UHPLC-Q-TOF-MS/MS and Network Pharmacology Analysis to Reveal Quality Markers of Xinjiang Cydonia oblonga Mill. for Antiatherosclerosis

Abstract

Cydonia oblonga Mill. (COM), mature fruit of genus Rosaceae, is consumed as a kind of traditional Chinese medicinal herb. Previous studies have shown that the components in COM extract have antioxidant, anti-inflammatory, blood pressure-lowering, blood lipid-lowering, antithrombotic, and other biological activities. However, the quality markers (Q-markers) of atherosclerosis (AS) have not been elucidated. The Q-marker is based on the five core principles of traceability, transferability, specificity, measurability, validity, and prescription dispensing. In this study, the quality markers of quince were investigated by applying the ultraperformance liquid chromatography-time-of-flight mass spectrometry (UHPLC/Q-TOF-MS/MS) method and network pharmacology method to highlight the three core elements which are, respectively, traceability transmission, measurability, and validity. At the first step, 72 components were identified by applying the ultraperformance liquid chromatography-time-of-flight mass spectrometry (UHPLC/Q-TOF-MS/MS) method. In the next step, 46 candidate components of COM anti-AS were obtained by network pharmacology, and then, 27 active components were filtered with the molecular docking assay. Finally, the 27 active components were intersected with 10 active components obtained by mass transfer and traceable quality markers. Four anti-AS Q-markers of COM were identified, including caffeic acid, chlorogenic acid, ellagic acid, and vanillic acid, which provided a reference for the quality control of quince. The methods and strategies can also be applied to other traditional Chinese medicines and their compound preparations, providing new ideas on the quantitative evaluation and identification of quality markers.

Figure 1

Network pharmacology workflow combined with UHPLC-Q-TOF-MS/MS.

Figure 2

Total ion chromatograms of COM extracts: (a) negative ion mode (-) ESI; (b) positive ion mode (+) ESI.

Figure 3

Preliminary screening of AS quality markers for COM intervention. (a) Venn diagram crossover: 737 targets are the common points of AS disease targets obtained from COM and TCMIP, CTD, DiSGeNET, and GeneCards databases; (b) combination of Venn diagram: 46 candidate targets were screened with the minimum required interaction score ≥ 0.999; (c) PPI network diagram: including 46 nodes and 518 edges; (d) component-target network, including 110 nodes and 437 edges, the yellow circle represents 46 candidate targets, the red square represents 64 COM components, and the size of the circle represents the node degree of the target protein.

Figure 4

COM intervention in the determination of AS quality markers. (a) The first 20 molecular functions; (b) composition of the first 20 cells; (c) the first 20 biological processes; (d) the first 25 signaling pathways; (e) CC-CT-CP network diagram, red quadrangle represents 63 components; yellow circle represents 39 candidate targets; green diamond represents 25 signaling pathways; the greater the circle size, the greater the degree of representative components, targets, and pathways, the more important the network.

Figure 5

Rescreening of AS quality markers by COM intervention. (a) The intersection of ligands: 46 components are the common points of components obtained by “C-T” and “C-T-P” network analysis. (b) The intersection of receptors: 5 targets are the common points of candidate targets obtained by “C-T” and “C-T-P” network analysis. (c) Docking results (BE ≤ −5.00 kcal/mol): docking of 25 components with 4 core targets, the darker the color, the stronger the receptor and ligand binding activity. (d) The main Q-marker intersection map: the common points of 10 components screened by physicochemical properties and 27 core components with good docking results.

Figure 6

Molecular models of the binding of caffeic acid to the predicted targets ESR1 (a, b), EGFR (c, d), CDK2 (e, f), and CDK1 (g, h) shown as 3D diagrams and 2D diagrams.

Figure 7

Molecular models of the binding of chlorogenic acid to the predicted targets ESR1 (a, b), EGFR (c, d), CDK2 (e, f), and CDK1 (g, h) shown as 3D diagrams and 2D diagrams.

Figure 8

Molecular models of the binding of ellagic acid to the predicted targets ESR1 (a, b), EGFR (c, d), CDK2 (e, f), and CDK1 (g, h) shown as 3D diagrams and 2D diagrams.

Figure 9

Molecular models of the binding of vanillic acid to the predicted targets ESR1 (a, b), EGFR (c, d), CDK2 (e, f), and CDK1 (g, h) shown as 3D diagrams and 2D diagrams.