Integration of multi-omics in investigations on the mechanisms of action of Chinese herbal medicine interventions in metabolic diseases

The rapid development of bioinformatics has provided novel approaches and methods for exploring the mechanisms of disease treatment via Chinese herbal medicines. Compound Chinese herbal medicines formulas have complex compositions and are characterized by their multiple constituents and diverse array of biological targets. Therefore, the mechanisms of action of most compound Chinese herbal medicines formulas cannot be adequately explained using a single pathway. Omics technologies describe high-throughput-based analytical and detection techniques, which include transcriptomics, proteomics, and metabolomics and provide multilayer parameter information that can be integrated to characterize the overall relationships involved in the therapeutic effects of Chinese herbal medicines formulas. Through their combination with network biology and drug effect networks, omics technologies also enable investigations into the mechanisms of disease treatment in traditional Chinese medicine. The integration of multiple omics technologies is in line with the concept of holism in traditional Chinese medicine and provides an approach for combining modern science and technology with traditional Chinese medicine theories. In recent years, omics technologies have been widely used to elucidate the mechanisms of action of Chinese herbal medicines. The latest studies employing multi-omics integration for investigating the mechanisms of action of Chinese herbal medicines interventions in metabolic diseases have devoted greater attention to in-depth explorations of disease pathogenesis. This paper provides a review of the following multi-omics technologies, which are used in research on the treatment of common metabolic diseases (e.g., type-2 diabetes mellitus, nonalcoholic fatty liver disease): network pharmacology combined with metabolomics, 16S rRNA sequencing combined with transcriptomics, 16S rRNA sequencing combined with metabolomics, and 16S rRNA sequencing combined with network pharmacology and metabolomics.