Login Register Cart 0
Generic placeholder image

Current Bioinformatics

Editor-in-Chief

ISSN (Print): 1574-8936
ISSN (Online): 2212-392X

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

Transcriptional Regulation Analysis of Alzheimer's Disease Based on FastNCA Algorithm

Author(s): Qianni Sun, Wei Kong*, Xiaoyang Mou and Shuaiqun Wang

Volume 14, Issue 8, 2019

Page: [771 - 782] Pages: 12

DOI: 10.2174/1574893614666190919150411

Price: $65

Abstract

Background: Understanding the relationship between genetic variation and gene expression is a central issue in genetics. Although many studies have identified genetic variations associated with gene expression, it is unclear how they perturb the underlying regulatory network of gene expression.

Objective: To explore how genetic variations perturb potential transcriptional regulation networks of Alzheimer’s disease (AD) to paint a more complete picture of the complex landscape of transcription regulation.

Methods: Fast network component analysis (FastNCA), which can capture the genetic variations in the form of single nucleotide polymorphisms (SNPs), is applied to analyse the expression activities of TFs and their regulatory strengths on TGs using microarray and RNA-seq data of AD. Then, multi-data fusion analysis was used to analyze the different TGs regulated by the same TFs in the different data by constructing the transcriptional regulatory networks of differentially expressed genes.

Results: the common TF regulating TGs are not necessarily identical in different data, they may be involved in the same pathways that are closely related to the pathogenesis of AD, such as immune response, signal transduction and cytokine-cytokine receptor interaction pathways. Even if they are involved in different pathways, these pathways are also confirmed to have a potential link with AD.

Conclusion: The study shows that the pathways of different TGs regulated by the same TFs in different data are all closely related to AD. Multi-data fusion analysis can form a certain complement to some extent and get more comprehensive results in the process of exploring the pathogenesis of AD.

Keywords: Alzheimer's Disease (AD), Fast Network Component Analysis (FastNCA), transcriptional regulation analysis, multi-data fusion analysis, Single Nucleotide Polymorphisms (SNPs), Cell Adhesion Molecules (CAMs).

