Herb-CMap: Novel Hot Diffusion Algorithm to Identify Bioactive Ingredients from Herbal Medicine Based on Gene Perturbation Profiling

Abstract

AbstractHerbal medicine, especially Traditional Chinese medicine (TCM), is a valuable resource of natural products for drug discovery with obvious therapeutic effects. However, the mechanisms of action (MOAs) of herbal medicine are often unknown due to limited target information and the complexity of multiple ingredients and targets. This study developed a Herb-CMap algorithm to prioritize active ingredients and targets within herbal medicine by integrating transcriptomics- based gene perturbation data with a random walk algorithm. This methodology bridges the gap between gene perturbation of herbal medicine and its therapeutic target. Using the Suhuang antitussive capsule (Suhuang) for treating cough variant asthma (CVA) as a case study, we identify and experimentally verify that quercetin and luteolin directly interact with Il17a, Pik3cb, Pik3cd, Akt1, and Tnf. These interactions inhibit the IL-17 signaling pathway and inactivate PI3K, Akt, and NF- κB, preventing lung inflammation and treating CVA. Our findings demonstrate the potential of the Herb-CMap methodology to provide insights into the molecular MOAs of herbal medicine, thereby advancing drug discovery from herbal medicine.