The components of SGD, i.e., White attractylodes rhizome (Baizhu) (Shang Hai De Hua GuoYao; Lot number: 2018061101), white peony root (Baishao) (Shang Hai Hua Pu Zhong Yao; Lot number: 2018042901), dried old orange peel (Chenpi) (Shang Hai Lei Yun Shang Zhong Yao; Lot number: 1805037), ledebouriella root (Fangfeng) (Shang Hai Yu Tian Cheng Zhong Yao; Lot number:2017022706), and Radix bupleuri (Chaihu) (Ma Chen Jiu Zhou; Lot number: E2018050101), were purchased as crude herbs from JinKe Pharmacy (Shanghai, China). Saikosaponin A (National Institute for Food and Drug Control; Lot number: 110777-201912), paeoniflorin (National Institute for Food and Drug Control; Lot number: 110736-201943), 5-O-Methylvisammioside (National Institute for Food and Drug Control; Lot number: 111523-201811), hesperidin (National Institute for Food and Drug Control; Lot number: 110721-201818), and cimicifugoside (National Institute for Food and Drug Control; Lot number: 111522-201913) were purchased from Shanghai Zhaorui Biological Technology Co. (Shanghai, China).

The quality ratios of White attractylodes rhizome (Baizhu), white peony root (Baishao), dried old orange peel (Chenpi), ledebouriella root (Fangfeng), and Radix bupleuri (Chaihu) are 6:4:3:4:6. SGD extract was prepared by decoction and water extraction in the Herbal Chemistry Lab in Shanghai University of TCM. The extraction process has been described previously (Wang Y. et al., 2020): herbal pieces were first soaked in distilled water for 30 min, then they were boiled in 6 times of water for 1 h. Next, the mixture was filtrated with 4 layers of gauze, and the filtrate was collected. The procedure was repeated twice, and the filtrate was freeze-dried to obtain the powder. The steps of freeze-drying are as follows: First, we freeze the drug into a solid state, and then sublimate and dry it to remove the ice crystals in the drug by sublimation. Next, we desorb and dry it to evaporate some of the water remaining in the product at a higher temperature, so that the residual water can meet the requirements. Finally, the dried products are sealed and packaged under vacuum or filled with inert gas for storage.

According to the procedure described in our previous study (Wang Y. et al., 2020): Saikosaponin A, paeoniflorin, 5-O-Methylvisammioside, hesperidin, and cimicifugoside was dissolved in methanol and obtained 1 mg/mL standard solution separately. 500 mg SGD extract power was weighed and dissolved in distilled water. After ultrasonic shock for 40 min, the SGD solution was fixed at a constant volume of 10 mL. Then, 1 mL solution was injected into the activated C₁₈ column, eluted with 10 mL water, and then eluted with 10 mL methanol. The methanol eluent was collected, concentrated to dry, dissolved with 1 mL methanol, and 50.89 mg/mL SGD sample solution was obtained through 0.45 μm microporous membrane. The standard solution and the SGD sample solution were analyzed using the Dionex UltiMate™ 3000 RSLC nano system (Thermo Scientific, MA, USA) equipped with a Corona® ultra™CAD detector, Luna^® C18 Column (Phenomenex, 250 mm × 4.6 mm, 5 mm), and a data station with analytical software (CHROMELEON^®). Mobile phases consisted of A-Purified water and B-acetonitrile. Gradient was set as follows: 0 min, 5% B; 35 min 65.5% B; 35.001 min, 100% B; 40 min, 100% B. Column temperature was set at 25°C, DAD detection wavelength: 203, 254, 366 nm.

Thirty male Sprague-Dawley (SD) rats, weighing 200g ± 20g, provided by Shanghai Bikai Experimental Animal Co., Ltd. [production license No.: SCXK (Shanghai) 2018-0006], raised in the Experimental Animal Center of Shanghai University of TCM under the standard temperature (21–24°C), humidity (50% ± 5%), light and dark cycle (12 h/12 h), and they had free access to standard rat chow and tap water. All the experiments in this study are in accordance with the regulations of the Animal Ethics Committee of Shanghai University of TCM (No. PZSHUTCM190906001). All the experiments were carried out between 8:00 and11:00 AM to minimize potential confounding effects of diurnal variations.

