1. Introduction

Intervertebral disc degeneration (IVDD), a prevalent and challenging chronic condition in orthopedics, affects middle-aged and elderly people and is strongly associated with the deterioration of the disc structure and the decrease in the body’s resistance to aging.[1] About 40% of people worldwide experience low back pain (LBP), which is now considered a severe public health problem.[2] Up to 90% of people in China over the age of 60 have IVDD, and as society develops and the number of people with unhealthy lifestyles rises, the disease is becoming prevalent among younger people. IVDD not only causes great suffering to the lives and spirits of patients, but it also places a heavy financial burden on those individuals, their families, and the entire nation.[3] Patients with IVDD are currently treated mostly with 2 forms of conservative and surgical ways. Physical therapy and medications can help early-stage IVDD patients with their long-term pain, but they cannot address the underlying causes of the disease or its mechanism of action.[4] Additionally, surgical excision of the projecting nucleus pulposus may provide temporary relief of LBP, but not all patients who have surgery have satisfactory results, and a considerable proportion of individuals experience persistent back pain after surgery.[5] As can be observed, IVDD clinical therapies and outcomes are fairly limited and focus on lowering local disc compression or inflammation to improve LBP.[6] Therefore, research into the pathophysiology of IVDD and the development of safe and efficient treatment methods are urgently required to halt the disease’s progression.

Chinese medicine is well known to play a significant role in the keeping of health in Asian nations like China.[7] Especially, herbal medicine has been utilized as an additional and alternative therapy for IVDD patients.[8] Many recent studies have partially established the usefulness of herbal medicine in the treatment of IVDD, with the primary mechanisms including reduction of oxidative stress, regulation of inflammatory response, and decrease of myeloid cell death, among others.[9,10] For instance, LiuWei DiHuang Decoction can cure IVDD by controlling Caspase-3, IL-1β, and other relevant targets to prevent intervertebral disc cells from apoptosis.[11] By promoting autophagy, the beneficial Qi and Blood mixture prevents IVDD,[12] as does the single herbal treatment. Through antioxidation, Salvia miltiorrhiza can slow IVDD in SD rats.[13]

In Bei Ji Qian Jin Yao Fang of the Tang Dynasty, Duhuo Jisheng Decoction (DHJSD) is a TCM recorded for the treatment of “Bi Zheng.”[14,15] Our team discovered that in patients with lumbar disc herniation, DHJSD dramatically lowers visual analogue scale and Japanese Orthopaedic Association ratings and has strong therapeutic efficacy. Additionally, DHJSD possesses antiinflammatory, analgesic, and immunological modulating properties that, by controlling immunity and thwarting inflammatory agents, can lessen the breakdown of the nucleus pulposus’ extracellular matrix and prevent the degeneration of the intervertebral disc.[16] But, herbal compounding is difficult to be recognized by the worldwide community for a number of reasons, including the lack of quantifiable and objective data to support the enormous number of biologically active components and complicated chemical systems that are included in it.[17]

Bioinformatics, which includes herbal network pharmacology, continues to use an a priori analytical method to investigate the connections between medications, chemicals, disorders, and targets.[18]Network pharmacology is frequently used to clarify the mechanism of action of herbal medicines since it has the ability to analyze multiple components, multiple targets, and multiple pathways of herbal medicines, giving researchers new ideas and approaches. In order to identify the essential genes and pathways of DHJSD for the treatment of IVDD, as well as the active DHJSD compounds, we will apply the network pharmacology technique in this study. This will enable further research and development (Fig. 1).

Figure 1.:

The schematic diagram of the present study to investigate potential mechanisms of DHJSD in treating IVDD. DHJSD = Duhuo Jisheng Decoction, IVDD = intervertebral disc degeneration.

2. Materials and methods 2.1. Screening for related targets and potential active compounds in DHJSD

To find the matching chemicals and accompanying data, we utilized the TCM Systematic Pharmacology Database (TCMSP, https://tcmspw.com/tcmsp.php) platform, and entered the names of 15 herbal remedies used to address DHJSD.[19] With criteria of oral bioavailability ≥ 30 and drug similarity ≥ 0.18, active compounds were evaluated in accordance with absorption, distribution, metabolism, and excretion procedures.[20,21] To create a potential target gene set for DHJSD, potential target proteins of the chosen active drugs were mined in the TCSMP database. The “Human” species was chosen in the UniProt database, and the specific relevant gene names and UniProt ID of the medication-related targets in DHJSD were acquired.[22]

2.2. Identification of targets associated with IVDD

Using “ Intervertebral disc degeneration” as a keyword, Search in the GeneCards (https://www.genecards.org/), MalaCards(https://www.malacards.org/), DisGeNet database(https://www.disgenet.org/), and Comparative Toxicogenomics Database (http://ctdbase.org/) disease database,[23–26] to access disease-acting target proteins. To obtain the gene set for IVDD, the search results from these databases were combined and removed duplication.

