Elucidating the anti-atherosclerosis mechanism of Si Jun Zi Decoction by integrating network pharmacology and transcriptomic with experimental validation in vivo

doi:10.21203/rs.3.rs-4872904/v1

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Elucidating the anti-atherosclerosis mechanism of Si Jun Zi Decoction by integrating network pharmacology and transcriptomic with experimental validation in vivo

https://doi.org/10.21203/rs.3.rs-4872904/v1

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Objective: Sijunzi Decoction (SJZD) is a kind of traditional Chinese medicine (TCM) formula,which has the contribution to anti-atherosclerosis.This study aims to explore the potential mechanism of the treatment of atherosclerosis (AS) with SJZD.

Methods: Comprehensive analysis with transcriptomics and network pharmacology,combining with in vivo experiment and therapeutic targets.

Results: Through the in vivo experiment,it was found that SJZD could significantly improve the blood lipid level and lipid deposition in ApoE−/− mice.Furthermore,4 biological processes(lipid localization,specific granule,positive regulation of lipid localization,positive regulation of lipid transport),7 targets (SPP1,EGF,OLR1,LDLR,PON1,SLP1,PLAU), and 6 active ingredients(including beta-sitosterol,kaempferol,quercetin,stigmasterol, naringenin and isorhamnetin) play an extremely important role in the treatment of AS with SJZD.We also found that the genes of OLR,SPP1 and EGF were involved in regulating oxidative stress during the progress of atherosclerosis and the improvement of endothelial dysfunction.Furthermore,SJZD could interfere the expression of mRNA and protein of OLR1,SPP1 and EGF,and respectively reduce and increase the density level of MDA and SOD in serum significantly.

Conclusion:Inhibiting excessively oxidative stress and improving endothelial dysfunction by regulating OLR1/SPP1/EGF pathway could be the mechanism,by which Sijunzi decoction resists AS.

network pharmacology

transcriptomic techniques

TCM

Sijunzi Decoction

atherosclerosis

OLR1/SPP1/EGF pathway

Atherosclerosis (AS) is one of the most common vascular disfunction all over the world and could lead to stroke and coronary heart disease.It is associated with health risks that are major causes of death ^[1].Previous studies have demonstrated that AS has a connection with endothelial cell damage,inflammation,lipid metabolism disorders,and immune dysfunction^[2].Endothelial cell injury,which results in plaque, leads to further instability and rupture of it, and secondary thrombosis will accelerate the progression of the disease^[3].Vascular endothelial cell injury caused by oxidative stress is an significant factor and an early pathological change in the development of AS^[4–5].In China,Chinese herbal medicine (CHM) has been used to treat atherosclerotic complications for a long time,such as angina pectoris,myocardial infarction and stroke^[6].However,the molecular mechanism is still unclear.Sijunzi Decoction (SJZD),consisting of Atractylodes macrocephala Panax ginseng,Glycyrrhiza uralensis and Poria cocos,has been applied for thousands of years^[7]. SJZD as a classic prescription for treating spleen deficiency syndrome,has obvious anti-oxidative stress effect^[8].However,the composition of traditional Chinese medicine (TCM) is complex,with many active components and function through multiple pathways.

To further explore its mechanism, network pharmacology and transcriptomic techniques are essential to be introduced. Network pharmacology,which is similar to the overall concept of TCM,constructs a drug-component-target-disease regulatory network through joint analysis of disease-related genes^[9].It is helpful to elucidate the mechanism of drug comprehensively as much as possible and interpret the synergistic effect of multiple components and targets of TCM^[10].It is also used to evaluate multi-target drugs and complex diseases^[11].Transcriptomics is a subject that elaborates gene transcription and regulation in tissues and cells with a holistic view,and could comprehensively explain the regulation mechanism of gene expression in complex diseases by integrating a variety of calculation ways.For the study of gene transcription and regulation in cells or tissues^[12], the combined application of network pharmacology and transcriptomics could preliminarily screen the active ingredients and molecular targets of drugs,and provide convenient conditions for subsequent research^[10].Based on the above, the aim of our research is to find the possible mechanism and molecular targets of SJZD,which could improve vascular endothelial injury and prevent AS,mainly through network pharmacology and transcriptomic technology.

2.1 Experimental animal

16 SPF grade male ApoE ^{− / −} mice (body weight, 18–20 g) and 8 male C57BL/6 mice were provided by Beijing Vital River Laboratory Animal Technology Co.Ltd. The Committee on the Ethics of Animal Experiments at Liaoning University of Traditional Chinese Medicine approved this study(permitted number SCXK(Jing) 2012-0001).All mice were raised in the Animal Experimental Center of Liaoning University of Traditional Chinese Medicine and allowed free movement with food and water ad libitum.

