# Systems Pharmacology and Microbiome Dissection of Shen Ling Bai Zhu San Reveal Multiscale Treatment Strategy for IBD

Authors: Wei-jie Lv; Cui Liu; Yue-fei Li; Wen-qian Chen; Zeng-quan Li; Yue Li; Ying Xiong; Li-min Chao; Xiao-long Xu; Shi-ning Guo
Journal: Oxidative Medicine and Cellular Longevity
 (2019)

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## Abstract

Generally, inflammatory bowel disease (IBD) can be caused by psychology, genes, environment, and gut microbiota. Therefore, IBD therapy should be improved to utilize multiple strategies. Shen Ling Bai Zhu San (SLBZS) adheres to the aim of combating complex diseases from an integrative and holistic perspective, which is effective for IBD therapy. Herein, a systems pharmacology and microbiota approach was developed for these molecular mechanisms exemplified by SLBZS. First, by systematic absorption-distribution-metabolism-excretion (ADME) analysis, potential active compounds and their corresponding direct targets were retrieved. Then, the network relationships among the active compounds, targets, and disease were built to deduce the pharmacological actions of the drug. Finally, an “IBD pathway” consisting of several regulatory modules was proposed to dissect the therapeutic effects of SLBZS. In addition, the effects of SLBZS on gut microbiota were evaluated through analysis of the V3-V4 region and multivariate statistical methods. SLBZS significantly shifted the gut microbiota structure in a rat model. Taken together, we found that SLBZS has multidimensionality in the regulation of IBD-related physiological processes, which provides new sights into herbal medicine for the treatment of IBD.


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## Body

## 1. Introduction

Recent studies have revealed several factors responsible for the digestive diseases such as irritable bowel syndrome/inflammatory bowel disease (IBS and IBD) [1–5]. However, the cellular mechanisms behind these diseases are complex and unclear. In recent years, much attention has been focused on the development of herbal medicine for the treatment of digestive diseases. Shen Ling Bai Zhu San (SLBZS), which is composed of 10 herbs, has been proven to have wide pharmacological effects on digestive diseases, including anti-inflammatory and gut microbiota modulation effect [6, 7].A majority of herbal medicines exert pharmacological effects by targeting multiple host molecules. However, it is difficult to identify these herbal medicine targets. Therefore, a new method that can identify the active compounds and pharmacological targets of herbal medicine is in urgent need of development [8].Systems pharmacology, which combines oral bioavailability prediction, multitarget prediction, and network analyses, is used to identify the active compounds and pharmacological targets of herbal medicine [9–11]. Herein, we applied the systems pharmacology method to explore the pharmacological mechanisms of SLBZS.Additionally, high-throughput sequencing has been used to promote our understanding of the role of gut microbiota in health and disease [12]. For example, small intestinal bacterial overgrowth [13] and altered intestinal microbiota [14] are implicated in subgroups of patients with functional bowel disorders. However, due to the inherent limitations of the diagnostic methods, the exact evidence of the causal role of microbiota composition on the pathogenesis of the disease remains elusive.In this study, a combination of systems pharmacology and 16S rRNA boosts our exploration of the potential relationship among drug-microbiota-target.


