Identification of a noncoding RNA‑mediated gene pair‑based regulatory module in Alzheimer's disease

Yu,Lin; Qian,Shi; Wei,Sun

doi:10.3892/mmr.2018.9190

Identification of a noncoding RNA‑mediated gene pair‑based regulatory module in Alzheimer's disease

Authors:
- Lin Yu
- Shi Qian
- Sun Wei
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Affiliations: Department of Neurology, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang 150081, P.R. China
Published online on: June 19, 2018 https://doi.org/10.3892/mmr.2018.9190
Pages: 2164-2170
Copyright: © Yu et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

Alzheimer's disease (AD) is the most common type of neurological disorder that results from brain cell death; however, not all brain regions are simultaneously affected to the same extent. Despite single biomarkers for AD having been determined on a genome‑wide scale, the differential co‑expression in gene pairs between regions and interactions with other types of cellular molecules, particularly non‑coding (nc)RNAs, are often overlooked in studies investigating the underlying mechanisms associated with AD. In the present study, based on 1,548 samples obtained from a cohort of 90 patients with AD spanning 19 brain regions, a gene‑pair based method was established for the classification of 19 brain regions into seven different groups, including marked disparate groupings of six single regions and a cluster of another 13 regions as revealed by principal component analysis (PCA). To further investigate the different underlying mechanisms associated with each group, five highly interconnected functional modules of the protein‑protein interaction network were demonstrated to characterize the seven region groups containing six single groups and 13 clustered regions based on 4,731 gene‑pairs. Genes in two of the functional modules exhibited a strong association with pathways associated with the nervous system, including cholinergic synapses, circadian entrainment and dopaminergic synapses. Notably, following integration of these two modules with a ncRNA‑mediated network, one module demonstrated a strong association with micro (mi)RNAs, which were revealed to interact with numerous long non‑coding (lnc)RNAs associated with AD, such as metastasis associated lung adenocarcinoma transcript 1 and taurine upregulated 1. This suggested that mRNAs and lncRNAs may represent competing endogenous RNAs for binding with miRNAs. Thus, these results indicated that the ncRNA‑mediated gene regulatory module detected by the established gene pair‑based method may further the understanding of underlying mechanisms associated with AD as well as aid the development of novel therapeutic strategies for the treatment of patients with AD.

