting of percentage improvements:
- We calculate the percentage improvement by first computing the average mAP across all compounds for both CytoSummaryNet and average profiling, and then comparing these averages. This approach is less susceptible to the influence of outlier improvements compared to calculating the average of individual compound percentage improvements.
- We report percentage improvements alongside their corresponding absolute improvements. For example, the mAP improvement for Stain4 (test set) is reported as 0.052 (60%). To further clarify this point, we have updated the caption of Table 1 to explicitly state how the percentage improvements are calculated:
“The improvements are calculated as mAP(CytoSummaryNet)-mAP(average profiling). The percentage improvements are calculated as (mAP(CytoSummaryNet)-mAP(average profiling))/mAP(average profiling).”
R1.1b. (Subjective) visual assessment of this figure does not show a convincing contribution of CytoSummaryNet representations of the average profiling on the test set (3.33 uM). This issue might also be relevant for the task of replicate retrieval. All in all, the mAP improvement reported in Table 1 and throughout the manuscript (including the Abstract), is not a proper evaluation metric for CytoSummaryNet contribution. We suggest reporting the following evaluations:
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Visualizing the results of cpg0001 (Figs. 1-3) similarly to cpg0004 (Fig. 4), i.e., plotting the matched mAP for CytoSummaryNet vs. average profile.
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In Table 1, we suggest referring to the change in the number of predictable MoAs (MoAs that pass a mAP threshold) rather than the improvement in percentages. Another option is showing a graph of the predictability, with the X axis representing a threshold and Y-axis showing the number of MoAs passing it. For example see (PMID: 36344834, Fig. 2B) and (PMID: 37031208, Fig. 2A), both papers included contributions from the corresponding author of this manuscript.
Regarding the suggestion to visualize the results for compound group cpg0001 similarly to cpg0004, unfortunately, this is not feasible due to the differences in data splitting between the two datasets. In cpg0001, an MoA might have one compound in the training set and another in the test or validation set. Reporting a single value per MoA would require combining these splits, which could be misleading as it would conflate performance across different data subsets.
However, we appreciate the suggestion to represent the number of predictable MoAs that surpass a certain mAP threshold, as it provides another intuitive measure of performance. To address this, we have created a graph that visualizes the predictability of MoAs across various thresholds, similar to the examples provided in the referenced papers (PMID: 36344834, Figure 2B and PMID: 37031208, Figure 2A). This graph, with the x-axis depicting the threshold and the y-axis showing the number of MoAs meeting the criterion, has been added to Supplementary Material K.
R1.1c.i. "a subset of 18 compounds were designated as validation compounds" - 5 cross-validations of 18 compounds can make the evaluation complete. This can also enhance statistical power in figures 1-3.
We appreciate your suggestion and acknowledge the potential benefits of employing cross-validation, particularly in enhancing statistical power. While we understand the merit of cross-validation for evaluating model performance and generalization to unseen data, we believe the results as presented already highlight the generalization characterics of our methods.
Specifically, (the new) Figure 3 demonstrates the model's improvement over average profiling in both training and validation plates, supporting its ability to generalize to unseen compounds (but not to unseen plates).
While cross-validation could potentially enhance our analysis, retraining five new models solely for different validation set results may not substantially alter our conclusions, given the observed trends in Suppl Figure A1 and (the new) Figure 4, both of which show results across multiple stain sets (but a single train-test-validation split).
R1.1c.ii. Clarify if the MoA results for cpg0001 are drawn from compounds from both the training and the validation datasets. If so, describe how the results differ between the sets in text and graphs.
We confirm that the Mechanism of Action (MoA) retrieval results for cpg0001 are derived from all available compounds. It's important to note that the training and validation dataset split for the replicate retrieval task is different from the MoA prediction task. For replicate retrieval, we train using all available compounds and validate on a held-out set (see Figure 2). For MoA prediction, we train using the replicate retrieval task as the objective on all available compounds but validate using MoA retrieval, which is a distinct task. We have added a brief clarification in the main text to highlight the distinction between these tasks and how validation is performed for each:
“We next addressed a more challenging task: predicting the mechanism of action class for each compound at the individual well level, rather than simply matching replicates of the exact same compound (Figure 5). It's also important to note that mechanism of action matching is a downstream task on which CytoSummaryNet is not explicitly trained. Consequently, improvements observed on the training and validation plates are more meaningful in this context, unlike in the previous task where only improvements on the test plate were meaningful. For similar reasons, we calculate the mechanism of action retrieval performance on all available compounds, combining both the training and validation sets. This approach is acceptable because we calculate the score on so-called "sister compounds" only—that is, different compounds that have the same mechanism of action annotation. This ensures there is no overlap between the mechanism of action retrieval task and the training task, maintaining the integrity of our evaluation. ”
R1.1c.iii. "Mechanism of action retrieval is evaluated by quantifying a profile's ability to retrieve the profile of other compounds with the same annotated mechanism of action.". It was unclear to us if the evaluation of mAP for MoA identification can include finding replicates of the same compound. That is, whether finding a close replicate of the same compound would be included in the AP calculation. This would provide CytoSummaryNet with an inherent advantage as this is the task it is trained to do. We assume that this was not the case (and thus should be more clearly articulated), but if it was - results need to be re-evaluated excluding same-compound replicates.
The evaluation excludes replicate wells of the same compound and only considers wells of other compounds with the same MoA. This methodology ensures that the model's performance on the MoA prediction task is not inflated by its ability to find replicates of the same compound, which is the objective of the replicate retrieval task. Please see the explanation we have added to the main text in our response to R1.1c.ii. Additionally, we have updated the Methods section to clearly describe this evaluation procedure:
“Mechanism of action retrieval is evaluated by quantifying a profile’s ability to retrieve the profile of different compounds with the same annotated mechanism of action.”
__R1.2a. __The description of Stain2-5 was not clear for us at first (and second) read. The information is there, but more details will greatly enhance the reader's ability to follow. One suggestion is explicitly stating that these "stains" partitioning was already defined in ref 26. Another suggestion is laying out explicitly a concrete example on the differences between two of these stains. We believe highlighting the differences between stains will strengthen the claim of the paper, emphasizing the difficulty of generalizing to the out-of-distribution stain.
We appreciate your feedback on the clarity of the Stain2-5 dataset descriptions; we certainly struggled to balance detail and concepts in describing these. We have made the following changes:
- Explicitly mentioned that the partitioning of the Stain experiments was defined in https://pubmed.ncbi.nlm.nih.gov/37344608/: “The partitioning of the Stain experiments have been defined and explained previously [21].”
- Moved an improved version of (now) Figure 2 from the Methods section to the main text to help visually explain how the stratification is done early on.
- Added a new section in the Experimental Setup: Diversity of stain sets, which includes a concrete example highlighting the differences between Stain2, and Stain5 to emphasize the diversity in experimental setups within the same dataset: “Stain2-5 comprise a series of experiments which were conducted sequentially to optimize the experimental conditions for image-based cell profiling. These experiments gradually converged on the most optimal set of conditions; however, within each experiment, there were significant variations in the assay across plates. To illustrate the diversity in experimental setups within the dataset, we will highlight the differences between Stain2 and Stain5.
Stain2 encompasses a wide range of nine different experimental protocols, employing various imaging techniques such as Widefield and Confocal microscopy, as well as specialized conditions like multiplane imaging and specific stains like MitoTracker Orange. This subset also includes plates acquired with strong pixel binning instead of default imaging and plates with varying concentrations of dyes like Hoechst. As a result, Stain2 exhibits greater variance in the experimental conditions across different plates compared to Stain5.
In contrast, Stain5, the last experiment in the series, follows a more systematic approach, consistently using either confocal or default imaging across three well-defined conditions. Each condition in Stain5 utilizes a lower cell density of 1,000 cells per well compared to Stain2's 2,500 cells per well. Being the final experiment in the series, Stain5 had the least variance in experimental conditions.
For training the models, we typically select the data containing the most variance to capture the broadest range of experimental variation. Therefore, we chose Stain2-4 for training, as they represented the majority of the data and captured the most experimental variation. We reserved Stain5 for testing to evaluate the model's ability to generalize to new experimental conditions with less variance.
All StainX experiments were acquired in different passes, which may introduce additional batch effects.”
These changes aim to provide a clearer understanding of the dataset's complexity and the challenges associated with generalizing to out-of-distribution data.
R1.2b. What does each data point in Figures 1-3 represent? Is it the average mAP for the 18 validation compounds, using different seeds for model training? Why not visualize the data similarly to Fig. 4 so the improvement per compound can be clearly seen?
The data points in (the new) Figures 3,4,5 represent the average mAP for each plate, calculated by first computing the mAP for each compound and then averaging across compounds to obtain the average mAP per plate. We have updated the figure captions to clarify this:
"... (each data point is the average mAP of a plate) ..."
While visualizing the mAP per compound, similar to (the new) Figure 6 for cpg0004, could provide insights into compound-level improvements, it would require creating numerous additional figures or one complex figure to adequately represent all the stratifications we are analyzing (plate, compound, Stain subset). By averaging the data per plate across different stratifications, we aim to provide a clearer and more comprehensible overview of the trends and improvements while allowing us to draw conclusions about generalization.
Please note: this comment is related to the comment R1.1b (Subjective)
R1.2.c [On the topic of enhancing clarity and readability:] Justification and interpretation of the evaluation metrics.
Please refer to our response to comment R1.1b, where we have addressed your concerns regarding the justification and interpretation of the evaluation metrics.
R1.2d. Explicitly mentioning the number of MoAs for each datasets and statistics of number of compounds per MoA (e.g., average\median, min, max).
We have added the following to the Experimental Setup: Data section:
“A subset of the data was used for evaluating the mechanism of action retrieval task, focusing exclusively on compounds that belong to the same mechanism class. The Stain plates contained 47 unique mechanisms of action, with each compound replicated four times. Four mechanisms had only a single compound; the four mechanisms (and corresponding compounds) were excluded, resulting in 43 unique mechanisms used for evaluation. In the LINCS dataset, there were 1436 different mechanisms, but only 661 were used for evaluation because the remaining had only one compound.”
R1.2e. The data split in general is not easily understood. Figure 8 is somewhat helpful, however in our view, it can be improved to enhance understanding of the different splits. Specifically, the training and validation compounds need to be embedded and highlighted within the figure.
Thank you for highlighting this. We have completely revised the figure, now Figure 2 which we hope more clearly conveys the data split strategy.
Please note: this comment is related to the comment R1.2a.
R1.3a. Why was stain 5 used for the test, rather than the other stains?
Stain2-5 were part of a series of experiments aimed at optimizing the experimental conditions for image-based cell profiling using Cell Painting. These experiments were conducted sequentially, gradually converging on the most optimal set of conditions. However, within each experiment, there were significant variations in the assay across plates, with earlier iterations (Stain2-4) having more variance in the experimental conditions compared to Stain5. As Stain5 was the last experiment in the series and consisted of only three different conditions, it had the least variance. For training the models, we typically select the data containing the most variance to capture the broadest range of experimental variation. Therefore, Stain2-4 were chosen for training, while Stain5 was reserved for testing to evaluate the model's ability to generalize to new experimental conditions with less variance.
We have now clarified this in the Experimental Setup: Diversity of stain sets section. Please see our response to comment R1.2a. for the full citation.
R1.3b How were the 18 validation compounds selected?
20% of the compounds (n=18) were randomly selected and designated as validation compounds, with the remaining compounds assigned to the training set. We have now clarified this in the Results section:
“Additionally, 20% of the compounds (n=18) were randomly selected and designated as validation compounds, with the remaining compounds assigned to the training set (Supplementary Material H).”
R1.3c. For cpg0004, no justification for the specific doses selected (10uM - train, 3.33 uM - test) for the analysis in Figure 4. Why was the data split for the two dosages? For example, why not perform 5-fold cross validation on the compounds (e.g., of the highest dose)?
We chose to use the 10 μM dose point as the training set because we expected this higher dosage to consist of stronger profiles with more variance than lower dose points, making it more suitable for training a model. We decided to use a separate test set at a different dose (3.33 μM) to assess the model's ability to generalize to new dosages. While cross-validation on the highest dose could also be informative, our approach aimed to balance the evaluation of the model's generalization capability with its ability to capture biologically relevant patterns across different dosages.
This explanation has been added to the text:
“We chose the 10 μM dose point for training because we expected this high dosage to produce stronger profiles with more variance than lower dose points, making it more suitable for model training.”
“The multiple dose points in this dataset allowed us to create a separate hold-out test set using the 3.33 μM dose point data. This approach aimed to evaluate the model's performance on data with potentially weaker profiles and less variance, providing insights into its robustness and ability to capture biologically relevant patterns across dosages. While cross-validation on the 10 μM dose could also be informative, focusing on lower dose points offers a more challenging test of the model's capacity to generalize beyond its training conditions, although we do note that all compounds’ phenotypes would likely have been present in the 10 μM training dataset, given the compounds tested are the same in both.”
R1.3d. A more detailed explanation on the logic behind using a training stain to test MoA retrieval will help readers appreciate these results. In our first read of this manuscript we did not grasp that, we did in a second read, but spoon-feeding your readers will help.
This comment is related to the rationale behind training on one task and testing on another, which is addressed in our responses to comments R1.1.cii and R1.1.ciii.
R1.4 Assessment of interpretability is always tricky. But in this case, the authors can directly confirm their interpretation that the CytoSummaryNet representation prioritizes large uncrowded cells, by explicitly selecting these cells, and using their average profile re
We progressively filtered out cells based on a quantile threshold for Cells_AreaShape features (MeanRadius, MaximumRadius, MedianRadius, and Area), which were identified as important in our interpretability analysis, and then computed average profiles using the remaining cells before determining the replicate retrieval mAP. In the exclusion experiment, we gradually left out cells as the threshold increased, while in the inclusion experiment, we progressively included larger cells from left to right.
The results show that using only the largest cells does not significantly increase the performance. Instead, it is more important to include the large cells rather than only including small cells. The mAP saturates after a threshold of around 0.4, indicating that larger cells define the profile the most, and once enough cells are included to outweigh the smaller cell features, the profile does not change significantly by including even larger cells.
These findings support our interpretation that CytoSummaryNet prioritizes large, uncrowded cells. While this approach could potentially be used as a general outlier removal strategy for cell profiling, further investigation is needed to assess its robustness and generalizability across different datasets and experimental conditions.
We have created Supplementary Material L to report these findings and we additionally highlight them in the Results:
“To further validate CytoSummaryNet's prioritization of large, uncrowded cells, we progressively filtered cells based on Cells_AreaShape features and observed the impact on replicate retrieval mAP (Supplementary Material L). The results support our interpretation and highlight the key role of larger cells in profile strength.”
__R1.5. __Placing this work in context of other weakly supervised representations. Previous papers used weakly supervised labels of proteins / experimental perturbations (e.g., compounds) to improve image-derived representations, but were not discussed in this context. These include PMID: 35879608, https://www.biorxiv.org/content/10.1101/2022.08.12.503783v2 (from the same research groups and can also be benchmarked in this context), https://pubs.rsc.org/en/content/articlelanding/2023/dd/d3dd00060e , and https://www.biorxiv.org/content/10.1101/2023.02.24.529975v1. We believe that a discussion explicitly referencing these papers in this specific context is important.
While these studies provide valuable insights into improving cell population profiles using representation learning, our work focuses specifically on the question of single-cell aggregation methods. We chose to use classical features for our comparisons because they are the current standard in the field. This approach allows us to directly assess the performance of our method in the context of the most widely used feature extraction pipeline in practice. However, we see the value in incorporating them in future work and have mentioned them in the Discussion:
“Recent studies exploring image-derived representations using self-supervised and self-supervised learning [35][36] could inspire future research on using learned embeddings instead of classical features to enhance model-aggregated profiles.”
R1.minor1. "Because the improved results could stem from prioritizing certain features over others during aggregation, we investigated each cell's importance during CytoSummaryNet aggregation by calculating a relevance score for each" - what is the relevance score? Would be helpful to provide some intuition in the Results.
We have included more explanation of the relevance score in the Results section, following the explanation of sensitivity analysis (SA) and critical point analysis (CPA):
“SA evaluates the model's predictions by analyzing the partial derivatives in a localized context, while CPA identifies the input cells with the most significant contribution to the model's output. The relevance scores of SA and CPA are min-max normalized per well and then combined by addition. The combination of the two is again min-max normalized, resulting in the SA and CPA combined relevance score (see Methods for details).”
R1.minor2. Figure 1:
- Colors of the two methods too similar
- The dots are too close. It will be more easily interpreted if they were further apart.
- What do the dots stand for?
- We recommend considering moving this figure to the supp. material (the most important part of it is the results on the test set and it appears in Fig.2).
- We chose a lighter and darker version of the same color as a theme to simplify visualization, as this theme is used throughout (the new) Figures 3,4,5.
- We agree; we have now redrawn the figure to fix this.
- Each data point is the average mAP of a plate. Please see our answer for R1.2b as well.
- We believe that (the new) Figures 3,4,5 serve distinct purposes in testing various generalization hypotheses. We have added the following text to emphasize that the first figures are specifically about generalization hypothesis testing: “We first investigated CytoSummaryNet’s capacity to generalize to out-of-distribution data: unseen compounds, unseen experimental protocols, and unseen batches. The results of these investigations are visualized in Figures 3, 4, and 5, respectively.”
R1.minor3 Figure 4: It is somewhat misleading to look at the training MoAs and validation MoAs embedded together in the same graph. We recommend showing only the test MoAs (train MoAs can move to SI).
We addressed this comment in R1.1c.ii. To reiterate briefly, there are no training, validation, or test MoAs because these are not used as labels during the training process. There is an option to split them based on training and validation compounds, which is addressed in R1.1c.ii.
R1.minor4 Figure 5
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Why only Stain3? What happens if we look at Stains 2,3 and 4 together? Stain 5?
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Should validation compounds and training compounds be analyzed separately?
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Subfigure (d): it is expected that the data will be classified by compound labels as it is the training task, but for this to be persuasive I would like to see this separately on the training compounds first and then and more importantly on the validation compounds.
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For subfigures (b) and (d): it appears there are not enough colors for d, which makes it partially not understandable. For example, the pink label in (d) shows a single compound which appears to represent two different MoAs. This is probably not the case, and it has two different compounds, but it cannot be inferred when they are represented by the same color.
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For the Subfigure (e) - only 1 circle looks justified (in the top left). And for that one, is it not a case of an outlier plate that would perhaps need to be removed from analysis? Is it not good that such a plate will be identified?
We have addressed this point in the text, stating that the results are similar for Stain2 and Stain4. Stain5 represents an out-of-distribution subset because of a very different set of experimental conditions (see Experimental Setup: Diversity of stain sets). To improve clarity, we have revised the figure caption to reiterate this information:
“... Stain2 and Stain4 yielded similar results (data not shown). …”
- For replicate retrieval, analyzing validation and training compounds separately is appropriate. However, this is not the case for MoA retrieval, as discussed in our responses to R1.1c.ii and R1.1c.i.
- We have created the requested plot (below) but ultimately decided not to include it in the manuscript because we believe that (the new) Figures 3 and 4 are more effective for making quantitative comparative claims.
[Please see the full revision document for the figures]
Top: training compounds (validation compounds grayed out); not all compounds are listed in the legend.
*Bottom: validation compounds (training compounds grayed out). *
Left: average profiling; Right: CytoSummaryNet
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We agree with your observation and have addressed this issue by labeling the center mass as a single class (gray) and highlighting only the outstanding pairs in color. Please refer to the updated figure and our response to R3.6 for more details.
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In the updated figure, we have revised the figure caption to focus solely on the annotation of same mechanism of action profile clusters, as indicated by the green ellipses. The annotation of isolated plate clusters has been removed (Figures 7e and 7f) to maintain consistency and avoid potential confusion. Despite being an outlier for Stain3, the plate (BR00115134bin1) clusters with Stain4 plates (Supplementary Figure F1, green annotated square inside the yellow annotated square), indicating it is not merely a noisy outlier and can provide insights into the out-of-sample performance of our model.
R1.minor5a. Discussion: "perhaps in part due to its correction of batch effects" - is this statement based on Fig. 5F - we are not convinced.
We appreciate the reviewer's scrutiny regarding our statement about batch effect correction. Upon reevaluation, we agree that this claim was not adequately substantiated by empirical data. We quantified the batch effects using comparison mean average precision for both average profiles and CytoSummaryNet profiles, and the statistical analysis revealed no significant difference between these profiles in terms of batch effect correction. Therefore, we have removed this theoretical argument from the manuscript entirely to ensure that all claims are strongly supported by the data presented.
R1.minor5b. "Overall, these results improve upon the ~20% gains we previously observed using covariance features" - this is not the same dataset so it is hard to reach conclusions - perhaps compare performance directly on the same data?
We have now explicitly clarified this is a different dataset. Please see our response to R1.1a for why a direct comparison was not performed. The following clarification can be found in the Discussion:
“These results improve upon the ~20% gains previously observed using covariance features [13] albeit on a different dataset, and importantly, CytoSummaryNet effectively overcomes the challenge of recomputation after training, making it easier to use.”
Reviewer 2
R2.1 The authors present a well-developed and useful algorithm. The technical motivation and validation are very carefully and clearly explained, and their work is potentially useful to a varied audience.
That said, I think the authors could do a better job, especially in the figures, of putting the algorithm in context for an audience that is unfamiliar with the cell painting assay. (a) For example, a figure towards the beginning of the paper with example images might help to set the stage. (b) Similarly a schematic of the algorithm earlier in the paper would provide a graphical overview. (c) For the sake of a biologically inclined audience, I would consider labeling the images in the caption by cell type and label.
Thank you for your valuable suggestions on improving the accessibility of our figures for readers unfamiliar with the Cell Painting assay. We have made the following changes to address your comments:
- and b. To provide visual context and a graphical overview of the algorithm, we have moved the original Figure 7 to Figure 1. This figure now includes example images that help readers new to the Cell Painting assay.
- We have added relevant details to the example images in (the new) Figure 1
R2.2 The interpretability results were intriguing. The authors might consider further validating these interpretations by removing weakly informative cells from the dataset and retraining. Are the cells so uninformative that the algorithm does better without them, or are they just less informative than other cells?
Please see our responses to R1.4 and R3.0
R2.3 As far as I can tell, the authors only oblique state whether the code associated with the manuscript is openly available. Posting the code is needed for reproducibility. I would provide not only a github, but a doi linked to the code, or some other permanent link.
We have now added a Code Availability and Data Availability section, clearing stating that the code and data associated with the manuscript are openly available.
R2.4 Incorporating biological heterogeneity into machine-learning driven problems is a critical research question. Replacing means/modes and such with a machine learning framework, the authors have identified a problem with potentially wide significance. The application to cell painting and related assays is of broad enough significance for many journals, However, the authors could further broaden the significance by commenting on other possible cell biology applications. What other applications might the algorithm be particularly suited for? Are there any possible roadblocks to wider use. What sorts of data has the code been tested on so far?
We have added the following paragraph to discuss the broader applicability of CytoSummaryNet:
“The architecture of CytoSummaryNet holds significant potential for broader applications beyond image-based cell profiling, accommodating tabular, permutation-invariant data and enhancing downstream task performance when applied to processed population-level profiles. Its versatility makes it valuable for any omics measurements where downstream tasks depend on measuring similarity between profiles. Future research could also explore CytoSummaryNet's applicability to genetic perturbations, expanding its utility in functional genomics.”
Reviewer 3
R3.0 The authors have done a commendable job discussing the method, demonstrating its potential to outperform current models in profiling cell-based features. The work is of considerable significance and interest to a wide field of researchers working on the understanding of cell heterogeneity's impact on various biological phenomena and practical studies in pharmacology.
One aspect that would further enhance the value of this work is an exploration of the method's separation power across different modes of action. For instance, it would be interesting to ascertain if the method's performance varies when dealing with actions that primarily affect size, those that affect marker expression, or compounds that significantly diminish cell numbers.
Thank you for encouraging comments!
We have added the following to Results: Relevance scores reveal CytoSummaryNet's preference for large, isolated cells:
“Statistical t-tests were conducted to identify the features that most effectively differentiate mechanisms of action from negative controls in average profiles, focusing on the three mechanisms of action where CytoSummaryNet demonstrates the most significant improvement and the three mechanisms where it shows the least. Consistent with our hypothesis that CytoSummaryNet emphasizes larger, more sparse cells, the important features for the CytoSummaryNet-improved mechanisms of action (Supplementary Material I1) often involve the radial distribution for the mitochondria and RNA channels. These metrics capture the fraction of those stains near the edge of the cell versus concentric rings towards the nucleus, which are more readily detectable in larger cells compared to small, rounded cells.
In contrast, the important features for mechanisms of action not improved by CytoSummaryNet (Supplementary Material I) predominantly include correlation metrics between brightfield and various fluorescent channels, capturing spatial relationships between cellular components. Some of these mechanisms of action included compounds that were not individually distinguishable from negative controls, and CytoSummaryNet did not overcome the lack of phenotype in these cases. This suggests that while CytoSummaryNet excels in identifying certain cellular features, its effectiveness is limited when dealing with mechanisms of action that do not exhibit pronounced phenotypic changes.”
We have also added supplementary material to support (I. Relevant features for CytoSummaryNet improvement).
R3.0 Another test on datasets that are not concerned with chemical compounds, but rather genetic perturbations would greatly increase the reach of the method into the functional genomics community and beyond. This additional analysis could provide valuable insights into the versatility and applicability of the proposed method.
We agree that testing the method's behavior on genetic perturbations would be interesting and could provide insights into its versatility. However, the efficacy of the methodology may vary depending on the specific properties of different genetic perturbation types.
For example, the penetrance of phenotypes may differ between genetic and chemical perturbations. In some experimental setups, a selection agent ensures that nearly all cells receive a genetic perturbation (though not all may express a phenotype due to heterogeneity or varying levels of the target protein). Other experiments may omit such an agent. Additionally, different patterns might be observed in various classes of reagents, such as overexpression, CRISPR-Cas9 knockdown (CRISPRn), CRISPR-interference (CRISPRi), and CRISPR-activation (CRISPRa).
We believe that selecting a single experiment with one of these technologies would not adequately address the question of versatility. Instead, we propose future studies that may conclusively assess the method's performance across a variety of genetic perturbation types. This would provide a more comprehensive understanding of CytoSummaryNet's applicability in functional genomics and beyond. We have update the Discussion section to reflect this:
“Future research could also explore CytoSummaryNet's applicability to genetic perturbations, expanding its utility in functional genomics.”
R3.1. The datasets were stratified based on plates and compounds. It would be beneficial to clarify the basis for data stratification applied for compounds. Was the data sampled based on structural or functional similarity of compounds? If not, what can be expected from the model if trained and validated using structurally or functionally diverse and non-diverse compounds?
Thank you for raising the important question of data stratification based on compound similarity. In our study, the data stratification was performed by randomly sampling the compounds, without considering their structural or functional similarity.
This approach may limit the generalizability of the learned representations to new structural or functional classes not captured in the training set. Consequently, the current methodology may not fully characterize the model’s performance across diverse compound structures.
In future work, it would be valuable to explore the impact of compound diversity on model performance by stratifying data based on structural or functional similarity and comparing the results to our current random stratification approach to more thoroughly characterize the learned representations.
R3.2. Is the method prioritizing a particular biological reaction of cells toward common chemical compounds, such as mitotic failure? Could this be oncology-specific, or is there more utility to it in other datasets?
Our analysis of CytoSummaryNet's performance in (the new) Figure 6 reveals a strong improvement in MoAs targeting cancer-related pathways, such as MEK, HSP, MDM, dehydrogenase, and purine antagonist inhibitors. These MoAs share a common focus on cellular proliferation, survival, and metabolic processes, which are key characteristics of cancer cells.
Given the composition of the cpg0004 dataset, which contains 1,258 unique MoAs with only 28 annotated as oncology-related, the likelihood of randomly selecting five oncology-related MoAs that show strong improvement is extremely low. This suggests that the observed prioritization is not due to chance.
Furthermore, the prioritization cannot be solely attributed to the frequency of oncology-related MoAs in the dataset. Other prevalent disease areas, such as neurology/psychiatry, infectious disease, and cardiology, do not exhibit similar improvements despite having higher MoA counts.
While these findings indicate a potential prioritization of oncology-related MoAs by CytoSummaryNet, further research is necessary to fully understand the extent and implications of this bias. Future work should involve conducting similar analyses across other disease areas and cell types to assess the method's broader utility and identify areas for refinement and application. However, given the speculative nature of these observations, we have chosen not to update the manuscript to discuss this potential bias at this time.
R3.3 Figures 1 and 2 demonstrate that the CytoSummaryNet profiles outperform average-aggregated profiles. However, the average profiling results seem more consistent when compared to CytoSummaryNet profiling. What further conditions or approaches can help improve CytoSummaryNet profiling results to be more consistent?
The observed variability in CytoSummaryNet's performance is primarily due to the intentional technical variance in our datasets, where each plate tested different staining protocol variations. It's important to note that this level of technical variance is not typical in standard cell profiling experiments. In practice, the variance across plates would be much lower. We want to emphasize that while a model capable of generalizing across diverse experimental conditions might seem ideal, it may not be as practically useful in real-world scenarios. This is because such non-uniform conditions are uncommon in typical cell profiling experiments. In normal experimental settings, where technical variance is more controlled, we expect CytoSummaryNet's performance to be more consistent.
R3.4 Can the poor performance on unseen data (in the case of stain 5) be overcome? If yes, how? If no, why not?
We believe that the poor performance on unseen data, such as Stain 5, can be overcome depending on the nature of the unseen data. As shown in Figure 4 (panel 3), the model improves upon average profiling for unseen data when the experimental conditions are similar to the training set.
The issue lies in the different experimental conditions. As explained in our response to R3.3, this could be addressed by including these experimental conditions in the training dataset. As long as CytoSummaryNet is trained (seen) and tested (unseen) on data generated under similar experimental conditions, we are confident that it will improve or perform as well as average profiling.
It's important to note that the issue of generalization to vastly different experimental conditions was considered out of scope for this paper. The main focus is to introduce a new method that improves upon average profiling and can be readily used within a consistent experimental setup.
R3.5 It needs to be mentioned how the feature data used for CytoSummaryNet profiling was normalized before training the model. What would be the impact of feature data normalization before model training? Would the model still outperform if the skewed feature data is normalized using square or log transformation before model training?
We have clarified in the manuscript that we standardized the feature data on a plate-by-plate basis to achieve zero mean and unit variance across all cells per feature within each plate. We have added the following statement to improve clarity:
“The data used to compute the average profiles and train the model were standardized at the plate-level, ensuring that all cell features across the plate had a zero mean and unit variance. The negative control wells were then removed from all plates."
We chose standardization over transformations like squaring or logging to maintain a balanced scale across features while preserving the biological and morphological information inherent in the data. While transformations can reduce skewness and are useful for data spanning several orders of magnitude, they might distort biological relevance by compressing or expanding data ranges in ways that could obscure important cellular variations.
Regarding the potential impact of square or log transformations on skewed feature data, these methods could improve the model's learning efficiency by making the feature distribution more symmetrical. However, the suitability and effectiveness of these techniques would depend on the specific data characteristics and the model architecture.
Although not explored in this study, investigating various normalization techniques could be a valuable direction for future research to assess their impact on the performance and adaptability of CytoSummaryNet across diverse datasets and experimental setups.
R3.6. In Figure 5 b and c, MoAs often seem to be represented by singular compounds and thus, the test (MoA prediction) is very similar to the training (compound ID). Given this context, a discussion about the extent this presents a circular argument supported by stats on the compound library used for training and testing would be beneficial.
Clusters in (the new) Figure 7 that contain only replicates of a single compound would not yield an improved performance on the MoA task unless they also include replicates of other compounds sharing the same MoA in close proximity. Please see our response to R1.1c.iii. for details. To improve visual clarity and avoid misinterpretation, we have recomputed the colors for (the new) Figure 7 and grayed out overlapping points.
R3.7 Can you estimate the minimum amount of supervision (fuzzy/sparse labels, often present in mislabeled compound libraries with dirty compounds and polypharmacology being present) that is needed for it to be efficiently trained?
It's important to note that the metadata used by the model is only based on identifying replicates of the same compound. Mechanism of action (MoA) annotations, which can be erroneous due to dirty compounds, polypharmacology, and incomplete information, are not used in training at all. MoA annotations are only used in our evaluation, specifically for calculating the mAP for MoA retrieval.
We have successfully trained CytoSummaryNet on 72 unique compounds with 4 replicates each. This is the current empirical minimum, but it is possible that the model could be trained effectively with even fewer compounds or replicates.
Determining the absolute minimum amount of supervision required for efficient training would require further experimentation and analysis. Factors such as data quality, feature dimensionality, and model complexity could influence the required level of supervision.
R3.minor1 Figure 5: The x-axis and y-axis tick values are too small, and image resolution/size needs to be increased.
We have made the following changes to address the concerns:
- Increased the image resolution and size to improve clarity and readability.
- Removed the x-axis and y-axis tick values, as they do not provide meaningful information in the context of UMAP visualizations. We believe these modifications enhance the visual presentation of the data and make it easier for readers to interpret the results.
R3.minor2 The methods applied to optimize hyperparameters in supplementary data need to be included.
We added the following to Supplementary Material D:
“We used the Weights & Biases (WandB) sweep suite in combination with the BOHB (Bayesian Optimization and HyperBand) algorithm for hyperparameter sweeps. The BOHB algorithm [47] combines Bayesian optimization with bandit-based strategies to efficiently find optimal hyperparameters.
Additionally Table D1 provides an overview of all tunable hyperparameters and their chosen values based on a BOHB hyperparameter optimization.”
R3.minor3 Figure 5(c, d): The names of compound 2 and Compound 5 need to be included in the labels.
These compounds were obtained from external companies and are proprietary, necessitating their anonymization in our study. This has now been added in the caption of (the new) Figure 7:
“Note that Compound2 and Compound5 are intentionally anonymized.”
R3.minor4 Table C1: Plate descriptions need to be included.
*Table C1: The training, validation, and test set stratification for Stain2, Stain3, Stain4, and Stain5. Five training, four validation, and three test plates are used for Stain2, Stain3, and Stain4. Stain5 contains six test set plates only. *
__Stain2 __
Stain3
Stain4
Stain5
Training plates
Test plates
BR00113818
BR00115128
BR00116627
BR00120532
BR00113820
BR00115125highexp
BR00116631
BR00120270
BR00112202
BR00115133highexp
BR00116625
BR00120536
BR00112197binned
BR00115131
BR00116630highexp
BR00120530
BR00112198
BR00115134
200922_015124-Vhighexp
BR00120526
Validation plates
BR00120274
BR00112197standard
BR00115129
BR00116628highexp
BR00112197repeat
BR00115133
BR00116629highexp
BR00112204
BR00115128highexp
BR00116627highexp
BR00112201
BR00115127
BR00116629
Test plates
BR00112199
BR00115134bin1
200922_044247-Vbin1
BR00113819
BR00115134multiplane
200922_015124-V
BR00113821
BR00115126highexp
BR00116633bin1
We have added a reference to the metadata file in the description of Table C1: https://github.com/carpenter-singh-lab/2023_Cimini_NatureProtocols/blob/main/JUMPExperimentMasterTable.csv
R3.minor5 Figure F1: Does the green box (stain 3) also involve training on plates from stain 4 (BR00116630highexp) and 5 (BR00120530) mentioned in Table C1? Please check the figure once again for possible errors.
We have carefully re-examined Figure F1 and Table C1 to ensure their accuracy and consistency. Upon double-checking, we can confirm that the figure is indeed correct. We intentionally omitted the training and validation plates from Figure F1 to maintain clarity and readability, as including them resulted in a cluttered and difficult-to-interpret figure.
Regarding the specific plates mentioned:
- BR00116630highexp (Stain4) is used for training, as correctly stated in Table C1. This plate is considered an outlier within the Stain4 dataset and happens to cluster with the Stain3 plates in Figure F1.
- BR00120530 (Stain5) is part of the test set only and correctly falls within the Stain5 cluster in Figure F1. To improve the clarity of the training, validation, and test split in Table C1, we have added a color scheme that visually distinguishes the different data subsets. This should make it easier for readers to understand the distribution of plates across the various splits.
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Referee #3
Evidence, reproducibility and clarity
Summary:
In the manuscript by Van Dijk et al., a novel deep learning technique is introduced that aims to summarize informative cells from heterogeneous populations in image-based profiling. This technique is based on a network that utilizes contrastive learning with a multiple-instance learning framework, a significant departure from existing average-based cell profiling models.
The authors have done a commendable job discussing the method, demonstrating its potential to outperform current models in profiling cell-based features. The work is of considerable significance and interest to a wide field of researchers working on the understanding of cell heterogeneity's impact on various biological phenomena and practical studies in pharmacology.
One aspect that would further enhance the value of this work is an exploration of the method's separation power across different modes of action. For instance, it would be interesting to ascertain if the method's performance varies when dealing with actions that primarily affect size, those that affect marker expression, or compounds that significantly diminish cell numbers. Another test on datasets that are not concerned with chemical compounds, but rather genetic perturbations would greatly increase the reach of the method into the functional genomics community and beyond. This additional analysis could provide valuable insights into the versatility and applicability of the proposed method. Please find my detailed comments below:
Major Comments:
- The datasets were stratified based on plates and compounds. It would be beneficial to clarify the basis for data stratification applied for compounds. Was the data sampled based on structural or functional similarity of compounds? If not, what can be expected from the model if trained and validated using structurally or functionally diverse and non-diverse compounds?
- Is the method prioritizing a particular biological reaction of cells toward common chemical compounds, such as mitotic failure? Could this be oncology-specific, or is there more utility to it in other datasets?
- Figures 1 and 2 demonstrate that the CytoSummaryNet profiles outperform average-aggregated profiles. However, the average profiling results seem more consistent when compared to CytoSummaryNet profiling. What further conditions or approaches can help improve CytoSummaryNet profiling results to be more consistent?
- Can the poor performance on unseen data (in the case of stain 5) be overcome? If yes, how? If no, why not?
- It needs to be mentioned how the feature data used for CytoSummaryNet profiling was normalized before training the model. What would be the impact of feature data normalization before model training? Would the model still outperform if the skewed feature data is normalized using square or log transformation before model training?
- In Figure 5 b and c, MoAs often seem to be represented by singular compounds and thus, the test (MoA prediction) is very similar to the training (compound ID). Given this context, a discussion about the extent this presents a circular argument supported by stats on the compound library used for training and testing would be beneficial.
- Can you estimate the minimum amount of supervision (fuzzy/sparse labels, often present in mislabeled compound libraries with dirty compounds and polypharmacology being present) that is needed for it to be efficiently trained?
Minor Comments:
- Figure 5: The x-axis and y-axis tick values are too small, and image resolution/size needs to be increased.
- The methods applied to optimize hyperparameters in supplementary data need to be included.
- Figure 5(c, d): The names of compound 2 and Compound 5 need to be included in the labels.
- Table C1: Plate descriptions need to be included.
- Figure F1: Does the green box (stain 3) also involve training on plates from stain 4 (BR00116630highexp) and 5 (BR00120530) mentioned in Table C1? Please check the figure once again for possible errors.
Significance
This work presents a significant move forward in the ways we deal with cellular heterogeneity in all single-cell assays. Though the model in its current state has trouble extrapolating to out of distribution data, I am confident that it provides a considerable step forward in the process of extracting "informative" knowledge from data in the form of optimized profiles.
The optimization is yet based on optimizing a similarity metric for group assignments, I will be interesting to see if other objectives could be more effective in developing aggregation techniques.
The work is of considerable significance and interest to a wide field of researchers working on the understanding of cell heterogeneity's impact on various biological phenomena and practical studies in pharmacology.
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Referee #2
Evidence, reproducibility and clarity
The authors present a well-developed and useful algorithm. The technical motivation and validation are very carefully and clearly explained, and their work is potentially useful to a varied audience.
That said, I think the authors could do a better job, especially in the figures, of putting the algorithm in context for an audience that is unfamiliar with the cell painting assay. For example, a figure towards the beginning of the paper with example images might help to set the stage. Similarly a schematic of the algorithm earlier in the paper would provide a graphical overview. For the sake of a biologically inclined audience, I would consider labeling the images in the caption by cell type and label.
The interpretability results were intriguing. The authors might consider further validating these interpretations by removing weakly informative cells from the dataset and retraining. Are the cells so uninformative that the algorithm does better without them, or are they just less informative than other cells?
As far as I can tell, the authors only oblique state whether the code associated with the manuscript is openly available. Posting the code is needed for reproducibility. I would provide not only a github, but a doi linked to the code, or some other permanent link.
Significance
Incorporating biological heterogeneity into machine-learning driven problems is a critical research question. Replacing means/modes and such with a machine learning framework, the authors have identified a problem with potentially wide significance. The application to cell painting and related assays is of broad enough significance for many journals, However, the authors could further broaden the significance by commenting on other possible cell biology applications. What other applications might the algorithm be particularly suited for? Are there any possible roadblocks to wider use. What sorts of data has the code been tested on so far?
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Referee #1
Evidence, reproducibility and clarity
Summary:
Provide a short summary of the findings and key conclusions (including methodology and model system(s) where appropriate).
Cell (non-genetic) heterogeneity is an important concept in cell biology, but there are currently only a few studies that try to incorporate this information to represent cell populations in the field of high-content image-based phenotypic profiling. The authors present CytoSummaryNet, a machine learning approach for representing heterogeneous cell populations, and apply it to a high-content image-based Cell Painting dataset to demonstrate superior performance in predicting a compound's mechanism of action (MoA), in relation to the average profile representation. CytoSummaryNet relies on Cell Profiler morphological features and simultaneous optimization of two components, both novel in the cell profiling field: (i) learning representations using weakly supervised contrastive learning according to the perturbation identifications (i.e., the compound), (ii) using a representation method called Deep Sets to create permutation-invariant population representations. The authors evaluate their representation on the task of replicate retrieval and of MoA retrieval using the public dataset cpg0001 (and cpg0004), and report superior performance in respect to the average-aggregated profiles for the experimental protocols and compounds seen on training (that do not generalize to out-of-distribution compounds + experimental protocols). By interpreting which cells were most important for the MoA model predictions, the authors propose that their representation prioritizes large uncrowded cells.
Major comments:
The strength of the manuscript is the new idea of combining contrastive learning and sets representations for better representation of heterogeneous cell populations. However, we are not convinced that the conclusion that this representation improves MoA prediction is fully supported by the data, for several reasons.
- Evaluations. This is the most critical point in our review.
a. CytoSummaryNet is evaluated in comparison to aggregate-average profiling, although previous work has already reported representations that capture heterogeneity and self-supervision independently. To argue that both components of contrastive learning and sets representations are contributing to MoA prediction we believe that a separate evaluation for each component is required. Specifically, the authors can benchmark their previous work to directly evaluate a simpler population representation (PMID: 31064985, ref #13) - we are aware that the authors report a 20% improvement, but this was reported on a separate dataset. The authors can also compare to contrastive learning-based representations that rely on the aggregate (average) profile to assess and quantify the contribution of the sets representation.
b. The evaluation metric of mAP improvement in percentage is misleading, because a tiny improvement for a MoA prediction can lead to huge improvement in percentage, while a much larger improvement in MoA prediction can lead to a small improvement in percentage. For example, in Fig. 4, MEK inhibitor mAP improvement of ~0.35 is measured as ~50% improvement, while a much smaller mAP improvement can have the same effect near the origins (i.e., very poor MoA prediction). (Subjective) visual assessment of this figure does not show a convincing contribution of CytoSummaryNet representations of the average profiling on the test set (3.33 uM). This issue might also be relevant for the task of replicate retrieval. All in all, the mAP improvement reported in Table 1 and throughout the manuscript (including the Abstract), is not a proper evaluation metric for CytoSummaryNet contribution. We suggest reporting the following evaluations:
i. Visualizing the results of cpg0001 (Figs. 1-3) similarly to cpg0004 (Fig. 4), i.e., plotting the matched mAP for CytoSummaryNet vs. average profile. ii. In Table 1, we suggest referring to the change in the number of predictable MoAs (MoAs that pass a mAP threshold) rather than the improvement in percentages. Another option is showing a graph of the predictability, with the X axis representing a threshold and Y-axis showing the number of MoAs passing it. For example see (PMID: 36344834, Fig. 2B) and (PMID: 37031208, Fig. 2A), both papers included contributions from the corresponding author of this manuscript.
c. Additional evaluation-related concerns were: i. "a subset of 18 compounds were designated as validation compounds" - 5 cross-validations of 18 compounds can make the evaluation complete. This can also enhance statistical power in figures 1-3.
ii. Clarify if the MoA results for cpg0001 are drawn from compounds from both the training and the validation datasets. If so, describe how the results differ between the sets in text and graphs.
iii. "Mechanism of action retrieval is evaluated by quantifying a profile's ability to retrieve the profile of other compounds with the same annotated mechanism of action.". It was unclear to us if the evaluation of mAP for MoA identification can include finding replicates of the same compound. That is, whether finding a close replicate of the same compound would be included in the AP calculation. This would provide CytoSummaryNet with an inherent advantage as this is the task it is trained to do. We assume that this was not the case (and thus should be more clearly articulated), but if it was - results need to be re-evaluated excluding same-compound replicates. 2. Lack of clarity in the description of the data and evaluation. While the concept of constructive learning + sets representation is elegant and intuitive, we found it very hard to follow the technical aspects of data and performance evaluation, even after digging in deep into the Methods. Figuring out these important aspects required us for vast investment in time, more than the vast majority of manuscripts we reviewed in the last couple of years. It is highly recommended that the authors provide more details to make this manuscript easier to follow. Some examples include:
a. The description of Stain2-5 was not clear for us at first (and second) read. The information is there, but more details will greatly enhance the reader's ability to follow. One suggestion is explicitly stating that these "stains" partitioning was already defined in ref 26. Another suggestion is laying out explicitly a concrete example on the differences between two of these stains. We believe highlighting the differences between stains will strengthen the claim of the paper, emphasizing the difficulty of generalizing to the out-of-distribution stain.
b. What does each data point in Figures 1-3 represent? Is it the average mAP for the 18 validation compounds, using different seeds for model training? Why not visualize the data similarly to Fig. 4 so the improvement per compound can be clearly seen?
c. Justification and interpretation of the evaluation metrics.
d. Explicitly mentioning the number of MoAs for each datasets and statistics of number of compounds per MoA (e.g., average\median, min, max).
e. The data split in general is not easily understood. Figure 8 is somewhat helpful, however in our view, it can be improved to enhance understanding of the different splits. Specifically, the training and validation compounds need to be embedded and highlighted within the figure. 3. Lack of justification of design choices. There were multiple design choices that were not justified. This adds to the lack of clarity and makes it harder to evaluate the merits of the new method. For example:
a. Why was stain 5 used for the test, rather than the other stains?
b. How were the 18 validation compounds selected?
c. For cpg0004, no justification for the specific doses selected (10uM - train, 3.33 uM - test) for the analysis in Figure 4. Why was the data split for the two dosages? For example, why not perform 5-fold cross validation on the compounds (e.g., of the highest dose)?
d. A more detailed explanation on the logic behind using a training stain to test MoA retrieval will help readers appreciate these results. In our first read of this manuscript we did not grasp that, we did in a second read, but spoon-feeding your readers will help. 4. The interpretability analysis is speculative. Assessment of interpretability is always tricky. But in this case, the authors can directly confirm their interpretation that the CytoSummaryNet representation prioritizes large uncrowded cells, by explicitly selecting these cells, and using their average profile representation to demonstrate that they achieve improved results. If this works, it could be applied as a general outlier removal strategy for cell profiling.
a. "We identified the likely mechanism by which the learned CytoSummaryNet aggregates cells: the most salient cells are generally larger and more isolated from other cells, while the least salient cells appear to be smaller and more crowded, and tend to contain spots of high-intensity pixels (whether dying, debris or in some stage of cell division)." - doesn't such a mechanism should generalize to out-of-distribution data? 5. Placing this work in context of other weakly supervised representations. Previous papers used weakly supervised labels of proteins / experimental perturbations (e.g., compounds) to improve image-derived representations, but were not discussed in this context. These include PMID: 35879608, https://www.biorxiv.org/content/10.1101/2022.08.12.503783v2 (from the same research groups and can also be benchmarked in this context),https://pubs.rsc.org/en/content/articlelanding/2023/dd/d3dd00060e , and https://www.biorxiv.org/content/10.1101/2023.02.24.529975v1. We believe that a discussion explicitly referencing these papers in this specific context is important.
Minor comments:
In our opinion, evaluation of the training task using the training data (Figure 1) is not contributing to the manuscript and could be excluded. Also we feel that the subjectiveness of the UMAP analysis (Figure 5) is not contributing much and could be excluded, especially if the authors follow our suggestions regarding quantification. Of course, this is up to the authors to decide (along with most of the other suggestions below).
Suggested clarifications:
- "Because the improved results could stem from prioritizing certain features over others during aggregation, we investigated each cell's importance during CytoSummaryNet aggregation by calculating a relevance score for each" - what is the relevance score? Would be helpful to provide some intuition in the Results.
- Figure 1:
a. Colors of the two methods too similar
b. The dots are too close. It will be more easily interpreted if they were further apart.
c. What do the dots stand for?
d. We recommend considering moving this figure to the supp. material (the most important part of it is the results on the test set and it appears in Fig.2). 3. Figure 4: It is somewhat misleading to look at the training MoAs and validation MoAs embedded together in the same graph. We recommend showing only the test MoAs (train MoAs can move to SI). 4. Figure 5
a. Why only Stain3? What happens if we look at Stains 2,3 and 4 together? Stain 5?
b. Should validation compounds and training compounds be analyzed separately?
c. Subfigure (d): it is expected that the data will be classified by compound labels as it is the training task, but for this to be persuasive I would like to see this separately on the training compounds first and then and more importantly on the validation compounds.
d. For subfigures (b) and (d): it appears there are not enough colors for d, which makes it partially not understandable. For example, the pink label in (d) shows a single compound which appears to represent two different MoAs. This is probably not the case, and it has two different compounds, but it cannot be inferred when they are represented by the same color.
e. For the Subfigure (e) - only 1 circle looks justified (in the top left). And for that one, is it not a case of an outlier plate that would perhaps need to be removed from analysis? Is it not good that such a plate will be identified? 5. Discussion:
a. "perhaps in part due to its correction of batch effects" - is this statement based on Fig. 5F - we are not convinced.
b. "Overall, these results improve upon the ~20% gains we previously observed using covariance features" - this is not the same dataset so it is hard to reach conclusions - perhaps compare performance directly on the same data?
Significance
Cell profiling is an emerging field with many applications in academia and industry. Finding better representations for heterogeneous cell populations is important and timely. However, unless convinced otherwise after a rebuttal/revision, the contribution of this paper, in our opinion, is mostly conceptual, but in its current form - not yet practical. This manuscript combined two concepts that were previously reported in the context of cell profiling, weakly supervised representations. Our expertise is in computational biology, and specifically applications of machine learning in microscopy.
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Reply to the reviewers
The authors do not wish to post a response at this time. This is because this is not the submission of the revised version, which we have not completed yet. This is a preliminary revision together with a revision plan instead.
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Referee #3
Evidence, reproducibility and clarity
In this manuscript, Singh et al. demonstrate that infection of D. melanogaster flies with B. bassiana fungus induces neurodegeneration via Toll/wek/sarm signalling. It is already known that fungal infection can be associated with neurodegeneration, but the exact mechanism is unclear. The authors demonstrate that the fungus enters the brain, causes hallmark symptoms of neurodegeneration, and requires Toll-1, Wek, and Sarm in order to do so. This is an important step forward as it demonstrates specific genes in the fly immune pathways that are involved in fungus-induced neurodegeneration, which could be informative for infections in humans. Overall, the manuscript is thorough and well-written and the conclusions are broadly supported. A few mostly minor comments and questions are below, which could mostly be addressed by including additional details in the methods or discussion. The only major comments would be 1) that the control fly genotypes used in experiments were not always the most ideal controls (eg, compared WT genotypes to RNAi against a gene of interest; ideally would be RNAi against a control gene compared to RNAi against a gene of interest), and 2) negative controls of fluorescence microscopy imaging were not always included. It would be important to address these through clarification in the figures/methods, and/or discussion of the potential caveats, even though it is likely the conclusions would still hold. Notably, these comments are relatively easily addressed through edits in the text.
Major comments:
- For fluorescence imaging, were negative controls included (no infection or no gene expression etc.) for all stains (as with Figure 1H)? If so, it would help to include representative images as supplemental figures. Also, for all positive samples, was presence of the fungus noted in all samples?
- Figure 4: Here, it appears that the control fly genotypes are wildtype vs an RNAi line (similar for some other figures/assays as well, but using this one as an example). The best control would be RNAi against a control gene compared to RNAi against a gene of interest, rather than just a control WT genotype with no RNAi compared to RNAi against a gene of interest. This should be included as a caveat in the discussion since the experiments do not all account for the effect of RNAi (or other gene expression) on the phenotypes regardless of the gene.
Minor comments:
- It would help to have line numbers throughout
- Figure 1- what are the arrows in panels D-G?
- Methods: A few details are unclear:
- Was only one fly sex used or were both used for the various assays? If both were used, were they statistically assessed for differences? Sex is only mentioned in a couple of the methods sections.
- How old were the flies at the start of the experiments? A few experiments noted age, but it was not clear for all
- For longevity, was the fungal culture ever replaced during the experiment?
- For the climbing assay when the flies were initially flipped, how much time was there between flips?
- Figures 2A, 2D, 2E, 3E, & 3H: If multiple replicates or samples are represented in the data, it would help to be able to see the data points underlying these bars. If so, please add them to the graphs to see the spread of data points.
- Figure 3F- what do arrows indicate?
- It is interesting that Wek-RNAi with infection not only rescues loss caused by the infection alone, but also increases YFP cells beyond the uninfected controls (Figure 5C). The same is true with toll-1 RNAi (Figure 4C). Why might this be?
- It would be ideal if data underlying data points and full statistical models and outputs could be included through a public repository such as Dryad. This would be ideal for full assessment of statistical approaches
Very Minor comments:
- Check italicizing throughout- missed a few "Drosophila" or "B. bassiana" in main text or figures
- Looks like no space between C. and elegans in C. elegans in a few cases
- Word missing: "No effect was seen after three days exposure to B. bassiana, but seven days exposure impaired climbing"... seven days of exposure?
- Toll-1 misspelled pg 6 last paragraph
Referee Cross-Commenting
Regarding the major comments, I agree with Reviewer 1 that more thorough proof of spores entering the brain (and what proportion of exposed flies this happens to) would be beneficial. I also agree with Reviewer 2 that a rescue experiment for the climbing assay and my earlier suggestion for more controls in the microscopy could help address this concern, at least in part. Other responses or experiments may also be appropriate to address some of the major concerns- maybe additional assay(s) of brain function other than climbing?
Reviewer 1 also brought up the point that flies with advanced infection were used for the experiments- it would be helpful to know if earlier time points were ever checked for BBB damage, loss of brain cells, or presence of fungus etc. This would clarify if the same phenotypes are present in flies that die early, along with other concerns from Reviewer 1.
However, whether directly or indirectly, several later figures show loss of brain cells with infection followed by rescue with RNAi against genes of interest. This does lend support to the conclusions that fungal infection negatively impacts brain cells and the fungus requires these host genes to do so.
Other concerns Reviewer 2 and I raised about the fly genetic controls being unclear should also be addressed. What is the full genotype of the flies in each case? What is considered "+" in each case? Were these driver background strains, WT (like Oregon R), or RNAi against control genes (best controls)?
Significance
General assessment: The manuscript by Singh et al. is a thorough investigation into the fungus-host interactions in the brain, demonstrating that the common insect fungal pathogen B. bassiana requires the host genes Toll, wek, and sarm to induce negative phenotypes in the brain. The strengths are in the multi-pronged approaches that use several independent techniques (fly behavior assays, gene expression, microscopy, etc.) and multiple genes, conducted with many replicates, that all show clear and consistent trends supporting the conclusions of the authors. The weaknesses include some cases where controls are either not completely clear or not the most ideal controls. This weakness could be addressed with either edits to the text, where appropriate, or addition of supplemental figures. However, the conclusions are still broadly supported.
Advance: Although it is known that fungal infections can impair brain function, it is not fully understood how this happens. This manuscript identifies Toll-associated molecules that are required for fungus-mediated neurodegeneration, which is a critical first step to understanding the process and for future development of therapies.
Audience: This finding would be of broad interest to scientists in immunology, microbiology, neuroscience, and other areas.
Expertise of reviewer: Drosophila, fly genetics, invertebrate immunology, insect-fungal interactions
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Referee #2
Evidence, reproducibility and clarity
Summary
The authors describe a role for Toll signaling in detrimental neuronal loss associated with B. bassiana fungal infection in Drosophila melanogaster model. They show that this effect is mediated by wek/sarm as silencing either of them prevents neuronal loss after the infection. Similar results are obtained with Toll-1 RNAi, suggesting that the response is dependent on the activation of Toll signaling by B. bassiana. The study is well executed,main conclusions are backed up by the data presented and experiments are conducted with adequate numbers of replications and individuals. Below I give some comments that I think would help in further improving the manuscript.
Major comments
As the initial experiments (including the effect on survival and climbing assay) have been performed using OR/CantonS, it would be interesting to investigate if the same is seen with a more similar background to that what is used in the genetic experiments. In addition, I'd suggest an experiment to see if the Toll (or wek/sarm) RNAi in the brain rescues the climbing defect caused by the fungal infection.
It is somewhat unclear what are the controls in the genetic experiments. For example, in Figure 2, the control is UAS-TrpA1/+. Does this mean that the UAS-TrpA1 flies have been crossed to something (like the driver background strain) or used as it is? In Figure 4, controls are ">+". Again, are MyD88>histoneYFP;tubulinGal80ts flies crossed to something (in this case, maybe the w1118 background of the KK library RNAi strains) or used as homozygous? And same for the subsequent figures. I'd ask the authors to clarify these points in the manuscript.
Could the authors please explain why they opted for MyD88-GAL4 in the experiments in Figures 5-7? What is the overall expression pattern of MyD88-GAL4? Is there a possibility that some of the effects seen could arise from the Toll/sarm/wek knockdown elsewhere in the fly? How do the flies survive the infection with Toll knockdown in MyD88-epressing cells (expressed at least in all immunogenic tissues)? A bit more explanation would clarify the situation.
Minor comments
Page 3: Full species name should be given here (Drosophila melanogaster)
A short description of the FM4-64 dye (what it stains etc) would be useful for the readers unfamiliar with it.
Page 6: Please explain shortly why TrpA1 overexpression was used to activate the neurons.
Figure 2E: What is the genotype of the flies? mtk is lacking statistics
Page 8: third row refers to Figure 2 but should be Figure 3.
Although antibody stainings are performed using "standard methods", a short overview on the process should be presented also in the current manuscript. Also, I imagine fungal spores are all over the flies retrieved from the infection chamber. I'd like to know (and this could also be described in the materials) how the flies (and the brains) were treated/washed prior to preparing brains for immunostaining and imaging?
Some typos and inconsistencies at various places. For example, at some occasions B. bassiana written without a space in between "B" and "bassiana" and not in italics (both in figures and in text); on page 5, first line: "mimicked" misspelled
Referee Cross-Commenting
As the fungal infiltration into the brain is central to the conclusions made in the manuscript, I agree that care should be taken in making this argument solid. I believe this can be achieved adding controls as reviewer #3 suggests together with additional experiment(s) verifying that Toll/wek/sarm in the brain is mediating the neuronal loss caused by the fungal infection (rescue experiments). Of note, I wonder if, similarly to mammalian macrophages, hemocytes could be responsible for delivering the fungal cells into the brain?
I agree with the reviewer #1 that the climbing defect could be because of multiple reasons other than the fungal spores in the brain causing neuronal loss (for instance flies being generally weak at this point, ). However, the authors do show convincingly that there is neuronal loss in fungal-infected flies.
Significance
Fungal infections are understudied in any research model considering the threat they pose to humans and other animals alike. Due to the high conservation of the signaling components studied here, the results provide a good basis for future research, extending to mammalian models. I think these results will be of interest to a wider audience because of the reasons stated above.
My fields of expertise are Drosophila melanogaster, innate immunity, cell-mediated immunity, blood cell homeostasis
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Referee #1
Evidence, reproducibility and clarity
Singh et al. report that, after exposure to the entomopathogenic fungus Beauveria bassiana, the Drosophila adults impaired fly locomotion and died within two weeks. During which time, the authors designed experiments and showed the decline of brain cells via a Toll-1/Wek/Sarm pathway, mimicking the neurodegenerative diseases in humans in association with fungal infections. Providing that the rather solid genetic evidence was shown for the pathway in mediating fly brain cell losses, critical issues of experiment design/setup and conclusion validity were concerned.
Specific comments:
The fungus-exposed flies died within two weeks were largely typical. However, it was unclear how those flies could be uniformly contaminated with fungal spores in the infection chamber shown in Fig. 1A, by landing on fungal "carpet"? It is publicly known that entomopathogenic fungi (EPF) like B. bassiana infect insects via spore germination on cuticle and then penetration of cuticles by fungal hyphae/mycelia (e.g., Trends Microbiol. 2024. 32, 302-316).
It is typical that EPF killed and mycosed insects within 5-14 days after topical infection by immersion in or spraying spore suspensions, or dusting on the sporulated plates. Fungal spores can be ingested by insects, largely those with chewing mouthparts. However, fungal spores can barely survive the highly-alkaline foreguts. It is questionable that flies could ingest spores and spores "infiltrated" the brains.
Regarding the detection of fungal cells in fly brains, on the one hand, the authors argued that detection of fungal SPORES in fly brain THREE days post exposure (page 5) by infiltration. It would be impossible that, even fungus could breach the blood brain barrier (BBB), it might be the fungal hyphae/mycelia but not the spores. One the other hand, the authors provided the evidence of the damaged BBB SEVEN days post exposure, a few days LATER than the detection of fungal spores in brains (THREE days) post treatment mentioned above. Did "spore infiltration" (even impossible) occur before BBB damage?
The authors stated that "by day seven more than half of the flies had died" (Fig. 1B). It is questionable therefore that the "other half" of the diseased and dying insects were used for the following experiments. There would be no wonder that the climbing of these diseased and dying flies was impaired, however, which could be due to muscle damage, hemocyte number decline and reduction of energy production etc. apart from brain cell loss. The brain function of dying animals could be compromised by multiple direct or indirect factors.
Issue of Fig. 2D labelling.
Referee Cross-Commenting
I agree with that Reviewers 2 and 3 that rather solid evidence of fly brain loss was shown in this work, however, at most in association with exposure to fungal cultures (volatiles could not be excluded etc.). "Spores" entry into fly brains were suspicious or impossible. If the dying flies had been used for these neurological experiments, the reliability of conclusions would be highly concerned.
Significance
Since there are critical concerns of experiment designs/setup in this work, it is questionable that fly brain cell loss was caused by fungal entry into brains.
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Reply to the reviewers
Manuscript number: RC-2024-02491
Corresponding author(s): Gilbert, Vassart
1. General Statements [optional]
We thank referees 1 and 2 for their in-depth analysis of our manuscript. They see interest in our study, with questions to be answered. Referee 3 is essentially negative, considering that there is nothing new ("novel finding is missing"). We respectfully disagree with him/her, comforted by the opinion of referee 2 that "the authors developed a protocol to reproducibly generate fetal-like spheroids from adult tissue is an important advancement in the field and ... the manuscript should attract a significant amount of attention in the intestinal field" and we provide evidence in our answers that he/she did not read the manuscript with the same attention as referees 1 and 2 (see in particular answer to his/her question 5).
Here is a summary of the main reason why we consider that our study represents valuable new information in the field of intestinal regeneration.
It is based on the serendipitous observation that dissociation of adult intestinal tissue by collagenase generates stably replatable spheroids upon culture in matrigel. Surprisingly and contrary to canonical EDTA-generated intestinal organoids and fetal spheroids, these spheroids are not traced in Rosa26Tomato mice harboring a VilCre transgene, despite expressing robustly endogenous Villin. Our interpretation is that adult intestinal spheroids originate from a cell lineage, distinct from the main developmental intestinal lineage, in which the VilCre transgene is unexpectedly not expressed, probaly due to the absence of cis regulatory sequences required for expression in this lineage.
Adult spheroid transcriptome shares a gene signature with the YAP/TAZ signature commonly expressed in models of intestinal regeneration. This led us to look for VilCre negative crypts in the regenerating intestine of Lgr5/DTR mice in which Lgr5-positive stem cells have been ablated by diphtheria toxin. Numerous VilCre negative clones were observed, identifying a novel lineage of stem cells implicated in intestinal regeneration.
FACS purification and scRNAseq analysis of the rare VilCre negative cells present at homeostasis identified a population of cells with characteristics of quiescent stem cells.
In sum, we believe that our study demonstrates the existence of a hitherto undescribed stem cell lineage involved in intestinal regeneration. It points to the existence of a hierarchical model of intestinal regeneration in addition to the well-accepted plasticity model.
2. Description of the planned revisions
See section 3 below.
3. Description of the revisions that have already been incorporated in the transferred manuscript
Here is a point-by-point reply to the queries of the three referees, with indication of the revisions introduced in the manuscript.
Reviewer #1 (Evidence, reproducibility and clarity (Required)):
- *In this manuscript, Marefati et al report an Lgr5-independent lineage in the regenerating intestine using in vitro organoids and in vivo injury-coupled lineage tracing model. In organoids, collagenase/dispase dissociated resulted in "immortal spheroids" that maintain a cystic and undifferentiated phenotype in the absence of standard growth factors (Rspondin/Noggin/EGF). Bulk RNAseq of spheroids demonstrates downregulation of classical CBC signatures and upregulation of fetal spheroid, mesenchymal, inflammation and regenerative signatures. In mice, Villin-Cre lineage tracing revealed some Villin- negative progenies that lack reporter tracing throughout crypt-villus ribbons after injury.
*The authors proposed that there is Lgr5-independent population support the regenerative response upon CBC depletion. A major caveat of this study is the identification of this population is based on absence of VilCre expression. *
We respectfully disagree. It is precisely this characteristic that makes the interest of our study. Whereas mosaicism of transgene expression is widespread and usually of little significance, our study shows that the rare VilCre-negative cells in the intestinal epithelium are not randomly showing this phenotype: they give specifically birth to what we call adult spheroids and regenerating crypts, which cannot be due to chance. The absence of VilCre expression allows tracing these cells from the zygote stage of the various VilCre/Ros26 reporter mice. We have modified our text to emphasize this point.
*It is surprising that there is no characterisation of Lgr5 expression throughout the manuscript whilst claiming of a Lgr5- independent lineage. *
We understand the perplexity of the referee not to see direct Lgr5 expression data in our manuscript, given our title. However, our point is that it is the cells at the origin of adult spheroids and the regenerating crypts we have identified that are Lgr5-negative, not the spheroids or the regenerated crypts themselves. Those are downstream offspring that may, and indeed have, gained some Lgr5 expression (e.g. figure 3F). We believe that our data showing that VilCre-negative spheroids are not traced in Lgr5-CreERT2/Rosa reporter mice convincingly demonstrate absence of Lgr5 expression in the cells at the origin of adult spheroids (figure 4G). We think that this experiment is better evidence than attempts to show absence of two markers (Tom and Lgr5) in the rare "white" cells present in the epithelium. Regarding the Lgr5 status of cells at the origin of the regenerating "white" crypts that we have identified, the early appearance of these crypts following ablation of CBC (i.e. Lgr5+ve) cells is a strong argument that they originate from Lgr5-negative cells. Regarding the scRNAseq experiment, Lgr5 transcripts are notoriously low and difficult to measure reliably in CBCs (Haber et al 2017). However, blowing up the pertinent regions of the merged UMAP allows showing some Lgr5 transcripts in clusters 5,6 and none in cluster 1 of figure 8GH. Given the very low level of detection, we had chosen not to include these data in the manuscript, but we hope they may help answer the point of the referee (see portion of UMAP below, with Olfm4 as a control, together with the corresponding violin plot). Several markers that gave significant signals in the CBC cluster (Smoc2, Axin2, Slc12a2) were virtually undetectable in the Olfm4-low /Tom-negative cluster of our scRNAseq data (figure 8I) supporting our conclusion.
Although the research question is potentially interesting, the concept of epithelial reprogramming upon injury is well documented in the field. The data generated in this manuscript also seem to be preliminary and lack of detailed characterisation. Below are specific comments.
We do not question the existence of epithelial reprogramming upon injury. We believe our data show, in addition to this well demonstrated phenomenon, the existence of rare cells traced by absence of VilCre expression that are at the origin of a developmental cell lineage distinct from Lgr5+ stem cells and also implicated in regeneration.
- Expression of Lgr5 should be properly characterised throughout the manuscript in both organoid models and injury-induced regeneration in vivo.
- *
See above for a detailed answer to this point.
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An important question is the origin of these "Lgr5-independent" adult spheroids. They look and appear like fetal organoids, which could be induced by injury (e.g. upon collagenase/dispase dissociation). Have the authors tried to culture fetal spheroids in BCM over extensive period of time? Do they behave the same? This would be a great way to directly compare the collagenase/dispase-derived organoids with fetal origin. * *Fetal spheroids require ENR for survival and die in BCM. We have chosen to illustrate this point in Fig2A by showing that, contrary to adult spheroid, they die even when only Rspondin is missing.
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Fig 1C, Why is the replating spheroid culture time different between mesenchymal cells and conditioned medium? We took the earliest time showing convincingly the return to the organoid phenotype. This timing difference does not modify the conclusion that EDTA organoids becoming spheroid-like when exposed to factors originating from mesenchymal cells revert to the organoid phenotype when returned to ENR medium without mesenchymal influence.
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*It is unclear how the bulk RNA-seq data in Fig. 3 were compared. How long were the adult organoids and spheroids cultured for (how many passages)? Were they culture in the same condition of were they in ENR vs BCM? * Both EDTA organoids and spheroids displaying a stable phenotype were used in this experiment. Organoids were collected at passage 4, day 5; spheroids were collected at passage passage 9 day 3.
As stated in the legend to the figure: "...to allow pertinent comparison spheroids and organoids were cultured in the same ENR-containing medium...".
These are important information to consider when interpreting the results. For instance, are Ptgs1 & Ptgs2 expression in adult spheroids the same in ENR vs BCM? Are the gene signatures (regenerative, fetal and YAP) changed in adult spheroids culturing in ENR vs BCM?
We did compare bulk RNAseq of EDTA organoids to ENR-cultured spheroids, short term (passage 6, day 6) BCM-cultured spheroids and long term BCM-cultured (passage 26, day 6) spheroids. To avoid overloading the manuscript these data were not shown in the original manuscript. In summary the BCM-cultured spheroids display a similar phenotype as those cultured in ENR, but with further de-differentiation. See in revision plan folder the results for PTGS, some differentiation markers and fetal regenerative markers including YAP induced genes.
We have included a brief description of these data in the new version of the manuscript and added an additional supplementary file (Suppl table 2) presenting the whole data set.
- It is stated: "In agreement with their aptitude to grow indefinitely, adult spheroids express a set of upregulated genes overlapping significantly with an "adult tissue stem cell module" [159/721 genes; q value 2.11 e-94) (Fig.S2F)].". What is the definition of "indefinitely"? Are they referring to the Fig 1B where spheroid were passaged to P10? The authors should avoid the term "indefinitely" but use a more specific time scale, e.g. passages, months etc.
We agree that the term indefinitely should be avoided, as it is vague. We have introduced the maximum number of passages during which we have maintained the stable spheroid phenotype (26 passages). Also worth noting, the spheroids could be frozen and cultured repeatedly over many months.
SuppFig 3D: Row Z-Score is missing the "e" in Score.
Corrected
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Fig 4E: Figure legend says QNRQ instead of CNRQ. Corrected
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Fig 4G: The brightfield image of adult spheroids 5 days after 3x TAM injections doesn't look like a spheroid. It seems to be differentiating. True, the choice was not the best as the spheroids started to darken. When further replated, however, the offspring of these spheroids showing a clear phenotype remain negative 30 days after tamoxifen administration as shown on the figure. We are sorry, but for reasons explained in section 4 below, we cannot redo the experiment to get a better picture.
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Fig 4: Most mouse model data are missing the number of mice & their respective age used for organoid isolation. We have introduced these data in the legend.
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*Fig 4A-D, H-G: How was fluorescent signal of organoids quantified? *
The settings of fluo imaging or time of LacZ staining were the same for organoids and spheroid pictures. This has been added to the material and methods of the figure and an example is shown below for Rosa26Tomato.
*How many images? * 2 per animal per condition.
*Were there equal numbers of organoids? *
No, see number of total elements counted added to the figure
This all needs to be included in methods/figure legends.
We have introduced additional pertinent information in the material and methods section.
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Figure 4B-D, G-H: Which culturing conditions were used for adult spheroids? Original method or sandwich method? These data were obtained with the original protocol
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Fig 6D-E: Please add the timepoint after DT administration these samples are from. It is not listed in text or figure legend. These samples were those obtained from mice sacrificed at the end of the 5 day period as indicated in panel A. This has been emphasized in the legend of the figure.
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SuppFig 6D: again timepoint is missing. In this experiment all samples were untreated as indicated. This has been emphasized in the legend of the figure.
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SuppFig 6: How were the crypts of these mice (DT WT & DT HE) isolated? Was this via EDTA? This was RNA extracted from total uncultured EDTA-released material (crypts). This has been emphasized in the legend of the figure.
Also, what is the timepoint for isolation for these samples? Even if untreated, the timepoint adds context to the data. Please add more context to describing these different experiments, either in the figure legends or methods section.
All these experiments were from 2 month old animals. We have indicated this in the legend of the figure.
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SuppFig 6E: The quality of the heatmap resolution is too poor to read gene names. We have improved the resolution of the figure and hope the name of the genes are readable now.
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5-7, are the regenerating crypt-villus units fully differentiated or are they maintained in the developmental state? Immunostaining of markers for stem cells (Lgr5), differentiated lineages (Alpi, Muc2, Lyz, ChgA etc.) and fetal state (Sca1, Trop2 etc) should be analysed in those "white" unrecombined crypt-villus units. The differentiation phenotype is shown by the clear presence of morphologically-identified Paneth and Goblet cells. We agree that specific immunostainings could have been performed to further explore this point. Regarding the fetal state, Clu expression was shown during the regeneration period (see figure 7D,E).
Unfortunately, for reasons explained in section 4 below, we are not in a position to perform these additional experiments.
- The following text needs clarification: "The kinetics of appearance of newly formed un-recombined ("white") crypts was studied after a single pulse of DT (Fig.7A). This demonstrated an increase at 48 hours, with further increase at day 10 and stable maintenance at day 30. The presence of newly formed white crypts one month after toxin administration indicates that the VilCre-negative lineage is developmentally stable and does not turn on the transgene during differentiation of the various epithelial lineages occurring after regeneration (Fig.7B).
*Comment: The "newly formed" is an overstatement, the data doesn't conclude that those are "new" crypts. *
Except if we do not understand the point, we think we can write that a fraction of "white" crypts must be "newly formed", since they are in excess of those present in untreated animals at the same time point.
*The end of the sentence states that these "white" crypts form developmentally stable lineages, thus these white crypts at day 30 could originate from the initial injury. *
As stated above, we consider that crypts found in excess of those present in untreated animals result from the initial injury.
*There was no characterisation of the various epitheial lineages. Are they fully differentiated? *
See above the point related to Paneth cells and Goblet cells.
Is Lgr5 expressed one month after toxin administration? Can the VilCre neg lineage give rise to CBCs?
We have tried hard to show presence or absence of Lgr5 in white crypts at the various times following DT administration. We tried double RFP / Lgr5-RNA scope labeling and double GFP/RFP immunolabeling. Unfortunately, we could not get these methods to produce convincing specific labeling of CBCs in homeostatic crypts, which explains why we could not reach a conclusion regarding the white crypts.
However, there is an indirect indication that "chronic" white crypts (i.e. those caused by DTR expression in CBC, plus those observed 30 days after DT administration) do not express Lgr5. Indeed, acute regeneration indicated by Clu expression at day 5 in Fig.7C is lower in white crypts than in red ones strongly suggesting that white crypts preexisting DT administration (the "chronic ones) do not express Lgr5DTR.
The relationship between white crypt generation and appearance of Clu-positive revival cells (Ayyaz et al., 2019) was then explored. In agreement with others and similar to what happens in the irradiation model, (Ayyaz et al., 2019; Yuan et al., 2023) Clu-positive cells were rare in crypts of untreated mice and their number transiently increased forty-eight hours after a single pulse of DT, and more so after three pulses of DT (Fig.7C,D).
Comment: Comparing 1 pulse at day 2 vs 3 pulses at day 5 makes the data hard to interpret. How is the Clu ISH level for 1 pulse at day 5? Are they equivalent?
After a single pulse of of DT, Clu is only transiently increased. As shown by Ayyaz et al it is back to the starting point at day 5 (supplementary figure 4 of Ayyaz et al).
Clu-positive cells were less frequently observed in white crypts (see "Total" versus "White" in Fig.7C). This fits with the hypothesis that Clu expression marks acutely regenerating crypts and that a proportion of the white crypts are chronically regenerating due to DTR expression in CBCs."
*Comment: I believe the authors suggested that the discrepancy of less Clu expression in white crypts is due to the ectopic expression of DTR in CBCs causing low grade injury without DT administration. This means that some white crypts could have been formed before the administration of DT, and thus are on a different regenerative timeline compared to the white crypts formed from DT administration. *
Yes, this is our interpretation. We have clarified it in the text.
Is there any proof of the chronic regeneration? Immunostaining of chronic regenerative markers such as Sca1, Anxa1 or Yap1 nuclear localization would support the claim. It'd be important to show only the white crypts, but not the RFP+ ones, show regenerative markers.
We think that the steady state higher number of white crypts in untreated Lgr5-DTR animals, compared to wild type siblings indicates chronical low-grade regeneration, which is supported by the RNAseq data (Suppl fig6). It must be noted, however, that this phenotype is mild compared to the well described fetal-like regeneration phenotype described in most injury models. Since these white crypts were made at undetermined earlier stages, the great majority of them are not expected to show markers of acute regeneration like Clu, Sca1....
Fig 7D-E: What are the timepoints of harvest for HE-WT-HE 1 pulse DT mice and HE- HE-HE PBS injected mice?
We have added this information in the figure.
- *Fig 8-9: Regarding the CBC-like Olfm4 low population, what is the status of Lgr5? This should be shown in the figure since the argument is that this is an Lgr5-independent lineage. * See response to the second point.
And what about the regenerative, Yap, mesenchymal and inflammatory signatures? Are they enriched in the white crypts similar to the in vitro spheroids?
In a portion of white crypts, those we believe are newly formed after CBC ablation (see above), there is a transient increase in Clu, which may be considered a marker of Yap activation. In the CBC-like Olfm4 low cells, as seen by scRNAseq, there is nothing like an actively regenerating phenotype. This is expected, since these cells are coming from homeostatic untreated VilCre/Rosa26Tom animals and are supposed to be quiescent "awaiting to be activated".
Reviewer #1 (Significance (Required)):
Strengths: The study employed a range of in vitro and in vivo models to test the hypothesis.
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*Limitations: Unfortunately, the models chosen did not provide sufficient evidence to draw the conclusions. Injury induced reprogramming, both in vivo and in vitro, has been well documented in the field. The new message here is to show that such reprogrammed state is continuous rather than transient; instead of regenerating Lgr5+ stem cells, it can continue to differentiate to all cell lineages in Lgr5-independent manner.
*
We respectfully disagree with this analysis of our results. What we show is not "that such reprogrammed state is continuous rather than transient; instead of regenerating Lgr5+ stem cells, it can continue to differentiate to all cell lineages in Lgr5-independent manner", but that a quiescent stem cell line, not previously identified, is activated to regenerate a portion of crypts following CBC ablation. These cells are not reprogrammed, they correspond to a developmental lineage waiting to be activated and keep their VilCre-negative state at least of 30 days. We believe that their "by default tracing" (VilCre negative from the zygote stage) is as strong an evidence for the existence of such a lineage as positive lineage tracing would be. The increase in crypts originating from this lineage after CBC ablation indicates that it is implicated in regeneration. We do not question the well-demonstrated plasticity-associated reprogramming taking place during regeneration; we simply suggest that this would coexist with the involvement of the quiescent VilCre-negative lineage we have identified.
*However, through the manuscript, there was no immunostaining of Lgr5 and other differentiation markers. The conclusion is an overstatement without solid proof. * We have provided the best answer we could to this point in our answer to the second question of the referee hereabove.
Reviewer #2 (Evidence, reproducibility and clarity (Required)):
In this manuscript, the Marefati et al. developed a novel approach to generate spheroids from adult intestinal epithelium using a collagenase/dispase based protocol. Adult spheroids were found to be distinct from classic budding-type organoids normally generated from EDTA based release of the crypt epithelium. Transcriptional profiling indicated that adult spheroids were undifferentiated and similar to regenerating crypts or fetal spheroids. To identify the cell of origin that generates adult spheroids, the authors labelled epithelial cells with VilCreERT-LSL-Tom, VilCre-LSL-GFP and Lgr5CreERT- LSLTom mice. From these experiments the authors conclude that that spheroids are only generated from Vil-Cre negative and Lgr5 negative cells. Next the authors deleted the anti- apoptotic gene Mcl1 using Vil-CreERT mice. This led to a strong apoptotic response throughout the crypt epithelium and tissues processed from knockout mice readily generated spheroids, and in vivo, replenishment of the gut epithelium was mediated by unrecombined cells. In a second model, CBCs were ablated using Lgr5DTR mice and VilCre negative cells were found again to contribute to regeneration of the crypt epithelium. Finally based on the absence of Vil-Cre reporter activity, the authors were able to sort out and perform scRNAseq to profile VilCre negative cells. These cells were found to be quiescent, express the stem cell marker Olfm4 and were also abundant in ribosomal gene expression.
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The fact that the authors developed a protocol to reproducibly generate fetal-like spheroids from adult tissue is an important advancement in the field. Previous reports have shown that treatment with various small molecule inhibitors can revert budding organoids into a spheroid morphology, but this manuscript demonstrates that spheroids can also be generated from otherwise untreated cells. This new methodology will provide new tools to dissect the molecular determinants of fetal/regenerative cells in the gut. Based on this, the manuscript should attract a significant amount of attention in the intestinal field.
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As pointed out by the authors themselves the study has important limitations that diminish enthusiasm. The primary issue relates to the inability of the team to identify markers of VilCre neg cells other than the fact that these cells are Olfm4+ and quiescent. Nonetheless, for the reasons stated above the manuscript should reach the target audience within the research community, if the authors can address the specific points below related to issues with methodology as well as defining more precisely the characteristics and growth requirements of adult spheroid cultures.
Thank you for this positive analysis of our study.
Major comments
The main conclusion of the study is that Vil-Cre neg cells are rare quiescent Olfm4+ crypt cells. If this is the case, then standard EDTA treatment should release these cells as well. Consequently, spheroids should also emerge from isolated crypts grown in the absence of ENR. If this is not the case how do the authors explain this?
We have tried hard to generate spheroids by culturing EDTA organoids in medium lacking ENR and by treating EDTA organoids with collagenase/dispase, without success. Therefore, we are left with the conclusion that spheroid-generating cells must be more tightly attached to the matrix than those released by EDTA, and that it is their release from this attachment by collagenase that triggers a regeneration-like phenotype. This hypothesis is supported by several models of regeneration in other tissues as indicated in our references (Gilbert et al., 2010; Machado et al., 2021; Montarras et al., 2005).
From the text the authors appear to suggest that growth of adult spheroids is dependent initially on "material" released by collagenase/dispase treatment. An obvious candidate would be mesenchymal cells, which are known to secrete factors such as Wnts and PGE2 that drive spheroid morphology. To test this, the authors should treat spheroid cultures with Porcupine and/or PGE2 inhibitors.
We followed similar reasoning, considering that spheroids express strongly Ptgs1 ,2 (Figure 3A). We thought their phenotype might be maintained by autocrine prostaglandin action. We tested aspirin, a Ptgs inhibitor, which was without effect on the spheroid phenotype. Besides, we explored a wide variety of conditions to test whether they would affect the spheroid phenotype [Aspirin-see above, cAMP agonists/antagonists, YapTaz inhibitors (verteporfin and CA3), valproic acid, Notch inhibitors (DAPT, DBZ, LY511455), all-trans retinoic acid, NFkB inhibitors (TCPA, BMS), TGFbeta inhibitor (SB431542)]. As these results were negative, we did not include them in the manuscript.
- If these inhibitors block growth then this would suggest that either stromal cells or autocrine signalling involving these pathways is important. Overall, more in-depth analysis of the growth requirements of adult spheroids is required.*
Figure 1d indicates that adult spheroids can be propagated for at least 10 passages. The abstract mentions they are "immortal". The text itself does not address this issue. More precise information as to how long spheroids can be propagated is required. If these cultures can be propagated for 10 passages or more it becomes important to determine what nutrients/mitogens in the basal media are driving growth? Alternatively, what is the evidence that spheroid cultures are completely devoid of mesenchymal cells. The text only mentions that "Upon replating, these spheroids could be stably cultured free of mesenchymal cells (Fig.1B)". No validation is shown to support this.
We agree that "immortal" is not a good way to characterize our spheroids, as also pointed out by referee nr 1. We have changed that in the text, indicating the maximal number of replating we tested was 26 and replacing immortal by stably replatable. Of note, the spheroids could frozen/thawed and recultured many times.
Related to the question whether mesenchymal cells could still contaminate the spheroid cultures, we can provide the following answers:
- No fibroblasts could be seen in replated cultures and multiple spheroids could be repeatedly propagated from a single starting spheroid.
- The bulk RNAseq experiment comparing organoids to ENR or BCM cultured spheroids show, despite expression of several mesenchymal markers (see matrisome in Fig3), absence of significant expression of Pdgfra (see in revision plan folder for CP20Millions results from the raw data of new suppl table 2, with Clu, Tacstd2 and Alpi shown as controls).
- Regarding the nutrients/mitogens in the medium driving spheroid growth, we did not explore the point further than showing that they grow in basal medium (i.e. advanced DMEM), given that the presence of Matrigel makes it difficult to pinpoint what is really needed. In Figure 2, the authors describe the growth requirements for adult spheroids and indicate that spheroids grown in ENR or EN became dark and shrink. The representative images showing this are clear, but this analysis should be quantified.
Added to the manuscript.
In SF3, the gene expression profile of organoids from the sandwich method only partially overlaps with that of organoids from the old protocol. What are the gene expression differences between the 2 culture systems? Secondly, the sandwich method appears to sustain growth of Tom+ spheroids based on RNAseq and the IF images. This suggest that Vil-Cre negative cells are not necessarily the only source of adult spheroids and thus this experiment seems to indicate that any cell may be converted to grow as a spheroid under the right conditions. These points should be addressed.
Looking back to our data in order to answer the point raised by the referee, we realized that we had inadvertently-compared organoids to ENR-cultured spheroids generated by the first protocol to BCM-cultured spheroids generated by the sandwich method. We have corrected this error in a new version of suppl fig3. This shows increased correspondence between genes up- or downregulated in the spheroids obtained in the two protocols (from 49/48% to 57/57% (Venn diagram on the new figure). We agree that, even after this correction, the spheroids obtained with the two protocols present sizeable differences in their transcriptome. However, considering the very different way these spheroids were obtained and cultured initially, we do not believe this to be unexpected. The important point in our opinion is that the core of the up- and down-regulated genes typical of the de-differentiation phenotype of adult spheroids is very similar, as shown in the heatmap (which was made with the correct samples!). Also, a key observation is that that both kind of spheroids survive and can be replated in basal medium. As already stated, this characteristic is only seen rare cases [spheroids obtained from rare FACS-purified cells (Smith et al 2018) or helminth-infected intestinal tissue (Nusse et al.2018)]. Together with the observation that the majority of them is not traced by VilCre constitutes what we consider the halmark of the spheroids described in our study. As shown in figure 4E (old protocol) and Suppl Fig.3 (sandwich protocol) both red and white spheroids were extremely low in VilCre expression. As stated in the text, the fact that some spheroids are nevertheless red is most probably related to the extreme sensitivity of the Rosa26Tom marker to recombination (Liu et al., 2013), but this does not mean that there are two phenotypically different kind of spheroids. It means that the arbitrary threshold of Rosa26Tom recombination introduces an artificial subdivision of spheroids with no phenotypical significance.
Regarding the point made by the referee that "that any cell may be converted to grow as a spheroid under the right conditions", we agree and have shown with others that organoids acquire indeed a spheroid phenotype when cultured for instance in fibroblasts-conditioned medium (see suppl fig1B and (Lahar et al., 2011; Roulis et al., 2020) quoted in the manuscript). However, these spheroids cannot be propagated in basal medium, and revert to an organoid phenotype when put back in ENR (Suppl fig1B).
*In Figure 4, the authors conclude that spheroids do not originate from Lgr5 cell derived clones even after 30days post Tam induction. Does this suggest that in vivo and under homeostatic conditions VilCre neg cells are derived from a distinct stem cell pool or are themselves a quiescent stem cell. Given the rarity of VilCre neg cells, the latter seems unlikely.
*
Despite their rarity, we believe VilCre-negative cells observed under homeostatic conditions are themselves quiescent stem cells. Actually, if they were derived from a larger stem cell pool, this pool should also be VilCre-negative. And we do not see such larger number of VilCre-neg cells under homeostatic conditions.
The problem with the original assertion is that Lgr5-CreERT mice are mosaic and therefore not all Lgr5+ cells are labelled in this model. "White" spheroids may thus derive from cells that in turn derive from these unlabelled Lgr5 cells.
We had considered the possibility that mosaicism [very low for VilCre (Madison et al., 2002); in the 40-50% range for Lgr5CreERT2 (Barker & Clevers. Curr Protoc Stem Cell Biol. 2010 Chapter 5)] could explain our data. We think, however that we can exclude this possibility on the basis that spheroids do not conform to the expected ratio of unrecombined cells, given the observed level of mosaicism. Indeed, for VilCre, a few percent, at most, of unrecombined cells in the epithelium translates into almost 100% unrecombined spheroids. For Lgr5CreERT2 mice, the mosaicism level is in the range of 40%, which is what we observe for EDTA organoids (Figure 4G), while spheroids were in their vast majority unrecombined.
We have included a discussion about the possible role of mosaicism in the new version.
ATACseq experiments were briefly mentioned in the manuscript but unfortunately little information was extracted from this experiment. What does this experiment reveal about the chromatin landscape of adult spheroids relative to normal organoids?
We only performed this experiment to search for an explanation to the paradoxical absence of expression of the VilCre transgene in spheroids, despite robust expression of endogenous villin (Suppl Fig.4). We chose to show the chromatin landscape of a gene equally expressed in both organoids and spheroids (Krt19), a gene specifically expressed in spheroids (Tacstd2) and the endogenous Villin gene also expressed in both. We believe that the observation of a clear difference in pattern of the chromatin accessibility around the endogenous villin gene in organoids and spheroids provides an explanation to the observed results. The cis regulatory sequences needed for expression of the endogenous villin gene seem to be different in organoids and spheroids, which may explain why the regulatory sequences present in the transgene (only 12.4kb) might not allow expression of the transgene in spheroids. We have added a sentence in the manuscript clarifying this point. Missing is obviously the chromatin landscape around the VilCre transgene, but this is beyond reach in such kind of experiments.
Reviewer #2 (Significance (Required)):
The fact that the authors developed a protocol to reproducibly generate fetal-like spheroids from adult tissue is an important advancement in the field. Previous reports have shown that treatment with various small molecule inhibitors can revert budding organoids into a spheroid morphology, but this manuscript demonstrates that spheroids can also be generated from otherwise untreated cells. This new methodology will provide new tools to dissect the molecular determinants of fetal/regenerative cells in the gut. Based on this, the manuscript should attract a significant amount of attention in the intestinal field.
Reviewer #3 (Evidence, reproducibility and clarity (Required)): CR-2024-02491
An Lgr5-independent developmental lineage is involved in mouse intestinal regeneration
Marefati et al.
Homeostatic maintenance of the intestinal epithelium has long been thought to rely upon Wnt signaling responsive Lgr5-expressing stem cells that reside at the crypt base.
However, myriad reported mechanisms or populations have been reported to underlie epithelial regeneration after injury. Many groups have reported that reacquisition of a fetal- link intestinal phenotype is an import part of the regenerative response, however the originating cell type has not been definitively identified. Herein, the authors demonstrate that cells from adult homeostatic intestine can generate immortal spheroids that resemble fetal spheroids and are derived independent of Lgr5+ intestinal stem cells (ISCs). The authors then draw the conclusion that this indicates that a hierarchical stem cell model applies to regeneration of the intestinal epithelium, in addition to the plasticity model.
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Comments:
- Please indicate what species is used for studies in Fig 1.
All experiments were performed in Mus musculus.
Please clarify if Figure 2 studies utilize Matrigel or not.
Yes
RNA-seq analyses of adult intestinal generated spheroids lack the granularity of single cell analyses and thus it is unclear if this is a homogeneous population or if the population has diversity across it (i.e., enteroids/organoids have a high level of diversity). Many of the conclusions from the RNA-seq study are broad and generalized-for example Fig 3F indicates that markers of the +4 ISC populations (Bmi1, tert, lrig1, hopx) were all expressed similarly in adult spheroids as compared to adult organoids. However, while this may be true in the bulk-RNA-seq analyses, clearly scRNA-seq would provide a better foundation to make this statement, as enteroids/organoids are comprised of heterogeneous subpopulations. . .and it might indicate that these +4 markers have only very low expression in the spheroids. Based upon these concerns, misconclusions are likely to be drawn.
We agree and it would be certainly worthwhile to perform scRNAseq of adult spheroid populations. This would certainly be worth doing in future studies to explore the possible heterogeneity of adult spheroids. We nevertheless believe that our scRNAseq performed on homeostatic intestinal tissue from VilCre/Rosa26Tom mice identify Olfm4-low VilCre-neg cells that are likely at the origin of adult spheroids and display a quite homogenous phenotype.
*The language around Figure 4 results is confusing. Please define "white" and "red". It might be simpler to designate recombined versus not recombined lineage.
*
We have clarified this in the figure.
The hypothesis that collagenase/dispase solution acts as a proxy for injury is not demonstrated and backed by data. Thus, it is difficult to make the conclusion that this approach could represent a "stable avatar" of intestinal regenerating cells. It is clear that subpopulations of crypt-based cells generate spheroids in culture without collagenase/dispase (see the cited reference Smith et al, 2018).
* *Smith et al demonstrate clearly the possibility to obtain spheroids with properties probably similar to ours from EDTA derived intestinal crypt cells. However they need to prepurify them by FACS. Besides, Nusse et al describe spheroids similar to ours after infection of the intestine by helminths (Nusse et al. 2018). In our case, and for most labs preparing enteroids with the EDTA protocol, the result is close to 100% organoids. Even if we treat EDTA organoids with collagenase, we do not obtain spheroids. This brought us to the conclusion that spheroid-generating cells must be more tightly attached to the matrix than CBCs and that it is their release from the matrix that activates the spheroid regeneration-like phenotype. This hypothesis is supported by several models of regeneration in other tissues as indicated in our references (Gilbert et al., 2010; Machado et al., 2021; Montarras et al., 2005)
A study based on the absence of recombination in a VilCre lineage tracing scenario is not well-established to be strong experimental approach, as there are many reasons why recombination may not cells may not be lineage marked. In order to use this system as the authors intend, they first need to demonstrate that villin is not expressed in the discrete cell population that they are targeting. For the presented observational studies, this would be difficult to do. While they do demonstrate differences in chromatin accessibility between cells from organoids versus spheroids (fig s4), some of these differences could merely be due to the bulk analytical nature of the study and the lack of comparing stem cell populations from spheroids to stem cell populations from organoids-since the spheroids are likely homogenous versus the organoids that only have a small fraction of stem cells-and thus represent a mix of stem cell and differentiated cell populations. The authors do not demonstrate that villin protein expression varies in these cells.
If it were found that villin is not expressed in their "novel" population, then one would expect that the downstream use of villin-based recombination would demonstrate the same recombination potential (i.e., Mcl1 would not be recombined). Both recombination studies in Fig 6 are difficult to interpret, and thus it is not clear if these studies support the stated conclusions. Quantification of number of crypts that are negative should be reported as a percentage of recombined crypts.
We are sorry but there seems to be a complete misunderstanding of our data regarding the point raised by the referee. The important point of our initial observation is that despite robust expression of villin in spheroids, the VilCre transgene is not expressed (see figure 4E). This in our opinion makes absence of VilCre expression (or of Rosa marker recombination) a trustful marker of a new developmental lineage. All the data in figure 4 constitute an answer.
*The reasoning about heterogeneity of cell type in organoids versus probable homogeneity of spheroids is well taken. However, as the endogenous villin gene is expressed in all cells of both organoids and spheroids, it is highly significant that only spheroids do not express the transgene. *
We performed the ATACseq experiment to search for an explanation to the paradoxical absence of expression of the VilCre transgene in spheroids, despite robust expression of endogenous villin (Suppl Fig.4). We chose to show the chromatin landscape of a gene equally expressed in both organoids and spheroids (Krt19), a gene specifically expressed in spheroids (Tacstd2) and the endogenous Villin gene also expressed in both. We believe that the observation of a clear difference in pattern of the chromatin accessibility around the endogenous villin gene in organoids and spheroids provides an explanation to the observed results. The cis regulatory sequences needed for expression of the endogenous villin gene seem to be different in organoids and spheroids, which may explain why the regulatory sequences present in the transgene (only 12.4kb) might not allow expression of the transgene in spheroids. We have added a sentence in the manuscript clarifying this point. Missing is obviously the chromatin landscape around the VilCre transgene, but this is beyond reach in such kind of experiments.
*Figure 8 indicates that the cell population identified by scRNA-seq may be quiescent. Companion IF or IHC should be conducted to confirm this finding, as well as other conclusions from the informatics conducted.
*
We agree that additional experiments could be performed to support this point. We are unfortunately not in a position to perform these experiments (see section 4 below).
Clearly the data is intriguing, however, the conclusion is strong and is an over interpretation of the presented data. There are a number of validation or extension data that would enhance the overall interpretation of the study: 1. validation of scRNA-seq or bulk RNA-seq concepts by protein staining of intestinal tissues in the damage model will serve as a secondary observation. 2. identification of the ISC that they are defining is critical and important. There is already the notion that this cell type exists and it has been shown with various different markers. 3. expand the analyses of the fetal-like expression profiling to injured intestines to demonstrate that the lineage negative cells indeed express fetal-like proteins. 4. expand the discussion of the Clu+ cell type. Is this cell the previously described revival cell? If so, how does this body of work provide unique aspects to the field?
We agree that all these suggested experiments could be performed and would be of interest. However, we consider that they would not modify the main message of our study and would only constitute an expansion of the present work. As already stated, we are not in the position to perform them (see section 4).
*There is some level of conflicting data, with the stem population being proliferative in culture stimulated by the stromal cells, but quiescent in vivo and also based upon scRNA- seq data in Fig 9.
*
We do not see any conflict in our observation regarding this point. The observation that cells that are quiescent in vivo become proliferative when subjected to culture (with or without addition of stromal cells) is routinely made in a multitude of cell culture systems. In particular, it has been shown that intestinal tissue dissociation activates the Yap/Taz pathway, resulting in proliferation (Yu et al. Hippo Pathway Regulation of Gastrointestinal Tissues. Annual Review of Physiology, 2015 Volume 77, 201-227).
Many of the findings have been previously reported: Population that grows as spheroids (Figure 2), Population that is Wnt independent (Figure 2), Lgr5 independent regenerative growth of the intestine (figure 3F, Figure 4), Clu+ ISCs drive regeneration (Figure 7).
Whereas these individual findings have indeed been reported, it was in a different context. We strongly disagree with the underlying suggestion that our study would not bring new information. We have identified here a developmental lineage involved in intestinal regeneration that has not been described up to now.
Minor comments:
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- The statement that spheroids must originate from collagenase/dispase digested material might be an overstatement. As spheroids generation from EDTA treated intestines have been previously reported (Smith et al, 2018). * See answer to point 4 above. *Overall while the study includes an extensive amount of work and different approaches, a foundationally supported novel finding is missing. Many of the statements have already been demonstrated by others in the fields. In addition, one of the most intriguing aspects of the study is that the stromal population impacts this stem cell population, however, interactions and factors stimulating the crosstalk are not addressed.
*
Reviewer #3 (Significance (Required)):
Overal while the study includes an extensive amount of work and different approaches, a foundationally supported novel finding is missing. Many of the statements have already been demonstrated by others in the fields. In addition, one of the most intriguing aspects of the study is that the stromal population impacts this stem cell population, however, interactions and factors stimulating the crosstalk are not addressed.
We can only disagree.
4. Description of analyses that authors prefer not to carry out
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We have answered most questions raised by the referees by explaining our view, by clarifying individual points and, in several cases, by providing additional information that was not included in the original manuscript.
In a limited number of cases when additional experiments were suggested, we were unfortunately obliged to write that we are not in a position to perform them. This is because my lab is closing after more than fifty years of uninterrupted activity. There will unfortunately be nobody to perform additional experiments.
Nevertheless, as written by referees 1 and 2, we believe that the revised manuscript, as it stands, contains data that will be of interest to the people in the field and may be the bases for future developments. We hope editors will find interest in publishing it.
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Referee #3
Evidence, reproducibility and clarity
RC-2024-02491
An Lgr5-independent developmental lineage is involved in mouse intestinal regeneration Marefati et al.
Homeostatic maintenance of the intestinal epithelium has long been thought to rely upon Wnt signaling responsive Lgr5-expressing stem cells that reside at the crypt base. However, myriad reported mechanisms or populations have been reported to underlie epithelial regeneration after injury. Many groups have reported that reacquisition of a fetal-link intestinal phenotype is an import part of the regenerative response, however the originating cell type has not been definitively identified. Herein, the authors demonstrate that cells from adult homeostatic intestine can generate immortal spheroids that resemble fetal spheroids and are derived independent of Lgr5+ intestinal stem cells (ISCs). The authors then draw the conclusion that this indicates that a hierarchical stem cell model applies to regeneration of the intestinal epithelium, in addition to the plasticity model.
Comments:
- Please indicate what species is used for studies in Fig 1.
- Please clarify if Figure 2 studies utilize Matrigel or not.
- RNA-seq analyses of adult intestinal generated spheroids lack the granularity of single cell analyses and thus it is unclear if this is a homogeneous population or if the population has diversity across it (i.e., enteroids/organoids have a high level of diversity). Many of the conclusions from the RNA-seq study are broad and generalized-for example Fig 3F indicates that markers of the +4 ISC populations (Bmi1, tert, lrig1, hopx) were all expressed similarly in adult spheroids as compared to adult organoids. However, while this may be true in the bulk-RNA-seq analyses, clearly scRNA-seq would provide a better foundation to make this statement, as enteroids/organoids are comprised of heterogeneous subpopulations. . .and it might indicate that these +4 markers have only very low expression in the spheroids. Based upon these concerns, misconclusions are likely to be drawn.
- The language around Figure 4 results is confusing. Please define "white" and "red". It might be simpler to designate recombined versus not recombined lineage.
- The hypothesis that collagenase/dispase solution acts as a proxy for injury is not demonstrated and backed by data. Thus, it is difficult to make the conclusion that this approach could represent a "stable avatar" of intestinal regenerating cells. It is clear that subpopulations of crypt-based cells generate spheroids in culture without collagenase/dispase (see the cited reference Smith et al, 2018).
- A study based on the absence of recombination in a VilCre lineage tracing scenario is not well-established to be strong experimental approach, as there are many reasons why recombination may not cells may not be lineage marked. In order to use this system as the authors intend, they first need to demonstrate that villin is not expressed in the discrete cell population that they are targeting. For the presented observational studies, this would be difficult to do. While they do demonstrate differences in chromatin accessibility between cells from organoids versus spheroids (fig s4), some of these differences could merely be due to the bulk analytical nature of the study and the lack of comparing stem cell populations from spheroids to stem cell populations from organoids-since the spheroids are likely homogenous versus the organoids that only have a small fraction of stem cells-and thus represent a mix of stem cell and differentiated cell populations. The authors do not demonstrate that villin protein expression varies in these cells. If it were found that villin is not expressed in their "novel" population, then one would expect that the downstream use of villin-based recombination would demonstrate the same recombination potential (i.e., Mcl1 would not be recombined). Both recombination studies in Fig 6 are difficult to interpret, and thus it is not clear if these studies support the stated conclusions. Quantification of number of crypts that are negative should be reported as a percentage of recombined crypts.
- Figure 8 indicates that the cell population identified by scRNA-seq may be quiescent. Companion IF or IHC should be conducted to confirm this finding, as well as other conclusions from the informatics conducted.
- Clearly the data is intriguing, however, the conclusion is strong and is an over interpretation of the presented data. There are a number of validation or extension data that would enhance the overall interpretation of the study:
- a. validation of scRNA-seq or bulk RNA-seq concepts by protein staining of intestinal tissues in the damage model will serve as a secondary observation.
- b. identification of the ISC that they are defining is critical and important. There is already the notion that this cell type exists and it has been shown with various different markers.
- c. expand the analyses of the fetal-like expression profiling to injured intestines to demonstrate that the lineage negative cells indeed express fetal-like proteins.
- d. expand the discussion of the Clu+ cell type. Is this cell the previously described revival cell? If so, how does this body of work provide unique aspects to the field?
- There is some level of conflicting data, with the stem population being proliferative in culture stimulated by the stromal cells, but quiescent in vivo and also based upon scRNA-seq data in Fig 9.
- Many of the findings have been previously reported: Population that grows as spheroids (Figure 2), Population that is Wnt independent (Figure 2), Lgr5 independent regenerative growth of the intestine (figure 3F, Figure 4), Clu+ ISCs drive regeneration (Figure 7).
Minor comments:
- The statement that spheroids must originate from collagenase/dispase digested material might be an overstatement. As spheroids generation from EDTA treated intestines have been previously reported (Smith et al, 2018).
Overall while the study includes an extensive amount of work and different approaches, a foundationally supported novel finding is missing. Many of the statements have already been demonstrated by others in the fields. In addition, one of the most intriguing aspects of the study is that the stromal population impacts this stem cell population, however, interactions and factors stimulating the crosstalk are not addressed.
Significance
Overall while the study includes an extensive amount of work and different approaches, a foundationally supported novel finding is missing. Many of the statements have already been demonstrated by others in the fields. In addition, one of the most intriguing aspects of the study is that the stromal population impacts this stem cell population, however, interactions and factors stimulating the crosstalk are not addressed.
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Referee #2
Evidence, reproducibility and clarity
In this manuscript, the Marefati et al. developed a novel approach to generate spheroids from adult intestinal epithelium using a collagenase/dispase based protocol. Adult spheroids were found to be distinct from classic budding-type organoids normally generated from EDTA based release of the crypt epithelium. Transcriptional profiling indicated that adult spheroids were undifferentiated and similar to regenerating crypts or fetal spheroids. To identify the cell of origin that generates adult spheroids, the authors labelled epithelial cells with VilCreERT-LSL-Tom, VilCre-LSL-GFP and Lgr5CreERT-LSLTom mice. From these experiments the authors conclude that that spheroids are only generated from Vil-Cre negative and Lgr5 negative cells. Next the authors deleted the anti-apoptotic gene Mcl1 using Vil-CreERT mice. This led to a strong apoptotic response throughout the crypt epithelium and tissues processed from knockout mice readily generated spheroids, and in vivo, replenishment of the gut epithelium was mediated by unrecombined cells. In a second model, CBCs were ablated using Lgr5DTR mice and VilCre negative cells were found again to contribute to regeneration of the crypt epithelium. Finally based on the absence of Vil-Cre reporter activity, the authors were able to sort out and perform scRNAseq to profile VilCre negative cells. These cells were found to be quiescent, express the stem cell marker Olfm4 and were also abundant in ribosomal gene expression.
The fact that the authors developed a protocol to reproducibly generate fetal-like spheroids from adult tissue is an important advancement in the field. Previous reports have shown that treatment with various small molecule inhibitors can revert budding organoids into a spheroid morphology, but this manuscript demonstrates that spheroids can also be generated from otherwise untreated cells. This new methodology will provide new tools to dissect the molecular determinants of fetal/regenerative cells in the gut. Based on this, the manuscript should attract a significant amount of attention in the intestinal field.
As pointed out by the authors themselves the study has important limitations that diminish enthusiasm. The primary issue relates to the inability of the team to identify markers of VilCre neg cells other than the fact that these cells are Olfm4+ and quiescent. Nonetheless, for the reasons stated above the manuscript should reach the target audience within the research community, if the authors can address the specific points below related to issues with methodology as well as defining more precisely the characteristics and growth requirements of adult spheroid cultures.
Major comments
The main conclusion of the study is that Vil-Cre neg cells are rare quiescent Olfm4+ crypt cells. If this is the case, then standard EDTA treatment should release these cells as well. Consequently, spheroids should also emerge from isolated crypts grown in the absence of ENR. If this is not the case how do the authors explain this?
From the text the authors appear to suggest that growth of adult spheroids is dependent initially on "material" released by collagenase/dispase treatment. An obvious candidate would be mesenchymal cells, which are known to secrete factors such as Wnts and PGE2 that drive spheroid morphology. To test this, the authors should treat spheroid cultures with Porcupine and/or PGE2 inhibitors. If these inhibitors block growth then this would suggest that either stromal cells or autocrine signalling involving these pathways is important. Overall, more in-depth analysis of the growth requirements of adult spheroids is required.
Figure 1d indicates that adult spheroids can be propagated for at least 10 passages. The abstract mentions they are "immortal". The text itself does not address this issue. More precise information as to how long spheroids can be propagated is required. If these cultures can be propagated for 10 passages or more it becomes important to determine what nutrients/mitogens in the basal media are driving growth? Alternatively, what is the evidence that spheroid cultures are completely devoid of mesenchymal cells. The text only mentions that "Upon replating, these spheroids could be stably cultured free of mesenchymal cells (Fig.1B)". No validation is shown to support this.
In Figure 2, the authors describe the growth requirements for adult spheroids and indicate that spheroids grown in ENR or EN became dark and shrink. The representative images showing this are clear, but this analysis should be quantified.
In SF3, the gene expression profile of organoids from the sandwich method only partially overlaps with that of organoids from the old protocol. What are the gene expression differences between the 2 culture systems? Secondly, the sandwich method appears to sustain growth of Tom+ spheroids based on RNAseq and the IF images. This suggest that Vil-Cre negative cells are not necessarily the only source of adult spheroids and thus this experiment seems to indicate that any cell may be converted to grow as a spheroid under the right conditions. These points should be addressed.
In Figure 4, the authors conclude that spheroids do not originate from Lgr5 cell derived clones even after 30days post Tam induction. Does this suggest that in vivo and under homeostatic conditions VilCre neg cells are derived from a distinct stem cell pool or are themselves a quiescent stem cell. Given the rarity of VilCre neg cells, the latter seems unlikely. The problem with the original assertion is that Lgr5-CreERT mice are mosaic and therefore not all Lgr5+ cells are labelled in this model. "White" spheroids may thus derive from cells that in turn derive from these unlabelled Lgr5 cells.
ATACseq experiments were briefly mentioned in the manuscript but unfortunately little information was extracted from this experiment. What does this experiment reveal about the chromatin landscape of adult spheroids relative to normal organoids?
Significance
The fact that the authors developed a protocol to reproducibly generate fetal-like spheroids from adult tissue is an important advancement in the field. Previous reports have shown that treatment with various small molecule inhibitors can revert budding organoids into a spheroid morphology, but this manuscript demonstrates that spheroids can also be generated from otherwise untreated cells. This new methodology will provide new tools to dissect the molecular determinants of fetal/regenerative cells in the gut. Based on this, the manuscript should attract a significant amount of attention in the intestinal field.
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Referee #1
Evidence, reproducibility and clarity
In this manuscript, Marefati et al report an Lgr5-independent lineage in the regenerating intestine using in vitro organoids and in vivo injury-coupled lineage tracing model. In organoids, collagenase/dispase dissociated resulted in "immortal spheroids" that maintain a cystic and undifferentiated phenotype in the absence of standard growth factors (Rspondin/Noggin/EGF). Bulk RNAseq of spheroids demonstrates downregulation of classical CBC signatures and upregulation of fetal spheroid, mesenchymal, inflammation and regenerative signatures. In mice, Villin-Cre lineage tracing revealed some Villin-negative progenies that lack reporter tracing throughout crypt-villus ribbons after injury. The authors proposed that there is Lgr5-independent population support the regenerative response upon CBC depletion. A major caveat of this study is the identification of this population is based on absence of VilCre expression. It is surprising that there is no characterisation of Lgr5 expression throughout the manuscript whilst claiming of a Lgr5-independent lineage. Although the research question is potentially interesting, the concept of epithelial reprogramming upon injury is well documented in the field. The data generated in this manuscript also seem to be preliminary and lack of detailed characterisation. Below are specific comments.
- Expression of Lgr5 should be properly characterised throughout the manuscript in both organoid models and injury-induced regeneration in vivo.
- An important question is the origin of these "Lgr5-independent" adult spheroids. They look and appear like fetal organoids, which could be induced by injury (e.g. upon collagenase/dispase dissociation). Have the authors tried to culture fetal spheroids in BCM over extensive period of time? Do they behave the same? This would be a great way to directly compare the collagenase/dispase-derived organoids with fetal origin.
- Fig 1C, Why is the replating spheroid culture time different between mesenchymal cells and conditioned medium?
- It is unclear how the bulk RNA-seq data in Fig. 3 were compared. How long were the adult organoids and spheroids cultured for (how many passages)? Were they culture in the same condition of were they in ENR vs BCM? These are important information to consider when interpreting the results. For instance, are Ptgs1 & Ptgs2 expression in adult spheroids the same in ENR vs BCM? Are the gene signatures (regenerative, fetal and YAP) changed in adult spheroids culturing in ENR vs BCM?
- It is stated: "In agreement with their aptitude to grow indefinitely, adult spheroids express a set of upregulated genes overlapping significantly with an "adult tissue stem cell module" [159/721 genes; q value 2.11 e-94) (Fig.S2F)].". What is the definition of "indefinitely"? Are they referring to the Fig 1B where spheroid were passaged to P10? The authors should avoid the term "indefinitely" but use a more specific time scale, e.g. passages, months etc.
- SuppFig 3D: Row Z-Score is missing the "e" in Score.
- Fig 4E: Figure legend says QNRQ instead of CNRQ.
- Fig 4G: The brightfield image of adult spheroids 5 days after 3x TAM injections doesn't look like a spheroid. It seems to be differentiating.
- Fig 4: Most mouse model data are missing the number of mice & their respective age used for organoid isolation.
- Fig 4A-D, H-G: How was fluorescent signal of organoids quantified? How many images? Were there equal numbers of organoids? This all needs to be included in methods/figure legends.
- Figure 4B-D, G-H: Which culturing conditions were used for adult spheroids? Original method or sandwich method?
- Fig 6D-E: Please add the timepoint after DT administration these samples are from. It is not listed in text or figure legend.
- SuppFig 6D: again timepoint is missing.
- SuppFig 6: How were the crypts of these mice (DT WT & DT HE) isolated? Was this via EDTA? Also, what is the timepoint for isolation for these samples? Even if untreated, the timepoint adds context to the data. Please add more context to describing these different experiments, either in the figure legends or methods section.
- SuppFig 6E: The quality of the heatmap resolution is too poor to read gene names.
- Fig.5-7, are the regenerating crypt-villus units fully differentiated or are they maintained in the developmental state? Immunostaining of markers for stem cells (Lgr5), differentiated lineages (Alpi, Muc2, Lyz, ChgA etc.) and fetal state (Sca1, Trop2 etc) should be analysed in those "white" unrecombined crypt-villus units.
- The following text needs clarification:
"The kinetics of appearance of newly formed un-recombined ("white") crypts was studied after a single pulse of DT (Fig.7A). This demonstrated an increase at 48 hours, with further increase at day 10 and stable maintenance at day 30. The presence of newly formed white crypts one month after toxin administration indicates that the VilCre-negative lineage is developmentally stable and does not turn on the transgene during differentiation of the various epithelial lineages occurring after regeneration (Fig.7B). Comment: The "newly formed" is an overstatement, the data doesn't conclude that those are "new" crypts. The end of the sentence states that these "white" crypts form developmentally stable lineages, thus these white crypts at day 30 could originate from the initial injury. There was no characterisation of the various epitheial lineages. Are they fully differentiated? Is Lgr5 expressed one month after toxin administration? Can the VilCre neg lineage give rise to CBCs?
The relationship between white crypt generation and appearance of Clu-positive revival cells (Ayyaz et al., 2019) was then explored. In agreement with others and similar to what happens in the irradiation model, (Ayyaz et al., 2019; Yuan et al., 2023) Clu-positive cells were rare in crypts of untreated mice and their number transiently increased forty-eight hours after a single pulse of DT, and more so after three pulses of DT (Fig.7C,D). Comment: Comparing 1 pulse at day 2 vs 3 pulses at day 5 makes the data hard to interpret. How is the Clu ISH level for 1 pulse at day 5? Are they equivalent?