2.3. Network construction and analysis

The intersection data acquired were the gene set for DHJSD in treating IVDD after the DHJSD gene and the IVDD gene were individually entered into the Vine online tool (https://bioinfogp.cnb.csic.es/tools/venny/). Cytoscape 3.9.1 program created the interaction between “DHJSD-Drug-Active Compound-Intersection Gene-IVDD” network diagram.[27]

2.4. Protein interaction analysis

The majority of biological processes (BP) in live cells are governed by protein interactions, which are crucial for comprehending cellular physiology in both healthy and pathological conditions. The collected set of intersecting genes in the current study was subjected to protein-protein interaction (PPI) network analysis utilizing the string database (http://string-db.org/), which was confined to “Homo sapiens” and had confidence values > 0.4.[28] Cytoscape 3.9.1 software was used to create the PPI network. Additionally, the top 10 nodes were chosen to locate hub genes using the CytoHubba algorithm (Density of Maximum Neighborhood Component, Maximum Neighborhood Component, Edge Percolated Component, Closeness, Betweenness, ClusteringCoefficient, EcCentricity, Radiality, Stress, BottleNeck, Degree, Maximal Clique Centrality), a Cytoscape plug-in.[29]

2.5. Analysis of biological process and pathway enrichment

Enter the DHJSD intersection gene for IVDD into the BioCloud platform, choose the enrichment analysis from the tool center, and restrict the species to “H. sapiens.” In the shared parameters, type the gene ID of the intersecting gene set and hit submit. Finally, we obtained Gene Ontology (GO) enrichment analysis, Recatome, WikiPathways, and Kyoto Encyclopedia of Genes and Genomes (KEGG) database pathway analysis for DHJSD treatment of IVDD crossover genes. Various graphs are used to display the results.

2.6. Molecular docking confirms expected targets

Obtain the 3D structure of the proposed docking target in mol2 format from the PubChem database, open the tiny ligand molecule with AutodockTools 1.5.6, hydrogenate, charge, identify the ligand root, search for and specify the rotatable bond, and save as a pdbqt file. Download the core 3D structure of the target protein as a docking protein from the RCSB Protein Data Bank (www.rcsb.org/). Add all hydrogen atoms to AutodockTools 1.5.6 to open, calculate Gasteiger charge, bind nonpolar hydrogen, define as receptor, and save as pdbqt file.[30] Set the coordinates and box size for Vina molecular docking, set the parameter exhaustiveness to 15, and use the default settings for the other parameters. Using Autodockvina 1.1.2, do semiflexible docking and choose the conformation with the greatest affinity as the final docked conformation.

3. Results 3.1. Active ingredient and target data for DHJSD

Through the TCMSP database, a total of 197 compounds in the DHJSD were ultimately retrieved, of which 9 were from Du Huo, 2 were from Sang Ji Sheng, 28 were from Du Zhong, 4 were from Niu Xi, 8 were from Xi Xin, 2 were from Qin Jiao, 15 were from Fu Ling, 18 were from Fang Feng, 7 were from Chuan Xiong, 22 were from Ren Shen, 92 were from Gan Cao, 2 were from Dang Gui, 13 were from Bai Shao, 2 were from Di Huang (Supplementary Table S1, Supplemental Digital Content, https://links.lww.com/MD/J61). The action targets of the medications were gathered for the active ingredients, and after screening and removing duplicate targets, a total of 306 useful action targets of DHJSD were attained. Two hundred twenty action targets were obtained for use in the last round of data analysis after the targets’ names were assigned in the UniProt database (Supplementary Table S2, Supplemental Digital Content, https://links.lww.com/MD/J62). Among them, there were 30 effective targets from Du Huo, 106 effective targets from Sang Ji Sheng, 23 effective targets from Du Zhong, 1 effective target from Niu Xi, 13 effective targets from Xi Xin, 2 effective targets from Fu Ling, 4 effective targets from Fang Feng, 5 effective targets from Ren Shen, 29 effective targets from Gan Cao, 6 effective targets from Bai Shao, and 1 effective target from Di Huang.