## 2. Results

In this study, a combination of oral bioavailability (OB) (≥30%) screening, Caco-2 permeability (Caco-2) (>-0.4), prediction of permeability, half-life (HL) (long), and drug-likeness (DL) (≥0.18) properties was applied to explore the active compounds of SLBZS. We also addedDL<0.18, OB<30%, Caco−2≤−0.4, and HL = short with bioactivity as candidate compounds. Finally, we screened 97 SLBZS as candidate compounds (Table 1). The number of active compounds in Dolicho LablabL, Atractylodes macrocephala, Wolfiporia cocos, Glycyrrhiza uralensis Fisch, Platycodon grandiflorus, Semen Nelumbinis, Panax Ginseng, Fructus Amomi, Dioscorea opposita, and Semen Coicis was 4, 7, 15, 20, 3, 8, 23, 9, 10, and 8, respectively.Table 1
Candidate information.
No.
Compound
Herb
OB
Caco-2
HL
DL
Degree
Structure
MOL1
Luteolin
Platycodon grandiflorusSemen Nelumbinis
36.16
0.19
Long
0.25
17
MOL2
12-Senecioyl-2E,8E,10E-atractylentriol
Atractylodes macrocephala
62.40
0.01
Short
0.22
2
MOL3
14-Acetyl-12-senecioyl-2E,8E,10E-atractylentriol
Atractylodes macrocephala
60.31
0.33
Short
0.31
1
MOL4
14-Acetyl-12-senecioyl-2E,8Z,10E-atractylentriol
Atractylodes macrocephala
63.37
0.42
Short
0.30
1
MOL5
Alpha-humulene
Atractylodes macrocephala
22.98
1.88
Short
0.06
1
MOL6
(3S,8S,9S,10R,13R,14S,17R)-10,13-Dimethyl-17-[(2R,5S)-5-propan-2-yloctan-2-yl]-2,3,4,7,8,9,11,12,14,15,16,17-dodecahydro-1H-cyclopenta[a]phenanthren-3-ol
Atractylodes macrocephala
36.23
1.45
Long
0.78
3
MOL7
Atractylenolide I
Atractylodes macrocephala
37.37
1.30
Long
0.15
2
MOL8
3β-Acetoxyatractylone
Atractylodes macrocephala
54.07
1.13
Long
0.22
2
MOL9
Palmitic acid
Dolicho LablabL
19.30
1.09
Short
0.10
4
MOL10
Quercetin
Glycyrrhiza uralensis FischSemen Nelumbinis
46.43
0.05
Short
0.28
28
MOL11
Linoleic acid
Dolicho LablabL
41.90
1.16
Short
0.14
3
MOL12
(2R)-2-[(3S,5R,10S,13R,14R,16R,17R)-3,16-Dihydroxy-4,4,10,13,14-pentamethyl-2,3,5,6,12,15,16,17-octahydro-1H-cyclopenta[a]phenanthren-17-yl]-6-methylhept-5-enoic acid
Wolfiporia cocos
30.93
0.01
Short
0.81
4
MOL13
Trametenolic acid
Wolfiporia cocos
38.71
0.52
Short
0.80
2
MOL14
7,9(11)-Dehydropachymic acid
Wolfiporia cocos
35.11
0.03
Short
0.81
7
MOL15
Cerevisterol
Wolfiporia cocos
37.96
0.28
Long
0.77
2
MOL16
(2R)-2-[(3S,5R,10S,13R,14R,16R,17R)-3,16-Dihydroxy-4,4,10,13,14-pentamethyl-2,3,5,6,12,15,16,17-octahydro-1H-cyclopenta[a]phenanthren-17-yl]-5-isopropyl-hex-5-enoic acid
Wolfiporia cocos
31.07
0.05
Short
0.82
5
MOL17
Ergosta-7,22E-dien-3beta-ol
Wolfiporia cocos
43.51
1.32
Short
0.72
1
MOL18
(2R)-2-[(5R,10S,13R,14R,16R,17R)-16-Hydroxy-3-keto-4,4,10,13,14-pentamethyl-1,2,5,6,12,15,16,17-octahydrocyclopenta[a]phenanthren-17-yl]-5-isopropyl-hex-5-enoic acid
Wolfiporia cocos
38.26
0.12
Short
0.82
5
MOL19
3beta-hydroxy-24-methylene-8-lanostene-21-oic acid
Wolfiporia cocos
38.70
0.61
Short
0.81
2
MOL20
Pachymic acid
Wolfiporia cocos
33.63
0.10
Short
0.81
4
MOL21
Poricoic acid A
Wolfiporia cocos
30.61
-0.14
Short
0.76
5
MOL22
Poricoic acid B
Wolfiporia cocos
30.52
-0.08
Short
0.75
4
MOL23
Poricoic acid C
Wolfiporia cocos
38.15
0.32
Short
0.75
4
MOL24
Hederagenin
Wolfiporia cocos
36.91
1.32
Short
0.75
3
MOL25
Tumulosic acid
Wolfiporia cocos
29.88
0.13
Short
0.81
2
MOL26
Dehydroeburicoic acid
Wolfiporia cocos
44.17
0.38
Short
0.83
3
MOL27
Denudatin B
Dioscorea opposita
61.47
0.90
Long
0.38
2
MOL28
Beta-sitosterol
Panax GinsengFructus Amomi
36.91
1.32
Short
0.75
3
MOL29
Sitosterol
Glycyrrhiza uralensis FischSemen Coicis
36.91
1.32
Short
0.75
2
MOL30
Docosanoic acid
Dolicho LablabL
15.69
1.21
Short
0.26
1
MOL31
Rutin
Glycyrrhiza uralensis FischSemen Nelumbinis
3.20
-1.93
Long
0.68
8
MOL32
Kaempferol
Glycyrrhiza uralensis FischPanax Ginseng
41.88
0.26
Long
0.24
15
MOL33
Stigmasterol
Panax GinsengFructus AmomiDioscorea oppositaSemen Coicis
43.83
1.44
Short
0.76
1
MOL34
Licochalcone A
Glycyrrhiza uralensis Fisch
40.79
0.82
Short
0.29
2
MOL35
Cholesterol
Dioscorea oppositaSemen Coicis
37.87
1.43
Short
0.68
3
MOL36
Sitosterol alpha1
Semen Coicis
43.28
1.41
Short
0.78
3
MOL37
Mandenol
Semen Coicis
42.00
1.46
Short
0.19
4
MOL38
24-Ethylcholest-4-en-3-one
Fructus Amomi
36.08
1.46
Short
0.76
1
MOL39
Poriferast-5-en-3beta-ol
Fructus Amomi
36.91
1.45
Short
0.75
3
MOL40
Isoliquiritigenin
Glycyrrhiza uralensis Fisch
85.32
0.44
Short
0.15
7
MOL41
Sitosteryl acetate
Fructus Amomi
40.39
1.39
Short
0.85
3
MOL42
[(2R)-2,3-Dihydroxypropyl] (Z)-octadec-9-enoate
Semen Coicis
34.13
0.34
Short
0.30
5
MOL43
Gynesine
Dolicho LablabL
60.07
0.58
Short
0.03
6
MOL44
Icosa-11,14,17-trienoic acid methyl ester
Fructus Amomi
44.81
1.52
Short
0.23
4
MOL45
Spinasterol
Platycodon grandiflorus
42.98
1.44
Short
0.76
3
MOL46
Hyperin
Semen Nelumbinis
6.94
-1.42
Short
0.77
4
MOL47
18beta-glycyrrhetinic acid
Glycyrrhiza uralensis Fisch
22.05
0.10
Long
0.74
6
MOL48
Isotrifoliol
Glycyrrhiza uralensis Fisch
31.94
0.53
Long
0.42
3
MOL49
(2S)-6-(2,4-Dihydroxyphenyl)-2-(2-hydroxypropan-2-yl)-4-methoxy-2,3-dihydrofuro[3,2-g]chromen-7-one
Glycyrrhiza uralensis Fisch
60.25
0.00
Long
0.63
4
MOL50
Licochalcone B
Glycyrrhiza uralensis Fisch
76.76
0.47
Short
0.19
5
MOL51
Licochalcone C
Glycyrrhiza uralensis Fisch
4.44
0.63
Long
0.29
2
MOL52
Glycyrrhizic acid
Glycyrrhiza uralensis Fisch
19.62
-2.66
Long
0.11
2
MOL53
Shinpterocarpin
Glycyrrhiza uralensis Fisch
80.30
1.10
Long
0.73
5
MOL54
Glabridin
Glycyrrhiza uralensis Fisch
53.25
0.97
Long
0.47
5
MOL55
Isoglycyrol
Glycyrrhiza uralensis Fisch
44.70
0.91
Long
0.84
1
MOL56
Icos-5-enoic acid
Glycyrrhiza uralensis Fisch
30.70
1.22
Short
0.20
3
MOL57
Gadelaidic acid
Glycyrrhiza uralensis Fisch
30.70
1.20
Short
0.20
2
MOL58
Gancaonin H
Glycyrrhiza uralensis Fisch
50.10
0.60
Long
0.78
8
MOL59
18α-Hydroxyglycyrrhetic acid
Glycyrrhiza uralensis Fisch
41.16
-0.29
Short
0.71
2
MOL60
Xambioona
Glycyrrhiza uralensis Fisch
54.85
1.09
Long
0.87
3
MOL61
Deoxyharringtonine
Panax Ginseng
39.27
0.19
Short
0.81
5
MOL62
Dianthramine
Panax Ginseng
40.45
-0.23
Short
0.20
3
MOL63
Arachidonate
Panax Ginseng
45.57
1.27
Short
0.20
3
MOL64
Ginsenoside Ro
Panax Ginseng
1.98
-2.86
Long
0.05
3
MOL65
Ginsenoside Rb1
Panax Ginseng
6.24
-3.99
Long
0.04
2
MOL66
Ginsenoside-Rb2
Panax Ginseng
6.02
-3.92
Long
0.04
2
MOL67
Ginsenoside-Rc
Panax Ginseng
8.16
-3.97
Long
0.04
2
MOL68
Ginsenoside-Rg3
Panax Ginseng
17.75
-2.02
Long
0.22
2
MOL69
Ginsenoside rh2
Panax Ginseng
36.32
-0.51
Long
0.56
2
MOL70
Ginsenoside-Rh3_qt
Panax Ginseng
13.09
0.97
Long
0.76
2
MOL71
Ginsenoside-Rh4
Panax Ginseng
5.22
-0.73
Short
0.60
2
MOL72
Ginsenoside-Rh4_qt
Panax Ginseng
31.11
0.50
Short
0.78
2
MOL73
Ginsenoside-Rs1
Panax Ginseng
6.27
-3.69
Long
0.04
3
MOL74
Ginsenoside-Rs2
Panax Ginseng
8.14
-4.03
Short
0.04
3
MOL75
Gomisin B
Panax Ginseng
31.99
0.60
Long
0.83
5
MOL76
Panaxadiol
Panax Ginseng
33.09
0.82
Long
0.79
2
MOL77
Panaxytriol
Panax Ginseng
33.76
0.06
Short
0.13
3
MOL78
Alexandrin_qt
Panax Ginseng
36.91
1.30
Long
0.75
1
MOL79
Ginsenoside Rg5
Panax Ginseng
6.15
-1.92
Long
0.23
1
MOL80
Ginsenoside Rg5_qt
Panax Ginseng
39.56
0.88
Long
0.79
2
MOL81
Hancinol
Dioscorea opposita
64.01
0.53
Long
0.37
2
MOL82
Hancinone C
Dioscorea opposita
59.05
0.74
Long
0.39
1
MOL83
24-Methylcholest-5-enyl-3belta-O-glucopyranoside_qt
Dioscorea opposita
37.58
1.33
Short
0.72
1
MOL84
Campesterol
Dioscorea opposita
37.58
1.34
Short
0.71
1
MOL85
Isofucosterol
Dioscorea opposita
43.78
1.36
Short
0.76
1
MOL86
Dioscoreside C_qt
Dioscorea opposita
36.38
0.39
Long
0.87
2
MOL87
Doradexanthin
Dioscorea opposita
38.16
0.52
Short
0.54
4
MOL88
Platycodin D
Platycodon grandiflorus
7.60
-4.99
Long
0.01
2
MOL89
Methyl icosa-11,14-dienoate
Fructus Amomi
39.67
1.47
Short
0.23
3
MOL90
(5S,8S,9S,10R,13R,14S,17R)-17-[(1R,4R)-4-Ethyl-1,5-dimethylhexyl]-10,13-dimethyl-2,4,5,7,8,9,11,12,14,15,16,17-dodecahydro-1H-cyclopenta[a]phenanthrene-3,6-dione
Fructus Amomi
33.12
0.90
Short
0.79
1
MOL91
Stigmasta-5,22-dien-3-beta-yl acetate
Fructus Amomi
46.44
1.41
Short
0.86
3
MOL92
Coixenolide
Semen Coicis
32.40
1.09
Short
0.43
1
MOL93
2-Monoolein
Semen Coicis
34.23
0.32
Short
0.29
2
MOL94
Galuteolin
Semen Nelumbinis
2.70
-1.50
Short
0.79
3
MOL95
14-Methyl-24-methylene-dihydromangiferodiol
Semen Nelumbinis
36.87
0.79
Short
0.80
2
MOL96
4′-Methyl-N-methylcoclaurine
Semen Nelumbinis
55.35
1.44
Long
0.26
2
MOL97
Gamabufotalin
Semen Nelumbinis
36.32
-0.29
Short
0.80
1