Clu-positive cells were less frequently observed in white crypts (see "Total" versus "White" in Fig.7C). This fits with the hypothesis that Clu expression marks acutely regenerating crypts and that a proportion of the white crypts are chronically regenerating due to DTR expression in CBCs." Comment: I believe the authors suggested that the discrepancy of less Clu expression in white crypts is due to the ectopic expression of DTR in CBCs causing low grade injury without DT administration. This means that some white crypts could have been formed before the administration of DT, and thus are on a different regenerative timeline compared to the white crypts formed from DT administration. Is there any proof of the chronic regeneration? Immunostaining of chronic regenerative markers such as Sca1, Anxa1 or Yap1 nuclear localization would support the claim. It'd be important to show only the white crypts, but not the RFP+ ones, show regenerative markers. - Fig 7D-E: What are the timepoints of harvest for HE-WT-HE 1 pulse DT mice and HE-HE-HE PBS injected mice? - Fig 8-9: Regarding the CBC-like Olfm4 low population, what is the status of Lgr5? This should be shown in the figure since the argument is that this is an Lgr5-independent lineage. And what about the regenerative, Yap, mesenchymal and inflammatory signatures? Are they enriched in the white crypts similar to the in vitro spheroids?
Significance
Strengths: The study employed a range of in vitro and in vivo models to test the hypothesis.
Limitations: Unfortunately, the models chosen did not provide sufficient evidence to draw the conclusions. Injury induced reprogramming, both in vivo and in vitro, has been well documented in the field. The new message here is to show that such reprogrammed state is continuous rather than transient; instead of regenerating Lgr5+ stem cells, it can continue to differentiate to all cell lineages in Lgr5-independent manner. However, through the manuscript, there was no immunostaining of Lgr5 and other differentiation markers. The conclusion is an overstatement without solid proof.
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Reply to the reviewers
The work would have significant impact in the cilia community, if the conclusion is correct. This reviewer, however, has a concern about the authors concluding the presence/absence of TZ, based on only B9D1 and the H-shaped body among nine doublet microtubules. First, is it really established how the structure of Xenopus embryo TZ is? While Chlamydomonas is well known to have a H-shaped TZ, other species have different form inside the 9+0 doublet, or no feature (Comparison of TZ from various species in Dennis Diener https://doi.org/10.1016/B978-0-12-822508-0.00007-1). Fig.2B of this manuscript shows visible densities in the panel "Pre", but it does not look like an H-shape. The tomogram of TZ before deciliation seems clearer (but judging from wavy MTs and membrane in this tomogram, there could be unevenness of embedding and staining), while the tomogram after deciliation is thin and does not cover the entire width. Therefore it is not sure that absence of TZ can be concluded. If the author claims Xenopus embryo cilia have a H-shaped TZ, they have to provide multiple micrographs (ideally tomogram or serial section TEM to cover the entire TZ structure) and/or past literature on Xenopus embryo TZ. B9D1 is likely a membrane associated protein (according to their deciliation by detergent and mechanical force). This may mean B9D1 is located on or near the membrane, in vicinity to TZ, and thus binds to TZ after the main part of TZ is built. In this case, it is risky to judge presence of TZ based on B9D1. Also in this point, TEM imaging will be helpful to confirm the authors' conclusion.
RESPONSE: We appreciate the reviewer’s thoughtful comments on the loss of TZ upon deciliation and its absence during the initial regeneration period. The reviewer is right in their assessment that the TZ of Xenopus cilia has not been well defined before in any manuscript. We want the reviewer to consider that our goal was not to define the TZ in Xenopus but to study deciliation and how cilia regenerate in a vertebrate model system for the first time. We unexpectedly discovered that cilia are deciliated distal to the basal body at the plasma membrane, and the “H-shaped structure,” similar to TZ, was also removed and did not come back for first hour during regeneration. Given this surprising observation, we felt obliged to study and explain our results. To that end, we explored different resources (antibodies and markers of TZ) and different methods over 6 years trying to define TZ in Xenopus.
Our conclusion about the TZ structure came from multiple lines of evidence from our experiments and published literature, including the similarity in structure compared to other organisms and its physical location in the cilium. Specifically, 1) In a review of the basal bodies, Mitchell indirectly suggested that the electron-dense “H-shaped” structure could be a TZ in Xenopus. 2) The electron-dense “H” shaped structure in Chlamydomonas is similar, if not identical, to that shown in Xenopus cilia. 3) The physical location of TZ is always shown to be distal to the basal body and transition fibers (except in clubmoss Phylloglosum) while proximal to the central pair. The electron-dense “H-shaped” structure in Xenopus fulfills these criteria, suggesting that this structure is the TZ in Xenopus. 4) The TZ bonafide protein B9D1 is localized distal to Chibby, which labels the distal end of the basal body, suggesting that the TZ is localized distal to the basal body. Moreover, the loss of an “H-shaped” structure determined using TEM and tomograms corresponds to the loss of the B9D1 signal, further strengthening the conclusion that the H-shaped structure is the TZ.
We will include serial sectioning and imaging of multiple Xenopus cilia in control and 0hr (after deciliation) to address this reviewer's concerns further. Our preliminary data has suggested that the ciliary membrane is tightened around this electron-dense structure, similar to what has been shown before for other organisms like Chlamydomonas. and thus boosts our confidence that this structure likely corresponds to the TZ in Xenopus.
The reviewer has raised a concern that “the tomogram after deciliation is thin and does not cover the entire width. Therefore, it is not sure that absence of TZ can be concluded”. We note that even if the tomograms do not go through the entire cilium (supplementary videos 2 and 3), it does go through more than the center of cilium as seen by the presence of central pair microtubules and we can observe that the electron-dense “H-shaped” structure is not present in these cilia. Further, in the supplementary videos 5 and 6, even if the tomogram again only covers half of the cilia, we can see the presence of the structure, confirming that our tomograms can demonstrate the presence or absence of the H-shaped structure confidently. We have also provided TEM sections in addition to the tomograms to show the same result.
The Reviewer has commented that “B9D1 is located on or near the membrane, in vicinity to TZ, and thus binds to TZ after the main part of TZ is built”. This reviewer is correct in their assessment. This is why we argue that the presence or absence of B9D1 may be a good marker for understanding the presence or absence of TZ assembly.
TIMELINE: We are performing additional serial TEM in the control and deciliated (0hr.) embryos to address the reviewer’s concern. We will need 1 month to finish these experiments.
Their discussion about length/number of cilia and force generated by cilia is interesting, but in the context of this research, this reviewer is skeptical about its value. The calcium induced deciliation is not a physiological phenomenon, but an artificial event (please correct if I am wrong). The argument how length and number of cilia are regulated upon deciliation makes sense only in case deciliation happens regularly and the species must optimize themselves to survive. The argument about possible passway of protein transport to control ciliary number and length (Line408-) seems, although it is an interesting topic in general, not suitable in this manuscript. For this reviewer's view, it is relatively straightforward to interpret the result of cilia number/length under normal growth, without new protein expression (CHX), with protein degradation blocked. Cilia will extend when components are provided. Growth will slow down when it is exhausted. Existing cilia start degrading, when they lack proteins, which are necessary for turn-over. With the current experimental output, there is no point to describe redistribution of proteins.
RESPONSE: We appreciate the reviewer’s comment; however, we would like to argue that different methods of deciliation have been used in different model systems, such as Chlamydomonas, to study cilia regeneration. Although this reviewer may not find some of the experiments and conclusions appropriate for this manuscript, other research groups have found these results interesting. For example, reviewer 2 states, “To support their observations that cilia length is favored over cilia number under conditions of limiting ciliary precursor availability, the authors use a mathematical model that leads to the conclusion that force generation is optimized by increasing cilia length. This is a convincing conclusion and in agreement with other comparable modeling studies performed in the field.” We have already had great discussions about these results with many cilia researchers at multiple conferences. Therefore, we prefer to keep these experiments and results in the manuscript and let readers come to their own conclusions about their importance.
Minor points:
Line65: do they mean "selected few basal bodies"? – we have removed the word “select”
Line73: extracellular flow is not limited to developmental system. – we have altered the statement to add “growth, development and homeostasis”
Line124: alpha-tubulin signal and SEM image – we have added “and scanning electron microscopy (SEM)”
Line139: Could you define explicitly the two hypotheses? – Now, we have reworded the sentences to clarify the two hypotheses. “Therefore, we considered two hypotheses: First, Xenopus MCCs regenerate cilia or second, Xenopus depend on stem cell-based replacement of damaged MCCs.”
Line164: 10,31-33 are not suitable citation for the location of calcium induced deciliation in Chlamydomonas. cite Sanders and Salisbury JCB 108, 1751 – We have changed the citation.
Line181: Later -> latter – We have changed the text.
Line195: by mechanical shearing, B9D1 remained with cilia. They concluded that TZ stays with the axoneme by deciliation. How can they exclude the possibility that mechanical separation works differently from calcium shock? – We do not intend to claim that both calcium-based and mechanical ripping of cilia from cells adopt the same deciliation mechanism, and we have mentioned in line 193 that ‘we adopted an alternative approach of mechanical deciliation’. Using these two methods as complimentary to each other, our aim was to show that TZ is lost by both ciliation methods. For the calcium method, because the membrane is ripped with detergent, we show the loss of TZ by examining the MCCs devoid of cilia. In the mechanical deciliation protocol, since no detergent is involved, we can examine cilia that are likely to have intact membranes and thus maintain a B9D1 signal.
Line214: 1.33uM -> 1.33um - We have made these changes to the text.
__RESPONSE: __All the minor points in the manuscript are addressed.
Overall, the results are well presented and allow strong conclusions to be drawn. The results are based on both immunofluorescence studies and EM analysis. To support their observations that cilia length is favored over cilia number under conditions of limiting ciliary precursor availability, the authors use a mathematical model that leads to the conclusion that force generation is optimized by increasing cilia length. This is a convincing conclusion, and in agreement with other comparable modeling studies performed in the field. It would be fascinating to be able to measure the flow parameters at the cell surface during cilia regeneration to see whether this regeneration actually leads to an increase in the overall flow or force generated by the cilia. But as the authors explain, this is probably a difficult experiment to carry out and appears to be optional in the context of this study.
__RESPONSE: __We thank the reviewer for recognizing and stating that “the results are well presented and allow strong conclusions to be drawn”. We also want to sincerely thank the reviewer for understanding the technical difficulties in performing these experiments.
The authors are apparently only able to detect a single TZ protein, B9D1, to follow the fate of the TZ during the deciliation and reciliation process. In some ways, this provides an incomplete demonstration that all the TZ is indeed removed during deciliation, although this is supported by EM observations. It also provides a limited understanding of the time course of TZ re-formation during reciliation. Given the limitations of antibody availability, could it be possible to express tagged proteins in the animal cap system to track more TZ proteins? In particular, would it be possible to track for example Cby and NPHP proteins. What is the behavior of Cep290? This would greatly reinforce the conclusions on the molecular reorganisation of the TZ after deciliation and during cilia regeneration.
__RESPONSE: __We appreciate this reviewer’s brilliant questions on understanding the time course of TZ re-formation during reciliation. When we started this project and observed that TZ was lost upon deciliation in our preliminary TEM experiment, our first goal was to confirm this outstanding result. Thus, we did more TEMs and EM tomography, used bonafide TZ protein B9D1 to label the structure, and observed its loss upon deciliation. Taken together, we feel highly confident that TZ is lost upon deciliation. To address this reviewer’s concerns, we will performing additional serial TEMs to confirm the loss of TZ after deciliation.
Our next goal was to understand what the reviewer has mentioned, the TZ assembly time course. We started with TEMs at different time points and again saw a surprising result: TZ assembly was delayed compared to cilia axoneme. We were driven by this question of understanding how cilia “put together” the complex structure of TZ structurally and molecularly using EM and fluorescence data. We first attempted a few antibodies, including B9D1, CEP290, MKS5, and NPHP4, to localize to the TZ in the Xenopus cilia. Despite our efforts with different fixation strategies, only B9D1 appeared to localize to the TZ, whereas others did not give any signal or localized at the basal body. Next, we tried localizing TMEM216, TMEM67, and NPHP4 using fluorescent tags, but we again found the same result: they localized to the basal body but not at the TZ. We are perplexed by this result and are pursuing the reasons behind them. However, these experiments are out of the scope of this paper. We want to note that we have used Chibby in our experiments and that it is not lost upon deciliation (Fig S1). This is because Chibby is a distal transition fiber protein (distal end of basal body) and does not extend up to the transition zone.
TIMELINE: To address the reviewer's concern, we are performing additional serial TEM in the control and deciliated (0hr.) embryos. We will attempt to localize CEP290-GFP, requiring approximately 1 month to finish the experiment. However, we would like to note that we cannot guarantee that this experiment will work, as similar experiments with other TZ markers have failed before.
Minor comments
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Figure 4: The images are poorly defined, and it is difficult to distinguish individual basal bodies and cilia. Therefore, it is not clear how the authors can confidently quantify the number of basal bodies in each condition to construct the graph at the bottom of the figure. In addition, it would be interesting to label the basal body with a centriolar marker to better define it. - Figure 4 labels the Transition Zone protein B9D1 and cilia marker acetylated tubulin and not basal bodies. The graph represents the number of cells with the presence or absence of elongated B9d1 signal.
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Figure 5: not clear why the graph on the lower right does not include the control at 3 and 6 hrs? Is it because the number is too high and difficult to quantify? – Yes, the reviewer is right. Cilia become too long and too many to quantify their number reliably.
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References: I would like to draw the authors' attention to studies of deciliation in Paramecia that could be cited in the introduction or discussion of the conservation of this pathway through evolution. – We have added multiple references to paramecia throughout the manuscript. Specifically, we mention that deciliation and regeneration in unicellular models like paramecia have added to our understanding of ciliogenesis. Line 102 “While it is important to remember that regeneration of cilia may not be identical to de novo assembly, cilia regeneration studies in Chlamydomonas reinhardtii, Paramecium and Tetrahymena etc., have provided significant insights into ciliogenesis, g., cargo transport, the presence of precursor pool, regulation of ciliary gene expression.18,23–26”. Further, we also added the reference to paramecia in results, line164 “Next, we determined the location where the deciliation treatment severed cilia. Unicellular models such as Chlamydomonas, Paramecium and Tetrahymena lose cilia distal to the TZ and below the central pair (CP) microtubules33.”. We also add discussion on the importance of TZ in paramecia, line 203 “Interestingly in Paramecium also a unicellular multiciliated cell, displays constant shedding of cilia when TZ proteins are depleted.25”. These statements have been supported by the following studies that are now cited in the manuscript: Machemer and Ogura 1979 Journal of Cell Physiology (10.1113/jphysiol.1979.sp012990) and Gogenddeau et al., Plos Biology (10.1371/journal.pbio.3000640).
RESPONSE: All the minor points in the manuscript are addressed.
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Referee #3
Evidence, reproducibility and clarity
The manuscript by Rao et al. focuses on determining the mechanism of cilia regeneration using Xenopus mucociliary epithelium. The authors employ a simple yet powerful approach to trigger deciliation of multiciliated cells, enabling them to study the mechanism of cilia regeneration. This research has a significant impact on the field of cilia biology and enhances our understanding of ciliopathies. Through detailed cell biological methodologies, the authors obtained intriguing results, including the finding that deciliation removes the transition zone and that cilia repair precedes the transition zone assembly. Additionally, the authors demonstrate that IFT proteins involved in cilia construction concentrate at selected basal bodies. Although there are open questions that the authors also highlight, this manuscript provides solid, pioneering insights into the process of cilia regeneration in vivo.
Significance
The manuscript characterizes the mechanism of cilia regeneration, providing new insights into processes that could be harnessed to restore ciliary function in patients suffering from chronic respiratory diseases.
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Referee #2
Evidence, reproducibility and clarity
Summary
This manuscript investigates how cilia regenerate in multi-ciliated cells. The authors have exploited an original multi-ciliated cell system derived from the Xenopus embryonic cap and use chemical and mechanical deciliation to understand the different steps of cilia regeneration. In this model, they show that cilia are excised just above the BB and below the ciliary transition zone. Their results indicate that during ciliary regeneration, axoneme reassembly precedes TZ formation and that ciliary reassembly relies on de novo protein synthesis. In the context of limited protein synthesis, cells regenerate fewer cilia, but of almost the same size as control cells, suggesting the existence of a cell control system to maximise force generation. Mathematical modelling of the forces exerted by defined numbers of cilia of different lengths supports this hypothesis.
Major comments
Overall, the results are well presented and allow strong conclusions to be drawn. The results are based on both immunofluorescence studies and EM analysis. To support their observations that cilia length is favored over cilia number under conditions of limiting ciliary precursor availability, the authors use a mathematical model that leads to the conclusion that force generation is optimized by increasing cilia length. This is a convincing conclusion, and in agreement with other comparable modeling studies performed in the field. It would be fascinating to be able to measure the flow parameters at the cell surface during cilia regeneration to see whether this regeneration actually leads to an increase in the overall flow or force generated by the cilia. But as the authors explain, this is probably a difficult experiment to carry out and appears to be optional in the context of this study.
The authors are apparently only able to detect a single TZ protein, B9D1, to follow the fate of the TZ during the deciliation and reciliation process. In some ways, this provides an incomplete demonstration that all the TZ is indeed removed during deciliation, although this is supported by EM observations. It also provides a limited understanding of the time course of TZ re-formation during reciliation. Given the limitations of antibody availability, could it be possible to express tagged proteins in the animal cap system to track more TZ proteins? In particular, would it be possible to track for example Cby and NPHP proteins. What is the behavior of Cep290? This would greatly reinforce the conclusions on the molecular reorganisation of the TZ after deciliation and during cilia regeneration.
Minor comments
Figure 4: The images are poorly defined and it is difficult to distinguish individual basal bodies and cilia. It is therefore not clear how the authors can confidently quantify the number of basal bodies in each condition to construct the graph at the bottom of the figure. In addition, it would be interesting to label the basal body with a centriolar marker to better define the basal body.
Figure 5: not clear why the graph on the lower right does not include the control at 3 and 6 hrs? Is it because the number is too high and difficult to quantify?
References: I would like to draw the authors' attention to studies of deciliation in Paramecia that could be cited in the introduction or discussion of the conservation of this pathway through evolution.
Significance
The mechanisms of deciliation and re-ciliation have mostly been studied in protozoa (Chlamydomonas, Paramecia) or in primary ciliated cell cultures. Only a few studies have described deciliation in multiciliated cells, such as sea urchins, or physiological deciliation in the oviduct. The Xenopus deciliation system described here has already been used to determine the dynamics of IFT proteins during ciliogenesis or to define the ciliary proteome. In this study, the authors go one step further by describing more precisely which part of the cilium is shed upon induction of deciliation and the dynamics of the recruitment of the Tip and of the TZ proteins.
This study provides a completely new perspective on the deciliation process:
- the authors show that, contrary to what is generally accepted from protozoan studies, the deciliation process, in Xenopus multiciliated cells, expels the TZ, leaving only the basal body in the cell;
- While ciliogenesis is described in various models to begin with the formation of the TZ, in this Xenopus system the TZ maturates after the onset of axonemal elongation, calling into question the precise function of the TZ in axonemal elongation. The observations could be further strengthened by analyzing more TZ proteins to better understand the time course of events involved in the deciliation-reciliation program.
The protocol used to deciliate Xenopus multiciliated cells has been described in previous manuscripts. Its use here reveals striking differences in the deciliation-reconciliation pathways from what is known in the field. It provides new conceptual perspectives for researchers working on the basic mechanisms of ciliogenesis. Note that, as a geneticist and specialist in ciliogenesis using various model organisms, I am not fully competent to critically evaluate the mathematical models developed in this study.
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Referee #1
Evidence, reproducibility and clarity
In this manuscript entitled "Machanisms of cilia regeneration in Xenopus multiciliated epithelium in vivo", the authors mostly focus on the question, whether TZ (transition zone of cilia) plays an essential role for ciliogenesis during cilia regeneration in multiciliated cells. They used Xenopus embryo as a system to examine this question. While cilia regeneration has been actively studies in unicellular green algae, Chlamydomonas reinhardtii, the mechanism of cilia regeneration is not known yet. Their approach is to investigate cells after deciliation by calcium shock, based on a TZ protein B9D1, as well as ultrastructure observation using conventional electron microscopy.
The authors observed loss of signal from B9D1 and H-shaped objects, which is typical for TZ, upon deciliation induced by calcium and also during the following re-growth of cilia. Based on these experiments they concluded that TZ formation is not necessary for cilia regeneration in multiciliated cells, differently from Chlamydomonas. They further conducted experiments to pursue source of component proteins for re-generation. They compared CHX-treated cells (lacking new protein production) and CHX/MG132 (reduced protein degradation) treated cells to find how the massive amount of protein components upon re-ciliation for multiple cilia will be supplied and regulated. This reviewer found the results of the experiments clearly presented and conducted properly.
The work would have significant impact in the cilia community, if the conclusion is correct. This reviewer, however, has a concern about the authors concluding the presence/absence of TZ, based on only B9D1 and the H-shaped body among nine doublet microtubules. First, is it really established how the structure of Xenopus embryo TZ is? While Chlamydomonas is well known to have a H-shaped TZ, other species have different form inside the 9+0 doublet, or no feature (Comparison of TZ from various species in Dennis Diener https://doi.org/10.1016/B978-0-12-822508-0.00007-1). Fig.2B of this manuscript shows visible densities in the panel "Pre", but it does not look like an H-shape. The tomogram of TZ before deciliation seems clearer (but judging from wavy MTs and membrane in this tomogram, there could be unevenness of embedding and staining), while the tomogram after deciliation is thin and does not cover the entire width. Therefore it is not sure that absence of TZ can be concluded. If the author claims Xenopus embryo cilia have a H-shaped TZ, they have to provide multiple micrographs (ideally tomogram or serial section TEM to cover the entire TZ structure) and/or past literature on Xenopus embryo TZ. B9D1 is likely a membrane associated protein (according to their deciliation by detergent and mechanical force). This may mean B9D1 is located on or near the membrane, in vicinity to TZ, and thus binds to TZ after the main part of TZ is built. In this case, it is risky to judge presence of TZ based on B9D1. Also in this point, TEM imaging will be helpful to confirm the authors' conclusion.
Their discussion about length/number of cilia and force generated by cilia is interesting, but in the context of this research, this reviewer is skeptical about its value. The calcium induced deciliation is not a physiological phenomena, but an artificial event (please correct if I am wrong). The argument how length and number of cilia are regulated upon deciliation makes sense only in case deciliation happens regularly and the species must optimize themselves to survive. The argument about possible passway of protein transport to control ciliary number and length (Line408-) seems, although it is an interesting topic in general, not suitable in this manuscript. For this reviewer's view, it is relatively straightforward to interpret the result of cilia number/length under normal growth, without new protein expression (CHX), with protein degradation blocked. Cilia will extend when components are provided. Growth will slow down when it is exhausted. Existing cilia start degrading, when they lack proteins, which are necessary for turn-over. With the current experimental output, there is no point to describe redistribution of proteins.
Minor points:
Line65: do they mean "selected few basal bodies"?
Line73: extracellular flow is not limited to developmental system.
Line124: alpha-tubulin signal and SEM image
Line139: Could you define explicitly the two hypotheses?
Line164: 10,31-33 are not suitable citation for the location of calcium induced deciliation in Chlamydomonas. cite Sanders and Salisbury JCB 108, 1751
Line181: Later -> latter
Line195: by mechanical shearing, B9D1 remained with cilia. They concluded that TZ stays with the axoneme by deciliation. How can they exclude the possibility that mechanical separation works differently from calcium shock?
Line214: 1.33uM -> 1.33um
Significance
The work would have significant impact in the cilia community, if the conclusion is correct. Their discussion about length/number of cilia and force generated by cilia is interesting, but in the context of this research, this reviewer is skeptical about its value.
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Reply to the reviewers
Manuscript number: RC-2024-02470
Corresponding author(s): Milán, Somogyvári; Csaba, Sőti
1. General Statements
We thank both Reviewers for their constructive comments, and we hope that our reply clarifies the concerns and the revised manuscript will be recommended for publication in Review Commons affiliated journals.
2. Description of the planned revisions
As the first major concern, Reviewer #1 raised the question of resolving the link between SIR-2.1 and HSF-1.
In order to address this issue, we plan to utilize a two-way approach:
- We plan to check the acetylation status of C. elegans HSF-1 using Mass Spectrometry.
- We aim to evaluate changes in its promoter binding by utilizing a form of Chromatin Immuno-Precipitation combined with RT-qPCR.
- As an alternative approach, we plan to utilize the cbp-1GoF mutant strain MH2430, that was described to acetylate HSF-1 and therefore change its transactivational function (Barrett & Westerheide, 2022). We'd like to see whether this can achieve a phenocopy of the sir-2.1(null) genetic background - on the level of the lipolysis phenotype and HSF-1-acetylation. These experiments are to be performed in both wildtype and sir-2.1-silenced animals under fed and starved conditions.
The second issue raised by Reviewer #1 was to confirm that the miR-53 activity on atgl-1 3' UTR is crucial for the described phenotype.
Our planned solution for this issue is to create novel C. elegans strains that expresses green fluorescent protein under the regulation of the atgl-1-promoter and with either the wildtype 3'UTR of atgl-1 attached or a mutated one, to which mir-53-binding is not possible. Through fluorescence microscopy experiments involving fed and starved animals, we hope to be able to sufficiently assess the necessity of mir-53 activity to changes in atgl-1 expression and function.* *
The following minor concern of Reviewer #1 regarding the Results section are addressed here:
(b) Supporting our ORO results with using the transgene idrIs1[dhs-3p::dhs-3::GFP] to label lipid droplets.
After acquiring the LIU1 strain harboring the aforementioned transgene, we plan to validate some of the results gained from ORO experiments.
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Reviewer #2 brought into our attention several concerns that need to be addressed:
1- Firstly, it was mentioned that the overabundance of histograms and the lack of indications on the representative images makes it difficult for the reader to assess the information on the panels.
We plan to include more images of stained worms while also indicating the changes that the histograms are meant to show. We hope these efforts will make our results more convincing.
2- Reviewer #2 mentioned that some of the results shown in our manuscript was already published in Zaarur N et al, 2019.
We thank the Reviewer for bringing this issue to our attention. We'd like to however point out that the mentioned paper by Zaarur et al. is indeed referenced in two places in the Introduction of our manuscript: first, when highlighting that longevity pathways, fat metabolism and lifespan determination are interconnected (line 56), and second, indicating that ATGL-1 mediates longevity in response to dietary restriction and reduced insulin-like signaling (line 65). The regulation of ATGL-1 by starvation and of lipid mobilization by ATGL-1 are not among the novel results of this study. The novelty of our data lies mainly in HSF-1 being involved through specific microRNAs - which to the best of our knowledge has not yet been published. We agree with Reviewer #2 that the visual representation of the data in Zaarur et al. is more pleasing, therefore we plan to incorporate representative images here as well.
5- Selection of mircoRNA genes
It was rightly pointed out by Reviewer #2 that mir-53 was reported to be upregulated by HSF-1 upon heat-shock, while there's no mention of it behaving so upon starvation. However, we considered examining it a potentially worthwhile direction given that in C. elegans it is a common observation that various stresses lead to the activation of similar/overlapping stress-response pathways. As seen at Figure 5E, our data supports the idea that mir-53 expression responds to starvation, as its pre-miRNA levels are elevated by starvation in sir-2.1-silenced animals. As for mir-60 and mir-75, we indeed do not have evidence for them being regulated by HSF-1, however their mutants have been associated with reduced body fat content (Brosnan, Palmer & Zuryn, 2021), thus we considered them also to be potential candidates for the role of mediator in the observed lipolysis phenomenon. We aim to make our reasons for choosing these specific miRs clearer in the manuscript.
3. Description of the revisions that have already been incorporated in the transferred manuscript
In accordance with the minor concerns of Reviewer #1, the following changes have already been made to the manuscript:
In the Abstract, two sentences were changed: (a) "In starving worms, a SIR-2.1-dependent suppression of specific HSF-1 transcriptional activity leads to the inhibition of lipolysis through mitigating miR-53-expression" & (b) "potential crosstalk" was introduced to the last sentence in order to better reflect the nature of our results.
In Introduction, the start of the first sentence was changed to "Lipids are a diverse group of cellular constituents".
In Results, the suggested changes in the C. elegans nomenclature were performed (a), where we substituted "sir-2.1 knockout" with "sir-2.1(null)" and "hsf-1 knockout" with "hsf-1(null)".
The following concerns of Reviewer #2 are addressed in the text of the transferred manuscript:
4- Stage of synchronized populations in the starvation protocol.
We thank the Reviewer for highlighting this issue. The life-stage of the animals should indeed be specified at this particular method. It may have been omitted due to RNAi treatments being described just above, where it is mentioned that L4 animals are washed onto RNAi plates where they spend 2 days. Any starvation protocol starts only after these RNAi treatments, since almost all our experiments include some form of RNAi. Therefore, in any trials that do not have RNAi in them, we still only applied starvation from 2 days after L4 stage - for comparability's sake. This issue is clarified in the revised manuscript.
4. Description of analyses that authors prefer not to carry out
The third major concern mentioned by Reviewer #1 is the epistatic relationships between our novel regulatory pathway and the KIN-1/PKA.
We'd like to thank Reviewer #1 for turning our attention towards the issue. We feel that the phosphorylation of ATGL-1 by KIN-1 - most probably on Serine 303 - was well established in Lee JH et al, 2014, as seen at Figure 5B - which, naturally, must occur downstream from any transcriptional regulation done by the SIR-2.1-HSF-1-MIR-53 axis. Nevertheless, it would be interesting to see if a non-phosphorylatable ATGL-1 will not support lipolysis upon starvation even if its mRNA expression is activated - which is something that was not tested for in Lee JH et al, 2014. However, since this was not the main subject of our manuscript - more of an addition to ATGL-1-regulation - while our work focused on the regulatory axis going through HSF-1, we do not consider it crucial to perform further experiments aimed at the KIN-1/PKA-mediated regulation of ATGL-1.
The following minor concerns of Reviewer #1 regarding the Results section are addressed here:
(c) Fatty acid profiling in the different experimental conditions.
Even though we feel the potential significance of such data, it does not fit under the scope of our current study. We feel this to be better fitting for a later, follow-up research project.
(d) Concern about double RNAi treatment in Figure 2D.
In such particular experiments, where the nematode strain is already a mutant one (where RNAi can only affect intestinal cells), it would require time-consuming crossings with the sir-2.1 and hsf-1 mutant animals. For this reason, we opted to use double RNAi, since according to literature - as well as to our previous experience - double RNAi can be a reliable method to silence the expression of two genes simultaneously. Since the effectivity of RNAi can be influenced by dosage, we compensated for this by mixing the single RNAi treatments with Empty Vector containing bacteria. The results themselves show the silencing-treatments to be effective - to a similar extent as the single RNAi treatments seen in Figure 2A.
(e) Improving statistical power.
We agree with Reviewer #1 that in some cases the addition of biological replicates may have the potential to strengthen our conclusions. We argue however, that even though in each case we applied statistical post-hoc tests in order to avoid a type I error, going above the customary 3 biological replicates at each and every experiment would increase the probability of such an error occurring.
The following concerns of Reviewer #2 are addressed here:
3- Reviewer #2 inquired about RNAi efficiency and the usage of knock-out mutants.
Here, we'd like to highlight that throughout the manuscript we utilized sir-2.1(null) mutants in Fig. 1A-B, Fig. 2A-B, Fig. 3D & Fig. S3A; while using hsf-1(null) mutants in Fig. 2A, Fig. 3B & Fig. 8D-F - among other mutants and transgenics. The list of strains can be found in Supplementary Table 1. Regarding the hsf-1(RNAi), it is a strain used for silencing hsf-1-expression reliably in the past by the lab of origin at ELTE University (Barna et al. 2012 BMC Developmental Biology). The silencing of hsf-1 does not lead to any noticeable phenotypes, but it does fully eliminate hsp-70-induction by heat-shock or starvation as shown on Fig. 5A-B.
6- KIN-1 & KIN-2's role and place in lipolysis regulation
As Reviewer #2 pointed out, both a mutation in kin-1 and kin-2 seemingly lead to the inhibition of lipolysis. However, since kin-2 codes for the regulatory subunit of KIN-1/PKA, a Loss of Function mutation in it leads to a constitutively active PKA - which in turn is expected (among other outcomes) to continuously phosphorylate and thus stabilize ATGL-1. In accordance with this, loss of KIN-1 resulted in an inability to utilize lipid-reserves - therefore the ORO staining levels of these mutants remained similar to wildtype and fed state even upon starvation - while loss of KIN-2 lead to a significantly decreased basal lipid staining, that could not be further decreased by starvation. We argue that the lack of any effect of sir-2.1(RNAi) (or hsf-1(RNAi)) on these phenotypes, while atgl-1(RNAi) was able to revert the kin-2(null)-related basal lipid loss, strongly supports the epistatic relationships proposed.
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Referee #2
Evidence, reproducibility and clarity
Review
An intestinal Sir2-HSF1-ATGL1 pathway regulates lipolysis in C. elegans
In the manuscript by Somogyvári et al., the authors focus on the differences between the fed and the fasted state using C. elegans. In particular the authors find that in the fasted state, the C. elegans SIRT1 ortholog, SIR-2.1, activates lipolysis by upregulation of ATGL-1. Further studies show that in fed worms regulation occurs in the intestine by HSF-1, ATGL-1, and the microRNA system. In contrast, in fasted worms, SIR-2.1 functions with the miR-53 microRNA to affect lipolysis and hsf-1. Further experiments attempt to implicate protein kinase A and proteostasis. Ultimately, the authors attempt to invoke a model for stress resilience and aging. Overall, the data as presented is not very convincing. All of the data is presented as histograms which show only mild effects. Images that are shown are not convincing. Some of the data has been previously published (mentioned below). Therefore, the manuscript needs extensive revisions prior to resubmission and should address the comments below.
- why is most of the data presented as a histogram?
Why are there not representative images that help readers examine the results? /
For example figure 1A does not really show anything but could guide the reader. The worm images throughout the manuscript do not give any indication of what the authors want the data to show the reader. 2. some of the data has already been published.
Mol Metab. 2019 Sep:27:75-82. doi: 10.1016/j.molmet.2019.07.001. Epub 2019 Jul 5. Nava Zaarur et al. fig 1
' ATGL-1 is up-regulated by fasting of C. elegans. (A) Wild type (N2) and atgl-1::gfp worms w
control (Fed) and Fasted groups and stained with Oil Red O.<br /> (B) Triglyceride content was measured in Fed and Fasted groups of N2 and atgl-1::gfp worms. (C) RNA was extracted from Fed and Fasted groups, and atgl-1 mRNA levels were measured by qRT-PCR; actin-1/3 was used for normalization. (D) Fed and Fasted L4 stage atgl-1::gfp worms were visualized by fluorescence microscopy (200X, equal exposure times). Bar e 50 mM. (E) Quantification of the results shown in panel D by ImageJ (10 randomly selected worms per group). '
this is not referenced or discussed and more convincing than simply a histogram 3. - why is there no analysis with mutants and simply Rnai? for example why is sir2 mutant not used.? - does the rnai show any phenotypes? ex hsf-1 rnai = hsf mutant? - Do you know the knockdown efficiency for the rnai clones? 4. Starvation protocol
560 Synchronized populations were washed 3 times with M9 buffer and placed either on 561 plates containing bacterial food source, or empty plates for 18 hours.
- what stage were the Synchronized populations?-
- Brunquell, J., Snyder, A., Cheng, F. & Westerheide, S. D. HSF-1 is a regulator of 699 miRNA expression in Caenorhabditis elegans. PLoS One 12, 1-24 (2017) This is the reference used to define the connection to micrornas. However, this manuscript describes miRNAs induced by heat shock. How does heat shock connect to starvation? The fed or the fasted state? Overall, the rationale for the specific microRNAs shown in the manuscript example mir-53 is unclear.
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Figure 6. The protein kinase A KIN-1 affects lipolysis and ATGL-1 function 330 downstream from SIR-2.1 and HSF-1.
- there is no difference between kin-1 knockout and Kin-2 knock out-so how does one say that it is only Kin-1?
- where are the differences between fig 6b and 6c?
- 'The complete 306 inhibition of lipolysis in the absence of sir-2.1 or kin-1 suggests that Sir2 and PKA pathways 307 are equally indispensable and cooperate in lipolysis regulation in the wildtype'
- does data really show this? ?- not much difference between kin-1 and kin-2- can you really separate the requirements?
Significance
Overall, the data as presented is not very convincing. All of the data is presented as histograms which show only mild effects. Images that are shown are not convincing. Some of the data has been previously published (mentioned below). Therefore, the manuscript needs extensive revisions prior to resubmission and should address the comments below.
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Referee #1
Evidence, reproducibility and clarity
Summary.
This study elucidates the contribution of sirtuin 1 ortholog SIR-2.1 in lipid mobilization upon starvation in the nematode Caenorhabditis elegans. The authors claim that HSF-1 controls the expression of adipose triglyceride lipase ATGL-1 in C. elegans gut. Furthermore, they show that SIR-2.1 modulates ATGL-1 activity by regulating the expression of microRNA miR-53 in an HSF-1-dependent manner. The manuscript also describes the interplay between SIR-2.1/HSF-1 and protein kinase A (KIN-1/PKA) in modulating ATGL-1 activity and proteostasis. Finally, the authors claim that lipid mobilization correlates with HSF-1-dependent proteostasis according to the feeding state of the organism.
Major concerns.
This manuscript consists of at least three parts that are relatively connected: - The impact of SIR-2.1 deficiency on ATGL-1 expression. Here, the main novelty is that HSF-1-dependent regulation of miR-53 defines atgl-1 expression during starvation. - The contribution of KIN-1/PKA in lipid mobilization and ATGL-1 activity downstream SIR-2.1 and HSF-1. This link was partially described in a few previous studies (e.g., Lee JH et al, 2014).<br /> - The contribution of HSF-1 in intestinal proteotoxic stress and fat metabolism. The role of HSF-1 in proteostasis has been well documented, whereas its participation to lipid metabolism has been described in a few studies (e.g., Oleson BJ, 2024).
The authors provide novel findings that give a better picture of the signaling cascade regulating these biological processes.
- However, this study does not conclusively resolve the link between SIR-2.1 and HSF-1. Does Sirtuin 1 influence HSF-1 through histone deacetylation and, therefore, HSF-1 deposition to target genes? Or does Sirtuin regulate HSF-1 acetylation state and therefore its activity? The authors attempted to address these questions with some experiments (Figure 5), however the data are indirect evidence.
- Furthermore, microRNAs have multiple targets with various biological functions. Although the authors provide first line of evidence demonstrating the impact of miR-53 on starvation-induced lipolysis, it may be important to confirm that the miR-53 activity on atgl-1 3' UTR is crucial for the described phenotype. Thus, the authors may consider to generate C. elegans strains carrying an atgl-1 3' UTR that is not recognized by the endogenous miR-53 (OPTIONAL).
- The role of KIN-1/PKA and ATGL-1 was previously reported (Lee JH et al, 2014) as mentioned in the manuscript. In the submitted manuscript, the authors tried to link KIN-1/PKA, ATGL-1, SIR-2.1 and HSF-1. The authors suggest that "KIN-1 acts downstream from the SIR-2.1 pathway" and "KIN-1 acts downstream of ATGL-1 post-transcriptional regulation". Most of the authors' conclusions are based on RNAi experiments. Could the authors support their claims by providing evidence that the downstream substrates are differentially posttranslationally modified according to the experimental conditions (starvation vs feeding)? Apart from RNAi methods, could the authors support their claims by using non-phosphorylatable ATGL-1 mutants?
Minor concerns.
Abstract.
(a) "SIR-2.1 suspends a miR-53-mediated suppression...". Please adjust the text to make it more understandable.
(b) "Our findings reveal a crosstalk between proteostasis and lipid/energy metabolism, which may modulate stress resilience and aging.". Which evidence do the authors have that this newly identified crosstalk influences aging? Figure 8 is not sufficient to make such a strong claim.
Introduction.
I would encourage the authors to re-word some of their sentences. For example, "lipids are diverse constitutes" sounds strange.
Results.
(a) Please, keep in mind the internationally accepted C. elegans nomenclature. For example, substitute "sir-2.1 knockout" with "sir-2.1(null)".
(b) The authors used Oil Red O (ORO staining) to assess lipid content in nematodes. However, the method has a few limitations and the accurate assessments of fat stores may be variable across experiments. One option is that the authors corroborate their findings with another approach. For example, they may consider to use the transgene idrIs1[dhs-3p::dhs-3::GFP] to label lipid droplets in intestinal cells.
(c) The authors assessed free-fatty acid content in fed and starved animals. It may be informative to report the individual fatty acid molecules that are mobilized in the different experimental conditions.
(d) It is always difficult to obtain reproducible results by using two RNAi clones (Figure 2D). The authors should corroborate their results with sir-2.1 and hsf-1 mutant worms.
(e) For some of the experiments, the statistics may be improved. Since some panels show tendency towards statistical significance (e.g., 8F), it may be important that the authors strengthen their analyses with additional biological replicates. This would help to consolidate their findings and conclusions.
Significance
This study reports how Sirtuin 1 can modulate ATGL-1 expression by regulating a microRNA (miR-53). It remains unclear if it is through a direct interaction or via epigenetic remodelling of histone acetylation of target genes. By building up on previous studies, the authors provide additional molecular players that take part in lipid mobilisation during starvation.. The audience can be defined as "specialised" and "basic research".
My fields of expertise are: metabolism, aging and epigenetics. I work with mice and C. elegans.
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Reply to the reviewers
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.
We thank the reviewers for their insights and helpful suggestions on the manuscript. Based on these, we have prepared a revision plan for this manuscript, which is outlined below. We believe these revisions will improve the overall quality of the manuscript.
2. Description of the planned revisions
Insert here a point-by-point reply that explains what revisions, additional experimentations and analyses are planned to address the points raised by the referees.
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Reviewer #1
(Evidence, reproducibility and clarity (Required)):
Summary:
This study builds on previous work from the same group, where they use Drosophila photoreceptors as a model system to investigate the role or ER-plasma membrane contact sites in an in vivo setting. The authors recently described a role of the ER-PM contact site protein dEsyt in regulating photoreceptor function in Drosophila. In this follow-up study, they explore whether this function of dEsyt is connected Ca2+ signaling downstream of photoreceptor activation. Using a dEsyt mutant that should be unable to bind Ca2+, they find that Ca2+ to some extent is required for dEsyt localization, membrane contact site formation and photoreceptor function.
Major comments:
The use of photoreceptor cells in Drosophila is an elegant model system that enable studies of membrane contact sites and associated proteins in a native condition. The data presented by the authors clearly shows that these structures are important for photoreceptor function, and that dEsyt plays a role at these sites. However, this was already known from previous studies by the same group. When it comes to whether these contacts are sensing Ca2+ changes and if these changes are acting through dEsyt, which is the focus of the current manuscript, the results are unclear to me and would need to be clarified by the authors both in text and with new experiments.
1) What is the role of cellular Ca2+ signaling in the regulation of dEsyt function? There are several aspects here that needs to be clarified. 1) How is WT dEsyt localization regulated by Ca2+? This could for example be evaluated in the mutant flies used in Fig. 1 (trpl302; trp343), where lack of light-induced Ca2+ influx would be predicted to result in a localization of dEsyt that resembles that observed for dEsytCaBM. 2) Is Ca2+ important for dEsyt localization, lipid exchange or both? The authors express a version of dEsyt with mutation made in all three C2 domains. In mammalian E-Syts, Ca2+ binding to the C2A domain is important for lipid exchange while binding to C2C (in E-Syt1) is important for interactions with lipids in the plasma membrane. Using more carefully designed mutants will allow the authors to determine how Ca2+ regulates dEsyt function in vivo. In addition, the authors must show experimentally that the mutant dEsytCaBM is unable to bind Ca2+ (could e.g. be done by acute Ca2+ changes in the cell-based model used in Fig. 3). Writing that "This transgene carrying a total of nine mutations should render the protein unable to bind calcium" (p. 6, line 173) is not sufficient.
1) How is WT dEsyt localization regulated by Ca2+?
We agree that further experimental evidence would be helpful in establishing the significance of cellular Ca2+ signaling in the control of dEsyt function. As suggested by the reviewer, the localization of wild type dEsyt will be examined in the mutants: norpAP24 (PLC null mutant) and trpl302; trp343 (protein null mutants of TRPL and TRP channels respectively) in which light induced calcium influx is eliminated. These data will be included in the revision.
2) Is Ca2+ important for dEsyt localization, lipid exchange or both?
We have already performed experiments to address the question of how important calcium binding to dEsyt is for lipid transport at the ER-PM interface in Drosophila photoreceptors. This results indicate a previously unexpected role for lipid exchange and will be included in the revision.
3). Writing that "This transgene carrying a total of nine mutations should render the protein unable to bind calcium" (p. 6, line 173) is not sufficient.
We concur with the reviewers that at present we do not have experimental data to demonstrate that dEsytCaBM can't bind Ca2+. However, as Reviewer 4 pointed out, it will be challenging to demonstrate this experimentally. A direct proof would only come from measurements of the calcium binding affinity of dEsyt (which involves protein purification that is beyond the scope of the current work). An indirect demonstration would be any cellular or in vivo experiment. In addition to the in silico analysis already included in Fig 2 C-F, we propose the following to provide additional evidence to strengthen our in silico analysis: Use AlphaFold model to demonstrate that the arrangement of the calcium binding residues in the C2 domain of dEsyt is compatible with Ca2+ binding.
2) The localization of dEsyt shown in Fig. 3B is a bit confusing. First of all, I would recommend including markers of the ER and the plasma membrane, because without these it is difficult to make statements about the localization of dEsyt to these structures.
As suggested, to better appreciate the localization of dEsyt in photoreceptors, we will perform colocalization of dEsyt with markers of the PM (Rhabdomere) and ER (Sub Microvillar Cisternae).
Second, it appears that WT dEsyt localize to the reticular ER, and that the CaBM version localize to the plasma membrane. This is somewhat opposite to mammalian ESyts, where mutations that prevent Ca2+ binding either had no effect (for ESyt2) or prevented (for ESyt1) the interaction with the plasma membrane. It also appears different from the localization in vivo (Fig. 3C). Clarifying this will be important. It will also be important to connect this localization to changes in Ca2+ and not just to the localization of a mutant that may or may not be deficient in Ca2+ binding (see comment above).
In considering this comment, we need to bear in mind the following:
- Mammalian cells have three genes that encode for Esyt: Esyt 1, 2 and 3 whereas the Drosophila genome encodes only a single gene for Esyt.
- In terms of sequence similarity and structure, dEsyt and hEsyt2 are very similar. However, in contrast to hEsyt2 and hEsyt3, which localize to the plasma membrane (PMID: 17360437), dEsyt acts like hEsyt1 and localizes to the ER-PM junctions.
- A single study (PMID: 27065097) has shown that the SMP domain of Esyt1 can transfer lipids in an in vitro assay. In our studies, we have noted an unexpected function for the SMP domain of dEsyt for in vivo function as measured through phenotypes in the eye (data will be presented in the revised ms).
- While knocking out the single dEsyt in Drosophila photoreceptor neurons results in phenotypes (Nath et.al PMID: 32716137) to date, knocking out all three Esyts in mammalian cell culture models or mice has not revealed an in vivo Bearing these points in mind it may not be reasonable to expect every observation on mammalian Esyt to be recapitulated in the fly system or vice versa. 3) I don't fully understand the time course of events. The authors show that dEsytCaBM is mislocalized already at day 1 in dark-reared flies (Fig. 3C) but this mislocalization is not accompanied by a change in MCS density or gap distance, and consistently does not influence the localization of RDGB. The authors next expose the flies to constant light illumination to trigger Ca2+ dependent signaling, and this leads to mislocalization of RDGB, perhaps indicating changes in MCS (this is not shown). From these results it is difficult to know what the role of dEsyt is. It would be necessary to also show a control where Ca2+ signaling is not induced, e.g. a parallel dark-control (same number of days but no illumination).
It is important to remember that even complete loss of Esyt does not result in altered MCS or mislocalization of RDGB on day 1 post eclosion. This has been published by us previously (Nath et.al PMID: 32716137). Since we show in this manuscript that dEsytCaBM exerts a dominant negative effect when expressed in wild type and phenocopies dEsytKO, one might expect expression of dEsytCaBM to also lead to altered MCS density and mislocalization of RDGB by 6D constant light.
Bearing this in mind, we will incorporate the following data in the manuscript: Addition of MCS density in dEsytKO photoreceptors at Day1 in Figure 3C.
- Electron Microscopy to check MCS density in Rh1>dEsytCaBM at Day 6CL with appropriate control genotypes.
- Confocal Imaging: RDGB staining in Rh1>dEsytCaBM- Day 6CD reared flies with appropriate control genotypes- dark control where only reduced Ca2+ signaling is induced due to dark noise or spontaneous PLC activation. This is particularly important given that the authors show in Fig. 1 that preventing Ca2+ influx had a dramatic impact on MCS density even at day 1 (which is in sharp contrast to dEsytCaBM-expressing flies, that show normal morphology at day 1, which rather implies that dEsyt is not a major Ca2+ effector).
In thinking about this comment, it is important to bear in mind the details of the experimental paradigm in use in each of the experiments while drawing comparisons between the observed results. It is to be noted that throughout the manuscript dEsytCaBM is expressed selectively in photoreceptors using the Rhodopsin enhancer which drives expression of the transgene during late eye development. By contrast, in germ line mutant strains such as trpl302;trp343 the channels are blocked throughout development. Thus the phenotypes of trpl302;trp343 might be broader than that of expressing dEsytCaBM. Therefore, mutating the calcium binding residues of dEsyt and expressing it using Rh1 enhancer at a specific developmental time window might not have the same impact on the contact site density as completely blocking the major calcium permeable channels, TRP and TRPL that is important to sustain the ongoing phototransduction cascade all through the development.
4) The experiments done in dEsyt KO flies are important, and here the authors show that dEsyt1 could to some extent rescue all phenotypes. Some results are a bit puzzling. For example, dEsyt1CaBM localization in dEsyt1 KO flies is identical to that of WT dEsyt (Fig. 5C), which is in sharp contrast to the data shown in Fig. 3C. What is the reason for this? I would have anticipated the opposite (i.e. that in WT flies, dEsytCaBM can form dimers with endogenous dEsyt through SMP-domain interactions which may have an impact on its localization and the function of endogenous dEsyt, but that in the dEsyt KO cells, dEsytCaBM would show a different localization due to the lack of endogenous dEyt to interact with). It is important to clarify as one of the major observations here is that dEsytCaBM no longer localize to MCS. Since the CaBM version of dEsyt could rescue, to some extent, MCS density and delay photoreceptor degeneration, this implies that Ca2+ may not be required for regulation of dEsyt function or that the mutant is still able to partially bind to Ca2+.
The localization shown in Fig 5C is not of dEsytCaBM in dEsytKO photoreceptors but the localization of RDGB in Rh1>dEsytCaBM; dEsytKO at Day 1 (Figure 5C i) and as a function of age and illumination- Day 6CL (Figure 5C ii).
One experiment that would help the authors determining the function of dEsyt in vivo would be to use a mutant that lacks functional SMP domain (ideally also with and without mutations in the C2-domains).
There is information available to address the question of how the lipid binding module, SMP is important to render dEsyt functional at the ER-PM interface in Drosophila photoreceptors. The same will be included in the revision.
5) PLC activation typically couples to rapid signaling and involved hydrolysis of PIP2 and release of Ca2+ from the ER. Mammalian Esyts also require PIP2 for plasma membrane binding (through interactions with C2-domains), so constitutive PLC activity would be expected to impair ESyt localization to MCS. Here, the authors expose flies for days of constant illumination. How does this influence plasma membrane PIP2 levels and could this be of relevance for how data is interpreted?
This is an interesting question from the reviewer. However, we would like to clarify the fact that constitutive activation of PLC is different from constant activation of PLC during illumination. Flies have robust mechanisms for controlling PLC turnover and PIP2 levels during continuous illumination and Ca2+ is a key regulator of this process; the underlying mechanisms have been described by Raghu and Hardie in multiple past papers (PMID: 11343651, PMID: 15355960). This is why, apart from adaptation, flies grown in constant light for many days do not show electrophysiological defects and neither do they undergo retinal degeneration. We will however measure the kinetics of PIP2 resynthesis in (i) wild type (Day 1 vs Day 6CD vs Day 6CL) and (ii) Control, Rh1>dEsyt and Rh1>dEsytCaBM (Day 1 vs Day6CL). This might reveal some interesting insight into the mutants.
Do the authors know whether the CaBM mutant has reduced affinity for PIP2?
The ability of wild type dEsyt to bind PIP2 has not been determined. We will test this and if it does so, the impact of CaBM on PIP2 binding can be tested.
Minor comments:
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The overexpression of WT dEsyt had a dramatic impact on MCS density and gap distance, while expression of dEsytCaBM did not. If these contacts are important for photoreceptor function, is it not surprising that such a dramatic change in photoreceptor structure was without effect on function? This should be further discussed. The establishment of more contact sites and reduction in contact site distance in Rh1>dEsyt::GFP photoreceptors is likely indicative of the proposed tethering role of the protein at the ER-PM MCS. Increase in contact site density or reduction in distance need not directly parallel to the increase in the levels of MCS proteins that are expressed at these contact sites to enhance the ongoing signal transduction. We will test this idea proposed by the reviewer and include the following data in a revision to strengthen our statement:
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RDGB levels in control vs Rh1>dEsyt::GFP - Western blot
- Electroretinograms from the genotypes indicated above as a functional readout of the ongoing signaling cascade.
- PIP2 kinetics in control vs Rh1>dEsyt::GFP to understand if establishing more contact sites can enhance the replenishment of the lipid at the PM. 2) How is quantification of MCS density and gap distance influenced by retinal degeneration (e.g. induced by dEsyt KO)?
Wherever we have analyzed MCS density or gap distance, these experiments have been done in flies at ages prior to the onset of retinal degeneration defined as collapse of the microvilli of the rhabdomere. Therefore, our measurements of MCS density and gap in this paper are not affected by retinal degeneration.
3) The graphical abstract is a bit confusing. It seems to suggest that changes in dEsyt is a consequence of ageing and does not show any role of this protein in photoreceptor function. I think that the abstract could be improved to more clearly highlight the findings in the manuscript. For example, it doesn't at all show the difference in localization between WT and CaBM.
We will modify the graphical abstract.
4) P. 5, line 135 the authors state that "The tethering and lipid transfer activity of mammalian Esyts are reported to be influenced by Ca2+". This is a massive understatement. Ca2+ is a critical regulator of Esyt function in mammalian cells.
The statement will be modified.
5) In figure legend 1B and C: correct µM to µm.
Changes will be incorporated as per the suggestion.
6) In figure legend 2A: should be red rectangles and not black rectangles.
Changes will be incorporated as per the suggestion.
7) In Fig. 2B: specify which isoform of human ESyt that is shown.
Changes will be incorporated as per the suggestion.
8) In Fig. 2C: do the authors mean D374 or D384 (as indicated in Fig. 2A)?
Changes will be incorporated as per the suggestion; the residue is D374.
Significance
Light-induced signal transduction in photoreceptor cells involves Ca2+ influx and signaling and also depends on correct formation of ER-plasma membrane contact sites. In mammalian cells, the Esyts (esp. Esyt1 and Esyt2) localize to ER-PM contacts in a Ca2+-dependent manner, and the ion has dual effects in both enriching the protein at the membrane contact sites and in promoting lipid transport. Mammalian Esyts form homo- and heterodimers, and the properties of the dimers depends on their composition (PMID: 26202220). Drosophila only have one Esyt (dEsyt) which is structurally most similar to mammalian Esyt2, and the authors have previously shown how this protein is required for photoreceptor function (PMID: 32716137), although the role of Ca2+ was not investigated in that study. However, an earlier study has shown that mutations of all Ca2+-coordinating residues in dEsyt impairs protein function in Drosophila neurons (PMID: 28882990), so a similar Ca2+-dependence in the retina would be expected. The results from the present study confirm the requirement of Ca2+ signaling for dEsyt function, and extends this Ca2+-dependent regulation to also involve photoreceptor-induced Ca2+ signaling, which corroborates many other studies showing the requirement of Ca2+ signaling for the regulation of Esyt function in mammalian cells (e.g. PMID: 23791178; PMID: 27065097; PMID: 29222176; PMID: 26202220; PMID: 24183667; PMID: 30589572). As such, the results from this study represent an incremental step towards understanding Esyt function in vivo. These results would be of greatest interest to researchers working of photoreceptor function, and of some interest to a broader audience working on membrane contact sites and signal transduction. My own background is in mammalian cell biology, with a focus on lipid and Ca2+ signaling and inter-organelle communication. I have limited understanding of the model system used here (Drosophila photoreceptor cells).
We would like to provide an alternative perspective on the reviewer’s view that “As such, the results from this study represent an incremental step towards understanding Esyt function in vivo.”
We are well aware of the content in several studies of Esyt in mammalian cells including the ones cited by the reviewer (e.g. PMID: 23791178; PMID: 27065097; PMID: 29222176; PMID: 26202220; PMID: 24183667; PMID: 30589572). These have been cited in our manuscript. However, it is important to recognize that each of these studies is an analysis of the properties of mammalian Esyt as a molecule in the context of Ca2+. However, none of these studies addresses the key question of whether the regulation of Esyt by Ca2+ is important for cellular function or to support cell physiology. The reason for this is quite straightforward and well known in the field. To date, there is no cellular or physiological phenotype that is reported to depend on endogenous Esyt function in mammalian cellular or animal models. As an illustrative example, deletion of all three mammalian Esyt does not affect cell signalling (PMID 23791178) including Ca2+ signalling and a triple knockout of all three Esyt in mice (PMID: 27348751) has no discernable phenotype.
By contrast, deletion of the single Esyt gene in Drosophila results in robust phenotypes in adult photoreceptors (PMID: 32716137). Using these phenotypes, in this manuscript we study the importance of Ca2+ dependent regulation of cellular functions mediated by dEsyt. Therefore, this study fills an important unfilled gap in establishing the mechanism by which dEsyt proteins regulate cellular functions in vivo, in a Ca2+ dependent manner. We respectfully ask that this not be caricatured as an incremental step.
Reviewer #2
Evidence, reproducibility and clarity
Esyt is a C domain (a Ca2+ binding domain) containing protein that localizes to the ER-MCS, playing a role in ER-mitochondria tethering and lipid transfer. At the same time, proteins at the ER-MCS are well-positioned to sense changing levels of Ca2+. Previous studies reported that loss of Esyt in Drosophila causes a loss of ER-PM integrity and retinal degeneration. Here, the authors report the consequence of disrupting the Esyt C domain in Drosophila photoreceptor cells. They used in-silico strategies to identify the Ca2+ contacting residues within the C domain and generated transgenic flies containing either the wild type or the Esyt-CaBM mutants. They show that the wild type transgene rescues several Esyt KO phenotypes in the Drosophila photoreceptors. In some cases, they report dominant negative effects of Esyt-CaBM overexpression.
This is a straightforward structure-function analysis of the Esyt C domain. Overall, the experiments are well executed. At the same time, a few aspects of the manuscript could be further improved. For example, the authors analyze multiple aspects of photoreceptor integrity. In some cases, they show that the mutant Esyt transgene shows dominant negative effects. In others, there is no evidence or even a partial function. Clarifying these points could be helpful. Below are a few specific points for the authors' consideration:
Major Comments
- RDGB is a protein that localizes to the ER-MCS. Esyt-CABM-GFP expression causes RDGB mis-localization even in the presence of wild type Esyt expression, suggestive of a dominant negative effect (Fig. 4C). But Esyt CaBM-GFP expression doesn't seem to have a dominant negative effect on contact site distance (Fig. 4D). Are the authors not seeing a dominant negative effect because they didn't examine older flies? Or, is there a distinct effect of Esyt CaBM on RDGB localization and contact site distance? If there is a distinct effect, what is the reason? As the reviewer correctly mentions, we are not seeing a dominant negative effect of dEsytCaBM::GFP expression on contact site distance because we didn't examine older flies.
Dominant negative effect of dEsytCaBM on the wild type protein is observed in all phenotypes analyzed. The contact site distance analysis shown in the paper is done on day 1 old constant dark reared flies. Contact site distance exhibited by dEsytCaBM is like that of dEsytKO photoreceptors at day 1 post eclosion. dEsyt deprived photoreceptors are comparable to its wild type counterpart at Day 1 in all aspects of phototransduction (PMID: 32716137). But as a function of age and illumination, the dEsytKO photoreceptors exhibit progressive loss in contact site integrity, followed by induction of retinal degeneration and RDGB mis-localisation (PMID: 32716137). These observations are consistent in dEsytCaBM.
During the revision, the following experiments will be included to strengthen this statement:
- Add the MCS density and gap distance in dEsytKO photoreceptors at Day1 in Figure 3C.
- Electron Microscopy to check MCS density and distance in Rh1>dEsytCaBM at Day 6CL with appropriate control genotypes.
Esyt-CABM-GFP partially rescues the Esyt KO phenotype in retinal degeneration (Fig 6). This is surprising since cellular assays in Fig 4 show a failure of Esyt-CaBM to localize to ER-MCS. The results here contrast with earlier data showing that Esyt-CABM has dominant negative effects. How will the authors interpret the results? Is it possible that Esyt-CAMB still has some residual Ca2+ binding activity? Alternatively, does this result imply that Esyt can still function (albeit at lower capacity) without binding Ca2+? Is there Esyt function unrelated to ER-MCS site maintenance when it comes to its role in retinal degeneration? A reasonable explanation is warranted.
Partial rescue of dEsytKO phenotypes by Rh1>dEsytCaBM; dEsytKO photoreceptors indicate that apart from calcium sensing, there might be another function for dEsyt at the ER-PM interface which is yet to be discovered.
Minor Comments:
Figure legends refer to "SMC" (I am guessing they are referring to Sub microvillar cisternae) without defining it in the text.
Changes will be incorporated as per the suggestion.
Significance
This study will be of interest to those generally interested in the ER mitochondria contact sites. The main significance here is in dissecting the role of the C-domain within the Esyt protein. The authors demonstrate a physiological role using Drosophila photoreceptors as a model.
We thank the reviewer for appreciating the significance of our study which seeks to show the in vivo significance of the Ca2+ regulation of dEsyt for in vivo function.
__Reviewer #3 __
(Evidence, reproducibility and clarity (Required)):
Summary
In the present work, the authors explore the role of Ca2+ binding to Esyt in the regulation of ER-PM contact sites using drosophila photoreceptors as a model system. By expressing in wild type or in EsytKO flies a mutated version of dEsyt which is predicted to lose Ca2+ binding, they highlight a potential role of Ca2+ binding to Esyt in the regulation of ER-PM contact sites density and the development of rhabdomeres. The data clearly show the effect of Esyt mutant during development of photoreceptors in Drosophila. However, as discussed below, one essential missing point is the experimental proof that the mutant has indeed lost its ability to bind Ca2+, and that PIP2 binding is not perturbed.
Major comments
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One major comment is the lack of experimental proof that the EsytCABM mutant is indeed unable to bind Ca2+. The MIB tool only gives a prediction and it is not sufficient to prove their statements throughout the manuscript on the requirement of Ca2+ binding for the regulation of MCS. We understand the reviewer’s comment that this manuscript does not contain experimental data demonstrating that dEsytCaBM does not bind Ca2+. However, as Reviewer 4 pointed out, it will be challenging to demonstrate this experimentally. A direct proof would likely come from measurements of the calcium binding affinity of dEsyt (which involves protein purification that is beyond the scope of this work). An indirect demonstration would be any cellular or in vivo experiment oar any additional in silico analysis. To provide additional indirect evidence to address this question, we will:
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Use the AlphaFold model to demonstrate that the arrangement of the calcium binding residues in dEsyt is compatible with Ca2+
- Evaluate if the wild type dEsyt is mislocalized in the photoreceptors upon eliminating the calcium entry to these specialized sensory neurons. The localization of wild type dEsyt will be examined in the mutants: norpAP24 (PLC null mutant) and trpl302; trp343 (protein null mutant of TRPL and TRP channels respectively) in which light induced calcium influx is eliminated. Moreover, they should check experimentally the potential differences in the capacity of EsytCABM mutant to bind PI(4,5)P2, which can potentially perturb its subcellular localization.
As recommended by the reviewer, it is important to determine the PIP2 binding capacity of dEsytCaBM. The ability of wild type dEsyt to bind PIP2 has not been determined. We will test this and if it does so, the impact of CaBM on PIP2 binding can be tested.
Figure 1A: the legend on the right side of the scheme is missing. On the left, RDGB and dEsyt don't associate with the PM.
Changes will be incorporated as per the suggestion.
line 125: the authors should describe more precisely the Trp mutant that they used.
The text will be modified.
Concerning the quantification of MCS density done throughout the paper, can the authors mention what they considered as an MCS, in other words, what distance they defined as the maximal distance between the ER and the PM.
We used fixation methods that allow enhanced membrane preservation and better visualization of membranes and MCS (PMID: 2496206). Such images allowed us to quantify the fraction of SMC that are present at the base of the microvilli in each ultrathin section of a photoreceptor. The MCS is the dark stretch that can be seen at the base of the rhabdomere in each TEM image (PMID: 32716137). Contact site distance measured is the absolute distance between the visible demarcation of the PM and SMC as indicated by the yellow arrows in Figure 4D iii, vi, and ix.
Figure 3: the localization of Esyt and EsytCABM in S2R cells and in vivo is not precisely analyzed: a co-staining with PM and ER markers should be added in order to state the localization at ER-PM MCS or at apical PM.
As suggested, to better understand the compartmental localization of dEsyt in photoreceptors, we will use markers of PM (Rhabdomere) and ER (Sub Microvillar Cisternae) and conduct co-localization assays.
line 181: the authors should precise in which membrane compartments Esyt is localized.
The text will be modified.
line 185-187: the conclusion here doesn't seem to fit the data, as the EsytCABM mutant looks enriched at ER-PM contact sites.
As previously answered, we will remark on whether there is an enrichment of dEsytCaBM at the ER-PM contact sites following the co-localization experiment that is recommended in Q5.
a paragraph on the production of Drosophila transgene mutants should be added to the Mat et Med section.
The text will be added as suggested.
considering the phenotypes observed for the EsytCABM mutant in vivo, the authors should provide an analysis of the level of expression of the exogenous proteins Esyt and EsytCABM by western blot in the different backgrounds. EsytCABM seems to be expressed at lower levels in Figure 3C.
As per the suggestion, western blot analysis will be conducted and better representative confocal images depicting the protein levels will be added in the manuscript.
Fig 4D: considering the perturbation of RDGB localization observed at Day 6, the authors should analyze the organization of MCS by TEM at Day 6, in addition to Day 1.
We agree that to support the observation of RDGB mis-localization, the decrease in contact site integrity as a function of age and illumination (Day6CL) should be evaluated in Rh1>dEsytCaBM photoreceptors. The manuscript revision will include data from this experiment.
the EsytCABM mutant exhibits strong dominant negative effects, but rescues completely or partially some of the phenotypes of Esyt KO: could the authors discuss and provide some hypothesis on this apparent discrepancy?
We are unsure what the reviewer means by “apparent discrepancy”. When dEsytCaBM is expressed in wild type photoreceptors, it exhibits a strong dominant negative effect presumably by inhibiting the function of wild type dEsyt protein.
dEsytKO is a protein null allele. Therefore, when dEsytCaBM is expressed in the dEsytKO background it does not exert a dominant negative effect as there is no wild type protein to interact with. The partial rescue of dEsytKO phenotypes by Rh1>dEsytCaBM; dEsytKO photoreceptors likely indicates that calcium binding is not the sole factor affecting dEsyt function at the ER-PM interface.
lines 230-233: the sentence is not clear. I don't see any consistency between data in Figure 5B, showing only very partial rescue by EsytCABM, and the data in Figure 5C (ii) showing complete rescue of RDGB localization by EsytCABM.
The time point (six days of continuous light exposure following eclosion) at which RDGB localization was analyzed becomes extremely important in thinking about this reviewer comment. If we look at the degeneration kinetics depicted in figure 5B, we can see that neurodegeneration begins in both dEsytKO and Rh1>dEsytCaBM on Day 8 post-eclosion; prior to which, on Day 6, RDGB is mislocalized from the base. However, in Rh1>dEsytCaBM; dEsytKO, the onset of degeneration is delayed, and the photoreceptors show intact structure until Day 8 or Day 10, and measurable retinal degeneration begins on Day 12. This may be the reason why, RDGB continues to be correctly localized in Rh1>dEsytCaBM; dEsytKO at Day 6CL.
Figure 6D: could the authors comment the increase of MCS density observed in Esyt-GFP expressing flies.
Esyt is proposed to function as a tether that connects the ER and PM (PMID: 23791178; PMID: 27065097; PMID: 29222176), bringing them closer together. Based on this idea, perhaps by expressing dEsyt::GFP we are drawing the membranes together thus establishing more MCS.
on several TEM images, some pictures illustrating different conditions look very similar, as if they were serial cuts: Fig 1B (Day 1 and Day 14), Fig 4D (Rh1 and Rh1>dEsytCABM::GFP), Fig 6B Day 1 and Day 14 and Fig 6C Day 1. Could the authors check if there was a mistake with these pictures?
The images are not taken from serial sections of the same TEM block as is evident from the arrangement of nucleus of each photoreceptor cell. As mentioned in the figure legends, all experiments are carried out using 3 independent blocks (N=3 fly heads) prepared from each genotype and 10 photoreceptors from each block/ fly retinae are used for quantification of contact site density/ contact site distance. Aside from the arrangement of the accessory cells and cellular nuclei, the TEM images will appear very similar since Drosophila photoreceptor neurons are symmetrically arranged, with around 700–800 ommatidia per eye each comprising 8 photoreceptors.
Minor comments:
- lines 84-88 : the sentence is not clear. Besides, the authors should precise what they mean by "extra-cellular Ca2+ influx enhance ER-PM contact sites". Which parameter exactly has been shown to be regulated by Ca2+?
The paper by Idevall-Hagren et al. proposes that following store operated Ca2+ influx, Esyt1 translocates to ER-PM junctions and the number of ER-PM contact sites increases. Please refer to this section of the publication from Idevall-Hagren et al. (2015) (PMID: 26202220):
“As detected by TIRF microscopy, the depletion of Ca2+ from the lumen of the ER occurring under these conditions led to a progressive accumulation of ER‐anchored STIM1 at the PM, where it activates Orai Ca2+ channels (Fig 4C). Subsequent addition of 1–10 mM Ca2+ to the extracellular medium, either in the absence or in the presence of SERCA inhibitors, caused a massive increase in cytosolic Ca2+ (SOCE) through the activated Ca2+ channels (Figs 4A and EV4D–G). Such increase induced a very robust translocation of E‐Syt1 to the PM (Figs 4B and EV4D–G), which, in the absence of SERCA inhibition (i.e., when a reversible inhibitor of the SERCA pump had been washed out), preceded the dissociation of STIM1 and the inactivation of SOCE (Fig 4D). Inspection of TIRF microscopy images during the manipulation showed that E‐Syt1 does not form new contacts but populates and expands contacts previously occupied by STIM1.”
- lines 108-110: can you give the reference?
Reference for the localization of dEsyt to ER-PM MCS is Nath et.al PMID PMID: 32716137
Reference for the localization of TRP and TRPL at the microvillar plasma membrane: Numerous primary research papers have shown this- for example see review PMID: 11557987, PMID: 22487656
- line 189: the authors should summarize the findings in one sentence. "Functional activity" would refer to lipid transfer.
The text will be modified as per the suggestion.
Reviewer #3 (Significance (Required)):
General assessment
The work relies on a model system that enables the exploration of the role of Esyt in vivo, in a fundamental process highly regulated during development. The data clearly show the effect of Esyt mutant during development of photoreceptors in Drosophila but as discussed before, some experimental evidences are missing to completely prove the statements.
Advance
This work brings new insights in the functional role of lipid transfer during development and explores how the dialog between lipid transfer and Ca2+ flux can influence MCS organization. The interesting points that could be explored in the paper are the effects of a Ca2+ influx on Esyt and EsytCABM localization, and on their lipid transfer activity.
Audience
This work would be of interest for the membrane contact sites community and for the Developmental biology community.
We thank the reviewer for highlighting the significance of our work and the clarity of the data. Additional data to address the points they have raised will be provided.
__Reviewer #4 __
(Evidence, reproducibility and clarity (Required)):
In this study, Nath et al., aim at understanding the role of dESyt Ca2+ binding activity on ER-PM MCS in D. melanogaster photoreceptors. Using a combination of transmission electron microscopy and fluorescence microscopy, the authors explore the ability of a dESyt mutant, supposedly unable to bind Ca2+ (based on homology with the human ortholog hESyt2), to recapitulate the function of the wild type version of the protein in establishing ER-PM MCS and modulating their density.
Findings:
1) MCS density depends on the activity of TRP and TRPL channels in aging photoreceptors.
2) Mutation of dESyt Ca2+ binding residues (dEsytCaBM::GFP) leads to a gross mis-localization of the protein, even in the presence of the endogenous protein.
3) Overexpression of the mutant affects the structure of photoreceptors upon constant illumination.
4) After 6 days of continuous illumination, RDGB is mis-localized in cells overexpressing dEsytCaBM::GFP.
5) Overexpressed dEsytCaBM::GFP fails to reduce the distance between ER and PM, meaning it fails to establish ER-PM contract sites, while overexpressed dEsyt::GFP show reduced MCS distance. Overexpressed dEsyt::GFP also leads to a 10% increase in MCS density compared to WT or cells expressing dEsytCaBM::GFP.
6) dEsytCaBM::GFP is not able to rescue the light dependent retinal degeneration of dESytKO, although it slightly delays the onset, but is able to rescue RDGB localization at day 6 of constant illumination.
7) Examining MCS density in dESytKO cells, rescues with dEsyt::GFP and dEsytCaBM::GFP show a slightly higher MCS density than dESytKO at day 1. At day 14, ER-PM MCS were non-existent in dESytKO, unchanged in dEsyt::GFP and reduced by 20% in dEsytCaBM::GFP compared to day1.
Specific comments:
My field of expertise is biochemistry and structural biology (including cellular cryo-electron tomography), but I have no experience with drosophila biology, so I am not able to judge the drosophila work per se.
While I find the confocal microscopy experiments compelling, I have some reservations regarding the quantification of the TEM images (MCS distances and density) as it was done manually, and therefore, to some extent subjective, especially, when differences between conditions are in the order of 10%. I would have found the quantification more convincing if done systematically, i.e. segmenting the MCS and computationally measuring distances and densities. Otherwise, the authors could expand a little bit on how their methodology is accurate.
As the reviewer correctly mentions, the quantification will be more convincing if done systematically, i.e. segmenting the MCS and computationally measuring distances and densities. For MCS measurements, we have experimented with the segmentation method using ImageJ and Imaris. As mentioned in the answer to Q4 of reviewer 3, we used fixation methods that allow enhanced membrane preservation and better visualization of membranes and MCS (Matsumoto‐Suzuki et al, 1989). However, this staining method does not selectively stain the ER which is part of the MCS but all the ER. Due to this, automated segmentation poses significant challenges.
The primary drawback of the segmentation method is that, in the process of training the software to predict/detect distinct cellular compartments, it recognizes all ER membranes, including SMC as well as the ER that is not part of the MCS. As a result, the software's minimum distance calculation may be between PM and SMC or PM and generic ER, which does not help the analysis we wish to perform. Similarly, to determine the contact site distance in images with obscure ER and PM boundaries, the software uses the border it can identify—which is typically inside the rhabdomere rather than at its edge. For the contact site density measurements, software is not able to distinguish between ER and pigment granules close to the rhabdomere as the gray scale value for both these compartments are comparable.
Advantages of manual approach:
To account for potential effects of photoreceptor depth on contact site density and distance, we have analyzed TEM sections obtained directly from the nuclear plane of the photoreceptors to calculate both contact site density and distance. Additionally, by utilizing the freehand line tool, manual analysis enables us to define the length of each little section of the MCS and the base of the rhabdomere. The entire length of the MCS at the base is then calculated by adding each segment together. An illustration of how the manual analysis is done will be included as part of methods in the revision.
Another point is whether the levels of expression of dESyt proteins (dESyt-GFP and dESytCABM-GFP) are comparable. In the overexpression experiments, what are the expression levels of the constructs compared to the endogenous protein? The authors should provide e.g. a Western blot.
As per the suggestion, western blot analysis will be conducted to compare the expression levels of the constructs utilized to the endogenous protein.
Concerning the modelling, while I do think that the identification of dESyt Ca2+ binding residues is correct (the sequence alignment is convincing and the sequence identity is very high), and that most likely the structural arrangement will be conserved, homology modelling (using MODELLER with a single reference) leads to models highly similar to the input reference (in particular when the sequence identity is very high). Therefore, rmsd will necessarily be low and the side chain arrangement of conserved residues will be identical. This is unlikely to happen, as protein structures will not be identical despite high sequence conservation. In addition, a crystal structure is a snapshot of a protein conformation that is favorable for crystal formation. It would have been more interesting to use an AlphaFold model and show that the arrangement on the residues is compatible with Ca2+ binding (i.e., the C positions are similar).
We agree with the reviewer that the data presented to demonstrate the inability of dEsytCaBM to bind Ca2+ is inadequate as is also pointed out by other reviewers. It would be crucial to prove this using multiple approaches. As suggested AlphaFold model will be used to answer the same.
Minor comments:
Line 102: indicate what PI and PA stand for (I don't think that there is a need for acronyms when they are not reused in the text later on).
Changes will be incorporated as per the suggestion.
Line 217-219: "When the same experimental set was examined for MCS density, we discovered that the density enhanced by 10% in Rh1>dEsyt::GFP while being comparable between wild type and dEsytCaBM::GFP flies." The authors don't comment on this finding. Does that imply that increase in the protein levels leads to increase in MCS density?
Yes. Increase in wild type dEsyt protein levels can establish more contact sites as well as reduce the contact site distance which further elucidates the protein's role in functional tethering as mentioned in line 215 as proposed by previous studies in other models (PMID: 23791178; PMID: 27065097; PMID: 29222176).
Lines 298-302: "...implying that dEsytCaBM exerts a dominant negative effect on wild type dEsyt. One possible mechanism for the phenotypes exhibited by dEsytCaBM expression in wild type cells is suggested by the findings of a structural and mass spectrometry investigation of hEsyt2 that reveals that the SMP domain dimerizes to create a 90Å long cylinder to facilitate the transfer of lipids (Schauder et al., 2014)." It is not clear to me what the authors suggest here: because of the dimerisation between wild type and mutant, the mutant has a negative effect or that the SMP dimerization is somehow impaired in dEsytCaBM?
SMP domain of Esyt proteins have previously been shown to dimerize (PMID: 23791178, PMID: 24847877). They are known to form either homodimers or heterodimers in mammalian system where there are three genes that code for the protein (Esyt1, 2 and 3). In Drosophila, since it is just one gene that codes for the protein, our hypothesis is that one copy of the functional wild type gene dimerizes with the CaBM mutant and thereby render the wild type gene product nonfunctional.
Line 304-305: "...protein expression was restricted to the cell body rather than the presynaptic terminals...". I am not sure that this is correct. The fact that a protein is localizing to a compartment does not mean that its expression is restricted to that compartment (one should measure mRNA levels to conclude this).
The statement is based on the findings made by Kikuma et al, 2017 (PMID: 28882990) when they tried to understand the role of dEsyt at the NMJs.
In figure 1B legend, indicate what SMC stands for (the acronym should be indicated in figure 1A legend).
The text will be added as suggested.
In figure 2A legend Ca binding in black box but in red boxes in figure.
Changes will be incorporated as per the suggestion.
**Referees cross-commenting**
I agree with the other reviewers that one of the premise of this study relies on the loss of calcium binding by the dESyt mutant and this is not experimentally proven by the authors. However, I find that this will be difficult to prove in vivo. Only measurements of dESyt calcium binding affinity would constitute a direct proof (which requires protein purification. Any in vivo or cellular experiment would be an indirect proof. I believe that based on the high sequence conservation with ESyt proteins, the calcium binding residues have been correctly identified.
Reviewer #4 (Significance (Required)):
ESyt proteins are known ER-PM tethers involved in lipid transfer at MCS in a Ca2+ dependent manner. Contrary to yeast and mammals, that have several ESyt orthologs, D. melanogaster has only one ESyt, making it an ideal model to study ESyt function in vivo. It has been previously shown that proper localization of ESyt at MCS depends on Ca2+ concentration: ESyts are anchors to the ER but translocate to the PM in response to elevation of Ca2+ levels in the cytosol (Fernández-Busnadiego et al., 2015). The finding that an ESyt mutant unable to bind calcium is not localized properly is therefore not surprising. The link between RDGB, a protein known to localize at MCS, and ESyt has been shown before but to my knowledge Nath et al., show for the first time that RDBG localization at MCS is directly dependent on the Ca2+ binding activity of ESyt. In addition, the authors convincingly demonstrate that the Ca2+ binding activity of dESyt is necessary to maintain the structure of aging photoreceptors.
The main finding of this study is that the Ca2+ binding activity of dESyt regulates the density of ER-PM MCS in photoreceptors. If true (see my comment below), that would be a novel finding, although the authors don't propose any mechanistic explanation for this.
3. Description of the revisions that have already been incorporated in the transferred manuscript
Please insert a point-by-point reply describing the revisions that were already carried out and included in the transferred manuscript. If no revisions have been carried out yet, please leave this section empty.
We haven't made any changes to the manuscript yet. However, we will be able to implement the changes mentioned in the pointwise response to reviewers above.
4. Description of analyses that authors prefer not to carry out
Please include a point-by-point response explaining why some of the requested data or additional analyses might not be necessary or cannot be provided within the scope of a revision. This can be due to time or resource limitations or in case of disagreement about the necessity of such additional data given the scope of the study. Please leave empty if not applicable.
- *
We feel that experiments to directly determine the calcium binding of dEsyt and the loss of this in dEsytCaBM are beyond the scope of this study. This is because of the huge work to heterologously express and purify the protein. We have proposed alternate ways to strengthen this conclusion.
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Referee #4
Evidence, reproducibility and clarity
In this study, Nath et al., aim at understanding the role of dESyt Ca2+ binding activity on ER-PM MCS in D. melanogaster photoreceptors. Using a combination of transmission electron microscopy and fluorescence microscopy, the authors explore the ability of a dESyt mutant, supposedly unable to bind Ca2+ (based on homology with the human ortholog hESyt2), to recapitulate the function of the wild type version of the protein in establishing ER-PM MCS and modulating their density.
Findings:
- MCS density depends on the activity of TRP and TRPL channels in aging photoreceptors.
- Mutation of dESyt Ca2+ binding residues (dEsytCaBM::GFP) leads to a gross mis-localization of the protein, even in the presence of the endogenous protein.
- Overexpression of the mutant affects the structure of photoreceptors upon constant illumination.
- After 6 days of continuous illumination, RDGB is mis-localized in cells overexpressing dEsytCaBM::GFP.
- Overexpressed dEsytCaBM::GFP fails to reduce the distance between ER and PM, meaning it fails to establish ER-PM contract sites, while overexpressed dEsyt::GFP show reduced MCS distance. Overexpressed dEsyt::GFP also leads to a 10% increase in MCS density compared to WT or cells expressing dEsytCaBM::GFP.
- dEsytCaBM::GFP is not able to rescue the light dependent retinal degeneration of dESytKO, although it slightly delays the onset, but is able to rescue RDGB localization at day 6 of constant illumination.
- Examining MCS density in dESytKO cells, rescues with dEsyt::GFP and dEsytCaBM::GFP show a slightly higher MCS density than dESytKO at day 1. At day 14, ER-PM MCS were non-existent in dESytKO, unchanged in dEsyt::GFP and reduced by 20% in dEsytCaBM::GFP compared to day1.
Specific comments:
My field of expertise is biochemistry and structural biology (including cellular cryo-electron tomography), but I have no experience with drosophila biology, so I am not able to judge the drosophila work per se. While I find the confocal microscopy experiments compelling, I have some reservations regarding the quantification of the TEM images (MCS distances and density) as it was done manually, and therefore, to some extent subjective, especially, when differences between conditions are in the order of 10%. I would have found the quantification more convincing if done systematically, i.e. segmenting the MCS and computationally measuring distances and densities. Otherwise, the authors could expand a little bit on how their methodology is accurate.
Another point is whether the levels of expression of dESyt proteins (dESyt-GFP and dESytCABM-GFP) are comparable. In the overexpression experiments, what are the expression levels of the constructs compared to the endogenous protein? The authors should provide e.g. a Western blot.
Concerning the modelling, while I do think that the identification of dESyt Ca2+ binding residues is correct (the sequence alignment is convincing and the sequence identity is very high), and that most likely the structural arrangement will be conserved, homology modelling (using MODELLER with a single reference) leads to models highly similar to the input reference (in particular when the sequence identity is very high). Therefore, rmsd will necessarily be low and the side chain arrangement of conserved residues will be identical. This is unlikely to happen, as protein structures will not be identical despite high sequence conservation. In addition, a crystal structure is a snapshot of a protein conformation that is favorable for crystal formation. It would have been more interesting to use an AlphaFold model and show that the arrangement on the residues is compatible with Ca2+ binding (i.e., the C positions are similar).
Minor comments:
Line 102: indicate what PI and PA stand for (I don't think that there is a need for acronyms when they are not reused in the text later on).
Line 217-219: "When the same experimental set was examined for MCS density, we discovered that the density enhanced by 10% in Rh1>dEsyt::GFP while being comparable between wild type and dEsytCaBM::GFP flies." The authors don't comment on this finding. Does that imply that increase in the protein levels leads to increase in MCS density?
Lines 298-302: "...implying that dEsytCaBM exerts a dominant negative effect on wild type dEsyt. One possible mechanism for the phenotypes exhibited by dEsytCaBM expression in wild type cells is suggested by the findings of a structural and mass spectrometry investigation of hEsyt2 that reveals that the SMP domain dimerizes to create a 90Å long cylinder to facilitate the transfer of lipids (Schauder et al., 2014)." It is not clear to me what the authors suggest here: because of the dimerisation between wild type and mutant, the mutant has a negative effect or that the SMP dimerization is somehow impaired in dEsytCaBM?
Line 304-305: "...protein expression was restricted to the cell body rather than the presynaptic terminals...". I am not sure that this is correct. The fact that a protein is localizing to a compartment does not mean that its expression is restricted to that compartment (one should measure mRNA levels to conclude this).
In figure 1B legend, indicate what SMC stands for (the acronym should be indicated in figure 1A legend).
In figure 2A legend Ca binding in black box but in red boxes in figure.
Referees cross-commenting
I agree with the other reviewers that one of the premise of this study relies on the loss of calcium binding by the dESyt mutant and this is not experimentally proven by the authors. However, I find that this will be difficult to prove in vivo. Only measurements of dESyt calcium binding affinity would constitute a direct proof (which requires protein purification. Any in vivo or cellular experiment would be an indirect proof. I believe that based on the high sequence conservation with ESyt proteins, the calcium binding residues have been correctly identified.
Significance
ESyt proteins are known ER-PM tethers involved in lipid transfer at MCS in a Ca2+ dependent manner. Contrary to yeast and mammals, that have several ESyt orthologs, D. melanogaster has only one ESyt, making it an ideal model to study ESyt function in vivo. It has been previously shown that proper localization of ESyt at MCS depends on Ca2+ concentration: ESyts are anchors to the ER but translocate to the PM in response to elevation of Ca2+ levels in the cytosol (Fernández-Busnadiego et al., 2015). The finding that an ESyt mutant unable to bind calcium is not localized properly is therefore not surprising. The link between RDGB, a protein known to localize at MCS, and ESyt has been shown before but to my knowledge Nath et al., show for the first time that RDBG localization at MCS is directly dependent on the Ca2+ binding activity of ESyt. In addition, the authors convincingly demonstrate that the Ca2+ binding activity of dESyt is necessary to maintain the structure of aging photoreceptors.
The main finding of this study is that the Ca2+ binding activity of dESyt regulates the density of ER-PM MCS in photoreceptors. If true (see my comment below), that would be a novel finding, although the authors don't propose any mechanistic explanation for this.
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Referee #3
Evidence, reproducibility and clarity
Summary
In the present work, the authors explore the role of Ca2+ binding to Esyt in the regulation of ER-PM contact sites using drosophila photoreceptors as a model system. By expressing in wild type or in EsytKO flies a mutated version of dEsyt which is predicted to lose Ca2+ binding, they highlight a potential role of Ca2+ binding to Esyt in the regulation of ER-PM contact sites density and the development of rhabdomeres. The data clearly show the effect of Esyt mutant during development of photoreceptors in Drosophila. However, as discussed below, one essential missing point is the experimental proof that the mutant has indeed lost its ability to bind Ca2+, and that PIP2 binding is not perturbed.
Major comments
- One major comment is the lack of experimental proof that the EsytCABM mutant is indeed unable to bind Ca2+. The MIB tool only gives a prediction and it is not sufficient to prove their statements throughout the manuscript on the requirement of Ca2+ binding for the regulation of MCS. Moreover, they should check experimentally the potential differences in the capacity of EsytCABM mutant to bind PI(4,5)P2, which can potentially perturb its subcellular localization.
- Figure 1A: the legend on the right side of the scheme is missing. On the left, RDGB and dEsyt don't associate with the PM.
- line 125: the authors should describe more precisely the Trp mutant that they used.
- concerning the quantification of MCS density done throughout the paper, can the authors mention what they considered as an MCS, in other words, what distance they defined as the maximal distance between the ER and the PM.
- Figure 3: the localization of Esyt and EsytCABM in S2R cells and in vivo is not precisely analyzed: a co-staining with PM and ER markers should be added in order to state the localization at ER-PM MCS or at apical PM.
- line 181: the authors should precise in which membrane compartments Esyt is localized.
- line 185-187: the conclusion here doesn't seem to fit the data, as the EsytCABM mutant looks enriched at ER-PM contact sites.
- a paragraph on the production of Drosophila transgene mutants should be added to the Mat et Med section.
- considering the phenotypes observed for the EsytCABM mutant in vivo, the authors should provide an analysis of the level of expression of the exogenous proteins Esyt and EsytCABM by western blot in the different backgrounds. EsytCABM seems to be expressed at lower levels in Figure 3C.
- Fig 4D: considering the perturbation of RDGB localization observed at Day 6, the authors should analyze the organization of MCS by TEM at Day 6, in addition to Day 1.
- the EsytCABM mutant exhibits strong dominant negative effects, but rescues completely or partially some of the phenotypes of Esyt KO: could the authors discuss and provide some hypothesis on this apparent discrepancy?
- lines 230-233: the sentence is not clear. I don't see any consistency between data in Figure 5B, showing only very partial rescue by EsytCABM, and the data in Figure 5C (ii) showing complete rescue of RDGB localization by EsytCABM.
- Figure 6D: could the authors comment the increase of MCS density observed in Esyt-GFP expressing flies.
- on several TEM images, some pictures illustrating different conditions look very similar, as if they were serial cuts: Fig 1B (Day 1 and Day 14), Fig 4D (Rh1 and Rh1>dEsytCABM::GFP), Fig 6B Day 1 and Day 14 and Fig 6C Day 1. Could the authors check if there was a mistake with these pictures?
Minor comments:
- lines 84-88 : the sentence is not clear. Besides, the authors should precise what they mean by "extra-cellular Ca2+ influx enhance ER-PM contact sites". Which parameter exactly has been shown to be regulated by Ca2+?
- lines 108-110: can you give the reference?
- line 189: the authors should summarize the findings in one sentence. "Functional activity" would refer to lipid transfer.
Significance
General assessment
The work relies on a model system that enables the exploration of the role of Esyt in vivo, in a fundamental process highly regulated during development. The data clearly show the effect of Esyt mutant during development of photoreceptors in Drosophila but as discussed before, some experimental evidences are missing to completely prove the statements.
Advance
This work brings new insights in the functional role of lipid transfer during development and explores how the dialog between lipid transfer and Ca2+ flux can influence MCS organization. The interesting points that could be explored in the paper are the effects of a Ca2+ influx on Esyt and EsytCABM localization, and on their lipid transfer activity.
Audience
This work would be of interest for the membrane contact sites community and for the Developmental biology community.
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Referee #2
Evidence, reproducibility and clarity
Esyt is a C domain (a Ca2+ binding domain) containing protein that localizes to the ER-MCS, playing a role in ER-mitochondria tethering and lipid transfer. At the same time, proteins at the ER-MCS are well-positioned to sense changing levels of Ca2+. Previous studies reported that loss of Esyt in Drosophila causes a loss of ER-PM integrity and retinal degeneration. Here, the authors report the consequence of disrupting the Esyt C domain in Drosophila photoreceptor cells. They used in-silico strategies to identify the Ca2+ contacting residues within the C domain and generated transgenic flies containing either the wild type or the Esyt-CaBM mutants. They show that the wild type transgene rescues several Esyt KO phenotypes in the Drosophila photoreceptors. In some cases, they report dominant negative effects of Esyt-CaBM overexpression.
This is a straightforward structure-function analysis of the Esyt C domain. Overall, the experiments are well executed. At the same time, a few aspects of the manuscript could be further improved. For example, the authors analyze multiple aspects of photoreceptor integrity. In some cases, they show that the mutant Esyt transgene shows dominant negative effects. In others, there is no evidence or even a partial function. Clarifying these points could be helpful. Below are a few specific points for the authors' consideration:
Major
- RDGB is a protein that localizes to the ER-MCS. Esyt-CABM-GFP expression causes RDGB mis-localization even in the presence of wild type Esyt expression, suggestive of a dominant negative effect (Fig. 4C). But Esyt CaBM-GFP expression doesn't seem to have a dominant negative effect on contact site distance (Fig. 4D). Are the authors not seeing a dominant negative effect because they didn't examine older flies? Or, is there a distinct effect of Esyt CaBM on RDGB localization and contact site distance? If there is a distinct effect, what is the reason?
- Esyt-CABM-GFP partially rescues the Esyt KO phenotype in retinal degeneration (Fig 6). This is surprising since cellular assays in Fig 4 show a failure of Esyt-CaBM to localize to ER-MCS. The results here contrast with earlier data showing that Esyt-CABM has dominant negative effects. How will the authors interpret the results? Is it possible that Esyt-CAMB still has some residual Ca2+ binding activity? Alternatively, does this result imply that Esyt can still function (albeit at lower capacity) without binding Ca2+? Is there Esyt function unrelated to ER-MCS site maintenance when it comes to its role in retinal degeneration? A reasonable explanation is warranted.
Minor:
Figure legends refer to "SMC" (I am guessing they are referring to Sub microvillar cisternae) without defining it in the text.
Significance
This study will be of interest to those generally interested in the ER mitochondria contact sites. The main significance here is in dissecting the role of the C-domain within the Esyt protein. The authors demonstrate a physiological role using Drosophila photoreceptors as a model.
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Referee #1
Evidence, reproducibility and clarity
Summary:
This study builds on previous work from the same group, where they use Drosophila photoreceptors as a model system to investigate the role or ER-plasma membrane contact sites in an in vivo setting. The authors recently described a role of the ER-PM contact site protein dEsyt in regulating photoreceptor function in Drosophila. In this follow-up study, they explore whether this function of dEsyt is connected Ca2+ signaling downstream of photoreceptor activation. Using a dEsyt mutant that should be unable to bind Ca2+, they find that Ca2+ to some extent is required for dEsyt localization, membrane contact site formation and photoreceptor function.
Major comments:
The use of photoreceptor cells in Drosophila is an elegant model system that enable studies of membrane contact sites and associated proteins in a native condition. The data presented by the authors clearly shows that these structures are important for photoreceptor function, and that dEsyt plays a role at these sites. However, this was already known from previous studies by the same group. When it comes to whether these contacts are sensing Ca2+ changes and if these changes are acting through dEsyt, which is the focus of the current manuscript, the results are unclear to me and would need to be clarified by the authors both in text and with new experiments.
- What is the role of cellular Ca2+ signaling in the regulation of dEsyt function? There are several aspects here that needs to be clarified. 1) How is WT dEsyt localization regulated by Ca2+? This could for example be evaluated in the mutant flies used in Fig. 1 (trpl302; trp343), where lack of light-induced Ca2+ influx would be predicted to result in a localization of dEsyt that resembles that observed for dEsytCaBM. 2) Is Ca2+ important for dEsyt localization, lipid exchange or both? The authors express a version of dEsyt with mutation made in all three C2 domains. In mammalian E-Syts, Ca2+ binding to the C2A domain is important for lipid exchange while binding to C2C (in E-Syt1) is important for interactions with lipids in the plasma membrane. Using more carefully designed mutants will allow the authors to determine how Ca2+ regulates dEsyt function in vivo. In addition, the authors must show experimentally that the mutant dEsytCaBM is unable to bind Ca2+ (could e.g. be done by acute Ca2+ changes in the cell-based model used in Fig. 3). Writing that "This transgene carrying a total of nine mutations should render the protein unable to bind calcium" (p. 6, line 173) is not sufficient.
- The localization of dEsyt shown in Fig. 3B is a bit confusing. First of all, I would recommend including markers of the ER and the plasma membrane, because without these it is difficult to make statements about the localization of dEsyt to these structures. Second, it appears that WT dEsyt localize to the reticular ER, and that the CaBM version localize to the plasma membrane. This is somewhat opposite to mammalian ESyts, where mutations that prevent Ca2+ binding either had no effect (for ESyt2) or prevented (for ESyt1) the interaction with the plasma membrane. It also appears different from the localization in vivo (Fig. 3C). Clarifying this will be important. It will also be important to connect this localization to changes in Ca2+ and not just to the localization of a mutant that may or may not be deficient in Ca2+ binding (see comment above).
- I don't fully understand the time course of events. The authors show that dEsytCaBM is mislocalized already at day 1 in dark-reared flies (Fig. 3C) but this mislocalization is not accompanied by a change in MCS density or gap distance, and consistently does not influence the localization of RDGB. The authors next expose the flies to constant light illumination to trigger Ca2+ dependent signaling, and this leads to mislocalization of RDGB, perhaps indicating changes in MCS (this is not shown). From these results it is difficult to know what the role of dEsyt is. It would be necessary to also show a control where Ca2+ signaling is not induced, e.g. a parallel dark-control (same number of days but no illumination). This is particularly important given that the authors show in Fig. 1 that preventing Ca2+ influx had a dramatic impact on MCS density even at day 1 (which is in sharp contrast to dEsytCaBM-expressing flies, that show normal morphology at day 1, which rather implies that dEsyt is not a major Ca2+ effector).
- The experiments done in dEsyt KO flies are important, and here the authors show that dEsyt1 could to some extent rescue all phenotypes. Some results are a bit puzzling. For example, dEsyt1CaBM localization in dEsyt1 KO flies is identical to that of WT dEsyt (Fig. 5C), which is in sharp contrast to the data shown in Fig. 3C. What is the reason for this? I would have anticipated the opposite (i.e. that in WT flies, dEsytCaBM can form dimers with endogenous dEsyt through SMP-domain interactions which may have an impact on its localization and the function of endogenous dEsyt, but that in the dEsyt KO cells, dEsytCaBM would show a different localization due to the lack of endogenous dEyt to interact with). It is important to clarify as one of the major observations here is that dEsytCaBM no longer localize to MCS. Since the CaBM version of dEsyt could rescue, to some extent, MCS density and delay photoreceptor degeneration, this implies that Ca2+ may not be required for regulation of dEsyt function or that the mutant is still able to partially bind to Ca2+. One experiment that would help the authors determining the function of dEsyt in vivo would be to use a mutant that lacks functional SMP domain (ideally also with and without mutations in the C2-domains).
- PLC activation typically couples to rapid signaling and involved hydrolysis of PIP2 and release of Ca2+ from the ER. Mammalian Esyts also require PIP2 for plasma membrane binding (through interactions with C2-domains), so constitutive PLC activity would be expected to impair ESyt localization to MCS. Here, the authors expose flies for days of constant illumination. How does this influence plasma membrane PIP2 levels and could this be of relevance for how data is interpreted? Do the authors know whether the CaBM mutant has reduced affinity for PIP2?
Minor comments:
- The overexpression of WT dEsyt had a dramatic impact on MCS density and gap distance, while expression of dEsytCaBM did not. If these contacts are important for photoreceptor function, is it not surprising that such a dramatic change in photoreceptor structure was without effect on function? This should be further discussed.
- How is quantification of MCS density and gap distance influenced by retinal degeneration (e.g. induced by dEsyt KO)?
- The graphical abstract is a bit confusing. It seems to suggest that changes in dEsyt is a consequence of ageing and does not show any role of this protein in photoreceptor function. I think that the abstract could be improved to more clearly highlight the findings in the manuscript. For example, it doesn't at all show the difference in localization between WT and CaBM.
- P. 5, line 135 the authors state that "The tethering and lipid transfer activity of mammalian Esyts are reported to be influenced by Ca2+". This is a massive understatement. Ca2+ is a critical regulator of Esyt function in mammalian cells.
- In figure legend 1B and C: correct µM to µm.
- In figure legend 2A: should be red rectangles and not black rectangles.
- In Fig. 2B: specify which isoform of human ESyt that is shown.
- In Fig. 2C: do the authors mean D374 or D384 (as indicated in Fig. 2A)?
Significance
Light-induced signal transduction in photoreceptor cells involves Ca2+ influx and signaling and also depends on correct formation of ER-plasma membrane contact sites. In mammalian cells, the Esyts (esp. Esyt1 and Esyt2) localize to ER-PM contacts in a Ca2+-dependent manner, and the ion has dual effects in both enriching the protein at the membrane contact sites and in promoting lipid transport. Mammalian Esyts form homo- and heterodimers, and the properties of the dimers depends on their composition (PMID: 26202220). Drosophila only have one Esyt (dEsyt) which is structurally most similar to mammalian Esyt2, and the authors have previously shown how this protein is required for photoreceptor function (PMID: 32716137), although the role of Ca2+ was not investigated in that study. However, an earlier study has shown that mutations of all Ca2+-coordinating residues in dEsyt impairs protein function in Drosophila neurons (PMID: 28882990), so a similar Ca2+-dependence in the retina would be expected. The results from the present study confirm the requirement of Ca2+ signaling for dEsyt function, and extends this Ca2+-dependent regulation to also involve photoreceptor-induced Ca2+ signaling, which corroborates many other studies showing the requirement of Ca2+ signaling for the regulation of Esyt function in mammalian cells (e.g. PMID: 23791178; PMID: 27065097; PMID: 29222176; PMID: 26202220; PMID: 24183667; PMID: 30589572). As such, the results from this study represent an incremental step towards understanding Esyt function in vivo. These results would be of greatest interest to researchers working of photoreceptor function, and of some interest to a broader audience working on membrane contact sites and signal transduction. My own background is in mammalian cell biology, with a focus on lipid and Ca2+ signaling and inter-organelle communication. I have limited understanding of the model system used here (Drosophila photoreceptor cells).
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Reply to the reviewers
Reviewer #1 (Evidence, reproducibility and clarity (Required)):
Summary In this study, Raja et al. found cytoplasmic condensates formed by the treatment of INFγ, investigated components of these condensates and identified p62, NBR1 and PARP14 as their components. INFγ treatment induced PARP14 expression, and PAPR14 inhibitor treatment inhibited condensation formation, suggesting that the amount of PARP14 and its enzymatic activity are important for the condensate formation. The ADPr-positive p62 condensates were independent of autophagic degradation, and proteasomal activity was required for their formation.
Major comment 1. The finding that the ubiquitin-proteasome, but not autophagy activity, is indispensable for the formation of p62 condensates is of interest. However, the molecular mechanism by which the ubiquitin-proteasome system (UPS) is involved in the regulation of the PARP14-p62 condensate is still unclear. Which step(s) is the UPS involved?
We appreciate the reviewer's acknowledgment of the novelty of our studies on the requirement of the ubiquitin-proteasome system (UPS) in the formation of ADPr-containing PARP14/p62 condensates. We have demonstrated that condensation formation requires the first and last steps of the UPS using two distinct classes of inhibitors: (1) TAK243 inhibits the E1 enzyme by forming a covalent adduct with ubiquitin that mimics the ubiquitin-adenylate complex, thereby blocking the initial step of ubiquitin conjugation (Fig. 6F-G). (2) Three different proteasome inhibitors with varying degrees of selectivity—MG132, epoxomicin, and Bortezomib—block the final step of the UPS by inhibiting the 26S proteasome (Fig. 6B, S6D). Given that blocking the early steps of the ubiquitin conjugation pathway or the late stages of the UPS inhibits the formation of ADPr condensates, we deduce that an active UPS is required. We will explore the involvement of additional steps in the UPS.
The p62 condensate serves as a scaffold for autophagosome formation through the assembling autophagy receptors including NBR1 and TAX1BP1, followed by recruiting ATG proteins such as FIP200. While ADPr-positive p62 condensates also contain NBR1 and polyubiquitinated proteins, they are unrelated to autophagic degradation. It is unclear what factors govern autophagy-independent function.
As we have identified the requirement of active UPS in regulating these condensates, determining the factors that govern autophagy-independent functions, though interesting, is beyond the scope of this manuscript. Our data indicate that the formation of ADPr-containing condensates, which include p62, other autophagy receptors such as NBR1, and polyubiquitinated proteins, but lack the autophagasome membrane protein LC3B (Fig. 3B, 5E-I, and S5D). Notably. this condensate formation is not inhibited by treatment with Bafilomycin A1 and chloroquine, which target the final step of autophagy involving lysosome interaction (Fig. 6A). In response to the reviewer's comments, we will further investigate whether these condensates also include other autophagy receptors, such as TAX1BP1, as well as the downstream autophagosome protein FIP200. Additionally, we will genetically deplete the critical autophagy factor ATG5 to confirm orthogonally that the formation of these condensates is indeed independent of autophagy.
The authors claim that the amount of PARP14 and its MAR activity are essential for the condensate formation. However, all experiments were performed only with PARP14 inhibitors, and further validation is needed. If the importance of PARP14 activity is to be directly demonstrated, experiments in which an enzyme activity mutant is introduced into PARP14 KO cells are needed.
We would like to clarify that we have not only used PARP14 chemical inhibitors to reduce MAR activity but also employed PROTAC to reduce the amount of PARP14 (Fig. 1H). Both approaches demonstrated that the inhibition of either the amount or MAR activity of PARP14 is critical for condensate formation. Additionally, we demonstrated that condensate formation is reduced upon PARP14 knockdown using siRNA and shRNA, as well as CRISPR-mediated knockout (Fig. 1G and S1C-H).
Furthermore, we showed that transient transfection of a PARP14 mutant deficient in ADP-ribosylhydrolase activity into U2OS cells leads to the formation of ADPr condensates that colocalize with PARP14, independent of IFNγ treatment. Notably, a subset of condensates—particularly the larger ones—that contain both PARP14 and ADPr showed strong colocalization with p62 (Fig. 4G). Treatment with PARP14 MAR activity inhibitor under these conditions resulted in the disappearance of ADPr/PARP14 condensates while p62 bodies remained (Fig. 4H), further indicating that ADPr enrichment in p62 bodies depends on the MAR activity of PARP14. To further confirm the dependence on MAR activity, we have now repeated the experiment using a PARP14 mutant deficient in MAR activity. PARP14 and ADPr condensates were not observed upon expression of this mutant, indicating that condensate formation depends on PARP14 MAR activity.
In Figure 2a, the heatmap alone is insufficient. Neither errors nor statistical comparisons are indicated.
We will incorporate our statistical data presented in Fig. S2A-D into Fig. 2A.
The statistical analysis of Figure S2 is inappropriate; instead of t-tests, multiple comparisons should be used to compare three or more groups.
We will perform multiple comparison analyses, as suggested.
Minor comment 1. What percentage of p62 condensates upon INFγ treatment are ADPr positive? Are all p62 bodies seen with INFγ stimulation unrelated to autophagy?
We will perform the quantification, as suggested.
Is ADPr condensation a PARA14-specific phenomenon? PARP9 and PARP12 were also upregulated by INFγ treatment. Are these factors also involved in condensate formation?
Amongst all catalytically active PARPs, ADPr condensation requires only PARP14. Russo et al., J Biol Chem 2021, have shown that genetic knockout of PARP9 affects the formation of ADPr condensates; however, PARP9 is catalytically inactive as an ADP-ribosyltransferase. Ribeiro et al., EMBO 2024, have further confirmed the requirement of PARP9 by siRNA knockdown and have also shown that condensate formation does not require PARP12. Based on the reviewers' comments, we will independently confirm this observation by performing knockdown experiments.
Figure 4D appears to be immunoprecipitation (IP) under non-denaturing conditions. If so, it is not possible to distinguish whether the MAR signal is derived from p62 or from the p62 interacting proteins (the associated ubiquitinated substrates). IP experiments should be performed under denaturing conditions.
We will perform denaturing IP or other experiments to confirm the p62 modification upon IFNγ treatment. Additionally, we would like to note that following our submission, Kubon et al. reported in a bioRxiv preprint that p62 is ADP-ribosylated in a PARP14-dependent manner upon treatment with type I interferon, IFNβ. This finding is consistent with our study involving type II interferon, IFNγ.
In Figure 5B, which band is HO1, the upper or lower?
Both bands are HO1, as shown by Biswas et al., J Biol Chem 2014. One band appears at 28 kDa and the other at 32 kDa. The 32-kDa isoform is predominantly constitutive in the cytoplasm, whereas the 28-kDa HO-1 is predominant and primarily localized to the nucleus.
There is no image for ubiquitin in S5D.
Our original statement, “However, when inhibited with the mTOR inhibitor Torin-1, autophagy is induced, leading to increased autophagosome formation marked by LC3B on the membranes, which facilitates the recruitment of p62 and ubiquitinated proteins (Fig. S5D),” contained a misplaced figure citation. The correct statement should be: “However, when inhibited with the mTOR inhibitor Torin-1, autophagy is induced, leading to increased autophagosome formation marked by LC3B on the membranes (Fig. S5D), which facilitates the recruitment of p62 and ubiquitinated proteins.” Our intention was to show that there are conditions, such as Torin-1 treatment, where p62 and LC3B colocalize.
Right panel in Figure 4F shows only IFγ + RBN, which should show all data sets in the same panel.
Given the complexity of the three conditions with extensive data points and error bars on the FRAP experiments, we aim to present the data clearly. Instead of merging the panel into one figure, we initially provided a summary table in Figure 4F. However, in response to the reviewer's comments, we will provide the composite image that includes all data sets in the same panel.
Reviewer #1 (Significance (Required)):
Liquid droplets, which have continuously being identified in cells, are a hot topic in cell biology. Droplet formation, structure, molecular dynamics, and degradation, as well as their abnormalities and disease development due to genetic mutation and stress, are of wide-ranging interest from basic to pathological aspects. Therefore, this research has the potential to attract interest from a wide range of fields.
General assessment Overall, the data are clear and the phenomenon is of interest. However, the molecular mechanism and biological significance of the condensate formation is unknown; It is unclear why proteasome activity is required for the formation of PARP14-mediated ADP ribosylation. It is also unclear what the consequences are for the cell if the ADPr-positive condensates are not formed. Thea authors should address these general and important issues and provide the data If not all.
We thank the reviewer for acknowledging that our condensate investigation is timely and important in cell biology and for recognizing that “data are clear and the phenomenon is of interest”. As mentioned in the Discussion, these condensates can be reversed by the SARS-CoV-2 macrodomain in lung A549 cells, whose activity to remove ADP-ribosylation is critical for viral replication and pathogenesis, indicating the biological significance of these condensates. In addition, similar IFNγ conditions can induce PARP14 expression in melanoma, where PARP14 inhibition resensitizes these cancers to immunotherapy. Given that these ADPr condensates are also observed in A375 melanoma cells beyond lung cells (Fig. S3B), this provides additional context to investigate their biological significance in the future.
We would like to note that we have already made significant advances by (1) revealing the identity of these condensates as related to p62 bodies (Fig. 3-5), (2) defining the responsible ADP-ribosyltransferase as PARP14 (Fig. 1-2), and (3) determining the requirements for condensation through ubiquitin-proteasome system (Fig. 6). The proposed exploration of the functional consequences and significance is beyond the scope of this manuscript. However, we will further define the mechanistic involvement of which step of ubiquitin-proteasome system.
Reviewer #2 (Evidence, reproducibility and clarity (Required)):
This manuscript investigates the formation of a novel cellular structure or condensate, similar to p62 bodies, that includes PARP14 and p62. The interferon-induced PARP14-mediated ADP-ribosylation of p62 in these condensates depends on an active ubiquitin-proteasome system. These condensates are characterized by the presence of PARP14 and ADPr and include some, but not all, components of the p62 bodies. Furthermore, their formation depends on both ubiquitin activation and proteasome activity, but it is unaffected by autophagy inhibition, unlike conventional p62 bodies.
The Introduction provides a well-delineated context of condensates, highlighting the importance of post-translational modifications in responding to environmental changes.
Although the manuscript is well-organized with apparent logical development, there are weaknesses that diminish the impact of the reported data. A more accurate review of the structures, both from a morphological and quantitative perspective, would strengthen the conclusions and the overall impact of this work. Additionally, while the authors have analyzed the contribution of PARP14 to condensate formation, the biological significance of these structures remains unclear. For instance, performing MS (mass spectrometry) analysis on the described structures could help identify their composition and functions.
The methodologies used in this study are standard for molecular and cellular biology research, including immunofluorescence assays, transient transfections, immunoprecipitations, and fluorescence recovery after photobleaching (FRAP) assays. These methods are described in detail and can be reproduced.
Below, please find a list of comments and suggestions to enhance the robustness of the data:
We thank the reviewer for acknowledging the logical progression of the manuscript and the detailed, reproducible methods. As detailed below, we will perform super-resolution microscopy experiments to examine morphology, improve data quantification, and conduct proteomics experiments to identify how the p62 interactome changes upon IFNγ treatment.
Major Points
- The IF analyses are central to the conclusions reported and are employed for each of the inhibitors or other tools used to investigate the formation of these condensates. The quality of the IF images needs to be improved; the shape and contacts of the condensates should be analyzed using either super-resolution or EM microscopy, or preferably both. The lack of morphometry and quantification from cell populations needs to be addressed for all experiments. These analyses are needed to support the claim that the condensates presented in this study are indeed novel structures, rather than being transient aggregates of a different nature.
We believe the reviewer may be referring to the size of the images rather than their quality, as they are of high resolution when zoomed in. However, we agree that larger images would enhance clarity. We will be discreet in choosing the figures to present, given that we have over 550 panels in the main figures and over 250 in the supplemental figures. We will resize our figures to ensure the images are clearly visible.
We will perform Airyscan imaging to provide super-resolution images for a better understanding of the morphology of these condensates. We will perform the morphometry quantification (circularity and ellipticity). For quantification, we would like to point out that nearly all of our experiments were analyzed from at least 4 fields, each containing 20-50 cells (depending on the magnification of 20x or 40x). In response to the reviewer's comment, we will also provide quantification on a per-cell basis.
Our data indicate that these are novel ADPr-containing condensates that colocalize with PARP14, p62, NBR1, and ubiquitin, but not LC3B (Fig. 1F, 3B, 5E-F, 5I). These structures are inducible by IFN treatment and can be inhibited by even 1 hour of PARP14 inhibition (Fig. 1A-B, 2D) They are dependent on PARP14 induction and its ADP-ribosyltransferase activity (Fig. 1G, 2B, 2D). These structures are not protein aggregates, as evidenced by their lack of staining with ProteoStat Dye (Fig. S6A), which stains for unfolded proteins.
The claim that PARP14 is essential for the formation of condensates requires support by the analyses indicated above. Minor points regarding Fig. 1 are indicated below. I suggest performing KD of PARP9 and/or PARP12 (whose expression is increased upon IFN treatment) and checking ADPr condensates to validate the central role of PARP14.
ADPr condensation requires PARP14, as demonstrated by multiple genetic depletion techniques (siRNA/shRNA/CRISPR; Fig. 1G, S1C-H) and chemical inhibitors (catalytic and PROTAC; Fig. 1H, 2B, 2D). We will provide additional image analyses to support the claim. In addition, Russo et al., J Biol Chem 2021, have shown that genetic knockout of PARP9 affects the formation of ADPr condensates; however, PARP9 is catalytically inactive as an ADP-ribosyltransferase. Ribeiro et al., EMBO 2024, have further confirmed the requirement of PARP9 by siRNA knockdown and have also shown that condensate formation does not require PARP12. Based on the reviewers' comments, we will independently confirm this observation by performing knockdown experiments.
According to the text, "PARP14 was pulled down by ADP-ribose binding Af1521 macrodomain following IFNγ treatment (Fig. 2H)", but the legend to the figure says otherwise. A Pan-ADPr binding reagent (MABE1016) is reported in the figure. Although the conclusion is similar for the results obtained with these two tools (but they must be described and reported properly), it is still insufficient to claim that PARP14 is ADP-ribosylated. This point should be at least discussed.
We apologize for the confusion. The Pan-ADPr binding reagent (MABE1016) is a His-tagged recombinant Af1521 macrodomain that binds to ADP-ribosylated protein (Gibson et al., Biochemistry 2017). Therefore, we used Ni-NTA resin to pull down His-tagged AF1521 for the subsequent analysis of PARP14. We will revise the text and figure legend for Fig. 2H to clarify this. We will further probe the eluted PARP14 is indeed ADP-ribosylated by western blot. Consistent with our studies, Kar et al. and Ribeiro et al. (EMBO J. 2024) also recently reported that PARP14 is ADP-ribosylated upon IFNγ treatment in A549 cells. Additionally, Higashi et al. (J. Proteome Res. 2019) reported PARP14 ADP-ribosylation in IFNγ-treated macrophage cells. We will include these references in our Discussion.
