Multi‐omic network analysis identified betacellulin as a novel target of omega‐3 fatty acid attenuation of western diet‐induced nonalcoholic steatohepatitis

  1. Jyothi Padiadpu
  2. Manuel Garcia‐Jaramillo
  3. Nolan K Newman
  4. Jacob W Pederson
  5. Richard Rodrigues
  6. Zhipeng Li
  7. Sehajvir Singh
  8. Philip Monnier
  9. Giorgio Trinchieri
  10. Kevin Brown
  11. Amiran K Dzutsev
  12. Natalia Shulzhenko
  13. Donald B Jump
  14. Andrey Morgun

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Abstract

Clinical and preclinical studies established that supplementing diets with ω3 polyunsaturated fatty acids (PUFA) can reduce hepatic dysfunction in nonalcoholic steatohepatitis (NASH) but molecular underpinnings of this action were elusive. Herein, we used multi‐omic network analysis that unveiled critical molecular pathways involved in ω3 PUFA effects in a preclinical mouse model of western diet induced NASH. Since NASH is a precursor of liver cancer, we also performed meta‐analysis of human liver cancer transcriptomes that uncovered betacellulin as a key EGFR‐binding protein upregulated in liver cancer and downregulated by ω3 PUFAs in animals and humans with NASH. We then confirmed that betacellulin acts by promoting proliferation of quiescent hepatic stellate cells, inducing transforming growth factor–β2 and increasing collagen production. When used in combination with TLR2/4 agonists, betacellulin upregulated integrins in macrophages thereby potentiating inflammation and fibrosis. Taken together, our results suggest that suppression of betacellulin is one of the key mechanisms associated with anti‐inflammatory and anti‐fibrotic effects of ω3 PUFA on NASH.

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  1. Version published to 10.15252/emmm.202318367
  2. Review Commons

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

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    Reply to the reviewers

    We would like to thank reviewers for their insightful comments.

    Overall, there were two major concerns/suggestions:

    • Applicability to humans of the increase of BTC in non-alcoholic steatohepatitis (NASH) and mechanisms of downregulation of BTC by omega-3. We now analyzed __3 __additional human gene expression datasets and show that BTC not only is increased in human NASH (as we have already shown for liver cancer meta-analysis), but is also decreased in livers of patients who received omega-3.

    • One of the reviewers suggested investigating a potential mechanism of how BTC is regulated by omega3 fatty acids. Although a complete answer to this question would require entirely new studies to be done, we still performed additional investigation that was possible within a reasonable timeframe. We found that transcription factor FOXO3 (well-known inhibitor of carcinogenesis) is a highly probable mediator of the DHA inhibitory effect on BTC.

    See all details of items 1 and 2 as well as answers to other (less critical concerns) below after each specific question.

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

    This work by Padiadpu and colleagues investigate the mechanism by which pufa of the n-3 series (mostly DHA) may influence NAFLD progression using systems biology analysis and multiple omics analysis. The work is interesting and may provide a novel view of the topic. However, there are a number of issues the authors may wish to consider in order to improve their manuscript.

    Major issues: Clarity: Since the authors refer to previously published experiments, they must refer to this work in the figure legends and improve the clarity of such legends. Here are a list of issues that must be fixed:

    Fig.1: First panel is not clear. What does the table tell the reader? What are the effects of the different diets on NAFLD?

    All the transcriptomic data are newly generated from the samples of previously published studies. The table shows the number of features changed by DHA and/or EPA in each of the -omics and phenotypic data used in the analysis.

    I understand that the results are published elsewhere, but the authors must provide information regarding the NAFLD/ NASH scores.

    We now added a supplementary table 1a showing the scores.

    Fig.4: Why is there sometimes a DHA diet, sometimes DHA and EPA. Legend is not clear. What does WD + Mean? I guess it is olive oil... But the legend must be improved.

    We added details in the legend for more clarity. Specifically, WD+O means WD + olive oil added as a control for WD+DHA, WD+EPA. As described in the 2nd paragraph of results, when both EPA and DHA had a similar and significant effects in reversing WD effect, it was defined as “EPA&DHA category” of parameters. When only WD+DHA or WD+EPA were significantly changed vs WD+O, those were assigned as “DHA category” or “EPA category”, respectively.

    One issue the authors may consider trying to fix is the specificity of the effect of DHA on BTC.

    Is it really specific? It seems to me that EPA has more or less the same effect. If the effect is DHA-specific, than make this clearer through the text.

    Although BTC expression was reduced by both DHA and EPA comparing to WD, DHA had a statistically significant stronger effect than EPA (Fig. 3D).

