Skip to main content
  • Research article
  • Open access
  • Published:

Exploring the pharmacological mechanism of Shengjing capsule on male infertility by a network pharmacology approach



Shengjing capsule (SJC) is a traditional Chinese medicine (TCM) and has gained widespread clinical application for the treatment of male infertility (MI). However, the pharmacological mechanism of SJC against MI remains vague to date.


The active ingredients of SJC and their targets were identified from the database, and MI-related genes were retrieved from several databases. Protein–protein interaction (PPI) data were obtained to construct the PPI networks. The candidate targets of SJC against MI were identified through topological analysis of the PPI network. Functional enrichment analysis of candidate targets was performed, and the key target genes were identified from the gene-pathway network.


We identified 154 active ingredients and 314 human targets of SJC, as well as 564 MI-related genes. Eight pharmacological network diagrams illustrating the interactions among herbs, active ingredients, targets, and pathways, were constructed. The four dominating network maps included a compound-target network of SJC, a compound-anti-MI targets network, a candidate targets PPI network, a pathway-gene network, and a drug-key compounds-hub targets-pathways network. Systematic analysis indicated that the targets of SJC in the treatment of MI mainly involved RPS6, MAPK1, MAPK3, MDM2, and DDX5. Pathway enrichment analysis showed that SJC had the potential to impact multiple biological pathways, such as cancer-related pathways, viral/bacterial infection-related pathways, and signal transduction-related pathways.


Our results preliminarily revealed the pharmacological basis and molecular mechanism SJC in treating MI, but further experimental research is required to verify these findings.

Peer Review reports


Infertility, a disorder of the reproduction system, is characterized by the failure of a couple to achieve a clinical pregnancy after at least one year of unprotected and regular sexual coition [1, 2]. Male infertility has been attracted great attention owing to the decline in semen quality among young healthy men and public awareness [3], it has been found to be deficient in no fewer than 50% of infertile couples [4]. It was reported that 90% of male infertility cases were caused by low sperm counts, poor sperm quality, or both [5, 6], and several other factors, such as ejaculation dysfunction, hormonal imbalances, and genetic defects, were believed to be responsible for the remaining cases [7,8,9,10]. Moreover, obesity and varicocele also contribute to some adverse effects on male fertility [11, 12]. Clinically, drug therapy and surgical approaches help many men with fertility problems achieve pregnancy [13, 14]. In addition, the application of assisted reproductive technologies (ARTs), including intrauterine insemination, in vitro fertilization (IVF), and even intracytoplasmic sperm injection (ICSI), has revolutionized the treatment of male infertility [15]. However, those treatments are sometimes ineffective, invasive, and expensive or have obvious adverse effects, which makes it necessary to develop more effective natural remedies to enhance fertility for most people affected by infertility.

In China, traditional Chinese medicine (TCM) has been wildly used in the treatment of male infertility for more than 2000 years with satisfactory results. Clinical studies on male infertility treated with TCM demonstrated the function of TCM to improve the quality of sperm and pregnancy rate of male interfile patients [16,17,18]. In addition, the combination of TCM with conventional medicine can also enhance the efficacy of conventional medicine and reduce its side effects [19]. Shengjing capsule, composed of RENSHEN (Panax ginseng C. A. Meyer), LURONG (Cornu Cervi Pantotrichum), BAQIA (Smilax china L.), GOUQIZI (Lycium chinense Miller), HUANGQI (Astragalus membranaceus (Fisch.) Bunge.), JINYINGZI (Rosae Laevigatae Fructus), YINYANGHUO (Epimedium brevicornu Maxim.), FUPENZI (Rubus idaeus L.), HUANGJING (Polygonatum kingianum Hemsl., Polygonatum sibiricum Red., or Polygonatum cyrtonema Hua), XIANMAO (Curculigo orchioides Gaertn.), TUSIZI (Cuscuta chinensis Lam.), and BUGUZHI (Psoralea corylifolia Linn.), has been wildly employed to treat male infertility in China. It was confirmed to improve oligozoospermia by enhancing spermatogenesis ability [20]. However, the mechanism of action underlying the therapeutic effect of SJC is not fully understood.