I have difficulties analyzing the colocalization with the different organelles, even enlarging the images as much as possible. In most cases, only one condensate per image is shown. Continuities with the nuclear envelope appear in some cases: has this been investigated?
We have provided images of at least two cells containing multiple cytoplasmic condensates in Figure S3A. We believe part of the confusion arises from the staining pattern of ADPr in the nucleus, which colocalizes with splicing speckles. However, this nuclear staining was not altered by IFNγ treatment. Therefore, we have focused on the cytoplasmic condensates in this study and will clarify this focus in the main text. However, in response to the reviewer’s question, we will also visit the possibilities of enrichment of signals at the nuclear envelope. For each colocalization study, we have analyzed at least four fields, each containing 20-30 cells. To further strengthen our claim for colocalization analyses, we will now provide quantification on a per-cell basis.
Minor Points
- Fig. 1A: The DAPI images at 3 and 6 hours are reversed. Additionally, for Fig. S1a and Fig. 1A, please include quantifications.
We apologize for the oversight and will provide quantification.
- Fig. 1B: Check PARP14 levels (and other IFN-PARPs) under the same experimental conditions.
As suggested, we will assess PARP9, PARP12, and PARP14 levels.
Fig. 1H: Explain why PARP14 IF staining is still visible upon RBN012811 treatment, while it is completely lost in WB analysis or upon PARP14 siRNA treatment (Fig. 1G). In addition, please include IF quantifications.
To clarify, for Figure 1H, the images provided correspond to those from IFNγ-treated cells while the western blot data include both with and without IFNγ treatment. We would like to point out that RBN012811 treatment indeed shows a similar dose-dependent signal with increasing hours of treatment, comparable to the western blot results. Specifically, we observed a small amount of PARP14 remaining at the 1-hour timepoint on western blots and IF images, with the highest intensity observed compared to other timepoints. In addition, we believe part of the discrepancy is due to background staining by PARP14 antibodies. Therefore, we will examine the level of background staining in PARP14 KO cells and provide corresponding IF quantification.
Fig. 2C: Please include quantifications.
We will provide quantification.
- Fig. 2E: The RBN treatment time is not indicated. Please include this information in the figure legend.
We apologize for the oversight and will add the treatment time (24 h) to the figure legend.
Fig. 2G: I am not convinced about the PARP14 staining. IF images do not show an increase in PARP14 levels, while WB analysis shows a strong increase in PARP14 protein levels (see Fig. 2E). Moreover, the RBN treatment time was not indicated; please include it in the figure legend. Does RBN alone affect PARP14 localization? The reported picture shows only 2 cells, each with a different subcellular localization of PARP14. As previously suggested, quantifications are required.
When presenting the data, our aim was to show the pattern rather than the relative intensity difference. Therefore, we used the autocontrast image function across different conditions, which resulted in an apparent change in pattern even with weak signals in control or RBN-treated samples. To address this, we will ensure the images presented across different conditions have the same exposure and are shown with consistent image contrast parameters. We will also include quantification of the condensates. Additionally, we apologize for the oversight and will add the RBN treatment time to the figure legend.
Fig. 3B: Pearson's correlation coefficient (PCC) is reported for n=3. The images show one condensate per cell. Under these conditions, the number of cells analyzed should be at least 100 for each experiment. Additionally, the PCC between PARP14 and p62 at steady state is shown to be 60% (which is quite high). However, the IF pictures do not support this quantification. Can the authors provide higher-resolution pictures? Does PARP14 always co-localize with p62? Lines 207-208 state: "these findings suggest that PARP14 is localized to p62 bodies upon IFNγ treatment when ADP-ribosylation occurs." According to the PCC value, the two proteins co-localize even in the absence of IFN. Can the authors clarify this aspect?
We apologize for the inaccurate description. The data should be n=4, representing different fields, each containing 20-30 cells (as indicated by the number of dots in the original graph panels in Fig. 3B). The Pearson's correlation coefficient was calculated across the cells, instead of focusing on the condensates—we will provide additional analyses on condensate colocalization analyses. We will also provide larger images and quantification to indicate the level of PARP14 colocalization with p62. For PARP14, we did not see a significant number of PARP14 condensates in control cells; PARP14 condensates were seen only after IFNγ treatment. A fraction of PARP14 condensates did not colocalize with p62. We will provide detailed quantification analyses.
Fig. S3B: Please include quantifications.
Quantification will be provided
Fig. S3C: How was the condensate size quantified? It would be useful to show a quantification mask.
We apologize for the omission in the Method Section. The condensate size quantification was performed with ImageJ. The nuclei were first identified with DAPI staining and masked out from the ADPr channel. The image was then thresholded with the “Maximum Entropy” method from ImageJ and the “Analyze Particles” function was used to identify condensates with size larger than 1 pixel. Quantification mask will be provided.
Fig. 3D: Does p62 KD affect PARP14 localization? The reported picture shows only 2 cells, each with different staining of PARP14.
p62 KD reduced the number of PARP14 condensates but did not change their localization. We will provide a representative image with more cells to illustrate this effect more clearly and provide quantification on the change in number of condensates.
Fig. 4A: Please quantify the PARP14 co-IP signal with p62, normalized to PARP14 total levels. In the reported WB, it is difficult to see the interaction between PARP14 and p62 in untreated conditions. Please provide clearer WB.
As suggested, we will quantify the PARP14 co-IP signal by normalizing it with PARP14 input levels. Additionally, we will provide a clearer WB in the revised manuscript.
Additionally, I would expect an increased interaction between PARP14 and p62 upon IFN treatment due to PARP14 recruitment to p62 condensates, not just because of increased PARP14 levels. Since the authors show that PARP14 is not recruited to ADPr condensates upon RBN treatment (Fig. 2G), why is the interaction between p62 and PARP14 so high under RBN treatment?
RBN treatment inhibits PARP14 catalytic activity but simultaneously increases PARP14 levels, as first described by Schenkel et al., Cell Chem Biol 2021. Western blot data indicate that the interaction between PARP14 and p62 is independent of this activity and instead depends on PARP14 protein levels. However, the formation of ADPr/PARP14-containing condensates requires catalytically active PARP14. Based on these data, we conclude that the colocalization of p62 and PARP14 depends on the catalytic activity of PARP14, which is reflected by its ADP-ribosylation.
Fig. 4C: Please quantify WB signals of ADP-ribosylated p62 for the different conditions analyzed. ADP-ribosylation of p62 is still present in cells lacking PARP14. Are there other enzymes that can modify p62? Moreover, the authors state: "We observed an increased MARylation of p62 upon IFNγ treatment" (line 230); is this dependent on the increase in PARP14 levels or the translocation of PARP14 to ADPr condensates? Quantifications should help clarify this aspect.
WB signals will be quantified. We agree with the reviewer's observation regarding the presence of ADP-ribosylated p62 in PARP14 KO cells. The basal levels of ADP-ribosylated p62 may be due to other PARP enzymes. However, PARP14 is critical for the increase in ADP-ribosylation under IFNγ treatment, as the increase was not observed in PARP14 KO cells. Given that PARP14 inhibitors increase PARP14 levels, we interpret that the increase in p62 MARylation requires an increase in active PARP14 levels, not just its total level. Since PARP14 activity is crucial for the localization of PARP14 to p62 condensates and its enrichment of ADPr signals, it is possible, as suggested by the reviewer, that the increase in MARylation of p62 is dependent on the translocation of PARP14 to the structure. However, the field currently lacks the tools to disrupt p62 bodies without knocking down p62 to definitively test whether colocalization is required for the MARylation increase.
Fig. 4G: Quantificationsare required.
Quantification will be provided
Reviewer #2 (Significance (Required)): The role of the PARP family in cellular processes is a very active and rapidly growing field. New information about the organization of PARPs in the nucleus, cytosol, or different types of bodies/structures is certainly relevant to the field. However, the present study is too preliminary at the moment to be considered highly relevant. Both the data analysis and conclusions need to be carefully reviewed. After major revisions, the manuscript might be of general interest if well contextualized within the fields of post-translational modification and protein degradation processes. It would remain in any case interesting for the field of ADP ribosylation. We thank the reviewer for recognizing the significance of our work in the rapidly evolving field of PARP biology. We apologize for the lack of clarity that we indeed quantified over 100 cells across at least 4 fields of images for the data reported. To further address the concerns raised, we will provide additional cell-based quantification to strengthen our claims. Furthermore, we will enhance the contextualization of our findings within the broader frameworks of post-translational modification and protein degradation processes in the Introduction and Discussion sections.
Reviewer #3 (Evidence, reproducibility and clarity (Required)):
In this manuscript, titled "Interferon-Induced PARP14-Mediated ADP-Ribosylation in p62 Bodies Requires an Active Ubiquitin-Proteasome System", Raja et al. perform fluorescence microscopy assays and molecular analyses on cultured cells ex vivo to further our understanding of ADP-ribose accumulations that form in the cytoplasm in response to IFγ stimulation. Guided by the operon-like linkage and co-expression patterns of PARP14, PARP9, and DTX3L and recent reports describing a SARS-CoV-2-dissolved cytoplasmic body induced by interferon-induced that is rich in ADP-ribose and the PARP9/DTX3L heterodimer form following IFγ stimulation, the authors provide clarity in this manuscript regarding two knowledge gaps - (i) what catalyzes mono(ADP-ribosyl)ation within these structures (as PARP9 lacks ADP-ribosyltransferase activity) and (ii) how these foci/condensates relate to similarly composed autophagy-associate "p62 bodies" that have been previously described. Using a combination of genetic depletion and inhibitor-based approaches, the authors show that these ADPr-condensates rely on the catalytic activity of PARP14. The authors also show that while these ADPr-condensates share componentry with "p62 bodies" like ubiquitin and p62 itself, these foci are distinct accumulations, as they lack both LC3B and sensitivity to autophagy inhibitors and require an active ubiquitn-mediated proteosomal degradation system.
While this report represents a more incremental advance in our understanding of these cell signaling structures, especially considering a pair of very recently published and similar reports (Kar et al., EMBO J [2024] and Chaves Ribiero et al EMBO J [2024]), the work here is well-written and reasoned and complements these works with some novelty and distinction to that reported literature. The experiments are definitive and of high quality and the authors interpretations/conclusions are largely well-supported by the results. Thus, it is my opinion that this work is appropriate for publication with predominantly minor revisions (outlined below) and a few more substantive experimental additions.
We thank the reviewer for recognizing the high quality of our work and for acknowledging that our interpretations are well-supported by the results. We appreciate that the reviewer deemed our work ready for publication with minor revisions. We believe the reviewer’s perception of our work as incremental arises because two related studies were published in June, after we submitted our work to Review Commons in May. According to the Scooping Protection Policy, our work should still be considered novel. The publication of these studies in EMBO J, which reported the discovery of PARP14 in ADPr-containing condensate formation, highlights the significance of our research. However, we further contribute to this discovery by demonstrating the critical role of PARP14 using multiple genetic manipulation techniques (siRNA, shRNA, and CRISPR-Cas9) and chemical inhibitors (catalytic and PROTAC), indicating the rigor of our studies. Moreover, we not only investigate PARP14 but also define the identity of these condensates related to p62 bodies and establish the requirement of an active ubiquitin-proteasome system for their formation.
Major Comment:
A major claim and novelty reported here is that the ADPr condensates are distinct from "p62 bodies". The evidence to support this rely largely on differences in their sensitivity to pharmacological treatments as well as somewhat subtle differences in FRAP recovery in p62 condensates after IFNgamma treatment. But, this claim would be better supported with more comprehensive mapping of differences in the componentry or functional outcomes of these condensates. The authors might consider:
-Mass spectrometry against p62 (a common component) in standard "p62 bodies" and ADPr Condensates, followed by IF to confirm significantly different composition in, what is argued here, these distinct structures. -Fine mapping of concentration dependence of components that give rise to these distinct condensates as has been demonstrated in papers like Riback et al Nature 2020 and others. -Methodology of the author's choosing to decipher functional outcomes from these condensates followed by demonstration that components unique to ADPr condensates are dispensable for functioning "p62 bodies" and, vice versa, components unique to "p62 bodies" are dispensable for ADPr Condensate function.
As rightly pointed out by the reviewer, our studies indicate the alteration in the composition and dynamics of p62 bodies upon IFNγ treatment. This was assessed using immunofluorescence against various known components of p62 bodies (Fig. 3B, 5E-I), quantification of the condensate size (Fig. S3C), and p62 mobility assessment by photokinetic experiments (Fig. 3C, 4F). In considering reviewer’s suggestions, we will perform p62 interactome studies with and without IFNγ treatment to identify potential changes. Additionally, we will analyze the concentration dependence of ADPr for condensate formation. However, we believe that investigating the functional outcomes is beyond the scope of this manuscript.
Minor Comments:
Overall, the representative microscopy images are far too small. For the benefit of future readers, please consider enlarging these images.
More of the quantitation of microscopy images, with accompanying statistics, that are found in abundance in the supplemental material should find their way into the main figures of the manuscript. This will give room for larger and more reader-friendly representative microscopy images in the main figures/text as discussed briefly above.
We appreciate the reviewer’s suggestions and rightly pointed out that our quantification and statistics were in supplementary materials. Given that we have provided over 800 image panels, we will restructure the manuscript so that the cell biology information is more readily available. We will move our quantification data and statistics currently in the supplementary materials to the main figures. Additionally, we will provide larger and more reader-friendly representative microscopy images in the main text as suggested.
Can the authors test whether or not the condensates are purely driven by mono(ADP-ribosyl)ation? Or does poly(ADP-ribose) co-occupy these condensates and play a substantive role?
We have tested for the presence of poly(ADP-ribose) in the condensates and found it is not present. We will provide the supporting data.
The manuscript would benefit from discussing very recent and related reports (Kar et al., EMBO J [2024] and Chaves Ribiero et al EMBO J [2024]), that I suspect were not available at the time of submission.
Yes, the reviewer is correct that our submission preceded the publication of these related reports. In light of the co-discovery, we will add a section to discuss their findings.
IFNalpha and IFNbeta, which are used in Figure S1, do not appear as reagents in Table 1.
We apologize for the oversight and will add the information on IFNa and IFNb to Table 1.
On lines 113-114, it would seem more appropriate to describe the increase of PARP-14 as statistically significant and largest in magnitude. "most significant" would just mean lowest p-value, which I expect is different that the authors intend here.
We thank the reviewer's suggestion will modify the text as follows:
"PARP9, PARP12, and PARP14 were statistically significantly upregulated at 6 and 24 hours post-treatment, with PARP14 showing the largest increase in mRNA expression levels (Fig. 1D and S1B)."
In Figure S1, better care should be taken to crop and align the western blots.
We thank the reviewer for pointing this out, and we will properly align and crop the western blots in Figure S1.
On line 154, it may be more appropriate to describe ITK as a "weaker" inhibitor of PARP14 relative to PARP11. It certainly is effective as an inhibitor (Figs. S2A and S2G) and its unclear how the authors (or anyone would) define what qualities make it "weak".
We thank the reviewer's suggestion and will modify the text as follows:
“…—specifically RBN012579 (hereafter RBN) and ITK7 (a potent PARP11 inhibitor with inhibitory effects on PARP14 that are weaker than RBN)—…"
The multiple bands for PARP14 in Figure 3E should be addressed. Why does this differ from other blots from the same cells?
We believe that the multiple bands seen can be due to insufficient blocking and using a different lot of PARP14 antibodies. We will address the issue by performing a new experiment with proper blocking conditions and using the same lot of PARP14 antibody as other blots. It should be noted that that variation is also observed in the reagent website: https://www.scbt.com/p/parp-14-antibody-c-1
Reviewer #3 (Significance (Required)):
I expect the advances in this work will appeal more to specialists who are interested in ADP-ribosylation as a signaling molecule and to those engaged in biotechnological efforts to drug immunological responses.
The advances reported here are incremental. The ADPr condensates that form in response to IFNgamma, the involvement of PARP9/DTX3L, and very recently the involvement of PARP14 and its MARylation activity are all known. Less known is the notion that this condensate is distinct from other kinds of "bodies", which is a clear point of novelty, especially if buttressed by the authors as suggested in this review.
We agree with the reviewer that our work is significant for multiple fields, including ADP-ribosylation and immunology. The perception of our work as incremental likely stems from the publication of two related recent studies in June, after our submission in May. According to the Scooping Protection Policy in Review Commons, our work should remain novel in editorial consideration. More importantly, the back-to-back EMBO J studies highlight the importance of reporting the critical role of PARP14 in ADPr-containing condensate formation. We further contribute to this discovery in three aspects: (1) we rigorously demonstrate the critical role of PARP14 through multiple genetic techniques (siRNA, shRNA, and CRISPR-Cas9) and chemical inhibitors (catalytic and PROTAC), (2) we reveal the identity of these condensates as related to p62 bodies, and (3) we define their requirement for an active ubiquitin-proteasome system.
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Referee #3
Evidence, reproducibility and clarity
In this manuscript, titled "Interferon-Induced PARP14-Mediated ADP-Ribosylation in p62 Bodies Requires an Active Ubiquitin-Proteasome System", Raja et al. perform fluorescence microscopy assays and molecular analyses on cultured cells ex vivo to further our understanding of ADP-ribose accumulations that form in the cytoplasm in response to IFγ stimulation. Guided by the operon-like linkage and co-expression patterns of PARP14, PARP9, and DTX3L and recent reports describing a SARS-CoV-2-dissolved cytoplasmic body induced by interferon-induced that is rich in ADP-ribose and the PARP9/DTX3L heterodimer form following IFγ stimulation, the authors provide clarity in this manuscript regarding two knowledge gaps - (i) what catalyzes mono(ADP-ribosyl)ation within these structures (as PARP9 lacks ADP-ribosyltransferase activity) and (ii) how these foci/condensates relate to similarly composed autophagy-associate "p62 bodies" that have been previously described. Using a combination of genetic depletion and inhibitor-based approaches, the authors show that these ADPr-condensates rely on the catalytic activity of PARP14. The authors also show that while these ADPr-condensates share componentry with "p62 bodies" like ubiquitin and p62 itself, these foci are distinct accumulations, as they lack both LC3B and sensitivity to autophagy inhibitors and require an active ubiquitn-mediated proteosomal degradation system.
While this report represents a more incremental advance in our understanding of these cell signaling structures, especially considering a pair of very recently published and similar reports (Kar et al., EMBO J [2024] and Chaves Ribiero et al EMBO J [2024]), the work here is well-written and reasoned and complements these works with some novelty and distinction to that reported literature. The experiments are definitive and of high quality and the authors interpretations/conclusions are largely well-supported by the results. Thus, it is my opinion that this work is appropriate for publication with predominantly minor revisions (outlined below) and a few more substantive experimental additions.
Major Comment:
A major claim and novelty reported here is that the ADPr condensates are distinct from "p62 bodies". The evidence to support this rely largely on differences in their sensitivity to pharmacological treatments as well as somewhat subtle differences in FRAP recovery in p62 condensates after IFNgamma treatment. But, this claim would be better supported with more comprehensive mapping of differences in the componentry or functional outcomes of these condensates. The authors might consider:
- Mass spectrometry against p62 (a common component) in standard "p62 bodies" and ADPr Condensates, followed by IF to confirm significantly different composition in, what is argued here, these distinct structures.
- Fine mapping of concentration dependence of components that give rise to these distinct condensates as has been demonstrated in papers like Riback et al Nature 2020 and others.
- Methodology of the author's choosing to decipher functional outcomes from these condensates followed by demonstration that components unique to ADPr condensates are dispensable for functioning "p62 bodies" and, vice versa, components unique to "p62 bodies" are dispensable for ADPr Condensate function.
Minor Comments:
Overall, the representative microscopy images are far too small. For the benefit of future readers, please consider enlarging these images.
More of the quantitation of microscopy images, with accompanying statistics, that are found in abundance in the supplemental material should find their way into the main figures of the manuscript. This will give room for larger and more reader-friendly representative microscopy images in the main figures/text as discussed briefly above.
Can the authors test whether or not the condensates are purely driven by mono(ADP-ribosyl)ation? Or does poly(ADP-ribose) co-occupy these condensates and play a substantive role?
The manuscript would benefit from discussing very recent and related reports (Kar et al., EMBO J [2024] and Chaves Ribiero et al EMBO J [2024]), that I suspect were not available at the time of submission.
IFNalpha and IFNbeta, which are used in Figure S1, do not appear as reagents in Table 1.
On lines 113-114, it would seem more appropriate to describe the increase of PARP-14 as statistically significant and largest in magnitude. "most significant" would just mean lowest p-value, which I expect is different that the authors intend here.
In Figure S1, better care should be taken to crop and align the western blots.
On line 154, it may be more appropriate to describe ITK as a "weaker" inhibitor of PARP14 relative to PARP11. It certainly is effective as an inhibitor (Figs. S2A and S2G) and its unclear how the authors (or anyone would) define what qualities make it "weak".
The multiple bands for PARP14 in Figure 3E should be addressed. Why does this differ from other blots from the same cells?
Significance
I expect the advances in this work will appeal more to specialists who are interested in ADP-ribosylation as a signaling molecule and to those engaged in biotechnological efforts to drug immunological responses.
The advances reported here are incremental. The ADPr condensates that form in response to IFNgamma, the involvement of PARP9/DTX3L, and very recently the involvement of PARP14 and its MARylation activity are all known. Less known is the notion that this condensate is distinct from other kinds of "bodies", which is a clear point of novelty, especially if buttressed by the authors as suggested in this review.
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Referee #2
Evidence, reproducibility and clarity
This manuscript investigates the formation of a novel cellular structure or condensate, similar to p62 bodies, that includes PARP14 and p62. The interferon-induced PARP14-mediated ADP-ribosylation of p62 in these condensates depends on an active ubiquitin-proteasome system. These condensates are characterized by the presence of PARP14 and ADPr and include some, but not all, components of the p62 bodies. Furthermore, their formation depends on both ubiquitin activation and proteasome activity, but it is unaffected by autophagy inhibition, unlike conventional p62 bodies.
The Introduction provides a well-delineated context of condensates, highlighting the importance of post-translational modifications in responding to environmental changes.
Although the manuscript is well-organized with apparent logical development, there are weaknesses that diminish the impact of the reported data. A more accurate review of the structures, both from a morphological and quantitative perspective, would strengthen the conclusions and the overall impact of this work. Additionally, while the authors have analyzed the contribution of PARP14 to condensate formation, the biological significance of these structures remains unclear. For instance, performing MS (mass spectrometry) analysis on the described structures could help identify their composition and functions.
The methodologies used in this study are standard for molecular and cellular biology research, including immunofluorescence assays, transient transfections, immunoprecipitations, and fluorescence recovery after photobleaching (FRAP) assays. These methods are described in detail and can be reproduced.
Below, please find a list of comments and suggestions to enhance the robustness of the data:
Major Points
- The IF analyses are central to the conclusions reported and are employed for each of the inhibitors or other tools used to investigate the formation of these condensates. The quality of the IF images needs to be improved; the shape and contacts of the condensates should be analyzed using either super-resolution or EM microscopy, or preferably both. The lack of morphometry and quantification from cell populations needs to be addressed for all experiments. These analyses are needed to support the claim that the condensates presented in this study are indeed novel structures, rather than being transient aggregates of a different nature.
- The claim that PARP14 is essential for the formation of condensates requires support by the analyses indicated above. Minor points regarding Fig. 1 are indicated below. I suggest performing KD of PARP9 and/or PARP12 (whose expression is increased upon IFN treatment) and checking ADPr condensates to validate the central role of PARP14.
- According to the text, "PARP14 was pulled down by ADP-ribose binding Af1521 macrodomain following IFNγ treatment (Fig. 2H)", but the legend to the figure says otherwise. A Pan-ADPr binding reagent (MABE1016) is reported in the figure. Although the conclusion is similar for the results obtained with these two tools (but they must be described and reported properly), it is still insufficient to claim that PARP14 is ADP-ribosylated. This point should be at least discussed.
- I have difficulties analyzing the colocalization with the different organelles, even enlarging the images as much as possible. In most cases, only one condensate per image is shown. Continuities with the nuclear envelope appear in some cases: has this been investigated?
Minor Points
- Fig. 1A: The DAPI images at 3 and 6 hours are reversed. Additionally, for Fig. S1a and Fig. 1A, please include quantifications.
- Fig. 1B: Check PARP14 levels (and other IFN-PARPs) under the same experimental conditions.
- Fig. 1H: Explain why PARP14 IF staining is still visible upon RBN012811 treatment, while it is completely lost in WB analysis or upon PARP14 siRNA treatment (Fig. 1G). In addition, please include IF quantifications.
- Fig. 2C: Please include quantifications.
- Fig. 2E: The RBN treatment time is not indicated. Please include this information in the figure legend.
- Fig. 2G: I am not convinced about the PARP14 staining. IF images do not show an increase in PARP14 levels, while WB analysis shows a strong increase in PARP14 protein levels (see Fig. 2E). Moreover, the RBN treatment time was not indicated; please include it in the figure legend. Does RBN alone affect PARP14 localization? The reported picture shows only 2 cells, each with a different subcellular localization of PARP14. As previously suggested, quantifications are required.
- Fig. 3B: Pearson's correlation coefficient (PCC) is reported for n=3. The images show one condensate per cell. Under these conditions, the number of cells analyzed should be at least 100 for each experiment. Additionally, the PCC between PARP14 and p62 at steady state is shown to be 60% (which is quite high). However, the IF pictures do not support this quantification. Can the authors provide higher-resolution pictures? Does PARP14 always co-localize with p62? Lines 207-208 state: "these findings suggest that PARP14 is localized to p62 bodies upon IFNγ treatment when ADP-ribosylation occurs." According to the PCC value, the two proteins co-localize even in the absence of IFN. Can the authors clarify this aspect?
- Fig. S3B: Please include quantifications.
- Fig. S3C: How was the condensate size quantified? It would be useful to show a quantification mask.
- Fig. 3D: Does p62 KD affect PARP14 localization? The reported picture shows only 2 cells, each with different staining of PARP14.
- Fig. 4A: Please quantify the PARP14 co-IP signal with p62, normalized to PARP14 total levels. In the reported WB, it is difficult to see the interaction between PARP14 and p62 in untreated conditions. Please provide clearer WB. Additionally, I would expect an increased interaction between PARP14 and p62 upon IFN treatment due to PARP14 recruitment to p62 condensates, not just because of increased PARP14 levels. Since the authors show that PARP14 is not recruited to ADPr condensates upon RBN treatment (Fig. 2G), why is the interaction between p62 and PARP14 so high under RBN treatment?
- Fig. 4C: Please quantify WB signals of ADP-ribosylated p62 for the different conditions analyzed. ADP-ribosylation of p62 is still present in cells lacking PARP14. Are there other enzymes that can modify p62? Moreover, the authors state: "We observed an increased MARylation of p62 upon IFNγ treatment" (line 230); is this dependent on the increase in PARP14 levels or the translocation of PARP14 to ADPr condensates? Quantifications should help clarify this aspect.
- Fig. 4G: Quantifications are required.
Significance
The role of the PARP family in cellular processes is a very active and rapidly growing field. New information about the organization of PARPs in the nucleus, cytosol, or different types of bodies/structures is certainly relevant to the field.
However, the present study is too preliminary at the moment to be considered highly relevant. Both the data analysis and conclusions need to be carefully reviewed. After major revisions, the manuscript might be of general interest if well contextualized within the fields of post-translational modification and protein degradation processes. It would remain in any case interesting for the field of ADP ribosylation.
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Referee #1
Evidence, reproducibility and clarity
Summary
In this study, Raja et al. found cytoplasmic condensates formed by the treatment of INFγ, investigated components of these condensates and identified p62, NBR1 and PARP14 as their components. INFγ treatment induced PARP14 expression, and PAPR14 inhibitor treatment inhibited condensation formation, suggesting that the amount of PARP14 and its enzymatic activity are important for the condensate formation. The ADPr-positive p62 condensates were independent of autophagic degradation, and proteasomal activity was required for their formation.
Major comment
- The finding that the ubiquitin-proteasome, but not autophagy activity, is indispensable for the formation of p62 condensates is of interest. However, the molecular mechanism by which the ubiquitin-proteasome system (UPS) is involved in the regulation of the PARP14-p62 condensate is still unclear. Which step(s) is the UPS involved?
- The p62 condensate serves as a scaffold for autophagosome formation through the assembling autophagy receptors including NBR1 and TAX1BP1, followed by recruiting ATG proteins such as FIP200. While ADPr-positive p62 condensates also contain NBR1 and polyubiquitinated proteins, they are unrelated to autophagic degradation. It is unclear what factors govern autophagy-independent function.
- The authors claim that the amount of PARP14 and its MAR activity are essential for the condensate formation. However, all experiments were performed only with PARP14 inhibitors, and further validation is needed. If the importance of PARP14 activity is to be directly demonstrated, experiments in which an enzyme activity mutant is introduced into PARP14 KO cells are needed.
- In Figure 2a, the heatmap alone is insufficient. Neither errors nor statistical comparisons are indicated.
- The statistical analysis of Figure S2 is inappropriate; instead of t-tests, multiple comparisons should be used to compare three or more groups.
Minor comment
- What percentage of p62 condensates upon INFγ treatment are ADPr positive? Are all p62 bodies seen with INFγ stimulation unrelated to autophagy?
- Is ADPr condensation a PARA14-specific phenomenon? PARP9 and PARP12 were also upregulated by INFγ treatment. Are these factors also involved in condensate formation?
- Figure 4D appears to be immunoprecipitation (IP) under non-denaturing conditions. If so, it is not possible to distinguish whether the MAR signal is derived from p62 or from the p62 interacting proteins (the associated ubiquitinated substrates). IP experiments should be performed under denaturing conditions.
- In Figure 5B, which band is HO1, the upper or lower?
- There is no image for ubiquitin in S5D. Right panel in Figure 4F shows only IFγ + RBN, which should show all data sets in the same panel.
Significance
Liquid droplets, which have continuously being identified in cells, are a hot topic in cell biology. Droplet formation, structure, molecular dynamics, and degradation, as well as their abnormalities and disease development due to genetic mutation and stress, are of wide-ranging interest from basic to pathological aspects. Therefore, this research has the potential to attract interest from a wide range of fields.
General assessment
Overall, the data are clear and the phenomenon is of interest. However, the molecular mechanism and biological significance of the condensate formation is unknown; It is unclear why proteasome activity is required for the formation of PARP14-mediated ADP ribosylation. It is also unclear what the consequences are for the cell if the ADPr-positive condensates are not formed. Thea authors should address these general and important issues and provide the data If not all.
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Reply to the reviewers
The authors do not wish to provide a response at this time.
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Referee #3
Evidence, reproducibility and clarity
The manuscript by Balachandra and Amodeo presents Bellymount-Pulsed Tracking as a technique for continuous long-term imaging of Drosophila oogenesis. This approach modifies the existing Bellymount technique by exposing restrained female flies to pulses of CO2 anesthesia in combination with image acquisition. Flies that survived the restraint were kept alive for many hours by addition of a liquid diet in the restraint apparatus. This allowed for imaging and tracking of egg chamber development over longer time periods than capable with ex vivo culturing methods. However, the authors did report a 40% mortality rate and decreased fecundity compared to unrestrained flies. Using this method the authors were able to image and measure the growth rate of developing egg chambers in living flies, and capture events like vitellogenesis which relies on the interactions of multiple organ systems.
This technique is a notable contribution to the fly community, as it could be useful for studying processes that require interactions between multiple tissues and organs, as well as for long-term imaging of other internal structures in the adult fly. The significance is somewhat reduced due to the relatively high mortality rate and the decreased fecundity and egg chamber growth rate reported. However, the authors should be commended for their diligence in documenting the limitations of the procedure, as this now provides a strong jumping off point to improve the technique if it becomes widely adopted by the fly community. Overall, the experiments appear to have been carefully performed and the manuscript is clearly written. However, there are several issues that should be addressed prior to publication.
Major concerns
- The movies of egg chamber development are challenging to interpret. They could be improved by the addition of timestamps and other annotations. Having multiple example movies of the same process would also be valuable. It could be helpful to potential users of this technique to show the process the authors used for identifying the same egg chamber between such long time points.
- Figure 4 - Given that the Bellymount PT technique slows oogenesis and reduces egg chamber growth in vitellogenic stages (Figure 3E), it is possible that Bellymount PT slows yolk protein uptake. It would be important to establish a baseline for how much to expect yolk protein levels to change across stages to compare to measurements obtained with Bellymount PT. It would be a relatively simple experiment to show the change in yolk protein uptake across stages in fixed samples. This could also be performed for His2Av dynamics during nurse cell dumping.
- Movie 11 - The authors propose that Bellymount-PT can be used to visualize the process of border cell migration. However, there is no obvious movement of the cluster relative to the nurse cell nuclei over the course of the 3 hour long movie. The authors should either show a better movie of border cell migration, or remove this claim from the manuscript.
- Movie 13 - The authors claim that they see egg chamber rotation continue in stage 9 and 10 egg chambers. This movie is not convincing. There is also very strong evidence in the literature that egg chamber rotation ends at stage 8. Chen et al., Cell Reports, 2017 showed using a method that tracks follicle cell migration in vivo that rotational migration ends during stage 8. The only movement of follicle cells after stage 8 is due to the epithelial reorganization that occurs during the posterior movement of the follicle cells as the stretch cells flatten. Additionally, after stage 8 follicle cells lose their circumferentially oriented actin protrusions that drive rotation. This claim should be removed from the manuscript.
Minor comments
- Line 104 - The authors mention that CO2 affects fertility in flies. They should also reference Sustar et al., Genetics, 2023 and Zimmerman and Berg, PLoS One, 2024 for wider ranging effects of CO2 on oogenesis.
- Line 244 - Although it is true that the original paper describing egg chamber rotation reported that it starts at 5, subsequent studies from multiple labs have confirmed that it begins much earlier. First shown by Cetera et al., Nature Communications, 2014 but later confirmed by Bilder, Dahmann, and Mirouse labs. Chen et al., Cell Reports, 2016 has even published a movie of an egg chamber initiating rotation as it buds from the germarium.
- Figures of egg chambers are generally oriented anterior on the left and posterior on the right. Reorienting all the figures would be challenging, so the recommendation is to be clear in the figure legends the orientation of the images. This is important given they are shown in different orientations in Figure 1 than throughout the rest of the paper, and also will be helpful for readers who may not be familiar with the structure of the ovary/egg chambers.
- Figure 1B and Methods line 334 - Should "Rely" be "Relay"?
- Figure 1E - Oocyte nuclei are missing from the diagrams of stage 7, 13 and 14 egg chambers. Also, "G" looks like a figure panel label, could just say Germarium
- Figure 3F-H - "Stagee" should be "Stage"
- Figure 4B - Why is the fluorescence for egg chamber #6 so much higher than the others? It makes the slopes of the other samples hard to see.
- Figure 4D,E,G - For clarity, the labeled boxes should be the same color as the lines on the associated graphs. In line 790 "Note the steady increase of H2Av in all three regions as it exits the nurse cell nuclei" - this is not actually shown without the nurse cell nuclei average intensity being on the graph as well.
- Line 787 - "Note the flow of H2Av" - "flow" is not actually shown in these static images. Consider a more precise description.
Referee Cross-commenting
The other reviewers make several excellent points. We personally feel that it is beyond the scope of this initial report to ask the authors to show that they can see all aspects of oogenesis with this technique. If the method becomes widely adopted by the oogenesis community, individual researchers can optimize it to suit the exact process they want to study. If the authors want to claim they can see a particular process, it needs to be well documented and convincing. For example, we agree that the movies that claim to show egg chamber rotation (both during established stages and later) and border cell migration need to be improved or the claims need to be removed. However, we feel that the authors have documented enough other interesting processes to make the study worthy of publication. Likewise, asking the authors to determine the minimal time window that can be used for imaging could take months of open-ended work and is something that could be better tackled by subsequent users depending on the requirements of the biological process they want to study. It seems better to get the work out into the public sooner rather than later so that improvements can be crowd sourced.
Finally, although Flp-out clones were used for cell tracking in the original Belly mount paper, this technique will be less effective during the first half of oogenesis when the egg chamber is rotating, as the clone is likely to rotate into and out of sight between imaging time points.
Significance
This technique is a notable contribution to the fly community, as it could be useful for studying processes that require interactions between multiple tissues and organs, as well as for long-term imaging of other internal structures in the adult fly. The significance is somewhat reduced due to the relatively high mortality rate and the decreased fecundity and egg chamber growth rate reported. However, the authors should be commended for their diligence in documenting the limitations of the procedure, as this now provides a strong jumping off point to improve the technique if it becomes widely adopted by the fly community. Overall, the experiments appear to have been carefully performed and the manuscript is clearly written. However, there are several issues that should be addressed prior to publication.
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Referee #2
Evidence, reproducibility and clarity
Summary:
The authors describe an improvement of the Bellymount imaging method for internal tissues of the fly's abdomen. They are able to increase the total duration of the imaging by introducing pulsed anesthesia. This allows the immobilized flies to take up food in between the imaging; this increases survival rate and allows for longer total imaging times. The authors illustrate the technique by tracking the development of egg chambers.
Major Points
- The Bellymount PT method results in decreased fecundity, which might affect the processes (oogenesis) the authors looked at. Indeed, the authors conclude that "oogenesis is not completely stalled under the Bellymount-PT protocol" (line 140). The authors do provide some data indicating that egg chambers develop (Fig. 2G,H; Fig. 3F,H), in particular a stage 10 egg chamber proceeding to a stage where dorsal appendages seem to form. However, for early stage egg chambers this is less convincing. The egg chambers show an increase in (cross-sectional) area, however, what is the evidence that they also mature? For example, during egg chamber maturation, the ratio of oocyte/nurse cell volume changes, follicle cells re-arrange, etc. The authors should test whether any of these characteristics can be observed in egg chambers imaged using Bellymount PT. This may include the imaging of egg chambers in which both nuclei and plasma membranes are visualized.
- A potential advantage of the Bellymount PT method is the ability to follow the dynamics of processes. A current drawback, however, is the rather low temporal resolution as the fly needs to wake up between single images. The authors should provide an estimate for the minimal possible cycle time and should test whether flies imaged at 10 minutes interval show lower survival/fecundity than flies imaged at 2 hours interval.
- The authors claim that they can track on a cellular level (based on nuclei), but it is unclear how accurate the tracking is. Especially cell tracking over very long times might be challenging here, as the time delay between two time points is big. The authors should test the accuracy of their tracking, potentially by creating Flip-out clones and using them as a control.
- The authors show that they can visualize cell membranes (Moesin-GFP, Fig. 2C). Tracking cells over time based on their membranes would greatly widen the applicability of the method as it would enable to analyze the complex cellular dynamics during egg chamber maturation. The authors should test whether cells can be tracked over time (e.g. using Moesin-GFP) using their technique.
- Movie 11. The authors claim that they can capture border cell migration. However, it is unclear whether the border cells actually migrate towards posterior. The authors should track and quantitatively analyze the migration path of the border cells in their movies.
- Movie 12. The authors claim that they can observe egg chamber rotation. However, it is unclear whether the egg chambers actually rotate. The authors should track cells and quantify the angular velocity of movement.
Minor Points
- Please move the labels of the scale bars to the legends.
- The figures (especially 2 and 3) would benefit from a clearer structuring. Moving part of them to supplementary figures would also help.
- "stage" typo in figure 3
Significance
The authors describe here an improvement of an existing technique. The advantage of the improved technique is the longer imaging time, which potentially allows users to track cells/organelles/proteins over time. However, tracking requires the user to connect single time points with each other, which is somewhat unclear at this time. Moreover, the potential applicability (and significance) of the technique would be widened if visualization and tracking of cell membranes/organelles/vesicles would be possible. With these further optimizations, the technique would add a useful tool to the Drosophila community.
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Referee #1
Evidence, reproducibility and clarity
Summary
The Drosophila ovary is an established model system for many aspects of development and cell biology. In vitro culture of live ovaries has provided valuable insight, yet these methods do not accurately mimic oogenesis in vivo for some stages. Here the authors develop a new method that allows for sustained imaging of ovaries in intact flies, maintaining normal physiology.
The method provides a valuable addition to the field. Processes such as growth, cell migration, egg chamber rotation, yolk uptake and nurse cell dumping can be observed in the intact fly. Time lapse and 3D reconstruction provide valuable tools. While the detail/resolution of the images is not as good as ex vivo or fixed samples, the ability to maintain normal development and homeostasis provides a novel advantage. The figures and movies are well-presented and sufficient detail is provided in the methods.
Major comments
- Why do the authors think that growth is slowed? The imaging process or the trapping/anesthesia of the fly? For example, if the frequency of imaging was varied, it could reveal whether it was the actual imaging that affected development. Did the length of time the fly had been in the trap make a difference? The sentence on lines 190-191 is not clear.
- In Movie 6, the nurse cell nuclear shape does not look normal - more ovoid than round. Perhaps some settings are off in the 3D reconstruction.
- Movie 11 - why do the border cells seem stalled?
- There is no discussion of the earliest stages of oogenesis. Is it possible to see egg chambers forming from the germarium?
Minor comments
- It would be helpful to mention if the egg chambers stay in similar locations or move around - is it challenging to locate the same egg chamber after 2 hours?
- Are any egg chambers degenerating? This could indicate stress in the fly.
- In Figure 4D, release of HisAV into the cytoplasm is described. Similar release of nuclear proteins was described by Cooley et al. 1992 so this paper could be cited.
- At 321 minutes in Figure 4D, a large nucleus is apparent in the oocyte. Is this an oocyte nucleus or evidence for nurse cell translocation to the oocyte as described in Ali-Murthy et al. 2021?
Significance
The technique provides a significant advance to the field, extending the time period currently possible to image ovaries through the Belly Mount method. It will immediately benefit researchers working on the ovary but could be extended to many other tissues in the fly abdomen such as the gut and tumor models.
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Reply to the reviewers
The authors do not wish to provide a response at this time.
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Referee #3
Evidence, reproducibility and clarity
Summary:
Parkin, a E3 ubiquitin ligase, is involved in the clearance of damaged mitochondrial via mitophagy. Upon mitochondrial damage, the activated Parkin ubiquitinates many mitochondrial substrates, leading to the recruitment of mitophagy effectors. However, the mechanism of substrate recognition by Parkin is still not known.
In this manuscript, Koszela et al. utilized diverse biochemical assays and biophysical approaches, combined with AlphaFold prediction, to identify a conserved region in the flexible linker between the Ubl and RING0 domains of Parkin that recognizes mitochondrial GTPase Miro1 via a stretch of hydrophobic residues and is critical for its ubiquitination activity on Miro1. This manuscript reveals the mechanisms by which Parkin recognizes and ubiquitinates substrate Miro1, providing a biochemical explanation for the presence of Parkin at the mitochondrial membrane prior to activation by mitochondrial damage. This study also provides insights into mitochondrial homeostasis and may facilitate new therapeutic approaches for Parkinson's disease.
Major Comments:
- The authors should expand the background introduction to include the biological function of Miro1, the domain architecture of Miro1 and more context of Miro1 K572 ubiquitination in mitophagy.
- Figure 1B is confusing. Due to the presence of various bands, it is hard to assign specific bands in each lane. In addition, there are various unlabeled bands that makes things unclear. The authors should include loading controls to clearly discern pParkin, Ube1, Ube2L3, and all substrates.
- In Figure 1B, it was not possible to identify the ubiquitination bands of E2 enzyme UBE2L3 and the E1 enzyme UBE1. Please indicate these bands on the gel.
- Since ubiquitinated Miro1 and Mfn1 are similar in molecular weight (Fig. 1b), the authors should show a western blot against the Miro1 and Mfn1 tag as done in the supplementary information, At least for the competition assays involving both Miro1 and Mfn1.
- The conclusion that Miro1 is pParkin's preferred substrate is not convincing. In the competition assay used to show substrate preference, Miro1 is at a five-fold higher concentration than the other substrates and 25-fold higher than FANCI/D2. This would ultimately drive pParkin's interaction with Miro1. This is further highlighted by the fact that it adding Mfn1 in excess has a similar effect. The competition assay should be done at equimolar concentrations of Miro1 and substrate. More convincing would be a competition assay where substrate ubiquitination is quantified at several different concentrations of Miro1.
- In Figure 1F, it is unclear what is defined as "high" or "low" ubiquitination levels statistically. Some of the changes in ubiquitination levels are extremely subtle (ex. mitoNEET and FancI/D2 in the presence and absence of Miro1 and Mfn1). In some cases, I find it extremely difficult to tell if there is any change in the ubiquitination levels when comparing lanes containing excess of different substrates. I would like to see band quantifications of this experiment in triplicate to support the conclusions drawn from the competition assay.
- The authors used both unmodified and phosphorylated Parkin for the crosslinking experiments and observe no difference in the intensity of the bands. However, this is not sufficient to draw any conclusion about the affinity between phosphorylated Parkin and Miro1 (which was done in lines 341-343). The authors should comment on why they did not test pParkin binding with Miro1, especially given the statement:
"In our assays in the absence of pUb, pParkin must interact with its substrates without the action of pUb, likely through 158 transient, low affinity interactions" - The reference to Parkin115-124 as a "Substrate Targeting Region (STR)" is misleading. This would imply that this motif in Parkin is responsible for general substrate recognition when there is no direct evidence of this. In Figure 5F, the authors create a synthetic peptide based off the STR sequence. Although this sequence was effective in inhibiting the ubiquitination of Miro1, it was ineffective against Mfn1. This would indicate that Mfn1 relies on a completely different set of interactions for ubiquitination by Parkin. I suggest that the authors tone down the language in describing this region and rename this region (perhaps "Miro1 Targeting Region (MTR)"?). - The authors appear to confuse plDDT and PAE scores in Figure 5B. The PAE describes the expected positional error of each residue in the model. The plot should be colored in terms of Expected Position Error (Ångstrom), not plDDT scores.
Minor Comments:
- Figure 1A would benefit from a schematic showing the domain architecture. If the goal is to appreciate the length of the linker, then showing the actual amino acid length would be beneficial.
- In Supplementary Figure 2D, the authors performed the MST experiment with His6-Smt3-tagged Parkin. The group had previously shown that the presence of the tag artificially interferes with autoubiquitination, potentially by forming intramolecular interactions. The SEC, Native Page, and ITC data of untagged Parkin with Miro1 provide sufficient evidence that the interaction between the two are weak. The authors should consider removing the MST data, since they are not congruent with the other experiments.
- The ITC data in Supplementary Figure 2C look promising. It would be nice if the authors could try to quantify the Kd of their STR peptides to Miro1
- Are STR peptides 1 and/or 2 unable to inhibit ubiquitination of other Parkin substrates besides Mfn1? Do these other substrates utilize the STR for recognition? AlphaFold modeling may provide some insight on Parkin recognition of other substrates.
- The authors shold consider using AlphaFold3 to model the interaction of pParkin with Miro1 compares to unmodified Parkin.
- Please label the protein names in Figure 4A for a better presentation.
- Page 2, line 37. "...by a 65-residue flexible region (linker) to a unique to Parkin RING0 domain..." should be "...by a 65-residue flexible region (linker) to a unique Parkin RING0 domain...". The second "to" should be omitted.
- Page 3, Line 48: "fulfill", not "fulfil"
- Page 5, line 110. In sentence, "...phosphorylation at Ser65 of Parkin...", it is better to explicitly state that this phosphorylation happens on the Parkin Ubl domain.
- Page 7, line151. Figure 1F should be Figure 1G.
- Page 11, line 241. In sentence "...Miro1 residues R263, R265 and D228...", do the authors mean R261 and not R265?
Significance
Parkin is an E3 ubiquitin ligase that is activated to ubiquitinate diverse substrates on the mitochondrial membrane in response to mitochondrial damage, thereby recruiting mitophagy effectors. This study reveals the mechanisms by which Parkin recognizes and ubiquitinates Miro1, providing insights into mitochondrial homeostasis and facilitating new therapeutic approaches for Parkinson's disease.
Readers with a background in protein ubiquitination and mitochondrial homeostasis might be interested in this study. My expertise includes protein ubiquitination and structural biology. However, I do not have sufficient expertise to evaluate the NMR experiments in this manuscript.
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Referee #2
Evidence, reproducibility and clarity
Koszela et al. have submitted this manuscript demonstrating the molecular mechanism of interaction between Parkin and one of its known substrates, Miro1. While the interaction and ubiquitination of Miro1 by Parkin (and it's role in mitochondrial quality control) has been known since 2011, as demonstrated by the Schwarz group and others, the mechanism of action has remained unknown. The ability of Parkin to ubiquitinate multiple proteins upon mitochondrial damage has indeed led many groups to speculate that Parkin is a promiscuous E3 ligase upon activation; this manuscript tries to provide a rationale for the interaction with one of its known substrates through a combination of biochemical and biophysical studies.
The authors demonstrate that Miro1 is efficiently ubiquitinated in in vitro biochemical assays in comparison to a few mitochondrial and non-mitochondrial proteins in an attempt to show that Miro1 is a preferred substrate for Parkin. Cross-linking coupled with mass spectrometry, SAXS and NMR experiments were used to provide compelling evidence for a direct and specific interaction between Parkin and Miro1. Molecular modelling using Colabfold and biochemical assays with mutants of the proposed interaction site were then used to provide further proof for the specificity of the interaction. This interaction is shown to occur between the conserved a.a.115-122 (referred to in this study as STR; located in the linker connecting the Ubl to RING0) and the EF domain of Miro1. Interestingly, the authors show that peptides corresponding to 115-122 competitively inhibit ubiquitination of Miro1 by Parkin. Overall, this article constitutes an important addition to our understanding of Parkin's mechanism of action. However, some of the key claims remain unsubstantiated, as described below.
Major issues:
- In line 151 the authors claim, 'these data strongly support the hypothesis that Miro1 is the preferred substrate of pParkin...'. Arguably, the biggest issue with this study is the lack of substantial proof that Miro1 is the preferred parkin substrate in a cellular or physiological context. This claim cannot be made based on a biochemical assay with three other proteins. The Harper group has performed in-depth proteomics studies on the kinetics of Parkin-mediated ubiquitination and proposed that VDACs and Mfn2 (among a few others) are most efficiently ubiquitinated upon mitochondrial damage in induced neurons (Ordureau et al, 2018,2020). Interestingly, neither of these papers have been mentioned by the authors in this manuscript. The Trempe group has shown that Mfn2 is efficiently targeted by Parkin through mitochondrial reconstitution assays and proximity ligation assays (Vranas et al, 2022). The authors need to substantiate their claim through cellular or mitochondrial assays to prove that Miro1 is the preferred physiological substrate of Parkin. Cellular experiments also account for cellular abundance and proximity of Parkin to the substrate, which is not possible in biochemical assays of the kind presented here. In the absence of strong experimental proof for this claim, these claims should be tampered down to Miro1 being "the preferred substrate compared to the other proteins in this assay", and the manuscript should focus more on the molecular mechanism of interaction between Miro1 and Parkin.
- In addition to the point above, the authors do not describe the rationale for specifically choosing Mfn1 and MitoNEET for their comparison with Miro1 as substrates. Interestingly, Miro1, MitoNEET and Mfn1 are not among the most efficiently ubiquitinated substrates of Parkin (Ordureau et al, 2018). Additionally, the authors have used a construct of Mfn1 that lacks the full HR1 domain for their assays. Previously, it has been shown that the HR1 of mitofusins is targeted by Parkin (McLelland et al. 2018). Can the authors prove that their Mfn1 construct is as efficiently ubiquitinated as full-length Mfn1 by Parkin? If it is not possible to obtain soluble full-length Mfn1 or other membrane proteins for these assays, then I strongly recommend the authors should perform mitochondrial reconstitution assays as others have performed previously (Vranas et al, 2022) and use this opportunity to also report the ubiquitination kinetics of multiple mitochondrial substrates compared to Miro1 to make a more compelling case for substrate preference.
- The authors show that both pParkin-Miro1 and Parkin-Miro1 complexes can be captured by chemical cross-linking. It is well-established in the field that pUbl binds to RING0 (Gladkova et al, 2018) (Sauve et al, 2018) while non-phosphorylated Ubl binds RING1 (Trempe et al, 2013). The Komander group has also shown that the ACT (adjacent to the STR) element binds RING2 in the activated Parkin structure (Gladkova et al, 2018). This suggests that STR could occupy different positions in the Parkin and pParkin. The authors have only reported the cross-link/MS data and model of the Parkin-Miro1 complex. Arguably, the pParkin-Miro1 data is just as, if not more, relevant given that pParkin represents the activated form the ligase. The authors need to robustly establish that Miro1 binds to the STR element in both cases by demonstrating the following:
A. Mass spectrometry data from cross-linked pParkin-Miro1 complex suggesting the same interaction site.
B. Colabfold modelling with the pParkin structure to show that Miro1 would bind to the same element. 4. Does Parkin only bind to Miro1, or can it bind to Miro2 as well? Are there differences between the binding site and Ub target sites between the two proteins? The author should also show experimentally if both proteins get ubiquitinated as efficiently by Parkin and if the STR element is involved in recognizing both proteins. Interestingly, the Harper group reports that Miro2 gets more efficiently ubiquitinated than Miro1 (Ordureau et al, 2018). 5. In Figure 5D, the level of unmodified Miro1 seems to be similar in assays with WT or I122Y Parkin, though the former seems to form longer chains while the latter forms shorter chains. Is there an explanation for this? Perhaps, the authors need to perform this assay at shorter time points to show that there is more unmodified Miro1 remaining when treated with I122Y Parkin (and similarly for the L221R mutant of Miro1)? Also, why is the effect of Miro1 L221R and Parkin I122Y not additive?
Minor comments:
- The authors should report the full cross-linking/MS data report from Merox including the full peptide table and decoy analysis report.
- The authors should report statistics for the fit of the Colabfold model to the experimental SAXS curve.
- Why is the Parkin-Miro1 interaction only captured by NMR and not by ITC? The authors should at least attempt to show the interaction of the STR peptide with Miro1 by an orthogonal technique like ITC.
- The authors should report the NMR line broadening data quantitatively i.e. reporting the reduction in signal intensity for the peaks upon peptide Miro1 binding to quantitatively demonstrate that the 115-122 peak intensity reduction is more significant than other regions.
- Figure 4 (structure figure) and B (PAE plot) should be annotated with the names of domains and elements in Parkin and Miro1 to make these figures clearer and more informative.
Referees cross-commenting
I am in agreement with reviewers 1 and 2. Both of them raise valid and interesting points in their reviews.
Specifically, I would like to highlight the following:
- Reviewer 1 makes a very good point (5/6) highlighting that L119A does not impair Parkin recruitment in the previously reported study. I second this concern and believe that the authors need to re-frame their discussion and make it much more nuanced with regards to the role of Miro1-Parkin interaction in mitophagy (if any at all). Additionally, the authors should also note that previous studies in the field from the Youle group (Narendra et al, 2008) and multiple other groups have shown a complete absence of Parkin recruitment to healthy mitochondria. Parkin recruitment to healthy mitochondria hence remains a controversial idea at best, with no evidence for it outside of Parkin overexpression systems (Safiulina et al, 2018) which can also lead to artifacts. The discussion should take all major studies/observations into account to propose a more nuanced picture of the role of Parkin-Miro1 interaction. Perhaps, this interaction plays more of a role in mitochondrial quarantine (Wang et al. 2011) as suggested by the Schwarz group than in Parkin recruitment?
- Reviewer 3 raises a valid concern about the lack of quantification in ubiquitination assays and alludes to the difficulty in visualizing ubiquitination of multiple proteins. That was a concern I also had but did not include in my review. Perhaps, the authors should also show western blots for each of the protein (in a time course experiment) demonstrating the difference in ubiquitination kinetics of each of proteins instead of busy SDS-PAGE gels for the assay.
Significance
The key strength of this study is the strong biophysical evidence of a direct interaction between Parkin and Miro1 and the discovery of the Miro1 binding site on Parkin. The biophysical and biochemical experiments in this study have been well-designed and executed. The evidence for a specific interaction between Parkin and Miro1 has been provided through multiple approaches. The authors should be commended for this effort. The biggest limitation of this study is the lack of proof that Miro1 is the preferred Parkin substrate in a cellular/physiological context since in biochemical assays Parkin can ubiquitinate multiple proteins non-specifically. Substrate preference claims need to be established in more physiologically relevant experimental settings.
Overall, the study represents a mechanistic advance in terms of our understanding of the interaction between Parkin and one of its substrates i.e. Miro1, showing that Parkin can indeed specifically bind its substrates before targeting them for ubiquitination. This might also inspire others to investigate the molecular mechanism of action of Parkin with other substrates. This paper would likely appeal specialized audiences i.e. biochemists and structural biologists studying Parkin in mitochondrial quality control.
Reviewer expertise: Expert biochemist and biophysicist with a number highly cited works in the field of mitochondrial quality control and Parkin.
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Referee #1
Evidence, reproducibility and clarity
The manuscript by Koszela et al. explores the substrate preference of the Parkinson's disease associated ubiquitin E3-ligase, Parkin. They conclude that Miro1 is a preferred substrate of Parkin and go on to further characterize a binding site of Parkin to Miro1 using a range of biochemical approaches. This site is identical to previously reported (see point 2). The experimental work is strong with many high-quality assays supporting their ideas; however, there are several major points that should be considered:
- The majority (perhaps all) of their biochemical work on Miro1 uses a truncated form of Miro1 lacking the first GTPase domain. It isn't at all clear why this is the case, as no justification is given. Moreover, functional full-length Miro1 has been purified in several papers (e.g. PMID: 33132189). If ubiquitination kinetics are different between the full-length and truncated form of Miro1, this would call into question the significance of the findings in vivo, where the truncation does not exist.
- As the manuscript is currently written, there are areas which do not do justice to previous work. Firstly, the authors state throughout the manuscript that no previous work has identified a binding interface between Parkin and one of its substrates, e.g., in the abstract "no substrate interaction site in Parkin has been reported". This is not true as a recent paper already described the binding interface (DOI: 10.1038/s44318-024-00028-1). "we identify a conserved region in the flexible linker", again this interface is identical to that identified previously. Therefore, this study does not "identify" this interface. Given the timing, it is likely that this discovery has been "scooped" by the previous study, but since the present study goes much further in the biochemical characterization of the interface, it would not diminish the paper's importance to rewrite it, giving proper credit where due. Secondly, the authors spend a large part of their discussion speculating on the significance of non-activated Parkin being able to bind Miro e.g., "Importantly, our results suggest that Parkin can interact with Miro1 independently of its activation state, as Parkin phosphorylation does not detectably increase its interaction with Miro1...". Again, this was already known as Parkin has been shown to be recruited to mitochondria upon Miro1 overexpression in the absence of PINK1 (DOI: 10.15252/embj.201899384 and DOI: 10.15252 /embj. 2018100715). The further biochemical characterisation of the Parkin-Miro1 interaction is important and therefore, in both cases the work contained within the manuscript is still a significant contribution, which should, however, be properly discussed in the light of published work.
- The Miro L221R mutation is used to disrupt Miro-Parkin interaction. Yet, this non-conservative mutation in the midst of a folded domain might have other effects, like affecting calcium binding or preventing the folding of the domain. This is not tested. The complementary Parkin-I122Y used for the same purpose decreases but does not abolish Parkin-Miro1 binding. Parkin-L119A is proposed to abolish the Parkin-Miro1 interaction. The inclusion of this mutant might be important to fully ascertain the role of Parkin-Miro1 binding in Miro1 ubiquitination.
- The effect of Miro competition on other substrates' ubiquitylation is marginal and its reproducibility is questionable (whether mitoNEET ubiquitylation is affected at all in figure 1G is unclear. This blot is anyway over processed with an unnaturally uniform grey background). If the authors wish to make a point about it, these experiments should be repeated and quantified. Moreover, since the model is that the specific Miro-Parkin interaction is involved, the mutants above should be used in the same competition experiments and shown to be unable to compete.
- Related to the previous point, one important factor about the kinetics that the authors do not discuss is how any of it relates to mitophagy in vivo. There very well might be a slight intrinsic preference at a given concentration of substrate and Parkin; however, how this plays out in the cell is not clear, e.g., Miro1 may be many times more, or less, abundant than Mfn1, and so a preference might not have much of an effect. So ubiquitination kinetics would need to be considered in a broader cellular context.
- Related to the above point, the authors state "Parkin translocation was diminished upon L119A mutation, supporting the importance of the Parkin Miro1-interacting site in mitophagy.". However, the study (not cited but which this reviewer assumes to be DOI: 10.1038/s44318-024-00028-1 since the L119A mutation has only ever been used here) finds no change in Parkin recruitment upon damage. So, it cannot be used to support "the importance of the Parkin Miro1-interacting site in mitophagy".
Referees cross-commenting
The reviews align well together with many overlaping point and similar assessment of the significance. Reviewer 2 brings in interesting points pertaining to literature that we were not aware of, explaining why we didn't make these points.
One comment on reviewer's 3 last major points
It does not appear that there is a confusion between pIDDT and PAE scores. The plot is coloured according to PAE (which is a residue x residue 2D matrix, figure 4B), while the protein ribbon is coloured according to pIDDT, which is a 1D per-residue confidence score.
Significance
This study provides an in-depth in vitro assessment of a specific binding interface between the E3-ligase Parkin and one of its substrate Miro1. Although this interface has been recently described, this study goes well beyond previous knowledge by showing that the interface is important for complete Miro ubiquitylation by Parkin, therefore showing that interactions involving unstructured linkers participate in substrate recognition by the E3-ligase. The importance of this interaction remains to be assessed in vivo. This study is of interest to basic mitochondrial dynamics, quality control and mitophagy researcher as well as translational Parkinson's Disease researchers.
The reviewer's expertise is in mitochondrial membrane dynamics.
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Reply to the reviewers
Manuscript number: RC- 2024-02497
Corresponding author(s): Tourriere, Hélene and Maraver, Antonio
1. General Statements [optional]
We sincerely thank the Editors and Reviewers for the time devoted to our manuscript. We found their critiques interesting and very helpful. After careful examination and thanks to a large collaborative effort, we will be able to answer to all the reviewers’ comments by adding significantly new experimental data.
We are also encouraged by the positive comments of the Reviewers:
“This manuscript will likely engage oncologists who investigate the chemotherapy-resistant mechanisms of platinum compounds in NSCLC treatment” (Reviewer 1);
“Overall, the authors have conducted experiments that sufficiently elucidate their claims, and the description of the experiments is detailed.”; and “Overall, this work unveils a novel mechanism for Notch activation in response to platinum chemotherapy, providing a renewed outlook on overcoming chemotherapy resistance in NSCLC” (Reviewer 2).
We are also aware that both reviewers agreed that there is room for improvement, and we are sure that upon accomplishment of all proposed experiments both reviewers will be fully satisfied.
Please bear in mind that although it was known that platinum-based chemotherapy induced the Notch pathway in lung cancer cells, the underlying molecular mechanism was largely unknown. Thanks to the molecular dissection we performed in our study, we propose an innovative treatment for patients with lung cancer, the main cause of death by cancer in the world. Hence, we agree with both reviewers that our study will be appealing for a large number of cancer researchers, and we feel it will be also the case for those interested in DNA damage, Notch and MDM2 pathways.
2. Description of the planned revisions
Reviewer #1 (Evidence, reproducibility and clarity (Required)):
Summary: This manuscript from Maraver and co-authors investigates the putative resistance mechanisms that hinder the efficacy of platinum-based therapies (e.g., carboplatin) against non-small cell lung carcinoma (NSCLC). Using in vitro lung cancer cell lines, shRNA-based knockdown, and exogenous overexpression systems, the research describes a DNA damage-induced resistance mechanism involving the NOTCH signaling pathway and the E3 ligase MDM2. The authors show that carboplatin treatment induces DNA damage and promotes ATM activation, which in turn activates the NOTCH signaling pathway via ubiquitination and stabilization of the Notch Intracellular Domain (NICD). New findings include the MDM2-mediated ubiquitination and stabilization of NICD. Using in vivo NSCLC-PDX models, they demonstrate that combining carboplatin with Notch and MDM2 inhibitors can enhance tumor killing, suggesting that targeting the MDM2/NICD axis in conjunction with carboplatin may be a viable therapeutic alternative. Furthermore, they show that NICD and MDM2 levels are elevated among tumor samples from chemo-resistant patients. Consistent with these findings, high MDM2 levels correlate with poor progression-free survival (PFS) in NSCLC patients.
[Authors] We thank this reviewer for her/his fair summary of our work that highlights our new findings.
Major comments:
Some of the key conclusions may not be convincing.
[Authors] We understand the concerns that reviewer might have and we are sure that upon accomplishment of all experiments detailed below, she/he will be convinced that the manuscript will be ready for publication.
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One significant weakness of the manuscript is the lack of exploration into the underlying mechanism of how MDM2 mediates the stabilization of NICD. While the observation of MDM2-mediated NICD stabilization is intriguing, it is important to provide a more convincing explanation for the reviewers. This could be achieved by offering a detailed molecular mechanism, especially considering that MDM2 typically targets proteins for degradation.
[Authors] After reading this reviewer’s comment, we realize we did a poor job discussing better the previous study demonstrating that MDM2 induced ubiquitination on NICD but not for degradative purposes (Pettersson et al., 2013). In particular, they performed it using a mutated form of ubiquitin in lysine 48, i.e., the K48R mutant. Like this, the authors of this seminal study demonstrated that MDM2 was still able to induce ubiquitination in NICD, and hence it was not degradative.
Still, and to confirm that this is the case also upon DNA damage, we will perform experiments using same K48R mutant to formally prove that MDM2 upon DNA damage does not ubiquitinate NICD via lysine 48-linked polymers, and hence it is not degradative. Even more, upon discussion with Laetitia Linares, author of our study and long-lasting expert in ubiquitination (for instance see (Riscal et al., 2016) and (Arena et al., 2018)), we will use another ubiquitin mutant in lysine 63. This different type of ubiquitination does not mark proteins for degradation but promote an association of the targeted protein with DNA helping for DNA repair (Liu et al., 2018). Using a ubiquitin mutated in this lysine, i.e., K63R, this type of ubiquitination cannot occur. Taking into account that we observe NICD increase ubiquitination upon DNA damage, the use of K63R will be very informative.
Hence, we will repeat experiments of current Figure 3A with the same WT ubiquitin as before, and now also with K48R and K63R mutants. Even more, we will also include mutant forms of ubiquitin which can only form ubiquitin chains on lysine 48 (K48 only) or lysine 63 (K63 only) and we anticipate that in the presence of K48 only mutant, NICD will not be ubiquitinated upon DNA damage, while the use of K63 only mutant will be very useful. All these data will be part of the new Figure 3A.
Of note, Dr Linares has all tools required to perform these experiments and hence we will start them soon.
Another weakness lies in the unclear role and the underlying mechanism of ATM in the MDM2-mediated NICD stabilization. While the data presented (Fig. 3B, 3C) suggest that carboplatin could elevate MDM2 levels for NICD stabilization, a more precise method to induce MDM2 overexpression specifically for targeting NICD is required. It appears that ATM plays a crucial role in this regulatory process. The following questions must be addressed: Does ATM induce the phosphorylation of MDM2 for its protein stabilization and/or E3 ligase activity?
[Authors] There are several points here.
For the first one, the use of a more precise method to induce MDM2 overexpression, it is exactly what we did in Figure 4A, i.e., ectopic expression of MDM2 to demonstrate that MDM2 is sufficient to increase NICD levels.
For the second one, i.e., the phosphorylation status of MDM2 by ATM in our system, we will perform different experiments. There are up to six proposed residues in MDM2 to be phosphorylated by ATM upon DNA damage: S386, S395, S407, T419, S425, and S429 (Cheng et al., 2011). Among all of them, S395 is the most well-known and again Dr Linares has interesting tools we will use to answer to this specific reviewer’s point. We will use an MDM2 mutant harboring an aspartate instead of the serine in this position, i.e., S395D, that mimics the serine 395 phosphorylation induced by ATM upon DNA damage. We will use this mutant together with the WT and 464A MDM2 proteins already used, and if this residue is important in our phenotype, total levels of NICD will be even higher and/or localize more in the nuclei when compared with WT MDM2. All these new data will appear as the new Figure 4A __and new Figure 4B__.
Furthermore, we will also use an antibody that recognizes this phosphorylation site by WB after carboplatin treatment and it will be part of the new Figure 3B.
Finally, we will also express WT MDM2 and purify it by immunoprecipitation in different experimental conditions: steady state, upon carboplatin treatment and also in combination of carboplatin and ATM inhibitor, to perform phospho-proteomics analysis upon all these conditions. Of note, and to show the feasibility of this approach, the proteomic platform at Biocampus in Montpellier has experience using this technique (Kassouf et al., 2019).
The combination therapy of carboplatin with MDM2 and NICD inhibitors may lack compelling rationale (see below).
[Authors] This is a very important point but we discuss it below, where more information is provided by the reviewer. Still, we anticipate we will perform a new in vivo experiment to answer to this point.
In lines 275-276, the authors stated that their preclinical data establish the enhancement of carboplatin's therapeutic effect in NSCLC in vivo through MDM2-NICD axis inhibition. However, it's important to note that this finding remains preliminary at this stage.
[Authors] We consider that our statement is not exaggerated, but we will tone down the message as proposed by the reviewer in the next submission.
Minor comments:
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The observed loss of NICD during ATMi + carboplatin treatment in Figures 2A and 2B raises the question of whether ATM regulates the gene transcription of NOTCH. In addition to the CHX assay conducted in Figures 2C and 2D, quantifying NOTCH mRNA upon ATM inhibition could provide further insights. Alternatively, referencing relevant studies on this topic may strengthen the discussion.
[Authors] This is an interesting experiment and we will perform it.
In Figures 4A and 4B, the noticeable discrepancy between the exogenous expression of wild-type (WT) MDM2 and catalytically inactive MDM2-464A raises concerns. It is essential to consider if the reduced ubiquitination and stability of NICD might be attributed to varying levels of MDM2-464A in the cells rather than its catalytic inactivity. While p53 ubiquitination was utilized as a control, ensuring comparable levels of MDM2 and MDM2-464A expression could enhance the experimental rigor. Compared to the smear poly-ubiquitination bands observed for MDM2 in Figure 4B, the ubiquitination of NICD appears simpler. What distinguishes the feature of MDM2-mediated NICD ubiquitination? Could it potentially involve mono-ubiquitination?
[Authors] The point of the reviewer is well taken, and importantly, as mentioned above in main point 2, we will repeat these experiments and will appear as new Figure 4A and new Figure 4B.
Regarding the type of ubiquitination, as explained in detail in major point 1 to same reviewer, we will fully characterize the type of ubiquitination on NICD induced by DNA damage, and we will confirm that MDM2 is required for this specific ubiquitination in future new Figure 4C where we will overexpress the required ubiquitin forms and WT MDM2.
In Figure 5A, the authors need to consider conducting additional NOTCH-associated factors to definitively demonstrate the activation of NOTCH signaling beyond HES1. Alternatively, in Figure 5B, the NICD Western blot could be complemented by detecting HES1 or other NOTCH-associated factors.
[Authors] To answer to this particular point, we will test for other downstream targets of Notch as NRARP and it will appear as part of new Figure 5C.
In Figures 5C and 5D, crucial control groups are missing, specifically mice treated solely with SP141+DBZ, carboplatin+SP141, and SP141+DBZ. It is essential to include these groups to demonstrate that the enhanced tumor killing results from the combination of carboplatin with SP141 and/or DBZ, rather than from SP141 and DBZ alone. Furthermore, in addition to the currently used NSCLC-PDX model harboring the p53 (P151R) mutation, it would be informative to include a NSCLC-PDX model expressing WT p53.
[Authors] This is a crucial point in this rebuttal as mentioned before in major point 3 and we detail it in here.
We did only 3 groups because preliminary data indicated that SP141 in combination with carboplatin was not showing any benefit compared to carboplatin alone while upon combination of carboplatin with Notch inhibition there was only a slight increase in therapeutic carboplatin benefit but otherwise not very potent, and for simplicity we preferred to don’t show these data. But, after reading this point from Reviewer 1, even if we will propose later only the triple combination for patients, we clearly need to demonstrate that the other combinations are not potent enough or not at all.
The reviewer asked to include: “SP141+DBZ, carboplatin+SP141, and SP141+DBZ”. We imagine that she/he meant: SP141+DBZ, carboplatin+SP141, and carboplatin +DBZ, that together with the vehicle, carboplatin and carboplatin+SP141+DBZ makes 6 groups of treatments. Putting together the 8 mice devoted for tumor growth and survival, plus 4 mice for the acute treatment for IHC and WB purposes (for current Figures 5A and 5B) makes a total of 72, that is a substantial number of mice. Of note, since we performed the in vivo experiment presented in the current manuscript, a new Notch inhibitor called nirogacestat, appear in the market being the first in class Notch inhibitor to treat solid cancer patients (desmoid tumors) after demonstrating a significant therapeutic effect in clinical trials (Gounder et al., 2023).
Hence, we will take advantage of the repetition of this experiment to substitute this new molecule instead of DBZ, that is an interesting molecule for preclinical research, but without any clinical relevance. Therefore, the use of nirogacestat will further increase the medical impact of our data. Importantly, nirogacestat is better tolerated than DBZ, meaning that mice can be treated for longer periods of time and we propose in here to treat up to 12 weeks. Finally, after discussion with Quentin Thomas, author of the manuscript and clinical researcher in the lab, we will provide 4 carboplatin cycles as it is proposed today to NSCLC patients in an attempt of getting closer to the clinical setting. In particular we will provide carboplatin to mice on weeks 1, 4, 7 and 10, while treating with MDM2 inhibitor (SP141) and Notch inhibitor (nirogacestat) from Monday to Friday for the 12 weeks.
This experiment will be long and will require an important use of resources both human and financial, but we are sure that the effect in tumor growth and survival will be more dramatic than the one presented now.
On the contrary and as explained in the 4th subheading part of this “revision plan”, including another 72 mice to treat a p53 proficient NSCLC PDX, when we already demonstrated in vitro that p53 is not required for the phenotype described in this study, for us it is totally unfeasible by ethical reasons, i.e., the use of animals in research (please see below for further details).
All the new data will appear as new Figure 5 (B to E). For new Figure 5A please see below the major comment 2 of Reviewer 2.
Though beyond the current study's scope, in the discussion section, the authors may want to propose or hypothesize on how MDM2-mediated NICD stabilization contributes to carboplatin resistance. This could provide valuable insights for future research directions.
[Authors] We will discuss this part as proposed by the reviewer.
In the Western blot results, the total ATM and ATR controls were absent.
[Authors] The reviewer is totally right and we will repeat experiments to include all the totals as requested.
Authors may choose to include a graphical abstract at the end of their study to visually illustrate the mechanisms they have described.
[Authors] Very good idea thanks, we will do it.
Reviewer #1 (Significance (Required)):
Advance: The authors aim to present a novel perspective on the resistance mechanisms to platinum compounds in NSCLC therapy. They explore platinum compounds-induced DNA damage, ATM activation, and MDM2-mediated stabilization of the active form of NOTCH (NICD). However, to strengthen their claims, they must provide more conclusive results.
Audience: This manuscript will likely engage oncologists who investigate the chemotherapy-resistant mechanisms of platinum compounds in NSCLC treatment, as well as scientists specializing in NOTCH and MDM2 pathways. However, the manuscript's central claims lack robust support from the available data, and the current approaches employed are not sufficiently thoughtful and rigorous; there is room for improvement.
My expertise is molecular medicine, cancer biology, and epigenetics.
[Authors] We want to thank again this reviewer for her/his helpful comments that will increase the impact and the relevance of our study while keeping the original message.
We are also very satisfied when she/he said: “This manuscript will likely engage oncologists who investigate the chemotherapy-resistant mechanisms of platinum compounds in NSCLC treatment”.
Reviewer #2 (Evidence, reproducibility and clarity (Required)):
In this manuscript, Sara Bernardo et al. investigated the molecular mechanisms underlying the activation of the Notch signaling in response to DNA damage induced by platinum-based chemotherapeutic agents in non-small cell lung cancer (NSCLC). They demonstrated that carboplatin treatment induces DNA double-strand breaks (DSBs) and stabilizes NICD, a process dependent on ATM and mediated by MDM2. In vivo experiments in patient-derived xenografts (PDX) showed that inhibition of NICD and MDM2 enhanced platinum effectiveness. Furthermore, clinical analysis revealed a correlation between MDM2 expression and poor prognosis in NSCLC patients treated with platinum compounds, emphasizing the clinical relevance of the MDM2-NICD axis in platinum resistance.
[Authors] We thank this reviewer for her/his nice synopsis of our study.
Major comments:
Overall, the authors have conducted experiments that sufficiently elucidate their claims, and the description of the experiments is detailed. However, there is still room for the improvement.
[Authors] We are very pleased that reviewer finds our experimental work “…sufficiently elucidate their claims, and the description of the experiments is detailed.” And we are sure that after all the new experiments we are proposing in here, she/he will be fully satisfied.
1.The finding that MDM2 promoted NICD stability through non degradative ubiquitination is interesting and in line with a previous study. As it is also known that NICD is regulated by various post-translational modifications, including ubiquitination that promotes NICD degradation. It is unclear what's the potential difference between these two types of ubiquitination. For example, do these two differ in specific ubiquitination sites? Can the authors provide some discussion?
[Authors] We agree with the reviewer and hence we will perform a new set of experiments to determine the role of 2 key lysine residues in the ubiquitin protein promoting either degradation or DNA binding. As explained in detail in major point 1 from reviewer 1, we will determine if DNA damage promotes ubiquitination in position 48, i.e., to degrade, or in position 63, i.e., to facilitate the binding to DNA for repairing upon DNA damage, or in any of these 2 positions. And as mentioned above, we will then confirm that MDM2 is responsible of the specific ubiquitination type we will uncover. We are sure that the reviewer will be satisfied by these new data once is generated.
As for the specific ubiquitination sites in NICD, there are up to 17 lysine residues susceptible of being ubiquitinated. Hence unveiling what residues are targeted by MDM2 and if they differ from others inducing degradation as those promoted by the E3 ligase FBXW7, we feel is out of the scope of the current manuscript. Still, we will discuss all this part as kindly proposed by the reviewer.
Could the overexpression of MDM2 or NICD lead to carboplatin resistance in A549 or H358 cells?
[Authors] This is a very interesting experiment and prompted by the reviewer’s comment we started the subcloning of inducible NICD into lentiviral vectors to generate stable cells and test the carboplatin sensitivity in presence of different levels of NICD. These new data will be the new Figure 5A.
The trends observed in the western blot data within the manuscript appear inconsistent. While the authors propose that NICD levels increased upon incubation with carboplatin, the discrepancy arises when considering the NICD levels without cycloheximide (CHX) treatment in Figure 1E, where no significant elevation is observed (Lane 6 vs. Lane 1).
[Authors] The point of the reviewer is well taken. Please bear in mind that in here we are handling several signaling pathways that interact among them while having each one different kinetics. Our finding of increased NICD upon carboplatin treatment is highly consistent in vitro and in vivo, but it is true that in the experiment mentioned by the reviewer is not obvious, probably due to some kinetic issue. We are repeating this experiment to have the increased in NICD upon carboplatin as it is in the rest of the manuscript (up to 9 times only in main figures).
The quality of western blots needs to be improved, especially Fig. 1C and S1C, also Figure 3B. Moreover, the NICD western blot sometimes appears as one band and sometimes as two bands. Please provide an explanation. If possible, please quantify the bands in western blots.
[Authors] We agree with the reviewers that not all WB have the same quality and we will repeat some of them to homogenize the quality all over the manuscript, and particularly, we will repeat the ones kindly pointed out by the reviewer.
The two bands it is something we also noticed and we will pay attention while reproducing the WB, since it might be related to discrepancies in the percentage of acrylamide. If this is not the case, i.e., upon repetition we still observe in some conditions and not in others, we will provide explanations for this in the new submission as kindly proposed by the reviewer.
Finally, and also as proposed by the reviewer we will quantify the WB bands.
Please provide a necessary discussion on whether the targeted treatment approach towards the MDM2-NICD axis is applicable to all patients or only to those with high expression of MDM2/NICD.
[Authors] In the discussion of the current manuscript, we focused into the MDM2 high expression subset of patients for this issue, but in the next submission we will enlarge to patients with high levels of NICD also.
How to interpret the significance of the simultaneous increase in NICD ubiquitination and stability mediated by MDM2? Please provide a relevant discussion.
[Authors] We will provide strong experimental data to go beyond discussion (please see above the experiments with ubiquitin mutants), but we will also provide discussion of this particular point.
In Figure 5B, please also check the level of MDM2. In Figure 5C, carboplatin appears to have little impact on tumor growth. How to explain the increase of Ki-67 in the carboplatin treatment group in Figure 5A?
[Authors] We will measure also levels of MDM2 in the future new Figure 5C as requested by the reviewer.
As for the interesting observation of the Ki67, since we will repeat the whole experiment, we will pay special attention to this point if ever it is repeated. Should be this the case, we will elaborate an explanation.
Minor comments:
1.Please include scale bars in Figure 1B and Supplemental Figure 1B.
[Authors] We thank the reviewer for this comment. We will include the scale bars where required.
2.Figure 5D, the P values of the survival curve should be indicated in the figures.
[Authors] We will include the P values in the future new Figure 5E.
3.The presentation of survival curve data in Figures 5D and 6A should be consistent.
[Authors] The point of the reviewer is well taken and we will use Prism to draw the PFS for patients in Figure 6A as we did for the mice in current Figure 5D.
4.It seems that supplemental figure 2 is missing.
[Authors] We actually jumped from supplemental figure 1 to 3 because we do not have any associated supplemental figure to main Figure 2. We will clarify this point in the next submission.
5.Please carefully check the spelling of the entire text, for example, on page 20, line 426 it should be 'western'. Also, please spell out the abbreviations DDR and ATM.
[Authors] We will double check all spelling and provide the abbreviations kindly suggested by the reviewer.
6.The abbreviation for Cleaved caspase 3 should be CC3.
[Authors] We thank the reviewer for this information, we will use CC3 in the next submission.
Reviewer #2 (Significance (Required)):
Notch signaling is associated with the occurrence and development of non-small cell lung cancer (NSCLC). Previous study indicates that the expression of Notch protein is significantly higher in NSCLC tissues compared to normal tissues (PMID: 31170211). Additionally, the upregulation of Notch1 is correlated with higher tumor grades, lymph node metastasis, tumor-node-metastasis (TNM) staging, and poor prognosis (PMID: 25996086). Abnormal activation of Notch signaling pathway is frequently observed in chemotherapy-resistant NSCLC, and some studies have aimed to address NSCLC drug resistance via modulating Notch signaling (PMID: 30087852, 38301911). This manuscript firstly proposes that MDM2-mediated stabilization of NICD upon DNA damage plays a major role in NSCLC response to platinum chemotherapy. It further suggests that targeting the MDM2-NICD axis could prove to be an effective therapeutic strategy. Overall, this work unveils a novel mechanism for Notch activation in response to platinum chemotherapy, providing a renewed outlook on overcoming chemotherapy resistance in NSCLC. This manuscript will attract those interested in the mechanisms of chemotherapy resistance and novel treatment approaches.
[Authors] We sincerely thank the reviewer for finding that our “…work unveils a novel mechanism for Notch activation in response to platinum chemotherapy, providing a renewed outlook on overcoming chemotherapy resistance in NSCLC”. We are also very satisfied when she/he says: “This manuscript will attract those interested in the mechanisms of chemotherapy resistance and novel treatment approaches.”
Finally, we are convinced that the reviewer will appreciate all the new proposed experimental data, and also that upon finishing all experiments, she/he will think that the manuscript will be suitable for publication.
3. Description of the revisions that have already been incorporated in the transferred manuscript
For simplicity, we decided to introduce all changes in next submission upon conclusion of all experimental approaches proposed above.
4. Description of analyses that authors prefer not to carry out
While we will perform almost all experiments proposed by reviewers, there is one we feel is not possible to do due to ethical reasons. Reviewer 1 wanted us to perform a new in vivo experiment with the same PDX using up to 6 treatment groups. We use 8 mice per condition (for tumor growth and survival) plus 4 for the “acute” treatment for WB and IHC purposes, hence 12 mice x 6 groups = 72 mice, and we will perform this experiment as indicated above and proposed by the reviewer.
On the contrary, the reviewer asked us also to repeat the same experiment with a PDX p53 proficient. While we understand the possible interest, since we demonstrated in vitro that p53 is not required for the protective phenotype of MDM2 and Notch upon DNA damage, we honestly believe that using another 72 mice to confirm this aspect in vivo, is against the rational use of animals in research going against the 3Rs rule. Hence, we will not perform this experiment unless Editors believe is strictly required.
REFERENCES
Arena, G., Cisse, M. Y., Pyrdziak, S., Chatre, L., Riscal, R., Fuentes, M., Arnold, J. J., Kastner, M., Gayte, L., Bertrand-Gaday, C., et al. (2018). Mitochondrial MDM2 Regulates Respiratory Complex I Activity Independently of p53. Mol Cell 69, 594-609 e598.
Cheng, Q., Cross, B., Li, B., Chen, L., Li, Z., and Chen, J. (2011). Regulation of MDM2 E3 ligase activity by phosphorylation after DNA damage. Mol Cell Biol 31, 4951-4963.
Gounder, M., Ratan, R., Alcindor, T., Schoffski, P., van der Graaf, W. T., Wilky, B. A., Riedel, R. F., Lim, A., Smith, L. M., Moody, S., et al. (2023). Nirogacestat, a gamma-Secretase Inhibitor for Desmoid Tumors. N Engl J Med 388, 898-912.
Kassouf, T., Larive, R. M., Morel, A., Urbach, S., Bettache, N., Marcial Medina, M. C., Merezegue, F., Freiss, G., Peter, M., Boissiere-Michot, F., et al. (2019). The Syk Kinase Promotes Mammary Epithelial Integrity and Inhibits Breast Cancer Invasion by Stabilizing the E-Cadherin/Catenin Complex. Cancers (Basel) 11.
Liu, P., Gan, W., Su, S., Hauenstein, A. V., Fu, T. M., Brasher, B., Schwerdtfeger, C., Liang, A. C., Xu, M., and Wei, W. (2018). K63-linked polyubiquitin chains bind to DNA to facilitate DNA damage repair. Sci Signal 11.
Pettersson, S., Sczaniecka, M., McLaren, L., Russell, F., Gladstone, K., Hupp, T., and Wallace, M. (2013). Non-degradative ubiquitination of the Notch1 receptor by the E3 ligase MDM2 activates the Notch signalling pathway. Biochem J 450, 523-536.
Riscal, R., Schrepfer, E., Arena, G., Cisse, M. Y., Bellvert, F., Heuillet, M., Rambow, F., Bonneil, E., Sabourdy, F., Vincent, C., et al. (2016). Chromatin-Bound MDM2 Regulates Serine Metabolism and Redox Homeostasis Independently of p53. Mol Cell 62, 890-902.
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Referee #2
Evidence, reproducibility and clarity
In this manuscript, Sara Bernardo et al. investigated the molecular mechanisms underlying the activation of the Notch signaling in response to DNA damage induced by platinum-based chemotherapeutic agents in non-small cell lung cancer (NSCLC). They demonstrated that carboplatin treatment induces DNA double-strand breaks (DSBs) and stabilizes NICD, a process dependent on ATM and mediated by MDM2. In vivo experiments in patient-derived xenografts (PDX) showed that inhibition of NICD and MDM2 enhanced platinum effectiveness. Furthermore, clinical analysis revealed a correlation between MDM2 expression and poor prognosis in NSCLC patients treated with platinum compounds, emphasizing the clinical relevance of the MDM2-NICD axis in platinum resistance.
Major comments:
Overall, the authors have conducted experiments that sufficiently elucidate their claims, and the description of the experiments is detailed. However, there is still room for the improvement.
1.The finding that MDM2 promoted NICD stability through non degradative ubiquitination is interesting and in line with a previous study. As it is also known that NICD is regulated by various post-translational modifications, including ubiquitination that promotes NICD degradation. It is unclear what's the potential difference between these two types of ubiquitination. For example, do these two differ in specific ubiquitination sites? Can the authors provide some discussion? 2. Could the overexpression of MDM2 or NICD lead to carboplatin resistance in A549 or H358 cells? 3. The trends observed in the western blot data within the manuscript appear inconsistent. While the authors propose that NICD levels increased upon incubation with carboplatin, the discrepancy arises when considering the NICD levels without cycloheximide (CHX) treatment in Figure 1E, where no significant elevation is observed (Lane 6 vs. Lane 1). 4. The quality of western blots needs to be improved, especially Fig. 1C and S1C, also Figure 3B. Moreover, the NICD western blot sometimes appears as one band and sometimes as two bands. Please provide an explanation. If possible, please quantify the bands in western blots. 5. Please provide a necessary discussion on whether the targeted treatment approach towards the MDM2-NICD axis is applicable to all patients or only to those with high expression of MDM2/NICD. 6. How to interpret the significance of the simultaneous increase in NICD ubiquitination and stability mediated by MDM2? Please provide a relevant discussion. 7. In Figure 5B, please also check the level of MDM2. In Figure 5C, carboplatin appears to have little impact on tumor growth. How to explain the increase of Ki-67 in the carboplatin treatment group in Figure 5A?
Minor comments:
1.Please include scale bars in Figure 1B and Supplemental Figure 1B. 2.Figure 5D, the P values of the survival curve should be indicated in the figures. 3.The presentation of survival curve data in Figures 5D and 6A should be consistent. 4.It seems that supplemental figure 2 is missing. 5.Please carefully check the spelling of the entire text, for example, on page 20, line 426 it should be 'western'. Also, please spell out the abbreviations DDR and ATM. 6.The abbreviation for Cleaved caspase 3 should be CC3.
Significance
Notch signaling is associated with the occurrence and development of non-small cell lung cancer (NSCLC). Previous study indicates that the expression of Notch protein is significantly higher in NSCLC tissues compared to normal tissues (PMID: 31170211). Additionally, the upregulation of Notch1 is correlated with higher tumor grades, lymph node metastasis, tumor-node-metastasis (TNM) staging, and poor prognosis (PMID: 25996086). Abnormal activation of Notch signaling pathway is frequently observed in chemotherapy-resistant NSCLC, and some studies have aimed to address NSCLC drug resistance via modulating Notch signaling (PMID: 30087852, 38301911). This manuscript firstly proposes that MDM2-mediated stabilization of NICD upon DNA damage plays a major role in NSCLC response to platinum chemotherapy. It further suggests that targeting the MDM2-NICD axis could prove to be an effective therapeutic strategy. Overall, this work unveils a novel mechanism for Notch activation in response to platinum chemotherapy, providing a renewed outlook on overcoming chemotherapy resistance in NSCLC. This manuscript will attract those interested in the mechanisms of chemotherapy resistance and novel treatment approaches.
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Referee #1
Evidence, reproducibility and clarity
Summary:
This manuscript from Maraver and co-authors investigates the putative resistance mechanisms that hinder the efficacy of platinum-based therapies (e.g., carboplatin) against non-small cell lung carcinoma (NSCLC). Using in vitro lung cancer cell lines, shRNA-based knockdown, and exogenous overexpression systems, the research describes a DNA damage-induced resistance mechanism involving the NOTCH signaling pathway and the E3 ligase MDM2. The authors show that carboplatin treatment induces DNA damage and promotes ATM activation, which in turn activates the NOTCH signaling pathway via ubiquitination and stabilization of the Notch Intracellular Domain (NICD). New findings include the MDM2-mediated ubiquitination and stabilization of NICD. Using in vivo NSCLC-PDX models, they demonstrate that combining carboplatin with Notch and MDM2 inhibitors can enhance tumor killing, suggesting that targeting the MDM2/NICD axis in conjunction with carboplatin may be a viable therapeutic alternative. Furthermore, they show that NICD and MDM2 levels are elevated among tumor samples from chemo-resistant patients. Consistent with these findings, high MDM2 levels correlate with poor progression-free survival (PFS) in NSCLC patients.
Major comments:
Some of the key conclusions may not be convincing.
- One significant weakness of the manuscript is the lack of exploration into the underlying mechanism of how MDM2 mediates the stabilization of NICD. While the observation of MDM2-mediated NICD stabilization is intriguing, it is important to provide a more convincing explanation for the reviewers. This could be achieved by offering a detailed molecular mechanism, especially considering that MDM2 typically targets proteins for degradation.
- Another weakness lies in the unclear role and the underlying mechanism of ATM in the MDM2-mediated NICD stabilization. While the data presented (Fig. 3B, 3C) suggest that carboplatin could elevate MDM2 levels for NICD stabilization, a more precise method to induce MDM2 overexpression specifically for targeting NICD is required. It appears that ATM plays a crucial role in this regulatory process. The following questions must be addressed: Does ATM induce the phosphorylation of MDM2 for its protein stabilization and/or E3 ligase activity?
- The combination therapy of carboplatin with MDM2 and NICD inhibitors may lack compelling rationale (see below).
- In lines 275-276, the authors stated that their preclinical data establish the enhancement of carboplatin's therapeutic effect in NSCLC in vivo through MDM2-NICD axis inhibition. However, it's important to note that this finding remains preliminary at this stage.
Minor comments:
- The observed loss of NICD during ATMi + carboplatin treatment in Figures 2A and 2B raises the question of whether ATM regulates the gene transcription of NOTCH. In addition to the CHX assay conducted in Figures 2C and 2D, quantifying NOTCH mRNA upon ATM inhibition could provide further insights. Alternatively, referencing relevant studies on this topic may strengthen the discussion.
- In Figures 4A and 4B, the noticeable discrepancy between the exogenous expression of wild-type (WT) MDM2 and catalytically inactive MDM2-464A raises concerns. It is essential to consider if the reduced ubiquitination and stability of NICD might be attributed to varying levels of MDM2-464A in the cells rather than its catalytic inactivity. While p53 ubiquitination was utilized as a control, ensuring comparable levels of MDM2 and MDM2-464A expression could enhance the experimental rigor. Compared to the smear poly-ubiquitination bands observed for MDM2 in Figure 4B, the ubiquitination of NICD appears simpler. What distinguishes the feature of MDM2-mediated NICD ubiquitination? Could it potentially involve mono-ubiquitination?
- In Figure 5A, the authors need to consider conducting additional NOTCH-associated factors to definitively demonstrate the activation of NOTCH signaling beyond HES1. Alternatively, in Figure 5B, the NICD Western blot could be complemented by detecting HES1 or other NOTCH-associated factors.
- In Figures 5C and 5D, crucial control groups are missing, specifically mice treated solely with SP141+DBZ, carboplatin+SP141, and SP141+DBZ. It is essential to include these groups to demonstrate that the enhanced tumor killing results from the combination of carboplatin with SP141 and/or DBZ, rather than from SP141 and DBZ alone. Furthermore, in addition to the currently used NSCLC-PDX model harboring the p53 (P151R) mutation, it would be informative to include a NSCLC-PDX model expressing WT p53.
- Though beyond the current study's scope, in the discussion section, the authors may want to propose or hypothesize on how MDM2-mediated NICD stabilization contributes to carboplatin resistance. This could provide valuable insights for future research directions.
- In the Western blot results, the total ATM and ATR controls were absent.
- Authors may choose to include a graphical abstract at the end of their study to visually illustrate the mechanisms they have described.
Significance
Advance: The authors aim to present a novel perspective on the resistance mechanisms to platinum compounds in NSCLC therapy. They explore platinum compounds-induced DNA damage, ATM activation, and MDM2-mediated stabilization of the active form of NOTCH (NICD). However, to strengthen their claims, they must provide more conclusive results.
Audience: This manuscript will likely engage oncologists who investigate the chemotherapy-resistant mechanisms of platinum compounds in NSCLC treatment, as well as scientists specializing in NOTCH and MDM2 pathways. However, the manuscript's central claims lack robust support from the available data, and the current approaches employed are not sufficiently thoughtful and rigorous; there is room for improvement."
My expertise is molecular medicine, cancer biology, and epigenetics.
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Reply to the reviewers
Manuscript number: RC-2024-02394
Corresponding author(s): Altman, Brian J
1. General Statements [optional]
We thank all three Reviewers for their insightful and helpful feedback and suggestions. We strongly believe that addressing these comments has now resulted in a much-improved manuscript. We appreciate that the Reviewers found the manuscript "interesting" with "valuable insights and... obvious novelty", "an important study that is well-done", and "an important understanding of the crosstalk between cancer cells and immune cells as well as the understanding of how the TME disrupts circadian rhythms". All three Reviewers requested a significant revision, which we provide here. We carefully and completely responded to each Reviewer question or suggestion, in most cases with new experiments and text, and in a very few cases with changes or additions to the Discussion section. This includes new data in seven of the original Figures and Supplementary Figures, and one new main Figure and three new Supplementary Figures. Highlights of these new data include testing the role of low pH in cancer cell supernatant on macrophage rhythms, and analysis of single-cell RNA-sequencing data for heterogeneity in macrophage circadian gene expression. Additional experiments were also performed that were not included in the manuscript, and these data are presented in this Response. A detailed point-by-point response to each comment is included below with excerpts of the data and updated text for the reviewers. Please note that the PDF version of this Response includes images of the new Figures inserted in to the manuscript.
2. Point-by-point description of the revisions
__Reviewer #1 __
Evidence, reproducibility and clarity
The manuscript by Knudsen-Clark et al. investigates the novel topic of circadian rhythms in macrophages and their role in tumorigenesis. The authors explore how circadian rhythms of macrophages may be influenced by the tumor microenvironment (TME). They utilize a system of bone marrow-derived macrophages obtained from transgenic mice carrying PER2-Luciferase (PER2-Luc), a trackable marker of rhythmic activity. The study evaluates how conditions associated with the TME, such as polarizing stimuli (to M1 or M2 subtype), acidic pH, and elevated lactate, can each alter circadian rhythms in macrophages. The authors employ several approaches to explore macrophage functions in cancer-related settings. While the manuscript presents interesting findings and may be the first to demonstrate that tumor stimuli alter circadian rhythms in macrophages and impact tumor growth, it lacks a clear conclusion regarding the role of altered circadian rhythms in suppressing tumor growth. Several discrepancies need to be addressed before publication, therefore, the manuscript requires revision before publication, addressing the following comments:
We thank Reviewer #1 for the comments regarding the quality of our work and are pleased that the Reviewer finds that this manuscript "presents interesting findings and may be the first to demonstrate that tumor stimuli alter circadian rhythms in macrophages and impact tumor growth". We have addressed all comments and critiques from Reviewer #1 below. To summarize, we added new data on how different macrophage polarization states affect media pH (Supplementary Figure 4), further characterized gene expression in our distinct macrophage populations (Supplementary Figure 1), provided clarity in the data and text on the universal nature of Clock Correlation Distance (CCD) across macrophage populations (Figure 6), included human tumor-associated macrophage (TAM) data for CCD (Figure 7) analyzed single-cell RNA-sequencing data of TAMs to demonstrate heterogeneity in circadian gene expression (Figure 9), and used tumor-conditioned media to show that low pH still affects macrophage rhythms in this context *Supplementary Figure 5". Thanks to the helpful suggestions of the Reviewer, we also made numerous clarifications and fixed a critical referencing error that the Reviewer identified.
Major comments: 1. It is well known that pro-inflammatory macrophages primarily rely on glycolysis during inflammation, exhibiting dysregulated tricarboxylic acid (TCA) cycle activity. These pro-inflammatory macrophages are commonly referred to as 'M1' or pro-inflammatory, as noted in the manuscript. In contrast, M2 macrophages, or pro-resolution macrophages, are highly dependent on active mitochondrial respiration and oxidative phosphorylation (OXPHOS). Given that M1 macrophages favor glycolysis, they create an acidic environment due to elevated lactate levels and other acidifying metabolites. However, the study does not address this effect. The authors' hypothesis revolves around the acidic environment created by glycolytic tumors, yet they overlook the self-induced acidification of media when culturing M1 macrophages. This raises the question of how the authors explain the reduced circadian rhythms observed in pro-inflammatory macrophages in their study, while low pH and higher lactate levels enhance the amplitude of circadian rhythms. I would encourage the authors to incorporate the glycolytic activity of pro-inflammatory macrophages into their experimental setup. Otherwise the data look contradictory and misleading in some extent.
We appreciate the important point Reviewer #1 made that macrophages polarized toward a pro-inflammatory phenotype such as those stimulated with IFNγ and LPS (M1 macrophages) prioritize metabolic pathways that enhance glycolytic flux, resulting in increased release of protons and lactate as waste products from the glycolysis pathway. In this way, polarization of macrophages toward the pro-inflammatory phenotype can lead to acidification of the media, which may influence our observations given that we are studying the effect of extracellular pH on rhythms in macrophages. To address this point, we have performed additional experiments in which we measured pH of the media to capture changes in media pH that occur during the time in which we observe changes in rhythms of pro-inflammatory macrophages.
In line with the documented enhanced glycolytic activity of pro-inflammatory macrophages, the media of pro-inflammatory macrophages is acidified over time, in contrast to media of unstimulated or pro-resolution macrophages. Notably, while pH decreased over time in the pro-inflammatory group, the pH differential between the pH7.4, pH6.8, and pH6.5 sample groups was maintained over the period in which we observe and measure changes in circadian rhythms of pro-inflammatory macrophages. Additionally, media that began at pH 7.4 was acidified only to pH 7 by day 2, above the acidic pH of 6.8 or 6.5. As a result, there remained a difference in pH between the two groups (pH 7.4 and pH 6.5) out to 2 days consistent with the changes in rhythms that we observe between these two groups. This indicates that the difference in circadian rhythms observed in pro-inflammatory macrophages cultured at pH 7.4 compared to pH 6.5 were indeed due to the difference in extracellular pH between the two conditions. We have incorporated these data, shown below, into Supplementary Figure 4 and added the following discussion of these data to the Results section:
"In line with their documented enhanced glycolytic capacity, pro-inflammatory macrophages acidified the media over time (Supplementary Figure 4C). Notably, while pH of the media the pro-inflammatory macrophages were cultured in decreased over time pH, the pH differential between the pH 7.4, pH 6.8, and pH 6.5 samples groups of pro-inflammatory macrophages was maintained out to 2 days, consistent with the changes in rhythms that we observe and measure between these groups."
The article examines the role of circadian rhythms in tumor-associated macrophages, yet it lacks sufficient compelling data to support this assertion. Two figures, Figure 7 and Figure 9, are presented in relation to cancer. In Figure 7, gene expression analysis of Arg1 (an M2 marker) and Crem (a potential circadian clock gene) is conducted in wild-type macrophages, BMAL1-knockout macrophages with dysregulated circadian rhythms, and using publicly available data on tumor-associated macrophages from a study referenced as 83. However, it is noted that this referenced study is actually a review article by Geeraerts et al. (2017) titled "Macrophage Metabolism as Therapeutic Target for Cancer, Atherosclerosis, and Obesity" published in Frontiers in Immunology. This raises concerns about the reliability of the results. Furthermore, comparing peritoneal macrophages from healthy mice with macrophages isolated from lung tumors is deemed inaccurate. It is suggested that lung macrophages from healthy mice and those from mice with lung tumors should be isolated separately for a more appropriate comparison. Consequently, Figure 7B is further questioned regarding how the authors could compare genes from the circadian rhythm pathway between these non-identical groups. As a result, the conclusion drawn from these data, suggesting that tumor-associated macrophages exhibit a gene expression pattern similar to BMAL1-KO macrophages, is deemed incorrect, affecting the interpretation of the data presented in Figure 8.
We thank Reviewer #1 for pointing out our error in the reference provided as the source of the TAM data used for CCD in Figure 7. While we took care to provide the GEO ID for the data set (GSE188549) in the Methods section, we mistakenly cited Geeraerts (2017) Front Immunol when we should have cited Geeraerts (2021) Cell Rep. We have corrected this citation error in the main text.
We also appreciate Reviewer #1's concern that we are comparing circadian gene expression of peritoneal macrophages to tumor-associated macrophages derived from LLC tumors, which are grown ectopically in the flank for the experiment from which the data set was produced. To ensure an accurate comparison of gene expression, we downloaded the raw FASTQ files from each dataset and processed them in identical pipelines. Our main comparison between these cell types is Clock Correlation Distance (CCD), which compares the pattern of co-expression of circadian genes (Shilts et al PeerJ 2018). CCD was built from multiple mouse and human tissues to be a "universal" tool to compare circadian rhythms, and designed to compare between different tissues and cell types. Each sample is compared to a reference control built from these multiple tissues. To better convey this concept to readers to give confidence the suitability of CCD for comparing data sets across different tissues, we have added the reference control to Figure 7 (now Figure 6B), We have also expanded our analysis to include bone marrow-derived macrophages, to further demonstrate that the organization of clock gene co-expression is not specific to peritoneal macrophages; we have added this data to Figure 7 (now Figure 6C,D). Finally, we have included an abbreviated explanation of the points made above in the results section.
Due to the universal nature of the CCD tool, we disagree with Reviewer #1's assertion that "the conclusion drawn from these data, suggesting that tumor-associated macrophages exhibit a gene expression pattern similar to BMAL1-KO macrophages, is deemed incorrect". Indeed, this finding mirrors findings in the original CCD paper, which showed that tumor tissues universally exhibit a disordered molecular clock as compared to normal tissue. Notably, the original CCD paper also compared across cell and tumor types.
As an additional note to the review, we would like to clarify that nowhere in the manuscript do we propose that Crem is a potential circadian clock gene. We are clear throughout the manuscript that we are using Crem as a previously established biomarker for acidic pH-sensing in macrophages. Please see below for the modified Figure and text.
"To understand the status of the circadian clock in TAMs, we performed clock correlation distance (CCD) analysis. This analysis has previously been used to assess functionality of the circadian clock in whole tumor and in normal tissue[102]. As the circadian clock is comprised of a series of transcription/translation feedback loops, gene expression is highly organized in a functional, intact clock, with core clock genes existing in levels relative to each other irrespective of the time of day. In a synchronized population of cells, this ordered relationship is maintained at the population level, which can be visualized in a heatmap. CCD is designed to compare circadian clock gene co-expression patterns between different tissues and cell types. To accomplish this, CCD was built using datasets from multiple different healthy tissues from mouse and human to be a universal tool to compare circadian rhythms. Each sample is compared to a reference control built from these multiple tissues (Figure 6B)[102]. To validate the use of this analysis for assessing circadian disorder in macrophages, we performed CCD analysis using publicly available RNA-sequencing data from bone marrow-derived macrophages and wild type peritoneal macrophages, as a healthy control for functional rhythms in a synchronized cell population, and BMAL1 KO peritoneal macrophages, as a positive control for circadian disorder[44]."
And in the Discussion:
"Interestingly, analysis of TAMs by clock correlation distance (CCD) presents evidence that rhythms are disordered in bulk TAMs compared to other macrophage populations (Figure 6). CCD is one of the most practical tools currently available to assess circadian rhythms due to its ability to assess rhythms independent of time of day and without the need for a circadian time series, which is often not available in publicly available data from mice and humans[102]."
If the authors aim to draw a clear conclusion regarding the circadian rhythms of tumor-associated macrophages (TAMs), they may need to analyze single-sorted macrophages from tumors and corresponding healthy tissues. Such data are publicly available (of course not in #83)
We agree with Reviewer #1 that while our interpretation of the data is that there may be heterogeneity in circadian rhythms of tumor-associated macrophages, we cannot prove this without assessing circadian rhythms at the single cell level. While single-cell RNA-sequencing data of freshly isolated tumor associated macrophages of sufficient read depth for circadian gene expression analysis has historically been unavailable, fortunately a dataset was released recently (May 2024) which we were able to use to address this point. We have analyzed publicly available single-cell RNAseq data of tumor-associated macrophages (GSE260641, Wang 2024 Cell) to determine whether there are differences in expression of circadian clock genes between different TAM populations. We have added these data as a new Figure 9. Please see the figure and updated text below.
"Tumor-associated macrophages exhibit heterogeneity in circadian clock gene expression.
__ Our findings suggested that heterogeneity of the circadian clock may lead to disorder in bulk macrophage populations, but did not reveal if specific gene expression changes exist in tumor-associated macrophages at the single-cell level. To determine whether heterogeneity exists within the expression of circadian clock genes of the tumor-associated macrophage population, we analyzed publicly available single-cell RNA sequencing data of macrophages isolated from B16-F10 tumors[107]. To capture the heterogeneity of macrophage subsets within the TAM population, we performed unbiased clustering (Figure 9A). We then performed differential gene expression to determine if circadian clock genes were differentially expressed within the TAM subpopulations. The circadian clock genes Bhlhe40 (DEC1), Bhlhe41 (DEC2), Nfil3 (E4BP4), Rora (RORα), Dbp (DBP), and Nr1d2 (REV-ERBβ) were significantly (adj.p We next sought to determine whether differences in circadian clock gene expression between TAM subpopulations were associated with exposure to acidic pH in the TME. To this end, we first assessed Crem expression in the TAM subpopulations that were identified by unbiased clustering. Crem expression was significantly higher in TAM clusters 4, 5, and 6 compared to TAM clusters 1-3 and 7-9 (Figure 9C). Clusters were subset based on Crem expression into Crem high (clusters 4-6) and Crem low (clusters 1-3, 7-9) (Figure 9D), and differential gene expression analysis was performed. The circadian clock genes Nfil3, Rora, Bhlhe40, and Cry1 (CRY1) were significantly (adj.p __And in the Discussion:
"Supporting the notion that population-level disorder may exist in TAMs, we used scRNA-sequencing data and found evidence of heterogeneity between the expression of circadian clock genes in different TAM subpopulations (Figure 9A, B). Phenotypic heterogeneity of TAMs in various types of cancer has previously been shown[20, 21, 125, 126], and we have identified distinct TAM subpopulations by unbiased clustering (Figure 9A). Within those TAM subpopulations, we identified differential expression of circadian clock genes encoding transcription factors that bind to different consensus sequences: DEC1 and DEC2 bind to E-boxes, NFIL3 and DBP binds to D-boxes, and RORα and REV-ERBβ binds to retinoic acid-related orphan receptor elements (ROREs)[127, 128]. While little is known about regulation of macrophages by E-box and D-box elements beyond the circadian clock, aspects of macrophage function have been shown to be subject to transcriptional regulation through ROREs[129, 130]. Thus, we speculate that variations in these transcription factors may exert influence on expression of genes to drive diversity between TAM subpopulations. Differential expression of circadian clock genes between TAM subpopulations was also associated with Crem expression (Figure 9C-E), suggesting that exposure of TAMs to acidic pH within the TME can alter the circadian clock. However, there remained significant variation in expression of circadian clock genes within the Crem high and Crem low groups (Figure 9B), suggesting that acidic pH is not the only factor in the TME that can alter the circadian clock. Together, these data implicate the TME in driving heterogeneity in TAM circadian rhythms just as it drives heterogeneity in TAM phenotype.
Interestingly, in contrast to our observations of circadian disorder in TAMs isolated from LLC tumors (Figure 6), rhythmicity in expression of circadian genes was observed in bulk TAMs isolated from B16 tumors[107]. This suggests that circadian rhythms of TAMs are maintained differently in different types of cancer. Notably, both of these observations were at the population level. Upon separation of the B16 TAM population into subsets by unbiased clustering of single-cell RNA sequencing data, we measured differences in expression of circadian clock genes between TAM subpopulations (Figure 9A,B). This suggests that even within a rhythmic TAM population, there is heterogeneity in the circadian clock of TAM subpopulations."
Additionally, it is widely acknowledged that human and mouse macrophages exhibit distinct gene expression profiles, both in vitro and in vivo. While assuming that genes involved in circadian rhythms are conserved across species, the authors could consider extending their funding to include analyses of single-sorted macrophages from cancer patients, such as those with lung cancer or pancreatic ductal adenocarcinoma (PDAC). These experiments would provide relevant insights into TAM biology.
We agree that with Reviewer #1 that ultimately, being able to relate findings in mice to humans is critical. It is important to assess if circadian disorder is observed in TAMs in human cancers as it is for LLC tumor-derived macrophages in mice. To address this point, we have performed CCD using a human data set (GSE116946; Garrido 2020 J Immunother Cancer) suitable for use with CCD (wherein macrophages were isolated from bulk tumor in humans, with a high enough samples size, and not cultured prior to sequencing). We have added these data as a new Figure 7, shown below. Please see the added data and updated text below.
"We next assessed the status of the circadian clock in human TAMs from NSCLC patients. We performed CCD with publicly available RNA-seq data of tumor-adjacent macrophages and tumor-associated macrophages from NSCLC patients, using alveolar macrophages from healthy donors as a control[104, 105]. To assess the contribution of the acidic TME to circadian disorder, we subset TAM NSCLC patient samples into groups (Crem high TAMs and Crem low TAMs) based on median Crem expression. Notably, in macrophages from human NSCLC there was a trend toward disorder in Crem low but not Crem high TAM samples (Figure 7A,B). Additionally, the co-variance among core clock genes observed in alveolar macrophages from healthy donors was absent within Crem low and Crem high TAM samples (Figure 7C). In all, these data indicate that there is population-level disorder in the circadian rhythms of tumor-associated macrophages in humans and mice, suggesting that circadian rhythms are indeed altered in macrophages within the TME."
And in the Discussion:
"Indeed, we observed differences in the circadian clock of Crem low human TAM samples compared to Crem high human TAM samples, suggesting that acidic pH influences circadian disorder in TAMs (Figure 7). Interestingly, Crem low TAM samples exhibited a trend toward disorder while Crem high TAM samples did not. This is of particular interest, as we have observed that acidic pH can enhance circadian rhythms in macrophages, raising the question of whether acidic pH promotes or protects against circadian disorder."
Minor comments: 1. Figure 2C needs clarification. It's unclear why pro-inflammatory macrophages treated with lactic acid would have a shorter amplitude and period, while acidic pH would increase amplitude and period in M2 macrophages.
We thank Reviewer #1 for this important observation. Based on the comment, it is our understanding that the Reviewer is referring to the data in Figure 2 (low pH) compared to Figure 4 (lactate). We also find it very interesting that lactate alters rhythms in a manner distinct from the way in which acidic pH alters rhythms. Reviewer 3 asked for clarification on how lactate affected circadian gene expression in pH 7.4 or 6.5. We have added these data as Figure 4C (data and text below). It is notable that lactate opposing effects on circadian gene expression in pH 6.5, enhancing the effects of low pH in some cases (Nr1d1) while blunting them in other cases (Cry1). This is mentioned in the text.
"Lactate was also observed to alter expression of the circadian clock genes Per2, Cry1, and Nr1d1 over time in BMDMs cultured at pH 6.5, while having more subtle effects at pH 7.4 (Figure 4C). Notably, lactate blunted the effect of pH 6.5 on Cry1 expression, while enhancing the effect of low pH on Nr1d1 expression."
Why these two stimuli alter rhythms differently remains an open question that is discussed in the Discussion section and is prime to be a topic of future investigation. We have added to the Discussion section potential reasons why these conditions may alter rhythms differently, such as the different pathways downstream of sensing these two different conditions. Please see the updated text, below.
"Although lactate polarizes macrophages toward a pro-resolution phenotype similar to acidic pH[30, 93], exposure to lactate had different effects on circadian rhythms - and in some cases, circadian clock gene expression - than exposure to acidic pH (Figure 4). Sensing of lactate occurs through different pathways than acid-sensing, which may contribute to the different ways in which these two stimuli modulate circadian rhythms of macrophages[111]. One previously published finding that may offer mechanistic insight into how phenotype can influence circadian rhythms is the suppression of Bmal1 by LPS-inducible miR-155[54]. It has also been observed that RORα-mediated activation of Bmal1 transcription is enhanced by PPARγ co-activation[112]. In macrophages, PPARγ expression is induced upon stimulation with IL-4 and plays a key role in alternative activation of macrophages, promoting a pro-resolution macrophage phenotype, and supporting resolution of inflammation[113-115]. Such observations prompt the question of whether there are yet-unidentified factors induced downstream of various polarizing stimuli that can modulate expression of circadian genes at the transcriptional and protein levels. Further work is required to understand the interplay between macrophage phenotype and circadian rhythms."
The scale in Figure 2C should be equal for all conditions (e.g., -200).
We appreciate Reviewer #1's preference for the axes to be scaled similarly to enable cross-comparison between graphs. However, due to the different amplitude of pro-inflammatory macrophages compared to the others, we feel that making all axes the same will make it hard to see the rhythms of pro-inflammatory macrophages, hindering the reader's ability to observe the data. Thus, we have put the matched-axis plots, shown below, in Supplementary Figure 4A.
Absolute values of amplitude, damping, and period differ between Figure 1 and Figure 2A, B, C. The authors should explain these discrepancies.
As with many experimental approaches, there is slight variation in absolute values between independent experiments, which Reviewer #1 correctly notes. However, while the absolute values vary slightly, the relationship between the values in each of these conditions remains the same across the panels mentioned by Reviewer #1.
The authors should consider modulating the acidic environment of macrophages in settings more representative of cancer. For example, by adding conditioned media from tumor cells with pronounced glycolysis.
We appreciate Reviewer #1's desire to more closely mimic the tumor microenvironment. To address Reviewer #1's point, we cultured macrophages in RPMI or cancer cell (KCKO) supernatant at pH 6.5 or pH-adjusted to pH 7.4 and assessed rhythms by measuring rhythmic activity of Per2-Luc with LumiCycle analysis. We then compared changes in rhythms between macrophages cultured normal media to cancer cell supernatant in pH-matched conditions to assess how cancer cell-conditioned media may influence circadian rhythms of macrophages, and the contribution of acidic pH. We have added these data, shown below, as a new Supplementary Figure 5, and included a discussion of these data in the manuscript. Please see the new Figure and updated text below.