2.2 Establishment of AS model mice and treatments

After 1 week of adaptive feeding in SPF grade mice,8 C57BL/6 mice were considered as a normal group(NG),and 16 ApoE^−/−mice were randomly assigned to the model group(MG) and SJZ Decoction group(SJZD).The normal group was fed with basal diet daily,while the other groups were fed with high-fat diet(containing 21% fat, 10% lard, 1% cholesterol, etc.)with free access to food and water for 12 weeks.The drug intervention was given to the group,which the ApoE^−/− mice treated with 20 g/kg/d SJZ Decoction for the last 4 weeks.The normal group and model group were given an equal volume of normal saline daily by gavage once a day.

2.3 Serum and tissue samples

After the treatment period,all mice were fasted overnight and anesthetized with chloral hydrate.Eyeball blood was collected from sacrificed mice after resting for 30 min and subjected to centrifugation at 3000× g and 4°C for 15 min in order to obtain serum, and stored in aliquots at -80°C for further analysis.Quickly separate the aortic arch,thoracic-abdominal aorta,and rinse 3 times with ice-cold saline.The aorta was fixed in 4% paraformaldehyde for atherosclerotic lesion analysis.The remaining part is frozen at -80°C until it is used for biochemical determination,iron content analysis and RNA extraction.

2.4 Serum lipids

The levels of serum TC,TG,HDL-C,and LDL-C were measured using an automated biochemical analyzer(SIEMENS,Munich,DE,Germany).

2.5 Histopathology test

The arterial tissue was fixed in 4% paraformaldehyde solution overnight,and stained using the conventional method of hematoxylin-eosin(HE)staining.The specific steps are as follows:ⅰ.The tissue is dehydrated,transparent,embedded in paraffin, and then sectioned(thickness:~5 microns). Ⅱ.Paraffin sections are dewaxed with xylene, rehydrated with gradient alcohol,stained with hematoxylin,differentiated with hydrochloric acid alcohol,counterstained with eosin, then dehydrated,transparent with xylene,and mounted with neutral gum. Ⅲ.Observe the morphology of rat aorta under a microscope.

Oil Red O staining of aorta: complete aorta was isolated and fixed in 10% neutral formalin for 24 h.The complete aorta was in 60% isopropanol for 10min,stained in oil red O (Solarbio,China) working fluid (3:2) for 3h,washed in 60% isopropanol for 6 times till the background color became white,and then photographed.

2.6 Transcriptomic sequencing and data analysis

2.6.1 RNA extraction and library preparation

The aorta tissues samples were randomly pooled from the mice in the NG,MG,and SJZD (n = 3).The total RNA was extracted using 1mL of Trizol Reagent (Invitrogen,Carlsbad,CA,USA) according to the manufacturer’s instructions.The total RNA was reverse transcribed into cDNA.NEBNext® UltraTM RNA Library Preparation Kit for Illumina(NEB,USA) was used to generate sequencing libraries.HiSeq X instrument was used for whole transcriptome sequencing.Cut adapt was used to filter the raw data to get clean data for further analysis.

2.6.2 Differential expression genes (DEGs) analysis

Two comparisons-MG versus NG, and SJZD vs MG were executed application of the DESeq2 R package (1.16.1) to determine differential expression genes.DESeq2 provided statistical routines for defining various expression in digital gene expression data application model according to the negative binomial distribution.The significance of DESeq2 was determined by p-value < 0.05 and |log2(fold-change)|>1,which was implemented to define DEGs.

2.6.3 PPI network construction and analysis

We used the Venn map to scalp the DEGs,which were down-regulated by MG and up-regulated by SJZD, or DEGs up-regulated by MG and down-regulated by SJZD. The screened DEGs might be the latent targets of SJZD to treat AS.

2.6.4 Functional enrichment analysis

The GO and KEGG pathways analysis were enriched by the R software package to realize the functions of candidate DEGs.In our research,we used the cluster Profiler to analyze the GO and KEGG pathways which was focused on were presented.

2.6 Transcriptomic sequencing and data analysis

2.6.1 RNA extraction and library preparation

2.6.2 Differential expression genes (DEGs) analysis

2.6.3 PPI network construction and analysis

2.6.4 Functional enrichment analysis

2.7 Network pharmacology

2.7.1 Gatheration of active compounds and acquisition of corresponding targets of SJZ decoction

The active ingredients of four herbal medicines in SJZ decoction were obtained from TCMSP and the BATMAN-TCM database.According to the recommended drug screening criteria of the TCMSP database,chemical compounds with OB ≥ 30% and DL ≥ 0.18 may considered active,and they were choosed as effective constituents for further analysis through the TCMSP database.Targets of active ingredients were acquired from the TCMSP database.The target protein names corresponding to active compounds were normalized in UniProt.