After a week of adaptive feeding, rats were randomly divided into 3 groups (n = 10 in each group). SGD group: 10 days WAS and gavage with SGD (1.28 g/kg body weight, lyophilized powder dosage, once per day) since the 4th day; WAS group: 10 days WAS and gavage with the same dose of saline; Control group: gavage with the same dose of saline.

Refer to the method pioneered by Bradesi et al. and used in our previous studies (Bradesi et al., 2005), rats were placed on the platform (10 cm long, 8 cm wide, 8 cm high) which was fixed in the center of a organic glass pool (45 cm long, 25 cm wide, 25 cm high) filled with water (25°C) to suffer from WAS for 1 h every day in 10 consecutive days.

As described before (Bradesi et al., 2006), fecal pellets output in the one hour of WAS were counted to assess colonic motility every day for 10 consecutive days.

On the 10th day after WAS, the pressure threshold to induce abdominal withdrawal reflex (AWR) in rats was measured by colorectal distension (CRD) test. The methods were as previously described (Spence and Moss-Morris, 2007): a balloon (5 mm diameter and 1 cm long) with catheter (2 mm diameter) was inserted into the colorectum 1 cm above the anus. The catheter was fixed to the root of the rat tail with adhesive tape. Then the balloon was inflated gradually by one experimenter; the abdominal wall reactions of the rats were observed by another experimenter and a voice command was issued by him when the first AWR appeared; then the pressure value at this moment was recorded. Every two measurements were done with an interval of 3 min, and the average value was calculated after 3 times of measurement.

After SGD treatment and WAS, feces from the Control, WAS, and SGD groups were obtained under sterile conditions and stored at −80°C. The fecal samples were divided into two parts, one part was used to perform Metagenomics analysis, and another part was used for untargeted metabolomics analysis.

The colon tissues were fixed in 4% paraformaldehyde for 48 h after the luminal content was washed off with ice normal saline. Then the paraffin sections were made by dehydration, transparency, wax soaking, embedding and sectioning. Hematoxylin eosin (H&E) solution staining, neutral gum sealing, and observation under ordinary optical microscope (Nikon Corporation, Japan) were done in sequence.

DNA was extracted from rat fecal samples, then microbial DNA was fragmented, metagenomic sequencing was performed based on Illumina NovaSeq high-throughput sequencing platform, Whole Genome Shotgun (WGS) strategy was adopted. The extracted metagenomic total DNA was randomly interrupted into short fragments and inserted fragment libraries of appropriate length were constructed. These libraries were paired with PE sequencing. FastQC was used to test data quality. MEGAHIT was used for metagenomic sequence splicing. Meta GeneMark¹ was used for gene prediction and the identification of Open Reading Frame (ORF), the corresponding gene prediction files and protein sequences were obtained. The non-redundant protein sequence set was compared with the common protein database to annotate and analyze the gene function in each sample. QIIME (Quantitative Insights Into Microbial Ecology) software was used to obtain the relative abundance distribution table of each sample corresponding to each functional level of each database. By using the software MEGAN,² each sample and taxonomy of species abundance information of data can map to NCBI Taxonomy provided by the microbial classification tree,³ which can be in a standard classification system, uniformly present the specific composition of all samples at each classification level. Next, with the help of the “random forest” toolkit of R software, the random forest algorithm is used to select the functions/species with significant differences in abundance distribution among different groups. Specifically, in order to compare the diversity of different samples, the abundance spectrum of underlying functional groups or the composition spectrum of species level annotated in each functional database of all samples were firstly randomly resampled according to the lowest sequencing depth (i.e., “sequence volume leveling”), so as to correct the diversity differences caused by sequencing depth. Subsequently, QIIME software was used to calculate four diversity indices including Shannon index for each sample. On the basis of the above analysis, we conducted Beta diversity analysis on the abundance spectrum of functional annotation and the composition spectrum of species annotation respectively, so as to investigate the differences between samples at the two levels of bacterial flora function and species composition. Mainly through three methods: Principal Component analysis (PCA), Multidimensional Scaling analysis (MDS) and Clustering analysis, the metagenomic multi-dimensional data structure was decomposed naturally and the samples were ordinated to observe the differences between samples. R software and QIIME software were used to perform PCA analysis on the abundance spectrum or species level composition spectrum of the underlying functional groups annotated in each functional database of metagenomic samples, and 2D and 3D images were used to describe the natural distribution characteristics among samples. QIIME software was used to map the first two- or three-dimensional data obtained from PCoA analysis, so as to know the spatial distribution characteristics of community samples based on metagenomic functional abundance spectrum or species composition spectrum, and quantify the size of differences between samples (groups). R software was used to perform NMDS analysis on the Bray-Curtis distance matrix obtained, and the structure distribution of community samples was described by two-dimensional ranking map. Using QIIME software, the Bray-Curtis distance matrix obtained was analyzed by UPGMA clustering and visualized by R software. According to the abundance spectrum or species-level composition spectrum of the underlying functional groups annotated in the functional database of each sample (group), R software was used to calculate the number of common taxa of each sample (group), and the number of common and unique functions/species of each sample (group) was visually presented by Venn diagram.