"Cancer cell supernatant alters circadian rhythms in macrophages in a manner partially reversed by neutralization of pH.
We have observed that polarizing stimuli, acidic pH, and lactate can alter circadian rhythms. However, the tumor microenvironment is complex. Cancer cells secrete a variety of factors and deplete nutrients in the environment. To model this, we cultured BMDMs in RPMI or supernatant collected from KCKO cells, which are a murine model of pancreatic ductal adenocarcinoma (PDAC)[94, 95], at pH 6.5 or neutralized to pH 7.4 (Supplementary Figure 5). Circadian rhythms of BMDMs cultured in cancer cell supernatant at pH 7.4 or pH 6.5 exhibited increased amplitude and lengthened period compared to RPMI control at pH 7.4 or 6.5, respectively, indicating that cancer cell supernatant contains factors that can alter circadian rhythms of BMDMs. Notably, BMDMs cultured in cancer cell supernatant at pH 6.5 had increased amplitude and shortened period compared to BMDMs cultured in cancer cell-conditioned media at pH7.4, indicating that pH-driven changes in rhythms were maintained in BMDMs cultured in cancer cell supernatant. When the pH of cancer cell supernatant was neutralized to pH7.4, the increased amplitude was decreased, and the shortened period was lengthened, indicating that neutralizing acidic pH partially reverses the changes in rhythms observed in macrophages cultured in cancer cell supernatant at pH 6.5. These data further support our observations that acidic pH can alter circadian rhythms of macrophages both alone and in combination with various factors in the TME."
And, in the Discussion:
"We have shown that various stimuli can alter rhythms of macrophages in a complex and contributing manner, including polarizing stimuli, acidic pH, and lactate. TGFβ is produced by a variety of cells within the TME, and was recently identified as a signal that can modulate circadian rhythms[123, 124]. Additionally, when we exposed macrophages to cancer cell-conditioned media, rhythms were modulated in a manner distinct from acidic pH or lactate, with these changes in rhythms partially reversed by neutralization of the cancer cell-conditioned media pH (Supplementary Figure 5). It is conceivable that, in addition to acidic pH, other stimuli in the TME are influencing circadian rhythms to drive population-level disorder that we observed by CCD."
Arg1 alone is not sufficient as an M2 polarization marker. The authors should include additional markers.
We thank Reviewer #1 for bringing up this critical point in experimental rigor. While Arg1 is a commonly-used marker for M2 polarization, Reviewer #1 points out that polarization of macrophages is typically assessed by a full panel of markers characteristic of the M2 state. To address this point, we have expanded our panel to include several other markers of M2 polarization in mice such as Retnla, Ym1, MGL1, and CD206. In response to Reviewer 2's major point 2 and Reviewer 3's major point 4 below, we have also expanded our panel of markers used to assess the M1 polarization state with Tnfa, Il1b. and Il6. We have added these data, shown below, to Supplementary Figure 1 and updated the text appropriately. Please see the new Figure and updated text below.
"Consistent with previous studies, we found that genes associated with anti-inflammatory and pro-resolution programming characteristic of IL-4 and IL-13-stimulated macrophages such as Arg1, Retnla, Chil3 (Ym1), Clec10a (MGL1), and Mrc1 (CD206) were induced in IL-4 and IL-13-stimulated macrophages, but not IFNγ and LPS-stimulated macrophages. In contrast, genes associated with pro-inflammatory activity characteristic of IFNγ and LPS-stimulated macrophages such as Nos2 (iNOS), Tnfa, Il1b, and Il6 were induced in IFNγ and LPS-stimulated macrophages, but not IL-4 and IL-13-stimulated macrophages (Supplementary Figure 1)[28, 30, 65, 71, 74, 75]. This indicates that macrophages stimulated with IL-4 and IL-13 were polarized toward a pro-resolution phenotype, while macrophages stimulated with IFNγ and LPS were polarized toward a pro-inflammatory phenotype."
__ Significance__
While the manuscript provides valuable insights and has obvious novelty, it requires a significant revision
We thank Reviewer #1 for their deep read of our manuscript, and their helpful feedback and suggestions. As shown by the comments above, we are confident we have fully addressed each of the points that were made to result in a much-improved revised manuscript.
__ Reviewer #2 __
Evidence, reproducibility and clarity
Knudsen-Clark et al. showed that the circadian rhythm of bone marrow-derived macrophages (BMDM) can be affected by polarization stimuli, pH of the microenvironment, and by the presence of sodium-lactate. Mechanistically, the acidic pH of cell microenvironment is partly regulated by intracellular cAMP-mediated signaling events in BMDM. The authors also showed that the circadian clock of peritoneal macrophages is also modified by the pH of the cell microenvironment. Using publicly available data, the authors showed that the circadian rhythm of tumor-associated macrophages is similar to that of Bmal1-KO peritoneal macrophages. In a murine model of pancreatic cancer, the authors showed that the tumor growth is accelerated in C57BL/6 mice co-injected with cancer cells and Bmal1-KO BMDM as compared to mice co-injected with cancer cell and wild type BMDM.
We thank Reviewer #2 for their insightful and helpful comments and feedback. Their Review guided key clarifying experiments and additions to the Discussion and Methods. To summarize, we added new data to Supplementary Figure 1 to characterize distinct gene expression in our different polarized macrophage populations, showed in Supplementary Figure 2 that serum shock independently induces cAMP and Icer, discussed the limitations of the artificial polarization models more clearly, and updated our Methods to better explain how macrophages were isolated from the peritoneum. We also quantified multiple immunoblots of pCREB, provided clarity in the Methods and Reviewer-only data on how our protein-extraction protocol isolates nuclear protein, better introduced the BMAL1-KO mouse model, and showed in Supplementary Figure 6 that low pH can induce oscillations in the absence of a serum shock.
Major points of criticism: 1. Nine main figures include different experimental models on a non-systematic manner in the manuscript, and only literature-based correlation is used to link the results each other. The authors used in vitro BMDM and peritoneal cell-based model systems to study the effects of IL4+IL13, IFNg+LPS, low pH, sodium-lactate, adenylate cyclase inhibitors on the circadian clock of macrophages. The link between these microenvironment conditions of the cells is still correlative with the tumor microenvironment: publicly available data were used to correlate the increased expression level of cAMP-activated signaling events with the presence of acidic pH of tumor microenvironment. Notably, the cell signaling messenger molecule cAMP is produced by not only low extracellular pH by activated GPCRs, but also starvation of the cell. The starvation is also relevant to this study, since the BMDM used in the in vitro culture system were starving for 24 hours before the measurement of Per2-Luc expression to monitor circadian rhythm.
We agree with the important point that Reviewer #2 makes that our synchronization protocol of serum starvation followed by serum shock can impact the cAMP signaling pathway. Indeed, it has previously been shown that serum shock induces phosphorylation of CREM in rat fibroblasts, which is indicative of signaling through the cAMP pathway. To address this point, we have added a schematic of our synchronization protocol to Supplementary Figure 2B for additional clarity. We have also performed additional experiments to test whether cAMP signaling is induced in macrophages by our synchronization protocol. For this, we assessed downstream targets of the cAMP signaling pathway, Icer and pCREB, after serum starvation but before serum shock, and at several time points post-treatment with serum shock (Supplementary Figures 2D,E). We observed that Icer and phosphorylation of Creb are induced rapidly in macrophages upon exposure to serum shock, as early as 10 minutes for pCREB and 1 hour post-exposure for Icer. Notably, this signaling is transient and rapidly returns to baseline, with pCREB levels fully returned to baseline by 2 hours post-treatment, at which time media is replaced and the experiment begins (CT 0). These data, shown below, have been added to Supplementary Figure 2 and a discussion of these data has been added to the manuscript - please see the modified text below."The synchronization protocol we use to study circadian rhythms in BMDMs involves a 24-hour period of serum starvation followed by 2 hours of serum shock. It has previously been shown that serum shock can induce signaling through the cAMP pathway in rat fibroblasts[98]. To determine whether the synchronization protocol impacts cAMP signaling in macrophages, we harvested macrophages before and after serum shock. We then assessed Icer expression and phosphorylation of cyclic AMP-response element binding protein (CREB), which occur downstream of cAMP and have been used as readouts to assess induction of cAMP signaling in macrophages[29, 96, 100]. Serum shock of macrophages following serum starvation led to rapid phosphorylation of CREB and Icer expression that quickly returned to baseline (Supplementary Figure 2D,E). This indicates that serum starvation followed by serum shock in the synchronization protocol we use to study circadian rhythms in BMDMs induces transient signaling through the cAMP signaling pathway. "
The definition of pre-resolution macrophages (MF) used across the manuscript could be argued. The authors defined BMDM polarized with IL-4 and IL-13 as pre-resolution MF. Resolution is followed by inflammation, but the IL-4 secretion does not occur in every inflammatory setting. Moreover, IL-4 and IL-13 are secreted during specific tissue environment and immunological settings involving type 2 inflammation or during germinal center reactions of the lymph nodes. • What are the characteristics of pre-resolution macrophages (MF)? The authors indicated that IL-4 and IL-13 cytokines were used to model the pre-resolution macrophages. In which pathological context are these cytokines produced and induce pre-resolution macrophages? IL-4 polarized BMDM can also produce pro-inflammatory protein and lipid mediators as compared to LPS-stimulated BMDM, and IL-4 polarized BMDM still have potent capacity to recruit immune cells and to establish type 2 inflammation.
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The authors showed Arg1 and Vegfa qPCR data from BMDM only. Based on the literature, these MFs are anti-inflammatory cells particularly. Resolution-related MFs followed by acute inflammation are a specific subset of MFs, and the phenotype of pre-resolution MF should be described, referred, and measured specifically.
We thank Reviewer #2 for bringing up this important point that clarity is required in describing our in vitro macrophage models. We chose the most commonly used models of in vitro macrophage polarization in the tumor immunology field, M2 (IL-4+IL-13) and M1 (IFNγ+LPS). These polarization conditions have been used for over two decades in the field, and have been well-characterized to drive a pro-inflammatory (for M1) and pro-resolution or anti-inflammatory (for M2) macrophage phenotype (Murray 2017 Annu Rev Phys). Each of these cell states have similarities in phenotype to pro-inflammatory and pro-resolution (pro-tumorigenic) macrophages found in tumors. In fact, in the literature, pro-inflammatory and pro-resolution TAMs will frequently be categorized as "M1" or "M2", respectively, even though this is a gross oversimplification (Ding 2019 J Immunol, Garrido-Martin 2020 J Immunother Cancer).
As Reviewer #2 points out, IL-4 and IL-13 play a role in inflammatory settings, mediating protective responses to parasites and pathological responses to allergens. Importantly, IL-4 and IL-13 are also key regulators and effectors of resolution and wound repair (Allen 2023 Annu Rev Immunol). In line with this, M2 macrophages show many of the characteristics of pro-resolution programming in their gene expression profile, expressing genes associated with wound healing (ex. Vegf) and immunoregulation (ex. Arg1) (Mantovani 2013 J Pathol). These cells have frequently been used as a model for studying TAMs in vitro, due to the similarity in pro-resolution programming that is dysregulated/hijacked in TAMs (Biswas 2006 Blood). M2 macrophages have also been referred to as anti-inflammatory, and this is in line with their role in the type 2 response driven by IL-4 and IL-13, as this is primarily a response induced by allergy or parasites where tissue damage drives an anti-inflammatory and pro-resolution phenotype in macrophages (Pesce 2009 Plos Pathogens and Allen 2023 Annu Rev Immunol).
We do not assert that these in vitro models recapitulate the macrophage polarization cycle that Reviewer #2 astutely describes, and indeed, stimuli polarizing macrophages in tumor are much more diverse and complex (Laviron 2022 Cell Rep). We also fully agree with Reviewer #2 that, while IL4 and IL13 may exist in the tumor and be secreted by Th2 CD4 T cells (see Shiao 2015 Cancer Immunol Res), there may be multiple reasons why macrophages may be polarized to a pro-resolution, M2-like state in a tumor (in fact, exposure to low pH and lactate each independently do this, as we show in Supplementary Figure 2 and Figure 4, and was previously shown in Jiang 2021 J Immunol and Colegio 2014 Nature). Nonetheless, using the well-described M1 and M2 in vitro models allows our findings to be directly comparable to the vast literature that also uses these models, and to understand how distinct polarization states respond to low pH.
We fully agree with Reviewer #2 that these cells must be defined more clearly in the text. We have taken care to discuss the limitations of using in vitro polarization models to study macrophages in our Limitations of the Study section. To better address Reviewer #2's concern, we have more thoroughly introduced the M2 macrophages as a model, and are clear that that these are type 2-driven macrophages that share characteristics of pro-resolution macrophages. We have also added additional citations to the manuscript, including those highlighted above in our response. Finally, we have expanded our panel to better characterize the IL-4/IL-13 stimulated macrophages using more markers that have been characterized in the literature, in line with both Reviewer #2's comments and that of Reviewer #1 and Reviewer #3. Please see the updated data and text, below.
"As macrophages are a phenotypically heterogeneous population in the TME, we first sought to understand whether diversity in macrophage phenotype could translate to diversity in circadian rhythms of macrophages. To this end, we used two well-established in vitro polarization models to study distinct macrophage phenotypes[5, 60-63]. For a model of pro-inflammatory macrophages, we stimulated macrophages with IFNγ (interferon γ) and LPS (lipopolysaccharide) to elicit a pro-inflammatory phenotype[60, 64]. These macrophages are often referred to as 'M1' and are broadly viewed as anti-tumorigenic, and we will refer to them throughout this paper as pro-inflammatory macrophages[65, 66]. For a model at the opposite end of the phenotypic spectrum, we stimulated macrophages with IL-4 and IL-13[60, 67]. While these type 2 stimuli play a role in the response to parasites and allergy, they are also major drivers of wound healing; in line with this, IL-4 and IL-13-stimulated macrophages have been well-characterized to adopt gene expression profiles associated with wound-healing and anti-inflammatory macrophage phenotypes[68-71]. As such, these macrophages are often used as a model to study pro-tumorigenic macrophages in vitro and are often referred to as 'M2' macrophages; throughout this paper, we will refer to IL-4 and IL-13-stimulated macrophages as pro-resolution macrophages[66, 72, 73]. Consistent with previous studies, we found that genes associated with anti-inflammatory and pro-resolution programming characteristic of IL-4 and IL-13-stimulated macrophages such as Arg1, Retnla, Chil3 (Ym1), Clec10a (MGL1), and Mrc1 (CD206) were induced in IL-4 and IL-13-stimulated macrophages, but not IFNγ and LPS-stimulated macrophages. In contrast, genes associated with pro-inflammatory activity characteristic of IFNγ and LPS-stimulated macrophages such as Nos2 (iNOS), Tnfa, Il1b, and Il6 were induced in IFNγ and LPS-stimulated macrophages, but not IL-4 and IL-13-stimulated macrophages (Supplementary Figure 1)[28, 30, 65, 71, 74, 75]. This indicates that macrophages stimulated with IL-4 and IL-13 were polarized toward a pro-resolution phenotype, while macrophages stimulated with IFNγ and LPS were polarized toward a pro-inflammatory phenotype.
In the Limitations of the Study section, we now write the following:
"Our observations of rhythms in macrophages of different phenotypes are limited by in vitro polarization models. It is important to note that while our data suggest that pro-inflammatory macrophages have suppressed rhythms and increased rate of desynchrony, it remains unclear the extent to which these findings apply to the range of pro-inflammatory macrophages found in vivo. We use IFNγ and LPS co-treatment in vitro to model a pro-inflammatory macrophage phenotype that is commonly referred to as 'M1', but under inflammatory conditions in vivo, macrophages are exposed to a variety of stimuli that result in a spectrum of phenotypes, each highly context-dependent. The same is true for for 'M2'; different tissue microenvironment are different and pro-resolution macrophages exist in a spectrum."
The authors used IFNg and LPS, or IL-4 and IL-13 and co-treatments to polarize BMDM in to type 1 (referred as pro-inflammatory MF) and type 2 (referred as pre-resolution MF) activation state. The comparison between these BMDM populations has limitations, since LPS induces a potent inflammatory response in MF. The single treatment with MF-polarizing cytokines enable a more relevant comparison to study the circadian clock in classically and alternatively activated MF.
We thank Reviewer #2 for bringing up this important point to provide additional clarity on our polarization conditions. The use of IFNγ and LPS to polarize macrophages toward a pro-inflammatory, M1 phenotype, and the use of IL-4 an IL-13 to polarize macrophages toward a pro-resolution, M2 phenotype have been commonly used for over two decades, and thus are well-characterized in the literature (please see Murray 2017 Annu Rev Phys for an extensive review on the history of these polarization models, as well as Hörhold 2020 PLOS Computational Biology, Binger 2015 JCI, McWhorter 2013 PNAS, Ying 2013 J Vis Exp for more recent studies using these models). The use of LPS alone or in combination with IFNγ, and IL-13 along with IL-4, was introduced in 1998 (Munder 1998 J Immunol). This approach was originally designed to mimic what could happen when macrophages were exposed to CD4+ Th1 cells, which produce IFNγ, or Th2 cells, which produce IL-4 and IL-13 (Munder 1998 J Immunol, Murray 2017 Annu Rev Phys). As Reviewer #2 points out, these stimuli induce potent responses, driving macrophages to adopt pro-inflammatory or pro-resolution/anti-inflammatory phenotypes that are two extremes at opposite ends of the spectrum of macrophage phenotypes (Mosser 2008 Nat Rev Immunol). Since our goal was to study rhythms of distinct macrophage phenotypes in vitro, and how TME-associated conditions such as acidic pH and lactate affect their rhythms, these cell states were appropriate for our questions. Thus, the polarization models used in this paper allowed us to achieve this goal. We include a section in the Discussion on the limitations of in vitro polarization models.
"A critical question in understanding the role of circadian rhythms in macrophage biology is determining how different polarization states of macrophages affect their internal circadian rhythms. This is especially important considering that tumor-associated macrophages are a highly heterogeneous population. Our data indicate that compared to unstimulated macrophages, rhythms are enhanced in pro-resolution macrophages, characterized by increased amplitude and improved ability to maintain synchrony; in contrast, rhythms are suppressed in pro-inflammatory macrophages, characterized by decreased amplitude and impaired ability to maintain synchrony (Figure 1). These agree with previously published work showing that polarizing stimuli alone and in combination with each other can alter rhythms differently in macrophages[80, 81]. In a tumor, macrophages exist along a continuum of polarization states and phenotypes[18-21, 24]. Thus, while our characterizations of rhythms in in vitro-polarized macrophages provide a foundation for understanding how phenotype affects circadian rhythms of macrophages, further experiments will be needed to assess macrophages across the full spectrum of phenotypes. Indeed, alteration of rhythms may be just as highly variable and context-dependent as phenotype itself."
There are missing links between the results of showing the circadian rhythm of polarized BMDM, sodium-lactate treated BMDM, and tumor growth. Specifically, do the used pancreatic ductal adenocarcinoma cells produce IL-4 and sodium-lactate? In the LLC-based experimental in silico analysis of tumors, the LLC do not produce IL-4.
Reviewer #2 raises important points about the source of lactate and IL-4 in tumors as relevance for our investigation of how these factors can alter rhythms in macrophages. Tumor-infiltrating Th2 CD4 T cells are potential sources of IL-4 and IL-13 in the tumor (see Shiao 2015 Cancer Immunol Res). Various cells in the tumor can produce lactate. We discuss this in both the Introduction and the Results: poor vascularization of tumors results in hypoxia areas, where cells are pushed toward glycolysis to survive and thus secrete increased glycolytic waste products such as protons and lactate. As lactate is lactic acid, ionized it is sodium l-lactate.
How can the circadian rhythm affect the function of BMDM? The Authors should provide evidence that circadian rhythm affects the function of polarized MF.
We agree with Reviewer #2 that the next step is to determine how altered rhythms influence function of macrophages. This will be the topic of future work, but is outside the scope of this paper. Our contribution with this paper is providing the first evidence that rhythms are altered in the TME and the TME-associated conditions can alter rhythms in macrophages. We have added what is currently known about how circadian rhythms influence macrophages function to the discussion section to facilitate a conversation about this important future direction. Please see the updated text below.
"Considering our observations that conditions associated with the TME can alter circadian rhythms in macrophages, it becomes increasingly important to understand the relevance of macrophage rhythms to their function in tumors. It has been shown that acidic pH and lactate can each drive functional polarization of macrophages toward a phenotype that promotes tumor growth, with acidic pH modulating phagocytosis and suppressing inflammatory cytokine secretion and cytotoxicity[28, 30, 93]. However, how the changes in circadian rhythms of macrophages driven by these conditions contributes to their altered function remains unknown. Current evidence suggests that circadian rhythms confer a time-of-day-dependency on macrophage function by gating the macrophage response to inflammatory stimuli based on time-of-day. As such, responses to inflammatory stimuli such as LPS or bacteria are heightened during the active phase while the inflammatory response is suppressed during the inactive phase. An important future direction will be to determine how changes in circadian rhythms of macrophages, such as those observed under acidic pH or high lactate, influences the circadian gating of their function."
In Figure 3, the authors show data from peritoneal cells. The isolated peritoneal cells are not pure macrophage populations. Based on the referred article in the manuscript, the peritoneal cavity contains more then 50% of lymphocytes, and the myeloid compartment contains 80% macrophages.
Reviewer #2 raises important concerns about the purity of the peritoneal population used in our experiments. We enrich for peritoneal macrophages from the peritoneal exudate cells by removing non-adherent cells in culture. This is described in our Methods section and is a method of isolation that is commonly used in the field, as lymphocytes are non-adherent. In addition to the source cited in the paper within our Methods section (Goncalves 2015 Curr Prot Immunol), please see Layoun 2015 J Vis Exp, de Jesus 2022 STAR Protocols, and Harvard HLA Lab protocol - macrophages enriched in this manner have been shown to be over 90% pure. We have modified our Methods section to make this clear, and added the additional references in this response to this section of our Methods. Please see the modified text below.
"Peritoneal exudate cells were harvested from mice as previously published[137]. To isolate peritoneal macrophages, peritoneal exudate cells were seeded at 1.2*106 cells/mL in RPMI/10% HI FBS supplemented with 100U/mL Penicillin-Streptomycin and left at 37⁰C for 1 hour, after which non-adherent cells were rinsed off[136]. Isolation of peritoneal macrophages using this method has been shown to yield a population that is over 90% in purity[138, 139]. Peritoneal macrophages were then cultured in Atmospheric Media at pH 7.4 or 6.5 with 100μM D-luciferin, and kept at 37⁰C in atmospheric conditions."
The figure legend of Figure 3 describes the effects of pH on the circadian rhythm of bone marrow-derived macrophages ex vivo. Peritoneal macrophages involve tissue resident peritoneal macrophages with yolk sac and fetal liver origin, and also involve small peritoneal MF with bone marrow origin. The altered description of results and figure legends makes confusion.
We are very grateful to Reviewer #2 for pointing out our typo. We have fixed the caption of Figure 3 to properly describe the data as "peritoneal macrophages ex vivo".
In Figure 6C, one single Western blot is shown with any quantification. The authors should provide data of the relative protein level of p-CREB from at least 3 independent experiments. In the Western-blot part of the methods, the authors described that the pellet was discarded after cell lysis. The p-CREB is the activated form of the transcription factor CREB and there is increased binding to the chromatin to regulate gene expression. By discarding the pellet after cell lysis, the chromatin-bond p-CREB could be also removed at the same time.
We thank Reviewer 2 for bringing up this point. We agree that quantification is an important aspect of western blot. We have repeated the experiment again for n=3 and provide quantification of pCREB normalized to total protein. We have added these data, shown below, to Figure 5.
Reviewer #2 also expressed concern that we may not be capturing all of the CREB due to nuclear localization and chromatin binding. We specifically chose the lysis buffer M-Per, which is formulated to lyse the nucleus and solubilize nuclear and chromatin-bound proteins. To demonstrate this, we show in the below Figure to the Reviewer that the nuclear protein p85 is solubilized and readily detectable by western blot using our protein extraction method.
We have also added an additional sentence in the Methods section for clarity - please see the modified text below.
"Cells were lysed using the M-Per lysis reagent (Thermo Scientific, CAT#78501), supplemented with protease and phosphatase inhibitor cocktail (1:100; Sigma, CAT#PPC1010) and phosphatase inhibitor cocktail 2 (1:50; Sigma, CAT#P5726), with 200μM deferoxamine (Sigma, CAT#D9533). M-Per is formulated to lyse the nucleus and solubilize nuclear and chromatin-bound proteins, allowing isolation of nuclear proteins as well as cytosolic proteins. Lysates were incubated on ice for 1 hour, then centrifuged at 17,000 xg to pellet out debris; supernatant was collected."
It is confusing that adenylate-cyclase inhibitor MDL-12 elevated the phospho-CREB levels in BMDM. How can the authors exclude any other inducers of CREB phosphorylation?
We agree with Reviewer #2 that it is surprising pCREB was elevated with MDL-12 treatment alone, and we do indeed think that there are other pathways contributing to this. We have addressed this point in the Discussion - please see the text below.
"The mechanism through which acidic pH can modulate the circadian clock in macrophages remains unclear. Evidence in the literature suggests that acidic pH promotes a pro-resolution phenotype in macrophages by driving signaling through the cAMP pathway[29]. It has previously been shown that cAMP signaling can modulate the circadian clock[99]. However, our data indicated that cAMP signaling was not fully sufficient to confer pH-mediated changes in circadian rhythms of macrophages (Figure 5A,B). Treatment with MDL-12, commonly known as an inhibitor of adenylyl cyclase[29, 117], resulted in suppression of pH-induced changes in amplitude of circadian rhythms but did not inhibit signaling through the cAMP signaling pathway (Figure 5C,D). While MDL-12 is commonly used as an adenylyl cyclase inhibitor, it has also been documented to have inhibitory activity toward phosphodiesterases (PDEs) and the import of calcium into the cytosol through various mechanisms[118, 119]. This is of particular interest, as calcium signaling has also been shown to be capable of modulating the circadian clock[120]. Furthermore, while acid-sensing through GPCRs have been the most well-characterized pathways in macrophages, there remain additional ways in which acidic pH can be sensed by macrophages such as acid-sensing ion channels[121, 122]. Further work is required to understand the signaling pathways through which pH can influence macrophage phenotype and circadian rhythms."
It is described in the methods that BMDM were starving for 24 hours in serum-free culture media followed by serum shock (50% FBS). The cAMP production can be induced during cell starvation which should be considered for the data representation.
We appreciate that Reviewer #2 points out that our synchronization protocol of serum starvation followed by serum shock may impact the cAMP signaling pathway in macrophages, as serum shock has been shown to induce pCREB, a downstream mediator of cAMP signaling, in rat fibroblasts. Indeed, we show in additional experiments performed (in response to Reviewer #2's major comment 1) evidence that cAMP signaling is induced in macrophages following the serum shock phase of our synchronization protocol, as indicated by elevation of Icer and pCREB. As we note above, this induction is transient and returns to baseline by 2 hours post-serum shock, the time at which we replace media and begin our experiments (CT 0).
Despite the transient nature of cAMP induction by our synchronization protocol, we agree wholeheartedly with Reviewer #2 that this must be considered in light of our experimental system in which we are studying the effect of acidic pH on circadian rhythms of macrophages, which in itself induces signaling through the cAMP signaling pathway. To address Reviewer #2's point, we have performed experiments in which we culture unstimulated BMDMs in neutral pH 7.4 or acidic pH 6.5, without prior serum starvation and serum shock (i.e. we do not submit these BMDMs to the synchronization protocol). We then observed circadian rhythms of Per2-Luc by LumiCycle to determine whether acidic pH alters circadian rhythms of BMDMs in the absence of prior serum starvation followed by serum shock. Similar to our observations in Figure 2, circadian rhythms of macrophages at pH 6.5 had increased amplitude and shortened period compared to rhythms of macrophages at pH 7.4. This indicates that pH-driven changes in circadian rhythms observed in our system are not due to the synchronization protocol. The data, shown below, have been placed in a new Supplementary Figure 6, and a discussion of these results has been added to the Results section - please see the updated text below.
"As acidic pH induces signaling through the cAMP pathway, we sought to determine whether acidic pH independently contributed to the pH-driven changes in circadian rhythms we observe in BMDMs. To test this, we omitted the synchronization step and observed BMDM rhythms by LumiCycle when cultured in neutral pH 7.4 or acidic pH 6.8 or pH 6.5 (Supplementary Figure 6). Circadian rhythms of BMDMs cultured at pH 6.5 exhibited similar changes as previously observed, with enhanced amplitude and shortened period relative to BMDMs at pH 7.4. This indicates pH-driven changes observed in circadian rhythms of BMDMs occur in the absence of prior serum starvation and serum shock. "As acidic pH independently induces signaling through the cAMP pathway, we sought to determine whether acid pH could also independently contribute to the pH-driven changes in circadian rhythms we observe in BMDMs. To test this, we omitted the synchronization step and observed BMDM rhythms by LumiCycle when cultured in neutral pH 7.4 or acidic pH 6.8 or pH 6.5 (Supplementary Figure 6). Circadian rhythms of BMDMs cultured at pH 6.5 exhibited similar changes as previously observed, with enhanced amplitude and shortened period relative to BMDMs at pH 7.4. This indicates pH-driven changes observed in circadian rhythms of BMDMs occur in the absence of prior serum starvation and serum shock."
How can the authors explain and prove that the wild type and Bmal1-KO BMDM co-injected with pancreatic cancer cells subcutaneously survive, present, and have effector functions at the same extent in the subcutaneous tissue, before and during tumor growth (Figure 9)? In other words, what kind of MF-derived parameters could be modified by disrupting the circadian rhythm of MF during tumor development? The production of MF-derived regulatory enzymes, cytokines, growth factors are affected by the disrupted circadian clock in MF?
Review #2 poses the very important question of why we see differences in tumor growth in our co-injection model, and what might be driving it. Of note, co-injection models of tumor growth are commonly used to determine macrophage-specific roles in tumor growth (Colegio 2014 Nature, Mills 2019 Cell Rep, Lee 2018 Nat Comm). We observed that tumor growth is altered when macrophages with disrupted circadian rhythms (BMAL1 KO) are co-injected compared to when macrophages with intact circadian rhythms (WT) are co-injected in a murine model of pancreatic cancer using KCKO cells. Our observation is supported by a previously published paper in which they used a co-injection model of melanoma, which we cite in the manuscript(Alexander 2020 eLife). What drives this difference in tumor growth remains an open question that is the subject of future work and is outside the scope of this paper, which focuses on our discovery that factors associated with the tumor microenvironment can alter circadian rhythms in macrophages. We have included a discussion on what is currently known about how circadian rhythms alter macrophage function, acknowledging that we have yet to answer these important questions and identifying it as interest for future work. Please see the text below."Considering our observations that conditions associated with the TME can alter circadian rhythms in macrophages, it becomes increasingly important to understand the relevance of macrophage rhythms to their function in tumors. It has been shown that acidic pH and lactate can each drive functional polarization of macrophages toward a phenotype that promotes tumor growth, with acidic pH modulating phagocytosis and suppressing inflammatory cytokine secretion and cytotoxicity[28, 30, 93]. However, how the changes in circadian rhythms of macrophages driven by these conditions contributes to their altered function remains unknown. Current evidence suggests that circadian rhythms confer a time-of-day-dependency on macrophage function by gating the macrophage response to inflammatory stimuli based on time-of-day. As such, responses to inflammatory stimuli such as LPS or bacteria are heightened during the active phase while the inflammatory response is suppressed during the inactive phase. An important future direction will be to determine how changes in circadian rhythms of macrophages, such as those observed under acidic pH or high lactate, influences the circadian gating of their function. Data from our lab and others suggest that disruption of the macrophage-intrinsic circadian clock accelerates tumor growth, indicating that circadian regulation of macrophages is tumor-suppressive in models of PDAC (our work) and melanoma [109]. This agrees with complementary findings that behavioral disruption of circadian rhythms in mice (through chronic jetlag) disrupts tumor macrophage circadian rhythms and accelerates tumor growth[56]. It remains unclear whether this is through the pro-tumorigenic functions of macrophages such as extracellular matrix remodeling or angiogenesis, through suppression of the anti-tumor immune response, or a combination of both functions. Further work will be needed to tease apart these distinctions."
Minor points of criticism: 1. The figure legends of the graphs and diagrams are missing in Figure 2D,E,F
We thank Reviewer #2 for pointing out that figure legends were missing. We have added legends for Figure 2D,E,F.
The BMAL1-based in vivo murine model of circadian rhythm is not introduced in the manuscript.
We thank Reviewer #2 for bringing to our attention that the BMAL1 KO macrophage model was not well-introduced in the manuscript. To address this point, we have modified the text to better introduce this model. Please see the modified text below.
"As a positive control for circadian clock disruption, we used data from BMAL1 KO peritoneal macrophages [44]. BMAL1 KO macrophages have a genetic disruption of the circadian clock due to the loss of Bmal1, the central clock gene. As a result, circadian rhythms of BMAL1 KO macrophages are disrupted, lacking rhythmicity and downstream circadian regulation of macrophage function (Supplementary Figure 8)[45, 54]. "As a positive control for circadian clock disruption, we used data from BMAL1 KO peritoneal macrophages [44]. BMAL1 KO macrophages have a genetic disruption of the circadian clock due to the loss of Bmal1, the central clock gene. As a result, circadian rhythms of BMAL1 KO macrophages are disrupted, lacking rhythmicity and downstream circadian regulation of macrophage function (Supplementary Figure 8)[45, 54]."__ __
Significance
Knudsen-Clark et al. showed that the circadian rhythm of bone marrow-derived macrophages (BMDM) can be affected by polarization stimuli, pH of the microenvironment, and by the presence of sodium-lactate. Mechanistically, the acidic pH of cell microenvironment is partly regulated by intracellular cAMP-mediated signaling events in BMDM. The authors also showed that the circadian clock of peritoneal macrophages is also modified by the pH of the cell microenvironment. Using publicly available data, the authors showed that the circadian rhythm of tumor-associated macrophages is similar to that of Bmal1-KO peritoneal macrophages. In a murine model of pancreatic cancer, the authors showed that the tumor growth is accelerated in C57BL/6 mice co-injected with cancer cells and Bmal1-KO BMDM as compared to mice co-injected with cancer cell and wild type BMDM.
We are grateful to Reviewer #2 for their very helpful comments and suggestions, which we believe have greatly enhanced the clarity and reproducibility of this manuscript.
__Reviewer #3 (Evidence, reproducibility and clarity (Required)): __
Review for Knudsen-Clark et al.
"Circadian rhythms of macrophages are altered by the acidic pH of the tumor microenvironment"
Knudsen-Clark and colleagues explore the impact of TME alterations on macrophage circadian rhythms. The authors find that both acidic pH and lactate modulate circadian rhythms which alter macrophage phenotype. Importantly, they define circadian disruption of tumor-associated macrophages within the TME and show that circadian disruption in macrophages promotes tumor growth using a PDAC line. This represents an important understanding of the crosstalk between cancer cells and immune cells as well as the understanding of how the TME disrupts circadian rhythms. The study is well-done, however, authors need to address several important points below.
We thank Reviewer #3 for their in-depth and insightful comments and suggestions, which have resulted in a much-improved manuscript. We were pleased that Reviewer #3 found the work to be "an important study that is well-done" and that it "represents an important understanding of the crosstalk between cancer cells and immune cells as well as the understanding of how the TME disrupts circadian rhythms.". In response to Reviewer #3's comments, we have added several new key experiments and changes to the text. To summarize, we added new data to Supplementary Figure 1 to better characterize our macrophage polarization states, showed in Figure 3 that low pH affects peritoneal macrophage circadian gene expression in a similar fashion as bone marrow-derived macrophages, added new data in Figure 4 to show how lactate and low pH affect circadian gene expression over time, and new computational analysis to Figures 6, 7, and Supplementary Figure 9 to probe circadian gene covariance from publicly available data. We also made several key additions to the Discussion to discuss the functional implications of macrophage circadian rhythm disruption by low pH and potential mechanisms of this disruption. Finally, at the request of Reviewer #3, we consolidated several existing Figures and added new data, where appropriate, to existing figures, and we worked to describe new findings succinctly.
Major comments:
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In Figures 3 and 4, the authors can include additional clock genes that can be run by qPCR. This was done in Figure 2 and was a nice addition to the data.
We agree with Reviewer #3's suggestion that an analysis of clock gene expression at the mRNA level would enhance our data in Figures 3 and 4. To address this point, we have performed short time course experiments to assess circadian clock gene expression over time in BMDMs cultured with or without lactate at neutral or acidic pH (for Figure 4). In line with the difference in circadian rhythms of Per2-Luc levels between BMDMs cultured in the presence or absence of lactate which we observed by Lumicycle analysis, we measured changes in expression of the circadian clock genes Per2, Nr1d1, and Cry1 between macrophages cultured with 25 mM sodium-L-lactate compared to those cultured with 0 mM sodium-L-lactate at pH 6.5. We have added these data, shown below, to Figure 4, and updated the manuscript accordingly to discuss these results. Please see below for the new Figure Panel and modified text.
"Lactate was also observed to alter expression of the circadian clock genes Per2, Cry1, and Nr1d1 over time in BMDMs cultured at pH 6.5, while having more subtle effects at pH 7.4 (Figure 4C). Notably, lactate blunted the effect of pH 6.5 on Cry1 expression, while enhancing the effect of low pH on Nr1d1 expression. In all, these data indicate that concentration of lactate similar to that present in the TME can influence circadian rhythms and circadian clock gene expression of macrophages."
As an additional measure to address Reviewer #3's point about Figure 3 (peritoneal macrophages), we have compared expression of circadian clock genes in peritoneal macrophages cultured at neutral pH 7.4 or acidic pH 6.8 for 24 hours using a publicly available RNA-seq data set from Jiang 2021 J Immunol (GSE164697). In line with previous observations in macrophages cultured under acidic compared to neutral pH conditions, including the clock gene expression data from Figure 2 in BMDMs and the Per2-Luc levels observed in peritoneal macrophages in Figure 3, we found that peritoneal macrophages exhibited differences in expression of circadian clock genes when cultured at acidic pH 6.8 compared to neutral pH 7.4. We have added these data, shown below, as Figure 3B, and have updated the manuscript accordingly - please see below for the new Figure panel and modified text.
"To test whether pH-driven changes in circadian rhythms of peritoneal macrophages were reflected at the mRNA level, we compared expression of circadian clock genes in peritoneal macrophages cultured at neutral pH 7.4 or acidic pH 6.8 for 24 hours using publicly available RNA-sequencing data [30]. In line with altered circadian rhythms observed by Lumicycle, peritoneal macrophages cultured at pH 6.8 expressed different levels of circadian clock genes than peritoneal macrophages culture at pH 7.4 (Figure 3B). The trends in changes of gene expression in peritoneal macrophages cultured at pH 6.8 matched what we observed in BMDMs, where low pH generally led to higher levels of circadian clock gene expression (Figure 2D-F). These data support our observations by LumiCycle and indicate that acidic pH drives transcriptional changes in multiple components of the circadian clock. In all, these data are evidence that pH-dependent changes in circadian rhythms are relevant to in vivo-differentiated macrophages."
We have also updated the Methods section appropriately
"FASTQ files from a previously published analysis of peritoneal macrophages cultured under neutral pH 7.4 or acidic pH 6.8 conditions were downloaded from NCBI GEO (accession #GSE164697) [30]."
2) There are far too many figures with minimal data in each. Please consolidate the figures. For example, Figures 1-3 can be fully combined, Figures 4-6 can be combined, and Figures 7-8 can be combined. Additionally, it is unclear if Figure 5 needs to be in the main, it can be moved to the supplement.
We appreciate the preference of Reviewer #3 to see some of the figures consolidated. We have combined Figures 5 and 6 into a single new Figure 5. Additionally, we have added new data from revisions to current figures to increase the amount of data in each figure and minimize the amount of new figures generated. In all, despite the large amount of new data added to the paper in response to Reviewer comments and suggestions (including additional data in Figure 4 and new Figures 6 and 8), our manuscript now contains 10 main Figures, only one more than the initial submission.
3) The observation that conditions like pH and lactate alter macrophage phenotype and rhythmicity are important. However, macrophage phenotype via gene expression does not always correlate to function. It is important for authors to demonstrate the effect of pH or lactate on macrophage function. This can be done using co-culture assays with cancer cells. If these experiments cannot be performed, it is suggested that authors discuss these limitations and consideration in the discussion.
Reviewer #3 correctly points out that changes in phenotype does not always correlate to changes in function. Others have shown that acidic pH and lactate can each alter macrophage phenotype, and also alter macrophage function and the ability to promote tumor growth (please see El-Kenawi 2019 Br J Cancer, Jiang 2021 J Immunol, Colegio 2014 Nature). How changes in rhythms influence macrophage function remains unknown and we agree with Reviewer #3 that this is an important future direction, We have added a section in the Discussion to facilitate the discussion of this important future direction. Please see the text below.
"Considering our observations that conditions associated with the TME can alter circadian rhythms in macrophages, it becomes increasingly important to understand the relevance of macrophage rhythms to their function in tumors. It has been shown that acidic pH and lactate can each drive functional polarization of macrophages toward a phenotype that promotes tumor growth, with acidic pH modulating phagocytosis and suppressing inflammatory cytokine secretion and cytotoxicity[28, 30, 93]. However, how the changes in circadian rhythms of macrophages driven by these conditions contributes to their altered function remains unknown. Current evidence suggests that circadian rhythms confer a time-of-day-dependency on macrophage function by gating the macrophage response to inflammatory stimuli based on time-of-day. As such, responses to inflammatory stimuli such as LPS or bacteria are heightened during the active phase while the inflammatory response is suppressed during the inactive phase. An important future direction will be to determine how changes in circadian rhythms of macrophages, such as those observed under acidic pH or high lactate, influences the circadian gating of their function."
4) On line 119-122, authors describe a method for polarization of macrophages. They then reference one gene to confirm each macrophage polarization state. To more definitively corroborate proper macrophage polarization, authors should perform qPCR for additional target genes that are associated with each phenotype. For example, Socs3, CD68, or CD80 for M1, and CD163 or VEGF for M2. Alternatively, the authors should cite previous literature validating this in vitro polarization model.
We appreciate Reviewer #3's suggestion to better the phenotypic identity of our polarization models with additional canonical markers. To address this point, we have expanded our panel using transcriptional markers commonly used in the murine polarization model for M1 macrophages such as Tnfa, Il6, and Il1b. As discussed in the response to Reviewer #1's minor point 5 and Reviewer #2's major point 2, we have also expanded our panel to include additional markers for M2 such as Vegf, Retnla, Ym1, Mgl1, and CD206. We have added these new data to Supplementary Figure 1. Finally, we have added additional citations for the in vitro polarization models. Please see the modified text and new data, below.
"As macrophages are a phenotypically heterogeneous population in the TME, we first sought to understand whether diversity in macrophage phenotype could translate to diversity in circadian rhythms of macrophages. To this end, we used two well-established in vitro polarization models to study distinct macrophage phenotypes[5, 60-63]. For a model of pro-inflammatory macrophages, we stimulated macrophages with IFNγ (interferon γ) and LPS (lipopolysaccharide) to elicit a pro-inflammatory phenotype[60, 64]. These macrophages are often referred to as 'M1' and are broadly viewed as anti-tumorigenic, and we will refer to them throughout this paper as pro-inflammatory macrophages[65, 66]. For a model at the opposite end of the phenotypic spectrum, we stimulated macrophages with IL-4 and IL-13[60, 67]. While these type 2 stimuli play a role in the response to parasites and allergy, they are also major drivers of wound healing; in line with this, IL-4 and IL-13-stimulated macrophages have been well-characterized to adopt gene expression profiles associated with wound-healing and anti-inflammatory macrophage phenotypes[68-71]. As such, these macrophages are often used as a model to study pro-tumorigenic macrophages in vitro and are often referred to as 'M2' macrophages; throughout this paper, we will refer to IL-4 and IL-13-stimulated macrophages as pro-resolution macrophages[66, 72, 73]. Consistent with previous studies, we found that genes associated with anti-inflammatory and pro-resolution programming characteristic of IL-4 and IL-13-stimulated macrophages such as Arg1, Retnla, Chil3 (Ym1), Clec10a (MGL1), and Mrc1 (CD206) were induced in IL-4 and IL-13-stimulated macrophages, but not IFNγ and LPS-stimulated macrophages. In contrast, genes associated with pro-inflammatory activity characteristic of IFNγ and LPS-stimulated macrophages such as Nos2 (iNOS), Tnfa, Il1b, and Il6 were induced in IFNγ and LPS-stimulated macrophages, but not IL-4 and IL-13-stimulated macrophages (Supplementary Figure 1)[28, 30, 65, 71, 74, 75]. This indicates that macrophages stimulated with IL-4 and IL-13 were polarized toward a pro-resolution phenotype, while macrophages stimulated with IFNγ and LPS were polarized toward a pro-inflammatory phenotype.
5) Several portions of the manuscript are unnecessarily long, including the intro and discussion. Please consolidate the text. The results section is also very lengthy, please consider consolidation.
We appreciate Reviewer #3's preference for a shorter manuscript. The revised manuscript, in response to the many Reviewer comments and requests, contains many new pieces of data, and we have taken care to describe these new data as briefly and simply as possible. In preparation for this Revision, we also removed and shortened several sections of the Results and Discussion where we felt extra explanation was not necessary. We will work with the editor of the journal we submit to ensure the length of the manuscript sections is compliant with the journal's guidelines.
6) The authors find that macrophage phenotype impacts rhythmicity. However, there is no mechanistic understanding of why this occurs. The authors should provide some mechanistic insight on this topic in the discussion.
We agree with Reviewer #3 that while the mechanism by which macrophage phenotype alters rhythms remains unknown, this is an important topic of discussion. While there is some literature on how circadian rhythms modulate inflammatory response (and hints at how it may influence phenotype) in macrophages, there is very little on the converse: how phenotype may influence circadian rhythms. We address this point by expanding on our Discussion - please see the modified text below.
"Elucidating the role of circadian rhythms in regulation of macrophage biology necessitates a better understanding of the crosstalk between phenotype and circadian rhythms. Although lactate polarizes macrophages toward a pro-resolution phenotype similar to acidic pH[30, 93], exposure to lactate had different effects on circadian rhythms - and in some cases, circadian clock gene expression - than exposure to acidic pH (Figure 4). Sensing of lactate occurs through different pathways than acid-sensing, which may contribute to the different ways in which these two stimuli modulate circadian rhythms of macrophages[111]. One previously published finding that may offer mechanistic insight into how phenotype can influence circadian rhythms is the suppression of Bmal1 by LPS-inducible miR-155[54]. It has also been observed that RORα-mediated activation of Bmal1 transcription is enhanced by PPARγ co-activation[112]. In macrophages, PPARγ expression is induced upon stimulation with IL-4 and plays a key role in alternative activation of macrophages, promoting a pro-resolution macrophage phenotype, and supporting resolution of inflammation[113-115]. Such observations prompt the question of whether there are yet-unidentified factors induced downstream of various polarizing stimuli that can modulate expression of circadian genes at the transcriptional and protein levels. Further work is required to understand the interplay between macrophage phenotype and circadian rhythms."
7) The data presented in Figure 9 is very intriguing and arguably the strongest aspect of the paper. To strengthen the point, the authors could repeat this experiment with an additional cell model, another PDAC line or a different cancer line.
We appreciate Reviewer #3's comment about the impact of tumor growth data. Indeed, our finding that deletion of Bmal1 in co-injected macrophages accelerated PDAC growth has been recapitulate by others in different cancer models. This lends strength to our observations. We discuss and cite complementary work on macrophage rhythms and tumor growth in other models of cancer the Discussion, please see below.
"Data from our lab and others suggest that disruption of the macrophage-intrinsic circadian clock accelerates tumor growth, indicating that circadian regulation of macrophages is tumor-suppressive in models of PDAC (our work) and melanoma [109]. This agrees with complementary findings that behavioral disruption of circadian rhythms in mice (through chronic jetlag) disrupts tumor macrophage circadian rhythms and accelerates tumor growth[56]."
Minor Comments:
1) Data is Figure 2 is interesting and the impact on circadian rhythms is clear based on changes in amplitude and period. However, though the impact on period and amplitude is clear from Figures 2A-C, the changes in circadian gene expression are less clear. For instance, though amplitude is up in 2B, amplitude is suppressed in 2C. However, that does not appear to be reflected in the gene expression data in Figures 2E and F. The authors should comment on this.
Reviewer #3 correctly points out that there appear to be discrepancies between the LumiCycle data in Figure 2 and the circadian gene expression data in Figure 2. This discrepancy is perhaps unsurprising given that the gene expression data is only a short time course over 12 hours, while the LumiCycle data are collected over a course of 3 days. The gene expression data do not allow us to determine changes in period or rhythm. Another point of interest is that it's been shown that circadian regulation occurs on many different levels (transcriptional, post-transcriptional, translational, post-translational). As result of this, circadian patterns observed in gene transcripts don't always match those of their encoded proteins; just the same, circadian patterns of proteins aren't always reflected in their encoding gene transcripts (Collins 2021 Genome Res). Due to this multi-level regulation, we propose that the results of the LumiCycle analysis, which measures PER2-Luc levels, are a more robust readout of rhythms because they are further downstream of the molecular clock than transcriptional readouts. That said, observing changes at both the protein (by Lumicycle) and transcriptional level confirm that all components of the clock are altered by acidic pH, even if the way in which they are altered appears to differ. We have incorporated the points we raised above into the Results section.
Please see the modified text below.
"Low pH was also observed to alter the expression of the circadian clock genes Per2, Cry1, and Nr1d1 (REV-ERBα) over time across different macrophage phenotypes, confirming that multiple components of the circadian clock are altered by acidic pH (Figure 2D-F). Notably, the patterns in expression of circadian genes did not always match the patterns of PER2-Luc levels observed by LumiCycle. This is perhaps unsurprising, as circadian rhythms are regulated at multiple levels (transcriptional, post-transcriptional, translational, post-translational); as a result, circadian patterns observed in circadian proteins such as PER2-Luc do not always match those of their gene transcripts[77]."
2) On line 156-158, authors describe damping rate. I believe the authors are trying to say that damping rate increases as the time it takes cells to desynchronize decreases and vice versa. However, this point needs to be better explained.
We thank Reviewer #3 for bringing to our attention that this was not communicated clearly in the text. We have adjusted our explanation to be clearer. Please see the modified text below.
"Damping of rhythms in most free-running cell populations (defined as populations cultured in the absence of external synchronizing stimuli) occurs naturally as the circadian clocks of individual cells in the population become desynchronized from each other; thus, damping can be indicative of desynchrony within a population[84]. The damping rate increases as the time it takes for rhythms to dissipate decreases; conversely, as damping rate decreases as the time it takes for rhythms to dissipate increases."
3) Data presented in Figures 3 and 4 are different in terms of the impact of changing the pH. The source of the macrophages is different, but the authors could clarify this further.
We thank Reviewer #3 for this comment. Our conclusion is that the impact of low pH is largely similar in Figure 3 (peritoneal macrophages) and Figure 4 (BMDMs exposed to low pH and lactate). In both Figures 3 and 4, exposure to acidic pH by culturing macrophages at pH 6.5 increased amplitude, decreased period, and increased damping rate compared to macrophages cultured at neutral pH 7.4.
4) For heatmaps shown in Figures 7 and 8, please calculate covariance and display asterisks where P We thank Reviewer #3 for the excellent suggestion to use an additional approach to asses circadian clock status in samples by measuring co-variance in the circadian clock gene network. To address this point, we have performed weighted gene co-expression network analysis (WGCNA) to calculate covariance, as was originally performed in Chun and Fortin et al Science Advances 2022. For the samples analyzed in Figure 7 (now Figure 6), we have added these data to the figure. We have applied this analysis to a new set of human data that we analyzed and added it to the new Figure 7. Finally, for the samples analyzed in Figure 8, we have added these data as a new Supplementary Figure 9. Please see the data and modified text below.
Figure 6
"Weighted gene co-expression network analysis (WGCNA) has been used as an alternate approach to measure the co-variance between clock genes and thus assess bi-directional correlations among the core clock gene network in healthy tissue and tumor samples [103]. In line with the circadian disorder observed by CCD, while many bi-directional correlations among the core clock gene network were significant and apparent in wild type peritoneal macrophages, these relationships were altered or abolished within BMAL1 KO peritoneal macrophages and TAM samples, and in some cases replaced by new relationships (Figure 6E). This indicates that there is population-level disorder in the circadian rhythms of tumor-associated macrophages in murine lung cancer."
Figure 7
"We next assessed the status of the circadian clock in human TAMs from NSCLC patients. We performed CCD with publicly available RNA-seq data of tumor-adjacent macrophages and tumor-associated macrophages from NSCLC patients, using alveolar macrophages from healthy donors as a control[104, 105]. To assess the contribution of the acidic TME to circadian disorder, we subset TAM NSCLC patient samples into groups (Crem high TAMs and Crem low TAMs) based on median Crem expression. Notably, in macrophages from human NSCLC there was a trend toward disorder in Crem low but not Crem high TAM samples (Figure 7A,B). Additionally, the co-variance among core clock genes observed in alveolar macrophages from healthy donors was absent within Crem low and Crem high TAM samples (Figure 7C). In all, these data indicate that there is population-level disorder in the circadian rhythms of tumor-associated macrophages in humans and mice, suggesting that circadian rhythms are indeed altered in macrophages within the TME."
Supplementary Figure 9
"CCD score worsened as populations became increasingly desynchronized, with the 12hr desynchronized population having a significantly worse CCD score than synchronized, homogenous macrophage population (Figure 8C). This indicates that as circadian rhythms of individual macrophages within a population become more different from each other, circadian disorder increases at the population-level. This is further supported by WGCNA, which revealed that the significant co-variance of circadian clock genes in the synchronized population was progressively altered and lost as the population is increasing desynchronized to 12 hours (Supplementary Figure 9)."
Reviewer #3 (Significance (Required)):
This is an important study that is well-done. It is the feeling of the reviewer that the study warrants a revision, at the discretion of the editor. The study represents an important understanding of the crosstalk between cancer cells and immune cells as well as the understanding of how the TME disrupts circadian rhythms.
We thank Reviewer #3 for their comments regarding the impact and significance of our work. As shown by the comments above, we are confident we have fully addressed each of the points that were made to result in a much-improved revised manuscript.
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Referee #3
Evidence, reproducibility and clarity
Review for Knudsen-Clark et al. "Circadian rhythms of macrophages are altered by the acidic pH of the tumor microenvironment"
Knudsen-Clark and colleagues explore the impact of TME alterations on macrophage circadian rhythms. The authors find that both acidic pH and lactate modulate circadian rhythms which alter macrophage phenotype. Importantly, they define circadian disruption of tumor-associated macrophages within the TME and show that circadian disruption in macrophages promotes tumor growth using a PDAC line. This represents an important understanding of the crosstalk between cancer cells and immune cells as well as the understanding of how the TME disrupts circadian rhythms. The study is well-done, however, authors need to address several important points below.
Major comments:
- In Figures 3 and 4, the authors can include additional clock genes that can be run by qPCR. This was done in Figure 2 and was a nice addition to the data.
- There are far too many figures with minimal data in each. Please consolidate the figures. For example, Figures 1-3 can be fully combined, Figures 4-6 can be combined, and Figures 7-8 can be combined. Additionally, it is unclear if Figure 5 needs to be in the main, it can be moved to the supplement.
- The observation that conditions like pH and lactate alter macrophage phenotype and rhythmicity are important. However, macrophage phenotype via gene expression does not always correlate to function. It is important for authors to demonstrate the effect of pH or lactate on macrophage function. This can be done using co-culture assays with cancer cells. If these experiments cannot be performed, it is suggested that authors discuss these limitations and consideration in the discussion.
- On line 119-122, authors describe a method for polarization of macrophages. They then reference one gene to confirm each macrophage polarization state. To more definitively corroborate proper macrophage polarization, authors should perform qPCR for additional target genes that are associated with each phenotype. For example, Socs3, CD68, or CD80 for M1, and CD163 or VEGF for M2. Alternatively, the authors should cite previous literature validating this in vitro polarization model.
- Several portions of the manuscript are unnecessarily long, including the intro and discussion. Please consolidate the text. The results section is also very lengthy, please consider consolidation.
- The authors find that macrophage phenotype impacts rhythmicity. However, there is no mechanistic understanding of why this occurs. The authors should provide some mechanistic insight on this topic in the discussion.
- The data presented in Figure 9 is very intriguing and arguably the strongest aspect of the paper. To strengthen the point, the authors could repeat this experiment with an additional cell model, another PDAC line or a different cancer line.
Minor Comments:
- Data is Figure 2 is interesting and the impact on circadian rhythms is clear based on changes in amplitude and period. However, though the impact on period and amplitude is clear from Figures 2A-C, the changes in circadian gene expression are less clear. For instance, though amplitude is up in 2B, amplitude is suppressed in 2C. However, that does not appear to be reflected in the gene expression data in Figures 2E and F. The authors should comment on this.
- On line 156-158, authors describe damping rate. I believe the authors are trying to say that damping rate increases as the time it takes cells to desynchronize decreases and vice versa. However, this point needs to be better explained.
- Data presented in Figures 3 and 4 are different in terms of the impact of changing the pH. The source of the macrophages is different, but the authors could clarify this further.
- For heatmaps shown in Figures 7 and 8, please calculate covariance and display asterisks where P < 0.001. This will more clearly demonstrate the loss of co-variance between the clock network as a result of clock disruption, the TME, and cell desynchrony.
Significance
This is an important study that is well-done. It is the feeling of the reviewer that the study warrants a revision, at the discretion of the editor. The study represents an important understanding of the crosstalk between cancer cells and immune cells as well as the understanding of how the TME disrupts circadian rhythms.
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Referee #2
Evidence, reproducibility and clarity
Knudsen-Clark et al. showed that the circadian rhythm of bone marrow-derived macrophages (BMDM) can be affected by polarization stimuli, pH of the microenvironment, and by the presence of sodium-lactate. Mechanistically, the acidic pH of cell microenvironment is partly regulated by intracellular cAMP-mediated signaling events in BMDM. The authors also showed that the circadian clock of peritoneal macrophages is also modified by the pH of the cell microenvironment. Using publicly available data, the authors showed that the circadian rhythm of tumor-associated macrophages is similar to that of Bmal1-KO peritoneal macrophages. In a murine model of pancreatic cancer, the authors showed that the tumor growth is accelerated in C57BL/6 mice co-injected with cancer cells and Bmal1-KO BMDM as compared to mice co-injected with cancer cell and wild type BMDM.
Major points of criticism: 1. Nine main figures include different experimental models on a non-systematic manner in the manuscript, and only literature-based correlation is used to link the results each other. The authors used in vitro BMDM and peritoneal cell-based model systems to study the effects of IL4+IL13, IFN+LPS, low pH, sodium-lactate, adenylate cyclase inhibitors on the circadian clock of macrophages. The link between these microenvironment conditions of the cells is still correlative with the tumor microenvironment: publicly available data were used to correlate the increased expression level of cAMP-activated signaling events with the presence of acidic pH of tumor microenvironment. Notably, the cell signaling messenger molecule cAMP is produced by not only low extracellular pH by activated GPCRs, but also starvation of the cell. The starvation is also relevant to this study, since the BMDM used in the in vitro culture system were starving for 24 hours before the measurement of Per2-Luc expression to monitor circadian rhythm. 2. The definition of pre-resolution macrophages (MF) used across the manuscript could be argued. The authors defined BMDM polarized with IL-4 and IL-13 as pre-resolution MF. Resolution is followed by inflammation, but the IL-4 secretion does not occur in every inflammatory setting. Moreover, IL-4 and IL-13 are secreted during specific tissue environment and immunological settings involving type 2 inflammation or during germinal center reactions of the lymph nodes. • What are the characteristics of pre-resolution macrophages (MF)? The authors indicated that IL-4 and IL-13 cytokines were used to model the pre-resolution macrophages. In which pathological context are these cytokines produced and induce pre-resolution macrophages? IL-4 polarized BMDM can also produce pro-inflammatory protein and lipid mediators as compared to LPS-stimulated BMDM, and IL-4 polarized BMDM still have potent capacity to recruit immune cells and to establish type 2 inflammation. • The authors showed Arg1 and Vegfa qPCR data from BMDM only. Based on the literature, these MFs are anti-inflammatory cells particularly. Resolution-related MFs followed by acute inflammation are a specific subset of MFs, and the phenotype of pre-resolution MF should be described, referred, and measured specifically. 3. The authors used IFN and LPS, or IL-4 and IL-13 and co-treatments to polarize BMDM in to type 1 (referred as pro-inflammatory MF) and type 2 (referred as pre-resolution MF) activation state. The comparison between these BMDM populations has limitations, since LPS induces a potent inflammatory response in MF. The single treatment with MF-polarizing cytokines enable a more relevant comparison to study the circadian clock in classically and alternatively activated MF. 4. There are missing links between the results of showing the circadian rhythm of polarized BMDM, sodium-lactate treated BMDM, and tumor growth. Specifically, do the used pancreatic ductal adenocarcinoma cells produce IL-4 and sodium-lactate? In the LLC-based experimental in silico analysis of tumors, the LLC do not produce IL-4. 5. How can the circadian rhythm affect the function of BMDM? The Authors should provide evidence that circadian rhythm affects the function of polarized MF. 6. In Figure 3, the authors show data from peritoneal cells. The isolated peritoneal cells are not pure macrophage populations. Based on the referred article in the manuscript, the peritoneal cavity contains more then 50% of lymphocytes, and the myeloid compartment contains 80% macrophages. 7. The figure legend of Figure 3 describes the effects of pH on the circadian rhythm of bone marrow-derived macrophages ex vivo. Peritoneal macrophages involve tissue resident peritoneal macrophages with yolk sac and fetal liver origin, and also involve small peritoneal MF with bone marrow origin. The altered description of results and figure legends makes confusion. 8. In Figure 6C, one single Western blot is shown with any quantification. The authors should provide data of the relative protein level of p-CREB from at least 3 independent experiments. In the Western-blot part of the methods, the authors described that the pellet was discarded after cell lysis. The p-CREB is the activated form of the transcription factor CREB and there is increased binding to the chromatin to regulate gene expression. By discarding the pellet after cell lysis, the chromatin-bond p-CREB could be also removed at the same time. 9. It is confusing that adenylate-cyclase inhibitor MDL-12 elevated the phospho-CREB levels in BMDM. How can the authors exclude any other inducers of CREB phosphorylation? 10. It is described in the methods that BMDM were starving for 24 hours in serum-free culture media followed by serum shock (50% FBS). The cAMP production can be induced during cell starvation which should be considered for the data representation. 11. How can the authors explain and prove that the wild type and Bmal1-KO BMDM co-injected with pancreatic cancer cells subcutaneously survive, present, and have effector functions at the same extent in the subcutaneous tissue, before and during tumor growth (Figure 9)? In other words, what kind of MF-derived parameters could be modified by disrupting the circadian rhythm of MF during tumor development? The production of MF-derived regulatory enzymes, cytokines, growth factors are affected by the disrupted circadian clock in MF?
Minor points of criticism: 1. The figure legends of the graphs and diagrams are missing in Figure 2D,E,F 2. The BMAL1-based in vivo murine model of circadian rhythm is not introduced in the manuscript.
Significance
Knudsen-Clark et al. showed that the circadian rhythm of bone marrow-derived macrophages (BMDM) can be affected by polarization stimuli, pH of the microenvironment, and by the presence of sodium-lactate. Mechanistically, the acidic pH of cell microenvironment is partly regulated by intracellular cAMP-mediated signaling events in BMDM. The authors also showed that the circadian clock of peritoneal macrophages is also modified by the pH of the cell microenvironment. Using publicly available data, the authors showed that the circadian rhythm of tumor-associated macrophages is similar to that of Bmal1-KO peritoneal macrophages. In a murine model of pancreatic cancer, the authors showed that the tumor growth is accelerated in C57BL/6 mice co-injected with cancer cells and Bmal1-KO BMDM as compared to mice co-injected with cancer cell and wild type BMDM.
Major points of criticism:
- Nine main figures include different experimental models on a non-systematic manner in the manuscript, and only literature-based correlation is used to link the results each other. The authors used in vitro BMDM and peritoneal cell-based model systems to study the effects of IL4+IL13, IFN+LPS, low pH, sodium-lactate, adenylate cyclase inhibitors on the circadian clock of macrophages. The link between these microenvironment conditions of the cells is still correlative with the tumor microenvironment: publicly available data were used to correlate the increased expression level of cAMP-activated signaling events with the presence of acidic pH of tumor microenvironment. Notably, the cell signaling messenger molecule cAMP is produced by not only low extracellular pH by activated GPCRs, but also starvation of the cell. The starvation is also relevant to this study, since the BMDM used in the in vitro culture system were starving for 24 hours before the measurement of Per2-Luc expression to monitor circadian rhythm.
- The definition of pre-resolution macrophages (MF) used across the manuscript could be argued. The authors defined BMDM polarized with IL-4 and IL-13 as pre-resolution MF. Resolution is followed by inflammation, but the IL-4 secretion does not occur in every inflammatory setting. Moreover, IL-4 and IL-13 are secreted during specific tissue environment and immunological settings involving type 2 inflammation or during germinal center reactions of the lymph nodes.
- What are the characteristics of pre-resolution macrophages (MF)? The authors indicated that IL-4 and IL-13 cytokines were used to model the pre-resolution macrophages. In which pathological context are these cytokines produced and induce pre-resolution macrophages? IL-4 polarized BMDM can also produce pro-inflammatory protein and lipid mediators as compared to LPS-stimulated BMDM, and IL-4 polarized BMDM still have potent capacity to recruit immune cells and to establish type 2 inflammation.
- The authors showed Arg1 and Vegfa qPCR data from BMDM only. Based on the literature, these MFs are anti-inflammatory cells particularly. Resolution-related MFs followed by acute inflammation are a specific subset of MFs, and the phenotype of pre-resolution MF should be described, referred, and measured specifically.
- The authors used IFN and LPS, or IL-4 and IL-13 and co-treatments to polarize BMDM in to type 1 (referred as pro-inflammatory MF) and type 2 (referred as pre-resolution MF) activation state. The comparison between these BMDM populations has limitations, since LPS induces a potent inflammatory response in MF. The single treatment with MF-polarizing cytokines enable a more relevant comparison to study the circadian clock in classically and alternatively activated MF.
- There are missing links between the results of showing the circadian rhythm of polarized BMDM, sodium-lactate treated BMDM, and tumor growth. Specifically, do the used pancreatic ductal adenocarcinoma cells produce IL-4 and sodium-lactate? In the LLC-based experimental in silico analysis of tumors, the LLC do not produce IL-4.
- How can the circadian rhythm affect the function of BMDM? The Authors should provide evidence that circadian rhythm affects the function of polarized MF.
- In Figure 3, the authors show data from peritoneal cells. The isolated peritoneal cells are not pure macrophage populations. Based on the referred article in the manuscript, the peritoneal cavity contains more then 50% of lymphocytes, and the myeloid compartment contains 80% macrophages.
- The figure legend of Figure 3 describes the effects of pH on the circadian rhythm of bone marrow-derived macrophages ex vivo. Peritoneal macrophages involve tissue resident peritoneal macrophages with yolk sac and fetal liver origin, and also involve small peritoneal MF with bone marrow origin. The altered description of results and figure legends makes confusion.
- In Figure 6C, one single Western blot is shown with any quantification. The authors should provide data of the relative protein level of p-CREB from at least 3 independent experiments. In the Western-blot part of the methods, the authors described that the pellet was discarded after cell lysis. The p-CREB is the activated form of the transcription factor CREB and there is increased binding to the chromatin to regulate gene expression. By discarding the pellet after cell lysis, the chromatin-bond p-CREB could be also removed at the same time.
- It is confusing that adenylate-cyclase inhibitor MDL-12 elevated the phospho-CREB levels in BMDM. How can the authors exclude any other inducers of CREB phosphorylation?
- It is described in the methods that BMDM were starving for 24 hours in serum-free culture media followed by serum shock (50% FBS). The cAMP production can be induced during cell starvation which should be considered for the data representation.
- How can the authors explain and prove that the wild type and Bmal1-KO BMDM co-injected with pancreatic cancer cells subcutaneously survive, present, and have effector functions at the same extent in the subcutaneous tissue, before and during tumor growth (Figure 9)? In other words, what kind of MF-derived parameters could be modified by disrupting the circadian rhythm of MF during tumor development? The production of MF-derived regulatory enzymes, cytokines, growth factors are affected by the disrupted circadian clock in MF?
Minor points of criticism:
- The figure legends of the graphs and diagrams are missing in Figure 2D,E,F
- The BMAL1-based in vivo murine model of circadian rhythm is not introduced in the manuscript.
-
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Referee #1
Evidence, reproducibility and clarity
The manuscript by Knudsen-Clark et al. investigates the novel topic of circadian rhythms in macrophages and their role in tumorigenesis. The authors explore how circadian rhythms of macrophages may be influenced by the tumor microenvironment (TME). They utilize a system of bone marrow-derived macrophages obtained from transgenic mice carrying PER2-Luciferase (PER2-Luc), a trackable marker of rhythmic activity. The study evaluates how conditions associated with the TME, such as polarizing stimuli (to M1 or M2 subtype), acidic pH, and elevated lactate, can each alter circadian rhythms in macrophages. The authors employ several approaches to explore macrophage functions in cancer-related settings. While the manuscript presents interesting findings and may be the first to demonstrate that tumor stimuli alter circadian rhythms in macrophages and impact tumor growth, it lacks a clear conclusion regarding the role of altered circadian rhythms in suppressing tumor growth. . Several discrepancies need to be addressed before publication, therefore, the manuscript requires revision before publication, addressing the following comments:
Major comments:
- It is well known that pro-inflammatory macrophages primarily rely on glycolysis during inflammation, exhibiting dysregulated tricarboxylic acid (TCA) cycle activity. These pro-inflammatory macrophages are commonly referred to as 'M1' or pro-inflammatory, as noted in the manuscript. In contrast, M2 macrophages, or pro-resolution macrophages, are highly dependent on active mitochondrial respiration and oxidative phosphorylation (OXPHOS). Given that M1 macrophages favor glycolysis, they create an acidic environment due to elevated lactate levels and other acidifying metabolites. However, the study does not address this effect. The authors' hypothesis revolves around the acidic environment created by glycolytic tumors, yet they overlook the self-induced acidification of media when culturing M1 macrophages. This raises the question of how the authors explain the reduced circadian rhythms observed in pro-inflammatory macrophages in their study, while low pH and higher lactate levels enhance the amplitude of circadian rhythms. I would encourage the authors to incorporate the glycolytic activity of pro-inflammatory macrophages into their experimental setup. Otherwise the data look contradictory and misleading in some extent.
- The article examines the role of circadian rhythms in tumor-associated macrophages, yet it lacks sufficient compelling data to support this assertion. Two figures, Figure 7 and Figure 9, are presented in relation to cancer. In Figure 7, gene expression analysis of Arg1 (an M2 marker) and Crem (a potential circadian clock gene) is conducted in wild-type macrophages, BMAL1-knockout macrophages with dysregulated circadian rhythms, and using publicly available data on tumor-associated macrophages from a study referenced as 83. However, it is noted that this referenced study is actually a review article by Geeraerts et al. (2017) titled "Macrophage Metabolism as Therapeutic Target for Cancer, Atherosclerosis, and Obesity" published in Frontiers in Immunology. This raises concerns about the reliability of the results. Furthermore, comparing peritoneal macrophages from healthy mice with macrophages isolated from lung tumors is deemed inaccurate. It is suggested that lung macrophages from healthy mice and those from mice with lung tumors should be isolated separately for a more appropriate comparison. Consequently, Figure 7B is further questioned regarding how the authors could compare genes from the circadian rhythm pathway between these non-identical groups. As a result, the conclusion drawn from these data, suggesting that tumor-associated macrophages exhibit a gene expression pattern similar to BMAL1-KO macrophages, is deemed incorrect, affecting the interpretation of the data presented in Figure 8.
- If the authors aim to draw a clear conclusion regarding the circadian rhythms of tumor-associated macrophages (TAMs), they may need to analyze single-sorted macrophages from tumors and corresponding healthy tissues. Such data are publicly available (of course not in #83)
- Additionally, it is widely acknowledged that human and mouse macrophages exhibit distinct gene expression profiles, both in vitro and in vivo. While assuming that genes involved in circadian rhythms are conserved across species, the authors could consider extending their funding to include analyses of single-sorted macrophages from cancer patients, such as those with lung cancer or pancreatic ductal adenocarcinoma (PDAC). These experiments would provide relevant insights into TAM biology.
Minor comments:
- Figure 2C needs clarification. It's unclear why pro-inflammatory macrophages treated with lactic acid would have a shorter amplitude and period, while acidic pH would increase amplitude and period in M2 macrophages.
- The scale in Figure 2C should be equal for all conditions (e.g., -200).
- Absolute values of amplitude, damping, and period differ between Figure 1 and Figure 2A, B, C. The authors should explain these discrepancies.
- The authors should consider modulating the acidic environment of macrophages in settings more representative of cancer. For example, by adding conditioned media from tumor cells with pronounced glycolysis.
- Arg1 alone is not sufficient as an M2 polarization marker. The authors should include additional markers.
Significance
While the manuscript provides valuable insights and has obvious novelty, it requires a significant revision
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Reply to the reviewers
Reviewer #1
Major comments
- In Fig. 3B, the dramatic (~20%) change in EGFR mobility upon EGF stimulation (i.e. from fast-mobile to confined) implies EGFR-EGF binding in excess of what is typically reported in the literature. How do the authors reconcile this and are there features of their cell model/analysis pipeline that are artificially contributing to this observation?
We tagged the cytoplasmic tail of EGFR with GFP (EGFR-GFP) (p. 11, l.198-201) instead of using antibody-mediated labeling, which has been used in previous studies. Compared with antibody-mediated labeling in the extracellular region of EGFR, a greater change in EGFR mobility upon EGF stimulation was observed, which is consistent with previous studies (Hiroshima et al., 2018; Yasui et al., 2018) (p. 11, l.209-211). The differences in labeling may cause differences in the characteristics of EGFR or binding efficiency to EGF. We have mentioned this possibility in the Discussion section (p. 25, l.501-505).
The detection of PIP2 nanodomains in the plasma membrane is somewhat controversial, especially using the PH domain of PLCd to detect PIP2 or using similar strategies. The recent study by Pacheco et al (JCB 2023, PMID: 36416724) uses a variety of measurements of fluorescent labeling of PIP2 by protein-based biosensors (similar to this study) and concludes that PIP2 is free diffusing in the plasma membrane, which would be inconsistent with PIP2 nanodomains. Pacheco et al propose that while engagement of PIP2 to effectors via the inositol headgroup may serve to immobilize this lipid, allowing clustering, the use of relatively large protein domains as fluorescent ligands that bind to the PIP2 headgroup to track PIP2, as performed here, displaces any intrinsic clustering mechanism, leading to free diffusion of PIP2. How can the clustering observed here for PIP2 be reconciled? Is it possible that additional, non lipid-based interactions function alongside PH domain-PIP2 interactions as a form of coincidence detection? It would be quite helpful to support the data shown in this manuscript with a different PIP2 binding domain, such as the Tubby domain used by Pacheco et al. It would not be necessary to repeat all experiments with such a complementary probe, but some key experiments that assess the apparent clustering of PIP2 would be important to consider repeating with this complementary PIP2 probe.
As suggested by the reviewer, we performed SMLM and SMT analyses using TubbyC. No significant differences were observed between PLCd-PH and TubbyC probes. These results are shown in Figs. 2C (p. 9, l.167-172), and S3E and S3F (p. 12, l.226-229).
It is unclear if and how stimulation with EGF or overexpression of synaptojanin modulates PIP2 in the plasma membrane. Some studies found that EGF stimulation does not change PIP2 levels in the PM, including Delos Santos et al. (Mol Biol Cell. 2017, PMID: 28814502). Others found that the regulation of PIP2 levels in the plasma membrane is tightly controlled and the total levels of PIP2 can resist alterations of PIP2 by changes in lipid enzymes (Wills et al. JCS 2023, PMID: 37534432). Hence, it is not clear if the stimulation with EGF or the overexpression of synaptojanin changes plasma membrane PIP2 levels, or may only alter the nanoscale dynamnics of this lipid. If the effects of synaptojanin may be restricted to alterations of the nanoscale organization of PIP2 in the membrane, it would be important to consider that synaptojanin is strongly localized to clathrin-coated pits in the plasma membrane (e.g. Perera et al. PNAS 2007. PMID: 17158794), and that EGFR only exhibits strong recruitment to clathrin-coated pits following EGF stimulation, which would suggest that the non-ligand-bound EGFR is distant to synaptojanin-containing structures. Some consideration of the possibility of broad action of PIP2 depletion vs nanoscale localized effects by these treatments should be considered when interpreting the results of this study.
Here, we show that EGF stimulation decreases the degree of PI(4,5)P2 nanodomain clustering, mainly by reducing the density of small nanodomains. As noted by the reviewer, EGF stimulation does not induce a significant change in PI(4,5)P2 levels in the plasma membrane (Delos Santos et al., 2017). These results suggest that PI(4,5)P2 may be hydrolyzed by PLCγ only in the region around EGFR. In contrast, synaptojanin expression reduced PI(4,5)P2 levels by 45% (Field et al., 2005), leading to defects in cytokinesis (Abe et al., 2012; Field et al., 2005). We previously found that synaptojanin expression diminishes the localization of PI(4,5)P2-binding proteins to the plasma membrane (Abe et al., 2012; Abe et al., 2021). These results suggest that synaptojanin expression decreases the amount of PI(4,5)P2 throughout the plasma membrane rather than in the restricted region around EGFR. We have added this information to the Discussion section (p. 22, l.443-452).
Minor comments
- Please quantify the extent to which endogenous EGFR was knocked down.
We knocked out rather than knocked down the EGFR gene with CRISPR/Cas9 gene editing. Therefore, there was no intrinsic EGFR in the cells used (Fig. S2A). The detailed methods is described in the Methods section (p. 28, l.546-564).
Fig. 2C - please provide the entire field of view, including the area chosen for the zoomed in images.
As suggested by the reviewer, we have added the entire and enlarged images to the new Fig. 2D (p. 9, l.173-174).
Regarding the time points chosen to measure EGFR area and others. Why were the 1 mins and 2 mins time points chosen to examine EGFR-PIP2 and EGFR-GRB2 interactions, respectively? Is there evidence that these interactions peak at these time points? Alternatively, please provide evidence of their interactions at earlier time points (e.g. 15-30 seconds for EGFR-PIP2 and 1 mins for EGFR-GRB2).
SMT analysis indicated that the colocalization rate of EGFR and PI(4,5)P2 significantly decreased after 0.5 min of EGF stimulation. For EGFR and GRB2, the rate was reduced after 0.5 min of EGF stimulation. These results suggest that the binding of EGF to EGFR and the subsequent reaction occurred within 0.5 min under our experimental conditions. We have added the relevant data to Fig. S4 (p. 12, l.229-234). In addition, we have provided the bivariate H(r) values of EGFR and PI(4,5)P2 at 0.5 min after EGF stimulation, as obtained with SMLM analysis (Fig. 2B, green) (p. 9, l.166-167).
Please demonstrate with immunoblot the extent to which EGFR-EGFP construct can be stimulated by EGF (EGFR Y1068) vs. control cells.
As suggested by the reviewer, we have added the results to the new Fig. S3A. Although we reduced the expression level of EGFR-GFP for SMT, it was phosphorylated to the same level after EGF stimulation as the intrinsic EGFR in the parental cells (p. 11, l.201-203).
Fig. S3B (right) - how do the authors explain the apparent decrease in diffusion coefficient for the fast mobile fraction of EGFR. Presumably, these receptors are not engaged with ligand, so what is causing the decrease in diffusion coefficient?
It is not necessary to assume that the EGFR in the fast mobile fraction was EGF-free. In the Variational Bayesian-Hidden Markov Model analysis, all time points in the single trajectories were assigned to one of the three states, and we frequently observed state transitions within a single trajectory (Hiroshima et al. 2018). These transitions suggest that the difference in the mobility states is not caused solely by ligand binding, as differences in the membrane environment might also influence the mobility. Therefore, ligand-free and ligand-bound molecules have different diffusion coefficients in the same fast-mobile fraction.
Fig. 6A - it is unclear which method was used to probe pAKT (S473) inhibition by wortmannin. Please specify.
The cells were incubated overnight in serum-free medium, treated with 10 µM wortmannin for 1 h, and stimulated with 20 nM EGF. pAKT was detected using anti-AKT (S473) (#4060, Cell Signaling Technology) as the primary antibody. This is described in the Methods section (p. 30, l.591-592; p. 31, l.623-627).
Fig. 6E (middle) - The authors explain that wortmannin treatment causes PIP2 dispersal. This interpretation would be strengthened with a quantification, as another interpretation of the representative image is that wortmannin appears to reduce the abundance of PIP2. This discrepancy requires explanation. There also appears to be an increase in pEGFR Y1068 relative to control.
The extent of PI(4,5)P2 distribution after wortmannin treatment was not a focus of this study; consequently, we deleted the text "wortmannin treatment causes PI(4,5)P2 dispersal." In addition, we replaced the images and changed the pseudocolor to avoid the incorrect impression that the amount of pEGFR was increased in wortmannin-treated cells (Fig. 6E).
Please provide uncropped immunoblot images without contrast adjustment. Some immunoblots appear to have lane-to-lane differences in background.
For all immunoblot images in this study, we did not adjust the contrast or brightness of the original images. The figure source data files show the images before they were trimmed.
Given the conclusion on line 421 re: PI3K localization, can you provide data to support that this is the case (i.e. that PI3K acts at distinct sites on the membrane away from the specific PIP2-EGFR nanodomains)? This should be possible given the methods described in the manuscript.
We want to say that PLCγ hydrolyzes PI(4,5)P2 around EGFR, whereas PI3K phosphorylates PI(4,5)P2 around the heterodimer of ERBB3 and EGFR in the plasma membrane. We have added the results of the SMT analysis, which indicated that the colocalization rate of PLCγ-PI3K was lower than that of EGFR-PLCγ or EGFR-PI3K (Fig. S7E and S7F) (p. 18, l.366-369). We have also added this information to the Discussion section (p. 22, l.428-442).
Likewise, showing whether this specific pool of PIP2-EGFR nanodomains are within or away from the well-characterized EGFR-tetraspanin nanodomains would add value to the interpretation of the results. However, this reviewer notes that this would add significant experiments to the study and this could be considered in future studies.
We were also interested in the local content of PI(4,5)P2 in EGFR-tetraspanin nanodomains. In the revised Discussion section we state that further studies will reveal the relationship between these molecules (p. 24, l.465-468).
Please explicitly state whether statistical considerations were made for multiple comparisons in the methods.
As the reviewer suggested, we have added the description in the Methods section (p.38, l.753-754).
Reviewer #2
Minor comments:
- From the original paper (Rosenbloom et al), it seems that rsKame still requires photoactivation at 405 nm. Was the done here for superresolution imaging? It is not listed in the methods for rsKame.
Similar to the photoactivation of Dronpa (Mizuno et al., 2010), we photoactivated rsKame with a 488 nm laser for excitation and turning off the fluorescence, and we attributed this to the spontaneous recovery of the stochastic turning on of the fluorescence, instead of the illumination at 405 nm. We have added this information to the Methods section (p. 35, l.705-709).
- The stimulation conditions vary throughout, in both EGF concentration and time (1-5 min), possible differences due to various stimulation conditions should be discussed. Furthermore, superresolution samples were fixed after 1 min of EGF stimulation. The lack of EGFR reorganization may be due to the time required for EGF to diffuse to the adherent cell surface. Other superresolution imaging has demonstrated that EGFR forms oligomers on the cell after EGF stimulation (e.g., Mudumbi et al, Cell Reports 2024; Needham et al Nat Comm 2016). Comment if your results are consistent or not with these other works.
For SMT and SMLM analyses throughout this study, the cells were treated with 20 nM EGF, which is sufficiently above the dissociation constant of 2-6 nM (Sugiyama et al., 2023). In the revised manuscript, we examined the time course of colocalization between EGFR and PI(4,5)P2 or GRB2 (Fig. S4) (p. 12, l.229-234). The colocalization rate of EGFR and PI(4,5)P2 decreased significantly by 0.5 min after EGF stimulation and remained low for at least 5 min (Fig. S4A). Under the same experimental conditions, the colocalization rate of EGFR and GRB2 increased by 0.5 min after EGF stimulation and remained high for at least 5 min (Fig. S4B). These results suggest that the binding of EGF to EGFR and the subsequent reaction were almost in a steady state, at least during 0.5-5 min of EGF stimulation.
As noted by the reviewer, Mudumbi et al. showed that the cluster size of EGFR increased after EGF stimulation, which may be different from our results (Fig. 1B). However, their methods and aims were different from those of the SMLM analysis in this study. They used very sparse conditions for single-cluster analysis, whereas in SMLM we used dense conditions to detect the coaggregation of nanodomains, which may contain multiple clusters. We also used sparse conditions for single-molecule imaging (Fig. 4) and observed dimer/oligomer formation with an increase in the fluorescence signal in the single EGF spots. We observed a similar increase in the single-spot fluorescence signal in our previous studies. Our results are at least qualitatively consistent with those reported previously (Mudumbi et al., 2024; Needham et al., 2016). We have added this information to the Discussion section (p. 25, l.506-512).
- Single molecule tracking was performed at room temperature. At what temperature were the superresolution and western blot samples stimulated? Fluidity and organization of the plasma membrane is altered by temperature. Possible caveats should be discussed. If biochemistry was performed at 37 C, then EGFR signaling cannot be correlated between samples dues to faster activation at physiological temperature.
Cells were stimulated with EGF at 25{degree sign}C in all experiments. We have added this information to the Methods section (p. 27, l.525-526).
- Many experiments are performed using transient transfection, with no control for or quantification of expression level. The frequency of EGFR:domain overlap and colocalization during SMT could be dependent on the relative expression levels of proteins/lipid markers. Was this accounted for?
As noted by the reviewer, the expression levels of EGFR and probes differ among cells. To consider the differences in expression levels among cells, we measured the density of particles (particle number/cell area). We then normalized the original colocalization rates, which were calculated using our custom-made software (Yanagawa and Sako, 2021), to the densities of both the EGFR and the probes. We normalized all data for the colocalization rates and presented them as relative colocalization rates (p. 12, l. 220-223; p. 34, l. 669-673)
- Please describe why some experiments performed with HeLa EGFR knockout cells and other with CHO cells?
As noted by the reviewer, we used CHO-K1 cells in the experiment shown in Fig. 4, whereas HeLa cells were used in other experiments. We performed a similar experiment using HeLa cells and we obtained similar results. The results for HeLa cells are presented in Fig. S5A (p. 13, l.256-257).
- The author state that "...dimerized EGFR was mainly found in the immobile fraction..." How did they determine this? This interpretation of the SMT data is important for suggesting that PIP2 EGFR stabilization (Fig. 4), and should therefore be explain/justified.
As shown in Figs. 4A and S5A, SMT analysis revealed that EGFR monomers decreased and dimers increased in the immobile fraction of control CHO-K1 cells after EGF stimulation (Fig. 4A). The changes in oligomer size were smaller in the slow- and fast-mobile fractions than in the immobile fraction. In addition, the fraction size of the immobile state was not reduced after EGF stimulation (Fig. 3B). These results suggest that stable EGFR dimer/oligomers were mostly increased in the immobile fraction after EGF stimulation. In contrast, the colocalization rate in the slow-mobile and fast-mobile fractions decreased after EGF stimulation, whereas the rate in the immobile fraction did not change significantly (Fig. S3D). The PI(4,5)P2 probes employed in this study can detect PI(4,5)P2 near EGFR but they might not bind to PI(4,5)P2 associated with EGFR due to steric hindrance. It is plausible that concentrated PI(4,5)P2 molecules help to stabilize EGFR dimer/oligomers in the immobile fraction. However, we cannot exclude the possibility that the dimer/oligomers in the slow- and fast-mobile fractions were also stabilized by PI(4,5)P2, which was not detected by the PI(4,5)P2 probes. We have added this information to the Discussion section (p.25, l.487-500).
- In Fig 3, it is shown that the immobile fraction of EGFR increases with EGF, while PIP2 diffusion is unchanged. If PIP2 interacts with EGFR and stabilizes dimers, would you expect to also see an increase in the PIP2 immobile fraction?
Our results suggest that the interaction between EGFR and PI(4,5)P2 is transient (Fig. 3D) (p.11, l.218-220). Therefore, we did not observe an increase in the PI(4,5)P2 immobile fraction. We have added a movie to the revised manuscript. Movie1 shows the lateral colization of EGFR and PI(4,5)P2 before and after EGF stimulation. As mentioned above, despite the decrease in PI(4,5)P2 concentration after EGF stimulation, colocalization between EGFR and PI(4,5)P2 was maintained in the immobile fraction.
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Referee #2
Evidence, reproducibility and clarity
The work presented in "Bilateral regulation of EGFR activity and local PI dynamics observed with superresolution microscopy" by Abe et al studies the role of PI(4,5)P2 in EGFR signaling. This is an important question since the interplay of lipids and membrane receptors is known to be important for signaling, but the underlying mechanisms are not fully understood. The authors use multicolor superresolution and single molecule tracking coupled with biochemical approaches to understand how EGFR and PIP2 interplay on the plasma membrane. This work focuses on the biophysical mechanism of EGFR signaling and will be relevant to journals in the areas of biophysics, cell biology, cell signaling and microscopy. Overall, this an important study that identifies PIP2 as playing a functional role in EGFR signaling. However, there are some caveats to the experimental conditions that need to be discussed.
Minor comments:
From the original paper (Rosenbloom et al), it seems that rsKame still requires photoactivation at 405 nm. Was the done here for superresolution imaging? It is not listed in the methods for rsKame.
The stimulation conditions vary throughout, in both EGF concentration and time (1-5 min), possible differences due to various stimulation conditions should be discussed. Furthermore, superresolution samples were fixed after 1 min of EGF stimulation. The lack of EGFR reorganization may be due to the time required for EGF to diffuse to the adherent cell surface. Other superresolution imaging has demonstrated that EGFR forms oligomers on the cell after EGF stimulation (e.g., Mudumbi et al, Cell Reports 2024; Needham et al Nat Comm 2016). Comment if your results are consistent or not with these other works.
Single molecule tracking was performed at room temperature. At what temperature were the superresolution and western blot samples stimulated? Fluidity and organization of the plasma membrane is altered by temperature. Possible caveats should be discussed. If biochemistry was performed at 37 C, then EGFR signaling cannot be correlated between samples dues to faster activation at physiological temperature.
Many experiments are performed using transient transfection, with no control for or quantification of expression level. The frequency of EGFR:domain overlap and colocalization during SMT could be dependent on the relative expression levels of proteins/lipid markers. Was this accounted for?
Please describe why some experiments performed with HeLa EGFR knockout cells and other with CHO cells?
The author state that "...dimerized EGFR was mainly found in the immobile fraction..." How did they determine this? This interpretation of the SMT data is important for suggesting that PIP2 EGFR stabilization (Fig. 4), and should therefore be explain/justified.
In Fig 3, it is shown that the immobile fraction of EGFR increases with EGF, while PIP2 diffusion is unchanged. If PIP2 interacts with EGFR and stabilizes dimers, would you expect to also see an increase in the PIP2 immobile fraction?
Significance
This study address an important question since the interplay of lipids and membrane receptors is known to be important for signaling, but the underlying mechanisms are not fully understood. The authors are able to make a conceptual advance in our understanding of EGFR biology by using advanced imaging techniques that allow for quantification of protein distribution and dynamics on intact cells. The elegant application of superresolution and single molecule tracking are important strengths of this work, while the clever use of receptor mutant and phosphates reveals novel insights. This work focuses on the biophysical mechanism of EGFR signaling and will be relevant to journals in the areas of biophysics, cell biology, cell signaling and microscopy.
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Referee #1
Evidence, reproducibility and clarity
The epidermal growth factor receptor (EGFR) is a central regulator of cell function, with important roles in development as well as tissue homeostasis in adults. The upregulation of EGFR expression or activity drives tumor progression in several types of cancer. This study examines the regulation of EGFR activity by compartmentalization of EGFR into unique plasma membrane nanodomains demarked by enrichment of phosphatidylinositol-4,5-bisphosphate (PIP2) and phosphatidylserine (PS). To do so, EGFR was labeled via C-terminal fusion with rsKame and labeling of PIP2 with PLCdelta-PH fused with PAmCherry or PS with evectin-2-PH-HaloTag. This was examined following fixation, using SMLM and statistical analysis using Ripley's univariate H-function (to determine the size and distribution of each nanocluster) and Ripley's bivariate H-function (to determine the distribution of different nanoclusters relative to one another). This revealed that the overlap of EGFR and PS did not change following stimulation of EGF, but this stimulation did reduce the overlap of EGFR and PIP2.
These experiments in fixed cells were complemented with live cell single molecule imaging studies, tracking EGFR and PIP2. EGF stimulation increased the immobile fraction of EGFR, and decreased the overlap of EGFR and PIP2. Experiments with overexpression of the inositol 5-phosphatase synaptojanin and expression of mutants of EGFR (3RN) with a disrupted putative PIP2 binding site were used to assess the function of PIP2 in EGFR regulation. Expression of synaptojanin and mutation of EGFR (3RN) had similar results in that the confinement, dimerization, and c-terminal pY1068 of EGFR following EGF stimulation was altered. Perturbation of PIP2 availability by expression of a dominant interfering PLCgamma led to a reduction of the EGF-stimulated pT654 on EGFR, known to negatively regulate EGFR activation. From this emerges a model where clustering of EGFR with PIP2 nanodomains at the cell surface is required to support initial EGFR activation, and then the hydrolysis of PIP2 to generate DAG is required for eventual deactivation of EGFR.
The manuscript is by Abe et al. is well-written, presenting a compelling investigation into the spatiotemporal dynamics of PIP2 and its regulatory role in EGFR activity in living cells. The use of super-resolution single-molecule microscopy to visualize the interactions between EGFR and PIP2 nanodomains and single particle tracking methodologies are complimentary, yielding important insights and underscoring the manuscript's contribution to advancing our understanding in this area. The experimental workflow is sophisticated and novel and is strengthened by the careful consideration of critical controls throughout the manuscript. For example, studying the expected Grb2 localization with EGFR, showing the expected gain in spatial correlation of EGFR and Grb2 upon EGF stimulation strengthens the use of this workflow to study interaction of EGFR and other nanoclusters. The results not only enhance our understanding of the intricate mechanisms governing EGFR regulation by lipids but also highlight the importance of PIP2 in the modulation of EGFR dimerization and autophosphorylation. While the experiments conducted in the study are largely well done and of high quality, there are several outstanding issues that must be addressed before considering publication. It is essential that the authors provide further clarification on certain aspects of their methodology for replicability and transparency. Additionally, a more detailed discussion on the implications of their findings within the broader context of cell signaling and potential impacts on related research areas would enhance the manuscript's significance. This could also require some additional experiments to align the observations made in this study with that of previous studies, in particular as it relates to PIP2 dynamics and clustering. Addressing these concerns will not only strengthen the conclusions drawn but also provide the scientific community with a more comprehensive understanding of the complex interplay between putative PIP2 nanodomains and EGFR activity. The resolution of these issues is crucial for the manuscript to fully meet the publication standards of contributing novel and impactful insights to the field.
Major comments
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In Fig. 3B, the dramatic (~20%) change in EGFR mobility upon EGF stimulation (i.e. from fast-mobile to confined) implies EGFR-EGF binding in excess of what is typically reported in the literature. How do the authors reconcile this and are there features of their cell model/analysis pipeline that are artificially contributing to this observation?
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The detection of PIP2 nanodomains in the plasma membrane is somewhat controversial, especially using the PH domain of PLCd to detect PIP2 or using similar strategies. The recent study by Pacheco et al (JCB 2023, PMID: 36416724) uses a variety of measurements of fluorescent labeling of PIP2 by protein-based biosensors (similar to this study) and concludes that PIP2 is free diffusing in the plasma membrane, which would be inconsistent with PIP2 nanodomains. Pacheco et al propose that while engagement of PIP2 to effectors via the inositol headgroup may serve to immobilize this lipid, allowing clustering, the use of relatively large protein domains as fluorescent ligands that bind to the PIP2 headgroup to track PIP2, as performed here, displaces any intrinsic clustering mechanism, leading to free diffusion of PIP2. How can the clustering observed here for PIP2 be reconciled? Is it possible that additional, non lipid-based interactions function alongside PH domain-PIP2 interactions as a form of coincidence detection? It would be quite helpful to support the data shown in this manuscript with a different PIP2 binding domain, such as the Tubby domain used by Pacheco et al. It would not be necessary to repeat all experiments with such a complementary probe, but some key experiments that assess the apparent clustering of PIP2 would be important to consider repeating with this complementary PIP2 probe.
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It is unclear if and how stimulation with EGF or overexpression of synaptojanin modulates PIP2 in the plasma membrane. Some studies found that EGF stimulation does not change PIP2 levels in the PM, including Delos Santos et al. (Mol Biol Cell. 2017, PMID: 28814502). Others found that the regulation of PIP2 levels in the plasma membrane is tightly controlled and the total levels of PIP2 can resist alterations of PIP2 by changes in lipid enzymes (Wills et al. JCS 2023, PMID: 37534432). Hence, it is not clear if the stimulation with EGF or the overexpression of synaptojanin changes plasma membrane PIP2 levels, or may only alter the nanoscale dynamnics of this lipid. If the effects of synaptojanin may be restricted to alterations of the nanoscale organization of PIP2 in the membrane, it would be important to consider that synaptojanin is strongly localized to clathrin-coated pits in the plasma membrane (e.g. Perera et al. PNAS 2007. PMID: 17158794), and that EGFR only exhibits strong recruitment to clathrin-coated pits following EGF stimulation, which would suggest that the non-ligand-bound EGFR is distant to synaptojanin-containing structures. Some consideration of the possibility of broad action of PIP2 depletion vs nanoscale localized effects by these treatments should be considered when interpreting the results of this study.
Minor comments 1. Please quantify the extent to which endogenous EGFR was knocked down. 2. Fig. 2C - please provide the entire field of view, including the area chosen for the zoomed in images. 3. Regarding the time points chosen to measure EGFR area and others. Why were the 1 mins and 2 mins time points chosen to examine EGFR-PIP2 and EGFR-GRB2 interactions, respectively? Is there evidence that these interactions peak at these time points? Alternatively, please provide evidence of their interactions at earlier time points (e.g. 15-30 seconds for EGFR-PIP2 and 1 mins for EGFR-GRB2). 4. Please demonstrate with immunoblot the extent to which EGFR-EGFP construct can be stimulated by EGF (EGFR Y1068) vs. control cells. 5. Fig. S3B (right) - how do the authors explain the apparent decrease in diffusion coefficient for the fast mobile fraction of EGFR. Presumably, these receptors are not engaged with ligand, so what is causing the decrease in diffusion coefficient? 6. Fig. 6A - it is unclear which method was used to probe pAKT (S473) inhibition by wortmannin. Please specify. 7. Fig. 6E (middle) - The authors explain that wortmannin treatment causes PIP2 dispersal. This interpretation would be strengthened with a quantification, as another interpretation of the representative image is that wortmannin appears to reduce the abundance of PIP2. This discrepancy requires explanation. There also appears to be an increase in pEGFR Y1068 relative to control. 8. Please provide uncropped immunoblot images without contrast adjustment. Some immunoblots appear to have lane-to-lane differences in background. 9. Given the conclusion on line 421 re: PI3K localization, can you provide data to support that this is the case (i.e. that PI3K acts at distinct sites on the membrane away from the specific PIP2-EGFR nanodomains)? This should be possible given the methods described in the manuscript. 10. Likewise, showing whether this specific pool of PIP2-EGFR nanodomains are within or away from the well-characterized EGFR-tetraspanin nanodomains would add value to the interpretation of the results. However, this reviewer notes that this would add significant experiments to the study and this could be considered in future studies. 11. Please explicitly state whether statistical considerations were made for multiple comparisons in the methods.
Significance
General strengths:
- This study examines an important question in the cell biology of a key regulator of cell physiology, EGFR. While the mobility and nanodomain clustering of EGFR has emerged as critical for the regulation of this receptor's ligand binding, dimerization, and downstream signaling output, there remains much to be understood about the nanoscale organization of EGFR relative to other signaling lipids and proteins in the plasma membrane. This study examines a novel link between nanodomains demarked by PIP2 and EGFR mobility and signaling.
- This study makes use of sophisticated multiplexed labeling of EGFR and lipids such as PIP2 and PS, along with super-resolution microscopy and single molecule imaging coupled to cutting-edge image quantification.
- Controls are generally well-considered and appropriate to support and validate the experimental workflows that are
General limitations:
- The labeling of PIP2 with fluorescently-labelled protein domains that recognize this lipid has some limitations, as described above in the comments.
Advance: This study fills a knowledge gap in how the central regulator of cell physiology, EGFR, is organized at the cell surface. It is well appreciated that EGFR exhibits confinement at the plasma membrane and that this receptor exhibits nanoscale clustering that regulates receptor function. However, the nature of the nanoscale clusters in which EGFR is detected in the ligand-bound and non-ligand-bound states, and how this defines receptor output is only beginning to be resolved. This study examined how clustering of the lipid PIP2 in the plasma membrane relates to EGFR clusters, and how this may functionally impact EGFR signaling. This fills a knowledge gap of the molecules within the plasma membrane that impact receptor nanoscale clustering and function. This study also advances how mechanisms that impact EGFR nanoscale organization also in turn affect signaling output, which provides compelling evidence for the significance of EGFR-PIP2 interactions (especially with the EGFR mutant that is predicted to have reduced PIP2 interactions).
Audience: This study will be of significant interest to fundamental cell biology researchers in general, and in particular those interested in cell signaling and lipid cell biology.
Reviewer expertise: This reviewer has expertise in cell biology of receptor signaling, phosphoinositides, single-particle tracking, and plasma membrane nanodomains.
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Reply to the reviewers
__Reviewer #1 (Evidence, reproducibility, and clarity (Required)):____ __ Summary: Viruses exploit host endoplasmic reticulum (ER)-resident chaperones to support new protein synthesis during viral replication. Here, Najarro et al. study the role of the ER-resident HSP70 family member Binding immunoglobulin protein (BiP) during lytic infection by the Kaposi's sarcoma-associated herpesvirus (KSHV). Using the established doxycycline-inducible lytic reactivation infection model cell line iSLK-BAC16, they showed that KSHV reactivation leads to an upregulation of total BiP protein but not RNA, and is independent of the unfolded protein response. siRNA knockdown or pharmacological inhibition by HA15 of BiP significantly reduced global viral gene expression and infectious virus production. The authors attribute this to at least the reduction of levels of the K1 gene which is required for efficient viral replication. Finally, they showed that HA15 has cytostatic activity in KSHV-transformed B cells and cytotoxic effects in KSHV-infected lymphatic endothelial cells arguing for BiP inhibition as a potential therapeutic strategy to treat KSHV-driven malignancies. The manuscript is well-written and the conclusions were generally supported by the data with a few exceptions below.
Major comments:
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They propose in lines 196-199 that the reduction of K1 from HA15 treatment partially explains the defect in virion production during lytic reactivation. I am not convinced that this statement is fully supported by their data. Reduction of K1 is likely a downstream consequence and not the cause of the inhibition of lytic replication.
We thank the reviewer for this comment. We conducted a more detailed analysis of our RNAseq data in iSLK.219 cells and confirmed the downregulation of the K1 transcript in latently infected cells treated with HA15 (See Fig 3 and Sup Fig 5). It is likely that the drop in transcript levels results from IRE1-mediated degradation in a recently-described process known as RIDDLE (IRE1-mediated RNA decay lacking endomotif), in which IRE1 depletes mRNAs1*. We have included this hypothesis in the discussion. *
Unfortunately, we cannot confirm the downregulation of K1 at the protein level in iSLK.219 cells since the antibodies are highly specific for K1 variants in PEL cells. To overcome this technical limitation, we conducted mass spectrometry analysis of the viral proteome from whole cell lysates of latent and lytic cells undergoing HA15 treatment. While we detect the expected global downregulation of viral proteins in lytic cells treated with HA15, we were not able to detect any viral proteins except for LANA in the latently infected cells, and our detection of several lytic proteins was limited. We speculate that the levels of latent viral proteins expressed in iSLK.219 cells are below the limits of detection of our assay, or that extensive modification of some of these viral proteins may hinder their detection. Due to these limitations, we decided not to include these data in the manuscript.
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Additionally, we note that the lower levels of K1 detected in latent iSLK.219 and TREx-BCBL-1 cells treated with HA15 may affect viral reactivation, which is consistent with findings from the Damania lab showing K1's crucial role in viral replication2.*
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*
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The quantification of the K1 blots in Fig. 3C only has n=2. With subtle differences by eye, large error bars, and no statistical analysis, it is hard to conclude here with confidence. *
We agree with the reviewer. We have moved the K1 blot to the Sup. Fig. 3E and adjusted the text accordingly.* *
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Like K1, ORF45, and K8.1 proteins are similarly decreased at 24 h in Fig. 2E, suggesting that the defect is upstream of K1. Does HA15 affect the amount of endogenous and/or transgene copy of RTA being produced (hence the broader effect in early gene expression at 24h?)?
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**To answer the Reviewer's query, we re-evaluated the impact of HA15 treatment on the activity of dox-inducible RTA. However, we think it is unlikely for HA15 to alter RTA activity since RTA does not enter the secretory pathway. *
To evaluate the activity of RTA in HA15 treated cells, we measured the expression of the viral episome-encoded RFP reporter, driven by the viral PAN promoter4*, at 24h post-doxycycline treatment of iSLK.219 cells. We compared the response of the PAN promoter to RTA in cells treated with or without HA15 at this early timepoint, to avoid any potential confounding effects stemming from elevated endogenous RTA expression at later times post-reactivation. We demonstrate that the levels of RFP in iSLK.219 cells treated with Dox are identical in presence or absence of HA15. This result, included in Sup. Fig. 3, indicates that the activity of RTA, crucial for initiating the lytic cycle in this context, is unaffected by BiP inhibition at early times post reactivation. *
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*
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K1 levels appear to decrease even during latency. Are the other latent proteins also affected? What about latent genome copies?
To address this query, we compared the Log2 fold change of latent transcripts (K1, K2, K12, ORF71, ORF72, ORF73) in the iSLK.219 RNAseq data set (Fig 3). Only the K1 transcript is reduced in HA15-treated cells. We include these data in Sup Fig 5A.
Regarding differences in genome copies, the consistent levels of the viral genome-encoded GFP in HA15 -/+ iSLK-219 cells (Sup Fig 3) indicate no significant changes in the levels of viral genomes at 24h post-treatment (prior to DNA replication). Previous studies by our lab and others show that knockdown of the major latency protein LANA results in episomal loss and lower levels of GFP5*. These results validate the use of GFP fluorescence in iSLK.219 as a proxy for genome copies. *
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*
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Fig. 3C was performed in a PEL cell line which they showed to enter cytostasis upon HA15 treatment (Fig. 5). This cytostasis (rather than K1) may be the root cause of the defect in viral replication as cells could be arrested at a different stage compared to the G2 requirement for lytic replication in PEL cells (Balisteri et al., PLOS Pathogens 2016, PMID: 26891221).
See point 2. below
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The cytostatic effect in PEL cell lines (Fig. 5) should be demonstrated using more direct methods that measure cell cycle (e.g. PI-BrdU).
We thank the reviewer for this comment. While more direct methods to measure the cell cycle stage affected by HA15 treatment will inform on its mechanism of action, these experiments lie outside of the scope of this manuscript and we consider are better suited for future studies on the anticancer properties of HA15. The data presented in Fig. 5 demonstrates that HA15 treatment of PEL cells causes a reduction in cell numbers without cytotoxicity, thus supporting our conclusion of a net negative effect on proliferation rather than cell death. The loss of our LN2 tank and PEL cell lines currently limits our ability to do these more detailed analyses. At the moment, we do not have an accurate estimate of how long it will take to replace these cell lines for our subsequent studies.
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*
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While having an uninfected B cell as a matched negative control for PEL is challenging, primary peripheral B cells (mostly of mature memory B cell stage) may not be the appropriate negative control. PEL cells are of plasma cell lineage which have unusually high protein translation and overloaded ER. The plasma cell lineage may explain the sensitivity of PEL cells to HA15. It is possible that HA15 may be toxic to plasma cells when used as a therapeutic agent.
We agree with the reviewer on the potential impact of HA15 on plasma cell viability. Indeed, HA15 (>2uM) treatment reduces the viability of plasma cell myeloma lines (NCI-H929 and U266 cells), substantiating its use as a potential anti-cancer drug6. Although HA15 has not been tested as a therapeutic agent in humans, studies in mice have demonstrated tolerability without evident toxicity, measured as normal body weight7*. The potential therapeutic application of HA15 for cancer warrants further investigation and is beyond the scope of our manuscript. *
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Does HA15 have cytostatic effects in uninfected or latently infected iSLK cells?
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*
We observed no cytostatic or cytotoxic effects in uninfected or latently infected iSLK cells exposed to up to 30uM of HA15. Although HA15 has been tested on various cancer types8*, it has not been evaluated in Renal Carcinoma Cells (RCC), the cellular background of iSLK.219 cells. The mechanism behind the resistance of these cells to HA15 eludes us, but its link to the cellular background of iSLK.219s merits exploration in future studies. *
Minor comments: 1. Consider changing the title of line 98 to specify cell type since BiP levels do not increase in BCBL-1 (Supp. Fig. 3).
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Revised in the manuscript
Fig. 3A may benefit from using z-scores instead of log2TPM so differences are more obvious per gene.
Since the data have already been collected, can the authors include both latent and lytic cells with and without HA15 treatment in Fig. 3A? It may give more information for the reader. *
*We have reanalyzed all the RNAseq data and included a z-score plot for all samples in Fig. 3. We also providing three new supplementary tables with the raw counts, the z-scores for viral genes, and the log2 of the normalized counts.
*
*Reviewer #1 (Significance (Required)):
Significance: Here, the authors convincingly demonstrate the proviral role of the ER chaperone BiP during KSHV reactivation. This manuscript will be relevant to researchers in the gammaherpesvirus field. Although the authors did present some interesting data, the scope is narrow, and mechanistic studies were not pursued that would have added more insight in BiP and/or KSHV biology. For instance, how do BiP protein levels increase during reactivation (is this at the level of RNA sequestration/export, translation, or protein stability?)? How does BiP promote lytic replication?
Field of expertise: KSHV, molecular and cell biology
*
* __Reviewer #2 (Evidence, reproducibility and clarity (Required)): __ Many viruses have complex relationships with cellular ER proteostasis machinery that remain poorly understood. Here Najarro, et al. report on studies of the oncogenic gammaherpesvirus KSHV. They report that the ER chaperone BiP is upregulated in epithelial cells during KSHV lytic replication. Unexpectedly, BiP upregulation is independent of the unfolded protein response, which stimulates transcriptional activation of BiP to meet the protein folding demand in the ER. Using a combination of genetic and pharmacologic approaches (CRISPRi and selective chemical inhibitor) they demonstrate that BiP inhibition interferes with the replication of diverse enveloped viruses including poxviruses and several herpesviruses, and reduces proliferation of KSHV-infected cells.
Figure-by-figure:
Fig. 1: This figure convincingly demonstrates the selective upregulation of BiP at the protein level during the course of KSHV lytic replication, and that KSHV late genes are dispensable for this upregulation. It further demonstrates that BiP is not upregulated at the mRNA level at all during KSHV infection, despite the fact that the UPR-dependent BiP mRNA upregulation pathway (presumably via ATF6 and IRE1) remains functional.
Fig. 2: This figure convincingly demonstrates that BiP ATPase activity is required to support KSHV lytic replication in both epithelial and B cell models on infection, even though it is also clear that BiP is not upregulated in the B cell model.
Fig. 3: This data demonstrates that steady-state levels of KSHV lytic gene products are reduced following HA15-treatment, whereas later gene expression was unaffected. As an interesting side note, v-IL6 bucks the trend of HA15-mediated downregulation of viral mRNA levels, suggesting that it may be regulated in a different manner. One thing that the authors may consider is the report from Drs. Yuan Chang and Patrick Moore (PMID: 12434062) that demonstrated that the v-IL6 gene is transactivated by type I interferon. Considering the poor replication of this virus during HA15 treatment, it may be valuable to investigate IFN production by these cells, and the extent to which it is impacted by inhibition of BiP ATPase activity.*
We thank the reviewer for bringing this report to our attention. We also found intriguing the specific transcriptional upregulation of IL6 in IFN-a treated BCP-1 cells. Although we see a dramatic upregulation of the vIL6 in HA15 treated cells, we still detect the expression of most viral genes, albeit at significantly lower levels than in untreated cells, which indicates that the viral transcriptional program in lytic+HA15 iSLK.219 cells is different from the one seen in IFN-treated BCP-1 cells. Preliminary analyses of the host transcriptome from our RNAseq results show the expression of several ISGs (OAS1, 2 and 3, IFI6, IFIT1, IFIT3, IFITM1) in lytic-untreated iSLK.219 cells, but not in those treated with HA15. Together, these observations substantiate the notion that there is no IFN-driven expression of vIL6 in HA15-treated iSLK.219 cells.
Fig. 4: This figure demonstrates that HA15 has broad, non-cytotoxic, antiviral activity against diverse enveloped viruses.
Figs. 5/6: These figure shows cytotoxic effects of HA15 on latently infected PEL cells, either solely infected with KSHV or co-infected with KSHV and EBV, whereas normal B cells were unaffected. HA15 was also cytotoxic to KSHV infected lymphatic endothelial cells.
**Referees cross-commenting**
I appreciate the insightful comments from Reviewer #1 and Reviewer #3. I think we are largely on the same page. The data is generally supportive of author's conclusions, with a few exceptions that are straightforward to address in revisions. The manuscript is limited in scope, which could also be addressed by additional experimentation if the authors are motivated to explore mechanism in greater depth. Of particular note is the lack of mechanistic insight into how BiP is upregulated at the protein level during lytic replication, if the mRNA is unchanged. The experimental approaches to this are straightforward.
*
*
We appreciate the reviewers' comments on the scope of our study. The mechanism of BiP upregulation remains an outstanding question for the following technical reasons: We hypothesized that the upregulation of BiP may depend on the IRES element present in its 5' UTR9. We tested this hypothesis by transfecting iSLK.219 cells with a bicistronic Renilla-(BiP)IRES-Firefly luciferase reporter from Licursi et. al10*. Unfortunately, for reasons that still elude us, our reactivation rates in transfected cells were consistently low in all of our experiments and therefore, we were not able to measure luciferase changes consistently and reliably. A potential workaround this technical limitation is to use a lentivirus-encoded IRES reporter to a lentiviral vector, as transduction of iSLK.219 cells does not alter viral reactivation, in our experience. At the moment, we do not have access to these reporters due to our lab's move to a different institution, and the first author of our study has started the next stage of their career. Therefore, we will not be able to pursue these experiments in a timely manner. *
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*As for the scope of this manuscript, even when the mechanism of BiP upregulation in KSHV infected cells remains unsolved, we consider that the broad-spectrum antiviral effect of BiP inhibition is an exciting finding that advances the field and benefits the virology community-the proteostasis network has been seldomly explored as a potential node for broad-spectrum antiviral intervention. Our results provide important proof-of-concept to continue the investigation of factors involved in protein synthesis, folding and transport as potential targets for the development of versatile broad-spectrum antivirals. *
Reviewer #2 (Significance (Required)):
Strengths: This is a well-written manuscript. The text and figures are clear and accurate and the methods are sufficiently informative that the study can be reproduced. The data generally supports the authors' conclusions. BiP appears to be a druggable target with minimal off-target cytotoxicity in normal, uninfected cells, although this study does not go beyond cell culture studies to validate in vivo.
Weaknesses: The study is somewhat limited in scope. The authors make the case for UPR transcription-independent upregulation of BiP during KSHV infection, and that late gene synthesis is dispensable, but the mechanism is not investigated further.
Point by point discussion:
Could an early KSHV gene product involved in this phenotype be identified by screening an ORF library or viral genome-wide CRISPRi screen?
The question of the viral protein responsible for the upregulation of BiP during lytic infection is indeed a fascinating one. However, we suspect that the mechanism may be not specifically directed to BiP, but rather general modulation of IRES-related translation. Identifying the gene product(s) affected and corroborating IRES involvement is a major undertaking and a long-term goal requiring considerable effort. These analyses are outside the scope of this manuscript, but we will pursue them in the future.
Or, beyond implicating viral factors in the mechanism of BiP upregulation, can some simple biochemical studies be performed to investigate BiP protein? Is the BiP mRNA more efficiently spliced and exported in KSHV infected cells?
Do alternative translation initiation mechanisms like eIF2A play a role in boosting BiP levels during infection?
What is the normal BiP protein turnover mechanism, and is this hindered during KSHV lytic replication? Is BiP AMPylation/de-AMPylation by FICD affected (PMID: 36041787)? These kinds of mechanistic studies are well within reach and would help extend the impact and interest to a broad audience.
