
==== Front
Heliyon
Heliyon
Heliyon
2405-8440
Elsevier

S2405-8440(24)13522-0
10.1016/j.heliyon.2024.e37491
e37491
Research Article
Investigation of the pharmacological mechanisms of Shenfu injection in acute pancreatitis through network pharmacology and experimental validation
Xu Liming
Wang Tianpeng
Xu Yingge
Jiang Chenghang jiangch2308@163.com
⁎
Emergency and Critical Care Center, Department of Emergency Medicine, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, 314408, Zhejiang, China
⁎ Corresponding author. jiangch2308@163.com
05 9 2024
30 9 2024
05 9 2024
10 18 e3749126 8 2023
4 9 2024
4 9 2024
© 2024 The Author(s)
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Background

Shenfu Injection (SFI) has emerged as a prevalent therapeutic intervention in clinical practice for the management of acute pancreatitis (AP). The purpose of this research was to investigate and validate the potential mechanisms of SFI in the treatment of AP through network pharmacology.

Methods

Network pharmacology was adopted to investigate the potential targets and mechanisms of SFI in the treatment of AP. Molecular docking was employed to evaluate the binding affinity between active components and targets. Single-cell transcriptome analysis was conducted to explore the cell types associated with SFI treatment in AP. In vitro and in vivo models of AP were induced by caerulein. The histopathological changes were observed by HE staining. Cell apoptosis was detected using flow cytometry and Tunel staining. Cell viability was assessed using CCK-8 assay. Western blot and ELISA were used to detect the protein expression and inflammatory cytokines, respectively.

Results

A total of 104 SFI active components were obtained, of which 29 targeted 76 genes. After intersecting with 3370 AP-related genes, 42 SFI treatment AP potential targets were identified. Enrichment analysis revealed that these targets were associated with cell apoptosis, necroptosis, and multiple signal transduction pathways, such as p53, IL-17 and TNF signal pathways, etc. Molecular docking demonstrated that the active components of SFI had good binding affinity with the corresponding targets and the binding ability of NGF and aromadendrene was the strongest. Bioinformatics analysis revealed that SFI treatment in AP is associated with various cell types, including acinar cells, endothelial cells, T cells, dendritic cells, ductal cells, and mesenchymal cells. Furthermore, in vitro experiments demonstrated that SFI induces acinar cell apoptosis in a dose-dependent manner, accompanied by increased expression of cleaved-caspase3/caspase3 and cleaved-caspase8/caspase8 proteins, and inhibition of inflammatory cytokine (TNF-ɑ, IL-1β, and PTGS2) expression. In vivo experiments demonstrated that SFI improved histopathological alterations, reduces inflammation, and promotes apoptosis and the expression of cleaved-casp3 and cleaved-casp8 in AP rats.

Conclusions

This study elucidated the multi-component, multi-target, and multi-cellular characteristics of SFI in the treatment of AP, and confirmed its mechanism of promoting acinar cell apoptosis.

Keywords

Shenfu injection
Acute pancreatitis
Netwwork paharmacology
Molecular docking
Acinar cell
==== Body
pmc1 Introduction

Acute pancreatitis (AP) is a common exocrine pancreatitis syndrome characterized by tissue damage and necrosis, which can result in severe abdominal pain, multiple organ dysfunctions, pancreatic necrosis, persistent organ failure and a mortality rate of 2–5% [1]. Gallstones and alcohol abuse are the main pathogenic factors in the mechanism of pancreatitis [2]. The incidence varies by region and gender, and the global incidence is increasing, although studies show that the incidence in Asia is relatively stable [3]. Furthermore, acute pancreatitis can cause significant short-term and long-term morbidity, and in very few cases can lead to long-term debilitation, recurrent diseases, and exocrine and/or endocrine dysfunction of the pancreas [4]. Importantly, there is currently no single therapeutic agent that can alter the course of this disease. At present, the clinical treatments for AP mainly include supportive treatments, nutritional support, prophylactic antibiotics, cholecystectomy, symptomatic treatments for complications [5,6]and novel medications (including antisecretory agents, protease inhibitors, anti-inflammatory drugs, and antioxidants, etc. [7]. However, due to the rapid progression of the disease, many of the medications have not yet shown therapeutic efficacy, and there is a great demand and prospect for the development of effective AP/SAP medication therapies.

