A Survey on Molecular Biological Processes of Cannabinoids by Functional Module-Based Network Analysis
Abstract
Cannabinoids are a group of bioactive compounds found in the genus cannabis. There are two important and well-studied bioactive compounds in medicine: CBD (Cannabidiol) and THC (∆9-Tetrahydrocannabinol). Using available data of the molecular mechanisms studies of cannabinoids can reduces time and costs, further in silico analysis of these compounds. The objective of this research was to explore the molecular biological processes of CBD and THC with functional module-based network analysis within the protein interaction network obtained from public databases by relying on bioinformatics tools and processes involved in the analysis of biological networks. The results showed that both CBD and THC networks are biological networks that can be analyzed by functional modules. When interpreting the biological effects of functional modules, 5 modules containing the CBD-targeting proteins which were involved in the mechanism of action via signal pathways, affecting the changes in calcium levels, relating to the structural proteins of the skin and drug metabolism. Functional interpretation for 8 modules containing THC-targeting proteins include the neuron formation involved in the nervous system, brain function, perception, emotion, endocrine, including stimulating appetite, response to psychoactive, drug metabolism, cell death and positive regulation of gene transcription. In summary, functional module-base network analysis of the protein interaction network of CBD and THC, the target proteins and associated protein interaction networks can be conducted, which can lead to further selection and testing for validating by in vitro laboratory. Keywords : functional module-based network analysis ; cannabinoids ; CBD ; THC ; cannabisReferences
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Lauckner, J. E., Jensen, J. B., Chen, H-Y, Lu, H-C, Hille, B. and Mackie, K. (2008). GPR55 is a cannabinoid receptor that increases intracellular calcium and inhibits M current. Proceedings of the National Academy of Sciences USA, 105(7), 2699-2704. https://doi.org/10.1073/pnas.0711278105.
Lukhele, S. T., and Motadi, L. R. (2016). Cannabidiol rather than Cannabis sativa extracts inhibit cell growth and induce apoptosis in cervical cancer cells. BMC Complementary and Alternative Medicine, 16(335), 1-16, https://doi.org/10.1186/s12906-016-1280-0.
Maere, S., Heymans, K. and Kuiper, M. (2005). BiNGO: a Cytoscape plugin to assess overrepresentation of gene ontology categories in biological networks. Bioinformatics, 21(16), 3448-3449. https://doi.org/10.1093/bioinformatics/bti551.
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Qi, Y. and Ge, H. (2006). Modularity and dynamics of cellular networks. PLoS Computational Biology 2(12), e174, https://doi:10.1371/journal.pcbi.0020174.
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 Research, 13(11), 2498-2504. http://www.genome.org/cgi/doi/10.1101/gr.1239303.
Śledziński, P. Zeyland, J., Słomski, R. and Nowak, A. (2017). The current state and future perspectives of cannabinoids in cancer biology. Cancer Medicine, 7(3), 765-775. https://doi.org/10.1002/cam4.1312.
Sornsiri, J. and Sootanan, P. (2020). Exploring anti-inflammatory mechanisms of onion bioactive compounds with module-based protein interaction network analysis. Burapha Science Journal, 25(2). 416-436. (in Thai).
Sornsiri, J., Srisook, K., Pornngam, P. and Sootanan, P. (2018). Prediction of biochemical mechanism of anti-inflammation explained from two marine-derived bioactive compounds. Agriculture and Natural Resources, 52(6), 588-595. https://doi.org/10.1016/j.anres.2018.11.016.
Srisubat, A and Thanasittichai, S. (2019). Marijuana and modern medicine. Journal of Medical Department, 44(6), 5-8. (in Thai).
Stelzer, G., Rosen, R., Plaschkes, I., Zimmerman, S., Twik, M., Fishilevich, S., Iny Stein, T., Nudel, R., Lieder, I., Mazor, Y., Kaplan, S., Dahary, D., Warshawsky, D., Guan – Golan, Y., Kohn, A., Rappaport, N., Safran, M. and Lancet, D. (2016). The GeneCards suite: from gene data mining to disease genome sequence analysis. Current Protocols in Bioinformatics, 54, 1.30.1-1.30.33. https://doi.org/10.1002/cpbi.5.
Sulcova, A. (2019). Pharmacodynamics of cannabinoids. Archives of Pharmacy and Pharmaceutical Sciences, 3, 011-018. https://doi.org/10.29328/journal.apps.1001013.
Szklarczyk, D, Gable, A. L., Lyon, D., Junge, A., Wyder, S., Huerta-Cepas, J., Simonovic, M., Doncheva, N. T., Morris, J. H., Bork, P., Jensen, L. J., and von Mering, C. (2019). STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Research. 47, D607-D613. https://doi.org/10.1093/nar/gky1131.
