Convergent insulin and TGF ‐β signalling drives cancer cachexia by promoting aberrant fat body ECM accumulation in a Drosophila tumour model

  1. Daniel Bakopoulos
  2. Sofya Golenkina
  3. Callum Dark
  4. Elizabeth L Christie
  5. Besaiz J Sánchez‐Sánchez
  6. Brian M Stramer
  7. Louise Y Cheng

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Abstract

In this study, we found that in the adipose tissue of wildtype animals, insulin and TGF‐β signalling converge via a BMP antagonist short gastrulation (sog) to regulate ECM remodelling. In tumour bearing animals, Sog also modulates TGF‐β signalling to regulate ECM accumulation in the fat body. TGF‐β signalling causes ECM retention in the fat body and subsequently depletes muscles of fat body‐derived ECM proteins. Activation of insulin signalling, inhibition of TGF‐β signalling, or modulation of ECM levels via SPARC, Rab10 or Collagen IV in the fat body, is able to rescue tissue wasting in the presence of tumour. Together, our study highlights the importance of adipose ECM remodelling in the context of cancer cachexia.

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  1. Version published to 10.15252/embr.202357695
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    Reply to the reviewers

    Reviewer #1

    Major:

    - The statement (line 149'Together, our data suggest that systemic ecdysone levels are unlikely to be involved in modulating tumour-induced muscle detachment or to mediate the role of fatbody Insulin signalling in regulating muscle detachment.') is derived from an experiment with sterol free diet (in which 20HE is genetically addressed) and a pleiotropic experiment (PG>RasG12V). In neither paper nor the current manuscript, 20HE levels have been directly addressed.

    Therefore, this statement needs further experimental support and discussion. Ecdysone is a critical hormone during development and especially growth-related effects central to this study. The authors should consider doing pharmacology or augment their claims here with genetic manipulation experiments of 20HE related genes in larvae (Leopold, Rewitz, Rideout, Drummond-Barbosa, Schuldiner labs) and adult animals using genetics, pharmacology or direct assessment of 20HE levels (RIPA, Edgar and Reiff labs).

    The main point we were trying to convey is that we do not think global ecdysone levels plays a role in modulating fatbody insulin or tgfb signalling, which in turn affects muscle detachment. We are not claiming that edysone levels is not changing in control vs. tumour bearing animals. In fact, we predict that 20HE levels will be different in tumour bearing vs. control animals (as tumour bearing animals undergo developmental delay), but this is not the main point of our conclusions. We believe that our conclusions are supported by the experiment demonstrating global ecdysone alterations (via feeding sterol-free food) did not affect how fatbody Akt activation altered tgfb signalling and enhanced muscle integrity (Figure S1). Therefore, we don’t think measuring 20HE helps to support our conclusions. Pharmacological inhibition via feeding ecdysone inhibitors effectively demonstrate a similar point to feeding sterol-free food which we have already performed. We are happy to try direct manipulation of 20HE related genes (eip75B-RNAi) in the fatbody to see if this affects muscle detachment or pAkt and pMad levels in tumour bearing animals.

    - In Fig.7 the authors used a sog-LacZ stock to show transcriptional activation in fatbody cells. This stock is based on P-element insertion in the according regulatory regions and supposed to express lacZ with an nls. I can clearly see lacZ in nuclei in Fig. 7H, whereas this is very hard to see in nuclei in Fig7i in the tumour model. In addition, lacZ is known for its high stability and not the best option. As this finding is vital for central claims of this study, it should be complemented by either qPCR for sog on fat body cells or using another readout by converting one of the two Mimic lines (BL42189/44958) into GFP sensors for sog.

    We will add a counterstain to these images. We will also perform qPCR in the fatbody of control and cachectic animals to assess whether Sog transcription is altered. We agree converting one of the Mimic lines to a GFP sensor would be a good option, but this experiment would require getting new fly lines into Australia, which takes at least 2 months because of quarantine laws. We don’t believe this experiment would change the general conclusions of the paper, therefore would prefer not to do this experiment.

    - I have similar problems with Fig.7B-F, as phosphorylated Mad should be translocated to the nucleus. In 7F the authors measure pMad over Dapi, which is the right way but it is hard to see pMad in the nucleaus apart from Fig7B, wheras in D and E, where the authors measure higher levels, I cannot identify clear pMad in nuclei. These images either need to show the Dapi channel or more representative images should be chosen like in Fig.4 with arrows pointing to measured nuclei. Fig.7C something went wrong with the compression of this image.

    We will show more representative examples and fix Fig 7C.

