Mechanism Assay of Honeysuckle for Heat-Clearing Based on Metabolites and Metabolomics

Abstract

Nonsteroidal anti-inflammatory drugs (NSAIDs), such as cyclooxygenase (Cox)-1/2 inhibitor, have emerged as potent antipyretics and analgesics. However, few herbs with Cox-1/2 inhibitory activity are commonly used for heat-clearing in China. Although these are known to have antipyretic activity, there is a lack of molecular data supporting their activity. Using the traditional Chinese medicine herb honeysuckle (Hon) as an example, we explored key antipyretic active compounds and their mechanisms of action by assessing their metabolites and metabolomics. Mitogen-activated protein kinase (MAPK) 3 and protein kinase B (AKT) 1 were suggested as key targets regulated primarily by chlorogenic acid (CA) and swertiamarin (SWE). CA and SWE synergistically inhibited the production of interleukin (IL)-1 and IL-6, alleviated generation of prostaglandin E2, and played an antipyretic role equivalent to honeysuckle extract at the same dose contents within 3 h. Collectively, these findings indicated that lipopolysaccharide-induced fever can be countered by CA with SWE synergistically, allowing the substitution of a crude extract of complex composition with active compounds. Our findings demonstrated that, unlike the traditional NSAIDs, the Hon extract showed a remote and indirect mechanism for alleviating fever that depended on the phosphatidylinositol-3-kinase-AKT and MAPK pathways by regulating the principal mediator of inflammation.

Keywords: anti-inflammatory; antipyretic; chlorogenic acid; metabolites; metabolomics; swertiamarin.

Figure 1

The identification of absorbable ingredients in HEP based on the MN assay. (A) Whole MN profiling of HEP, blank plasma sample, and administration plasma sample after oral HEP. (B) The network association diagram showed a clustering of the absorbable ingredients and their metabolites (top panel), as well as the relative contents of the absorbable ingredients and their metabolites. The minimum concentration was expressed as dark blue and the maximum concentration was expressed as dark red (bottom panel). (C) The structure of the key prototype compounds.

Figure 2

(A) Volcano plot of the quantitative metabolites in plasma after HEP administration according to fold change and corrected p-value. Metabolites with significant variation in abundance are shown as red (up) or blue (down) dots. (B) Heat map of cluster analysis illustrates the key related metabolites. (C) Metabonomic analysis of plasma. Statistical analysis of differential metabolites among experimental groups (top panel). Venn diagram analysis of the differential metabolites (bottom panel). (D) The 23 significantly enriched KEGG pathways. The x-axis shows rich factor; the y-axis corresponds to the KEGG pathway. The fever and inflammation pathways are shown in red. (E) The systematic integrative analysis of all the proteins associated with fever based on metabolites and metabolomics data. The x-axis represents the protein’s fit factor for fever from GeneCards; the y-axis represents the protein’s rich factor on the key pathway from KEGG; the z-axis represents the target protein’s docking score rich from PharmMapper. Adjusted p-values of different correlation scores were used to enable robust statistical interpretation.

Figure 3

(A,B) Anal temperature curve of Aspirin, HEP, CA+SWE (left panel) and CA, SWE (right panel) in the first 3 h after intraperitoneal injection of LPS. Values are given as means ± standard deviation (SD) (n = 10). (C,D) Anal temperature at 0.5 h and 2.25 h for each group. ^### p < 0.001, Aspirin vs. LPS; ** p < 0.01, *** p < 0.001, drug intervention group vs. LPS; ^ p < 0.05, ^^^ p < 0.001, CA vs. SWE; ns, not significant (n = 10).

Figure 4

HEP, CA, and SWE downregulated the expression of pro-inflammatory cytokines in LPS-induced fever rats. The protein expression levels of PGE2 (A), IL-1 (B), and IL-6 (C) in the plasma at 0.5 h and 2.25 h were tested with ELISA kits. ^### p < 0.001, LPS vs. self-control; * p < 0.005,** p < 0.01, *** p < 0.001, drug intervention group vs. LPS; ^ p < 0.05, ^^ p < 0.01, ^^^ p < 0.001, CA vs. SWE; ns, not significant (n = 6).

Figure 5

Workflow of the integrated analysis approach based on metabolites and metabolomics assay approach. The process including LC–MS combined MN screening to obtain the prototype compounds and its metabolites; the enrichment of the key target proteins from metabonomics assay; target prediction of potential bio-active compounds; and integrative analysis and validation of biological effect.