Evidence-Based Complementary and Alternative Medicine

Evidence-Based Complementary and Alternative Medicine / 2020 / Article

Research Article | Open Access

Volume 2020 |Article ID 4956329 | https://doi.org/10.1155/2020/4956329

Juan Lu, Xinkai Lyu, Ruiping Chai, Yue Yu, Minghui Deng, Xia Zhan, Zhengqi Dong, Xi Chen, "Investigation of the Mechanism of Shengmai Injection on Sepsis by Network Pharmacology Approaches", Evidence-Based Complementary and Alternative Medicine, vol. 2020, Article ID 4956329, 11 pages, 2020. https://doi.org/10.1155/2020/4956329

Investigation of the Mechanism of Shengmai Injection on Sepsis by Network Pharmacology Approaches

Academic Editor: Jeng-Ren Duann
Received02 Mar 2020
Accepted18 Jun 2020
Published03 Aug 2020

Abstract

Shengmai injection (SMI) contains Ginsen Radix et Rhizoma Rubra, Ophiopogon japonicus, and Schisandrae Chinensis Fructus. It is used as a supportive herbal medicine in the management of sepsis, systemic inflammatory response syndrome, and septic or hemorrhagic shock. An UPLC method was established to identify and evaluate SMI fingerprints. Fingerprint similarities of 9 batches of SMI were compared. The network platform, “TCM-components-core targets-key pathways,” was established, and the mechanism of SMI in the treatment of sepsis was investigated. The similarity of 9 batches of SMI fingerprints was greater than 0.91. 44 peaks were selected as the common peaks, of which 11 peaks were identified. KEGG functional pathway analysis showed SMI was mainly involved in the pathways of cancer, cell cycle, and p53 signaling, suggesting SMI protects multiple organs via regulating immunity, inflammation, apoptosis, and energy metabolism. GO enrichment analysis showed active SMI components regulated various biological processes and altered the pathophysiology of sepsis. The interplays between SMI and multiple energy metabolism signaling cascades confer protection from life-threatening multiple organ failure in sepsis.

1. Introduction

Sepsis is a deregulated body response to infection, triggering inflammatory reactions that can cause systemic symptoms and damage multiple organs. Release of cytokines mediates uncontrolled inflammatory cascades that result in dysfunction and failure of multiple major organs and septic shock [1, 2]. Managing infection is the most critical strategy for sepsis therapy. However, these treatments could cause various side effects [3]. Clinically, sepsis is managed with early use of antibiotics and glucocorticoids. Traditional Chinese medicine, including Xuebijing injection [2, 4], Shenfu injection [5], and SMI [6], provides supportive effects in sepsis treatment.

Shengmai injection (SMI) origins from the ancient prescription of Chinese medicine Shengmaiyin; it contains Ginsen Radix et Rhizoma Rubra, Ophiopogon japonicus, and Schisandrae Chinensis Fructus. It invigorates Qi, nourishes Yin, and promotes blood circulation. SMI is used as add-on for supportive treatment in managing patients with sepsis, systemic inflammatory syndrome, and septic or hemorrhagic shock [79]. The frequency of adverse events associated with SMI is low [10, 11]. There have been few reports on the evaluation of SMI effective components and their underlying mechanisms despite they are used as supportive interventions for sepsis treatment. It is a compound with multiple components targeting multiple molecular networks; exploring its complex antisepsis mechanism in a suitable model is of great importance for sepsis treatment [12]. Network pharmacology is a useful tool for systemic investigation of the mechanisms of multiple component drugs [13, 14]. Its approach has been used to study “compound-protein/gene-disease” pathways which reveal complexities among drugs, biological systems, and diseases from a network perspective. Network pharmacology provides insights into the complex interrelationships between the active ingredients of traditional Chinese compounds and molecular mechanisms [15].

We established a fingerprint method to detect and represent chemical information of SMI. We mapped potential targets of SMI bioactive ingredients that may regulate the progress of sepsis using a network pharmacology approach. Our findings shed light on further understanding of the mechanisms of SMI in treating complex diseases such as sepsis.

