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:: Volume 30, Issue 1 (3-2022) ::
Journal of Ilam University of Medical Sciences 2022, 30(1): 29-41 Back to browse issues page
Bioinformatic Prediction of Non-Coding Genes related to the Mouse FGF8, NOG, and BMP4 Ectodermal Differentiation Pathway Genes and Mapping of Related Network
Somayeh Moghaddam1 , Esmaeil Babaei * 2
1- Dept of Animal Biology, School of Natural Sciences, University of Tabriz, Tabriz, Iran
2- Dept of Animal Biology, School of Natural Sciences, University of Tabriz, Tabriz, Iran , babaei@tabrizu.ac.ir
Abstract:   (908 Views)
Introduction: Long non-coding RNAs (lncRNAs) and miRNAs belong to a class of non-coding RNAs (ncRNAs) that play important roles and functions in the regulation of the expression of genes in main biological processes, such as cell proliferation, apoptosis, and differentiation. LncRNAs can potentially affect miRNAs in the forms of cis/trans to modulate their regulatory role. In this study, mRNA, miRNA, and lncRNA gene networks were predicted by web-based programs for three ectodermal pathway markers (BMP4, NOG, FGF8) in the mouse embryonic stem cells.
Material & Methods: In this theoretical bioinformatics study, the miRNAs of the target genes (BMP4, NOG, and FGF8) were extracted and examined by MirWalk and TARGETSCAN databases to finally obtain the common miRNAs of these three genes. Following that, the target lncRNAs for common miRNAs were then extracted from the DIANA-Tool database.
(Ethic code: 100/21560/2/پ)
Findings: MiRs mmu-miR-92a-2-5p, mmu-miR-129b-5p, mmu-miR-130b-5p, mmu-miR-692, mmu-miR-7009-3P, mmu-miR-7116-3p, and mmu-miR-7689-3p may affect the function of lncRNAs, including Kcnq1ot1, Gm26812, Gm4117, Gm11837, 4930423MO2Rik, Malat1, Gm12594, Gm3414, 5830444B04Rik, Gm2464, and NEAT1.
Discussion & Conclusion: Due to the mutual relationships among lncRNA, miRNA, and mRNA, our results provided a novel perspective on lncRNAs for future research and experimental studies on ectodermal differentiation pathways and molecular mechanisms.
Keywords: Bioinformatics, Biological system, BM4, Ectoderm, FGF8, Non-coding RNAs
Full-Text [PDF 1602 kb]   (320 Downloads)    
Type of Study: Research | Subject: Molecular Genetics
Received: 2021/03/7 | Accepted: 2021/10/11 | Published: 2023/03/6
1. Zhou R-S, Zhang E-X, Sun Q-F, Ye Z-J, Liu J-W, Zhou D-H, et al. Integrated analysis of lncRNA-miRNA-mRNA ceRNA network in squamous cell carcinoma of tongue. BMC cancer. 2019;19:1:1-10. doi: 10.1186/s12885-019-5983-8.
2. Huang R, Wu J, Zheng Z, Wang G, Song D, Yan P, et al. The construction and analysis of ceRNA network and patterns of immune infiltration in mesothelioma with bone metastasis. Front Bioeng Biotechnol. 2019;7:257. doi: 10.3389/fbioe.2019.00257.
3. He J-H, Han Z-P, Zou M-X, Wang L, Lv YB, Zhou JB, et al. Analyzing the LncRNA, miRNA, and mRNA regulatory network in prostate cancer with bioinformatics software. J Comput Biol. 2018;25:2:146-57. doi: 10.1089/cmb.2016.0093.
4. Zhang X, Wang W, Zhu W, Dong J, Cheng Y, Yin Z, et al. Mechanisms and functions of long non-coding RNAs at multiple regulatory levels. Int J Mol Sci. 2019;20:22:5573. doi: 10.3390/ijms20225573.
5. Menolfi D, Zha S. ATM, ATR and DNA-PKcs kinases—the lessons from the mouse models: inhibition≠ deletion. Cell Biosci. 2020;10:1:1-15. doi:10.1186/s13578-020-0376-x.
6. Schmierer B, Hill CS. TGFβ–SMAD signal transduction: molecular specificity and functional flexibility. Nat rev Mol cell bio. 2007;8:12:970-82. doi: 10.1038/nrm2297.
7. Gerrard L, Rodgers L, Cui W. Differentiation of human embryonic stem cells to neural lineages in adherent culture by blocking bone morphogenetic protein signaling. Stem cells. 2005;23(9):1234-41. doi: 10.1634/stemcells.2005-0110.
8. Britton G, Heemskerk I, Hodge R, Qutub AA, Warmflash A. A novel self-organizing embryonic stem cell system reveals signaling logic underlying the patterning of human ectoderm. Development. 2019;146:20:dev179093. doi: 10.1242/dev.179093.
9. Zhang Y, Yu F, Bao S, Sun J. Systematic characterization of circular RNA-associated CeRNA network identified novel circRNA biomarkers in Alzheimer's disease. Front Bioeng Biotechnol. 2019;7:2.2.. doi: 10.3389/fbioe.2019.00222
10. Kitano H. Systems biology: a brief overview. science. 2002;295(5560):1662-4.doi: 10.1126/science.1069492.
11. Riffo-Campos ÁL, Riquelme I, Brebi-Mieville P. Tools for sequence-based miRNA target prediction: what to choose? Int J Mol Sci. 2016;1712:1987.. doi: 10.3390/ijms17121987
12. Friedman RC, Farh KK-H, Burge CB, Bartel DP. Most mammalian mRNAs are conserved targets of microRNAs. Genome Res. 2009;19:1:92-105. doi: 10.1101/gr.082701.108
13. Agarwal V, Bell GW, Nam J-W, Bartel DP. Predicting effective microRNA target sites in mammalian mRNAs. elife. 2015;4:e05005. doi: 10.7554/eLife.05005.