2.7.2 Collect the common targets of SJZ Decoction and AS

Using “atherosclerosis” as a keyword,the predicted genes of AS were collected from Gene Cards database, CTD database and TTD database.The search results found in three database were merged and deleted duplicate targets to obtain all target genes of AS.The common targets of SJZ Decoction and AS were identified by the bioinformatics online tool (http://www.bioinformatics.com.cn/).In short,the Venn map was used to draw the targets of SJZ Decoction and the disease targets of AS.Next,the core targets were considered as the potential targets for SJZ Decoction in treatment of AS.

2.7.3 Network construction and Functional enrichment

The core targets were entered into String online tool to obtain the interrelated information of protein-protein interactions (PPI).Next, Cytoscape 3.7.2 was used to establish the visualized PPI network and calculate the degree centrality. Moreover, the key proteins of the PPI network were acquired. The GO and KEGG pathways of the potential proteins were enriched and analyzed by R 3.6.3. The results were analized by using p-value and count values.

2.8 Structure ingredient-gene-pathway network (IGP)

We assessed pathways in the GO-biological process,which appears together in network pharmacology and transcriptome research.The genes related to this category were found in the transcriptome results,and the gene-pathway network was established.According to the network pharmacology results,the related active molecules of genes in this part were analyzed.Ingredients,genes,and GO-biological process were also put into Cytoscape tool to demonstrate a visualized Ingredient-gene-pathway (IGP) network.

2.9 Validation of compound-target interaction

To validate the compound-target association,the molecular docking program was performed with Auto Dock Tools software(version 4.2).All the 3D structures of core target proteins were obtained from the PDB database (http://www.rcsb.org/),so that proteins and ligands could be prepared in the Auto Dock before performing the docking progress.The whole molecular docking process included setup of proteins,determination of docking sites and docking of proteins to small molecules.Firstly, during the process of protein preparation,water molecules were removed from the protein structures as well as the region arround the amide portion and atomic groups.Next,the protein binding sites were defined and edited.Finally,insert the compounds into the protein-binding site.If the small molecule docked with the protein successfully,it showed a labelled interaction site and a corresponding docking score.Hence,target proteins and compounds with high docking scores were choosed for further analysis.

2.10 Quantitative real-time PCR to examine mRNA expression

TRIZOL reagent (Solarbio,China) was used to extract total RNA from the sample.The cDNA template was synthesized using a commercially available reverse transcription reaction (Bio-Rad,USA),and the quantitative RT-PCR experiment was performed using an ABI7500 rapid quantitative PCR instrument (Applied Biosystems, USA).The reaction procedure is:pre-denaturation at 95°C for 5 min,denaturation at 95°C for 20 s,annealing at 58°C for 30 s,extension at 72°C for 20 s,40 cycles.The relative quantification (2^−ΔΔCt) method was used to calculate the relative amount of mRNA. The PCR primers were as follows:EGF,5’-ACAGAAGGAGTAGATACGCTTG-3’-(forward),and 5’-GATTATTCGATGATGCTTCCCG-3’-(reverse).OLR1,5’-GAAGCCTGCGAATGACGAGC-3’-(forward),5’-ACACCAGGCAGAGGATGACC-3’-(reverse).COL1,5’-TGAACGTGGTGTACAAGGTC-3’-(forward),and 5’-CCATCTTTACCAGGAGAACCAT-3’-(reverse).PON1,5’-AAGAGGAAAGATCACTCTTGCA-3’-(forward),and 5’-GGTCCAATAGCAGCTATATCGT-3’-(reverse).SLP1,5’-ATGTATGATGCTTAACCCTCCC-3’-(forward),and 5’-AGGCAGACTTTCCCACATATAC-3’-(reverse).SPP1,5’-AAACACACAGACTTGAGCATTC-3’-(forward),and 5’-TTAGGGTCTAGGACTAGCTTGT-3’-(reverse).PLAU,5’-GCTTGTTTCTCATGAACAGTGT-3’-(forward),and 5’-TTCGATGTTACAGATAAGCGGT-3’-(reverse).MMP2,5’-ACTTTGAGAAGGATGGCAAGTA-3’-(forward),and 5’-CTTCTTATCCCGGTCATAGTCC-3’-(reverse).LDLR1,5’-GAAGCCTGCGAATGACGAGC-3’-(forward),and 5’-ACACCAGGCAGAGGATGACC-3’-(reverse).And GAPDH,5’-GACATGCCGCCTGGAGAAAC-3’-(forward),and 5’-AGCCCAGGATGCCCTTTAGT-3’-(reverse).