Network pharmacology is a novel tool for discovering the mechanism of novel medicines and herbal medicines [21, 22]. In the present study, a network pharmacology approach was applied to systematically investigate the mechanism of SJC against MI. Firstly, the active ingredients of SJC and their corresponding targets were obtained, and MI-related targets were also identified from databases. PPI networks of compounds-targets and MI-related targets were built and merged to identify the candidate targets of SJC. GO and KEGG pathway enrichment analyses of candidate targets were further performed. Finally, the hub targets were screened from the pathway-gene network and used to construct the drug-key compounds-hub targets-pathways network. The detailed workflow was illustrated in Fig. 1.

Fig. 1
figure 1

The flowchart of network pharmacology-based strategy for deciphering the mechanisms of SJC acting on MI


Identification of active compounds and their targets for SJC

We mined the chemical constituents of SJC from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform [23] (TCMSP, and the Chinese Academy of Sciences chemistry database (CASC, The active compounds were identified if the chemical constituents meet the following criteria: oral bioavailability (OB) ≥ 40% and drug-likeness (DL) ≥ 0.2 [24]. The targets of active compounds were identified from the DrugBank database ( [25].

MI-related genes

MI-related genes were obtained from the following 3 existing resources in March 2020 using “male infertility” as searching keywords: Comparative Toxicogenomics Database (CTD,, DisGeNET (, and GeneCards (

Networks construction and candidate target identification

The PPI information of SJC targets and MI-related genes were retrieved from six databases using the Bisogenet plugin [26], and the PPI networks were then built and visualized using Cytoscape 3.9.1 software. To identify the candidate targets of SJC against MI, we merged the PPI network of SJC targets and the PPI network of MI-related genes, and the candidate targets were then obtained by limiting topological parameters, including betweenness centrality (BC), degree centrality (DC), closeness centrality (CC), eigenvector centrality (EC), network centrality (NC), and local average connectivity (LAC).

Functional enrichment analysis

Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed using the Database for Annotation, Visualization and Integrated Discovery (DAVID,, v6.8) [27] online tool. GO terms include three categories: biological process (BP), molecular function (MF), and cellular component. GO terms and KEGG pathways with a false discovery rate of less than 0.05 were considered to be statistically significant. The top ten GO terms in each category and the top 20 KEGG pathways were selected for visualization.


Screening of the active ingredients and their targets of SJC

A total of 154 compounds in SJC were obtained from databases, including 7 in BAQIA, 44 in GOUQIZI, 10 in HUANGQI, 7 in JINYINGZI, 23 in YINYANGHUO, 7 in FUPENZI, 12 in HUANGJING, 7 in XIANMAO, 11 in TUSIZI, 22 in RENSHEN, 2 in LURONG, and 2 in BUGUZHI. Eventually, 123 active compounds were identified after removing the duplications, and 97 of the 123 active compounds had human targets (Table 1). We identified 314 human targets for the 97 active compounds and the detailed compound-target pairs were shown in Table S1.

Table 1 The final selected compounds in Shengjing capsule for analysis

Generating a compound-target network for SJC

In order to intuitively display and understand the interaction between components and targets, a compound-target network was constructed, as shown in Fig. 2. The network was composed of 411 nodes and 1253 edges. Topological analysis showed that the active ingredients in the compound-target network had a median of 4 degrees, indicating the multi-target characteristics of active ingredients. Quercetin, kaempferol and luteolin were associated with 154, 63, and 57 targets, respectively. Meanwhile, the OB values of quercetin, kaempferol, and luteolin were 46.43, 41.88, and 36.16%, respectively. Given the favorable OB characteristics and numerous targets of these active compounds, they might play more important roles in the treatment of MI by SJC.

Fig. 2
figure 2

Compound-target network of SJC. The Purple hexagons represent compounds; the Blue quadrilaterals represent the compounds from herbs of SJC

Identification of MI-Related Targets

MI-related targets were identified from various databases, including GeneCard, CTD, and DisGeNET databases. After the removal of duplications, 564 genes were finally considered to be associated with MI (Table S2). We observed 43 common targets between MI-related genes and SJC targets, which might directly mediate the anti-MI activity of SJC. The detailed connections between active compounds and these 43 common targets were shown in Fig. 3A and Table S3. The top six active compounds with a higher degree value in the active compound-common targets network were quercetin, kaempferol, luteolin, formononetin, palmitic acid, and baicalein, of which degree was 32, 17, 11, 9, 8, and 8, respectively.

