This study was reviewed by the Ethics Committee of the Heilongjiang Academy of Chinese Medicine Sciences (No. 2015-64) and the experiments were conducted by the Chinese Government’s “Guidelines on the Care and Use of Laboratory Animals” to minimize their suffering.

Reagents included Qinbaiqingfei concentrated pills (Heilongjiang Academy of Chinese Medicine, 20200715 China), azithromycin (Pfizer, United States), methanol, acetonitrile (chromatography pure, Merck, Germany), formic acid (chromatography pure, Fisher, United States), distilled water (Watsons, China), methanol, acetonitrile (analysis of pure, Kemiou, China), the rat serum IL-10, TNF-α ELISA kit (Boster, China), mycoplasma pneumoniae nucleic acid detection kit (Da-an gene, China), NF-κB p65 rabbit polyclonal antibody, IKK-α rabbit polyclonal antibody, IκB-α rabbit polyclonal antibody, MyD88 rabbit polyclonal antibody, β-actin antibody, and horseradish peroxidase–labeled goat anti-rabbit IgG (H+L) (Beyotime Biotechnology, China).

Materials included Acquity UPLC (Waters, United States, including binary high-pressure gradient pump, vacuum degasser, autosampler, and column oven), AB SCIEX Triple-TOFTM 5600⁺ mass spectrometer (AB SCIEX, United States, equipped with ESI Source and APCI source), data acquisition software was analyst TF 1.6 software (AB SCIEX, United States), data processing software system: Peakview 2.0/masterview1.0 software (AB SCIEX, United States), Progenesis QI 2.0 software (Waters, United States), Waters Acquity UPLC BEH C₁₈ column (1.7 μm, 2.1 mm × 100 mm), Aquity UPLC BEH C₁₈ VanGuard Pre-Column (1.7 μm, 2.1 mm × 5 mm), Bioer Line gene 9600 fluorescence Quantitative PCR instrument (Hangzhou Bioeri, China), Infinite M200 PRO (Tecan, Switzerland), Orthopolar photographic microscope (Nikon, Japan), and Amersham Imagery 600 gel imaging system (GE Company, United States).

60 SPF Waster rats were raised in a constant temperature and humid environment. After 7 days of adaptive rearing, the rats were randomly divided into groups. Rats were anesthetized with pentobarbital during model building. Using the blind insertion method, the left lung cannula was inserted through the mouth and trachea to inject 200 μl of mycoplasma bacterial solution. The control group (CN) was injected with the same amount of sterile culture medium. For the next 6 days, the model was maintained by a 200 μl nasal drip after ether anesthesia every day. After modeling, the QB low-dose group (L) (0.8 g/kg) was administered intragastrically for 14 days, the QB medium-dose group (M) (1.6 g/kg), for 14 days and the QB high-dose group (H) (3.2 g/kg), for 14 days, and the azithromycin group (AZM) (45 mg/kg on the first day, 22.5 mg/kg), for 10 days. AZM and M correspond to adult doses. The adult dose of QB is 18 g/d, and the rat dose is calculated by the body surface area method. CN and MPP were given the same dose of purified water daily. On the last day, the rat’s urine was collected in a metabolic cage and then anesthetized with pentobarbital. The blood was taken from the abdominal aorta, and the supernatant was centrifuged after standing. The lung tissue was washed with normal saline, and the lower lobe of the right lung was fixed in formalin. Inside, the rest of the tissue is frozen in an ultra-low temperature refrigerator.

10 SPF Waster rats were reared in a constant temperature and humid environment. After being adaptively reared for 7 days, the rats were randomly divided into five groups. QB was given by intragastric administration at 16.2 g/kg, and CN was given equal volume of purified water. 1 h later, the rats were anesthetized with pentobarbital. Blood was collected from the hepatic portal vein and centrifuged, and the supernatant was frozen and stored. The lung tissue was washed and frozen.