    - The proper function of RNAi stocks targeting genes like sog, mad, etc. is vital for this study as these lines are used throughout the study. Functional evidence of specific knockdown efficiency should be provided or references given in which these stocks were shown to provide functional knockdown on transcript or protein level.

    We agree with the reviewer that this is an important point. We will demonstrate the knockdown of sog and mad (and other RNAis) used in the study by either referring to published data or demonstrate knockdown ourselves.

    - Fig.S7 discusses appearance of gbb/Bmp7 and Sog/CHRD in human patients. The analysis the authors performed shows a correlation between both factors, but is hampered by the fact that datasets for peripheral tissues of cachexia patients are unavailable. The authors may consider sorting these after tumor entities in which cachexia occurs frequently vs. low occurrence and then check for both genes.

    We will try this analysis.

    Fig.5 M-P pMAd is not indicated in the Panels only the legend.

    We will fix this error.

    - Please follow FlyBase nomenclature, e.g. dlg1 for discs large 1 and unify in the whole manuscript and figure for all genes.

    We will fix this error.

    - For endogenous fusion proteins like Viking-GFP (e.g. vkg::GFP) choose a format to clearly decipher them from transcriptional readout stocks like sog-lacZ.

    We will fix this error.

    - The quantifications in most figures are quite small with tiny lettering and XY axis are difficult to read in letter/A4 size.

    We will enlarge font size.

    Minor:

    1. Adjust in-figure caption alignments

    2. Line 104: add comma RasV12, dlgRNAi

    3. Line 114: replace little  not significant (n.s.)

    4. Line 334: 'sogRNAi overexpression' to my knowledge, RNAi are expressed, not overexpressed.

    5. Line 454: italicize r4>

    6. Fig S4E: remove frame

    7. Figures 6: It would be better to number and explain the pathway presented in the figure in text and fig legend.

    8. Just a personal preference. Lettering of images in images is commonly done horizontally, here it appears like a mix between vertical and horizontal.

    We will fix these minor errors.

    Reviewer #2

    Major comment

    Their genetic experiments clearly showed that the reduction of insulin signaling activity in the fatbody induces upregulation of TGF-β signaling and Collagen accumulation. Then, how does TGF-β signaling induce Collagen accumulation?

    From the experiments we have carried out, we do not have insights into how TGF-B signalling induce Collagen accumulation.

    They showed that Rab10 knockdown and SPARC overexpression reduced the accumulation of fatbody ECM. Are Rab10 and SPARC expression regulated by TGF-β signaling?

    We can address this point by assessing if Rab10 and SPARC expression is altered in cachectic fatbody.

    Minor comments

    Line 90: "Disc Large (Dlg) RNAi in the eye" must be "Discs Large (Dlg1) RNAi in the eye imaginal discs".

    we will fix this error.

    Figures 1D and 1L are from the same image. Also, Figures 1C and 1M are from the same image. Are both of them necessary to be shown in the different panels?

    The duplication of 1C and 1M, was an error, we thank the reviewer for picking this up. We will fix this error. We will use different images for 1D and 1L.

    Why are the staining patterns of anti-pAkt shown in Figures 1L and 1U so different? pAkt is not detected in the nuclei in Fig. 1L but its nuclear signal is clear in Fig. 1U.

    We will show more representative images of these staining.

    Figure 1: Images of counter staining for nuclei like DAPI should be also included for all these fatbody images.

    We will show counter staining for DAPI.

    Line 101: "Tumour specific ImpL2 inhibition was sufficient to reduce fatbody pAkt levels." Is this correct? ImpL2 inhibition in tumors should elevate the pAKT level in fatbody.

    This was a mistake, we will fix this error.

    Figure S1~S4: These figures and their legends do not correspond to each other. We thank the reviewer in picking up this error, there was an error in inserting the images into the text. S2 and S3 were swapped.

    We will fix this error.

    Line 189: The pAkt level in the muscle of tumour-bearing animals should be examined to confirm the activity of the insulin signaling is downregulated.

    We will include this data.

    Line 189: If the authors conclude that muscle insulin signaling predominantly regulates translation and atrophy, OPP assay for the muscle cells should be examined in the same experimental settings.

    We will carry out OPP assay upon Akt overexpression in the muscle.

    Line 247: The expression level of Rab10 and SPARC should be examined in the fatbody of tumour-bearing animals to see whether Rab10 is upregulated and SPARC is downregulated.

    Line 247: If Rab10 upregulation and SPARC downregulation are the causes of the accumulation of ECM proteins in the fatbody of tumour-bearing animals, how the overexpressed Collagen proteins can be secreted from the fatbody cells?

