Quantitative and Systems Pharmacology 3. Network-Based Identification of New Targets for Natural Products Enables Potential Uses in Aging-Associated Disorders

Abstract

Aging that refers the accumulation of genetic and physiology changes in cells and tissues over a lifetime has been shown a high risk of developing various complex diseases, such as neurodegenerative disease, cardiovascular disease and cancer. Over the past several decades, natural products have been demonstrated as anti-aging interveners via extending lifespan and preventing aging-associated disorders. In this study, we developed an integrated systems pharmacology infrastructure to uncover new indications for aging-associated disorders by natural products. Specifically, we incorporated 411 high-quality aging-associated human genes or human-orthologous genes from mus musculus (MM), saccharomyces cerevisiae (SC), caenorhabditis elegans (CE), and drosophila melanogaster (DM). We constructed a global drug-target network of natural products by integrating both experimental and computationally predicted drug-target interactions (DTI). We further built the statistical network models for identification of new anti-aging indications of natural products through integration of the curated aging-associated genes and drug-target network of natural products. High accuracy was achieved on the network models. We showcased several network-predicted anti-aging indications of four typical natural products (caffeic acid, metformin, myricetin, and resveratrol) with new mechanism-of-actions. In summary, this study offers a powerful systems pharmacology infrastructure to identify natural products for treatment of aging-associated disorders.

Keywords: aging; natural products; network-based; quantitative and systems pharmacology; target identification.

Figure 1

Schematic diagram of the systems pharmacology infrastructure for identification of aging-associated indications by natural products. (A) Construction of drug-target network of natural products. (B) Manual curation of aging-associated genes. (C) Discovery of new anti-aging indications for natural products via network-based prediction. (D) Identification of new anti-aging mechanism-of-action via network analysis.

Figure 2

Overlaps among four gene sets of human-orthologous aging-associated genes (AAGs) from 4 non-human organisms: Caenorhabditis elegans (CE), Drosophila melanogaster (DM), mus musculus (MM), and saccharomyces cerevisiae (SC). The detailed AAGs are provided in Table S1.

Figure 3

A bipartite drug–target interaction network for FDA-approved or clinically investigational natural products. This network contains 2,408 interactions connecting 224 natural products to 494 target proteins, including proteins encoded by 70 aging-associated genes (AAGs) and 424 non-AAGs. The label font size and node size are proportional to degree (connectivity).

Figure 4

Chemical diversity analysis of natural products targeting aging-associated proteins. (A) Chemical structure clustering of 1,877 natural products via FCFP_6 fingerprint; (B) The representative structures of 10 cluster centers during chemical structural clustering analysis.

Figure 5

A bipartite drug-target network for 4 typical natural products. This network includes 90 experimentally validated and 16 computationally predicted drug-target interactions connecting 4 natural products (caffeic acid, hesperetin, myricetin and resveratrol) and 70 targets (21 aging-associated proteins and 49 non-aging proteins).

Figure 6

A discovered anti-aging drug-target network for 3 typical natural products. This network displays the predicted anti-aging indications as well as the known and predicted drug targets for three typical natural products: metformin, vitamin E, and huperzine A. The thickness of blue line between natural products and anti-aging indication is proportional to the predicted Z-score (Equation 2, see section Materials and Methods).