« Previous Next »
Graphical Abstract

[1]
Castellani RJ, Perry G. The complexities of the pathology-pathogenesis relationship in Alzheimer disease. Biochem Pharmacol 2014; 88(4): 671-6.
[2]
Hardy J, Selkoe DJ. The amyloid hypothesis of Alzheimer’s disease: progress and problems on the road to therapeutics. Science 2002; 297(5580): 353-6.
[3]
Heinitz MF. Magnesium and Alzheimer’s Disease: The Cholinergic Hypothesis. Schweizerische Zeitschrift Für Ganzheitsmedizin 2012; 24(6): 371-4.
[4]
Liu J, Ye X, Wu FX. Characterizing dynamic regulatory programs in mouse lung development and their potential association with tumourigenesis via miRNA-TF-mRNA circuits. BMC Syst Biol 2013; 7(Suppl. 2): S11.
[5]
Acquaah-Mensah GK, Taylor RC. Brain in situ hybridization maps as a source for reverse-engineering transcriptional regulatory networks: Alzheimer’s disease insights. Gene 2016; 586(1): 77-86.
[6]
Zhang L, Feng XK, Ng YK, Li SC. Reconstructing directed gene regulatory network by only gene expression data. BMC Genomics 2016; 17(Suppl. 4): 430.
[7]
Butter F, Davison L, Viturawong T, et al. Proteome-wide analysis of disease-associated SNPs that show allele-specific transcription factor binding. PLoS Genet 2012; 8(9)e1002982
[8]
Ye C, Galbraith SJ, Liao JC, Eskin E. Using network component analysis to dissect regulatory networks mediated by transcription factors in yeast. PLOS Comput Biol 2009; 5(3)e1000311
[9]
Liao JC, Boscolo R, Yang YL, Tran LM, Sabatti C, Roychowdhury VP. Network component analysis: reconstruction of regulatory signals in biological systems. Proc Natl Acad Sci USA 2003; 100(26): 15522-7.
[10]
Chang C, Ding Z, Hung YS, Fung PC. Fast network component analysis (FastNCA) for gene regulatory network reconstruction from microarray data. Bioinformatics 2008; 24(11): 1349-58.
[11]
Sutherland GT, Janitz M, Kril JJ. Understanding the pathogenesis of Alzheimer’s disease: will RNA-Seq realize the promise of transcriptomics? J Neurochem 2011; 116(6): 937-46.
[12]
Warren AS, Aurrecoechea C, Brunk B, et al. RNA-Rocket: an RNA-Seq analysis resource for infectious disease research. Bioinformatics 2015; 31(9): 1496-8.
[13]
Li B, Tsoi LC, Swindell WR, et al. Transcriptome analysis of psoriasis in a large case-control sample: RNA-seq provides insights into disease mechanisms. J Invest Dermatol 2014; 134(7): 1828-38.
[14]
Wang R, Sun L, Bao L, et al. Bulk segregant RNA-seq reveals expression and positional candidate genes and allele-specific expression for disease resistance against enteric septicemia of catfish. BMC Genomics 2013; 14(1): 929.
[15]
Qi YX, Liu YB, Rong WH. RNA-Seq and its applications: a new technology for transcriptomics. Yi Chuan 2011; 33(11): 1191-202.
[16]
Soneson C, Delorenzi M. A comparison of methods for differential expression analysis of RNA-seq data. BMC Bioinformatics 2013; 14(1): 91.
[17]
Oshlack A, Robinson MD, Young MD. From RNA-seq reads to differential expression results. Genome Biol 2010; 11(12): 220.
[18]
Law CW, Chen Y, Shi W, Smyth GK. voom: Precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol 2014; 15(2): R29.
[19]
Liang WS, Reiman EM, Valla J, et al. Alzheimer’s disease is associated with reduced expression of energy metabolism genes in posterior cingulate neurons. Proc Natl Acad Sci USA 2008; 105(11): 4441-6.
[20]
Scheckel C, Drapeau E, Frias MA, et al. Regulatory consequences of neuronal ELAV-like protein binding to coding and non-coding RNAs in human brain. eLife 2016; 5: 5.
[21]
Chen HF, Wang JK. The databases of transcription factors. Yi Chuan 2010; 32(10): 1009-17.
[22]
Deng J, Kong W, Mou X, et al. Pathway Crosstalk Analysis based on Signaling Pathway Impact Analysis in Alzheimer’s Disease. Curr Proteomics 2018; 15(2): 142-50.
[23]
Caltagarone J, Jing Z, Bowser R. Focal adhesions regulate Abeta signaling and cell death in Alzheimer’s disease. Biochim Biophys Acta 2007; 1772(4): 438-45.
[24]
Arnold SE, Lee EB, Moberg PJ, et al. Olfactory epithelium amyloid-beta and paired helical filament-tau pathology in Alzheimer disease. Ann Neurol 2010; 67(4): 462-9.
[25]
Liu Y, Jiancheng HE. Study and mechanism of expression of immune molecules in Alzheimer. J Apoplexy Nervous Dis 2002; 19(6): 378-80.
[26]
Ho AW, Garg AV, Monin L, Simpson-Abelson MR, Kinner L, Gaffen SL. The anaphase-promoting complex protein 5 (AnapC5) associates with A20 and inhibits IL-17-mediated signal transduction. PLoS One 2013; 8(7)e70168
[27]
Latchman DS. Transcription factors: an overview. Int J Exp Pathol 1993; 74(5): 417-22.
[28]
Li C. Alzheimer’s disease and signal transduction. Chem Life 2000; 20(6): 278-81.
[29]
Lukiw WJ. NF-κB-regulated, proinflammatory miRNAs in Alzheimer’s disease. Alzheimers Res Ther 2012; 4(6): 47.
[30]
Silva ART, Santos ACF, Farfel JM, et al. Repair of oxidative DNA damage, cell-cycle regulation and neuronal death may influence the clinical manifestation of Alzheimer’s disease. PLoS One 2014; 9(6)e99897
[31]
Pascall JC, Lyons V, Curtis A, et al. Northern blot analysis of 7SL RNA indicates increased RNA degradation in Alzheimer’s disease. Biochem Soc Trans 1986; 14(1): 104-5.
[32]
Chen S, Ge X, Chen Y, Lv N, Liu Z, Yuan W. Advances with RNA interference in Alzheimer’s disease research. Drug Des Devel Ther 2013; 7(3): 117-25.

Rights & Permissions Print Cite
Article Metrics
31
1
© 2024 Bentham Science Publishers | Privacy Policy