Drawing upon the unique theoretical system and effective treatment methods of traditional Chinese medicine, it has been used to prevent and treat diseases for centuries, with increasing research focus in recent years. Traditional Chinese medicine has long been established as a superior treatment method for inflammatory diseases such as AP in China. Compatible with modern medical concepts, its joint treatment of AP has been increasingly gaining attention. Shenfu injection (SFI), a Chinese medicine extract routinely employed in the clinical management of coronary heart diseases and congestive heart failure in China, has been found to exhibit protective effects against ischemia-reperfusion injury (IRI) in multiple organs, including the heart, liver, kidney and brain [[8], [9], [10], [11]]. In addition, numerous traditional Chinese medicine clinical studies have reported satisfactory efficacy of SFI in the treatment of acute pancreatitis [12]. Animal experiments showed that SFI protects rats against AP induced by bilirubin by regulating the activity of cell factors, oxidative stress, and nuclear factor kappa B (NF-κB) [12]. Nevertheless, the molecular mechanism of SF1 in the treatment of acute pancreatitis remains largely unknown.

Recently, there has been a surge of interest in network pharmacology worldwide. Network pharmacology aims to comprehensively understand the interactions between drugs and diseases by constructing a “drug-target-disease” network. In this study, our objective is to uncover the potential mechanisms of SFI in the treatment of acute pancreatitis using network pharmacology, molecular docking, and single-cell transcriptomics analysis. To support our findings, we conducted cellular experiments to validate some of the most promising results.

2 Materials and methods

2.1 Network pharmacology

The bioactive components and targets of SFI were identified using the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP, https://lsp.nwu.edu.cn/tcmsp.php). Genes related to AP were obtained from multiple databases including GeneCards (https://www.genecards.org/), Online Mendelian Inheritance in Man (OMIM, https://www.omim.org/), PharmGKB database (https://www.pharmgkb.org/), therapeutic target database (https://db.idrblab.net/ttd/), and DisGeNET (https://www.disgenet.org/), using “pancreatitis” as a keyword for data retrieval. The integrated results from these databases yielded the AP-related genes. The intersection of SFI targets and AP-related genes was used to identify candidate targets of SFI against AP, and the protein-protein interaction (PPI) data of these candidate targets were retrieved from STRING database (https://string-db.org/) to construct a PPI network, which was subsequently analyzed using Cytoscape software. The functional enrichment analysis was performed using the “clusterprofiler” package in R.

2.2 Molecular docking

The network topology analysis was used to identify hub targets, which were further subjected to molecular docking analysis. The structures of the target proteins and bioactive compounds were obtained from RCSB PDB (https://www.rcsb.org/) and PubChem (https://pubchem.ncbi.nlm.nih.gov), respectively. PyMOL software was utilized to extract the grid box for docking by removing water molecules and organic solvents from the protein structures. Autodock software was employed for molecular docking analysis, utilizing the Lamarckian Genetic algorithm to perform conformational search and generate 100 conformations. The final docking conformation was selected based on the best affinity. LigPlus (version 2.24) was used to generate 2D diagrams of the SIN-targets complex, while PyMol was employed for visualization of the 3D complex.

2.3 Single-cell transcriptomic analysis

The dataset GSE198183 was retrieved from the Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/) repository. This dataset encompasses single-cell transcriptomic profiles of pancreatic cells subjected to treatment with either physiological saline or cerulein for 2 days (S2D and C2D) or 6 weeks (S6W and C6W). The Seurat package was employed for comprehensive data analysis and visualization. Preceding analysis, cells demonstrating low expression levels (<200) or excessively high expression levels (>5000) of genes, as well as those with mitochondrial genes accounting for more than 10 % of the total genes, were excluded. The FindVariableFeatures function was utilized to identify the top 2000 genes exhibiting substantial variation. Principal component analysis (PCA) was performed on a set of 2000 genes, subsequently reducing the dimensionality and identifying clusters via uniform manifold approximation and projection (UMAP). Cell annotation was achieved by leveraging previously published marker genes [13]. The graph-based cluster method was employed to generate unsupervised cell cluster assignments based on the top 20 principal components. To evaluate the differential expression of potential targets across distinct cell groups, single-cell gene set variation analysis (ssGSVA) was conducted using the GSVA package, and statistical t-tests were employed for comparisons of ssGSVA scores.

2.4 Animals

Specific pathogen-free male Sprague-Dawley rats (200 ± 20 g, 8-week-old) were purchased from were purchased from Hangzhou Ziyuan Experimental Animal Technology Co., Ltd, China. All rats were maintained at the laboratory of the Laboratory Medicine Center, Zhejiang Provincial People's Hospital. Rats were housed individually in cages on a 12 h dark/12 h light cycle at 23 ± 2 °C under standard environmental conditions and had free access to pellet diet and tap water. All studies were conducted in accordance with the Principles of Laboratory Animal Care. This study was also approved by the Animal Ethics Committee of Zhejiang Provincial People's Hospital (No. 2022–139).

