Abstract
Background
The antineoplastic activity of Chelidonium majus has been reported, but its mechanism of action (MoA) is unsuspected. The emerging theory of systems pharmacology may be a useful approach to analyze the complicated MoA of this multi-ingredient traditional Chinese medicine (TCM).
Methods
We collected the ingredients and related compound-target interactions of C. majus from several databases. The bSDTNBI (balanced substructure-drug-target network-based inference) method was applied to predict each ingredient’s targets. Pathway enrichment analysis was subsequently conducted to illustrate the potential MoA, and prognostic genes were identified to predict the certain types of cancers that C. majus might be beneficial in treatment. Bioassays and literature survey were used to validate the in silico results.
Results
Systems pharmacology analysis demonstrated that C. majus exerted experimental or putative interactions with 18 cancer-associated pathways, and might specifically act on 13 types of cancers. Chelidonine, sanguinarine, chelerythrine, berberine, and coptisine, which are the predominant components of C. majus, may suppress the cancer genes by regulating cell cycle, inducing cell apoptosis and inhibiting proliferation.
Conclusions
The antineoplastic MoA of C. majus was investigated by systems pharmacology approach. C. majus exhibited promising pharmacological effect against cancer, and may consequently be useful material in further drug development. The alkaloids are the key components in C. majus that exhibit anticancer activity.
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References
Chen, S.L., Song, J.Y., Sun, C., Xu, J., Zhu, Y.J. and Verpoorte, R. (2015) Herbal genomics: examining the biology of traditional medicine. Science/AAAS Custom Publishing Office, 347, S27–S29
Yin, N., Ma, W., Pei, J., Ouyang, Q., Tang, C. and Lai, L. (2014) Synergistic and antagonistic drug combinations depend on network topology. PLoS One, 9, e93960
Colombo, M. L. and Bosisio, E. (1996) Pharmacological activities of Chelidonium majus L. (Papaveraceae). Pharmacol. Res., 33, 127–134
Gilca, M., Gaman, L., Panait, E., Stoian, I. and Atanasiu, V. (2010) Chelidonium majus–an integrative review: traditional knowledge versus modern findings. Forsch. Komplementarmed., 17, 241–248
Danhof, M. (2016) Systems pharmacology–towards the modeling of network interactions. Eur. J. Pharm. Sci., 94, 4–14
Xie, L., Draizen, E. J. and Bourne, P. E. (2017) Harnessing big data for systems pharmacology. Annu. Rev. Pharmacol. Toxicol., 57, 245–262
Cheng, F., Liu, C., Jiang, J., Lu, W., Li, W., Liu, G., Zhou, W., Huang, J. and Tang, Y. (2012) Prediction of drug-target interactions and drug repositioning via network-based inference. PLoS Comput. Biol., 8, e1002503
Wu, Z., Cheng, F., Li, J., Li, W., Liu, G. and Tang, Y. (2017) SDTNBI: an integrated network and chemoinformatics tool for systematic prediction of drug–target interactions and drug repositioning. Brief. Bioinform., 18, 333–347
Wu, Z., Lu, W., Wu, D., Luo, A., Bian, H., Li, J., Li, W., Liu, G., Huang, J., Cheng, F., et al. (2016) In silico prediction of chemical mechanism of action via an improved network-based inference method. Br. J. Pharmacol., 173, 3372–3385
Wang, Y., Li, J., Wu, Z., Zhang, B., Yang, H., Wang, Q., Cai, Y., Liu, G., Li, W. and Tang, Y. (2017) Insights into the molecular mechanisms of Polygonum multiflorum Thunb-induced liver injury: a computational systems toxicology approach. Acta Pharmacol. Sin., 38, 719–732