2.11 Western blot analysis

Total proteins were extracted from tissues using RIPA lysis buffer containing 1% phenylmethylsulphonyl fuoride (PMSF) for 30 minute on ice.The supernatants were collected after being clarified by centrifuging at 12000 rpm for 15 minutes at 4°C. Protein concentrations were measured by the BCA protein assay.The same amounts of protein (60 µg) were separated by electrophoresis on 10%-12% sodium dodecyl sulphate polyacrylamide gels and transferred onto PVDF membranes.Next,PVDF membranes were incubated overnight at 4°C with the antibody(proteintech,China). After washing with TBST 3 times,the membranes were incubated for 1 h with fluorescent secondary antibody at room temperature.Then be washed with TBST again, the membranes were scanned using a fluorescent scanner.

2.12 Immunohistochemistry(IHC)analysis

IHC was performed on aorta tissue sections.Each sample was fixed in formalin and embedded in paraffin.The blocks were sliced into 5 µm-thick sections,After the tissue sections were dewaxed,antigen retrieval was performed,hydrated in a graded series of alcohols,and subjected to heat-activated antigen retrieval.After blocking endogenous peroxidase activity,the tissue was incubated with OLR1,SPP1 and EGF antibodies at room temperature for 4 h.Subsequently,the sections were washed and incubated with biotinylated secondary antibody at room temperature for 30 min.The reaction complexes were visualized with diaminobenzidine and counterstained with hematoxylin.Observe the morphology under the microscope.

2.13 Enzyme-linked immunosorbent assay (ELISA) analysis

The serum SOD and MDA were determined using an ELISA kit according to the manufacturer's instructions. Specifically, after separating and collecting mice blood sample, the serum was obtained by centrifugation. Use the following commercial kits to evaluate various substances in serum by ELISA analysis: MDA (Mouse MDA ELISA KIT; Shanghai Enzyme Linked Biotechnology Co., Ltd.), SOD (Mouse SOD ELISA KIT; Shanghai Enzyme Linked Biotechnology Co., Ltd.).

2.14 Statistical analysis

Unless otherwise specified, all experiments are conducted in no less than three independent parallel studies, and each parallel study consists of three repeated measurements. All data were analyzed using IBM's SPSS Statistics 21.0 software and expressed as mean ± standard error. One-way analysis of variance (ANOVA) is used for statistical analysis of comparisons between different groups. For all tests, a P-value < 0.05 was considered statistically significant. Unless marked with an asterisk, differences between groups can be considered statistically insignificant.

3.1 SJZD regulates Serum lipid profile and Aortic lesion areas in ApoE^−/−mice.

In order to observe the anti-atherosclerotic effect of SIZD, we first measured the level of blood lipids.In the MG, the serum levels of TC and LDL-C increased significantly than those in the NG(P < 0.01, Fig. 1A), while those of HDL-C decreased significantly(P < 0.01).The ApoE^−/− mice in SJZD exhibited significantly lower serum levels of TC and LDL-C than the ApoE^−/− mice in the model group (P < 0.01).

Then we carried out HE and Oil Red O staining.HE staining results (Fig. 1B) showed that the endothelium of NG arranged regularly. No obvious atherosclerotic lesion was observed. In the MG, a greater amount of the number of aortic atherosclerotic plaque was observed. However, SJZD treatment significantly reduced the lipid deposition in the aorta of ApoE^−/− mice and the area of atherosclerotic plaque was smaller.

Furthermore, Oil Red O staining (Fig. 1C). In the MG, atherosclerotic plaque development was observed, with the intimal thickening of the aorta, and an increased amount of massive Oil Red O-stained lipid. The amount of Oil Red O-stained lipids in the SJZD decreased significantly.

3.2Analysis of DEGs altered after treatment by Transcriptomic sequencing

A total of 631.24 million raw reads were obtained in the sequencing. After filtering, 619.38 (98.12%) clean reads were obtained for following analysis. 600.13 million (95.07%) clean reads mapped to reference genome and 19.25 million clean reads were uniquely mapped. In order to further explore the mechanism of SJZD anti-AS, we performed transcriptome sequencing of mouse aorta samples from NG, MG and SJZD.