### 2.1. Target Identification and Network Analysis

SLBZS exerts a pharmacological effect by targeting several host molecules [15, 16]. To identify the drug-target direct interactions on a large scale, we hypothesized that the ensemble features of the ligand group can accurately reflect the direct binding information of a specific target based on ligand-target interaction data to establish the WES model. In the present study, 74 potential targets were predicted for the 97 candidate compounds (Supp Table S1 and S2).


### 2.2. Network Construction

#### 2.2.1. Compound-Target Network

The compounds from SLBZS acted on multiple targets, and each target was involved with a variety of compounds. Figure1(a) shows that the compound-target network contains 171 nodes and 330 compound-target interactions. We screened 74 target proteins from the potential target list related to intestinal disease processes, including inflammatory bowel disease, duodenal ulcer, and colitis. Additionally, multiple relationships between the compounds and targets were illustrated in this network. For instance, quercetin exhibited the highest number of target candidate-target interactions (degree=28), followed by luteolin (degree=7) and kaempferol (degree=15), thereby indicating the multitarget properties of SLBZS ingredients.Figure 1
Compound-target-disease-pathway network. (a) Compound-target network of SLBZS consisting of 171 nodes (97 compounds and 74 potential targets) and 330 edges. (b) Target-disease network including 73 candidate targets and 4 diseases. (c) Target-pathway network including 59 candidate targets and 47 KEGG pathways.
(a)
(b)
(c)


#### 2.2.2. Target-Disease Network

To identify the relationship between SLBZS and disease, the DrugBank, TTD, and PharmGKB databases were searched. As shown in Figure1(b) and Supp Table S3, 73 SLBZS compounds were related to 4 types of diseases, including digestive system disease (degree=44), pathology processes (degree=28), signs and symptoms (degree=10), and cancer (degree=19).


#### 2.2.3. Target-Pathway Network

A total of 59 candidate targets and 47 KEGG pathways constituted the target-pathway network (Figure1(c)). Apparently, most targets were related to different pathways, indicating that SLBZS may exert synergistic effects through these different pathways. Additionally, one active compound may target different molecular targets, illustrating the multitarget mechanism of SLBZS. Moreover, to investigate the synergic effects of these 10 herbal medicines on IBD, an integrated “digestive-related pathway” approach was applied based on the current research of digestive disease pathology including the MAPK signaling pathway, NF-kappa B signaling pathway, calcium signaling pathway, and chemokine signaling pathway (Figure 2 and Supp Table S4 and S5).Figure 2
Intestinal disease-related pathway including MAPK signaling pathway, NF-kappa B signaling pathway, calcium signaling pathway, and chemokine signaling pathway.


#### 2.2.4. Target Tissue Location

Supp FigureS1 shows the tissue location network based on these 70 targets, which can be divided into six tissue modules, including the liver, heart, spleen, lung, kidney, and gut. Thus, the candidate compounds reflect multiple targets that can be localized to more than one tissue (Supp Table S6).


### 2.3. Structural Changes of Intestinal Microbiota by SLBZS

To analyze the alteration in the intestinal microbiota structure in rats treated with herbal medicine for IBD, the Illumina sequencing system was used to generate high-quality sequences from stool samples. Theα diversity of the intestinal microflora indicates that DSS can significantly decrease the Chao1 and Shannon diversity indices in both experimental groups (MOD and SLBZS), whereas the SLBZS group showed a higher Shannon index when compared to the Chao1 index after treatment with SLBZS (Figures 3(a)–3(d)). Principal component analysis (PCA) and principal coordinate analysis (PCoA) showed that SLBZS significantly altered the intestinal microbiota structure of rats (Figures 3(e) and 3(f)). Similarly, the unweighted UniFrac distance and unweighted pair-group method with arithmetic means (UPGMA) showed that DSS and SLBZS treatment can clearly separate rats (Figures 3(g) and 3(h)).Figure 3
The effect on the gut microbiota structure of SLBZS. (a–d) Rarefaction curves showing microbial richness based on the Chao1 index and microbial richness and evenness on the Shannon index. (e, f) Microbiome clustering based on unweighted principal component analysis (PCA) and principal coordinate analysis (PCoA) UniFrac metrics of fecal gut microbiota. (g, h) Unweighted UniFrac distance and unweighted pair-group method with arithmetic means (UPGMA) showed that DSS and SLBZS treatment can separate rats clearly. Statistical significant difference was assessed through one-way ANOVA with LSD post hoc test∗P<0.05, ∗∗P<0.01, and ∗∗∗P<0.001; n=6.
(a)
(b)
(c)
(d)
(e)
(f)
(g)
(h)To find key system types related to the efficacy of SLBZS, MetaStat analysis was used in this study. Using mothur software, the statistical algorithm of MetaStat [17] was used to perform a pairwise comparison test on the differences between the samples (groups) of each taxon at the gate and genus levels. A detailed phylogenetic analysis of the taxonomic composition of the microbiome of mice treated with DSS showed that the increased inflammatory conditions observed upon DSS administration were associated with variations in the abundance of specific taxa, including Firmicutes, Bacteroidetes, Proteobacteria, and Actinobacteria [18, 19] (Figure 4(a)).Figure 4
Gut microbiota analysis upon SLBZS treatment in colitic rats. (a, b) Bar plots of the taxonomic composition showingrelativeabundances>1% of bacterial phyla (a) and families (b). (c) Comparison of the relative abundances of different taxa among control, model, and SLBZS-treated rats.
(a)
(b)
(c)Significant changes towards restoration of normobiosis were detected among the less abundant genera belonging to theFirmicutes phylum in the DSS+SLBZS-derived microbiota. For instance, Corynebacteriaceae, Lactobacillaceae, Paraprevotellaceae, Veillonellaceae, Prevotellaceae, and Clostridiaceae which were expanded in colitic rats were reduced upon SLBZS treatment and returned to levels comparable to those observed in control group rats (Figure 4(b)).SLBZS-derived samples showed significant increases in known commensals, includingAdlercreutzia and Dorea, and in the SCFA-producing taxa Ruminococcus spp., Blautia sp., Clostridium spp., Veillonella spp., Coprococcus spp., and Roseburia spp. (Figure 4(c) and Supp Figure S2), which are reported to be reduced in IBD patients [20–22]. Additionally, PICRUSt was applied to predict microbiota metabolism [23]. 328 pathways were involved in the microbiota metabolism and 176 pathways were significantly changed by SLBZS (Figure 5 and Supp Figure S3). After treatment with SLBZS, the relative abundance of functional genes in the following categories significantly (P<0.01) increased: cell motility, signal transduction, membrane transport, and amino acid metabolism, all of which were reduced in the DSS-treated group. Meanwhile, cell growth and death, replication and repair, infectious diseases, immune system diseases, glycan biosynthesis and metabolism, and digestive system genes were significantly (P<0.01) decreased but were increased in the DSS group.Figure 5
KEGG statistics for the functional genes. The horizontal coordinates represent the relative abundance of functional genes in different groups; the vertical coordinates represent the functions of genes. The relative abundance of functional genes in the following categories was significantly changed (P<0.01).