We agree on the putative involvement of translation initiation factors like eIF2A on promoting the translation of BiP (see discussion). We tested the effect of siRNA-mediated KD of eIF2A on BiP expression and found that, interestingly, the levels of BiP rose above those of controls in latent iSLK.219 cells (Data included in the manuscript and the discussion has been modified accordingly). This finding aligns with previous reports suggesting that eIF2A may suppress IRES-mediated translation in yeast cells and in mammalian in vitro translation assays. Moreover, Starck et. al11, observed a 50% increase of endogenous BiP levels in HeLa cells transfected with siRNAs against eIF2A, supporting the IRES-suppressor role for eIF2A in mammalian cells. Future work will be required to address the role of eIF2A on BiP translation. These analyses are beyond the scope our manuscript.
Reviewer #3 (Evidence, reproducibility and clarity (Required)):
The manuscript by Najarro et al. investigates the contribution of BiP/GRP78 to double-stranded DNA virus infection, primarily focusing on the oncogenic gammaherpesvirus Kaposi's sarcoma-associated herpesvirus (KSHV). The authors observe that BiP expression is increased in lytic iSLK.219 cells as well as in KSHV-infected LECs. Interestingly, the authors data suggest a post-translational regulation of BiP in the iSLK.219 cells. Using various knockdown approaches and chemical inhibitors the authors demonstrate that inhibition of BiP impacts KSHV reactivation in multiple cells lines. Importantly, the authors also find that BiP inhibition can selectively kill KSHV-infected cells, while sparing primary B cells. Overall, this is a very well controlled and presented manuscript. My comments for the manuscript are minor, and largely cosmetic to aid the presentation of the data.
• Fig 1C, It would be ideal to show that PAA treatment did indeed prevent the virus from entering the late stage of gene expression.
*We have included an immunoblot for K8.1 in Figure 1C to confirm the effect of PFA on arresting the KSHV lytic cycle. *
Sup Fig2, should show KD efficiency of XBP1, same goes for ATF6.
- *
Sup. Fig. 2D shows the expression of XBP1s in NS vs. XBP1KD cells in the presence or absence of Tg. In Sup Fig. 2G we have also included a bar graph showing the efficiency of downregulation of ATF6 mRNA in the presence of the targeting sgRNA.
Sup Fig 3. It is interesting that the authors do not see increased BiP in TREx-BCBL1-RTA cells. A potential caveat is that lytic reactivation in TREx-BCBL1-RTA cells is not as efficient as in iSLK.219 cells. Therefore, it may simply be a result of the reduced population entering the lytic cycle. It may be worth adding a comment regarding this.
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Images of the microscopy for Figure 4 would be useful.
Images have been included in Fig. 4
• Add label of the cell types for Figure 5.
DONE
• Does HSV1, HCMV, or VacV increase BiP expression compared to mock-infected cells?
Yes, we have included a comment on this in the discussion
Reviewer #3 (Significance (Required)):
Overall, this is a very well controlled and presented manuscript.
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Referee #3
Evidence, reproducibility and clarity
The manuscript by Najarro et al. investigates the contribution of BiP/GRP78 to double-stranded DNA virus infection, primarily focusing on the oncogenic gammaherpesvirus Kaposi's sarcoma-associated herpesvirus (KSHV). The authors observe that BiP expression is increased in lytic iSLK.219 cells as well as in KSHV-infected LECs. Interestingly, the authors data suggest a post-translational regulation of BiP in the iSLK.219 cells. Using various knockdown approaches and chemical inhibitors the authors demonstrate that inhibition of BiP impacts KSHV reactivation in multiple cells lines. Importantly, the authors also find that BiP inhibition can selectively kill KSHV-infected cells, while sparing primary B cells. Overall, this is a very well controlled and presented manuscript. My comments for the manuscript are minor, and largely cosmetic to aid the presentation of the data.
- Fig 1C, It would be ideal to show that PAA treatment did indeed prevent the virus from entering the late stage of gene expression.
- Sup Fig2, should show KD efficiency of XBP1, same goes for ATF6.
- Sup Fig 3. It is interesting that the authors do not see increased BiP in TREx-BCBL1-RTA cells. A potential caveat is that lytic reactivation in TREx-BCBL1-RTA cells is not as efficient as in iSLK.219 cells. Therefore, it may simply be a result of the reduced population entering the lytic cycle. It may be worth adding a comment regarding this.
- Images of the microscopy for Figure 4 would be useful.
- Add label of the cell types for Figure 5.
- Does HSV1, HCMV, or VacV increase BiP expression compared to mock-infected cells?
Significance
Overall, this is a very well controlled and presented manuscript.
-
Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.
Learn more at Review Commons
Referee #2
Evidence, reproducibility and clarity
Many viruses have complex relationships with cellular ER proteostasis machinery that remain poorly understood. Here Najarro, et al. report on studies of the oncogenic gammaherpesvirus KSHV. They report that the ER chaperone BiP is upregulated in epithelial cells during KSHV lytic replication. Unexpectedly, BiP upregulation is independent of the unfolded protein response, which stimulates transcriptional activation of BiP to meet the protein folding demand in the ER. Using a combination of genetic and pharmacologic approaches (CRISPRi and selective chemical inhibitor) they demonstrate that BiP inhibition interferes with the replication of diverse enveloped viruses including poxviruses and several herpesviruses, and reduces proliferation of KSHV-infected cells.
Figure-by-figure:
Fig. 1: This figure convincingly demonstrates the selective upregulation of BiP at the protein level during the course of KSHV lytic replication, and that KSHV late genes are dispensable for this upregulation. It further demonstrates that BiP is not upregulated at the mRNA level at all during KSHV infection, despite the fact that the UPR-dependent BiP mRNA upregulation pathway (presumably via ATF6 and IRE1) remains functional.
Fig. 2: This figure convincingly demonstrates that BiP ATPase activity is required to support KSHV lytic replication in both epithelial and B cell models on infection, even though it is also clear that BiP is not upregulated in the B cell model.
Fig. 3: This data demonstrates that steady-state levels of KSHV lytic gene products are reduced following HA15-treatment, whereas later gene expression was unaffected. As an interesting side note, v-IL6 bucks the trend of HA15-mediated downregulation of viral mRNA levels, suggesting that it may be regulated in a different manner. One thing that the authors may consider is the report from Drs. Yuan Chang and Patrick Moore (PMID: 12434062) that demonstrated that the v-IL6 gene is transactivated by type I interferon. Considering the poor replication of this virus during HA15 treatment, it may be valuable to investigate IFN production by these cells, and the extent to which it is impacted by inhibition of BiP ATPase activity.
Fig. 4: This figure demonstrates that HA15 has broad, non-cytotoxic, antiviral activity against diverse enveloped viruses.
Figs. 5/6: These figure shows cytotoxic effects of HA15 on latently infected PEL cells, either solely infected with KSHV or co-infected with KSHV and EBV, whereas normal B cells were unaffected. HA15 was also cytotoxic to KSHV infected lymphatic endothelial cells.
Referees cross-commenting
I appreciate the insightful comments from Reviewer #1 and Reviewer #3. I think we are largely on the same page. The data is generally supportive of author's conclusions, with a few exceptions that are straightforward to address in revisions. The manuscript is limited in scope, which could also be addressed by additional experimentation if the authors are motivated to explore mechanism in greater depth. Of particular note is the lack of mechanistic insight into how BiP is upregulated at the protein level during lytic replication, if the mRNA is unchanged. The experimental approaches to this are straightforward.
Significance
Strengths: This is a well-written manuscript. The text and figures are clear and accurate and the methods are sufficiently informative that the study can be reproduced. The data generally supports the authors' conclusions. BiP appears to be a druggable target with minimal off-target cytotoxicity in normal, uninfected cells, although this study does not go beyond cell culture studies to validate in vivo.
Weaknesses: The study is somewhat limited in scope. The authors make the case for UPR transcription-independent upregulation of BiP during KSHV infection, and that late gene synthesis is dispensable, but the mechanism is not investigated further. Could an early KSHV gene product involved in this phenotype be identified by screening an ORF library or viral genome-wide CRISPRi screen? Or beyond implicating viral factors in the mechanism of BiP upregulation, can some simple biochemical studies be performed to investigate BiP protein? Is the BiP mRNA more efficiently spliced and exported in KSHV infected cells? Do alternative translation initiation mechanisms like eIF2A play a role in boosting BiP levels during infection? What is the normal BiP protein turnover mechanism, and is this hindered during KSHV lytic replication? Is BiP AMPylation/de-AMPylation by FICD affected (PMID: 36041787)? These kinds of mechanistic studies are well within reach and would help extend the impact and interest to a broad audience.
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Referee #1
Evidence, reproducibility and clarity
Summary:
Viruses exploit host endoplasmic reticulum (ER)-resident chaperones to support new protein synthesis during viral replication. Here, Najarro et al. study the role of the ER-resident HSP70 family member Binding immunoglobulin protein (BiP) during lytic infection by the Kaposi's sarcoma-associated herpesvirus (KSHV). Using the established doxycycline-inducible lytic reactivation infection model cell line iSLK-BAC16, they showed that KSHV reactivation leads to an upregulation of total BiP protein but not RNA, and is independent of the unfolded protein response. siRNA knockdown or pharmacological inhibition by HA15 of BiP significantly reduced global viral gene expression and infectious virus production. The authors attribute this to at least the reduction of levels of the K1 gene which is required for efficient viral replication. Finally, they showed that HA15 has cytostatic activity in KSHV-transformed B cells and cytotoxic effects in KSHV-infected lymphatic endothelial cells arguing for BiP inhibition as a potential therapeutic strategy to treat KSHV-driven malignancies. The manuscript is well-written and the conclusions were generally supported by the data with a few exceptions below.
Major comments:
- They propose in lines 196-199 that the reduction of K1 from HA15 treatment partially explains the defect in virion production during lytic reactivation. I am not convinced that this statement is fully supported by their data. Reduction of K1 is likely a downstream consequence and not the cause of the inhibition of lytic replication. Consider revising this statement in light of my comments below:
- a. The quantification of the K1 blots in Fig. 3C only has n=2. With subtle differences by eye, large error bars, and no statistical analysis, it is hard to draw conclusions from here with confidence.
- b. Like K1, ORF45 and K8.1 proteins are similarly decreased at 24 h in Fig. 2E suggesting that the defect is upstream of K1. Does HA15 affect the amount of endogenous and/or transgene copy of RTA being produced (hence the broader effect in early gene expression at 24h?)?
- c. K1 levels appear to decrease even during latency. Are the other latent proteins also affected? What about latent genome copies?
- d. Fig. 3C was performed in a PEL cell line which they showed to enter cytostasis upon HA15 treatment (Fig. 5). This cytostasis (rather than K1) may be the root cause of the defect in viral replication as cells could be arrested at a different stage compared to the G2 requirement for lytic replication in PEL cells (Balisteri et al., PLOS Pathogens 2016, PMID: 26891221).
- The cytostatic effect in PEL cell lines (Fig. 5) should be demonstrated using more direct methods that measure cell cycle (e.g. PI-BrdU).
- While having an uninfected B cell as a matched negative control for PEL is challenging, primary peripheral B cells (mostly of mature memory B cell stage) may not be the appropriate negative control. PEL cells are of plasma cell lineage which have unusually high protein translation and overloaded ER. The plasma cell lineage may explain the sensitivity of PEL cells to HA15. It is possible that HA15 may be toxic to plasma cells when used as a therapeutic agent.
- Does HA15 have cytostatic effects in uninfected or latently infected iSLK cells?
Minor comments:
- Consider changing the title of line 98 to specify cell type since BiP levels do not increase in BCBL-1 (Supp. Fig. 3).
- Fig. 3A may benefit from using z-scores instead of log2TPM so differences are more obvious per gene.
- Since the data have already been collected, can the authors include both latent and lytic cells with and without HA15 treatment in Fig. 3A? It may give more information for the reader.
Significance
Here, the authors convincingly demonstrate the proviral role of the ER chaperone BiP during KSHV reactivation. This manuscript will be relevant to researchers in the gammaherpesvirus field. Although the authors did present some interesting data, the scope is narrow, and mechanistic studies were not pursued that would have added more insight in BiP and/or KSHV biology. For instance, how do BiP protein levels increase during reactivation (is this at the level of RNA sequestration/export, translation, or protein stability?)? How does BiP promote lytic replication?
Field of expertise: KSHV, molecular and cell biology
- They propose in lines 196-199 that the reduction of K1 from HA15 treatment partially explains the defect in virion production during lytic reactivation. I am not convinced that this statement is fully supported by their data. Reduction of K1 is likely a downstream consequence and not the cause of the inhibition of lytic replication. Consider revising this statement in light of my comments below:
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Reply to the reviewers
Response to the reviewer's questions
__Reviewer #1 (Evidence, reproducibility and clarity (Required)):
Using the S protein from 14 different sarbecoviruses isolated from bats or pangolin, Zhang et al. makes in this manuscript several points on sabecovirus entry. These points include ACE2 independent entry, trypsin-driven entry, RBD-dependence of trypsin-mediated entry, use of soluble proteases and TRMPRSS-family transmembrane proteases in trypsin-mediated and trypsin-independent entry, and neutralizing antibody evasion in trypsin-mediated entry. Some of these points are supported by the data presented; although there are some discrepancies, they are largely within the range of experimental error. However, some of the statements in the Title, Abstract, and main text, appear to be more than what the data support. Nonetheless, the data authors presented are informative and will help understanding sarbecovirus entry processes. __
Thank you very much for the positive assessment of our study and for the suggestions for improvement.__
Major points:
Below are only a few examples of inaccurate sentences. The authors should rewrite similar statements throughout the manuscript.
Q1: The title: "ACE2-independent sarbecovirus cell entry is supported by TMPRSS2-related enzymes and reduces sensitivity to antibody-mediated neutralization" does not correctly reflect the presented data (1) because the contribution by TMPRSS2-like enzymes was shown only when they were co-transfected during PV production, but not when they are expressed on the target cell surface, and (2) because "reduces sensitivity to antibody-mediated neutralization" was observed only for one S protein but was not observed for the other two trypsin-dependent S proteins. In addition, this point was made using one monoclonal Ab for trypsin-dependent entry, but not for the entry mediated by TMPRSS2-related enzymes as the title implies. The title sounds like the three points are interconnected and represent general phenomena. Perhaps a more accurate title could be "ACE2-independent sarbecovirus cell entry is supported by trypsin and may reduce sensitivity to a neutralizing antibody". __
A1: __We appreciate the critique. From our perspective, the statement that ACE2-independent entry is supported by TMPRSS2-related enzymes is correct irrespective of whether these enzymes cleave the viral S protein during entry into uninfected cells or during S protein biogenesis in infected cells (in order to allow for subsequent ACE2-independent entry into uninfected cells). The reviewer is correct that rescue from antibody-mediated neutralization was only observed for one monoclonal antibody. However, we also obtained evidence that ACE2-independent entry allowed for evasion of neutralizing antibodies induced upon infection or vaccination. In order to avoid generalization, we phrased the title in a more careful fashion: "ACE2-independent sarbecovirus cell entry can be supported by TMPRSS2-related enzymes and can reduce sensitivity to antibody-mediated neutralization".
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__Q2: In the Abstract, the authors state "Several TMPRSS2-related cellular proteases but not the insertion of a multibasic cleavage site into the S protein allowed for ACE2-independent entry in the absence of trypsin and may support viral spread in the respiratory tract" (lines 38-41) and "In sum, our study reports a pathway for entry into human cells that is ACE2-independent, supported by TMPRSS2-related proteases...." (lines 44-46). These sentences should be rewritten for the same reason described above for the Title. __
__A2: __We feel that the statement that TMPRSS2-related enzymes can support ACE2-independent entry is correct. Thus, either trypsin pretreatment of particles or expression of TMPRSS2-related enzymes in particle producing cells allows for ACE2-independent entry. We rephrased our concluding sentence in a more careful fashion and now state: "In sum, our study reports a pathway for entry into human cells that is ACE2-independent, can be supported by TMPRSS2-related proteases and may be associated with antibody evasion."
__Q3: The lines 102-105 say "...ACE2-independent, trypsin-dependent entry can modulate neutralization by the pan sarbecovirus antibody S2H97..." and the lines 427-9 say "...trypsin-dependent usage of an ACE2-independent entry pathway may result in slightly reduced susceptibility to neutralization by antibodies induced upon infection or vaccination." Because Fig 8 (S2H97 Ab) and Fig 9 (immune plasma) use Vero-ACE2-TMPRSS2 and A549-ACE2-TMPRSS2, respectively, "ACE2-independent," is incorrect here. __
__A3: __We respectfully disagree. We have shown that certain spikes can facilitate entry into ACE2-expressing cell lines in an ACE2-dependent manner but switch to an ACE2-independent entry route upon pre-treatment of particles with trypsin and blockade of ACE2 by an antibody (Supplementary Figure 4C). In figure 8 and 9, we show that when the ACE2-dependent entry route is blocked by neutralizing antibodies, opening the ACE2-independent route reduces antibody-mediated neutralization. As a consequence, it is fair to conclude that our data indicate that usage of the ACE2-independent entry route may reduce neutralization sensitivity. We feel that this argument is further supported by our most recent data, shown as new figure 3C, which demonstrate that trypsin treatment not only allows for entry into ACE2+ cells pretreated with anti-ACE2 antibody but, more importantly, also permits entry into ACE2 KO cells.
__
Q4: The line 46 says "...and associated with antibody evasion", the lines 104-5 says "...and allows for partial antibody evasion in the context of plasma from COVID-19 vaccinees." and the lines 427-9 say "...may result in slightly reduced susceptibility to neutralization by antibodies..." The authors should rewrite them because the resistance to S2H97 Ab was observed with one S protein but all other trypsin-mediated entry was sensitive to S2H97 or immune plasma. __
__A4: __We have phrased the sentences in question in a more careful fashion and now state:
"Finally, the pan-sarbecovirus antibody S2H97 enhanced cell entry driven by two S proteins and this effect was reversed by trypsin while trypsin protected entry driven by a third S protein from neutralization by S2H97. Similarly, plasma from quadruple vaccinated individuals neutralized entry driven by all S proteins studied, and availability of the ACE2-independent, trypsin-dependent pathway reduced neutralization sensitivity. In sum, our study reports a pathway for entry into human cells that is ACE2-independent, can be supported by TMPRSS2-related proteases and may be associated with antibody evasion." (Abstract)
"Finally, we obtained evidence that ACE2-independent, trypsin-dependent entry can modulate neutralization by the pan sarbecovirus antibody S2H97 in a spike-dependent fashion and allows for partial antibody evasion in the context of plasma from COVID-19 vaccinees." (end of introduction).
"In sum, these results suggest that availability of the trypsin-dependent, ACE2-independent entry pathway may result in slightly reduced susceptibility to neutralization by antibodies induced upon infection or vaccination." (end of results section).
__
Q5: If trypsin- independent entry is still controlled by RBD, why LYRa11 and Rs7327 entry is enhanced by and RsSHC014 entry is resistant to S2H97 Ab? The authors may want to discuss possible explanations. __
__A5: __It is at present unclear why trypsin-treatment increased S2H97-mediated inhibition of LYRa11- and Rs7327-S protein driven entry while it conferred S2H97-resistance to RsSHC014-S. One could speculate that slight differences in the S2H97 epitope of the three spike proteins alter antibody affinity and thus determine whether the antibody enhances or blocks entry.
__
Q6: Fig. 2B. The entry supported by ACE2 orthologs was normalized to that utilizing hACE2 after hACE2-supported entry was normalized to background entry (no-S PV). First, it is unclear why background entry is used for normalization instead of being subtracted. Second, two times of such normalization likely created huge experimental errors and might have skewed the outcomes. Thus, 14 PVs should be quantified by RT-qPCR and same genome copy number should be used to directly assess their usage of ACE2 orthologs. This way, normalization by hACE2 entry is not necessary. Background entry should be subtracted, not used for normalization. __
__A6: __We respectfully disagree. It is fair to ask how much more efficient single cycle particles bearing a viral envelope protein enter target cells as compared to identical particles bearing no viral glycoprotein. Normalization of the data presented as a heat map (Figure 2C) was performed based on the raw data (not the "Fold over Background"-normalized data). Thus, data were only normalized once. Regarding the possibility that different particle numbers were used for the respective pseudoviruses, we would like to state that particle production efficiency was analyzed by immunoblot (based on VSV matrix protein levels) and no major differences for the different pseudoviruses were observed (please see new Supplementary figure 4A). Thus, we are confident that our results are not skewed by gross differences in pseudovirus particle numbers.
__
Q7: Because VSV PVs were harvested in culture media, there were serum and divalent cations. Were PVs purified before trypsin digestion? Digestion by trypsin or other proteases should be described in detail. __
__A7: __Medium without serum was used for PV production to avoid inhibition of trypsin activity by serum components. For immunoblot samples, VSV PVs were further harvested from the culture medium and concentrated using 20% sucrose. The concentrated VSV PVs were aliquoted into separate tubes, each containing an equal volume, and treated with the specified concentrations of proteases at 37{degree sign}C, as detailed in the Materials and Methods section. Subsequently, the treated VSV PVs were mixed with an equal volume of 2x SDS loading buffer and heated at 96{degree sign}C for 10 minutes.
__
Q8: How was S2' fragment on the blot determined? Should be described. __
A8: __The S2' fragment was determined based on the molecular size of the corresponding bands. This information has been added to the respective figure legends.
Minor points.
Q9: The line 129 says "...14 S proteins, representing all clades, were selected for detailed analyses". Correct the sentence because the S protein representing clade 5 is not included in the study. __
A9: __We now state ""...14 S proteins, representing all clades except clade 5, were selected for detailed analyses"
__
__Q10: Fig 2. Because 14 S proteins and several TFR1 orthologs were used, a table describing which S isolate is derived from which animal species will help. Organizing Fig 2A and B in the same order will help reading the result. Also, indicate which clades those S proteins belong to. __
__A10: __We have added a table providing detailed information on the spike proteins under study.
Supplemental table 1: Information on the spike proteins under study.
Spike
Virus
Identifier
RBD clade
Host
Region
SARS-2-S
Human SARS-CoV-2 hCoV-19/Wuhan/Hu-1/2019
GISAID: EPI_ISL_402125
1b
Human (Homo sapiens)
Asia (China)
RaTG13-S
Bat SARSr-CoV hCoV-19/bat/Yunnan/RaTG13/2013
GISAID: EPI_ISL_402131
1b
Bat (Rhinolophus affinis)
Asia (China)
P5L-S
Pangolin SARSr-CoV hCoV-19/pangolin/Guangxi/P5L/2017
GISAID: EPI_ISL_410540
1b
Malayan pangolin (Manis javanica)
Asia (China)
cDNA8-S
Pangolin SARSr-CoV hCoV-19/pangolin/Guangdong/cDNA8-S/2019
GISAID: EPI_ISL_471461
1b
Malayan pangolin (Manis javanica)
Asia (China)
Rs4081-S
Bat SARSr-CoV Rs4081
GenBank: KY417143.1
2
Bat (Rhinolophus sinicus)
Asia (China)
Rs4237-S
Bat SARSr-CoV RS4237
GenBank: KY417147.1
2
Bat (Rhinolophus sinicus)
Asia (China)
SARS-1-S
Human SARS-CoV-1/Frankfurt-1
GenBank: AY291315.1
1a
Human (Homo sapiens)
Europe (Germany)
WIV1-S
Bat SARSr-CoV WIV1
GenBank: KF367457.1
1a
Bat (Rhinolophus sinicus)
Asia (China)
LYRa11-S
Bat SARSr-CoV LYRa11
GenBank: KF569996.1
1a
Bat (Rhinolophus affinis)
Asia (China)
RsSHC014-S
Bat SARSr-CoV RsSHC014
GenBank: KC881005.1
1a
Bat (Rhinolophus sinicus)
Asia (China)
Rs4231-S
Bat SARSr-CoV Rs4231
GenBank: KY417146.1
1a
Bat (Rhinolophus sinicus)
Asia (China)
Rs4874-S
Bat SARSr-CoV Rs4874
GenBank: KY417150.1
1a
Bat (Rhinolophus sinicus)
Asia (China)
Rs7327-S
Bat SARSr-CoV Rs7327
GenBank: KY417151.1
1a
Bat (Rhinolophus sinicus)
Asia (China)
BM48-31-S
Bat SARSr-CoV BM48-31/BGR/2008
GenBank: GU190215.1
3
Rhinolophus blasii
Europe (Bulgaria)
__
Q11: Fig S5. Describe cell lines used. __
__A11: __We have added a table providing information on the cell lines used.
Supplemental table 2: Information on the cell lines used.
Cell line
Species
Organ
Modification
Culture medium
Vero
African green monkey (Cercopithecus aethiops)
Kidney
n.a.
DMEM + 10% FCS + Pen/Strep
Vero-ACE2+TMPRSS2
African green monkey (Cercopithecus aethiops)
Kidney
Stable expression of human ACE2 and human TMPRSS2
DMEM + 10% FCS + Pen/Strep + Blasticidin (2 µg/ml) + Puromycin (1 µg/ml)
Vero-TMPRSS2
African green monkey (Cercopithecus aethiops)
Kidney
Stable expression of human TMPRSS2
DMEM + 10% FCS + Pen/Strep + Blasticidin (2 µg/ml)
MyDauLu/47
Bat (Myotis daubentonii)
Lung
n.a.
DMEM + 10% FCS + Pen/Strep
PipNi/3
Bat (Pipistrellus pipistrellus)
Kidney
n.a.
DMEM + 10% FCS + Pen/Strep
Caco-2
Human (Homo sapiens)
Intestine
n.a.
MEM + 10% FCS 1% NEA + 10 mM sodium pyruvate + Pen/Strep + Puromycin (1 µg/ml)
293T
Human (Homo sapiens)
Kidney
n.a.
DMEM + 10% FCS + Pen/Strep
293T-ACE2
Human (Homo sapiens)
Kidney
Stable expression of human ACE2
DMEM + 10% FCS + Pen/Strep + Puromycin (1 µg/ml)
Huh-7
Human (Homo sapiens)
Liver
n.a.
DMEM + 10% FCS + Pen/Strep
Li7
Human (Homo sapiens)
Liver
n.a.
DMEM + 10% FCS + Pen/Strep
A549-ACE2
Human (Homo sapiens)
Lung
Stable expression of human ACE2
DMEM/F-12 + 10% FCS + Pen/Strep + Puromycin (1 µg/ml)
A549-ACE2+TMPRSS2
Human (Homo sapiens)
Lung
Stable expression of human ACE2 and human TMPRSS2
DMEM/F-12 + 10% FCS + Pen/Strep + Blasticidin (2 µg/ml) + Puromycin (1 µg/ml)
Calu-3
Human (Homo sapiens)
Lung
n.a.
DMEM/F-12 + 10% FCS 1% NEA + 10 mM sodium pyruvate + Pen/Strep
Calu-3-ACE2
Human (Homo sapiens)
Lung
Stable expression of human ACE2
DMEM/F-12 + 10% FCS 1% NEA + 10 mM sodium pyruvate + Pen/Strep + Puromycin (1 µg/ml)
NCI-H522
Human (Homo sapiens)
Lung
n.a.
RPMI + 10% FCS 1% NEA + 10 mM sodium pyruvate + Pen/Strep
BHK-21
Syrian golden hamster (Mesocricetus auratus)
Kidney
n.a.
DMEM + 10% FCS + Pen/Strep
__ Q12: Fig 3 legend should indicate trypsin digestion condition (concentration and length). __
__A12: __We have added the requested information to the respective figure legends.
__
Reviewer #1 (Significance (Required)):
Because overwhelming amount of data bear large experimental errors, there are some discrepancies among the data presented. Nonetheless, most of each point the authors claim is largely supported by the data. The problem happened when the authors tried to connect the dots too much and thus overstated some conclusions. If the overstated conclusions are amended throughout the manuscript, presented data provide sufficiently useful information on sarbecovirus entry.
__
Thank you. We have rephrased our conclusions in a more careful fashion.
__Reviewer #2 (Evidence, reproducibility and clarity (Required)):____
SUMMARY: Recent work from several groups has shown that the majority of bat sarbecoviruses infect cells independent of ACE2, the receptor primarily used by sarbecoviruses that infect humans, and instead infect cells in the presence of exogenous protease including trypsin. In this study, Zhang and colleagues build on these earlier findings by demonstrating that ACE2-independent sarbecovirus entry can be mediated by other exogenous proteases and several different TMPRSS11 enzymes. Using in vitro based methods and viral pseudotypes, the authors reproduce previous findings with trypsin, demonstrate similar effects with alternative proteases and provide lines of evidence suggesting (1) trypsin treatment can impart ACE2-independence and that (2) ACE2-independence provides resistance to neutralizing antibodies. __
Many thanks for evaluation our manuscript and for the constructive critique.__
MAJOR COMMENTS:
Q1: Defining sarbecovirus RBDs into clades by in del features has already been established by other groups and many studies across different disciplines now use these previously-established clades. The authors use slightly different nomenclature without any acknowledgment of the previously defined sarbecovirus RBD clades, which will lead to confusion between studies. For example, SARS-CoV-2 is generally regarded as a clade 1 RBD (with ACE2 use and both loops in tact), clade 3 includes BM48-31 and Khosta-2, clade 4 includes RatG15. __
__A1: __We have changed the nomenclature of the different groups to "clusters" to avoid confusion. Further, we added for each cluster information on the RBD clade. Please see revised Figure 1.
__
Q2: Why did the authors select BM48-31 as the representative of its clade when other members of the clade have known receptors and clear phenotypes in lab assays? BM48-31 has largely failed in every lab assay by every group that has studied it. On the other hand, Khosta2 uses human ACE2, BtKY72 and other African sarbecoviruses can also use ACE2 from their host species and have low but detectable human ACE2 compatibility. It would be interesting to see how the antibody-resistance results compare with other ACE2-dependent sarbecoviruses. __
__A2: __We have selected BM48-31 at a time when the information stated above was not available. We agree that testing additional spikes for neutralization sensitivity should be considered within future studies but also feel that solid conclusions can be drawn from the 13 spikes tested within this study.
__
Q3: What is the aurthors' proposed mechanism for how protease is functioning for ACE2-independent entry? For ACE2-dependent entry, TMPRSS2 cleaves spike after RBD engagement. However, in this study, TMPRSS11 enzymes only function when included in producer cells- prior to RBD engagement. Is TMPRSS11 cleaving spike during spike biogenesis (similar to furin for SARS-CoV-2) or is an alternative mechanism at play? Is TMPRSS11 secreted? If this is the case, then the enzyme may be functioning similar to the other exogenous proteases in this study. __
__A3: __It is possible that pre-cleavage by a TMPRSS2-like enzymes (or trypsin) is needed for subsequent S protein activation by another protease, likely cathepsin B/L, for ACE2-independent entry. This would be similar to SARS-CoV-2 entry into lung cells, which depends on spike pre-cleavage by furin and spike cleavage-activation by TMPRSS2. Alternatively, the TMPRSS2-like enzymes may cleave spike at the RBD, with the cleavage eluding detection by the methods applied here, and this cleavage might be needed for engagement of the so far unknown receptor responsible for ACE2-independent entry. TMPRSS2-like enzymes can be shed into the extracellular space. However, we feel that extracellular TMPRSS-activity was not responsible for ACE2-independent entry since expression of TMPRSS2-like enzymes in target cells should have also resulted in protease shedding but failed to allow for ACE2-independent entry.
__
Q4: Related to comment 3: the authors study trypsin as a pre-treatment, but other studies have shown trypsin exerts activity during entry. How do the authors propose trypsin is functioning prior to RBD engagement? Is it possible that trypsin is not fully inactivated and remains partially active during entry? __
A4: __For most experiments, trypsin was present/active during the whole entry process. Only for Figures 3B, 8 and 9 trypsin inhibitor was added prior to inoculation of target cells in order to discriminate effects of trypsin on virus particles and cells and to exclude that trypsin compromised the integrity of the antibodies under study. We speculate that trypsin cleavage even before receptor engagement can allow for ACE2-independent entry.
__
__Q5: I am not convinced that trypsin is driving ACE2-independent entry for ACE2-dependent viruses. The experiment performed in figure 3C is performed in African green monkey cells using an antibody directed toward human ACE2. The difference in species between antibody and antigen may influence how well the antibody binds ACE2 on the Vero cells, which may only block some ACE2-dependent viruses but not all. Curiously, the only ACE2-dependent spikes that gain "ACE2-independence" are also activated by trypsin. These blocking assay results would be more convincing in a human cell line, or a non-permissive cell line like BHKs that express the human receptor. Alternatively, knocking out ACE2 in the Vero cells may be another way to assess ACE2-independent entry. __
__A5: __We have now examined entry into 293T WT and 293T ACE2 KO cells. Importantly, the same spikes that allow for trypsin-dependent entry into Vero-TMPRSS2 cells treated with anti-ACE2 antibody also allow for robust entry into 293T ACE2 KO cells when pretreated with trypsin, please see new figure 3C. These results confirm our previous data and validate our conclusion that some spikes facilitate ACE2-dependent entry but can switch to the ACE2-independent entry route upon pre-treatment with trypsin.
__
MINOR COMMENTS:
Q6: line 148: Rs4237 is missing a clade designation __
__A6: __Rs4237 belongs to the Asian bat cluster (RBD clade 2). This information has been added to the revised figure 1 and is further provided in the new supplemental table 1.
__ Q7: Figure 3. The figure's main message could be improved by visually grouping the viruses according to clade. __
__A7: __We modified all figures and now indicate for each spike to which RBD clade they belong.
__
Q8: Some details are missing for reproducibility, including the accession numbers of the TMPRSS enzymes used in this study __
__A8: __We added the requested information to the Materials and methods section.
__
Q9: Contrary to claims in the text, this study includes a fairly small panel of spike proteins. Prior studies by Letko 2020, Starr 2022 and Roelle 2022 (cited by the authors) all measured entry for between 20-40 spikes - more twice the number in this study. __
A9: __We apologize for the mistake and removed the statement that "...these analyses were confined to small numbers of S proteins and.."
__
__Q10: Line 472-473: the data presented in figure 2B shows SARS-CoV-2 has slightly better entry with pangolin ACE2 than raccoon dog. I am not sure the authors should cite this data in support of raccoon dogs as an intermediate for SARS-CoV-2. __
A10: We feel that our statement that - based on ACE2 usage - raccoon dogs should be considered as intermediate hosts is valid since it refers to the finding that diverse sarbecoviruses used this ACE2 orthologue with highest efficiency.
__
Reviewer #2 (Significance (Required)):
SIGNIFICANCE: This study provides some novel insights into proteases and sarbecovirus cell entry and highlights previously unappreciated entry factors that are key for some viruses. A major limitation of this study is its lack of mechanistic exploration. The authors data do not really elucidate how TMPRSS11 proteins mediate ACE2-independent entry, nor do the results explain how ACE2-independence is shielding viruses from neutralizing antibodies. Another limitation is in the choice of using a non-human cell line to study the blocking effect of an antibody directed toward a human protein. __
We feel that our findings that TMPRSS2-related enzymes can support ACE2-independent entry and that ACE2-independnet entry might allow for some level of antibody evasion are novel and important. We would also like to point out that we employed a human ACE2 KO cell line to address the reviewer's reservations regarding use of a non-human primate cell line. The data obtained with the human KO cell line confirmed those obtained with anti-ACE2 antibody treated non-human primate cell line, validating our conclusions.
__
ADVANCE: This study nicely reproduces a number of previous findings, including: 1. sarbecovirus RBDs can be categorized into clades based on deletions in surface exposed loops 2. ACE2-independent, trypsin-dependent sarbecovirus entry - notably for Rs4081 3. the RBD in ACE2-independent sarbecoviruses controls entry 4. anti-ACE2 antibodies do not block entry for ACE2-independent sarbecoviruses as well as some ACE2-dependent sarbecoviruses 5. trypsin does not increase S proteins binding to cells 6. protease expression in target cells does not increase S-driven entry 7. a multi-basic cleavage site in spike does not compensate for exogenous protease in ACE2-independent entry
This study has many novel advancements as well: 1. identification of other exogenous proteases that mediate ACE2-independent entry (elastase, thermolysin) 2. identification of TMPRSS11 family members that mediate trypsin-free entry for ACE2-independent viruses when produced in cells producing spike proteins but not target cells 3. ACE2-independent entry may reduce spike susceptibility to antibody neutralization __
Thank you.__
AUDIENCE: This study will appeal to the coronavirus research community.
__
__Reviewer #3 (Evidence, reproducibility and clarity (Required)):____
Zhang et al. analyzed the infection mechanisms of various Sarbecovirus primarily using VSV pseudoviruses with individual Sarbecovirus S proteins. The study demonstrated that many Sarbecoviruses, similar to two Sarbecoviruses that do not exhibit infectivity without trypsin, gain infectivity in human cells after processing virus particles with trypsin. This trypsin treatment is closely associated with the cleavage of the S1/S2 site of the S protein. This study demonstrated that the infection of the two viruses is not dependent on ACE2 expression, suggesting infection through receptors other than ACE2. Indeed, this study indicates that the receptor-binding domain of the S protein determines these properties. Furthermore, this study shows that some ACE2-using Sarbecoviruses also acquire ACE2-independent infectivity after trypsin treatment of virus particles. Although similar phenomena have already been reported in some Sarbecoviruses, the data in this study are more extensive, systematically conducted, and thoroughly analyzed, providing sufficient and additional evidence for the points mentioned above. The weaknesses, if pointed out, are that little progress has been made in elucidating the detailed molecular mechanism of this ACE2-independent and trypsin-dependent infection. __
Thank you very much for reviewing our manuscript and for the positive comments.__
Q1: To improve the study, the authors may consider the following points: • The Immunoblot data showing the expression level of ACE2-expressing cells used in the analysis of Figure 2 should be presented rather than indicated as "data not shown." __
__A1: __The immunoblot data are now shown as new supplemental figure 3, panel B, and reveal robust expression of all ACE2 orthologues analyzed.
__ Q2: In the explanation of Figure 2, it is stated, "all S proteins studied efficiently employed human ACE2 (lines 165-166)," but since there are significant differences in utilization levels, this description needs modification. Is it appropriate to normalize the utilization ability of human ACE2 as "1" in Figure 2B? Supplementary Figure 4 may be more relevant, and it should be considered to use it as a regular figure. __
__A2: __We modified the text to indicate that although most spike proteins readily interacted with human ACE2, interaction efficacies greatly varied among the spike proteins ("*Thus, all S proteins studied employed human ACE2 for entry with the exception of the aforementioned S proteins of BM48-31, Rs4081 and Rs4237, which had also failed to bind to ACE2 (Figure 2B). However, although most sarbecovirus S proteins were able to readily utilize human ACE2 as an entry receptor, notable differences were observed. For instance, while *
Particles bearing SARS-2-S, P5L-S, SARS-1-S, WIV-1-S, or Rs4874-S robustly entered BHK-21 cells expressing human ACE2, entry of particles carrying RaTG13-S, cDNA8-S, LYRa11-S, RsSHC014-S, Rs4231-S, or Rs7327-S was roughly 10- to 500-fold less efficient (Figure 2B).", see pages 7-8, lines 182-197). Further, we agree that Figure S4 contains important information for the reader and thus moved the data to main Figure 2 (as new panel B).
__ Q3: It is concluded that Raccoon dog ACE2 is the most functional ACE2, but is it possible to quantitatively evaluate the level of difference in expression, which is challenging to adjust experimentally? It may be necessary to present data on expression levels or to pay attention to the interpretation of the data. __
__A3: __The immunoblot data on ACE2 expression are now shown as new supplemental figure 3, panel B-C, and reveal roughly comparable expression of all ACE2 orthologues analyzed.
__ Q4: No data are presented indicating the functionality of the BM48-31 S protein. While it is assumed that this S protein cannot function as a receptor, it cannot be denied that it may not be adequately expressed. __
__A4: __Expression of all S proteins studied was readily detectable including BM48-31 S protein, although expression of P5L-S, cDNA8-S and BM4831-S was decreased. Please see new supplementary figure 4, panel A. Consequently, lack of cell entry by pseudoviruses bearing BM48-31-S may in fact be due to inefficient S protein incorporation into particles. This is now stated on page 8, lines 201-202.
__ Q5: What is meant by "little impact" compared to what is mentioned? (line 306) __
__A5: __We modified the text for clarity. The paragraph now states: "Expression of TMPRSS11A, TMPRSS11E and furin in cells producing SARS-1-S bearing particles as well as trypsin-treatment slightly improved generation of the S2 fragment (which results from cleavage at the S1/S2 site) (Figure 5E, left panel). Further, TMPRSS11D expression strongly increased production of the S2 fragment and the S2' fragment (which results from cleavage at the S2' site) while TMPRSS2 and TMPRSS13 expression and trypsin treatment only augmented production of the S2' fragment and decreased production of the S2 fragment (Figure 5E)." (please see page 14, lines 346-354).
__
__
__Q6: Although VSV pseudoviruses are used to evaluate infectivity, in experiments using different conditions (e.g., Figure 5F), how is the amount of VSV pseudovirus for infection adjusted to a similar level? __
__A6: __For infection of target cells, VSV pseudoviruses were normalized for volume. Immunoblot analysis revealed the particle preparations contained comparable amounts of VSV M protein, please see new supplemental figure 4, panel A.
__ Q7: Citation of the paper. (lines 474-476) __
__A7: __The requested citations have been inserted.
__ Q8: What does "(-)" in Supplementary Figure 4 indicate? __
A8: "(-) in former figure S4 (now Figure 2B) indicates empty vector. For clarity (and conformity with the other figures), we have changed the label to "No Spike".
__ Q9: Is it appropriate to indicate the value of 'Pseudovirus Entry' with background fold ratio ('Fold over Background') in Figure 4B, etc (for example)? __
__A9: __We feel that adding numerical values indicating the fold change ratios to our graphs would "overload" the figures and reduce clarity of the presented data.
__ Reviewer #3 (Significance (Required)):
This study is a comprehensive investigation into the function of the S protein of various Sarbecoviruses within the Coronaviridae family. The S protein is one of the most crucial proteins determining the infectivity of coronaviruses, and understanding the receptors and host cell proteases involved in cleaving the S protein is essential. The importance of furin and TMPRSS2 as proteases, and ACE2 as a receptor, has been clearly demonstrated in the infection of SARS-CoV-2, making them the foremost molecules to understand about SARS-CoV-2. However, in this study, the authors have clearly shown the existence of other significant modes of infection (independent of ACE2 and reliant on other proteases), thereby providing clear significance in this regard. Nevertheless, the current weakness, if point out, lies in the need for more depth of understanding of the specific molecular mechanisms underlying this novel mode of infection. __
Thank you.
-
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Learn more at Review Commons
Referee #3
Evidence, reproducibility and clarity
Zhang et al. analyzed the infection mechanisms of various Sarbecovirus primarily using VSV pseudoviruses with individual Sarbecovirus S proteins. The study demonstrated that many Sarbecoviruses, similar to two Sarbecoviruses that do not exhibit infectivity without trypsin, gain infectivity in human cells after processing virus particles with trypsin. This trypsin treatment is closely associated with the cleavage of the S1/S2 site of the S protein. This study demonstrated that the infection of the two viruses is not dependent on ACE2 expression, suggesting infection through receptors other than ACE2. Indeed, this study indicates that the receptor-binding domain of the S protein determines these properties. Furthermore, this study shows that some ACE2-using Sarbecoviruses also acquire ACE2-independent infectivity after trypsin treatment of virus particles. Although similar phenomena have already been reported in some Sarbecoviruses, the data in this study are more extensive, systematically conducted, and thoroughly analyzed, providing sufficient and additional evidence for the points mentioned above. The weaknesses, if pointed out, are that little progress has been made in elucidating the detailed molecular mechanism of this ACE2-independent and trypsin-dependent infection.
To improve the study, the authors may consider the following points:
- The Immunoblot data showing the expression level of ACE2-expressing cells used in the analysis of Figure 2 should be presented rather than indicated as "data not shown."
- In the explanation of Figure 2, it is stated, "all S proteins studied efficiently employed human ACE2 (lines 165-166)," but since there are significant differences in utilization levels, this description needs modification. Is it appropriate to normalize the utilization ability of human ACE2 as "1" in Figure 2B? Supplementary Figure 4 may be more relevant, and it should be considered to use it as a regular figure.
- It is concluded that Raccoon dog ACE2 is the most functional ACE2, but is it possible to quantitatively evaluate the level of difference in expression, which is challenging to adjust experimentally? It may be necessary to present data on expression levels or to pay attention to the interpretation of the data.
- No data are presented indicating the functionality of the BM48-31 S protein. While it is assumed that this S protein cannot function as a receptor, it cannot be denied that it may not be adequately expressed.
- What is meant by "little impact" compared to what is mentioned? (line 306)
- Although VSV pseudoviruses are used to evaluate infectivity, in experiments using different conditions (e.g., Figure 5F), how is the amount of VSV pseudovirus for infection adjusted to a similar level?
- Citation of the paper. (lines 474-476)
- What does "(-)" in Supplementary Figure 4 indicate?
- Is it appropriate to indicate the value of 'Pseudovirus Entry' with background fold ratio ('Fold over Background') in Figure 4B, etc (for example)?
Significance
This study is a comprehensive investigation into the function of the S protein of various Sarbecoviruses within the Coronaviridae family. The S protein is one of the most crucial proteins determining the infectivity of coronaviruses, and understanding the receptors and host cell proteases involved in cleaving the S protein is essential. The importance of furin and TMPRSS2 as proteases, and ACE2 as a receptor, has been clearly demonstrated in the infection of SARS-CoV-2, making them the foremost molecules to understand about SARS-CoV-2. However, in this study, the authors have clearly shown the existence of other significant modes of infection (independent of ACE2 and reliant on other proteases), thereby providing clear significance in this regard. Nevertheless, the current weakness, if point out, lies in the need for more depth of understanding of the specific molecular mechanisms underlying this novel mode of infection.
-
Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.
Learn more at Review Commons
Referee #2
Evidence, reproducibility and clarity
Summary:
Recent work from several groups has shown that the majority of bat sarbecoviruses infect cells independent of ACE2, the receptor primarily used by sarbecoviruses that infect humans, and instead infect cells in the presence of exogenous protease including trypsin. In this study, Zhang and colleagues build on these earlier findings by demonstrating that ACE2-independent sarbecovirus entry can be mediated by other exogenous proteases and several different TMPRSS11 enzymes. Using in vitro based methods and viral pseudotypes, the authors reproduce previous findings with trypsin, demonstrate similar effects with alternative proteases and provide lines of evidence suggesting (1) trypsin treatment can impart ACE2-independence and that (2) ACE2-independence provides resistance to neutralizing antibodies.
Major comments:
- Defining sarbecovirus RBDs into clades by in del features has already been established by other groups and many studies across different disciplines now use these previously-established clades. The authors use slightly different nomenclature without any acknowledgment of the previously defined sarbecovirus RBD clades, which will lead to confusion between studies. For example, SARS-CoV-2 is generally regarded as a clade 1 RBD (with ACE2 use and both loops in tact), clade 3 includes BM48-31 and Khosta-2, clade 4 includes RatG15.
- Why did the authors select BM48-31 as the representative of its clade when other members of the clade have known receptors and clear phenotypes in lab assays? BM48-31 has largely failed in every lab assay by every group that has studied it. On the other hand, Khosta2 uses human ACE2, BtKY72 and other African sarbecoviruses can also use ACE2 from their host species and have low but detectable human ACE2 compatibility. It would be interesting to see how the antibody-resistance results compare with other ACE2-dependent sarbecoviruses.
- What is the aurthors' proposed mechanism for how protease is functioning for ACE2-independent entry? For ACE2-dependent entry, TMPRSS2 cleaves spike after RBD engagement. However, in this study, TMPRSS11 enzymes only function when included in producer cells- prior to RBD engagement. Is TMPRSS11 cleaving spike during spike biogenesis (similar to furin for SARS-CoV-2) or is an alternative mechanism at play? Is TMPRSS11 secreted? If this is the case, then the enzyme may be functioning similar to the other exogenous proteases in this study.
- Related to comment 3: the authors study trypsin as a pre-treatment, but other studies have shown trypsin exerts activity during entry. How do the authors propose trypsin is functioning prior to RBD engagement? Is it possible that trypsin is not fully inactivated and remains partially active during entry?
- I am not convinced that trypsin is driving ACE2-independent entry for ACE2-dependent viruses. The experiment performed in figure 3C is performed in African green monkey cells using an antibody directed toward human ACE2. The difference in species between antibody and antigen may influence how well the antibody binds ACE2 on the Vero cells, which may only block some ACE2-dependent viruses but not all. Curiously, the only ACE2-dependent spikes that gain "ACE2-independence" are also activated by trypsin. These blocking assay results would be more convincing in a human cell line, or a non-permissive cell line like BHKs that express the human receptor. Alternatively, knocking out ACE2 in the Vero cells may be another way to assess ACE2-independent entry.
Minor comments:
- line 148: Rs4237 is missing a clade designation
- Figure 3. The figure's main message could be improved by visually grouping the viruses according to clade.
- Some details are missing for reproducibility, including the accession numbers of the TMPRSS enzymes used in this study
- Contrary to claims in the text, this study includes a fairly small panel of spike proteins. Prior studies by Letko 2020, Starr 2022 and Roelle 2022 (cited by the authors) all measured entry for between 20-40 spikes - more twice the number in this study.
- Line 472-473: the data presented in figure 2B shows SARS-CoV-2 has slightly better entry with pangolin ACE2 than raccoon dog. I am not sure the authors should cite this data in support of raccoon dogs as an intermediate for SARS-CoV-2.
Significance
This study provides some novel insights into proteases and sarbecovirus cell entry and highlights previously unappreciated entry factors that are key for some viruses. A major limitation of this study is its lack of mechanistic exploration. The authors data do not really elucidate how TMPRSS11 proteins mediate ACE2-independent entry, nor do the results explain how ACE2-independence is shielding viruses from neutralizing antibodies. Another limitation is in the choice of using a non-human cell line to study the blocking effect of an antibody directed toward a human protein.
Advance
This study nicely reproduces a number of previous findings, including: 1. sarbecovirus RBDs can be categorized into clades based on deletions in surface exposed loops 2. ACE2-independent, trypsin-dependent sarbecovirus entry - notably for Rs4081 3. the RBD in ACE2-independent sarbecoviruses controls entry 4. anti-ACE2 antibodies do not block entry for ACE2-independent sarbecoviruses as well as some ACE2-dependent sarbecoviruses 5. trypsin does not increase S proteins binding to cells 6. protease expression in target cells does not increase S-driven entry 7. a multi-basic cleavage site in spike does not compensate for exogenous protease in ACE2-independent entry
This study has many novel advancements as well:
- identification of other exogenous proteases that mediate ACE2-independent entry (elastase, thermolysin)
- identification of TMPRSS11 family members that mediate trypsin-free entry for ACE2-independent viruses when produced in cells producing spike proteins but not target cells
- ACE2-independent entry may reduce spike susceptibility to antibody neutralization
Audience:
This study will appeal to the coronavirus research community.
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Referee #1
Evidence, reproducibility and clarity
Using the S protein from 14 different sarbecoviruses isolated from bats or pangolin, Zhang et al. makes in this manuscript several points on sabecovirus entry. These points include ACE2 independent entry, trypsin-driven entry, RBD-dependence of trypsin-mediated entry, use of soluble proteases and TRMPRSS-family transmembrane proteases in trypsin-mediated and trypsin-independent entry, and neutralizing antibody evasion in trypsin-mediated entry. Some of these points are supported by the data presented; although there are some discrepancies, they are largely within the range of experimental error. However, some of the statements in the Title, Abstract, and main text, appear to be more than what the data support. Nonetheless, the data authors presented are informative and will help understanding sarbecovirus entry processes.
Major points:
Below are only a few examples of inaccurate sentences. The authors should rewrite similar statements throughout the manuscript.
- The title: "ACE2-independent sarbecovirus cell entry is supported by TMPRSS2-related enzymes and reduces sensitivity to antibody-mediated neutralization" does not correctly reflect the presented data (1) because the contribution by TMPRSS2-like enzymes was shown only when they were co-transfected during PV production, but not when they are expressed on the target cell surface, and (2) because "reduces sensitivity to antibody-mediated neutralization" was observed only for one S protein but was not observed for the other two trypsin-dependent S proteins. In addition, this point was made using one monoclonal Ab for trypsin-dependent entry, but not for the entry mediated by TMPRSS2-related enzymes as the title implies. The title sounds like the three points are interconnected and represent general phenomena. Perhaps a more accurate title could be "ACE2-independent sarbecovirus cell entry is supported by trypsin and may reduce sensitivity to a neutralizing antibody".
In the Abstract, the authors state "Several TMPRSS2-related cellular proteases but not the insertion of a multibasic cleavage site into the S protein allowed for ACE2-independent entry in the absence of trypsin and may support viral spread in the respiratory tract" (lines 38-41) and "In sum, our study reports a pathway for entry into human cells that is ACE2-independent, supported by TMPRSS2-related proteases...." (lines 44-46). These sentences should be rewritten for the same reason described above for the Title.<br /> 2. The lines 102-105 say "...ACE2-independent, trypsin-dependent entry can modulate neutralization by the pan sarbecovirus antibody S2H97..." and the lines 427-9 say "...trypsin-dependent usage of an ACE2-independent entry pathway may result in slightly reduced susceptibility to neutralization by antibodies induced upon infection or vaccination." Because Fig 8 (S2H97 Ab) and Fig 9 (immune plasma) use Vero-ACE2-TMPRSS2 and A549-ACE2-TMPRSS2, respectively, "ACE2-independent," is incorrect here.
The line 46 says "...and associated with antibody evasion", the lines 104-5 says "...and allows for partial antibody evasion in the context of plasma from COVID-19 vaccinees." and the lines 427-9 say "...may result in slightly reduced susceptibility to neutralization by antibodies..." The authors should rewrite them because the resistance to S2H97 Ab was observed with one S protein but all other trypsin-mediated entry was sensitive to S2H97 or immune plasma. 3. If trypsin- independent entry is still controlled by RBD, why LYRa11 and Rs7327 entry is enhanced by and RsSHC014 entry is resistant to S2H97 Ab? The authors may want to discuss possible explanations. 4. Fig. 2B. The entry supported by ACE2 orthologs was normalized to that utilizing hACE2 after hACE2-supported entry was normalized to background entry (no-S PV). First, it is unclear why background entry is used for normalization instead of being subtracted. Second, two times of such normalization likely created huge experimental errors and might have skewed the outcomes. Thus, 14 PVs should be quantified by RT-qPCR and same genome copy number should be used to directly assess their usage of ACE2 orthologs. This way, normalization by hACE2 entry is not necessary. Background entry should be subtracted, not used for normalization. 5. Because VSV PVs were harvested in culture media, there were serum and divalent cations. Were PVs purified before trypsin digestion? Digestion by trypsin or other proteases should be described in detail. 6. How was S2' fragment on the blot determined? Should be described.
Minor points.
- The line 129 says "...14 S proteins, representing all clades, were selected for detailed analyses". Correct the sentence because the S protein representing clade 5 is not included in the study.
- Fig 2. Because 14 S proteins and several TFR1 orthologs were used, a table describing which S isolate is derived from which animal species will help. Organizing Fig 2A and B in the same order will help reading the result. Also, indicate which clades those S proteins belong to.
- Fig S5. Describe cell lines used.
- Fig 3 legend should indicate trypsin digestion condition (concentration and length).
Significance
Because overwhelming amount of data bear large experimental errors, there are some discrepancies among the data presented. Nonetheless, most of each point the authors claim is largely supported by the data. The problem happened when the authors tried to connect the dots too much and thus overstated some conclusions. If the overstated conclusions are amended throughout the manuscript, presented data provide sufficiently useful information on sarbecovirus entry.
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Reply to the reviewers
Reviewer #1 (Evidence, reproducibility and clarity (Required)): Throughout, the authors claim that there is a cross-talk between UPRmt and SG. This is unsubstantiated and unclear.
We strongly disagree this comment. Throughout the manuscript, we show how manipulating UPRmt signalling affects SG formation, and how manipulating SG assembly alters mitochondrial functions and UPRmt-associated mitochondrial ouputs. In addition, both other reviewers are supportive of our conclusions.
Major: Link between UPRmt and stress granules:
The authors claim a link between the UPRmt and stress granule formation based on the finding that the loss of ATF5 affects the expression of UPRmt markers, but not ISR markers. Yet, the authors actually show that GTPP-induced SGs form in a manner independent of ATF5 (Supp. Fig. 2). Thus, there is no data in the manuscript that substantiates this claim.
In the revised manuscript, we show that reducing ATF5 level results in defective SG assembly, with SGs displaying small size and more numerous, reflecting a maturation defect (Sup Figure 6B, 6C and 6D). In addition, we show a clear dependence of SGs to PERK activation (see comment below) and a specific increase of the ISR main negative regulator GADD34 (Figure 2A and 2B). Therefore, we disagree with this reviewer's conclusion and provide data supporting a link between UPRmt and SG formation.
PERK-mediated activation of the ISR. The authors claim that PERK mediates activation of the ISR following GTPP treatment. However, the experiments in Fig. 2E were done 1h after treatment. The authors in Fig. 1C nicely show that SG formation begins at 2h. Thus, it is possible that following a longer GTPP treatment (i.e. >2h) the ISR is activated by different branches; for example, the mitochondrial branch that is mediated by HRI. Thus, the authors should determine which kinase mediates ISR activation at the time point that SG formation is maximal.
We apologise if the description of the experimental procedure was unclear. These experiments are performed at 2h post GTPP treatment as explained in the text (see line 222) and legend (see lines 715-717, Figure 2 legend), and therefore performed at a time of maximal SG induction. Therefore, the identification of PERK as the driver for eIF2α-P and SG formation is performed at a time point where SG formation is maximal.
Role of SG-linked decrease in cellular adaptation to stress. The finding that SGs limit mitochondrial respiration is interesting. Presumably this promotes cellular adaptation to mitochondrial stresses. The authors should test whether G3BP1/2 DKO cells are more susceptible to death following longer GTPP treatments.
We thank the reviewer for this comment. These data are presented in Figure 8, where we show that G3BP1/2 dKO cells are less viable compared to wild-type cells following GTPP treatment for up to 28 hours.
Minor: Fig. 2C should be moved to supplemental as well as the data indicated the lack of ISR inhibition.
Figure 2C is now supplementary Figure 3.
Fig. 3A should have representative images of all conditions from Fig. 3B.
This has now been included as supplementary Figure 4.
IFAs in Fig. 3 and 4 are hard to interpret given both DAPI and G3BP1 are in shades of blue. Ideally, insets of a merged panel should show each individual panel.
We adopted the combination cyan, magenta and clue for our images to make scientific figures accessible to readers with red/green color-blindness. For these figures, G3BP1 is in light cyan and DAPI in dark blue, a colour we adopted previously in three publications (PMID 36965618, PMID 35098996, PMID 31905230), allowing colour blind reader to appreciate the results.
Reviewer #1 (Significance (Required)): The link between the UPRmt and SGs is interesting and would be an advance. However, the authors put forward data that indicates SGs form in an UPRmt (ATF5)- independent manner. An interesting aspect of this story for which there is data is that SGs limit mitochondrial function. This should be explored further (i.e. although it limits mitochondrial respiration, perhaps SGs protect mitochondria against chronic ISR stress).
As suggested we now provided an extensive amount of additional data supporting a role in mitochondrial functions, with data demonstrating that the absence of SGs rescues cell viability (Figure 8A and 8B), restoring mitochondrial functions such as respiration, ATP production (Figure 6D, 6E and 6F) or translation (Figure 7A), and reducing the production mitochondrial ROS (Figure 6C) or mitochondrial fragmentation (Figure 6A and 6B).
Reviewer #2 (Evidence, reproducibility and clarity (Required)): Summary: The article by Lopez-Nieto Jordana et al entitled "Activation of the mitochondrial unfolded protein response regulates the dynamic formation of stress granules" describes the identification of a novel cross talk between the mitochondrial unfolded protein response (UPRmt) and the integrated stress response (ISR) and the contributory role SG regulation plays in mitochondrial function and adaptation to stress. This manuscript presents data highlighting that activation of the UPRmt results in the temporal modulation of SG formation via GADD34 levels and further this analysis by suggesting that these levels of GADD34 may enable cells to be protected from prolonged stress.
Minor comments: This is a very well written manuscript with beautifully presented data. There are some inconsistencies/typos with the abbreviation GTPP- this needs to be checked within the manuscript but examples are on Lines: 204/206/214/324/328/357.
This has now been corrected throughout.
Check reference list for inconsistencies; line 680 reference has no page numbers, line 718 reference has no issue or page numbers
This has now been corrected, references curated throughout.
Line 255 - is it correct to say induction here? I think impairment should be used.
This has now been corrected, see lines 283-284.
Cell type not mentioned in Fig 2 legend.
This has now been corrected, see line 707.
Errors in Fig 4 legend - 4F, G do not exist.
This has now been corrected, see lines 748-750.
Major comments: In figure 1- the GTPP treatment only results in 25% of cells showing SGs compared with 80% in Ars treated cells. While the activation of ISR markers by GTPP treatment is convincing (in Figure 2A), What happens to overall protein synthesis levels in these cells? Puromycin incorporation assays would be a useful addition here.
We now show in Figure 1D that GTPP treatment result in a global reduction in translation, and that cells displaying SGs present with a stronger shut-off when compared with treated cell lacking SGs.
Fig. 1A - ATF4 upregulation is lower in ATF5 siRNA treated cells - what is % uptake of the siRNA in these cells - also see comment below. If possible, it would be nice to see the re-localisation of ATF5 to the nucleus to confirm the UPRmt activation of this protein
These are experiments that we had planned to perform, however in our hands none of the commercially available antibodies allowed us to determine with confidence the localisation of ATF5. We have not determined the uptake of ATF5 siRNA but show by qPCR a reduction in ATF5 mRNA levels following siRNA treatment (see Figure 1A).
Does the dispersal of SGs also correlate with a recovery of protein synthesis- there is still a relatively high level of eIF2alph-P at the 8h (from Figure 2A).
We have not performed these experiments as we do not believe they would have added depth to our study. It is well accepted that SG disassembly results in mRNA re-entry in polysomes and the restart of translation (PMID: 30664789). SGs disappear a few minutes before translation is resumed.
In Figure 2A the 30 min treatment of GTPP induces a robust level of eIF2α-P yet SGs are only observed following the induction of ATF4/GADD34 at 2h. Puromycin incorporation assays may also be able to shed light on the lack of SG inductions at this stage. The formation of SGs around the time when ATF4 and GADD34 are induced seems counterintuitive and should be commented on.
As commented in response to an earlier point, our analysis shows that GTPP result in a global reduction in translation level, the assembly of SGs in a subpopulation of cells (as reported also in the context of many viral infection) may reflect cell-specific differences in the levels of eIF2α kinases and/or differences in reaching the threshold needed for eIF2α phosphorylation to induce SG assembly (as shown in PMID 30674674 and PMID 35319985).
In line 207-208 you state that "PERK is the main eIF2α kinase responsive to GTTP. Overall, these results suggest that induction of the UPRmt is associated with an early SG assembly and ISR activation through PERK." Does the PERK inhibitor inhibit the formation of SG following GTTP treatment? # This is now shown in Figures 2E and 2F. Indeed pharmacological inhibition of PERK following GTPP treatment resulted in inhibition of SG assembly.
Additionally, does GTPP activation of the UPRmt also induce an oxidative stress and therefore activate an additional EIF2AK such as HRI? If so could be the reason you don't get formation of SGs following Ars treatment? Have you considered what would happen if you used the UV stress which activates GCN2 followed by Ars treatment?
As shown on Figures 2D and 2E, we could not detect contribution from the other eIF2a kinases GCN2 and PKR following GTPP treatment; and Figures 2E, 2F demonstrate that PERK inhibition is sufficient to revert eIF2a phosphorylation and ablate SG induction, as noted in the response to the point above. This strongly suggest that the eIF2a kinase HRI does not contribute to eIF2a signalling, however we do not exclude in the broader sense (beyond eIF2a signalling) an induction of oxidative during UPRmt activation. Furthermore, as shown in Figure 2D, A-92 treatment reduced p-eIF2a levels in response to UV treatment but not those induced by GTPP therefore we can exclude a contribution from GCN2. If we understand correctly, this reviewer asks what would happen if cells were UV-stressed to activate GCN2 followed by oxidative stress with arsenite. This is outside the scope of this manuscript, but based on our previous work showing that mRNA GADD34 mRNA levels act as the molecular memory of the ISR and drives cell adaptation to acute and chronic stress, we would expect that the response to a second pulse of stress would be dampened by the sustained level of GADD34 mRNA induced following the first stress (see PMID 35319985). In these previous studies we already demonstrated that induction of p-eIF2a and SGs by a first acute stress (heat shock or thapsigargin) impairs the induction of p-eIF2a and SGs by a second acute (heat shock or arsenite) or chronic (HCV infection) stress (PMID 35319985, see Figure 6; PMID: 38602876, see Figure 7).
Overall, this and the response to the previous comment strongly support that PERK activation, and the resulting induction of GADD34, are responsible for SG regulation following GTPP treatment.
In Figure 3, for the paraquat experiments have you missed the transient induction of SGs by only looking at 48h? You already have GADD34 levels high here so SGs/eIF2α-P levels will already be lowered.
We have now included additional timepoints, see supplementary Figure 5, showing the absence of SGs at 1, 2, 6 and 24h post paraquat treatment, to complement the 48h treatment previously shown.
In addition, when analysing GTPP + Ars treatment impact on SG formation (Fig 2B), could the 2 h GTPP + Ars data also be included, as this is the peak time for SG induction by GTPP
This is now included in Figure 3B.
In line 211 you refer to the early and late stages of the stress, how have these been defined? It seems that the ability of the UPRmt to be protective to an additional stressor is time dependent- the number of SGs that are present following the additional stress increases from 4-8h. Does this correlate with a decrease in the level of GADD34?
We define early and late to the time points corresponding to induction (early) or disassembly (late) of SGs. Also see lines 227-230.
In line 254 you state that ATF5 silencing didn't impact the ISR or SG formation? These data suggest that the formation of SGs is not a direct impact of activation of the UPRmt but rather activation of the cellular ISR possibly due to the proteotoxic and/or oxidative stress? Can the authors comment on this?
We now show in supplementary Figure 6 that reducing the expression of ATF5 results in defects in SG maturation with GTPP treatment resulting in more numerous and smaller SGs. Moreover, it should be noted that HSF1, in addition to ATF5, is a key controller of UPRmt induction and future studies could aimed at dissecting the role of HSF1 in the SG-UPRmt crosstalk (discussed in lines 459-461).
In Figure 4, If GADD34 was driving the loss of SGs in GTPP treated cells why are SGs not persistent in these KO cells. Please comment on this.
Two phosphatases are known to catalyse eIF2a-P dephosphorylation, GADD34 and CReP. The current model proposes that GADD34, which is induced following stress, acts in a negative feedback loop to resolve cellular stress. In contrast, CReP is constitutively expressed and controls basal P-eIF2α levels independently from stress levels (PMID 27161320). In recent work, we have shown that when GADD34 expression is silenced, CReP takes over to revert eIF2a -P and therefore disassemble SGs (PMID: 38602876). This work also showed that CreP is stress-induced in the absence of GADD34. Therefore, in Figure 4 we can speculate that the absence of SGs in GTPP treated KO cells is due to the ability of CReP to compensate for the absence of GADD34. In the context of GTPP treatment followed by arsenite, GADD34 is important to increase the threshold at which SGs can form, altering the response to a second pulse of stress.
In addition, in these GADD34KO cells there should also be a persistent level of eIF2α-P when treated with GTPP and Pq, there is some as evidenced by the quantification but this is not very convincing
As noted here, we do provide evidence of sustained levels of eIF2a-P in cells treated with GTPP at least, the results of independent experiments (n=3) showing persistent phosphorylation when compared treatment in GADD34 KO relative to WT cells. But as noted in the point above the likely activity of CReP can compensate for the lack GADD34, and therefore dampen the amount of eIF2a phosphorylation observed.
Fig 4B shows no cells exhibiting SG following 4h GTPP treatment, which does not correlate with other experiments in the original cell line, e.g. supp 2B - please explain. Can GTPP still activate the UPR-mt in this CRISPR control cell line
GTPP still activates the UPRmt in the CRISPR control cell line has shown by the inhibition of arsenite-induced SGs assembly when cells are pre-treated with GTPP for 4h (Figure 4A). However, we have noted that the timings of the response to GTPP can vary slightly, impacting on the exact SG kinetics, depending on the purity of the drug (synthetised through organic routes by our collaborator Dr Altieri), with the SG peak either at 2 h or at 4 h post-GTPP treatment. Potentially live imaging of SGs in control and GADD34 KO cells would alleviate this caveat, however in the time frame of the rebuttal, further engineering of GADD34 KO and parental lines into G3BP1/2 knock-outs / GFP-G3BP1 knock-ins was not achievable.
In Figure 5, of the 80% of SG still present in GTPP treated Sil SGs- was size or frequency impacted here too as in Pq treatment? # These data are now provided, see Figure 5C and in the result section lines 325-329. These show that GTPP treatment resulted in a reduction in average size of silvestrol-induced SGs, from 0.98 μm2 to 0.9 μm2, and increased average number of SGs, from 18 to 22, when compared to non-treated cells. Additionally, we also quantified features of Ars-induced SGs in GTPP-pretreated cells, data provided in Figure 3C and in the result section lines 245-250. The analysis showed that as paraquat, GTPP pre-treatment also impacts size and frequency of arsenite-induced SGs.
This is just for clarification but If GTPP is a hsp90 inhibitor, is it specific to mitochondrial Hsp90 proteins?
Indeed GTPP is specific to mitochondrial Hsp90.
In the last results section the authors suggest that G3BP1/2 KO cells unable to assemble SGs present with improved mitochondrial function during stress. Firstly, is the UPRmt activated in these KO cells? Could the increased activity just be a consequence of the cells not being able to sense the stress and adapt? Are these cells able to recover from the GTPP stress to the same extent as the wt? Do they die at later timepoints? If you inhibited the disassembly of SGs using DYRK3 inhibitors would you decrease mitochondrial activity? # The figure below confirms the upregulation of UPRmt genes mRNA levels after GTPP treatment in U2OS G3BP1/2 dKO (rebuttal Figure 1). We did not include this in the main manuscript given it is figure heavy already and this did not add depth to our results. Our extensive additional analysis shows that cells unable to assemble SGs present with multiple restored mitochondrial functions following UPRmt induction, including increased ATP production (Fig 6D), and respiration (FIG 6E, 6F), reduced mitochondrial ROS level (Fig 6C) and fragmentation (Fig 6A, 6B). These all support a model in which SG assembled following UPRmt induction contribute to impaired mitochondrial function and that their inhibition/disassembly is necessary to restore mitochondrial homeostasis.
Rebuttal Figure 1: RT-qPCR analysis of the UPRmt and ISR markers DNAJA3, HSPD1, CHOP and ATF4 mRNA levels in U2OS cells treated with GTPP for up to 6 h. Results shown representative of n=3, normalised to RPL9 mRNA and shown relative to DMSO.
Reviewer #2 (Significance (Required)): Significance: This is an interesting and clearly important observation providing mechanistic insight into the role SGs may play in the cells control of mitochondrial function during stress. The functional role of SGs in disease and stress is still widely unknown and this manuscript therefore sheds light on how the cell may use SGs to modulate and adapt to mitochondrial stress. This is an exciting area of research that will be applicable to a large audience as SGs are implicated in a wide range of diseases. While the data is significant there are currently a number of important experiments required to strengthen the current observational analysis. Below are some minor and major comments linked to the manuscript. # We thank the reviewer for highlighting the importance of our work in an 'exciting area of research'.
Reviewer #3 (Evidence, reproducibility and clarity (Required)): As it stands, this study will be suited for a specialized cell biology journal. In order to be published in a journal of a broader readership, the authors would need to address two major points:
- Mitochondrial dysfunction affects cellular function in many ways. Reduced levels of ATP, oxidative stress by increased ROS levels and mitochondrial precursor proteins that challenge proteostasis in the cytosol are just three major consequences of mitochondrial defects. Arguably, for the generation of stress granules, it will be important which of these consequences of mitochondrial dysfunction are prevalent. Since mitochondrial dysfunction is an ill-defined umbrella term, this study would be stronger if the authors could link stress granule formation to the specific molecular defects that arise from specific inhibition of mitochondrial functions.
We agree with this reviewer that mitochondrial dysfunction can take many shapes and therefore to address their comment we have now performed an extensive amount of additional experiments probing various aspects of mitochondrial functions. In addition to the data previously included we can now show to that inhibition of SG formation during UPRmt induction result in increased cell viability (Figure 8A-B), restoring mitochondrial functions such as respiration, ATP production (Figure 6C-F) or translation (Figure 7A), and reduce mitochondrial ROS (Figure 6C) or fragmentation (Figure 6A-B). These all support a model in which SGs assembled following UPRmt induction contribute to impaired mitochondrial function and that their inhibition/disassembly is necessary to restore mitochondrial homeostasis.
- Also stress granules are an umbrella term. Different treatments will presumably change the spectrum of transcripts that are sequestered in these granules. As mitochondrial defects remodel the transcription and translation of mitochondrial precursor proteins, the study would benefit from a comprehensive analysis of the spectrum of transcripts that are contained in granules induced by GTPP and sodium arsenite, respectively.
Previous studies, including our own, have demonstrated that indeed different stress (or infections) can result in the assembly of compositionally distinct SGs (or SG-like foci) that sequester specific subset of mRNAs or proteins. These studies are based on affinity purification or proximity ligation approaches followed by multi-omics analysis of SG components by RNA-seq and mass spectrometry. While we agree with this reviewer that determining the composition of UPRmt-induced SGs could help understand their function, we believe these studies are outside the scope of the current manuscript, and this would instead form the basis of subsequent study and manuscript.
Reviewer #3 (Significance (Required)): The study is interesting but descriptive. It confirms previous observations. The advance in mechanistic insights is limited. Nevertheless, the study is technically sound and of interest for a specialized readership. As it stands, the study might be published in a specialized journal. In order to be of general interest for a large and general readership, the authors will have to provide much more mechanistic and molecular insight, which will require at least another six months of work.
We have now produced an extensive additional body of work to answer specific comments made by all three reviewers, bolstering our hypothesis, and delving deeper into the impact of SG assembly on mitochondrial functions.
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Referee #3
Evidence, reproducibility and clarity
Mitochondrial dysfunction induces a complex remodeling of gene expression. One defined branch in this response is known as the mitochondrial unfolded protein response (UPRmt). The transcription factor ATF5 acts as a key mediator of the mammalian UPRmt signaling. Moreover, mitochondrial defects also mute protein synthesis and trigger the integrated stress response (ISR). ISR is a well-characterized anti-stress routine characterized by eIF2alpha phosphorylation. The induction of cytosolic stress granules is one hallmark of ISR. In the present study, the authors observe that induction of UPRmt by inhibition of the mitochondrial HSP90 chaperone induces cytosolic stress granules. This is not unexpected given the well-established UPRmt/ISR and ISR/stress granules links. Still, the study is technically sound and extends our understanding of the effects of mitochondrial problems on the reactions in the cytosol.
The authors compare two different inhibitors of mitochondrial functions: gamitrinib-triphenylphosphonium (GTPP) which interferes with HSP90 and whose effect on extra-mitochondrial proteostasis was well characterized by studies from Wade Harper and Christian Munch (Sutandy et al. 2023 Nature; Munch and Harper 2016 Nature); and paraquat which induces the generation of superoxide radicals from the respiratory chain. They found considerable differences of these two drugs in respect to stress granule formation which is consistent with previous observations. GTPP induces the accumulation of mitochondrial precursor proteins in the cytosol, which induces UPRmt. Defects in respiration however do not necessarily block mitochondrial protein import.
In general, this is an interesting study that confirms previous observations. The molecular and mechanistic insights are limited and the authors neither identified the cascade of events that triggers stress granule formation upon HSP90 inhibition, nor did they analyze the transcripts that are sequestered by the cytosolic stress granules. Nevertheless, despite its rather descriptive nature, the study will be of interest for researchers studying the consequences of mitochondrial dysfunction.
As it stands, this study will be suited for a specialized cell biology journal. In order to be published in a journal of a broader readership, the authors would need to address two major points:
- Mitochondrial dysfunction affects cellular function in many ways. Reduced levels of ATP, oxidative stress by increased ROS levels and mitochondrial precursor proteins that challenge proteostasis in the cytosol are just three major consequences of mitochondrial defects. Arguably, for the generation of stress granules, it will be important which of these consequences of mitochondrial dysfunction are prevalent. Since mitochondrial dysfunction is an ill-defined umbrella term, this study would be stronger if the authors could link stress granule formation to the specific molecular defects that arise from specific inhibition of mitochondrial functions.
- Also stress granules are an umbrella term. Different treatments will presumably change the spectrum of transcripts that are sequestered in these granules. As mitochondrial defects remodel the transcription and translation of mitochondrial precursor proteins, the study would benefit from a comprehensive analysis of the spectrum of transcripts that are contained in granules induced by GTPP and sodium arsenite, respectively.
Significance
The study is interesting but descriptive. It confirms previous observations. The advance in mechanistic insights is limited.
Nevertheless, the study is technically sound and of interest for a specialized readership. As it stands, the study might be published in a specialized journal. In order to be of general interest for a large and general readership, the authors will have to provide much more mechanistic and molecular insight, which will require at least another six months of work.
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Referee #2
Evidence, reproducibility and clarity
Summary:
The article by Lopez-Nieto Jordana et al entitled "Activation of the mitochondrial unfolded protein response regulates the dynamic formation of stress granules" describes the identification of a novel cross talk between the mitochondrial unfolded protein response (UPRmt) and the integrated stress response (ISR) and the contributory role SG regulation plays in mitochondrial function and adaptation to stress. This manuscript presents data highlighting that activation of the UPRmt results in the temporal modulation of SG formation via GADD34 levels and further this analysis by suggesting that these levels of GADD34 may enable cells to be protected from prolonged stress.
Minor comments:
This is a very well written manuscript with beautifully presented data.
There are some inconsistencies/typos with the abbreviation GTPP- this needs to be checked within the manuscript but examples are on Lines: 204/206/214/324/328/357.
Check reference list for inconsistencies; line 680 reference has no page numbers, line 718 reference has no issue or page numbers
Line 255 - is it correct to say induction here? I think impairment should be used.
Cell type not mentioned in Fig 2 legend.
Errors in Fig 4 legend - 4F, G do not exist.
Major comments:
In figure 1- the GTPP treatment only results in 25% of cells showing SGs compared with 80% in Ars treated cells. While the activation of ISR markers by GTPP treatment is convincing (in Figure 2A), What happens to overall protein synthesis levels in these cells? Puromycin incorporation assays would be a useful addition here.
Fig. 1A - ATF4 upregulation is lower in ATF5 siRNA treated cells - what is % uptake of the siRNA in these cells - also see comment below.
If possible it would be nice to see the re-localisation of ATF5 to the nucleus to confirm the UPRmt activation of this protein oes the dispersal of SGs also correlate with a recovery of protein synthesis- there is still a relatively high level of eIF2alph-P at the 8h (from Figure 2A).
In Figure 2A the 30 min treatment of GTPP induces a robust level of eIF2α-P yet SGs are only observed following the induction of ATF4/GADD34 at 2h. Puromycin incorporation assays may also be able to shed light on the lack of SG inductions at this stage. The formation of SGs around the time when ATF4 and GADD34 are induced seems counterintuitive and should be commented on.
In line 207-208 you state that "PERK is the main eIF2α kinase responsive to GTTP.Overall, these results suggest that induction of the UPRmt is associated with an early SG assembly and ISR activation through PERK." Does the PERK inhibitor inhibit the formation of SG following GTTP treatment?
Additionally, does GTPP activation of the UPRmt also induce an oxidative stress and therefore activate an additional EIF2AK such as HRI? If so could be the reason you don't get formation of SGs following Ars treatment? Have you considered what would happen if you used the UV stress which activates GCN2 followed by Ars treatment?
In Figure 3, for the paraquat experiments have you missed the transient induction of SGs by only looking at 48h? You already have GADD34 levels high here so SGs/eIF2α-P levels will already be lowered.
In addition, when analysing GTPP + Ars treatment impact on SG formation (Fig 2B), could the 2 h GTPP + Ars data also be included, as this is the peak time for SG induction by GTPP
In line 211 you refer to the early and late stages of the stress, how have these been defined? It seems that the ability of the UPRmt to be protective to an additional stressor is time dependent- the number of SGs that are present following the additional stress increases from 4-8h. Does this correlate with a decrease in the level of GADD34?
In line 254 you state that ATF5 silencing didn't impact the ISR or SG formation? These data suggest that the formation of SGs is not a direct impact of activation of the UPRmt but rather activation of the cellular ISR possibly due to the proteotoxic and/or oxidative stress? Can the authors comment on this?
In Figure 4, If GADD34 was driving the loss of SGs in GTPP treated cells why are SGs not persistent in these KO cells. Please comment on this.
In addition, in these GADD34KO cells there should also be a persistent level of eIF2α-P when treated with GTPP and Pq, there is some as evidenced by the quantitation but this is not very convincing/
Fig 4B shows no cells exhibiting SG following 4h GTPP treatment, which does not correlate with other experiments in the original cell line, e.g. supp 2B - please explain. Can GTPP still activate the UPR-mt in this CRISPR control cell line
In Figure 5, of the 80% of SG still present in GTPP treated Sil SGs- was size or frequency impacted here too as in Pq treatment? This is just for clarification but If GTPP is a hsp90 inhibitor, is it specific to mitochondrial Hsp90 proteins?
In the last results section the authors suggest that G3BP1/2 KO cells unable to assemble SGs present with improved mitochondrial function during stress. Firstly, is the UPRmt activated in these KO cells? Could the increased activity just be a consequence of the cells not being able to sense the stress and adapt? Are these cells able to recover from the GTPP stress to the same extent as the wt? Do they die at later timepoints?
If you inhibited the disassembly of SGs using DYRK3 inhibitors would you decrease mitochondrial activity?
Significance
This is an interesting and clearly important observation providing mechanistic insight into the role SGs may play in the cells control of mitochondrial function during stress. The functional role of SGs in disease and stress is still widely unknown and this manuscript therefore sheds light on how the cell may use SGs to modulate and adapt to mitochondrial stress. This is an exciting area of research that will be applicable to a large audience as SGs are implicated in a wide range of diseases. While the data is significant there are currently a number of important experiments required to strengthen the current observational analysis. Below are some minor and major comments linked to the manuscript.
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Referee #1
Evidence, reproducibility and clarity
The integrated stress response is activated by kinases that sense diverse stresses including viral infection and ER-linked stress and phosphorylate eIF2. This leads to the inhibition of translation initiation, the disassembly of polysomes, and the phase separation of mRNas and RNA binding proteins into stress granules (SG). Here, the authors show that treatment with GTPP, a previously established activator of the UPRmt, activates the ISR and induces the formation of stress granules. Following induction of the ISR, cells become more resistant to SG formation. The authors pinpoint this resistance to GADD34 dephosphorylation of eIF2a. Finally, the authors show that SGs limit mitochondrial respiration. These findings demonstrate the importance of putting the breaks on the ISR. Throughout, the authors claim that there is a cross-talk between UPRmt and SG. This is unsubstantiated and unclear.
Major:
Link between UPRmt and stress granules:
The authors claim a link between the UPRmt and stress granule formation based on the finding that the loss of ATF5 affects the expression of UPRmt markers, but not ISR markers. Yet, the authors actually show that GTPP-induced SGs form in a manner independent of ATF5 (Supp. Fig. 2). Thus, there is no data in the manuscript that substantiates this claim.
PERK-mediated activation of the ISR.
The authors claim that PERK mediates activation of the ISR following GTPP treatment. However, the experiments in Fig. 2E were done 1h after treatment. The authors in Fig. 1C nicely show that SG formation begins at 2h. Thus, it is possible that following a longer GTPP treatment (ie. >2h) the ISR is activated by different branches; for example the mitochondrial branch that is mediated by HRI. Thus, the authors should determine which kinase mediates ISR activation at the time point that SG formation is maximal.
Role of SG-linked decrease in cellular adaptation to stress.
The finding that SGs limit mitochondrial respiration is interesting. Presumably this promotes cellular adaptation to mitochondrial stresses. The authors should test whether G3BP1/2 DKO cells are more susceptible to death following longer GTPP treatments.
Minor:
Fig. 2C should be moved to supplemental as well as the data indicated the lack of ISR inhibition.
Fig. 3A should have representative images of all conditions from Fig. 3B.
IFAs in Fig. 3 and 4 are hard to interpret given both DAPI and G3BP1 are in shades of blue. Ideally, insets of a merged panel should show each individual panel.
Significance
The link between the UPRmt and SGs is interesting and would be an advance. However, the authors put forward data that indicates SGs form in an UPRmt (ATF5)- independent manner. An interesting aspect of this story for which there is data is that SGs limit mitochondrial function. This should be explored further (i.e. although it limits mitochondrial respiration, perhaps SGs protect mitochondria against chronic ISR stress).
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Reply to the reviewers
__Dear Reviewers, __
We would like to thank you for the time and attention dedicated to reviewing our manuscript. We have taken all the questions and comments into consideration, which we believe have helped us to improve the paper.
Reviewer #1 (Evidence, reproducibility and clarity (Required)):
The study by Aguirre-Botero et al. shows the dynamics of 3D11 anti-CSP monoclonal antibody (mAb) mediated elimination of rodent malaria Plasmodium berghei (Pb) parasites in the liver. The authors show that the anti-CSP mAb could protect against intravenous (i.v.) Pb sporozoite challenge along with the cutaneous challenge, but requires higher concentration of antibody. Importantly, the study shows that the anti-CSP mAb not only affects sporozoite motility, sinusoidal extravasation, and cell invasion but also partially impairs the intracellular development inside the liver parenchyma, indicating a late effect of this antibody during liver stage development. While the study is interesting and conducted well, the only novel yet very important observation made in this manuscript is the effect of the anti-CSP mAb on liver stage development.
Major This observation is highlighted in the manuscript title but is supported by only limited data. A such it needs to be substantiated and a mechanism should be investigated. The phenomenon of intracellular effects of the anti-CSP mAb should be analyzed in much more detail. For example, can the authors demonstrate uptake of the Ab together with the parasite during hepatocyte invasion? What cellular mechanism leads to elimination?
Lines 234 - 243; 308 - 325: These results are the gist of the entire study and also defined the title of the manuscript. Thus, it would be pre-mature to claim the substantial effect of 3D11 antibody in late killing of the parasite in the infected hepatocytes just by looking at the decreased GFP fluorescence. The authors need to at least verify the fitness of the liver stages by measuring the size of the developing parasites as well as using different parasite specific markers (UIS4, MSP1, HSP70 etc.) in immunofluorescence assays on the infected liver sections and in vitro infections.
Response
We greatly appreciate the comments. We have taken the suggestions into consideration and will add data, perform as well as improve the text to clarify some key concepts. We hope that these changes will increase the impact of our results. We are adding extra data showing the percentage of UIS4+ intracellular parasites at 2, 4, and 44h in control and 3D11 groups. In addition, the effect of 2h incubation of sporozoites with 3D11 on the parasite size, GFP intensity and UIS4-staining at 44h post-infection was quantified. Briefly, the percentage of UIS4-negative intracellular parasites is significantly higher than the control, but not at 44h, indicating that part of the parasite clearance is due to the absence of UIS4 secretion that could be related to the neutralizing Ab effect or the inhibition of parasites during HepG2 cell traversal. At 44h, the in vitro data show that 3D11 only significantly decreases GFP intensity without affecting size and UIS4-staining. Since (i) the inhibition of invasion parallels parasite killing in the titration curve (Fig. 4A), (ii) the post-invasion parasite elimination occurs as early as 15h (Fig. 4C), (iii) the decrease in parasite GFP intensity occurs without a change in UIS4-staining and parasite size at 48h, and (iv) we cannot dissociate non-cytotoxic from cytotoxic 3D11 effects, we concluded for the moment that the 3D11 post-invasion “late effect” is probably due to mAb cytotoxicity, which is decreasing the SPZ fitness with measurable consequences in the percentage of surviving EEF and EEF GFP intensity. However, since we cannot exclude a non-cytotoxic effect of 3D11, which its cytotoxic activity could mask, we are addressing this question in another manuscript using hmAbs against PfCSP with different cytotoxicity levels.
We will also test the potential post-invasion neutralizing effect of 3D11 in vitro. However, since the mAb does not affect the in vivo parasite growth from 24h to 44h, it is unlikely that it affects late intracellular development.
____Reviewer #1__ Minor comments __
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Line 44 - 43: The statement is applicable only to the rodent infecting Plasmodium parasites. The authors need to clarify that.
Responses
This is an important clarification. We have modified the text that now reads:
“The sporozoite surface is covered by a dense coat of the immunodominant circumsporozoite protein (CSP), shown to be an immunodominant protective antigen using a rodent malaria model”.
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Line 68: Replace the second 'against' after the CSP with 'of'.
It is done.
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Line 141 - 143: The 3D11 mAb does affect the homing and killing in the blood of cutaneous injected sporozoites. The authors need to clearly state that the statement is true only for i.v. injected sporozoites.
Thank you for the comment. Now the text reads “Altogether, these data indicate that 3D11 rather than having an early effect on i.v. inoculated sporozoites, e.g. in the homing or killing in the blood, requires more than 4 h to eliminate most parasites in the liver.”
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Figure 3B: The numbers of sporozoites detected in the experiment varies from 0 h (line 172) to 2 h (line 184). Therefore, the numbers need to be mentioned on all the bars of each timepoint.
This information was missing but now we have added the numbers to Figure 3B.
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Figure 3C: If the authors have used flk1-GFP mice, then how well they were able to detect the Pb-PfCSP GFP parasites in the vessel vs. parenchyma in the intravital imaging? The representative images for Pb-PfCSP GFP should also be included.
Since 3D11 does not target PbPf parasites most of them are motile in the movies, making them easily distinguishable from the endothelial cells. In addition, the stronger GFP intensity of sporozoites make them easily detectable in the sinusoids. Representative images were added in the supplementary figures (now Figure S3).
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It is not mentioned anywhere how the viability of the sporozoites was determined. This has to be described especially in the methods section.
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Also, the flow acquisition and data analysis of the sporozoites and infected HepG2 cells must be described in the method section.
We briefly mentioned it in the results (line 228- 230): “In addition, by comparing the total number of recovered GFP+ sporozoites at 2 h in the two studied conditions, we measured the early lethality (%viable sporozoites, Figure 4B) of the anti-CSP Ab on the extracellular forms of the parasite (Figure 4A).”
A more detailed description has been added in the methods section that now reads:
“After 2 h, the supernatant and the trypsinized cells were analyzed by flow cytometry to quantify the amount of GFP+ events inside and outside the cells. Viability was then quantified by adding the total number of sporozoites (GPF+ events) in the supernatant, inside and outside the cells. We calculated the percentage viability by comparing the average of the total number of sporozoites in the treated samples to the average in controls using three technical replicates in each condition. Additionally, we quantify the percentage of infected cells using the total number of GFP+ events in the HepG2 gate (Figure S4). To compare the biological replicates, we further normalized to the control of each experiment. For the samples used to analyze parasite development, the cells were incubated for 15 or 44 h after sporozoite addition, and the medium was changed after 2 and 24 h. The cells were trypsinized and the percentage of intracellular parasites was determined by flow cytometry as described above (Figure S4). The prolonged effect between 2 h - 15/44 h was calculated by normalizing to the percentage of infected cells at 2 h.”
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Figure 4: The flow layouts should be included for at least comparing the 0 vs. 5 μg/ml of 3D11 mAb concentrations.
Flow layouts were added in the supplementary figures.
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Line 651 (Figure S1 legend): Typographical error '14'.
Thank you for noticing. We corrected it.
Reviewer #2 (Evidence, reproducibility and clarity (Required____)):
Aguirre-Botero and collaborators report on the dynamics of Plasmodium parasite elimination in the liver using the 3D11 anti-CSP monoclonal antibody (mAb). By using microscopy and bioluminescence imaging in the P. berghei rodent malaria model, the authors first demonstrate that higher antibody concentrations are required for protection against intravenous sporozoite challenge, when compared to cutaneous challenge, which is not surprising. The study also shows that the 3D11 mAb reduces sporozoite motility, impairs hepatic sinusoidal barrier crossing, and more relevantly inhibits intracellular development of liver stages through its cytotoxic activity. These findings highlight the role of this specific monoclonal antibody, 3D11 mAb against CSP, in targeting sporozoites in the liver.
Major Comments
The study provides valuable insights into the mechanisms of protection conferred by the 3D11 anti-CSP monoclonal antibody against P. berghei sporozoites and this finding allow the field to speculate that other monoclonal antibodies against CSP of P. Falciparum may act similarly. However, an important experiment is missing that would significantly strengthen the conclusions. Specifically, the authors should perform experiments where the monoclonal antibody is added immediately after the sporozoites have completed invasion. This should be done both in vitro and in vivo to show whether the antibody has any effect on intracellular development of liver stages when added after invasion.
While the claims are generally supported by the data presented, to comprehensively conclude the late cytotoxic effects of 3D11, the additional experiment of post-invasion antibody application is relevant. This would help determine if the observed effects are due to the antibody's action during invasion or its continued action post-invasion.
The data and methods are presented in a manner that allows for reproducibility. The use of microscopy and bioluminescence imaging is well-documented. The experiments appear adequately replicated, and statistical analyses are appropriate.
Response
We thank reviewer 2 for important suggestions. To be sure that the effect might not come from the internalization of the antibodies after sporozoite invasion, we will test the potential post-invasion neutralizing effect of 3D11 in vitro. However, since the mAb does not affect the in vivo parasite growth from 24h to 44h, it is unlikely that it affects late intracellular development.
Indeed, it is important to corroborate this effect might have a different effect using antibodies targeting P. falciparum CSP. That is why we are working in parallel with anti-PfCSP antibodies, but these data will be included in a different manuscript.
__Reviewer #2 ____Minor Comments __
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The text and figures are clear and accurate. Some minor typographical errors should be corrected.
Thank you for the remark, we have worked on that and hope that the text reads better now.
Reviewer #3 (Evidence, reproducibility and clarity (Required)):
Aguirre-Botero et al have studied the effect of a potent monoclonal antibody against the circumsporozoite protein, the major surface protein of the malaria sporozoite. This is an elegantly designed, performed, and analyzed study. They have efficiently delineated the mode of action of anti-CSP repeat mAb and confirmed previous in vitro work (not cited) that demonstrated the same intracellular effect. Specific comments :
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Line 51: The authors claim a correlation between high antibody levels and protection. However, they did not provide direct proof that these antibodies were responsible for protection, nor did they establish a cut-off level of anti-CSP antibodies that would distinguish between protected and unprotected individuals.
We would first like to thank reviewer 3 for the comments. Indeed, we agree with reviewer 3, these are correlative studies where the causality cannot be established. We modified the ensuing sentence to specify the causality between anti-CSP mAbs and in vivo protection against sporozoite infection. Now the text reads: “Extensive research has demonstrated a positive correlation between high levels of anti-CSP antibodies (Abs) induced by the RTS,S/AS01 vaccine and its efficacy against malaria 11–13. Remarkably, anti-CSP monoclonal Abs (mAbs) have been proven to protect in vivo against malaria in various experimental settings, including, mice 14–21, monkeys 23, and humans 24–26”
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Line 326: The late intrahepatic effect of mAb against the CSP repeat has been previously reported (see Figure 2, Nudelman et al, J Immunol, 1989). The effect was shown to affect the transition from liver trophozoites to liver schizonts. This study should be cited and discussed.
Thank you for the remark. Now the text reads: Notably, a similar effect has been previously reported using sera from mice immunized with PfCSP or mAb against P. yoelii (Py) CSP. Incubation of Pf or Py sporozoites with the immune sera or mAbs not only affected sporozoite invasion in vitro but continued to affect intracellular forms for several days after invasion38,39. Additionally, using anti-PfCSP sera, it was also observed that late EEFs from sera-treated sporozoites had abnormal morphology38. Altogether, it was thus concluded that the anti-CSP Abs present in the sera had a long-term effect on the parasites38,39.
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Referee #3
Evidence, reproducibility and clarity
Aguirre-Botero et al have studied the effect of a potent monoclonal antibody against the circumsporozoite protein, the major surface protein of the malaria sporozoite. This is an elegantly designed, performed, and analyzed study. They have efficiently delineated the mode of action of anti-CSP repeat mAb and confirmed previous in vitro work (not cited) that demonstrated the same intracellular effect.
Specific comments
Line 51: The authors claim a correlation between high antibody levels and protection. However, they did not provide direct proof that these antibodies were responsible for protection, nor did they establish a cut-off level of anti-CSP antibodies that would distinguish between protected and unprotected individuals.
Lone 326: The late intrahepatic effect of mAb against the CSP repeat has been previously reported (see Figure 2, Nudelman et al, J Immunol, 1989). The effect was shown to affect the transition from liver trophozoites to liver schizonts. This study should be cited and discussed.
Significance
A well-done study that elucidates the mechanisms of a protective monoclonal antibody against malaria sporozoites. These data are important and will interest a large audience of researchers working in infectious diseases and immunology.
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Referee #2
Evidence, reproducibility and clarity
Aguirre-Botero and collaborators report on the dynamics of Plasmodium parasite elimination in the liver using the 3D11 anti-CSP monoclonal antibody (mAb). By using microscopy and bioluminescence imaging in the P. berghei rodent malaria model, the authors first demonstrate that higher antibody concentrations are required for protection against intravenous sporozoite challenge, when compared to cutaneous challenge, which is not surprising. The study also shows that the 3D11 mAb reduces sporozoite motility, impairs hepatic sinusoidal barrier crossing, and more relevantly inhibits intracellular development of liver stages through its cytotoxic activity. These findings highlight the role of this specific monoclonal antibody, 3D11 mAb against CSP, in targeting sporozoites in the liver.
Major Comments
The study provides valuable insights into the mechanisms of protection conferred by the 3D11 anti-CSP monoclonal antibody against P. berghei sporozoites and this finding allow the field to speculate that other monoclonal antibodies against CSP of P. Falciparum may act similarly. However, an important experiment is missing that would significantly strengthen the conclusions. Specifically, the authors should perform experiments where the monoclonal antibody is added immediately after the sporozoites have completed invasion. This should be done both in vitro and in vivo to show whether the antibody has any effect on intracellular development of liver stages when added after invasion.
While the claims are generally supported by the data presented, to comprehensively conclude the late cytotoxic effects of 3D11, the additional experiment of post-invasion antibody application is relevant. This would help determine if the observed effects are due to the antibody's action during invasion or its continued action post-invasion.
The data and methods are presented in a manner that allows for reproducibility. The use of microscopy and bioluminescence imaging is well-documented. The experiments appear adequately replicated, and statistical analyses are appropriate.
Minor Comments
The text and figures are clear and accurate. Some minor typographical errors should be corrected.
Significance
The study's strengths lie in its detailed analysis of the 3D11 mAb's effect on sporozoite motility and liver stage development. The use of advanced imaging techniques adds robustness to the findings. The main limitation is the lack of data on the antibody's effect post-invasion. Additionally, the study's conclusions are based on a single monoclonal antibody and its target region, which may not be representative of other anti-CSP antibodies. Still, the findings offer insights into the cytotoxic action of anti-CSP antibodies, which could inform the development of more effective malaria vaccines and therapeutic antibodies.
This research will primarily interest a specialized audience in malaria research, particularly those focused on vaccine development and antibody therapeutics. It also holds value for broader audiences in immunology and infectious disease research.
My expertise: Malaria research and liver invasion by Plasmodium sporozoites
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Referee #1
Evidence, reproducibility and clarity
The study by Aguirre-Botero et al. shows the dynamics of 3D11 anti-CSP monoclonal antibody (mAb) mediated elimination of rodent malaria Plasmodium berghei (Pb) parasites in the liver. The authors show that the anti-CSP mAb could protect against intravenous (i.v.) Pb sporozoite challenge along with the cutaneous challenge, but requires higher concentration of antibody. Importantly, the study shows that the anti-CSP mAb not only affects sporozoite motility, sinusoidal extravasation, and cell invasion but also partially impairs the intracellular development inside the liver parenchyma, indicating a late effect of this antibody during liver stage development. While the study is interesting and conducted well, the only novel yet very important observation made in this manuscript is the effect of the anti-CSP mAb on liver stage development.
Major
This observation is highlighted in the manuscript title but is supported by only limited data. A such it needs to be substantiated and a mechanism should be investigated.
- The phenomenon of intracellular effects of the anti-CSP mAb should be analyzed in much more detail. For example, can the authors demonstrate uptake of the Ab together with the parasite during hepatocyte invasion? What cellular mechanism leads to elimination?
Minor
- Line 44 - 43: The statement is applicable only to the rodent infecting Plasmodium parasites. The authors need to clarify that.
- Line 68: Replace the second 'against' after the CSP with 'of'.
- Line 141 - 143: The 3D11 mAb does affect the homing and killing in the blood of cutaneous injected sporozoites. The authors need to clearly state that the statement is true only for i.v. injected sporozoites.
- Figure 3B: The numbers of sporozoites detected in the experiment varies from 0 h (line 172) to 2 h (line 184). Therefore, the numbers need to be mentioned on all the bars of each timepoint.
- Figure 3C: If the authors have used flk1-GFP mice, then how well they were able to detect the Pb-PfCSP GFP parasites in the vessel vs. parenchyma in the intravital imaging? The representative images for Pb-PfCSP GFP should also be included.
- It is not mentioned anywhere how the viability of the sporozoites was determined. This has to be described especially in the methods section.
- Also, the flow acquisition and data analysis of the sporozoites and infected HepG2 cells must be described in the method section.
- Figure 4: The flow layouts should be included for at least comparing the 0 vs. 5 μg/ml of 3D11 mAb concentrations.
- Lines 234 - 243; 308 - 325: These results are the gist of the entire study and also defined the title of the manuscript. Thus, it would be pre-mature to claim the substantial effect of 3D11 antibody in late killing of the parasite in the infected hepatocytes just by looking at the decreased GFP fluorescence. The authors need to at least verify the fitness of the liver stages by measuring the size of the developing parasites as well as using different parasite specific markers (UIS4, MSP1, HSP70 etc.) in immunofluorescence assays on the infected liver sections and in vitro infections.
- Line 651 (Figure S1 legend): Typographical error '14'.
Significance
The phenomenon of intracellular effects of the anti-CSP Ab is the only novel observation and as such, should be analyzed in much more detail. For example, can the authors demonstrate uptake of the Ab together with the parasite during hepatocyte invasion? What cellular mechanism leads to elimination?
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Reply to the reviewers
1. General Statements
We are grateful for the valuable, constructive comments of the reviewers, which helped to substantially improve the quality of our manuscript. We particularly agree that the original structure of the manuscript was confusing and in parts misleading, since we followed the history of the project, which first identified the RBM39 mediated impact on IRF3 expression, whereas the -omics studies, identifying additional factors, were done at a far later point. Many discrepancies further arose from the low sensitivity of our initial proteomics analysis, which we now repeated, thereby obtaining far more sensitive detection of the key factors we also found in the transcriptomics data.
We have re-structured the entire manuscript by moving the -omics data from the end of the paper towards the middle and provide similar depth downstream analysis of all relevant key factors identified (RIG-I/MDA5, IFN receptors, STAT1/2), to reduce the focus on IRF3, as suggested. We further changed the title and abstract to reflect this major conceptual change. Thanks to this helpful comment, we think that our manuscript is now conceptually much clearer.
We further added new data to support the central claims of our manuscript, including a repetition of the proteomics study. Proteomics and transcriptomics now consistently demonstrate the impact of RMB39 knockdown as well as indisulam treatment on several key factors of innate immunity, including IRF3, STAT1/2, RIG-I and MDA5 (now in Fig. 5), with IFNAR2 and IL10RB additionally found in transcriptomics. We provide additional functional evidence that IRF3 is the key factor affected in the TLR3 pathway (IRF3 overexpression, Fig. 6B, C), whereas diminished abundance of RIG-I/MAD5 is equally important in the respective pathway, thereby also affecting NF-κB response (Fig. 6F-I). We further show the functional significance of IFN-receptor/STAT downregulation on type I and III IFN responses (Fig. 7E-G).
The reviewers also pointed to some datasets showing the expected trends, but in some cases lacking statistical significance, due to variability in knockdown efficiency. We repeated all mentioned datasets with new batches of siRNA with sufficient biological replicates (n=3). We thereby obtained consistent, statistically significant data in all cases. Importantly, all experiments implementing the RMB39.esc control now show consistent rescue (Fig2. A-E).
To generate a homogenous experimental design for virus infections, we further added new data showing a comparable impact of siRNA knockdown (Fig. 3F) and indisulam treatment (new Fig. 3G) on Sendai virus infection in A549 cells and took this as a rationale to consistently use indisulam for all other infections.
2. Point-by-point description of the revisions
__Reviewer #1 (Evidence, reproducibility and clarity (Required)): __ This manuscript by Li and colleagues examines the role of RBM39 in innate immune signaling. Splicing factor RBM39 was identified through a genome wide screen with a death reporter under control of the IFIT1 promoter that got stimulated with pIC in a TLR3-dependent manner. Besides IFIT1, further experiments showed that RBM39 is also involved in optimal expression of other innate immunity genes like IFNB, CXCL10, RIG-I or MDA5. While NFkB-dependent genes seem not to depend on RBM39, for IRF3 it was shown that protein levels decrease under conditions of RBM39 depletion, because IRF3 mRNAs are (slightly) reduced and spliced differently. The sulfonamid Indisulam could largely recapitulate the phenotype of RBM39 depletion. Further analyses using proteomics and transcriptomics showed that RBM39 is required for mRNA splicing and expression of a large set of other proteins. Altogether, this well designed and written study highlights the fundamental role played by RBM39 in in maintaining the pathways of immunity and metabolism. The key conclusions are convincing but some additional experiments would strengthen them further.
We are grateful for the very positive general comments of this reviewer.
Major comments: - For the statistics, authors seem not to have done multiple tests but rather tested individual datasets within larger graphs against each other. Please explain where this is the case and use corrections if multiple testing was done
We apologize for not have been clearer here, we indeed used multiple testing. In the proteomics, statistical significance was evaluated by "two-sample tests" (Student's T-test with permutation-based FDR 0.05 and 250 number of randomizations). For the analysis of RNAseq data, p values were calculated with the Wald test and corrected for multiple testing according to Benjamini-Hochberg. We have now included this information in the materials and methods section and in the respective figure legends.
- Fig. 4 shows that RBM39 depletion reduces IFIT expression in virus infected cells and slightly increases virus replication. RBM39 has a major effect on IRF3 levels, but also on other players in innate immunity. What happens if IRF3 is ectopically expressed as in figure 5? With this experiment one could measure how high the contribution of IRF3 miss-splicing is to innate immunity.
We thank this reviewer for the valuable suggestion. We restructured the entire manuscript, to address several reviewer comments regarding the focus on IRF3 and the lack of data on other factors in the pathway. We now clearly demonstrate that ectopic IRF3 expression entirely rescues the TLR3 response to poly(I:C) in PH5CH cells (Fig. 6B-C), which also explains the lack of impact on the NF-κB pathway (Fig. 2G-H). In contrast, overexpression of IRF3 does not rescue the RIG-I/MDA5 response in A549 cells (new data, Fig. 6F-I). Here, also the NF-κB pathway is affected by knockdown of RBM39, suggesting that reduced RIG-I/MDA5 abundance upon RMB39 knockdown substantially contributed to the diminished innate immune response.
- Fig. 4 A uses siRNAs but B, C and D only indisulam treatment. It would be better if siRNAs would also be used for the other viruses.
We agree that a homogenous setup for virus infection would be favorable, however, the use of different cell lines was authorative due to limited permissivess of the used cell types towards virus infection and it appeared challenging to achieve similar knockdown efficiencies. To generate a homogenous experimental design, we now added new data showing a comparable impact of siRNA knockdown (Fig. 3F) and indisulam treatment (new Fig. 3G) on Sendai virus infection in A549 cells and took this as a rationale to consistently use indisulam for all other infections.
- RBM39 depletion strongly reduces IRF3 levels in the WB, but not so much in RT-PCR and not at all in proteomics. Is the antibody used for WB perhaps recognizing a domain that is underrepresented in isoforms after disturbed splicing? Please clarify.
Our previous proteomics data suffered from a very low sensitivity, therefore we missed clear detection of many factors, including IRF3. We repeated the whole proteomics analysis with siRNA and indisulam treatment (new Fig. 5A, B) and now found significantly reduced IRF3 protein levels in both conditions (new Fig. S5C), in agreement with the WB data. The lower impact on IRF3 mRNA abundance is due to the additional contribution of alternative splicing (Fig. 6A, Fig. S6A-D), which both in combination affect protein abundance.
- Volcano plots in figure 7 show a lot of hits obtained after both RBM38 siRNA and indisulam (green dots), and some that are additionally identified in transcriptomes and in proteomes (red dots). Nonetheless only innate immunity and stress response genes are marked, although they do not belong to these highly conserved classes. Please elaborate more on the most RBM39-dependent genes, e.g. by presenting them in a heat map.
To our knowledge, our study is the first with a comprehensive comparison on the impact of RBM39 knockdown and indisulam treatment on the host cell proteome and transcriptome. However, several studies already did -omics studies on individual conditions/readouts (e.g. (Coomar et al, 2023; Dou et al, 2023; Mai et al, 2016; Nijhuis et al, 2022)). These studies already identified and described in detail key changes in transcriptome and proteome e.g. affecting genes involved in cell cycle control and metabolism, which we find as well. However, the novelty of our paper is the impact on innate immune response, we therefore rather decided to put an even stronger focus on these genes and to omit other factors, like stress response pathway components, etc.. This strategy is supported by the higher sensitivity of our new proteome analysis, which now generated a far better overlap with the transcriptomics, favoring a display setting on highlighting only those factors that were further analyzed in detail in the volcano blots (Fig. 5). Still, interested readers will find the comprehensive list of data in the supplementary Excel-datasheets as well as in our primary data in online depositories.
Minor comments: - Some abbreviations are not explained, like PGK, siNT, siVTN
We apologize and have added the missing explanation of abbreviations.
- Welsch should read Welch
Corrected.
- Fig. 2H: were cells also stimulated and if yes, how?
These were unstimulated conditions, to show the impact of RBM39 on basal expression of the IFNlambda receptor chains. However, we deleted this dataset due to the re-organisation of the manuscript. The analysis of the type I and type III receptor and STAT1/2 expression is now comprehensively shown in Fig. 7/S6E, F, solely based on the transcriptomic data for consistency reasons, along with the functional impact on the IFN response.
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Fig. 6E: I cannot see a difference between to IRF3-203 and 228 isoforms. And what are the white boxes?
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Also 6E: Location of the primers is barely visible
Due to the re-organization of the manuscript these data are now shown in Fig. S6D. Both isoforms are indeed very similar and only differ by a very small (16nt) additional exon in isoform 228. The white boxes are exons not translated in the respective isoforms. We have included this important information in the legend to Fig. S6 and increased the arrows indicating the positions of the primer.
- Some materials are not properly referenced, like the death reporter, the lentiviral system, or the Rift Valley fever luciferase virus
We are sorry for the missing information, which has now been added to the materials and methods section.
- Supplement has no page numbers
We have added page numbers to the supplementary information.
Reviewer #1 (Significance (Required)):
The study advances our knowledge about the regulation of innate immunity. Strengths are the discovery of a novel layer of innate immunity regulation by splicing and the in-depth analysis of the importance of RBM39 for cellular gene expression. A potential weakness might be the focus on innate immunity as other biological functions seem even more dependent on RBM39. However, this reviewer sees the necessity that covering all aspects of RBM39 finction would be beyond the scope of a single study. The relevant literature is appropriately cited (except for some materials, see minor comments). Results will be of interest not only to people doing basic research on innate immunity, but also to those interested in gene regulation in general or to cancer researchers using indisulam
__Reviewer #2 (Evidence, reproducibility and clarity (Required)): __ The authors performed a CRISPR-based screen for genes required for TLR3-mediated signaling and gene expression in Hepatoma cells. Interferon-stimulated expression of an apoptosis inducer was used as a read-out system. A number of candidate genes were identified and one of these, RBM39, investigated in detail. The protein has previously been linked to both transcriptional control and RNA processing. Validation studies confirm that reduction of cellular RBM39 results in less TLR3-mediated IFN-beta synthesis and lower levels of ISG mRNA synthesis. Initial studies suggest a role of RBM39 in regulating of IRF3 levels, the transcription factor activated by TLR3 signaling to induce IFN-beta synthesis. However, the effect is variable and poorly supported by transcriptomic and proteomic data. Moreover, only one out of four cell-based viral infection models reports a substantial effect of the RBM39 knockdown.
We apologize for the lack of consistency among several datasets, which was mainly due to the low sensitivity of the proteomic analysis. This has been repeated and now fully confirms all other data. In part due to the comments of this reviewer, we further broadened the scope of the manuscript away from IRF3, including a change of the title.
Major comments:
- The data do not support the claim that RBM39 is a broadly acting player in innate immune responses. In addition, they suggest that IRF3 may not be the only relevant RBM39 target. The most informative knockdown control in this regard would be IRF3 siRNA.
We have re-structured the entire manuscript and added new data to support the central claims of our manuscript, including a repetition of the proteomics study. Proteomics and transcriptomics now consistently demonstrate the impact of RMB39 knockdown as well as indisulam treatment on several key factors of innate immunity, including IRF3, STAT1/2, RIG-I and MDA5 (now in Fig. 5), with IFNAR2 and IL10RB additionally found in transcriptomics. We further provide functional evidence that IRF3 is the key factor affected in the TLR3 pathway (IRF3 overexpression, Fig. 6B, C), whereas diminished abundance of RIG-I/MAD5 is equally important in the respective pathway, thereby also affecting NF-κB response (Fig. 6F-I). We further show the functional significance of IFN-receptor/STAT downregulation on type I and III IFN responses (Fig. 7E-G). We hope this reviewer now agrees with our claim that RBM39 is a broadly acting player in innate immune responses.
- The structure of the manuscript is rather confusing because IRF3 is presented as the main RBM39 target in figures 3-6, but the -omics data in figures 7 and 8 do not support this view. The authors argue different sensitivities of the experimental approaches, but I think few people would agree that western blots are more sensitive than MS. To my opinion a narrative with less focus on IRF3 and a broader integration of candidates of the -omics approaches would be preferable.
We are grateful for this valuable comment and fully agree that the original structure of the manuscript was confusing and in parts misleading, which was mainly due to the fact that we followed the history of the project, which first identified the RBM39 mediated impact on IRF3 expression, whereas the -omics studies, identifying additional factors, were done at a far later point. Many discrepancies further arose from the low sensitivity of our proteomics analysis, which we now repeated, thereby obtaining far more sensitive detection of the key factors we also found in the transcriptomics data. We now moved the -omics data from the end of the paper towards the middle and provide similar depth downstream analysis of all relevant key factors identified (RIG-I/MDA5, IFN receptors, STAT1/2, to reduce the focus on IRF3, as suggested. We further changed the title and abstract to reflect this major conceptual change. Thanks to this helpful comment, we think that our manuscript is now conceptually much clearer.
Investigating the role of RBM39 by RNA-seq in pIC-treated cells would further strengthen the manuscript. It will yield a broader view of the protein's role in induced innate immunity.
We did not add pIC treatment to the RNA-seq analysis, since, based on own experience and numerous papers, this will change the expression of literally thousands of genes. Based on the key factors of the pIC response modulated by RBM39 (RLRs and IRF3), this would very likely simply result in reduced induction of the whole ISG panel (as exemplified for IFIT1, ISG15, MxA and CXCL10 in Fig. 2B-E).
3.The results in figures 6A-C are confusing for two reasons. First, the siRNA-mediated knockdown should result in reduced RBM39 protein as well (as shown in Fig. 3A) and, therefore, in an increase in RBM39 levels. Second, why was this effect not noted in the experiments shown in figs. 1-5? To avoid this confusion it might be good to mention which IRF3 splice isoforms are detected by the primers and antibodies used in these figures.
Unfortunately, the reviewer seems to have conceptually misinterpreted Fig. 6A-C of the original paper, which did not show protein, but transcriptome data. We now added the corresponding data of the proteomic analysis in the new Fig. S5, for all detectable, relevant candidates, showing consistency to all previous data. The confusing point in previous Fig. 6B, which the reviewer appears to refer to, is the upregulation of RBM39 transcript levels upon indisulam treatment, which was not apparent in previous experiments, since we always used WB to show diminished RBM39 protein levels upon indisulam treatment. This increase in RBM39 mRNA is due to an autoregulation of RBM39 mRNA by protein abundance, which has been reported in literature (Campagne et al, 2023). Since this is rather confusing and not relevant for our study, we removed previous Fig. 6B and show this aspect only in the volcano blot in Fig. 5D, mentioning and citing the paper on autoregulation.
Minor comments.
- Fig S1: the figure panels and legend are inconsistent. IFIT1 is labeled as ISG56 in panel S1A.
We apologie for this inconsistency and now use IFIT1 throughout the paper.
- Data with the siRNA escape mutant of RBM39 are inconsistent. For example, why is its effect significantly different only in 1 out of 4 ISG in figures S2A-D?