2. Materials and Methods

2.1. Equipment and Reagents

Water Acquity H-class UPLC, equipped with quaternion high-pressure pump, automatic sampler, and PDA detector (Milford, USA); METTLER AB265-S electronic analytical balance (Zurich, Switzerland); and SB25-12DT ultrasonic cleaner (Ningbo, China) were used. Standard ginsenoside Rb1 (no. 171018), ginsenoside Rb2 (no. 171009), ginsenoside Rd (no. 170530), ginsenoside Re (no. 170924), ginsenoside RF (no. 171126), ginsenoside Rg1 (no. 180105), Ophiopogon D (no. 171126), schisandrol A (no. 180109), and schisandrin A (no. 171231) were all purchased from Shanghai Ronghe Medical Pharmaceutical Technology Co., Ltd (Shanghai, China). Ginsenoside Rb3 (no. 111686-201504) and ginsenoside Rg2 (no. 111779-200801) were purchased from the National Institutes for Food and Drug Control (Beijing, China). The purity of all of the above standards was above 98.0%. Acetonitrile and methanol were purchased from Merck (chromatographically pure, Darmstadt, Germany). Distilled water was purchased from Watson (Hong Kong, China). There were 9 batches of SMI: S1, 16120401005; S2, 160502; S3, 17071014; S4, 17040423; S5, 1704252; S6, 17091302; S7, 17092903; S8, 17061103; and S9, 17053005.

2.2. Standards and Sample Solution Preparation

Standard stock solutions of ginsenoside Rb1 (9.49 mg), ginsenoside Rb2 (10.22 mg), ginsenoside Rb3 (6.69 mg), ginsenoside Rd (6.24 mg), ginsenoside Re (6.24 mg), ginsenoside Rf (7.35 mg), ginsenoside Rg1 (13.24 mg), ginsenoside Rg2 (6.58 mg), Ophiopogon D (5.12 mg), schisandrol A (5.60 mg), and schisantherin A (3.27 mg) were dissolved in 5 ml methanol followed by sonication, respectively. Mixture of standard solution was filtered through the 0.22 μm membrane in a 5 mL volumetric flask. Final concentration of each standard in the mixture was 37.96, 40.88, 26.76, 24.96, 24.96, 29.40, 52.96, 26.32, 20.48, 17.92, and 10.46 μg·mL−1, respectively. SMI solution of each batch was filtered through the 0.22 μm membrane before analysis.

2.3. UPLC Conditions

The analyte was separated by a Waters Acquity UPLC BEH C18 (2.1 mm × 50 mm, 1.7 μm) column. The mobile phases used were solvent A (acetonitrile) and solvent B (water) with gradient elution (Table 1). The analysis was carried out at a flow rate of 0.3 mL/min. The column temperature was set to 40°C. UV detection wavelength was over the range of 190 to 400 nm. 5 μL of the sample was injected. 210 nm was selected as the extraction wavelength of the fingerprints.


Time (min)A (%)B (%)

01981
31981
1226.873.2
153268
233268
23.14456
3566.833.2

2.4. Precision of the Method

Method precision was determined by analyzing the same sample SMI (S1, 16120101005) five consecutive times in a day. The peak of schisantherin A was used as the reference peak. Relative standard deviation (RSD) was calculated from the relative peak area (RPA) or relative retention time (RRT) of each characteristic peak.

2.5. Sample Stability

Sample stability was evaluated using the same SMI (S1, 16120401005) after 0, 2, 4, 6, 8, 12, and 24 hours. The peak of schisantherin A was used as the reference peak. RSD was calculated from RPA or RRT of each peak to the reference peak from the chromatographic profiles of samples.

2.6. Repeatability

Repeatability was evaluated by analyzing six independently prepared SMI samples. The peak of schisantherin A was used as the reference peak. RSD was calculated from RPA or RRT of each peak to the reference peak from the chromatographic profiles of samples.

2.7. Information about Databases and Software of Network Pharmacology

TCMSP database (https://tcmspw.com/tcmsp.php), PubChem CID (https://pubchem.ncbi.nlm.nih.gov/search/), STITCH (http://stitch.embl.de/), Human Phenotype Ontology (HPO, https://hpo.jax.org/app/), STRING database (https://string-db.org), OMIM database (https://omim.org/), and DAVID database (https://david.ncifcrf.gov/summary.jsp) were used. Cytoscape software v3.5.1 was used.