### 2.4. Alleviation of IL-1β, IL-10, and TNF-α and Pathological Changes by SLBZS

We analyzed the data from the experiment, and after one week of treatment, IL-1β and TNF-α were significantly decreased by SLBZS, whereas IL-10 was increased. As shown in Figures 6(a)–6(c), IL-1β and TNF-α were significantly decreased, while IL-10 increased after treatment with SLBZS. The histological changes in the colons of each group are shown in Figures 6(d)–6(f). The colon of the control group exhibited normal histological features. In the model group, there was infiltration of colon mucosal inflammatory cells, and the intestinal villus epithelial cells were degenerated, necrotic, and shed. Pathological changes were significantly reduced in the SLBZS group.Figure 6
IL-1β (a), IL-10 (b), and TNF-α (c) can be significantly improved by SLBZS. ∗P<0.05; ∗∗P<0.01. Histological changes of the colon in the control group (d), the model group (e), and the SLBZS group (f). The colon in the control group (d) presented the normal histological feature. In the model group (e), there was intestinal inflammatory cell infiltration, intestinal villus epithelial cell degeneration, necrosis, and shedding. In the SLBZS group (f), the pathological change was significantly reduced. HE staining (×100).
(a)
(b)
(c)
(d)
(e)
(f)


## 2.1. Target Identification and Network Analysis

SLBZS exerts a pharmacological effect by targeting several host molecules [15, 16]. To identify the drug-target direct interactions on a large scale, we hypothesized that the ensemble features of the ligand group can accurately reflect the direct binding information of a specific target based on ligand-target interaction data to establish the WES model. In the present study, 74 potential targets were predicted for the 97 candidate compounds (Supp Table S1 and S2).


## 2.2. Network Construction

### 2.2.1. Compound-Target Network

The compounds from SLBZS acted on multiple targets, and each target was involved with a variety of compounds. Figure1(a) shows that the compound-target network contains 171 nodes and 330 compound-target interactions. We screened 74 target proteins from the potential target list related to intestinal disease processes, including inflammatory bowel disease, duodenal ulcer, and colitis. Additionally, multiple relationships between the compounds and targets were illustrated in this network. For instance, quercetin exhibited the highest number of target candidate-target interactions (degree=28), followed by luteolin (degree=7) and kaempferol (degree=15), thereby indicating the multitarget properties of SLBZS ingredients.Figure 1
Compound-target-disease-pathway network. (a) Compound-target network of SLBZS consisting of 171 nodes (97 compounds and 74 potential targets) and 330 edges. (b) Target-disease network including 73 candidate targets and 4 diseases. (c) Target-pathway network including 59 candidate targets and 47 KEGG pathways.
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### 2.2.2. Target-Disease Network

To identify the relationship between SLBZS and disease, the DrugBank, TTD, and PharmGKB databases were searched. As shown in Figure1(b) and Supp Table S3, 73 SLBZS compounds were related to 4 types of diseases, including digestive system disease (degree=44), pathology processes (degree=28), signs and symptoms (degree=10), and cancer (degree=19).


### 2.2.3. Target-Pathway Network

A total of 59 candidate targets and 47 KEGG pathways constituted the target-pathway network (Figure1(c)). Apparently, most targets were related to different pathways, indicating that SLBZS may exert synergistic effects through these different pathways. Additionally, one active compound may target different molecular targets, illustrating the multitarget mechanism of SLBZS. Moreover, to investigate the synergic effects of these 10 herbal medicines on IBD, an integrated “digestive-related pathway” approach was applied based on the current research of digestive disease pathology including the MAPK signaling pathway, NF-kappa B signaling pathway, calcium signaling pathway, and chemokine signaling pathway (Figure 2 and Supp Table S4 and S5).Figure 2
Intestinal disease-related pathway including MAPK signaling pathway, NF-kappa B signaling pathway, calcium signaling pathway, and chemokine signaling pathway.


### 2.2.4. Target Tissue Location

Supp FigureS1 shows the tissue location network based on these 70 targets, which can be divided into six tissue modules, including the liver, heart, spleen, lung, kidney, and gut. Thus, the candidate compounds reflect multiple targets that can be localized to more than one tissue (Supp Table S6).


## 2.2.1. Compound-Target Network

The compounds from SLBZS acted on multiple targets, and each target was involved with a variety of compounds. Figure1(a) shows that the compound-target network contains 171 nodes and 330 compound-target interactions. We screened 74 target proteins from the potential target list related to intestinal disease processes, including inflammatory bowel disease, duodenal ulcer, and colitis. Additionally, multiple relationships between the compounds and targets were illustrated in this network. For instance, quercetin exhibited the highest number of target candidate-target interactions (degree=28), followed by luteolin (degree=7) and kaempferol (degree=15), thereby indicating the multitarget properties of SLBZS ingredients.Figure 1
Compound-target-disease-pathway network. (a) Compound-target network of SLBZS consisting of 171 nodes (97 compounds and 74 potential targets) and 330 edges. (b) Target-disease network including 73 candidate targets and 4 diseases. (c) Target-pathway network including 59 candidate targets and 47 KEGG pathways.
(a)
(b)
(c)


## 2.2.2. Target-Disease Network

To identify the relationship between SLBZS and disease, the DrugBank, TTD, and PharmGKB databases were searched. As shown in Figure1(b) and Supp Table S3, 73 SLBZS compounds were related to 4 types of diseases, including digestive system disease (degree=44), pathology processes (degree=28), signs and symptoms (degree=10), and cancer (degree=19).


## 2.2.3. Target-Pathway Network

A total of 59 candidate targets and 47 KEGG pathways constituted the target-pathway network (Figure1(c)). Apparently, most targets were related to different pathways, indicating that SLBZS may exert synergistic effects through these different pathways. Additionally, one active compound may target different molecular targets, illustrating the multitarget mechanism of SLBZS. Moreover, to investigate the synergic effects of these 10 herbal medicines on IBD, an integrated “digestive-related pathway” approach was applied based on the current research of digestive disease pathology including the MAPK signaling pathway, NF-kappa B signaling pathway, calcium signaling pathway, and chemokine signaling pathway (Figure 2 and Supp Table S4 and S5).Figure 2
Intestinal disease-related pathway including MAPK signaling pathway, NF-kappa B signaling pathway, calcium signaling pathway, and chemokine signaling pathway.


## 2.2.4. Target Tissue Location

Supp FigureS1 shows the tissue location network based on these 70 targets, which can be divided into six tissue modules, including the liver, heart, spleen, lung, kidney, and gut. Thus, the candidate compounds reflect multiple targets that can be localized to more than one tissue (Supp Table S6).