Szklarczyk, D., Santos, A., von Mering, C., Jensen, L. J., Bork, P. and Kuhn, M. (2016). STITCH 5: augmenting protein-chemical interaction networks with tissue and affinity data. Nucleic Acids Research, 44(D1), D380–D384. https://doi.org/10.1093/nar/gkv1277.
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Thomas, B. F. (2017). Interactions of cannabinoids with biochemical substrates. Substance Abuse: Research and Treatment, 11, 1-9. https://doi.org/10.1177/1178221817711418.
Todd, S. M., and Arnold, J. C. (2016). Neural correlates of interactions between cannabidiol and 9-tetrahydrocannabinol in mice: implications for medical cannabis. British Journal of Pharmacology, 173, 53-65. https://doi.org/10.1111/bph.13333.
Tournier, B. B., Dimiziani, A., Tsartsalis, S., Millet, P., and Ginovart, N. (2018). Different effects of chronic THC on the neuroadaptive response of dopamine D2/3 receptor-mediated signaling in roman high- and roman low-avoidance rats. Synapse, 72(4). https://doi.org/10.1002/syn.22023.
Volkow, N. D., Baler, R. D., Compton, W. M. and Weiss, S. R. B. (2014). Adverse health effects of marijuana use. The New England Journal of Medicine, 370(23), 2219-2227. https://doi:10.1056/NEJMra1402309.
Watts, D. J. and Strogatz, S. H. (1998). Collective dynamics of ‘small-world’ networks. Nature, 393, 440-442. https://doi.org/10.1038/30918.
Xiong, W., Cheng, K. J., Cui, T., Godlewski, G., Rice, K., Xu, Y. and Zhang, L. (2011). Cannabinoid potentiation of glycine receptors contributes to cannabis-induced analgesia. Nature Chemical Biology, 7(5), 296–303. https://doi.org/10.1038/nchembio.552.
Zendulka, O., Dovrtělová, G., Nosková, K., Turjap, M., Šulcová, A., Hanuš, L., and Juřica, J., (2016). Cannabinoids and cytochrome P450 interactions. Current Drug Metabolism. 17(3), 206-226. https://doi:10.2174/1389200217666151210142051.
Zou, S. and Kumar, U. (2018). Cannabinoid receptors and the endocannabinoid system: signaling and function in the central nervous system. International Journal of Molecular Sciences, 19(833), 23 pages, https://doi.org/10.3390/ijms19030833.
Allen, K., McGregor, I., Hunt, G., Singh, M., and Mallet, P. (2003). Regional differences in naloxone modulation of 9-THC induced Fos expression in rat brain. Neuropharmacology, 44, 264–274. https://doi.org/10.1016/S0028-3908(02)00364-7.
Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., Davis, A. P., Dolinski, K., Dwight, S. S., Eppig, J. T., Harris, M. A., Hill, D. P., Issel-Tarver, L., Kasarskis, A., Lewis, S., Matese, J. C., Richardson, J. E., Ringwald, M., Rubin, G. M. and Sherlock, G. (2000). Gene ontology: tool for the unification of biology. Nature Genetics, 25(1), 25-29. https://doi.org/10.1038/75556.
Bader, G. D. and Hogue C. W. (2003). An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinformatics, 4, 2. https://doi.org/10.1186/1471-2105-4-2.
Briant, J. A., Nielsen, D. A., Proudnikov, D., Londono, D., Ho, A., Ott, J., and Kreeka, M. J. (2013). Evidence for association of two variants of the nociceptin/orphanin FQ receptor gene OPRL1 with vulnerability to develop opiate addiction in Caucasians. Psychiatric Genetics, 20(2), https://doi:10.1097/YPG.0b013e328363f631.
Bridgeman, M. B. and Abazia, D. T. (2017). Medicinal cannabis: history, pharmacology, and implications for the acute care setting. Pharmacology and Therapeutics, 42(3), 180-188.
Casares, L., Garcia, V., Garrido-Rodriguez, M., Millán, E., Collado, J. A., García-Martín, A., Peñarando, J., Calzado, M. A., de la Vega, L., and Muñoz, E. (2020). Cannabidiol induces antioxidant pathways in keratinocytes by targeting BACH1. Redox Biology, 28, 2213-2317. https://doi.org/10.1016/j.redox.2019.101321.
