Twelve batches of CRP stored for different lengths of time were collected from the same place at Xinhui, Guangdong, China. Three batches were stored for 1 year (GCP1), and the other nine batches were stored for more than 1 year (GCP3, GCP5, GCP10). GCP3 = 1 < x < 3; GCP5 = 3 < x < 5; GCP10 = 5 < x < 10. X is the yeares of storage. Based on the principle that the longer the storage, the better quality of CRP, CRP stored for 1 year (GCP1) was used as the control group, and samples stored for more than 1 year (GCP3, GCP5, GCP10, respectively) were used as the experimental groups. The samples were purchased from Xin Baotang Pharmaceutical Co., Ltd. (Xinhui, China) and identified as CRP by Yan Zhuyun, an expert from the Medicinal Resources Department of Chengdu University of TCM. All information related to the batches of CRP sampled for this study is listed in Supplementary Table S1.

100 mg of each batch of the samples were weighed and dissolved in 1.0 ml methanol aqueous solution (70% methanol). The samples were sonicated for 0.5 h and placed at a low temperature for 12 h. During the period, the samples were vortex three times, centrifuged and supernatant was absorbed, filtered, and put into the injection bottle for LC-MS analysis (Li H et al., 2019).

The data acquisition instrument system mainly includes Ultra Performance Liquid Chromatography (UPLC) and Tandem mass spectrometry (MS/MS). The UPLC and ESI-Q TRAP-MS/MS methods were followed by the reported methods (Table 1) (Li J et al., 2019; Xia et al., 2021).

The screening criteria for all the differential metabolites were fold change values ≥ 2 or ≤ 0.5 and VIP values ≥ 1 (Yang R et al., 2021). The overlapping differential metabolites of all comparison groups were shown by Venn diagram. Among them, up-regulated metabolites were considered to be the key metabolites. The targets prediction of the key metabolites were obtained by the Traditional Chinese Medicine Systems Pharmacology (TCMSP) (Li Z. T et al., 2021; Song et al., 2021).

A search for genes related to the progression of COVID-19 was performed using “COVID-19” as the keyword in the GeneCards database and other database in the previous reports (Geer et al., 2010; Stelzer et al., 2016; Amberger et al., 2019; Gu et al., 2020; Ye et al., 2021).

The selected target genes were enriched by gene ontology and analyzed by Kyoto Encyclopedia of Genes and Genomes pathway using Metascape platform (Huang et al., 2009; Chen et al., 2017), and the important results are visualized by using the Weishengxin platform.

AutoDock Tools was used for molecular docking of isorhamnetin, limocitrin, syringetin, 1,2,3,7-Tetramethoxyxanthone, RS-Mevalonic acid, 7,3′,4′-Trihydroxyflavone, ladanein, 3-Aminosalicylic acid, and centaureidin with RdRp, Mpro, SP1, and ACE2 (Dhankhar et al., 2020). The hydrogen atoms and gasteiger charges were added and saved in pdbqt format. The molecular grid was set in between motif A (612–626) and motif G (499–511) covering the catalytic core of RdRp. For Mpro the grid was set in between domains 1 and 2 covering the catalytic dyad of His41 and Cys145. For SP1 and ACE2 refer to the literature method (Tao et al., 2020). The details of the grid are mentioned in Supplementary Table S6. The Lamarckian genetic algorithm was used to generate 25 docked conformations of each ligand (Singh et al., 2017). Discovery Studio (4.5 Client) software was used to perform molecular docking of the selected active components and targets, as well as dehydration of the structures. Hydrogenation and charge calculation on the proteins was then done using Autodock 4.2.6 software (Trott and Olson, 2010; Berman et al., 2019; Pinzi and Rastelli, 2019). Ebselen was docked with 3CL hydrolase, spike protein S1, angiotensin-converting enzyme II, and RNA-dependent RNA polymerase as a positive control and binding affinity was compared with isorhamnetin (Jin et al., 2020).

