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Network medicine framework shows that proximity of polyphenol targets and disease proteins predicts therapeutic effects of polyphenols

Abstract

Polyphenols, natural products present in plant-based foods, play a protective role against several complex diseases through their antioxidant activity and by diverse molecular mechanisms. Here we develop a network medicine framework to uncover mechanisms for the effects of polyphenols on health by considering the molecular interactions between polyphenol protein targets and proteins associated with diseases. We find that the protein targets of polyphenols cluster in specific neighbourhoods of the human interactome, whose network proximity to disease proteins is predictive of the molecule’s known therapeutic effects. The methodology recovers known associations, such as the effect of epigallocatechin-3-O-gallate on type 2 diabetes, and predicts that rosmarinic acid has a direct impact on platelet function, representing a novel mechanism through which it could affect cardiovascular health. We experimentally confirm that rosmarinic acid inhibits platelet aggregation and α-granule secretion through inhibition of protein tyrosine phosphorylation, offering direct support for the predicted molecular mechanism. Our framework represents a starting point for mechanistic interpretation of the health effects underlying food-related compounds, allowing us to integrate into a predictive framework knowledge on food metabolism, bioavailability and drug interaction.

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Fig. 1: Properties of polyphenol protein targets.
Fig. 2: Protein–protein interactions of polyphenol targets.
Fig. 3: Proximity between polyphenol targets and disease proteins is predictive of the therapeutic effects of the polyphenol.
Fig. 4: Relationships among gene expression perturbation, network proximity and the therapeutic effects of polyphenols on diseases.
Fig. 5: Diseases proximal to polyphenol targets have higher gene expression perturbation profiles.
Fig. 6: RA modulates platelet function.

Data availability

All data supporting the findings of this study are available at https://github.com/italodovalle/polyphenols and within the paper and its Supplementary Information files.

Code availability

Computer code is available at https://github.com/italodovalle/polyphenols.

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Acknowledgements

This study was supported, in part, by NIH grants 1P01HL132825, HG007690, HL108630 and HL119145; American Heart Association grants 151708 and D700382 and ERC grant 810115-DYNASET. We would like to thank P. Ruppert, G. Menichetti and I. Kovacs for support in this study, F. Cheng for assembling the human interactome and A. Grishchenko for help with data visualization.

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Authors and Affiliations

Authors

Contributions

I.F.d.V. and A.-L.B. designed the study. I.F.d.V. performed all computational analyses. H.G.R., M.W.M., E.B. and J.L. designed and performed experimental validation. J.L. guided I.F.d.V. in validation case studies. S.M. and D.B. guided I.F.d.V. in data interpretation and curation of disease associations obtained from the literature. I.F.d.V. and A.-L.B. wrote the paper with input from all authors. All authors read and approved the manuscript.

Corresponding author

Correspondence to Albert-László Barabási.

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Competing interests

J.L. and A.-L.B. are co-scientific founders of Scipher Medicine, Inc., which applies network medicine strategies to biomarker development and personalized drug selection; A.-L.B. is the founder of Datapolis Inc., which explores mobility patterns in urban planning, and Foodome, Inc., which applies data science to health. I.F.d.V. is a scientific consultant for Foodome, Inc.

Additional information

Peer review information Nature Food thanks Dariush Mozaffarian and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Figs. 1–13 and Notes.

Reporting Summary

Supplementary Table 1

Summary of polyphenols evaluated in this study.

Supplementary Table 2

Predicted gastrointestinal (GI) absorption and bioavailability.

Supplementary Table 3

Polyphenols proximal to vascular diseases.

Supplementary Data 1

Human interactome assembled in this study.

Supplementary Data 2

Network proximity calculations between 65 polyphenols and 299 diseases.

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do Valle, I.F., Roweth, H.G., Malloy, M.W. et al. Network medicine framework shows that proximity of polyphenol targets and disease proteins predicts therapeutic effects of polyphenols. Nat Food 2, 143–155 (2021). https://doi.org/10.1038/s43016-021-00243-7

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