Fig. 3
figure 3

Identification of candidate targets of SJC against MI. (A) Network of compounds-anti-MI targets;. (B) PPI network merge and identification of candidate SJC targets for MI treatment based on topological characteristics

Identification of hub targets for the treatment of MI by SJC

To identify hub targets that contribute to the anti-MI effects of SJC, we performed topological analysis on the PPI network of SJC targets and MI-related genes. It was generally believed that biological processes contain a variety of protein–protein interactions, and the elucidation of PPI is crucial for the understanding of system biology [28]. Firstly, we constructed a PPI network of SJC targets containing 8363 nodes and 185,044 edges and a PPI network of MI-related genes with 8544 nodes and 195,892 edges. Secondly, a merged network consisting of 6038 nodes and 157,614 edges was generated using the merge tool in Cytoscape software. Subsequently, after two-step of topological screening, a hub network composed of 413 hub targets were extracted. Figure 3B illustrated the screening process and parameters of the hub targets in detail. Meanwhile, the topological features of the hub network were shown in Table S4.

Functional enrichment analysis

Enrichment analysis results revealed that the hub targets were significantly associated with 248 GO terms including 144 BP terms, 49 cellular component terms, and 55 MF terms (FDR < 0.05) (Table S5). Meanwhile, we selected the top 10 terms in each category for visualization, as shown in Fig. 4. It showed that the hub targets had poly(A) binding and protein binding capabilities, and were mainly located in nucleoplasm and nucleus. Moreover, these hub targets participate in the regulation of gene transcription and viral infections. Forty-six significantly enriched pathways (FDR < 0.05) including viral carcinogenesis, Epstein-Barr virus infection, ribosome, cell cycle, and spliceosome were identified to be associated with the hub targets. The data of the KEGG pathway analysis were presented in Table S6, and we further visualized the top 20 KEGG pathways with a lower FDR value in Fig. 5.

Fig. 4
figure 4

Gene ontology terms of candidate targets of SJC against MI. The top 10 GO functional categories with FDR < 0.05 were selected

Fig. 5
figure 5

KEGG pathway enrichment of candidate targets of SJC against MI. Top 20 significantly enriched pathways were selected. Size of the spot represents number of genes and color represents -logFDR value

Construction of the pathway-target network

The significantly enriched pathways as well as involved genes were used to construct the gene-pathway network, which consists of 314 nodes and 1148 edges (Fig. 6). The topological analysis of the network revealed that RPS6 had the most maximum BC value and is regarded as the core gene. Meanwhile, several other genes presented with a larger BC, such as MAPK1, MAPK3, MDM2, DDX5, and TP53, also play important roles in this network They might be the key target genes for SJC against male infertility. Detailed topological characteristics were presented in Table S7.

Fig. 6
figure 6

Gene-Pathway Network of SJC against MI. The topological analysis of 46 pathways and 263 genes was carried out with BC. The green circles represent target genes and the purple triangles represent pathways. Big size represents the larger BC

The characteristics of the drug-key compounds-hub targets-pathway network

To systematically and holistically elucidate the pharmacological mechanism of SJC against MI, we constructed and visualized a drug-key compounds-hub targets-pathway network. As shown in Fig. 7, a total of 41 nodes and 141 edges were observed in this network. Firstly, according to the compound anti-MI targets network, quercetin, kaempferol, luteolin, formononetin, palmitic acid, and baicalein were identified as the key compounds. Owing to the lack of edges with enriched pathways, palmitic acid was removed. The rest 5 key compounds and their targets were extracted and merged with the pathway-gene network. Eleven hub targets involved in 24 pathways were eventually identified. In addition, these pathways mainly included cancer-related pathways, viral/bacterial infection-related pathways, signal transduction-related pathways, and other pathways.

Fig. 7
figure 7

Drug-key compounds-hub targets-pathways network. (circle indicated the SJC; triangles represented key compounds; rectangles indicated hub genes; and hexagons represented pathways)


TCM theory has been formed and developed over thousands of years in China in the treatment and prevention of various diseases. TCM formulations generally consist of multiple compatible herbs to improve therapeutic effects through synergistism [29]. SJC is one of the most common capsules used to treat male infertility in TCM, which has demonstrated significant clinical effects. It has been shown to enhance the activity of antioxidant enzymes and inhibit oxidative stress. Besides, SJC was able to repair testicular and epididymal pathological damages, protect spermatogenesis and improve sperm quality [20]. However, more detailed information about the mechanism of SJC for MI is not available. The concept of network pharmacology is compatible with TCM theory and is appropriate to be used for exploring the mechanism of complex TCM herbal formulations.