2.5 AP models and SFI treatment

Rats were divided into the following groups according to a random number table method: Normal, AP, LSFI and HSFI groups. The AP model was established by intraperitoneal injection of cerulein as previously described [14]. Briefly, the rat model of AP was established by intraperitoneal injections of cerulein (50 μg/kg) once an hour for a total of 7 times. With the exception of the normal group, rats in other groups were injected with cerulein to induce the AP. SFI was produced by Ya'an Sanjiu Pharmaceutical Co., Ltd. (Ya'an, China), and the LSFI and HSFI groups were given 1 mL or 10 mL/kg through once intraperitoneal injection at the time of the last injection of caerulein. Rats in the normal and AP groups received same volume of normal saline. All treatments lasted for 7 days. After the experiments, blood and pancreas samples were collected.

2.6 HE staining

Pancreatic tissue from rats was fixed in 4 % paraformaldehyde and embedded in paraffin for 5 μm sectioning. Sections underwent incubation at 37 °C for 1 h, followed by dewaxing with xylene and hydration in graded ethanol. Subsequently, HE staining was performed as per established methods [15]. Pathological evaluation of pancreatic tissue in AP rats was carried out microscopically on stained sections using an Olympus light microscope from Japan.

2.7 Terminal-deoxynucleotidyl transferase-mediated UTP nick end labeling (TUNEL) staining

After a 30-min exposure to a 0.3 % Triton X-100 buffer at room temp, pancreatic tissues were further processed with a 0.3 % H2O2 solution. Next, these tissues were immersed in a commercial TUNEL reaction mixture (Beyotime, Beijing, PRC) in a dark environment at 37 °C for 1 h, followed by an incubation with a Streptavidin-HRP conjugate prepared according to the manufacturer's protocol (Beyotime, Beijing, China) for 30 min. To stain nuclei, Hoechst 33,342 dye was utilized. Photographic documentation was accomplished using a fluorescence microscope system, specifically the Fluoview FV1000 (Olympus Corporation, Tokyo, Japan).

2.8 Cell culture

The AR42J pancreatic acinar cell line (obtained from the American Type Culture Collection, Rockville, Maryland, USA) was cultured in a 37 °C incubator with 5 % CO2 using Dulbecco's modified Eagle's medium (Gibco, Grand Island, New York, USA) supplemented with 10 % fetal bovine serum and antibiotics (100 U/ml penicillin and 100 μg/mL streptomycin). The AR42J cells were maintained until they reached logarithmic growth phase, with a confluence of approximately 80 %. Subsequently, the cells were treated with 10 nmol/L caerulein for 24 h to establish an in vitro AP cell model [16].

2.9 Cell counting kit-8 (CCK-8) assay

In order to assess the potential protective effect of SFI on AR42J cells, cellular viability tests were conducted. The number of viable cells in the proliferation and cytotoxicity assays was determined using CCK-8. The cells were evenly distributed into 96-well plates and subjected to pretreatment with varying concentrations of SFI (1, 5, 10, or 20 μL/mL) for a duration of 24 h. Subsequently, they were exposed to a medium containing 10 nmol/L caerulein for an additional 24 h. The CCK-8 assay was carried out following the provided instructions from the manufacturer.

2.10 Flow cytometry assay

An Annexin V-FITC/PI apoptosis detection kit (Beyotime Institute of Biotechnology) was used for flow cytometry analysis following the manufacturer's instructions. To begin, AR42J cells were seeded into 96-well plates at a density of 1 × 105 cells/well and incubated overnight at 37 °C. Following this, the cells were subjected to pretreatment with either LSFI (5 μL/mL) or HSFI (20 μL/mL) for 24 h, followed by incubation in a medium containing 10 nmol/L caerulein for an additional 24 h. Subsequently, the cells were collected and incubated with a solution containing 10 μl Annexin V-FITC and PI at room temperature for 10 min. Flow cytometry analysis was carried out using a FACSCalibur flow cytometer (Becton Dickinson) equipped with CellQuest Pro 3.0 software (Becton Dickinson). The apoptotic rate was calculated as the percentage of cells displaying positive Annexin V-FITC and positive/negative PI staining.

2.11 Enzyme-linked immunosorbent assay (ELISA)

The cell culture supernatant and rat serum was collected. The levels of pro-inflammatory cytokines TNF-α, IL-1β, and PTGS2 were measured using ELISA assay kits (Thermo Fisher Scientific) according to the provided instructions.

