Login Register Cart 0
Generic placeholder image

Current Alzheimer Research

Editor-in-Chief

ISSN (Print): 1567-2050
ISSN (Online): 1875-5828

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Research Article

Development of Computational Correlations among Known Drug Scaffolds and their Target-Specific Non-Coding RNA Scaffolds of Alzheimer's Disease

Author(s): Debjani Roy*, Shymodip Kundu and Swayambhik Mukherjee

Volume 20, Issue 8, 2023

Published on: 20 October, 2023

Page: [539 - 556] Pages: 18

DOI: 10.2174/0115672050261526231013095933

Price: $65

Abstract

Background: Alzheimer's disease is the most common neurodegenerative disorder. Recent development in sciences has also identified the pivotal role of microRNAs (miRNAs) in AD pathogenesis.

Objectives: We proposed a novel method to identify AD pathway-specific statistically significant miRNAs from the targets of known AD drugs. Moreover, microRNA scaffolds and corresponding drug scaffolds of different pathways were also discovered.

Material and Methods: A Wilcoxon signed-rank test was performed to identify pathway-specific significant miRNAs. We generated feed-forward loop regulations of microRNA-TF-gene-based networks, studied the minimum free energy structures of pre-microRNA sequences, and clustered those microRNAs with their corresponding structural motifs of robust transcription factors. Conservation analyses of significant microRNAs were done, and the phylogenetic trees were constructed. We identified 3’UTR binding sites and chromosome locations of these significant microRNAs.

Results: In this study, hsa-miR-4261, hsa-miR-153-5p, hsa-miR-6766, and hsa-miR-4319 were identified as key miRNAs for the ACHE pathway and hsa-miR-326, hsa-miR-6133, hsa-miR-4251, hsa-miR-3148, hsa-miR-10527-5p, hsa-miR-527, and hsa-miR-518a were identified as regulatory miRNAs for the NMDA pathway. These miRNAs were regulated by several AD-specific TFs, namely RAD21, FOXA1, and ESR1. It has been observed that anisole and adamantane are important chemical scaffolds to regulate these significant miRNAs.

Conclusion: This is the first study that developed a detailed correlation between known AD drug scaffolds and their AD target-specific miRNA scaffolds. This study identified chromosomal locations of microRNAs and corresponding structural scaffolds of transcription factors that may be responsible for miRNA co-regulation for Alzheimer's disease. Our study provides hope for therapeutic improvements in the existing microRNAs by regulating pathways and targets.