The metabolites in feces were extracted and analyzed by UHPLC (Ultra high-performance liquid chromatography) platform of Shanghai Paiseno Technology Co., LTD. The specific steps of untargeted metabolomics mainly include: sample preparation, QC preparation, sample LC-MS/MS mass spectrometry, data analysis and experimental report, etc. In order to control the quality of this experiment, the researchers prepared QC samples at the same time, and QC samples were samples mixed with equal amounts of all samples. QC samples were used in the balanced chromatography-mass spectrometry system and the state of the instrument, and were used to evaluate the stability of the system throughout the experiment. After the liquid nitrogen was ground, 400 μl of pre-cooled methanol/acetonitrile/aqueous solution (4:4:2, V/V) was added to the sample, mixed by vortexing, stood at −20°C for 60 min, centrifuged at 14,000 g at 4°C for 20 min, and the supernatant was dried under vacuum. For mass spectrometry analysis, 100 μL acetonitrile aqueous solution (acetonitrile: Water = 1:1, v/v) were redissolved, vortexed, centrifuged at 14,000 g for 15 min at 4°C, and 2 μL of the supernatant was taken for sample analysis. The samples were separated on Agilent 1290 Infinity LC ultra-high performance liquid chromatography (UHPLC) HILIC column. The column temperature is 25°C; Flow rate 0.3 mL/min; Injection volume 2 μL; Mobile phase composition: A: water + 25 mM ammonium acetate + 25 mM ammonia, B: acetonitrile; The gradient elution procedure was as follows: 0–1 min, 95%b; 1–14 min, B changed linearly from 95 to 65%; 14–16 min, B changed linearly from 65 to 40%; 16–18 min, B maintained at 40%; 18–18.1 min, B changed linearly from 40 to 95%; 18.1– 23 min, B maintained at 95%; The samples were placed in an autosampler at 4°C during the whole analysis. Electrospray ionization (ESI) positive ion mode and negative ion mode were used for detection. The samples were separated by UHPLC and analyzed by mass spectrometry using a Triple TOF 6600 mass spectrometer (AB SCIEX). The ESI Source conditions after HILIC chromatographic separation were as follows: Ion Source Gas1 (Gas1): 60, Ion Source Gas2 (Gas2): 60, Curtain Gas (CUR): 30, Source temperature: 600°C, IonSapary Voltage Floating (ISVF) ± 5,500 V (both positive and negative modes); TOF MS Scan M/Z Range: 60–1,000 Da, Product ION Scan M/Z Range: 25–1,000 Da, TOF MS scan accumulation time is 0.20 s/spectra, Product ion scan accumulation time is 0.05 s/spectra; The secondary mass spectra were obtained using Information dependent acquisition (IDA) and their high sensitivity mode, Declustering potential (DP): In situ: ± 60 V (positive and negative modes), Collision Energy: 35 ± 15 eV, IDA non-frontiers within 4 Da, Candidate ions to monitor per cycle: 6. The final Data set was imported into SIMCA 16.0.2 software using an internal standard normalization method (Sartorius Stedim Data Analytics AB, Umea, Sweden; RRID:SCR_014688) was applied to principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA). One-dimensional statistical analysis including Student’s t-test and multiple variation analysis, R software was used to draw the volcano map. VIP > 1 and P value < 0.05 in OPLS-DA model were used as screening criteria, and then cluster analysis and KEGG metabolic pathway analysis were performed on the differentially expressed metabolites.