In the present study, we identified 314 targets of 97 bioactive compounds in SJC and constructed a compound-target network to illustrate the detailed interaction. Our data showed that active compounds of SJC target multiple genes and compounds with the most targets were quercetin, kaempferol, and luteolin. Therefore, they were very likely to be the crucial pleiotropically active ingredients for SJC. In addition, the overlapping targets in different herbs suggested that multiple compounds of SJC may have the same target providing synergistic effects. Quercetin, luteolin, and kaempferol had various pharmacological effects, such as anti-cancer [30,31,32], antioxidant [33, 34], and anti-diabetic [35, 36]. Growing evidence has also confirmed the beneficial effects of quercetin [37,38,39] and kaempferol [40, 41] on reproductive dysfunction. In addition, it was reported that luteolin can ameliorate testis injury and blood-testis barrier disruption, and repair abnormal sperm morphology [42]. Generally, TCM exerts its anti-disease effect through its complex medicinal material compatibility and the synergistic effect of numerous active ingredients. Herein, quercetin, luteolin, and kaempferol had the most targets that contribute to the pathogenesis of MI and had potential improvement in sperm quality. Meanwhile, given the favorable OB feature of these compounds, they might contribute greatly to the anti-MI activity of SJC.

After topological analysis of PPI networks, we eventually obtained 413 hub targets that mediate the anti-MI effects of SJC. Functional enrichment analysis was performed and suggested that SJC might regulate several important biological processes, such as transcription, viral process, apoptotic process, and cell–cell adhesion. Spermatogenesis occurs in the testes and is regulated at the transcription and post-transcriptional levels [43]. MI is a reproductive system disorder associated with various genetic and environmental factors. Accumulated evidence has confirmed the role of viral infections in the pathogenesis of male infertility [44, 45]. Apoptosis occurs at a high rate in the testis and is also exhibited by spermatozoa in the human ejaculate [46]. Spermatogenic and Sertoli cells are required for spermatogenesis, and cell adhesion-mediated interaction of spermatogenic and Sertoli cells plays a crucial role in spermatogenesis [47]. Therefore, SJC might improve MI symptoms by ameliorating immunological function through above-mentioned processes. Accumulating evidence has proposed the involvement of apoptosis [46], RNA-binding proteins [48], and nuclear stability [49] in the pathogenesis of MI. Moreover, our data suggested that the cellular regulatory effects of SJC might occur in the nucleoplasm and nucleus and be mediated by RNA binding activity.

TCM is characterized by multi-component, multi-target, and multi-pathway in treating diseases. Therefore, these features also apply in SJC. Herein, we found 46 pathways were involved in the anti-MI activity by SJC, such as the thyroid hormone signaling pathway and the Hippo signaling pathway. It suggested that changes from normal thyroid function could lead to decreased sexual activity and fertility [50]. As a conserved growth pathway, the Hippo signaling pathway was involved in the regulation of the transition of testicular Sertoli cells from a proliferative state during infancy to a non-proliferative functionally mature state at the onset of puberty, which is essential for proper spermatogenic progression [51]. Viral infection is a risk of male infertility and can impair sperm parameters, DNA integrity, and in particular, reduces forward motility [44]. Therefore, SJC might exert regulatory effects on viral infection-induced impairment through relevant pathways, such as viral carcinogenesis, Epstein-Barr virus infection, Herpes simplex infection, HIF-1 signaling pathway, Hepatitis B, and HTLV-I infection. It was found that chronic alcoholism decreased male fertility hormones and semen quality [52]. In addition, accumulating evidence has confirmed the relationship between MI and cancer [53], suggesting that the therapeutic function of SJC against MI may result from the regulation of the following enriched cancer-related pathways, including prostate cancer, and transcriptional misregulation in cancer, pancreatic cancer, pathways in cancer. Although multiple pathways were found to be associated with the action of SJC on MI, it requires further in vivo and in vitro experiments to validate these connections.

