Abstract Introduction: Corni Fructus (CF) is a Chinese herbal medicine used for medicinal and dietary purposes. It is available commercially in two main forms: raw CF (unprocessed CF) and wine-processed CF. Clinical observations have indicated that wine-processed CF exhibits superior hypoglycemic activity compared to its raw counterpart. However, the mechanisms responsible for this improvement are not well understood. Methods: To address this gap in knowledge, we conducted metabolomics analysis using ultra-performance liquid chromatography-quadrupole/time-of-flight mass spectrometry (UPLC-QTOF-MS) to compare the chemical composition of raw CF and wine-processed CF. Subsequently, network analysis, along with immunofluorescence assays, was employed to elucidate the potential targets and mechanisms underlying the hypoglycemic effects of metabolites in CF. Results: Our results revealed significant compositional differences between raw CF and wine-processed CF, identifying 34 potential markers for distinguishing between the two forms of CF. Notably, wine processing led to a marked decrease in iridoid glycosides and flavonoid glycosides, which are abundant in raw CF. Network analysis predictions provided clues that eight compounds might serve as hypoglycemic metabolites of CF, and glucokinase (GCK) and adenylate cyclase (ADCYs) were speculated as possible key targets responsible for the hypoglycemic effects of CF. Immunofluorescence assays confirmed that oleanolic acid and ursolic acid, two bioactive compounds present in CF, significantly upregulated the expression of GCK and ADCYs in the HepG2 cell model. Discussion: These findings support the notion that CF exerted hypoglycemic activity via multiple components and targets, shedding light on the impact of processing methods on the chemical composition and hypoglycemic activity of Chinese herbal medicine. Keywords: Corni Fructus, metabolomics, network analysis, hypoglycemic, wine-processed markers 1 Introduction Corni Fructus (CF; shanzhuyu in Chinese), derived from the dried mature fruits of Cornus officinalis Sieb. et Zucc., which had been widely used in traditional Chinese medicine (TCM) in Asia to treat multiple diseases ([40]Cui et al., 2021). As a TCM, CF has been extensively utilized in China to treat diabetes by nourishing the liver and kidney, addressing kidney deficiency, regulating hypertension and other related diseases ([41]Gao et al., 2021). Moreover, CF is an important medicinal component in many classic TCM prescriptions, such as Liuwei Dihuang pill and Zuogui pill ([42]Zhou et al., 2020). Modern pharmacological studies have indicated that CF exhibits a broad spectrum of pharmacological activities, including hypoglycemic and hypolipidemic activity, liver and kidney protection, and other activities ([43]Huang et al., 2018). Phytochemical research revealed that active components of CF majorly include iridoids, flavonoids, and triterpenes, which are employed for antioxidative, antidiabetic, and antineoplastic activities ([44]Ma et al., 2014; [45]Dong et al., 2018). Processing is an essential step in TCM preparation, which can alter the properties of medicinal substances, reduce TCM toxicity, and enhance TCM efficacy ([46]Zhao et al., 2010). In addition, as two commercial products, there are differences in pharmacological activity between raw CF and wine-processed CF. Long-term clinical practice has shown that compared to raw CF, wine-processed CF has stronger effects on nourishing the liver and kidney, and exhibits superior hypoglycemic activity ([47]Zhang et al., 2016; [48]Bi et al., 2019). However, the bioactive chemical changes occurring during the wine processing of CF remain unclear. Currently, several studies have been carried out to analyze the changes in components between raw CF and wine-processed CF ([49]Cao et al., 2020; [50]Han et al., 2022). Previous studies revealed that several iridoids showed significant differences in raw CF and wine-processed CF by HPLC-MS ([51]Wang et al., 2018). However, these previous studies were only low throughput analyses, fail to systematically illustrate the chemical alteration involved in the wine processing of CF, and it is difficult to screen wine-processing associated markers due to the chemical complexity of CF. LC-MS based metabolomics is a valuable approach for high-throughput detection and analysis of secondary metabolites and active ingredients in medicinal plants ([52]Xie et al., 2022). Moreover, with the aid of multivariate statistical analysis, metabolomics could screen meaningful markers for reflecting chemical varieties caused by TCM processing ([53]Xia et al., 2020; [54]Gao et al., 2022). In the present study, an integrated strategy was established to unveil the changes in hypoglycemice metabolites in raw CF and wine-processed CF. Firstly, ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC/Q-TOF-MS) based metabolomics was performed to compare the plant metabolic profiling and metabolites changes of 20 batches CF samples, and differential metabolites responsible for distinguishing raw CF and wine-processed CF was screened. Then, the active ingredients of CF and their hypoglycemic potential targets were predicted by network analysis. Finally, immunofluorescence assays and quantitative analysis were applied to further verify the hypoglycemic mechanism of CF in the HepG2 cells model ([55]Figure 1). FIGURE 1. [56]FIGURE 1 [57]Open in a new tab The strategy to discover potential hypoglycemic metabolites based on metabolomics and network analysis. 