Take 100 mg rat lung tissue, add 100 μl of water to homogenize for 2 min, centrifuge at 6500 rpm for 10 min, take the supernatant, centrifuge at 12,000 rpm for 5 min, add 50 μl DNA extracted from the pellet, treat with 100°C for 10 min, centrifuge at 12,000 rpm for 10 min, and add 2 μl of the supernatant to the reaction tube. The amplification conditions were 93°C–2 min; 93°C, 45 s – 55°C, 60 s, 10 cycles; and 93°C 30 s – 55°C 45 s, 30 cycles.

The fixed lung tissue was washed with distilled water and dehydrated with ethanol gradient. Xylene was transparently treated twice, soaked in wax, and embedded in a paraffin embedding machine. The embedded tissue was fixed on a microtome for sectioning and placed on a clean carrier. On the glass slides, the slides are dried in a 60°C constant temperature oven, and then dewaxed, HE stained, dehydrated, and finally sealed with neutral gum.

After the rat serum was thawed, it was centrifuged at 2000 rpm for 10 min. The supernatant was taken for ELISA detection, and a microplate reader was used for detection.

Freeze urine samples are gradually heated and thawed; take 200 μl of urine, add 200 μl of distilled water, vortex and centrifuge at 4°C, 13,000 r/min for 10 min, and take the supernatant. Take 200 μl of the serum, add 800 μl of methanol (precooled at −20°C), vortex for 2 min, let it stand for 10 min, and centrifuge at 4°C, 13,000 r/min, 10 min. Take 800 μl of the supernatant, blow dry with nitrogen, add 200 μl of 80% methanol (precooled at −20°C) to reconstitute, vortex for 2 min, 4°C, 13,000r/min, centrifuge for 10 min, and take the supernatant to obtain. Take 50 mg of the lung tissue, add 0.25 ml of water for 2 min, add 1.5 ml of methanol: acetonitrile (1:1), vortex for 2 min, ultrasound for 2 min, and allow it to stand for 10 min. Then, take 1 ml of the supernatant and centrifuge at 13,000r/min for 10 min at 4°C, take 800 μl of the supernatant and dry it with nitrogen. Then, reconstitute it with 200 μl of 80% methanol, centrifuge at high speed, and collect the supernatant. Take a small amount of urine, serum, and lung tissue samples from each to make QC samples.

Take 400 μl of the rat blank serum and administration serum, add 10 μl of phosphoric acid to each, vortex for 60 s, add 2.4 ml methanol, vortex for 60 s at 4°C, 13,000 rpm for 10 min, blow-dry the supernatant with nitrogen and reconstitute the residue with 200 μl of methanol, vortex and shake for 60 s, and centrifuge at 13,000 rpm, 4°C for 10 min. Then, take the supernatant for UPLC-Q/TOF-MS analysis. Take 100 mg of the lung tissue, add 1 ml water, homogenize for 3 min, add 10 μl of phosphoric acid and vortex for 60 s, add 4 ml acetonitrile, vortex for 2 min, allow it to stand for 10 min, and centrifuge at 4°C, 13,000 rpm for 10 min.

Adopt an ESI ion source, the ionization mode is electric spray positive and negative ion mode, the positive and negative ion source voltage is 5500 V/−4500 V, ion source temperature is 550°C, cleavage voltage is 100 V/−100 V, collision energy are 35 eV/−35 eV, and collision energy extension are 15 eV/−15 eV. The atomizing gas is nitrogen, the auxiliary gas Gas1 is 55 PSI, the auxiliary gas Gas2 is 55 PSI, and the curtain gas CurGas is 35 PSI. The scanning range of the precursor ion of the primary mass spectrum is 80–1200 Da. IDA sets the eight highest peaks with a response value of over 100 cps to scan the secondary mass spectrum; the scanning range of the product ion is 50–1200 Da, and dynamic background subtraction is enabled.