2.8. Network Construction

Data acquisition and processing were done in databases which include SciFinder and TCMSP. Additionally, PubChem CID for each active ingredient of SMI was obtained from PubChem. We used SMILES format in STITCH chemical association networks and obtained the interaction complex between SMI bioactive components and the potential target protein in humans. Using HPO as a tool, we annotated and analyzed the core protein targets that participate in sepsis. The primary as well as predicted interactions between SMI target proteins and proteins involved in sepsis were analyzed in the STRING database. We collected core proteins that are highly associated with sepsis, while proteins with low correlation were filtered out [16]. The molecular interplays between SMI key targets and sepsis proteins were visualized in the Cytoscape platform. We calculated the degree, betweenness, and closeness of the targets; proteins with topological parameters greater than the corresponding median values were considered as major hits. The selected proteins were validated in the OMIM database to establish protein-disease association and construct “SMI Targets-Sepsis Targets” network.

2.9. Prediction of the SMI-Antisepsis Mechanism

A list of the selected top 20 key proteins was uploaded to the DAVID database for functional annotation and enrichment analysis to obtain the main pathways and network distribution that confer potential mechanisms for SMI treatment. Only pathways with were considered for mechanism prediction.

3. Results and Discussion

3.1. Establishment of SMI Fingerprints and the Results of Methodological Evaluation

The RSDs of RPA and RRT for precision, repeatability, and sample stability were lower than 3%, respectively. The results showed that the fingerprint method developed for analysis of SMI is reliable and applicable. Figures 1 and 2 show the UPLC chromatogram fingerprints of 9 batches of SMI.

3.2. Reference Peak and Common Peak

The peak of schisantherin A was used as the reference peak; it showed as an intense peak with preferable chromatographic peak resolution and RRT. Peaks that existed in all SMI samples were appointed as “common peaks.” 44 common peaks were detected in SMI samples, in which 11 peaks were identified (Figure 3): ginsenoside Rg1 (peak #10), ginsenoside Re (peak #11), ginsenoside Rf (peak #15), ginsenoside Rb1(peak #19), ginsenoside Rb2 (peak #20), ginsenoside Rb2 (peak #22), ginsenoside Rb3 (peak #23), schisandrol A (peak #24), ginsenoside Rd (peak #25), Ophiopogonin D (peak #37), and schisantherin A (peak #39).

3.3. Similarity of Fingerprints of 9 Batches of SMI

The similarities of all chromatographic patterns among the samples (Table 2) were calculated using software “Chromatographic Fingerprints of Traditional Chinese Medicine, version: 2004A.” The similarities of 9 SMI batches were greater than 0.91. Therefore, our method was precise, stable, reproducible, and reliable.


S1S2S3S4S5S6S7S8S9Control fingerprints

S110.9540.9830.9760.9160.9380.9440.9420.9530.968
S20.95410.9430.9450.8210.8690.890.8850.9220.914
S30.9830.94310.9920.9310.9620.9670.9650.9730.986
S40.9760.9450.99210.9270.9450.9520.950.9670.978
S50.9160.8210.9310.92710.9440.9440.9390.9310.963
S60.9380.8690.9620.9450.94410.9960.9960.9830.98
S70.9440.890.9670.9520.9440.99610.9980.9890.984
S80.9420.8850.9650.950.9390.9960.99810.9890.98
S90.9530.9220.9730.9670.9310.9830.9890.98910.98
Control fingerprints0.9680.9140.9860.9780.9630.980.9840.980.981

3.4. Putative Targets of SMI Ingredients

The SMI components were screened by TCMSP, and the criteria are OB (oral bioavailability) ≥ 30% and, meanwhile, DL (drug-like) ≥ 0.18 [1719]. All the components were confirmed in the PubChem database [19] (total 9 bioactive SMI components: ginsenoside Rb1, ginsenoside Rb2, ginsenoside Re, ginsenoside Rf, ginsenoside Rg1, ginsenoside Rg2, schisandrol A, schisantherin A, and Ophiopogon D. Their chemical structures and molecular properties were analyzed and uploaded to the STITCH database for predicting targets that interact with SMI ingredients [20]. A total of 62 targets (Figure 4 and Table 3) showed potential interaction with 9 SMI ingredients.