    We are not sure, but the overexpression of Collagen proteins is at an extremely high level, therefore, it is possible that some of it can be processed and secreted despite Rab10 upregulation and SPARC downregulation. We have carried out an experiment to overexpress Collagen proteins in the muscle, in this case, this manipulation did not rescue. This indicates that processing of Collagen in the fatbody is important, however, we do not know how the processing is regulated.

    Line 347: Sog is a secreted BMP antagonist. Thus, it can be expected that the Sog overexpression downregulates TGF-β signaling in fatbody and muscle tissues. If the rescued phenotypes with Sog overexpression can be explained by this logic, pMad level should be examined in these experiments.

    We have shown this data in Figure R-T. We will refer back to this data in Line 347.

    Reviewer #3

    Major comments:

    - Are the key conclusions convincing?

    Most of the conclusions are convincing. It is not clear however whether the ECM accumulation in the fat body of tumor animals is fibrotic and whether it is extracellular or in the cell cortex.

    - Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether?

    -The authors state in line 71 'This deposition of disorganized ECM leads to fibrotic ECM

    accumulation.' The authors haven't really provided evidence for the ECM being fibrotic. The authors could either rephrase this or provide additional experimental evidence of fibrosis in the fat body.

    We will tone down the claim that the ECM accumulation is fibrotic.

    - 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.

    -The authors state in line 147" Finally, in tumor-bearing animals fed a sterol-free diet, that underwent a prolonged 3rd instar stage due to reduced ecdysone levels (Parkin and Burnet, 1986), we activated insulin signalling in the fatbody via Akt overexpression (QRasV12, scribRNAi). We found that this manipulation caused a significant decrease in pMad levels in the fatbody and a rescue of muscle detachment (Figure S1 D-I), similar to animals fed a standard diet (Figure 1 O-Q, Figure 2 F-H)." Since it's not already known what the extent of muscle integrity defect there is in tumors with additional sterol free diet, it would be important to show a non-tumor control for comparison in FigS1F. This would also then make it clear to what extent the defect is rescued by Akt overexpression.

    We will include a non-tumour control for Fig S1F.

    -The authors state in line 158 'Upon the knockdown of Impl2, we found that tumor gbb was not significantly altered (Figure S3A).' Even though this shows an indication that Gbb levels are not reduced, the n number is too low to state that it is non-significant. The authors should increase the n number here.

    N=3 is generally enough to see a difference, we will include data done in parallel which shows Impl2 RNAi is sufficient to induce a reduction in Impl2 RNA levels. This will demonstrate that n=3 is sufficient to demonstrate a reduction in transcript levels if there is a reduction.

    -The authors state in line 171 'Conversely, knockdown of gbb alone or knockdown of gbb together with ImpL2 significantly rescued the Nidogen overaccumulation defects observed at the plasma membrane of fatbody from tumor-bearing animals, while ImpL2RNAi alone did not (Figure S2 Q-U).' This is a somewhat misleading representation, since again no non-tumor control was used, so the extent of the rescue by gbb knowdown is not obvious. In FigS2P Nidogen levels in the tumor seem ~100% higher than in control. But in FigS2U, in which no control was included, the tumor+gbb knowdown seems ~ 20% lower than tumor. So it is probably a more moderate rescue, but that's only possible to assess by including a non-tumor control in FigS2U. Also the images in FigS2Q-T don't seem representative since they appear to show a much bigger difference in fluorescence intensity than ~20%. Please show more representative images.

    We will include a non-tumour control for S2Q-T and show more representative pictures.

    -The authors state in line 174 'Finally, co-knockdown of gbb and ImpL2 in the tumor significantly rescued the reduction in OPP and Nidogen levels observed in the muscles of tumor-bearing animals (Figure S3 B-I).'

    Again, the single knockdowns and the non-tumor control are not shown in FigS3E and I and should be included for comparison and to see the contribution of each knockdown and to be able to judge the extent of the rescue.

    We will include the single knockdowns and a wildtype control

    -Regarding Fig3O: Is there a significant tumor muscle attachment defect here? In this graph the tumor only looks about 10% lower than the WT (rather than 40% in Fig2E). The other issue is the extremely low n number for WT. I would recommend increasing the n number for WT here and to indicate in the graph whether the tumor is significantly different to WT (or non-significant, in which case RabRNAi wouldn't actually 'rescue' the defect). In the present form, this graph is not very convincing.