Wang, T., Wu, Z., Sun, L., Li, W., Liu, G. and Tang, Y. (2018) A computational systems pharmacology approach to investigate molecular mechanisms of herbal formula Tian-Ma-Gou-Teng-Yin for treatment of alzheimer’s disease. Front. Pharmacol., 9, 668
Chen, C. Y.-C. (2011) TCM Database@Taiwan: the world’s largest traditional Chinese medicine database for drug screening in silico. PLoS One, 6, e15939
Ru, J., Li, P., Wang, J., Zhou,W., Li, B., Huang, C., Li, P., Guo, Z., Tao, W., Yang, Y., et al. (2014) TCMSP: a database of systems pharmacology for drug discovery from herbal medicines. J. Cheminform., 6, 13
Xue, R., Fang, Z., Zhang, M., Yi, Z., Wen, C. and Shi, T. (2013) TCMID: traditional Chinese medicine integrative database for herb molecular mechanism analysis. Nucleic Acids Res., 41, D1089–D1095
Gaulton, A., Bellis, L. J., Bento, A. P., Chambers, J., Davies, M., Hersey, A., Light, Y., Mcglinchey, S., Michalovich, D., Al-Lazikani, B., et al. (2012) ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Res., 40, D1100–D1107
Liu, T., Lin, Y., Wen, X., Jorissen, R. N. and Gilson, M. K. (2007) BindingDB: a web-accessible database of experimentally determined protein–ligand binding affinities. Nucleic Acids Res., 35, D198–D201
Southan, C., Sharman, J. L., Benson, H. E., Faccenda, E., Pawson, A. J., Alexander, S. P. H., Buneman, O. P., Davenport, A. P., McGrath, J. C., Peters, J. A., et al. (2016) The IUPHAR/BPS Guide to PHARMACOLOGY in 2016: towards curated quantitative interactions between 1300 protein targets and 6000 ligands. Nucleic Acids Res., 44, D1054–D1068
Roth, B. L., Lopez, E., Patel, S. and Kroeze, W. K. (2000) The multiplicity of serotonin receptors: uselessly diverse molecules or an embarrassment of riches? Neuroscientist, 6, 252–262
Huang, D. W., Sherman, B. T. and Lempicki, R. A. (2008) Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protoc., 4, 44–57
Huang, D. W., Sherman, B. T. and Lempicki, R. A. (2009) Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res., 37, 1–13
Lei, Q., Zhao, X., Xu, L., Peng, Y. and Xiao, P. (2014) Chemical constituents of plants from tribe Chelidonieae and their bioactivities. Chin. Herb. Med., 6, 1–21
Stork, P. J. S. and Schmitt, J. M. (2002) Crosstalk between cAMP and MAP kinase signaling in the regulation of cell proliferation. Trends Cell Biol., 12, 258–266
Mehlen, P. and Puisieux, A. (2006) Metastasis: a question of life or death. Nat. Rev. Cancer, 6, 449–458
Otrock, Z. K., Mahfouz, R. A. R., Makarem, J. A. and Shamseddine, A. I. (2007) Understanding the biology of angiogenesis: review of the most important molecular mechanisms. Blood Cells Mol. Dis., 39, 212–220
Lambert, A. W., Pattabiraman, D. R. and Weinberg, R. A. (2017) Emerging biological principles of metastasis. Cell, 168, 670–691
Sanchez-Vega, F., Mina, M., Armenia, J., Chatila,W. K., Luna, A., La, K. C., Dimitriadoy, S., Liu, D. L., Kantheti, H. S., Saghafinia, S., et al. (2018) Oncogenic signaling pathways in the Cancer Genome Atlas. Cell, 173, 321–337.e310
Uhlen, M., Zhang, C., Lee, S., Sjöstedt, E., Fagerberg, L., Bidkhori, G., Benfeitas, R., Arif, M., Liu, Z., Edfors, F., et al. (2017) A pathology atlas of the human cancer transcriptome. Science, 357, eaan2507
Sárközi, Á., Janicsák, G., Kursinszki, L. and Kéry, Á. (2006) Alkaloid composition of Chelidonium majus L. studied by different chromatographic techniques. Chromatographia, 63, S81–S86
