Network pharmacology combined with metabolomics and lipidomics to reveal the hypolipidemic mechanism of Alismatis rhizoma in hyperlipidemic mice.

Alismatis rhizoma (AR), the dried rhizome of Alisma orientale (Sam) Juzep, is effective in treating hyperlipidemia, but the mechanisms involved require further exploration. This study evaluated the hypolipidemic properties of AR using an integrated strategy combining network pharmacology with metabolomics and lipidomics. Firstly, a hyperlipidemia mouse model induced by a high-fat diet was established to evaluate the therapeutic effects of AR. Secondly, plasma metabolomics and lipidomics were used to identify differential metabolites and lipids, and metabolic pathway analysis was performed using MetaboAnalyst. Thirdly, network pharmacology, based on the metabolic profile of AR in vivo, was used to discover potential therapeutic targets. Finally, key targets were obtained through a compound-target-metabolite network, which was verified by molecular docking and quantitative real-time PCR (qPCR). Biochemistry analysis and histological examinations showed that AR exerted hypolipidemic effects on hyperlipidemic mice. Seventy potential biomarkers for the AR treatment of hyperlipidemia were identified by metabolomics and lipidomics, which were mainly involved in lipid metabolism, energy metabolism and amino acid metabolism. Eighteen potentially active compounds were identified in the plasma of mice after oral administration of AR, which were associated with 83 potential therapeutic targets. The PPAR signaling pathway was considered a crucial signaling pathway of AR against hyperlipidemia by KEGG analysis. The joint analysis showed that 6 upstream key targets were regulated by AR, including ALB, TNF, IL1B, MMP9, PPARA and PPARG. Molecular docking showed that active compounds of AR had high binding affinity with these key targets. qPCR further demonstrated that AR could reverse the mRNA expression of these key targets in hyperlipidemic mice. This study integrates network pharmacology with metabolomics and lipidomics to reveal the regulatory effects of AR on endogenous metabolites and validates key therapeutic targets, and represents the most systematic and in-depth study on the hypolipidemic activity of AR.

Improved structural annotation of triterpene metabolites of traditional Chinese medicine in vivo based on quantitative structure-retention relationships combined with characteristic ions: Alismatis Rhizoma as an example.

As a fast, sensitive and selective method, liquid chromatography-tandem high-resolution mass spectrometry (LC-HRMS) has been used for studying the in vivo metabolism of traditional Chinese medicine (TCM). However, the rapid discovery and characterization of metabolites, especially isomers, remain challenging due to their complexity and low concentration in vivo. This study proposed a strategy to improve the structural annotation of prototypes and metabolites through characteristic ions and a quantitative structure-retention relationship (QSRR) model, and Alismatis Rhizoma (AR) triterpenes were used as an example. This strategy consists of four steps. First, based on an in-house database reported previously, prototypes and metabolites in biosamples were preliminarily identified. Second, the candidate structures of prototype compounds and metabolites were determined by characteristic ions, databases or potential metabolic pathways. Then, a QSRR model was established to predict the retention times of the proposed structure. Finally, the structures of unknown prototypes and metabolites were determined by matching experimental retention times with the predicted values. The QSRR model built by the genetic algorithm-multiple linear regression (GA-MLR) has excellent regression correlation (R2 = 0.9966). Based on this strategy, a total of 118 compounds were identified, including 47 prototypes and 71 metabolites, among which 61 unknown compounds were reasonably characterized. The typical compound identified by this strategy was successfully validated using a triterpene standard. This strategy can improve the annotation confidence of in vivo metabolites of TCM and facilitate further pharmacological research.