Network Pharmacology and Multi-Omics Integration Reveal Anti-Diabetic Mechanisms of Phyllostachys nigra–Derived Polysaccharides
Keywords:
network pharmacology, multi-omics integration, Phyllostachys nigra, polysaccharides, type 2 diabetes, systems architecture, biomedical knowledge graphs, governance, gut microbiomeAbstract
The escalating global burden of type 2 diabetes mellitus has intensified the search for multi-target therapeutic agents derived from natural products. Phyllostachys nigra, a bamboo species with a long ethnopharmacological history, has recently yielded bioactive polysaccharides exhibiting pronounced glycolipid metabolism regulation. Unraveling the polypharmacological mechanisms of such complex macromolecules demands a departure from reductionist paradigms in favor of integrative systems-level frameworks. This paper explicates the confluence of network pharmacology and multi-omics integration as a coherent architectural approach to dissect the anti-diabetic effects of P. nigra-derived polysaccharides. We conceptually analyze the layered infrastructure required to harmonize transcriptomic, metabolomic, metagenomic, and network-based target prediction data. The discussion emphasizes structural trade-offs in data federation, algorithmic robustness in polypharmacological network inference, and the governance of heterogeneous biomedical knowledge graphs. By examining the deployment of computational pipelines that map polysaccharide interference on insulin signaling, gut microbial community restructuring, and host metabolic reprogramming, we highlight how such architectures enable the systematic identification of synergistic effector modules. We further address sustainability, reproducibility, fairness in natural product dataset representation, and the policy implications of translating integrative omics discoveries into equitable clinical and nutritional interventions. This perspective advocates a disciplined, infrastructure-aware systems science that treats botanical macromolecules as perturbations to a deeply interconnected biological network, offering a roadmap for future large-scale, transdisciplinary anti-diabetic discovery.
References
1. Barabási, A. L., Gulbahce, N., & Loscalzo, J. (2011). Network medicine: a network-based approach to human disease. Nature Reviews Genetics, 12(1), 56–68.
2. Hopkins, A. L. (2008). Network pharmacology: the next paradigm in drug discovery. Nature Chemical Biology, 4(11), 682–690.
3. Keiser, M. J., Setola, V., Irwin, J. J., Laggner, C., Abbas, A. I., Hufeisen, S. J., ... & Roth, B. L. (2009). Predicting new molecular targets for known drugs. Nature, 462(7270), 175–181.
4. Li, S., & Zhang, B. (2013). Traditional Chinese medicine network pharmacology: theory, methodology and application. Chinese Journal of Natural Medicines, 11(2), 110–120.
5. Ritchie, M. E., Phipson, B., Wu, D., Hu, Y., Law, C. W., Shi, W., & Smyth, G. K. (2015). limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Research, 43(7), e47.
6. Rohart, F., Gautier, B., Singh, A., & Lê Cao, K. A. (2017). mixOmics: An R package for ‘omics feature selection and multiple data integration. PLoS Computational Biology, 13(11), e1005752.
7. Kanehisa, M., & Goto, S. (2000). KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Research, 28(1), 27–30.
8. Wishart, D. S., Feunang, Y. D., Guo, A. C., Lo, E. J., Marcu, A., Grant, J. R., ... & Wilson, M. (2018). DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Research, 46(D1), D1074–D1082.
9. Xu, J., Chen, H. B., & Li, S. L. (2017). Understanding the molecular mechanisms of the interplay between herbal medicines and gut microbiota. Medicinal Research Reviews, 37(5), 1140–1185.
10. Zeevi, D., Korem, T., Zmora, N., Israeli, D., Rothschild, D., Weinberger, A., ... & Segal, E. (2015). Personalized nutrition by prediction of glycemic responses. Cell, 163(5), 1079–1094.
11. Wang, Y., Liu, Z., & Li, C. (2021). Natural polysaccharides for the treatment of type 2 diabetes mellitus: a review of their mechanisms and structure–activity relationships. Food & Function, 12(7), 2801–2823.
12. Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., ... & Mesirov, J. P. (2005). Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences, 102(43), 15545–15550.
13. Nicholson, J. K., Holmes, E., Kinross, J., Burcelin, R., Gibson, G., Jia, W., & Pettersson, S. (2012). Host-gut microbiota metabolic interactions. Science, 336(6086), 1262–1267.
14. Zhao, K., Wu, X., Han, G., Sun, L., Zheng, C., Hou, H., ... & Shi, Z. (2024). Phyllostachys nigra (Lodd. ex Lindl.) derived polysaccharide with enhanced glycolipid metabolism regulation and mice gut microbiome. International journal of biological macromolecules, 257, 128588.
15. Magger, A., Waldman, Y. Y., Ruppin, E., & Sharan, R. (2012). Enhancing the prioritization of disease-causing genes through tissue specific protein interaction networks. PLoS Computational Biology, 8(9), e1002690.
16. Greene, C. S., Krishnan, A., Wong, A. K., Ricciotti, E., Zelaya, R. A., Himmelstein, D. S., ... & Troyanskaya, O. G. (2015). Understanding multicellular function and disease with human tissue-specific networks. Nature Genetics, 47(6), 569–576.
17. Leek, J. T., Scharpf, R. B., Bravo, H. C., Simcha, D., Langmead, B., Johnson, W. E., ... & Irizarry, R. A. (2010). Tackling the widespread and critical impact of batch effects in high-throughput data. Nature Reviews Genetics, 11(10), 733–739.
18. Baker, M. (2016). 1,500 scientists lift the lid on reproducibility. Nature, 533(7604), 452–454.
19. Popejoy, A. B., & Fullerton, S. M. (2016). Genomics is failing on diversity. Nature, 538(7624), 161–164.
20. Buck, M., & Hamilton, C. (2011). The Nagoya Protocol on access to genetic resources and the fair and equitable sharing of benefits arising from their utilization to the Convention on Biological Diversity. Review of European Community & International Environmental Law, 20(1), 47–61.
21. Vivian, J., Rao, A. A., Nothaft, F. A., Ketchum, C., Armstrong, J., Novak, A., ... & Paten, B. (2017). Toil enables reproducible, open source, big biomedical data analyses. Nature Biotechnology, 35(4), 314–316.
22. Langmead, B., & Nellore, A. (2018). Cloud computing for genomic data analysis and collaboration. Nature Reviews Genetics, 19(4), 208–219.
23. Szklarczyk, D., Gable, A. L., Lyon, D., Junge, A., Wyder, S., Huerta-Cepas, J., ... & Mering, C. V. (2019). STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Research, 47(D1), D607–D613.
24. Committee on the Framework for Evaluating the Safety of Dietary Supplements, National Academies of Sciences, Engineering, and Medicine. (2019). Dietary supplements for weight loss: limited regulation and inadequate safety monitoring. In S. H. Woolf & L. D. Meyers (Eds.), Health and Medicine. National Academies Press.
25. Topol, E. J. (2019). High-performance medicine: the convergence of human and artificial intelligence. Nature Medicine, 25(1), 44–56.
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