« Previous Next »
[1]
Ceyzériat K, Zilli T, Millet P, Frisoni GB, Garibotto V, Tournier BB. Learning from the past: A review of clinical trials targeting amyloid, tau and neuroinflammation in Alzheimer disease. Curr Alzheimer Res 2020; 17(2): 112-25.
[http://dx.doi.org/10.2174/1567205017666200304085513] [PMID: 32129164]
[2]
Ardura-Fabregat A, Boddeke GMEW, Boza-Serrano A. Targeting neuroinflammation to treat Alzheimer disease. CNS Drug 2017; 31(12): 1057-82.
[http://dx.doi.org/10.1007/s40263-017-0483-3]
[3]
Selkoe DJ. Alzheimer’s disease: Genes, proteins, and therapy. Physiol Rev 2001; 81(2): 741-66.
[http://dx.doi.org/10.1152/physrev.2001.81.2.741] [PMID: 11274343]
[4]
Hardy JA, Higgins GA. Alzheimer’s disease: The amyloid cascade hypothesis. Science 1992; 256(5054): 184-5.
[http://dx.doi.org/10.1126/science.1566067] [PMID: 1566067]
[5]
Barbier P, Zejneli O, Martinho M, et al. Role of tau as a microtubule-associated protein: Structural and functional aspects. Front Aging Neurosci 2019; 11: 204.
[http://dx.doi.org/10.3389/fnagi.2019.00204] [PMID: 31447664]
[6]
Rodriguez-Vieitez E, Saint-Aubert L, Carter SF, et al. Diverging longitudinal changes in astrocytosis and amyloid PET in autosomal dominant Alzheimer’s disease. Brain 2016; 139(3): 922-36.
[http://dx.doi.org/10.1093/brain/awv404] [PMID: 26813969]
[7]
Nyakas C, Granic I, Halmy LG, Banerjee P, Luiten PGM. The basal forebrain cholinergic system in aging and dementia. Rescuing cholinergic neurons from neurotoxic amyloid-β42 with memantine. Behav Brain Res 2011; 221(2): 594-603.
[http://dx.doi.org/10.1016/j.bbr.2010.05.033] [PMID: 20553766]
[8]
Tan L, Yu JT, Hu N, Tan L. Non-coding RNAs in Alzheimer’s disease. Mol Neurobiol 2013; 47(1): 382-93.
[http://dx.doi.org/10.1007/s12035-012-8359-5] [PMID: 23054683]
[9]
Idda ML, Munk R, Abdelmohsen K, Gorospe M. Noncoding RNAs in Alzheimer’s disease. Wiley Interdiscip Rev RNA 2018; 9(2): e1463.
[http://dx.doi.org/10.1002/wrna.1463] [PMID: 29327503]
[10]
Bartel DP. MicroRNAs. Cell 2004; 116(2): 281-97.
[http://dx.doi.org/10.1016/S0092-8674(04)00045-5] [PMID: 14744438]
[11]
Imperatore JA, Then ML, McDougal KB, Mihailescu MR. Characterization of a G-Quadruplex structure in pre-miRNA-1229 and its Alzheimer disease-associated variant rs2291418: implications for miRNA-1229 maturation. Int J Mol Sci 2020; 21(3): 767.
[http://dx.doi.org/10.3390/ijms21030767] [PMID: 31991575]
[12]
Barabási AL, Oltvai ZN. Network biology: Understanding the cell’s functional organization. Nat Rev Genet 2004; 5(2): 101-13.
[http://dx.doi.org/10.1038/nrg1272] [PMID: 14735121]
[13]
Chatterjee P, Roy D. Insight into the epigenetics of Alzheimer disease: A computational study from human interactome. Curr Alzheimer Res 2016; 13(12): 1385-96.
[http://dx.doi.org/10.2174/1567205013666160803151101] [PMID: 27492077]
[14]
Barabási AL, Albert R. Emergence of scaling in random networks. Science 1999; 286(5439): 509-12.
[http://dx.doi.org/10.1126/science.286.5439.509] [PMID: 10521342]
[15]
Rankin SA, Zorn AM. Gene regulatory networks governing lung specification. J Cell Biochem 2014; 115(8): 1343-50.
[http://dx.doi.org/10.1002/jcb.24810] [PMID: 24644080]
[16]
Crespo I, Roomp K, Jurkowski W, Kitano H, del Sol A. Gene regulatory network analysis supports inflammation as a key neurodegeneration process in prion disease. BMC Syst Biol 2012; 6(1): 132.
