Insights into the antineoplastic mechanism of Chelidonium majus via systems pharmacology approach

Xinzhe Xiao , Zehui Chen , Zengrui Wu , Tianduanyi Wang , Weihua Li , Guixia Liu , Bo Zhang , Yun Tang

Quant. Biol. ›› 2019, Vol. 7 ›› Issue (1) : 42 -53.

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Quant. Biol. ›› 2019, Vol. 7 ›› Issue (1) : 42 -53. DOI: 10.1007/s40484-019-0165-x
RESEARCH ARTICLE
RESEARCH ARTICLE

Insights into the antineoplastic mechanism of Chelidonium majus via systems pharmacology approach

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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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Keywords

systems pharmacology / mechanism of action / traditional Chinese medicine / Chelidonium majus

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Xinzhe Xiao, Zehui Chen, Zengrui Wu, Tianduanyi Wang, Weihua Li, Guixia Liu, Bo Zhang, Yun Tang. Insights into the antineoplastic mechanism of Chelidonium majus via systems pharmacology approach. Quant. Biol., 2019, 7(1): 42-53 DOI:10.1007/s40484-019-0165-x

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References

[1]

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

[2]

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

[3]

Colombo, M. L. and Bosisio, E. (1996) Pharmacological activities of Chelidonium majus L. (Papaveraceae). Pharmacol. Res., 33, 127–134

[4]

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

[5]

Danhof, M. (2016) Systems pharmacology – towards the modeling of network interactions. Eur. J. Pharm. Sci., 94, 4–14

[6]

Xie, L., Draizen, E. J. and Bourne, P. E. (2017) Harnessing big data for systems pharmacology. Annu. Rev. Pharmacol. Toxicol., 57, 245–262

[7]

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

[8]

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

[9]

Wu, Z., Lu, W., Wu, D., Luo, A., Bian, H., Li, J., Li, W., Liu, G., Huang, J., Cheng, F., (2016) In silico prediction of chemical mechanism of action via an improved network-based inference method. Br. J. Pharmacol., 173, 3372–3385

[10]

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

[11]

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

[12]

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

[13]

Ru, J., Li, P., Wang, J., Zhou, W., Li, B., Huang, C., Li, P., Guo, Z., Tao, W., Yang, Y., (2014) TCMSP: a database of systems pharmacology for drug discovery from herbal medicines. J. Cheminform., 6, 13

[14]

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

[15]

Gaulton, A., Bellis, L. J., Bento, A. P., Chambers, J., Davies, M., Hersey, A., Light, Y., Mcglinchey, S., Michalovich, D., Al-Lazikani, B., (2012) ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Res., 40, D1100–D1107

[16]

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

[17]

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., (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

[18]

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

[19]

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

[20]

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

[21]

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

[22]

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

[23]

Mehlen, P. and Puisieux, A. (2006) Metastasis: a question of life or death. Nat. Rev. Cancer, 6, 449–458

[24]

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

[25]

Lambert, A. W., Pattabiraman, D. R. and Weinberg, R. A. (2017) Emerging biological principles of metastasis. Cell, 168, 670–691

[26]

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., (2018) Oncogenic signaling pathways in the Cancer Genome Atlas. Cell, 173, 321–337.e310

[27]

Uhlen, M., Zhang, C., Lee, S., Sjöstedt, E., Fagerberg, L., Bidkhori, G., Benfeitas, R., Arif, M., Liu, Z., Edfors, F., (2017) A pathology atlas of the human cancer transcriptome. Science, 357, eaan2507

[28]

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

[29]

Hanahan, D. and Weinberg, R. A. (2011) Hallmarks of cancer: the next generation. Cell, 144, 646–674

[30]

Hanahan, D. and Weinberg, R. A. (2000) The hallmarks of cancer. Cell, 100, 57–70

[31]

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

[32]

Miller, C. and Koeffler, H. P. (1993) P53 mutations in human cancer. Leukemia, 7, S18–S21

[33]

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

[34]

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

[35]

Vogt, P. K. (2001) Jun, the oncoprotein. Oncogene, 20, 2365–2377

[36]

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

[37]

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

[38]

Michalik, L., Desvergne, B. and Wahli, W. (2004) Peroxisome-proliferator-activated receptors and cancers: complex stories. Nat. Rev. Cancer, 4, 61–70

[39]

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

[40]

Hui, L., Bakiri, L., Mairhorfer, A., Schweifer, N., Haslinger, C., Kenner, L., Komnenovic, V., Scheuch, H., Beug, H. and Wagner, E. F. (2007) p38α suppresses normal and cancer cell proliferation by antagonizing the JNK-c-Jun pathway. Nat. Genet., 39, 741–749

[41]

Gustafson, A. M., Soldi, R., Anderlind, C., Scholand, M. B., Qian, J., Zhang, X., Cooper, K., Walker, D., Mcwilliams, A., Liu, G., (2010) Airway PI3K pathway activation is an early and reversible event in lung cancer development. Sci. Transl. Med., 2, 26ra25

[42]

Malumbres, M. (2014) Cyclin-dependent kinases. Genome Biol., 15, 122

[43]

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

[44]

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

[45]

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

[46]

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

[47]

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.

[48]

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

[49]

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

[50]

Vrba, J., Doležel, P., Vičar, 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

[51]

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

[52]

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

[53]

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

[54]

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

[55]

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

[56]

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

[57]

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

[58]

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

[59]

Small-Molecule Drug Discovery Suite 2015-2, Schrödinger, LLC, New York, 2015

[60]

Bairoch, A., Apweiler, R., Wu, C. H., Barker, W. C., Boeckmann, B., Ferro, S., Gasteiger, E., Huang, H., Lopez, R., Magrane, M., (2005) The Universal Protein Resource (UniProt). Nucleic Acids Res., 33, D154–D159

[61]

Klekota, J. and Roth, F. P. (2008) Chemical substructures that enrich for biological activity. Bioinformatics, 24, 2518–2525

[62]

Yap, C. W. (2011) PaDEL-descriptor: an open source software to calculate molecular descriptors and fingerprints. J. Comput. Chem., 32, 1466–1474

[63]

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

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