SPSS version 25.0 (SPSS, Chicago, IL, USA) and GraphPad Prism 9.0 (La Jolla, CA, USA) were used for data analysis. Each value was expressed as mean ± SE. If data were subject to normality and homogeneity of variance, one-way analysis of variance (One-way ANOVA) and followed LSD-t test was use for analyzing the differences among the groups. If disobedient, the rank-sum test was used. P < 0.05 was considered statistically significant.

It was found that compared with control group, the amount of fecal pellets output of rats in WAS group was significantly increased. Compared with WAS group, the SGD group had reduced amount of fecal pellets output (P < 0.05, Figure 1A). In addition, we found that compared with control group, visceral sensitivity threshold of rats in WAS group was decreased. Compared with WAS group, visceral sensitivity threshold of rats increased in SGD group (P < 0.001, Figure 1B). No significant pathological changes were found in colonic mucosa in WAS and SGD groups, compared with control group (Figure 1C).

We used a sequencing platform to conduct metagenomic sequencing of rat fecal bacteria DNA, aiming to study the changes of intestinal microbiota species in IBS-D model rats before and after SGD treatment. The results showed that shannon index in WAS group was significantly higher than that in control and SGD group, while there is no significant difference among the three groups in Simpson index (Figure 2A). In addition, Venn diagram analysis indicated that the three groups shared 19,748 OTUs, with 857 OTUs peculiar to control group, 1,050 OTUs peculiar to WAS group and 1,166 OTUs peculiar to SGD group (Figure 2B). The results of principal coordinate analysis (PCA) and systematic clustering tree both manifested that the intestinal microbiota of the three groups were significantly different (Figures 2C,D).

Next, we studied the changes of intestinal microbiota and its abundance at phylum and species level in each group. Firstly, at the phylum level, 20 phyla could be found in each group (Figure 3A) and the most abundant phyla in each group were Bacteroidetes and Firmicutes. Compared with control group, the relative abundance of Bacteroidetes in WAS group decreased (P < 0.05, Figure 3B) and Firmicutes increased (P < 0.05, Figure 3C). Compared with WAS group, the relative abundance of Bacteroidetes in SGD group increased (P < 0.05, Figure 3B). Secondly, at the species level, we found that, compared to control group, the relative abundance of Parabacteroides sp. CAG:409, Akkermansia muciniphila, Bacteroides sp. CAG:714 and Bacteroides Barnesiae decreased, and the relative abundance of Bacterium D42-87 increased in WAS group. Compared with WAS group, the relative abundance of Bacteroides sp. CAG:714, Lactobacillus reuteri and Bacteroides Barnesiae increased, and the relative abundance of Bacterium D42-87 decreased in SGD group (P < 0.05 and P < 0.01, Figures 4A,B). Besides, the possible function related to the differential intestinal microbiota has been analyzed. The results are shown in Figure 5.

To further investigate whether the improvement of VH and intestinal motility by SGD is related to the effect of significantly altered intestinal microbiota, we conducted correlation analysis between these significantly changed intestinal strains and visceral sensitivity threshold and amount of fecal pellets output in rats, respectively. On this basis, the heatmap is used to further analyze the correlation between significantly altered intestinal microbiota and rat phenotype parameters. As illustrated in the correlation heatmap, the abundances of Parabacteroides sp. CAG:409, Akkermansia muciniphila, Bacteroides sp. CAG:714, Bacterium D42-87, Lactobacillus reuteri and Bacteroides Barnesiae were positively correlated with visceral sensitivity threshold. Moreover, except for Bacterium D42-87, the abundances of Parabacteroides sp. CAG:409, Akkermansia muciniphila, Bacteroides sp. CAG:714, Lactobacillus reuteri and Bacteroides Barnesiae were all negatively associated with the amount of fecal pellets output (Figure 6).