[http://dx.doi.org/10.1186/1752-0509-6-132] [PMID: 23068602]
[17]
Ideker T, Sharan R. Protein networks in disease. Genome Res 2008; 18(4): 644-52.
[http://dx.doi.org/10.1101/gr.071852.107] [PMID: 18381899]
[18]
Shen-Orr SS, Milo R, Mangan S, Alon U. Network motifs in the transcriptional regulation network of Escherichia coli. Nat Genet 2002; 31(1): 64-8.
[http://dx.doi.org/10.1038/ng881] [PMID: 11967538]
[19]
Song A, Yan J, Kim S, et al. Network-based analysis of genetic variants associated with hippocampal volume in Alzheimer’s disease: A study of ADNI cohorts. BioData Min 2016; 9(1): 3.
[http://dx.doi.org/10.1186/s13040-016-0082-8] [PMID: 26788126]
[20]
Pérez-Palma E, Bustos BI, Villamán CF, et al. Overrepresentation of glutamate signaling in Alzheimer’s disease: Network-based pathway enrichment using meta-analysis of genome-wide association studies. PLoS One 2014; 9(4): e95413.
[http://dx.doi.org/10.1371/journal.pone.0095413] [PMID: 24755620]
[21]
Talwar P, Silla Y, Grover S, et al. Genomic convergence and network analysis approach to identify candidate genes in Alzheimer’s disease. BMC Genomics 2014; 15(1): 199.
[http://dx.doi.org/10.1186/1471-2164-15-199] [PMID: 24628925]
[22]
Caberlotto L, Lauria M, Nguyen TP, Scotti M. The central role of AMP-kinase and energy homeostasis impairment in Alzheimer’s disease: A multifactor network analysis. PLoS One 2013; 8(11): e78919.
[http://dx.doi.org/10.1371/journal.pone.0078919] [PMID: 24265728]
[23]
Liu G, Bao X, Jiang Y, et al. Identifying the association between Alzheimer disease and Parkinson’s disease using genome-wide association studies and protein-protein interaction network. Mol Neurobiol 2015; 52(3): 1629-36.
[http://dx.doi.org/10.1007/s12035-014-8946-8] [PMID: 25370933]
[24]
Roth W, Hecker D, Fava E. System’s biology approaches to the study of biological networks underlying Alzheimer disease: role of miRNAs in Systems Biology of Alzheimer Disease. Springer 2016; pp. 349-77.
[http://dx.doi.org/10.1007/978-1-4939-2627-5_21]
[25]
Bhattacharyya M, Bandyopadhyay S. Studying the differential co-expression of microRNAs reveals significant role of white matter in early Alzheimer’s progression. Mol Biosyst 2013; 9(3): 457-66.
[http://dx.doi.org/10.1039/c2mb25434d] [PMID: 23344858]
[26]
Meng F, Dai E, Yu X, et al. Constructing and characterizing a bioactive small molecule and microRNA association network for Alzheimer’s disease. J R Soc Interface 2014; 11(92): 20131057.
[http://dx.doi.org/10.1098/rsif.2013.1057] [PMID: 24352679]
[27]
Wishart DS, Knox C, Guo AC, et al. DrugBank: A comprehensive resource for in silico drug discovery and exploration. Nucleic Acids Res 2006; 34(90001): D668-72.
[http://dx.doi.org/10.1093/nar/gkj067] [PMID: 16381955]
[28]
Csizmadia P. MarvinSketch and MarvinView: Molecule Applets for the World Wide. 1999. Available from: https://chemaxon.com/blog/presentation/marvinsketch-and-marvinview-molecule-applets-for-the-world-wide-web
[29]
Wong N, Wang X. miRDB: an online resource for microRNA target prediction and functional annotations. Nucleic Acids Res 2015; 43(D1): D146-52.
[http://dx.doi.org/10.1093/nar/gku1104] [PMID: 25378301]
[30]