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1	Kumar A, Singh A and Ekavali: A review on Alzheimer's disease pathophysiology and its management: An update. Pharmacol Rep. 67:195–203. 2015. View Article : Google Scholar : PubMed/NCBI
2	Maiese K: Late onset Alzheimer's disease: Novel clinical prospects for the future. Curr Neurovasc Res. 14:892017. View Article : Google Scholar : PubMed/NCBI
3	Wang M, Roussos P, McKenzie A, Zhou X, Kajiwara Y, Brennand KJ, De Luca GC, Crary JF, Casaccia P, Buxbaum JD, et al: Integrative network analysis of nineteen brain regions identifies molecular signatures and networks underlying selective regional vulnerability to Alzheimer's disease. Genome Med. 8:1042016. View Article : Google Scholar : PubMed/NCBI
4	Wang X, Michaelis ML and Michaelis EK: Functional genomics of brain aging and Alzheimer's disease: Focus on selective neuronal vulnerability. Curr Genomics. 11:618–633. 2010. View Article : Google Scholar : PubMed/NCBI
5	Collins FS, Morgan M and Patrinos A: The Human Genome Project: Lessons from large-scale biology. Science. 300:286–290. 2003. View Article : Google Scholar : PubMed/NCBI
6	Constantinides VC, Paraskevas GP, Emmanouilidou E, Petropoulou O, Bougea A, Vekrellis K, Evdokimidis I, Stamboulis E and Kapaki E: CSF biomarkers β-amyloid, tau proteins and a-synuclein in the differential diagnosis of Parkinson-plus syndromes. J Neurol Sci. 382:91–95. 2017. View Article : Google Scholar : PubMed/NCBI
7	Lin PP, Chen WL, Yuan F, Sheng L, Wu YJ, Zhang WW, Li GQ, Xu HR and Li XN: An UHPLC-MS/MS method for simultaneous quantification of human amyloid beta peptides Aβ1-38, Aβ1-40 and Aβ1-42 in cerebrospinal fluid using micro-elution solid phase extraction. J Chromatogr B Analyt Technol Biomed Life Sci. 1070:82–91. 2017. View Article : Google Scholar : PubMed/NCBI
8	Chopra P, Lee J, Kang J and Lee S: Improving cancer classification accuracy using gene pairs. PLoS One. 5:e143052010. View Article : Google Scholar : PubMed/NCBI
9	Jin N, Wu H, Miao Z, Huang Y, Hu Y, Bi X, Wu D, Qian K, Wang L, Wang C, et al: Network-based survival-associated module biomarker and its crosstalk with cell death genes in ovarian cancer. Sci Rep. 5:115662015. View Article : Google Scholar : PubMed/NCBI
10	Knoll M, Lodish HF and Sun L: Long non-coding RNAs as regulators of the endocrine system. Nat Rev Endocrinol. 11:151–160. 2015. View Article : Google Scholar : PubMed/NCBI
11	ENCODE Project Consortium, . Birney E, Stamatoyannopoulos JA, Dutta A, Guigó R, Gingeras TR, Margulies EH, Weng Z, Snyder M, Dermitzakis ET, et al: Identification and analysis of functional elements in 1% of the human genome by the ENCODE pilot project. Nature. 447:799–816. 2007. View Article : Google Scholar : PubMed/NCBI
12	Bertone P, Stolc V, Royce TE, Rozowsky JS, Urban AE, Zhu X, Rinn JL, Tongprasit W, Samanta M, Weissman S, et al: Global identification of human transcribed sequences with genome tiling arrays. Science. 306:2242–2246. 2004. View Article : Google Scholar : PubMed/NCBI
13	Wang KC and Chang HY: Molecular mechanisms of long noncoding RNAs. Mol Cell. 43:904–914. 2011. View Article : Google Scholar : PubMed/NCBI
14	Sheinerman KS, Toledo JB, Tsivinsky VG, Irwin D, Grossman M, Weintraub D, Hurtig HI, Chen-Plotkin A, Wolk DA, McCluskey LF, et al: Circulating brain-enriched microRNAs as novel biomarkers for detection and differentiation of neurodegenerative diseases. Alzheimers Res Ther. 9:892017. View Article : Google Scholar : PubMed/NCBI
15	Cesana M, Cacchiarelli D, Legnini I, Santini T, Sthandier O, Chinappi M, Tramontano A and Bozzoni I: A long noncoding RNA controls muscle differentiation by functioning as a competing endogenous RNA. Cell. 147:358–369. 2011. View Article : Google Scholar : PubMed/NCBI
16	Tay Y, Kats L, Salmena L, Weiss D, Tan SM, Ala U, Karreth F, Poliseno L, Provero P, Di Cunto F, et al: Coding-independent regulation of the tumor suppressor PTEN by competing endogenous mRNAs. Cell. 147:344–357. 2011. View Article : Google Scholar : PubMed/NCBI