We apologize for the inconsistency, which is due to variability of silencing efficiency. We repeated the entire set of experiments (n=3) with a new batch of siRNA and obtained comparable, significant differences for all ISGs analyzed (new Fig. 2B-E).
- Line 164: the statement that TRIF and RBM39 siRNAs produce effects of similar magnitude is incorrect for the IFIT1 gene in figure S2A.
This experiment was repeated (see previous point), now obtaining significant, more homogenous data. We have modified the text accordingly.
4.Fig. 2H: In absence of additional evidence for functional implications, the data showing reduced IL10RB expression should be omitted.
We omitted the data, as suggested by the reviewer, however, we provide a more in depth analysis of the type I and III IFN response in Fig. 7, based on the transcriptomic data and a functional analysis.
5.Fig. 3: More datapoints would be needed in panel A to sustain the lack of significant difference between the untreated and escape mutant samples. Are the viability data in panels B and C normalized to untreated cells to control for Indisulam toxicity? In figure S3A the effect of the mutant is rather small. To allow for comparison, the Indisulam titration curves should be adapted to the concentrations used in Fig. 3.
Fig. 3 (now Fig. 4) was replaced by another representative experiment, now also containing the quantification of the shown western blots, however, the statistical analysis shown in the previous version was and is based on three independent biological replicates, as indicated in the figure legend. Viability data was normalized to controls and this information is now added to the figure lengend as well. The mutant analyzed in Fig. S3A (now S4A) confers only partial resistance, which explains the limited but clear rescue. We did not include higher indisulam concentrations here due to the increased cytotoxicity of concentration above 5 µM in PH5CH, in the absence of pronounced additional effects on RBM39 abundance (Fig. 4B).
6.RNA-seq measures steady-state RNA, not transcription.
This is of course correct, we changed all sentences, where our wording might have indicated that we are measuring transcription by RNAseq. However, we still need to differentiate between the role of RBM39 in transcriptional regulation and splicing, where changes in RNA abundance found in RNAseq rather point to transcriptional regulation.
Reviewer #2 (Significance (Required)):
The identification of RBM39 as a candidate player in innate immune responses is of interest to a large scientific community with interest in signalling by pattern recognition receptors. Its role should be strengthened with additional infection models. It is puzzling that three out of four viruses don't benefit from the reduced IFN-beta synthesis in the RBM39 knockdown. Moreover, the data are not convincing (or too diverse) to nail down IRF3 as a major, or the most relevant, RBM39 target.
__Reviewer #3 (Evidence, reproducibility and clarity (Required)): __ CRISPR Screen for factors that are required for dsRNA-dependent ISG production. Found a large number of hits but most did not validate in subsequent assays. The authors follow up the one candidate that did pass secondary screening criteria, RBM39, although re-expression of RBM39 only rescues the phenotype of the siRNAs against RBM39 (siRBM39) in one of the two cell lines tested. Additionally, siRBM39 impacts only a subset of polyIC-induced ISGs and does not regulate NFkB-driven gene expression. They go on to attempt to investigate the impact of siRBM39 on other key innate immune genes and proteins, although many key controls and appropriate methods are missing.
We thank this reviewer for pointing at inconsistencies and missing controls in our manuscript. We have critically re-evaluated the respective datasets.
Major comments: 1) The authors propose some rationale for the limited success of the screen, however, while RBM39 may have a role in dsRNA-induced innate immunity, in general the screen seems to have limited value.
The aim of our CRISPR/Cas9 death reporter screen was the identification of so far unknown contributors to innate immune response. This was achieved by identifying a critical role of RBM39, followed by an in depth validation focusing on RBM39. We further found known components of the TLR3 pathway in our candidate list (e.g. TRIF and UNC93B1), pointing to the overall quality of the experimental setup. At no point of the manuscript we claim that our screen aimed for or delivered a comprehensive overview on innate immunity pathways. Honestly, no comparable screen (e.g. on cytopathic viruses) has delivered such data.
2) Given that the siRBM39 clearly has off-target effects (since expression of a resistant RBM39 cDNA only gives limited rescue in many cases - Fig S2), each of the experiments in which siRBM39 is used (i.e. Fig 2) should have the RBM39.esc control - especially those that drive subsequent experiments such as the expression of IFNbeta and IFNLR1 (Fig 2a, h)
The inconsistency in some datasets, showing all the same trends, but in some cases lacking statistical significance was due to variability in knockdown efficiency. We repeated all mentioned datasets with new batches of siRNA with sufficient biological replicates (n=3) with now all of them revealing consistent, statistically significant data. Importantly, all experiments implementing the RMB39.esc control now show consistent rescue.
3) Since RBM39 reduction has an apparent impact even if IFNLR1-deficient cells (although need the rescue control to know if this is real) the authors conclude that RBM39 regulates the initial wave of dsRNA signaling-events, but this should be tested with the use of Ruxilitinib to block JAK-STAT signaling.
Due to the general major re-organization of the manuscript, aiming for a less confusing data presentation and consistency towards depth of candidate evaluation, we have removed the data on the IFNLR-deficient cell line. The claim that RBM39 affects the initial wave of ISG responses is based on reduced IFNb expression, which is exclusively induced by the initial wave of ISG response and by the general impact on ISG expression, which we measure at 6h after induction, too early for autocrine IFN stimulation (Burkart et al, 2023). However, we further demonstrate that downregulation of type I and type III IFN receptors in conjunction with STAT1/2 affect the type I and the type III IFN response as well (Fig. 7E-G, in part new data). Therefore, RBM39 affects both, the intial wave and the auto-/paracrine IFN response, and we therefore undertook no further efforts to separate these effects.
4) IRF3 expression in the Indisulam-treated cells more closely tracks cell viability than RBM39 expression. For example in Fig 3C 10 microM gives 50% IRF3 expression and 50% viability but still 95% RBB39 expression - arguing that the impact of siRBM39 on IRF3 might be very indirect (and error bars on rescue are large so unclear if the rescue really worked in Fig 3A).
Based on this reviewer comment we re-evaluated the quantification in previous Fig. 3C (now Fig. 4C), which combines data from three independent experiments. We deeply apologize, but the initial quantification proved to be wrong, due erroneous background subtraction, which was relatively high in one of the PHH-replicates (Replicate 1, see Reviewer Fig. 1 in uploaded file). The re-evaluated quantification revealed 55% for the RBM39 abundance at 10µM indisulam, which better reflects the data shown and is now in line with the impact on cytotoxicity and IRF3 abundance.
5) It is unclear in Fig 4 why some cell/virus combinations are tested with siRBM39 and others are tested with Indisulam. Also the conclusion that RBM39 "substantially contributes to the cell intrinsic innate immune response to viral infections" is greatly overstated given that the differences are between ~3 fold and non-significant.
We agree that a homogenous setup for virus infection would be favorable, however, the use of different cell lines was authoritave due to limited permissivess of the used cell types towards virus infection and it appeared challenging to achieve similar knockdown efficiencies. To generate a homogenous experimental design, we now added new data showing a comparable impact of siRNA knockdown (Fig. 3F) and indisulam treatment (new Fig. 3G) on Sendai virus infection in A549 cells and took this as a rationale to consistently use indisulam for all other infections. Overall, the aim of the virus infection experiments was using a variety of natural triggers of innate immunity beyond synthetic poly(I:C). Here we found indeed significant reductions of ISG induction for all viruses tested, similar to poly(I:C), this is the basis for the statement that RBM39 contributes the cell intrinsic innate immune response to viral infections. Our experimental design did not intend to see pronounced effects on viral replication, this was only measured to secure that reduced ISG induction was not due to inhibition of viral replication. We have explained this strategy now clearer and tuned down corresponding statements, to exclude potential overinterpretation of the data.
6) Neither DTU/DRIMseq or qPCR are valid methods to measure splice isoform differences. The authors need to use rMATS or MAJIQ and validate by gel-based RT-PCR.
Output generated by modern alignment algorithms like salmon is suitable for studies on an isoform level (Love et al, 2018) and has been used in a variety of studies (e.g.(Jabs et al, 2020; Xiong et al, 2023). MAJIQ and rMATS are only superior tools if the detection of so far unknown isoforms is of interest (Love et al., 2018), which is beyond the scope of this project. We have validated the data for IRF3 in RT-qPCR, showing close to identical results to the DTU analysis (compare Fig. 6A and S6D). We disagree that a gel-based RT-PCR analysis would be superior here, due to the lack of quantification.
7) The conclusions from the proteomic and transcriptomic analyses should be treated with extreme caution given the caveats of methodology and controls discussed above.
We are aware of the caveats of these technologies. The previous proteomic analysis indeed suffered from low sensitivity, failing to detect essential candidates like IRF3. The repetition of the experiment (new Fig. 5A, B, new Fig. S5) now revealed data very consistent with the transcriptomic data. Overall, the strength of our approach is the direct comparison of siRNA based RBM39 knockdown and RBM39 depletion by indisulam throughout transcriptomics and proteomics analyses. The wide overlap argues for the validity of our data and suggests that we thereby circumvented many caveats.
Reviewer #3 (Significance (Required)):
Innate immune signaling is a complex and essential pathway for maintaining health. While much is known about key components of this pathway, additional regulators are likely to exist. This manuscript describes an attempt to identify new regulators of dsRNA-mediated gene expression.
References
Burkart SS, Schweinoch D, Frankish J, Sparn C, Wust S, Urban C, Merlo M, Magalhaes VG, Piras A, Pichlmair A et al (2023) High-resolution kinetic characterization of the RIG-I-signaling pathway and the antiviral response. Life Sci Alliance 6
Campagne S, Jutzi D, Malard F, Matoga M, Romane K, Feldmuller M, Colombo M, Ruepp MD, Allain FH (2023) Molecular basis of RNA-binding and autoregulation by the cancer-associated splicing factor RBM39. Nat Commun 14: 5366
Coomar S, Mota P, Penson A, Schwaller J, Abdel-Wahab O, Gillingham D (2023) Overlaid Transcriptional and Proteome Analyses Identify Mitotic Kinesins as Important Targets of Arylsulfonamide-Mediated RBM39 Degradation. Mol Cancer Res 21: 768-778
Dou Z, Zhang X, Su W, Zhang T, Ye F, Zhao D, Chen X, Li Q, Zhang H, Di C (2023) Indisulam exerts anticancer effects via modulation of transcription, translation and alternative splicing on human cervical cancer cells. Am J Cancer Res 13: 2922-2937
Jabs S, Biton A, Becavin C, Nahori MA, Ghozlane A, Pagliuso A, Spano G, Guerineau V, Touboul D, Giai Gianetto Q et al (2020) Impact of the gut microbiota on the m(6)A epitranscriptome of mouse cecum and liver. Nat Commun 11: 1344
Love MI, Soneson C, Patro R (2018) Swimming downstream: statistical analysis of differential transcript usage following Salmon quantification. F1000Res 7: 952
Mai S, Qu X, Li P, Ma Q, Cao C, Liu X (2016) Global regulation of alternative RNA splicing by the SR-rich protein RBM39. Biochim Biophys Acta 1859: 1014-1024
Nijhuis A, Sikka A, Yogev O, Herendi L, Balcells C, Ma Y, Poon E, Eckold C, Valbuena GN, Xu Y et al (2022) Indisulam targets RNA splicing and metabolism to serve as a therapeutic strategy for high-risk neuroblastoma. Nat Commun 13: 1380
Xiong L, Liu J, Han SY, Koppitch K, Guo JJ, Rommelfanger M, Miao Z, Gao F, Hallgrimsdottir IB, Pachter L et al (2023) Direct androgen receptor control of sexually dimorphic gene expression in the mammalian kidney. Dev Cell 58: 2338-2358 e2335
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Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.
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Referee #3
Evidence, reproducibility and clarity
CRISPR Screen for factors that are required for dsRNA-dependent ISG production. Found a large number of hits but most did not validate in subsequent assays. The authors follow up the one candidate that did pass secondary screening criteria, RBM39, although re-expression of RBM39 only rescues the phenotype of the siRNAs against RBM39 (siRBM39) in one of the two cell lines tested. Additionally, siRBM39 impacts only a subset of polyIC-induced ISGs and does not regulate NFkB-driven gene expression. They go on to attempt to investigate the impact of siRBM39 on other key innate immune genes and proteins, although many key controls and appropriate methods are missing.
Major comments:
- The authors propose some rationale for the limited success of the screen, however, while RBM39 may have a role in dsRNA-induced innate immunity, in general the screen seems to have limited value.
- Given that the siRBM39 clearly has off-target effects (since expression of a resistant RBM39 cDNA only gives limited rescue in many cases - Fig S2), each of the experiments in which siRBM39 is used (i.e. Fig 2) should have the RBM39.esc control - especially those that drive subsequent experiments such as the expression of IFNbeta and IFNLR1 (Fig 2a, h)
- Since RBM39 reduction has an apparent impact even if IFNLR1-deficient cells (although need the rescue control to know if this is real) the authors conclude that RBM39 regulates the initial wave of dsRNA signaling-events, but this should be tested with the use of Ruxilitinib to block JAK-STAT signaling.
- IRF3 expression in the Indisulam-treated cells more closely tracks cell viability than RBM39 expression. For example in Fig 3C 10 microM gives 50% IRF3 expression and 50% viability but still 95% RBB39 expression - arguing that the impact of siRBM39 on IRF3 might be very indirect (and error bars on rescue are large so unclear if the rescue really worked in Fig 3A).
- It is unclear in Fig 4 why some cell/virus combinations are tested with siRBM39 and others are tested with Indisulam. Also the conclusion that RBM39 "substantially contributes to the cell intrinsic innate immune response to viral infections" is greatly overstated given that the differences are between ~3 fold and non-significant.
- Neither DTU/DRIMseq or qPCR are valid methods to measure splice isoform differences. The authors need to use rMATS or MAJIQ and validate by gel-based RT-PCR.
- The conclusions from the proteomic and transcriptomic analyses should be treated with extreme caution given the caveats of methodology and controls discussed above.
Significance
Innate immune signaling is a complex and essential pathway for maintaining health. While much is known about key components of this pathway, additional regulators are likely to exist. This manuscript describes an attempt to identify new regulators of dsRNA-mediated gene expression.
-
Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.
Learn more at Review Commons
Referee #2
Evidence, reproducibility and clarity
The authors performed a CRISPR-based screen for genes required for TLR3-mediated signaling and gene expression in Hepatoma cells. Interferon-stimulated expression of an apoptosis inducer was used as a read-out system. A number of candidate genes were identified and one of these, RBM39, investigated in detail. The protein has previously been linked to both transcriptional control and RNA processing. Validation studies confirm that reduction of cellular RBM39 results in less TLR3-mediated IFN-beta synthesis and lower levels of ISG mRNA synthesis. Initial studies suggest a role of RBM39 in regulating of IRF3 levels, the transcription factor activated by TLR3 signaling to induce IFN-beta synthesis. However, the effect is variable and poorly supported by transcriptomic and proteomic data. Moreover, only one out of four cell-based viral infection models reports a substantial effect of the RBM39 knockdown.
Major comments:
- The data do not support the claim that RBM39 is a broadly acting player in innate immune responses. In addition, they suggest that IRF3 may not be the only relevant RBM39 target. The most informative knockdown control in this regard would be IRF3 siRNA.
- The structure of the manuscript is rather confusing because IRF3 is presented as the main RBM39 target in figures 3-6, but the -omics data in figures 7 and 8 do not support this view. The authors argue different sensitivities of the experimental approaches, but I think few people would agree that western blots are more sensitive than MS. To my opinion a narrative with less focus on IRF3 and a broader integration of candidates of the -omics approaches would be preferable. Investigating the role of RBM39 by RNA-seq in pIC-treated cells would further strengthen the manuscript. It will yield a broader view of the protein's role in induced innate immunity.
- The results in figures 6A-C are confusing for two reasons. First, the siRNA-mediated knockdown should result in reduced RBM39 protein as well (as shown in Fig. 3A) and, therefore, in an increase in RBM39 levels. Second, why was this effect not noted in the experiments shown in figs. 1-5? To avoid this confusion it might be good to mention which IRF3 splice isoforms are detected by the primers and antibodies used in these figures.
Minor comments.
- Fig S1: the figure panels and legend are inconsistent. IFIT1 is labeled as ISG56 in panel S1A.
- Data with the siRNA escape mutant of RBM39 are inconsistent. For example, why is its effect significantly different only in 1 out of 4 ISG in figures S2A-D?
- Line 164: the statement that TRIF and RBM39 siRNAs produce effects of similar magnitude is incorrect for the IFIT1 gene in figure S2A.
- Fig. 2H: In absence of additional evidence for functional implications, the data showing reduced IL10RB expression should be omitted.
- Fig. 3: More datapoints would be needed in panel A to sustain the lack of significant difference between the untreated and escape mutant samples. Are the viability data in panels B and C normalized to untreated cells to control for Indisulam toxicity? In figure S3A the effect of the mutant is rather small. To allow for comparison, the Indisulam titration curves should be adapted to the concentrations used in Fig. 3.
- RNA-seq measures steady-state RNA, not transcription.
Significance
The identification of RBM39 as a candidate player in innate immune responses is of interest to a large scientific community with interest in signalling by pattern recognition receptors. Its role should be strengthened with additional infection models. It is puzzling that three out of four viruses don't benefit from the reduced IFN-beta synthesis in the RBM39 knockdown. Moreover, the data are not convincing (or too diverse) to nail down IRF3 as a major, or the most relevant, RBM39 target.
-
Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.
Learn more at Review Commons
Referee #1
Evidence, reproducibility and clarity
This manuscript by Li and colleagues examines the role of RBM39 in innate immune signaling. Splicing factor RBM39 was identified through a genome wide screen with a death reporter under control of the IFIT1 promoter that got stimulated with pIC in a TLR3-dependent manner. Besides IFIT1, further experiments showed that RBM39 is also involved in optimal expression of other innate immunity genes like IFNB, CXCL10, RIG-I or MDA5. While NFkB-dependent genes seem not to depend on RBM39, for IRF3 it was shown that protein levels decrease under conditions of RBM39 depletion, because IRF3 mRNAs are (slightly) reduced and spliced differently. The sulfonamid Indisulam could largely recapitulate the phenotype of RBM39 depletion. Further analyses using proteomics and transcriptomics showed that RBM39 is required for mRNA splicing and expression of a large set of other proteins.
Altogether, this well designed and written study highlights the fundamental role played by RBM39 in in maintaining the pathways of immunity and metabolism. The key conclusions are convincing but some additional experiments would strengthen them further.
Major comments:
- For the statistics, authors seem not to have done multiple tests but rather tested individual datasets within larger graphs against each other. Please explain where this is the case and use corrections if multiple testing was done
- Fig. 4 shows that RBM39 depletion reduces IFIT expression in virus infected cells and slightly increases virus replication. RBM39 has a major effect on IRF3 levels, but also on other players in innate immunity. What happens if IRF3 is ectopically expressed as in figure 5? With this experiment one could measure how high the contribution of IRF3 miss-splicing is to innate immunity.
- Fig. 4 A uses siRNAs but B, C and D only indisulam treatment. It would be better if siRNAs would also be used for the other viruses.
- RBM39 depletion strongly reduces IRF3 levels in the WB, but not so much in RT-PCR and not at all in proteomics. Is the antibody used for WB perhaps recognizing a domain that is underrepresented in isoforms after disturbed splicing? Please clarify.
- Volcano plots in figure 7 show a lot of hits obtained after both RBM38 siRNA and indisulam (green dots), and some that are additionally identified in transcriptomes and in proteomes (red dots). Nonetheless only innate immunity and stress response genes are marked, although they do not belong to these highly conserved classes. Please elaborate more on the most RBM39-dependent genes, e.g. by presenting them in a heat map.
Minor comments:
- Some abbreviations are not explained, like PGK, siNT, siVTN
- Welsch should read Welch
- Fig. 2H: were cells also stimulated and if yes, how?
- Fig. 6E: I cannot see a difference between to IRF3-203 and 228 isoforms. And what are the white boxes?
- Also 6E: Location of the primers is barely visible
- Some materials are not properly referenced, like the death reporter, the lentiviral system, or the Rift Valley fever luciferase virus
- Supplement has no page numbers
Significance
The study advances our knowledge about the regulation of innate immunity. Strengths are the discovery of a novel layer of innate immunity regulation by splicing and the in-depth analysis of the importance of RBM39 for cellular gene expression. A potential weakness might be the focus on innate immunity as other biological functions seem even more dependent on RBM39. However, this reviewer sees the necessity that covering all aspects of RBM39 finction would be beyond the scope of a single study.
The relevant literature is appropriately cited (except for some materials, see minor comments). Results will be of interest not only to people doing basic research on innate immunity, but also to those interested in gene regulation in general or to cancer researchers using indisulam
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Reply to the reviewers
Reply to reviewers
We thank the reviewers for their constructive comments. We appreciate the insights that they have shared. The comments were very helpful and will be addressed in the following sections
Reviewer 1:
As this field is very specific and this study may not have a broadened readership, it would benefit to add some more layers of complexity hence potential interest for Notch signaling in general and/or T-ALL pathology: e.g. do the induced mutated cell lines are more aggressive than the parental cell lines in vivo? How well do these PSEN1 mutated cell lines respond to other drugs like CB-103 (in vivo and in vitro), especially the cell lines where more Notch1 cleavage was observed
Response to reviewer 1:
- Reviewer 1 suggested to test the sensitivity of resistant T-ALL cell lines with PSEN1 mutations to other NOTCH inhibitors, such as CB-103. In response, we plan to assess the sensitivity of our DND-41 Cas9, HPB-ALL Cas9 and RPMI-8402 Cas9 cell lines (WT and PSEN1 mutants: 275(A>Y), 275(A>Y)+276(Q>E), 421_422(->ILS)) to CB-103 using a proliferation assay (ATPlite luminescence assay) in the coming weeks. These data will provide insight in the resistance profile and will determine if the mutations conferring resistance to the PSEN1-selective gamma-secretase inhibitor also confer resistance to other inhibitors that target NOTCH1 directly. We will incorporate this data in the manuscript once the experiments are completed.
- Reviewer 1 was wondering whether the induced mutated T-ALL cell lines are more aggressive than the parental cell line in vivo. We did not observe a significant change in proliferation in vitro for the mutated cell lines after 10-day culture with DMSO compared to parental cell line (Fig. 3). As shown in Fig. 5, certain PSEN1 mutation can enhance affinity for the NOTCH1 substrate, thereby increasing the amount of cleaved NOTCH1. Here, we could hypothesize that these PSEN1 mutations could maybe lead to a more aggressive phenotype in patients. However, the focus of this article was to determine if PSEN1 mutations lead to MRK-560 resistance. Consequently, we believe that including these additional experiments would not significantly improve the study and animal experiments would not offer a major improvement.
*Reviewer 2: *
The study reports an important mechanism of resistance to THE MRK-560 inhibitor. The study might benefit from a few considerations: -Test the effect of the mutations in resistance using in vivo setting (xenograft model) -The authors should ideally introduce the mutations in patient samples and repeat some of the studies using this more relevant model. -"We identified 3 types of resistance mutations.": Could mutations in the control elements of the gene (that might affect gene expression) also lead to resistance to MRK-560? Please discuss.
*Response to reviewer 2: *
Reviewer 2 suggested discussing whether mutations in the control elements of the PSEN1 gene could also lead to MRK-560 resistance. In this paper, we focused on a specific resistance mechanism involving mutations in the target protein. However, the reviewer's point is valid. Therefore, we will add a new section in the discussion of the revised manuscript to explore other potential resistance mechanism to MRK-560 (PTEN deletion, PSEN2 upregulation). However, we do believe that alterations of PSEN1 expression will not be an important resistance mechanism: PSEN1 expression cannot be completely silenced because NOTCH1 cleavage is necessary for cell proliferation, and higher PSEN1 expression levels will not affect drug binding/affinity.
*Reviewer 2 suggested validating the resistance mutations in vivo using cell line xenograft or patient-derived xenograft mouse models. Our study aimed to investigate if PSEN1 mutations could confer resistance to MRK-560. We demonstrated in 2 different cell models (mouse embryonic fibroblasts and T-ALL cell lines) that the identified PSEN1 mutations resulted in higher levels of cleaved NOTCH1 compared to WT cells following MRK-560 treatment, confirming resistance. Additionally, we validated the resistance mechanisms, including the direct disruption of the drug binding pocket, a well-established resistance mechanism observed in patients treated with other targeted therapies. While in vivo validation would likely confirm the in vitro findings, we believe it requires extensive resources and would add only a marginal value to the study. *
*Reviewer 3: *
- Major comments
- The authors have limited their CRISPR mutant analysis to PS1 in this paper. The molecular studies are fine, but it is unclear whether such mutants can be generated by MRK560 treatment. To clarify the significance of these mutants in vivo, they should discuss whether the mutations identified in this study are observed in cancer patients or cultured cells treated with MRK560.
- Many of the mutants examined are similar to familial Alzheimer's disease, such as those that increase Aβ42 production (Fig. 5F). Concerning this mechanism, we would appreciate the discussion on how to establish cancer treatment strategies for patients with familial Alzheimer's disease.
- The analysis in this paper shows that mutations in a single PSEN1 allele are sufficient to acquire resistance to MRK560. On the other hand, since duplication of genes can occur in cancer cells, there is a possibility that cancer cells with multiple PSEN1 alleles or mutants with elevated PSEN1 expression (mutations in the promoter region) may arise. In such cases, we would like to see experimental evidence as to whether resistance to MRK560 is canceled or whether mutant PS1 is selectively incorporated into functional γ-secretase.*
*Response to reviewer 3: *
Reviewer 3 raised a valid concern regarding the use of MRK-560 in patients with pre-existing PSEN1 mutations, particularly those with familial Alzheimer's disease, who can also develop leukemia. This is a good comment, as these patients may exhibit primary resistance to PSEN1-selective inhibitors. Consequently, we will expand our discussion to address the possibility of primary resistance to MRK-560 due to inherent PSEN1 mutations.
Reviewer 3 suggested to discuss whether the identified mutations could be acquired during MRK-560 treatment in cell lines and/or patients. Currently, no T-ALL patients have been treated with PSEN1-selective g-secretase inhibitors and hence, no data on PSEN1 mutations in patients is available (PSEN1 is also not screened at diagnosis of T-ALL patients). However, it is known that patients treated with other targeted drugs, such as imatinib (B-ALL patients) or IDH inhibitors (AML), acquired point mutations in the target gene/protein in response to treatment, leading to resistance.1,2,3,4 Here, we also demonstrated that mutations in PSEN1 can result in MRK-560 resistance, indicating a similar resistance mechanism to those previously described and further increasing the likelihood that PSEN1 mutations will arise in patients treated with PSEN1-selective g-secretase inhibitors. Predicting these resistance mutations offers the possibility to test already other inhibitors that can overcome or prevent such resistance.
Reviewer 3 inquired about the impact of PSEN1 gene duplications on MRK-560 resistance. First, it is important to note that PSEN1 gene duplications are not observed in T-ALL patients. Additionally, PSEN1 mutations are predominantly heterozygous and dominant. Consequently, duplication of either WT or mutated PSEN1 allele is unlikely to influence resistance to MRK-560. We believe that investigating the effect of gene duplication on MRK-560 resistance is out of scope for this paper
1Lyczek, A., Berger, B. T., Rangwala, A. M., et al. Mutation in Abl kinase with altered drug-binding kinetics indicates a novel mechanism of imatinib resistance. Proc. Natl. Acad. Sci. U. S. A. 2021; 118 (46);.
2Melo, J. V. & Chuah, C. Resistance to imatinib mesylate in chronic myeloid leukaemia. Cancer Lett. 2007; 249 (2); 121-132
3Issa, G. C. & DiNardo, C. D. Acute myeloid leukemia with IDH1 and IDH2 mutations: 2021 treatment algorithm. Blood Cancer J. 2021; 11 (107); 1-7.
4Zhuang, X., Pei, H. Z., Li, T., et al. The Molecular Mechanisms of Resistance to IDH Inhibitors in Acute Myeloid Leukemia. Front. Oncol. 2022; 12 (931462);.
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Referee #3
Evidence, reproducibility and clarity
In this paper, the authors comprehensively investigated the mechanism of resistance acquisition to MRK560, a PS1-specific γ-secretase inhibitor, in T-ALL cells by CRISPR screening. They found multiple mutants and explored their molecular mechanisms based on the 3D structure of γ-secretase. They found that the mutants can be classified into three groups: those that inhibit the binding of PSEN1 to the compound, those that inhibit the binding of PSEN1 to the substrate, and those that inhibit the binding of MRK560.
Major comments
- The authors have limited their CRISPR mutant analysis to PS1 in this paper. The molecular studies are fine, but it is unclear whether such mutants can be generated by MRK560 treatment. To clarify the significance of these mutants in vivo, they should discuss whether the mutations identified in this study are observed in cancer patients or cultured cells treated with MRK560.
- Many of the mutants examined are similar to familial Alzheimer's disease, such as those that increase Aβ42 production (Fig. 5F). Concerning this mechanism, we would appreciate the discussion on how to establish cancer treatment strategies for patients with familial Alzheimer's disease.
- The analysis in this paper shows that mutations in a single PSEN1 allele are sufficient to acquire resistance to MRK560. On the other hand, since duplication of genes can occur in cancer cells, there is a possibility that cancer cells with multiple PSEN1 alleles or mutants with elevated PSEN1 expression (mutations in the promoter region) may arise. In such cases, we would like to see experimental evidence as to whether resistance to MRK560 is canceled or whether mutant PS1 is selectively incorporated into functional γ-secretase.
Significance
The results of this paper advance our understanding of the molecular mechanisms by which mutations in the PSEN1 gene may lead to the acquisition of γ-secretase inhibitor resistance in T-ALL treatment strategies. On the other hand, this study alone cannot be generalized to the development of T-ALL treatment strategies in terms of gene mutation acquisition in cancer cells, because mutations in the non-coding region of the PSEN1 gene and mutants of other γ-secretase components as well as PSEN1 can occur.
Some of the mutations found have not been previously identified and provide new insights into our understanding of the mechanisms by which PSEN1 exerts its activity. However, the mutants obtained are based on structural analysis of the MRK560 complex PSEN1, which has already been analyzed and does not provide major advances in mechanistic insights of γ-secretase.
Given that this paper is primarily a pharmacological analysis and is limited to γ-secretase and T-ALL, the intended audience for this paper is likely to be researchers involved in cancer-related research and pharmacological research.
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Referee #2
Evidence, reproducibility and clarity
The study reports an important mechanism of resistance to THE MRK-560 inhibitor. The study might benefit from a few considerations:
- Test the effect of the mutations in resistance using in vivo setting (xenograft model)
- The authors should ideally introduce the mutations in patient samples and repeat some of the studies using this more relevant model.
- "We identified 3 types of resistance mutations.": Could mutations in the control elements of the gene (that might affect gene expression) also lead to resistance to MRK-560? Please discuss.
Significance
It is a well-designed study
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Referee #1
Evidence, reproducibility and clarity
In this manuscript, Vandersmissen et al., performed a CRISPR-mediated mutagenesis screen to identify presenilin (PSEN1) mutations that could lead to the resistance of PSEN1-selective gamma-secretase inhibitors such as MRK-560, in T-ALL. In general, the manuscript is well-written, and the experiments performed do support their claims and are well done. The authors even went on to interrogate that the mutations that confer the drug resistance were located at the enzyme-substrate interface that caused a shift in relative binding affinities towards MRK-560 and/or substrate. Another resistance mechanism involved a mutation at the enzyme-substrate interface that hindered the entrance of MRK-560 to the binding pocket. This study is quite unusual in the sense that PSEN1 mutations that confer resistance to MRK-560 in T-ALL have yet to be reported, as far as this reviewer is aware. Hence, the authors created a potential problem that has yet to exist. Although cancers do develop resistance to drugs, whether naturally occurring MRK-560-resistant T-ALL samples would be the same as described in this study is unknown. Nevertheless, it is an interesting study and can set the foundation for future studies.
Significance
As this field is very specific and this study may not have a broadened readership, it would benefit to add some more layers of complexity hence potential interest for Notch signaling in general and/or T-ALL pathology: e.g. do the induced mutated cell lines are more aggressive than the parental cell lines in vivo? How well do these PSEN1 mutated cell lines respond to other drugs like CB-103 (in vivo and in vitro), especially the cell lines where more Notch1 cleavage was observed?
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Reply to the reviewers
#Reviewer 1 (Evidence, reproducibility and clarity):
This manuscript by Deshmukh et al is aimed at generating chimeric antigens that can be useful for making next generation vaccines that block blood stage infection by malaria parasite. Given that there is no blood stage vaccine against malaria and available liver stage vaccine shows only limited efficacy that too only in Africa, there is dire need for having novel approaches to generate successful vaccines. In the past attempts have been made to make multivalent vaccines but have not been successful. Nevertheless, it is still a good option as single target blood-stage vaccines have failed. Authors propose to target cytoadhesion and host erythrocyte invasion. For this purpose, they have selected epitopes from PfEMP1/VarB family members, which poses a major challenge as at least 60 genes encode them and they exhibit variations which facilitate the escape from the immune system. The other two chimeras target invasion related proteins like MSPs and adhesins shed by micronemes and rhoptries, which are critical for invasion. The reported work is interesting and provides a useful approach towards developing vaccines against blood stage infection.
We appreciate the time and effort given by our reviewer in thoroughly reading the manuscript. We are thankful for all the comments and suggestions for better shaping the article.
Comments:
- __ The peptides used in InvB chimera did not show good reactivity especially when compared to VarB or MSP peptides. Please discuss the possible reasons.__
Response: Thank you for pointing out the difference in the explanation. With chimeric InvP, we see a strong response against a few peptides of SERA-5 and RH-5, while other peptides, in comparison, have lesser antibody responses. We have now included the following statement detailing this difference with possible explanations in the revised manuscript (Page 8, Line 25 to 30).
The IgG responses to chimeric InvP were slightly different from those to chimeric varB and MSP. The intensity of IgG to peptides of SERA-5 and RH-5 was very high in comparison to the rest of the peptides used in the construct, whereas in chimeric varB and MSP, the IgG titers were comparable between the peptides. This could be a result of antigen exposure in the cohort of 19 patient samples that we used, and may change when a larger sample size is considered.
__ It will be interesting to determine if blocking a specific VarB/PfEMP1 alters expression of other members. Based on the data provided in Fig. 4E, can a chimera be designed which only includes PfEMP1 that are represented well in HBEC-5i population?__
Response: We agree that observing the altered expression of PfEMP1 would be an interesting phenomenon to study. The blocking of PfEMP1 using anti-chimeric varB antibodies is a transient process in our assays (just enough to quantify the cytoadhesion). It may take multiple cycles with negative selection pressure on parasites for the switching to take place. Also, it will be interesting to design chimeras based on the HBEC-5i binding PfEMP1. We can certainly plan these as prospective future experiments.
__ Some of the invasion related proteins like RH5 and EBA175 are not present at parasite surface, instead, secreted from rhoptries and micronemes. It will be nice to perform Western blots on condition medium and see if InvP (or even MSP and VarB) antibodies recognizes the secreted version of these proteins.__
Response: We thank the reviewer for this valuable comment and the suggestive experiment. We will perform a western blot on spent media and probe using anti-chimeric MSP and InvP antibodies to detect the proteins selected in chimeric MSP and InvP antigens.
__ Fig. 6E- Statistics need to be provided for inhibition at 12.3 and 25ug.__
Response: We apologize for the missed statistics. It is now included in the figure panel.
__ Plasmodium uses multiple ligand-receptor interaction, which could depend (e.g. EBA-glycohophorins) or operate independent (e.g. RH5-basigin) of sialic acid. While there is representation from candidates from both of these families, most studies especially growth rate assays (Fig. 6E) have been carried using 3D7 strain, which does not require sialic acid. It is possible that if similar experiments were performed using sialic acid-sensitive strains, InvP and MSP antibodies may cause greater inhibition of parasite growth, which may be worth testing.__
Response: We are grateful for the suggestion of using a sialic acid-dependent strain. Indeed, the pathway of reinvasion chosen by the parasite may determine the growth inhibition assay (GIA) outcome. We will perform the GIA assay on the Dd2 strain and 3D7 with neuraminidase treatment (Sialic acid-dependent invasion). We will also note the difference in growth inhibition potential of chimeric antibodies in sialic-acid dependent and independent pathways.
__ The direct effect of InvP and MSP Abs should be tested directly on host erythrocyte invasion.__
Response: We thank the reviewer for this comment. We certainly can determine the inhibitory potential of anti-chimeric MSP and InvP antibodies through invasion assays. We will include the invasion inhibition potential of these antibodies in 3D7, Dd2, with neuraminidase treatment along with GIA data.
Reviewer #1 Significance:
Present study proposes novel strategies for the development of anti-malarial vaccine.
#Reviewer 2 (Evidence, reproducibility and clarity):
The manuscript describes the vaccine potential of unstructured P. falciparum merozoite protein fragments 25 amino acid long belonging to 3 different protein families. The work is well performed, easily reproducible and clearly described.
We appreciate the time and effort given by our reviewer in thoroughly reading the manuscript. We are thankful for all the comments and suggestions for better shaping the article.
Reviewer #2 (Significance):
- The use of protein fragments whose structure can be predicted by their sequence has been exploited in many studies for the development of vaccines or other biologicals. In this studies the authors selected 3 different families belonging to the red blood stage of the parasite. The table showing the sequences selected is not readable and should be clearly provided in the supplementary section.
Response: We apologize for the readability of the sequences. The supplementary Table 1 has the proteins selected, the sequences taken, and the precise order for the stitching.
In addition, polymorphic residues should be highlighted.
Response: We thank the reviewer for pointing this out. We will analyze and compile the protein sequences in 3D conformation, highlighting polymorphic residues and the peptides selected in our study.
In addition, it is not to mention why the authors used immune rabbit sera obtained by injection of the 3 poly-epitopes instead of obtaining by affinity chromatography antigen specific human antibodies from sera of individuals living in endemic regions which could provide a direct and clear answer whether a protective vaccine could be obtained.
Response: We agree that the clear answer to the protective function of antibodies could have been answered using human antibodies. However, we did not have a sufficient volume of patient sera to perform affinity enrichment. The use of rabbits here was to ensure the generation of antigen-specific antibody responses in ample amounts. The patient sera in quantities available were used in ELISA, epitope mapping, and IP, followed by mass-spectrometry. The IP-MS clearly shows the presence of antibodies against the proteins taken in the generation of chimeric antigens (Supplementary Figure 1 D).
#Reviewer 3 (Evidence, reproducibility and clarity):
Multi-protein chimeric antigens... by: Deshmukh et al
This article addresses an extremely important objective, the development of an effective prophylactic vaccine for Malaria. The disease continues to be widespread claiming the lives of hundreds of thousands of people annually, many of them children. Despite efforts towards producing Malaria vaccines, none thus far have been sufficiently protective or long term. As the authors point out vaccines can target the parasite per se, and possibly more attractive would be to focus on parasite derived antigens expressed on the surface of infected erythrocytes, hence targeting the Blood stage of the infection, which is most directly associated with Malaria pathogenesis. The authors propose a somewhat novel approach in which they have selected an array of short (25 amino acids) segments of Plasmodium derived proteins stitched together to produce 3 chimeric recombinant proteins as potential immunogens. Although a considerable amount of work is described, the results are not compelling in proving the efficacy or advantage of using chimeric antigens as worthy vaccine candidates for Malaria.
Unfortunately, the rationale behind the experiments are not clearly defined which is a matter of concern. In addition, details of the work done and the technical aspects needs to better explained to fully understand how and why the target segments were selected and the chimeras produced. This review focuses first on scientific issues and then format and editing, both aspects demonstrate that the manuscript in its present form requires major changes for it to be of relevance to the field. This review focuses first on issues of substance and then format and editing, both aspects disqualify the publication of the manuscript in its present form.
We appreciate the time and effort given by our reviewer in thoroughly reading the manuscript. We are thankful for all the comments and suggestions for better shaping the article.
Experiments and Results:
- The underlying proposal claims that chimeric antigens might be advantageous in eliciting protective antibodies. The authors produced three chimeras: var, MSP and InvP. __The var chimera contains 29 segments of PfEMP1 derived from 8 alleles. The hypothesis is that by expressing 29 different segments one will produce antibodies that can better cope with the antigenic diversity of this target. Indeed, serial monoallelic expression of anyone of the 60 PfEMP1 variants of a given P. falciparum strain has been thought to mediate immune evasion. The parasite is presumed to be able to escape immune defenses, by switching and serially expressing PfEMP1 alleles. Hence, one might assume that by introducing different segments, derived from different alleles, one will gain better protection. The authors have not really tested this idea. They have produced a single chimera and tested it without controlled comparison of performance to any single segment, or for that matter compared to alternative structural domain(s) of PfEMP. This brings me to the question of how the segments were selected and why. The authors implement IEDB-AR to identify presumably preferred B-cell epitopes. The methodology relies on a number of computational methods that predict the propensity of linear segments of proteins to have, for example, secondary structures, or be surface accessible, or relatively hydrophilic or flexible, etc. IEDB-AR is a tool to assist the identification of segments (5-25 amino acids in length), that might be associated with B-cell epitopes, or at least segments comprising linear aspects of B-cell epitopes. The input is a linear sequence of an antigen, proposing linear aspects of what could be associated with B-cell epitopes. B-cell epitopes, however, are typically conformational and discontinuous. They certainly can and do contain linear segments, but even these may require 3D conformations dictated by spatial constraints imposed by the native surrounding aspects of the natural antigen. It is hard to assume that by simply stitching 29 segments, one after the other, one can provide them with the native environment for them to assume a somewhat physiologically relevant conformation. Unfortunately, the authors have not addressed the unique characteristics of the antigen they have selected. PfEMP1, for example, is a family of antigens with discrete sub-domain structures and features (DBL and CIDR for example). It would be relevant and useful to relate the segments that they chose to the natural unique domains of the antigen and how they might best present common vs variant aspects of the antigen. There are at least 30 crystal atomic structures for PfEMP1 in complex with various physiologically relevant proteins (eg ICAM etc). The authors might have considered the 3D structure of PfEMP in their analyses and at least indicated on an atomic structure where the 29 segments lie. __
More concerning is the fact that the expression of the chimera does not produce a crisp single protein, but rather a complex of products as illustrated in the Supplement Figure 1 B. The authors simply claim that they produce the antigen for immunization of rabbits (or one rabbit?) and they collect gel-derived band(s) of what MW?? Assuming that a 25aa segment should be about 2500-2800 daltons and so 29 such segments strung together should be about 80kDa. The gel shows bands at 124kDa, and a slew of bands shorter than 71kDa. There is no mention what the expected MW should be and there is no explanation why the protein pattern contains so many bands of different sizes and what exact bands were taken for the immunogen or why.
Response: We thank the reviewer for this comment, as it tells us the reader's perspective on how the chimeric construct part is underexplained. We have now expanded the section on chimeric construct design, the sequences used, the functional domains they belong to in the PfEMP1 protein (Supplementary Tables 1 and 2), and the expected sizes of the proteins created. As for the B-cell epitope prediction, we have used the linear epitope prediction tool. However, we will include a 3D conformational study highlighting the placement of peptides that we have used to generate chimeric antigens.
The sequences for chimeric constructs were synthesized commercially and confirmed using Sanger sequencing. The antigens run higher than their expected molecular weights, and we have confirmed them through western blot and mass spectrometry (Supplementary Figure 1 B and C). The chimeric varB antigen specifically shows a cleaving pattern, hence the multiple bands in western blotting (we have considered the top-most band with the highest anti-his intensity). After these confirmations, the antigens were independently injected in rabbits to generate antibodies.
Similar considerations can be made regarding the selection of the segments for the two other chimeras, although they seem to produce a single polypeptide.
Response: The antigens were confirmed using Sanger sequencing, expression using anti-his western blot, and proteins were confirmed using mass spectrometry for all three chimeric constructs (Supplementary Figure 1 B and C).
If the point was to test a "chimera" modality as an improved vaccine, it would have been more useful to focus on one chimera and carefully characterize it and compare it to its components used separately.
Response: The idea of chimera arises from the fact that individual proteins/components are insufficient to generate optimal responses. The proteins considered in our study have already been validated in the field (as separate components) and show that the efficacy observed was sub-optimal. Since our rationale is to include multiple proteins to tackle the redundancy and parasite virulence, we have focused on generating three chimeric constructs covering the entire blood stage of Plasmodium falciparum. Our objective is to demonstrate that a multi-protein, multi-factorial vaccine, as a proof of concept, works better in tackling malaria. We believe that in proving so, a comparison of chimera with their individual components is an unnecessary and economically unviable.
The authors devote much effort to the fluidics system and their assay. This might warrant a paper dedicated to the methodology they have developed.
Response: The Plasmodium virulence genes are extensively studied for their interactions with human endothelial receptors. Unfortunately, these studies fail to take human physiological conditions into account. We wanted to test our anti-chimeric varB antibodies in the best mimicking environment possible. Hence, the efforts were devoted to developing, standardizing, and quantifying the fluidic cytoadherence system. We thank the reviewer for their kind words of encouragement on our methodology.
Format and Editing:
- The manuscript is very poorly written with multiple errors throughout. The authors use abbreviations that are not defined, eg iRBC (pg 5 line 22) or sometimes incorrectly defined, eg MSP ("merozoite-specific proteins - pg 6 line 18).
Response: We apologize for the abbreviation error. The abbreviation for iRBC is defined in the introduction section (page no 4, Line 15); hence, it is not redefined on page 5, line 22. We have corrected merozoite-specific proteins on page 6, line 18.
The Figures are of low resolution to the extent that they can not be read (for example Figure 3 pg 34). Figure 1 is somewhat useless and misleading. In Fig1 C - the diagram illustrates 5 hypothetical chimeras where in fact only three were produced. There really is no detail or explanation as to how the chimeras were produced.
Response: We apologize for the low resolution of the images. We have now improved the image quality. Figure 1C represents the idea of designing the construct, not the number of chimeras we generated. We apologize for this confusion and have explicitly mentioned this in the figure panel for Figure 1C. As for the design and generation of chimeric antigens, we understand that the materials and methods section is underexplained, and we have now expanded on it with all details included.
In the construction of the chimeras there is no mention as to whether short linkers were introduced between the segments or not. What was the expected weight of the chimera? Was the order of segments random or precise and consistent? Were the constructs sequence validated in addition to the MassSpec?
Response: We understand that the section on the chimeric construct is underexplained for the readers, and we thank the reviewer for pointing it out. We have now expanded the section on chimeric antigen design and included the details. Chimera was tested with GSGSGS linkers and without linkers for expression. The final antigen injected in rabbits was serially attached peptides without linkers. The segments stitched were in precise order, as mentioned in Supplementary Sheet 1. The construct was commercially synthesized and sequence validated along with the anti-his western blot and mass spectrometry analysis.
The figures of the Supplement are not numbered.
Response: We thank the reviewer for pointing this out. The figures are now numbered.
Note that the headings in Supplement Figure 1 B and C have overlapping text.
Response: Thank you for pointing this out. We have now rearranged the supplementary figures 1B, and 1C.
Most disturbing is that multiple references that are incomplete. For example: in References 15, 16, 25, 26, 27 there is no indication of the Journal.
Response: We apologize for the mistakes in referencing. These references did not have full citations in Endnote. We have now manually checked all the references and corrected the incomplete formats of the references.
The authors mention reference 13 [2006] in claiming that the antibodies can be protective, and then support this by referring to refs 14, 15 and 16 published in 1961, 1963 and 1962 respectively. Although, old articles can be useful, but the authors should attempt to provide current proof of such basic claims.
Response: We thank the reviewer for pointing this out. We have now separated these two statements and not mentioned the latter as a support to the former. As for references 14, 15, and 16, these were the early studies in the field that show the protective nature of antibodies through the passive immunization process and are foundations for the idea of blood stage vaccination. Current proofs of antibodies against blood-stage antigens are included for blood-stage vaccine candidates.
Reviewer #3 (Significance):
The goal of the study is very important.
The hypothesis that a chimeric presentation of select peptides could be advantageous was not rigorously tested nor well controlled in a meaningful evaluation and thus no conclusion can be made. There are no comparative analyses to test their hypothesis.
The method for selection of epitope segments is not well justified. There is little attempt to provide rationale or description of the segments chosen and how they fit within the antigens, thus justifying segments over multiple antigens.
The grammatical errors, lack of clarity accompanied by little attention to style and readability render the manuscript quite illegible.
There is no excuse for so many errors in the references.
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Referee #3
Evidence, reproducibility and clarity
Multi-protein chimeric antigens... by: Deshmukh et al
This article addresses an extremely important objective, the development of an effective prophylactic vaccine for Malaria. The disease continues to be widespread claiming the lives of hundreds of thousands of people annually, many of them children. Despite efforts towards producing Malaria vaccines, none thus far have been sufficiently protective or long term. As the authors point out vaccines can target the parasite per se, and possibly more attractive would be to focus on parasite derived antigens expressed on the surface of infected erythrocytes, hence targeting the Blood stage of the infection, which is most directly associated with Malaria pathogenesis. The authors propose a somewhat novel approach in which they have selected an array of short (25 amino acids) segments of Plasmodium derived proteins stitched together to produce 3 chimeric recombinant proteins as potential immunogens. Although a considerable amount of work is described, the results are not compelling in proving the efficacy or advantage of using chimeric antigens as worthy vaccine candidates for Malaria.
Unfortunately, the rationale behind the experiments are not clearly defined which is a matter of concern. In addition, details of the work done and the technical aspects needs to better explained to fully understand how and why the target segments were selected and the chimeras produced. This review focuses first on scientific issues and then format and editing, both aspects demonstrate that the manuscript in its present form requires major changes for it to be of relevance to the field. This review focuses first on issues of substance and then format and editing, both aspects disqualify the publication of the manuscript in its present form.
Experiments and Results:
The underlying proposal claims that chimeric antigens might be advantageous in eliciting protective antibodies. The authors produced three chimeras: var, MSP and InvP.
The var chimera contains 29 segments of PfEMP1 derived from 8 alleles. The hypothesis is that by expressing 29 different segments one will produce antibodies that can better cope with the antigenic diversity of this target. Indeed, serial monoallelic expression of anyone of the 60 PfEMP1 variants of a given P. falciparum strain has been thought to mediate immune evasion. The parasite is presumed to be able to escape immune defenses, by switching and serially expressing PfEMP1 alleles. Hence, one might assume that by introducing different segments, derived from different alleles, one will gain better protection. The authors have not really tested this idea. They have produced a single chimera and tested it without controlled comparison of performance to any single segment, or for that matter compared to alternative structural domain(s) of PfEMP. This brings me to the question of how the segments were selected and why. The authors implement IEDB-AR to identify presumably preferred B-cell epitopes. The methodology relies on a number of computational methods that predict the propensity of linear segments of proteins to have, for example, secondary structures, or be surface accessible, or relatively hydrophilic or flexible, etc. IEDB-AR is a tool to assist the identification of segments (5-25 amino acids in length), that might be associated with B-cell epitopes, or at least segments comprising linear aspects of B-cell epitopes. The input is a linear sequence of an antigen, proposing linear aspects of what could be associated with B-cell epitopes. B-cell epitopes, however, are typically conformational and discontinuous. They certainly can and do contain linear segments, but even these may require 3D conformations dictated by spatial constraints imposed by the native surrounding aspects of the natural antigen. It is hard to assume that by simply stitching 29 segments, one after the other, one can provide them with the native environment for them to assume a somewhat physiologically relevant conformation. Unfortunately, the authors have not addressed the unique characteristics of the antigen they have selected. PfEMP1, for example, is a family of antigens with discrete sub-domain structures and features (DBL and CIDR for example). It would be relevant and useful to relate the segments that they chose to the natural unique domains of the antigen and how they might best present common vs variant aspects of the antigen. There are at least 30 crystal atomic structures for PfEMP1 in complex with various physiologically relevant proteins (eg ICAM etc). The authors might have considered the 3D structure of PfEMP in their analyses and at least indicated on an atomic structure where the 29 segments lie. More concerning is the fact that the expression of the chimera does not produce a crisp single protein, but rather a complex of products as illustrated in the Supplement Figure 1 B. The authors simply claim that they produce the antigen for immunization of rabbits (or one rabbit?) and they collect gel-derived band(s) of what MW?? Assuming that a 25aa segment should be about 2500-2800 daltons and so 29 such segments strung together should be about 80kDa. The gel shows bands at 124kDa, and a slew of bands shorter than 71kDa. There is no mention what the expected MW should be and there is no explanation why the protein pattern contains so many bands of different sizes and what exact bands were taken for the immunogen or why.
Similar considerations can be made regarding the selection of the segments for the two other chimeras, although they seem to produce a single polypeptide.
If the point was to test a "chimera" modality as an improved vaccine, it would have been more useful to focus on one chimera and carefully characterize it and compare it to its components used separately. The authors devote much effort to the fluidics system and their assay. This might warrant a paper dedicated to the methodology they have developed.
Format and Editing:
The manuscript is very poorly written with multiple errors throughout. The authors use abbreviations that are not defined, eg iRBC (pg 5 line 22) or sometimes incorrectly defined, eg MSP ("merozoite-specific proteins - pg 6 line 18).
The Figures are of low resolution to the extent that they can not be read (for example Figure 3 pg 34). Figure 1 is somewhat useless and misleading. In Fig1 C - the diagram illustrates 5 hypothetical chimeras where in fact only three were produced. There really is no detail or explanation as to how the chimeras were produced.
In the construction of the chimeras there is no mention as to whether short linkers were introduced between the segments or not. What was the expected weight of the chimera? Was the order of segments random or precise and consistent? Were the constructs sequence validated in addition to the MassSpec?
The figures of the Supplement are not numbered.
Note that the headings in Supplement Figure 1 B and C have overlapping text.
Most disturbing is that multiple references that are incomplete. For example: in References 15, 16, 25, 26, 27 there is no indication of the Journal.
The authors mention reference 13 [2006] in claiming that the antibodies can be protective, and then support this by referring to refs 14, 15 and 16 published in 1961, 1963 and 1962 respectively. Although, old articles can be useful, but the authors should attempt to provide current proof of such basic claims.
Significance
The goal of the study is very important.
The hypothesis that a chimeric presentation of select peptides could be advantageous was not rigorously tested nor well controlled in a meaningful evaluation and thus no conclusion can be made. There are no comparative analyses to test their hypothesis.
The method for selection of epitope segments is not well justified. There is little attempt to provide rationale or description of the segments chosen and how they fit within the antigens, thus justifying segments over multiple antigens.
The grammatical errors, lack of clarity accompanied by little attention to style and readability render the manuscript quite illegible.
There is no excuse for so many errors in the references.
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Referee #2
Evidence, reproducibility and clarity
The manuscript describes the vaccine potential of unstructured P. falciparum merozoite protein fragments 25 amino acid long belonging to 3 different protein families. The work is well performed, easily reproducible and clearly described.
Referees cross-commenting
The polymorphic residues should be highlighted in the supplementary figure.
Significance
The use of protein fragments whose structure can be predicted by their sequence has been exploited in many studies for the development of vaccines or other biologicals. In this studies the authors selected 3 different families belonging to the red blood stage of the parasite. The table showing the sequences selected is not readable and should be clearly provided in the supplementary section. In addition, polymorphic residues should be highlighted. In addition, it is not to mention why the authors used immune rabbit sera obtained by injection of the 3 poly-epitopes instead of obtaining by affinity chromatography antigen specific human antibodies from sera of individuals living in endemic regions which could provide a direct and clear answer whether a protective vaccine could be obtained.
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Referee #1
Evidence, reproducibility and clarity
This manuscript by Deshmukh et al is aimed at generating chimeric antigens that can be useful for making next generation vaccines that block blood stage infection by malaria parasite. Given that there is no blood stage vaccine against malaria and available liver stage vaccine shows only limited efficacy that too only in Africa, there is dire need for having novel approaches to generate successful vaccines. In the past attempts have been made to make multivalent vaccines but have not been successful. Nevertheless, it is still a good option as single target blood-stage vaccines have failed. Authors propose to target cytoadhesion and host erythrocyte invasion. For this purpose, they have selected epitopes from PfEMP1/VarB family members, which poses a major challenge as at least 60 genes encode them and they exhibit variations which facilitate the escape from the immune system. The other two chimeras target invasion related proteins like MSPs and adhesins shed by micronemes and rhoptries, which are critical for invasion. The reported work is interesting and provides a useful approach towards developing vaccines against blood stage infection.
Comments:
- The peptides used in InvB chimera did not show good reactivity especially when compared to VarB or MSP peptides. Please discuss the possible reasons.
- It will be interesting to determine if blocking a specific VarB/PfEMP1 alters expression of other members. Based on the data provided in Fig. 4E, can a chimera be designed which only includes PfEMP1 that are represented well in HBEC-5i population?
- Some of the invasion related proteins like RH5 and EBA175 are not present at parasite surface, instead, secreted from rhoptries and micronemes. It will be nice to perform Western blots on condition medium and see if InvP (or even MSP and VarB) antibodies recognizes the secreted version of these proteins.
- Fig. 6E- Statistics need to be provided for inhibition at 12.3 and 25ug.
- Plasmodium uses multiple ligand-receptor interaction, which could depend (e.g. EBA-glycohophorins) or operate independent (e.g. RH5-basigin) of sialic acid. While there is representation from candidates from both of these families, most studies especially growth rate assays (Fig. 6E) have been carried using 3D7 strain, which does not require sialic acid. It is possible that if similar experiments were performed using sialic acid-sensitive strains, InvP and MSP antibodies may cause greater inhibition of parasite growth, which may be worth testing.
- The direct effect of InvP and MSP Abs should be tested directly on host erythrocyte invasion.
Significance
Present study proposes novel strategies for the development of anti-malarial vaccine.
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www.biorxiv.org www.biorxiv.org
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Reply to the reviewers
Manuscript number: RC-2024-02516
Corresponding author(s): Christopher Shoemaker
__1. __General Statements [optional]
Thank you to all the reviewers for their helpful efforts on behalf of our manuscript. We appreciate the time and effort they have invested in providing valuable feedback.
Overall, the positive reception from our reviewers highlighted their appreciation for our approach and findings. Moreover, their comments underscored the relevance and potential impact of our findings, particularly within the fields of autophagy and protein interaction networks. Their detailed and constructive critiques will also help refine both the content and presentation of our work.
In response to the reviews, we have proposed targeted revisions to the manuscript, all of which are well within our lab's capabilities and can be executed efficiently. We have detailed our responses to each specific point raised by the reviewers below. * *
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__2. __Description of the planned revisions
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Reviewer #1
Evidence, reproducibility and clarity
- EVIDENCE, REPRODUCIBILITY AND CLARITY Summary:
Selective autophagy receptors (SARs) of the Sequestosome-1 like receptor group (SLRs) including SQSTM1(Sequestosome-1)/p62, NBR1, TAX1BP1, NDP52, CALCOCO1 and Optineurin are soluble SARs that engage cargo and ATG8 family proteins as well as components of the core autophagy machinery like FIP200/RBCC1 to bring about the autophagic degradation of the cargo and themselves. In the autophagic degradation of protein aggregates (aggrephagy) the most studied SAR p62 collaborates with the archetypal autophagy receptor NBR1 and also TAX1BP1 to bring about effective turnover of ubiquitinated cargos sequestered into p62 bodies or droplets by liquid-liquid phase separation. How this intricate co-operation of these SARs is orchestrated is incompletely understood. In the paper by North et al entitled "The LC3-interacting region of NBR1 is a protein interaction hub enabling optimal flux" the authors use peptide arrays to map the binding sites for ATG8-family proteins LC3A and GABARAPL1, FIP200 and TAX1BP1 to the autophagy receptor NBR1. The authors find that three short linear interaction motifs (SLiMs), the LIR, FIR and TIR interacting with ATG8 family proteins, FIP200 and TAX1BP1, respectively, partly overlap in a short region of NBR1 that can adopt different conformations to accommodate the different binding partners. In short, the different interactions are mediated by distinct overlapping determinants, rather than a single, convergent, SLiM. While the important binding determinants for ATG8 proteins and FIP200 show more overlap and it was not possible here to find mutations that distinguish LIR and FIR binding, TAX1BP1 bound more to a region downstream of the LIR and a specific mutation in NBR1 and in TAX1BP1 could abolish binding. Checking the role of phosphorylations in augmenting binding using phosphomimetic mutations it was seen that while FIP200 and Atg8-family binding were generally augmented by phosphorylation, TAX1BP1 binding did not respond to these mutations. Very interestingly, the authors found that co-expression of TAX1BP1 with tandem-tagged NBR1 in pentaKO cells (not expressing the SLRs p62, NBR1, NDP52, TAX1BP1 and OPTN) increased significantly the autophagic turnover of NBR1. None of the other SLRs could do this. Instead, this over-expression assay revealed a competition.
Major points:
1) In Fig 4 the peptide array binding assay is not sufficient as it is only semiquantitative. The data shown should be accompanied by a more direct binding assay allowing the determination of kDs for the binding where the WT peptides are directly compared to the phosphor mimicking mutant peptides. Here the fluorescence anisotropy assay the authors use in Suppl Fig. 1E or ITC, OctetRed96 or another assay suitable for kD determinations should be used.
Response: Thank you for the constructive comments regarding our peptide array binding assay. We agree that the semi-quantitative nature of this method limits its ability to provide detailed binding affinity measurements. To address this, we will purify multiple peptides and assess the binding affinities between phosphomimetic+/- LIR peptides and Atg8s, FIP200, and TAX1BP1. While testing all peptides may be cost and time prohibitive, we will prioritize a representative range for this validation effort.
2) As this paper is already dominated by the use of peptides it would significantly enhance the quality of the data if the authors had included studied with peptides phosphorylated at the specific positions to allow comparison with the phosphomimetic substitutions to aspartate.
Response: Thank you for your insightful comment. We agree that incorporating studies with peptides phosphorylated at specific positions could provide a more nuanced comparison with the phosphomimetic substitutions to aspartate. Previous studies, including Popelka and Klionsky (2022) and Kliche et al. (2022), have indeed suggested that phosphomimetic substitutions do not perfectly replicate phosphorylation events.
In response, we plan to order a peptide array containing phosphorylated peptides, not merely phosphomimetics, and will conduct additional experiments with TAX1BP1, FIP200, and LC3A. This approach will allow us to directly assess the effects of actual phosphorylation compared to phosphomimetic substitutions.
While we acknowledge the possibility of subtle differences in binding affinity or regulatory interactions, we anticipate that the primary conclusions of our study—namely, that TAX1BP1 is largely insensitive to phosphorylation, whereas FIP200 and LC3A binding activities are affected—will remain unchanged. These experiments will provide valuable data to confirm the robustness of our conclusions under the conditions of true phosphorylation.
3) The quality of the 2D peptide array probing of GST-LC3A binding in Fig 3A is poor. Is this a stripped and re-probed membrane? I do not think these data are publication quality and the experiment should be redone unless the authors have very good arguments against my suggestion. It would also be nice to see a 2D peptide array of GABARAPL1 binding too to make the comparative study complete.
Response: Thank you for your constructive feedback regarding the quality of the 2D peptide array probing of GST-LC3A in Figure 3A. As you rightly pointed out, the membrane was indeed stripped and reprobed, with LC3A being the final probe. This method sometimes introduces artifacts, such as the 'ring' effect observed, which are common with this technique. However, the results consistently aligned with established consensus sequences for LC3, reinforcing the reliability of our findings despite the suboptimal image quality.
Recognizing the concerns about the quality of the blot, we are prepared to repeat this experiment using a new commercial vendor, as our previous collaborator is no longer available. We anticipate some differences in the appearance of the blots due to changes in dot size and spacing from the new supplier. Given these variations, we propose adding the revised blot to the supplementary materials rather than the main figures to avoid disrupting the visual continuity of the data presentation.
Additionally, in response to the reviewer’s suggestion, we will include a 2D peptide array probing for GABARAPL1. This will enhance the comparative analysis within our study.
One alternative (related to Reviewer 3, comment 3) that we can deliver is using our LIR arrays to derive consensus sequences for LC3 binders and GABARAPL1 binders. In doing this, we find the same differences in LC3 and GABARAP binding preferences that were reported previously in Rogov et al 2017. Recovering these known, and somewhat subtle, differences in binding preference further bolster the validity of our approach.
4) For the data shown in Fig 6 it should be noted that although these are very interesting results a clear limitation of the study is that the results on the autophagic turnover is based on overexpressing the SLRs in the pentaKO cells. In a physiological setting with all relevant actors in place and with a different stoichiometry the effects could likely be different.
Response: We appreciate the observation regarding the limitations of our study due to the use of overexpressed SLRs in pentaKO cells. As the reviewer rightly points out, the stoichiometry and interaction dynamics in a physiological setting might differ significantly. Critically, after submission of this manuscript, a recent preprint by Sascha Martens’ group (Bauer et al. BioRxiv) has shown similar results using endogenously tagged p62, TAX1BP1, and NBR1. This study corroborates our results, suggesting that the interactions we observed are not merely artifacts of overexpression but reflect genuine biological phenomena. We will incorporate a detailed discussion of this study in the Discussion section of our manuscript to contextualize our findings within a more physiologically relevant framework.
Therefore, we believe that our reductionist approach, while not fully reflective of physiological conditions, offers valuable and generalizable insights into the intricate cooperation of SARs in autophagy.
Minor points:
1) It would be beneficial for the reader to show a cartoon of the domain organization of both TAX1BP1 and NBR1 in Figure 1. NBR1 is shown in supplemental figure 1, but there is no depiction of the domain organization of TAX1BP1.
Response: As suggested, a domain schematic for NBR1 and TAX1BP1 will be included.
2) The authors say at the bottom of page 4 "Complementary in vivo studies reveal that while SLRs typically compete". But do they actually typically compete? Is this not a result of the experimental strategies employed? There is more a shortage of SLRs based on cargo competition as shown recently by Peter Kim's group that excessive pexophagy may reduce mitophagy etc. (Germain et al. 2023).
Response: Thank you for pointing out this overstatement. We will soften this statement.
3) In Fig. 3D it should be shown that D, E, A and V are preferred residues at position +1 for LC3A binding.
Response: As suggested, we will amend the figure to include these residues at the +1 position.
4) In such a 2D mutational analysis it is often just as important to determine which residues are not allowed for binding. It would therefore be nice if the authors could summarize/visualize their results in a better way in Fig 3D to also show the residues that lead to loss of binding. These could be shown below the sequence and the use of color to distinguish basic, acidic, hydrophobic and aromatic residues could be attempted.
Response: As suggested, we will add to this figure to make it more comprehensive by including residues that are both preferred and lead to loss of binding. Furthermore, we have incorporated the use of color to distinguish the traits of different residues (basic, acidic, hydrophobic and aromatic) that are dis(favored) at each position.
5) Line 327: To be clear about the fact that this is an overexpression assay "simultaneous expression" should be corrected to simultaneous overexpression".
Response: We will make the suggested change.
6) There are LIRs and FIRs that overlap and those that do not. To check the degree of overlaps that may occur among known LIRs the authors made a peptide array with 100 established LIR sequences taken from the LIR-Central database (Chatzichristofi et al., 2023). The peptide array was probed with LC3A (29 bound), GABARAPL1 (49 bound), the FIP200 Claw domain (57 bound) and the TAX1BP1 CC2 domain (49 bound). As much as one third (32) of the LIR peptides were not bound by any of the four probes. Do the authors have a good explanation for the fact that so many peptides did not bind?
Response: Thank you for highlighting the significant number of LIR peptides that did not bind to any of the probes in our study. At first, we were similarly surprised by this. In our manuscript, we will expand on several factors that might explain this observation:
- Specificity of Atg8 Family Proteins: The LIR-Central database indicates that these sequences bind at least one Atg8-family protein, but not necessarily all. Our assay might not have included the specific Atg8 proteins that some LIRs preferentially bind.
- Peptide Solubility and Conformation: The solubility and conformational stability of peptides printed on an array can vary, affecting binding efficiency. Certain sequences may not adopt the optimal conformation for binding under these assay conditions.
- Sequence Context and Accessibility: The native context in which the LIR motif is contained, including neighboring amino acids, can influence binding. Peptide arrays strip these peptides of their physiological context. As short linear interaction motifs, the assumption is that context will not strongly affect binding, but it’s known that many LIRs adopt partially structured motifs that influence binding (e.g. a C-terminal helix). Our peptide array approach is likely to impede such secondary structures from forming and may limit binding.
- Misannotated sequences. The LIRs included from the database have varying levels of validation. Some sequences might be misannotated and, therefore, do not bind any of the probes. These discussion points will be included in the manuscript to provide a comprehensive explanation for the observed data.
7) Strangely enough, the NBR1 peptide used in Figure 2A did not bind any of the probes while the NBR1 peptides used in Fig. 1C bound very well. Do the authors have any explanation for this?
Response: Thank you for noting the discrepancy in NBR1 peptide binding observed in Figure 2A compared to Figure 1C. This observation was noted by all reviewers. The difference likely arises from the solubility issues associated with the NBR1 peptide in the format used for Figure 2A, where the peptide sequence included the LIR motif plus 10 amino acids on each side. The core LIR sequence of NBR1 (YIII) is highly hydrophobic, which can affect its solubility and, consequently, its observed binding in our peptide array.
To overcome this, we optimized the LIR sequence of NBR1 for peptide arrays (amino acids 725-749), which includes seven residues before the LIR and 14 residues after. This shift enhanced solubility and facilitated more reliable probing in our experiments (notably Fig 3). In Fig2A and other assays, both the standard and the optimized formats of the NBR1 LIR were included: the standard format to maintain consistency with other LIRs extracted from the LIR-Central database and the optimized version as a control to validate our results.
We will detail this explanation in the manuscript, clarifying the rationale behind the observed binding differences.
Significance
SIGNIFICANCE
I found this paper very interesting to read with a lot of interesting new detailed and useful information on binding specificity for the proteins and motifs involved. It is a generally well performed study with interesting results. I also very much enjoyed the Discussion section which opens up for several interesting possible scenarios. The study also produced important point mutants that can be used in future studies to selectively abolish TAX1BP1 binding to NBR1. I think this is a "must read" paper for researchers interested in selective autophagy and co-operation between SARs, and more generally for getting some insight into how SLiMs may work. As such, this paper will be of interest for all interested in autophagy research and for a wider audience too as it is in essence about how overlapping SLiMs may be employed to orchestrate multiple protein-protein interactions using distinct overlapping determinants, rather than a single, convergent, SLiM. It is also one of the very few papers I have come across exploiting the power of the peptide array method so extensively with success for mapping protein binding sites.
It could perhaps be interesting if the authors discussed their results in relation to another study from the group of Sascha Martens on the role of TAX1BP1 in p62 bodies or condensates (doi: https://doi.org/10.1101/2024.05.17.594671). These two papers should be read together as they are both very interesting and important contributions.
Response: Thank you for pointing out this important reference that was posted shortly after our manuscript was submitted. As mentioned above, we will include an expanded discussion section to discuss these corroborating findings. We will also include a citation to Ferrari et al (PMID: ) on Tau evasion of autophagy through exclusion of TAX1BP1.
Reviewer #2
Evidence, reproducibility and clarity
Summary In this manuscript, North et al. examined how short linear interaction motifs (SLiMs) help to orchester selective autophagy receptors (SARs) function during cargo engulfment in autophagosomes. In particular, the authors focused on NBR1 as a model SAR to address the role of its role in the clearance of protein aggregates (aggrephagy). Using binding assays, the authors showed that a SLiM harboring NBR1's LIR motif also mediates binding to FIP200 and TAX1BP1. Intrigued by these overlapping binding sites, the authors probed 100 LIRs for their binding to TAX1BP1's coiled-coil 2 region (CC2), FIP200's claw domain and two different ATG8 family members and found heterogenous binding pattern and distinct correlation between these four binding partners. Using mutational peptide arrays of NBR1's SLiM, the authors revealed unique binding determinants of these NBR1 partners and their potential differential regulation by phosphorylation. Taking advantage of their new NBR1 binding insights, the authors structurally modeled the binding of TAX1BP1's CC2 to NBR1's SLiM and identified crucial residues in both proteins for this interaction. Lastly, the authors turned to autophagy flux assays in cells and showed that TAX1BP1 acts synergistically with NBR1 to increase its lysosomal delivery. Overall, the claims and the conclusions are largely supported by the data. However, a few critical issues should be addressed.
Are the data and the methods presented in such a way that they can be reproduced?
Are the experiments adequately replicated and statistical analysis adequate?
Major comments
1) What are the expression levels of the different tf-SAR fusions compared to the endogenous levels of the respective SAR? And are tf-NBR1 protein levels changed upon co-expression of the other SARs?
__Response: __We appreciate the questions concerning the expression levels of tf-SAR fusions relative to the endogenous levels of the respective SARs, similar to inquiries from Reviewer 1 (major comment 4). In our study, the levels of tf-NBR1 are notably higher than the endogenous levels. Interestingly, we observed that the co-expression of autophagy-competent NBR1 and TAX1BP1 generally leads to a decrease in the levels of both proteins, likely due to enhanced autophagic turnover. This pattern is not seen with autophagy-deficient mutants, suggesting a functional interaction affecting protein stability.
Furthermore, a recent preprint by Sascha Martens’ group (Bauer et al., BioRxiv) has presented findings that echo our results using endogenously tagged versions of p62, TAX1BP1, and NBR1. This study supports our observations, indicating that the interactions and effects we report are not artifacts of overexpression but are reflective of genuine biological processes. These findings will be thoroughly discussed in the Discussion section of our manuscript to provide context for our results within a physiologically relevant framework.
Therefore, we believe that our reductionist approach, while not fully reflective of physiological conditions, offers valuable and generalizable insights into the intricate cooperation of SARs in autophagy.
2) Which of the 100 LIRs have been shown to specifically bind LC3A or GABARAPL1? The authors should include this information from the literature in Figure 2 (e.g., highlighted by color or else).
__Response: __Thank you for your suggestion to detail the specific interactions between the 100 LIRs and Atg8 homologs like LC3A and GABARAPL1 in Figure 2. While each LIR in the LIR-Central database has been validated, detailed information on which LIRs bind specific Atg8 homologs—and with what relative affinity—is often lacking in the literature. This gap makes it challenging to present comprehensive binding preferences in a visually coherent way within Figure 2.
Nevertheless, we recognize the value of such information. We plan to conduct a thorough literature review on all 100 LIRs included in our study. Should we find sufficient and reliable data regarding binding specificities, we will incorporate this into Figure 2, potentially using color coding or another method to highlight these relationships clearly.
We can also perform the reciprocal experiment by using our LIR arrays to derive consensus sequences for LC3 binders and GABARAPL1 binders. In doing this, we find the same differences in LC3 and GABARAP preferences that were reported previously in Rogov et al 2017. Recovering these known, and somewhat subtle, differences in binding preference further bolster the validity of our approach. These new data will be added to the manuscript.
3) How effective is the stripping of the peptide array? The authors should provide evidence that there is no carry over binding from sequential probing the array. As a control, the authors should at least repeat probing for the last binder in their sequential binding assay with a new peptide array that has not yet been incubated with a different binder and then stripped.
__Response: __This is an important question, related to Reviewer 1 (comment 3), as the stripping of the peptide array can be variably affective. Prior to performing any of the arrays included in this manuscript, we did several validation arrays to identify the proper ordering of probes (e.g. what proteins can be stripped, which cannot). FIP200 and TAX1BP1 probing was performed on fresh or successfully stripped blots. LC3A probing was done last, as there is substantial previous literature defining the LC3 motif. However, the results of the LC3A binding consistently aligned with established consensus sequences for LC3, reinforcing the reliability of our findings despite the stripping process. Therefore, while stripping sometimes introduces artifacts, such as the 'ring effect’ observed in Figure 3A, the results did not appear to be influenced by prior probes.
As suggested, we are prepared to repeat the LC3A probing on a new array to fully cement this interpretation. We note, however, that this will be done using a new commercial vendor, as our previous collaborator is no longer available (The original blots were ordered over 3 years ago). We anticipate some differences in the appearance of the blots due to changes in dot size and spacing from the new supplier. Given these variations, we propose adding the revised blot to the supplementary materials rather than the main figures to avoid disrupting the visual continuity of the data presentation.
4) What is the number of replicates for the peptide array assays?
__Response: __Due to cost considerations, peptide array assays in our study were conducted as one or two replicates. We understand the limitations this presents in terms of statistical robustness and variability assessment. However, where possible, we supplemented these assays with additional validation experiments and controls to ensure reliability of our findings. For critical experiments, including key interaction validations, we used independent biochemical assays to confirm the results obtained from the peptide arrays.
5) The authors should test whether the enhancement of NBR1 flux by TAX1BP1 is only due to the contribution of an additional LIR or potential other functions of TAX1BP1 (e.g. ubiquitin binding or FIP200 binding). The authors should expand the panel shown in Figure 6E with TAX1BP1 mutant which are deficient in ubiquitin or FIP200 binding.
__Response: __We thank the reviewer for their suggestion. We will include data with TAX1BP1 mutants that are deficient in ubiquitin or FIP200 binding
Minor comments
6) Molecular weight markers are missing on immunoblots.
__Response: __We apologize for this oversight. We will amend figure to include molecular weight markers.
7) It would be more informative (since some proteins have more than one LIR) if the actual LIR motif would be displayed next to the peptide array (as e.g. done for NBR1) and not only in the supplements.
__Response: __We appreciate this thoughtful input and will consider its implementation carefully. We will explore the feasibility of integrating this detail in a manner that maintains figure clarity.
8) Along this line in Figure 2A, NBR1's LIR (marked with a red star) is among the LIRs for which no binding was observed. The authors should explain this.
Response: Thank you for noting the discrepancy in NBR1 peptide binding observed in Figure 2A compared to Figure 1C. This observation was noted by all reviewers. The difference likely arises from the solubility issues associated with the NBR1 peptide in the format used for Figure 2A, where the peptide sequence included the LIR motif plus 10 amino acids on each side. The core LIR sequence of NBR1 (YIII) is highly hydrophobic, which can affect its solubility and, consequently, its observed binding in our peptide array.
To overcome this, we optimized the LIR sequence of NBR1 for peptide arrays (amino acids 725-749), which includes seven residues before the LIR and 14 residues after. This shift enhanced solubility and facilitated more reliable probing in our experiments (notably Fig 3). In Fig2A and other assays, both the standard and the optimized formats of the NBR1 LIR were included: the standard format to maintain consistency with other LIRs extracted from the LIR-Central database and the optimized version as a control to validate our results.
We will detail this explanation in the manuscript, clarifying the rationale behind the observed binding differences.
Significance
Collectively, the work of North and colleagues provide valuable new mechanistic insights into the network of interaction that governs the function of SARs. Importantly, this works extends the knowledge in the field that SARs are acting in an orchestrated manner which reinforces their delivery to lysosomes. However, given the involvement of several SARs in the same process, it is crucial to dissect the binding modalities among these factors. In this regard, the current study on fine mapping binding sites provides an important contribution. In particular, in probing the in vitro findings in reconstituted KO cells. This part is really strong. In addition, the identification of critical residues for these bindings events represents important tools for the autophagy community which will be among the basic research audience most interested in this technical study.
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Referee #3
Evidence, reproducibility and clarity
North et al., using NBR1 as a model, found that three ATG8-family proteins, FIP200, and TAX1BP1 - each bind to a short linear interaction motif (SLiM) within NBR1. Mutational peptide arrays showed that these binding events are mediated by distinct overlapping determinants, rather than a single, convergent, SLiM. They performed peptide binding arrays on >100 established LC3-interacting regions (LIRs), and showed that that FIP200 and/or TAX1BP1 binding to LIRs is a common phenomenon and suggesting LIRs as protein interaction hotspots. Comparative analysis of phosphomimetic peptides showed that while FIP200 and Atg8-family binding are generally augmented by phosphorylation, TAX1BP1 binding is nonresponsive. In vivo studies confirmed that LIR-mediated interactions with TAX1BP1 enhance NBR1 activity, increasing autophagosomal delivery by leveraging an additional LIR from TAX1BP1.
Suggestions for further improvement of the paper:
- Figure 1: Data in figure 1 would be strengthened with cellular localization studies of the various constructs. What is the localization pattern of TIR mutants?
- Figure 2: Some more elaborate analysis and discussion is needed to explain the reason of 'never-binders'
- GIM (GABARAP interaction motifs) have been previously identified (Rogov et al., 2017). Can the authors extend/comment/discuss their findings in the context of GIMs?
- Figure 3: Data in figure 3 would be strengthened with cellular localization studies of the various constructs.
- The statement : 'LIR motif of NBR1 is a protein interaction hub enabling optimal flux' is not well discussed in the discussion and does not come through very clearly throughout the paper.
Significance
This is a very interesting and well structured study with clear and convincing data.
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Referee #2
Evidence, reproducibility and clarity
Summary
In this manuscript, North et al. examined how short linear interaction motifs (SLiMs) help to orchester selective autophagy receptors (SARs) function during cargo engulfment in autophagosomes. In particular, the authors focused on NBR1 as a model SAR to address the role of its role in the clearance of protein aggregates (aggrephagy). Using binding assays, the authors showed that a SLiM harboring NBR1's LIR motif also mediates binding to FIP200 and TAX1BP1. Intrigued by these overlapping binding sites, the authors probed 100 LIRs for their binding to TAX1BP1's coiled-coil 2 region (CC2), FIP200's claw domain and two different ATG8 family members and found heterogenous binding pattern and distinct correlation between these four binding partners. Using mutational peptide arrays of NBR1's SLiM, the authors revealed unique binding determinants of these NBR1 partners and their potential differential regulation by phosphorylation. Taking advantage of their new NBR1 binding insights, the authors structurally modeled the binding of TAX1BP1's CC2 to NBR1's SLiM and identified crucial residues in both proteins for this interaction. Lastly, the authors turned to autophagy flux assays in cells and showed that TAX1BP1 acts synergistically with NBR1 to increase its lysosomal delivery. Overall, the claims and the conclusions are largely supported by the data. However, a few critical issues should be addressed.
Are the data and the methods presented in such a way that they can be reproduced? Are the experiments adequately replicated and statistical analysis adequate?
Major comments
- What are the expression levels of the different tf-SAR fusions compared to the endogenous levels of the respective SAR? And are tf-NBR1 protein levels changed upon co-expression of the other SARs?
- Which of the 100 LIRs have been shown to specifically bind LC3A or GABARAPL1? The authors should include this information form the literature in Figure 2 (e.g., highlighted by color or else).
- How effective is the stripping of the peptide array? The authors should provide evidence that there is no carry over binding from sequential probing the array. As a control, the authors should at least repeat probing for the last binder in their sequential binding assay with a new peptide array that has not yet been incubated with a different binder and then stripped.
- What is the number of replicates for the peptide array assays?
- The authors should test whether the enhancement of NBR1 flux by TAX1BP1 is only due to the contribution of an additional LIR or potential other functions of TAX1BP1 (e.g. ubiquitin binding or FIP200 binding). The authors should expand the panel shown in Figure 6E with TAX1BP1 mutant which are deficient in ubiquitin or FIP200 binding.
Minor comments
- Molecular weight markers are missing on immunoblots.
- It would be more informative (since some proteins have more than one LIR) if the actual LIR motif would be displayed next to the peptide array (as e.g. done for NBR1) and not only in the supplements.
- Along this line in Figure 2A, NBR1's LIR (marked with a red star) is among the LIRs for which no binding was observed. The authors should explain this.
Referee Cross-Commenting
I find that all reviewers raised valid and important points that the authors should address to increase the quality and impact of their manuscript.
Significance
Collectively, the work of North and colleagues provide valuable new mechanistic insights into the network of interaction that governs the function of SARs. Importantly, this works extends the knowledge in the field that SARs are acting in an orchestered manner which reinforces their delivery to lysosomes. However, given the involvement of several SARs in the same process, it is crucial to dissect the binding modalities among these factors. In this regard, the current study on fine mapping binding sites provides an important contribution. In particular, in probing the in vitro findings in reconstituted KO cells. This part is really strong. In addition, the identification of critical residues for these bindings events represents important tools for the autophagy community which will be among the basic research audience most interested in this technical study.
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Referee #1
Evidence, reproducibility and clarity
Summary:
Selective autophagy receptors (SARs) of the Sequestosome-1 like receptor group (SLRs) including SQSTM1(Sequestosome-1)/p62, NBR1, TAX1BP1, NDP52, CALCOCO1 and Optineurin are soluble SARs that engage cargo and ATG8 family proteins as well as components of the core autophagy machinery like FIP200/RBCC1 to bring about the autophagic degradation of the cargo and themselves. In the autophagic degradation of protein aggregates (aggrephagy) the most studied SAR p62 collaborates with the archetypal autophagy receptor NBR1 and also TAX1BP1 to bring about effective turnover of ubiquitinated cargos sequestered into p62 bodies or droplets by liquid-liquid phase separation. How this intricate co-operation of these SARs is orchestrated is incompletely understood. In the paper by North et al entitled "The LC3-interacting region of NBR1 is a protein interaction hub enabling optimal flux" the authors use peptide arrays to map the binding sites for ATG8-family proteins LC3A and GABARAPL1, FIP200 and TAX1BP1 to the autophagy receptor NBR1. The authors find that three short linear interaction motifs (SLiMs), the LIR, FIR and TIR interacting with ATG8 family proteins, FIP200 and TAX1BP1, respectively, partly overlap in a short region of NBR1 that can adopt different conformations to accommodate the different binding partners. In short, the different interactions are mediated by distinct overlapping determinants, rather than a single, convergent, SLiM. While the important binding determinants for ATG8 proteins and FIP200 show more overlap and it was not possible here to find mutations that distinguish LIR and FIR binding, TAX1BP1 bound more to a region downstream of the LIR and a specific mutation in NBR1 and in TAX1BP1 could abolish binding. Checking the role of phosphorylations in augmenting binding using phosphomimetic mutations it was seen that while FIP200 and Atg8-family binding were generally augmented by phosphorylation, TAX1BP1 binding did not respond to these mutations. Very interestingly, the authors found that co-expression of TAX1BP1 with tandem-tagged NBR1 in pentaKO cells (not expressing the SLRs p62, NBR1, NDP52, TAX1BP1 and OPTN) increased significantly the autophagic turnover of NBR1. None of the other SLRs could do this. Instead, this over-expression assay revealed a competition.
Major points:
In Fig 4 the peptide array binding assay is not sufficient as it is only semiquantitative. The data shown should be accompanied by a more direct binding assay allowing the determination of kDs for the binding where the WT peptides are directly compared to the phosphor mimicking mutant peptides. Here the fluorescence anisotropy assay the authors use in Suppl Fig. 1E or ITC, OctetRed96 or another assay suitable for kD determinations should be used.
As this paper is already dominated by the use of peptides it would significantly enhance the quality of the data if the authors had included studied with peptides phosphorylated at the specific positions to allow comparison with the phosphomimetic substitutions to aspartate.
The quality of the 2D peptide array probing of GST-LC3A binding in Fig 3A is poor. Is this a stripped and re-probed membrane? I do not think these data are publication quality and the experiment should be redone unless the authors have very good arguments against my suggestion. It would also be nice to see a 2D peptide array of GABARAPL1 binding too to make the comparative study complete.
For the data shown in Fig 6 it should be noted that although these are very interesting results a clear limitation of the study is that the results on the autophagic turnover is based on overexpressing the SLRs in the pentaKO cells. In a physiological setting with all relevant actors in place and with a different stoichiometry the effects could likely be different.
Minor points:
It would be beneficial for the reader to show a cartoon of the domain organization of both TAX1BP1 and NBR1 in Figure 1. NBR1 is shown in supplemental figure 1, but there is no depiction of the domain organization of TAX1BP1.
The authors say at the bottom of page 4 "Complementary in vivo studies reveal that while SLRs typically compete". But do they actually typically compete? Is this not a result of the experimental strategies employed? There is more a shortage of SLRs based on cargo competition as shown recently by Peter Kim's group that excessive pexophagy may reduce mitophagy etc. (Germain et al. 2023).
In Fig. 3D it should be shown that D, E, A and V are preferred residues at position +1 for LC3A binding.
In such a 2D mutational analysis it is often just as important to determine which residues are not allowed for binding. It would therefore be nice if the authors could summarize/visualize their results in a better way in Fig 3D to also show the residues that lead to loss of binding. These could be shown below the sequence and the use of color to distinguish basic, acidic, hydrophobic and aromatic residues could be attempted.
Line 327: To be clear about the fact that this is an overexpression assay "simultaneous expression" should be corrected to simultaneous overexpression".
There are LIRs and FIRs that overlap and those that do not. To check the degree of overlaps that may occur among known LIRs the authors made a peptide array with 100 established LIR sequences taken from the LIR-Central database (Chatzichristofi et al., 2023). The peptide array was probed with LC3A (29 bound), GABARAPL1 (49 bound), the FIP200 Claw domain (57 bound) and the TAX1BP1 CC2 domain (49 bound). As much as one third (32) of the LIR peptides were not bound by any of the four probes. Do the authors have a good explanation for the fact that so many peptides did not bind? Strangely enough, the NBR1 peptide used in Figure 2A did not bind any of the probes while the NBR1 peptides used in Fig. 1C bound very well. Do the authors have any explanation for this?
Significance
I found this paper very interesting to read with a lot of interesting new detailed and useful information on binding specificity for the proteins and motifs involved. It is a generally well performed study with interesting results. I also very much enjoyed the Discussion section which opens up for several interesting possible scenarios. The study also produced important point mutants that can be used in future studies to selectively abolish TAX1BP1 binding to NBR1. I think this is a "must read" paper for researchers interested in selective autophagy and co-operation between SARs, and more generally for getting some insight into how SLiMs may work. As such, this paper will be of interest for all interested in autophagy research and for a wider audience too as it is in essence about how overlapping SLiMs may be employed to orchestrate multiple protein-protein interactions using distinct overlapping determinants, rather than a single, convergent, SLiM. It is also one of the very few papers I have come across exploiting the power of the peptide array method so extensively with success for mapping protein binding sites. It could perhaps be interesting if the authors discussed their results in relation to another study from the group of Sascha Martens on the role of TAX1BP1 in p62 bodies or condensates (doi: https://doi.org/10.1101/2024.05.17.594671). These two papers should be read together as they are both very interesting and important contributions.
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Reply to the reviewers
We thank the reviewers for their positive and constructive criticism. We answer their points one by one below.
Reviewer #1
1.) In the baf-1 G12T mutants the authors find reduced levels of lamin in hypodermal nuclei. It would be good to also examine the dynamics of lamin in the second tissue that was subjected to DamID (intestinal cells).
We provide a complete analysis of GFP::LMN-1 and EMR-1::mCh in control and baf-1(G12T) day 1 adults in intestine and hypodermis and at 20°C and 25°C. These data demonstrate that GFP::LMN-1 expression is reduced in baf-1(G12T) mutants in both tissue and at both temperatures. In contrast, for EMR-1::mCh a significant reduction was only observed in hypodermal nuclei at 20°C.
The effects on GFP::LMN-1 and EMR-1::mCh in the hypodermis 20°C were reported in Figure 2E-F in the original version of our manuscript. We have moved these data to the new Supplementary Figure S5 and represent instead the data obtained for hypodermis at 25°C in Figure 2E-F for consistency with the data represented in Figure 2A-D. Data on intestine for both markers and both temperatures are also included in the new Supplementary Figure S5.
We have modified the text as follows:
“To test the impact of baf-1(G12T) on LMN-1, EMR-1, and BAF-1 localization in vivo, we quantified these factors at the NE of hypodermal and intestinal cells. We observed a significantly lower median GFP::LMN-1 signal at the NE in baf-1(G12T) mutants in both tissues at 20°C and 25°C (Figure ____2E; Supplementary Figure S____5A-C). In contrast, accumulation of EMR-1 at the NE was unaffected by the baf-1(G12T) mutation in both tissues at 25°C and reduced in the hypodermis at 20°C (Figure ____2F; Supplementary Figure S____5D-F). In human NGPS cells, emerin was observed to be delocalized to the ER (Janssen et al., 2022; Puente et al., 2011), but we detected no increase in cytoplasmic EMR-1::mCh signal in the mutant, indicating that this NGPS phenotype is not present in the C. elegans model. In agreement with these microscopy data, analysis of whole-worm mRNA levels by quantitative RT-PCR also revealed a significant reduction in lmn-1 expression whereas emr-1 was unaffected (Supplementary Figure S4E-F).”
2.) The authors make a statement that EMR-1 expression was reduced in the baf-1 G12T mutant, but do not comment on LMN-1 expression. Can a statement on this be made by RT PCR?
Our gene expression analysis by RAPID determined a significant reduction in emr-1 expression in the intestine of baf-1(G12T) mutants, using a fold change of 2 as threshold. In contrast, expression of emr-1 in hypodermis as well as baf-1 and lmn-1 expression in both tissues were not significantly different between wild type and baf-1(G12T) mutants in our RAPID data.
We performed qRT-PCR on bulk mRNA to compare the expression of baf-1, emr-1 and lmn-1 in control versus baf-1(G12T) mutants. No differences were detected for baf-1 and emr-1 (new Supplementary Figure S4E-F). Considering that the qRT-PCR is on bulk mRNA, the emr-1 result is compatible with the RAPID data that suggest deregulation of emr-1 only in intestine and unaffected expression in the hypodermis. For baf-1 there is agreement between qRT-PCR and RAPID data from both tissues (no difference in the mutant). For lmn-1, the qRT-PCR analysis suggests a modest reduction (23%; not reaching the threshold applied in the RAPID analysis) in baf-1(G12T) mutants, which is concordant with the reduction observed in GFP::LMN-1 intensity in hypodermis and intestine by confocal microscopy (e.g. 14% reduction in median GFP::LMN-1 intensity in hypodermis at 25C; Figure 2E).
The discordance between RAPID and live imaging for emr-1/EMR-1::mCh (a reduction in the intestine or the hypodermis according to RAPID or live imaging, respectively) is not surprising. Although mRNA and protein levels in general correlate well, often, variation in transcription can only explain We have added these two sentences to the manuscript:
“In agreement with these microscopy data, analysis of whole-worm mRNA levels by quantitative RT-PCR also revealed a significant reduction in lmn-1 expression whereas emr-1 was unaffected (Supplementary Figure S4E-F).”
“As described above, the amount of endogenously tagged EMR-1::mCh at the NE of intestinal cells was normal in baf-1(G12T) mutants (Supplementary Figure S5F), suggesting a cellular capacity to buffer the downregulation of emr-1 transcription (Vogel & Marcotte, 2012).”
3.) The authors find few alterations in gene regulation of the loci which have different occupancy WT BAF-1 versus BAF-1 G12T. It was surprising to see the DamID and RNA polymerase DamID experiments be done with worms grown at 20°C, because the more penetrant phenotypes at the organismal level were observed at 25°C. Could this be the reason for the little change of chromatin occupancy of BAF-1 and BAF-1 G12T or few changes in gene expression? Would it make sense to examine the expression of some selected BAF-1 bound loci by single molecule Fish at 25°C and compare expression wt versus baf-1 G12T?
We performed the DamID experiments at 20°C to avoid potential artifacts and/or toxicity by higher expression levels of Dam fusion proteins (Greil, Moorman, & van Steensel, 2006; Schuster et al., 2010). We note that altered UV and tert-butyl hydroperoxide was observed at 20°C, indicating that the baf-1(G12T) allele affects physiology at several temperatures. The original version of our manuscript described the expression of fluorescently tagged LMN-1 and EMR-1 in the hypodermis at 20°C (Figure 2E-F). As described above, in the revised version, we report the expression in the intestine at 20°C and in both tissues at 25°C. For GFP::LMN-1, a similar reduction in the baf-1(G12T) mutant was observed at the two temperatures in both tissues, whereas for EMR-1::mCh a reduction was only seen in the hypodermis at 20°C. Taken together, we conclude that 20°C is a suitable temperature for the DamID experiments.
We appreciate the suggestion to study expression of genes bound by BAF-1 by smFISH. However, we anticipate that because the hypodermis is composed mostly of large syncytia covering the round body of the animal, smFISH would be difficult to quantify. Regarding loci with different occupancy of WT BAF-1 versus BAF-1(G12T), the emr-1 locus was bound in the intestine by Dam::BAF-1 but not by Dam::BAF-1(G12T) (Figure 6B). As mentioned above, we observed that emr-1 expression was reduced in intestine of baf-1(G12T) mutants, suggesting that BAF-1 binding has a positive effect of transcription of this locus.
4.) The finding that BAF-1 nuclear envelope localization remains unchanged in the mutant stems from detection of the inserted GFP epitope. Given that the tag has an influence on the BAF-1 G12 12T mutant viability, this statement should be phrased with more care. The tag could influence the turnover of the protein for example. Maybe Western blots comparing the signal of WT BAF-1 worms and BAF-1 G12T mutant worms would be instructive to compare the levels of the protein (at 20oC and at 25oC, day 1 adults and day 8 adults).
We performed Western blot experiments to address this. As controls, we included strains expressing equal amounts of GFP::BAF-1 and GFP::BAF-1(G12T) strains (Figure 3E and Supplementary Figure 7 in original manuscript reported equal expression of the two proteins). Surprisingly, the polyclonal anti-BAF-1 serum raised against recombinant, full-length wild type BAF-1 (Gorjanacz et al., 2007) has significantly lower affinity for mutant GFP::BAF-1(G12T) than for GFP::BAF-1, which precludes the evaluation of untagged proteins:
Figure 1. [png file provided to reviewers - not possible to include here for technical reasons] Western blot analyses with anti-BAF-1 serum (Gorjanacz et al, 2007). (A) Embryonic extracts. A band of the expected size is observed in wildtype embryos (*), but not in baf-1(G12T) embryos. (B) Extracts from young adults. A faint band of the expected size is observed in wildtype embryos (* in lane 1; longer exposure is shown below), whereas a more prominent band is present corresponding to endogenously tagged GFP::BAF-1 (** in lane 2). The intensity of the potential GFP::BAF-1(G12T) is reduced by >80% (lane 4; >90% reduction was observed in a second experiment).
We point out in the revised manuscript that the conclusion on equal BAF-1 and BAF-1(G12T) expression was based on endogenously tagged proteins: “Quantifying the intensity at the NE or in the nucleoplasm of hypodermal cells did not demonstrate any difference between endogenously GFP-tagged wild-type and mutant BAF-1 (Figure 3E). A small reduction in cytoplasmic signal was observed for BAF-1(G12T), however, no difference was detected in the ratio between nucleoplasmic/cytoplasmic signal (Figure 3E). Quantitative RT-PCR analysis of whole-worm RNA samples also indicated that baf-1 and baf-1(G12T) are expressed at identical levels (Supplementary Figure S4E-F).”
5.) Line 105: typo: remove "s"
Corrected.
6.) Line 154: A conclusion is missing for the fog-2 experiment.
We have modified the text as follows: “To test this possibility, we incubated baf-1(G12T) males with fog-2(q71) feminized worms that only produce oocytes and counted daily offspring. At 25°C, the fog-2(q71) allele prevents spermatogenesis specifically in XX hermaphrodites whereas X0 males are unaffected (Schedl & Kimble, 1988). We observed a reduction in brood size of approximately one third when sperm came from baf-1(G12T) males (Supplementary Figure S2B, C). Thus, we concluded that the baf-1(G12T) mutation has a negative impact on spermatogenesis. The male/female ratio in the progeny was ~1, suggesting that meiotic segregation of chromosomes was normal in baf-1(G12T) males.”
7). Would it make sense to discuss a possible influence of altered lamin binding to the nuclear envelope in the mutant in the context of the gene expression results?
We agree that this point is relevant, and we have added the following text to the Discussion: “At current, we can only speculate about how the NGPS mutation might affect gene expression. Proteomics analyses indicate that BAF interacts with several histones and transcription factors (Montes de Oca, Shoemaker, Gucek, Cole, & Wilson, 2009), and the differences between BAF-1 and BAF-1(G12T)’s chromatin binding profiles reported here might be accompanied by changes in the association of chromatin factors at the deregulated loci. A particularly interesting candidate is GCL-1/germ cell-less 1, a repressive factor involved in spermatogenesis (Holaska, Lee, Kowalski, & Wilson, 2003). Moreover, it is plausible that the diminished recruitment of LMN-1 to the NE in baf-1(G12T) mutants modifies its interaction with the genome and with chromatin factors.”
8). In a nutshell, the authors have established a convincing accessible model system for studying aging, ready for consecutive testing interventions to reduce the pace of premature aging.
We appreciate and share the opinion of the reviewer.
Reviewer #2
1). The value of this work is two-fold: First, it is a very robust characterization of NGPS worms. Second, this will be a very useful model for the study of NGPS. Overall, the study is well-designed, technically strong, and the results are carefully and thoughtfully interpreted, which is nicely exemplified by the discussion of the relatively small number of genes which are differentially bound by BAF1 and are also differentially expressed and the authors do a good job of not overinterpreting the data, but simply state them. The results are convincing and informative.
We thank the reviewer for her/his positive evaluation.
My only minor point that may make this paper marginally better is that it would be nice to have a paragraph in the Discussing elaborating on the potential and the limitations of using the worm model to understand human NGPS, for example, humans have multiple lamin proteins etc.
We agree with the reviewer and have added the following text to the Discussion: “We note that the simplicity of invertebrates also implies certain limitations. For instance, while both human and C. elegans genomes contain a single BAF gene, humans, but not C. elegans, express multiple lamin isoforms in tissue-specific ratios that regulate chromatin organization and nuclear mechanics (Swift et al., 2013). Thus, C. elegans is not suitable to explore potential differences in how wild type and NGPS BAF interacts differently with the various lamin isoforms.”
Reviewer #3
1). Overall, this manuscript strongly supports the major conclusion that this C. elegans line is a powerful model for human NGPS that complements a previously reported Drosophila model. Equally importantly, from the viewpoint of fundamental discovery, this manuscript also reports major advances in understanding how BAF influences gene expression at the molecular level.
We thank the reviewer for her/his positive evaluation.
2). DamID-Baf-1 access to chromatin was unaffected by the G12T mutation (Fig. S7), but they successfully identified subsets of genes 'occupied' by baf-1 in specific cell types, some of which were significantly affected in opposite ways by the NGPS mutation (Fig. 4, Fig. 5). However, these important new results are described too briefly, and discussion is inadequate. E.g., in hypodermal cells, the baf-1 G12T mutation dysregulated genes encoding proteins in five categories (ribosomal, proton transport, cuticle components, cell surface, lysine acetylation), by downregulating genes in three categories (ribosomal, proton transport, histone acetylation) and upregulating three other categories (cuticle components, cell surface, apical region). In intestinal cells, the mutation dysregulated genes in 8 categories (ribosomal, response to X-ray, proton transport, proteasome binding, mitochondrial protein import, endopeptidase activity, carboxy-lyase activity, ATP generation), by downregulating genes in 5 categories (ribosomal, proton transport, peptidase activity, NAD binding, metal cluster binding) and upregulating 3 categories (ribosomal, response to external stimulation, histone acetylation). Opposite results for "ribosomal genes" is confusing. Examples of genes in each affected category are shown in Fig. 6. To fully interpret this data, and address apparently-conflicting results, further analysis is needed to determine if any affected groups of genes have shared regulators. For example, Fig 5E shows "ribosomal protein genes" are both up- and down-regulated by the mutation. The authors should consider: (a) WHICH ribosomal genes are in each category, and (b) does either group of genes have known regulators that might be differentially affected by the baf-1 mutation? Similar consideration of other sets of differentially-affected genes might provide novel insight into specific chromatin-regulatory proteins (e.g., potential baf-1 partners; see next paragraph) affected by the NGPS mutation.
At first it may seem confusing that some ribosomal genes are downregulated while others are upregulated. However, the baf-1(G12T) mutant represents a disease situation and not a process of natural selection where one might expect “meaningful” groups of up- and down-regulated genes. We have looked closer at the individual deregulated ribosomal genes and found genes encoding structural components of large ribosomal subunits that are either upregulated (rpl-10, rpl-29, rpl-36) or downregulated (rpl-1, rpl-3, rpl-30) in the intestine. Although these opposite behaviors might seem confusing, we propose that they reflect deregulation of ribosome biosynthesis, which is in concordance with the observations in NGPS fibroblasts (Breusegem et al., 2022). We agree that it will be important to investigate how the NGPS mutation induces these oppositely directed effects on gene expression. We found a significant higher association of the 13 deregulated ribosomal genes to BAF-1(G12) than to BAF-1 in the intestine, but we believe it goes beyond the scope of this manuscript to focus on the underlying mechanisms.
3). The current manuscript is too strictly focused on establishing C. elegans as a model for NGPS, and neglects the novel discoveries. The authors did not consider or discuss HOW a baf-1 mutation might cause such complex gene expression outcomes- given that baf-1 binds dsDNA nonspecifically. One plausible molecular explanation is that the NGPS mutation might affect baf-1 interactions with: (a) transcription factors (Requiem, RBBP4, DDB1) or chromatin-regulators (PARP1; UV-regulated interactions with DDB2 and CUL4A) identified as BAF-associated in a proteomic study (Montes de Oca et al., 2009), or (b) histone modifiers such as SET/I2PP2A (blocks H3 dephosphorylation) or H3K9 methyltransferase 'G9a' (Montes de Oca et al., 2011), or (c) other regulators that control affected genes identified in this manuscript.
We agree that this point is very relevant, but at this point we do not have experimental support for any of these possibilities. As indicated in the response to Reviewer #2, we have added the following text to the Discussion: “At current, we can only speculate about how the NGPS mutation might affect gene expression. Proteomics analyses indicate that BAF interacts with several histones and transcription factors (Montes de Oca et al., 2009), and the differences between BAF-1 and BAF-1(G12T)’s chromatin binding profiles reported here might be accompanied by changes in the association of chromatin factors at the deregulated loci. A particularly interesting candidate is GCL-1/germ cell-less 1, a repressive factor involved in spermatogenesis (Holaska et al., 2003). Moreover, it is plausible that the diminished recruitment of LMN-1 to the NE in baf-1(G12T) mutants modifies its interaction with the genome and with chromatin factors.”
4). Figures 1, 2, 4, 5: the graphs in Fig 1A,B,D-F and Fig 2B,D and the colorscales in Fig 4F and Fig 5E are uninterpretable when printed in black-and-white. Please fix Figs 1 and 2 using black/light-gray/white/stippled for bar graphs, and black/light-gray/solid/dotted/dashed for line graphs. Fig 2B can be fixed by direct-labeling of class numbers within each bar (instead of 'color-coding' separately).
We thank the reviewer for this suggestion. We have modified the figures to enable better visualization when printed in BW.
5). Revise abstract lines 40-42 ("suggesting a direct relationship between BAF-1 binding [to what?] and gene expression") to reflect the deeper analysis.
We have rephrased this sentence, so it now reads: “Most genes deregulated by the baf-1(G12T) mutation were characterized by a change in BAF-1 association, suggesting a direct relation between association of a gene to BAF-1 and its expression.” However, we prefer to not extend into speculations in the abstract because of lack of experimental evidence.
6). Lines 132-155 (Figure 1): The impact on sperm production suggests the NGPS mutation might affect association with Germ cell-less (GCL), a transcription repressor that competes with BAF for binding to emerin in mammalian cells (Holaska et al., 2003 JBC).
This is indeed an interesting possibility and we have incorporated it into to Discussion (see answer to point 3 above).
7). Lines 151-154: Did not understand the fog-2 'feminized worm' experiments. Please briefly explain for non-worm experts.
Please see our response to Reviewer #1’s point 6.
8). Line 190: Clarify that nuclear shapes were categorized manually by single-blind observer.
We have amended the text: “Nuclei were manually classified by single-blind observer based on their morphology as previously described (Perez-Jimenez, Rodriguez-Palero, Rodenas, Askjaer, & Munoz, 2014), except that we introduced a fourth class to describe the most irregular nuclei (see Materials and Methods).”
9). Line 237-252: Abnormal chromosome segregation and postmitotic nuclear assembly in all gfp::baf-1(G12T) embryos is fully consistent (not 'presumably causative'; line 251) with the embryonic loss-of-function phenotype for baf-1 (Margalit et al., 2005, PNAS) and is consistent with mutational disruption of binding to lamin (Liu J et al., 2000, MBC) and/or LEM-domain proteins (Liu J, Lee KK et al., 2003, PNAS).
We thank the reviewer for pointing this out. We have added the following sentence: “These phenotypes are consistent with the effects of embryonic depletion of BAF-1 or LMN-1 (Liu et al., 2000; Margalit, Segura-Totten, Gruenbaum, & Wilson, 2005).”
10). Lines 530-533: Baf-1 localization (mobility) in intestinal cells is known to change profoundly in response to heat shock, caloric restriction or food deprivation (Bar et al., 2014, MBC). It would be worthwhile testing, in future, whether the NGPS mutation affects baf-1 localization in response to these stresses.
We appreciate this suggestion, and we agree with the reviewer that it would be important to test this in future studies.
Other changes:
Missing column in Table S3 added.
Mistake if column heading in Table S4 corrected.
Breusegem, S. Y., Houghton, J., Romero-Bueno, R., Fragoso-Luna, A., Kentistou, K. A., Ong, K. K., . . . Larrieu, D. (2022). A multiparametric anti-aging CRISPR screen uncovers a role for BAF in protein translation. bioRxiv. doi:10.1101/2022.10.07.509469
Gorjanacz, M., Klerkx, E. P., Galy, V., Santarella, R., Lopez-Iglesias, C., Askjaer, P., & Mattaj, I. W. (2007). Caenorhabditis elegans BAF-1 and its kinase VRK-1 participate directly in post-mitotic nuclear envelope assembly. Embo J, 26(1), 132-143. doi:10.1038/sj.emboj.7601470
Greil, F., Moorman, C., & van Steensel, B. (2006). DamID: mapping of in vivo protein-genome interactions using tethered DNA adenine methyltransferase. Methods Enzymol, 410, 342-359. doi:10.1016/S0076-6879(06)10016-6
Holaska, J. M., Lee, K. K., Kowalski, A. K., & Wilson, K. L. (2003). Transcriptional repressor germ cell-less (GCL) and barrier to autointegration factor (BAF) compete for binding to emerin in vitro. J Biol Chem, 278(9), 6969-6975.
Janssen, A., Marcelot, A., Breusegem, S., Legrand, P., Zinn-Justin, S., & Larrieu, D. (2022). The BAF A12T mutation disrupts lamin A/C interaction, impairing robust repair of nuclear envelope ruptures in Nestor-Guillermo progeria syndrome cells. Nucleic Acids Res. doi:10.1093/nar/gkac726
Liu, J., Rolef Ben-Shahar, T., Riemer, D., Treinin, M., Spann, P., Weber, K., . . . Gruenbaum, Y. (2000). Essential roles for Caenorhabditis elegans lamin gene in nuclear organization, cell cycle progression, and spatial organization of nuclear pore complexes. Mol Biol Cell, 11(11), 3937-3947.
Margalit, A., Segura-Totten, M., Gruenbaum, Y., & Wilson, K. L. (2005). Barrier-to-autointegration factor is required to segregate and enclose chromosomes within the nuclear envelope and assemble the nuclear lamina. Proc Natl Acad Sci U S A, 102(9), 3290-3295. doi:10.1073/pnas.0408364102
Montes de Oca, R., Shoemaker, C. J., Gucek, M., Cole, R. N., & Wilson, K. L. (2009). Barrier-to-autointegration factor proteome reveals chromatin-regulatory partners. PLoS ONE, 4(9), e7050. doi:10.1371/journal.pone.0007050
Perez-Jimenez, M. M., Rodriguez-Palero, M. J., Rodenas, E., Askjaer, P., & Munoz, M. J. (2014). Age-dependent changes of nuclear morphology are uncoupled from longevity in Caenorhabditis elegans IGF/insulin receptor daf-2 mutants. Biogerontology, 15(3), 279-288. doi:10.1007/s10522-014-9497-0
Puente, X. S., Quesada, V., Osorio, F. G., Cabanillas, R., Cadinanos, J., Fraile, J. M., . . . Lopez-Otin, C. (2011). Exome sequencing and functional analysis identifies BANF1 mutation as the cause of a hereditary progeroid syndrome. Am J Hum Genet, 88(5), 650-656. doi:10.1016/j.ajhg.2011.04.010
Schedl, T., & Kimble, J. (1988). fog-2, a germ-line-specific sex determination gene required for hermaphrodite spermatogenesis in Caenorhabditis elegans. Genetics, 119(1), 43-61. doi:10.1093/genetics/119.1.43
Schuster, E., McElwee, J. J., Tullet, J. M., Doonan, R., Matthijssens, F., Reece-Hoyes, J. S., . . . Gems, D. (2010). DamID in C. elegans reveals longevity-associated targets of DAF-16/FoxO. Mol Syst Biol, 6, 399. doi:10.1038/msb.2010.54
Swift, J., Ivanovska, I. L., Buxboim, A., Harada, T., Dingal, P. C., Pinter, J., . . . Discher, D. E. (2013). Nuclear lamin-A scales with tissue stiffness and enhances matrix-directed differentiation. Science, 341(6149), 1240104. doi:10.1126/science.1240104
Vogel, C., & Marcotte, E. M. (2012). Insights into the regulation of protein abundance from proteomic and transcriptomic analyses. Nat Rev Genet, 13(4), 227-232. doi:10.1038/nrg3185
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Referee #3
Evidence, reproducibility and clarity
The mechanisms of rare human progeria syndromes caused by mutations in nuclear lamina proteins (lamins or BANF1) are still poorly understood, mainly because these proteins are complicated: they interact and are structurally essential for mitosis and nuclear assembly; hence, disrupting either protein can (and often does) disrupt the other. Lamins and BANF1 also have multiple interwoven roles with other partners involved in 3D genome organization, chromatin regulation, and tissue-specific gene regulation during interphase. To focus on Nestor-Guillermo progeria syndrome (NGPS), caused by the homozygous A12T missense mutation in human BANF1, the authors inserted the corresponding G12T mutation in C. elegans baf-1. They tested potential phenotypes at multiple levels (molecular, transcriptional, cellular and organismal) extensively and rigorously, and did careful controls to determine whether BAF, a tiny (89-aa) protein, was disrupted by fusion to proteins such as GFP or TurboID. Animals carrying the G12T mutation exhibited reduced lifespan (Fig. 1, S1), lower responses to UV irradiation and heat-stress (Fig 7B, 7C), and revealed unexpected germline-specific defects in male worms (Fig. S2, S3), and altered gene expression in two tissues affected by human HGPS (Figs. 4 and 5). Overall, this manuscript strongly supports the major conclusion that this C. elegans line is a powerful model for human NGPS that complements a previously reported Drosophila model.
Equally importantly, from the viewpoint of fundamental discovery, this manuscript also reports major advances in understanding how BAF influences gene expression at the molecular level. Through careful attention to controls, and experimental design, the authors overcome many complications that make BAF difficult to study: its essential roles in mitosis and early embryogenesis, the 'tag'-sensitivity of endogenous BAF, and the absolute necessity to study BAF in native cell types. The authors carefully compared the impacts of tagging either baf-1 or lamin, and compared wildtype versus G12T-mutated baf-1 interactions with lamin and emerin (Fig. S5, S6, S8, S9; videos S1 and S2). DamID-Baf-1 access to chromatin was unaffected by the G12T mutation (Fig. S7), but they successfully identified subsets of genes 'occupied' by baf-1 in specific cell types, some of which were significantly affected in opposite ways by the NGPS mutation (Fig. 4, Fig. 5). However, these important new results are described too briefly, and discussion is inadequate. E.g., in hypodermal cells, the baf-1 G12T mutation dysregulated genes encoding proteins in five categories (ribosomal, proton transport, cuticle components, cell surface, lysine acetylation), by downregulating genes in three categories (ribosomal, proton transport, histone acetylation) and upregulating three other categories (cuticle components, cell surface, apical region). In intestinal cells, the mutation dysregulated genes in 8 categories (ribosomal, response to X-ray, proton transport, proteasome binding, mitochondrial protein import, endopeptidase activity, carboxy-lyase activity, ATP generation), by downregulating genes in 5 categories (ribosomal, proton transport, peptidase activity, NAD binding, metal cluster binding) and upregulating 3 categories (ribosomal, response to external stimulation, histone acetylation). Opposite results for "ribosomal genes" is confusing. Examples of genes in each affected category are shown in Fig. 6. To fully interpret this data, and address apparently-conflicting results, further analysis is needed to determine if any affected groups of genes have shared regulators. For example, Fig 5E shows "ribosomal protein genes" are both up- and down-regulated by the mutation. The authors should consider: (a) WHICH ribosomal genes are in each category, and (b) does either group of genes have known regulators that might be differentially affected by the baf-1 mutation? Similar consideration of other sets of differentially-affected genes might provide novel insight into specific chromatin-regulatory proteins (e.g., potential baf-1 partners; see next paragraph) affected by the NGPS mutation.
The current manuscript is too strictly focused on establishing C. elegans as a model for NGPS, and neglects the novel discoveries. The authors did not consider or discuss HOW a baf-1 mutation might cause such complex gene expression outcomes- given that baf-1 binds dsDNA nonspecifically. One plausible molecular explanation is that the NGPS mutation might affect baf-1 interactions with: (a) transcription factors (Requiem, RBBP4, DDB1) or chromatin-regulators (PARP1; UV-regulated interactions with DDB2 and CUL4A) identified as BAF-associated in a proteomic study (Montes de Oca et al., 2009), or (b) histone modifiers such as SET/I2PP2A (blocks H3 dephosphorylation) or H3K9 methyltransferase 'G9a' (Montes de Oca et al., 2011), or (c) other regulators that control affected genes identified in this manuscript.