Ahrens P, Andersen LOB, Lilje B, et al. Changes in the vaginal microbiota following antibiotic treatment for Mycoplasma genitalium, Chlamydia trachomatis and bacterial vaginosis. PLoS One 2020; 15(7): e0236036.
[http://dx.doi.org/10.1371/journal.pone.0236036] [PMID: 32722712]
[31]
Zhang Q, Liu W, Zhang HM, et al. hTFtarget: A comprehensive database for regulations of human transcription factors and their targets. Genom Proteom Bioinform 2020; 18(2): 120-8.
[http://dx.doi.org/10.1016/j.gpb.2019.09.006] [PMID: 32858223]
[32]
Tong Z, Cui Q, Wang J, Zhou Y. TransmiR v2.0: An updated transcription factor-microRNA regulation database. Nucleic Acids Res 2019; 47(D1): D253-8.
[http://dx.doi.org/10.1093/nar/gky1023] [PMID: 30371815]
[33]
Shannon P, Markiel A, Ozier O, et al. Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res 2003; 13(11): 2498-504.
[http://dx.doi.org/10.1101/gr.1239303] [PMID: 14597658]
[34]
Gruber AR, Bernhart SH, Hofacker IL, Washietl S. Strategies for measuring evolutionary conservation of RNA secondary structures. BMC Bioinformatics 2008; 9(1): 122.
[http://dx.doi.org/10.1186/1471-2105-9-122] [PMID: 18302738]
[35]
Johnson M, Zaretskaya I, Raytselis Y, Merezhuk Y, McGinnis S, Madden TL. NCBI BLAST: A better web interface. Nucleic Acids Res 2008; 36(S2): W5–W9.
[http://dx.doi.org/10.1093/nar/gkn201]
[36]
Kumar S, Stecher G, Li M, Knyaz C, Tamura K. MEGA X: molecular evolutionary genetics analysis across computing platforms. Mol Biol Evol 2018; 35(6): 1547-9.
[http://dx.doi.org/10.1093/molbev/msy096] [PMID: 29722887]
[37]
Hofacker IL. Vienna RNA secondary structure server. Nucleic Acids Res 2003; 31(13): 3429-31.
[http://dx.doi.org/10.1093/nar/gkg599] [PMID: 12824340]
[38]
Silvestro S, Bramanti P, Mazzon E. Molecular sciences role of miRNAs in Alzheimer disease and possible fields of application. Int J Mol Sci 2019; 20: 3979.
[http://dx.doi.org/10.3390/ijms20163979] [PMID: 31443326]
[39]
Higaki S, Muramatsu M, Matsuda A, et al. Defensive effect of microRNA-200b/c against amyloid-beta peptide-induced toxicity in Alzheimer’s disease models. PLoS One 2018; 13(5): e0196929.
[http://dx.doi.org/10.1371/journal.pone.0196929] [PMID: 29738527]
[40]
Campos-Esparza MR, Sánchez-Gómez MV, Matute C. Molecular mechanisms of neuroprotection by two natural antioxidant polyphenols. Cell Calcium 2009; 45(4): 358-68.
[http://dx.doi.org/10.1016/j.ceca.2008.12.007] [PMID: 19201465]
[41]
Zhou X, Dai E, Song Q, et al. In silico drug repositioning based on drug-miRNA associations. Brief Bioinform 2020; 21(2): 498-510.
[http://dx.doi.org/10.1093/bib/bbz012] [PMID: 30753359]
[42]
Chan YH. Biostatistics 102: quantitative data–parametric & non-parametric tests. Blood Press 2003; 24(08): 79.
[43]
Blenkiron C, Goldstein LD, Thorne NP, et al. MicroRNA expression profiling of human breast cancer identifies new markers of tumor subtype. Genome Biol 2007; 8(10): R214.
[http://dx.doi.org/10.1186/gb-2007-8-10-r214] [PMID: 17922911]
[44]
Qin G, Mallik S, Mitra R, et al. MicroRNA and transcription factor co-regulatory networks and subtype classification of seminoma and non-seminoma in testicular germ cell tumors. Sci Rep 2020; 10(1): 852.
[http://dx.doi.org/10.1038/s41598-020-57834-w] [PMID: 31965022]
[45]