Topological analysis was applied to the gene-pathway network to identify key targets of SJC in treating MI. RPS6 was regarded as the core target due to its highest BC value, and the other 5 genes, including MAPK1, MAPK3, MDM2, DDX5, and TP53, were identified as the key target genes thanks to their higher BC value too. The blood-testis barrier (BTB) is crucial for the development and maturation of meiotic and postmeiotic germ cells in seminiferous tubes because it provided a unique microenvironment for these processes. [54]. RPS6 participates in many pathways, including the mTOR and MAPK pathways. It has been revealed that RPS6 regulates the BTB dynamics spermatogenetic function in the testis [55,56,57], and the expression of spermatozoal RPS6 in recurrent pregnancy loss (RPL) patients was significantly lower than in healthy control [58], implying that decreased spermatozoal RPS6 might contribute to MI and RPS6 can be a potential target for the treatment of MI. Mitogen-activated protein kinases (MAPKs) play a crucial role in the regulation of spermatogenesis and spermatozoa functions [59], and MAPK1 and MAPK3 were also recognized as key target genes of SJC for male infertility due to their higher BC. A recent study found that DDX5 is expressed by spermatogonia and plays essential transcriptional and post-transcriptional roles in the maintenance and function of spermatogonia [60]. In addition, TP53 has been confirmed to mediate the spontaneous testicular germ cell apoptosis and germ cell quality control in spermatogenesis [61], and TP53 knockout can result in spontaneous testicular atrophy in rats [62]. By integrating the compound anti-MI targets network and pathway-gene network, several hub targets with related pathways associated with key compounds of SJC were also identified, including RELA, EGFR, MYC, AKT1, and so on, which might directly mediate the action of SJC on MI.


In our study, we investigated the potential pharmacology mechanism of SJC in treating MI using a network pharmacology-based approach. Firstly, a total of 314 targets affected by 97 bioactive compounds in the SJC were obtained. Quercetin, kaempferol, and luteolin regulated the most targets associated with MI. Secondly, 564 MI-related genes were collected and 413 candidate targets of SJC against MI were identified based on the analysis of the PPI network. Thirdly, GO and KEGG analysis suggested that SJC may treat male infertility through multiple biological processes including transcription, viral process, apoptotic process, and cell–cell adhesion and the related pathways including thyroid hormone signaling pathway and Hippo signaling pathway. Finally, pathway-gene network analysis indicated that RPS6, MAPK1, MAPK3, MDM2, DDX5, and TP53 might be the key target genes of SJC in the treatment of MI, and the drug-key compounds-hub targets-pathways network was constructed. However, these findings were not validated by in vivo and in vitro experiments, which need to be carried out in future studies.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.



Male infertility


Shengjing capsule


Protein–protein interaction


Gene ontology


Kyoto Encyclopedia of Genes and Genomes


Assisted reproductive technologies


in vitro Fertilization


Intracytoplasmic sperm injection


Traditional Chinese medicine


Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform


Oral bioavailability




Comparative Toxicogenomics Database


Database for Annotation, Visualization and Integrated Discovery


False discovery rate


Betweenness centrality


Degree centrality


Closeness centrality


Eigenvector centrality


Network centrality


Local average connectivity


Blood-testis barrier


  1. Mascarenhas MN, Flaxman SR, Boerma T, et al. National, regional, and global trends in infertility prevalence since 1990: a systematic analysis of 277 health surveys[J]. PLoS Med. 2012;9(12):e1001356.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Zegers-Hochschild F, Adamson GD, de Mouzon J, et al. The international committee for monitoring assisted reproductive technology (ICMART) and the world health organization (WHO) revised glossary on ART terminology, 2009[J]. Hum Reprod. 2009;24(11):2683–7.

    Article  CAS  PubMed  Google Scholar 

  3. Alrabeeah K, Yafi F, Flageole C, et al. Testicular sperm aspiration for nonazoospermic men: sperm retrieval and intracytoplasmic sperm injection outcomes[J]. Urology. 2014;84(6):1342–6.

    Article  PubMed  Google Scholar 

  4. Niederberger C. WHO manual for the standardized investigation, diagnosis and management of the infertile male[J]. Urology. 2001;57(1):208.

    Article  Google Scholar 

  5. Bensdorp A, Cohlen B J, Heineman M J, et al. Intra‐uterine insemination for male subfertility[J]. Cochrane Database Syst Rev. 2007(4).

  6. Levine BA, Grifo JA. Intrauterine insemination and male subfertility[J]. Urol Clin North Am. 2008;35(2):271–6.

    Article  PubMed  Google Scholar 

  7. Corona G, Rastrelli G, Limoncin E, et al. Interplay between premature ejaculation and erectile dysfunction: a systematic review and meta-analysis[J]. J Sex Med. 2015;12(12):2291–300.

    Article  PubMed  Google Scholar 

  8. Pitteloud N, Dwyer A. Hormonal control of spermatogenesis in men: Therapeutic aspects in hypogonadotropic hypogonadism[C]. Elsevier, 2014.