2.12 Western blot

The cells and pancreatic tissues from each experimental group were subjected to Western blot analysis. Protein extraction was performed, and the protein concentration was determined using the BCA Kit (Beyotime, Shanghai, China). A total of 60 μg protein was loaded onto a 10 % SDS-PAGE gel under constant pressure for protein separation. Subsequently, the proteins were transferred onto PVDF membranes using a constant flow method. The transferred proteins were blocked with 5 % skimmed milk at room temperature for 1 h and then incubated with the following primary antibodies: anti-CASP3 (ab13585, 1:1000), anti-cleaved CASP3 (ab214430, 1:5000), anti-CASP8 (ab227430, 1:3000), anti-cleaved CASP8 (Immunoway, YC0011, 1:2000), and anti-GAPDH (1:1000, ab181602) at 4 °C for 12 h. After washing with Tris-buffered saline, the membranes were incubated with the appropriate secondary antibody for 2 h at room temperature. Finally, protein detection was performed using the Enhanced Chemiluminescence Kit (Absin Biotechnology, Shanghai, China).

2.13 Statistic analysis

The data obtained were presented as mean ± standard deviation and statistically analyzed using two-tailed unpaired Student's t-test. GraphPad Prism 8 was used to construct statistical graphs and to perform statistical analyses. Results were considered statistically significant when p < 0.05.

3 Results

3.1 The active ingredients and targets of SFI

Based on the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database, we conducted a search for the chemical components of SFI, yielding 65 compounds from Aconitum carmichaelii Debx. and 74 compounds from Panax ginseng C.A. Mey. Due to their administration by injection, we focused solely on the Druglikeness (DL) parameter in the selection of active ingredients, which were chosen based on a DL value greater than 0.1. Fig. 1A illustrates that there are 48 active ingredients in Panax ginseng C.A. Mey. and 57 in Aconitum carmichaelii Debx., with one shared active ingredient, MOL00012 (Arachic acid). Furthermore, the targets of these active ingredients were obtained from the TCMSP database, resulting in 76 targets for 29 active ingredients. The target relationships among them are presented in Fig. 1B, where node size indicates the number of target relationships. MOL000358 (beta-sitosterol) exhibits the most targets (37), with PTGS2 being the most affected target, linked to 18 active ingredients. The average degree value of the active ingredients is 6.59, and that of the targets is 2.67, suggesting that the synergistic interactions between these components and targets may contribute to the therapeutic effects of the drug.Fig. 1 The active components and their targets of SFI. (A) Network graph of active components of SFI. (B) Network graph of active components - targets of SFI.

Fig. 1

3.2 The potential targets of SFI for the treatment of AP

In order to identify potential targets for SFI therapy in AP, we conducted a comprehensive search of six major databases to obtain AP-related genes, as depicted in Fig. 2A. A total of 3370 AP-related genes were identified, comprising 49 from OMIM, 3176 from GeneCards, 5 from TTD, 502 from DisGeNET, and 10 from PharmGKB. By intersecting AP-related genes with SFI targets, we identified 42 potential targets for SFI therapy in AP. Of these targets, 38 exhibited 179 protein-protein interactions, as shown in Fig. 2B. Notably, several protein targets with significant interactions, including TNF, CASP3, ESR1, IL1B, PTGS2, and BDNF, were observed after visualizing node size according to degree value, indicating their potential importance in AP. In addition, we performed enrichment analysis on these potential targets, revealing associations with 1125 biological processes, 27 cellular components, 54 molecular functions, and 187 KEGG pathways. The top 10 KEGG pathways with the highest number of genes are depicted in Fig. 2C, while Fig. 2D displays the top 10 Gene Ontology terms with the highest number of genes. These findings suggest that these potential targets are closely linked to apoptosis and may represent a key mechanism for SFI therapy in AP.Fig. 2 Potential targets and enrichment analysis of SFI treatment for AP. (A) Venn diagram of AP-related genes. (B) Protein interaction map of potential targets of SFI treatment for AP. (C) Related pathway enrichment dot plot of potential targets of SFI treatment for AP. (D) Related enrichment GO dot plot of potential targets of SFI treatment for AP.