Consequently,in order to identify the potential genes associated with AS, we analyzed DEGS. As shown in Fig. 2A, a heat map distribution of those DEGs. The DEGs shown in red indicate a high expression, whereas those in blue indicate a low expression. The heat map demonstrated that several regions with highly altered (up-regulated or down-regulated) expression were shown in SJZD, NG, and MG groups.

DEGs were further filtered by applying volcano plots, where the x-axis shows the Log2FC value and the y-axis corresponds to the value of log10 (p-value). The red dots correspond to significantly up-regulated DEGs (P < 0.05; |Log2FC|>1); the green dots correspond to significantly downregulated DEGs (P < 0.05; | Log2FC|>1); and the black dots indicate DEGs that were no statistical significance (P > 0.05; |Log2FC|<1). There were 2754 DEGs in the MG/NG comparison (total gene counts: DEG-up = 2162; DEG-down = 592; Fig. 2B). Moreover, in the SJZD/AS comparison, there were 1680 DEGs (total gene counts: DEG-up = 540, DEG-down = 1140; Fig. 2C).

3.3 PPI network analysis of common SJZT targets to determine the core targets and bioinformatics analysis of predictive targets.

As indicated in the Venn diagrams, in the mice treatment by SJZD, 33 genes downregulated by AS and upregulated by SJZD, and 306 genes upregulated by AS and downregulated by SJZD. Fortunately, 339 genes were rescued, indicating that SJZD reversed some AS-induced genes. Those 339 overlapping genes that indicate the opposite trends were marked as latent genes of interest (Fig. 3A-B). The PPI network of the 339 latent targets was established by Cytoscape 3.7.2. As shown in Fig. 3C, some of the remarkable genes in the PPI network included PON1, EGF, SLPI were initially upregulated in AS, but later afterwards reversed by SJZD.

The GO enrichment results showed that 339 latent targets of SJZD were involved in biological processes, including lipid localization, eukocyte cell-cell adhesion, and leukocyte proliferation. The cellular component involved, including the membrane raft, nuclear chromatin, and nuclear transcription factor complex. It was closely related to molecular function, such as receptor ligand activity and cytokine receptor binding. As shown in Fig. 3D-F, the GO enrichment results were scalped. The results of KEGG pathways enrichment showed that 339 DEGs were enriched into nine pathways, in which the pathways with P < 0.05 were defined as the key pathways (Fig. 3G). Among them, Including AGE-RAGE signaling pathway in diabetic complications, Th17 cell differentiation, and Non-alcoholic fatty liver disease are AS-related signal pathways.

3.4 Determination of effective components and common anti-AS indexes of SJZD.

A total of 136 active ingredients of four herbal medicines in SJZ decoction were identified based on threshold values of OB > 30% and DL > 0.18, including 22 constituents in ginseng, 15 constituents in poria cocos, 7 constituents in atractylodes, 92 constituents in licorice after deleting the duplicate data. Details of the active ingredients are listed in (supplement Table S1). In addition, 258 targets of active constituents of SJZ decoction were obtained from the TCMSP database and the gene names of these targets were collected via the Uniprot database (Supplementary Table S2).

Furthermore, we had collected AS-related genes/targets from Gene Cards database, CTD database, TTD database. And we used TCMSP to supplement them and then added targets. Finally, we totally collected 3729 AS-related targets. Information on these targets is provided in Supplementary Table S3.

After mapping SJZD targets and AS targets, a total of 171 common targets were obtained (Fig. 4A). To better understand the interactions between common targets, PPI network was established to better interpret the mechanisms of SJZD in AS treatment by using STRING software (combined score ≥ 0.9). As shown in Fig. 4B, the network consisted of 165 nodes and 1283 edges with an average node degree of 7.776, network centralization of 0.342, and an average number of neighbors of 15.552. According to the degree principle of each target, AKT1, IL6, MAPK1, JUN, and MAPK8 were determined as the hub targets (Fig. 4C).

To reflect the interactions intuitively between the active compounds of SJZD and their potential targets, the C–T network was constructed by mapping 114 active compounds to their 208 corresponding potential targets. As shown in Fig. 4D, the network consisted of 322 nodes (114 active compound nodes, and 208 compound associated target nodes) and 1400 interaction edges.In the network, quercetin (degree 136), kaempferol (degree 85), and 7-Methoxy-2-methyl isoflavone (degree 31) presented the maximum interactions with potential targets, indicating that these active compounds with high degree values could play an significant role in the latent pharmacological effects of SJZD. The C-T network revealed intimate communications between active constituents and related targets, which provided a reference to further investigate the pharmacological mechanisms of SJZD.