3.2. IVDD-relevant target data

Regarding the action targets of IVDD, we found 1127 in GeneCards, 43 in MalaCards, 667 in the DisGeNet database, and 13002 in Comparative Toxicogenomics Database when we searched disease-related databases. We eventually discovered 12905 IVDD action targets through filtering (Supplementary Table S3, Supplemental Digital Content, https://links.lww.com/MD/J63).

3.3. Screening and network construction of DHJSD targets for IVDD treatment

After analyzing Venny online tool, we were able to identify 209 overlapping genes for DHJSD and IVDD (Fig. 2). In order to create the data for the medicines, elements, and intersecting genes of DHJSD for IVDD, we imported it into the Cytoscope program. We ultimately created the “DHJSD-Drug-Active Compound-Intersection Gene-IVDD” effect network diagram after calculations (Fig. 3).

Figure 2.:

Venny diagram of DHJSD-related targets and IVDD-related targets. DHJSD = Duhuo Jisheng Decoction, IVDD = intervertebral disc degeneration.

Figure 3.:

“DHJSD-active compounds-target genes-IVDD” network. The green square represents this disease, the red octagon represents DHJSD, the brown diamond represents the herbal composition of DHJSD, the small green rectangle represents the active ingredient in the herb, the purple oval and the inverted small triangle represent the potential targets of action of DHJSD for IVDD, and the inverted small triangle represents the most core 10 targets. DHJSD = Duhuo Jisheng Decoction, IVDD = intervertebral disc degeneration.

3.4. PPI data and enrichment analysis results

The network interaction protein map of DHJSD for the treatment of IVDD was obtained using the 209 intersecting genes that were imported into the string online data analysis tool. The protein interaction data were imported into Cytoscope 3.9.1 once more, and the top 10 genes of each set of data were taken separately following CytoHubba calculation (Supplementary Table S4, Supplemental Digital Content, https://links.lww.com/MD/J64). After combining the data, 36 key targets of DHJSD for IVDD were obtained, where the top 10 pivotal genes played a significant role, which were TNF, IL6, IL1β, VEGFA, STAT3, PTGS2, CASP3, EGFR, MAPK3, and IL4 (Table 1Fig. 4).

Table 1 - Basic information of some key targets of DHJSD against IVDD. UniProt ID Gene symbol Protein names Degree P01375 TNF Tumor necrosis factor 33 P05231 IL6 Interleukin-6 33 P01584 IL1B Interleukin-1 beta 32 P15692 VEGFA Vascular endothelial growth factor A 31 P40763 STAT3 Signal transducer and activator of transcription 3 31 P35354 PTGS2 Prostaglandin G/H synthase 2 30 P42574 CASP3 Caspase-3 28 P00533 EGFR Epidermal growth factor receptor 28 Q16644 MAPK3 Mitogen-activated protein kinase 3 28 P05112 IL4 Interleukin-4 28 P31749 AKT1 RAC-alpha serine/threonine-protein kinase 28 P04637 TP53 Cellular tumor antigen p53 27 P01579 IFNG Interferon-gamma 27 Q16665 HIF1A Hypoxia-inducible factor 1-alpha 26 P03956 MMP1 Interstitial collagenase 26 P35222 CTNNB1 Catenin beta-1 25 P37231 PPARG Peroxisome proliferator-activated receptor gamma 25 P01583 IL1A Interleukin-1 alpha 25 P60568 IL2 Interleukin-2 25 P01106 MYC Myc proto-oncogene protein 24 P10451 SPP1 Osteopontin 23 P04040 CAT Catalase 23 P03372 ESR1 Estrogen receptor 22 P16581 SELE E-selectin 22 P29466 CASP1 Caspase-1 21 P45983 MAPK8 Mitogen-activated protein kinase 8 21 P02778 CXCL10 C-X-C motif chemokine 10 21 P19875 CXCL2 C-X-C motif chemokine 2 16 P17252 PRKCA Protein kinase C alpha type 15 O14625 CXCL11 C-X-C motif chemokine 11 13 P09238 MMP10 Stromelysin-2 12 P18428 LBP Lipopolysaccharide-binding protein 6 Q14209 E2F2 Transcription factor E2F2 5 P03973 SLPI Antileukoproteinase 4 Q92819 HAS2 Hyaluronan synthase 2 2 P08709 F7 Coagulation factor VII 2

DHJSD = Duhuo Jisheng Decoction, IVDD = intervertebral disc degeneration, LBP = low back pain.

