# Integrated Network Pharmacology and Gut Microbiota Study on the Mechanism of Huangqin Decoction in Treatment Diabetic Enteritis

Authors: Xiaomin Xu; Cheng Fang; Fang Lu; Shumin Liu
Journal: Applied Bionics and Biomechanics
 (2022)

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## Abstract

Objective. Using network pharmacology and gut microbiota sequencing to investigate the probable mechanism of Huangqin decoction in the treatment of Diabetic enteritis (DE). Methods. The mechanism of Huangqin decoction on DE was studied by combining network pharmacology and gut microbiota sequencing analysis. The core components and possible targets of Huangqin decoction were analyzed by network pharmacology. The effect of Huangqin decoction on microorganisms was investigated by gut microbiota sequencing. Results. The results of gut microbiota sequencing analysis showed the abundance of TM7, Tenericutes, Chloroflexi, Cyanobacteria, Acidobacteria, WS6, [Prevotella], Helicobacter, Prevotella, Lactococcus, and Anaeroplasma in the Huangqin decoction group had a significant downward. Using a network pharmacology-related database, 141 main active components of Huangqin decoction were identified, as well as 256 corresponding component targets and 1777 corresponding disease targets; the disease targets and component targets were mapped, and topological analysis was used to determine the potential of Huangqin decoction in the treatment of DE. There were 156 targets, of which the top 20 genes were selected for GO and KEGG. The KEGG results showed that 134 pathways were enriched, which was partially consistent with the metabolic pathways of gut microbiota sequencing analysis. Conclusion. The results show that Huangqin decoction can inhibit the expression of inflammatory factors and related inflammatory pathways in intestinal epithelial cells, thereby regulating the structure of intestinal flora. Using picurst2 for functional prediction and metabolic pathway statistics, seven metabolic pathways were obtained consistent with gut microbiota sequencing, and the NOD-like receptor signaling pathway may be its potential molecular mechanism. These results help to understand the mechanism of Huangqin decoction on DE and provide the theoretical basis for further study of Huangqin decoction.


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## Body

## 1. Introduction

Diabetes mellitus is a widespread disease. According to the International Diabetes Federation, it affects 463 million people worldwide with an increasing prevalence [1]. Diabetes is an important public health burden, mainly because of its cardiovascular, renal, and neurological complications. Furthermore, many people with diabetes have upper gastrointestinal (GI) symptoms as well as motor abnormalities. Up to 50% of individuals with type 1 and type 2 diabetes suffer dyspepsia and gastroparesis or are asymptomatic in certain cases, impacting 50% of delayed gastric emptying [2]. These two clinical symptoms share similar pathogenic mechanisms, including autonomic neuropathy, changes in the enteric nervous system, and histological abnormalities. Dyspeptic symptoms are common in people with diabetes, and they are part of the so-called diabetic bowel disease [3]. Studies have shown that the intestinal environment of T2DM patients is in a chronic low-grade inflammatory response [4]. Some literature has shown that intestinal flora is closely related to the systemic chronic inflammatory response. Oral antibiotics can regulate the intestinal flora of diabetic patients, reduce inflammation in the body, and improve the phenotype of T2DM. It is worth noting that oral antibiotics can inhibit intestinal flora. Antibiotics may also cause damage to the beneficial intestinal flora, leading to an imbalance of intestinal flora, which may have adverse effects on diabetic patients [5].Huangqin decoction is a traditional Chinese medicine formula in the classic Chinese medical book “Treatise on Febrile Diseases” written by Zhang Zhongjing of the Eastern Han Dynasty. It has a history of nearly 1800 years and is widely used in the clinical treatment of intestinal diseases such as ulcerative colitis [6–9]; it is made by frying four traditional Chinese medicines: Scutellaria baicalensis, Paeonia lactiflora, jujube, and licorice. In Huangqin decoction, according to traditional Chinese theory, Scutellariae is the king, which is bitter, cold, hardy yin, and clears heat in the interior; Shaoyao is the minister in this medicine, slightly bitter and sour, relieving acute pain, astringing yin, and nourishing. Preserving yin to stop dysentery is essential for treating dysentery; licorice and jujube benefit qi and neutralize the middle. Adjust and supplement the righteousness [10]. Scutellaria baicalensis decoction is precise and has less medicinal flavor. Scutellaria baicalensis has the effect of clearing heat and relieving dysentery, Paeonia lactiflora has the functions of astringing Yin, nourishing and relieving pain, and licorice and jujube can help neutralize the stomach, invigorate the spleen and stop diarrhea, and nourish qi and nourish liquid. On one side, it also has the methods of clearing heat, detoxifying, drying dampness, cooling blood, and nourishing sweetness. Therefore, it is called “the ancestral prescription for treating dysentery.”Pharmacological studies have shown that Huangqin decoction has anti-inflammatory, antibacterial, analgesic, antipyretic, sedative, and other effects. In recent years, with extensive research on Huangqin decoction, it has been used to treat acute lung injury, colon cancer, gastric cancer, leukemia, and other diseases according to its anti-inflammatory, antiproliferation, and mucosal protective effects [11–14]. It is mainly used to treat intestinal inflammation and is considered to have a significant effect on intestinal inflammation and mucosal protection in the intestinal tract. It was found that gut flora played a critical role in the investigation of its mechanism of action [15]. It was discovered that the intestinal flora can not only act directly on the intestinal mucosa, exerting anti-inflammatory and mucosal protective effects, but can also affect the metabolism of the components in Huangqin decoction, metabolizing the more difficult-to-absorb components such as baicalin into the more easily-absorbable components such as baicalein, thereby augmenting the therapeutic effect of Huangqin decoction [7]. Simultaneously, as experimental animal research has progressed and clinical use of Huangqin decoction has expanded, it has been discovered that it has a favorable therapeutic impact in the treatment of ulcerative colitis and other disorders [16].Therefore, based on network pharmacology and gut microbiota, this study will investigate the mechanism of Huangqin decoction in the treatment of DE.


## 2. Materials and Methods

### 2.1. Experimental Materials

Experimental materials are as follows: Scutellaria baicalensis; Baishao; Jujube; Licorice (all purchased from Hebei Quantai Pharmaceutical Co., Ltd.); DNeasy PowerSoil Kit (QIAGEN, Netherlands); Quant-iT PicoGreen dsDNA Assay Kit (Invitrogen, USA); Agcout AMPure Beads (Beckman Coulter, USA); PicoGreen dsDNA Kit (Invitrogen, USA); CD45 antibody (Santa, USA); PV9004 secondary antibody (Zhongshan, Beijing); DAB chromogen (Zhongshan, Beijing); Quantifluor-ST fluorometer (Promega); high-throughput sequencer (Illumina, USA); low-temperature medical freezer (Haier Co., Ltd.); gdsAUTO520 gel imaging system (BG Corp., USA); DYY-6C electrophoresis instrument (ABI, USA); paraffin embedding machine (LeicaEG1150H); paraffin microtome (Leica RM 2125 RTS); paraffin microtome (Leica HI1220); and fluorescence microscope (OLYMPUS DP80).Preparation of Huangqin decoction: weigh the decoction pieces of Radix Scutellariae, Radix Paeoniae Alba, jujube, and licorice in the ratio of 3 : 2 : 2 : 2, add 10 times the total weight of water, soak for 30 minutes, decoct for 1 hour, filter the filtrate, add 8 times the volume of water to decoct for 1 hour, filter the filtrate, combine the two filtrates, and concentrate to 1.5 g/mL, and the low-dose concentration of Huangqin decoction is 0.5 g/mL.


### 2.2. Experimental Animals

The 48 male db/db mice and 8 db/m mice were purchased from Aiermaite Technology Co., Ltd. (animal certificate number: no. 202009670). The mice were housed in a single cage in a clean-grade barrier system with free food. Drinking water, temperature controlled at 20-26°C, humidity controlled at 40-70% kept 12/12 hours of animal lighting alternating in the light and dark cycle, changing cages, and bedding once a week. All animal experiment-related operations were followed relevant regulations of the Laboratory Animal Ethics Committee of Heilongjiang University of Traditional Chinese Medicine. The db transgenic mice were adaptively fed for one week after purchase, and 2 random blood glucose tests were performed. db/db mice with blood glucose values higher than 11.1 mol/L were selected for grouping and divided into a model group and a low-dose Huangqin decoction group (3.75 g/kg) and Huangqin decoction high-dose group (11.25 g/kg), and db/m mice were the blank group; after grouping, the mice were administered once a day, and the administration volume was 7.5 mg/kg for 8 weeks.


### 2.3. Sample Collection and Processing

Before the sacrifice, body weights of each mice in tested groups were recorded. After the mice were sacrificed, the cecum contents were taken and placed in a sterile tube in time. After sealing, they were quickly frozen with liquid nitrogen and transferred to a refrigerator at -80°C for storage for subsequent analysis. The epididymal adipose tissue was stored at −75°C. A small portion of the liver tissue was placed in formaldehyde solution of 4% concentration, and the remaining liver tissue was stored at −80°C for further tests. Similarly, intestinal tissues were collected and frozen for further experimental purposes.


### 2.4. H&E Staining

The cecal tissues of mice were dehydrated and waxed; slice and paste the cut film on the cover glass to dry; before staining, the paraffin in the section must be removed with xylene, then through high concentration to low concentration alcohol, and finally, into distilled water to dye; put the slices that have been put into distilled water into hematoxylin aqueous solution for dyeing for several minutes; color separation in acid water and ammonia water for several seconds, respectively; after washing with running water for 1 h, add distilled water for a moment; dehydrate in 70% and 90% alcohol for 10 minutes, respectively; add alcohol eosin staining solution for 2~3 minutes.


### 2.5. Immunohistochemistry

According to the consensus criteria developed for type 1 diabetes, an individual can be diagnosed with insulitis when ≥15 CD45+ cells are found within the parenchyma or in the islet-exocrine interface in ≥3 islets. The changes of expression of CD45 in the cecal tissue of diabetic cardiomyopathy mice were taken from the cecum tissues, fixed with wax blocks, and then, cut and sealed for 78 degrees in the oven. After the morning was taken out, the xylene was fixed for 10 min, dehydrated with alcohol, then dehydrated 5~10 min; distilled water was washed for 2 times, 3~5 min/times, 3% H2O2 blocked 10 min; distilled water was cleaned 2 times, 3~5 min/times. Citric acid repair (add the tissue after boiling water, stop heating after steam, open the air valve after 2~3 minutes, open the cover after 7 minutes, and take out the tissue), cool for about 50 minutes, wash the tissue with PBS for 3 times after cooling, 5 min/time, and add primary antibody (100 μ 50). Incubate overnight at 4°C, wash PBS for 3 times the next day, 5 min/time, and add secondary antibody (100 μ 50). Incubate at room temperature for 30 min, wash with PBS for 3 times, 5 min/time, DAB color development for 5-10 minutes (block the color development after observing the color development under the microscope), hematoxylin reappears for 1 min after washing with water for 3-5 minutes, wash with water for 10-15 minutes after observing the dyeing degree under the microscope (if it is not ideal, it can be dyed repeatedly), dehydrate and dealcoholic for 2-3 min/time after washing with distilled water, and finally, seal the neutral gum in the incubator at 87°C for 2-3 days. The film is taken under the microscope and stored in the computer.


### 2.6. 16S rRNA Gene Sequencing

#### 2.6.1. DNA Extraction from Stool Samples

The Dneasy powerOil kit of QIAGEN company was used for extraction and DNA detection. The absorbance values of the extracted DNA were measured at 260 nm and 280 nm, respectively, by fluorescence spectrophotometer, the concentration of DNA was calculated, and the quality of DNA was detected by agarose gel electrophoresis of 1%. Adjust the concentration of DNA solution according to the results, and then, store the adjusted DNA working solution in a 4°C refrigerator and the storage solution in a -20°C refrigerator.


#### 2.6.2. 16S rRNA Gene Amplicon Sequencing

The v3-v4 region of the 16S rRNA gene of flora was amplified by PCR with forward primer (ACTCCTACGGGAGGCAGCA) and reverse primer (GGACTACHVGGGTWTCTAAT). The sample-specific 7 bp barcode was integrated into primers for multiple sequencing. The PCR component contains 5μL Q5 reaction buffer (5) ×, 5 μL Q5 high fidelity GC buffer (5) ×, zero point two five μL Q5 high fidelity DNA polymerase (5μ)/μL), 2 μL (2.5 mM) dNTPs, 1 μL (10 μM), DNA template solution 2 μL, and ddH2O 8.75 μL. The thermal cycle includes initial denaturation at 98°C for 2 min, then denaturation at 98°C for 15 s, annealing at 55°C for 30 s, extension at 72°C for 30 s, 25 cycles, and final extension at 72°C for 5 min. PCR amplicons were purified with Agcout AMPure Beads and quantified using PicoGreen dsDNA detection kit (Invitrogen, Carlsbad, CA, USA). After a single quantification step, equal amounts of amplified products were collected, and paired-end 2×300bp sequencing was performed using the Illumina MiSeq platform and MiSeq Kit v3 at Shanghai Parsono Biotechnology (Shanghai, China) Co., Ltd.


#### 2.6.3. Sequencing Data Processing

The data obtained were dedried by DADA2 and Vsearch using the Parsono gene cloud platform. The R language script was used to calculate the high-quality sequences’ length distribution in all samples. The QIIME2 analysis software was used to perform species taxonomic annotation, taxonomic composition analysis, and diversity analysis, and random forest analysis between multiple groups of samples was used to find the different flora between groups. Finally, the PICRUSt2 analysis software was used to predict functional potential [17].


### 2.7. Data Processing

Statistical analysis was performed using the SPSS 20.0 software. Wilcoxon rank sum test was used to compare the two groups of samples, and Kruskal Wallis test was used to compare the three groups of samples.


### 2.8. Network Pharmacology Analysis

Using the TCMSP database, the active components of Huangqin decoction were screened under the conditions of oral absorption and utilization (OB) ≥30% anddrugclassDL≥18%, and the screened components were supplemented in combination with literature reports. The obtained components were sequentially imported into the TCMSP database, the target was predicted, and the protein name of the relevant target was obtained, which was imported into the Uniprot database, and the gene name was transformed to obtain the relevant target of traditional Chinese medicine. The GeneCards database was used to search “DE,” and the intersection of the disease target and the drug target was used as the prediction target of the drug acting on the disease. Gene and protein names were standardized using the Uniprot database (http://www.uniprot.org/). The target information of the interaction between Huangqin decoction and DE obtained above was imported into the STRING database (https://string-db.org/) to obtain the relationship between potential targets. The Cytoscape v3.8.2 software was used to visualize and analyze the protein interaction analysis results of the STRING database to construct a protein-protein interaction network. The clusterProfiler package in R language was used to perform GO functional annotation enrichment analysis and KEGG pathway analysis for common targets. The enriched pathways are the potential pathways for the drug to play a therapeutic role, and a histogram is drawn to visualize it.


## 2.1. Experimental Materials

Experimental materials are as follows: Scutellaria baicalensis; Baishao; Jujube; Licorice (all purchased from Hebei Quantai Pharmaceutical Co., Ltd.); DNeasy PowerSoil Kit (QIAGEN, Netherlands); Quant-iT PicoGreen dsDNA Assay Kit (Invitrogen, USA); Agcout AMPure Beads (Beckman Coulter, USA); PicoGreen dsDNA Kit (Invitrogen, USA); CD45 antibody (Santa, USA); PV9004 secondary antibody (Zhongshan, Beijing); DAB chromogen (Zhongshan, Beijing); Quantifluor-ST fluorometer (Promega); high-throughput sequencer (Illumina, USA); low-temperature medical freezer (Haier Co., Ltd.); gdsAUTO520 gel imaging system (BG Corp., USA); DYY-6C electrophoresis instrument (ABI, USA); paraffin embedding machine (LeicaEG1150H); paraffin microtome (Leica RM 2125 RTS); paraffin microtome (Leica HI1220); and fluorescence microscope (OLYMPUS DP80).Preparation of Huangqin decoction: weigh the decoction pieces of Radix Scutellariae, Radix Paeoniae Alba, jujube, and licorice in the ratio of 3 : 2 : 2 : 2, add 10 times the total weight of water, soak for 30 minutes, decoct for 1 hour, filter the filtrate, add 8 times the volume of water to decoct for 1 hour, filter the filtrate, combine the two filtrates, and concentrate to 1.5 g/mL, and the low-dose concentration of Huangqin decoction is 0.5 g/mL.