A549 cells were placed in a 37°C water bath. The cryo-storage tube was gently shaken to melt. After melting, the cells were transferred to a 15 ml centrifuge tube, and 5 ml of preheated complete medium was added slowly. The cells were collected by centrifugation, centrifuged at 1,000 rpm for 5min at room temperature, and the supernatant was discarded. The cells were suspended with complete medium, inoculated into Petri dishes, gently blown and mixed, and cultured at 37°C, 5% CO₂ saturated humidity. When the suspended cells had grown to 80%–90%, the cells were then subcultured.

CCK-8 assay was used to determine the cell viability. A549 cells were inoculated into 96 well plate, and after the cells were completely adherented, different concentrations of isorhamnetin (5 μM, 10 μM, 20 μM, 40 μM and 100 μM) were added and cultured for 24 h. After 24 h of drug intervention, 10 μl of CCK-8 solution were added to each well and incubated at 37°C for 1 h. Finally, the absorbance of each well was detected at 450 nm using iMARK microplate reader (Bole, United States), and the cell viability was calculated.

The inflammatory factors IL-1β, IL-6 and TNF-ɑ in A549 cell supernatants were determined by kit methods.

Six-week old BALB/c mices were obtained from Beijing Medical Laboratory Animal Center. The mices were injected intraperitoneally with 100 g/L chloral hydrate (4 ml/kg) and placed on a plate of 45° after anesthesia. The tongue of the mice was pulled with forceps, and 50 µl of LPS with a concentration of 1 g/L was absorbed into the posterior pharyngeal wall of the mice with a pipette gun. After 20 s, the tongue and nose were released and the mices were placed in the cage to wake up naturally. The animal experiments were approved by the Animal Ethics Committee of Chengdu University of Traditional Chinese Medicine (SYXK 2020–124).

The animals were separated into five groups (n = 6/group): Control group, LPS group, LPS + isorhamnetin group (10 mg/kg), LPS + isorhamnetin group (30 mg/kg), and LPS + isorhamnetin group (90 mg/kg) (Bo et al., 2022). 30 min after modeling, mices in the administration group were injected with different doses of drugs intraperitoneally. The mices were sacrificed 24 h after modeling, and the lungs were collected for detection. The control group was given corresponding normal saline.

The left lobular lung tissue was cut in half, fixed with picric acid solution for 24 h, embedded in paraffin, and sectioned with 5 μm thickness. Then HE staining was performed to observe the histopathological changes of mouse lung tissue under microscope.

After the mice were sacrificed, lung tissue was immediately collected and weighed. The dry weight was obtained by heating at 80°C for 48 h. Lung wet to dry ratio (W/D) was calculated by dividing lung wet weight by lung dry weight.

An appropriate amount of lung tissue was weighed and ground with 9 times homogenization medium, then the grinding solution was centrifuged at 3000–4000 rpm for 10 min, and the supernatant was taken to prepare 10% tissue homogenate, and the levels of IL-1β, IL-6 and TNF-α were detected according to ELISA kit instructions.

Paraffin sections of 5 µ m lung tissue were prepared for IL-1 β, IL-6 and TNF-α expression by immunohistochemistry, and then observed and photographed under a ×400 magnification microscope.

100 mg of lung tissue was put into test tube, cut into pieces, lysate was added for 1 h, centrifuged at 12,000 r/min for 15 min, and the protein concentration was measured by Bio-Rad protein quantification kit. Protein expression was detected by western blot analysis. Antibodies used herein including anti-SYK, anti-MAPK14, anti-AKT, anti-p-AKT, anti-MAPK8, anti-EGFR, anti-IL6, anti-IL-1β, anti-PIK3CA and β-actin were obtained from Affinity Biosciences LTD. The intensity of stained bands was detected with a Bio-Rad gel imaging analyzer.

SPSS 17.0 statistical software was used for analysis. Data are expressed as mean ± standard deviation. The SNK-Q method was used for comparison between groups. A p-value of less than 0.05 was considered statistically significant.

