Abstract

Network Medicine has improved the mechanistic understanding of disease, offering quantitative insights into disease mechanisms, comorbidities, and novel diagnostic tools and therapeutic treatments. Yet, most network-based approaches rely on a comprehensive map of protein-protein interactions, ignoring interactions mediated by non-coding RNAs (ncRNAs). Here, we systematically combine experimentally confirmed binding interactions mediated by ncRNA with protein-protein interactions, constructing the first comprehensive network of all physical interactions in the human cell. We find that the inclusion of ncRNA, expands the number of genes in the interactome by 46% and the number of interactions by 107%, significantly enhancing our ability to identify disease modules. Indeed, we find that 132 diseases, lacked a statistically significant disease module in the protein-based interactome, but have a statistically significant disease module after inclusion of ncRNA-mediated interactions, making these diseases accessible to the tools of network medicine. We show that the inclusion of ncRNAs helps unveil disease-disease relationships that were not detectable before and expands our ability to predict comorbidity patterns between diseases. Taken together, we find that including non-coding interactions improves both the breath and the predictive accuracy of network medicine.

Citation

Gysi DM, Barabasi AL. Non-Coding RNAs Improve the Predictive Power of Network Medicine. arXiv preprint arXiv:2211.14800. 2022 Nov 27. https://arxiv.org/abs/2211.14800