## 2.2. Experimental Animals

The 48 male db/db mice and 8 db/m mice were purchased from Aiermaite Technology Co., Ltd. (animal certificate number: no. 202009670). The mice were housed in a single cage in a clean-grade barrier system with free food. Drinking water, temperature controlled at 20-26°C, humidity controlled at 40-70% kept 12/12 hours of animal lighting alternating in the light and dark cycle, changing cages, and bedding once a week. All animal experiment-related operations were followed relevant regulations of the Laboratory Animal Ethics Committee of Heilongjiang University of Traditional Chinese Medicine. The db transgenic mice were adaptively fed for one week after purchase, and 2 random blood glucose tests were performed. db/db mice with blood glucose values higher than 11.1 mol/L were selected for grouping and divided into a model group and a low-dose Huangqin decoction group (3.75 g/kg) and Huangqin decoction high-dose group (11.25 g/kg), and db/m mice were the blank group; after grouping, the mice were administered once a day, and the administration volume was 7.5 mg/kg for 8 weeks.


## 2.3. Sample Collection and Processing

Before the sacrifice, body weights of each mice in tested groups were recorded. After the mice were sacrificed, the cecum contents were taken and placed in a sterile tube in time. After sealing, they were quickly frozen with liquid nitrogen and transferred to a refrigerator at -80°C for storage for subsequent analysis. The epididymal adipose tissue was stored at −75°C. A small portion of the liver tissue was placed in formaldehyde solution of 4% concentration, and the remaining liver tissue was stored at −80°C for further tests. Similarly, intestinal tissues were collected and frozen for further experimental purposes.


## 2.4. H&E Staining

The cecal tissues of mice were dehydrated and waxed; slice and paste the cut film on the cover glass to dry; before staining, the paraffin in the section must be removed with xylene, then through high concentration to low concentration alcohol, and finally, into distilled water to dye; put the slices that have been put into distilled water into hematoxylin aqueous solution for dyeing for several minutes; color separation in acid water and ammonia water for several seconds, respectively; after washing with running water for 1 h, add distilled water for a moment; dehydrate in 70% and 90% alcohol for 10 minutes, respectively; add alcohol eosin staining solution for 2~3 minutes.


## 2.5. Immunohistochemistry

According to the consensus criteria developed for type 1 diabetes, an individual can be diagnosed with insulitis when ≥15 CD45+ cells are found within the parenchyma or in the islet-exocrine interface in ≥3 islets. The changes of expression of CD45 in the cecal tissue of diabetic cardiomyopathy mice were taken from the cecum tissues, fixed with wax blocks, and then, cut and sealed for 78 degrees in the oven. After the morning was taken out, the xylene was fixed for 10 min, dehydrated with alcohol, then dehydrated 5~10 min; distilled water was washed for 2 times, 3~5 min/times, 3% H2O2 blocked 10 min; distilled water was cleaned 2 times, 3~5 min/times. Citric acid repair (add the tissue after boiling water, stop heating after steam, open the air valve after 2~3 minutes, open the cover after 7 minutes, and take out the tissue), cool for about 50 minutes, wash the tissue with PBS for 3 times after cooling, 5 min/time, and add primary antibody (100 μ 50). Incubate overnight at 4°C, wash PBS for 3 times the next day, 5 min/time, and add secondary antibody (100 μ 50). Incubate at room temperature for 30 min, wash with PBS for 3 times, 5 min/time, DAB color development for 5-10 minutes (block the color development after observing the color development under the microscope), hematoxylin reappears for 1 min after washing with water for 3-5 minutes, wash with water for 10-15 minutes after observing the dyeing degree under the microscope (if it is not ideal, it can be dyed repeatedly), dehydrate and dealcoholic for 2-3 min/time after washing with distilled water, and finally, seal the neutral gum in the incubator at 87°C for 2-3 days. The film is taken under the microscope and stored in the computer.


## 2.6. 16S rRNA Gene Sequencing

### 2.6.1. DNA Extraction from Stool Samples

The Dneasy powerOil kit of QIAGEN company was used for extraction and DNA detection. The absorbance values of the extracted DNA were measured at 260 nm and 280 nm, respectively, by fluorescence spectrophotometer, the concentration of DNA was calculated, and the quality of DNA was detected by agarose gel electrophoresis of 1%. Adjust the concentration of DNA solution according to the results, and then, store the adjusted DNA working solution in a 4°C refrigerator and the storage solution in a -20°C refrigerator.


### 2.6.2. 16S rRNA Gene Amplicon Sequencing

The v3-v4 region of the 16S rRNA gene of flora was amplified by PCR with forward primer (ACTCCTACGGGAGGCAGCA) and reverse primer (GGACTACHVGGGTWTCTAAT). The sample-specific 7 bp barcode was integrated into primers for multiple sequencing. The PCR component contains 5μL Q5 reaction buffer (5) ×, 5 μL Q5 high fidelity GC buffer (5) ×, zero point two five μL Q5 high fidelity DNA polymerase (5μ)/μL), 2 μL (2.5 mM) dNTPs, 1 μL (10 μM), DNA template solution 2 μL, and ddH2O 8.75 μL. The thermal cycle includes initial denaturation at 98°C for 2 min, then denaturation at 98°C for 15 s, annealing at 55°C for 30 s, extension at 72°C for 30 s, 25 cycles, and final extension at 72°C for 5 min. PCR amplicons were purified with Agcout AMPure Beads and quantified using PicoGreen dsDNA detection kit (Invitrogen, Carlsbad, CA, USA). After a single quantification step, equal amounts of amplified products were collected, and paired-end 2×300bp sequencing was performed using the Illumina MiSeq platform and MiSeq Kit v3 at Shanghai Parsono Biotechnology (Shanghai, China) Co., Ltd.


### 2.6.3. Sequencing Data Processing

The data obtained were dedried by DADA2 and Vsearch using the Parsono gene cloud platform. The R language script was used to calculate the high-quality sequences’ length distribution in all samples. The QIIME2 analysis software was used to perform species taxonomic annotation, taxonomic composition analysis, and diversity analysis, and random forest analysis between multiple groups of samples was used to find the different flora between groups. Finally, the PICRUSt2 analysis software was used to predict functional potential [17].


## 2.6.1. DNA Extraction from Stool Samples

The Dneasy powerOil kit of QIAGEN company was used for extraction and DNA detection. The absorbance values of the extracted DNA were measured at 260 nm and 280 nm, respectively, by fluorescence spectrophotometer, the concentration of DNA was calculated, and the quality of DNA was detected by agarose gel electrophoresis of 1%. Adjust the concentration of DNA solution according to the results, and then, store the adjusted DNA working solution in a 4°C refrigerator and the storage solution in a -20°C refrigerator.


## 2.6.2. 16S rRNA Gene Amplicon Sequencing

The v3-v4 region of the 16S rRNA gene of flora was amplified by PCR with forward primer (ACTCCTACGGGAGGCAGCA) and reverse primer (GGACTACHVGGGTWTCTAAT). The sample-specific 7 bp barcode was integrated into primers for multiple sequencing. The PCR component contains 5μL Q5 reaction buffer (5) ×, 5 μL Q5 high fidelity GC buffer (5) ×, zero point two five μL Q5 high fidelity DNA polymerase (5μ)/μL), 2 μL (2.5 mM) dNTPs, 1 μL (10 μM), DNA template solution 2 μL, and ddH2O 8.75 μL. The thermal cycle includes initial denaturation at 98°C for 2 min, then denaturation at 98°C for 15 s, annealing at 55°C for 30 s, extension at 72°C for 30 s, 25 cycles, and final extension at 72°C for 5 min. PCR amplicons were purified with Agcout AMPure Beads and quantified using PicoGreen dsDNA detection kit (Invitrogen, Carlsbad, CA, USA). After a single quantification step, equal amounts of amplified products were collected, and paired-end 2×300bp sequencing was performed using the Illumina MiSeq platform and MiSeq Kit v3 at Shanghai Parsono Biotechnology (Shanghai, China) Co., Ltd.


## 2.6.3. Sequencing Data Processing

The data obtained were dedried by DADA2 and Vsearch using the Parsono gene cloud platform. The R language script was used to calculate the high-quality sequences’ length distribution in all samples. The QIIME2 analysis software was used to perform species taxonomic annotation, taxonomic composition analysis, and diversity analysis, and random forest analysis between multiple groups of samples was used to find the different flora between groups. Finally, the PICRUSt2 analysis software was used to predict functional potential [17].


## 2.7. Data Processing

Statistical analysis was performed using the SPSS 20.0 software. Wilcoxon rank sum test was used to compare the two groups of samples, and Kruskal Wallis test was used to compare the three groups of samples.


## 2.8. Network Pharmacology Analysis

Using the TCMSP database, the active components of Huangqin decoction were screened under the conditions of oral absorption and utilization (OB) ≥30% anddrugclassDL≥18%, and the screened components were supplemented in combination with literature reports. The obtained components were sequentially imported into the TCMSP database, the target was predicted, and the protein name of the relevant target was obtained, which was imported into the Uniprot database, and the gene name was transformed to obtain the relevant target of traditional Chinese medicine. The GeneCards database was used to search “DE,” and the intersection of the disease target and the drug target was used as the prediction target of the drug acting on the disease. Gene and protein names were standardized using the Uniprot database (http://www.uniprot.org/). The target information of the interaction between Huangqin decoction and DE obtained above was imported into the STRING database (https://string-db.org/) to obtain the relationship between potential targets. The Cytoscape v3.8.2 software was used to visualize and analyze the protein interaction analysis results of the STRING database to construct a protein-protein interaction network. The clusterProfiler package in R language was used to perform GO functional annotation enrichment analysis and KEGG pathway analysis for common targets. The enriched pathways are the potential pathways for the drug to play a therapeutic role, and a histogram is drawn to visualize it.


## 3. Results

### 3.1. H&E Staining

The degree of damage to the integrity of the intestinal barrier reflects the barrier function of the intestinal mucosa to a certain extent. In this study, the length of the intestinal mucosal villi and the thickness of the muscularis layer were observed by H&E staining to determine the change of the intestinal mucosal barrier function, as shown in Figure1. H&E staining showed that the villus length and muscle thickness of the intestinal mucosa in the blank group were normal (Figure 1(a)); the villi were shortened, and the muscle layer thickness was decreased in the model group (Figure 1(b)); the symptoms of the Huangqin decoction group were more severe than those of the model group. The villus length was similar to that of the blank group, but the thickness of the muscle layer was still significantly lower than that of the blank group (Figure 1(c)).Figure 1
Influence of intestinal mucosal barrier integrity in DE model mice. Note: histomorphological changes in the cecum of db mice (H&E staining) (×20). (a) Intestinal mucosal structure of blank group; (b) intestinal mucosal structure of model group; (c) intestinal mucosal structure of Huangqin decoction group.
(a)(b)(c)


### 3.2. Immunohistochemistry

The intestinal mucosa was sectioned, and the differences in leukocyte infiltration were observed by CD45 staining, as shown in Figure2. In the blank group, only a few CD45+ cells were infiltrated. Compared with the blank group, the infiltration of CD45+ cells in the model group were significantly increased, while that in the Huangqin decoction group was significantly less than that in the model group (Figures 2(a)–2(c)).Figure 2
Immunohistochemistry of CD45 in the intestinal mucosa of DE mice. Note: immunohistochemical staining of cecal tissue of db mice to observe the changes of CD45 (×40); (a) CD45+ in the intestinal mucosa of the blank group; (b) CD45+ of intestinal mucosa in the model group; (c) effect of Huangqin decoction group on intestinal mucosa CD45+.
(a)(b)(c)


### 3.3. 16S rRNA Gene Sequencing

#### 3.3.1. Classification and Analysis of OTUs (Operational Taxonomic Units) of Gut Microbiota in Samples from Each Group

The blank group has a total of 5450 OTUs and 3297 unique OTUs, the model group has a total of 5678 OTUs and 3561 unique OTUs, the Scutellaria baicalensis soup has a total of 5580 OTUs and 3747 unique OTUs, and the three groups have a total of 837 OTUs, as shown in Figure3. Compared with the blank group, the number of OTUs in the model group was significantly increased; compared with the model group, the number of OTUs in the administration group was significantly downregulated.Figure 3
(a) Venn diagram of OTUs of gut microbiota in each group (K is blank group; M is model group; Q is Huangqin decoction group); (b–d) sparse curve of the intestinal flora of rat samples in each group (b), species accumulation curve (c), and rank-abundance curve (d).
(a)(b)(c)(d)


#### 3.3.2. Alpha Diversity Analysis of Gut Microbiota in Each Group

As shown in Figures3(b)–3(d), the diversity of samples in each group is almost saturated, indicating that the sequencing depth is sufficient and the sample size of each group is sufficient to reflect the richness of the community. The biodiversity indexes of the three groups of samples were compared, as shown in Table 1. Compared with the blank group, the intestinal flora Chao1, Faith_pd, Goods_coverage, Observed_species, Pielou_e, Shannon, and Simpson indexes of mice in the model group all showed a downward trend. After the administration of Huangqin decoction, all diversity indices showed a downward trend, as shown in Table 1.Table 1
Microbial diversity index of gut microbiota in each group.
GroupChao1Faith_pdGoods_coverageObserved_speciesPielou_eShannonSimpsonControl group2012.33494.26890.98898721731.780.73173987.827020.9813612Model group1875.29888.866880.98861641586.60.71248867.565110.9707218Huangqin decoction group1921.7591.231560.9894441669.040.71562887.6923420.97627


#### 3.3.3. Taxonomic Composition Analysis of Gut Microbiota in Each Group

Through the analysis of Figures4(a) and 4(b), it is found that Bacteroides and Firmicutes are the most important phyla at the phylum level, followed by tenerictes, Proteobacteria, and actinobacteria. These five phyla account for a very high proportion of the whole phyla and belong to the dominant phyla. Compared with the blank group, in the model group, bacteroidea (54.25% → 43.77%), Firmicutes (38.06% → 47.37%), Firmicutes (2.48% → 5.55%), proteus (2.98% → 0.01%), actinomycetes (0.89% → 0.73%), and Firmicutes/Bacteroidetes ratio in the blank group was 0.70, and Firmicutes/Bacteroidetes ratio in the model group was 1.08. It is suggested that the dominant flora of model group mice and blank group mice has changed significantly in the structure; compared with the model group, the ratio of Firmicutes/Bacteroidetes in Huangqin decoction group was 1.14, suggesting that Huangqin decoction could regulate the dominant flora of db/db mice.Figure 4
Abundance distribution of intestinal flora in each group of rat samples at phylum (a) and genus (b) levels; analysis of the distribution of bacterial community structure in each group of rat samples (c).
(a)(b)(c)From the subordinate level, through cluster analysis of the absolute dominant bacterial genera in the top 20, find and analyze the relative abundance of different bacterial genera, namely, Bacteroides, oscillospira, anaeroplasma, Lactobacillus, odoribacter Ruminococcus, Desulfovibrio, Lactococcus, dehalobacterium, rikenella, alistipes, adlercreutzia, candidatus_arthromitus, coprococcus Helicobacter, parabacteroides, and Staphylococcus.