Fig. 2

3.3 Identification of hub targets and pathways of SFI therapy for AP

In order to identify crucial targets and pathways for SFI therapy in AP, we constructed a pathway-target-ingredient-herb network (Fig. 3). Firstly, we screened the top 10 KEGG pathways with low P-values that were non-disease-related, and constructed a pathway-gene network (not depicted). Subsequently, by utilizing the entire pathway-gene network, protein-protein interaction network, herb-ingredient network, and ingredient-target network, we established the key pathway-target-ingredient-herb network for SFI therapy in AP. This comprehensive network encompasses 10 pivotal pathway nodes, 104 active ingredient nodes, 38 target nodes, and 2 herb nodes, consisting of 539 interaction information. The average degree value of the network is 5.61, with the top 3 target nodes being PTGS2, TNF, and IL1B, and the top 3 active ingredient nodes being MOL000358 (beta-sitosterol), MOL000131 (linoleic acid), and MOL002417 (fuzitine), which may all play significant roles in the SFI therapy process for AP.Fig. 3 The herb-component-target-pathway network of SFI in the treatment of AP.

Fig. 3

3.4 The key active components of SFI have a good binding affinity with hub target

Analysis of the affinity between the key components and targets of SFI therapy for AP can be conducted by means of molecular docking technology. As shown in Fig. 4A, we carried out molecular docking analysis on nine targets with high degree values and ten components and combined them to draw a heat map. The results revealed that these components and targets had low binding energy (6.61 kcal/mol), indicating their potential binding energy. The lowest binding energy was found in the NGF-aromadendrene complex, while the highest binding energy was found in the NFKBIA-ginsenoside rh2 complex (−4.0 kcal/mol). Additionally, we analyzed the average binding energy of these targets and components (Fig. 4B and C), and the results showed that aromadendrene had the lowest average binding energy with these targets, suggesting it to be the most active component; PTGS2 had the lowest average binding energy, indicating its influence by active components to be the deepest and being one of the most critical targets. Fig. 4D and E respectively show the 3D and 2D structures of the NGF-aromadendrene complex, and the results indicate a wide range of van der Waals forces between aromadendrene and NGF, which is the main source of their binding force.Fig. 4 Molecular docking analysis of the key targets and active components of SFI therapy for AP (A) Heat map of binding affinity of the key targets and active components of SFI therapy for AP. (B) Average energy of the binding of the key active components of SFI therapy for AP with targets (C) Average energy of the binding of the key targets of SFI therapy for AP with active components (D) 3D structure of NGF binding to aromadendrene (E) 2D structure of NGF binding to aromadendrene.

Fig. 4

3.5 Dissection of SFI's influence on AP-related cell subtypes

We conducted a comprehensive analysis to classify and annotate the GSE198183 dataset, resulting in the identification of 10 distinct cell types (Fig. 5A). The expression patterns of key marker genes characterizing these cell subtypes are depicted in Fig. 5B. To assess the functional relevance of potential targets, we employed the ssGSVA algorithm to evaluate the enrichment scores across diverse cell types, as illustrated in Fig. 5C. Our statistical analysis revealed a prominent elevation in GSVA scores among macrophages, whereas endocrine cells exhibited the lowest GSVA scores (Fig. 5D). In the C2D group, there was a statistically significant diminution in GSVA scores across multiple cell lineages, including acinar, endothelial, T lymphocyte, dendritic, ductal, and mesenchymal cells when contrasted with the S2D group, indicative of potential gene set repression in AP. Conversely, in the C6W group, these same gene sets demonstrated a marked elevation in their GSVA scores within these aforementioned cellular compartments relative to C2D, suggesting that the activation of these potential gene sets may contribute favorably to the amelioration of AP conditions (Fig. 5E). Collectively, these findings implicate specific cell types and their diversity as critical targets of SFI therapy in the treatment of AP.Fig. 5 Cellular mechanisms of potential target genes for SFI therapy in AP. (A) UMAP plot showing the visualization of all cell subtypes from the GSE198183 cohort. (B) Bubble plot illustrating the average and percent expression of model genes in different cell subtypes. (C) UMAP plot displaying the distribution of GSVA scores for potential targets. (D) Violin plot depicting the GSVA scores of potential targets in various cell types. (E) Comparison of GSVA scores of potential targets across different groups within each cell type. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001.

Fig. 5

3.6 SFI enhances acinar cell apoptosis

Cell viability assessment revealed a significant suppression of acinar cell activity following CAE intervention. Conversely, SFI exhibited a dose-dependent enhancement of acinar cell activity (Fig. 6A). Subsequently, we conducted further experiments employing two SFI dosages: a low dose (5 μL/mL, LSFI) and a high dose (20 μL/mL, HSFI). Flow cytometry analysis demonstrated a notable reduction in acinar cell apoptosis levels within the CAE group compared to the control group. Notably, SFI pretreatment effectively augmented the inhibitory effect on acinar cell apoptosis induced by CAE, with the pro-apoptotic impact of the high-dose SFI surpassing that of the low-dose SFI (Fig. 6B and C).Fig. 6 Effects of SFI on the viability and apoptosis of AR42J cells. (A) Viability of AR42J cells stimulated with CAE in the presence of SFI. (B) Flow cytometry analysis of apoptosis in AR42J cells. (C) Comparison of apoptosis in AR42J cells among different groups. ###p < 0.001, compared to the control group, *p < 0.05, **p < 0.01, ***p < 0.001, compared to the CAE group.