## 2.3. Structural Changes of Intestinal Microbiota by SLBZS

To analyze the alteration in the intestinal microbiota structure in rats treated with herbal medicine for IBD, the Illumina sequencing system was used to generate high-quality sequences from stool samples. Theα diversity of the intestinal microflora indicates that DSS can significantly decrease the Chao1 and Shannon diversity indices in both experimental groups (MOD and SLBZS), whereas the SLBZS group showed a higher Shannon index when compared to the Chao1 index after treatment with SLBZS (Figures 3(a)–3(d)). Principal component analysis (PCA) and principal coordinate analysis (PCoA) showed that SLBZS significantly altered the intestinal microbiota structure of rats (Figures 3(e) and 3(f)). Similarly, the unweighted UniFrac distance and unweighted pair-group method with arithmetic means (UPGMA) showed that DSS and SLBZS treatment can clearly separate rats (Figures 3(g) and 3(h)).Figure 3
The effect on the gut microbiota structure of SLBZS. (a–d) Rarefaction curves showing microbial richness based on the Chao1 index and microbial richness and evenness on the Shannon index. (e, f) Microbiome clustering based on unweighted principal component analysis (PCA) and principal coordinate analysis (PCoA) UniFrac metrics of fecal gut microbiota. (g, h) Unweighted UniFrac distance and unweighted pair-group method with arithmetic means (UPGMA) showed that DSS and SLBZS treatment can separate rats clearly. Statistical significant difference was assessed through one-way ANOVA with LSD post hoc test∗P<0.05, ∗∗P<0.01, and ∗∗∗P<0.001; n=6.
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(h)To find key system types related to the efficacy of SLBZS, MetaStat analysis was used in this study. Using mothur software, the statistical algorithm of MetaStat [17] was used to perform a pairwise comparison test on the differences between the samples (groups) of each taxon at the gate and genus levels. A detailed phylogenetic analysis of the taxonomic composition of the microbiome of mice treated with DSS showed that the increased inflammatory conditions observed upon DSS administration were associated with variations in the abundance of specific taxa, including Firmicutes, Bacteroidetes, Proteobacteria, and Actinobacteria [18, 19] (Figure 4(a)).Figure 4
Gut microbiota analysis upon SLBZS treatment in colitic rats. (a, b) Bar plots of the taxonomic composition showingrelativeabundances>1% of bacterial phyla (a) and families (b). (c) Comparison of the relative abundances of different taxa among control, model, and SLBZS-treated rats.
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(c)Significant changes towards restoration of normobiosis were detected among the less abundant genera belonging to theFirmicutes phylum in the DSS+SLBZS-derived microbiota. For instance, Corynebacteriaceae, Lactobacillaceae, Paraprevotellaceae, Veillonellaceae, Prevotellaceae, and Clostridiaceae which were expanded in colitic rats were reduced upon SLBZS treatment and returned to levels comparable to those observed in control group rats (Figure 4(b)).SLBZS-derived samples showed significant increases in known commensals, includingAdlercreutzia and Dorea, and in the SCFA-producing taxa Ruminococcus spp., Blautia sp., Clostridium spp., Veillonella spp., Coprococcus spp., and Roseburia spp. (Figure 4(c) and Supp Figure S2), which are reported to be reduced in IBD patients [20–22]. Additionally, PICRUSt was applied to predict microbiota metabolism [23]. 328 pathways were involved in the microbiota metabolism and 176 pathways were significantly changed by SLBZS (Figure 5 and Supp Figure S3). After treatment with SLBZS, the relative abundance of functional genes in the following categories significantly (P<0.01) increased: cell motility, signal transduction, membrane transport, and amino acid metabolism, all of which were reduced in the DSS-treated group. Meanwhile, cell growth and death, replication and repair, infectious diseases, immune system diseases, glycan biosynthesis and metabolism, and digestive system genes were significantly (P<0.01) decreased but were increased in the DSS group.Figure 5
KEGG statistics for the functional genes. The horizontal coordinates represent the relative abundance of functional genes in different groups; the vertical coordinates represent the functions of genes. The relative abundance of functional genes in the following categories was significantly changed (P<0.01).


## 2.4. Alleviation of IL-1β, IL-10, and TNF-α and Pathological Changes by SLBZS

We analyzed the data from the experiment, and after one week of treatment, IL-1β and TNF-α were significantly decreased by SLBZS, whereas IL-10 was increased. As shown in Figures 6(a)–6(c), IL-1β and TNF-α were significantly decreased, while IL-10 increased after treatment with SLBZS. The histological changes in the colons of each group are shown in Figures 6(d)–6(f). The colon of the control group exhibited normal histological features. In the model group, there was infiltration of colon mucosal inflammatory cells, and the intestinal villus epithelial cells were degenerated, necrotic, and shed. Pathological changes were significantly reduced in the SLBZS group.Figure 6
IL-1β (a), IL-10 (b), and TNF-α (c) can be significantly improved by SLBZS. ∗P<0.05; ∗∗P<0.01. Histological changes of the colon in the control group (d), the model group (e), and the SLBZS group (f). The colon in the control group (d) presented the normal histological feature. In the model group (e), there was intestinal inflammatory cell infiltration, intestinal villus epithelial cell degeneration, necrosis, and shedding. In the SLBZS group (f), the pathological change was significantly reduced. HE staining (×100).
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## 3. Discussion