    We will increase the n number for WT for this experiment. The reduction in muscle detachment is 10% rather than 40% here is because this experiment was done at day 6, which we will indicate in the figure legend. The 40% reduction in Fig2E is because these samples were processed at day7. Rab10RNAi experiment was carried out at day 6, because by day7, the Rab10RNAi rescue is so good, most of the tumour bearing animals have pupated, thus the experiment could only be carried out at day6.

    - Regarding Fig3W: A non-tumor control would be important to include to be able to judge the extent of muscle attachment defects and the extent of the rescue for UAS-Sparc. This will allow to assess the severity of muscle integrity defect in this particular experiment (since it appears to vary in different experiments e.g. muscle defect in tumor 40% in Fig2E and ~10% in Fig3O) and to assess the extent of rescue for the various genotypes.

    We will include a non-tumour control for 3W.

    -The authors show an accumulation of ECM in the fat body of tumors. It is not clear, whether this ECM accumulates intracellularly near the cell surface or extracellularly. The authors should assess this, maybe by doing electron microscopy.

    We do not have an EM facility that can accommodate this experiment, thus doing EM is not an option for us. However, we can address whether the accumulation of ECM is intracellular or extracellular by performing an experiment, where we try perform antibody staining against Viking-GFP without permeabilizing the cells. If Viking is detected without permeabilization, it would indicate the accumulations are extracellular. This approach has been previously used to address this question in Zang et al., elife, 2015.

    - 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.

    -These suggested experiments should be quite straightforward since they are mostly just repeating previous experiments with the appropriate controls and n numbers. I would think that they can be done within a few months. The electron microscopy should not take more than a few weeks and not be costly.

    - Are the data and the methods presented in such a way that they can be reproduced?

    -The details on how old animals used in each experiment were, are not easy to find and not written very clearly. They should be included in the each figure legend rather than summarising those details in the methods.

    We will add the number of days in the figure legend.

    -Also, in line 788 in the methods, several stocks are indicated as coming from particular labs (e.g. UAS-FOXO (Kieran Harvey), UAS-GFP (Kieran Harvey), UAS-lacZRNAi (Kieran Harvey), UAS-RasV12 (Helena Richardson), UAS-cg25C;UAS-Vkg (Brian Stramer)).

    However, it is not clear whether these labs actually made these stocks and if so whether it has already been described in their papers how the lines were made. If the lines are unpublished, the detailed information should be given on how the lines were made. Or if the lines are published, the authors should provide the reference.

    We will fix these references.

    - Are the experiments adequately replicated and statistical analysis adequate?

    In general, the n number is rather low in several experiments, especially n of 3 for many controls. And as I mentioned before, rescues of tumor phenotypes are often shown without including a non-tumor control, making it hard to judge the extent of the rescue. Sometimes this information can be found in other figures, but the reader should not have to search for it. And also the severity of the phenotype can vary from experiment to experiment.

    We will include a non-tumour control when appropriate to address this.

    Minor comments:

    - Specific experimental issues that are easily addressable.

    - Are prior studies referenced appropriately?

    Yes, as far as I can tell.

    - Are the text and figures clear and accurate?

    -In the literature, people usually call it 'fat body' rather than 'fatbody'.

    We will fix this error.

    -The authors state in line 265 "Vkg accumulated in the membranes of fatbody where p60 was overexpressed using r4-GAL4 (Figure 5 A-C)."

    This must be a typo. I think it is shown in Fig5E-G. Unless it's labelled wrongly in the figure and B, C and D show p60 rather than TorDN.

    We will fix this error.

    -The authors state in line 188 'This manipulation significantly rescued muscle integrity (Figure S4 A-C) and muscle atrophy (Figure S4 D-F), without affecting muscle ECM levels (Figure S4 G-H).' According to the graph in FigS4H this does actually 'affect muscle ECM levels' significantly, as in that it reduced Nidogen levels further. The authors could rephrase this.

    We will reword this statement.

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    Referee #3

    Evidence, reproducibility and clarity

    Summary:

    Provide a short summary of the findings and key conclusions (including methodology and model system(s) where appropriate).

