Transcriptome inference and systems approaches to polypharmacology and drug discovery in herbal medicine

Publication date: 4 January 2017
Source:Journal of Ethnopharmacology, Volume 195
Author(s): Peng Li, Jianxin Chen, Wuxia Zhang, Bangze Fu, Wei Wang
Ethnopharmacological relevanceHerbal medicine is a concoction of numerous chemical ingredients, and it exhibits polypharmacological effects to act on multiple pharmacological targets, regulating different biological mechanisms and treating a variety of diseases. Thus, this complexity is impossible to deconvolute by the reductionist method of extracting one active ingredient acting on one biological target.Aim of the studyTo dissect the polypharmacological effects of herbal medicines and their underling pharmacological targets as well as their corresponding active ingredients.Materials and methodsWe propose a system-biology strategy that combines omics and bioinformatical methodologies for exploring the polypharmacology of herbal mixtures. The myocardial ischemia model was induced by Ameroid constriction of the left anterior descending coronary in Ba-Ma miniature pigs. RNA-seq analysis was utilized to find the differential genes induced by myocardial ischemia in pigs treated with formula QSKL. A transcriptome-based inference method was used to find the landmark drugs with similar mechanisms to QSKL.ResultsGene-level analysis of RNA-seq data in QSKL-treated cases versus control animals yields 279 differential genes. Transcriptome-based inference methods identified 80 landmark drugs that covered nearly all drug classes. Then, based on the landmark drugs, 155 potential pharmacological targets and 57 indications were identified for QSKL.ConclusionOur results demonstrate the power of a combined approach for exploring the pharmacological target and chemical space of herbal medicines. We hope that our method could enhance our understanding of the molecular mechanisms of herbal systems and further accelerate the exploration of the value of traditional herbal medicine systems.

Graphical abstract

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