To our knowledge, this is the first study to evaluate the efficacy of TCM on IBD with a systems pharmacology and microbiota approach. Chinese herbs are a complex system of multicomponent, multitarget, and synergistic effects among its components. This systematic pharmacological technique is used to study TCM active compounds and target identification and identify targets and the relationship between drugs and diseases.In this study, some herbal medicines may contain the same compound, such asGlycyrrhiza uralensis Fisch and Panax Ginseng, Panax Ginseng and Fructus Amomi, and Panax Ginseng and Glycyrrhiza uralensis Fisch. Among these, Atractylodes macrocephala, Glycyrrhiza uralensis Fisch, and Panax Ginseng have been found to have extensive pharmacological effects, such as antioxidant, immunomodulation, and anti-inflammatory [24–29]. In particular, the low molecular weight Glycyrrhiza uralensis Fisch can inhibit tumor cell proliferation to exert anticancer and immunomodulatory effects. They also increase the thymus/spleen index and T lymphocyte population. In addition, Glycyrrhiza uralensis Fisch can increase the expression of antitumor factors such as IL2, IL6, and IL7 and reduce the protumor cytokine TNFα [24]. Shimato et al. have demonstrated that Atractylodes macrocephala can promote the production of G-CSF, clinically used to treat neutropenia and prevent chemotherapy-induced immunodeficiency [27]. Also, Panax Ginseng has been demonstrated to regulate multiple types of immune cells including macrophages, NK cells, DCs, T cells, and B cells [30]. Moreover, luteolin and quercetin, which existed widely in Glycyrrhiza uralensis Fisch, Platycodon grandiflorus, and Semen Nelumbinis, have been proven to have broad biological and pharmacological effects [31, 32].Despite the different numbers of each herbal-related targets, significant target overlap occurred between these herbals. The results indicate that these targets can be regulated by different herbs in SLBZS to play a synergistic role [15, 16]. For example, several compounds including alexandrin_qt, campesterol, isofucosterol, and panaxadiol were involved in modulating the activation of peroxisome proliferator-activated receptor gamma (Pparg), which might be ascribed to lipid and glucose metabolism [33, 34].Based on network construction, the results indicate that most compounds modulate multiple targets to exert their pharmacological effects. For example,quercetin exhibits not only antitumor [35] and anticancer [31] activity but also antiproliferative effects [36]. Antiproliferative effects of conjugated metabolites of quercetin have been evaluated against three different cancer cell lines [36]. In addition, luteolin presents anti-inflammatory effects, serves as a neuroprotective agent [37], and also exhibits antitumor effect [32, 38]. The antitumor effect of luteolin may be transmitted by cMet/Akt/ERK signaling [38]. Furthermore, the results indicated that several targets are hit by multiple ingredients in the compound-target network. Pparg showed the highest degree (degree=39), along with Sphk1 (Sphingosine kinase 1, degree=31), Klf5 (Kruppel-like factor 5, degree=19), and Akr1b10 (Aldo-keto reductase family 1 member B10, degree=19), demonstrating the potential therapeutic effect of each drug contained in SLBZS for combating intestinal disease via modulation of these relevant proteins. These targets play crucial pathological rules in disease related to cancer [39–42]. AKR1B10 has been shown to be closely related to tumor size and cell metastasis of gastric cancer, and AKR1B10 can be used as a good prognostic indicator for gastric cancer [39]. Deletion of KLF5 can result in a decrease in PI3K/AKT signaling and accumulate HIF1α in prostate tumors to promote tumor angiogenesis [40]. Previous research has illustrated that the PPARγ allele may be involved in the development, differentiation, and metastasis of gastric cancer in Turkey [43]. Sphk1 has been shown to be involved in the pathogenesis of human hepatocellular carcinoma (HCC), and DMS can inhibit the effects of Sphk1. Therefore, Sphk1 can be used as a potential target for the treatment of HCC [42].Reportedly, the proteins Mmp12, Pparg, and Ptgs2 are related to digestive system disease and cancer. Biancheri et al. demonstrated that Mmp12 contributes to the responsiveness of patients with IBD to anti-TNF agents [44] and might be involved in the remodeling of injured gut tissue with respect to the migration, proliferation, and differentiation of endothelial cells [45]. Pparg is involved in the inhibition of tissue damage associated with immune activation through the inhibition of the NF-κB pathway [46]. Ptgs2 can repair the injured intestinal mucosa and exert a critical role in the pathophysiology of Salmonella typhimurium-induced ulcerative colitis [47].Some pathways, such as the NF-κB pathway [46], MAPK pathway [48, 49], calcium signaling pathway [50], and chemokine signaling pathway [51], have been improved in relation to digestive disease. In addition, these pathways are reflected in several modules such as inflammation, proliferation apoptosis, survival, and proliferation.The MAPK cascade is a highly conserved module that is involved in various cellular functions, including cell proliferation, differentiation, and migration. A study on the pharmacological approach showed that stevioside exerts antiapoptotic and anti-inflammatory effects through the inhibition of the release of cytokines and the activation of the MAPK signaling pathway [52]. Another pharmacological study showed that berberine might execute an antiapoptotic function by inhibiting the MAPK pathway [53]. In macrophages, carbon monoxide (CO) reduces lipopolysaccharide-induced proinflammatory cytokines effectuated by the MAPK pathway [48]. Recent studies have highlighted that oxidative stress activates MAPKs and MT2A (a mediator of MAPKs) that play a crucial role in antiapoptosis and anti-inflammation [54]. Studies have shown that black raspberries (BRB) have a preventive effect on rat esophageal cancer, and the mechanism may be that BRB reverse oxidative stress and inhibit NF-κB/MAPK pathway [55]. Rutin exerts neuroprotective effects by increasing superoxide dismutase and glutathione peroxidase levels in the peripheral blood and inhibiting the p38 MAPK pathway [56]. This finding reveals the importance of MAPKs in the normal digestive function.NF-κB is found in almost all animal cell types and is involved in cellular responses to stimuli such as stress, cytokines, free radicals, heavy metals, ultraviolet irradiation, oxidized LDL, and bacterial or viral antigens. NF-κB also regulates the expression of antiapoptotic genes which play an important role in cell survival and play various roles in cell development, proliferation, differentiation, and metabolism [57]. Furthermore, the degree of NF-κB activation induced by LPS is significantly increased in Atg7-deficient intestinal epithelium. This also shows that autophagy can relieve endotoxin-induced inflammatory responses in intestinal endothelial cells, thereby maintaining intestinal homeostasis [58]. Both innate and adaptive immune responses are associated with NF-κB, and the development and maintenance of cells and tissues associated with the immune system are under the control of the NF-κB transcription factor family in multiple pathways. Initially, the role of NF-κB in the thymus was limited to the important role of RelB [59], but it became clearer in the development of medullary thymic epithelial cells [60–62].Calcium (Ca2+) ions are important for cell signaling, and they exert an allosteric regulation of many enzymes and proteins once they enter the cytosol of the cytoplasm. Studies have shown that intracellular Ca2+ release is accomplished by Zn+ triggering inositol 1,4,5-triphosphate (IP3) [63, 64]. Activation of mitogen-activated protein (MAP) and phosphoinositide 3 (PI3) kinase pathways protects colonic epithelial cells. In experimental colitis, metabolic calcium signaling in colonocytes is induced by zinc-induced receptors [65]. In Caco-2 cell monolayers, DSS increases the intracellular Ca2+ concentration and depletes intracellular Ca2+ by BAPTA/AM, disrupts tight junctions, and causes barrier dysfunction [66]. These findings reveal the importance of the calcium signaling pathway, which might be instrumental to the success of future human trials of a new strategy to treat digestive disease.Chemokine receptors are cytokine receptors found on the surface of certain cells that interact with a type of cytokine called a chemokine. Chemokines can activate a range of signaling pathways to mediate their biological effects by binding to G-protein-coupled receptors (GPCRs). Duffy and D6 bind to CXC and CC inflammatory chemokines, respectively [67]. Chemokines can be divided into “inflammatory” chemokines and “steady state” chemokines [68]. Inflammatory chemokines can play important roles in recruiting leukocytes to the site of inflammation, such as neutrophils, monocyte macrophages, dendritic cells (DC), and natural killer (NK), which play key roles in the innate immune response. The results demonstrated that the chemokine signaling pathway can be further divided into modules such as survival, migration, apoptosis, cellular growth, and cytokine production. In the immune system, chemokines are mainly produced and transported by leukocytes and play an important role in the immune system [69]. Chemokines act by binding to chemokine receptors. Activation of chemokine receptors induces proliferation and differentiation of immune cells, and both are essential processes in innate and adaptive immune responses [70].In summary, these pathways are regulated by different compounds, suggesting that digestive diseases may affect different pathways. In addition, multiple protein targets belong to a variety of signaling pathways, suggesting that certain proteins can simultaneously affect multiple signaling pathways.In this experiment, IL-1β, IL-10, and TNF-α were analyzed. IL-1β and TNF-α participate in the MAPK and NF-κB pathways, and the two pathways share crosstalk. Recent studies have shown that IL-1β and TNF-α can be proinflammatory cytokines in IBD [71–74], whereas IL-10 has been demonstrated to play a vital role in the control of inflammation and prevention of enteritis [75]. In patients with colitis and inflammatory bowel disease, damaged mitochondria accumulate in macrophages when IL-10 signal is deficient. This leads to abnormal activation of NLRP3 inflammasome and IL-1β production [76, 77].In our research, changes to the intestinal microbial structure by SLBZS were observed. SLBZS treatment inhibits two genera that contain potential pathogens,Corynebacterium and Helicobacter, which are strongly associated with peptic ulcers, chronic gastritis, duodenitis, and stomach cancer [78]. Blautia is a group of bacteria containing various acetate and butyrate producers [79, 80]. This research observed that an increase in the SCFA producer Blautia in the SLBZS treatment group was consistent with previous animal studies [81]. Roseburia and Lachnospira, as SCFA producers, were reported to recover a balanced community after diet treatment in type 2 diabetic patients [82]. Ruminococcus is present in the digestive tract of ≥90% of people and is involved in diseases associated with the intestines, such as IBD. A recent study demonstrated the ability of Ruminococcus to produce propanol and propionate as the end products of metabolism [83]. These new findings provide us with insights into the specificity of the genera’s adaptability to the gut environment and promote our understanding of the role of gut commensals in health and disease.Based on the 16S rRNA results, we found that SLBZS treatment can alter the microbial structure of the intestine, which has an enhanced effect on the intestinal microbiota richness and diversity in DSS model rats. Therefore, SLBZS seems to offset the structural changes caused by DSS. As a result, the identified bacterial phylotypes in response to SLBZS treatment in DSS rats may be related to the development and improvement of DSS-induced metabolic abnormalities. The overrepresentation of the KEGG gene in the patient’s microbiome can reflect the deleterious metabolism of the neurotransmitter pathway and the host gut protection glycosaminoglycan mucin, in contrast to the beneficial counterpart in the control [84]. Our PICRUSt analyses revealed that SLBZS can significantly increase amino acid metabolism, which may indicate SCFA. This is consistent with the increased SCFA-producing genera. Infectious disease, immune system disease, and digestive system disease were all reduced after treatment with SLBZS, which had a positive effect on IBD.However, a large number of validation tests are required. The systems pharmacology results can directly prove the effect of the active compounds of SLBZS on the target, while a change in the microbiota structure can supplement the interaction between the Chinese herbal compounds and gut commensals, then revealing the underlying mechanism. Taken together, the combination of the two methods can systematically illustrate the relationship among active compounds, targets, and the microbiota and contribute to the study of the mechanism of IBD.


