Investigating the active substance and mechanism of Jing-Fu-Kang granules via mass spectrometry technology and network pharmacology method

Jing-Fu-Kang granules (JFKG) is a famous Chinese patent medicine for the treatment of cervical spondylosis around the China, whereas the active substance and mechanism are not completely investigated clearly. In the current study, a rapid separation and identification method using UPLC-QE-Orbitrap-MS was established, 97 chemical constituents from JFKG were identified, and 16 prototype components from plasma samples after administration of JFKG were observed within 16 min. The structures of typical compounds were preliminarily speculated by comparing the retention time and fragmentation pattern. Furthermore, multiple databases were used to integrate the compound targets of JFKG, and the disease targets related to cervical spondylosis. After the intersection of the two sets of targets, a protein-protein interaction (PPI) network and a TCM-component-target-pathway-disease network were established, then using the DAVID database to perform gene ontology analysis and Kyoto Encyclopedia of Genes and Genomes analysis on the common targets to find related pathways. Finally, a total of 531 common targets and 136 pathways were found to participate in the mechanism. Our findings will help to further confirm the mechanism of JFKG for relieving cervical spondylosis, which will improve the scientific rationality of JFKG in clinical use, and can also assist in guiding doctors.

The JFKG sample was dissolved in distilled water. Rats were randomized by body 113 weight and orally administered under the dose of 6.84 g/kg. At the same time, the blank 114 group was orally administered with the same dose of distilled water. 3 rats in the 115 experimental group were given the suspension prepared by intragastric administration 116 at a dose of 6.84 g/kg, and 3 rats in the blank group were intragastrical drunk twice a 117 day for 3 days. After the last administrations, a 0.2 mL volume of blood was collected 118 in a heparinized microcentrifuge tube from tail vein at the following time points: 15 119 min, 30 min, 1 h, 2 h, 4 h, 6 h, and then centrifuged at 4000 rpm for 10 min; the 120 supernatant was frozen at -80 • C before analysis.          judged to be berberine from the Cortex Phellodendri (Fig 2A).     Target Prediction website to predict targets; a total of 2818 targets were obtained. Using the keyword "cervical spondylosis/ Cervical spondylopathy/ cervical syndrome" to 304 search for disease targets in OMIM and GeneGards databases; screening and merging to 305 remove duplicates; a total of 6923 disease targets were obtained.

PPI construction and topological analysis 307
The Venn map is made through WeChat website(http://www.bioinformatics.com.cn/); 308 and 531 common targets are obtained (Fig 6). was exported for backup. The topological characteristics of the protein network structure were 317 analyzed by Cytoscape3.8.2 software; and 13 core targets for the treatment of CS were screened 318 (Fig 7 and Table 3).

Fig 7 PPI network construction diagram of common targets
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Enrichment analysis 323
The GO analysis of the corresponding targets of the active components of JFKG was 324 carried out by using DAVID database. The screening condition was FDR < 0.001; and the 26 325 biological process (BP); molecular function (MF) and cellular components (CC) were screened.

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The drawing was made by bioinformatics (Fig 8D1). Among them; the top one in BP analysis

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The top MF is protein binding; binding; protein kinase activity; protein serine/threonine kinase 333 activity; zinc ion binding; which speculates that JFKG in the treatment of CS may be the result 334 of a complex multi-way synergistic effect.

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The results of KEGG signal pathway analysis: a total of 136 signal pathways (P<0.05) 336 were identified and the first 10 pathways with many enriched genes were selected (Fig 8D2).

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The mechanism of JFKG in treating CS mainly involves Pathways in cancer; Neuroactive

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To better clarify the relationship among components; targets; and pathways; we use 346 Cytoscape3.8.2 to construct a "TCM-component-target-pathway-disease" network (Fig 9).  ignore some non-blood-entering ingredients with strong pharmacological activity. Secondly; 424 even at the maximum dose; the concentration of some components in serum is still too low; 425 how to ensure the detection of these components is still a difficulty. In addition; in the current 426 study; too much attention has been paid to the transitional components in the serum at the peak 427 of blood concentration; ignoring the effective components before and after the peak [42]. So to 428 clarify the effective components of traditional Chinese medicine more comprehensively and 429 accurately; this study used the extracted components of JFKG to construct the "TCM-430 component-target-pathway-disease" network; but did not select the blood ingredient.