Untargeted metabolomics analysis was performed by ultra-high performance liquid chromatography-Q-TOF MS. Volcanic map showed that there were 26 significantly upregulated metabolites in WAS group (The red dots in the figure are metabolites with FC > 2.0 and P value <0.05, that is, the difference metabolites screened by univariate statistical analysis) compared with control group (Figure 7A), 14 metabolites were significantly up-regulated in the feces of the SGD group compared with the control group (Figure 7B), and 44 significantly upregulated metabolites in SGD compared with the WAS group (Figure 7C).

In addition, we performed cluster analysis on all metabolites detected and constructed a heat map (Figure 8). The results showed that, compared with control group, N-acetyl-D-Galactosamine 4-sulfate, PG (18:0/22:6 (4Z,7Z,10Z,13Z,16Z,19Z), Brassylic acid, gibberellinA51-catabolite were significantly up-regulated in WAS group, while 5-amino-4-imidazolecarboxylate, (8R,9R,11Z)-1-carboxy-9-hydroxy-11-heptadecen-8-yl alpha- L-talopyranosiduronic acid, 2_2-iminodipropanoate, Piceatan-nol, 4-indolecarbaldehyde, ?-Cyano-3-hydroxycinnamic acid, 3″-hydroxy-geranylhydroquinone, GibberellinA8, Gentisic acid, Gallic acid, 1-methylProlinamide, Creatinine, 1-Linoleoyl-sn-glycero-3-phosphoethanolamine, Dezaguanine, 5-[(Z)-2-(3-hydroxy-4-methoxyphenylvinyl-1,3-benzenediol, Oleocanthal, Isorhapontigenin decreased significantly (Figure 8A). In addition, compared with control group, the 3-Hydroxysebacic acid, Letosteine, Dezaguanine, (2S,3S)-2,3-dihydro-3-hydroxyanthranilic acid zwitterion, trans-4-Hydroxy-L-proline, (8R,9R,11Z)-1-Carboxy-9-hydroxy-11-heptadecen-8-yl alpha-L-talopy- ranosiduronic acid, Creatinine, 1-linoleoyl-sn-glycero-3-phosphoethanolamine, Piceatannol, [FAhydroxy(22:0)]13-hydroxy-docosanoicacid in SGD group decreased significantly, while (5beta)-Chola-7,9(11)-dien-24-oic acid, Adrenic acid, Ethylenediaminetetraacetic acid, Karwinaphthol B, Brassylic acid, Gynocardin, 2-Hydroxyquinoline, (4S)-Cholest-5-ene-3beta_7alpha_24-triol, 5-Hydroxy-8-methoxy-2,2-dimethyl-7-(3-methyl-2-buten-1-yl)-2H,6H-pyrano[3,2-blxanthen-6-one, 3-tert-Butyladipic acid were significantly up-regulated (Figure 8B). Besides, compared with WAS group, the levels of 1H-lMidazole-4-carboxylic acid, D-Mannose 6-phosphate barium salt hydrate and Creatinine were significantly decreased, while Pentadecanedioic acid, Digitoxigenin, 3beta, 17beta-diacetoxy-5alpha-androstane, 2R_4S-2_4-Diaminopentanoate, [FAmethyl (14:O)]12-methyl-tetradecanoicacid, (9Z)-(13S)-12_13-Epoxyoctadeca-9_11-dienoicacid, 2-Hydroxyethanesulfonate, Sarmentosin, 3_4-Dihydroxy-L-phenylalanine (L-DOPA), Fluocinolone, Uric acid, Fluvastatin, Fortimicin FU-10, N-Acetyl-D-glucosamine and Neuraminicacid increased significantly in SGD group (Figure 8C).

Finally, Bioinformatics analysis showed that compared to control group, the metabolic pathways with significant differences in WAS group were as follows: endocrine resistance, tryptophan metabolism, prostate cancer and prolactin signaling pathway (Figure 9A and Table 1). In addition, compared to control group, the metabolic pathways that were significantly different in SGD group were as follows: purine metabolism, nicotinate and nicotinamide metabolism, biosynthesis of unsaturated fatty acids, and arginine and proline metabolism (Figure 9B and Table 1). What’s more, compared to WAS group, the metabolic pathways with significant differences in SGD group were as follows: taurine and hypotaurine metabolism, purine metabolism, sulfur metabolism, ABC transporters and bile secretion (Figure 9C and Table 1).