No.Targets

1E2F5
2HDAC2
3VPS33A
4TFDP2
5MSN
6KCNE1
7RB1
8CDK1
9TFDP1
10MDM2
11VPS16
12VPS39
13E2F4
14E2F2
15CDK2
16VPS41
17KCNA4
18HSP90AA1
19CDK4
20CDK6
21HDAC1
22CCNA2
23E2F1
24RBBP4
25ABL1
26SP1
27CCND2
28E2F3
29RBL2
30CCNE1
31KCNA3
32KCNA1
33RAF1
34GNAS
35LRRK1
36STMN4
37HMOX1
38PPP1R3A
39RHAG
40PTGES3
41GNAL
42CCND1
43CYP3A4
44PPIB
45LRRK2
46GALE
47PPIC
48RHCE
49VPS18
50ANGPTL4
51ICT1
52DLG4
53CKS2
54TMPRSS11D
55KCNQ1
56CCNB1
57CCNB2
58CCND3
59CDC20
60CDC37
61CDC6
62CDKN1B

3.5. Acquisition of Known Therapeutic Sepsis Targets

Sepsis targets were collected from the HPO database. The keyword “sepsis” was used to search known therapeutic targets for sepsis in humans [21]. A total of 58 sepsis targets (Table 4) were acquired from the HPO database, and targets were further verified in the NCBI database.


No.Targets

1CYBB
2RMRP
3RAG1
4RAG2
5TGM1
6LIG4
7SEMA3D
8NIPAL4
9SEMA3C
10TCF3
11MYH11
12ATP7A
13WAS
14WIPF1
15GALT
16HLA-B
17G6PC3
18DCLRE1C
19NRTN
20ABCA12
21PIK3R1
22IGHM
23NFKB2
24BTK
25NCF1
26BLNK
27APC
28LRRC8A
29ELANE
30ACTG2
31CYP4F22
32AK2
33CD79A
34CD79B
35NCF2
36IKZF1
37ALOXE3
38NCF4
39SERAC1
40LIPN
41CHD7
42IGLL1
43RET
44PLEC
45CTNNB1
46ECE1
47ADA
48IL2RG
49ITGB4
50GDNF
51MUT
52ALOX12B
53EDN3
54EDNRB
55IL7R
56FERMT3
57TFRC
58CYBA

3.6. Results of Network Construction

The putative targets of SMI active ingredients and sepsis disease targets were determined based on the protein-protein interactions [22]. The interplays amongst SMI targets, known therapeutic targets for sepsis, and interactional human targets were combined to construct the network. The network illustrates the relationship between SMI targets and sepsis targets. The overall interaction network (Figure 5) was visualized using Cytoscape (sepsis targets in red circles and SMI targets in blue squares); the larger a node, the more targets it contains and more important in sepsis management. Targets with higher values of “degree,” “betweenness,” “closeness,” and “coreness” (above the median value of all the network nodes) were identified [23, 24]. Targets which might play unimportant roles in the network according to the topological features were discarded [25]. Median value of “degree,” “betweenness,” and “closeness” was 19, 0.014, and 0.449, respectively. Top twenty proteins were selected as key sepsis therapeutic targets (Figure 6 and Table 5), including ABL1, CCND1, CDK family (CDK1, CDK2, CDK6, and CDKN1B), RB1, HSP90AA1, SMARCA4, RBL2, CTNNB1, MDM2, SP1, LRRK1, BTK, PIK3R1, TMPRSS11D, ACTG2, CD79A, and RET.