#### 3.3.4. Influence of Gut Microbiota on Beta Diversity Analysis

In this study, PCoA analysis was used to investigate the differences in the beta diversity of rat gut microbiota. There was no significant difference in the diversity of bacterial community structure in the blank, model, and Huangqin decoction groups, and the communities in each group fell within their respective ranges, with strong similarity. The results showed that compared with the blank group, the microflora of the mice in the model group were significantly separated, indicating that the beta diversity of the two groups was significantly different. After administration of Huangqin decoction, it tended to the blank group, indicating that the beta diversity of the Huangqin decoction group was similar to that of the normal group. After administration of Huangqin decoction, it had a certain effect on the body of DE mice, as shown in Figure4(c).Differences between groups were analyzed by (permutational multivariate analysis of variance (PERMANOVA)). The results showed that the differences in the bacterial community structure diversity within the blank group, the model group, and the Huangqin decoction administration group were significantly smaller than the differences between the groups, suggesting that the blank group, the model group, and the Huangqin decoction administration group had significant effects on the bacterial community structure diversity. There were significant between-group differences, see Table2.Table 2
Statistical chart of difference analysis between groups.
Group 1Group 2Sample sizePermutationsPseudoFP valueq valueAll—159992.9354130.001—KM109992.4081840.0110.012KQ109993.4503990.0110.012MQ109992.9866950.0120.012


#### 3.3.5. Gut Microbiota Difference Analysis

Using the method of random forest analysis, we screened the different gut microbiota among the experimental groups. We found that the gut microbiota with differences at the phylum level included deferribacteris, Proteobacteria, tenericutes, Firmicutes, acidobacteria, cyanobacteria, Actinobacteria, Bacteroidetes, fusobacteria, tm7-3, Chloroflexi, and WS6. The gut microbiota with differences at the class level include Mollicutes, epsilon Proteobacteria, deferribacterias, deltaproteobacteria, Actinobacteria, tm7-3, gamma Proteobacteria, bacteroidia, coriobacteria, clostridia, erysipelotrichi, betaproteobacteria, Alphaproteobacteria, synechococcophycedeae, bacilli, [chloracidobacteria], Flavobacteria Chloroflexi,TM7-1, and Fusobacteriia. The gut microbiota with differences at the order level include deferribacteriales, desulfovibrionales, bacillales, actinomyceteales, campylobacterales, bifidobacteria, caulobacterales, enterobacteria, erysipelotrichales, neisseriales, cw040, Chlorophyta, clostridiales, burkholderiales, Rickettsiales, bacteroidales, mycoplastiales, coriobacteriales Anaeroplasmatales, and Pseudanabaenales. The gut microbiota with differences at the family level include [paraprevotellaceae], corynebactriaceae, Veillonella CEAE, [odoribacterae], prevotellaceae, deferribacterae, staphylococcaceae, Rhodospirillaceae, anaeroplasmataceae, caulobacteraceae, streptococcaceae, dehalobactriaceae, christensenellaceae, desulfovibrionaceae, ruminococcaceae, F16 Bifidobacteriaceae, Helicobacteraceae, S24-7, and Pianococcacese; gut microbiota groups that differ at the genus level include [Prevotella], blautia, bilophila, Corynebacterium, Prevotella, Helicobacter, Lactococcus, rikenella, parabacteroides, anaeroplasma, Megasphaera, dehalobacterium, oscillospira, Clostridium, Streptococcus, Shigella, AF12, Desulfovibrio, and Candidatus_Arthromitus; the gut microbiota that produce differences at the species level Parabacteroides_distasonis, Mucispirillum_schaedleri, Alistipes_indistinctus, Bacteroides_uniformis, Corynebacterium_stationis, Lactobacillus_helveticus, Desulfovibrio_C21_c20, [Ruminococcus]_gnavus, Alistipes_finegoldii, Alistipes_onderdonkii, Sulfuricurvum_kujiense, Cellulomonas_uda, Butyricicoccus_pullicaecorum, Lactobacillus_vaginalis, Silene_vulgaris, Staphylococcus_sciuri, Helicobacter_hepaticus, Cetobacterium_somerae, Alistipes_massiliensis, Clostridium_cocleatum; by observing the gut microbiota with different levels of phyla and genus, it is found that there are 6 species of gut microbiota with callback effect at phyla level, namely, TM7, tenericutes, Chloroflex, cyanobacteria, acidobacteria, and WS6. There are 5 gut microbiota species with callback effect at the genus level, namely, [Prevotella], Helicobacter, Prevotella, Lactococcus, and anaeroplasma, as shown in Figure5.Figure 5
Random forest plot of differential gut microbiota of samples in each group. Note: (a) phylum-level differential gut microbiota; (b) class-level differential gut microbiota; (c) order-level differential gut microbiota; (d) family-level differential gut microbiota; (e) genus-level differential gut microbiota; (f) species-level differential gut microbiota.
(a)(b)(c)(d)(e)(f)


#### 3.3.6. Prediction of Metabolic Pathways in Microbiota Sample Communities

In order to identify the signaling pathway of Huangqin decoction in the treatment of DE, this study used picurst2 to perform functional analysis on the treatment group and identified 170 signaling pathways through enrichment, as shown in Table3 below.Table 3
Signal pathway of DE.
PathwayDescriptionko00010Glycolysis/gluconeogenesisko00020Citrate cycle (TCA cycle)ko00030Pentose phosphate pathwayko00040Pentose and glucuronate interconversionsko00051Fructose and mannose metabolismko00052Galactose metabolismko00053Ascorbate and aldarate metabolismko00061Fatty acid biosynthesisko00071Fatty acid metabolismko00072Synthesis and degradation of ketone bodiesko00100Steroid biosynthesisko00120Primary bile acid biosynthesisko00121Secondary bile acid biosynthesisko00130Ubiquinone and other terpenoid-quinone biosynthesisko00140Steroid hormone biosynthesisko00190Oxidative phosphorylationko00195Photosynthesisko00196Photosynthesis-antenna proteinsko00230Purine metabolismko00240Pyrimidine metabolismko00250Alanine, aspartate, and glutamate metabolismko00253Tetracycline biosynthesisko00260Glycine, serine, and threonine metabolismko00270Cysteine and methionine metabolismko00280Valine, leucine, and isoleucine degradationko00281Geraniol degradationko00290Valine, leucine, and isoleucine biosynthesisko00300Lysine biosynthesisko00310Lysine degradationko00311Penicillin and cephalosporin biosynthesisko00312Beta-lactam resistanceko00330Arginine and proline metabolismko00340Histidine metabolismko00350Tyrosine metabolismko00360Phenylalanine metabolismko00361Chlorocyclohexane and chlorobenzene degradationko00362Benzoate degradationko00363Bisphenol degradationko00364Fluorobenzoate degradationko00380Tryptophan metabolismko00400Phenylalanine, tyrosine, and tryptophan biosynthesisko00410Beta-alanine metabolismko00430Taurine and hypotaurine metabolismko00440Phosphonate and phosphinate metabolismko00450Selenocompound metabolismko00460Cyanoamino acid metabolismko00471D-glutamine and D-glutamate metabolismko00472D-arginine and D-ornithine metabolismko00473D-alanine metabolismko00480Glutathione metabolismko00500Starch and sucrose metabolismko00510N-glycan biosynthesisko00511Other glycan degradationko00520Amino sugar and nucleotide sugar metabolismko00521Streptomycin biosynthesisko00523Polyketide sugar unit biosynthesisko00524Butirosin and neomycin biosynthesisko00531Glycosaminoglycan degradationko00540Lipopolysaccharide biosynthesisko00550Peptidoglycan biosynthesisko00561Glycerolipid metabolismko00562Inositol phosphate metabolismko00564Glycerophospholipid metabolismko00590Arachidonic acid metabolismko00591Linoleic acid metabolismko00600Sphingolipid metabolismko00601Glycosphingolipid biosynthesis-lacto and neolacto seriesko00620Pyruvate metabolismko00621Dioxin degradationko00622Xylene degradationko00623Toluene degradationko00624Polycyclic aromatic hydrocarbon degradationko00625Chloroalkane and chloroalkene degradationko00627Aminobenzoate degradationko00630Glyoxylate and dicarboxylate metabolismko00633Nitrotoluene degradationko00640Propanoate metabolismko00642Ethylbenzene degradationko00643Styrene degradationko00650Butanoate metabolismko00660C5-branched dibasic acid metabolismko00670One carbon pool by folateko00680Methane metabolismko00710Carbon fixation in photosynthetic organismsko00720Carbon fixation pathways in prokaryotesko00730Thiamine metabolismko00740Riboflavin metabolismko00750Vitamin B6 metabolismko00760Nicotinate and nicotinamide metabolismko00770Pantothenate and CoA biosynthesisko00780Biotin metabolismko00785Lipoic acid metabolismko00790Folate biosynthesisko00791Atrazine degradationko00830Retinol metabolismko00860Porphyrin and chlorophyll metabolismko00900Terpenoid backbone biosynthesisko00903Limonene and pinene degradationko00906Carotenoid biosynthesisko00908Zeatin biosynthesisko00909Sesquiterpenoid biosynthesisko00910Nitrogen metabolismko00920Sulfur metabolismko00930Caprolactam degradationko00941Flavonoid biosynthesisko00943Isoflavonoid biosynthesisko00960Tropane, piperidine, and pyridine alkaloid biosynthesisko00965Betalain biosynthesisko00970Aminoacyl-tRNA biosynthesisko00980Metabolism of xenobiotics by cytochrome P450ko00983Drug metabolism-other enzymesko01040Biosynthesis of unsaturated fatty acidsko01051Biosynthesis of ansamycinsko01053Biosynthesis of siderophore group nonribosomal peptidesko01055Biosynthesis of vancomycin group antibioticsko01056Biosynthesis of type II polyketide backboneko02010ABC transportersko02020Two-component systemko02030Bacterial chemotaxisko02040Flagellar assemblyko02060Phosphotransferase system (PTS)ko03008Ribosome biogenesis in eukaryotesko03010Ribosomeko03013RNA transportko03015mRNA surveillance pathwayko03018RNA degradationko03020RNA polymeraseko03030DNA replicationko03040Spliceosomeko03050Proteasomeko03060Protein exportko03070Bacterial secretion systemko03410Base excision repairko03420Nucleotide excision repairko03430Mismatch repairko03440Homologous recombinationko03450Nonhomologous end-joiningko04020Calcium signaling pathwayko04112Cell cycle-Caulobacterko04113Meiosis-yeastko04122Sulfur relay systemko04141Protein processing in endoplasmic reticulumko04142Lysosomeko04144Endocytosisko04145Phagosomeko04146Peroxisomeko04210Apoptosisko04310Wnt signaling pathwayko04621NOD-like receptor signaling pathwayko04622RIG-I-like receptor signaling pathwayko04626Plant-pathogen interactionko04722Neurotrophin signaling pathwayko04910Insulin signaling pathwayko04962Vasopressin-regulated water reabsorptionko04974Protein digestion and absorptionko05010Alzheimer’s diseaseko05012Parkinson’s diseaseko05100Bacterial invasion of epithelial cellsko05110Vibrio cholerae infectionko05111Vibrio cholerae pathogenic cycleko05120Epithelial cell signaling in Helicobacter pylori infectionko05130Pathogenic Escherichia coli infectionko05131Shigellosisko05143African trypanosomiasisko05145Toxoplasmosisko05146Amoebiasisko05150Staphylococcus aureus infectionko05200Pathways in cancerko05322Systemic lupus erythematosusko05410Hypertrophic cardiomyopathy (HCM)