Column temperature: 35°C; mobile phase: A: 0.1% formic acid water and B: 0.1% formic acid acetonitrile. The gradient elution sequence is as follows and only write the proportion of B. Urine: 5–45% from 0 to 16 min, 45–100% from 16 to 18 min, and 5% from 18.1 to 20 min. Serum: 5–55% from 0 to 5 min, 55–65% from 5 to 9 min, 65–75% from 9 to 13 min, 75–100% from 13 to 18 min, and 5% from 18.1 to 20 min. Lung tissue: 5–55% from 0 to 3 min, 55–65% from 3 to 6 min, 65% from 6 to 8 min, 65–70% from 8 to 10 min, 70–100% from 10 to 18 min, and 5% from 18.1 to 20 min. Blood components: 5–55% from 0 to 5 min, 55–65% from 5 to 9 min, 65–75% from 9 to 13 min, 75–100% from 13 to 18 min s, and 5% from 18.1 to 20 min. Lung component: 5–55% from 0 to 3 min, 55–65% from 3 to 6 min, 65% from 6 to 8 min, 65–70% from 8 to 10 min, 70–100% from 13 to 18 min, and 5% from 18.1 to 20 min. The flow rate is 0.3 ml/min and the injection volume is 5 μl.

Import the collected metabolic profile data onto Progenesis QI software, and go through peak matching, peak extraction, normalization, data dimension reduction, and mass spectrum matrix information acquisition. Establish the variable projection importance VIP file. Select VIP>1 variables as potential biomarkers preliminary screening for these potential markers, further analysis of the trend of changes in the content of these potential markers for the sample. The second potential marker screening is performed based on the principle of significant differences in metabolites between groups (p < 0.05) to obtain potential biomarkers things. In this experiment, we first screened between the control group and the MPP group and preliminarily screened based on the principles of VIP>1 and p < 0.05. Then, we reviewed the initially selected compounds with the administration group to initially determine the difference.

Use the Masterview function in Peakview2.0 software to import the liquid quality acquisition data of the administered serum and the blank serum. Set the mass error <5 ppm; weight 30%, isotope difference <10%; weight 40%, Formula finder score >70%; weight 40%, sample/control is three times to deduct the interference of the blank serum endogenous components. Compare the remaining detected ions with the retention time, isotope kurtosis ratio, the accurate mass of the precursor ion, and the secondary mass spectrum of the compound detected in the extract of QB. If they are consistent, it is confirmed as the prototype component in QB.

Target screening of the obtained ingredients, in SwissTargetPrediction (principle: ligand structure similarity), DrugBank (principle: ligand structure similarity), PharmMapper (principle: pharmacophore matching), and BATMAN-TCM (principle: query component Possible component target interactions and the similarity of component target interactions known in the database). Set Probability ≥0.70 in SwissTargetPrediction, select “Homo sapiens” in “Choose an organism.” Select “0.70” in “Similarity threshold” in DrugBank, set “20” in BATMAN-TCM “Score cutoff,” and NormFit ≥0.70 in PharmMapper to get the final possible target.

In the eight databases of NCBI Gene, DrugBank, GeneCards, DisGeNET, TTD, PharmGKB, OMIM, and KEGG, search with “Pneumonia” and “Mycoplasma pneumonia” as keywords. In addition, the relevant gene chips were retrieved in GEO with “Pneumonia” and “Mycoplasma pneumonia” as keywords, and impute package v1.62.0 and limma package v3.44.3 of R v4.0.0 were used to impute missing values. Standardize processing and pass experience Bayesian model is used to determine the differential gene, and the differential gene threshold is set to p < 0.05 and |log2 fold change|≥1. In order to ensure the accuracy of disease target prediction, the disease targets of the abovementioned sources are intersected to obtain the disease target and the intersection target is obtained.

The contribution of the components to the C-T network is evaluated by introducing the Contribution Index. The PubMed search strategy is to use “Pneumonia” and “Mycoplasma pneumonia” in the Abstract/Title section. The English name of the active ingredient on the Internet and the commonly used keywords are jointly searched for documents from 1990 to December 1, 2020. Build a C-T network through screening.