Novikova G, Kapoor M, Tcw J, et al. Integration of Alzheimer’s disease genetics and myeloid genomics identifies disease risk regulatory elements and genes. Nat Commun 2021; 12(1): 1610.
[http://dx.doi.org/10.1038/s41467-021-21823-y] [PMID: 33712570]
[46]
Martin A, Tegla CA, Cudrici CD, et al. Role of SIRT1 in autoimmune demyelination and neurodegeneration. Immunol Res 2015; 61(3): 187-97.
[http://dx.doi.org/10.1007/s12026-014-8557-5] [PMID: 25281273]
[47]
Siegrist SE, Haque NS, Chen CH, Hay BA, Hariharan IK. Inactivation of both Foxo and reaper promotes long-term adult neurogenesis in Drosophila. Curr Biol 2010; 20(7): 643-8.
[http://dx.doi.org/10.1016/j.cub.2010.01.060] [PMID: 20346676]
[48]
Domanskyi A, Alter H, Vogt MA, Gass P, Vinnikov IA. Transcription factors Foxa1 and Foxa2 are required for adult dopamine neurons maintenance. Front Cell Neurosci 2014; 8: 275.
[http://dx.doi.org/10.3389/fncel.2014.00275] [PMID: 25249938]
[49]
Luckhaus C, Sand PG. Estrogen Receptor 1 gene (ESR1) variants in Alzheimer’s disease. Results of a meta-analysis. Aging Clin Exp Res 2007; 19(2): 165-8.
[http://dx.doi.org/10.1007/BF03324684] [PMID: 17446729]
[50]
Ma SL, Tang NLS, Tam CWC, et al. Polymorphisms of the estrogen receptor α (ESR1) gene and the risk of Alzheimer’s disease in a southern Chinese community. Int Psychogeriatr 2009; 21(5): 977-86.
[http://dx.doi.org/10.1017/S1041610209990068] [PMID: 19586561]
[51]
Ross-Innes CS, Stark R, Teschendorff AE, et al. Differential oestrogen receptor binding is associated with clinical outcome in breast cancer. Nature 2012; 481(7381): 389-93.
[http://dx.doi.org/10.1038/nature10730] [PMID: 22217937]
[52]
Leeke B, Marsman J, O’Sullivan JM, Horsfield JA. Cohesin mutations in myeloid malignancies: Underlying mechanisms. Exp Hematol Oncol 2014; 3(1): 13.
[http://dx.doi.org/10.1186/2162-3619-3-13] [PMID: 24904756]
[53]
Mazumdar C, Shen Y, Xavy S, et al. Leukemia-associated cohesin mutants dominantly enforce stem cell programs and impair human hematopoietic progenitor differentiation. Cell Stem Cell 2015; 17(6): 675-88.
[http://dx.doi.org/10.1016/j.stem.2015.09.017] [PMID: 26607380]
[54]
Liu J, Zhang Z, Bando M, et al. Transcriptional dysregulation in NIPBL and cohesin mutant human cells. PLoS Biol 2009; 7(5): e1000119.
[http://dx.doi.org/10.1371/journal.pbio.1000119] [PMID: 19468298]
[55]
Li DJ, Verma D, Mosbruger T, Swaminathan S. CTCF and Rad21 act as host cell restriction factors for Kaposi’s sarcoma-associated herpesvirus (KSHV) lytic replication by modulating viral gene transcription. PLoS Pathog 2014; 10(1): e1003880.
[http://dx.doi.org/10.1371/journal.ppat.1003880] [PMID: 24415941]
[56]
Chatterjee P, Roy D. Insight into the epigenetics of Alzheimer’s disease: a computational study from human interactome. Curr Alzheimer Res 2016; 13(12): 1385-96.
[http://dx.doi.org/10.2174/1567205013666160803151101] [PMID: 27492077]
[57]
Gao Z, Fu HJ, Xue JJ, Wu ZX, Zhao LB. RETRACTED ARTICLE: Genetic polymorphisms in VDR, ESR1 and ESR2 genes may contribute to susceptibility to Parkinson’s disease: A meta-analysis. Mol Biol Rep 2014; 41(7): 4463-74.
[http://dx.doi.org/10.1007/s11033-014-3317-0] [PMID: 24595449]
[58]
Antony J, Chin CV, Horsfield JA. Cohesin mutations in cancer: Emerging therapeutic targets. Int J Mol Sci 2021; 22(13): 6788.