    This paper uses a Drosophila tumor model induced by the expression of RasV12+Scrib-IR or RasV12+Dlg-IR in the eye imaginal disc to understand how inter-organ communication affects cachexia in the fat body and muscle. The tumor has previously been shown to secrete the factors ImpL2 and Gbb which decreases insulin signalling and increases TGF-beta signalling in the fat body, respectively, and results in fat body and muscle defects. Here they dissect the role of insulin and TGF-beta signalling in the fat body in regulating muscle integrity further. They show that these two pathways converge via Sog in the fat body of tumor-bearing animals and result in aberrant ECM accumulation in the fat body which hinders ECM secretion. This then results in the muscle receiving less fat body-derived ECM which causes muscle attachment defects. Interestingly, these muscle defects can be ameliorated by activating insulin signalling or inhibiting TGF-beta signalling or even by increasing ECM secretion in the fat body. The authors also provide some evidence that the insulin and TGF-beta signalling pathways can converge in non-tumor settings.

    Major comments:

    • Are the key conclusions convincing?

    Most of the conclusions are convincing. It is not clear however whether the ECM accumulation in the fat body of tumor animals is fibrotic and whether it is extracellular or in the cell cortex.

    • Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether?
    • The authors state in line 71 'This deposition of disorganized ECM leads to fibrotic ECM
      accumulation.' The authors haven't really provided evidence for the ECM being fibrotic. The authors could either rephrase this or provide additional experimental evidence of fibrosis in the fat body.
    • 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.
    • The authors state in line 147" Finally, in tumor-bearing animals fed a sterol-free diet, that underwent a prolonged 3rd instar stage due to reduced ecdysone levels (Parkin and Burnet, 1986), we activated insulin signalling in the fatbody via Akt overexpression (QRasV12, scribRNAi). We found that this manipulation caused a significant decrease in pMad levels in the fatbody and a rescue of muscle detachment (Figure S1 D-I), similar to animals fed a standard diet (Figure 1 O-Q, Figure 2 F-H)." Since it's not already known what the extent of muscle integrity defect there is in tumors with additional sterol free diet, it would be important to show a non-tumor control for comparison in FigS1F. This would also then make it clear to what extent the defect is rescued by Akt overexpression.
    • The authors state in line 158 'Upon the knockdown of Impl2, we found that tumor gbb was not significantly altered (Figure S3A).' Even though this shows an indication that Gbb levels are not reduced, the n number is too low to state that it is non-significant. The authors should increase the n number here.
    • The authors state in line 171 'Conversely, knockdown of gbb alone or knockdown of gbb together with ImpL2 significantly rescued the Nidogen overaccumulation defects observed at the plasma membrane of fatbody from tumor-bearing animals, while ImpL2RNAi alone did not (Figure S2 Q-U).' This is a somewhat misleading representation, since again no non-tumor control was used, so the extent of the rescue by gbb knowdown is not obvious. In FigS2P Nidogen levels in the tumor seem ~100% higher than in control. But in FigS2U, in which no control was included, the tumor+gbb knowdown seems ~ 20% lower than tumor. So it is probably a more moderate rescue, but that's only possible to assess by including a non-tumor control in FigS2U. Also the images in FigS2Q-T don't seem representative since they appear to show a much bigger difference in fluorescence intensity than ~20%. Please show more representative images.
    • The authors state in line 174 'Finally, co-knockdown of gbb and ImpL2 in the tumor significantly rescued the reduction in OPP and Nidogen levels observed in the muscles of tumor-bearing animals (Figure S3 B-I).'
      Again, the single knockdowns and the non-tumor control are not shown here in Fig3E and I and should be included for comparison and to see the contribution of each knockdown and to be able to judge the extent of the rescue.
    • Regarding Fig3O: Is there a significant tumor muscle attachment defect here? In this graph the tumor only looks about 10% lower than the WT (rather than 40% in Fig2E). The other issue is the extremely low n number for WT. I would recommend increasing the n number for WT here and to indicate in the graph whether the tumor is significantly different to WT (or non-significant, in which case RabRNAi wouldn't actually 'rescue' the defect). In the present form, this graph is not very convincing.
    • Regarding Fig3W: A non-tumor control would be important to include to be able to judge the extent of muscle attachment defects and the extent of the rescue for UAS-Sparc. This will allow to assess the severity of muscle integrity defect in this particular experiment (since it appears to vary in different experiments e.g. muscle defect in tumor 40% in Fig2E and ~10% in Fig3O) and to assess the extent of rescue for the various genotypes.
    • The authors show an accumulation of ECM in the fat body of tumors. It is not clear, whether this ECM accumulates intracellularly near the cell surface or extracellularly. The authors should assess this, maybe by doing electron microscopy.
    • 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.
    • These suggested experiments should be quite straightforward since they are mostly just repeating previous experiments with the appropriate controls and n numbers. I would think that they can be done within a few months. The electron microscopy should not take more than a few weeks and not be costly.
    • Are the data and the methods presented in such a way that they can be reproduced?
    • The details on how old animals used in each experiment were, are not easy to find and not written very clearly. They should be included in the each figure legend rather than summarising those details in the methods.
    • Also, in line 788 in the methods, several stocks are indicated as coming from particular labs (e.g. UAS-FOXO (Kieran Harvey), UAS-GFP (Kieran Harvey), UAS-lacZRNAi (Kieran Harvey), UAS-RasV12 (Helena Richardson), UAS-cg25C;UAS-Vkg (Brian Stramer)).
      However, it is not clear whether these labs actually made these stocks and if so whether it has already been described in their papers how the lines were made. If the lines are unpublished, the detailed information should be given on how the lines were made. Or if the lines are published, the authors should provide the reference.
    • Are the experiments adequately replicated and statistical analysis adequate?
      In general, the n number is rather low in several experiments, especially n of 3 for many controls. And as I mentioned before, rescues of tumor phenotypes are often shown without including a non-tumor control, making it hard to judge the extent of the rescue. Sometimes this information can be found in other figures, but the reader should not have to search for it. And also the severity of the phenotype can vary from experiment to experiment.