3.5 Gene ontology enrichment analysis and targetpathway network construction

The GO functional analyses is carried out in the David database to reveal the function of 171overlapping targets. GO enrichment analysis included 197biological processes (BP), 59 cellular components (CC) and 126 molecular functions (MF) with a threshold value of P < 0.05. The GO analysis results are shown in Fig. 5A–C. The BP results mainly comprised of positive regulation of transcription from RNA polymerase II promoter, positive regulation of transcription, DNA-templated, response to drug. The CC analysis indicated that the overlapping targets were mainly related to cytosol, nucleoplasm, extracellular space. The MF results included protein binding, enzyme binding, identical protein binding.

The results of the KEGG enrichment analysis indicated that 124 pathways meeting the threshold value of P < 0.05 were significantly enriched. As depicted in Fig. 5D, the KEGG pathways of SJZD against atherosclerosis were mainly related to P13K-Akt signaling pathway, TNF signaling pathway, MAPK signaling pathway, HIF-1 signaling pathway, and FpxO signaling pathway.

3.6 Structuration of ingredient-gene-pathway (IGP) network and verification of core genes by qRT-PCR.

In order to find a reliable signal pathway for SZJD to improve AS, we chose DEGs and GO-biological process pathway, in which the pathways with P < 0.05, by intersecting results of network pharmacology with transcriptome (Fig. 6A). The results of GO-biological processes pathway revealed that 339 DEGs were enriched including lipid localization. Ingredients, genes, and pathways were also introduced into Cytoscape to establish a visualized Ingredient-gene-pathway (IGP) network for further analysis (Fig. 6B). We performed qRT-PCR analyses to investigate whether SJZD affected the transcriptional regulation of the genes screened above. As shown in Fig. 2, EGF, OLR1, COL1, SLP, SPP1, PLAU and MMP2 mRNA were significantly increased in ApoE^−/−mice, while PON-1mRNA significantly reduced. compared with those in normal mice (P < 0,01). The expression of LDLR mRNA was not statistically significant. The expression of LDLR mRNA was no significant difference (P>0.05).The expressions of PON-1and LDLR mRNA was significantly increased and the expressions of EGF, OLR1, COL1, SPP1, PLAU and MMP2 mRNA was significantly reduced under SJZD treatment (P < 0.05 or (P < 0.01). The expression of SLP1 mRNA was no significant difference (P>0.05, Fig. 6C).

3.7SJZD regulates OLR1/SPP1/EGF pathway to affect endothelial injury and prevent atherosclerosis

Among the genes screened above, we found that there is a link between OLR1,SPP1 and EGF, which is related to endothelial injury and oxidative stress. To examine whether antioxidative effect of SJZD is mediated by OLR1,SPP1 and EGF, we detected their protein expression in mice aorta. The protein levels of OLR1,SPP1 and EGF were significantly increased in the model group, compared to the normal mice (P < 0.01). SJZD intervention decreased the expression levels of these proteins(P < 0.01, Figs. 7A). The above results suggest that SJZD can improve vascular endothelial injury in atherosclerotic mice by down-regulating the expression of OLR1,SPP1 and EGF protein.

In order to further explore whether SJZD can improve endothelial function and reduce oxidative stress in ApoE^−/− mice, the expression of OLR1, SPP1 and EGF were detected by immunehistochemistry. As shown in Figs. 7B, immunohistochemical(IHC)analysis of OLR1,SPP1 and EGF showed that the area occupied by these proteins in the lesions of ApoE^−/− mice was significantly higher than that of normal mice ( OLR1 was30.21vs. 18.39%, SPP1 was 25.40 vs.10.16%, EGF was 29.13 vs.11.80%, P < 0.01) However, after SJZD administration, this situation was obviously reversed, and plaque deposition and these proteins occupied area of aorta decreased significantly (P < 0.01 or P < 0.05,Fig. 7C). These results were consistent with the changes in protein levels in mouse aortas, as revealed by Western Blot. All in all, these results indicate that SJZD can reduce the level of OLR1, SPP1 and EGF in atherosclerotic lesions and improve oxidative stress.

According to the above results, we speculate that the therapeutic effect of Sijunzi decoction on ApoE^−/−mice may be related to oxidative stress. In order to further verify the effect of SJZD treatment on oxidative stress in ApoE^−/− mice, we detected the levels of SOD and MDA in serum of mice. compared to that from normal mice, the serum levels of anti-oxidative SOD was significantly reduced and pro-oxidative MDA was increased in ApoE^−/− mice (P < 0.01). However, the serum level of SOD increased and the level of MDA decreased in ApoE^−/− mice treated with SJZD (P < 0.01, Figs. 7D). These results indicated that SJZD treatment results in a marked suppression in oxidative stress in ApoE^−/− mice through OLR1/SPP1/EGF pathway.