[http://dx.doi.org/10.3390/ijms22136788] [PMID: 34202641]
[59]
Kim K, Bohnen NI, Müller MLTM, Lustig C. Compensatory dopaminergic-cholinergic interactions in conflict processing: Evidence from patients with Parkinson’s disease. Neuroimage 2019; 190: 94-106.
[http://dx.doi.org/10.1016/j.neuroimage.2018.01.021] [PMID: 29337277]
[60]
Bian S, Zhang X, Lin L, et al. Exosomal MiR-4261 mediates calcium overload in RBCs by downregulating the expression of ATP2B4 in multiple myeloma. Front Oncol 2022; 12: 978755.
[http://dx.doi.org/10.3389/fonc.2022.978755] [PMID: 36091107]
[61]
Li Z, Zhao S, Zhu S, Fan Y. MicroRNA-153-5p promotes the proliferation and metastasis of renal cell carcinoma via direct targeting of AGO1. Cell Death Dis 2021; 12(1): 33.
[http://dx.doi.org/10.1038/s41419-020-03306-y] [PMID: 33414440]
[62]
Wang N, Li X, Zhong Z, et al. 3D hESC exosomes enriched with miR-6766-3p ameliorates liver fibrosis by attenuating activated stellate cells through targeting the TGFβRII-SMADS pathway. J Nanobiotechnology 2021; 19(1): 437.
[http://dx.doi.org/10.1186/s12951-021-01138-2] [PMID: 34930304]
[63]
Huang L, Zhang Y, Li Z, et al. MiR‐4319 suppresses colorectal cancer progression by targeting ABTB1. United European Gastroenterol J 2019; 7(4): 517-28.
[http://dx.doi.org/10.1177/2050640619837440] [PMID: 31065369]
[64]
Liang X, Li Z, Men Q, Li Y, Li H, Chong T. miR-326 functions as a tumor suppressor in human prostatic carcinoma by targeting Mucin1. Biomed Pharmacother 2018; 108: 574-83.
[http://dx.doi.org/10.1016/j.biopha.2018.09.053] [PMID: 30243091]
[65]
Hamada-Tsutsumi S, Onishi M, Matsuura K, et al. Inhibitory effect of a human microRNA, miR-6133-5p, on the fibrotic activity of hepatic stellate cells in culture. Int J Mol Sci 2020; 21(19): 7251.
[http://dx.doi.org/10.3390/ijms21197251] [PMID: 33019495]
[66]
Vishnubalaji R, Elango R, Manikandan M, et al. MicroRNA-3148 acts as molecular switch promoting malignant transformation and adipocytic differentiation of immortalized human bone marrow stromal cells via direct targeting of the SMAD2/TGFβ pathway. Cell Death Discov 2020; 6(1): 79.
[http://dx.doi.org/10.1038/s41420-020-00312-z] [PMID: 32922961]
[67]
Xiao Z, Feng X, Zhou Y, et al. Exosomal miR-10527-5p inhibits migration, invasion, lymphangiogenesis and lymphatic metastasis by affecting Wnt/β-catenin signaling via Rab10 in esophageal squamous cell carcinoma. Int J Nanomedicine 2023; 18: 95-114.
[http://dx.doi.org/10.2147/IJN.S391173] [PMID: 36636641]
[68]
Xian Q, Zhao R, Fu J. MicroRNA-527 induces proliferation and cell cycle in esophageal squamous cell carcinoma cells by repressing PH domain leucine-rich-repeats protein phosphatase 2. Dose Response 2020; 18(2)
[http://dx.doi.org/10.1177/1559325820928687] [PMID: 32547334]
[69]
Qu LL, He L, Zhao X, Xu W. Downregulation of miR-518a-3p activates the NIK-dependent NF-κB pathway in colorectal cancer. Int J Mol Med 2015; 35(5): 1266-72.
[http://dx.doi.org/10.3892/ijmm.2015.2145] [PMID: 25812680]
[70]
Chatterjee P, Roy D, Rathi N. Epigenetic drug repositioning for alzheimer disease based on epigenetic targets in human interactome. J Alzheimer's Dis 2018; 61(1): 53-65.
[http://dx.doi.org/10.3233/JAD-161104] [PMID: 29199645]

Rights & Permissions Print Cite
© 2024 Bentham Science Publishers | Privacy Policy