    Minor comments:

    Specific experimental issues that are easily addressable.

    • Are prior studies referenced appropriately?

    Yes, as far as I can tell.

    • Are the text and figures clear and accurate?
    • In the literature, people usually call it 'fat body' rather than 'fatbody'.
    • The authors state in line 265 "Vkg accumulated in the membranes of fatbody where p60 was overexpressed using r4-GAL4 (Figure 5 A-C)."
      This must be a typo. I think it is shown in Fig5E-G. Unless it's labelled wrongly in the figure and B, C and D show p60 rather than TorDN.
    • The authors state in line 188 'This manipulation significantly rescued muscle integrity (Figure S4 A-C) and muscle atrophy (Figure S4 D-F), without affecting muscle ECM levels (Figure S4 G-H).' According to the graph in FigS4H this does actually 'affect muscle ECM levels' significantly, as in that it reduced Nidogen levels further. The authors could rephrase this.
    • Do you have suggestions that would help the authors improve the presentation of their data and conclusions?

    Significance

    • Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field.

    The field of inter-organ communication in cancer is a very interesting and trending research field. Several labs including this one have provided new insights into how the tumor, the fat body and the muscle communicate and affect each other and how this can cause cachexia. Previous work from the Chen lab already showed that the tumor secretes the factors ImpL2 and Gbb which decreases insulin signalling and increases TGF-beta signalling in the fat body, respectively and results in fat body and muscle defects. Here they dissect this role of insulin and TGF-beta signalling in the fat body in regulating muscle integrity during cachexia further. They show that these two pathways converge via Sog in the fat body of tumor-bearing animals and result in aberrant ECM accumulation in the fat body which hinders ECM secretion. As a result of this, the muscle receives less fat body-derived ECM and displays muscle attachment defects. Interestingly, the authors show that these muscle defects can be ameliorated by activating insulin signalling or inhibiting TGF-beta signalling or even by increasing ECM secretion in the fat body. This has potentially important implications for the clinic since it suggests that targeting ECM secretion or ECM remodeling in the fat tissue could be a promising treatment for cachexia.
    Moreover, the authors also provide some evidence that the insulin and TGF-beta signalling pathways can converge in tumor and non-tumor settings. This might also reveal new drug targets to treat cachexia.

    • Place the work in the context of the existing literature (provide references, where appropriate).

    The Chen lab showed previously that MMP1 secreted from the tumor induces ECM disruption in the fat body as well as muscle, ultimately causing fat body remodeling and muscle wasting (Lodge et al. 2021). They showed that this is via TGF-beta activation in the fat body. Another contributing factor is tumor-secreted Impl2 which decreases Insulin signalling in the fat body and tumor. However, it remained unknown, how ECM accumulation in the fat body might cause muscle wasting. In this paper, the authors look into this.

    • State what audience might be interested in and influenced by the reported findings.

    This paper would be of interest for scientists and clinicians interested in inter-organ communication in cancer, particularly in the context of cachexia.

    • 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 lies in the field of Drosophila fat body and ECM, and to some extent tumors but less so signalling pathways.