In the present study, the metabolites of CRP from GCP1, GCP3, GCP5, and GCP10 were investigated based on widely targeted metabolomics. Finally, 399 metabolites were characteried in all the samples, including 6 terpenoids, 51 alkaloids, 80 phenolic acids, 4 quinones, 3 tannins, 147 flavonoids, 8 lignans and coumarins, and 90 other compounds (Supplementary Table S2). In the heatmap (Figures 2A–C), the relative content of metabolites in GCP1 was the lowest compared with GCP3, GCP5, and GCP10. Although the samples of GCP1, GCP3, GCP5, and GCP10 were grouped, the content of the metabolites was also quite different within each set. Our findings demonstrated that a longer storage time improves the metabolite content of CRP (Zheng et al., 2021).

Principal component analysis (PCA) is a multivariate statistical method to investigate the correlation between multiple variables, and study how to reveal the internal structure of multiple variables through a few principal components. This method is often used to explore the differences between samples from different sources (Giuliani, 2017).

In the present study, PCA was preformed to investigate the metabolites differences of CRP with different storage years. As shown in Figures 3D–F, GCP3, GCP5, and GCP10 were clearly separated from GCP1, thus indicating that there are great differences between different samples. At the same time, the clustering of QC samples into one category also indicates the repeatability and reliability of this experiment. Similarly, orthogonal partial least squares discriminant analysis (OPLS-DA) is widely used in metabolomics analysis. Partial least squares regression is used to establish the relationship model between metabolite expression levels and sample categories, and it can also effectively separate samples and predict sample categories (Triba et al., 2015). All the differential metabolite analysis parameters of comparison groups in this paper are shown in Supplementary Tables S3–S5, including logFC, p-value, and VIP. Parameters of OPLS-DA model for all comparison groups in this paper are shown in Figures 3A–C. As can be seen from Figures 3A–C, the Q² values of the three comparison groups were 0.999, 1,1, respectively. Since the Q² value in all comparison groups was greater than 0.9, indicating that these OPLS-DA models were reliable and could be used to screen the differential metabolites in each comparison group.

A comparison between GCP1 and GCP3 showed that 40 metabolites increased and 39 decreased (Figure 2D). In the comparison between GCP1 and GCP5, 46 and 23 metabolites were up-regulated and down-regulated, respectively (Figure 2E). In the comparison between GCP1 and GCP10, 34 and 60 metabolites were down-regulated and up-regulated, respectively (Figure 2F). Notably, the number of up-regulated compounds was more than that of down-regulated compounds in all the comparison groups, which confirmed that metabolites were enriched in the longer storaged samples.

The Venn diagram shows the differential metabolites shared by the comparison groups of GCP1 vusus GCP3, GCP1 vusus GCP5, and GCP1 vusus GCP10. From Figure 4, 32 overlapping differential metabolites were obtained. Among them, 9 of these 32 metabolites (Table 2 and Figure 5) were screened out, and they were belonging to the upregulated overlapping differential metabolites and were identified as active metabolites of CRP. Then, we identified 225 COVID-19 related targets of the above active metabolites from the TCMSP database.

We obtained 1,238, 1956 and 1567 COVID-19 targets from the NCBI, GeneCards and GenCLiP3 Databases, respectively. Finally, 1,587 potential treatment targets remained after removing duplicate targets. Using Venny 2.1 drawing software, 225 drug targets of the identified active metabolites of CRP were mapped to 1587 COVID-19-related disease targets. Finally, 48 drug targets (Figure 7A) were chosen for further analysis as potential targets in the treatment of COVID-19.

Based on the data obtained, a component-target network was constructed. 48 nodes, 192 interactions make up the network (Figure 7B). Further analysis indicated that nine compounds targeted COVID-19-related targets (Figure 7C). And the genes associated with these nine compounds were constructed into a PPI network. Among them, ATK1, EGFR, MAPK8, and MAPK14 were the main targets (Figure 7D). GO enrichment analysis (Figure 6) suggested that the compounds found in CRP are mainly involved in regulating inflammatory response, oxidative stress, and the channel protein activities in host-virus interactions.