Fig. 6

3.7 SFI-induced attenuation of inflammatory cytokines and activation of apoptosis-associated target proteins

In vitro experiments substantiated the facilitative influence of SFI on apoptotic modulation of acinar cells, while network pharmacology analysis disclosed the involvement of pivotal apoptosis-associated hub targets, namely CASP3 and CASP8, in the therapeutic efficacy of SFI for AP. Subsequently, Western blotting was implemented to assess the expression profiles of these proteins (Fig. 7A, Supplementary materials: Fig. S1). The results exhibited a significant reduction in the levels of cleaved-CASP3/Casp3 and cleaved-Casp8/Casp8 within the CAE group when compared to the control group. Furthermore, SFI intervention effectively counteracted the diminished expression of the aforementioned markers induced by CAE, with the high-dose regimen showcasing a more pronounced effect than the low-dose regimen (Fig. 7B). Additionally, ELISA measurements demonstrated a notable elevation in the levels of pro-inflammatory cytokines (TNF-α, IL-1β, PTGS2) within the acinar cells of the CAE group relative to the control group, whereas SFI dose-dependently attenuated the expression of these inflammatory factors in CAE group adenoid cells (Fig. 7C).Fig. 7 Regulation of apoptosis-related targets and inflammatory factor-related targets by SFI. (A) Western blot assay was performed to detect the expression of cleaved-casp3, casp3, cleaved-casp8, casp8 in AR42J cells. (B) Statistical analysis of cleaved-casp3/casp3 and cleaved-casp8/casp8 between different groups. (C) Statistical analysis of TNF-ɑ, IL-1β, PTGS2 levels in different groups using ELISA. ###p < 0.001, compared to the control group, *p < 0.05, **p < 0.01, ***p < 0.001, compared to the CAE group.

Fig. 7

3.8 In vivo functional and mechanistic validation of SFI in treating AP

Compared to the Normal group, H&E staining reveals diffuse pancreatic tissue damage in the AP model group, characterized by focal expansion of pancreatic lobular septa, acinar atrophy, and inflammatory cell infiltration. SFI pretreatment significantly ameliorates the histopathological changes induced by AP in rats, indicating a protective effect of SFI against AP (Fig. 8A). TUNEL staining shows that cerulein administration suppresses apoptosis in acinar cells, whereas SFI treatment promotes cell apoptosis compared to the Normal group (Fig. 8B). Serum concentrations of IL-1β, TNF-α, and PTGS2 in rats from the AP group were notably higher than those in the Normal group (Fig. 8C). Notably, in a dose-dependent manner, SFI treatment in rats led to a decrease in the levels of all three inflammatory cytokines compared to the AP group (P < 0.01). Furthermore, Western blot analysis disclosed that the levels of cleaved-caspase-3 and cleaved-caspase-8 were significantly reduced in the AP group compared to the control group, and SFI treatment reversed this decline (Fig. 8D–F, Supplementary materials: Fig. S2).Fig. 8 In vivo functional validation and target assessment of SFI treatment in AP. (A) Representative H&E stained images of pancreatic sections (scale bar: 100 μm). (B) Representative TUNEL images of pancreatic sections (scale bar: 50 μm). (C) Statistical analysis of TNF-ɑ, IL-1β, and PTGS2 levels in various groups using ELISA assays. (D) Representative Western blotting images of cleaved-casp3, casp3, cleaved-casp8, and casp8 proteins in pancreatic tissue. (E, F) Corresponding relative protein expression levels. *p < 0.05, **p < 0.01, ***P < 0.001 compared to the AP group; ###P < 0.001 versus the Normal group.

Fig. 8

4 Discussion

Network Pharmacology, a newly emerged branch of Pharmacology, integrates the methods of Computer Science, Bioinformatics and Pharmacology to investigate the mechanisms of drugs and how they impact the functions of cells and tissue. This study reveals the molecular mechanism of SFI treatment of AP through integration of Network Pharmacology, Molecular Docking and experimental verification. According to the drug-likeness parameters, SFI has 104 active ingredients, of which 79 potential targets of 29 active ingredients have been identified. Out of those, 42 are related to AP and could potentially be the targets of SFI for treating AP. Enrichment analysis was then conducted to further analyze the biological functions and pathways of these genes. Network topology analysis indicates the key targets of SFI in treating AP, such as PTGS2, TNF, CASP8, etc., and these targets have demonstrated good binding affinity with active ingredients. Single-cell transcriptomic analysis further identified key cellular subpopulations targeted by SFI in the treatment of AP.