2 Materials and methods 2.1 Chemicals and reagents Geniposide (Lot: 110749-201919), loganin (Lot: 110640-201707), morroniside (Lot:111998-201703), rutin (Lot: 100080-201811), quercetin (Lot: 100081-201610), kaempferol (Lot: 110861-202013), and caffeic acid (Lot: 110885-201703) were all bought from China National Institutes for Food and Drug Control (Beijing, China). Gallic acid (Lot: 190715-008), 5-hydroxymethylfurfural (Lot: 191015-037) and cornuside (Lot: 190917-066) were purchased from Beijing Ya Xi’er Technology Co., Ltd. (Beijing, China). Astragalin (Lot: 19092602) was purchased from Chengdu Herbpurify (Chengdu, China). LC-MS-grade acetonitrile and methanol were supplied by Merck (Darmstadt, Germany), and LC-MS-grade formic acid was purchased from Acslabchem (ACS, United States). Ultrapure water was supplied by Shenzhen Watsons Distilled Water Co., Ltd. CF samples were purchased from five herbal pieces factories in China and identified by Professor Zhiguo Zhang from The First Hospital of Hunan University of Chinese Medicine. The specific information is shown in [58]Supplementary Table S1. 2.2 Sample preparation According to the wine-processing methods of CF recorded in the Chinese Pharmacopoeia (National Commission of Chinese Pharmacopoeia, 2020 version), the raw CF was mixed with wine, saturated, and the temperature set up at 115°C, then steamed with high-pressure wine for 1 h, dried for 4 h at 60°C. Finally, it is removed for cooling, wine-processed CF was prepared. Each batch of CF sample was weighed 100 g, extracted with 8 volumes of water and refluxed twice for 1 h each at 100°C. And the CF water extracts was merged, concentrated in vacuum, and subsequently lyophilized to prepare a CF extract powder. The 3 g powder was weighed precisely, and then was dissolved in a 30 mL 50% methanol. The solution was sonicated for 30 min, centrifugated, filtered and obtained the CF sample solution which was used for LC/MS analysis. Eleven reference standards, including gallic acid, geniposide, loganin, morroniside, rutin, quercetin, kaempferol, 5-hydroxymethylfurfural, caffeic acid, cornuside and astragalin, were accurately weighed 10 mg, added in a 25 mL volumetric bottle, and dissolved in methanol yielding a standard solution at 0.4 mg/mL. 2.3 UPLC-Q-TOF-MS conditions LC-MS/MS (1290UPLC-6540-QTOF, Agilent, United States) was applied to qualitatively analyze the metabolites in CF, a high-efficiency C18 column (3.0 × 100 mm, 1.8 μm, Agilent) was used to separate metabolites, the flow rate was set at 0.4 mL/min, and the separation was subjected to gradient elution mode. The mobile phase consisted of water (included 0.1% formic acid, A) and acetonitrile (B), the elution conditions are described in the [59]Supplementary Materials. ESI positive and negative ion mode were adopted in the mass spectrum, LC-MS analysis methods were used according to our previously published article ([60]Wang et al., 2020b). Molecule Feature Extractor of Masshunter Qualitative Analysis (Agilent, United States) was applied to analyze the primary and secondary mass spectrometry data. The identification of metabolites in CF were conducted through comparison with standards and MS/MS fragmentation and GNPS platform. UPLC-DAD was further performed to quantitatively analyze metabolites. 2.4 Multivariate statistical analysis screened The LC-MS raw data of raw CF and wine-processed CF samples were imported to MassHunter Profinder (Agilent, United States), and converted mass spectrometry data into the matrix format of metabolite peak area, and peak alignment and matching was performed. Moreover, multivariate statistical analysis (Simca-p14.0 software, Umetrics AB, Sweden) was adopted to analyze to all the resultant data matrix. Hierarchical cluster analysis (HCA) and principal component analysis (PCA) as unsupervised pattern recognition methods were used to cluster analysis to distinguish metabolic phenotypes between raw CF and wine-processed CF. To screen markers associated with wine processing more effectively, OPLS-DA was used to observe the main characteristic ingredient for the data variance. The variable importance parameter (VIP > 1) value of the validated OPLS-DA model and p < 0.05 in the Student’s test were taken as candidate distinguishing markers. Finally, the structures of metabolites were determined by analyzing the elemental compositions and MS/MS fragmentation, and compared the retention time of samples with authentic standards. GNPS ([61]https://gnps.ucsd.edu/ProteoSAFe/static/gnps-splash.jsp), PubChem ([62]https://pubchem.ncbi.nlm.nih.gov/) and references were used for