Corchero, J., Avila, M. A., Fuentes, J. A., Manzanares, J. (1997). delta-9-Tetrahydrocannabinol increases prodynorphin and proenkephalin gene expression in the spinal cord of the rat. Life Sciences, 61(4), 39-43. https://doi.org/10.1016/S0024-3205(97)00405-0.
Corchero, J., Fuentes, J. A., and Manzanares, J. (1997). 9-Tetrahydrocannabinol increases proopiomelanocortin gene expression in the arcuate nucleus of the rat hypothalamus. European Journal of Pharmacology, 323, 193-195. https://doi.org/10.1016/S0014-2999(97)00144-1.
Davies, M., Nowotka, M., Papadatos, G., Dedman, N., Gaulton, A., Atkinson, F., Bellis, L. and Overington, J. P. (2015). ChEMBL web services: streamlining access to drug discovery data and utilities. Nucleic Acids Research, 43, W612-W620. https://doi.org/10.1093/nar/gkv352.
Elikottil, J., MBBS, Gupta, P., and Gupta, K. (2009). The analgesic potential of cannabinoids. Journal of Opioid Management, 5(6), 341–357.
Ferreira, F. F., Ribeiro, F. F., Rodrigues, R. S., Sebastião, A. M., and Xapelli, S. (2018). Brain-derived neurotrophic factor (BDNF) role in cannabinoid-mediated neurogenesis. Frontiers in Cellular Neuroscience, 12, 441. https://doi.org/10.3389/fncel.2018.00441.
Gan, Y., Zheng, S., Baak, J.P., Zhao, S., Zheng, Y., Luo, N., Liao, W. and Fu, C. (2015). Prediction of the anti-inflammatory mechanisms of curcumin by module-Based protein interaction network analysis. Acta Pharmaceutica Sinica B, 5(6), 590-595. https://doi.org/10.1016/j.apsb.2015.09.005.
Hartsel, J. A., Eades, J., Hickory, B. and Makriyannis A. (2016). Cannabis sativa and Hemp. In Nutraceutical: Efficacy, Safety and Toxicity. (pp. 735-754). Elsevier Inc.
Izzo, A. A., Borrelli, F., Capasso, R., di Marzo, V. and Mechonulam, R. (2009). Non-psychotropic plant cannabinoids: new therapeutic opportunities from an ancient herb. Trends in Pharmacological Sciences, 30(10), 515-527. https://doi.org/10.1016/j.tips.2009.07.006.
Koch, M., Varela, L., Kim, J. G., Kim, J. D., Hernandez, F., Simonds, S. E., Castorena, C. M., Vianna, C. R., Elmquist, J. K., Morozov, Y. M., Rakic, P., Bechmann, I., Cowley, M. A. Szigeti-Buck, K., Dietrich, M. O. Gao, X.-B., Diano, S., and Horvath, T. L. (2015). Hypothalamic POMC neurons promote cannabinoid-induced feeding. Nature, 519(7541), 45–50. https://doi.org/10.1038/nature14260.
Lauckner, J. E., Jensen, J. B., Chen, H-Y, Lu, H-C, Hille, B. and Mackie, K. (2008). GPR55 is a cannabinoid receptor that increases intracellular calcium and inhibits M current. Proceedings of the National Academy of Sciences USA, 105(7), 2699-2704. https://doi.org/10.1073/pnas.0711278105.
Lukhele, S. T., and Motadi, L. R. (2016). Cannabidiol rather than Cannabis sativa extracts inhibit cell growth and induce apoptosis in cervical cancer cells. BMC Complementary and Alternative Medicine, 16(335), 1-16, https://doi.org/10.1186/s12906-016-1280-0.
Maere, S., Heymans, K. and Kuiper, M. (2005). BiNGO: a Cytoscape plugin to assess overrepresentation of gene ontology categories in biological networks. Bioinformatics, 21(16), 3448-3449. https://doi.org/10.1093/bioinformatics/bti551.
Pellati, F., Borgonetti, V., Brighenti, V., Biagi, M., Benvenuti, S. and Corsi, L. (2018). Cannabis sativa L. and nonpsychoactive cannabinoids: their chemistry and role against oxidative stress, inflammation, and cancer. BioMed Research International, 15 pages. https://doi.org/10.1155/2018/1691428.
Qi, Y. and Ge, H. (2006). Modularity and dynamics of cellular networks. PLoS Computational Biology 2(12), e174, https://doi:10.1371/journal.pcbi.0020174.
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 Research, 13(11), 2498-2504. http://www.genome.org/cgi/doi/10.1101/gr.1239303.
Śledziński, P. Zeyland, J., Słomski, R. and Nowak, A. (2017). The current state and future perspectives of cannabinoids in cancer biology. Cancer Medicine, 7(3), 765-775. https://doi.org/10.1002/cam4.1312.
Sornsiri, J. and Sootanan, P. (2020). Exploring anti-inflammatory mechanisms of onion bioactive compounds with module-based protein interaction network analysis. Burapha Science Journal, 25(2). 416-436. (in Thai).
Sornsiri, J., Srisook, K., Pornngam, P. and Sootanan, P. (2018). Prediction of biochemical mechanism of anti-inflammation explained from two marine-derived bioactive compounds. Agriculture and Natural Resources, 52(6), 588-595. https://doi.org/10.1016/j.anres.2018.11.016.
Srisubat, A and Thanasittichai, S. (2019). Marijuana and modern medicine. Journal of Medical Department, 44(6), 5-8. (in Thai).
Stelzer, G., Rosen, R., Plaschkes, I., Zimmerman, S., Twik, M., Fishilevich, S., Iny Stein, T., Nudel, R., Lieder, I., Mazor, Y., Kaplan, S., Dahary, D., Warshawsky, D., Guan – Golan, Y., Kohn, A., Rappaport, N., Safran, M. and Lancet, D. (2016). The GeneCards suite: from gene data mining to disease genome sequence analysis. Current Protocols in Bioinformatics, 54, 1.30.1-1.30.33. https://doi.org/10.1002/cpbi.5.
Sulcova, A. (2019). Pharmacodynamics of cannabinoids. Archives of Pharmacy and Pharmaceutical Sciences, 3, 011-018. https://doi.org/10.29328/journal.apps.1001013.
Szklarczyk, D, Gable, A. L., Lyon, D., Junge, A., Wyder, S., Huerta-Cepas, J., Simonovic, M., Doncheva, N. T., Morris, J. H., Bork, P., Jensen, L. J., and von Mering, C. (2019). STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Research. 47, D607-D613. https://doi.org/10.1093/nar/gky1131.
Szklarczyk, D., Santos, A., von Mering, C., Jensen, L. J., Bork, P. and Kuhn, M. (2016). STITCH 5: augmenting protein-chemical interaction networks with tissue and affinity data. Nucleic Acids Research, 44(D1), D380–D384. https://doi.org/10.1093/nar/gkv1277.
The Gene Ontology Consortium. (2019) The gene ontology resource: 20 years and still GOing strong. Nucleic Acids Research. 47(D1), D330-D338. https://doi.org/10.1093/nar/gky1055.
Thomas, B. F. (2017). Interactions of cannabinoids with biochemical substrates. Substance Abuse: Research and Treatment, 11, 1-9. https://doi.org/10.1177/1178221817711418.
Todd, S. M., and Arnold, J. C. (2016). Neural correlates of interactions between cannabidiol and 9-tetrahydrocannabinol in mice: implications for medical cannabis. British Journal of Pharmacology, 173, 53-65. https://doi.org/10.1111/bph.13333.
Tournier, B. B., Dimiziani, A., Tsartsalis, S., Millet, P., and Ginovart, N. (2018). Different effects of chronic THC on the neuroadaptive response of dopamine D2/3 receptor-mediated signaling in roman high- and roman low-avoidance rats. Synapse, 72(4). https://doi.org/10.1002/syn.22023.
Volkow, N. D., Baler, R. D., Compton, W. M. and Weiss, S. R. B. (2014). Adverse health effects of marijuana use. The New England Journal of Medicine, 370(23), 2219-2227. https://doi:10.1056/NEJMra1402309.
Watts, D. J. and Strogatz, S. H. (1998). Collective dynamics of ‘small-world’ networks. Nature, 393, 440-442. https://doi.org/10.1038/30918.
Xiong, W., Cheng, K. J., Cui, T., Godlewski, G., Rice, K., Xu, Y. and Zhang, L. (2011). Cannabinoid potentiation of glycine receptors contributes to cannabis-induced analgesia. Nature Chemical Biology, 7(5), 296–303. https://doi.org/10.1038/nchembio.552.
Zendulka, O., Dovrtělová, G., Nosková, K., Turjap, M., Šulcová, A., Hanuš, L., and Juřica, J., (2016). Cannabinoids and cytochrome P450 interactions. Current Drug Metabolism. 17(3), 206-226. https://doi:10.2174/1389200217666151210142051.
Zou, S. and Kumar, U. (2018). Cannabinoid receptors and the endocannabinoid system: signaling and function in the central nervous system. International Journal of Molecular Sciences, 19(833), 23 pages, https://doi.org/10.3390/ijms19030833.
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2022-01-10
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