### 3.4. Network Pharmacology Analysis

After entering the keyword, “Scutellaria baicalensis, Paeonia alba, jujube and licorice” in the TCMSP database, 34 effective components of Scutellaria baicalensis, 8 effective components of Paeonia alba, 20 effective components of jujube, and 90 effective components of licorice were screened according to the screening conditions ofOB≥30% and DL≥0.18. After merging and deleting multiple items, 141 components were obtained, as shown in Table 4. Using PubChem database (https://pubchem.ncbi.nlm.nih.gov/), obtain the SMILE standard structural formula of active ingredients and import it into the swisstargetprediction database (http://swisstargetprediction.ch/). Target prediction was carried out, duplicate targets were deleted, and a total of 265 targets related to active components were obtained.Table 4
Effective ingredients of HQD.
MOL IDIngredientOB (%)DLSourceMOL000422Kaempferol41.882249540.24066Licorice, white peony rootMOL000359Sitosterol36.913905830.7512Licorice, Scutellaria baicalensis, white peony rootMOL000098Quercetin46.433348120.27525Jujube, licoriceMOL000211Mairin55.377073380.7761Jujube, licorice, white peony rootMOL000358Beta-sitosterol36.913905830.75123Jujube, Scutellaria baicalensis, white peony rootMOL000449Stigmasterol43.829851580.75665Jujube, Scutellaria baicalensisMOL000492(+)-catechin54.826434050.24164Jujube, white peony rootMOL000096(-)-catechin49.67638680.24162JujubeMOL000627Stepholidine33.106250740.54083MOL000787Fumarine59.262504580.82694MOL001454Berberine36.861245040.77665MOL001522S-coclaurine42.350642170.23518MOL002773Beta-carotene37.184333370.58358MOL004350Ruvoside_qt36.121019530.75671MOL007213Nuciferin34.431028830.40475MOL012921Stepharine31.547866910.33376MOL012946Zizyphus saponin I_qt32.691135070.61923MOL012976Coumestrol32.487029290.33733MOL012981Daechuine S744.817744870.82806MOL012986Jujubasaponin V_qt36.989631090.63448MOL012992Mauritine D89.125093810.45286MOL000263Oleanolic acid29.020.76MOL000354Isorhamnetin49.604377050.306LicoriceMOL003656Lupiwighteone51.635691810.36739MOL004808Glyasperin B65.224386080.43851MOL004810Glyasperin F75.836800130.53514MOL004811Glyasperin C45.563806620.39947MOL004820Kanzonols W50.480075990.51704MOL004824(2S)-6-(2,4-dihydroxyphenyl)-2-(2-hydroxypropan-2-yl)-4-methoxy-2,3-dihydrofuro[3,2-g]chromen-7-one60.250409080.63433MOL004827Semilicoisoflavone B48.777551940.54732MOL004828Glepidotin A44.721874650.34685MOL004829Glepidotin B64.462923860.34485MOL0048493-(2,4-Dihydroxyphenyl)-8-(1,1-dimethylprop-2-enyl)-7-hydroxy-5-methoxy-coumarin59.622474980.42894MOL004855Licoricone63.578459380.4712MOL004856Gancaonin A51.075191070.40378MOL004857Gancaonin B48.794402010.44924MOL0048645,7-Dihydroxy-3-(4-methoxyphenyl)-8-(3-methylbut-2-enyl)chromone30.488776730.41002MOL004879Glycyrin52.606571660.47466MOL004883Licoisoflavone41.610218850.41646MOL004884Licoisoflavone B38.928708880.54714MOL004885Licoisoflavanone52.466247060.54488MOL004904Licopyranocoumarin80.360013310.6535MOL0049591-Methoxyphaseollidin36.565372330.32291MOL0049663′-Hydroxy-4′-O-methylglabridin43.714951410.57406MOL0049743′-Methoxyglabridin46.161509290.57393MOL005000Gancaonin G60.435205060.39404MOL005001Gancaonin H50.103723270.78416MOL005007Glyasperins M72.670809840.59274MOL005008Glycyrrhiza flavonol A41.275277330.59512MOL000497Licochalcone a40.789651990.28517MOL004328Naringenin59.293897730.21128MOL004903Liquiritin65.690111650.73893MOL000392Formononetin69.673880610.21202MOL000500Vestitol74.655189120.20935MOL001792DFV32.762723750.18316MOL002565Medicarpin49.219817610.3351MOL0038967-Methoxy-2-methyl isoflavone42.564741480.19946MOL004835Glypallichalcone61.597062270.18993MOL004941(2R)-7-hydroxy-2-(4-hydroxyphenyl)chroman-4-one71.122989010.18303MOL004957HMO38.36542380.21067MOL0049782-[(3R)-8,8-dimethyl-3,4-dihydro-2H-pyrano[6,5-f]chromen-3-yl]-5-methoxyphenol36.214292080.52122MOL000239Jaranol50.828816770.29148MOL001484Inermine75.183060380.53754MOL004806Euchrenone30.287260990.57386MOL004815(E)-1-(2,4-dihydroxyphenyl)-3-(2,2-dimethylchromen-6-yl)prop-2-en-1-one39.616855370.35077MOL004833Phaseolinisoflavan32.008107720.44538MOL0048662-(3,4-Dihydroxyphenyl)-5,7-dihydroxy-6-(3-methylbut-2-enyl)chromone44.151961260.41482MOL004891Shinpterocarpin80.295276880.72746MOL004908Glabridin53.245143280.46967MOL004910Glabranin52.895655080.31208MOL004911Glabrene46.266857210.43902MOL004912Glabrone52.512174190.49645MOL004915Eurycarpin A43.277284250.37429MOL004945(2S)-7-hydroxy-2-(4-hydroxyphenyl)-8-(3-methylbut-2-enyl)chroman-4-one36.565372330.32291MOL004961Quercetin der.46.44938840.3343MOL004980Inflacoumarin A39.709095980.32613MOL0049896-Prenylated eriodictyol39.223830180.41259MOL0049917-Acetoxy-2-methylisoflavone38.923331050.26217MOL0049938-Prenylated eriodictyol53.794763180.40383MOL005003Licoagrocarpin58.813902870.58498MOL005012Licoagroisoflavone57.282240980.48679MOL005016Odoratin49.948218170.30487MOL005020Dehydroglyasperins C53.823260140.37006MOL000417Calycosin47.751827830.24278MOL0048388-(6-Hydroxy-2-benzofuranyl)-2,2-dimethyl-5-chromenol58.437280910.38106MOL0048633-(3,4-Dihydroxyphenyl)-5,7-dihydroxy-8-(3-methylbut-2-enyl)chromone66.371250460.41392MOL002311Glycyrol90.775782230.66819MOL004805(2S)-2-[4-hydroxy-3-(3-methylbut-2-enyl)phenyl]-8,8-dimethyl-2,3-dihydropyrano[2,3-f]chromen-4-one31.787033530.72403MOL004814Isotrifoliol31.944787240.42422MOL004841Licochalcone B76.757354850.1935MOL004848Licochalcone G49.254963320.32325MOL004898(E)-3-[3,4-dihydroxy-5-(3-methylbut-2-enyl)phenyl]-1-(2,4-dihydroxyphenyl)prop-2-en-1-one46.267922560.3062MOL004907Glyzaglabrin61.068886310.35347MOL004924(-)-Medicocarpin40.993971990.95059MOL004935Sigmoidin-B34.881086160.41455MOL004948Isoglycyrol44.699225680.83845MOL004949Isolicoflavonol45.169990580.41859MOL004988Kanzonol F32.468333640.89364MOL0049907,2′,4′-trihydroxy-5-methoxy-3-arylcoumarin83.714367440.27136MOL005017Phaseol78.766219250.57867MOL005018Xambioona54.849162420.87419MOL0049131,3-Dihydroxy-9-methoxy-6-benzofurano[3,2-c]chromenone48.141542350.42831MOL0049141,3-Dihydroxy-8,9-dimethoxy-6-benzofurano[3,2-c]chromenone62.901354860.52759MOL004985Icos-5-enoic acid30.702942550.19725MOL004996Gadelaidic acid30.702942550.19725MOL004882Licocoumarone33.210850680.3568MOL001789Isoliquiritigenin85.320.15MOL00480418Beta-glycyrrhetinic acid22.050.74MOL000073Ent-epicatechin48.959841140.24162Scutellaria baicalensisMOL000173Wogonin30.684567060.22942MOL000228(2R)-7-hydroxy-5-methoxy-2-phenylchroman-4-one55.233173890.20163MOL000525Norwogonin39.403971840.20723MOL0005525,2′-Dihydroxy-6,7,8-trimethoxyflavone31.712464930.35462MOL001458Coptisine30.6718520.85647MOL001490Bis[(2S)-2-ethylhexyl] benzene-1,2-dicarboxylate43.593325470.34531MOL001689Acacetin34.973572730.24082MOL002714Baicalein33.518918690.20888MOL002879Diop43.593325470.39247MOL002897Epiberberine43.092332280.7761MOL0029095,7,2,5-Tetrahydroxy-8,6-dimethoxyflavone33.815825990.44739MOL002910Carthamidin41.150962730.24189MOL002913Dihydrobaicalin_qt40.037781030.20722MOL002914Eriodyctiol (flavanone)41.350427130.2436MOL002915Salvigenin49.065926060.33279MOL0029175,2′,6′-Trihydroxy-7,8-dimethoxyflavone45.047428020.33057MOL0029255,7,2′,6′-Tetrahydroxyflavone37.013486880.24382MOL002927Skullcapflavone II69.510433980.4379MOL002928Oroxylin a41.3675690.23233MOL002932Panicolin76.257049890.2915MOL0029335,7,4′-Trihydroxy-8-methoxyflavone36.562004690.26666MOL002934Neobaicalein104.34460520.43917MOL002937Dihydrooroxylin66.061738720.23057MOL008206Moslosooflavone44.087959590.25331MOL01041511,13-Eicosadienoic acid, methyl ester39.275344220.2289MOL0122455,7,4′-trihydroxy-6-methoxyflavanone36.626886280.26833MOL0122465,7,4′-Trihydroxy-8-methoxyflavanone74.235220010.26479MOL012266Rivularin37.940233550.3663MOL002776Baicalin40.120.75MOL001918Paeoniflorgenone87.593120840.36678White peony rootMOL001919(3S,5R,8R,9R,10S,14S)-3,17-dihydroxy-4,4,8,10,14-pentamethyl-2,3,5,6,7,9-hexahydro-1H-cyclopenta[a]phenanthrene-15,16-dione43.556201670.53276MOL001924Paeoniflorin53.870375160.78709To identify the main genes of Huangqin decoction’s anti-DE (Table5), Cytoscape 3.8.2 was used for visual analysis, and a protein-protein interaction network was constructed (Figure 6(c)). At the same time, the cytohubba plug-in was used to screen out the core targets. Combined with the score of the calculation method, the top 10 genes were considered as core genes (IL-6, TNF, TP53, IL1B, CASP3, JUN, PPARG, MAPK3, EGFR, and PTGS2) Table 6. GO annotation and KEGG pathway enrichment of the obtained potential target genes of Huangqin decoction against DE were performed by R language (Figures 6(d) and 6(e)). GO enrichment analysis showed that there was mainly positive regulation of nitric oxide biosynthetic process, response to ethanol, response to hypoxia, etc. According to KEGG enrichment analysis, the significantly affected pathways are bladder cancer, colorectal cancer, pancreatic cancer, leishmaniasis, hepatitis B, etc.Table 5
Target information of DE in network pharmacology.
No.Gene namesProtein namesUniprot ID1TGFB1Transforming growth factor beta-1 proprotein [cleaved into: latency-associated peptideP011372SLC6A4Sodium-dependent serotonin transporterP316453PTGS2Prostaglandin G/H synthase 2P353544PTGS1Prostaglandin G/H synthase 1P232195PRKCAProtein kinase C alpha typeP172526PRKACAcAMP-dependent protein kinase catalytic subunit alphaP176127PON1Serum paraoxonase/arylesterase 1P271698PIK3CGPhosphatidylinositol 4,5-bisphosphate 3-kinase catalytic subunit gamma isoformP487369OPRM1Mu-type opioid receptorP3537210JUNTranscription factor AP-1P0541211HTR2A5-Hydroxytryptamine receptor 2AP2822312HSP90AA1Heat shock protein HSP 90-alphaP0790013CASP9Caspase-9P5521114CASP8Caspase-8Q1479015CASP3Caspase-3P4257416BCL2Apoptosis regulator Bcl-2P1041517BAXApoptosis regulator BAXQ0781218ADRB2Beta-2 adrenergic receptorP0755019NR3C2Mineralocorticoid receptorP0823520XDHXanthine dehydrogenase/oxidase [includes: xanthine dehydrogenaseP4798921VCAM1Vascular cell adhesion protein 1P1932022TNFTumor necrosis factorP0137523STAT1Signal transducer and activator of transcription 1-alpha/betaP4222424SLPIAntileukoproteinaseP0397325SLC2A4Solute carrier family 2, facilitated glucose transporter member 4P1467226SELEE-selectinP1658127RELATranscription factor p65Q0420628PRSS1Trypsin-1P0747729PPARGPeroxisome proliferator-activated receptor gammaQ0820930NOS3Nitric oxide synthase, endothelialP3723131NOS2Nitric oxide synthase, inducibleQ1499432MMP1Interstitial collagenaseO7546933MAPK8Mitogen-activated protein kinase 8P2947434INSRInsulin receptorP3522835IKBKBInhibitor of nuclear factor kappa-B kinase subunit betaP0395636ICAM1Intercellular adhesion molecule 1P4598337HMOX1Heme oxygenase 1P0621338GSTM1Glutathione S-transferase Mu 1O1492039F7Coagulation factor VIIP0536240F2ProthrombinP0960141DPP4Dipeptidyl peptidase 4P0948842CYP3A4Cytochrome P450 3A4P0870943CYP1A1Cytochrome P450 1A1P0073444CDK1Cyclin-dependent kinase 1P2748745ARAndrogen receptorP0868446ALOX5Polyunsaturated fatty acid 5-lipoxygenaseP0479847AKT1RAC-alpha serine/threonine-protein kinaseP0649348AHRAryl hydrocarbon receptorP1027549ACHEAcetylcholinesteraseP0991750ESR1Estrogen receptorP3174951IL6Interleukin-6P3586952CD14Monocyte differentiation antigen CD14P2230353FASNFatty acid synthaseP1979354TP53Cellular tumor antigen p53P0337255THBDThrombomodulinP1842856SPP1OsteopontinP0523157SOD1Superoxide dismutase [Cu-Zn]P0857158SERPINE1Plasminogen activator inhibitor 1P4932759RUNX2Runt-related transcription factor 2P0463760RB1Retinoblastoma-associated proteinP0720461RAF1RAF protooncogene serine/threonine-protein kinaseP1045162PTENPhosphatidylinositol 3,4,5-trisphosphate 3-phosphatase and dual-specificity protein phosphatase PTENP0044163PRKCBProtein kinase C beta typeP0512164PPARDPeroxisome proliferator-activated receptor deltaQ1395065PPARAPeroxisome proliferator-activated receptor alphaP0640066PLAUUrokinase-type plasminogen activatorP0404967PLATTissue-type plasminogen activatorP6048468PARP1Poly [ADP-ribose] polymerase 1P0577169ODC1Ornithine decarboxylaseQ0318170NQO1NADQ0786971NFKBIANF-kappa-B inhibitor alphaP0074972NFE2L2Nuclear factor erythroid 2-related factor 2P0075073NCF1Neutrophil cytosol factor 1P0987474MYCMyc protooncogene proteinP1192675MPOMyeloperoxidaseP1555976MMP9Matrix metalloproteinase-9P2596377MMP3Stromelysin-1Q1623678MMP272 kDa type IV collagenaseP0110679MGAMMaltase-glucoamylase, intestinal [includes: maltaseP0516480MAPK1Mitogen-activated protein kinase 1P1478081IL2Interleukin-2P0825482IL1BInterleukin-1 betaP0825383IL1AInterleukin-1 alphaO4345184IL10Interleukin-10P2848285IGFBP3Insulin-like growth factor-binding protein 3P6056886IGF2Insulin-like growth factor IIP0158487IFNGInterferon gammaP0158388HSPB1Heat shock protein beta-1P2230189HSPA5Endoplasmic reticulum chaperone BiPP1793690HK2Hexokinase-2P0134491HIF1AHypoxia-inducible factor 1-alphaP0157992GJA1Gap junction alpha-1 proteinP0479293FOSProtooncogene c-FosP1102194F3Tissue factorP5278995ERBB3Receptor tyrosine-protein kinase erbB-3Q1666596ERBB2Receptor tyrosine-protein kinase erbB-2P1730297EGFREpidermal growth factor receptorP0110098CXCL8Interleukin-8P1372699CXCL10C-X-C motif chemokine 10P00742100CTSDCathepsin DP21860101CRPC-reactive protein [cleaved into: C-reactive protein]P04626102CLDN4Claudin-4P00533103CHEK2Serine/threonine-protein kinase Chk2Q01094104CDKN1ACyclin-dependent kinase inhibitor 1P10145105CD40LGCD40 ligandP19875106CCND1G1/S-specific cyclin-D1P02778107CCL2C-C motif chemokine 2P07339108CAV1Caveolin-1P02741109BIRC5Baculoviral IAP repeat-containing protein 5P02461110AKR1B1Aldo-keto reductase family 1 member B1O96017111ACACAAcetyl-CoA carboxylase 1P38936112ADRB1Beta-1 adrenergic receptorP29965113ADRA2AAlpha-2A adrenergic receptorP24385114HTR2C5-Hydroxytryptamine receptor 2CP13500115DRD4DQ03135116ADRA2BAlpha-2B adrenergic receptorO15392117PDE4DcAMP-specific 3′,5′-cyclic phosphodiesterase 4DQ07817118KDRVascular endothelial growth factor receptor 2P15121119HTR3A5-Hydroxytryptamine receptor 3AQ13085120CTNNB1Catenin beta-1P21397121DRD2DP08588122ESR2Estrogen receptor betaP35968123CDK2Cyclin-dependent kinase 2P35222124PTPN1Tyrosine-protein phosphatase nonreceptor type 1P55210125OLR1Oxidized low-density lipoprotein receptor 1P14416126MAPK14Mitogen-activated protein kinase 14P20813127GSK3BGlycogen synthase kinase-3 betaQ92731128SIRT1NAD-dependent protein deacetylase sirtuin-1P24941129MT-ND6NADH-ubiquinone oxidoreductase chain 6P18031130IL4Interleukin-4P78380131STAT3Signal transducer and activator of transcription 3Q16539132CDK4Cyclin-dependent kinase 4P49841133JAK2Tyrosine-protein kinase JAK2Q96EB6134SLC2A1Solute carrier family 2, facilitated glucose transporter member 1P03923135MAPK10Mitogen-activated protein kinase 10P05112136SREBF1Sterol regulatory element-binding protein 1P40763137SOAT1Sterol O-acyltransferase 1P11802138MTTPMicrosomal triglyceride transfer protein large subunitO60674139MAPK3Mitogen-activated protein kinase 3P11166140LDLRLow-density lipoprotein receptorP53779141HMGCR3-Hydroxy-3-methylglutaryl-coenzyme A reductaseP36956142GSRGlutathione reductase, mitochondrialP55157143CYP19A1AromataseP27361144BADBcl2-associated agonist of cell deathP01130145APOBApolipoprotein B-100P04035146ADIPOQAdiponectinP00390147CATCatalaseP11511148UGT1A1UDP-glucuronosyltransferase 1A1P23141149HSD11B2Corticosteroid 11-beta-dehydrogenase isozyme 2Q92934150CYP2E1Cytochrome P450 2E1P04114151KCNMA1Calcium-activated potassium channel subunit alpha-1Q15848152FN1FibronectinP33527153PRKCDProtein kinase C delta typeP04040154FASLGTumor necrosis factor ligand superfamily member 6P22309155CYCSCytochrome cP05181156CYP2C9Cytochrome P450 2C9P02751Figure 6
Network pharmacology analysis of Huangqin decoction in the treatment of DE. (a) Venn diagram of the intersection targets of Huangqin decoction and DE; (b) the component-target-disease interaction network of Huangqin decoction in the treatment of DE; (c) PPI network diagram of Huangqin decoction for anti-DE; (d, e) GO enrichment analysis and KEGG pathway enrichment analysis;P<0.05 for all pathways.
(a)(b)(c)(d)(e)Table 6
CytoHubba key genes screened.
Gene symbolRank methods in CytoHubbaMCCMNCDegreeEPCBottleNeckEcCentricityClosenessRadialityBetweennessStressIL-69.22E+1312324625.989240.51393.825811344.7337283272TNF9.22E+1311723425.10110.51363.7871874.0127978512TP539.22E+1311623223.73220.5135.53.78065771.8204770320IL1B9.22E+1311022025.05910.33333132.333333.73548506.510650800CASP39.22E+1310621223.82750.33333130.333333.70968520.9158153936JUN9.22E+1310420823.70110.33333129.333333.69677383.4897942960PPARG9.22E+1310120223.5510.33333127.833333.67742309.0781336088MAPK39.22E+139919824.044370.51273.67097555.1306749336EGFR9.22E+139819623.68910.5126.53.66452354.8507840368PTGS29.22E+139519023.30110.51253.64516700.31459256


