Transcriptomics- and metabolomics-based integration analyses revealed the potential pharmacological effects and functional pattern of in vivo Radix Paeoniae Alba administration

Background Radix Paeoniae Alba (RPA) and other natural medicines have remarkable curative effects and are widely used in traditional Chinese Medicine (TCM). However, due to their multi-component and multi-target characteristics, it is difficult to study the detailed pharmacological mechanisms for those natural medicines in vivo. Therefore, their real effects on organisms is still uncertain. Methods RPA was selected as research object, the present study was designed to study the complex mechanisms of RPA in vivo by integrating and interpreting the transcriptomic based RNA-seq and metabolomic based NMR spectrum after RPA administration in mice. A variety of dimension-reduction algorithms and classifier models were applied to the processing of high-throughput data. Results Among serum metabolites, the contents of PC and glucose were significantly increased, while the contents of various amino acids, lipids and their metabolites were significantly decreased in mice after RPA administration. Based on the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases, differential analysis showed that the liver was the site where RPA exerted a significant effect, which confirmed the rationality of “meridian tropism” in the theory in TCM. In addition, RPA played a role in lipid metabolism by regulating genes encoding enzymes of the glycerolipid metabolism pathway, such as 1-acyl-sn-glycerol-3-phosphate acyltransferase (Agpat), phosphatidate phosphatase (Lpin), phospholipid phosphatase (Plpp) and endothelial lipase (Lipg). We also found that RPA regulates several substance addiction pathways in the brain, such as the cocaine addiction pathway, and the related targets were predicted based on the sequencing data from pathological model in the GEO database. The overall effective pattern of RPA was intuitively presented with a multidimensional radar map through a self-designed model which found that liver and brain were mainly regulated by RPA compared with the traditional meridian tropism theory. Conclusions Overall this study expanded the potential application of RPA and provided possible targets and directions for further mechanism study, meanwhile, it also established a multi-dimensional evaluation model to represent the overall effective pattern of TCM for the first time. In the future, such study based on the high-throughput data sets can be used to interpret the theory of TCM and to provide a valuable research model and clinical medication reference for the TCM researchers and doctors.