Figure 4.:

The PPI network of potential targets of DHJSD in the treatment of IVDD. (A) The PPI network from STRING was further analyzed using Cytoscape software. (B) Information on 36 key targets for the gene DHJSD’s possible target screening for IVDD. (C) The top 10 hub genes were identified by CytoHubba algorithm. DHJSD = Duhuo Jisheng Decoction, IVDD = intervertebral disc degeneration, PPI = protein-protein interaction.

Using 36 key targets of action, we conducted GO, Reactome, WikiPathways, and KEGG database enrichment analysis to clarify the biological mechanism of DHJSD for IVDD. Ultimately, we had 1220 GO analysis items, of which 950 were for BP, 162 were for molecular functions, and 108 were for cellular components (CCs) (Supplementary Table S5, Supplemental Digital Content, https://links.lww.com/MD/J65). The top 10 items from BP, CCs, and molecular functions were selected after the ranking was determined by P value. As a result, we came to the conclusion that the biological process of DHJSD for IVDD mostly includes the cytokine-mediated signaling pathway (GO:0019221), positive regulation of gene expression (GO:0010628), positive regulation of transcription by RNA polymerase II (GO:0045944), inflammatory response (GO:0006954), positive regulation of transcription, DNA-templated (GO:0045893), cellular response to lipopolysaccharide (GO:0071222), positive regulation of tyrosine phosphorylation of STAT protein (GO:0042531), negative regulation of apoptotic process (GO:0043066), positive regulation of interleukin-6 production (GO:0032755), and vascular endothelial growth factor production (GO:0010573). Molecular function is mainly concerned with enzyme binding (GO:0019899), cytokine activity (GO:0005125), transcription factor binding (GO:0008134), nitric-oxide synthase regulator activity (GO:0030235), protein phosphatase binding (GO:0019903), CXCR chemokine receptor binding (GO:0045236), identical protein binding (GO:0042802), DNA-binding transcription factor activity (GO:0003700), CXCR3 chemokine receptor binding (GO:0048248), estrogen receptor binding (GO:0030331), CC is mainly concerned with extracellular space (GO:0005615), extracellular region (GO:0005576), protein-containing complex (GO:0032991s), RNA polymerase II transcription regulator complex (GO:0090575), transcription regulator complex (GO:0005667), membrane raft (GO:0045121), perinuclear region of cytoplasm (GO:0048471), Chromatin (GO:0000785), endoplasmic reticulum lumen (GO:0005788), and focal adhesion (GO:0005925) (Table 2, Fig. 5). The examination of Reactome data revealed 557 connected pathways in total (Supplementary Table S6, Supplemental Digital Content, https://links.lww.com/MD/J66), and the top 10 most highly correlated pathways were found in descending order of enrichment score. These pathways include cytokine signaling in immune system, cellular responses to stress, cellular responses to stimuli, immune system, generic transcription pathway, RNA polymerase II transcription, gene expression (transcription), signaling by GPCR, developmental biology, signal transduction (Table 3, Fig. 6). The WikiPathways data analysis produced a total of 392 linked pathways (Supplementary Table S7, Supplemental Digital Content, https://links.lww.com/MD/J67), which were ranked by enrichment score in order to determine the top 10, including TCA cycle nutrient use and invasiveness of ovarian cancer, coronavirus disease 2019 adverse outcome pathway, mammary gland development pathway – involution (Stage 4 of 4), cytokines and inflammatory response, nanomaterial-induced inflammasome activation, activation of NLRP3 inflammasome by severe acute respiratory syndrome coronavirus 2, photodynamic therapy-induced NF-kB survival signaling, hepatitis C and hepatocellular carcinoma, HIF1A and PPARG regulation of glycolysis, caloric restriction, and aging (Table 4). The KEGG data analysis yielded 206 correlated pathways (Supplementary Table S8, Supplemental Digital Content, https://links.lww.com/MD/J68), and the top 20 most correlated pathways were obtained in descending order of enrichment score, which are bladder cancer, IL-17 signaling pathway, inflammatory bowel disease, AGE-RAGE signaling pathway in diabetic complications, pertussis, pancreatic cancer, TNF signaling pathway, Toll-like receptor signaling pathway, C-type lectin receptor signaling pathway, Th17 cell differentiation, HIF-1 signaling pathway, hepatitis C, fluid shear stress and atherosclerosis, Kaposi sarcoma-associated herpesvirus infection, hepatitis B, human cytomegalovirus infection, proteoglycans in cancer, Influenza A, coronavirus disease 2019, and pathways in cancer (Table 5, Fig. 7).