### 3.5. Integrating 16S rRNA Gene Sequencing and Network Pharmacology Analysis

In order to identify the signaling pathway of Huangqin decoction in the treatment of DE, this study used picurst2 to analyze the function of the treatment group and identified 170 signaling pathways through enrichment. Similar signaling pathways are followed by apoptosis (apoptosis), calcium signaling pathway (calcium signaling pathway), cell cycle-Caulobacter (cell cycle-Caulobacter), insulin signaling pathway (insulin signaling pathway), neurotrophin signaling pathway (neurotrophin signaling pathway), NOD-like receptor signaling pathway, and RIG-I-like receptor signaling pathway. Based on the core genes obtained from these seven signaling pathways and network pharmacology, an integrated network map of Huangqin decoction for the treatment of DE was drawn. As shown in Figure7, the NOD-like receptor signaling pathway has the most significant node, and it has the strongest correlation with TNF and quercetin. Therefore, it is speculated that the mechanism of Huangqin decoction in the treatment of DE may be that the core component of Huangqin decoction, quercetin inhibits the expression of the TNF gene, thereby inhibiting the expression of NOD-like receptor signaling pathway and thereby achieving the therapeutic effect of the disease.Figure 7
Comprehensive network diagram of Huangqin decoction in the treatment of DE.


## 3.1. H&E Staining

The degree of damage to the integrity of the intestinal barrier reflects the barrier function of the intestinal mucosa to a certain extent. In this study, the length of the intestinal mucosal villi and the thickness of the muscularis layer were observed by H&E staining to determine the change of the intestinal mucosal barrier function, as shown in Figure1. H&E staining showed that the villus length and muscle thickness of the intestinal mucosa in the blank group were normal (Figure 1(a)); the villi were shortened, and the muscle layer thickness was decreased in the model group (Figure 1(b)); the symptoms of the Huangqin decoction group were more severe than those of the model group. The villus length was similar to that of the blank group, but the thickness of the muscle layer was still significantly lower than that of the blank group (Figure 1(c)).Figure 1
Influence of intestinal mucosal barrier integrity in DE model mice. Note: histomorphological changes in the cecum of db mice (H&E staining) (×20). (a) Intestinal mucosal structure of blank group; (b) intestinal mucosal structure of model group; (c) intestinal mucosal structure of Huangqin decoction group.
(a)(b)(c)


## 3.2. Immunohistochemistry

The intestinal mucosa was sectioned, and the differences in leukocyte infiltration were observed by CD45 staining, as shown in Figure2. In the blank group, only a few CD45+ cells were infiltrated. Compared with the blank group, the infiltration of CD45+ cells in the model group were significantly increased, while that in the Huangqin decoction group was significantly less than that in the model group (Figures 2(a)–2(c)).Figure 2
Immunohistochemistry of CD45 in the intestinal mucosa of DE mice. Note: immunohistochemical staining of cecal tissue of db mice to observe the changes of CD45 (×40); (a) CD45+ in the intestinal mucosa of the blank group; (b) CD45+ of intestinal mucosa in the model group; (c) effect of Huangqin decoction group on intestinal mucosa CD45+.
(a)(b)(c)


## 3.3. 16S rRNA Gene Sequencing

### 3.3.1. Classification and Analysis of OTUs (Operational Taxonomic Units) of Gut Microbiota in Samples from Each Group

The blank group has a total of 5450 OTUs and 3297 unique OTUs, the model group has a total of 5678 OTUs and 3561 unique OTUs, the Scutellaria baicalensis soup has a total of 5580 OTUs and 3747 unique OTUs, and the three groups have a total of 837 OTUs, as shown in Figure3. Compared with the blank group, the number of OTUs in the model group was significantly increased; compared with the model group, the number of OTUs in the administration group was significantly downregulated.Figure 3
(a) Venn diagram of OTUs of gut microbiota in each group (K is blank group; M is model group; Q is Huangqin decoction group); (b–d) sparse curve of the intestinal flora of rat samples in each group (b), species accumulation curve (c), and rank-abundance curve (d).
(a)(b)(c)(d)


### 3.3.2. Alpha Diversity Analysis of Gut Microbiota in Each Group

As shown in Figures3(b)–3(d), the diversity of samples in each group is almost saturated, indicating that the sequencing depth is sufficient and the sample size of each group is sufficient to reflect the richness of the community. The biodiversity indexes of the three groups of samples were compared, as shown in Table 1. Compared with the blank group, the intestinal flora Chao1, Faith_pd, Goods_coverage, Observed_species, Pielou_e, Shannon, and Simpson indexes of mice in the model group all showed a downward trend. After the administration of Huangqin decoction, all diversity indices showed a downward trend, as shown in Table 1.Table 1
Microbial diversity index of gut microbiota in each group.
GroupChao1Faith_pdGoods_coverageObserved_speciesPielou_eShannonSimpsonControl group2012.33494.26890.98898721731.780.73173987.827020.9813612Model group1875.29888.866880.98861641586.60.71248867.565110.9707218Huangqin decoction group1921.7591.231560.9894441669.040.71562887.6923420.97627


### 3.3.3. Taxonomic Composition Analysis of Gut Microbiota in Each Group

Through the analysis of Figures4(a) and 4(b), it is found that Bacteroides and Firmicutes are the most important phyla at the phylum level, followed by tenerictes, Proteobacteria, and actinobacteria. These five phyla account for a very high proportion of the whole phyla and belong to the dominant phyla. Compared with the blank group, in the model group, bacteroidea (54.25% → 43.77%), Firmicutes (38.06% → 47.37%), Firmicutes (2.48% → 5.55%), proteus (2.98% → 0.01%), actinomycetes (0.89% → 0.73%), and Firmicutes/Bacteroidetes ratio in the blank group was 0.70, and Firmicutes/Bacteroidetes ratio in the model group was 1.08. It is suggested that the dominant flora of model group mice and blank group mice has changed significantly in the structure; compared with the model group, the ratio of Firmicutes/Bacteroidetes in Huangqin decoction group was 1.14, suggesting that Huangqin decoction could regulate the dominant flora of db/db mice.Figure 4
Abundance distribution of intestinal flora in each group of rat samples at phylum (a) and genus (b) levels; analysis of the distribution of bacterial community structure in each group of rat samples (c).
(a)(b)(c)From the subordinate level, through cluster analysis of the absolute dominant bacterial genera in the top 20, find and analyze the relative abundance of different bacterial genera, namely, Bacteroides, oscillospira, anaeroplasma, Lactobacillus, odoribacter Ruminococcus, Desulfovibrio, Lactococcus, dehalobacterium, rikenella, alistipes, adlercreutzia, candidatus_arthromitus, coprococcus Helicobacter, parabacteroides, and Staphylococcus.


### 3.3.4. Influence of Gut Microbiota on Beta Diversity Analysis

In this study, PCoA analysis was used to investigate the differences in the beta diversity of rat gut microbiota. There was no significant difference in the diversity of bacterial community structure in the blank, model, and Huangqin decoction groups, and the communities in each group fell within their respective ranges, with strong similarity. The results showed that compared with the blank group, the microflora of the mice in the model group were significantly separated, indicating that the beta diversity of the two groups was significantly different. After administration of Huangqin decoction, it tended to the blank group, indicating that the beta diversity of the Huangqin decoction group was similar to that of the normal group. After administration of Huangqin decoction, it had a certain effect on the body of DE mice, as shown in Figure4(c).Differences between groups were analyzed by (permutational multivariate analysis of variance (PERMANOVA)). The results showed that the differences in the bacterial community structure diversity within the blank group, the model group, and the Huangqin decoction administration group were significantly smaller than the differences between the groups, suggesting that the blank group, the model group, and the Huangqin decoction administration group had significant effects on the bacterial community structure diversity. There were significant between-group differences, see Table2.Table 2
Statistical chart of difference analysis between groups.
Group 1Group 2Sample sizePermutationsPseudoFP valueq valueAll—159992.9354130.001—KM109992.4081840.0110.012KQ109993.4503990.0110.012MQ109992.9866950.0120.012


### 3.3.5. Gut Microbiota Difference Analysis

Using the method of random forest analysis, we screened the different gut microbiota among the experimental groups. We found that the gut microbiota with differences at the phylum level included deferribacteris, Proteobacteria, tenericutes, Firmicutes, acidobacteria, cyanobacteria, Actinobacteria, Bacteroidetes, fusobacteria, tm7-3, Chloroflexi, and WS6. The gut microbiota with differences at the class level include Mollicutes, epsilon Proteobacteria, deferribacterias, deltaproteobacteria, Actinobacteria, tm7-3, gamma Proteobacteria, bacteroidia, coriobacteria, clostridia, erysipelotrichi, betaproteobacteria, Alphaproteobacteria, synechococcophycedeae, bacilli, [chloracidobacteria], Flavobacteria Chloroflexi,TM7-1, and Fusobacteriia. The gut microbiota with differences at the order level include deferribacteriales, desulfovibrionales, bacillales, actinomyceteales, campylobacterales, bifidobacteria, caulobacterales, enterobacteria, erysipelotrichales, neisseriales, cw040, Chlorophyta, clostridiales, burkholderiales, Rickettsiales, bacteroidales, mycoplastiales, coriobacteriales Anaeroplasmatales, and Pseudanabaenales. The gut microbiota with differences at the family level include [paraprevotellaceae], corynebactriaceae, Veillonella CEAE, [odoribacterae], prevotellaceae, deferribacterae, staphylococcaceae, Rhodospirillaceae, anaeroplasmataceae, caulobacteraceae, streptococcaceae, dehalobactriaceae, christensenellaceae, desulfovibrionaceae, ruminococcaceae, F16 Bifidobacteriaceae, Helicobacteraceae, S24-7, and Pianococcacese; gut microbiota groups that differ at the genus level include [Prevotella], blautia, bilophila, Corynebacterium, Prevotella, Helicobacter, Lactococcus, rikenella, parabacteroides, anaeroplasma, Megasphaera, dehalobacterium, oscillospira, Clostridium, Streptococcus, Shigella, AF12, Desulfovibrio, and Candidatus_Arthromitus; the gut microbiota that produce differences at the species level Parabacteroides_distasonis, Mucispirillum_schaedleri, Alistipes_indistinctus, Bacteroides_uniformis, Corynebacterium_stationis, Lactobacillus_helveticus, Desulfovibrio_C21_c20, [Ruminococcus]_gnavus, Alistipes_finegoldii, Alistipes_onderdonkii, Sulfuricurvum_kujiense, Cellulomonas_uda, Butyricicoccus_pullicaecorum, Lactobacillus_vaginalis, Silene_vulgaris, Staphylococcus_sciuri, Helicobacter_hepaticus, Cetobacterium_somerae, Alistipes_massiliensis, Clostridium_cocleatum; by observing the gut microbiota with different levels of phyla and genus, it is found that there are 6 species of gut microbiota with callback effect at phyla level, namely, TM7, tenericutes, Chloroflex, cyanobacteria, acidobacteria, and WS6. There are 5 gut microbiota species with callback effect at the genus level, namely, [Prevotella], Helicobacter, Prevotella, Lactococcus, and anaeroplasma, as shown in Figure5.Figure 5
Random forest plot of differential gut microbiota of samples in each group. Note: (a) phylum-level differential gut microbiota; (b) class-level differential gut microbiota; (c) order-level differential gut microbiota; (d) family-level differential gut microbiota; (e) genus-level differential gut microbiota; (f) species-level differential gut microbiota.
(a)(b)(c)(d)(e)(f)