    Another issue the authors may wish to investigate is the relationship between W3 consumption and BTC expression in studies performed by other labs (if available on Gene expression omnibus?).

    Thanks for the suggestion. We used publicly available data of human and mouse studies that showed significant increase in liver BTC gene expression in NASH in multiple datasets while a human trial with Omega 3 treatment for one year showed its significant reduction (Figures 3F - human data, S3G-mouse data).

    Finally, a key issue would be to identify the mechanism by which DHA inhibits BTC expression? How does this happen? could such inhibition be induced by other fatty acids of the W3 series? I understand that this is not easy to address but it would significantly strengthen the manuscript.

    Thanks to your question we investigated and found at least one of potential mechanisms contributing to how “DHA inhibits BTC expression”. See details in the answer to next question. As for “other fatty acids” while we agree this is important question, it is outside of the scope of the current study but will be investigated in future studies.

    Moreover, it might be possible to identify the set of genes highly co-regulated with BTC expression and to investigate the possible transcription factors at play in the control of such gene set.

    We really appreciate this question as our efforts in this direction provided one potential mechanism. A direct screen of transcription factor (TF) motifs in genes co-regulated with BTC did not provide any clear results. Therefore, we implemented a combination of network analysis and screen for motifs in BTC gene with the in vivo and in vitro treatment results and found FOXO3 as a candidate TF regulated by DHA upstream of BTC.

    See details of the analysis and results in a new Supplementary Figure S6 and corresponding text located at the end of the results.

    Minor: the authors use the term "beneficial" transcriptome alterations by DHA.

    I do not think it is correct to use "beneficial".

    We agree and removed the word "beneficial”.

    Reviewer #1 (Significance (Required)):

    Strength: This paper uses new approaches to investigate the relationship between W3 consumption and liver gene expression and its relevance to chronic metabolic liver diseases.

    The experiments and data set used to perform systems biology are from an excellent lab (the authors lab) who has published a lot of important and reproducible discoveries in the field of regulation of gene expression by dietary fatty acids.

    The work has high translational relevance in medicine / hepatology / metabolism.

    I am not a qualified reviewer to assess the systems biology that has been done.

    Limitation: The mechanistic link between DHA consumption and BTC expression is not very clear. The specificity of this effect could also be tested (DHA vs other W3 and/or W6).

    Although BTC expression was reduced by both DHA and EPA comparing to WD, DHA had a significantly stronger effect than EPA (Fig. 3D). Other omega fatty acids were not tested but it can be done in future studies.

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

    The authors files a manuscript describing the impact of the suppression of betacellulin as a key mechanism to counteract fibrosis and inflammation in NASH by modulating fatty acids in WD-fed mice.

    Major Comments: (i) No histological analysis was presented and indeed this is of clinical relevance for NASH since diagnosis is still based on biopsy.

    While histological evaluation was presented in the originally published papers (PMID: 28422962, 23303872), it is now provided in Supplementary Table S1a.

    (ii) Human comparative analysis: is done with HCC not with NASH patients.

    This cancer-related dataset is most likely obtained from different etiologies.

    I would suggest comparing these mouse datasets with GSE48452 (human NAFLD-NASH spectra).

    Thanks for this important question. We now analyzed available human data of NASH and show significant increase of BTC expression in two datasets while a human trial with omega-3 treatment for one year showed its significant reduction of BTC expression (Figure 3F) resembling our observations in mice.

    (iii) to compare the inflammation and fibrosis (also lipid metabolism), one can compare these mouse datasets with GSE222576 and cite this preprint (https://doi.org/10.21203/rs.3.rs-2009380/v1)

    Using the suggested dataset (of a chemically induced liver fibrosis), we first observed that Btc gene expression was significantly increased over 10 weeks of the model and now included this result in Fig. S3G.

    We also queried the 66 genes from the network modules described by the authors to check their changes in our NASH model. We observed that 28 genes were differentially expressed in NASH with 14 of them belonging to the module that authors named as “Pathways in Cancer”. Other genes were from the lipid metabolism (4 genes), immunity (2) and inflammation (2 genes). In addition, we observed that several genes we found regulated by omega-3 and changed in this fibrosis model contained other inflammatory genes such as classical macrophage genes (Mmp12, Lgals3, Cd68, Trem2), fibrosis (Col4a1, Col27a1, Itga2b, Itga8) and lipid metabolism (Scd2, Lpl, Soat1). Of note, the preprint has been published and we now cite the corresponding article.

    Minor comments:

    (i) The heatmap in Figure 1B and another heatmap should show all mice not the average to see the variability

    The supplementary figure with all the individual mouse data as another heatmap is added to show the variability and similarity (Figure S1D).