## 4. Materials and Methods

### 4.1. Dataset Construction

The current data were obtained from the TCM pharmacology analysis platform (TCMSP,http://lsp.nwu.edu.cn/tcmsp.php) and a large amount of literature mining. We collected 980 compounds and their physicochemical properties from SLBZS: 14 compounds of Dolicho LablabL, 55 compounds of Atractylodes macrocephala, 34 compounds of Wolfiporia cocos, 280 compounds of Glycyrrhiza uralensis Fisch, 102 compounds of Platycodon grandiflorus, 31 compounds of Semen Nelumbinis, 190 compounds of Panax Ginseng, 165 compounds of Fructus Amomi, 71 compounds of Dioscorea opposita, and 38 compounds of Semen Coicis. Detailed information is freely available from the TCMSP analysis platform.


### 4.2. Active Compound Screening Model

#### 4.2.1. Oral Bioavailability (OB)

In the present study, oral bioavailability (OB) was estimated based on OBioavail 1.1 [85] and the IntegOB. OB is one of the major pharmacokinetic parameters in the ADME (absorption, distribution, metabolism, excretion) profile of a drug. The molecule with suitable OB≥30% served as the candidate compound for further research.


#### 4.2.2. Caco-2 Permeability

The oral absorption of drugs was primarily completed via intestinal epithelial cells (IEC). In this study, a computer Caco-2 permeability prediction model was applied to predict intestinal permeability of all TCM components in TCMSP. The number ofCaco−2molecules>−0.4 is considered to exhibit adequate intestinal epithelial permeability.


#### 4.2.3. DL (Drug-Likeness)

Drug-likeness is a qualitative concept used in drug design for how “druglike” a substance is with respect to factors like bioavailability. It is estimated from the molecular structure before the substance is even synthesized and tested.


#### 4.2.4. HL (Half-Life)

The half-life of a substance is the duration required for a drug to lose half of its pharmacological activity. HL = long is defined as a suitable half-life range.


### 4.3. Drug Targeting

Drug targeting was implemented by a novel computational model designed to detect the direct drug targets based on an in-house weighted ensemble similarity (WES) method [86] with satisfactory validation of both internal and external data.


### 4.4. Network Construction

TCM is a complex material system comprised of the effective active compound, the target of the action, and the related diseases; it does not correspond to the theory of “single gene, single target, and single disease.” In order to resolve these issues, we constructed a compound-target network to reveal the association between the drug and the target protein. The construction of the network aided in identifying the protein targets of each compound in Chinese medicine, understanding the mechanism underlying the activity of the drug in the treatment of the disease, and studying the target in the disease network. Thus, in the generated network, nodes represent compounds, targets, signaling pathways, or diseases, while edges represent compound-target, target-disease, and target-pathway interactions. The bipartite graphs were generated using Cytoscape 2.8.1 [87].


### 4.5. Compound Organ Location

In order to elucidate the gut disorder at the organ level, firstly, the GO targets were analyzed for most of the selected targets, followed by the analysis of distribution in tissues and organs. Determination of target tissue distribution was based on microarray analysis data of different tissue types in BioGPS database (http://biogps.org).
(1)Ω=t1,t2,⋯,tn,Hi=hit1,hit2,⋯,hitn,hi¯=∑i=1nhin,Ai=t∈Ω∣t>hi¯,wherein t represents human tissue, Ω represents tissue location, h represents a tissue-specific pattern of mRNA expression of one target, Hi represents the expression position of mRNA of one target in Ω, hi¯ represents one target in the tissue of the average expression, n is the number of organizations, and Ai indicates a target tissue location.


### 4.6. Animal Management

A rat model test was performed to test the effect of SLBZS on the IBD model. After a week of acclimation period, 30 rats (male and 10 weeks) were randomly divided into 3 groups—control group (CON), model group (MOD), and SLBZS group (SLBZS). The MOD and SLBZS group rats were administered 3% DSS (40 kDa; MP Biomedicals) for 7 days, while the CON was administered with equal volume of saline. After 7 days, the SLBZS group was intragastrically administered with SLBZS 2 mL (1.2 g) for 7 days, and the other two groups were orally given an equal volume of saline. The condition of the rats was monitored twice per day. After 24 hours of the last administration, feces, serum, and colon were collected and stored at -80°C. The animal experiments were approved by the Institutional Animal Care and Use Committee of South China Agricultural University (Approval No. CNAS BL0011).


### 4.7. Preparation of SLBZS

SLBZS was prepared using the published method [7]. SLBZS is a traditional Chinese patent medicine, composed of Panax Ginseng (10 g), Wolfiporia cocos (10 g), Atractylodes macrocephala (10 g), Dioscorea opposita (10 g), Dolichos Lablab (7.5 g), Semen Nelumbinis (5 g), Semen Coicis (5 g), Fructus Amomi (5 g), Platycodon grandiflorus (5 g), and Glycyrrhiza uralensis Fisch (5 g). The ten Chinese herbs were purchased from qualified suppliers based on standards specified in the Chinese Pharmacopoeia (Guangzhou, China). The herb materials were mixed and extracted twice at 80°C by stirring it for 1 h using 10 vols of distilled water (v/m). Then, we centrifuged the extract at 1500 ×g at room temperature. The SLBZS decoction was filtered and then concentrated to 1 g mL−1 (net content) with deionized water.


### 4.8. 16S rRNA Gene Sequence Analysis of Intestinal Flora in Fecal Samples

Total genomic DNA of fecal samples was extracted by the InviMag Stool DNA Kit (Invitek, Germany) as previously described [7]. Fecal microbial DNA was extracted using Fast DNA SPIN extraction kits (MP Biomedicals, Santa Ana, CA, USA) and applied to amplification of the V3-V4 region of 16S rDNA. Fecal microbiota composition was assessed using Illumina HiSeq sequencing of 16S rDNA Amplicon and QIIME-based microbial analysis. The procedures for fecal microbial DNA extraction, sequencing and library construction, and microbial analysis are described in the supplementary methods.


### 4.9. Histologic Observation of the Colon

Histologic colon samples were prepared as previously described [7]. The colon samples were collected and fixed in 10% formalin, dehydrated with a sequence of ethanol solutions, embedded in paraffin, sliced (4-5 μm), and stained with hematoxylin and eosin (HE), then observed with Olympus BH22 Microscope (Japan).


### 4.10. Measurement of Serum Cytokines

Interleukin- (IL-) 1β, IL-10, and tumor necrosis factor-alpha (TNF-α) were tested using an ELISA kit (Cusabio, Houston, TX, USA; https://www.cusabio.com/). Indices were tested according to the manufacturer’s instructions.


### 4.11. Quantification and Statistical Analysis

Statistical analysis was performed with GraphPad Prism 5 (GraphPad Software). Statistical significance was calculated using Kruskal-Wallis test with Dunn’s multiple comparison correction.∗P<0.05, ∗∗P<0.01, and ∗∗∗P<0.001 were regarded as statistically significant.