### 3.3.6. Prediction of Metabolic Pathways in Microbiota Sample Communities

In order to identify the signaling pathway of Huangqin decoction in the treatment of DE, this study used picurst2 to perform functional analysis on the treatment group and identified 170 signaling pathways through enrichment, as shown in Table3 below.Table 3
Signal pathway of DE.
PathwayDescriptionko00010Glycolysis/gluconeogenesisko00020Citrate cycle (TCA cycle)ko00030Pentose phosphate pathwayko00040Pentose and glucuronate interconversionsko00051Fructose and mannose metabolismko00052Galactose metabolismko00053Ascorbate and aldarate metabolismko00061Fatty acid biosynthesisko00071Fatty acid metabolismko00072Synthesis and degradation of ketone bodiesko00100Steroid biosynthesisko00120Primary bile acid biosynthesisko00121Secondary bile acid biosynthesisko00130Ubiquinone and other terpenoid-quinone biosynthesisko00140Steroid hormone biosynthesisko00190Oxidative phosphorylationko00195Photosynthesisko00196Photosynthesis-antenna proteinsko00230Purine metabolismko00240Pyrimidine metabolismko00250Alanine, aspartate, and glutamate metabolismko00253Tetracycline biosynthesisko00260Glycine, serine, and threonine metabolismko00270Cysteine and methionine metabolismko00280Valine, leucine, and isoleucine degradationko00281Geraniol degradationko00290Valine, leucine, and isoleucine biosynthesisko00300Lysine biosynthesisko00310Lysine degradationko00311Penicillin and cephalosporin biosynthesisko00312Beta-lactam resistanceko00330Arginine and proline metabolismko00340Histidine metabolismko00350Tyrosine metabolismko00360Phenylalanine metabolismko00361Chlorocyclohexane and chlorobenzene degradationko00362Benzoate degradationko00363Bisphenol degradationko00364Fluorobenzoate degradationko00380Tryptophan metabolismko00400Phenylalanine, tyrosine, and tryptophan biosynthesisko00410Beta-alanine metabolismko00430Taurine and hypotaurine metabolismko00440Phosphonate and phosphinate metabolismko00450Selenocompound metabolismko00460Cyanoamino acid metabolismko00471D-glutamine and D-glutamate metabolismko00472D-arginine and D-ornithine metabolismko00473D-alanine metabolismko00480Glutathione metabolismko00500Starch and sucrose metabolismko00510N-glycan biosynthesisko00511Other glycan degradationko00520Amino sugar and nucleotide sugar metabolismko00521Streptomycin biosynthesisko00523Polyketide sugar unit biosynthesisko00524Butirosin and neomycin biosynthesisko00531Glycosaminoglycan degradationko00540Lipopolysaccharide biosynthesisko00550Peptidoglycan biosynthesisko00561Glycerolipid metabolismko00562Inositol phosphate metabolismko00564Glycerophospholipid metabolismko00590Arachidonic acid metabolismko00591Linoleic acid metabolismko00600Sphingolipid metabolismko00601Glycosphingolipid biosynthesis-lacto and neolacto seriesko00620Pyruvate metabolismko00621Dioxin degradationko00622Xylene degradationko00623Toluene degradationko00624Polycyclic aromatic hydrocarbon degradationko00625Chloroalkane and chloroalkene degradationko00627Aminobenzoate degradationko00630Glyoxylate and dicarboxylate metabolismko00633Nitrotoluene degradationko00640Propanoate metabolismko00642Ethylbenzene degradationko00643Styrene degradationko00650Butanoate metabolismko00660C5-branched dibasic acid metabolismko00670One carbon pool by folateko00680Methane metabolismko00710Carbon fixation in photosynthetic organismsko00720Carbon fixation pathways in prokaryotesko00730Thiamine metabolismko00740Riboflavin metabolismko00750Vitamin B6 metabolismko00760Nicotinate and nicotinamide metabolismko00770Pantothenate and CoA biosynthesisko00780Biotin metabolismko00785Lipoic acid metabolismko00790Folate biosynthesisko00791Atrazine degradationko00830Retinol metabolismko00860Porphyrin and chlorophyll metabolismko00900Terpenoid backbone biosynthesisko00903Limonene and pinene degradationko00906Carotenoid biosynthesisko00908Zeatin biosynthesisko00909Sesquiterpenoid biosynthesisko00910Nitrogen metabolismko00920Sulfur metabolismko00930Caprolactam degradationko00941Flavonoid biosynthesisko00943Isoflavonoid biosynthesisko00960Tropane, piperidine, and pyridine alkaloid biosynthesisko00965Betalain biosynthesisko00970Aminoacyl-tRNA biosynthesisko00980Metabolism of xenobiotics by cytochrome P450ko00983Drug metabolism-other enzymesko01040Biosynthesis of unsaturated fatty acidsko01051Biosynthesis of ansamycinsko01053Biosynthesis of siderophore group nonribosomal peptidesko01055Biosynthesis of vancomycin group antibioticsko01056Biosynthesis of type II polyketide backboneko02010ABC transportersko02020Two-component systemko02030Bacterial chemotaxisko02040Flagellar assemblyko02060Phosphotransferase system (PTS)ko03008Ribosome biogenesis in eukaryotesko03010Ribosomeko03013RNA transportko03015mRNA surveillance pathwayko03018RNA degradationko03020RNA polymeraseko03030DNA replicationko03040Spliceosomeko03050Proteasomeko03060Protein exportko03070Bacterial secretion systemko03410Base excision repairko03420Nucleotide excision repairko03430Mismatch repairko03440Homologous recombinationko03450Nonhomologous end-joiningko04020Calcium signaling pathwayko04112Cell cycle-Caulobacterko04113Meiosis-yeastko04122Sulfur relay systemko04141Protein processing in endoplasmic reticulumko04142Lysosomeko04144Endocytosisko04145Phagosomeko04146Peroxisomeko04210Apoptosisko04310Wnt signaling pathwayko04621NOD-like receptor signaling pathwayko04622RIG-I-like receptor signaling pathwayko04626Plant-pathogen interactionko04722Neurotrophin signaling pathwayko04910Insulin signaling pathwayko04962Vasopressin-regulated water reabsorptionko04974Protein digestion and absorptionko05010Alzheimer’s diseaseko05012Parkinson’s diseaseko05100Bacterial invasion of epithelial cellsko05110Vibrio cholerae infectionko05111Vibrio cholerae pathogenic cycleko05120Epithelial cell signaling in Helicobacter pylori infectionko05130Pathogenic Escherichia coli infectionko05131Shigellosisko05143African trypanosomiasisko05145Toxoplasmosisko05146Amoebiasisko05150Staphylococcus aureus infectionko05200Pathways in cancerko05322Systemic lupus erythematosusko05410Hypertrophic cardiomyopathy (HCM)


## 3.3.1. Classification and Analysis of OTUs (Operational Taxonomic Units) of Gut Microbiota in Samples from Each Group

The blank group has a total of 5450 OTUs and 3297 unique OTUs, the model group has a total of 5678 OTUs and 3561 unique OTUs, the Scutellaria baicalensis soup has a total of 5580 OTUs and 3747 unique OTUs, and the three groups have a total of 837 OTUs, as shown in Figure3. Compared with the blank group, the number of OTUs in the model group was significantly increased; compared with the model group, the number of OTUs in the administration group was significantly downregulated.Figure 3
(a) Venn diagram of OTUs of gut microbiota in each group (K is blank group; M is model group; Q is Huangqin decoction group); (b–d) sparse curve of the intestinal flora of rat samples in each group (b), species accumulation curve (c), and rank-abundance curve (d).
(a)(b)(c)(d)


## 3.3.2. Alpha Diversity Analysis of Gut Microbiota in Each Group

As shown in Figures3(b)–3(d), the diversity of samples in each group is almost saturated, indicating that the sequencing depth is sufficient and the sample size of each group is sufficient to reflect the richness of the community. The biodiversity indexes of the three groups of samples were compared, as shown in Table 1. Compared with the blank group, the intestinal flora Chao1, Faith_pd, Goods_coverage, Observed_species, Pielou_e, Shannon, and Simpson indexes of mice in the model group all showed a downward trend. After the administration of Huangqin decoction, all diversity indices showed a downward trend, as shown in Table 1.Table 1
Microbial diversity index of gut microbiota in each group.
GroupChao1Faith_pdGoods_coverageObserved_speciesPielou_eShannonSimpsonControl group2012.33494.26890.98898721731.780.73173987.827020.9813612Model group1875.29888.866880.98861641586.60.71248867.565110.9707218Huangqin decoction group1921.7591.231560.9894441669.040.71562887.6923420.97627


## 3.3.3. Taxonomic Composition Analysis of Gut Microbiota in Each Group

Through the analysis of Figures4(a) and 4(b), it is found that Bacteroides and Firmicutes are the most important phyla at the phylum level, followed by tenerictes, Proteobacteria, and actinobacteria. These five phyla account for a very high proportion of the whole phyla and belong to the dominant phyla. Compared with the blank group, in the model group, bacteroidea (54.25% → 43.77%), Firmicutes (38.06% → 47.37%), Firmicutes (2.48% → 5.55%), proteus (2.98% → 0.01%), actinomycetes (0.89% → 0.73%), and Firmicutes/Bacteroidetes ratio in the blank group was 0.70, and Firmicutes/Bacteroidetes ratio in the model group was 1.08. It is suggested that the dominant flora of model group mice and blank group mice has changed significantly in the structure; compared with the model group, the ratio of Firmicutes/Bacteroidetes in Huangqin decoction group was 1.14, suggesting that Huangqin decoction could regulate the dominant flora of db/db mice.Figure 4
Abundance distribution of intestinal flora in each group of rat samples at phylum (a) and genus (b) levels; analysis of the distribution of bacterial community structure in each group of rat samples (c).
(a)(b)(c)From the subordinate level, through cluster analysis of the absolute dominant bacterial genera in the top 20, find and analyze the relative abundance of different bacterial genera, namely, Bacteroides, oscillospira, anaeroplasma, Lactobacillus, odoribacter Ruminococcus, Desulfovibrio, Lactococcus, dehalobacterium, rikenella, alistipes, adlercreutzia, candidatus_arthromitus, coprococcus Helicobacter, parabacteroides, and Staphylococcus.


## 3.3.4. Influence of Gut Microbiota on Beta Diversity Analysis

In this study, PCoA analysis was used to investigate the differences in the beta diversity of rat gut microbiota. There was no significant difference in the diversity of bacterial community structure in the blank, model, and Huangqin decoction groups, and the communities in each group fell within their respective ranges, with strong similarity. The results showed that compared with the blank group, the microflora of the mice in the model group were significantly separated, indicating that the beta diversity of the two groups was significantly different. After administration of Huangqin decoction, it tended to the blank group, indicating that the beta diversity of the Huangqin decoction group was similar to that of the normal group. After administration of Huangqin decoction, it had a certain effect on the body of DE mice, as shown in Figure4(c).Differences between groups were analyzed by (permutational multivariate analysis of variance (PERMANOVA)). The results showed that the differences in the bacterial community structure diversity within the blank group, the model group, and the Huangqin decoction administration group were significantly smaller than the differences between the groups, suggesting that the blank group, the model group, and the Huangqin decoction administration group had significant effects on the bacterial community structure diversity. There were significant between-group differences, see Table2.Table 2
Statistical chart of difference analysis between groups.
Group 1Group 2Sample sizePermutationsPseudoFP valueq valueAll—159992.9354130.001—KM109992.4081840.0110.012KQ109993.4503990.0110.012MQ109992.9866950.0120.012


## 3.3.5. Gut Microbiota Difference Analysis

Using the method of random forest analysis, we screened the different gut microbiota among the experimental groups. We found that the gut microbiota with differences at the phylum level included deferribacteris, Proteobacteria, tenericutes, Firmicutes, acidobacteria, cyanobacteria, Actinobacteria, Bacteroidetes, fusobacteria, tm7-3, Chloroflexi, and WS6. The gut microbiota with differences at the class level include Mollicutes, epsilon Proteobacteria, deferribacterias, deltaproteobacteria, Actinobacteria, tm7-3, gamma Proteobacteria, bacteroidia, coriobacteria, clostridia, erysipelotrichi, betaproteobacteria, Alphaproteobacteria, synechococcophycedeae, bacilli, [chloracidobacteria], Flavobacteria Chloroflexi,TM7-1, and Fusobacteriia. The gut microbiota with differences at the order level include deferribacteriales, desulfovibrionales, bacillales, actinomyceteales, campylobacterales, bifidobacteria, caulobacterales, enterobacteria, erysipelotrichales, neisseriales, cw040, Chlorophyta, clostridiales, burkholderiales, Rickettsiales, bacteroidales, mycoplastiales, coriobacteriales Anaeroplasmatales, and Pseudanabaenales. The gut microbiota with differences at the family level include [paraprevotellaceae], corynebactriaceae, Veillonella CEAE, [odoribacterae], prevotellaceae, deferribacterae, staphylococcaceae, Rhodospirillaceae, anaeroplasmataceae, caulobacteraceae, streptococcaceae, dehalobactriaceae, christensenellaceae, desulfovibrionaceae, ruminococcaceae, F16 Bifidobacteriaceae, Helicobacteraceae, S24-7, and Pianococcacese; gut microbiota groups that differ at the genus level include [Prevotella], blautia, bilophila, Corynebacterium, Prevotella, Helicobacter, Lactococcus, rikenella, parabacteroides, anaeroplasma, Megasphaera, dehalobacterium, oscillospira, Clostridium, Streptococcus, Shigella, AF12, Desulfovibrio, and Candidatus_Arthromitus; the gut microbiota that produce differences at the species level Parabacteroides_distasonis, Mucispirillum_schaedleri, Alistipes_indistinctus, Bacteroides_uniformis, Corynebacterium_stationis, Lactobacillus_helveticus, Desulfovibrio_C21_c20, [Ruminococcus]_gnavus, Alistipes_finegoldii, Alistipes_onderdonkii, Sulfuricurvum_kujiense, Cellulomonas_uda, Butyricicoccus_pullicaecorum, Lactobacillus_vaginalis, Silene_vulgaris, Staphylococcus_sciuri, Helicobacter_hepaticus, Cetobacterium_somerae, Alistipes_massiliensis, Clostridium_cocleatum; by observing the gut microbiota with different levels of phyla and genus, it is found that there are 6 species of gut microbiota with callback effect at phyla level, namely, TM7, tenericutes, Chloroflex, cyanobacteria, acidobacteria, and WS6. There are 5 gut microbiota species with callback effect at the genus level, namely, [Prevotella], Helicobacter, Prevotella, Lactococcus, and anaeroplasma, as shown in Figure5.Figure 5
Random forest plot of differential gut microbiota of samples in each group. Note: (a) phylum-level differential gut microbiota; (b) class-level differential gut microbiota; (c) order-level differential gut microbiota; (d) family-level differential gut microbiota; (e) genus-level differential gut microbiota; (f) species-level differential gut microbiota.
(a)(b)(c)(d)(e)(f)


## 3.3.6. Prediction of Metabolic Pathways in Microbiota Sample Communities

In order to identify the signaling pathway of Huangqin decoction in the treatment of DE, this study used picurst2 to perform functional analysis on the treatment group and identified 170 signaling pathways through enrichment, as shown in Table3 below.Table 3
Signal pathway of DE.
PathwayDescriptionko00010Glycolysis/gluconeogenesisko00020Citrate cycle (TCA cycle)ko00030Pentose phosphate pathwayko00040Pentose and glucuronate interconversionsko00051Fructose and mannose metabolismko00052Galactose metabolismko00053Ascorbate and aldarate metabolismko00061Fatty acid biosynthesisko00071Fatty acid metabolismko00072Synthesis and degradation of ketone bodiesko00100Steroid biosynthesisko00120Primary bile acid biosynthesisko00121Secondary bile acid biosynthesisko00130Ubiquinone and other terpenoid-quinone biosynthesisko00140Steroid hormone biosynthesisko00190Oxidative phosphorylationko00195Photosynthesisko00196Photosynthesis-antenna proteinsko00230Purine metabolismko00240Pyrimidine metabolismko00250Alanine, aspartate, and glutamate metabolismko00253Tetracycline biosynthesisko00260Glycine, serine, and threonine metabolismko00270Cysteine and methionine metabolismko00280Valine, leucine, and isoleucine degradationko00281Geraniol degradationko00290Valine, leucine, and isoleucine biosynthesisko00300Lysine biosynthesisko00310Lysine degradationko00311Penicillin and cephalosporin biosynthesisko00312Beta-lactam resistanceko00330Arginine and proline metabolismko00340Histidine metabolismko00350Tyrosine metabolismko00360Phenylalanine metabolismko00361Chlorocyclohexane and chlorobenzene degradationko00362Benzoate degradationko00363Bisphenol degradationko00364Fluorobenzoate degradationko00380Tryptophan metabolismko00400Phenylalanine, tyrosine, and tryptophan biosynthesisko00410Beta-alanine metabolismko00430Taurine and hypotaurine metabolismko00440Phosphonate and phosphinate metabolismko00450Selenocompound metabolismko00460Cyanoamino acid metabolismko00471D-glutamine and D-glutamate metabolismko00472D-arginine and D-ornithine metabolismko00473D-alanine metabolismko00480Glutathione metabolismko00500Starch and sucrose metabolismko00510N-glycan biosynthesisko00511Other glycan degradationko00520Amino sugar and nucleotide sugar metabolismko00521Streptomycin biosynthesisko00523Polyketide sugar unit biosynthesisko00524Butirosin and neomycin biosynthesisko00531Glycosaminoglycan degradationko00540Lipopolysaccharide biosynthesisko00550Peptidoglycan biosynthesisko00561Glycerolipid metabolismko00562Inositol phosphate metabolismko00564Glycerophospholipid metabolismko00590Arachidonic acid metabolismko00591Linoleic acid metabolismko00600Sphingolipid metabolismko00601Glycosphingolipid biosynthesis-lacto and neolacto seriesko00620Pyruvate metabolismko00621Dioxin degradationko00622Xylene degradationko00623Toluene degradationko00624Polycyclic aromatic hydrocarbon degradationko00625Chloroalkane and chloroalkene degradationko00627Aminobenzoate degradationko00630Glyoxylate and dicarboxylate metabolismko00633Nitrotoluene degradationko00640Propanoate metabolismko00642Ethylbenzene degradationko00643Styrene degradationko00650Butanoate metabolismko00660C5-branched dibasic acid metabolismko00670One carbon pool by folateko00680Methane metabolismko00710Carbon fixation in photosynthetic organismsko00720Carbon fixation pathways in prokaryotesko00730Thiamine metabolismko00740Riboflavin metabolismko00750Vitamin B6 metabolismko00760Nicotinate and nicotinamide metabolismko00770Pantothenate and CoA biosynthesisko00780Biotin metabolismko00785Lipoic acid metabolismko00790Folate biosynthesisko00791Atrazine degradationko00830Retinol metabolismko00860Porphyrin and chlorophyll metabolismko00900Terpenoid backbone biosynthesisko00903Limonene and pinene degradationko00906Carotenoid biosynthesisko00908Zeatin biosynthesisko00909Sesquiterpenoid biosynthesisko00910Nitrogen metabolismko00920Sulfur metabolismko00930Caprolactam degradationko00941Flavonoid biosynthesisko00943Isoflavonoid biosynthesisko00960Tropane, piperidine, and pyridine alkaloid biosynthesisko00965Betalain biosynthesisko00970Aminoacyl-tRNA biosynthesisko00980Metabolism of xenobiotics by cytochrome P450ko00983Drug metabolism-other enzymesko01040Biosynthesis of unsaturated fatty acidsko01051Biosynthesis of ansamycinsko01053Biosynthesis of siderophore group nonribosomal peptidesko01055Biosynthesis of vancomycin group antibioticsko01056Biosynthesis of type II polyketide backboneko02010ABC transportersko02020Two-component systemko02030Bacterial chemotaxisko02040Flagellar assemblyko02060Phosphotransferase system (PTS)ko03008Ribosome biogenesis in eukaryotesko03010Ribosomeko03013RNA transportko03015mRNA surveillance pathwayko03018RNA degradationko03020RNA polymeraseko03030DNA replicationko03040Spliceosomeko03050Proteasomeko03060Protein exportko03070Bacterial secretion systemko03410Base excision repairko03420Nucleotide excision repairko03430Mismatch repairko03440Homologous recombinationko03450Nonhomologous end-joiningko04020Calcium signaling pathwayko04112Cell cycle-Caulobacterko04113Meiosis-yeastko04122Sulfur relay systemko04141Protein processing in endoplasmic reticulumko04142Lysosomeko04144Endocytosisko04145Phagosomeko04146Peroxisomeko04210Apoptosisko04310Wnt signaling pathwayko04621NOD-like receptor signaling pathwayko04622RIG-I-like receptor signaling pathwayko04626Plant-pathogen interactionko04722Neurotrophin signaling pathwayko04910Insulin signaling pathwayko04962Vasopressin-regulated water reabsorptionko04974Protein digestion and absorptionko05010Alzheimer’s diseaseko05012Parkinson’s diseaseko05100Bacterial invasion of epithelial cellsko05110Vibrio cholerae infectionko05111Vibrio cholerae pathogenic cycleko05120Epithelial cell signaling in Helicobacter pylori infectionko05130Pathogenic Escherichia coli infectionko05131Shigellosisko05143African trypanosomiasisko05145Toxoplasmosisko05146Amoebiasisko05150Staphylococcus aureus infectionko05200Pathways in cancerko05322Systemic lupus erythematosusko05410Hypertrophic cardiomyopathy (HCM)