    Reviewer #2 (Significance (Required)): The authors files a manuscript describing the impact of the suppression of betacellulin as a key mechanism to counteract fibrosis and inflammation in NASH by modulating fatty acids.

    This is well designed experiment, and the results are of interest to hepatologists and should be indeed published after consideration of the following points

    Strength is multiOMICs approach.

    Weakness is human applicability.

    We improved human applicability by investigating 3 additional human datasets of NASH (Fig. 3F) and finding consistent changes in BTC expression closely resembling our observations in mouse NASH model, including one trial with omega-3 treatment of patients for one year showing significant reduction in BTC gene expression.

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    Referee #2

    Evidence, reproducibility and clarity

    The authors files a manuscript describing the impact of the suppression of betacellulin as a key mechanism to counteract fibrosis and inflammation in NASH by modulating fatty acids in WD-fed mice.

    Major Comments:

    • (i) No histological analysis was presented and indeed this is of clinical relevance for NASH since diagnosis is still based on biopsy.
    • (ii) Human comparative analysis: is done with HCC not with NASH patients. This cancer-related dataset is most likely obtained from different etiologies. I would suggest comparing these mouse datasets with GSE48452 (human NAFLD-NASH spectra).
    • (iii) to compare the inflammation and fibrosis (also lipid metabolism), one can compare these mouse datasets with GSE222576 and cite this preprint (https://doi.org/10.21203/rs.3.rs-2009380/v1)

    Minor comments:

    • (i) The heatmap in Figure 1B and another heatmap should show all mice not the average to see the variability

    Significance

    The authors files a manuscript describing the impact of the suppression of betacellulin as a key mechanism to counteract fibrosis and inflammation in NASH by modulating fatty acids. This is well designed experiment and the results are of interest to hepatologists and should be indeed published after consideration of the following points

    Strength is multiOMICs approach

    Weakness is human applicability

  4. Review Commons

    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 work by Padiadpu and colleagues investigate the mechanism by which pufa of the n-3 series (mostly DHA) may influence NAFLD progression using systems biology analysis and multiple omics analysis.

    The work is interesting and may provide a novel view of the topic.

    However, there are a number of issues the authors may wish to consider in order to improve their manuscript.

    Major issues:

    Clarity:

    Since the authors refer to previously published experiments they must refer to this work in the figure legends and improve the clarity of such legends. Here are a list of issues that must be fixed: Fig.1 : Firts panel is not clear. What does the table tell the reader? What are the effects of the different diets on NAFLD? I understand that the results are published elsewhere, but the authors must provide information regarding the NAFLD/ NASH scores. Fig.4: Why is there sometimes a DHA diet, sometimes DHA and EPA. Legend is not clear. What does WD + Mean? I guess it is olive oil... But the legend must be improved.

    One issue the authors may consider trying to fix is the specificity of the effect of DHA on BTC. Is it really specific? It seems to me that EPA has more or less the same effect. If the effect is DHA-specific, than make this clearer through the text. In this current version of the manusript, the authors alternatively use the term DHA or W3. Related to this issue, it would be nice to know what the composition of the WD is? More specifically, it would be important to know whether it might be W3 deficient.

    Another issue the authors may wish to investigate is the relationship between W3 consumption and BTC expression in studies performed by other labs (if available on Gene expression omnibus?).

    Finally, a key issue would be to identify the mechanism by which DHA inhibits BTC expression? How does this happen? could such inhibition be induced by other fatty acids of the W3 series? I understand that this is not easy to address but it would significantly strengthen the manuscript. Moreover, it might be possible to identify the set of genes highly co-regulated with BTC expression and to investigate the possible transcription factors at play in the control of such gene set.

    Minor: the authors use the term "beneficial" transcriptome alterations by DHA. I do not think it is correct to use "beneficial".

    Significance

    Strenght:

    This paper uses new approaches to investigate the relationship between W3 consumption and liver gene expression and its relevance to chronic metabolic liver diseases. The experiments and data set used to perform systems biology are from and excellent lab (the authors lab) who has published a lot of important and reproducible discoveries in the field of regulation of gene expression by dietary fatty acids.

    Limitation:

    The mechanistic link between DHA consumption and BTC expression is not very clear. The specificity of this effect could also be tested (DHA vs other W3 and/or W6).

    The work has high translational relevance in medicine / hepatology / metabolism.

    I am not a qualified reviewer to assess the systems biology that has been done.

  5. Version published to 10.1101/2022.10.03.510635v2 on bioRxiv
  6. Version published to 10.1101/2022.10.03.510635v1 on bioRxiv