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    Referee #2

    Evidence, reproducibility and clarity

    Summary

    In this paper, the authors show how the interaction of two signaling pathways, insulin/PI3K and TGF-β signaling, in the fatbody plays an important role in cachectic muscle detachment in tumor-bearing animals. The Drosophila tumor models and the genetic experimental tools are sophisticated and the conclusion is well supported by the data from these genetic experiments. They found that the TGF-β signaling activation (phosphorylation of Mad) is negatively regulated by insulin/PI3K signaling in the fatbody. They also identified the functional involvements of two molecules secreted from tumour tissues, Impl2 (a negative regulator of insulin signaling) and Gbb (one of the TGF-β ligands), in protein synthesis and ECM accumulation in the fatbody, respectively. They also showed that the cachectic fatbody traps ECM proteins and prevents ECM secretion to the muscle, causing muscle degradation. Finally, they identified a secreted BMP antagonist, Sog, as an important player in this process. They found that Sog is reduced in the hemolymph of tumour-bearing animals and that Sog expression is regulated by insulin signaling. Furthermore, Sog overexpression in the tumours, fatbody, and muscle rescues cachectic muscle detachment.

    Major comment

    Their genetic experiments clearly showed that the reduction of insulin signaling activity in the fatbody induces upregulation of TGF-β signaling and Collagen accumulation. Then, how does TGF-β signaling induce Collagen accumulation? They showed that Rab10 knockdown and SPARC overexpression reduced the accumulation of fatbody ECM. Are Rab10 and SPARC expression regulated by TGF-β signaling?

    Minor comments

    1. Line 90: "Disc Large (Dlg) RNAi in the eye" must be "Discs Large (Dlg1) RNAi in the eye imaginal discs".
    2. Figures 1D and 1L are from the same image. Also, Figures 1C and 1M are from the same image. Are both of them necessary to be shown in the different panels?
    3. Why are the staining patterns of anti-pAkt shown in Figures 1L and 1U so different? pAkt is not detected in the nuclei in Fig. 1L but its nuclear signal is clear in Fig. 1U.
    4. Figure 1: Images of counter staining for nuclei like DAPI should be also included for all these fatbody images.
    5. Line 101: "Tumour specific ImpL2 inhibition was sufficient to reduce fatbody pAkt levels." Is this correct? ImpL2 inhibition in tumors should elevate the pAKT level in fatbody.
    6. Figure S1~S4: These figures and their legends do not correspond to each other.
    7. Line 189: The pAkt level in the muscle of tumour-bearing animals should be examined to confirm the activity of the insulin signaling is downregulated.
    8. Line 189: If the authors conclude that muscle insulin signaling predominantly regulates translation and atrophy, OPP assay for the muscle cells should be examined in the same experimental settings.
    9. Line 247: The expression level of Rab10 and SPARC should be examined in the fatbody of tumour-bearing animals to see whether Rab10 is upregulated and SPARC is downregulated.
    10. Line 247: If Rab10 upregulation and SPARC downregulation are the causes of the accumulation of ECM proteins in the fatbody of tumour-bearing animals, how the overexpressed Collagen proteins can be secreted from the fatbody cells?
    11. Line 347: Sog is a secreted BMP antagonist. Thus, it can be expected that the Sog overexpression downregulates TGF-β signaling in fatbody and muscle tissues. If the rescued phenotypes with Sog overexpression can be explained by this logic, pMad level should be examined in these experiments.

    Significance

    I found these results from their genetic experiments described here very interesting and of high quality. Although the mechanism by which the TGF-β signaling induces ECM accumulation in fatbody is not clear, this study represents several important advances to understand the key processes in tumor-induced muscle degradation. These data will attract broad audiences not only from cancer biology but also from the research fields including interorgan interactions, systemic signaling in homeostasis, and developmental biology.

  5. Review Commons

    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 #1

    Evidence, reproducibility and clarity

    In this manuscript, Bakopolous et al. investigated on the function of Insulin and TGF beta signaling in the converging regulation of sog (BMP antagonist) and how it controls ECM remodeling. Therefore, the authors used a Drosophila model of cachexia established in Lodge et al., 2021. The authors have shown that the tumors increase Impl2 and Gbb in the fatbody leading to the inhibition of insulin signaling and activation of TGF-β signaling respectively. This lead to the accumulation of ECM proteins that contributes to muscle ECM deficit and muscle detachment. These findings are a major advance in the field of cachexia and of broad interest. The authors demonstrate that state-of-the-art genetics in flies allows acquisition of genetically precise data along with important and complex discoveries on signaling pathways with relevance not only for basic, but for biomedical research as well.

    The manuscript is concise and very well written. The experiments overt a clear logical order and are comprehensively described. The authors provide exhaustive data to support their novel claims of broad interest to the scientific community. Please find below some minor recommendations and experiments that could shed further light on some aspects of this manuscript.