The T-D network obtains the interaction between them through STRING. Set “organism” and “minimum required interaction score” to “Homo sapiens” and “medium confidence (>0.400).” Construct a T-D network by importing Cytoscape. Combine coreness, closeness, degree, average shortest path length, and betweenness to judge the importance of nodes. The filter condition is first> the mean degree. Then, it meets the mean values of degree, closeness, betweenness, and coreness at the same time and less than the mean value of the average shortest path length.

For molecular docking of the core target and core components, use GEMDOCK and select “200,” “Population size,” “Generation,” “Number of solutions,” and “Default setting” in the software operation interface. “Docking Accuracy Settings.” “7,” “2,” and “Docking standard,” the other docking parameters, are all default.

Using Progenesis QI 2.0 software, import the experimentally obtained disease treatment targets and biomarkers with significant callback effects to http://impala.molgen.mpg.de/ for comprehensive molecular pathway level analysis. Analyze genes and metabolites that carry out integration and association. Through the screening of the p value and Q value, the path with biological significance is screened to the limit of satisfying p < 0.05 and Q < 0.05 at the same time.

Take 50 mg of the rat lung tissue, add 500 μl of PBS and homogenize on ice bath, and centrifuge at 500 g for 3 min. Remove the supernatant, add 100 μl of the nucleoprotein extraction reagent and extract and centrifuge, take the supernatant, and add the loading buffer. It can be pyrolyzed and stored in aliquots. Prepare a separation gel and concentrated gel (accelerating coagulation at 37°C) in accordance with the requirements of the kit and load a small amount of the sample. Then use 80 mV for concentrated gel separation, 120 mV for separation gel separation, and 200 mA, 1.5 h for CN membrane transfer and membrane washing. It is then blocked with 5% skim milk for 0.5 h, blocked with the primary antibody overnight, and incubated with the secondary antibody for 2 h. After washing the membrane, add luminescent solution to take pictures.

The amount of mycoplasma in rats in the MPP group reached 1.15 × e⁷. Both the QB and AZM groups decreased, which was below the minimum detection limit of the kit (Figure 2A). In this case, it can be considered that there is no infectious ability.

A representative photomicrograph of HE staining is shown in Figure 3B. In the control group, the surface of the lung tissue was smooth and the alveolar wall structure was clear. In the pneumonia group, alveolar walls were thickened and eosinophils were occasionally increased. There are some foam cells and the inflammatory infiltration near the blood vessels forms a ring. These two conditions indicate that this has caused damage to the vascular structure. Both QB and AZM alveolar wall thickness decreased, and alveolar stenosis was not seen. The QB group recovered better, with tissue fluid exudation and inflammatory infiltration significantly contracted. This is related to its composition. We have confirmed that QB has good tissue repair ability (Meng et al., 2014), which is a unique advantage over AZM.

Using IL-10, TNF-α, and Th1/Th2 ratios as evaluation indicators (Figure 3A), the MPP, CN, QB, and AZM groups were collected to evaluate the effect of QB on MPP. Compared with the control group, the levels of IL-10 and TNF-α in the MPP group were significantly increased. The Th1/Th2 ratio showed an upward trend (p < 0.01). After QB and AZM treatment, the above indicators recovered (Figure 3B). It should be noted that the Th1/Th2 ratio is higher in the control group than in the pneumonia group, but IL-10 also has a rising trend in the MPP group. The differentiation of Th0 cells depends on the expression of the transcription factors T-bet and GATA-3 (Tibbitt et al., 2019). In the early stage of the disease, there is a Th1 advantage (Wu et al., 2019). With the development of the disease, the activation of innate immunity requires the activation of Th2 cells and is used to deactivate the precursor of IgE (Wynn, 2015). At the same time, it promotes the secretion of eosinophils, airway hyperresponsiveness, and mucus production (Hamilos, 2015). As a sensitizing inflammatory factor, long-term high expression of Th2 can not only inhibit the inflammatory response but also cause allergic tissue damage (Tibbitt et al., 2019). Th2 dominance exists in the later stage of the disease, as a means for the body to resist foreign invasion (Koh et al., 2001).