## 3.4. Network Pharmacology Analysis

After entering the keyword, “Scutellaria baicalensis, Paeonia alba, jujube and licorice” in the TCMSP database, 34 effective components of Scutellaria baicalensis, 8 effective components of Paeonia alba, 20 effective components of jujube, and 90 effective components of licorice were screened according to the screening conditions ofOB≥30% and DL≥0.18. After merging and deleting multiple items, 141 components were obtained, as shown in Table 4. Using PubChem database (https://pubchem.ncbi.nlm.nih.gov/), obtain the SMILE standard structural formula of active ingredients and import it into the swisstargetprediction database (http://swisstargetprediction.ch/). Target prediction was carried out, duplicate targets were deleted, and a total of 265 targets related to active components were obtained.Table 4
Effective ingredients of HQD.
MOL IDIngredientOB (%)DLSourceMOL000422Kaempferol41.882249540.24066Licorice, white peony rootMOL000359Sitosterol36.913905830.7512Licorice, Scutellaria baicalensis, white peony rootMOL000098Quercetin46.433348120.27525Jujube, licoriceMOL000211Mairin55.377073380.7761Jujube, licorice, white peony rootMOL000358Beta-sitosterol36.913905830.75123Jujube, Scutellaria baicalensis, white peony rootMOL000449Stigmasterol43.829851580.75665Jujube, Scutellaria baicalensisMOL000492(+)-catechin54.826434050.24164Jujube, white peony rootMOL000096(-)-catechin49.67638680.24162JujubeMOL000627Stepholidine33.106250740.54083MOL000787Fumarine59.262504580.82694MOL001454Berberine36.861245040.77665MOL001522S-coclaurine42.350642170.23518MOL002773Beta-carotene37.184333370.58358MOL004350Ruvoside_qt36.121019530.75671MOL007213Nuciferin34.431028830.40475MOL012921Stepharine31.547866910.33376MOL012946Zizyphus saponin I_qt32.691135070.61923MOL012976Coumestrol32.487029290.33733MOL012981Daechuine S744.817744870.82806MOL012986Jujubasaponin V_qt36.989631090.63448MOL012992Mauritine D89.125093810.45286MOL000263Oleanolic acid29.020.76MOL000354Isorhamnetin49.604377050.306LicoriceMOL003656Lupiwighteone51.635691810.36739MOL004808Glyasperin B65.224386080.43851MOL004810Glyasperin F75.836800130.53514MOL004811Glyasperin C45.563806620.39947MOL004820Kanzonols W50.480075990.51704MOL004824(2S)-6-(2,4-dihydroxyphenyl)-2-(2-hydroxypropan-2-yl)-4-methoxy-2,3-dihydrofuro[3,2-g]chromen-7-one60.250409080.63433MOL004827Semilicoisoflavone B48.777551940.54732MOL004828Glepidotin A44.721874650.34685MOL004829Glepidotin B64.462923860.34485MOL0048493-(2,4-Dihydroxyphenyl)-8-(1,1-dimethylprop-2-enyl)-7-hydroxy-5-methoxy-coumarin59.622474980.42894MOL004855Licoricone63.578459380.4712MOL004856Gancaonin A51.075191070.40378MOL004857Gancaonin B48.794402010.44924MOL0048645,7-Dihydroxy-3-(4-methoxyphenyl)-8-(3-methylbut-2-enyl)chromone30.488776730.41002MOL004879Glycyrin52.606571660.47466MOL004883Licoisoflavone41.610218850.41646MOL004884Licoisoflavone B38.928708880.54714MOL004885Licoisoflavanone52.466247060.54488MOL004904Licopyranocoumarin80.360013310.6535MOL0049591-Methoxyphaseollidin36.565372330.32291MOL0049663′-Hydroxy-4′-O-methylglabridin43.714951410.57406MOL0049743′-Methoxyglabridin46.161509290.57393MOL005000Gancaonin G60.435205060.39404MOL005001Gancaonin H50.103723270.78416MOL005007Glyasperins M72.670809840.59274MOL005008Glycyrrhiza flavonol A41.275277330.59512MOL000497Licochalcone a40.789651990.28517MOL004328Naringenin59.293897730.21128MOL004903Liquiritin65.690111650.73893MOL000392Formononetin69.673880610.21202MOL000500Vestitol74.655189120.20935MOL001792DFV32.762723750.18316MOL002565Medicarpin49.219817610.3351MOL0038967-Methoxy-2-methyl isoflavone42.564741480.19946MOL004835Glypallichalcone61.597062270.18993MOL004941(2R)-7-hydroxy-2-(4-hydroxyphenyl)chroman-4-one71.122989010.18303MOL004957HMO38.36542380.21067MOL0049782-[(3R)-8,8-dimethyl-3,4-dihydro-2H-pyrano[6,5-f]chromen-3-yl]-5-methoxyphenol36.214292080.52122MOL000239Jaranol50.828816770.29148MOL001484Inermine75.183060380.53754MOL004806Euchrenone30.287260990.57386MOL004815(E)-1-(2,4-dihydroxyphenyl)-3-(2,2-dimethylchromen-6-yl)prop-2-en-1-one39.616855370.35077MOL004833Phaseolinisoflavan32.008107720.44538MOL0048662-(3,4-Dihydroxyphenyl)-5,7-dihydroxy-6-(3-methylbut-2-enyl)chromone44.151961260.41482MOL004891Shinpterocarpin80.295276880.72746MOL004908Glabridin53.245143280.46967MOL004910Glabranin52.895655080.31208MOL004911Glabrene46.266857210.43902MOL004912Glabrone52.512174190.49645MOL004915Eurycarpin A43.277284250.37429MOL004945(2S)-7-hydroxy-2-(4-hydroxyphenyl)-8-(3-methylbut-2-enyl)chroman-4-one36.565372330.32291MOL004961Quercetin der.46.44938840.3343MOL004980Inflacoumarin A39.709095980.32613MOL0049896-Prenylated eriodictyol39.223830180.41259MOL0049917-Acetoxy-2-methylisoflavone38.923331050.26217MOL0049938-Prenylated eriodictyol53.794763180.40383MOL005003Licoagrocarpin58.813902870.58498MOL005012Licoagroisoflavone57.282240980.48679MOL005016Odoratin49.948218170.30487MOL005020Dehydroglyasperins C53.823260140.37006MOL000417Calycosin47.751827830.24278MOL0048388-(6-Hydroxy-2-benzofuranyl)-2,2-dimethyl-5-chromenol58.437280910.38106MOL0048633-(3,4-Dihydroxyphenyl)-5,7-dihydroxy-8-(3-methylbut-2-enyl)chromone66.371250460.41392MOL002311Glycyrol90.775782230.66819MOL004805(2S)-2-[4-hydroxy-3-(3-methylbut-2-enyl)phenyl]-8,8-dimethyl-2,3-dihydropyrano[2,3-f]chromen-4-one31.787033530.72403MOL004814Isotrifoliol31.944787240.42422MOL004841Licochalcone B76.757354850.1935MOL004848Licochalcone G49.254963320.32325MOL004898(E)-3-[3,4-dihydroxy-5-(3-methylbut-2-enyl)phenyl]-1-(2,4-dihydroxyphenyl)prop-2-en-1-one46.267922560.3062MOL004907Glyzaglabrin61.068886310.35347MOL004924(-)-Medicocarpin40.993971990.95059MOL004935Sigmoidin-B34.881086160.41455MOL004948Isoglycyrol44.699225680.83845MOL004949Isolicoflavonol45.169990580.41859MOL004988Kanzonol F32.468333640.89364MOL0049907,2′,4′-trihydroxy-5-methoxy-3-arylcoumarin83.714367440.27136MOL005017Phaseol78.766219250.57867MOL005018Xambioona54.849162420.87419MOL0049131,3-Dihydroxy-9-methoxy-6-benzofurano[3,2-c]chromenone48.141542350.42831MOL0049141,3-Dihydroxy-8,9-dimethoxy-6-benzofurano[3,2-c]chromenone62.901354860.52759MOL004985Icos-5-enoic acid30.702942550.19725MOL004996Gadelaidic acid30.702942550.19725MOL004882Licocoumarone33.210850680.3568MOL001789Isoliquiritigenin85.320.15MOL00480418Beta-glycyrrhetinic acid22.050.74MOL000073Ent-epicatechin48.959841140.24162Scutellaria baicalensisMOL000173Wogonin30.684567060.22942MOL000228(2R)-7-hydroxy-5-methoxy-2-phenylchroman-4-one55.233173890.20163MOL000525Norwogonin39.403971840.20723MOL0005525,2′-Dihydroxy-6,7,8-trimethoxyflavone31.712464930.35462MOL001458Coptisine30.6718520.85647MOL001490Bis[(2S)-2-ethylhexyl] benzene-1,2-dicarboxylate43.593325470.34531MOL001689Acacetin34.973572730.24082MOL002714Baicalein33.518918690.20888MOL002879Diop43.593325470.39247MOL002897Epiberberine43.092332280.7761MOL0029095,7,2,5-Tetrahydroxy-8,6-dimethoxyflavone33.815825990.44739MOL002910Carthamidin41.150962730.24189MOL002913Dihydrobaicalin_qt40.037781030.20722MOL002914Eriodyctiol (flavanone)41.350427130.2436MOL002915Salvigenin49.065926060.33279MOL0029175,2′,6′-Trihydroxy-7,8-dimethoxyflavone45.047428020.33057MOL0029255,7,2′,6′-Tetrahydroxyflavone37.013486880.24382MOL002927Skullcapflavone II69.510433980.4379MOL002928Oroxylin a41.3675690.23233MOL002932Panicolin76.257049890.2915MOL0029335,7,4′-Trihydroxy-8-methoxyflavone36.562004690.26666MOL002934Neobaicalein104.34460520.43917MOL002937Dihydrooroxylin66.061738720.23057MOL008206Moslosooflavone44.087959590.25331MOL01041511,13-Eicosadienoic acid, methyl ester39.275344220.2289MOL0122455,7,4′-trihydroxy-6-methoxyflavanone36.626886280.26833MOL0122465,7,4′-Trihydroxy-8-methoxyflavanone74.235220010.26479MOL012266Rivularin37.940233550.3663MOL002776Baicalin40.120.75MOL001918Paeoniflorgenone87.593120840.36678White peony rootMOL001919(3S,5R,8R,9R,10S,14S)-3,17-dihydroxy-4,4,8,10,14-pentamethyl-2,3,5,6,7,9-hexahydro-1H-cyclopenta[a]phenanthrene-15,16-dione43.556201670.53276MOL001924Paeoniflorin53.870375160.78709To identify the main genes of Huangqin decoction’s anti-DE (Table5), Cytoscape 3.8.2 was used for visual analysis, and a protein-protein interaction network was constructed (Figure 6(c)). At the same time, the cytohubba plug-in was used to screen out the core targets. Combined with the score of the calculation method, the top 10 genes were considered as core genes (IL-6, TNF, TP53, IL1B, CASP3, JUN, PPARG, MAPK3, EGFR, and PTGS2) Table 6. GO annotation and KEGG pathway enrichment of the obtained potential target genes of Huangqin decoction against DE were performed by R language (Figures 6(d) and 6(e)). GO enrichment analysis showed that there was mainly positive regulation of nitric oxide biosynthetic process, response to ethanol, response to hypoxia, etc. According to KEGG enrichment analysis, the significantly affected pathways are bladder cancer, colorectal cancer, pancreatic cancer, leishmaniasis, hepatitis B, etc.Table 5
Target information of DE in network pharmacology.
No.Gene namesProtein namesUniprot ID1TGFB1Transforming growth factor beta-1 proprotein [cleaved into: latency-associated peptideP011372SLC6A4Sodium-dependent serotonin transporterP316453PTGS2Prostaglandin G/H synthase 2P353544PTGS1Prostaglandin G/H synthase 1P232195PRKCAProtein kinase C alpha typeP172526PRKACAcAMP-dependent protein kinase catalytic subunit alphaP176127PON1Serum paraoxonase/arylesterase 1P271698PIK3CGPhosphatidylinositol 4,5-bisphosphate 3-kinase catalytic subunit gamma isoformP487369OPRM1Mu-type opioid receptorP3537210JUNTranscription factor AP-1P0541211HTR2A5-Hydroxytryptamine receptor 2AP2822312HSP90AA1Heat shock protein HSP 90-alphaP0790013CASP9Caspase-9P5521114CASP8Caspase-8Q1479015CASP3Caspase-3P4257416BCL2Apoptosis regulator Bcl-2P1041517BAXApoptosis regulator BAXQ0781218ADRB2Beta-2 adrenergic receptorP0755019NR3C2Mineralocorticoid receptorP0823520XDHXanthine dehydrogenase/oxidase [includes: xanthine dehydrogenaseP4798921VCAM1Vascular cell adhesion protein 1P1932022TNFTumor necrosis factorP0137523STAT1Signal transducer and activator of transcription 1-alpha/betaP4222424SLPIAntileukoproteinaseP0397325SLC2A4Solute carrier family 2, facilitated glucose transporter member 4P1467226SELEE-selectinP1658127RELATranscription factor p65Q0420628PRSS1Trypsin-1P0747729PPARGPeroxisome proliferator-activated receptor gammaQ0820930NOS3Nitric oxide synthase, endothelialP3723131NOS2Nitric oxide synthase, inducibleQ1499432MMP1Interstitial collagenaseO7546933MAPK8Mitogen-activated protein kinase 8P2947434INSRInsulin receptorP3522835IKBKBInhibitor of nuclear factor kappa-B kinase subunit betaP0395636ICAM1Intercellular adhesion molecule 1P4598337HMOX1Heme oxygenase 1P0621338GSTM1Glutathione S-transferase Mu 1O1492039F7Coagulation factor VIIP0536240F2ProthrombinP0960141DPP4Dipeptidyl peptidase 4P0948842CYP3A4Cytochrome P450 3A4P0870943CYP1A1Cytochrome P450 1A1P0073444CDK1Cyclin-dependent kinase 1P2748745ARAndrogen receptorP0868446ALOX5Polyunsaturated fatty acid 5-lipoxygenaseP0479847AKT1RAC-alpha serine/threonine-protein kinaseP0649348AHRAryl hydrocarbon receptorP1027549ACHEAcetylcholinesteraseP0991750ESR1Estrogen receptorP3174951IL6Interleukin-6P3586952CD14Monocyte differentiation antigen CD14P2230353FASNFatty acid synthaseP1979354TP53Cellular tumor antigen p53P0337255THBDThrombomodulinP1842856SPP1OsteopontinP0523157SOD1Superoxide dismutase [Cu-Zn]P0857158SERPINE1Plasminogen activator inhibitor 1P4932759RUNX2Runt-related transcription factor 2P0463760RB1Retinoblastoma-associated proteinP0720461RAF1RAF protooncogene serine/threonine-protein kinaseP1045162PTENPhosphatidylinositol 3,4,5-trisphosphate 3-phosphatase and dual-specificity protein phosphatase PTENP0044163PRKCBProtein kinase C beta typeP0512164PPARDPeroxisome proliferator-activated receptor deltaQ1395065PPARAPeroxisome proliferator-activated receptor alphaP0640066PLAUUrokinase-type plasminogen activatorP0404967PLATTissue-type plasminogen activatorP6048468PARP1Poly [ADP-ribose] polymerase 1P0577169ODC1Ornithine decarboxylaseQ0318170NQO1NADQ0786971NFKBIANF-kappa-B inhibitor alphaP0074972NFE2L2Nuclear factor erythroid 2-related factor 2P0075073NCF1Neutrophil cytosol factor 1P0987474MYCMyc protooncogene proteinP1192675MPOMyeloperoxidaseP1555976MMP9Matrix metalloproteinase-9P2596377MMP3Stromelysin-1Q1623678MMP272 kDa type IV collagenaseP0110679MGAMMaltase-glucoamylase, intestinal [includes: maltaseP0516480MAPK1Mitogen-activated protein kinase 1P1478081IL2Interleukin-2P0825482IL1BInterleukin-1 betaP0825383IL1AInterleukin-1 alphaO4345184IL10Interleukin-10P2848285IGFBP3Insulin-like growth factor-binding protein 3P6056886IGF2Insulin-like growth factor IIP0158487IFNGInterferon gammaP0158388HSPB1Heat shock protein beta-1P2230189HSPA5Endoplasmic reticulum chaperone BiPP1793690HK2Hexokinase-2P0134491HIF1AHypoxia-inducible factor 1-alphaP0157992GJA1Gap junction alpha-1 proteinP0479293FOSProtooncogene c-FosP1102194F3Tissue factorP5278995ERBB3Receptor tyrosine-protein kinase erbB-3Q1666596ERBB2Receptor tyrosine-protein kinase erbB-2P1730297EGFREpidermal growth factor receptorP0110098CXCL8Interleukin-8P1372699CXCL10C-X-C motif chemokine 10P00742100CTSDCathepsin DP21860101CRPC-reactive protein [cleaved into: C-reactive protein]P04626102CLDN4Claudin-4P00533103CHEK2Serine/threonine-protein kinase Chk2Q01094104CDKN1ACyclin-dependent kinase inhibitor 1P10145105CD40LGCD40 ligandP19875106CCND1G1/S-specific cyclin-D1P02778107CCL2C-C motif chemokine 2P07339108CAV1Caveolin-1P02741109BIRC5Baculoviral IAP repeat-containing protein 5P02461110AKR1B1Aldo-keto reductase family 1 member B1O96017111ACACAAcetyl-CoA carboxylase 1P38936112ADRB1Beta-1 adrenergic receptorP29965113ADRA2AAlpha-2A adrenergic receptorP24385114HTR2C5-Hydroxytryptamine receptor 2CP13500115DRD4DQ03135116ADRA2BAlpha-2B adrenergic receptorO15392117PDE4DcAMP-specific 3′,5′-cyclic phosphodiesterase 4DQ07817118KDRVascular endothelial growth factor receptor 2P15121119HTR3A5-Hydroxytryptamine receptor 3AQ13085120CTNNB1Catenin beta-1P21397121DRD2DP08588122ESR2Estrogen receptor betaP35968123CDK2Cyclin-dependent kinase 2P35222124PTPN1Tyrosine-protein phosphatase nonreceptor type 1P55210125OLR1Oxidized low-density lipoprotein receptor 1P14416126MAPK14Mitogen-activated protein kinase 14P20813127GSK3BGlycogen synthase kinase-3 betaQ92731128SIRT1NAD-dependent protein deacetylase sirtuin-1P24941129MT-ND6NADH-ubiquinone oxidoreductase chain 6P18031130IL4Interleukin-4P78380131STAT3Signal transducer and activator of transcription 3Q16539132CDK4Cyclin-dependent kinase 4P49841133JAK2Tyrosine-protein kinase JAK2Q96EB6134SLC2A1Solute carrier family 2, facilitated glucose transporter member 1P03923135MAPK10Mitogen-activated protein kinase 10P05112136SREBF1Sterol regulatory element-binding protein 1P40763137SOAT1Sterol O-acyltransferase 1P11802138MTTPMicrosomal triglyceride transfer protein large subunitO60674139MAPK3Mitogen-activated protein kinase 3P11166140LDLRLow-density lipoprotein receptorP53779141HMGCR3-Hydroxy-3-methylglutaryl-coenzyme A reductaseP36956142GSRGlutathione reductase, mitochondrialP55157143CYP19A1AromataseP27361144BADBcl2-associated agonist of cell deathP01130145APOBApolipoprotein B-100P04035146ADIPOQAdiponectinP00390147CATCatalaseP11511148UGT1A1UDP-glucuronosyltransferase 1A1P23141149HSD11B2Corticosteroid 11-beta-dehydrogenase isozyme 2Q92934150CYP2E1Cytochrome P450 2E1P04114151KCNMA1Calcium-activated potassium channel subunit alpha-1Q15848152FN1FibronectinP33527153PRKCDProtein kinase C delta typeP04040154FASLGTumor necrosis factor ligand superfamily member 6P22309155CYCSCytochrome cP05181156CYP2C9Cytochrome P450 2C9P02751Figure 6
Network pharmacology analysis of Huangqin decoction in the treatment of DE. (a) Venn diagram of the intersection targets of Huangqin decoction and DE; (b) the component-target-disease interaction network of Huangqin decoction in the treatment of DE; (c) PPI network diagram of Huangqin decoction for anti-DE; (d, e) GO enrichment analysis and KEGG pathway enrichment analysis;P<0.05 for all pathways.
(a)(b)(c)(d)(e)Table 6
CytoHubba key genes screened.
Gene symbolRank methods in CytoHubbaMCCMNCDegreeEPCBottleNeckEcCentricityClosenessRadialityBetweennessStressIL-69.22E+1312324625.989240.51393.825811344.7337283272TNF9.22E+1311723425.10110.51363.7871874.0127978512TP539.22E+1311623223.73220.5135.53.78065771.8204770320IL1B9.22E+1311022025.05910.33333132.333333.73548506.510650800CASP39.22E+1310621223.82750.33333130.333333.70968520.9158153936JUN9.22E+1310420823.70110.33333129.333333.69677383.4897942960PPARG9.22E+1310120223.5510.33333127.833333.67742309.0781336088MAPK39.22E+139919824.044370.51273.67097555.1306749336EGFR9.22E+139819623.68910.5126.53.66452354.8507840368PTGS29.22E+139519023.30110.51253.64516700.31459256