## 4.1. Dataset Construction

The current data were obtained from the TCM pharmacology analysis platform (TCMSP,http://lsp.nwu.edu.cn/tcmsp.php) and a large amount of literature mining. We collected 980 compounds and their physicochemical properties from SLBZS: 14 compounds of Dolicho LablabL, 55 compounds of Atractylodes macrocephala, 34 compounds of Wolfiporia cocos, 280 compounds of Glycyrrhiza uralensis Fisch, 102 compounds of Platycodon grandiflorus, 31 compounds of Semen Nelumbinis, 190 compounds of Panax Ginseng, 165 compounds of Fructus Amomi, 71 compounds of Dioscorea opposita, and 38 compounds of Semen Coicis. Detailed information is freely available from the TCMSP analysis platform.


## 4.2. Active Compound Screening Model

### 4.2.1. Oral Bioavailability (OB)

In the present study, oral bioavailability (OB) was estimated based on OBioavail 1.1 [85] and the IntegOB. OB is one of the major pharmacokinetic parameters in the ADME (absorption, distribution, metabolism, excretion) profile of a drug. The molecule with suitable OB≥30% served as the candidate compound for further research.


### 4.2.2. Caco-2 Permeability

The oral absorption of drugs was primarily completed via intestinal epithelial cells (IEC). In this study, a computer Caco-2 permeability prediction model was applied to predict intestinal permeability of all TCM components in TCMSP. The number ofCaco−2molecules>−0.4 is considered to exhibit adequate intestinal epithelial permeability.


### 4.2.3. DL (Drug-Likeness)

Drug-likeness is a qualitative concept used in drug design for how “druglike” a substance is with respect to factors like bioavailability. It is estimated from the molecular structure before the substance is even synthesized and tested.


### 4.2.4. HL (Half-Life)

The half-life of a substance is the duration required for a drug to lose half of its pharmacological activity. HL = long is defined as a suitable half-life range.


## 4.2.1. Oral Bioavailability (OB)

In the present study, oral bioavailability (OB) was estimated based on OBioavail 1.1 [85] and the IntegOB. OB is one of the major pharmacokinetic parameters in the ADME (absorption, distribution, metabolism, excretion) profile of a drug. The molecule with suitable OB≥30% served as the candidate compound for further research.


## 4.2.2. Caco-2 Permeability

The oral absorption of drugs was primarily completed via intestinal epithelial cells (IEC). In this study, a computer Caco-2 permeability prediction model was applied to predict intestinal permeability of all TCM components in TCMSP. The number ofCaco−2molecules>−0.4 is considered to exhibit adequate intestinal epithelial permeability.


## 4.2.3. DL (Drug-Likeness)

Drug-likeness is a qualitative concept used in drug design for how “druglike” a substance is with respect to factors like bioavailability. It is estimated from the molecular structure before the substance is even synthesized and tested.


## 4.2.4. HL (Half-Life)

The half-life of a substance is the duration required for a drug to lose half of its pharmacological activity. HL = long is defined as a suitable half-life range.


## 4.3. Drug Targeting

Drug targeting was implemented by a novel computational model designed to detect the direct drug targets based on an in-house weighted ensemble similarity (WES) method [86] with satisfactory validation of both internal and external data.


## 4.4. Network Construction

TCM is a complex material system comprised of the effective active compound, the target of the action, and the related diseases; it does not correspond to the theory of “single gene, single target, and single disease.” In order to resolve these issues, we constructed a compound-target network to reveal the association between the drug and the target protein. The construction of the network aided in identifying the protein targets of each compound in Chinese medicine, understanding the mechanism underlying the activity of the drug in the treatment of the disease, and studying the target in the disease network. Thus, in the generated network, nodes represent compounds, targets, signaling pathways, or diseases, while edges represent compound-target, target-disease, and target-pathway interactions. The bipartite graphs were generated using Cytoscape 2.8.1 [87].


## 4.5. Compound Organ Location

In order to elucidate the gut disorder at the organ level, firstly, the GO targets were analyzed for most of the selected targets, followed by the analysis of distribution in tissues and organs. Determination of target tissue distribution was based on microarray analysis data of different tissue types in BioGPS database (http://biogps.org).
(1)Ω=t1,t2,⋯,tn,Hi=hit1,hit2,⋯,hitn,hi¯=∑i=1nhin,Ai=t∈Ω∣t>hi¯,wherein t represents human tissue, Ω represents tissue location, h represents a tissue-specific pattern of mRNA expression of one target, Hi represents the expression position of mRNA of one target in Ω, hi¯ represents one target in the tissue of the average expression, n is the number of organizations, and Ai indicates a target tissue location.


## 4.6. Animal Management

A rat model test was performed to test the effect of SLBZS on the IBD model. After a week of acclimation period, 30 rats (male and 10 weeks) were randomly divided into 3 groups—control group (CON), model group (MOD), and SLBZS group (SLBZS). The MOD and SLBZS group rats were administered 3% DSS (40 kDa; MP Biomedicals) for 7 days, while the CON was administered with equal volume of saline. After 7 days, the SLBZS group was intragastrically administered with SLBZS 2 mL (1.2 g) for 7 days, and the other two groups were orally given an equal volume of saline. The condition of the rats was monitored twice per day. After 24 hours of the last administration, feces, serum, and colon were collected and stored at -80°C. The animal experiments were approved by the Institutional Animal Care and Use Committee of South China Agricultural University (Approval No. CNAS BL0011).


## 4.7. Preparation of SLBZS

SLBZS was prepared using the published method [7]. SLBZS is a traditional Chinese patent medicine, composed of Panax Ginseng (10 g), Wolfiporia cocos (10 g), Atractylodes macrocephala (10 g), Dioscorea opposita (10 g), Dolichos Lablab (7.5 g), Semen Nelumbinis (5 g), Semen Coicis (5 g), Fructus Amomi (5 g), Platycodon grandiflorus (5 g), and Glycyrrhiza uralensis Fisch (5 g). The ten Chinese herbs were purchased from qualified suppliers based on standards specified in the Chinese Pharmacopoeia (Guangzhou, China). The herb materials were mixed and extracted twice at 80°C by stirring it for 1 h using 10 vols of distilled water (v/m). Then, we centrifuged the extract at 1500 ×g at room temperature. The SLBZS decoction was filtered and then concentrated to 1 g mL−1 (net content) with deionized water.


## 4.8. 16S rRNA Gene Sequence Analysis of Intestinal Flora in Fecal Samples

Total genomic DNA of fecal samples was extracted by the InviMag Stool DNA Kit (Invitek, Germany) as previously described [7]. Fecal microbial DNA was extracted using Fast DNA SPIN extraction kits (MP Biomedicals, Santa Ana, CA, USA) and applied to amplification of the V3-V4 region of 16S rDNA. Fecal microbiota composition was assessed using Illumina HiSeq sequencing of 16S rDNA Amplicon and QIIME-based microbial analysis. The procedures for fecal microbial DNA extraction, sequencing and library construction, and microbial analysis are described in the supplementary methods.


## 4.9. Histologic Observation of the Colon

Histologic colon samples were prepared as previously described [7]. The colon samples were collected and fixed in 10% formalin, dehydrated with a sequence of ethanol solutions, embedded in paraffin, sliced (4-5 μm), and stained with hematoxylin and eosin (HE), then observed with Olympus BH22 Microscope (Japan).


## 4.10. Measurement of Serum Cytokines

Interleukin- (IL-) 1β, IL-10, and tumor necrosis factor-alpha (TNF-α) were tested using an ELISA kit (Cusabio, Houston, TX, USA; https://www.cusabio.com/). Indices were tested according to the manufacturer’s instructions.


## 4.11. Quantification and Statistical Analysis

Statistical analysis was performed with GraphPad Prism 5 (GraphPad Software). Statistical significance was calculated using Kruskal-Wallis test with Dunn’s multiple comparison correction.∗P<0.05, ∗∗P<0.01, and ∗∗∗P<0.001 were regarded as statistically significant.



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*Source: 8194804-2019-06-23.xml*