3.8 Validation of compound-target interaction

To improve the accuracy of the connection between small molecular compounds and the core target proteins, we used molecular docking to evaluate the interactions between compounds and target proteins. It is generally believed that when the docking binding energy less than − 4.0 kcal/mol, it could be considered to have potential binding activity between the ligand and receptor ^[13]. A binding energy score less than − 5 kcal/mol suggests strong binding between the ligand and receptor ^{[14, 15]}. Molecular docking indicated that kaempferol (A), quercetin (B) and naringenin (C) had good affinity for the three important compounds, SPP1, EGF, OLR1, in SJZT.

Many studies have shown that SJZD is a promising formula for treatment of cardiovascular related diseases. Preliminary basic research shows that SJZD has the effect of anti-atherosclerosis, however, the pharmacological mechanism of SJZD against AS is not yet fully understood, and the effects of its bioactive compounds and targets remain elusive. Therefore, by applying network pharmacology and transcriptomic methods, this study, which is combined with in vivo experiment, was carried out to identify the underlying mechanism and therapeutic targets of SJZD against AS effect.

Firstly,the results about serum lipids and atherosclerotic plaques of aorta by HE and Oil Red O suggested that ApoE^−/− AS mice model is conducted successfully and SJZT has a regulatory effect on them,which is consistent with previous studies.Then, transcriptomics sequencing was used to obtain active targets of AS.Through transcriptomics results of aorta tissue,339 genes were screened out,which indicated that SJZD may reverse some AS-induced genes.The PPI network analysis showed that some of the notable genes were reversed by SJZD,which means these genes may serve as potential targets and play an important role in the development of AS.The data of GO and KEGG showed that the importance of AS related terms was extremely high, which supports our previous hypothesis that the reliability of SJZD in the treatment of AS.Furthermore,a network pharmacology approach was used to identify 114 active compounds and 208 hub targets.The component-target network indicated that SJZD compounds might affect multiple targets and possess overlapping some,which results in synergistic effects.GO enrichment analysis showed that 208 targets were enriched in some biological processes,such as inflammatory response,angiogenesis and anti-apoptosis.KEGG analysis demonstrated that through the inhibition of some signal pathways,such as PI3K-Akt,TNF, MAPK and HIF-1, SJZD could resist AS. Finally, to make further analysis, we coupled network pharmacology with known transcriptomic results, and a visualized IGP network was established. According to the results,4 biological process (lipid localization, specific granule, positive regulation of lipid transport, positive regulation of lipid localization), 7 targets (SPP1, EGF, OLR1, LDLR, PON1, SLP1, PLAU), and 6 active ingredients (including beta-sitosterol, kaempferol, quercetin, stigmasterol, naringenin and isorhamnetin) play an extremely important role in the treatment of AS with SJZD.

From above, we find that four biological processes involved in AS are mainly related to lipids and specific granules. As it is known, AS is a process caused by multiple factors, and lipid accumulation and subsequent inflammation have been shown to play a critical role. During further progression of atherosclerotic lesion, many other specific granules, such as low-density lipoprotein (LDL), come into play, which results in, locally or systemically, chronic low-grade inflammatory state. The infiltration and retention of atheroprone lipoproteins, predominately LDL, in the vessel wall, is considered as an initial step in the development of AS ^[16]. The transportation of atheroprone particles, like LDL, across the endothelium, is critical to the initiation, progression, and regression of AS ^[17].

In this study, we attempted to explore a prospective novel therapeutic for AS based on the bioinformatic methods. In vivo and some animal models have shown that quercetin has a wide range of biological actions, including anti-carcinogenic, antiviral,anti-inflammatory,antioxidant,and psychostimulant activities,as well as the ability to inhibit lipid peroxidation,capillary permeability and platelet aggregation, and to stimulate mitochondrial biogenesis^[21].Recently,the mechanism of the anti-atherosclerotic effects of quercetin have been investigated extensively^[22].In addition,quercetin suppressed vascular ROS formation endothelial dysfunction in HFD-fed ApoE-/- mice,with beneficial effects on atherosclerotic plaque formation. Naringenin is a major flavanone in citrus fruits that has multiple pharmacological attributes, such as anticancer and antiatherogenic antioxidant activity and free radical scavenging effects.Naringenin plays an important role in resisting cardiovascular diseases^[23].Naringenin attenuates endothelial dysfunction by reducing ROS accumulation and increasing nitric oxide production in endothelial cells ^[24].Finally, we performed molecular docking to identify the reliability of the interactions of 3 active compounds and 3 core targets based on the previous study.We found that quercetin, kaempferol and naringenin bind most closely to the EGF, SPP1 and OLR1 proteins. In terms of secondary structure,quercetin and kaempferol are similar in structure.This shows that the binding pockets of kaempferol and quercetin are almost the same on protein,but the binding free energy and binding site are not necessarily the same. These results, which are consistent with our previous findings, show that the strategy of applying network pharmacology to find potential active compounds is reliable and feasible.