## 3.5. Integrating 16S rRNA Gene Sequencing and Network Pharmacology Analysis

In order to identify the signaling pathway of Huangqin decoction in the treatment of DE, this study used picurst2 to analyze the function of the treatment group and identified 170 signaling pathways through enrichment. Similar signaling pathways are followed by apoptosis (apoptosis), calcium signaling pathway (calcium signaling pathway), cell cycle-Caulobacter (cell cycle-Caulobacter), insulin signaling pathway (insulin signaling pathway), neurotrophin signaling pathway (neurotrophin signaling pathway), NOD-like receptor signaling pathway, and RIG-I-like receptor signaling pathway. Based on the core genes obtained from these seven signaling pathways and network pharmacology, an integrated network map of Huangqin decoction for the treatment of DE was drawn. As shown in Figure7, the NOD-like receptor signaling pathway has the most significant node, and it has the strongest correlation with TNF and quercetin. Therefore, it is speculated that the mechanism of Huangqin decoction in the treatment of DE may be that the core component of Huangqin decoction, quercetin inhibits the expression of the TNF gene, thereby inhibiting the expression of NOD-like receptor signaling pathway and thereby achieving the therapeutic effect of the disease.Figure 7
Comprehensive network diagram of Huangqin decoction in the treatment of DE.


## 4. Discussion

This study combined network pharmacology and 16S rRNA sequencing. This study screened 156 active ingredients of Huangqin decoction in treating DE. Among them, quercetin as the core active ingredient, also known as quercetin, is a flavonoid compound with various biological activities. Quercetin and its derivatives are widely distributed in the plant kingdom, mostly in flowers, leaves, and fruits. Exist in the form of glycosides. It has expectorant, antitussive, antiasthmatic, anti-inflammatory, antiallergic, antispasmodic, cardiotonic, blood pressure lowering, coronary artery dilation, blood lipid lowering, antiarrhythmic, antiplatelet aggregation, antioxidant, antitumor, antioxidant, antidiabetic complications, and other pharmacological effects [18]. Ling and others showed that the mechanism of quercetin treatment in type 2 diabetic rats was that the substance activated the FGF21/MAPK signaling pathway in the pancreatic tissue increased the expression level of FGF21 and MAPK.In contrast, the high level of FGF21 could significantly reduce the bodyweight of type 2 diabetic rats and accelerate the reabsorption of blood glucose, thereby lowering the level of blood sugar.β function of cells was maintained and played a role in improving insulin resistance. MAPK is the key kinase downstream of FGF21. This enzyme accelerates the absorption and utilization of sugars by increasing the expression of GLUT4, and the increase of insulin receptor sensitivity and insulin resistance is achieved through MAPK phosphorylation to achieve the effect of treating type 2 diabetes [19]. Mao Xiaoming et al. found that quercetin can inhibit the activity of aldose reductase in the diabetic kidney by measuring the urinary protein in the kidney tissue of experimental diabetic rats, and early application can prevent or delay the occurrence of diabetes [20]; in the research on the protective mechanism of quercetin on the kidneys of diabetic rats, it was found that quercetin can improve oxidative stress and have anti-inflammatory effects, thereby exerting a protective effect on early diabetes [21].Tumor necrosis factor (TNF) has typical cytokine properties and is a major inflammatory factor and pleiotropic cellular regulatory protein. The excessive local release can trigger an inflammatory response and the body’s immune response process. This factor is closely related to insulin and promotes insulin resistance by interfering with the insulin signal transduction pathway, resulting in the clinical manifestations of insulin resistance [22]. The KEGG signaling pathway enrichment results showed that the most prominent signaling pathway was NOD-like receptor signaling pathway. Relevant studies have shown that the innate immune system can recognize a variety of pathogenic microorganisms and is the body’s first line of defense against pathogenic microorganisms. It recognizes invading pathogens by sensing pathogen-associated molecular patterns through specialized pattern recognition receptors (PRRs). They can be recognized by PRR-containing immune cells to initiate an immune response [23]. Among them, the NOD-like receptor family (NLR) in the cytoplasm, as one of the pattern recognition receptors, greatly influences the disease. NOD1 and NOD2 are members of the NODs subtypes of the NLRs family. NOD1 and NOD2 are cytoplasmic receptors for innate immunity that sense peptidoglycan from Gram-negative bacteria. Their functions have been extensively studied [24–26], revealing that they play a key role in host defense against pathogens such as Listeria monocytogenes, Helicobacter pylori, and Staphylococcus. In humans, dysregulation of NOD signaling pathways caused by mutations in NOD receptors, especially NOD2 receptors, is associated with inflammatory bowel disease. At the same time, related studies have found that NOD2 can recognize bacterial-derived cell muramyl dipeptide, which can induce the release of antimicrobial peptides and inflammatory signals required to maintain the homeostasis of intestinal flora, thereby protecting the host from bacterial invasion and thereby playing a role in preventing and treating diabetes. The expression of NOD2 is induced by bacterial components (lipopolysaccharide LPS), short-chain fatty acids (butyric acid), hormone vitamin D (1,25-dihydroxyvitamin D3), and proinflammatory cytokines (TNF-α), among others [27]. This is also consistent with the results of this experiment.Some human diseases, including inflammatory bowel disease (IBD), diabetes, obesity, metabolic syndrome, fatty liver, and some neurological diseases, have been confirmed to be related to intestinal flora imbalance. The continuous exposure of intestinal tissue to microorganisms puts the intestinal mucosa in a state of physiological inflammation, where proinflammatory and anti-inflammatory responses are in balance to maintain body homeostasis [28]. If this relationship is unbalanced, it will lead to dysbiosis, where pathogenic bacteria dominate the commensal bacteria, causing damage to the intestinal epithelial barrier, bacterial invasion, and inflammation [29, 30]. To date, multiple studies have highlighted the important role of Nod2 in maintaining the balance between the microbial community and the host immune response [31, 32]. At the same time, the 16S rRNA results were consistent with it. In this study, we found 6 species of bacteria with callback effect at the phylum level, namely, TM7, Tenericutes, Chloroflexi, Cyanobacteria, Acidobacteria, and WS6. Among them, TM7 and Tenericutes are in the callback function. The most important value ranks among the phyla, and the research species related to diabetes are considered to be related to the inflammatory response. Tenericutes is the third dominant bacterial phylum in this study, which has been reported to be involved in the occurrence and development of diabetes. This study is closely related to the inflammatory response caused by high glucose, and the inhibition of Softwallia helps control the inflammatory response related to diabetes [33]. The proportions of TM7 were 2.48%, 5.55%, and 0.88%, respectively, and there was an obvious correction. TM7 was the sixth bacterial phylum in this study, which was confirmed to be positively correlated with the occurrence of inflammation in diabetes [34]; the genus level had a callback. There are 5 species of bacteria that act, namely, [Prevotella], Helicobacter, Prevotella, Lactococcus, and Anaeroplasma. Prevotella is the genus with the highest significant value in this study, and its level reduction is considered to be related to the inhibition of diabetes. These changes are related to the inflammatory responses [35, 36]. These changes indicate that Huangqin decoction can regulate the intestinal flora of diabetic mice. By regulating the abundance of bacteria, it protects the intestinal mucosa, improves the intestinal barrier function, and inhibits the inflammatory response generated in the high glucose state.


## 5. Conclusion

Through network pharmacology and 16S rRNA sequencing analysis, it was found that the mechanism of Huangqin decoction in the treatment of DE may be the prevention and treatment of the disease by inhibiting the expression of inflammatory factors in intestinal epithelial cells, thereby regulating the intestinal flora, but the exact molecular mechanism remains to need further verified.



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*Source: 5080191-2022-04-07.xml*