Hanahan, D. and Weinberg, R. A. (2011) Hallmarks of cancer: the next generation. Cell, 144, 646–674
Hanahan, D. and Weinberg, R. A. (2000) The hallmarks of cancer. Cell, 100, 57–70
Crowley, C. W., Cohen, R. L., Lucas, B. K., Liu, G., Shuman, M. A. and Levinson, A. D. (1993) Prevention of metastasis by inhibition of the urokinase receptor. Proc. Natl. Acad. Sci. USA, 90, 5021–5025
Miller, C. and Koeffler, H. P. (1993) P53 mutations in human cancer. Leukemia, 7, S18–S21
Achbarou, A., Kaiser, S., Tremblay, G., Ste-Marie, L.-G., Brodt, P., Goltzman, D. and Rabbani, S. A. (1994) Urokinase overproduction results in increased skeletal metastasis by prostate cancer cells in vivo. Cancer Res., 54, 2372–2377
Xing, R. H. and Rabbani, S. A. (1996) Overexpression of urokinase receptor in breast cancer cells results in increased tumor invasion, growth and metastasis. Int. J. Cancer, 67, 423–429
Vogt, P. K. (2001) Jun, the oncoprotein. Oncogene, 20, 2365–2377
Stead, E., White, J., Faast, R., Conn, S., Goldstone, S., Rathjen, J., Dhingra, U., Rathjen, P., Walker, D. and Dalton, S. (2002) Pluripotent cell division cycles are driven by ectopic Cdk2, cyclin A/E and E2F activities. Oncogene, 21, 8320–8333
Chang, F., Lee, J. T., Navolanic, P. M., Steelman, L. S., Shelton, J. G., Blalock, W. L., Franklin, R. A. and Mccubrey, J. A. (2003) Involvement of PI3K/Akt pathway in cell cycle progression, apoptosis, and neoplastic transformation: a target for cancer chemotherapy. Leukemia, 17, 590–603
Michalik, L., Desvergne, B. and Wahli, W. (2004) Peroxisomeproliferator-activated receptors and cancers: complex stories. Nat. Rev. Cancer, 4, 61–70
Ciocca, D. R. and Calderwood, S. K. (2005) Heat shock proteins in cancer: diagnostic, prognostic, predictive, and treatment implications. Cell Stress Chaperones, 10, 86–103
Hui, L., Bakiri, L., Mairhorfer, A., Schweifer, N., Haslinger, C., Kenner, L., Komnenovic, V., Scheuch, H., Beug, H. and Wagner, E. F. (2007) p38a suppresses normal and cancer cell proliferation by antagonizing the JNK-c-Jun pathway. Nat. Genet., 39, 741–749
Gustafson, A. M., Soldi, R., Anderlind, C., Scholand, M. B., Qian, J., Zhang, X., Cooper, K., Walker, D., Mcwilliams, A., Liu, G., et al. (2010) Airway PI3K pathway activation is an early and reversible event in lung cancer development. Sci. Transl. Med., 2, 26ra25
Malumbres, M. (2014) Cyclin-dependent kinases. Genome Biol., 15, 122
Chen, X.-M., Zhang, M., Fan, P.-L., Qin, Y.-H. and Zhao, H.-W. (2016) Chelerythrine chloride induces apoptosis in renal cancer HEK-293 and SW-839 cell lines. Oncol. Lett., 11, 3917–3924
Noureini, S. K. and Esmaili, H. (2014) Multiple mechanisms of cell death induced by chelidonine in MCF-7 breast cancer cell line. Chem. Biol. Interact., 223, 141–149
Lee, J. S., Jung, W.-K., Jeong, M. H., Yoon, T. R. and Kim, H. K. (2012) Sanguinarine induces apoptosis of HT-29 human colon cancer cells via the regulation of Bax/Bcl-2 Ratio and caspase-9-dependent pathway. Int. J. Toxicol., 31, 70–77
Ahmad, N., Gupta, S., Husain, M. M., Heiskanen, K. M. and Mukhtar, H. (2000) Differential antiproliferative and apoptotic response of sanguinarine for cancer cells versus normal cells. Clin. Cancer Res., 6, 1524–1528
Adhami, V. M., Aziz, M. H., Reagan-Shaw, S. R., Nihal, M., Mukhtar, H. and Ahmad, N. (2004) Sanguinarine causes cell cycle blockade and apoptosis of human prostate carcinoma cells via modulation of cyclin kinase inhibitor-cyclin-cyclin-dependent kinase machinery. Mol. Cancer Ther., 3, 933–940.