No.Gene nameProtein nameDegreeCloseness centralityBetweenness centrality

1ABL1Tyrosine-protein kinase ABL1430.5480.101
2CCND1G1/S-specific cyclin-D1420.5220.046
3RB1Retinoblastoma-associated protein410.5090.037
4CDK2Cyclin-dependent kinase 2410.520.026
5HSP90AA1Heat-shock protein HSP 90-alpha400.5310.077
6CDK1Cyclin-dependent kinase 1390.5090.023
7CDK6Cyclin-dependent kinase 6390.5020.015
8SMARCA4Transcription activator BRG1370.5110.037
9CDKN1BCyclin-dependent kinase inhibitor 1B370.5060.031
10RBL2Retinoblastoma-like protein 2360.4820.04
11CTNNB1Catenin beta-1360.5090.036
12MDM2E3 ubiquitin-protein ligase Mdm2350.4880.025
13SP1Transcription factor Sp1340.5040.027
14LRRK1Leucine-rich repeat serine/threonine-protein kinase 1310.5150.115
15BTKTyrosine-protein kinase BTK230.4860.038
16PIK3R1Phosphatidylinositol 3-kinase regulatory subunit alpha210.470.054
17TMPRSS11DTransmembrane protease serine 11D200.4630.041
18ACTG2Actin, gamma-enteric smooth muscle200.4540.025
19CD79AB-cell antigen receptor complex-associated protein alpha chain190.4580.058
20RETProto-oncogene tyrosine-protein kinase receptor Ret190.4630.032

Sepsis causes life-threatening organ dysfunction due to a host’s complex systemic inflammatory response to infection [26]. In the present study, we identified core proteins that may play important roles in SMI-supportive treatment in sepsis. ABL1 is a tyrosine-protein kinase which is important for cell growth and survival, cytoskeleton remodeling in response to extracellular stimuli, autophagy, and apoptosis [2729]. It also regulates multiple pathological signaling cascades during infection that alter vascular permeability and the endothelium barrier in inflammation [30].

Cyclin-dependent kinase 1 (CDK1) is a member of the Ser/Thr protein kinase family. Its kinase activity is controlled by cyclin [31, 32]. CCND1/CDK4 and CDK2 are critical for G1/S phase transition. It has been shown rat kidney injury is associated with G1 phase arrest in cecal ligation and puncture- (CLP-) induced sepsis, while upregulation of CCND1/CDK4 and CCNE/CDK2 activates Rb leading to revival of cell cycle progress and recovery of kidney function 48 hours after CLP [33, 34]. The findings demonstrate that cell cycle arrest occurs in sepsis, and drugs that regulate cell cycle proteins may be a means to rescue organ injury [35]. In addition, the targets of SMI are more involved in DNA replication and transcription; for example, MDM2, E3 ubiquitin ligase, mediates ubiquitination and degradation of p53. It mediates apoptosis in organ injury and malignant transformation [36]. SMI may inhibit MDM2 and keep p53 active; therefore, it promotes cells staying in the G1/G2 phase and alleviates cell injury in sepsis.

Molecular chaperone heat-shock protein (HSP 90) is extensively expressed by cells, and its expression increases upon stimulation [37]. HSPs are associated with multiple organ failure in sepsis [38]. In the vast immune response in sepsis, stressed cells release HSPs that are regarded as “danger signal” to neighboring and immune cells [39]. HSP90-α has been shown to interact with about 200 client proteins, including signal proteins in the inflammatory pathway such as NF-κB, Akt, and IKK, to interfere inflammation [4042]. Moreover, HSP90-α, as abundant “chaperone,” is one of the main mediators that activates bacteria lipopolysaccharide. It interacts with proteins in the PI3K/Akt pathway and is essential in promoting the immune response and improving host defense to pathogens. Inhibition of HSP90-α prevents severe sepsis-associated acute lung injury; therefore, block HSP90-α offers a novel treatment for lung injury in sepsis [4345].

BTK (tyrosine-protein kinase) is a component of the toll-like receptor (TLR) pathway and plays important roles in innate and adaptive immunity. Key target CD79A is required for efficient differentiation of pro- and pre-B-cells. It cooperates with CD79B and bounds to the B-cell antigen receptor complex (BCR) for initiation of the signal transduction cascade. It is pivotal in regulating immunity and inflammation [46]. Network pharmacology revealed SMI represses BTK expression/activation, blocks signals through multiple pathways (TLR, B-cell antigen receptor signal, and apoptosis), and consequently ameliorates cell apoptosis and organ injury. SMI acts as a whole, and each formula has its corresponding targets/syndromes; thus, SMI prescription acts on multiple key targets, and the network pharmacology study of SMI provides insights into understanding its fundamental mechanisms.