It is revealed that different active components affect multiple targets, among which the ones with the most targets include beta-sitosterol, linoleic acid, ginsenoside Rh2, Fuzitine, deltoin, etc. These components may possess multiple pharmacological functions. For instance, beta-sitosterol is a common plant sterol in traditional Chinese medicine, which has been proven to possess antioxidant and anti-inflammatory activities [17]. Oxidative stress is considered as an important regulator of inflammatory related signal pathways, recruiting inflammatory cells, releasing inflammatory factors, etc., and thus is implicated in the occurrence and development of AP. Therefore, beta-sitosterol may be a potential drug to improve oxidative stress and inflammatory response in AP, although a recent study reported a case of beta-sitosterol induced acute pancreatitis [18]. Moreover, linoleic acid is identified as one of the most influential antioxidants for AP patients [19], which is a natural PPAR activator [20] and can control the inflammation of pancreatitis and improve prognosis [21,22]. Additionally, ginsenoside Rh2 is an active substance in red ginseng that has strong pharmacological effects (6), such as immunoregulation, antioxidant, anti-inflammatory, etc. [23,24]. Deltoin is reported as a liver protective agent and TNF-α inhibitor [25]. TNF-α has been found to play a critical role in the multiple adverse effects of acute pancreatitis, and is the primary determinant of systemic progression and terminal organ damage (such as acute lung injury and liver failure) of this pathological condition. Therefore, blocking the action of this mediator is considered to be an attractive therapeutic option [26]. Nevertheless, these sporadic studies are insufficient to fully explain the mechanism of SFI treatment in AP, and more research is needed to explore the pharmacological effects and molecular mechanisms of various active components.

Topological analysis has identified some key targets of SFI therapy for AP, such as prostaglandin synthase-2 (PTGS2, also known as COX-2), TNF, interleukin-1B (IL1B), nuclear factor kappa B inhibitor alpha (NFKBIA), CASP3, and CASP8. Recent experimental studies have demonstrated that COX-2 is upregulated in rats with AP [27]. Furthermore, mice lacking the COX-2 gene displayed a reduced severity of pancreatitis and pancreatitis-associated lung injury [28]. Additionally, COX-2 inhibitors were found to effectively suppress the activation of NF-κB and the expression of TNF-α messenger ribonucleic acid in the pancreas of AP rats, thereby decreasing the levels of TNF-α, IL-1, and IL-6 in serum [29]. Furthermore, COX-2 inhibitors have been shown to ameliorate the inflammatory process of AP and improve renal and respiratory function [30]. Lornoxicam, a COX-1/COX-2 inhibitor, was found to effectively reduce the expression of toll-like receptors (TLRs) and production of pro-inflammatory cytokines in AP patients [31]. This was also confirmed in a recent clinical trial [32]. Inflammation and parenchymal cell death are two key pathological responses and determinants of the severity of disease in AP [33]. NF-κB mediates the expression of hundreds of genes involved in inflammation, immunity, cell survival and proliferation, and the NFKBIA-encoded protein interacts with REL dimer to repress the NF-κB/REL complex implicated in an inflammatory response. Hence, regulation of NFKBIA may offer potential for the treatment of AP. CASP3 and CASP8, two apoptosis-related proteins, have been found to play important roles in mediating apoptotic damage of acinar cells [34,35]. However, further research is needed to elucidate the coordination of these targets by the various active ingredients of SFI to exert a beneficial effect on AP.

In this study, we have identified acinar cells, endothelial cells, T lymphocytes, dendritic cells, and ductal cells as potential effector cells targeted by SFI in the treatment of AP. In AP, pancreatic acinar cells undergo damage leading to autodigestion by activated pancreatic enzymes, exacerbating inflammation and tissue injury [36]. Endothelial cells, forming the walls of pancreatic blood vessels, when compromised in AP, give rise to microvascular dysfunction and impaired blood perfusion, thus amplifying inflammation and hypoxia, facilitating the cascade of inflammatory reactions, and potentially contributing to local thrombus formation and pancreatic necrosis [37]. T cells play a regulatory role in the immune response during AP, where they might mediate defense mechanisms at early stages of inflammation, while overactivation or dysregulation of T cells can escalate inflammatory responses and fibrotic processes in the chronic phase [38]. Dendritic cells actively participate in the induction of adaptive immune responses during AP, particularly by stimulating T cell activation, thereby influencing the course and outcome of the inflammatory process [39]. SFI appears to exert distinct effects on these various cell types, collectively working to ameliorate AP symptoms. Further experimental investigation is warranted to elucidate the underlying molecular mechanisms by which SFI modulates the functions of these different cell populations to achieve its therapeutic effect.