14. Dweep H, Gretz N. miRWalk2. 0: a comprehensive atlas of microRNA-target interactions. Nature methods. 2015;12(8):697-. doi:10.1038/nmeth.3485
15. Vlachos IS, Hatzigeorgiou AG. Functional analysis of miRNAs using the DIANA tools online suite. Drug Target miRNA: Springer; 2017. p. 25-50. doi: 10.1007/978-1-4939-6563-2_2.
16. Gallo A, Agnese V, Coronnello C, Raffa GM, Bellavia D, Conaldi PG, et al. On the prospect of serum exosomal miRNA profiling and protein biomarkers for the diagnosis of ascending aortic dilatation in patients with bicuspid and tricuspid aortic valve. Int J Cardiol. 2018;273:230-6. doi: 10.1016/j.ijcard.2018.10.005.
17. Valenti MT, Deiana M, Cheri S, Dotta M, Zamboni F, Gabbiani D, et al. Physical exercise modulates miR-21-5p, miR-129-5p, miR-378-5p, and miR-188-5p expression in progenitor cells promoting osteogenesis. Cells. 2019;8:7:742. doi: 10.3390%2Fcells8070742
18. Brandon-Warner E, Benbow JH, Swet JH, Feilen NA, Culberson CR, McKillop IH, et al. Adeno-Associated Virus Serotype 2 Vector–Mediated Reintroduction of microRNA-19b Attenuates Hepatic Fibrosis. Hum gene ther. 2018;29:6:674-86. doi: 10.1089/hum.2017.035.
19. Rey F, Barzaghini B, Nardini A, Bordoni M, Zuccotti GV, Cereda C, et al. Advances in tissue engineering and innovative fabrication techniques for 3-D-structures: translational applications in neurodegenerative diseases. Cells. 2020;9:7:1636. doi: 10.3390/cells9071636.
20. Gu Q-H, Yu D, Hu Z, Liu X, Yang Y, Luo Y, et al. miR-26a and miR-384-5p are required for LTP maintenance and spine enlargement. Nat Commun. 2015;6:1:1-15.doi: 10.1038%2Fncomms7789
21. Duarte Ramos Matos G. Free Energy Calculations in Action: Theory, Applications and Challenges of Solvation Free Energies: UC Irvine; 2018.
22. Weng J, Zhang P, Yin X, Jiang B. The whole transcriptome involved in denervated muscle atrophy following peripheral nerve injury. Front Bioeng Biotechnol. 2018;11:69. doi: [DOI:10.3389/fnmol.2018.00069]
23. He Q, Wang Q, Yuan C, Wang Y. Downregulation of miR‐7116‐5p in microglia by MPP+ sensitizes TNF‐α production to induce dopaminergic neuron damage. Glia. 2017; 65:8:1251-63. doi: 10.1002/glia.23153.
24. Pei W, Tao L, Zhang LW, Zhang S, Cao J, Jiao Y, et al. Circular RNA profiles in mouse lung tissue induced by radon. Environ Health Prev Med. 2017;22:1:1-10. doi: 10.1186/s12199-017-0627-6.
25. Wang S, Xu Z, Wang L. Shuanghuang Shengbai granule cures myelosuppression and suppresses lung cancer progression: Mechanism and therapeutic targets from the aspect of microRNAs. Oncotarget. 2017;8:37:62154. doi: 10.18632/oncotarget.19129.
26. Bhat SA, Ahmad SM, Mumtaz PT, Malik AA, Dar MA, Urwat U, et al. Long non-coding RNAs: Mechanism of action and functional utility. Noncoding RNA Res. 2016;1. doi: 10.1016/j.ncrna.2016.11.002
27. Lin Y, Schmidt BF, Bruchez MP, McManus CJ. Structural analyses of NEAT1 lncRNAs suggest long-range RNA interactions that may contribute to paraspeckle architecture. Nucleic Acids Res. 2018;46:7:3742-52. doi: 10.1093/nar/gky046.
28. Антонова Е. Морфометрические показатели ультраструктурных проявлений репаративной регенерации в печени черепах вида Trachemys scripta elegans после действия гипертермии. Вестник Балтийского федерального униве рситета им И Канта Серия: Естественные и медицинские науки. 2008;7.
29. Clemson CM, Hutchinson JN, Sara SA, Ensminger AW, Fox AH, Chess A, et al. An architectural role for a nuclear noncoding RNA: NEAT1 RNA is essential for the structure of paraspeckles. Mol Cell. 2009;33:6:717-26. doi: 10.1016/j.molcel.2009.01.026.
30. Thakur N, Tiwari VK, Thomassin H, Pandey RR, Kanduri M, Göndör A, et al. An antisense RNA regulates the bidirectional silencing property of the Kcnq1 imprinting control region. Mol cell biol. 2004;24:18:7855-62. doi: 10.1128%2FMCB. 24.18.7855-7862.2004
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Moghaddam S, Babaei E. Bioinformatic Prediction of Non-Coding Genes related to the Mouse FGF8, NOG, and BMP4 Ectodermal Differentiation Pathway Genes and Mapping of Related Network. J. Ilam Uni. Med. Sci. 2022; 30 (1) :29-41
URL: http://sjimu.medilam.ac.ir/article-1-7022-en.html

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Volume 30, Issue 1 (3-2022) Back to browse issues page
مجله دانشگاه علوم پزشکی ایلام Journal of Ilam University of Medical Sciences
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