In order to further understand the relationship between the identified targets and the corresponding pathways, target-pathway interactions was constructed (Figure 7E). From the top 20 pathways (Table 3), the TNF signaling pathway, PI3K-Akt signalling pathway, and the T cell receptor signaling pathway might be the main pathways associated with these compounds in treating COVID-19.

In the above analyses, we showed that isorhamnetin, limocitrin, syringetin, 1,2,3,7-tetramethoxyxanthone, RS-mevalonic acid, 7,3′,4′-trihydroxyflavone, ladanein, 3-aminosalicylic acid, and centaureidin are the main active metabolites of CRP. We also identified the SARS-CoV-2 proteins 3CL hydrolase (3CL; PDB ID: 6LU7), angiotensin-converting enzyme II (ACE2; PDB ID: 1R42), spike protein (S1; PDB ID: 3BGF), and RNA-dependent RNA polymerase (RdRp; PDB ID: 6M71) as the main functional targets. Molecular docking was then performed to verify whether the active components and the targets can interact.

The molecular docking results showed that, except for RS-mevalonic acid, all the identified active metabolites of CRP had a docking score of less than −5.0 with the four main functional targets, indicating that they have strong binding activity (Table 4). Among them, the docking scores of isorhamnetin with all four targets were significantly lower than the other compounds. And the binding affinity of CRP compound isorhamnetin was also lower than the positive control ligands. This suggests a greater binding affinity and thus potentially higher biological activity. The molecular docking modes of isorhamnetin with key targets of the disease are shown in Figure 8. Molecular docking results illustrated that isorhamnetin bind with amino acid residues (G143, E166, S144, V3, T87, Q88, K211, E213, A125, S122) of RdRp, 3CL, S1, and ACE2 with a binding affinity in the range of that −8.1 to −6.1 kcal/mol. Overall, molecular docking results suggested that isorhamnetin can bind with a considerable binding affinity at the critical sites of RdRp, 3CL, S1, and ACE2.

CCK-8 assay was used to detect the effect of isorhamnetin on the activity of A549 cells. As shown in Figure 7, the cell activity of LPS group was significantly decreased (p < 0.01) compared with the blank group, indicating that LPS had a significant inhibitory effect on cell growth. Meanwhile, with the increase of isorhamnetin concentration, the cell viability was significantly enhanced, suggesting that isorhamnetin could protect A549 cells from LPS-induced injury. At the same time, the levels of inflammatory factors IL-1, IL-6, and TNF-ɑ in each treatment group were significantly lower than those in the model group (Figures 9B–D), indicating that isorhamnetin could inhibit inflammatory response in A549 cells and has anti-inflammatory activity.

As shown in Figure 10A, the lung tissue in the blank group was clear, the alveolar structure was intact, there was no secretion and inflammatory cell infiltration in the alveolar cavity, and the alveolar wall thickness was normal. The lung tissue in LPS group was seriously diseased, with inflammatory cell infiltration and red blood cell exudation in alveolar cavity, thickened alveolar septum, and blood vessel congestion. Compared with LPS group, the lung tissue lesions were significantly improved, the alveolar wall was thinner, the inflammatory cells in alveolar cavity were reduced, and congestion was alleviated in the treatment group. In addition, the W/D ratio of lung and lung injury score of mices in each group were compared. The results showed that W/D ratio and lung injury were significantly decreased in the treatment group (Figures 10B,C).

Compared with the control group, the protein levels of SYK, IL-6, MAPK14, MAPK8, TNF-α, AKT, p-Akt, PIK3CA, EGFR and IL-1β in the model group were increased (p < 0.05). Compared with the model group, isorhamnetin reduced the protein expressions of SYK, IL-6, MAPK14, MAPK8, TNF-α, AKT, p-Akt, PIK3CA, EGFR and IL-1β in lung tissues of pneumonia mice (Figure 11). Immunofluorescence results also confirmed the expression of IL-1β, IL-6, and TNF-ɑ proteins (Figure 12A). In addition, isorhamnetin could also reduce the levels of inflammatory factors IL-1β, IL-6, and TNF-ɑ in lung tissue of mice in the treatment group (Figures 12B–D), suggesting that isorhamnetin inhibited the inflammatory response.