Other clarifications and revisions to improve the manuscript:
Figures 1, 2, 4, 5: the graphs in Fig 1A,B,D-F and Fig 2B,D and the colorscales in Fig 4F and Fig 5E are uninterpretable when printed in black-and-white. Please fix Figs 1 and 2 using black/light-gray/white/stippled for bar graphs, and black/light-gray/solid/dotted/dashed for line graphs. Fig 2B can be fixed by direct-labeling of class numbers within each bar (instead of 'color-coding' separately).
Revise abstract lines 40-42 ("suggesting a direct relationship between BAF-1 binding [to what?] and gene expression") to reflect the deeper analysis.
Lines 132-155 (Figure 1): The impact on sperm production suggests the NGPS mutation might affect association with Germ cell-less (GCL), a transcription repressor that competes with BAF for binding to emerin in mammalian cells (Holaska et al., 2003 JBC). Lines 151-154: Did not understand the fog-2 'feminized worm' experiments. Please briefly explain for non-worm experts.
Line 190: Clarify that nuclear shapes were categorized manually by single-blind observer.
Line 237-252: Abnormal chromosome segregation and postmitotic nuclear assembly in all gfp::baf-1(G12T) embryos is fully consistent (not 'presumably causative'; line 251) with the embryonic loss-of-function phenotype for baf-1 (Margalit et al., 2005, PNAS) and is consistent with mutational disruption of binding to lamin (Liu J et al., 2000, MBC) and/or LEM-domain proteins (Liu J, Lee KK et al., 2003, PNAS).
Line 424: Agree that this new C. elegans model is important and strongly complements the Drosophila NGPS model.
Lines 463-464: Agree that future suppressor analysis in this C. elegans model will be powerfully informative.
Lines 530-533: Baf-1 localization (mobility) in intestinal cells is known to change profoundly in response to heat shock, caloric restriction or food deprivation (Bar et al., 2014, MBC). It would be worthwhile testing, in future, whether the NGPS mutation affects baf-1 localization in response to these stresses.
Referees cross-commenting
I agree with the comments from both other reviewers.
Significance
Overall, this manuscript strongly supports the major conclusion that this C. elegans line is a powerful model for human NGPS that complements a previously reported Drosophila model.
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Referee #2
Evidence, reproducibility and clarity
Romero-Bueno et al. have generated a C.elegans model of Nestor-Guillermo Progeria Syndrome (NGPS) caused by a point mutation in the BAF1 gene and they characterize the worm model. They find reduced fertility, reduced longevity and earlier aging symptoms in mutant animals compared to wild type animals. Looking at the molecular level, the authors find reduced accumulation of lamin A and emerin at the nuclear periphery in cells from mutant animals. They also find mitotic chromosome segregation defects. Using tissue-specific DamID, they show altered binding patters of the mutant protein to genome regions and they identify some gene groups which show both changes in gene expression and BAF1 binding. Finally, they show that BAF1 mutants are more resistant to oxidative stress than wild type animals.
The value of this work is two-fold: First, it is a very robust characterization of NGPS worms. Second, this will be a very useful model for the study of NGPS. Overall, the study is well-designed, technically strong, and the results are carefully and thoughtfully interpreted, which is nicely exemplified by the discussion of the relatively small number of genes which are differentially bound by BAF1 and are also differentially expressed and the authors do a good job of not overinterpreting the data, but simply state them. The results are convincing and informative.
My only minor point that may make this paper marginally better is that it would be nice to have a paragraph in the Discussing elaborating on the potential and the limitations of using the worm model to understand human NGPS, for example, humans have multiple lamin proteins etc.
Referees cross-commenting
I am glad to see that there is strong agreement that this is a valuable study.
Significance
The study is significant as it introduces a new animal model system to study an ultra-rare disease. The presented results are robust and convincing. This is the first worm model for this disease and it will be of interest to those studying laminopathies. The worm model is expected to reflect some of the human disease phenotypes but not all and a discussion of the potential and the limitation of the worm model to study NGPS would be welcomed.
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Referee #1
Evidence, reproducibility and clarity
Summary:
Specific mutations in nuclear lamina proteins such as lamin A or BAF1 can cause premature aging syndromes (Huchinson Gilford progeria or Nestor Guillermo syndrome), which show age related deterioration of nuclear morphology as a hallmark and affected individuals have a severely shortened life span. In this study Romero-Bueno et al established an animal model system for the Nestor Guillermo syndrome, by generating C. elegans strains harboring homozygous baf-1::G12A mutations. They nicely recapitulate the expected cellular and organismal phenotypes: decreased life spans of the mutant animals and faster nuclear deterioration. In addition, the authors find reduced fertility in the mutant when BAF-1 was combined with a GFP tag and synthetic lethality when the baf-1::G12T mutant is introduced into strain carrying an epitope tagged Lamin allele. At the organismal level the authors report increased resistance to oxidative stress, but reduced thermotolerance and decreased UV resistance. By conducting tissue specific DamID together with tissue specific RNA polymerase DamID, the authors find that in the baf-1::G12T mutant the overall chromatin association of chromosome arms remains largely unchanged, however a few individual loci either lost or gained BAF-1 association. The authors report that loss of BAF-1 association with chromatin correlates with gene expression in some instances, however there was no strict uniform correlation. This is not surprising, because the changes in expression could be a secondary consequence of a BAF-1-mediated change of another locus or a consequence of altered lamin nucelar envelope association. The global pattern of BAF-1 and BAF-1 G12T binding to chromatin was very similar in both genotypes. The authors find unaltered localization of BAF-1 G 12T protein at the nuclear envelope, in contrast to reduced levels of lamin and emerin. Interestingly, BAF-1 is found on sperm, in contrast to the absence of other lamina proteins, like LMN-1 or Emerin.
Major comments:
- In the baf-1 G12T mutants the authors find reduced levels of lamin in hypodermal nuclei. It would be good to also examine the dynamics of lamin in the second tissue that was subjected to DamID (intestinal cells).
- The authors make a statement that EMR-1 expression was reduced in the baf-1 G12T mutant, but do not comment on LMN-1 expression. Can a statement on this be made by RT PCR?
- The authors find few alterations in gene regulation of the loci which have different occupancy WT BAF-1 versus BAF-1 G12T. It was surprising to see the DamID and RNA polymerase DamID experiments be done with worms grown at 20oC, because the more penetrant phenotypes at the organismal level were observed at 25oC. Could this be the reason for the little change of chromatin occupancy of BAF-1 and BAF-1 G12T or few changes in gene expression? Would it make sense to examine the expression of some selected BAF-1 bound loci by single molecule Fish at 25oC and compare expression wt versus baf-1 G12T?
Minor comments:
The finding that BAF-1 nuclear envelope localization remains unchanged in the mutant stems from detection of the inserted GFP epitope. Given that the tag has an influence on the BAF-1 G12 12T mutant viability, this statement should be phrased with more care. The tag could influence the turnover of the protein for example. Maybe Western blots comparing the signal of WT BAF-1 worms and BAF-1 G12T mutant worms would be instructive to compare the levels of the protein (at 20oC and at 25oC, day 1 adults and day 8 adults)
Line 105: typo: remove "s"
Line 154: A conclusion is missing for the fog-2 experiment would it make sense to discuss a possible influence of altered lamin binding to the nuclear envelope in the mutant in the context of the gene expression results?
Referees cross-commenting I also see this as a valuable study. I regretted a bit that the analysis was not done at the higher temperature when the authors saw the most prominent phenotypes--but I suppose the analysis is very expensive and time consuming.
Significance
Since a long time, it has remained a matter of debate whether the progeria T to G transition in BAF-1 reduces binding of BAF-1 to lamin or whether the mutation affects the binding of BAF-1 to chromatin and thereby alters chromatin organization. Conflicting results emerged from the studies of BAF1 mutants in tissue culture cells. For this reason, this study-conducted in the context of a whole animal-is very important: it allowed the author to do their experiments with cells with unaltered ploidy, expression from the endogenous promoters and in the context of defined tissues. A second conflicting finding concerned the localization of BAF-1 G 12 to T mutant protein at the nuclear envelope: some labs find it reduced at the nuclear envelope, others find unchanged amounts at the nuclear envelope. With this work the authors contributed novel and interesting findings to those ongoing discussions, they found both altered affinity of BAF-1 with chromatin (not on a global scale, but on a local scale) and reduced affinity to lamin.
Furthermore, this is one of the first studies mapping BAF-1 association with individual gene loci in a specific tissue and the authors showed that in a given tissue BAF-1 tends to be associated with not expressed genes. In a nutshell, the authors have established a convincing accessible model system for studying aging, ready for consecutive testing interventions to reduce the pace of premature aging. Strengths: convincing presentation of a novel genetic model system to study progeria, first study where BAF-1 bound loci were shown from the analysis of a tissue (there is a correlation of BAF-1 bound loci, which are not expressed in the examined tissue), introduction of an easy-to-handle model to search for compounds suitable for clinical intervention for progeria patients or anti-aging drugs. This study adds some clarity to conflicting views in the filed: the NGS mutation in BAF-1 both reduces the amount of lamin at the nuclear periphery and affects the affinity of BAF-1 to chromatin.
Limitations: the observed transcriptional changes in the mutant can be either a direct consequence of BAF-1 chromatin association or a consequence of an altered lamina since lamin is less stable at the nuclear envelope. The transcriptomic analysis was not conducted at the temperature at which penetrant phenotypes at the organismal level were observed.
Advance: Previous studies presented conflicting results about the nuclear envelope localization of BAF-1 G12T protein: this study clearly shows that the localization of the protein remains unaltered. This study also clearly demonstrates that there is less lamin at the nuclear envelope in the mutant, lending support to the in vitro findings that the mutant is compromised in Lamin binding.
Audience: The study will be of interest to anyone who studies the nuclear lamina, the nuclear envelope, progeria, aging and stress response of an organism. Beyond this a convincing powerful novel genetic system is being presented to study progeria, which is of interest to clinicians. It is also of interest for translational research, because the system can be used to screen for compounds, which could be useful for therapeutic intervention for progeria patients or for the identification of compounds that combat aging in general. Some of the presented synthetic effects with tagged strains open the opportunity to conduct genetic suppressor screens, which would be a wonderful entry point to collect more mechanistic insights in the phenomena of aging and stress response. This genetic system is an awesome starting point for further studies and advancements of elucidating the molecular mechanism of progeria by genetic screens.
Expertise: I am a C. elegans geneticist and appreciate that all the conflicting results from tissue culture studies can now be compared to an analysis in a more physiological setting and the context of a real tissue and a living animal. I am not really competent to judge the sensitivity of RAPID assays.
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Reply to the reviewers
Reviewer #1 (Evidence, reproducibility and clarity (Required)):
The paper by Salinas-Rebolledo et al. describes a novel PROTAC approach in which the UBX domain of FAF1 is fused to a nanobody that recognizes the target protein. The idea is that the UBX domain will bind the fusion protein to the p97 ATPase, a major ATPase involved in the unfolding of many proteins. The target protein recognized by the UBX-nanobody fusion (UBX-Nb) is then supposed to be unfolded in a ubiquitin-independent manner and subsequently degraded by the proteasome.
The authors provide evidence that Ubx-Nb, containing a nanobody recognizing GFP, can colocalize with GFP fusion proteins in the nucleus and to liquid-liquid phase separation structures. Importantly, the fusion can reduce the cellular levels of the target proteins. The authors confirm that degradation triggered by Ubx-Nb is proteasome dependent. Ubx-Nb can also promote the degradation of model proteins that form aggregates relevant to neurodegenerative diseases.
__* The major issue with the paper is that it does not provide mechanistic insight into the degradation mechanism. First, the data implicating p97 in degradation are conflicting. On the one hand, siRNA of p97 compromises degradation (although degradation is not completely inhibited; see Figure 4G), but on the other hand, an inhibitor of p97 does not have an effect. The authors have not shown that target proteins are actually unfolded by their artificial adaptor (in vitro experiments would be required). __
__In addition, it would be important to show co-localization in vivo with p97. __
Thus, the role of p97 is not convincingly established. Another major question is how the unfolded, non-ubiquitinated proteins would be degraded by the 26S proteasome. Is there a ubiquitin ligase required after substrate unfolding?
Overall, the paper reports some intriguing effects of their designed PROTAC adaptor, but the mechanism by which it functions remains unclear. The findings of the manuscript appears too preliminary in its current version for it to be of value to the community.
Responses #1
We appreciate the detailed feedback provided and acknowledge the reviewers' concerns regarding the mechanistic insight into the degradation process. Below, we address each point raised:
We acknowledge that our current data do not fully elucidate the degradation mechanism involving p97. Our preliminary findings suggest that while siRNA of p97 compromises degradation, this effect is not absolute. Additionally, we recognize the inconsistency observed with the p97 inhibitor, which did not affect degradation. It is important to note that the selective inhibitor used in our study binds to the D2 domains of p97, as reported by Zhou et al. (J Med Chem. 2015). There are molecules that bind to the D1 domain and enhance p97-mediated degradation (Figuerola-Conchas et al., ACS Chem Biol. 2020). The primary ATPase function of p97 is associated with the D2 domain, but ATP binding to the D1 domain is crucial for hexamer formation and N-terminal domain conformation, which regulates cofactor binding and various functions of p97. Therefore, it is possible that our p97-PROTAC activates the D1 domain or interacts with cofactors enhancing protein degradation mediated by p97.
We plan to conduct in vitro experiments to demonstrate that target proteins are unfolded by our artificial adaptor. We have already cloned the degradation system into a bacterial expression vector to express and purify the system for these in vitro studies, which will be carried out in California. Furthermore, we aim to show in vivo co-localization with p97 in future studies.
*We understand the importance of establishing the role of p97 convincingly. Our preliminary data indicate the presence of residual p97 in siRNA experiments. Regarding the degradation of unfolded, non-ubiquitinated proteins by the 26S proteasome, there are studies indicating that various proteins are degraded by the proteasome independently of ubiquitin. Moreover, evidence suggests that different pools of the same protein can be directed to the proteasome via both ubiquitin-dependent and ubiquitin-independent mechanisms under the same cellular conditions. Also, prior research by Butler et al. (2016) demonstrated that fusing NbSyn87 with the mouse Ornithine Decarboxylase (ODC) PEST degron effectively reduced protein levels and this reduction was achieved by harnessing the innate cellular machinery responsible for ubiquitin-independent proteolysis. *
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We also speculate that the degradation may also occur via the autophagy-lysosome pathway. These speculations will be added to the discussion section, and we plan to investigate this pathway in detail in a subsequent scientific article focused on the mechanism of action of p97-PROTAC.
We recognize the need for further mechanistic studies and plan to publish these preliminary findings while continuing our research to elucidate the degradation mechanism. Our future work includes determining the crystal structure and conducting mass spectrometry to identify other proteins interacting with this complex, potentially aiding in degradation. We also plan to test the system in vivo using murine models with alpha-synuclein overexpression, in collaboration with researchers in Spain. This long-term project will form the basis of a subsequent publication.
We believe that our findings present an innovative and unique tool, and this preliminary data warrant publication. We appreciate the reviewers' comments and hope that our detailed response and future research plans address their concerns.
Minor points:
Fig. 1B: Although emerin is reported to be a nuclear envelope protein, it is not localized to the NE, but throughout the ER, likely because the protein was too highly expressed.
We agree with the reviewer, the overexpression of the NE could leak into the ER. We do not have specific Nanobodies to directly degrade Emerin. However, we would like to make the point that in both cases the protein will conserve a single transmembrane domain and even then, the GFP Nanobody fused to the UBX domain is able to trigger degradation
Fig. 1B: ETV co-localization is not obvious from the figure.
We are going to repeating the experiment and quantify the colocalization as suggested
Fig. 4: The depletion of p97 leads to cell death, so it is unclear whether the siRNA effect is specific.
We appreciate your comment and would like to clarify that there are published studies where the p97 gene has been silenced in HeLa cells without causing complete cell death. While it is true that a significant number of cells die post-transfection, we have observed that by changing the cell culture medium daily, the remaining cells start to grow again.
Studies supporting our findings include:
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- Wójcik C, Yano M, DeMartino GN. RNA interference of valosin-containing protein (VCP/p97) reveals multiple cellular roles linked to ubiquitin/proteasome-dependent proteolysis. J Cell Sci. 2004 Jan 15;117(Pt 2):281-92. doi: 10.1242/jcs.00841. Epub 2003 Dec 2. PMID: 14657277. *
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- Beskow A, Grimberg KB, Bott LC, Salomons FA, Dantuma NP, Young P. A conserved unfoldase activity for the p97 AAA-ATPase in proteasomal degradation. J Mol Biol. 2009 Dec 11;394(4):732-46. doi: 10.1016/j.jmb.2009.09.050. Epub 2009 Sep 24. PMID: 19782090.*
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- Yahiro K, Tsutsuki H, Ogura K, Nagasawa S, Moss J, Noda M. Regulation of subtilase cytotoxin-induced cell death by an RNA-dependent protein kinase-like endoplasmic reticulum kinase-dependent proteasome pathway in HeLa cells. Infect Immun. 2012 May;80(5):1803-14. doi: 10.1128/IAI.06164-11. Epub 2012 Feb 21. PMID: 22354021; PMCID: PMC3347452. Additionally, although our figure does not clearly show the band corresponding to p97, upon overexposing the film, we can detect a very faint band of the protein. This indicates that there is still residual expression of p97, albeit at a lower concentration, which is consistent with a significant reduction but not a complete elimination of the protein.*
Fig. 5C: The colocalization of GFP-HTT Q23 with UBX-Nb(GFP) is not entirely convincing.
We are going to repeating the experiment and quantify the colocalization as suggested
Reviewer #1 (Significance (Required)):
Overall, the paper reports some intriguing effects of their designed PROTAC adaptor, but the mechanism by which it functions remains unclear. The findings of the manuscript appears too preliminary in its current version for it to be of value to the community.
Reviewer #2 (Evidence, reproducibility and clarity (Required)):
Summary:
The authors have developed a p97-directed proteolysis-targeting chimera (PROTAC) that operates independently of ubiquitin. This system employs a camelid nanobody to selectively recognize target proteins, tethered to p97 through the UBX domain of the p97 adapter FAF1. The anti-GFP nanobody effectively targets various GFP-fusion proteins for degradation via the proteasome, relying on p97 for its mechanism of action. The authors validate the presence of p97 in brain tissues of Non-Human Primates (Nhp) Macaca fascicularis, rat (Sprague Dawley), and mouse (C57BL6/C), supported by proteasome inhibition and p97 RNA silencing data. Importantly, the p97-PROTAC mechanism operates independently of ubiquitination, demonstrated through degradation of clinically relevant proteins such as alpha-synuclein using a camelid nanobody (NbSyn87).
Major comments:
* Anti-GFP Nanobody clarification: Details about the original anti-GFP nanobody are unclear, which makes reproducing the current work a challenge for outside labs.
o 20. Fulcher LJ, Macartney T, Bozatzi P, Hornberger A, Rojas-Fernandez A, Sapkota GP. An affinity-directed protein missile system for targeted proteolysis. Open Biol. Oct 2016;6(10)doi:10.1098/rsob.160255
o I assume that the anti-GFP nanobody is aGFP from supplementary figure #2 in the Fulcher manuscript but is unclear as they also have used anti-GFP nanobody aGFP16.
* Amino acids from FAF1-UBX domain: Further clarity is needed regarding the amino acid details from the FAF1-UBX domain, which may have been disclosed in a patent application but should be explicitly outlined in the methods section.
We appreciate your comment regarding the need for further clarity on the amino acid details from the FAF1-UBX domain. To address this, we have added a supplementary figure which includes a table outlining the amino acid sequences of our construct and the FAF1-UBX domain. This supplementary figure provides a detailed representation of the amino acid sequences. We hope this addition meets your requirements and provides the necessary information for a comprehensive understanding of our work.
__* Degradation of Proteins of Clinical Interest: The data presented is not convincing enough to support the stated claims that the PROTAC is clearing aggregated mutant HTT. In Figure 5, there is an abundance of GFP-HTT Q74 puncta. While the western blot data suggests a reduction of soluble GFP-HTT-Q74 protein levels, it does not account for aggregated HTT. Aggregated HTT does not efficiently enter the separating gel during electrophoresis. To make these claims the authors need to 1. Show the level of mHTT Q74 aggregation in the empty control groups so that a comparison can be visually made between empty control groups and UBX-Nb(GFP) treated groups. A similar comparison would be useful with the GFP-HTT Q23 treated cells as well. __
* Visualization of Aggregated Proteins: The continued visibility of puncta raises doubts about the system's efficacy in degrading aggregated proteins. Including comparisons between untreated and treated cells for all test systems would strengthen the argument. It would be useful to show a comparison between the untreated controls and UBX-Nb (GFP) treated cells for all the test systems shown.
*We acknowledge that the data presented in Figure 5 may not be sufficient to support the claim that the PROTAC is clearing aggregated mutant HTT. The western blot data suggests a reduction in soluble GFP-HTT-Q74 protein levels, but it does not account for aggregated HTT, which does not efficiently enter the separating gel during electrophoresis. We agree that more experiments will need to be performed to evaluate the impact of the p97 PROTAC on the direct turnover of the aggregates once those are form. *
Minor comments:
* The In-text citations should be placed outside of the sentence. Example: The ubiquitin-proteasome system (UPS) regulates protein abundance by specific E3 ubiquitin ligases, which catalyze ubiquitin chain formation on the substrates, inducing their proteasome mediated degradation. 1-4
* Sentence two in the introduction is missing a period. It is unclear whether sentence three is a heading or part of paragraph one.
* There are additional formatting issues. It would be easier to read the paper if there was a space between paragraphs. Page numbers would be helpful.
* Page 2. Missing word. Inclusion body myopathy associated with Paget's disease.
* Figure 1. D, F, and E. Missing annotation to denote significance.
* Figure 2G Missing annotation to denote significance.
* Figure 4. Missing annotation to denote significance for figures 4D, 4F, 4H.
* Is Figure 4I significantly different or not? In Figure 6, you use ns to denote not significant. This feels like it is an important point that you would want to make that the effect is dependent on p97. When you knock out p97 the degradation capacity of UBX-Nb is lost.
Response
These changes are going to be apply
Reviewer #2 (Significance (Required)):
* The p97-PROTAC system is an ubiquitin-independent approach to degrade intracellular proteins. This system was able to target proteins for degradation at diverse subcellular locations, integral membrane protein residing at the inner nuclear membrane, chromatin located, and liquid-liquid phase separated compartments. The ability to clear alpha-synuclein builds on previous research suggesting that ubiquitin-independent degradation of alpha-synuclein could be a therapeutic approach to treat synucleinopathies such as Parkinson's Disease. However, the ability of this approach to clear aggregated proteins is not convincing, given the presence of visible aggregates in the treated cells.
* The investigations with NbSyn87 build upon prior research by Butler et al. (2016), who fused NbSyn87 with the mouse Ornithine decarboxylase (ODC) PEST degron. This fusion strategy not only facilitates the targeting of alpha-synuclein but also harnesses the innate cellular machinery responsible for ubiquitin-independent proteolysis. The current approach demonstrates and alternative mechanism to direct alpha-synuclein (and other proteins) into the proteasome for ubiquitin-independent clearance.
* At the current state of development, this research is of interest to specialized audience with antibody engineering backgrounds; however, it holds translational potential for clearance of toxic proteins.
* My research interests is in the development of therapeutics for the treatment of neurodegenerative diseases including Huntington's Disease, Parkinson's Disease, and Alzheimer's Disease.
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Referee #2
Evidence, reproducibility and clarity
Summary:
The authors have developed a p97-directed proteolysis-targeting chimera (PROTAC) that operates independently of ubiquitin. This system employs a camelid nanobody to selectively recognize target proteins, tethered to p97 through the UBX domain of the p97 adapter FAF1. The anti-GFP nanobody effectively targets various GFP-fusion proteins for degradation via the proteasome, relying on p97 for its mechanism of action. The authors validate the presence of p97 in brain tissues of Non-Human Primates (Nhp) Macaca fascicularis, rat (Sprague Dawley), and mouse (C57BL6/C), supported by proteasome inhibition and p97 RNA silencing data. Importantly, the p97-PROTAC mechanism operates independently of ubiquitination, demonstrated through degradation of clinically relevant proteins such as alpha-synuclein using a camelid nanobody (NbSyn87).
Major comments:
- Anti-GFP Nanobody clarification: Details about the original anti-GFP nanobody are unclear, which makes reproducing the current work a challenge for outside labs.
-
- Fulcher LJ, Macartney T, Bozatzi P, Hornberger A, Rojas-Fernandez A, Sapkota GP. An affinity-directed protein missile system for targeted proteolysis. Open Biol. Oct 2016;6(10)doi:10.1098/rsob.160255
- I assume that the anti-GFP nanobody is aGFP from supplementary figure #2 in the Fulcher manuscript but is unclear as they also have used anti-GFP nanobody aGFP16.
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- Amino acids from FAF1-UBX domain: Further clarity is needed regarding the amino acid details from the FAF1-UBX domain, which may have been disclosed in a patent application but should be explicitly outlined in the methods section.
- Degradation of Proteins of Clinical Interest: The data presented is not convincing enough to support the stated claims that the PROTAC is clearing aggregated mutant HTT. In Figure 5, there is an abundance of GFP-HTT Q74 puncta. While the western blot data suggests a reduction of soluble GFP-HTT-Q74 protein levels, it does not account for aggregated HTT. Aggreggated HTT does not efficiently enter the separating gel during electrophoresis. To make these claims the authors need to 1. Show the level of mHTT Q74 aggregation in the empty control groups so that a comparison can be visually made between empty control groups and UBX-Nb(GFP) treated groups. A similar comparison would be useful with the GFP-HTT Q23 treated cells as well.
- Visualization of Aggregated Proteins: The continued visibility of puncta raises doubts about the system's efficacy in degrading aggregated proteins. Including comparisons between untreated and treated cells for all test systems would strengthen the argument. It would be useful to show a comparison between the untreated controls and UBX-Nb (GFP) treated cells for all the test systems shown.
Minor comments:
- The In-text citations should be placed outside of the sentence. Example: The ubiquitin-proteasome system (UPS) regulates protein abundance by specific E3 ubiquitin ligases, which catalyze ubiquitin chain formation on the substrates, inducing their proteasome mediated degradation. 1-4
- Sentence two in the introduction is missing a period. It is unclear whether sentence three is a heading or part of paragraph one.
- There are additional formatting issues. It would be easier to read the paper if there was a space between paragraphs. Page numbers would be helpful.
- Page 2. Missing word. Inclusion body myopathy associated with Paget's disease.
- Figure 1. D, F, and E. Missing annotation to denote significance.
- Figure 2G Missing annotation to denote significance.
- Figure 4. Missing annotation to denote significance for figures 4D, 4F, 4H.
- Is Figure 4I significantly different or not? In Figure 6, you use ns to denote not significant. This feels like it is an important point that you would want to make that the effect is dependent on p97. When you knock out p97 the degradation capacity of UBX-Nb is lost.
Significance
- The p97-PROTAC system is an ubiquitin-independent approach to degrade intracellular proteins. This system was able to target proteins for degradation at diverse subcellular locations, integral membrane protein residing at the inner nuclear membrane, chromatin located and liquid-liquid phase separated compartments. The ability to clear alpha-synuclein builds on previous research suggesting that ubiquitin-independent degradation of alpha-synuclein could be a therapeutic approach to treat synucleinopathies such as Parkinson's Disease. However, the ability of this approach to clear aggregated proteins is not convincing, given the presence of visible aggregates in the treated cells.
- The investigations with NbSyn87 build upon prior research by Butler et al. (2016), who fused NbSyn87 with the mouse Ornithine decarboxylase (ODC) PEST degron. This fusion strategy not only facilitates the targeting of alpha-synuclein but also harnesses the innate cellular machinery responsible for ubiquitin-independent proteolysis. The current approach demonstrates and alternative mechanism to direct alpha-synuclein (and other proteins) into the proteasome for ubiquitin-independent clearance.
- At the current state of development, this research is of interest to specialized audience with antibody engineering backgrounds; however, it holds translational potential for clearance of toxic proteins.
- My research interests is in the development of therapeutics for the treatment of neurodegenerative diseases including Huntington's Disease, Parkinson's Disease, and Alzheimer's Disease.
- Anti-GFP Nanobody clarification: Details about the original anti-GFP nanobody are unclear, which makes reproducing the current work a challenge for outside labs.
-
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Referee #1
Evidence, reproducibility and clarity
The paper by Salinas-Rebolledo et al. describes a novel PROTAC approach in which the UBX domain of FAF1 is fused to a nanobody that recognizes the target protein. The idea is that the UBX domain will bind the fusion protein to the p97 ATPase, a major ATPase involved in the unfolding of many proteins. The target protein recognized by the UBX-nanobody fusion (UBX-Nb) is then supposed to be unfolded in a ubiquitin-independent manner and subsequently degraded by the proteasome.
The authors provide evidence that Ubx-Nb, containing a nanobody recognizing GFP, can colocalize with GFP fusion proteins in the nucleus and to liquid-liquid phase separation structures. Importantly, the fusion can reduce the cellular levels of the target proteins. The authors confirm that degradation triggered by Ubx-Nb is proteasome dependent. Ubx-Nb can also promote the degradation of model proteins that form aggregates relevant to neurodegenerative diseases.
The major issue with the paper is that it does not provide mechanistic insight into the degradation mechanism. First, the data implicating p97 in degradation are conflicting. On the one hand, siRNA of p97 compromises degradation (although degradation is not completely inhibited; see Figure 4G), but on the other hand, an inhibitor of p97 does not have an effect. The authors have not shown that target proteins are actually unfolded by their artificial adaptor (in vitro experiments would be required). In addition, it would be important to show co-localization in vivo with p97. Thus, the role of p97 is not convincingly established. Another major question is how the unfolded, non-ubiquitinated proteins would be degraded by the 26S proteasome. Is there a ubiquitin ligase required after substrate unfolding?
Overall, the paper reports some intriguing effects of their designed PROTAC adaptor, but the mechanism by which it functions remains unclear. The findings of the manuscript appears too preliminary in its current version for it to be of value to the community.
Specific points:
Fig. 1B: Although emerin is reported to be a nuclear envelope protein, it is not localized to the NE, but throughout the ER, likely because the protein was too highly expressed.
Fig. 1B: ETV co-localization is not obvious from the figure.
Fig. 4: The depletion of p97 leads to cell death, so it is unclear whether the siRNA effect is specific.
Fig. 5C: The colocalization of GFP-HTT Q23 with UBX-Nb(GFP) is not entirely convincing.
Significance
Overall, the paper reports some intriguing effects of their designed PROTAC adaptor, but the mechanism by which it functions remains unclear. The findings of the manuscript appears too preliminary in its current version for it to be of value to the community.
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www.biorxiv.org www.biorxiv.org
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Reply to the reviewers
Reviewer 1
The paper is overall convincing. However, a little more attention to data presentation and possibly the addition of at least another technique (see below) would greatly strengthen the findings.
As we hope to demonstrate below, we have taken steps to improve our manuscript on both fronts (data presentation and experimental evidence).
The absence of statistics catches immediately the eye. I am sure that the shown differences are statistically significant (thanks to the number of analyzed cells), but reporting the result of some statistical test would help the reader in identify the relevant data in a plot. This is somehow necessary considering that sometimes in the text something is deemed to be "significant" or "not significant", and I felt that I really needed that when looking at the plot in Fig. 3D.
To facilitate the interpretation of figures that contain data from multiple strains (such as the one mentioned by the reviewer), we have carried out a nonparametric single-step multiple comparison test (Games-Howell) to identify mutants whose means differ significantly from each other. To avoid overcrowding the figures, we have graphically summarized the p-values of all pairwise comparisons in a small matrix within the corresponding panel, and provided 99% confidence intervals and p-values of all differences in the Supplement.
Related to the previous point: for every N/C distribution analysis, a number of analyzed cells is reported. By the way it is written, it seems that the replication relies solely by the cells in that specific population, i.e.: each cell is treated as a replicate. At least I could not find if that is not the case in the legends or in the methods. I wonder what the results would be (and their significance) if each replicate would be a new assay on another population.
Cell populations exhibit significant variability in their phenotypic characteristics. Consequently, the quantification of a specific feature (e.g., the Sfp1 nuclear/cytoplasmic ratio) across a sample of cells from a given population results in a distribution rather than a single fixed value. For each quantification, we report the number of cells that were used to construct the corresponding distribution, i.e. the sample size. To compare samples from different populations (e.g., different Sfp1 mutant strains), we run them in parallel during microscopy experiments and compare their means, as described above. Throughout our study, we have tried to ensure that we quantify a sufficiently large number of cells to overcome cell-to-cell variability and enhance the reliability of our results.
In this context, the question of the reviewer is not entirely clear to us, as individual measurements of a sample are not replicates. However, one can replicate the entire experiment on a different day by re-growing the different strains, running microscopy, quantifying the new movies etc. In this sense, the experiments shown in the manuscript consist of single replicates, i.e. experiments that were carried out on the same day, with all the relevant mutants and controls quantified together. However, we have monitored many of our mutants multiple times over the course of our work. For example, Fig. 1 below shows replicates of the Sfp1 N/C ratio distributions at steady-state in the analog-sensitive (A) and wild-type (B) background, which were quantified several times across various experiments. While day-to-day variability in the empirical distributions of the same mutant exists to a small extent, it is quite small.
The scale of x axes in N/C ratio plots. Besides not being consistent throughout the figures, it originates from 1, visually enhancing the differences.
We believe the reviewer was referring to the y-axes, as the x-axes represent time. Summarizing the N/C ratio dynamics of different Sfp1 mutants has been challenging. First, the average N/C ratios at steady-state vary considerably across different mutants, as shown in the panels that summarize steady-state N/C ratios. To compare the magnitude and features of their responses, normalization is necessary. We chose to normalize the time series of each mutant to have a mean of 1 prior to the onset of a perturbation. This allows the normalized time series to represent the percentage-wise changes in the Sfp1 N/C ratio upon perturbation.
Using a common y-axis scale for all plots of N/C ratio dynamics not ideal, as some responses are subtler than others. Additionally, we do not believe that N/C dynamics across different figures need to (or should) be compared to each other. However, within a figure, panels that require comparison are placed in the same row and share the same y-axis scale. We believe that this approach optimizes data visualization and facilitates important visual comparisons.
Related to the previous point: it is evident from the plots that the N/C ratio is always positive, even in the most deficient of the analyzed mutants. This implies that a relevant fraction of Sfp1 is still nuclear. I thus wonder what the impact of these mutations would be on the actual function of Sfp1. For this reason, I feel that qPCR evaluation of transcripts of Sfp1 target genes is particularly needed. Since lack of Sfp1 is known to yield some of the smallest cells possible, it would also be cool to have an estimate of the size of mutants where Sfp1 is less nuclear. These analyses could confer phenotypical relevance to the data, but would also help in assessing a currently unexplored possibility, that phosphorylation events by PKA influence Sfp1 function besides its localization, i.e.: the still somehow nuclear fraction is not as functional as wt Sfp1 in promoting transcription.
It is indeed the case that the recorded N/C ratios are larger than 1 in all strains that we have monitored. We have never observed an N/C ratio smaller than 1 using widefield microscopy for two main reasons: first, out-of-focus light from the cytosol above and below the nucleus is added to the nuclear signal, causing the nuclear signal to always be non-zero, even for predominantly cytosolic proteins. Second, both in- and out of focus vacuoles are devoid of the fluorescent protein fusions that we quantify, which reduces the average brightness of the cytosol. For these reasons, even when a protein is largely cytosolic, the average N/C ratio over a cell population is no lower than around 1.5. Keeping these points in mind, one can observe that our most delocalized Sfp1 mutants have an N/C ratio that is around 1.6-1.7, which is very close to the lower limit. This means that these Sfp1 mutants are largely cytosolic, and the nuclear fraction (if non-zero) is quite small.
We agree that assessing the phenotypic relevance of Sfp1 mutations is of interest. However, this was impossible with our original strains, as we introduced each Sfp1 mutant as an extra copy in the HO locus while leaving the endogenous Sfp1 locus intact. This was done in order to avoid any phenotypic changes that might result from changes in Sfp1 activity.
To address the suggestion of the reviewer, we therefore deleted the endogenous Sfp1 copy in strains carrying sfp1PKA2A, sfp1PKA2D and sfp113A, leaving only the mutated Sfp1 copy at the HO locus. Surprisingly, the growth rate and drug sensitivity (determined by halo assays) of these single-copy mutants did not differ much in comparison to the mutants carrying the functional Sfp1 copy and from the wild-type (Supp. Figs. 4J and 7). This observation aligns with findings for the single-copy sfp1-1 mutant in [Lempiäinen et al. 2009], which corresponds to sfp1TOR7A in our work. [Lempiäinen et al. 2009] had suggested that Sch9 compensates for the loss of Sfp1 activity via a feedback mechanism, which could explain our results as well. If this is the case, acute depletion of wild-type Sfp1 could unveil transient changes in cell growth, before the compensatory effect of Sch9 was established. Unfortunately, we were unable to efficiently degrade wild-type Sfp1 carrying a C-terminal auxin-inducible degron. Instead, we followed the same approach with [Lempiäinen et al. 2009] and deleted SCH9.
As we describe in the last section of Results, the difference was dramatic for sfp113A __mutants, which were extremely slow-growing in the absence of Sch9 (doubling time was around 4 hours, but it was hard to estimate because we could not grow the cells consistently). Interestingly, SCH9 deletion had a negative impact on sfp1__PKA2D __but not sfp1__PKA2A __cells (__Supp. Fig. 7). Overall, these results demonstrate that Sch9 can compensate for loss of Sfp1 activity, which makes it challenging to study the impact of Sfp1 mutations on cellular phenotypes.
To further understand to what extent Sch9 compensates for loss of Sfp1 phosphorylation, we carried out RNA-seq on WT and cells carrying a single copy of sfp113A (with the endogenous SFP1 copy removed). Despite the fact that sfp113A __grow as well as WT, RNA-seq picked up several differentially expressed genes related to amino acid biosynthesis. This surprising finding is presented in the last section of Results, and in __Supplementary Figures 8, 9 and 10. We explore the relevance of these results and their connection with past literature on Sfp1 and Sch9 in the Discussion section.
I found some typos here and there, and it would greatly help to report them if in the manuscript line numbers were included.
We apologize for the typos. We have tried to eliminate them, and we have also added line numbers to the manuscript.
Reviewer 2
There is no biochemical evidence presented that the putative PKA sites (S105 and S136) are genuinely phosphorylated by PKA. The fact that they match the PKA consensus motif, alone, does not guarantee this. In order to claim that they are looking at the effect of PKA by mutagenizing these residues, the authors have to demonstrate the PKA-dependency of S105 and S136 phosphorylation by, for example, mass spec experiments or western blotting with phospho-specific antibodies (Cell Signaling Technology #9624 for example). Also, does the band-shift caused by PKA inhibition (Fig 3C) is canceled by the S105A/S136A mutation?
We took several actions to demonstrate that the putative PKA sites are indeed phosphorylated by PKA. We first tried to detect Sfp1 phosphorylation using the antibody mentioned by the reviewer, but failed as the sensitivity of this antibody appears to be quite low. On the other hand, mass spectrometry did not produce the right fragments to detect the sites of interest. We therefore resorted to an in vitro kinase assay using [γ-32P]ATP together with purified PKA and Sfp1. Unfortunately, bacterial overexpression of MBP-tagged Tpk1, Tpk2 and Tpk3 (the catalytic subunits of PKA) was quite challenging and we were unable to produce soluble protein. We therefore resorted to commercially available bovine PKA (bPKA, PKA catalytic subunit, Sigma-Aldrich 539576), which shows high homology to the yeast Tpk kinases [Toda et al. 1987]. Moreover 87% of bPKA substrates have been shown to also be Tpk1 substrates [Ptacek et al. 2005], and bPKA has been used to identify new Tpk substrates in budding yeast [Budovskaya et al. 2005__]. As we show in the revised manuscript, bovine PKA does phosphorylate Sfp1. Moreover, phosphorylation is reduced by 50% in the double S105A, S136A mutant (Fig.1F), and becomes undetectable in the 13A mutant__ (Supp Fig. 6). Together with the rapid response of Sfp1 localization to acute PKA inhibition which we had already reported, we believe that these results provide strong evidence that Sfp1 is a direct PKA substrate, and that the two phosphosites that we identified are functional.
As the above in vivo experiments do not exclude S105/S136 phosphorylation by other kinases downstream of PKA, in order to claim the direct phosphorylation, the authors need in vitro PKA kinase assay. These biochemical experiments are not trivial, but I think absolutely necessary for this story.
One cannot exclude that S105/S136 are also phosphorylated by other kinases of the AGC family (note that [Lempiäinen et al. 2009] has already excluded Sch9). However, as we hope to have shown, PKA indeed phosphorylates Sfp1. Examining if other kinases besides PKA and TORC1 target Sfp1 is a very interesting question that should be addressed in future work.
The authors only look at the localization of Sfp1. To assess its functionality and so physiological impact, it would be informative to measure the mRNA level of target ribosomal genes in various Sfp1 mutants they created.
As we described in our response to Reviewer 1 above, we did perform RNA-seq on WT and cells carrying a single copy of sfp113A. We observed a notable absence of differentially expressed ribosomal genes and ribosome-related categories in the GO analysis (Supp. Figs. 8, 9 and 10). Together with our observations on SCH9 deletion (Supp. Fig. 7), these results suggest that Sch9 can largely compensate for the loss of Sfp1 activity. On the other hand, the emergence of differentially expressed amino acid biosynthesis genes is a finding that merits further investigation, as it connects with previous observations made with Sch9 deletion mutants and the [ISP+] prion form of Sfp1 (cf. Discussion).
In the experiments using analog-sensitive PKA (Fig 1D and E for example), they directly compare wildtype-PKA versus analog sensitive-PKA, or with 1-NM-PP1 versus without 1-NM-PP1. This makes interpretation difficult, particularly because 1-NM-PP1 itself has a significant impact even in the wild PKA strain. The real question is the difference between wild-type Sfp1 versus mutant Sfp1. In the current form, they compare Fig 1D versus 1E, these two do not look like a single, side-by-side experiment. They should compare wild-type Sfp1 versus mutant Sfp1 side-by-side.
Figure 1D shows that 1-NM-PP1 has a transient off-target effect on Sfp1 localization in WT cells, which could also affect Sfp1 mutants. This observation prompted us to use wild-type PKA as a control when testing the effect of 1-NM-PP1 on sfp1PKA2D in cells carrying PKAas (Figure 1E). As Fig. 1E shows, the effect of 1-NM-PP1 on sfp1PKA2D localization in PKAas cells is quite similar to the off-target effect in cells carrying sfp1__PKA2D __and wild-type PKA. This behavior of sfp1__PKA2D __is clearly different from the response of wild-type Sfp1 to PKAas inhibition, which results in sustained delocalization. We have made the latter observation repeatedly, both in this study and our previously published work [Guerra et al. 2021].
In Figure 3, the argument around the additive effects of PKA and TORC1 is confusing. The authors say they are additive referring Figure 3E, but say they are not additive referring Figure 3B. Which is true? In fact, Figure 3B appears to show an additive effect as well.
We did not use the word "additive" in the text, because we find it difficult to interpret. Instead, we state that PKA and TORC1 appear to control Sfp1 phosphorylation independently of each other. PKA and TORC1 phosphorylation converges to the same response, affecting Sfp1 localization. It appears that loss of either kinase delocalizes Sfp1, while loss of both kinases may only have a small additional effect.
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Referee #2
Evidence, reproducibility and clarity
Summary:
The authors investigated how Sfp1, a transcription factor for ribosomal genes, integrates signals from TORC1 and PKA pathways. They did so by analyzing the nuclear localization of the GFP-tagged Sfp1 variants harboring unphosphorylatable or phosphomimetic mutations on either TORC1 target sites, putative PKA target sites, or a combination of both. This approach was complemented by examining the effect of pharmacological inhibition of either pathway on Sfp1 localization. The obtained results support that TORC1 and PKA independently promote nuclear localization of Snf1, provided that the putative PKA sites are genuinely PKA sites (see Major point). In course of their investigation, the authors made two novel findings about the regulatory mechanism of Sfp1 localization. First, they identified the 98-106aa region as a nuclear export signal (NES). Because this region overlaps with a putative PKA site, it is conceivable that PKA regulates Sfp1 localization via altering the functionality of NES. In addition, they found that the nuclear localization of Snf1 requires its C-terminal zinc fingers, although this domain appears to work independently from TORC1- and PKA-dependent regulations.
Major points:
- There is no biochemical evidence presented that the putative PKA sites (S105 and S136) are genuinely phosphorylated by PKA. The fact that they match the PKA consensus motif, alone, does not guarantee this. In order to claim that they are looking at the effect of PKA by mutagenizing these residues, the authors have to demonstrate the PKA-dependency of S105 and S136 phosphorylation by, for example, mass spec experiments or western blotting with phospho-specific antibodies (Cell Signaling Technology #9624 for example). Also, does the band-shift caused by PKA inhibition (Fig 3C) is canceled by the S105A/S136A mutation?
- As the above in vivo experiments do not exclude S105/S136 phosphorylation by other kinases downstream of PKA, in order to claim the direct phosphorylation, the authors need in vitro PKA kinase assay. These biochemical experiments are not trivial, but I think absolutely necessary for this story.
Minor points:
- The authors only look at the localization of Sfp1. To assess its functionality and so physiological impact, it would be informative to measure the mRNA level of target ribosomal genes in various Sfp1 mutants they created.
- In the experiments using analog-sensitive PKA (Fig 1D and E for example), they directly compare wildtype-PKA versus analog sensitive-PKA, or with 1-NM-PP1 versus without 1-NM-PP1. This makes interpretation difficult, particularly because 1-NM-PP1 itself has a significant impact even in the wild PKA strain. The real question is the difference between wild-type Sfp1 versus mutant Sfp1. In the current form, they compare Fig 1D versus 1E, these two do not look like a single, side-by-side experiment. They should compare wild-type Sfp1 versus mutant Sfp1 side-by-side.
- In Figure 3, the argument around the additive effects of PKA and TORC1 is confusing. The authors say they are additive referring Figure 3E, but say they are not additive referring Figure 3B. Which is true? In fact, Figure 3B appears to show an additive effect as well.
Significance
TORC1 and PKA are major pro-growth signaling pathways widely conserved in eukaryotes, that often converge on the same target proteins. How the information from the two pathways is integrated is an interesting question, which the authors directly and meticulously address here with yeast Sfp1 as an example. Provided that they can demonstrate that the putative PKA sites are the real ones (this is really important- TORC1 sites are already known, what is new here is PKA sites), their data and conclusion should be of interest to the signal transduction field.
Their additional discovery of NES and the role of zinc fingers in Sfp1 localization should be of interest to those working on Sfp1, or transcriptional regulation of ribosomal genes in general.
My area of expertise: yeast TOR
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Referee #1
Evidence, reproducibility and clarity
Summary:
In this paper, Vuillemenot and Milias-Argeitis investigate in budding yeast the role of Protein Kinase A (PKA) in regulating through phosphorylation the subcellular localization of the transcription factor Sfp1, known for controlling transcription of RP genes. Sfp1 is very well known for being regulated by another signaling pathway, centered on the kinase TORC1. Thus, regulation of Sfp1 by PKA raises the intriguing possibility of a downstream crosstalk between the two pathways. Indeed, the authors find that Sfp1 is regulated by PKA independently from TORC1. In the study, the authors employ mainly single-cell microscopy to monitor the nucleo/cytosolic localization of Sfp1 mutants, an experimental set-up they established in a previous paper, with some contribution by PhosTag bandshift assays.
Major comments:
The paper is overall convincing. However, a little more attention to data presentation and possibly the addition of at least another technique (see below) would greatly strengthen the findings. Summarizing my major concerns: - The absence of statistics catches immediately the eye. I am sure that the shown differences are statistically significant (thanks to the number of analyzed cells), but reporting the result of some statistical test would help the reader in identify the relevant data in a plot. This is somehow necessary considering that sometimes in the text something is deemed to be "significant" or "not significant", and I felt that I really needed that when looking at the blot in Fig. 3D. - Related to the previous point: for every N/C distribution analysis, a number of analyzed cells is reported. By the way it is written, it seems that the replication relies solely by the cells in that specific population, i.e.: each cell is treated as a replicate. At least I could not find if that is not the case in the legends or in the methods. I wonder what the results would be (and their significance) if each replicate would be a new assay on another population. - The scale of x axes in N/C ratio plots. Besides not being consistent throughout the figures, it originates from 1, visually enhancing the differences. - Related to the previous point: it is evident from the plots that the N/C ratio is always positive, even in the most deficient of the analyzed mutants. This implies that a relevant fraction of Sfp1 is still nuclear. I thus wonder what the impact of these mutations would be on the actual function of Sfp1. For this reason, I feel that qPCR evaluation of transcripts of Sfp1 target genes is particularly needed. Since lack of Sfp1 is known to yield some of the smallest cells possible, it would also be cool to have an estimate of the size of mutants where Sfp1 is less nuclear. These analyses could confer phenotypical relevance to the data, but would also help in assessing a currently unexplored possibility, that phosphorylation events by PKA influence Sfp1 function besides its localization, i.e.: the still somehow nuclear fraction is not as functional as wt Sfp1 in promoting transcription.
Minor comments:
Experimental issues and suggestions on data presentation are reported in the major comments section, since I felt those were major issues.
Just a side remark: I found some typos here and there, and it would greatly help to report them if in the manuscript line numbers were included.
Significance
The finding that both PKA and TORC1 impinge on Sfp1, and therefore presumably on protein synthesis, is a valuable conceptual addition to the field of cell signaling. The audience potentially interested by the findings of the study include not only yeast cell biologists, but also computational biologists interested in modeling crucial cellular processes. One example is the regulation of cell size, where TORC1, PKA and Sfp1 are already know to play a role, but were potentially missing a crosstalk link.
As requested by Review Commons, I specify that my expertise is on TORC1/AMPK/PKA pathways, on their crosstalk and their regulation by metabolic intermediates.
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www.biorxiv.org www.biorxiv.org
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Reply to the reviewers
Reviewer #1:
We thank the reviewer for his/her time and for the constructive comments. Below please find our detailed responses to your points.
STING is a key signalling hub in the innate immune system, receiving multiple inputs from upstream activators (such as cGAS) and in turn triggering multiple downstream events (such as IFN induction, NF-kB signalling, autophagy, cell death). Mutations in the STING gene cause a rare inflammatory disease called SAVI. Using a previously established STING ki mouse that recapitulates some of the clinical observations in SAVI patients, this manuscript tests the hypothesis that TNF signalling drives pathology. Using anti-TNF antibody and TNF receptor knockout, the authors show that TNF indeed plays important roles in causing disease in this mouse model. For example, the loss of T cells and neurons is prevented when TNF signalling is blocked, and lung pathology is rescued in STING ki mice lacking TNF receptors. Overall, the manuscript is well written and laid out, and the experimental work is of a high technical standard.
Major comments
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- Most figures show pooled data from two independent experiments including a total of 5-8 mice. Given the variability in some of the readouts, this raises the question of whether there is sufficient statistical power to draw conclusions. For example, in Figure 2, the conclusion that "Infliximab did not alter the expression of inflammatory mediators" seems questionable given the results in Figure 2F and G. Did the authors perform a power calculation? What effect size can the authors detect given the variability and number of replicates? Similarly, in Figure 3, the authors conclude that "Disruption of TNFR signaling did not significantly prevent T cell lymphopenia"; however, with some more replicates, the data in Figure 3D would likely reach significance. Similar concerns apply to several panels in Figures 4 and 6 and to Figure S5M. Ideally, the authors should perform additional repeat experiments to increase the number of replicates. If that is not possible, power calculations need to be provided and conclusions should explicitly mention the minimum effect size that the author can detect given the small sample size (for example "Infliximab did not alter the expression of inflammatory mediators more than x-fold").* Thank you for this suggestion. However, it is not possible to repeat the treatment of mice with Infliximab for generation of more replicates. The blockade of TNF signalling by treatment with drugs did not cure the murine SAVI disease. According to animal welfare restrictions, we cannot perform additional treatment experiments with Infliximab or Etanercept.
We analysed the effect size d, f and power of all these presented results and collected them in table S4. Additional explanations about effect sizes were added in the corresponding text to Figures 2 and 3. The demonstrated results in Figure 4 and 6 already contain significant data. We did not include the calculation of effects sizes here. All effect size and power calculations are summarized in table S4.
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The authors should not make unjustified overstatements. For example, STING KI; TNFR1/2 KO mice should not be referred to as a "new mouse model". The manuscript simply tests the role of TNFR1/2 in the already published STING N153S model. In line 687, avoid using "impressively" and in line 734 avoid using "massively".*
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Thank you for this suggestion. We changed this sentence into:…”these newly generated mouse lines of TNFR”…., see line 796. Additionally, in line 687 (actual line 705) we omitted “impressively” and in line 734 “massively produced” into “elevated” (actual line 752).
Minor comments
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Line 767-769: The statement that spike activates cGAS is misleading, because this effect is an indirect consequence of cell-to-cell fusion (Liu et al 2022).*
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Thank you for this suggestion. We changed this sentence into: Cell fusion caused by the SARS-CoV-2 spike protein is a potent… (actual line 785).
Reviewer #1 (Significance (Required)):
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The main strengths of this study are (1) the use of complementary antibody-based and genetic methods to test the role of TNF signalling; (2) the use of multiple different readouts; and (3) the analysis of many different cell types / organ systems. The main weaknesses are (1) small sample sizes limiting statistical power (see above) and (2) the exclusive use of mouse models.
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Overall, my opinion is that the advance is important, both fundamentally and clinically. Studies of this and the related V154M mouse model previously showed an important role of non-IFN pathways in driving disease. This study indicates that TNF signalling may cause pathology. This not only extends our understanding of STING's role in autoinflammation but also opens a direct therapeutic avenue using approved TNF targeting drugs.
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This study will be primarily of interest to specialised audiences working on STING and SAVI, and secondarily to the wider innate immunity field.
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This reviewer has expertise in the field of nucleic acid sensing, including cGAS-STING.
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Reviewer #2:
We thank the reviewer for his/her time and for the constructive comments. Below please find our detailed responses to your points.
*In this paper, Luksch et al (2024) examines the role of TNF signaling in STING-associated vasculopathy with onset in infancy (SAVI). By using pharmacological inhibition and genetic inactivation of TNF receptors in a murine SAVI model (STING ki), the research found that pharmacologically inhibiting TNF signaling improved T cell lymphopenia but had limited effects on lung disease. Genetic inactivation of TNFR signaling, particularly TNFR1, enhanced thymocyte survival and expanded the peripheral T cell pool, reducing inflammation and neurodegeneration. The development and progression of severe lung disease in STING ki mice are also reliant on TNFR1 signaling, while TNFR2 deletion did not alleviate lung inflammation. The authors also explored the severe inflammatory lung disease manifestation, showing that primary lung endothelial cells in STING ki mice allowed more neutrophil attachment compared to those in STING WT mice, indicating chronic STING activity in endothelial cells disrupts the endothelial barrier and promotes severe lung disease. The study highlights TNFR signaling as crucial in SAVI and COVID-19 progression and suggests blocking TNFR1 signaling as a potential therapeutic approach for both diseases. *
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Major comments:
The paper establishes a strong connection between TNFR1 depletion and the reduction of SAVI disease severity in lung and neuroinflammation, suggesting TNFR1 blockade as a viable therapeutic strategy for SAVI. To strengthen the arguments and improve the therapeutic potential, the authors should address the following major comments:
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- The authors conclude that TNFR1 signaling drives murine SAVI disease, as evidenced by the reduced severity of lung disease in TNFR1 -/- mice. While the genetic model is convincing, the discrepancy between pharmacological inhibition and genetic models needs clarification. Before attributing the pharmacological failure to late administration, have the authors considered that Infliximab might not sufficiently deplete TNF to achieve therapeutic benefits? In figure 2H, serum TNF levels were not significantly altered in STING ki mice treated with Infliximab. Have the authors considered using other TNF inhibitors or alternative methods to measure TNF depletion efficacy in STING ki murine models, such as qPCR, flow cytometry, or immunohistochemistry in lymph nodes or lung tissues?* Thank you for this suggestion. In a preliminary experiment, we already treated STING WT and STING ki mice with Etanercept which is not included in the paper. 3-week-old mice were treated with subcutaneously injection of 25 mg/kg Etanercept or saline, twice per week, for 7 weeks. After treatment, all mice were euthanized and single cell suspensions of blood and spleen were used for flow cytometry analysis. Lung tissue was harvested for histological analysis. Quantification of gene expression was performed by snap frozen lung and kidney tissue and quantification of secreted proteins was analysed by snap frozen serum.
The transcription of ISGs and proinflammatory mediators in lung tissue was not significantly improved by the Etanercept treatment of mice, see additional figure below (A – D). Interestingly, the amount of secreted CXCL9 in the serum was reduced in Etanercept treated mice compared to vehicle treated mice (E). We concluded that our treatment strategy had no impact in the manifestation and progression of murine SAVI disease, in highly inflamed tissues / organs. However, we found a reduction (partially significant) of proinflammatory mediator transcriptions in the kidney of Etanercept treated mice compared to vehicle control mice. Murine SAVI disease is a systemic autoinflammatory disease without histological alteration in kidney tissue of 10 weeks old mice. Remarkably, transcription of ISGs and proinflammatory mediators is highly upregulated in SAVI mice. Treatment with Etanercept improved this aberrant gene expression in murine SAVI influenced tissue / organ (I – K). These results encouraged us to perform the treatment with infliximab because we expected a more pronounced effect since infliximab can bind the monomeric and trimeric form while etanercept can only bind to the active trimeric from of TNF.
Etanercept treatment of STING WT (in black) and ____STING ki (in red)____ mice.
(A) Relative expression level of Cxcl10, (B) Mx1, (C) Tnf and (D) Il1b in lung tissue of Etanercept or saline treated STING WT and STING ki mice. (E) Quantification of CXCL9, (F) CXCL10, (G) IL-6 and (H) TNF in serum samples from STING WT and STING ki mice after treatment. (I) Relative expression level of Cxcl10, (J) Mx1, (K) Tnf and (L) Il1b in kidney tissue of treated mice.
- The TNF pathway exhibits redundancy, as multiple signaling molecules or pathways can compensate for the loss of TNF function to maintain cellular processes and immune responses. The authors showed that thymocytes of STING ki mice lacking TNFR1/2 expressed significantly lower levels of IFN-related genes (Cxcl10, Sting1), and mice lacking TNFR1 and TNFR1/2 expressed reduced levels of NF-κB-related genes. Does this imply that IFN and NF-κB pathways are downstream of TNF signaling driving SAVI progression? It would be valuable to hear the authors' comments or postulations on the potential mechanisms of TNF driving SAVI progression in the discussion, and the methods to dissect the mechanisms further using genetic or pharmacological methods.*
Thank you for this suggestion. STING is a key player in various proinflammatory mechanism and is directly involved in IFN and NF-κB signalling. We assume that these signalling pathways are adaptable to various proinflammatory situations. Knock out of TNFR1 and TNFR1/2 results in a strong inhibition of all inflammatory reactions in the whole organisms. We think, it is not possible to conclude mechanisms of murine SAVI manifestation and progression from the results of these mouse lines only. These observations provide new hypothesis, but cannot completely explain the mechanism.
- The authors mentioned that the pharmacological inhibition of TNF by Infliximab is ineffective due to late administration compared to the onset of SAVI. How would this affect the therapeutic treatment of TNF if the treatment is going to be later than the disease onset? Can the authors elaborate on the potential ways to circumvent the timing of treatment? Would TNFR1 antagonists experience the same issue? To understand disease progression and optimal targeting times, the creation of an inducible TNFR1/2 -/- mouse model could be beneficial. This is optional, but the authors are encouraged to comment on improving TNFR1/2 -/- mouse SAVI models to further study the therapeutic potential of TNF signaling blockage in treating SAVI.*
We agree with the suggestion. In the next project, we want to generate STING ki mice with inducible knock out.
Minor comments:
- The authors separate STING WT and STING ki into different graphs, which can sometimes make it hard to compare STING WT and STING ki baseline levels. It would be beneficial to merge the two genotypes into single graphs for easier comparison.*
Thank you for this suggestion. In the first version of this manuscript, we collected results from STING WT and STING ki mice in one graph with 8 bars in different colours and textures in the case of TNFR knock out lines. These graphs were overloaded and very confusing. It is was not possible to mark statistical calculations inside these graphs without losing the focus. Hence, we created the demonstrated design of graphs. We think this is the most convincing version.
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Figure S5 lacks statistical annotations, although the legends mention them. Are the statistics usually shown when a comparison is mentioned in the text, or are they only displayed when the differences are significant? It would be helpful if the authors could clarify this and ensure that all relevant statistical comparisons are clearly reflected in the graphs, regardless of the significance level. This consistency would improve the clarity and interpretation of the data presented.*
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Thank you for this suggestion. We removed the significance level from the legend of Figure S5 (actually line 1199).
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The authors did an excellent job discussing the study's implications, but some of this content could be moved to the introduction. The hypothesis that "tumor necrosis factor (TNF) signaling is involved in the manifestation and progression of murine SAVI disease" can be introduced more naturally once the authors present previous findings on TNF's association with various autoimmune disorders. This would set a clear context for the study's objectives and rationale.
We agree with this suggestion and inserted the sentence: “In our previous investigations, we observed an elevated transcription of Tnf in spleen and thymus of STING ki mice (Siedel et al., 2020).” (actual line 89/90).
General Assessment: The study identifies enhanced TNF signaling as a driver of SAVI and specifies TNFR1 blockage as a promising treatment to reduce disease severity. It thoroughly characterizes pharmacological inhibition and genetic perturbations of TNF signaling in murine SAVI models and creates a novel mouse model for studying TNF-targeted therapies in SAVI treatment.
*However, the study is limited in characterizing the discrepancy between pharmacological inhibition and genetic depletion of TNF and understanding the underlying mechanisms of TNF driving chronic STING activation and tissue inflammation. *
Advances: The study extends knowledge in the field by demonstrating that enhanced TNF signaling drives SAVI, establishing causation rather than mere correlation. The authors provide strong rationale for treating SAVI with TNF inhibitors/blockage, previously used in other autoimmune disorders like IBD or Crohn's disease, but not in SAVI. They also present a valuable genetic model for studying TNFR signaling blockage in SAVI progression, which is important for both the field of SAVI and future therapy development.
Audience: The research provides translational and clinical insights by suggesting that targeting TNFR1 signaling could inspire novel treatments for SAVI. The study also advances basic research on SAVI disease progression. Immunologists and clinicians studying and treating autoimmune disorders are the intended audience, but the findings have broader implications. The study highlights the potential role of TNF signaling in COVID-19 disease progression and treatment, thus attracting interest beyond the field of autoimmune disorders.
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Field of expertise:
cGAS-STING regulation in chromosomally unstable cancers, genomic instability, nuclear envelope rupture and repair
Do not have sufficient expertise in:
Immunological underpinning of autoimmune disorders, clinical models or manifestations of SAVI
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Reviewer #3:
We thank the reviewer for his/her time and for the constructive comments. Below please find our detailed responses to your points.
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Uncontrolled activation of STING is linked to autoinflammatory disease "STING-associated vasculopathy with onset in infancy (SAVI)". The authors had previously published a mouse model of SAVI, which was generated by knocking in the disease causing variant N153S into the endogenous murine Sting1 gene (STING ki) (Luksch et.al., 2019). In the current study, the author further investigated the role of tumor necrosis factor (TNF) signaling in manifestation and progression of murine SAVI disease by using the approach of pharmacologic and genetic inhibition of TNF receptors TNFR1 and TNFR2. Overall, the authors were able to demonstrate the following novel findings:
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1) Infliximab treatment of STING ki mice significantly increased the number of blood CD8+ T cells and thymic cells count. The authors claimed that the pharmacological inhibition of TNF signalling has a partial rescue effect of T cell lymphopenia. However, pharmacologic inhibition of TNF signalling however has no effect on lung disease.
2) On the other hand, STING ki;Tnfr1-/- (lacking TNFR1) showed the similar modest rescue of the CD8+ T and CD4+ T cells in blood compared to the WT C57BL/6 (BL6) but not with STING ki;Tnfr2-/- (lacking TNFR2). STING ki;Tnfr1-/-, STING ki;Tnfr2-/- and STING ki;Tnfr1/2-/- had modest rescue of thymic cell count and reduced spleen cell count (reduced splenomegaly). Along with the rescued thymic content and reduced splenomegaly, genetic ablation of TNF signalling (STING ki;Tnfr1-/-) also prevented manifestation of severe inflammatory lung disease.
3) To investigate the role of lung endothelial cells in the development of interstitial lung disease, primary murine lung endothelial cells from STING WT, STING ki and STING WT;Tnfr1/2-/- and STING ki;Tnfr1/2-/- mice were isolated and bulk RNAseq was performed. This showed decreased level of several proinflammatory cytokines (e.g. Tnf, Il1b) and chemokines (e.g. Cxcl1, Cxcl2, Cxcl9, Cxcl10, Ccl2, Ccl3 and Ccl4) in STING ki mice lacking TNFR1/2 compared to STING ki mice.
4) Neutrophils were isolated from bone marrow and were added to cultured primary lung endothelial cell monolayers. The experiments demonstrated that the attachment and transmigration of neutrophil cells were dependent on expression of STING gain-of-function mutation in endothelial cells.
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A few points require clarification before publication of this study.
- Tnfr1-/-, Tnfr2-/- and Tnfr1/2-/- did not show any statistical significant improvement of thymic cell count in STING ki mice. As such, the statement in the conclusion/summary section of discussion regarding Tnfr1 can restore thymocyte numbers should be toned-down.
- Thank you for this suggestion. In Figure 4 E, we demonstrated that knock out of TNFR1 leads to increasing of SP CD8 thymocyte count and partially of SP CD4 thymocyte count (Fig. 4 D). In agreement with this suggestion, we marked this subpopulation of thymocytes in the discussion and summary section, see actual line 684 and see actual line 794.
2) The section on Neuroinflammation and neurodegeneration and dependency of TNFR1/2 signaling is very currently difficult to follow (based on how the data are presented in figures and text). This section requires to be re-written for clarity.
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Thank you for this suggestion. We re-wrote this section, see line 472 - 499.
Neuroinflammation and neurodegeneration in dependency of TNFR1/2 signaling
The extent of inflammation in mouse brain resulting from constitutive activation of STING N153S was reported by quantifying the density of Iba1-positive microglia (Fig.5 A). Consistent with our previous findings (Szego et al., 2022), the density of Iba1-positive microglia in the substantia nigra was higher in STING ki;BL6 mice than in STING WT mice (Fig.5 B). TNFR deficiency did not affect neuroinflammation because there was no significant difference between the density of Iba1-positive microglia between STING ki;BL6 mice and STING ki;Tnfr1/2-/- mice (Fig.5 B). This suggests that the TNF pathway is not required for STING-induced microglia activation in the substantia nigra.
In addition, we measured the extent of STING-induced astrogliosis by quantifying the density of GFAP-positive cells (Fig. 5 A). Consistent with our previous findings, the density of GFAP-positive astroglia was higher in STING ki than in STING WT mice (Fig. 5C). Yet, as for microglia, there was no significant difference between the density of GFAP-positive astroglia between STING ki;BL6 mice and STING ki;Tnfr1/2-/- mice (Fig.5 C), suggesting that the TNF pathway is not required for STING-induced astrogliosis in the substantia nigra.
Finally, we measured the extent of STING-induced neurodegeneration by quantifying the density of TH-positive dopaminergic neurons in the substantia nigra (Fig. 5A). As in our previous findings, the density of TH-positive neurons was lower in STING ki;BL6 mice than in STING WT mice (Fig.5 D). The density of TH-positive neurons in the substantia nigra of STING ki;Tnfr1/2-/- mice was higher than the density of TH-positive neurons in the substantia nigra of STING ki;BL6 mice (Fig. 5 D), suggesting that the STING-induced degeneration of TH-positive neurons was blunted in Tnfr1/2-/- mice and that TNFR1/2 are involved in the STING-induced degeneration of dopaminergic neurons.
Hence, there is a discrepancy between STING-induced effects on glial cells as opposed to STING-induced effects on neurons. The dependence of STING-induced neurodegeneration but not glial response on TNFR1/2 suggests that the STING-induced degeneration of dopaminergic neurons is not a direct consequence of microglia or astroglia activation. This is consistent with the emerging concept of a neuron-specific inflammatory response (Welikovitch et al., 2020).
*The powerful use of in vivo genetic KO models and TNF inhibitor makes this study a valuable contribution to the field - helping further decipher the importance of the NF-KB/TNF branch of STING in SAVI (knowledge gap). The audience for this work would be specialised to STING biology and potential clinical treatments of SAVI. *
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Our expertise is in nucleic acids sensing (such as STING) and auto-immunity.
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Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.
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Referee #3
Evidence, reproducibility and clarity
Uncontrolled activation of STING is linked to autoinflammatory disease "STING-associated vasculopathy with onset in infancy (SAVI)". The authors had previously published a mouse model of SAVI, which was generated by knocking in the disease causing variant N153S into the endogenous murine Sting1 gene (STING ki) (Luksch et.al., 2019). In the current study, the author further investigated the role of tumor necrosis factor (TNF) signaling in manifestation and progression of murine SAVI disease by using the approach of pharmacologic and genetic inhibition of TNF receptors TNFR1 and TNFR2. Overall, the authors were able to demonstrate the following novel findings:
- Infliximab treatment of STING ki mice significantly increased the number of blood CD8+ T cells and thymic cells count. The authors claimed that the pharmacological inhibition of TNF signalling has a partial rescue effect of T cell lymphopenia. However, pharmacologic inhibition of TNF signalling however has no effect on lung disease.
- On the other hand, STING ki;Tnfr1-/- (lacking TNFR1) showed the similar modest rescue of the CD8+ T and CD4+ T cells in blood compared to the WT C57BL/6 (BL6) but not with STING ki;Tnfr2-/- (lacking TNFR2). STING ki;Tnfr1-/-, STING ki;Tnfr2-/- and STING ki;Tnfr1/2-/- had modest rescue of thymic cell count and reduced spleen cell count (reduced splenomegaly). Along with the rescued thymic content and reduced splenomegaly, genetic ablation of TNF signalling (STING ki;Tnfr1-/-) also prevented manifestation of severe inflammatory lung disease.
- To investigate the role of lung endothelial cells in the development of interstitial lung disease, primary murine lung endothelial cells from STING WT, STING ki and STING WT;Tnfr1/2-/- and STING ki;Tnfr1/2-/- mice were isolated and bulk RNAseq was performed. This showed decreased level of several proinflammatory cytokines (e.g. Tnf, Il1b) and chemokines (e.g. Cxcl1, Cxcl2, Cxcl9, Cxcl10, Ccl2, Ccl3 and Ccl4) in STING ki mice lacking TNFR1/2 compared to STING ki mice.
- Neutrophils were isolated from bone marrow and were added to cultured primary lung endothelial cell monolayers. The experiments demonstrated that the attachment and transmigration of neutrophil cells were dependent on expression of STING gain-of-function mutation in endothelial cells.
A few points require clarification before publication of this study.
- Tnfr1-/-, Tnfr2-/- and Tnfr1/2-/- did not show any statistical significant improvement of thymic cell count in STING ki mice. As such, the statement in the conclusion/summary section of discussion regarding Tnfr1 can restore thymocyte numbers should be toned-down.
- The section on Neuroinflammation and neurodegeneration and dependency of TNFR1/2 signaling is very currently difficult to follow (based on how the data are presented in figures and text). This section requires to be re-written for clarity.
Significance
The powerful use of in vivo genetic KO models and TNF inhibitor makes this study a valuable contribution to the field - helping further decipher the importance of the NF-KB/TNF branch of STING in SAVI (knowledge gap). The audience for this work would be specialised to STING biology and potential clinical treatments of SAVI.
Our expertise is in nucleic acids sensing (such as STING) and auto-immunity.
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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
Referee #2
Evidence, reproducibility and clarity
Short summary
In this paper, Luksch et al (2024) examines the role of TNF signaling in STING-associated vasculopathy with onset in infancy (SAVI). By using pharmacological inhibition and genetic inactivation of TNF receptors in a murine SAVI model (STING ki), the research found that pharmacologically inhibiting TNF signaling improved T cell lymphopenia but had limited effects on lung disease. Genetic inactivation of TNFR signaling, particularly TNFR1, enhanced thymocyte survival and expanded the peripheral T cell pool, reducing inflammation and neurodegeneration. The development and progression of severe lung disease in STING ki mice are also reliant on TNFR1 signaling, while TNFR2 deletion did not alleviate lung inflammation. The authors also explored the severe inflammatory lung disease manifestation, showing that primary lung endothelial cells in STING ki mice allowed more neutrophil attachment compared to those in STING WT mice, indicating chronic STING activity in endothelial cells disrupts the endothelial barrier and promotes severe lung disease. The study highlights TNFR signaling as crucial in SAVI and COVID-19 progression and suggests blocking TNFR1 signaling as a potential therapeutic approach for both diseases.
Major comments:
The paper establishes a strong connection between TNFR1 depletion and the reduction of SAVI disease severity in lung and neuroinflammation, suggesting TNFR1 blockade as a viable therapeutic strategy for SAVI. To strengthen the arguments and improve the therapeutic potential, the authors should address the following major comments: - The authors conclude that TNFR1 signaling drives murine SAVI disease, as evidenced by the reduced severity of lung disease in TNFR1 -/- mice. While the genetic model is convincing, the discrepancy between pharmacological inhibition and genetic models needs clarification. Before attributing the pharmacological failure to late administration, have the authors considered that Infliximab might not sufficiently deplete TNF to achieve therapeutic benefits? In figure 2H, serum TNF levels were not significantly altered in STING ki mice treated with Infliximab. Have the authors considered using other TNF inhibitors or alternative methods to measure TNF depletion efficacy in STING ki murine models, such as qPCR, flow cytometry, or immunohistochemistry in lymph nodes or lung tissues? - The TNF pathway exhibits redundancy, as multiple signaling molecules or pathways can compensate for the loss of TNF function to maintain cellular processes and immune responses. The authors showed that thymocytes of STING ki mice lacking TNFR1/2 expressed significantly lower levels of IFN-related genes (Cxcl10, Sting1), and mice lacking TNFR1 and TNFR1/2 expressed reduced levels of NF-κB-related genes. Does this imply that IFN and NF-κB pathways are downstream of TNF signaling driving SAVI progression? It would be valuable to hear the authors' comments or postulations on the potential mechanisms of TNF driving SAVI progression in the discussion, and the methods to dissect the mechanisms further using genetic or pharmacological methods. - The authors mentioned that the pharmacological inhibition of TNF by Infliximab is ineffective due to late administration compared to the onset of SAVI. How would this affect the therapeutic treatment of TNF if the treatment is going to be later than the disease onset? Can the authors elaborate on the potential ways to circumvent the timing of treatment? Would TNFR1 antagonists experience the same issue? To understand disease progression and optimal targeting times, the creation of an inducible TNFR1/2 -/- mouse model could be beneficial. This is optional, but the authors are encouraged to comment on improving TNFR1/2 -/- mouse SAVI models to further study the therapeutic potential of TNF signaling blockage in treating SAVI.
Minor comments:
- The authors separate STING WT and STING ki into different graphs, which can sometimes make it hard to compare STING WT and STING ki baseline levels. It would be beneficial to merge the two genotypes into single graphs for easier comparison.
- Figure S5 lacks statistical annotations, although the legends mention them. Are the statistics usually shown when a comparison is mentioned in the text, or are they only displayed when the differences are significant? It would be helpful if the authors could clarify this and ensure that all relevant statistical comparisons are clearly reflected in the graphs, regardless of the significance level. This consistency would improve the clarity and interpretation of the data presented.
- The authors did an excellent job discussing the study's implications, but some of this content could be moved to the introduction. The hypothesis that "tumor necrosis factor (TNF) signaling is involved in the manifestation and progression of murine SAVI disease" can be introduced more naturally once the authors present previous findings on TNF's association with various autoimmune disorders. This would set a clear context for the study's objectives and rationale.
Significance
General Assessment: The study identifies enhanced TNF signaling as a driver of SAVI and specifies TNFR1 blockage as a promising treatment to reduce disease severity. It thoroughly characterizes pharmacological inhibition and genetic perturbations of TNF signaling in murine SAVI models and creates a novel mouse model for studying TNF-targeted therapies in SAVI treatment. However, the study is limited in characterizing the discrepancy between pharmacological inhibition and genetic depletion of TNF and understanding the underlying mechanisms of TNF driving chronic STING activation and tissue inflammation.
Advances: The study extends knowledge in the field by demonstrating that enhanced TNF signaling drives SAVI, establishing causation rather than mere correlation. The authors provide strong rationale for treating SAVI with TNF inhibitors/blockage, previously used in other autoimmune disorders like IBD or Crohn's disease, but not in SAVI. They also present a valuable genetic model for studying TNFR signaling blockage in SAVI progression, which is important for both the field of SAVI and future therapy development.
Audience: The research provides translational and clinical insights by suggesting that targeting TNFR1 signaling could inspire novel treatments for SAVI. The study also advances basic research on SAVI disease progression. Immunologists and clinicians studying and treating autoimmune disorders are the intended audience, but the findings have broader implications. The study highlights the potential role of TNF signaling in COVID-19 disease progression and treatment, thus attracting interest beyond the field of autoimmune disorders.
Field of expertise:
cGAS-STING regulation in chromosomally unstable cancers, genomic instability, nuclear envelope rupture and repair
Do not have sufficient expertise in:
Immunological underpinning of autoimmune disorders, clinical models or manifestations of SAVI
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Referee #1
Evidence, reproducibility and clarity
Review Commons STING SAVI May 2024
STING is a key signalling hub in the innate immune system, receiving multiple inputs from upstream activators (such as cGAS) and in turn triggering multiple downstream events (such as IFN induction, NF-kB signalling, autophagy, cell death). Mutations in the STING gene cause a rare inflammatory disease called SAVI. Using a previously established STING ki mouse that recapitulates some of the clinical observations in SAVI patients, this manuscript tests the hypothesis that TNF signalling drives pathology. Using anti-TNF antibody and TNF receptor knockout, the authors show that TNF indeed plays important roles in causing disease in this mouse model. For example, the loss of T cells and neurons is prevented when TNF signalling is blocked, and lung pathology is rescued in STING ki mice lacking TNF receptors. Overall, the manuscript is well written and laid out, and the experimental work is of a high technical standard.
Major comments
- Most figures show pooled data from two independent experiments including a total of 5-8 mice. Given the variability in some of the readouts, this raises the question of whether there is sufficient statistical power to draw conclusions. For example, in Figure 2, the conclusion that "Infliximab did not alter the expression of inflammatory mediators" seems questionable given the results in Figure 2F and G. Did the authors perform a power calculation? What effect size can the authors detect given the variability and number of replicates? Similarly, in Figure 3, the authors conclude that "Disruption of TNFR signaling did not significantly prevent T cell lymphopenia"; however, with some more replicates, the data in Figure 3D would likely reach significance. Similar concerns apply to several panels in Figures 4 and 6 and to Figure S5M. Ideally, the authors should perform additional repeat experiments to increase the number of replicates. If that is not possible, power calculations need to be provided and conclusions should explicitly mention the minimum effect size that the author can detect given the small sample size (for example "Infliximab did not alter the expression of inflammatory mediators more than x-fold").
- The authors should not make unjustified overstatements. For example, STING KI; TNFR1/2 KO mice should not be referred to as a "new mouse model". The manuscript simply tests the role of TNFR1/2 in the already published STING N153S model. In line 687, avoid using "impressively" and in line 734 avoid using "massively".
Minor comments
- Line 767-769: The statement that spike activates cGAS is misleading, because this effect is an indirect consequence of cell-to-cell fusion (Liu et al 2022).
Significance
The main strengths of this study are (1) the use of complementary antibody-based and genetic methods to test the role of TNF signalling; (2) the use of multiple different readouts; and (3) the analysis of many different cell types / organ systems. The main weaknesses are (1) small sample sizes limiting statistical power (see above) and (2) the exclusive use of mouse models.
Overall, my opinion is that the advance is important, both fundamentally and clinically. Studies of this and the related V154M mouse model previously showed an important role of non-IFN pathways in driving disease. This study indicates that TNF signalling may cause pathology. This not only extends our understanding of STING's role in autoinflammation but also opens a direct therapeutic avenue using approved TNF targeting drugs.
This study will be primarily of interest to specialised audiences working on STING and SAVI, and secondarily to the wider innate immunity field.
This reviewer has expertise in the field of nucleic acid sensing, including cGAS-STING.
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Reply to the reviewers
Response to Reviewers
We thank the reviewers for their comments and suggestions, which we think are helpful and will improve the manuscript, and intend to address with the changes and planned revisions below.
Reviewer #1 (Evidence, reproducibility and clarity (Required)):
Bello et al look at the SNP rs28834970 associated with Alzheimer's disease (AD), with C being the risk allele, on chromatin accessibility and expression of a nearby gene, PTK2B, in microglia. Their contention is that the single SNP affects chromatin accessibility and binding of the transcription factor CEBP[beta] in an intronic region of PTK2B and thereby affects PTKB expression. I had a few questions that I think are critical to be addressed. Please note that my numbering of panels is based on the figures, not the legends, which do not seem to quite agree with each other. There are also some figure legends that say "IFNg" while the figures say "LPS", which should be fixed.
We apologise for the mistake in the figure legend that made this confusing, which we have now revised.
The abstract says that editing a line that is homozygous for protective alleles to homozygous for risk results in "subtle downregulation of PTK2B expression". It isn't clear to me that the presented data fully supports this contention, which is central to the argument of the paper. In figure 2e, the authors show in both RNAseq and ddPCR that there is numerically lower PTK2B expression but this is not indicated to be statistically significant by one-way paired ANOVA. If there is no nominally significant difference in the edited lines, compared to the proposed significant differences in lines carrying the full risk haplotype (figure 1), then it would not seem sensible to ascribe the effects to the single edited base pair.
We agree with the reviewer that given the effect of the SNP on PTK2B expression in the edited lines is small and only significant in macrophages, we should not interpret the effects to be mediated solely through PTK2B expression, and have substantially reworded the manuscript accordingly.
Whilst the effects in the eQTL analysis are significant, it is worth noting that this is likely due to the much larger number of donors (133-217) giving greater power to detect the subtle changes in expression (~1.1 to 2 fold in eQTL). This change is of a similar magnitude in our SNP edited lines (~1.2 fold in SNP edited lines) as would be expected of most common regulatory variants so we believe that it could be the primary causal variant. However, we cannot exclude that other variants in the haplotype could contribute to the effect, so have also reworded accordingly to make this clear.
Given this uncertainty about the overall strength of effect of the single base pair change it would seem important to evaluate the proposed mechanism of CEBPb binding. It wasn't clear whether the ATAC-seq data summarized in the volcano plot in 2C is proposed to be a cause or a consequence of the CEBPb binding change. I am assuming that the 'fold change' estimate here is CC compared to TT, which would be consistent with direction of effect in figure 1, but please clarify.
We apologise for the mistake in the figure legend that made this confusing, which we have now revised along with clarification in the revised text. It is difficult to be sure whether changes in chromatin accessibility are a cause or consequence of CEBPb binding, but the fact that the binding of CEBPb is increased in the CC allele (Fig 2a, Fig 2c), that the C allele better matches the consensus sequence (Fig 2b) and there is increased chromatin accessibility (Fig 2a, Supp Fig 3b) suggests that CEBPb binding is causing the formation of the region of chromatin accessibility.
In contrast to the subtle effects at PTK2B, the global transcriptional effects in figure 3 look quite strong. Are any of these changes dependent on PTK2B, that is to say, are they mimicked by partial suppression of PTK2B expression or activity?
We agree that the downstream effects of the SNP are much stronger than the effects on PTK2B expression, and we have substantially reworded the manuscript to make it clear that we are unsure that the effects of the SNP are all mediated via PTK2B.
However, we note that there is evidence in the literature of a loss in CCL4 and CCL5 expression upon PTK2B knockout in macrophages (https://www.nature.com/articles/s41467-021-27038-5) and inhibition of PTK2B in monocytes results in a reduction in CCL5 and CXCL1 (https://www.nature.com/articles/s41598-019-44098-2) consistent with our observations.
Experiments to manipulate PTK2B expression in microglia and readout changes at the RNA level would take a few months to complete, but we would be willing to do this if the reviewer felt this was necessary.
Finally, in figure 4, it should be clarified as to why lower expression of PTK2B would be expected to have a detrimental effect on Alzheimer's risk. If understood correctly, and again fixing the figure legends would be helpful, the CC edited lines (risk) have lower chemokine induction than the unedited TT lines.
We apologise for the error in this figure which we have corrected in the revised version. You are correct that the CC lines have a lower chemokine level in both unstimulated and stimulated cells, and we have now discussed further how this may be linked to increased disease risk.
"Even though overexpression of these chemokines is characteristic of neuroinflammation, correlated with disease progression and found in late stages of AD, knockout of chemokines, such as CCL2, and chemokine receptors, such as CCR2 and CCR5, in mice is associated with increased Aβ deposition and accumulation [47,50-52,107]. It has also been found that patients carrying CCR5Δ32 mutation, which prevents CCR5 surface expression, develop AD at a younger age[108]. Therefore, we hypothesize that in individuals carrying the C/C allele of rs28834970 downregulation of these chemokines in macrophages and microglia harbouring the C/C allele of rs28834970 affects Aβ-induced microglia chemotaxis, leukocytes recruitment and clearance of Aβ, and may increase the risk of developing symptomatic AD"
Reviewer #1 (Significance (Required)):
Going from GWAS hits, which represent blocks of high LD inherited variants, to single functional variants is a difficult problem in human genetics. The current paper attempts to isolate the effect of a single variant within an LD block on IPSC derived macrophages and microglia. This idea might be useful in nominating PTK2B as a therapeutic target for AD, although there is some question in my mind as to direction of effect.
Reviewer #2 (Evidence, reproducibility and clarity (Required)):
SUMMARY: In this manuscript the authors explore the biological effects of an intronic SNP in the PTK2B gene, previously shown to be associated with late onset Alzheimer's disease (AD) risk. Based on the likely effect of the SNP locus on PTK2B expression in the macrophage lineage, the authors explore the consequences of introducing with the Crispr/Cas9 technique the biallelic SNP base change (C/C vs T/T) in a human IPSC line that is then differentiated into macrophages or microglia. They observe that C/C increases chromatin accessibility and CEBPb binding in comparison to T/T, with a slight decrease in PTK2B expression, significant in macrophages but not in microglia. The authors then investigate the transcriptome changes induced by the C/C mutation and find alteration in many genes, including a decreased expression of a number of cytokine or receptor proteins involved in inflammatory responses. The authors also mention a decreased effect on IFNg-induced reduced mobility but the data are missing (see Figure errors below). Overall the authors propose that the risk SNP is associated with a decreased PTK2B expression and hypothesize a link between this change and a decreased function of macrophages/microglia that may contribute to AD pathology.
MAJOR COMMENTS
1- The authors claim that their results show that the investigated SNP has a causal effects in "microglial function" (Title) and in Alzheimer's disease (AD) (Abstract 2nd sentence "Here we validate a causal single nucleotide polymorphism (SNP) associated with an increased risk of Alzheimer's disease". The word "causal" is repeated many times. However the authors should qualify their claim with respect to AD. Their results do show that the SNP has an effect on chromatin accessibility, CEBP binding, PTK2B expression and transcriptome, but the link between these changes is not formally demonstrated and their potential role in AD-like phenotype is not explored. The "causal" role is not formally and logically demonstrated. It remains an interesting, plausible hypothesis and the results provide strong arguments in support of that hypothesis but do not prove it, yet.
Concerning the title, "causal effects on microglial function" is awkward, anything that has effects is logically "causal" in these effects. The title should be "... has effects on microglial functions" or "... alters microglial function".
We agree with the reviewer that given the effect of the SNP on PTK2B expression in the edited lines is small and only significant in macrophages, we should not interpret the effects to be mediated solely through PTK2B expression, or that they cause AD. We have substantially reworded the manuscript throughout to account for this.
2- One major difficulty in the results is to link the slight decrease in PTK2B transcript, which is only significant in macrophages, with the rest of the phenotype. Because what matters to make this link is not the mRNA but the protein, and because mRNA levels are often not strictly correlated with the protein levels, the authors should measure the PTK2B/PYK2 protein levels in their differentiated cell lines in basal conditions and following activation (as they do for other readouts) using immunoblotting. A robust and significant diminution in PYK2 protein would strongly support its role in linking PTK2B expression and transcriptome change.
We have performed preliminary analyses of PTK2B expression by Western blot in these cell lines after differentiation, but were unable to observe a significant change in abundance in the edited cell lines. This is not unexpected given the results at the RNA level, since the effect size of this common regulatory variant is likely very small (estimated to be ~1.2 fold from the eQTL analysis), and likely within the variability of this assay.
As mentioned above, we have reworded the manuscript to avoid interpreting that the effects of rs28834970 are mediated solely through effects on PTK2B expression. We think that an experiment to manipulate PTK2B levels (see next point) may be a better way to demonstrate whether these effects are mediated through PTK2B expression.
An optional additional key experiment would be to reverse the transcriptome phenotype by increasing the expression of PTK2B (e.g. by cDNA transfection). Note that these points are important because an alternative hypothesis to explain the effects of C/C mutation on macrophage function would be that the C/C mutation has a long distance effect on other chromatin regions with key role in regulating these cells.
We agree that this would be a valuable experiment, and are planning additional experiments to investigate the effect of manipulating PTK2B levels (through knockout) on microglia.
3- The manuscript contains several errors in the figures and figure legends. In Fig. 2 the legends for the figure items are shuffled. Figure 4 and Supplementary Figure 5 are duplicates of the same one. Consequently important data are not presented.
We apologise for the errors in these figures that were due to a mistake during uploading where the incorrect versions were used. The legends for figure 2 and panels in figure 4 have now been corrected, and show the effects of rs28834970 on microglial migration and chemokine release in the presence or absence of IFNg.
4- When the number of replicates is small (e.g. n = 3) it is preferable to use non parametric tests (rank analysis, e.g. Mann Whitney's test) rather than t test. This applies to Figures 2D (current legend 2A), 2E (current legend 2B), Figure 4A-C, Supplementary Figures 2A, 2B. In Supplementary Fig 4E (MARCO) the number of replicates (presumably 3 because based on RNAseq) and the used test are not indicated. Is it the RNAseq statistical analysis?
We thank the reviewer for this comment. We acknowledge that the t-test may lead to inflated false discovery rates. However, it has been shown that for small sample sizes parametric tests have a power advantage compared to non-parametric ones that may outweigh the possibly exaggerated false positives. See https://genomebiology.biomedcentral.com/articles/10.1186/s13059-022-02648-4#Sec3 which states:
"In conclusion, when the per-condition sample size is less than 8, parametric methods may be used because their power advantage may outweigh their possibly exaggerated false positives."
We have also modified the legend of supplementary figure 4E to clarify the number of replicates used.
5- In addition to the above comment on tests, when the number of replicates is small it is not appropriate (and misleading) to show box plots or bars with SEM. In the indicated figures the individual data points should be shown.
We now show individual replicates on box plots (Figure 2D, 2E and supp figure 4E).
MINOR COMMENTS:
a- Macrophages and microglia are very similar cell types. Could the authors comment more on the differences they observe and how they are related to those previously described?
We have now referenced the original papers and commented on the markers that we see differentially expressed, notably P2RY12 which is a key homeostatic microglia marker that distinguishes these cells from macrophages.
b- In Fig. 2A CEBPb cut and run plot, the differences are not limited to the SNP immediate vicinity, there are also visible differences between T/T and C/C plots in at least a 40-kb range. Is it due to multiple interactions of CEBPb? How can the point difference have broad consequences? Please explain this potentially interesting and relevant finding.
Whilst there may be small changes in CEBPb binding at the second intronic PTK2B chromatin peak, this is not statistically significant given the variability between repeats. In fact, the only significant change we see in CEBPb binding genome-wide is at the locus overlapping the SNP (Fig 2c).
c- Potentially cis-altered genes near the SNP include CHRNA2 and EPHX2 (see Sup. Fig. 3a). Their expression may not be detected in macrophage lineage. If this is the case please indicate in the text, otherwise please include the corresponding data in Sup. Fig. 3b to show the presence or absence of SNP-induced change.
You are correct that CHRNA2 and EPHX2 are not expressed in our macrophages or microglia, and we have now explicitly stated this in the revised text.
d- In general the Figures are not of very high quality and are difficult to read or understand without constantly going back and forth to the legends (which are mislabeled in some instances). To improve:
. Please increase font size whenever possible.
. Please improve Fig. 1d by indicating the position of the SNP, numbering the exons (an intermediate scale plot may be necessary and lines on bottom trace are hardly visible).
. Please indicate the correct color code for T/T and C/C in Fig 3a and b, left panels, which currently doesn't match.
. Please label the Venn's diagrams comparisons in Sup. Fig. 4b.
. In the text and legends the Figure items are identified with letters in upper case, in the figures they are in lower case. Please be consistent.
We have improved the resolution of the images in the pdf and Fig 1d has been revised to include the position of the SNP. The colour code for T/T and C/C is correct in fig 3a and 3b, but since the PCA plots are independently created, we would not always expect the position of the T/T and C/C alleles to be the same. The Venn diagrams in Sup Fig 4b have been updated, and the letters for the figure panels made consistently upper case throughout.
e- In Fig. 2D and 2E, the Y axes should start at zero to avoid artificially increasing the visual differences. If there is a strong reason not to do so (I don't see any here), the Y axis should be clearly interrupted to avoid confusion.
We have altered this accordingly.
f- In the introduction the authors provide some background about previous work about the potential role of PTK2B/PYK2 in AD pathophysiology. The cited preclinical results suggest that PTK2B activity could have a deleterious effect (references in the manuscript). In contrast, some other reports (PMID: 29803828, 33718872) suggest a protective effect of PTK2B/PYK2. Because the evidence in the current manuscript suggests that the risk-associated SNP results in a decreased function of PTK2B/PYK2 (through decreased levels), at least in cells of the macrophage lineage, the authors could broaden their discussion to include these results.
We have now discussed the conflicting evidence in the revised manuscript.
Reviewer #2 (Significance (Required)):
ADVANCE: Late onset Alzheimer's disease is a major medical issue. It has a complex genetic risk component with many associated loci identified in GWAS. Most of these have only a small individual impact on the risk. One of the SNPs associated with increased risk (rs28834970) is located in an intron of the PTK2B gene. Although various reports have investigated the role of the PTK2B gene product, the tyrosine kinase PYK2, in several AD models, the possible link with rs28834970, is unclear.
An important point is to determine whether TàC SNP corresponding to rs28834970 alters PTK2B expression and how it does so. An alternative hypothesis could be that the SNP has a strong linkage disequilibrium with an unidentified allele in human populations that could be responsible for AD risk. The current manuscript is a significant step forward in addressing that question. By generating a biallelic C/C SNP mutation in a human IPSC line the current study allows to eliminate such linked contribution.
The strength of the manuscript is to show an effect on chromatin accessibility, CEBP binding and possibly PTK2B transcripts. It also provides interesting evidence of a broad effect of the C/C mutation on the transcriptome of macrophage lineage cells. In its current form the manuscript presents weaknesses that could be improved. These flaws include issues with the presentation discussed above and the uncomplete demonstration that it is the decrease in PTK2B expression that causes the macrophage/microglia phenotype. If these flaws were overcome the paper would represent a significant advance.
AUDIENCE: The expected audience is specialized in AD with a possible broader range if all weaknesses are addressed.
REVIEWER EXPERTISE: Basic science close to the field.
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Referee #2
Evidence, reproducibility and clarity
Summary: In this manuscript the authors explore the biological effects of an intronic SNP in the PTK2B gene, previously shown to be associated with late onset Alzheimer's disease (AD) risk. Based on the likely effect of the SNP locus on PTK2B expression in the macrophage lineage, the authors explore the consequences of introducing with the Crispr/CAS9 technique the biallelic SNP base change (C/C vs T/T) in a human IPSC line that is then differentiated into macrophages or microglia. They observe that C/C increases chromatin accessibility and CEBPb binding in comparison to T/T, with a slight decrease in PTK2B expression, significant in macrophages but not in microglia. The authors then investigate the transcriptome changes induced by the C/C mutation and find alteration in many genes, including a decreased expression of a number of cytokine or receptor proteins involved in inflammatory responses. The authors also mention a decreased effect on IFNg-induced reduced mobility but the data are missing (see Figure errors below). Overall the authors propose that the risk SNP is associated with a decreased PTK2B expression and hypothesize a link between this change and a decreased function of macrophages/microglia that may contribute to AD pathology.
Major comments:
- The authors claim that their results show that the investigated SNP has a causal effects in "microglial function" (Title) and in Alzheimer's disease (AD) (Abstract 2nd sentence "Here we validate a causal single nucleotide polymorphism (SNP) associated with an increased risk of Alzheimer's disease". The word "causal" is repeated many times. However the authors should qualify their claim with respect to AD. Their results do show that the SNP has an effect on chromatin accessibility, CEBP binding, PTK2B expression and transcriptome, but the link between these changes is not formally demonstrated and their potential role in AD-like phenotype is not explored. The "causal" role is not formally and logically demonstrated. It remains an interesting, plausible hypothesis and the results provide strong arguments in support of that hypothesis but do not prove it, yet. Concerning the title, "causal effects on microglial function" is awkward, anything that has effects is logically "causal" in these effects. The title should be "... has effects on microglial functions" or "... alters microglial function".
- One major difficulty in the results is to link the slight decrease in PTK2B transcript, which is only significant in macrophages, with the rest of the phenotype. Because what matters to make this link is not the mRNA but the protein, and because mRNA levels are often not strictly correlated with the protein levels, the authors should measure the PTK2B/PYK2 protein levels in their differentiated cell lines in basal conditions and following activation (as they do for other readouts) using immunoblotting. A robust and significant diminution in PYK2 protein would strongly support its role in linking PTK2B expression and transcriptome change. An optional additional key experiment would be to reverse the transcriptome phenotype by increasing the expression of PTK2B (e.g. by cDNA transfection). Note that these points are important because an alternative hypothesis to explain the effects of C/C mutation on macrophage function would be that the C/C mutation has a long distance effect on other chromatin regions with key role in regulating these cells.
- The manuscript contains several errors in the figures and figure legends. In Fig. 2 the legends for the figure items are shuffled. Figure 4 and Supplementary Figure 5 are duplicates of the same one. Consequently important data are not presented.
- When the number of replicates is small (e.g. n = 3) it is preferable to use non parametric tests (rank analysis, e.g. Mann Whitney's test) rather than t test. This applies to Figures 2D (current legend 2A), 2E (current legend 2B), Figure 4A-C, Supplementary Figures 2A, 2B. In Supplementary Fig 4E (MARCO) the number of replicates (presumably 3 because based on RNAseq) and the used test are not indicated. Is it the RNAseq statistical analysis?
- In addition to the above comment on tests, when the number of replicates is small it is not appropriate (and misleading) to show box plots or bars with SEM. In the indicated figures the individual data points should be shown.
Minor comments:
- a. Macrophages and microglia are very similar cell types. Could the authors comment more on the differences they observe and how they are related to those previously described?
- b. In Fig. 2A CEBPb cut and run plot, the differences are not limited to the SNP immediate vicinity, there are also visible differences between T/T and C/C plots in at least a 40-kb range. Is it due to multiple interactions of CEBPb? How can the point difference have broad consequences? Please explain this potentially interesting and relevant finding.
- c. Potentially cis-altered genes near the SNP include CHRNA2 and EPHX2 (see Sup. Fig. 3a). Their expression may not be detected in macrophage lineage. If this is the case please indicate in the text, otherwise please include the corresponding data in Sup. Fig. 3b to show the presence or absence of SNP-induced change.
- d. In general the Figures are not of very high quality and are difficult to read or understand without constantly going back and forth to the legends (which are mislabeled in some instances). To improve:
- Please increase font size whenever possible.
- Please improve Fig. 1d by indicating the position of the SNP, numbering the exons (an intermediate scale plot may be necessary and lines on bottom trace are hardly visible).
- Please indicate the correct color code for T/T and C/C in Fig 3a and b, left panels, which currently doesn't match.
- Please label the Venn's diagrams comparisons in Sup. Fig. 4b.
- In the text and legends the Figure items are identified with letters in upper case, in the figures they are in lower case. Please be consistent.
- e. In Fig. 2D and 2E, the Y axes should start at zero to avoid artificially increasing the visual differences. If there is a strong reason not to do so (I don't see any here), the Y axis should be clearly interrupted to avoid confusion.
- f. In the introduction the authors provide some background about previous work about the potential role of PTK2B/PYK2 in AD pathophysiology. The cited preclinical results suggest that PTK2B activity could have a deleterious effect (references in the manuscript). In contrast, some other reports (PMID: 29803828, 33718872) suggest a protective effect of PTK2B/PYK2. Because the evidence in the current manuscript suggests that the risk-associated SNP results in a decreased function of PTK2B/PYK2 (through decreased levels), at least in cells of the macrophage lineage, the authors could broaden their discussion to include these results.
Significance
Advance: Late onset Alzheimer's disease is a major medical issue. It has a complex genetic risk component with many associated loci identified in GWAS. Most of these have only a small individual impact on the risk. One of the SNPs associated with increased risk (rs28834970) is located in an intron of the PTK2B gene. Although various reports have investigated the role of the PTK2B gene product, the tyrosine kinase PYK2, in several AD models, the possible link with rs28834970, is unclear.
An important point is to determine whether TC SNP corresponding to rs28834970 alters PTK2B expression and how it does so. An alternative hypothesis could be that the SNP has a strong linkage disequilibrium with an unidentified allele in human populations that could be responsible for AD risk. The current manuscript is a significant step forward in addressing that question. By generating a biallelic C/C SNP mutation in a human IPSC line the current study allows to eliminate such linked contribution.
The strength of the manuscript is to show an effect on chromatin accessibility, CEBP binding and possibly PTK2B transcripts. It also provides interesting evidence of a broad effect of the C/C mutation on the transcriptome of macrophage lineage cells. In its current form the manuscript presents weaknesses that could be improved. These flaws include issues with the presentation discussed above and the uncomplete demonstration that it is the decrease in PTK2B expression that causes the macrophage/microglia phenotype. If these flaws were overcome the paper would represent a significant advance.
Audience: The expected audience is specialized in AD with a possible broader range if all weaknesses are addressed.
Reviewer Expertise: Basic science close to the field.
-
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Referee #1
Evidence, reproducibility and clarity
Bello et al look at the SNP rs28834970 associated with Alzheimer's disease (AD), with C being the risk allele, on chromatin accessibility and expression of a nearby gene, PTK2B, in microglia. Their contention is that the single SNP affects chromatin accessibility and binding of the transcription factor CEBP[beta] in an intronic region of PTK2B and thereby affects PTKB expression. I had a few questions that I think are critical to be addressed. Please note that my numbering of panels is based on the figures, not the legends, which do not seem to quite agree with each other. There are also some figure legends that say "IFNg" while the figures say "LPS", which should be fixed.
The abstract says that editing a line that is homozygous for protective alleles to homozygous for risk results in "subtle downregulation of PTK2B expression". It isn't clear to me that the presented data fully supports this contention, which is central to the argument of the paper. In figure 2e, the authors show in both RNAseq and ddPCR that there is numerically lower PTK2B expression but this is not indicated to be statistically significant by one-way paired ANOVA. If there is no nominally significant difference in the edited lines, compared to the proposed significant differences in lines carrying the full risk haplotype (figure 1), then it would not seem sensible to ascribe the effects to the single edited base pair.
Given this uncertainty about the overall strength of effect of the single base pair change it would seem important to evaluate the proposed mechanism of CEBPb binding. It wasn't clear whether the ATAC-seq data summarized in the volcano plot in 2C is proposed to be a cause or a consequence of the CEBPb binding change. I am assuming that the 'fold change' estimate here is CC compared to TT, which would be consistent with direction of effect in figure 1, but please clarify.
In contrast to the subtle effects at PTK2B, the global transcriptional effects in figure 3 look quite strong. Are any of these changes dependent on PTK2B, that is to say, are they mimicked by partial suppression of PTK2B expression or activity?
Finally, in figure 4, it should be clarified as to why lower expression of PTK2B would be expected to have a detrimental effect on Alzheimer's risk. If understood correctly, and again fixing the figure legends would be helpful, the CC edited lines (risk) have lower chemokine induction than the unedited TT lines.
Significance
Going from GWAS hits, which represent blocks of high LD inherited variants, to single functional variants is a difficult problem in human genetics. The current paper attempts to isolate the effect of a single variant within an LD block on IPSC derived macrophages and microglia. This idea might be useful in nominating PTK2B as a therapeutic target for AD, although there is some question in my mind as to direction of effect.
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Reply to the reviewers
The manuscript " Phosphoproteomic analysis reveals the diversity of signaling behind ErbB inhibitor-induced phenotypes" authored by Drs. Katri Vaparanta, Anne Jokilammi, Johannes Merilahti, Johanna Örling, Noora Virtanen, Cecilia Sahlgren, Klaus Elenius and Ilkka Paatero was reviewed in Review Commons, and we carried out a full revision based on the received reviewer comments.
The comments from three reviewers and our point-by-point reply is here below. After each of reviewer´s comment, our reply is formatted in bold.
Reviewer #1 (Evidence, reproducibility and clarity (Required)):
In this study, Vaparanta and co-workers used zebrafish embryos as model to analyze the impact of ErbB tyrosine kinase inhibitors on signaling pathways at the whole organism level. Experimentally, zebrafish embryos were exposed for 1 hour to a single dose of 3 different ErbB tyrosine kinase inhibitors and the global phosphoproteome of the embryos was analyzed by MS/MS. The authors show that the 3 inhibitors differentially modulate the activity of PI3K/Akt, p38 MAPK, Notch, Hippo-YAP/TAZ and β-catenin signaling pathways, associated with different neurological and myocardial phenotypic changes. Using small molecule inhibitors of selective signaling pathways, they show that perturbation of different signaling pathways may induce similar phenotypes in zebrafish embryos.
Specific comments:
- The observation that exposure of zebrafish embryos to lapatinib, gefitinib and AG1478 leads to different global phosphoproteomic changes and to differential modulation of cellular signaling pathways was predictable and supported by an abundant literature. These 3 inhibitors differentially inhibit ErbB homo- and heterodimers and hit many other kinases. This point should be discussed in the paper.
Indeed, the kinase inhibitors do have different selectivity for the ErbB family kinases as pointed out by the reviewer. We have now discussed this point in the manuscript (new Supplemental Table 1) and added additional data from embryos treated with different ErbB kinase inhibitors with similar selectivity profiles into the manuscript (new Supplemental Figure 3-4). The ErbB family kinase selectivity profile of the inhibitors, however, does not fully explain why treatment with lapatinib (EGFR/ErbB2 inhibitor) induced the most unique phosphoproteomic changes from AG1478 (EGFR/ErbB2/ErbB4 inhibitor) and gefitinib (EGFR inhibitor) treatment in zebrafish embryos. This point is now discussed in the manuscript.
AG1478 is a first-generation tyrphostin while gefitinib and lapatinib are FDA-approved drugs. These compounds not only have different selectivity profiles, but also different pharmacological properties. Do the authors have any information about the permeability, distribution or concentration of the compounds in zebrafish embryos? Otherwise, how can they compare their effects?
__The reviewer points out correctly, that not only selectivity but also several other parameters could differ between compounds. The logic of our experimentation was to utilize differences in the properties of inhibitors to get new insights into underlying biological processes. These utilized differences could arise from not only selectivity but also as well from pharmacokinetic and –dynamic properties. Although it can be useful to understand these differences, this information per se is not needed to identify differentially regulated pathways that could affect the studied phenotypes. This is now better clarified in the discussion section. Our data indicates that ErbB inhibition profile explains a significant proportion, but not all, of observed signaling differences (Supplemental Fig. 3C). __
One major limitation of this study is that phosphoproteomic analysis was performed at a single time point and with a single dose of inhibitor, which compromises the interpretation of the findings. How was the dose of each inhibitor selected?
The doses were chosen based on our previous work (Paatero et al, 2019; Vaparanta et al, 2023), where with these inhibitor concentrations we were able to maximize the phenotypic effects without causing significant mortality. This is now mentioned in the results section of the manuscript. Higher dosages were lethal for the embryos, especially of AG1478, which is why a lower concentration of this inhibitor was used. The higher toxicity of AG1478 at lower concentrations compared to other ERBB inhibitors has also been previously noted by another group (Pruvot et al, 2014). Similar concentrations of the inhibitors have also been previously used by other groups with zebrafish embryos (Tran et al, 2007; Gallardo et al, 2015; Zhang et al, 2021; Du et al, 2024)__. __
One approach for better exploiting the data would be to correlate changes in phosphopeptides with the kinome selectivity of the inhibitors.
Indeed, we have now correlated our results from these inhibitors with other ErbB inhibitors of similar ErbB family kinase selectivity. The phosphoproteomic changes induced by inhibitors with similar ErbB family kinase selectivity significantly correlate (P = 0.0002, r:0.80 ,R2:0.65, Supplemental Fig. 3C) indicating that the ErbB selectivity plays a major role in determining the phosphoproteomic changes induced by these inhibitors. We also performed a correlation analysis between dimensionality-reduced phosphoproteomic changes and inhibitor selectivity. There was no significant correlation between the changes in the phosphoproteome and the ERBB selectivity of the inhibitors (P=0.1551, One-tailed Pearson correlation). Taken together, these results indicate that while the phosphoproteomic changes induced by these inhibitors can be reproduced by other inhibitors with similar ERBB selectivity profiles, inhibiting only a subset of the ERBB kinases (especially EGFR and ERBB2, but not ERBB4) produces a unique signaling signature that is not recapitulated with pan-ERBB inhibitor treatment. This information may be of interest since both lapatinib (EGFR/ERBB2 inhibitor) and neratinib (pan-ERBB inhibitor) are both used in the clinic to treat HER2-positive breast cancer. Our data indicates that the administration of these inhibitors to patients will likely have a differential global effect on cell signaling.
In the same vein, the signaling inhibitors used in Fig. 4 to dissect the phenotypic impact of distinct signaling pathways are non-selective, precluding any rigorous interpretation of the data. This confounding factor should at least be discussed in the manuscript. Again, the choice of the different doses of inhibitors is not justified.
Indeed, like all inhibitors, the inhibitors we utilized in Figure 4 can have some off-target effects. We aimed to use the concentration known by previous literature to have a measurable effect on the physiology of the zebrafish embryo (Fujii et al, 2000; Geling et al, 2002; Vasilyev et al, 2012; Jiang et al, 2023)__. These concentrations for different inhibitors were different in the literature, which is why different concentrations of the different inhibitors were used. We couldn’t find a reference for the concentration for VT-103, so a 30µM concentration was selected. With this concentration, the size of the embryo hearts was significantly reduced (P
The effect of inhibitors on the motility of embryos appears variable. For example, lapatinib markedly decreases motility in Fig. 4E but has no effect in Fig. 4F. Any explanation?
Different inhibitor concentrations were used in Figure 4E and Figure 4F. This has been now more clearly indicated in the manuscript in the results section and the figure legend. The lower inhibitor dosages in Figure 4F were to reduce the mortality and allow motility analyses of the embryos treated with a combination of the inhibitors analyses to facilitate observation of potential synergistic actions of inhibitors in co-treated embryos.
The conclusion that ErbB inhibitors induce similar phenotypes by perturbing different signaling pathways is not justified.
We have now softened our conclusions in the manuscript in the results section by replacing the sentence:” Taken together, these results suggest that AG1478 and lapatinib induce similar phenotypes by partially perturbing different signaling pathways in zebrafish embryos.” With the sentences: ” Taken together, these results suggest that AG1478 and lapatinib induce similar phenotypes but perturb different signaling pathways. Inhibition of these pathways induce similar phenotypes to lapatinib or AG1478 treatment in zebrafish embryos.”.
I have a few suggestions which could enhance the study's contribution to the field-
- The rationale for this study should be elaborated further. What new information is expected to emerge from these studies, independently of the conceptual and technical limitations outlined above?
We have now further elaborated the rationale of the study in the introduction section.
The advantage of studying the whole organism instead of selected tissues is questionable. Analyzing a mixture of organs may mask subtle and physiologically relevant alterations of signaling pathways in specific tissues.
We agree with the reviewer that if the researcher’s interests reside in a specific tissue then a more targeted approach should be applied to probe the phosphoproteome of this tissue. However, sometimes a more global view of the inhibitor effects is required especially when it is unknown which tissues are affected by the inhibitor treatment. Ideally, the global approach would be followed by a more targeted approach on the tissues that are indicated to be affected by the inhibitor. One must also consider the feasibility, time consumption and costs of probing all tissues separately. If only the targeted approach is applied, the information on what pathway activities are globally most affected in the organism by the inhibitor treatment can be hard to estimate.
Can the authors correlate neurological and myocardial phenotypes extrapolated from their study with pharmacological effects observed in mice or humans treated with these compounds?
__We have now correlated our findings in the discussion section with the previous literature on the phenotypes of ErbB inhibitor-treated and ErbB receptor knock-out mice and with the reported adverse effects of ErbB inhibitor treatment in the clinic. __
Reviewer #1 (Significance (Required)):
The authors show that the 3 inhibitors differentially modulate the activity of PI3K/Akt, p38 MAPK, Notch, Hippo-YAP/TAZ and β-catenin signaling pathways, associated with different neurological and myocardial phenotypic changes. Using small molecule inhibitors of selective signaling pathways, they show that perturbation of different signaling pathways may induce similar phenotypes in zebrafish embryos.
Reviewer #2 (Evidence, reproducibility and clarity (Required)):
In this study, the authors assess the effects of various ErbB receptor family tyrosine kinase inhibitors on the phosphoproteome of late embryonic and early larval stages of zebrafish. MS, Western blotting, and analysis of a transgenic zebrafish Notch signaling reporter line data suggest differential but overlapping effects of treatment with gefitinib, lapatinib and AG1478. Selected deregulated pathways are further assessed using a range of candidate downstream pathway-targeting inhibitors. Inhibitor treatment followed by quantification of spontaneous larval motility and heart ventricle wall area, which were previously found by the authors to be affected by AG1478 and lapatinib treatment, identifies involved downstream signaling pathways.
Major comments:
While I do not question the validity of the presented data showing phosphoproteome perturbations resulting from the performed ErbB inhibitor treatments, the treatment regimens used to assess the differential effects of the compounds may be insuffient to substantiate general statements comparing the phenotypic and phosphorylation effects of lapatinib, gefitinib and AG1478 beyond the effects of the specific doses applied to the embryo media. Unless directly quantified, it is difficult to reliably predict the in vivo dose resulting from drug administered to the embryo medium, and therefore a dose may be too high or too low for drug-to-drug comparison. Rationale for chosen dose of drugs should be provided. If available, inclusion of quantitative data on the drug-induced change in phosphorylation status of the drug target(s) is encouraged, and the discussion of the phosphoproteomic and phenotypical data should include this information.
The reviewer points out correctly, that not only selectivity but also several other parameters could differ between compounds. The logic of our experimentation was to utilize differences in the properties of inhibitors to get new insights into underlying biological processes. These utilized differences could arise from not only selectivity but also as well from pharmacokinetic and –dynamic properties. Although it can be useful to understand these differences, this information per se is not needed for the identification of differentially regulated pathways that could affect the studied phenotypes. This is now better clarified in the manuscript.
The rationale for the chosen drug doses has now been added to the manuscript in the results section. We used drug concentrations that were known to produce a phenotypic effect without causing significant mortality in the zebrafish embryos.
The ErbB receptors themselves are expressed at low levels, and unfortunately, we couldn´t reliably observe phosphopeptides of ErbB tyrosine autophosphorylation sites. To address this issue from a different angle, we treated embryos with other ErbB inhibitors exhibiting similar ErbB inhibition profiles as AG1478, lapatinib, and gefitinib (Supplemental Figure 3-4). This data indicates that the ErbB inhibition profile correlates quite well with the observed changes in the downstream signaling pathways p38, pAkt, pErk and Notch (Figure 3C and 4C).
Husbandry: The statement that "Zebrafish were maintained (...) following standard procedures." is insufficient without a specific reference. Please provide details on water quality parameters, temperature, light/darkness cycle and feeding regimen.
The requested information has now been added to the manuscript.
Western analysis: How many embryos were pooled in each sample? Please specify standard protocol or provide reference.
We have now amended the western analysis chapter in the materials and methods section as suggested by the reviewer. Five embryos were pooled for each sample.
Ventricle growth assay: The method of ventricle wall quantification is insufficiently described and might result in unnecessarily high variation. At which stage of the cardiac contraction-relaxation cycle were ventricle wall thickness and ventricle area measured? The confounding effect of contraction could be avoided altogether by stopping the heartbeat pharmacologically e.g. by administration of blebbistatin or verapamil. Subtracting ventricle lumen area from total ventricle area seems a much more direct measure of ventricle wall area than the estimation obtained by multiplying ventricle wall thickness with ventricle area.