17	Cai Y, Sun Z, Jia H, Luo H, Ye X, Wu Q, Xiong Y, Zhang W and Wan J: Rpph1 upregulates CDC42 expression and promotes hippocampal neuron dendritic spine formation by competing with miR-330-5p. Front Mol Neurosci. 10:272017. View Article : Google Scholar : PubMed/NCBI
18	Yi Y, Zhao Y, Li C, Zhang L, Huang H, Li Y, Liu L, Hou P, Cui T, Tan P, et al: RAID v2.0: An updated resource of RNA-associated interactions across organisms. Nucleic Acids Res. 45:(D1). D115–D118. 2017. View Article : Google Scholar : PubMed/NCBI
19	Irizarry RA, Hobbs B, Collin F, Beazer-Barclay YD, Antonellis KJ, Scherf U and Speed TP: Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics. 4:249–264. 2003. View Article : Google Scholar : PubMed/NCBI
20	Leek JT, Johnson WE, Parker HS, Jaffe AE and Storey JD: The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics. 28:882–883. 2012. View Article : Google Scholar : PubMed/NCBI
21	Leek JT and Storey JD: Capturing heterogeneity in gene expression studies by surrogate variable analysis. PLoS Genet. 3:1724–1735. 2007. View Article : Google Scholar : PubMed/NCBI
22	Diedenhofen B and Musch J: Cocor: A comprehensive solution for the statistical comparison of correlations. PLoS One. 10:e01219452015. View Article : Google Scholar : PubMed/NCBI
23	Bader GD and Hogue CW: An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinformatics. 4:22003. View Article : Google Scholar : PubMed/NCBI
24	Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B and Ideker T: Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res. 13:2498–2504. 2003. View Article : Google Scholar : PubMed/NCBI
25	Kanehisa M, Furumichi M, Tanabe M, Sato Y and Morishima K: KEGG: New perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res. 45:D353–D361. 2017. View Article : Google Scholar : PubMed/NCBI
26	Kanehisa M, Sato Y, Kawashima M, Furumichi M and Tanabe M: KEGG as a reference resource for gene and protein annotation. Nucleic Acids Res. 44:(D1). D457–D462. 2016. View Article : Google Scholar : PubMed/NCBI
27	Kanehisa M and Goto S: KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28:27–30. 2000. View Article : Google Scholar : PubMed/NCBI
28	Dennis G Jr, Sherman BT, Hosack DA, Yang J, Gao W, Lane HC and Lempicki RA: DAVID: Database for annotation, visualization, and integrated discovery. Genome Biol. 4:P32003. View Article : Google Scholar : PubMed/NCBI
29	Warnes G, Bolker B, Bonebakker L, Gentleman R, Liaw Andy WH, Lumley T, Maechler M, Magnusson A, Moeller S, Schwartz M and Venables B: gplots: Various R programming tools for plotting data. R package. version 3.0.3. 2016.
30	Braskie MN, Kohannim O, Jahanshad N, Chiang MC, Barysheva M, Toga AW, Ringman JM, Montgomery GW, McMahon KL, de Zubicaray GI, et al: Relation between variants in the neurotrophin receptor gene, NTRK3, and white matter integrity in healthy young adults. NeuroImage. 82:146–153. 2013. View Article : Google Scholar : PubMed/NCBI
31	Chavanas S, Adoue V, Méchin MC, Ying S, Dong S, Duplan H, Charveron M, Takahara H, Serre G and Simon M: Long-range enhancer associated with chromatin looping allows AP-1 regulation of the peptidylarginine deiminase 3 gene in differentiated keratinocyte. PLoS One. 3:e34082008. View Article : Google Scholar : PubMed/NCBI
32	Yao J, Wang XQ, Li YJ, Shan K, Yang H, Wang YN, Yao MD, Liu C, Li XM, Shen Y, et al: Long non-coding RNA MALAT1 regulates retinal neurodegeneration through CREB signaling. EMBO Mol Med. 8:346–362. 2016. View Article : Google Scholar : PubMed/NCBI
33	Costa V, Esposito R, Aprile M and Ciccodicola A: Non-coding RNA and pseudogenes in neurodegenerative diseases: ‘The (un)Usual suspects’. Front Genet. 3:2312012. View Article : Google Scholar : PubMed/NCBI
34	Johnson R: Long non-coding RNAs in Huntington's disease neurodegeneration. Neurobiol Dis. 46:245–254. 2012. View Article : Google Scholar : PubMed/NCBI
35	Zheng J, Yi D, Liu Y, Wang M, Zhu Y and Shi H: Long nonding RNA UCA1 regulates neural stem cell differentiation by controlling miR-1/Hes1 expression. Am J Transl Res. 9:3696–3704. 2017.PubMed/NCBI