Apoptosis, a process of programmed cell death, plays a pivotal role in determining the severity of acute pancreatitis. Mild cases of acute pancreatitis exhibit extensive apoptotic death of acinar cells, whereas severe cases are characterized by widespread necrosis of acinar cells with minimal apoptotic activity [40]. These findings strongly suggest that acinar cell apoptosis may confer a favorable response, potentially mitigating the severity of acute pancreatitis episodes. Our enrichment analysis reveals a significant correlation between the therapeutic mechanism of SFI (specific treatment) for acute pancreatitis and apoptosis. Notably, SFI targets key components of the apoptotic pathway, including caspase 3, caspase 8, caspase 9, Bax, Bcl-2, among others. Furthermore, our in vitro experiments provide empirical evidence that SFI augments the inhibition of acinar cell apoptosis mediated by CAE, which can be attributed to the regulatory effects of SFI on the aforementioned apoptosis-related genes. In addition, our experiments also showed that SFI could suppress the release of inflammatory factors of acinar cells, such as TNF-α, PTGS2 and IL-1β. In addition, the targets of SFI in treating AP were also related to necroptosis and multiple signal pathways, including interleukin-17 and p53 signal pathways. Necroptosis is a form of programmed cell death that is distinct from apoptosis and traditional necrosis, and animal studies have demonstrated that inhibition of acinar cell necroptosis can protect against AP [41]. IL-17 is a cytokine that may play a critical role in AP, and inhibition of the IL-17 cytokine family can ameliorate the pathogenesis of AP [42]. The p53 signaling pathway is involved in cell cycle arrest, cell senescence or apoptosis, and studies have shown that ATF6 can modulate p53/AIFM2 transcription to enhance severe acute pancreatitis acinar cell apoptosis and damage [43]. These findings suggest that SFI can impede the progression and prognosis of AP in a multi-faceted manner.

Despite our research providing many new insights into the mechanism of SFI therapy for AP, several limitations remain. Firstly, network pharmacology revealed many targets and related pathways of SFI therapy for AP, while we only verified the effects of SFI on apoptotic acinar cells and related targets; the effects of SFI on other AP-related cells, targets, and pathways still require a large amount of research for verification. Secondly, the existing methodology of network pharmacology is difficult to fully reveal all the mechanisms of SFI therapy for AP, and further integration of other omics techniques or development of advanced analytical pipelines is still needed in the future.

5 Conclusion

In conclusion, this study systematically elucidated the mechanisms of SFI treatment for AP at the target, pathway, and cellular levels. In vitro experiments have rigorously substantiated the notion that SFI exerts a pro-apoptotic influence on acinar cells while concurrently suppressing inflammatory responses during the initiation and progression of AP. The functional efficacy and therapeutic targets of SFI in treating AP were further substantiated through animal experimentation. Nevertheless, given the intricate nature of SFI treatment for AP, further rigorous experimental investigations are warranted to corroborate and validate these findings.

Ethics statement

This study was also approved by the Animal Ethics Committee of Zhejiang Provincial People's Hospital (No. 2022–139).

Consent for publication

Not applicable.

Funding

This study was supported by Zhejiang Research Fund of Traditional Chinese Medicine (2021ZA016 ), the Clinical Research and Application Project of Zhejiang Health Science and Technology Program (2022KY582 ) and the Zhejiang Research Fund of Traditional Chinese Medicine (2022ZB030 ).

Data availability statement

Data will be made available on request.

CRediT authorship contribution statement

Liming Xu: Writing – review & editing, Writing – original draft, Methodology, Formal analysis, Data curation, Conceptualization. Tianpeng Wang: Writing – original draft, Formal analysis, Data curation. Yingge Xu: Writing – original draft, Formal analysis, Data curation. Chenghang Jiang: Writing – review & editing, Methodology, Conceptualization.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix A Supplementary data

The following is/are the supplementary data to this article:Fig. S1 Full, unadjusted images of gels and blots from in vitro experiments.

Fig. S1

Fig. S2 Full, unadjusted images of gels and blots from in vivo experiments.

Fig. S2

Acknowledgements

Not applicable.

Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.heliyon.2024.e37491.
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