Table 2 - The top 10 Gene Ontology (GO) enrichment items. ID Term Category GO:0019221 Cytokine-mediated signaling pathway biological_process GO:0010628 Positive regulation of gene expression biological_process GO:0045944 Positive regulation of transcription by RNA polymerase II biological_process GO:0006954 Inflammatory response biological_process GO:0045893 Positive regulation of transcription, DNA-templated biological_process GO:0071222 Cellular response to lipopolysaccharide biological_process GO:0042531 Positive regulation of tyrosine phosphorylation of STAT protein biological_process GO:0043066 Negative regulation of apoptotic process biological_process GO:0032755 Positive regulation of interleukin-6 production biological_process GO:0010573 Vascular endothelial growth factor production biological_process GO:0005615 Extracellular space cellular_component GO:0005576 Extracellular region cellular_component GO:0032991 Protein-containing complex cellular_component GO:0090575 RNA polymerase II transcription regulator complex cellular_component GO:0005667 Transcription regulator complex cellular_component GO:0045121 Membrane raft cellular_component GO:0048471 Perinuclear region of cytoplasm cellular_component GO:0000785 Chromatin cellular_component GO:0005788 Endoplasmic reticulum lumen cellular_component GO:0005925 Focal adhesion cellular_component GO:0019899 Enzyme binding molecular_function GO:0005125 Cytokine activity molecular_function GO:0008134 Transcription factor binding molecular_function GO:0030235 Nitric-oxide synthase regulator activity molecular_function GO:0019903 Protein phosphatase binding molecular_function GO:0045236 CXCR chemokine receptor binding molecular_function GO:0042802 Identical protein binding molecular_function GO:0003700 DNA-binding transcription factor activity molecular_function GO:0048248 CXCR3 chemokine receptor binding molecular_function GO:0030331 Estrogen receptor binding molecular_function
Table 3 - The top 10 Reactome enrichment items. ID Pathway Enrichment_score R-HSA-1280215 Cytokine signaling in immune system 8.981376518 R-HSA-2262752 Cellular responses to stress 3.845780459 R-HSA-8953897 Cellular responses to stimuli 3.776128549 R-HSA-168256 Immune system 3.61613896 R-HSA-212436 Generic transcription pathway 3.522462633 R-HSA-73857 RNA polymerase II transcription 3.205286276 R-HSA-74160 Gene expression (transcription) 2.903462236 R-HSA-372790 Signaling by GPCR 2.885964912 R-HSA-1266738 Developmental biology 2.587719298 R-HSA-162582 Signal transduction 2.488060523
Table 4 - The top 10 WikiPathways enrichment items. ID Pathway Enrichment_score WP2868 TCA cycle nutrient use and invasiveness of ovarian cancer 133.3885714 WP4891 COVID-19 adverse outcome pathway 74.1047619 WP2815 Mammary gland development pathway – involution (Stage 4 of 4) 66.69428571 WP530 Cytokines and inflammatory response 65.87089947 WP3890 Nanomaterial-induced inflammasome activation 63.51836735 WP4876 Activation of NLRP3 inflammasome by SARS-CoV-2 63.51836735 WP3617 Photodynamic therapy-induced NF-kB survival signaling 63.51836735 WP3646 Hepatitis C and hepatocellular carcinoma 57.80171429 WP2456 HIF1A and PPARG regulation of glycolysis 55.57857143 WP4191 Caloric restriction and aging 55.57857143

COVID-19 = coronavirus disease 2019, SARS-CoV-2 = severe acute respiratory syndrome coronavirus 2.

Table 5 - The top 20 KEGG enrichment items. ID Term P value hsa05200 Pathways in cancer 7.54E-17 hsa04657 IL-17 signaling pathway 1.95E-15 hsa04933 AGE-RAGE signaling pathway in diabetic complications 4.23E-15 h