  9. Ray PF, Toure A, Metzler Guillemain C, et al. Genetic abnormalities leading to qualitative defects of sperm morphology or function[J]. Clin Genet. 2017;91(2):217–32.

    Article  CAS  PubMed  Google Scholar 

  10. Zhao S, Zhu W, Xue S, et al. Testicular defense systems: immune privilege and innate immunity[J]. Cell Mol Immunol. 2014;11(5):428.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Evers JL, Collins JA. Assessment of efficacy of varicocele repair for male subfertility: a systematic review[J]. The Lancet. 2003;361(9372):1849–52.

    Article  Google Scholar 

  12. Sallmén M, Sandler DP, Hoppin JA, et al. Reduced fertility among overweight and obese men[J]. Epidemiology. 2006;17(5):520–3.

    Article  PubMed  Google Scholar 

  13. Kumar R, Gautam G, Gupta NP. Drug therapy for idiopathic male infertility: rationale versus evidence[J]. J Urol. 2006;176(4):1307–12.

    Article  CAS  PubMed  Google Scholar 

  14. Pisipati S, Pearcy R. The role of urological surgery in male infertility[J]. Hum Fertil. 2010;13(4):233–41.

    Article  Google Scholar 

  15. Nayan M, Punjani N, Grober E, et al. The use of assisted reproductive technology before male factor infertility evaluation[J]. Translational andrology and urology. 2018;7(4):678.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Zhao M, Chan CPS, Cheung CWC, et al. A double-blinded, randomized placebo-controlled trial on the effect of traditional Chinese medicine formula Wuzi Yanzong pill on improving semen qualities in men with suboptimal parameters[J]. Trials. 2019;20(1):1–7.

    Article  Google Scholar 

  17. Wang Fu, Qing-He G, Qiang G, et al. effectiveness and safety evaluation of Qixiong Zhongzi Decoction in Idiopathic Asthenozoospermia treatment: a randomized controlled Trial[J]. Chin J Integr Med. 2020;26(2):146–51.

    Article  PubMed  Google Scholar 

  18. Hao X, Song F. Clinical study of Wuzi-Yanzong pill combined with compound Xuanju capsule for the male patients with infertility oligoasthenozoospermia[J]. International Journal of Traditional Chinese Medicine. 2017;39(6):496–9.

    Google Scholar 

  19. Ma W, Jia J. The effects and prospects of the integration of traditional Chinese medicine and Western medicine on andrology in China[J]. Asian J Androl. 2011;13(4):592–5.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Zhou S, Weng Z, Liang A, et al. Experimental research on therapeutic efficacy of traditional chinese medicine Shengjing Capsule extracts in treating spermatogenesis impairment induced by oxidative stress[J]. Medical science monitor: international medical journal of experimental and clinical research. 2016;22:50.

    Article  CAS  Google Scholar 

  21. Lalwani AK, Krishnan K, Bagabir SA, et al. Network theoretical approach to explore factors affecting signal propagation and stability in Dementia’s protein-protein interaction network[J]. Biomolecules. 2022;12(3):451.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Iqbal S, Malik MZ, Pal D. Network-based identification of miRNAs and transcription factors and in silico drug screening targeting δ-secretase involved in Alzheimer’s disease[J]. Heliyon. 2021;7(12):e8502.

    Article  Google Scholar 

  23. Ru J, Li P, Wang J, et al. TCMSP: a database of systems pharmacology for drug discovery from herbal medicines[J]. Journal of cheminformatics. 2014;6(1):13.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Li J, Zhao P, Li Y, et al. Systems pharmacology-based dissection of mechanisms of Chinese medicinal formula Bufei Yishen as an effective treatment for chronic obstructive pulmonary disease[J]. Sci Rep. 2015;5:15290.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Vivian L, Craig K, Yannick D, et al. DrugBank 4.0: shedding new light on drug metabolism[J]. Nucleic Acids Res. 2013;(D1):D1.

  26. Martin A, Ochagavia M E, Rabasa L C, et al. BisoGenet: a new tool for gene network building, visualization and analysis[J]. BMC Bioinformatics. 2010;11(1):91.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources[J]. Nat Protoc. 2009;4(1):44.