We apologize for the mistake in the materials methods section, where we had written area instead of perimeter. We have now amended the ventricle growth assay chapter in the materials and methods sections and added more details on the ventricle wall area estimation. The ventricle wall area was measured from high-speed movies in diastole and systole, and the average perimeter over these states was reported. The ventricle wall thickness was only measured in systole. We chose this quantification method since the lumen area is difficult to estimate in the systole.
Phosphopeptide enrichment: How many embryos per sample? Final DMSO concentration is not stated.
__Twenty embryos per sample and 1% DMSO was used. This information is now included in the materials and methods section. __
P-values are presented for comparison of select groups only and a statement that e.g. only P-values We have added the recommended statement and the mean/median value with deviation values for the data indicated by the reviewer in the figure legends.
Minor comments:
Overall, the manuscript is well written and data and methods are well presented.
The relevant targets within the ErbB family of receptors should be introduced including information on well-established functions and downstream signaling pathways to enable the non-specialist reader to place the presented data in the context of known gene and protein function. Furthermore, conservation of target proteins in zebrafish should be touched upon.
We have now rewritten the introduction and results sections to include information on the ErbB family kinase selectivity of these inhibitors, the well-established functions and the target downstream pathways of ErbB receptors. We have now performed a multiple sequence alignment on the kinase domain of the ErbB receptors in human and zebrafish to estimate the conservation of the inhibitor targets in the zebrafish model. Human ErbB kinase domains had a high 86+/-9% sequence identity with zebrafish counterparts (Supplemental Figure 2) compared to 67+/-14% identity with the other ErbB kinase domain sequences in zebrafish (P=0.012).
Given different target profiles of the tested drugs among receptors of the ErbB family, differences in protein phosphorylation perturbations and in treatment-induced phenotypes may not be unexpected. Statements such as: "An unexpectedly large cluster of phosphopeptides that were increased in lapatinib-treated embryos but reduced in AG1478 and gefitinib treated embryos was detected" and "AG1478 and lapatinib may induce similar phenotypes by partially perturbing different signaling pathways in zebrafish embryos" should be discussed in the context of known drug target(s) and their functions.
We have now rewritten these statements as suggested by the reviewer and the target profiles are now discussed in the manuscript.
**Referee Cross-commenting**
I agree with the other reviewers on almost all points.
1) While the sensitivity to smaller or highly local effects is most likely reduced using the whole organism approach compared to e.g. single tissue analysis, I do believe that it is highly relevant due to its ability to identify potential effects beyond a single tissue or organ.
2) I maintain that while the presented data nicely show the effects of each administered dose of the individual compounds, the data does not allow for meaningful drug-to-drug comparisons without quantitative information on in vivo dose or direct target effect. If such information cannot be included, cross-drug conclusions and discussion should be done very carefully.
Reviewer #2 (Significance (Required)):
The evaluation of systemic molecular and phenotypic consequences of anti-cancer drugs in a vertebrate model system represents a relevant advancement. Although drug effects are likely to differ somewhat between embryonic and larval zebrafish and human cancer patients, the authors' comparison of obtained zebrafish data with human data supports translatability of the presented phosphoproteomics data. Also, the presented data pose a relevant advancement facilitating the informed use of the tested inhibitors as tools in basic science.
Expertise: Molecular biology, signaling, zebrafish. Limited expertise in omics data analysis and pharmacology.
Reviewer #3 (Evidence, reproducibility and clarity (Required)):
The authors evaluated selected EGFR inhibitors developed as targeted cancer therapeutics, using zebrafish embryos and larvae as an in vivo model system. They performed mass spectrometry to analyze phosphorylation levels in target proteins, in combination with western blotting and gene set enrichment analyses; using this data, they assessed overlap between the inhibitors and overlap with known human data. They also performed imaging and locomotion analyses to assess alterations in phenotypes and phosphorylation-dependent signaling due to the inhibitor(s). The study generates novel information that is potentially relevant to the toxicity and efficacy of clinically used kinase inhibitors.
- The statistical analyses are appropriate to the data and the experimental design.
-
The claims made by the authors are consistent with the data. In my opinion, the following revisions are needed for the manuscript to be accepted for publication:
-
There is no mention of Gefitinib in the Abstract; please include it.
Gefitinib is now included in the Abstract.
Please state the target selectivity profiles (from known preclinical and/or clinical data) of the three inhibitors used.
__These are now presented in supplemental data (Supplemental Table 1), and analysed in relation to the observed signaling changes (Supplemental Figures 3 and 4). __
Please clarify whether the residues mentioned in the phospho-specific antibody data refer to zebrafish or human proteins.
Residues refer to human proteins as they are more widely used. This is now more clearly indicated in the materials and methods section.
Please state whether the pan-antibodies corresponding to the phospho-specific antibody targets were used, and mention any problems associated with their use. This will help readers not familiar with antibody use in zebrafish experiments. It will also help emphasize the value of mass spectrometric analysis in zebrafish protein work.
__As pointed out, the target specificity of antibodies is not often defined in zebrafish models on residue level, and phospho-specific antibodies may bind several closely related targets. The availability of robustly validated antibodies for zebrafish work, especially for phosphospecific epitopes, is quite limiting and therefore other, non-antibody-based techniques would be highly useful. This is now discussed in the manuscript. The phosphorylation site-specific antibodies used in this study indeed recognize the phosphorylated residue in several protein family members which further complicates the result interpretation. This is less of a limitation in the DIA-MS based phosphoproteomics approach which is now additionally discussed in the manuscript. __
Please attempt to describe the clinically documented cardiovascular and neurological effects of the inhibitors and any correlation(s) with your data. This will enhance the impact of the study.
See our reply for reviewer#1, comment 3.
**Referee Cross-commenting**
The common points raised in all the Reviews are the following:
- The rationale of the study should be described in more detail, especially the utility of zebrafish as an in vivo model, addressing its advantages and limitations.
This is now discussed more extensively in the manuscript.
The findings need to be described in the context of the target selectivity profiles and clinical effects of the inhibitors, especially the approved inhibitors (Gefitinib and Lapatinib).
We have added data on target selectivity profiles (Supplemental Table 1), target conservation (Supplemental Figure 2) and also compared our observations to zebrafish embryos treated with other ErbB inhibitors with similar ErbB selectivity profiles (Supplemental Figure 3 and 4).
- In my opinion, while the comments regarding target site drug concentration (within the embryos/larvae) and dose-response are relevant, I consider these experiments to be appropriate in a more detailed follow-up study.
We agree with the reviewer that the comprehensive pharmacokinetic studies fall outside the scope of this manuscript. As discussed before, in this manuscript we utilize differential inhibitor properties to gain new insight into phenotypes and underlying biological processes. This logic works even if the differences arise from properties other than the target selectivity.
One of the main value additions of the study is that it highlights a useful alternative to conventional strategies used in preclinical cellular and mammalian model studies of kinase inhibitors. I would urge the authors to discuss specific future directions, giving due importance to all the reviewers' comments.
This is now more extensively elaborated in the discussion section.
Reviewer #3 (Significance (Required)):
The experiments are well-described and provide sufficient information and detail for readers to understand and reproduce.
The study is highly relevant to the use of zebrafish as a whole-organism model for in vivo evaluation of drugs, specifically kinase inhibitors.
References
Du K, Liu Y, Zhang L, Peng L, Dong W, Jiang Y, Niu M, Sun Y, Wu C, Niu Y et al (2024) Lapatinib combined with doxorubicin causes dose-dependent cardiotoxicity partially through activating the p38MAPK signaling pathway in zebrafish embryos. Biomed Pharmacother 175. doi:10.1016/J.BIOPHA.2024.116637.
Fujii R, Yamashita S, Hibi M, Hirano T (2000) Asymmetric p38 activation in zebrafish: Its possible role in symmetric and synchronous cleavage. Journal of Cell Biology 150. doi:10.1083/jcb.150.6.1335.
Gallardo VE, Varshney GK, Lee M, Bupp S, Xu L, Shinn P, Crawford NP, Inglese J, Burgess SM (2015) Phenotype-driven chemical screening in zebrafish for compounds that inhibit collective cell migration identifies multiple pathways potentially involved in metastatic invasion. DMM Disease Models and Mechanisms 8. doi:10.1242/dmm.018689.
Geling A, Steiner H, Willem M, Bally-Cuif L, Haass C (2002) A γ-secretase inhibitor blocks Notch signaling in vivo and causes a severe neurogenic phenotype in zebrafish. EMBO Rep 3. doi:10.1093/embo-reports/kvf124.
Jiang Y, Zhao X, Chen J, Aniagu S, Chen T (2023) PM2.5 induces cardiac malformations via PI3K/akt2/mTORC1 signaling pathway in zebrafish larvae. Environmental Pollution 323. doi:10.1016/j.envpol.2023.121306.
Paatero I, Veikkolainen V, Mäenpää M, Schmelzer E, Belting HG, Pelliniemi LJ, Elenius K (2019) ErbB4 tyrosine kinase inhibition impairs neuromuscular development in zebrafish embryos. Mol Biol Cell 30. doi:10.1091/mbc.E18-07-0460.
Pruvot B, Curé Y, Djiotsa J, Voncken A, Muller M (2014) Developmental defects in zebrafish for classification of EGF pathway inhibitors. Toxicol Appl Pharmacol 274. doi:10.1016/j.taap.2013.11.006.
Tran TC, Sneed B, Haider J, Blavo D, White A, Aiyejorun T, Baranowski TC, Rubinstein AL, Doan TN, Dingledine R et al (2007) Automated, quantitative screening assay for antiangiogenic compounds using transgenic zebrafish. Cancer Res 67. doi:10.1158/0008-5472.CAN-07-3126.
Vaparanta K, Jokilammi A, Paatero I, Merilahti JA, Heliste J, Hemanthakumar KA, Kivelä R, Alitalo K, Taimen P, Elenius K (2023) STAT5b is a key effector of NRG ‐1/ ERBB4 ‐mediated myocardial growth . EMBO Rep 24. doi:10.15252/embr.202256689.
Vasilyev A, Liu Y, Hellman N, Pathak N, Drummond IA (2012) Mechanical stretch and PI3K signaling link cell migration and proliferation to coordinate epithelial tubule morphogenesis in the zebrafish pronephros. PLoS One 7. doi:10.1371/journal.pone.0039992.
Xin M, Kim Y, Sutherland LB, Qi X, McAnally J, Schwartz RJ, Richardson JA, Bassel-Duby R, Olson EN (2011) Development: Regulation of insulin-like growth factor signaling by Yap governs cardiomyocyte proliferation and embryonic heart size. Sci Signal 4. doi:10.1126/scisignal.2002278.
Zhang Y, Cai Y, Zhang SR, Li CY, Jiang LL, Wei P, He MF (2021) Mechanism of hepatotoxicity of first-line tyrosine kinase inhibitors: Gefitinib and afatinib. Toxicol Lett 343. doi:10.1016/j.toxlet.2021.02.003.
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Referee #3
Evidence, reproducibility and clarity
The authors evaluated selected EGFR inhibitors developed as targeted cancer therapeutics, using zebrafish embryos and larvae as an in vivo model system. They performed mass spectrometry to analyze phosphorylation levels in target proteins, in combination with western blotting and gene set enrichment analyses; using this data, they assessed overlap between the inhibitors and overlap with known human data. They also performed imaging and locomotion analyses to assess alterations in phenotypes and phosphorylation-dependent signaling due to the inhibitor(s). The study generates novel information that is potentially relevant to the toxicity and efficacy of clinically used kinase inhibitors.
- The statistical analyses are appropriate to the data and the experimental design.
- The claims made by the authors are consistent with the data.
In my opinion, the following revisions are needed for the manuscript to be accepted for publication:
- There is no mention of Gefitinib in the Abstract; please include it.
- Please state the target selectivity profiles (from known preclinical and/or clinical data) of the three inhibitors used.
- Please clarify whether the residues mentioned in the phospho-specific antibody data refer to zebrafish or human proteins.
- Please state whether the pan-antibodies corresponding to the phospho-specific antibody targets were used, and mention any problems associated with their use. This will help readers not familiar with antibody use in zebrafish experiments. It will also help emphasize the value of mass spectrometric analysis in zebrafish protein work.
- Please attempt to describe the clinically documented cardiovascular and neurological effects of the inhibitors and any correlation(s) with your data. This will enhance the impact of the study.
Referee Cross-commenting
The common points raised in all the Reviews are the following:
- The rationale of the study should be described in more detail, especially the utility of zebrafish as an in vivo model, addressing its advantages and limitations.
- The findings need to be described in the context of the target selectivity profiles and clinical effects of the inhibitors, especially the approved inhibitors (Gefitinib and Lapatinib).
- In my opinion, while the comments regarding target site drug concentration (within the embryos/larvae) and dose-response are relevant, I consider these experiments to be appropriate in a more detailed follow-up study.
- One of the main value additions of the study is that it highlights a useful alternative to conventional strategies used in preclinical cellular and mammalian model studies of kinase inhibitors. I would urge the authors to discuss specific future directions, giving due importance to all the reviewers' comments.
Significance
The experiments are well-described and provide sufficient information and detail for readers to understand and reproduce.
The study is highly relevant to the use of zebrafish as a whole-organism model for in vivo evaluation of drugs, specifically kinase inhibitors.
-
Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.
Learn more at Review Commons
Referee #2
Evidence, reproducibility and clarity
In this study, the authors assess the effects of various ErbB receptor family tyrosine kinase inhibitors on the phosphoproteome of late embryonic and early larval stages of zebrafish. MS, Western blotting, and analysis of a transgenic zebrafish Notch signaling reporter line data suggest differential but overlapping effects of treatment with gefitinib, lapatinib and AG1478. Selected deregulated pathways are further assessed using a range of candidate downstream pathway-targeting inhibitors. Inhibitor treatment followed by quantification of spontaneous larval motility and heart ventricle wall area, which were previously found by the authors to be affected by AG1478 and lapatinib treatment, identifies involved downstream signaling pathways.
Major comments:
While I do not question the validity of the presented data showing phosphoproteome perturbations resulting from the performed ErbB inhibitor treatments, the treatment regimens used to assess the differential effects of the compounds may be insuffient to substantiate general statements comparing the phenotypic and phosphorylation effects of lapatinib, gefitinib and AG1478 beyond the effects of the specific doses applied to the embryo media. Unless directly quantified, it is difficult to reliably predict the in vivo dose resulting from drug administered to the embryo medium, and therefore a dose may be too high or too low for drug-to-drug comparison. Rationale for chosen dose of drugs should be provided. If available, inclusion of quantitative data on the drug-induced change in phosphorylation status of the drug target(s) is encouraged, and the discussion of the phosphoproteomic and phenotypical data should include this information.
Husbandry: The statement that "Zebrafish were maintained (...) following standard procedures." is insufficient without a specific reference. Please provide details on water quality parameters, temperature, light/darkness cycle and feeding regimen.
Western analysis: How many embryos were pooled in each sample? Please specify standard protocol or provide reference. Ventricle growth assay: The method of ventricle wall quantification is insufficiently described and might result in unnecessarily high variation. At which stage of the cardiac contraction-relaxation cycle were ventricle wall thickness and ventricle area measured? The confounding effect of contraction could be avoided altogether by stopping the heartbeat pharmacologically e.g. by administration of blebbistatin or verapamil. Subtracting ventricle lumen area from total ventricle area seems a much more direct measure of ventricle wall area than the estimation obtained by multiplying ventricle wall thickness with ventricle area.
Phosphopeptide enrichment: How many embryos per sample? Final DMSO concentration is not stated. P-values are presented for comparison of select groups only and a statement that e.g. only P-values < 0.05 are plotted would be helpful if applicable. Also, please provide mean +/- standard deviation for data presented in figures 3A, 3B, 4C, 4E, and 4F.
Minor comments:
Overall, the manuscript is well written and data and methods are well presented. The relevant targets within the ErbB family of receptors should be introduced including information on well-established functions and downstream signaling pathways to enable the non-specialist reader to place the presented data in the context of known gene and protein function. Furthermore, conservation of target proteins in zebrafish should be touched upon.
Given different target profiles of the tested drugs among receptors of the ErbB family, differences in protein phosphorylation perturbations and in treatment-induced phenotypes may not be unexpected. Statements such as: "An unexpectedly large cluster of phosphopeptides that were increased in lapatinib-treated embryos but reduced in AG1478 and gefitinib treated embryos was detected" and "AG1478 and lapatinib may induce similar phenotypes by partially perturbing different signaling pathways in zebrafish embryos" should be discussed in the context of known drug target(s) and their functions.
Referee Cross-commenting
I agree with the other reviewers on almost all points.
- While the sensitivity to smaller or highly local effects is most likely reduced using the whole organism approach compared to e.g. single tissue analysis, I do believe that it is highly relevant due to its ability to identify potential effects beyond a single tissue or organ.
- I maintain that while the presented data nicely show the effects of each administered dose of the individual compounds, the data does not allow for meaningful drug-to-drug comparisons without quantitative information on in vivo dose or direct target effect. If such information cannot be included, cross-drug conclusions and discussion should be done very carefully.
Significance
The evaluation of systemic molecular and phenotypic consequences of anti-cancer drugs in a vertebrate model system represents a relevant advancement. Although drug effects are likely to differ somewhat between embryonic and larval zebrafish and human cancer patients, the authors' comparison of obtained zebrafish data with human data supports translatability of the presented phosphoproteomics data. Also, the presented data pose a relevant advancement facilitating the informed use of the tested inhibitors as tools in basic science.
Expertise: Molecular biology, signaling, zebrafish. Limited expertise in omics data analysis and pharmacology.
-
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Referee #1
Evidence, reproducibility and clarity
In this study, Vaparanta and co-workers used zebrafish embryos as model to analyze the impact of ErbB tyrosine kinase inhibitors on signaling pathways at the whole organism level. Experimentally, zebrafish embryos were exposed for 1 hour to a single dose of 3 different ErbB tyrosine kinase inhibitors and the global phosphoproteome of the embryos was analyzed by MS/MS. The authors show that the 3 inhibitors differentially modulate the activity of PI3K/Akt, p38 MAPK, Notch, Hippo-YAP/TAZ and β-catenin signaling pathways, associated with different neurological and myocardial phenotypic changes. Using small molecule inhibitors of selective signaling pathways, they show that perturbation of different signaling pathways may induce similar phenotypes in zebrafish embryos.
Specific comments:
- The observation that exposure of zebrafish embryos to lapatinib, gefitinib and AG1478 leads to different global phosphoproteomic changes and to differential modulation of cellular signaling pathways was predictable and supported by an abundant literature. These 3 inhibitors differentially inhibit ErbB homo- and heterodimers and hit many other kinases. This point should be discussed in the paper.
- AG1478 is a first-generation tyrphostin while gefitinib and lapatinib are FDA-approved drugs. These compounds not only have different selectivity profiles, but also different pharmacological properties. Do the authors have any information about the permeability, distribution or concentration of the compounds in zebrafish embryos? Otherwise, how can they compare their effects?
- One major limitation of this study is that phosphoproteomic analysis was performed at a single time point and with a single dose of inhibitor, which compromises the interpretation of the findings. How was the dose of each inhibitor selected?
- One approach for better exploiting the data would be to correlate changes in phosphopeptides with the kinome selectivity of the inhibitors.
- In the same vein, the signaling inhibitors used in Fig. 4 to dissect the phenotypic impact of distinct signaling pathways are non-selective, precluding any rigorous interpretation of the data. This confounding factor should at least be discussed in the manuscript. Again, the choice of the different doses of inhibitors is not justified.
- The effect of inhibitors on the motility of embryos appears variable. For example, lapatinib markedly decreases motility in Fig. 4E but has no effect in Fig. 4F. Any explanation?
- The conclusion that ErbB inhibitors induce similar phenotypes by perturbing different signaling pathways is not justified.
I have a few suggestions which could enhance the study's contribution to the field-
- The rationale for this study should be elaborated further. What new information is expected to emerge from these studies, independently of the conceptual and technical limitations outlined above?
- The advantage of studying the whole organism instead of selected tissues is questionable. Analyzing a mixture of organs may mask subtle and physiologically relevant alterations of signaling pathways in specific tissues.
- Can the authors correlate neurological and myocardial phenotypes extrapolated from their study with pharmacological effects observed in mice or humans treated with these compounds?
Significance
The authors show that the 3 inhibitors differentially modulate the activity of PI3K/Akt, p38 MAPK, Notch, Hippo-YAP/TAZ and β-catenin signaling pathways, associated with different neurological and myocardial phenotypic changes. Using small molecule inhibitors of selective signaling pathways, they show that perturbation of different signaling pathways may induce similar phenotypes in zebrafish embryos.
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Reply to the reviewers
Response to Reviewer #1:
We agree with Reviewer 1 that a function of ROPGEFs in this process was expected to some degree. However, we want to point out that this manuscript focuses on the requirement of ROPGEFs and especially the spatio-temporal description of ROP signalling polarisation and activation during pollen germination. Moreover, different to the downstream ROPs, we show ROPGEFs do not act strictly redundant, confirming results from root hair initiation and providing additional evidence that multiple signalling pathways are required for pollen germination and that ROPGEFs might be essential for bringing specificity to these signals.
Major comments:
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Only one GEF11 mutant line, gef11-t1, was analyzed for germination ratio. It is presumptuous to conclude that GEF11 has no function in the pollen germination of Arabidopsis thaliana (line 241- line 242).
After the initial negative results, we did not focus on GEF11 further. Thus, we fully agree that it is presumptuous to make such strong statements about the role of GEF11 during pollen germination. We generated additional gef11 mutant alleles for this revision plan using CRISPR/Cas9 as no other suitable lines were available. Moreover, we now have additional higher-order mutants available to demonstrate the function of GEF11 during pollen germination. These additional lines were generated and confirmed and are growing right now. Thus, we will be able to implement new results addressing this point timely, allowing us to make a more founded statement about the function of GEF11 (see Response to Reviewer #2).
Minor comments:
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In Figure 2A, pollen germination ratio was not provided for the single mutants gef8-c△3 and gef9-c△
This is due to the generation process of the CRISPR/Cas9 alleles. These alleles were generated by a construct mutating both genes simultaneously; thus, these mutants are unavailable as single mutant lines. Instead of separating these alleles by outcrossing, we included additional single mutant alleles for both GEFs with a similar deletion. As all these CRISPR/Cas9 mutants have a complete deletion of the GEF-ORF, we are sure about the loss of the according GEF function. Additional alleles account for possible unspecific effects.
In Figure 3D, the subcellular localization of GEF12GEF8C is fuzzy. Better imaging is needed.
We agree that the quality of these images is not ideal due to this specific line having less fluorescent signal. We screened for more lines of this construct and already performed more experiments. We will provide better images for this genotype.
In Figure 3E, it is intriguing that both GEF8-S518A and GEF8-S518D are not associated with the PM in germinating pollen grains. Does it mean that phosphorylation at S518 is not relevant to polar distribution of GEF8?
We also find this very intriguing as we did not expect this result. However, we interpret it slightly differently in the way that the S518 site is relevant for GEF polarisation, which might be conferred by RLK interaction. We think both mutant forms alter this potential association with RLKs, thus losing polarisation. We will include more imaging experiments of these constructs and additional lines to strengthen our results. Moreover, we generated lines to study these lines' functionality and complementation capacity, which will be included in a revised manuscript.
T-DNA insertion lines, gef11-t1 and gef12-t1, need to be verified by PCRs in Figure S3D.
Thanks for pointing this out. This control should be provided, and we will include the verification in the supplement.
Response to Reviewer #2:
Like Reviewer #2, we are also very intrigued by the biphasic accumulation of GEFs, as this is an entirely novel feature of this process. Like Reviewer #2, we also interpret this as an exploration and establishment phase, which could help us to understand how the pollen germination site is decided in species without aperture-dependent pollen germination.
Major comments:
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In line 241, the authors conclude that GEF11 has no function in pollen germination. However, it is likely that GEF11 also plays a redundant role as GEF12 does. I recommend the authors check the phenotypes of gef11,gef12 double mutant and gef8,gef9,gef11 triple mutant to confirm that GEF11 has indeed no function. Otherwise, this conclusion should be better rephrased.
This point is well justified and similar to the comment of Reviewer #1. As stated before, we had to generate additional lines for this. We will analyse an additional gef11 allele, gef8/gef11 and gef9/11 double mutants, and gef9/11/12 triple mutants to address the function of GEF11 in more detail. The conclusions of the original manuscript will, of course, be adjusted according to the new results.
Although GEF12 is in the cytosol, the strong pollen germination defects in gef8,gef9,gef12 triple mutants do indicate a critical role of GEF12. Is it possible that GEFs could function in the cytosol? The authors can test this possibility by examining the rescuing ability of several constructs that express, for example, GEF12, GEF12(+GEF8C), GEF8(SA), or GEF8(SD) in gef8. The authors may not perform all of these rescue experiments, but some of the mentioned lines are already in hands. They could readily check the phenotypes.
We thank the Reviewer for this great point. This information is crucial to discriminate the function of the individual GEFs. We have generated new lines expressing some of the mentioned constructs in the gef8 background to address this. We now have lines that complement gef8 with GEF12, GEF12GEF8C, GEF8S518A, GEF8S518D, and GEF8ΔC. We are currently performing experiments which determine the functionality of these constructs, which will allow us to make more conclusive statements about the function of GEFs in the cytosol and how important the PRONE domain alone, or the membrane attachment of GEFs, is for their function.
The authors conclude that the C-terminus of GEF8 and GEF9 is necessary and sufficient for membrane localization because GEF8/9C can target GEF12 PRONE domain to the membrane. It is intriguing whether the C-terminus alone could confer membrane targeting ability. Currently, it is not fully understood how GEFs localize to the membrane. Examining the localization of GEF8/9C itself would help clarify this and improve our understanding of GEF regulation. Alternatively, the authors may discuss evidence that supports or disagrees with this possibility.
This is a good suggestion by the reviewer and indeed intriguing if the C-Terminus alone could confer membrane attachment. Meanwhile, we obtained plants expressing such constructs, showing that the C-terminus alone is insufficient for membrane attachment. This is not surprising, as these domains are largely disordered, and we suspect that the context of an adjacent PRONE domain is required to carry out this function. We will include our new results in the revised manuscript.
Minor comments:
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The N- and C-terminus of GEF8 are predicted to inhibit complex formation. How is the prediction performed? Do the authors use monomer prediction or multimer prediction? Alphafold2 has a low accuracy in predicting non-conserved regions. How confident are the predicted inhibitory contacts?
We used multimer-prediction of Alphafold2 for the shown structures. However, we fully agree that the predicted structures of Alphafold have low accuracy in that regard, especially for disordered domains like this. We will provide confidence models and predicted aligned error (PAE) plots for this structure. Additionally, we will put our conclusions in a better perspective of these structure confidences and tone down our interpretations of this section.
Localization of ROPs and calcium reporter in Figure 4 appears to be variable. It would help clarify the specific effects on each reporter if the authors present these data more quantitatively.
We agree with the reviewer that some of the observations are variable. We will provide the data more quantitatively, including overviews of which percentage we observed the described phenomena and a more quantitative analysis of the strength and timing of signal accumulation (see also Response to Reviewer #3).
Response to Reviewer #3:
Major points:
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One of my major points is that the manuscript is now mainly based on the observations of individual pollen grains. These are then subjected to well-performed image analysis approaches but still represent somewhat anecdotal evidence (Fig 1A, B, Fig 3C-E, etc). The analysis and (numerical) presentation of a more robust data sample (which I presume the authors have acquired) would strengthen the ms considerably. This goes beyond the Figs - e.g. in l. 164-165 authors state rather vaguely, "we observed that mCit-GEF8 and mCit-GEF9 accumulated at a defined region in the cell periphery, which strongly correlated with the future germination site." Here, I would appreciate the data showing the actual correlation, if every germinated pollen grain displays GEF8/9 accumulation, whether there is a population of pollen grains showing the GEF8/9 transient but not germinating, etc...
We very much appreciate the reviewer's comment, as this version of the manuscript indeed seems like we made our conclusions based on observations made from individual pollen. However, this is not the case. As the reviewer suspected, more data is available but not included in the manuscript. We have multiple observations for each of the shown constructs and only show a representative one. Furthermore, we imaged more pollen germination events of lines that showed variability and included additional lines for some constructs. We will provide a more quantitative analysis of the results to better represent the variability of the individual constructs, and we will adjust the manuscript accordingly (see comment 2).
Where the authors analyse multiple cells, we are still missing some info - e.g. it is not stated what the error bars in Fig 1C, D represents (SD, SEM, CI?), size of the sample, etc. In any case, it is evident that there is quite substantial variability in the data, which is understandable. Maybe the authors can plot the individual profile lines along the average? Plus, GEF9 seem to have the maximum pre-germination localisation at -5 min rather than -9 min.
We agree with the Reviewer that information is missing or not obviously stated. We will correct this for the revised manuscript. Moreover, we agree that the suggested way of showing the data would provide more information and allow a better representation of the results and their variability. This can be seen in the reviewer's interpretation of the results of GEF9. In this case, we see some variability in the timing of GEF9 accumulation, leading to the peak maximum shift. In a revised manuscript, we will, as suggested, show the data as individual lines, providing a better representation of the data. Moreover, we will include such representations for other used constructs to provide a general, more quantitative data analysis (see comment 1).
I know it is very challenging, but the ms would be much stronger with the in vivo imaging of pollen germination on stigmatic papillae (i) GEF8/9 in wt, (ii) gef8/9 double mutant. This would bring crucial data about the role of the GEF polar domain and its functional relation to pollination.
This would indeed be great to see. We put an effort into establishing such in vivo imaging experiments with our fluorescent markers. However, we cannot image these events in an in vivo setup (at least with our resources). This has two reasons: 1. The events are very fast and limited to a small region at the pollen-papilla contact side, which we have issues resolving optically and timely. 2. The used marker lines only have a low fluorescent level due to the native promoter, and stronger expression would lead to overexpression artefacts. In vitro, it is difficult to see the observed signal accumulation. In the in vivo situation, we are facing additional diffraction of the papilla cells, which would make the observation of GEF accumulation impossible with our microscopes.
The phylogeny presented in Fig S1 is only rudimental and not very interesting. Given the author's results, I would love to see if GEF8/9 orthologs also exist in species with defined pollen apertures (where establishing a dynamic site makes little sense). The authors touch on this (L409-411), but it would deserve better analysis and discussion.
We agree with the reviewer that studying GEF function/accumulation in species with aperture-dependent germination would be interesting. However, we can not conclude functional orthologs in other species based on phylogeny. Such phylogenetic analyses were done, for example, by Kim et al. (BMC Plant Biology, 2020, doi: 10.1186/s12870-020-2298-5). The issue is that all Arabidopsis pollen-expressed GEFs form a closed phylogenetic group without allowing the interpretation of which rice homolog is the functional ortholog of the respective Arabidopsis GEF (this is the same for maize). Thus, such phylogenetic analyses are not conclusive, and they would require experimental data to prove orthology. However, we agree that this point can be interpreted and discussed better, and we will include this in the revised manuscript.
I am not entirely convinced by the authors' interpretation of rather strange S518 mutation data. Could S518A mutation affect overall GEF8 structure/stability?
We were also suspicious about these results, as they were unexpected (see also Response to Reviewer #1). To confirm these results, we made additional lines for these constructs, double-checked that the constructs were correct and made more observations for both GEF8S18A and GEF8S18D. Additionally, we started investigating the functionality of these constructs and have this data available timely. Preliminary results suggest that the constructs are partial to fully functional compared to the WT GEF8, arguing against these mutations' effect on structure or stability. We will include more data for these constructs in a revised manuscript to allow a more conclusive interpretation of these unexpected observations.
Although the authors cannot observe the localisation of ROPs in the plasma membrane, they see the apparent accumulation of active ROP marker CRIB4 there - implying that ROPs must localise to the pollen PM at the germination site. This discrepancy should be solved or at least discussed more.
The reviewer is correct in that we cannot observe ROP accumulation but rather the accumulation of ROP activity (as seen by CRIB4). This is in line with the observation made by Xiang et al. (2023, Plant Physiology, doi: 10.1093/plphys/kiad196), which also cannot find ROP accumulation. We are convinced that ROPs are present at the plasma membrane of the pollen germination site, but no accumulation is observable. We believe this is due to a high mobility of ROPs and that no accumulation is required, as only a few ROPs are sufficient to activate downstream signals. We will discuss these results in more detail in a revised manuscript to better explain the observed discrepancy.
Given that calcium oscillates very rapidly in pollen and pollen tubes (with frequency ~6-20s), the profound, long-term changes in calcium levels reported by the authors can hardly be referred to as oscillations. The phenomenon observed should again be analysed using a bigger sample.
We agree that the terminology is not good, as it suggests similarities to the oscillations found in pollen tubes. Thus, we will change the revised manuscript and refer to the changes in Ca2+ levels as “elevations”. Moreover, we will provide a more quantitative analysis and a bigger sample size, as stated in Response to Reviewer #2.
Minor points:
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In Fig 1F, GEF12 also seems to be polarly localised to the future site.
The chosen sample is not ideal, as it looks like GEF12 would also slightly accumulate. However, as seen in the quantification of this cell, GEF12 does not significantly accumulate at the pollen germination site, and we never observed any accumulation of GEF12 that is comparable to GEF8 or GEF9. We will include another sample of this colocalisation in the revised manuscript to avoid misinterpretation of the data.
It is difficult to make any assumptions based on the AlphaFold2 predictions without showing their confidence assessments (e.g., PAE plots). The authors state this themselves in the discussion (L. 447-449).
As the Response to Reviewer #2 stated, we will include structures with confidence values and PAE plots in the supplement. We additionally tone down our interpretation of these structure predictions to make clear that these structures should be interpreted carefully.
On one hand the authors repeatedly state that pollen GEFs do act in a redundant manner (and provide some evidence for it), on the other hand the absence of an in vivo phenotype for single and double knockout lines and only mild phenotype for a triple ko line does suggest a level of redundancy. This should be rephrased.
We agree that this is not clearly phrased. In a revised version, we will change the manuscript to indicate which type and level of redundancy are described. We will discriminate between genetic redundancy, as seen in the mild in vivo effects, and non-redundant molecular function, as observed by protein localisation.
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Referee #3
Evidence, reproducibility and clarity
This manuscript investigates the role of PRONE ROP GEFS in germinating Arabidopsis pollen. Given that the molecular mechanisms underlying cellular polarisation in pollen germinating pollen grains are still largely unknown (as opposed to the tip growth of elongating pollen tubes), this manuscript deals with an important topic. Moreover, it builds on the excellent previous research from the lead author, which uncovered ROP GEFs as principal polarisation players during root hair initiation. Here, the authors found that out of five pollen-expressed GEFS, two (GEF8 and 9) mark a future germination site with remarkable spatiotemporal dynamics. Using the genetic tools, GEF8 and 9 were shown to be important for pollen germination in vitro and participate in germination in vivo. Generally, this is an exciting topic, and I quite enjoyed reading the manuscript. However, there are several aspects of the work, which - when addressed - would significantly improve the overall message presented by the authors.
Major points
- One of my major points is that the manuscript is now mainly based on the observations of individual pollen grains. These are then subjected to well-performed image analysis approaches but still represent somewhat anecdotal evidence (Fig 1A, B, Fig 3C-E, etc). The analysis and (numerical) presentation of a more robust data sample (which I presume the authors have acquired) would strengthen the ms considerably. This goes beyond the Figs - e.g. in l. 164-165 authors state rather vaguely, "we observed that mCit-GEF8 and mCit-GEF9 accumulated at a defined region in the cell periphery, which strongly correlated with the future germination site." Here, I would appreciate the data showing the actual correlation, if every germinated pollen grain displays GEF8/9 accumulation, whether there is a population of pollen grains showing the GEF8/9 transient but not germinating, etc...
- Where the authors analyse multiple cells, we are still missing some info - e.g. it is not stated what the error bars in Fig 1C, D represents (SD, SEM, CI?), size of the sample, etc. In any case, it is evident that there is quite substantial variability in the data, which is understandable. Maybe the authors can plot the individual profile lines along the average? Plus, GEF9 seem to have the maximum pre-germination localisation at -5 min rather than -9 min.
- I know it is very challenging, but the ms would be much stronger with the in vivo imaging of pollen germination on stigmatic papillae (i) GEF8/9 in wt, (ii) gef8/9 double mutant. This would bring crucial data about the role of the GEF polar domain and its functional relation to pollination.
- The phylogeny presented in Fig S1 is only rudimental and not very interesting. Given the author's results, I would love to see if GEF8/9 orthologs also exist in species with defined pollen apertures (where establishing a dynamic site makes little sense). The authors touch on this (L409-411), but it would deserve better analysis and discussion.
- I am not entirely convinced by the authors' interpretation of rather strange S518 mutation data. Could S518A mutation affect overall GEF8 structure/stability?
- Although the authors cannot observe the localisation of ROPs in the plasma membrane, they see the apparent accumulation of active ROP marker CRIB4 there - implying that ROPs must localise to the pollen PM at the germination site. This discrepancy should be solved or at least discussed more.
- Given that calcium oscillates very rapidly in pollen and pollen tubes (with frequency ~6-20s), the profound, long-term changes in calcium levels reported by the authors can hardly be referred to as oscillations. The phenomenon observed should again be analysed using a bigger sample.
Minor points
- In Fig 1F, GEF12 also seems to be polarly localised to the future site.
- It is difficult to make any assumptions based on the AlphaFold2 predictions without showing their confidence assessments (e.g., PAE plots). The authors state this themselves in the discussion (L. 447-449).
- On one hand the authors repeatedly state that pollen GEFs do act in a redundant manner (and provide some evidence for it), on the other hand the absence of an in vivo phenotype for single and double knockout lines and only mild phenotype for a triple ko line does suggest a level of redundancy. This should be rephrased.
Significance
General assessment
I believe that both strenghts and limitations are evident form the list above. I feel this a study with great potential, which can be improved by textual ammendments and by several additional experiments that do not require the generation of new genetic material.
Advance
This ms builds on the results obtained previously by the lead author and does advance the knowledge of the field of plant cell polarity substantially.
Audience
The ms is targeted for the basic research audience, particularly for plant scientists.
Expertise of the reviewer
Pollen biology, membrane trafficking, phylogenetic analyses, protein biochemistry.
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Referee #2
Evidence, reproducibility and clarity
Summary:
In this study, Bouatta et al. report the function of RopGEFs in pollen germination. The authors analyzed all of the five RopGEFs, namely RopGEF8/9/11/12/13, that have been shown to be expressed in mature pollen tubes, and found that only GEF8/9/11/12 are detectable. In addition, GEF8 and GEF9 localize to germination sites, while GEF11 and GEF12 are cytosolic. Through a series of phenotype analyses and live-cell imaging, the authors show that GEF8, GEF9, and GEF12 are required for pollen germination while GEF11 is not. The authors also provide evidence that GEF8 and GEF9 are targeted to the membrane via the C-terminus, where they activate ROPs and calcium signaling.
Major comments:
- In line 241, the authors conclude that GEF11 has no function in pollen germination. However, it is likely that GEF11 also plays a redundant role as GEF12 does. I recommend the authors check the phenotypes of gef11,gef12 double mutant and gef8,gef9,gef11 triple mutant to confirm that GEF11 has indeed no function. Otherwise, this conclusion should be better rephrased.
- Although GEF12 is in the cytosol, the strong pollen germination defects in gef8,gef9,gef12 triple mutants do indicate a critical role of GEF12. Is it possible that GEFs could function in the cytosol? The authors can test this possibility by examining the rescuing ability of several constructs that express, for example, GEF12, GEF12(+GEF8C), GEF8(SA), or GEF8(SD) in gef8. The authors may not perform all of these rescue experiments, but some of the mentioned lines are already in hands. They could readily check the phenotypes.
- The authors conclude that the C-terminus of GEF8 and GEF9 is necessary and sufficient for membrane localization because GEF8/9C can target GEF12 PRONE domain to the membrane. It is intriguing whether the C-terminus alone could confer membrane targeting ability. Currently, it is not fully understood how GEFs localize to the membrane. Examining the localization of GEF8/9C itself would help clarify this and improve our understanding of GEF regulation. Alternatively, the authors may discuss evidence that supports or disagrees with this possibility.
Minor comments:
- The N- and C-terminus of GEF8 are predicted to inhibit complex formation. How is the prediction performed? Do the authors use monomer prediction or multimer prediction? Alphafold2 has a low accuracy in predicting non-conserved regions. How confident are the predicted inhibitory contacts?
- Localization of ROPs and calcium reporter in Figure 4 appears to be variable. It would help clarify the specific effects on each reporter if the authors present these data more quantitatively.
Significance
Advance:
ROP GTPases and RopGEFs are critical regulators of cell polarity, but how they initiate polarity remains unclear. This study uses pollen germination as a model to address this question. It systematically analyzed all pollen-specific GEFs and found that GEF8 and GEF9 are critical regulators of pollen germination and polarity initiation. Importantly, GEF8 and GEF9 undergo biphasic accumulation, suggesting polarity is established through a transient exploration phase. This study provides a comprehensive view of the functions of GEFs in polarity initiation, which will be of interest not only to readers who work on pollen germination and growth but also to those who study cell polarity and morphogenesis in general. In my view, the most novel part of this study is that GEFs play overlapping but non-identical roles in polarity establishment and undergo transient accumulation during the polarity initiation process.
Limitations:
This study shows that GEFs use the C-terminus for membrane targeting and GEFs can activate ROPs and calcium signaling during pollen germination. These mechanisms could be largely inferred from previous studies in mature pollen tubes or others. Advancements in the regulation of GEF such as how the C-terminus mediates GEF localization, e.g. whether through direct interaction with the PRONE domain in a phosphorylation-dependent manner, would further increase the novelty of this work.
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Referee #1
Evidence, reproducibility and clarity
In the manuscript, the Denninger group reported the identification of ROPGEF8/9 as the key ROPGEFs for ROP activation during pollen germination, a process of polarity establishment. By examining the subcellular localization of pollen-expressed/enriched GEFs using their own promoter and fluorescence protein fusions, the authors convincingly showed the spatiotemporal distribution of GEF8 and GEF9 during pollen germination. By characterizing pollen germination of gef mutants, the authors demonstrated that GEF8 and GEF9 are critical for the process, with GEF12 playing a redundant role likely as a compensation response. The authors further showed that C-termini of GEF8/9, previously demonstrated as an inhibitory domain for GDP-GTP exchange, was critical for the polar distribution. The C-termini of GEFs interact with PRK. The authors reported that the phosphorylation of GEFs at C-termini was critical for their polar distribution. By examining the dynamic localization of active ROP biosensor CRIBRIC4, the authors demonstrated that GEF8/9 were critical for polar distribution of active ROPs at future germination sites. By introducing a calcium biosensor, the authors showed that calcium gradient, a key downstream process of ROP signaling, was compromised by functional loss of GEF8/9 during pollen germination.
Major comments
Only one GEF11 mutant line, gef11-t1, was analyzed for germination ratio. It is presumptuous to conclude that GEF11 has no function in the pollen germination of Arabidopsis thaliana (line 241- line 242).
Minor comments
In Figure 2A, pollen germination ratio was not provided for the single mutants gef8-c△3 andgef9-c△2.
In Figure 3D, the subcellular localization of GEF12GEF8C is fuzzy. Better imaging is needed.
In Figure 3E, it is intriguing that both GEF8-S518A and GEF8-S518D are not associated with the PM in germinating pollen grains. Does it mean that phosphorylation at S518 is not relevant to polar distribution of GEF8?
T-DNA insertion lines, gef11-t1 and gef12-t1, need to be verified by PCRs in Figure S3D.
Significance
The identification of ROPGEF8/9 as the key ROPGEFs for ROP activation during pollen germination is a step forward in understanding ROP signaling. Useful but not unexpected.
Pollen germination is a process of polarity establishment, similar to root hair initiation. Compared to pollen tube growth and root hair growth, processes of polarity maintenance, the role of ROP signaling was less clear. Recently, Xiang et al. (2023, Plant Physiol) reported an essential role of ROP1/3/5 and their downstream components BDR8/9 in pollen germination. Consistently with polar ROP activation, Ca2+ and post-Golgi secretion were polar. The current work is one step ahead, showing GEF8/9 as the upstream GEFs for this process, comparable to GEF3/4 during root hair initiation (Denninger et al., 2019, Curr Biol). The identification of the C-terminal phosphorylate site in GEF8/9 is informative. It was reported previously that PRKs interact with the C-termini of GEFs to release their auto-inhibition (Gu et al., 2006, Plant Cell; Zhang and McCormick, et al., 2007, Proc Nat Acad Sci USA, Zhao et al., 2013, J Exp Bot) and PRKs were reported to phosphorylate GEF (Chang et al., 2013, Mol Plant). Thus, results reported in the current work indicate that phosphorylation of GEFs likely by PRKs is a critical step for the establishment of polarity domain for pollen germination. From this perspective, it would be more mechanistically sound to investigate the role of PRKs in spatiotemporal polarization of GEFs during pollen germination.
Researchers working on cell signaling and cell morphogenesis in plants will be interested.
My lab works on cell morphogenesis and ROP signaling. This manuscript exactly falls within the expertise of my field.
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Reply to the reviewers
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Reply to the Reviewers
We want to thank the three reviewers for their invaluable and constructive feedback. We respond to each comment individually, describing how we plan to address them in our revised manuscript.
Reviewer #1
- Given the emphasis on super-resolution imaging deep inside a sample, we were surprised to see no mention of other forms of structured illumination that allow super-resolution imaging in samples thicker than a single cell. These include the 'spot-scanning' implementations of SIM that offer better imaging at depth by virtue of pinholes, and include MSIM, iSIM, and rescan confocal technologies. The two-photon / AO implementation of iSIM seems particularly germane, e.g. https://pubmed.ncbi.nlm.nih.gov/28628128/ Please consider citing these works, as they help place the existing work into context.
Response:
We want to thank reviewer #1 for the good point. To address this comment, we plan to add to the discussion section a description of these super resolution techniques, together with other SIM methods, explaining how they compare to our approach.
- As we're sure the authors appreciate, besides aberrations, a major additional obstacle to 3D SIM in thick tissues is the presence of out-of-focus background. Indeed, this point was mentioned by Gustafsson in his classic 2008 paper on 3D SIM (https://pubmed.ncbi.nlm.nih.gov/18326650/): 'The application area of three-dimensional structured illumination microscopy overlaps with that of confocal microscopy, but the two techniques have different and complementary strengths. Structured illumination microscopy offers higher effective lateral resolution, because it concentrates much of the excitation light at the very highest illumination angles, which are most effective for encoding high-resolution information into the observed data, whereas confocal microscopy spreads out its illumination light more or-less uniformly over all available angles to form a focused beam. For very thick and compactly fluorescent samples, however, confocal microscopy has an advantage in that its pinhole removes out-of focus light physically. Structured illumination microscopy is quite effective at removing out-of-focus light computationally, because it is not subject to the missing-cone problem, but computational removal leaves behind the associated shot noise. Therefore confocal microscopy may be preferable on very thick and dense samples, for which the in-focus information in a conventional microscope image would be overwhelmed by out-of-focus light, whereas structured illumination microscopy may be superior in a regime of thinner or sparser samples.' This point is not mentioned at all in the manuscript, yet we are certain it is at least partially responsible for the residual image artifacts the authors mention. Please discuss the problem of out of focus light on 3D samples, particularly with an eye to the 'spot-scanning' papers mentioned above.
Response:
We appreciate this significant obstacle and we want to thank Reviewer #1 for emphasising its importance. To address the comment, we plan to add a discussion of the significance of out-of-focus light to SIM imaging to the introduction, results, and discussion sections of the manuscript.
- The authors use a water dipping lens, yet they image into samples that are mounted on coverslips, i.e. they use a dipping lens to image through a coverslip:
This almost certainly introduces spherical aberration, which the authors seem to observe: see attached pdf for reference
We find this troubling, as it seems that in the process of building their setup, the authors have made a choice of objective lens that introduces aberrations - that they later correct. At the very least, this point needs to be acknowledged in the manuscript (or please correct us if we're wrong) - as it renders the data in Figs. 3-4 somewhat less compelling than if the authors used an objective lens that allowed correction through a coverglass, e.g. a water dipping lens with a correction collar. In other words, in the process of building their AO setup, the authors have introduced system aberrations that render the comparison with 3D SIM somewhat unfair. Ideally the authors would show a comparison with an objective lens that can image through a glass coverslip.
Response:
We want to thank Reviewer #1 for raising this point, which we did not describe clearly enough, leading to confusion. We should have made it clearer that we used a water dipping/immersion objective lens with a correction collar which extends from no coverslip (dipping) up to well beyond a standard #1.5 (170 um thick) coverslip. We adjusted this collar before each image acquisition session, to ensure that the system is optimised for each experiment individually and that the spherical aberrations are minimal before any DM-based correction. We plan to elaborate and emphasise this point in several places in the revised manuscript, including in the figure legends, materials and methods and results sections, to avoid any ambiguity and confusion about the use of the correction collar and this particular water immersion/dipping objective lens.
- The authors tend to include numbers for resolution without statistics. This renders the comparisons meaningless in my opinion; ideally every number would have a mean and error bar associated with it. We have included specific examples in the minor comments below.
Response:
This is a good point, which we address below, in three minor comments. In summary, to address this comment, we plan to include statistical information in the revised manuscript.
- In Fig. 5, after the 'multipoint AO SIM', the SNR in some regions seems to decrease after AO: see attached pdf for reference
Please comment on this issue.
Response:
We want to thank Reviewer #1 for the insightful comment. There are multiple phenomena in effect here, which cause the drop in intensity. The most prominent one is photobleaching, as the AO image stack (right) was acquired after the bypass one (left). To address this comment, we plan to add additional data and to include a brief discussion about this issue and other related points.
- Please provide timing costs for the indirect AO methods used in the paper, so the reader understands how this time compares to the time required for taking a 3D SIM stack. In a similar vein, the authors in Lines 213-215, mention a 'disproportionate measurement time' when referring to the time required for AO correction at each plane - providing numbers here would be very useful to a reader, so they can judge for themselves what this means. What is the measurement time, why is it so long, and how does it compare to the time for 3D SIM? It would also be useful to provide a comparison between the time needed for AO correction at each (or two) planes without remote focusing (RF) vs. with RF, so the reader understands the relative temporal contributions of each part of the method. We would suggest, for the data shown in Fig. 5, to report a) the time to acquire the whole stack without AO (3D SIM only); b) the time to acquire the data as shown; c) the time to acquire the AO stack without RF. This would help bolster the case for remote focusing in general; as is we are not sure we buy that this is a capability worth having, at least for the data shown in this paper.
Response:
We agree that the timing (and other) costs can be an important consideration, and we want to thank Reviewer #1 for bringing up this good point. To address this issue, we plan to expand our description of the AO methods, also including numbers for the time it takes to perform the different parts. In terms of comparisons, the RF makes no contribution to the timing costs of the aberration correction, a point that we want to make clearer in the results and the methods and materials sections, as the two are independent processes in our approach. Instead, the RF can be compared to standard focusing with a piezo stage, a point which we discuss in the supplementary material. We plan to make this point clearer in the discussion section of the main manuscript, and to emphasise better the advantages of the RF in terms of imaging speed.
- Some further discussion on possibly extending the remote focusing range would be helpful. We gather that limitations arose from an older model of the DM being used, due to creep effects. We also gather from the SI that edge effects at the periphery of the DM was also problematic. Are these limitations likely non-issues with modern DMs, and how much range could one reasonably expect to achieve as a result? We are wondering if the 10 um range is a fundamental practical limitation or if in principle it could be extended with commercial DMs.
Response:
Regrettably, we were not able to try other DMs on the Deep3DSIM system. However, Jiahe and colleagues show in [1] that similar DM-based remote focusing, even with the same model deformable mirror, can be pushed to 120 um (Strehl ratio >0.8) with a 0.42 NA dry lens (20 mm WD) and close-loop wavefront compensation operation. While this is not directly translatable to high NA 3D-SIM imaging, we expect that with a stable version of the same DM the useable RF range could be easily increased twice or even more. We thank Reviewer #1 for the good comment, which we plan to address by revising the text to make the limitations clearer and by citing relevant studies.
[1] Cui, J., Turcotte, R., Emptage, N. J., & Booth, M. J. (2021). Extended range and aberration-free autofocusing via remote focusing and sequence-dependent learning. Optics Express, 29(22), 36660-36674.
Minor comments:
- The paper mentions Ephys multiple times, even putting micromanipulators into Fig. 1 - although it is not actually used in this paper. If including in Figure 1, please make it clear that these additional components are aspirational and not actually used in the paper.
Response:
Although not shown in the context of this paper, the Deep3DSIM system was built specifically around experiments such as electrophysiology, which can benefit from the upright configuration and the water-dipping-capable objective lens. To address this comment, we plan to clarify the role of the micromanipulators and to update Figure 1 accordingly.
- The abstract mentions '3D SIM microscopes', 'microscopes' redundant as the 'm' in 'SIM' stands for 'microscope'.
Response:
We accept that “3D SIM microscopes” sounds repetitious and we plan to revise the wording of the abstract to “3D SIM system”.
- 'fast optical sectioning', line 42, how can optical sectioning be 'fast'? Do they mean rapid imaging with optical sectinong?
Response:
Yes, we meant rapid imaging with optical sectioning. We plan to change the wording to make it less ambiguous.
- line 59, 'effective imaging depth may be increased to some extent using silicone immersion objectives', what about water immersion objectives? We would guess these could also be used.
Response:
Yes, water immersion objective lenses also fall in the same category and we plan to rephrase this part to state it explicitly.
- line 65 - evidence for 'water-dipping objectives are more sensitive to aberrations' ? Please provide citation or remove. They are certainly more prone to aberrations if used with a coverslip as done here.
Response:
The refractive index (RI) of cells and tissues [1] is closer to the RI of silicone oil (~1.4) than it is to water (~1.33). Therefore, because of the larger difference in RI, imaging with a water-dipping objective lens is more prone to aberrations from RI mismatch. We plan to rephrase this argument to make it clearer.
[1] Jacques, S. L. (2013). Optical properties of biological tissues: a review. Physics in Medicine & Biology, 58(11), R37.
- 'fast z stacks' is mentioned in line 103. How fast is fast?
Response:
The speed would depend on the way Z-stacks are being acquired. For example, acquisitions with two channels would be at least twice as fast, because of the ability to do simultaneous imaging on the Deep3DSIM system. Likewise, experiments that can benefit from the remote focusing can be several times faster than using a Z piezo stage, and this point is discussed in the supplementary material (section “Step response”). Finally, thanks to the electronic design of the imaging system, orchestrating everything via digital logic (e.g. TTL) signals, and thanks to the elaborate control software, we can ensure that all image acquisitions are carried out as quickly as possible, operating near the limit of the underlying hardware devices. We plan to explain these points in a clear way in the discussion section, and we plan to provide more numbers in the supplementary material.
- line 116 'we imaged 100 nm diameter green fluorescent beads'. Deposited on glass? Given that this paper is about imaging deep this detail seems worth specifying in the main text.
Response:
Yes, in this case the beads were deposited on glass. We plan to include this detail in the description of the experiment.
- lines 127-130, when describing changes in the bead shape with numbers for the FWHM, please provide statistics - quoting single numbers for comparison is almost useless and we cannot conclude that there is a meaningful improvement without statistics.
Response:
We agree with this comment. We plan to include statistical information for all the FWHM numbers.
- In the same vein, how can we understand that remote focus actually improves the axial FWHM of the widefield bead? Is this result repeatable, or it just noise?
Response:
The lower axial FWHM with remote focusing is likely caused by data fitting or quantification error. Together with the inclusion of statistical information, we plan to review all the resolution values and to ensure that they are accurate and sensible.
- line 155, 'Because of the high spatial information...' -> 'Because of the high resolution spatial information...'
Response:
We agree with this comment. To address it, we plan to rephase this part.
- When quoting estimated resolution #s from microtubules (lines 158-163) similarly please provide statistics as for beads.
Response:
We agree with this comment. To address it, we plan to include statistical information for the resolution values from microtubules.
- It seems worth mentioning the mechanism of AO correction (i.e. indirect sensing) in the main body of the text, not just the methods.
Response:
We agree with this comment. To address it, we plan to describe briefly the aberration correction method in the introduction or the results section.
- How long do the AO corrections take for the datasets in the paper?
Response:
The duration of the aberration correction routines is directly proportional to the number of Zernike modes, the number of iterations, the exposure time of the camera, and other parameters. In our experiments, it was usually in the order of tens of seconds. To address this comment, and in line with the sixth major comment, we plan to include more details about the timing of the different parts of the AO methods.
- Were the datasets in Fig. 2-4 acquired with remote focusing, or in conventional z stack mode? Please clarify this point in the main text and the figure captions.
Response:
The only data acquired with RF in Fig. 2-4 are one bead in Fig. 2A and another bead in Fig. 2B, both labelled accordingly. We plan to make it clearer in the text that the rest of Figure 2, as well as Figures 3 and 4, were acquired with the piezo Z stage.
- It would be helpful when showing z projections in Figs. 3-5 to indicate the direction of increasing depth (we assume this is 'down' due to the upright setup, but this would be good to clarify)
Response:
The direction is indicated by the arrows labelled with ‘Z’. We plan to clarify this in the figure captions.
- line 174, 'showed significant improvements in both intensity and contrast after reconstruction' - we see the improvements in contrast and resolution, it is harder to appreciate improvements in intensity. Perhaps if the authors showed some line profiles or otherwise quantified intensity this would be easier to appreciate.
Response:
We agree with this comment. To address it, we plan to change Figure 3 to illustrate the improvement in intensity, likely with line profiles, as suggested by the reviewer.
- line 195 'reduced artefacts' due to AO. We would agree with this statement - the benefit from AO is obvious, and yet there are still artefacts. If the authors could clarify what these (residual) artefacts are, and their cause (out of focus light, uncorrected residual aberrations, etc) this would be helpful for a reader that is not used to looking at 3D SIM images.
Response:
We agree with this comment. To address it, we plan to explain this point in both the results and the discussion sections.
- Line 197, 'expected overall structure', please clarify what is expected about the structure and why.
Response:
We agree with this comment. To address it, we plan to describe better the Canoe (Cno) protein, including an explanation of its expression pattern, which is the honeycomb-like structure observed in the images.
- Line 199, what is a 'pseudo structure'?
Response:
We used this expression to refer to unclear (e.g. dim, fuzzy) structures. We plan to improve the wording of that part of the results section.
- Fig. 4B, 'a resolution of ~200 nm is retained at depth', please clarify how this estimate was obtained, ideally with statistics.
Response:
We agree with this comment. To address it, we plan to clarify this point in the results section, including statistical information.
- Fig. 4D, please comment on the unphysical negative valued intensities in Fig. 4D, ideally explaining their presence in the caption. It would also be helpful to highlight where in the figure these plots arise, so the reader can visually follow along.
Response:
We agree with this comment. To address it, we plan to explain how negative intensities arise in SIM reconstruction, often a result of spherical aberrations, and we plan to indicate where the line profile in Figure 4D comes from.
- Line 245, 'rapid mitosis'. What does rapid mean, i.e. please provide the expected timescale for mitosis.
Response:
The mitotic cycles at this developmental stage are short, e.g. 5 minutes per mitosis, compared to those of somatic cells where it takes several hours. We plan to include this information in the main text.
- For the data in Fig. 6, was remote refocusing necessary?
Response:
Yes, it was necessary because the point of Figure 6 is to demonstrate the combination of remote focusing and SIM super-resolution in live samples. Drosophila embryos are a very good sample for this kind of demonstration, because they are often subject to micromanipulation (e.g. injection and electrophysiology), and these are the kind of experiments that can benefit greatly from the optical axial scanning of the remote focusing, where the sample can remain stationary. However, there is nothing preventing the imaging of this kind of sample with a piezo Z stage or with some other kind of mechanical actuator. In this sense, the remote focusing is not strictly necessary but still much more convenient in some applications. We plan to make this point clearer in the discussion section.
- What is the evidence for 'reduced residual aberrations', was a comparative stack taken without AO? In general we feel that the results shown in Fig. 6 would be stronger if there were comparative results shown without AO (or remote focusing).
Response:
We agree with this comment. In general, it is difficult to make direct comparisons (e.g. as in Figures 3-5) with live samples, because of the dynamic character of the samples, where it is often impossible to capture the same scene more than once. To address this comment, we plan to revise the wording of the relevant part of the results section, to ensure that the data in Figure 6 is properly described.
- Line 350, 'incorporation of denoising algorithms' - citations would be helpful here.
Response:
We agree with this comment. To address it, we plan to add references to the relevant statement, showing examples of denoising in 3D-SIM imaging and reconstruction.
- Line 411, 'All three were further developed and improved' - vague, how so?
Response:
A detailed breakdown of all the changes is available on the respective software repositories. We also plan to add a summary in the supplementary material.
- Sensorless AO description; how many Zernike modes were corrected?
Response:
We usually corrected 8 modes: Z5 to Z11 and Z22, using Noll indexing. We plan to add a table to the supplementary material, describing which modes were corrected for each dataset.
- Multi-position aberration correction. Was the assumption of linearity in the Zernike correction verified or met? Why is this a reasonable assumption?
Response:
By their very definition, some aberrations, such as defocus and spherical aberrations, change linearly with depth. Others are also proportional to the imaging depth, and first-order approximation (i.e. straight line) is the most sensible for just two correction points, as is the case with the dataset presented in Figure 5. We plan to explain this point better in the results section.
- Fig. S1B is not useful; if the idea is to give a visual impression of the setup, we would recommend providing more photos with approximate distances indicated so that the reader has a sense of the scale of the setup. As is - it looks like a photograph of some generic optical setup.
Response:
We agree with this comment. To address it, we plan on including more photos in the supplementary material, to give a better sense of the scale.
- SI pattern generation - 'the maximum achievable reconstruction resolution was only slightly reduced to about 95% of the theoretical maximum'. We don't understand this sentence, as the resolution obtained on the 100 nm beads is considerably worse than 95% of the theoretical maximum. Or do the authors mean 95% of the theoretical maximum given their pitch size of 317 nm for green and 367 nm for red?
Response:
Limiting the stripe width to about 90% of what is achievable leads to a reduction of the theoretical maximum resolution to 95% of what it could be. We plan to rephrase this part to make it clearer.
- SI Deformable mirror calibration 'spanning the range [0.1, 0.9]' - what are the units here?
Response:
These are normalised control amplitudes, i.e. [10%, 90%], which means that they are unitless. We plan to explain this in a clearer way.
- What are the units in Fig. S5C, S5D?
Response:
Errors are in radians, defined by the calibration interferometric wavefront sensor. We plan on updating the figure to include this information.
- It would be useful to define 'warmup' also in the caption of SI Fig. S6A.
Response:
We agree with this comment. We plan to change the caption of Figure S6A to clarify this point.
- SI Remote Focusing, 'four offsets, {-5 mm, -2.5 mm, 2.5 mm, 5 mm}...' are the units mm or um?
Response:
The units are supposed to be um (micrometres). We plan on fixing this error.
- '...whereas that of the 10 beads was...' here, do the authors mean the position of the beads derived from the movement of the piezo stage, as opposed to the remote focusing?
Response:
This is the average standard deviation between the 10 different beads, all from volumes acquired with remote focusing. We plan on rephrasing this part to make it clearer.
- The authors refer to the 'results from Chapter 3.2'. What are they talking about? Do they mean a supplementary figure, or earlier supplementary results? In general, we found the discussion in this paragraph difficult to follow.
Response:
This is a remnant from an earlier version of the document which used numbered sectioning. Chapter 3.2 is referring to the section titled “Characterisation of drift and temperature effects”. We plan on revising this paragraph to make it clearer.
- Supplementary Fig. 9 seems to be not referred to anywhere in the text.
Response:
We agree with this comment. To address this issue, we plan on referring to this figure in the main text.
- Since the paper emphasizes 3D SIM, OTFs along the axial direction would also be useful to show, in addition to the lateral OTFs shown in Fig. 2D.
Response:
We agree with this comment. To address it, we plan on adding orthogonal views of the OTFs to the supplementary material.
- When the sample is moved by the piezo, the axial phase of the 3D-SIM illumination pattern is stable as the sample is scanned through the illumination pattern. When remote focusing is performed, the sample is always stable so the axial phase of the 3D-SIM illumination pattern is presumably changing with remote focusing. Can the authors clarify if the 3D SIM illumination pattern is scanned when remote focusing is applied, or is the intensity pattern stable in z?
Response:
Yes, the illumination pattern is scanned. We plan on clarifying how the structured illumination works in the case of remote focusing in the supplementary material.
- In Supplementary Fig. 9, primary spherical is referred to twice, both at index 11 and 22. The latter is presumably secondary spherical?
Response:
Yes, it is supposed to be secondary spherical aberrations. We plan on fixing this error.
- we do not understand the x axis label, in Fig. S4D, is it really [0, 50, 50, 50] as written?
Response:
The labels of the x-axis are not well formatted. There are three range of [0, 50] where only the first zero is properly displayed. We will revise this part of the figure to make it clear.
Reviewer #2
- The authors have provided an incomplete description of the structured illumination microscopy (SIM) reconstruction process. It is unclear whether the approach is based on 2D interference SIM configurations or 3D interference patterns. Furthermore, the specific algorithm utilized for image reconstruction has not been elucidated. Elaborating on these aspects is crucial as they significantly influence the interpretation of the resulting data.
Response:
We want to thank Reviewer #2 for bringing our attention to the incomplete description of the reconstruction process. Our approach was based on 3D interference patterns and it was carried out using the Gustafsson’s reconstruction techniques as implemented by the softWoRx software, designed for the OMX 3D-SIM microscopes. To address this comment, we plan to revise the manuscript and to include more details about the 3D-SIM reconstruction techniques in the methods and materials section.
- The authors have stated that sample-induced aberrations caused by RI inhomogeneities within the specimen is another major reason for causing artifacts generation. Literature has demonstrated that RI inhomogeneities can lead to non-local distortions in the grid pattern, which suggests that applying uniform reconstruction parameters across the entire image may not be viable. Traditional artifact remediation using the classical Wiener method is likely insufficient under these conditions (PMID: 33896197). The existing adaptive optics (AO) approach, which employs a deformable mirror (DM) alongside an sCMOS camera, is inadequate for tackling the issue at hand. Actually the assertion made in the paper that "aberrations change approximately linearly with depth" is seemingly contradicted by simulations referenced in the cited literature (PMID: 33896197). Consequently, it appears that the current methodology might only achieve a partial mitigation of the problems associated with spherical aberration resulting from RI mismatches. It is advisable, therefore, that the authors explicitly acknowledge this limitation in their manuscript to prevent any potential misinterpretation by readers.
Response:
We are thankful for the thoughtful comment by Reviewer #2. The focus of our work was not the use of advanced 3D-SIM reconstruction and aberration correction methods; instead, we used standard ones which are not able to deal perfectly with anisoplanitism, i.e. when the aberrations vary laterally. As such, our approach provides an average reconstruction and correction across the field of view. In our particular setup this anisoplanitism was not very significant, but we agree that it could be an issue for optical systems with very wide field of view. To address this good point, we plan on clarifying these potential issues in the results and the discussion sections.
- In Figure 2, the use of COS-7 cells, which are known for their relatively thin axial dimension, for the experiments raises an eyebrow. Notably, there are ample instances in existing research where both 2D-SIM and 3D-SIM, without the integration of adaptive optics, have yielded high-quality super-resolution images of structures such as tubulin and the endoplasmic reticulum. In addition, the authors did not present a direct comparison between BP-SIM and AO-SIM here. Without this comparative analysis, it remains ambiguous whether the enhancements in resolution and contrast and the reduction in artifacts can genuinely be attributed to the mitigation of spherical aberration. To clarify this, it would be beneficial for the authors to include side-by-side comparisons of these modalities to demonstrate the specific improvements attributed to AO-SIM.
Response:
We are grateful to Reviewer #2 for this helpful comment. In Figure 2, we demonstrate the performance we get out of 3D-SIM in terms of optical resolution. We do not make any statements about the impact of the aberration correction on image quality. Nevertheless, to address this comment, we plan to revise the figure to explain more clearly and explicitly this point.
- In Figures 3 and 4, the authors have illustrated the enhancements achieved through the application of AO. However, there is a discernible presence of hammer-stroke and honeycomb artifacts in pre-AO imaged data, which seem to originate from the amplification of the incorrectly moved out-of-focal background in the frequency domain. Various strategies have been previously suggested to address these specific artifacts, encompassing methods like subtracting background noise in the raw images or employing selective frequency spectrum attenuation techniques, such as Notch filtering and High-Fidelity SIM. To facilitate a more comprehensive understanding, I would recommend that the authors incorporate into their study a comparison that includes BP-SIM data that has undergone either background subtraction or frequency spectrum attenuation. This added data would enable a more complete evaluation and comparison regarding the merits and impact of their AO approach.
Response:
We thank the reviewer for this excellent suggestion and we agree that a pre-processing step, such as background subtraction or frequency spectrum attenuation, can help with the reduction of artefacts. To address this comment, we will re-analyse our data and apply these techniques, and we will add the data to the manuscript, with an appropriate revision to the text.
Reviewer #3
- There is an overall reference in the manuscript of the novelty possible range of applications of using an upright microscope configuration. Examples mentioned are tissue-based imaging, access to whole-mount specimens for manipulation and electrophysiology. However, authors fail to present any such applications. There is not a single example presented which could not have been obtained with an inverted microscope. Could the authors provide an example where a water-dipping is used. Expanded samples could be one case, since the thickness of the gel makes it difficult to image with an inverted microscope. Another possible example would be to label the extracellular space and do shadow imaging of the tissue (SUSHI PMID: 29474910). ExM might be simpler to do as part of revising the manuscript than SUSHI.
Response:
We are thankful to Reviewer #3 for these interesting comments. To address this comment, we will emphasise more clearly that Figure 6 of our manuscript shows a sample that is often part of live imaging experiments that require microinjection and even electrophysiology. Our aim was to show the proof of principle and the potential of such experiments, rather than to carry out real and complex experiments using electrophysiology or microinjection. Regarding providing an example where water-dipping is used, this is already present in the same Figure 6, which we will describe more explicitly and fully in the revised manuscript. The reviewer’s comments on expansion microscopy and SUSHI are interesting, but the primary purpose of our microscope system is to facilitate super resolution live cell imaging experiments. Nevertheless, to address this comment, we will add an explanation of the relevance of our approach to improving deep super resolution imaging of expanded specimens.
- On the main text it is described a 5-fold volumetric resolution, which is confusing since authors only mention lateral and axial resolutions. Their measurements correspond to a ~1.6-fold lateral improvement and ~1.7-fold axial improvement. These are however not the 95% of the achievable resolution theoretical maximum, as stated in p7 SI (2 fold increase of 282nm), but only the 80-85%. This point should be rephrased in the manuscript.
Response:
We want to thank Reviewer #3 for bringing up this important point. To address it, we plan to make changes to the text, both in the main manuscript and in the supplementary material, to make it clearer what are the resolution improvement that we achieve and what are the limitations to our approach.
- [OPTIONAL] p4 and related to figure 2, it would be important to report also measurements of beads with SIM but without AO, just as done for WF. Is there an improvement of using AO on SIM? This is reported for the fixed cells but not for the beads.
Response:
We found no significant improvement in resolution when AO was applied to SIM. To address this comment, we plan to add the extra data to Figure 2, demonstrating this point.
- Figure 2, it is odd the comparison between WF+/- AO and SIM +/- AO are done using different cellular structures. Since wavelengths used are not the same it is difficult to interpret if there is any improvement of using AO on SIM compared to SIM without AO. Same questions arise as above, Is there an improvement of using AO on SIM?
Response:
We agree that the data in Figure 2C and 2D is presented in unusual way. Our intention was not to make a comparison between bypass and AO, but instead to characterise the super-resolution capabilities of the system. We use different channels because doing -/+ AO consecutively leads to noticeable intensity drop due to photobleaching. We are grateful to Reviewer #3 for the valuable comment, which we plan to address by revising Figure 2.
- "A significant benefit and uniqueness of the Deep3DSIM design is its upright configuration, whereas commercial SIM systems are built around inverted microscopes and are usually restricted to imaging thin samples, such as cultured cells." (p5) is not correct. The commercial DeepSIM module from CREST Optics can be mounted on an inverted microscope as well as image deep into tissue (seehttps://crestoptics.com/deepsim/ and application notes therein) and be used with essentially any objective. This point should be rephrased in the text.
Response:
We want to thank Reviewer #3 for bringing our attention to this error. Of course, we meant commercial 3D-SIM systems, such as GE Healthcare DeltaVision OMX and Nikon N-SIM. To address this issue, we plan to rephrase this part of the results section. Regarding the commercial DeepSIM module from CREST Optics, as far as we can tell, it uses a different method – 2D lattice multi-spot SIM – which comes at the cost of signal loss when sample-induced aberrations are strong. This is very different from our method, which uses a deformable mirror to manipulate the phase information of both the excitation and the emission light at the back-pupil plane of the objective lens, which can theoretically provide 2× resolution enhancement with no signal lost.
- Fig 3 reports the improvements of AO on SIM for imaging over 10um in tissue. What are the Zernike modes measured? Or how does the pupil look like before and after correction? It would be also good to report the Fourier amplitudes as done in Fig 2C as a quantitative measure of improvement. It would be good to point out the artifacts observed on the BP SIM image reconstruction (labelled with 3x, fringes are noticeable).
Response:
We thank Reviewer #3 for the good suggestions. We plan to add information about the measured Zernike modes to the results section, as well as to add a brief discussion about the noticeable reconstruction artefacts. In terms of pupil and Fourier amplitudes, we plan to change Figure 3 to include all this information or, alternatively, to include it in the supplementary material.
- Many key details relating to image acquisition and AO correction are missing for all figures. How is the AO optimization implemented? Is it implemented via a genetic algorithm (progressive optimization of parameters) or using more clever strategies? Not clear if the optimization is implemented using images obtained with flat illumination or after SIM imaging/processing of a given dataset. How long does the AO optimization take? How sensitive to noise is the process? What metric do they use to estimate the sensorless AO correction? On pag12, they say "Fourier domain image metric" for measurements with fine details; otherwise, ISOsense when not high frequencies are present. Could the authors report the formula used to calculate the first metric? What do they consider to be low and high frequencies in this case? Is there a reason why ISOsense is not always used, or is there an automatic way to choose between the two? How many images were acquired for AO correction? Which samples were corrected with ISOsense and which ones with Fourier domain image metric? (see for example the detailed experimental reporting in the Supp Mat from Lin et al Nat Commun 2021).
Response:
We are grateful to Reviewer #3 for the extensive list of questions. The optimisation is done via non-linear least square, it uses widefield images, and it is performed before the actual image acquisition, i.e. well before any SIM reconstruction takes place. The methods used for aberration correction are described in the Methods and materials section, and further in the cited literature, e.g. Antonello et al 2020 and Hall et al 2020. ISOsense needs to be manually chosen over the Fourier image metric, and this should be done when large mode biases lead to small changes in the metric value, which is likely to happen when there are little or no sharp features in the images. One of the disadvantages of our implementation of ISOsense is that the structured illumination pattern is continuously exposed over the sample, which leads to photobleaching and phototoxicity. None of the datasets shown in the manuscript use ISOsense. To address all of the questions from this comment, we plan to significantly expand our descriptions of the AO methods, both in the main text and in the supplementary material.
- Fig 4. Data presented for larval brain tissue is a very clear example of adding AO to image deep into tissue as the effect at ~130 cannot be understated. Here too, it would be also good to report the Fourier amplitudes as done in Fig 2C as a quantitative measure of improvement and possibly the SNR of reconstructed images. Having a way to quantitatively describe how much better are those images would be great. Also, what are the aberrations corrected? Can the wavefront or Zernike amplitude of the modes be reported? Same as for Fig 3, details about AO correction are missing.
Response:
We are grateful to Reviewer #3 for the helpful comment. We will address it by adding the Fourier amplitudes to Figure 4, as suggested, and by reporting the Zernike mode amplitudes of the aberration corrections.
- [OPTIONAL] "It is worth noting that aberrations can differ across larger fields, and therefore, after applying an average correction, residual aberrations can still be observed in some regions of the AO-corrected bead images. However, the overall PSF shapes were still dramatically improved with AO compared to the equivalent without AO." This point is very interesting although not result either in the main text or in the SI is presented.
Response:
The residual aberrations are present in the right image of Figure 4B, although we did not highlight them specifically. We are thankful to Reviewer #3 for the good suggestion and we plan to implement it by changing Figure 4 to show a few of the beads with residual aberrations.
- "As we found that the aberrations change approximately linearly in depth, we could measure the aberration in two planes and calculate the corrections in intermediate planes by interpolation, an approach which we termed "multi-position AO"." This is, personally, one of the major contributions of this work to the community. Unfortunately, it is not reported in detail. Not only for SIM but for imaging with WF or confocal, such linear change for aberrations with depth is not well known. Again, here the details of AO correction and image metrics are missing. To establish that for most thick biological structures 'aberrations change approximately linearly in depth' would be foundational to the widespread use of AO within standard imaging. Would it be possible for the authors to elaborate on this point and present detailed results? What is the error from measuring and correcting more than 2 planes? What is the error from just measuring and AO correcting at the deeper plane, i.e. from a single measurement? Authors could also show a case in which a linear assumption works nicely (or how well it works). For example, comparing an intermediate plane (or a plane beyond) imaged after AO optimization or after coefficient interpolation of the Zernike modes and compare it against correcting directly that plane.
Response:
Some aberrations, such as defocus and spherical aberrations, are mathematically defined as varying linearly with depth. The change in other aberrations with depth can also be estimated with a linear model, which is a standard first-order approximation in the case of two datapoints, such as corrections done in Figure 5. It is not possible to do regression analysis with just a single point, so it is impossible to apply our multi-position AO at a single plane. We are grateful to Reviewer #3 for the constructive comment. To address the questions in this comment, we plan to provide a more detailed description of the correction estimation methods to the results section, as well as a discussion on the accuracy of the linear model in the discussion section.
- The image of the cos-7 cell in metaphase, for Fig 5 is, however, very disappointing. See Fig 1 of Novak et al Nat Commun 2018 for an example of a single z-plane of a cell in metaphase. Having the possibility to correct for the entire 3D volume, I would expect amazing 3D volumes (movies and/or projections) associated with this imaging which are not presented.
Response:
We thank Reviewer #3 for the interesting comment. The example in Novak et al 2018 was acquired with STED microscopy, which is an entirely different imaging method and thus produces different results. Nevertheless, we will revise the discussion of Figure 5 to ensure that the right expectations are set.
- In Figure 6, they use AO in remote configuration mode to allow imaging of live specimens. It needs to be clarified if this is an a priori characterization that is then kept fixed while recording in time. The last acquired volume of fig 6A and B have a higher amount of artifacts with respect to time 00:00. Are those artifacts due to lower SNR (maybe due to sample bleaching) or due to some change in the aberrations of the specimen?
Response:
We want to thank Reviewer #3 for the valuable comment. We assume that by change in artefacts, Reviewer #3 is referring to the overall green fluorescent structure. Indeed, this last volume shows the anaphase to telophase transition where the mitotic spindle is being reorganised and disassembled. As such, the structure is much less well-defined than in the first volume. The changes in aberrations over time are not particularly significant in this case, and the photobleaching is not that impactful in such an experiment where relatively thin volumes are acquired with substantial time delay between them. To address this comment, we plan to revise the discussion of the figure and to ensure that the scene observed in the last volume is clearer.
- "These results demonstrate that the remote focusing functionality of the system can be successfully applied for live 3D-SIM experiments, allowing four-dimensional acquisition while keeping the specimen stationary, thus avoiding the usual agitation and perturbations associated with mechanical actuation." Generally, this statement is true, but for the specific example shown of drosophila embryogenesis is it relevant? If they use piezo-driven Z-stack imaging with AO, does that lead to incorrect reconstructions or motion-induced artifacts? Related to the results shown in Fig 6, the fair comparison would be AO SIM vs SIM (without AO), not AO SIM vs AO WF.
Response:
We are grateful to Reviewer #3 for the insightful comment. Drosophila embryos are quite robust to perturbations due to their shape and size, and the restrictions imposed by SIM experiments (e.g. small Z steps and Z levels held for long periods of time) make motion-induced artefacts not very impactful. Regarding the results, the point of Figure 6 is not to demonstrate the advantages of aberration correction, which we do not claim in the caption or in the relevant part of the discussion, but to demonstrate that remote focusing works well with 3D-SIM reconstruction, which is known to have stringent requirements about the image quality. To address this comment, we plan to revise the figure and its relevant part of the results section.
- When performing remote focusing, is the effective NA of the imaged plane changing with respect to the NA of the objective used at its focal plane?
Response:
We thank Reviewer #3 for the good question. The effective NA is not altered by the remote focusing. We plan to mention this detail in the results section.
- [OPTIONAL] Did the authors run calculations to explore whether a commercial upright microscope could be used instead of their design? Are there any fundamental flaws that would make impossible using a commercial base? If not, could an AO SIM module be designed such that it adds on a commercial base? It would be important to discuss this point.
Response:
We thank Reviewer #3 for bringing up this interesting point. A lot of considerations, calculations, and modelling were done in the design of the Deep3DSIM system. Of course, the use of a commercial upright microscope stand was part of the deliberation. One of the obvious limitations is the difficult access to the pupil-conjugated plane. On the other hand, a commercial microscope stand is not well compatible with many of the key parts of the system, which were designed around specific biological applications, such as dual camera system for fast live simultaneous imaging and the heavy-duty Z stage intended to support two heavy micromanipulators. To address this comment, we plan to add a discussion of the compatibility of Deep3DSIM with commercial microscope stands to the discussion section and the supplementary material.
Minor comments:
- Fig 2 lacks a color bar for D panels, which is in log scale. Authors should also show the Fourier transform along the z direction.
Response:
The colour mapping in Figure 2 uses the lookup tables called Cyan Hot and Orange Hot, as indicated in the caption, which come from the ImageJ software. To address this comment, we plan to improve the caption to reflect the fact that the plots are in log scale. We also want to include Fourier transforms along Z, either in the figure itself or in the supplementary material.
- p4, "Such minor aberrations tend to be insignificant in conventional microscopy modalities such as widefield and confocal (Wang and Zhang, 2021). Therefore..." If optical aberrations are insignificant for single cells in widefield and confocal why do experiments here? These sentences should be rephrased to motivate better the experiments performed.
Response:
We agree with this comment. To address it, we plan to rephrase this part of the results section to motivate better the experiments.
- Imaged microtubules look abnormal, 'dotty' (figure 2) in both WF and SIM. See https://sim.hms.harvard.edu/portfolio/microtubules/ or Fig 1 of Wegel, et al Dobbie Sci Rep 2016, for better examples of continuous microtubule structures as imaged with SIM.
Response:
The dottiness of the microtubule structures is not related to the SIM reconstruction, because the same dottiness is seen in the respective WF data, too. It is a product of the sample preparation and it has only aesthetic significance. Nevertheless, to address this comment we plan to mention the dottiness in the results section.
- Is also the remote focusing performed via optimization of metrics similar to the one used for compensating aberrations?
Response:
Yes, as mentioned in the Methods and materials (p. 13), the calibration of the remote focusing involved sensorless aberration correction of several Zernike modes, such as defocus and spherical aberrations.
- Figure 2, the order of names on the top right of the panel should match the order of curves presented.
Response:
We agree with this comment. To address it, we plan to reorder the curves in Figure 2.
- I value the efforts to improve open-source tools for system and AO control and GUI. And those tools seemed to have been modified for this work, although those modifications are not described. Would it be possible for the authors to describe those modifications?
Response:
A detailed breakdown is publicly available at the respective software repositories. To address this comment, we plan to add a summary of software changes to the supplementary material.
- Reported average values of the FWHM of imaged beads in 3D (p4) require also to report errors associated with those measurements.
Response:
We agree with this comment. To address it, we plan to add statistical information to the FWHM values on page 4.
- Page 13, second paragraph states that "The results from chapter 3.2..." I believe that was a copy/paste from a thesis but should be corrected for a peer-reviewed publication, as there is no chapter 3.2.
Response:
This is a leftover from an older version of the document which used numbered sectioning. In this case “chapter 3.2” refers to subsection “Characterisation of drift and temperature effects”. We plan on fixing this mistake in the revised manuscript.
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Referee #3
Evidence, reproducibility and clarity
Summary
The work of Wang et al entitled "Deep super-resolution imaging of thick tissue using structured illumination with adaptive optics" presents the use of a deformable mirror to simultaneously perform adaptive optics 'AO' and remote focusing 'RF' on a custom-designed upright microscope configuration. The work is novel and represents a timely application of this type of technology to imaging biological specimens. AO enables the correction of refractive index mismatch and sample-induced aberrations while remote focusing allows focusing through the sample without moving the specimen or the objective. The use of AO improves the final image reconstructed using a traditional SIM processing strategy. I greatly value the idea presented (SI pattern generation p7 Supp Inf) about maximizing the contrast of the projected structured illumination. This could be an excellent way to improve SIM imaging since image reconstructions suffer from artifacts when signal to noise ratio is low. However, since it is only one of the factors considered for reducing the stripe width it is unclear how it compares to imaging with the width that maximizes resolution.
Authors do also a very good job describing, characterizing and designing experiments to deal with instabilities exhibited by the deformable mirror.
One of the key aspects of the paper, that could be stressed more, is that including AO gives access to better-quality raw images that are then be used for standard reconstruction pipeline SIM processing. When aberrations are compensated, the illumination pattern closely matches what is expected by the SIM image formation model. Since these raw recorded images are sharper and closer to the actual assumption behind the SIM image reconstruction model, they will have a major positive impact in reducing artifacts that the inversion algorithm is returning. This is particularly evident in Figure 4.
Major comments:
- There is an overall reference in the manuscript of the novelty possible range of applications of using an upright microscope configuration. Examples mentioned are tissue-based imaging, access to whole-mount specimens for manipulation and electrophysiology. However, authors fail to present any such applications. There is not a single example presented which could not have been obtained with an inverted microscope. Could the authors provide an example where a water-dipping is used. Expanded samples could be one case, since the thickness of the gel makes it difficult to image with an inverted microscope. Another possible example would be to label the extracellular space and do shadow imaging of the tissue (SUSHI PMID: 29474910). ExM might be simpler to do as part of revising the manuscript than SUSHI.
- On the main text it is described a 5-fold volumetric resolution, which is confusing since authors only mention lateral and axial resolutions. Their measurements correspond to a ~1.6-fold lateral improvement and ~1.7-fold axial improvement. These are however not the 95% of the achievable resolution theoretical maximum, as stated in p7 SI (2 fold increase of 282nm), but only the 80-85%. This point should be rephrased in the manuscript.
- [OPTIONAL] p4 and related to figure 2, it would be important to report also measurements of beads with SIM but without AO, just as done for WF. Is there an improvement of using AO on SIM? This is reported for the fixed cells but not for the beads.
- Figure 2, it is odd the comparison between WF+/- AO and SIM +/- AO are done using different cellular structures. Since wavelengths used are not the same it is difficult to interpret if there is any improvement of using AO on SIM compared to SIM without AO. Same questions arise as above, Is there an improvement of using AO on SIM?
- "A significant benefit and uniqueness of the Deep3DSIM design is its upright configuration, whereas commercial SIM systems are built around inverted microscopes and are usually restricted to imaging thin samples, such as cultured cells." (p5) is not correct. The commercial DeepSIM module from CREST Optics can be mounted on an inverted microscope as well as image deep into tissue (see https://crestoptics.com/deepsim/ and application notes therein) and be used with essentially any objective. This point should be rephrased in the text.
- Fig 3 reports the improvements of AO on SIM for imaging over 10um in tissue. What are the Zernike modes measured? Or how does the pupil look like before and after correction? It would be also good to report the Fourier amplitudes as done in Fig 2C as a quantitative measure of improvement. It would be good to point out the artifacts observed on the BP SIM image reconstruction (labelled with 3x, fringes are noticeable).
- Many key details relating to image acquisition and AO correction are missing for all figures. How is the AO optimization implemented? Is it implemented via a genetic algorithm (progressive optimization of parameters) or using more clever strategies? Not clear if the optimization is implemented using images obtained with flat illumination or after SIM imaging/processing of a given dataset. How long does the AO optimization take? How sensitive to noise is the process? What metric do they use to estimate the sensorless AO correction? On pag12, they say "Fourier domain image metric" for measurements with fine details; otherwise, ISOsense when not high frequencies are present. Could the authors report the formula used to calculate the first metric? What do they consider to be low and high frequencies in this case? Is there a reason why ISOsense is not always used, or is there an automatic way to choose between the two? How many images were acquired for AO correction? Which samples were corrected with ISOsense and which ones with Fourier domain image metric? (see for example the detailed experimental reporting in the Supp Mat from Lin et al Nat Commun 2021).
- Fig 4. Data presented for larval brain tissue is a very clear example of adding AO to image deep into tissue as the effect at ~130 cannot be understated. Here too, it would be also good to report the Fourier amplitudes as done in Fig 2C as a quantitative measure of improvement and possibly the SNR of reconstructed images. Having a way to quantitatively describe how much better are those images would be great. Also, what are the aberrations corrected? Can the wavefront or Zernike amplitude of the modes be reported? Same as for Fig 3, details about AO correction are missing.
- [OPTIONAL] "It is worth noting that aberrations can differ across larger fields, and therefore, after applying an average correction, residual aberrations can still be observed in some regions of the AO-corrected bead images. However, the overall PSF shapes were still dramatically improved with AO compared to the equivalent without AO." This point is very interesting although not result either in the main text or in the SI is presented.
- "As we found that the aberrations change approximately linearly in depth, we could measure the aberration in two planes and calculate the corrections in intermediate planes by interpolation, an approach which we termed "multi-position AO"." This is, personally, one of the major contributions of this work to the community. Unfortunately, it is not reported in detail. Not only for SIM but for imaging with WF or confocal, such linear change for aberrations with depth is not well known. Again, here the details of AO correction and image metrics are missing. To establish that for most thick biological structures 'aberrations change approximately linearly in depth' would be foundational to the widespread use of AO within standard imaging. Would it be possible for the authors to elaborate on this point and present detailed results? What is the error from measuring and correcting more than 2 planes? What is the error from just measuring and AO correcting at the deeper plane, i.e. from a single measurement? Authors could also show a case in which a linear assumption works nicely (or how well it works). For example, comparing an intermediate plane (or a plane beyond) imaged after AO optimization or after coefficient interpolation of the Zernike modes and compare it against correcting directly that plane.
- The image of the cos-7 cell in metaphase, for Fig 5 is, however, very disappointing. See Fig 1 of Novak et al Nat Commun 2018 for an example of a single z-plane of a cell in metaphase. Having the possibility to correct for the entire 3D volume, I would expect amazing 3D volumes (movies and/or projections) associated with this imaging which are not presented.
- In Figure 6, they use AO in remote configuration mode to allow imaging of live specimens. It needs to be clarified if this is an a priori characterization that is then kept fixed while recording in time. The last acquired volume of fig 6A and B have a higher amount of artifacts with respect to time 00:00. Are those artifacts due to lower SNR (maybe due to sample bleaching) or due to some change in the aberrations of the specimen?
- "These results demonstrate that the remote focusing functionality of the system can be successfully applied for live 3D-SIM experiments, allowing four-dimensional acquisition while keeping the specimen stationary, thus avoiding the usual agitation and perturbations associated with mechanical actuation." Generally, this statement is true, but for the specific example shown of drosophila embryogenesis is it relevant? If they use piezo-driven Z-stack imaging with AO, does that lead to incorrect reconstructions or motion-induced artifacts? Related to the results shown in Fig 6, the fair comparison would be AO SIM vs SIM (without AO), not AO SIM vs AO WF.
- When performing remote focusing, is the effective NA of the imaged plane changing with respect to the NA of the objective used at its focal plane?
- [OPTIONAL] Did the authors run calculations to explore whether a commercial upright microscope could be used instead of their design? Are there any fundamental flaws that would make impossible using a commercial base? If not, could an AO SIM module be designed such that it adds on a commercial base? It would be important to discuss this point.
Minor comments
- Fig 2 lacks a color bar for D panels, which is in log scale. Authors should also show the Fourier transform along the z direction.
- p4, "Such minor aberrations tend to be insignificant in conventional microscopy modalities such as widefield and confocal (Wang and Zhang, 2021). Therefore..." If optical aberrations are insignificant for single cells in widefield and confocal why do experiments here? These sentences should be rephrased to motivate better the experiments performed.
- Imaged microtubules look abnormal, 'dotty' (figure 2) in both WF and SIM. See https://sim.hms.harvard.edu/portfolio/microtubules/ or Fig 1 of Wegel, et al Dobbie Sci Rep 2016, for better examples of continuous microtubule structures as imaged with SIM.
- Is also the remote focusing performed via optimization of metrics similar to the one used for compensating aberrations?
- Figure 2, the order of names on the top right of the panel should match the order of curves presented.
- I value the efforts to improve open-source tools for system and AO control and GUI. And those tools seemed to have been modified for this work, although those modifications are not described. Would it be possible for the authors to describe those modifications?
- Reported average values of the FWHM of imaged beads in 3D (p4) require also to report errors associated with those measurements.
- Page 13, second paragraph states that "The results from chapter 3.2..." I believe that was a copy/paste from a thesis but should be corrected for a peer-reviewed publication, as there is no chapter 3.2.
Referee Cross-Commenting
The other two reviewers raise relevant and important points that would contribute to the overall improvement of the work. I think that authors should try to address most, if not all, of the comments as long as they don't require more than 3-6 months to get done.
Significance
General assessment:
Although a very good and timely idea is presented the overall the manuscript still needs a lot of work. There is a lack of many key details of AO correction, all applications chosen could have been done in an inverted scope and some of the example images reported are suboptimal (Fig 2 and 5) that need further experimental work. Details and metrics, one example of the advantage of using an upright microscope and overall better examples of imaged cells could be provided.
This work builds upon recent work of implementing AO for 3D SIM (Lin et al Nat Commun 2021) to propose to use a deformable mirror to perfrom AO as well as remote focusing in an upright microscope configuration.
Audience: this work will be of interest for a specialized group of researchers, but it will contribute to the goal of adding AO tools to every microscope that will greatly impact the whole imaging community.
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Referee #2
Evidence, reproducibility and clarity
The authors want to develop a structured illumination microscopy (SIM) system for deep tissue superresolution imaging. Here they have developed a SIM system based on the upright configration, and use deformable mirror to compensate for the relection index (RI) mismatch and improve the resolution and contrast in deep tissues. They also showed examples of SR imaging of COS-7 cells and Drosophila larval brains and embryos.
However, I do have some concerns regarding the paper.
- The authors have provided an incomplete description of the structured illumination microscopy (SIM) reconstruction process. It is unclear whether the approach is based on 2D interference SIM configurations or 3D interference patterns. Furthermore, the specific algorithm utilized for image reconstruction has not been elucidated. Elaborating on these aspects is crucial as they significantly influence the interpretation of the resulting data.
- The authors have stated that sample-induced aberrations caused by RI inhomogeneities within the specimen is another major reason for causing artifacts generation. Literature has demonstrated that RI inhomogeneities can lead to non-local distortions in the grid pattern, which suggests that applying uniform reconstruction parameters across the entire image may not be viable. Traditional artifact remediation using the classical Wiener method is likely insufficient under these conditions (PMID: 33896197). The existing adaptive optics (AO) approach, which employs a deformable mirror (DM) alongside an sCMOS camera, is inadequate for tackling the issue at hand. Actually the assertion made in the paper that "aberrations change approximately linearly with depth" is seemingly contradicted by simulations referenced in the cited literature (PMID: 33896197). Consequently, it appears that the current methodology might only achieve a partial mitigation of the problems associated with spherical aberration resulting from RI mismatches. It is advisable, therefore, that the authors explicitly acknowledge this limitation in their manuscript to prevent any potential misinterpretation by readers.
- In Figure 2, the use of COS-7 cells, which are known for their relatively thin axial dimension, for the experiments raises an eyebrow. Notably, there are ample instances in existing research where both 2D-SIM and 3D-SIM, without the integration of adaptive optics, have yielded high-quality super-resolution images of structures such as tubulin and the endoplasmic reticulum. In addition, the authors did not present a direct comparison between BP-SIM and AO-SIM here. Without this comparative analysis, it remains ambiguous whether the enhancements in resolution and contrast and the reduction in artifacts can genuinely be attributed to the mitigation of spherical aberration. To clarify this, it would be beneficial for the authors to include side-by-side comparisons of these modalities to demonstrate the specific improvements attributed to AO-SIM.
- In Figures 3 and 4, the authors have illustrated the enhancements achieved through the application of AO. However, there is a discernible presence of hammer-stroke and honeycomb artifacts in pre-AO imaged data, which seem to originate from the amplification of the incorrectly moved out-of-focal background in the frequency domain. Various strategies have been previously suggested to address these specific artifacts, encompassing methods like subtracting background noise in the raw images or employing selective frequency spectrum attenuation techniques, such as Notch filtering and High-Fidelity SIM. To facilitate a more comprehensive understanding, I would recommend that the authors incorporate into their study a comparison that includes BP-SIM data that has undergone either background subtraction or frequency spectrum attenuation. This added data would enable a more complete evaluation and comparison regarding the merits and impact of their AO approach.
Significance
The authors want to develop a structured illumination microscopy (SIM) system for deep tissue superresolution imaging. Here they have developed a SIM system based on the upright configration, and use deformable mirror to compensate for the relection index (RI) mismatch and improve the resolution and contrast in deep tissues.
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Referee #1
Evidence, reproducibility and clarity
Review, 3D SIM + AO, Wang and coworkers
In this manuscript, Wang and coworkers report an upright 3D SIM system with adaptive optics (AO) correction. They demonstrate that AO improves imaging into thick 3D samples, including Drosophila larval brain. They also explore the use of remote focusing with their setup. The authors clearly demonstrate a gain with AO, and we are convinced that the microscope they build offers some utility over existing state of the art, particularly in samples thicker than a single cell. That said, we have concerns with the manuscript that we would like to see addressed before recommending publication:
- Given the emphasis on super-resolution imaging deep inside a sample, we were surprised to see no mention of other forms of structured illumination that allow super-resolution imaging in samples thicker than a single cell. These include the 'spot-scanning' implementations of SIM that offer better imaging at depth by virtue of pinholes, and include MSIM, iSIM, and rescan confocal technologies. The two-photon / AO implementation of iSIM seems particularly germane, e.g. https://pubmed.ncbi.nlm.nih.gov/28628128/ Please consider citing these works, as they help place the existing work into context.
- As we're sure the authors appreciate, besides aberrations, a major additional obstacle to 3D SIM in thick tissues is the presence of out-of-focus background. Indeed, this point was mentioned by Gustafsson in his classic 2008 paper on 3D SIM (https://pubmed.ncbi.nlm.nih.gov/18326650/): 'The application area of three-dimensional structured illumination microscopy overlaps with that of confocal microscopy, but the two techniques have different and complementary strengths. Structured illumination microscopy offers higher effective lateral resolution, because it concentrates much of the excitation light at the very highest illumination angles, which are most effective for encoding high-resolution information into the observed data, whereas confocal microscopy spreads out its illumination light more or-less uniformly over all available angles to form a focused beam. For very thick and compactly fluorescent samples, however, confocal microscopy has an advantage in that its pinhole removes out-of focus light physically. Structured illumination microscopy is quite effective at removing out-of-focus light computationally, because it is not subject to the missing-cone problem, but computational removal leaves behind the associated shot noise. Therefore confocal microscopy may be preferable on very thick and dense samples, for which the in-focus information in a conventional microscope image would be overwhelmed by out-of-focus light, whereas structured illumination microscopy may be superior in a regime of thinner or sparser samples.' This point is not mentioned at all in the manuscript, yet we are certain it is at least partially responsible for the residual image artifacts the authors mention. Please discuss the problem of out of focus light on 3D samples, particularly with an eye to the 'spot-scanning' papers mentioned above.
- The authors use a water dipping lens, yet they image into samples that are mounted on coverslips, i.e. they use a dipping lens to image through a coverslip: see attached pdf for reference
This almost certainly introduces spherical aberration, which the authors seem to observe: see attached pdf for reference
We find this troubling, as it seems that in the process of building their setup, the authors have made a choice of objective lens that introduces aberrations - that they later correct. At the very least, this point needs to be acknowledged in the manuscript (or please correct us if we're wrong) - as it renders the data in Figs. 3-4 somewhat less compelling than if the authors used an objective lens that allowed correction through a coverglass, e.g. a water dipping lens with a correction collar. In other words, in the process of building their AO setup, the authors have introduced system aberrations that render the comparison with 3D SIM somewhat unfair. Ideally the authors would show a comparison with an objective lens that can image through a glass coverslip. - The authors tend to include numbers for resolution without statistics. This renders the comparisons meaningless in my opinion; ideally every number would have a mean and error bar associated with it. We have included specific examples in the minor comments below. - In Fig. 5, after the 'multipoint AO SIM', the SNR in some regions seems to decrease after AO: see attached pdf for reference
Please comment on this issue.
- Please provide timing costs for the indirect AO methods used in the paper, so the reader understands how this time compares to the time required for taking a 3D SIM stack. In a similar vein, the authors in Lines 213-215, mention a 'disproportionate measurement time' when referring to the time required for AO correction at each plane - providing numbers here would be very useful to a reader, so they can judge for themselves what this means. What is the measurement time, why is it so long, and how does it compare to the time for 3D SIM? It would also be useful to provide a comparison between the time needed for AO correction at each (or two) planes without remote focusing (RF) vs. with RF, so the reader understands the relative temporal contributions of each part of the method. We would suggest, for the data shown in Fig. 5, to report a) the time to acquire the whole stack without AO (3D SIM only); b) the time to acquire the data as shown; c) the time to acquire the AO stack without RF. This would help bolster the case for remote focusing in general; as is we are not sure we buy that this is a capability worth having, at least for the data shown in this paper.
- Some further discussion on possibly extending the remote focusing range would be helpful. We gather that limitations arose from an older model of the DM being used, due to creep effects. We also gather from the SI that edge effects at the periphery of the DM was also problematic. Are these limitations likely non-issues with modern DMs, and how much range could one reasonably expect to achieve as a result? We are wondering if the 10 um range is a fundamental practical limitation or if in principle it could be extended with commercial DMs.
Minor comments
- The paper mentions Ephys multiple times, even putting micromanipulators into Fig. 1 - although it is not actually used in this paper. If including in Figure 1, please make it clear that these additional components are aspirational and not actually used in the paper.
- The abstract mentions '3D SIM microscopes', 'microscopes' redundant as the 'm' in 'SIM' stands for 'microscope'.
- 'fast optical sectioning', line 42, how can optical sectioning be 'fast'? Do they mean rapid imaging with optical sectinong?
- line 59, 'effective imaging depth may be increased to some extent using silicone immersion objectives', what about water immersion objectives? We would guess these could also be used.
- line 65 - evidence for 'water-dipping objectives are more sensitive to aberrations' ? Please provide citation or remove. They are certainly more prone to aberrations if used with a coverslip as done here.
- 'fast z stacks' is mentioned in line 103. How fast is fast?
- line 116 'we imaged 100 nm diameter green fluorescent beads'. Deposited on glass? Given that this paper is about imaging deep this detail seems worth specifying in the main text.
- lines 127-130, when describing changes in the bead shape with numbers for the FWHM, please provide statistics - quoting single numbers for comparison is almost useless and we cannot conclude that there is a meaningful improvement without statistics.
- In the same vein, how can we understand that remote focus actually improves the axial FWHM of the widefield bead? Is this result repeatable, or it just noise?
- line 155, 'Because of the high spatial information...' -> 'Because of the high resolution spatial information...'
- When quoting estimated resolution #s from microtubules (lines 158-163) similarly please provide statistics as for beads.
- It seems worth mentioning the mechanism of AO correction (i.e. indirect sensing) in the main body of the text, not just the methods.
- How long do the AO corrections take for the datasets in the paper?
- Were the datasets in Fig. 2-4 acquired with remote focusing, or in conventional z stack mode? Please clarify this point in the main text and the figure captions.
- It would be helpful when showing z projections in Figs. 3-5 to indicate the direction of increasing depth (we assume this is 'down' due to the upright setup, but this would be good to clarify)
- line 174, 'showed significant improvements in both intensity and contrast after reconstruction' - we see the improvements in contrast and resolution, it is harder to appreciate improvements in intensity. Perhaps if the authors showed some line profiles or otherwise quantified intensity this would be easier to appreciate.
- line 195 'reduced artefacts' due to AO. We would agree with this statement - the benefit from AO is obvious, and yet there are still artefacts. If the authors could clarify what these (residual) artefacts are, and their cause (out of focus light, uncorrected residual aberrations, etc) this would be helpful for a reader that is not used to looking at 3D SIM images.
- Line 197, 'expected overall structure', please clarify what is expected about the structure and why.
- Line 199, what is a 'pseudo structure'?
- Fig. 4B, 'a resolution of ~200 nm is retained at depth', please clarify how this estimate was obtained, ideally with statistics.
- Fig. 4D, please comment on the unphysical negative valued intensities in Fig. 4D, ideally explaining their presence in the caption. It would also be helpful to highlight where in the figure these plots arise, so the reader can visually follow along.
- Line 245, 'rapid mitosis'. What does rapid mean, i.e. please provide the expected timescale for mitosis.
- For the data in Fig. 6, was remote refocusing necessary?
- What is the evidence for 'reduced residual aberrations', was a comparative stack taken without AO? In general we feel that the results shown in Fig. 6 would be stronger if there were comparative results shown without AO (or remote focusing).
- Line 350, 'incorporation of denoising algorithms' - citations would be helpful here.
- Line 411, 'All three were further developed and improved' - vague, how so?
- Sensorless AO description; how many Zernike modes were corrected?
- Multi-position aberration correction. Was the assumption of linearity in the Zernike correction verified or met? Why is this a reasonable assumption?
- Fig. S1B is not useful; if the idea is to give a visual impression of the setup, we would recommend providing more photos with approximate distances indicated so that the reader has a sense of the scale of the setup. As is - it looks like a photograph of some generic optical setup.
- SI pattern generation - 'the maximum achievable reconstruction resolution was only slightly reduced to about 95% of the theoretical maximum'. We don't understand this sentence, as the resolution obtained on the 100 nm beads is considerably worse than 95% of the theoretical maximum. Or do the authors mean 95% of the theoretical maximum given their pitch size of 317 nm for green and 367 nm for red? SI Deformable mirror calibration
'spanning the range [0.1, 0.9]' - what are the units here?
What are the units in Fig. S5C, S5D?
It would be useful to define 'warmup' also in the caption of SI Fig. S6A. SI Remote Focusing, 'four offsets, {-5 mm, -2.5 mm, 2.5 mm, 5 mm}...' are the units mm or um? '...whereas that of the 10 beads was...' here, do the authors mean the position of the beads derived from the movement of the piezo stage, as opposed to the remote focusing? The authors refer to the 'results from Chapter 3.2'. What are they talking about? Do they mean a supplementary figure, or earlier supplementary results? In general, we found the discussion in this paragraph difficult to follow. Supplementary Fig. 9 seems to be not referred to anywhere in the text. - Since the paper emphasizes 3D SIM, OTFs along the axial direction would also be useful to show, in addition to the lateral OTFs shown in Fig. 2D. - When the sample is moved by the piezo, the axial phase of the 3D-SIM illumination pattern is stable as the sample is scanned through the illumination pattern. When remote focusing is performed, the sample is always stable so the axial phase of the 3D-SIM illumination pattern is presumably changing with remote focusing. Can the authors clarify if the 3D SIM illumination pattern is scanned when remote focusing is applied, or is the intensity pattern stable in z? - In Supplementary Fig. 9, primary spherical is referred to twice, both at index 11 and 22. The latter is presumably secondary spherical? - we do not understand the x axis label, in Fig. S4D, is it really [0, 50, 50, 50] as written? see attached pdf for reference
Referee Cross-Commenting
I don't have much to add; the other reviewers raise good points and I think it would be good if the authors could respond to their feedback in a revised manuscript.
Significance
Nearly all fluorescence images deteriorate as a function of depth. Methods to ameliorate this depth-dependent degradation are thus of great practical value, as they improve the information content of images and thus (hopefully) biological insight. In this work, the authors develop a method to improve super-resolution imaging (3D SIM) at depth, by combining it with adaptive optics.
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Reply to the reviewers
Reviewer #1:
This study provides negative in vivo evidence for the use of two PERK inhibitors and of TUDCA for the treatment of Sli1-related Marinesco-Sjögren syndrome (MSS).
Overall, the manuscript reports a substantial amount of work and the study could be published in its present format. The experiments are well described in terms of methodology and appropriate analysis has been applied. Claims are proportionate and not overstated
I would have only minor comments related to some clarifications that the authors could make in the present manuscript and a suggestion for experiments that could improve the manuscript.
First, although this is not my expertise, the in vitro analysis of CHOP luciferase assays suggests that very high concentrations, in particular of TUDCA, are needed to observe an effect. The authors may wish to clarify their opinion and whether this could be the reason why in vivo they have been unable to obtain any inhibition of the PERK pathway.
The reviewer is correct in pointing out that high concentrations of trazodone, DBM and TUDCA were required to inhibit the PERK pathway in the CHOP::luciferase reporter cell lines. However, as we state in the Discussion, we do not think that their lack of effect in vivo was due to insufficient drug levels, since woozy mice were treated with trazodone, DBM or TUDCA according to dose regimens and administration routes that have proved effective in other neurodegenerative disease mouse models. Moreover, our analysis did not find major differences in drug bioavailability between mice with the woozy genetic background (CXB5/ByJ) and C57BL/6J mice in which these drugs had shown neuroprotective effects (see also the response to the next point).
Second, it seems to me that when measuring the Trazodone metabolism there is a difference between acute and chronic treatment. It would be worth discussing what the authors make of that and what is more relevant (I assume chronic) to the disease model outcome.
We realized that the nomenclature used in Figures 6 and 7 was confusing, leading the reader to think there were differences in trazodone levels between chronically and acutely treated mice.
The experiment shown in Figure 6 was designed to test whether there were differences in trazodone pharmacokinetics and metabolism between mice of the woozy strain, which have the CXB5/ByJ genetic background, and C57BL/6J mice in which trazodone had shown neuroprotective effects in previous studies. In contrast, Figure 7 illustrates the levels of trazodone and m-CPP in control and woozy mice (both of which have the CXB5/ByJ genetic background) that had been chronically treated with trazodone for 5 weeks. These are the same animals as in Figure 3, as we state in Figure 7 legend. Therefore one should compare the levels of trazodone and m-CPP in Figure 7 with those of the "woozy" group (CXB5/ByJ genetic background) in Figure 6. This comparison shows that trazodone and m-CPP levels are comparable after chronic and acute (6h) treatment.
To avoid confusion, we have changed the mouse nomenclature. We have renamed the control group of mice as "CT" (previously "WT") throughout the text and figures. In Figure 6, we have used CXB5/ByJ instead of "woozy" to emphasize the comparison between the different genetic backgrounds (CXB5/ByJ vs C57BL/6J). Finally, we have replaced the colors of symbols in Figure 7 in order to match those of Figure 3. We have also made the description and discussion of these results clearer in the revised manuscript.
With respect to the experiments a simple and informative addition would be the evaluation of the PERK pathway in mice treated with TUDCA, as this is missing.
The effect of TUDCA treatment on the PERK pathway is shown in Figure 5, where we measured CHOP mRNA levels in Purkinje cells microdissected from mice treated with 0.4% TUDCA in the chow, and in Figure 9C and D, where we measured the percentage of CHOP-immunopositive Purkinje cells in the cerebellum of same groups of mice by immunohistochemistry.
Figure 10 illustrates the results of an additional experiment in which woozy mice were treated with 500 mg/kg TUDCA intraperitoneally (ip), to test whether this alternative dosing regimen was any better. Like the treatment per os, TUDCA ip had no beneficial effect on motor dysfunction. Therefore we deemed it unnecessary to check the effect on PERK pathway inhibition in this group of mice.
A more difficult but potentially more interesting line of investigation is that of searching for potential actions of Trazodone that are PERK independent and might be responsible for the partial rescue observed in the beam walking test, which is much more sensitive and specific than rotarod, so worth considering. Assuming authors want to go down this route and add significance to their study my suggestion would be an unbiased RNA seq from the brain samples they already have. However, this is a suggestion to steer the study towards a more positive outcome and it is not necessary to support their current conclusions.
We agree with the reviewer that it would be interesting to investigate the mechanism by which trazodone slightly ameliorated the motor performance of woozy mice in the beam walking test. In the Discussion, we speculated that this could be due to an effect of trazodone on cerebellar serotonergic neurotransmission, which would require electrophysiological investigations to demonstrate. Of course, other mechanisms may also be operative, which RNA seq may help identify, as the reviewer suggests. However, this would be a complex and lengthy investigation, the results of which would not change the main conclusions of the present paper. We plan to explore this line of investigation in a future study.
Reviewer #2:
Lavigna et al. described the effect of Trazodone in Marinesco-Sjögren syndrome model mice. Although the results are somewhat disappointing, this research has provided fundamental evidence for the future development of MSS therapeutics. There are few minor comments to further improve the manuscript
Major comment<br /> P14<br /> "Trazodone metabolism to m-CPP was slightly impaired in woozy mice compared to C57BL/6J mice. This was evident from the m-CPP/trazodone ratio, calculated on the AUC0-t in the plasma, which was 0.34 in woozy and 0.67 in C57BL/6J mice."
Why was the concentration different between WT and woozy mice? Which organ mainly contributes to the metabolism of trazodone? Is the function of this target organ different between WT and woozy mice?<br /> Similar to trazodone, m-CPP clearance from plasma was slightly faster in woozy than in C57BL/6J mice.<br /> Is m-CPP eliminated via the kidney? Or liver? Why is there a difference? Does SIL1 functions in liver or kidney? Needs discussion. This is the same for brain m-CPP levels.
As explained in the response to the second comment of reviewer #1, "woozy" in Figure 6 referred to mice with the CXB5/ByJ genetic background, and in this experiment we compared trazodone pharmacokinetics and metabolism between CXB5/ByJ and C57BL/6J mice. We have modified the nomenclature of Figure 6 and the Results to make this clear.
Trazodone undergoes extensive hepatic metabolism, and only a small percentage is excreted unchanged in the urine. Metabolism involves hydroxylation, oxidation and dealkylation reactions, forming in particular the 5HT-active metabolite m-CPP (by CYP3A4). This and other metabolites are mainly excreted in urine, as conjugates [1-3]. The slight differences in trazodone pharmacokinetics and metabolism between the CXB5/ByJ and C57BL6/J mice shown in Figure 6 is not attributable to loss of SIL1 function, since both groups of mice carried wild-type Sil1 alleles, but is most likely due to subtle differences between the two strains, for example in the binding to plasma proteins, metabolic enzymes, transporters and/or the excretion processes. The available data do not allow to clarify this issue.
The main point, however, is that no major differences were found in the plasma and brain concentrations of trazodone between these two strains of mice, which could have explained the lack of efficacy of trazodone in woozy mice, as we now further stress in the revised Discussion.
Minor comments
P3 L5 mutation should be variant.
This has been changed.
P4 L1 eIF2a-P should be phosphorylated eIF2α (p-eIF2α). The reviewer prefers (p-eIF2α) than (eIF2α-p) throughout the manuscript.
There is no standard rule for indicating phosphorylated proteins, and phosphorylated eIF2α is referred to in various ways in different papers, with the "p" in capital or lowercase, preceding or following the protein name, separated by a dash or not. We would prefer to maintain the current nomenclature for consistency with our previous publications, unless the Editor deems otherwise.
P9 L11 M-CPP should be fully spelled out the first time it appears. m-Chlorophenylpiperazine (m-CPP)
M-CPP is spelled out the first time it appears in the Material and Methods, subheading Drug treatments and bioanalysis.
Please explain the difference between the expected function of trazodone and its metabolite m-CPP. Why m-CPP is not effective.
Based on the observation that mice of the woozy strain had lower brain levels of m-CPP than C57BL6/J mice (Figure 6), we hypothesized that the lack of effect of trazodone in woozy mice could be due to m-CPP mediating the PERK signaling inhibitory activity of trazodone. However, experiments in CHOP::luciferase reporter cells demonstrated that m-CPP does not inhibit PERK signaling (Figure 2D). The precise mechanism by which trazodone inhibits PERK signaling is not known [4], which makes it difficult to speculate why its main metabolite, m-CPP, does not exhibit this activity.
P11 L3 Fig. 3 Fig. 3A and B?
Yes, we specifically refer to panels A and B of Figure 3. We have indicated this in the revised manuscript.
P11 L6 at 7 weeks of age?
We have re-done the statistical analysis by two-way ANOVA and reported the results in the legend to Figure 3. There is a significant difference between vehicle- and trazodone-treated woozy mice in the number of missteps when the two groups are compared globally. No statistically significant difference in the number of missteps is detected at specific time points by post-hoc analysis. There is no statistically significant difference between vehicle- and trazodone-treated woozy mice in the time to traverse the beam. The Results section has been revised accordingly.
P12 L17 ~4 times, 4 times? Please state the exact value.
Done.
Figure 7 Why are brain m-CPP levels higher than plasma levels? Is trazodone metabolized in brain tissue?
Trazodone is extensively metabolized in the liver through Cytochrome P450 (Rotzinger et al., 1999). It is well documented that m-CPP readily passes the blood-brain barrier, much better than the parent compound, explaining its high brain levels [2].
P19 L7 ISRIB; please fully spell out the first time it appears.
Done.
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