Herbert, J. M., Augereau, J. M., Gleye, J. and Maffrand, J. P. (1990) Chelerythrine is a potent and specific inhibitor of protein kinase C. Biochem. Biophys. Res. Commun., 172, 993–999
Malikova, J., Zdarilova, A. and Hlobilkova, A. (2006) Effects of sanguinarine and chelerythrine on the cell cycle and apoptosis. Biomed. Pap. Med. Fac. Univ. Palacky Olomouc Czech Repub., 150, 5–12
Vrba, J., Doležel, P., Vicar, J., Modrianský, M. and Ulrichová, J. (2008) Chelerythrine and dihydrochelerythrine induce G1 phase arrest and bimodal cell death in human leukemia HL-60 cells. Toxicol. In Vitro, 22, 1008–1017
Sun, Y., Xun, K., Wang, Y. and Chen, X. (2009) A systematic review of the anticancer properties of berberine, a natural product from Chinese herbs. Anticancer Drugs, 20, 757–769
Li, J., Qiu, D. M., Chen, S. H., Cao, S. P. and Xia, X. L. (2014) Suppression of human breast cancer cell metastasis by coptisine in vitro. Asian Pac. J. Cancer Prev., 15, 5747–5751
Rao, P. C., Begum, S., Sahai, M. and Sriram, D. S. (2017) Coptisine-induced cell cycle arrest at G2/M phase and reactive oxygen species–dependent mitochondria-mediated apoptosis in non-small-cell lung cancer A549 cells. Tumour Biol., 39
Nadova, S., Miadokova, E., Alfoldiova, L., Kopaskova, M., Hasplova, K., Hudecova, A., Vaculcikova, D., Gregan, F. and Cipak, L. (2008) Potential antioxidant activity, cytotoxic and apoptosis-inducing effects of Chelidonium majus L. extract on leukemia cells. Neuroendocrinol. Lett., 29, 649–652
Deljanin, M., Nikolic, M., Baskic, D., Todorovic, D., Djurdjevic, P., Zaric, M., Stankovic, M., Todorovic, M., Avramovic, D. and Popovic, S. (2016) Chelidonium majus crude extract inhibits migration and induces cell cycle arrest and apoptosis in tumor cell lines. J. Ethnopharmacol., 190, 362–371
Zhang, L., Chen, Z. H., Chen, H. Y., Wang, X. Q., Zhang, B. and University, S. (2018) Study on antitumor molecular mechanism of Chelidonium majus based on network pharmacology. Chin. Tradit. Herbal Drugs, 49, 646–657
Yu, H., Chen, J., Xu, X., Li, Y., Zhao, H., Fang, Y., Li, X., Zhou, W., Wang, W. and Wang, Y. (2012) A systematic prediction of multiple drug-target interactions from chemical, genomic, and pharmacological data. PLoS One, 7, e37608
O’Boyle, N. M., Banck, M., James, C. A., Morley, C., Vandermeersch, T. and Hutchison, G. R. (2011) Open Babel: an open chemical toolbox. J. Cheminform., 3, 33
Small-Molecule Drug Discovery Suite 2015–2, Schrödinger, LLC, New York, 2015
Bairoch, A., Apweiler, R., Wu, C. H., Barker, W. C., Boeckmann, B., Ferro, S., Gasteiger, E., Huang, H., Lopez, R., Magrane, M., et al. (2005) The Universal Protein Resource (UniProt). Nucleic Acids Res., 33, D154–D159
Klekota, J. and Roth, F. P. (2008) Chemical substructures that enrich for biological activity. Bioinformatics, 24, 2518–2525
Yap, C. W. (2011) PaDEL-descriptor: an open source software to calculate molecular descriptors and fingerprints. J. Comput. Chem., 32, 1466–1474
Shannon, P., Markiel, A., Ozier, O., Baliga, N. S., Wang, J. T., Ramage, D., Amin, N., Schwikowski, B. and Ideker, T. (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res., 13, 2498–2504
Acknowledgements
This work was supported by the National Key Research and Development Program of China (No. 2016YFA0502304), the National Natural Science Foundation of China (Nos. 81673356 and U1603122) and the 111 Project (No. B07023).
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Author summary: Tremendous and persistent efforts have been made to seek novel antineoplastic drugs in the everlasting fight against cancers. In this work, we integrated several systems pharmacology approaches to uncover the MoA of C. majus’ antineoplastic activity. We have found that the alkaloids in C. majus may be the significant components for its anticarcinogenic effect. Our research unlocked the antineoplastic mechanism of this medicinal herb that was employed as an analgesic in TCM. The polypharmacological character of C. majus can be utilized for further drug research and development.
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Xiao, X., Chen, Z., Wu, Z. et al. Insights into the antineoplastic mechanism of Chelidonium majus via systems pharmacology approach. Quant Biol 7, 42–53 (2019). https://doi.org/10.1007/s40484-019-0165-x
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DOI: https://doi.org/10.1007/s40484-019-0165-x