    Article  PubMed  Google Scholar 

  28. Rao VS, Srinivas K, Sujini GN, et al. Protein-protein interaction detection: methods and analysis[J]. Int J Proteomics. 2014;2014:147648.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Li Y, Zhou H, Xie J, et al. A novel method for evaluating the cardiotoxicity of traditional chinese medicine compatibility by using support vector machine model combined with metabonomics[J]. Evid Based Complement Alternat Med. 2016;2016:6012761.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Ezzati M, Yousefi B, Velaei K, et al. A review on anti-cancer properties of Quercetin in breast cancer[J]. Life Sci. 2020;248:117463.

    Article  CAS  PubMed  Google Scholar 

  31. Zhu L, Xue L. Kaempferol suppresses proliferation and induces cell cycle arrest, apoptosis, and DNA damage in breast cancer cells[J]. Oncol Res Featuring Preclinical Clin Cancer Ther. 2019;27(6):629–34.

    Google Scholar 

  32. Lin Y, Shi R, Wang X, et al. Luteolin, a flavonoid with potential for cancer prevention and therapy[J]. Curr Cancer Drug Targets. 2008;8(7):634–46.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Lesjak M, Beara I, Simin N, et al. Antioxidant and anti-inflammatory activities of quercetin and its derivatives[J]. J Funct Foods. 2018;40:68–75.

    Article  CAS  Google Scholar 

  34. Santos J, Monte A, Lins T, et al. Kaempferol can be used as the single antioxidant in the in vitro culture medium, stimulating sheep secondary follicle development through the phosphatidylinositol 3-kinase signaling pathway[J]. Theriogenology. 2019;136:86–94.

    Article  CAS  PubMed  Google Scholar 

  35. Chen S, Jiang H, Wu X, et al. Therapeutic effects of quercetin on inflammation, obesity, and type 2 diabetes[J]. Mediators Inflamm. 2016;2016:9340637.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Özay Y, Güzel S, Yumrutaş Ö, et al. Wound healing effect of kaempferol in diabetic and nondiabetic rats[J]. J Surg Res. 2019;233:284–96.

    Article  PubMed  Google Scholar 

  37. Taepongsorat L, Tangpraprutgul P, Kitana N, et al. Stimulating effects of quercetin on sperm quality and reproductive organs in adult male rats[J]. Asian J Androl. 2008;10(2):249–58.

    Article  CAS  PubMed  Google Scholar 

  38. Ranawat P, Pathak CM, Khanduja KL. A new perspective on the quercetin paradox in male reproductive dysfunction[J]. Phytother Res. 2013;27(6):802–10.

    Article  CAS  PubMed  Google Scholar 

  39. Zribi N, Chakroun NF, Abdallah FB, et al. Effect of freezing–thawing process and quercetin on human sperm survival and DNA integrity[J]. Cryobiology. 2012;65(3):326–31.

    Article  CAS  PubMed  Google Scholar 

  40. Jamalan M, Ghaffari MA, Hoseinzadeh P, et al. Human sperm quality and metal toxicants: protective effects of some flavonoids on male reproductive function[J]. Int J Fertil Steril. 2016;10(2):215.

    CAS  PubMed  PubMed Central  Google Scholar 

  41. Zini A, San Gabriel M, Baazeem A. Antioxidants and sperm DNA damage: a clinical perspective[J]. J Assist Reprod Genet. 2009;26(8):427–32.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Ma B, Zhang J, Zhu Z, et al. Luteolin Ameliorates Testis Injury and Blood-Testis Barrier Disruption through the Nrf2 Signaling Pathway and by Upregulating Cx43[J]. Mol Nutr Food Res. 2019;63(10):1800843.

    Article  Google Scholar 

  43. Bettegowda A, Wilkinson MF. Transcription and post-transcriptional regulation of spermatogenesis[J]. Phil Trans R Soc B Biol Sci. 2010;365(1546):1637–51.

    Article  CAS  Google Scholar 

  44. Garolla A, Pizzol D, Bertoldo A, et al. Sperm viral infection and male infertility: focus on HBV, HCV, HIV, HPV, HSV, HCMV, and AAV[J]. J Reprod Immunol. 2013;100(1):20–9.

    Article  PubMed  Google Scholar 

  45. Rohde V, Erles K, Sattler HP, et al. Detection of adeno-associated virus in human semen: does viral infection play a role in the pathogenesis of male infertility?[J]. Fertil Steril. 1999;72(5):814–6.

    Article  CAS  PubMed  Google Scholar 

  46. Shukla KK, Mahdi AA, Rajender S. Apoptosis, spermatogenesis and male infertility[J]. Front Biosci (Elite Ed). 2012;4(1):746–54.