3.7. KEGG and GO Analysis

To cluster the biological functions of SMI and its targets, data were uploaded to KEGG, and results revealed SMI active formulae target pathways including cancer, cell cycle, p53, B-cell receptor, and ErbB pathways (Table 6). SMI regulates the interplay and synergy among the pathways of immunity, inflammation, and apoptosis to protect cellular and organ injury in sepsis. p53 pathway regulates mitochondrial fission and mitochondrial biogenesis via AMPK, and it alters PKM2-dependent glycolysis. Global deletion of PKM2 results in systemic inflammation in mice [47]. Our GO analysis (Table 7) showed that SMI ingredients regulate multiple biological processes including cell cycle, energy metabolism, cellular signal transduction, transcription regulation, and immunity development. Our data indicate the putative role of SMI in alleviating systemic inflammation and deregulating immunity in the host; moreover, it regulates energy utilization and promotes energy homeostasis and therefore ameliorates multiple organ failure associated with sepsis. It is also in agreement with the idea of SMI used in the early phase of sepsis.


No.NameCount value

1Pathways in cancer116.2 × 10−9
2Cell cycle91.2 × 10−9
3p53 signaling pathway54.7 × 10−5
4Melanoma55.6 × 10−5
5Non-small cell lung cancer45.8 × 10−4
6B-cell receptor signaling pathway32.3 × 10−2
7Colorectal cancer32.8 × 10−2
8ErbB signaling pathway33 × 10−2


No.NameCount value

1Cell cycle103.5 × 10−7
2Phosphorylation96.1 × 10−6
3Phosphorus metabolic process92.6 × 10−5
4Intracellular signaling cascade91.6 × 10−4
5Regulation of transcription91.8 × 10−2
6Mitotic cell cycle84.1 × 10−7
7Protein amino acid phosphorylation82.1 × 10−5
8Positive regluation of macromolecule metabolic process81.0 × 10−4
9Interphase75.0 × 10−9
10Regulation of cell proliferation75.4 × 10−4
11Regulation of transcription, DNA-dependent73.0 × 10−2
12Regulation of RNA metabolic process73.3 × 10−2
13Regulation of mitotic cell cycle62.6 × 10−15
14Hemopoiesis61.5 × 10−5
15Hemopoietic or lyphoid organ development62.4 × 10−5
16Immune system development63.1 × 10−5
17Positive regluation of nitrogen compound metabolic process61.6 × 10−3
18Positive regluation of celluar biosynthetic process62.1 × 10−3
19Positive regluation of biosynthetic process62.2 × 10−3
20Regulation of transcription from RNA polymerase II promoter62.7 × 10−3

4. Conclusion

An UPLC method was developed for analysis of SMI fingerprints. Forty-four peaks were selected as common peaks, of which 11 peaks were identified. The consistency in the chromatograms of 9-batch samples reflects the presence of similar chemical constituents (similarities greater than 0.91). The technique was proven to be useful in SMI quality control. A total of 9 active components of SMI target 20 key proteins including ABL1, CDK, HSP90, BTK, PIK3R1, and CD79A. These proteins are enriched in cell cycle, p53 signaling pathway, B-cell receptor signaling pathway, and ErbB pathway. It is likely that the pharmacological mechanisms of SMI in sepsis treatment are of multiple dimensions that are associated with regulation of cell cycle, energy metabolism, cellular signal transduction, transcription regulation, and immunity development. Further experiments are needed to validate our prediction.

Data Availability

The data used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest

The authors declare no conflicts of interest.

Authors’ Contributions

Juan Lu and Xinkai Lyu contributed equally to this work. XC, ZD, and JL conceived, designed, and supervised the project. RC, YY, XL, MD, and XZ performed the experiments. JL and XL integrated all the data and wrote the paper.

Acknowledgments

This research was funded by the National Natural Science Foundation of China (no. 81673667), the CAMS Innovation Fund for Medical Science (CIFMS) (nos. 2016-I2M-3-015 and 2017-I2M-B&R-09), Beijing Natural Science Committee-Beijing Education Committee Joint Foundation (no. KZ201910011012), and Science and Technology Projects in Liaoning Province (no. 20170540263).

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