In conclusion,benefiting by the integration of network pharmacology, transcriptomics,further experimental verification and validation of compound-target interaction,our study clarified that LOX-1/SPP1/EGF pathway may be one of the possible targets of SJZD to antiatherogenic.Kaempferol,quercetin and naringenin are the effective constitutes of SJZD to antiatherogenic.These findings are expected to guide the therapeutic research of AS in the future,and to provide a methodological reference for the pharmacodynamic material basis and targets of TCM in treatment of diseases by the integrated analysis combined with network pharmacology and transcriptomics.Although we have examined the possible role of SJZD in treating AS,there are some limitations in the present study.Further in vivo experimental validation is needed to support our research.

Compliance with ethical standards

All animal experiments and methods were performed in accordance with the Health Guide for the Care and Use of Laboratory Animals published by the National Institute of Health.

Financial support and sponsorship

This study was supported by National Natural Science Foundation of China grant (No. 81874372 to Wenna Chen);the Natural Science Foundation of Liaoning Province (20180550767).

Conflicts of interest

There are no conflicts of interest.

Author Contribution

Weiyan Chen were responsible for conception and design. Tianmin Ji and Ying Yang was responsible for manuscript writing. Zhuo Zhao, Hao Gao, were responsible for collection and assembly of data. Lijiang Zhou and Ying Wang were responsible for data analysis and interpretation. All authors were responsible for manuscript writing. All authors were responsible for the final approval of the manuscript.

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Supplementary Tables S1 to S3 are not available with this version.

No competing interests reported.

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You are reading this latest preprint version

Elucidating the anti-atherosclerosis mechanism of Si Jun Zi Decoction by integrating network pharmacology and transcriptomic with experimental validation in vivo

Status:

Version 1

Abstract

Figures

1. Introduction

2. Materials and Methods

2.1 Experimental animal

2.2 Establishment of AS model mice and treatments

2.3 Serum and tissue samples

2.4 Serum lipids

2.5 Histopathology test

2.6 Transcriptomic sequencing and data analysis

2.6.1 RNA extraction and library preparation

2.6.2 Differential expression genes (DEGs) analysis

2.6.3 PPI network construction and analysis

2.6.4 Functional enrichment analysis

2.6 Transcriptomic sequencing and data analysis

2.6.1 RNA extraction and library preparation

2.6.2 Differential expression genes (DEGs) analysis

2.6.3 PPI network construction and analysis

2.6.4 Functional enrichment analysis

2.7 Network pharmacology

2.7.1 Gatheration of active compounds and acquisition of corresponding targets of SJZ decoction

2.7.2 Collect the common targets of SJZ Decoction and AS

2.7.3 Network construction and Functional enrichment

2.8 Structure ingredient-gene-pathway network (IGP)

2.9 Validation of compound-target interaction

2.10 Quantitative real-time PCR to examine mRNA expression

2.11 Western blot analysis

2.12 Immunohistochemistry(IHC)analysis

2.13 Enzyme-linked immunosorbent assay (ELISA) analysis

2.14 Statistical analysis

3. Results

3.1 SJZD regulates Serum lipid profile and Aortic lesion areas in ApoE−/−mice.

3.2Analysis of DEGs altered after treatment by Transcriptomic sequencing

3.4 Determination of effective components and common anti-AS indexes of SJZD.

3.5 Gene ontology enrichment analysis and targetpathway network construction

3.6 Structuration of ingredient-gene-pathway (IGP) network and verification of core genes by qRT-PCR.

3.7SJZD regulates OLR1/SPP1/EGF pathway to affect endothelial injury and prevent atherosclerosis

3.8 Validation of compound-target interaction

4. Discussion

5. Conclusions

Declarations

Author Contribution

References

Supplementary Tables

Additional Declarations

Supplementary Files

Status:

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3.1 SJZD regulates Serum lipid profile and Aortic lesion areas in ApoE^−/−mice.