By constructing the OPLS-DA diagram, it is found that QB and MPP are well separated. QB and AZM are well separated from the MPP group and tend to be close to the CN group (Figure 4). MetaboAnalyst screened 105 compounds, and 56 were dosing callbacks (Table 1 and Figure 5).

Data collection of the rat serum and lung tissue was conducted, and 31 prototype components belonging to Qinbaiqingfei concentrated pills were identified. The results are shown in Table 2.

The components that enter the lungs and blood are mostly flavonoids and alkaloids. Flavonoids and alkaloids have strong effects of inhibiting microorganisms, antioxidation, anti-inflammatory, protecting blood vessels, and neuroprotection (Birt et al., 2001; Parnell et al., 2012; Meng et al., 2014; Xie F. et al., 2020; Ku et al., 2020; Rui et al., 2020; Porras et al., 2021). Take the flavonoid “wogonin” and the alkaloid “tuberostemoninol A” as examples to illustrate the identification process.

The wogonin quasi-molecular ion is m/z 285[M+H]⁺. This fragment has a relatively high abundance, and the fragmentation is divided into three ways. The quasi-molecular ion A-ring removes the methyl group attached to the methoxy group, and the oxygen forms at one end of the ion to form an end that connects to the benzene ring. Another cleavage pathway is the combination of the hydroxyl group of the A-ring and the methyl hydrogen to remove a molecule of water, and the remaining ethyl group will then fall off to form the fragment ion of m/z253. Continue to remove the carbonyl group to form m/z225 fragment ions. The third way is that under the bombardment of high-energy particles, the A-ring is connected with the methoxy group and the hydroxyl group to break off the carbon atoms to form a ring-opening rearrangement to form m/z213 fragment ions and continue to deoxidize to form m/z197 fragment ions or directly remove the B-ring benzene ring to form m/z137 fragments. The specific cracking method is shown in Figure 6A.

The cleavage of tuberostemoninol A is relatively simple. The two ways are first to continuously remove H₂O and CO to form m/z388 and m/z360 fragment ions. The other is to remove two carbon rings to form m/z200 rearranged ions, and continue to remove water molecules to form m/z182 fragment ions. The specific cracking method is shown in Figure 6B.

414 (Four hundred and fourteen) component targets were obtained by predicting the component targets of the four databases. And one related gene chip was retrieved from the database (Chen JW. et al., 2020): GSE40012 is provided by the Intensive Care Lab of the Nepean Clinical School of University of Sydney, Kingswood, NSW, Australia. The chip contains 190 gene expression data sets of influenza pneumonia patients, bacterial pneumonia patients, mixed bacterial and influenza pneumonia patients, systemic inflammatory response patients, and healthy control. We selected 61 cases of bacterial pneumonia patients and 36 cases of healthy control, a total of 97 cases for research. Use impute package v1.62.0 and limma package v3.44.3 of R v4.0.0 to impute missing values, standardize, and determine differential genes through empirical Bayesian models. The threshold of differential genes is set to p < 0.05 and |log2 fold change| ≥1. To ensure the accuracy of disease target prediction, the disease targets from the above sources were obtained by intersecting the disease targets, and 778 targets were obtained. Finally, the component targets and disease targets were re-intersected. Finally, 67 therapeutic targets were obtained (the above retrieval date ended at 2020.12.1).

After the C-T network deletes the isolated nodes and their connected edges, there is a total of 89 nodes, representing 22 compound components and 67 therapeutic targets; 129 edges represent 129 component–target relationships (Figure 7). The results show that the total CI of luteolin, guanosine, wogonin, and isorhamnetin is 93.79% (greater than 85%), which is the core component that contributes the most to the C-T network.