    Article  Google Scholar 

  47. Wakayama T, Iseki S. Role of the spermatogenic–Sertoli cell interaction through cell adhesion molecule-1 (CADM1) in spermatogenesis[J]. Anat Sci Int. 2009;84(3):112–21.

    Article  CAS  PubMed  Google Scholar 

  48. Venables JP, Eperon IC. The roles of RNA-binding proteins in spermatogenesis and male infertility[J]. Curr Opin Genet Dev. 1999;9(3):346–54.

    Article  CAS  PubMed  Google Scholar 

  49. Jager S. Sperm nuclear stability and male infertility[J]. Arch Androl. 1990;25(3):253–9.

    Article  CAS  PubMed  Google Scholar 

  50. Krajewska-Kulak E, Sengupta P. Thyroid function in male infertility[J]. Front Endocrinol. 2013;4:174.

    Article  Google Scholar 

  51. Sharma SS, Vats A, Majumdar S. Regulation of Hippo pathway components by FSH in testis[J]. Reprod Biol. 2019;19(1):61–6.

    Article  PubMed  Google Scholar 

  52. Muthusami KR, Chinnaswamy P. Effect of chronic alcoholism on male fertility hormones and semen quality[J]. Fertil Steril. 2005;84(4):919–24.

    Article  CAS  PubMed  Google Scholar 

  53. Dohle GR. Male infertility in cancer patients: review of the literature[J]. Int J Urol. 2010;17(4):327–31.

    Article  PubMed  Google Scholar 

  54. Jiang X, Bukhari I, Zheng W, et al. Blood-testis barrier and spermatogenesis: lessons from genetically-modified mice[J]. Asian J Androl. 2014;16(4):572.

    Article  PubMed  PubMed Central  Google Scholar 

  55. Li SY, Yan M, Chen H, et al. mTORC1/rpS6 regulates blood-testis barrier dynamics and spermatogenetic function in the testis in vivo[J]. Am J Physiol Endocrinol Metab. 2018;314(2):E174–90.

    Article  PubMed  Google Scholar 

  56. Xu H, Shen L, Chen X, et al. mTOR/P70S6K promotes spermatogonia proliferation and spermatogenesis in Sprague Dawley rats[J]. Reprod Biomed Online. 2016;32(2):207–17.

    Article  CAS  PubMed  Google Scholar 

  57. Siwen Wu, Ming Y, Linxi Li, et al. mTORC1/rpS6 and spermatogenic function in the testis insights from the adjudin model[J]. Reprod Toxicol. 2019;89:54–66.

    Article  Google Scholar 

  58. Dhawan V, Kumar M, Deka D, et al. Paternal factors and embryonic development: Role in recurrent pregnancy loss[J]. Andrologia. 2019;51(1): e13171.

    Article  PubMed  Google Scholar 

  59. Almog T, Naor Z. Mitogen activated protein kinases (MAPKs) as regulators of spermatogenesis and spermatozoa functions[J]. Mol Cell Endocrinol. 2008;282(1–2):39–44.

    Article  CAS  PubMed  Google Scholar 

  60. Legrand J, Chan AL, La HM, et al. DDX5 plays essential transcriptional and post-transcriptional roles in the maintenance and function of spermatogonia[J]. Nat Commun. 2019;10(1):2278.

    Article  PubMed  PubMed Central  Google Scholar 

  61. Yin Y, Stahl BC, Dewolf WC, et al. p53-mediated germ cell quality control in spermatogenesis[J]. Dev Biol. 1998;204(1):165–71.

    Article  CAS  PubMed  Google Scholar 

  62. Dai MS, Hall SJ, Vantangoli PM, et al. Spontaneous testicular atrophy occurs despite normal spermatogonial proliferation in a Tp53 knockout rat[J]. Andrology. 2017;5(6):1141–52.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references


Not applicable.


Not applicable.

Author information

Authors and Affiliations



MW and XSZ formulated the idea of the article and supervised the research. MW, QW and HJ performed the research, analyzed the data and wrote the manuscript. HJ and YQD analyzed the DAVID enrichment results. MW, YQD and XSZ participated in revising the data and improving manuscript writing. All authors reviewed the manuscript, and all authors read and approved the final version of the manuscript.

Corresponding author

Correspondence to Xiansheng Zhang.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, M., Wang, Q., Jiang, H. et al. Exploring the pharmacological mechanism of Shengjing capsule on male infertility by a network pharmacology approach. BMC Complement Med Ther 22, 299 (2022).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: