تعداد نشریات | 23 |
تعداد شمارهها | 383 |
تعداد مقالات | 3,036 |
تعداد مشاهده مقاله | 2,760,793 |
تعداد دریافت فایل اصل مقاله | 1,950,053 |
Decoding the Biosynthetic Pathway of the Alkaloid Morphine with Bioinformatics | ||
Agrotechniques in Industrial Crops | ||
مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 06 خرداد 1404 اصل مقاله (886.3 K) | ||
نوع مقاله: Original Article | ||
شناسه دیجیتال (DOI): 10.22126/atic.2025.11523.1182 | ||
نویسندگان | ||
Nazila Bagheri* 1؛ Alireza Tarinejad1؛ Karim Hasanpur2؛ Mohammad Majidi1 | ||
1Agricultural Biotechnology Department, Faculty of Agriculture, Azarbaijan Shahid Madani University, Tabriz, Iran | ||
2Department of Animal Sciences, University of Tabriz, Tabriz, Iran | ||
چکیده | ||
In 1803, the opium poppy was the source of Morphine, the first alkaloid extracted. Due to its diverse use and therapeutic applications, it is considered the most notable alkaloid and accounts for 42 out of all alkaloid substances. This study aimed to use bioinformatics techniques to investigate the biosynthesis pathway of morphine. It included the compilation of nine genes associated with this pathway, based on a thorough literature review. The genes were later confirmed with the NCBI BLAST tool. For examining gene interactions, the research used STRING, and Cytoscape was utilized to visualize the molecular interaction network. Additionally, CytoHubba was applied to pinpoint hub proteins in this network. The hub genes were examined for enrichment with the Kyoto Encyclopedia of Genes and Genomes (KEGG) using STRING, while Gene Ontology (GO) analysis was conducted through gprofiler. Furthermore, the promoter regions of important genes were analyzed using MEME. The metabolic processes involved in morphine production highlight that the gene network associated with the morphine pathway has wider functions beyond merely generating primary metabolites. An examination of the KEGG pathway highlighted the importance of metabolic pathways and the production of secondary metabolites. Additionally, a review of the promoter suggested that signal transduction might be involved in morphine synthesis. The main genes involved in the production of morphine are linked to several key plant pathways, given that morphine is categorized as a secondary metabolite. This study employs various bioinformatics tools to pinpoint and evaluate gene interactions and metabolic pathways, providing a better understanding of how morphine alkaloids are synthesized. This approach could help develop new methods for producing and extracting morphine, as well as improve agricultural practices related to medicinal plants. | ||
تازه های تحقیق | ||
| ||
کلیدواژهها | ||
Analysis؛ Bioinformatics؛ Hub genes؛ Morphine؛ Poppy؛ Secondary metabolites | ||
مراجع | ||
Aghaali Z., Naghavi M.R. 2024. Developing benzylisoquinoline alkaloid-enriched opium poppy via CRISPR-directed genome editing: A review. BMC Plant Biology 24(1): 700. https://doi.org/10.1186/s12870-024-05412-x
Aglas L., Soh W.T., Kraiem A., Wenger M., Brandstetter H., Ferreira F. 2020. Ligand binding of PR-10 proteins with a particular focus on the Bet v 1 allergen family. Current Allergy and Asthma Reports 20: 25. https://doi.org/10.1007/s11882-020-00918-4
Aleksander S.A., Balhoff J., Carbon S., Cherry J.M., Drabkin H.J., Ebert D., Feuermann M., Gaudet P., Harris N.L., Hill D.P., et al. 2023. The Gene Ontology knowledgebase in 2023. Genetics 224(1): iyad03. https://doi.org/10.1093/genetics/iyad031
Allen R.S., Miller J.A., Chitty J.A., Fist A.J., Gerlach W.L., Larkin P.J. 2008. Metabolic engineering of morphinan alkaloids by over‐expression and RNAi suppression of salutaridinol 7‐O‐acetyltransferase in opium poppy. Plant Biotechnology Journal 6(1): 22-30. https://doi.org/10.1111/j.1467-7652.2007.00293.x
Aykanat S., Türktaş M. 2024. Divergent proteomic profiles of opium poppy cultivars. Turkish Journal of Biology 48(1): 80-90. https://doi.org/10.55730/1300-0152.2684
Bharadwaj R., Kumar S.R., Sharma A., Sathishkumar R. 2021. Plant metabolic gene clusters: Evolution, organization, and their applications in synthetic biology. Frontiers in Plant Science 12: 697318. https://doi.org/10.3389/fpls.2021.697318
Desgagné-Penix I., Farrow S.C., Cram D., Nowak J., Facchini P.J. 2012. Integration of deep transcript and targeted metabolite profiles for eight cultivars of opium poppy. Plant Molecular Biology 79: 295-313. https://doi.org/10.1007/s11103-012-9913-2
Dorafshan M., Soltani Howyzeh M., Shariati V. 2019. Identification of terpenoid backbone biosynthetic pathway genes in fruit of Citrullus colocynthis (L.) Schrad. medical plant by RNA sequencing. Iranian Journal of Medicinal and Aromatic Plants Research 35(4): 691-702. (In Farsi). https://doi.org/10.22092/ijmapr.2019.125294.2510
Fazili M.A., Bashir I., Ahmad M., Yaqoob U., Geelani S.N. 2022. In vitro strategies for the enhancement of secondary metabolite production in plants: A review. Bulletin of the National Research Centre 46(1): 35. https://doi.org/10.1186/s42269-022-00717-z
Filiault D.L., Ballerini E.S., Mandáková T., Aköz G., Derieg N.J., Schmutz J., Jenkins J., Grimwood J., Shu S., Hayes R.D. 2018. The Aquilegia genome provides insight into adaptive radiation and reveals an extraordinarily polymorphic chromosome with a unique history. eLife 7: e36426. https://doi.org/10.7554/eLife.36426
Garg A., Agrawal L., Misra R.C., Sharma S., Ghosh S. 2015. Andrographis paniculata transcriptome provides molecular insights into tissue-specific accumulation of medicinal diterpenes. BMC Genomics 16: 659. https://doi.org/10.1186/s12864-015-1864-y
Ghorbani A., Rostami M., Izadpanah K. 2023. Gene network modeling and pathway analysis of maize transcriptomes in response to Maize Iranian mosaic virus. Genomics 115(3): 110618. https://doi.org/10.1016/j.ygeno.2023.110618
Grutsch S., Fuchs J.E., Ahammer L., Kamenik A.S., Liedl K.R., Tollinger M. 2017. Conformational flexibility differentiates naturally occurring Bet v 1 isoforms. International Journal of Molecular Sciences 18(6): 1192. https://doi.org/10.3390/ijms18061192
Hao L. 2023. Evaluation of biosynthetic pathway and engineered biosynthesis of morphine with CRISPR. 5th International Conference on Biotechnology and Biomedicine 59: 01022. https://doi.org/10.1051/bioconf/20235901022
Higashi Y., Kutchan T.M., Smith T.J. 2011. Atomic structure of salutaridine reductase from the opium poppy (Papaver somniferum). Journal of Biological Chemistry 286(8): 6532-6541. https://doi.org/10.1074/jbc.M110.168633
Huacachino A.A., Joo J., Narayanan N., Tehim A., Himes B.E., Penning T.M. 2024. Aldo-keto reductase (AKR) superfamily website and database: An update. Chemico-Biological Interactions 398: 111111. https://doi.org/10.1016/j.cbi.2024.111111
Jha K., Saha S., Dutta P. 2024. Incorporation of gene ontology in identification of protein interactions from biomedical corpus: A multi-modal approach. Annals of Operations Research 339(3): 1793-1811.
Karimizadeh E., Sharifi-Zarchi A., Nikaein H., Salehi S., Salamatian B., Elmi N., Gharibdoost F., Mahmoudi M. 2019. Analysis of gene expression profiles and protein-protein interaction networks in multiple tissues of systemic sclerosis. BMC Medical Genomics 12: 199. https://doi.org/10.1186/s12920-019-0632-2
Krishnamurthy P., Pothiraj R., Suthanthiram B., Somasundaram S.M., Subbaraya U. 2022. Phylogenomic classification and synteny network analyses deciphered the evolutionary landscape of aldo–keto reductase (AKR) gene superfamily in the plant kingdom. Gene 816: 146169. https://doi.org/10.1016/j.gene.2021.146169
Kuiper C., Vissers M.C. 2014. Ascorbate as a co-factor for Fe-and 2-oxoglutarate dependent dioxygenases: Physiological activity in tumor growth and progression. Frontiers in Oncology 4: 359. https://doi.org/10.3389/fonc.2014.00359
Li M., Li D., Tang Y., Wu F., Wang J. 2017. CytoCluster: A cytoscape plugin for cluster analysis and visualization of biological networks. International Journal of Molecular Sciences 18(9): 1880. https://doi.org/10.3390/ijms18091880
Liu X., Hong Z., Liu J., Lin Y., Rodríguez-Patón A., Zou Q., Zeng X. 2020. Computational methods for identifying the critical nodes in biological networks. Briefings in Bioinformatics 21(2): 486-497. https://doi.org/10.1093/bib/bbz011
Malik C., Dwivedi S., Rabuma T., Kumar R., Singh N., Kumar A., Yogi R., Chhokar V. 2023. De novo sequencing, assembly, and characterization of Asparagus racemosus transcriptome and analysis of expression profile of genes involved in the flavonoid biosynthesis pathway. Frontiers in Genetics 14: 1236517. https://doi.org/10.3389/fgene.2023.1236517
Manni M., Berkeley M.R., Seppey M., Simão F.A., Zdobnov E.M. 2021. BUSCO update: Novel and streamlined workflows along with broader and deeper phylogenetic coverage for scoring of eukaryotic, prokaryotic, and viral genomes. Molecular Biology and Evolution 38(10): 4647-4654. https://doi.org/10.1093/molbev/msab199
Mogensen J.E., Wimmer R., Larsen J.N., Spangfort M.D., Otzen D.E. 2002. The major birch allergen, Bet v 1, shows affinity for a broad spectrum of physiological ligands. Journal of Biological Chemistry 277(26): 23684-23692. https://doi.org/10.1074/jbc.M202065200
Osorio-Concepción M., Lax C., Navarro E., Nicolás F.E., Garre V. 2021. DNA methylation on N6-adenine regulates the hyphal development during dimorphism in the early-diverging fungus Mucor lusitanicus. Journal of Fungi 7(9): 738. https://doi.org/10.3390/jof7090738
Ozber N., Yu L., Hagel J.M., Facchini P.J. 2023. Strong feedback inhibition of key enzymes in the morphine biosynthetic pathway from opium poppy detectable in engineered yeast. ACS Chemical Biology 18(2): 419-430. https://doi.org/10.1021/acschembio.2c00873
Radauer C., Lackner P., Breiteneder H. 2008. The Bet v 1 fold: An ancient, versatile scaffold for binding of large, hydrophobic ligands. BMC Evolutionary Biology 8: 286. https://doi.org/10.1186/1471-2148-8-286
Runguphan W., Glenn W.S., O'Connor S.E. 2012. Redesign of a dioxygenase in morphine biosynthesis. Chemistry & Biology 19(6): 674-678. https://doi.org/10.1016/j.chembiol.2012.04.017
Shanbhag A.P., Bhowmik P. 2024. Cancer to cataracts: The mechanistic impact of aldo-keto reductases in chronic diseases. The Yale Journal of Biology and Medicine 97(2): 179. https://doi.org/10.59249/VTBV6559
Tahmasebi A., Ebrahimie E., Pakniyat H., Ebrahimi M., Mohammadi-Dehcheshmeh M. 2019. Tissue-specific transcriptional biomarkers in medicinal plants: Application of large-scale meta-analysis and computational systems biology. Gene 691: 114-124. https://doi.org/10.1016/j.gene.2018.12.056
Tolchin Z.A., Dukes D.M., Gharbaoui L.M., Smith J.M. 2023. Dearomative access to (−)-thebaine and derivatives. Organic Letters 25(47): 8424-8428. https://doi.org/10.1021/acs.orglett.3c03270
van Dorp E.L., Yassen A., Dahan A. 2007. Naloxone treatment in opioid addiction: The risks and benefits. Expert Opinion on Drug Safety 6(2): 125-132. https://doi.org/10.1517/14740338.6.2.125
Vorontsov I.E., Kozin I., Abramov S., Boytsov A., Jolma A., Albu M., Ambrosini G., Faltejskova K., Gralak A.J., Gryzunov N., et al. 2024. Cross-platform DNA motif discovery and benchmarking to explore binding specificities of poorly studied human transcription factors. bioRxiv. https://doi.org/10.1101/2024.11.11.619379
Yucebilgili Kurtoglu K., Unver T. 2021. Integrated omics analysis of benzylisoquinoline alkaloid (BIA) metabolism in opium poppy (papaver somniferum L.). In: Tombuloglu H., Unver T., Tombuloglu G., Hakeem K.R. (eds) Oil Crop Genomics. Springer, Cham. https://doi.org/10.1007/978-3-030-70420-9_13 | ||
آمار تعداد مشاهده مقاله: 10 تعداد دریافت فایل اصل مقاله: 15 |