The T-D network finally screened and obtained six core therapeutic targets: SIRT1, IL-10, MMP9, CTNNB1, CRP, and EGFR (Table 3). The therapeutic target and the interaction relationship between the targets are imported into Cytoscape to construct a T-D network. After deleting the isolated nodes and connected edges, there are 67 nodes and 384 edges in the network (Figure 8A). Extract the target points greater than the mean value of the degree (11.463) to form the core target point network 1. After deleting the isolated nodes and connected edges, there are 25 nodes and 184 edges in the network (Figure 8B). Extract six core targets to form a core target network2. After deleting the isolated nodes and connected edges, there are six nodes and 13 edges in the network (Figure 8C). Extract six core targets and their directly connected nodes to form a core target network 3, which has 56 nodes and 341 edges (Figure 8D). There are only six core targets, but they are connected to 83.58% of the nodes (56/67) and 88.80% of the edges (341/384) of the T-D network. It shows that these six nodes play an extremely important role in maintaining the stability of the T-D network.

The four core components luteolin, guanosine, wogonin, and isorhamnetin and the six core targets SIRT1, IL-10, MMP9, CTNNB1, CRP, and EGFR were screened for molecular docking according to their binding affinity. It is generally believed that the absolute value of the connection affinity energy value is close to or greater than the original protein docking energy value, which means that the docking is completed. In this experiment, good results were obtained except for CRP and the protein configuration of IL-10 was not found in the database, considering that it plays a significant role as a signal factor involved in inhibiting inflammation, promoting humoral immunity, and tissue repair (Hendriks et al., 2004; Zhang et al., 2019; Singh et al., 2020). Finally, as shown in Figure 8, it demonstrated good binding ability (Figure 9A). The docking effect between the core target and the core component is shown in Figure 9B.

The selected core biomarkers and core target enrichment analysis are integrated with pathway over-representation analysis. The biologically significant pathways satisfying p < 0.05 and Q < 0.05 are shown in the following table (Table 4). Through the combined analysis of metabolomics and network pharmacology, it can be seen that QB mainly interfere with the G alpha (q) signaling events, GPCR downstream signaling, signal transduction, signaling by GPCR, spinal cord injury, GnRH signaling pathway, relationship between inflammation COX-2 and EGFR, and other interventions in the disease process, mainly the intervention of G-protein–coupled receptors, and EGFR. Through the analysis of the combined path of the two, G-protein–coupled receptors can directly promote the activation of the PI3K pathway (Li et al., 2021), while EGFR directly promotes the expression of Twist2 (Lehman et al., 2020). Both the STAT3 and PI3K pathways act as the direct upstream of the NF-κB pathway, directly or through Twist2, promoting the activation of the NF-κB pathway (Figure 10).

Through the analysis of the combined path of biomarkers and core disease targets, it is further obtained that its main function and the intracellular NF-κB pathway are found through further analysis, and TLR-MyD88-NF-κB p65 is the most critical signaling pathway for the pathogenesis and treatment of MPP. The TLR4/MyD88/NF-κB signaling pathway is involved in the body’s immune response and alveolar inflammation. TLR4 is activated after body injury, which further promotes the expression of downstream factors MyD88 and NF-κB and promotes the expression of inflammatory factors. This causes the alveolar epithelial cells to synthesize and secrete pulmonary inflammation–related cytokines and alveolar epithelial cell apoptosis, leading to the occurrence of pneumonia (Wang et al., 2020).

The protein expression detection was carried out according to the Western Blot method. The results are shown in Figure 11A. Compared with the blank group, the protein band of the model group was deepened and there was a trend of callback after administration. The callback trend of M and H is similar or better than that of the CN.

Using the gray value to analyze the protein expression level, the high dose has a better callback effect on NF-κB p65 than azithromycin (p < 0.05). The regulation of the upstream protein shows an obvious trend of superiority with increasing dose. The middle dose inhibited MyD88 the strongest. This reflects that a high dose has the advantage of strongly inhibiting the NF-κB pathway, but does not directly inhibit the agonistic ability of toll-like receptors. It may even have a certain activation ability. In this case, the middle dose is considered the optimal dose (Figure 11B).