    Major:

    • The statement (line 149'Together, our data suggest that systemic ecdysone levels are unlikely to be involved in modulating tumour-induced muscle detachment or to mediate the role of fatbody Insulin signalling in regulating muscle detachment.') is derived from an experiment with sterol free diet (in which 20HE is genetically addressed) and a pleiotropic experiment (PG>RasG12V). In neither paper nor the current manuscript, 20HE levels have been directly addressed.
      Therefore, this statement needs further experimental support and discussion. Ecdysone is a critical hormone during development and especially growth-related effects central to this study. The authors should consider doing pharmacology or augment their claims here with genetic manipulation experiments of 20HE related genes in larvae (Leopold, Rewitz, Rideout, Drummond-Barbosa, Schuldiner labs) and adult animals using genetics, pharmacology or direct assessment of 20HE levels (RIPA, Edgar and Reiff labs).
    • In Fig.7 the authors used a sog-LacZ stock to show transcriptional activation in fatbody cells. This stock is based on P-element insertion in the according regulatory regions and supposed to express lacZ with an nls. I can clearly see lacZ in nuclei in Fig. 7H, whereas this is very hard to see in nuclei in Fig7i in the tumour model. In addition, lacZ is known for its high stability and not the best option. As this finding is vital for central claims of this study, it should be complemented by either qPCR for sog on fat body cells or using another readout by converting one of the two Mimic lines (BL42189/44958) into GFP sensors for sog.
    • I have similar problems with Fig.7B-F, as phosphorylated Mad should be translocated to the nucleus. In 7F the authors measure pMad over Dapi, which is the right way but it is hard to see pMad in the nucleaus apart from Fig7B, wheras in D and E, where the authors measure higher levels, I cannot identify clear pMad in nuclei. These images either need to show the Dapi channel or more representative images should be chosen like in Fig.4 with arrows pointing to measured nuclei. Fig.7C something went wrong with the compression of this image.
    • The proper function of RNAi stocks targeting genes like sog, mad, etc. is vital for this study as these lines are used throughout the study. Functional evidence of specific knockdown efficiency should be provided or references given in which these stocks were shown to provide functional knockdown on transcript or protein level.
    • Fig.S7 discusses appearance of gbb/Bmp7 and Sog/CHRD in human patients. The analysis the authors performed shows a correlation between both factors, but is hampered by the fact that datasets for peripheral tissues of cachexia patients are unavailable. The authors may consider sorting these after tumor entities in which cachexia occurs frequently vs. low occurrence and then check for both genes.
    • Fig.5 M-P pMAd is not indicated in the Panels only the legend.
    • Please follow FlyBase nomenclature, e.g. dlg1 for discs large 1 and unify in the whole manuscript and figure for all genes.
    • For endogenous fusion proteins like Viking-GFP (e.g. vkg::GFP) choose a format to clearly decipher them from transcriptional readout stocks like sog-lacZ.
    • The quantifications in most figures are quite small with tiny lettering and XY axis are difficult to read in letter/A4 size.

    Minor:

    1. Adjust in-figure caption alignments
    2. Line 104: add comma RasV12, dlgRNAi
    3. Line 114: replace little  not significant (n.s.)
    4. Line 334: 'sogRNAi overexpression' to my knowledge, RNAi are expressed, not overexpressed.
    5. Line 454: italicize r4>
    6. Fig S4E: remove frame
    7. Figures 6: It would be better to number and explain the pathway presented in the figure in text and fig legend.
    8. Just a personal preference. Lettering of images in images is commonly done horizontally, here it appears like a mix between vertical and horizontal.

    Significance

    In this manuscript, Bakopolous et al. investigated on the function of Insulin and TGF beta signaling in the converging regulation of sog (BMP antagonist) and how it controls ECM remodeling. Therefore, the authors used a Drosophila model of cachexia established in Lodge et al., 2021. The authors have shown that the tumors increase Impl2 and Gbb in the fatbody leading to the inhibition of insulin signaling and activation of TGF-β signaling respectively. This lead to the accumulation of ECM proteins that contributes to muscle ECM deficit and muscle detachment. These findings are a major advance in the field of cachexia and of broad interest. The authors demonstrate that state-of-the-art genetics in flies allows acquisition of genetically precise data along with important and complex discoveries on signaling pathways with relevance not only for basic, but for biomedical research as well.

    The manuscript is concise and very well written. The experiments overt a clear logical order and are comprehensively described. The authors provide exhaustive data to support their novel claims of broad interest to the scientific community

  6. Version published to 10.1101/2023.06.10.544444v1 on bioRxiv