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. 2016 Aug 2;5(8):e343.
doi: 10.1038/mtna.2016.57.

Systematic Analysis of Long Noncoding RNAs in the Senescence-accelerated Mouse Prone 8 Brain Using RNA Sequencing

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Free PMC article

Systematic Analysis of Long Noncoding RNAs in the Senescence-accelerated Mouse Prone 8 Brain Using RNA Sequencing

Shuai Zhang et al. Mol Ther Nucleic Acids. .
Free PMC article

Abstract

Long noncoding RNAs (lncRNAs) may play an important role in Alzheimer's disease (AD) pathogenesis. However, despite considerable research in this area, the comprehensive and systematic understanding of lncRNAs in AD is still limited. The emergence of RNA sequencing provides a predictor and has incomparable advantage compared with other methods, including microarray. In this study, we identified lncRNAs in a 7-month-old mouse brain through deep RNA sequencing using the senescence-accelerated mouse prone 8 (SAMP8) and senescence-accelerated mouse resistant 1 (SAMR1) models. A total of 599,985,802 clean reads and 23,334 lncRNA transcripts were obtained. Then, we identified 97 significantly upregulated and 114 significantly downregulated lncRNA transcripts from all cases in SAMP8 mice relative to SAMR1 mice. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes analyses revealed that these significantly dysregulated lncRNAs were involved in regulating the development of AD from various angles, such as nerve growth factor term (GO: 1990089), mitogen-activated protein kinase signaling pathway, and AD pathway. Furthermore, the most probable AD-associated lncRNAs were predicted and listed in detail. Our study provided the systematic dissection of lncRNA profiling in SAMP8 mouse brain and accelerated the development of lncRNA biomarkers in AD. These attracting biomarkers could provide significant insights into AD therapy in the future.

Figures

Figure 1
Figure 1
Learning and memory deficits in senescence-accelerated mouse prone 8 (SAMP8) mice. Morris water maze test was conducted in SAMP8 and senescence-accelerated mouse resistant 1 (SAMR1) mice at 7 months of age (n = 8/group). (a) Mean escape latency in the place navigation test (days 1–5). (b) Swimming paths in the probe trial test. (c) Number of crossings in the probe trial test. (d) Time spent in the target quadrant in the probe trial test. (e) Average swimming speeds of mice in the visible-platform test. *P < 0.05.
Figure 2
Figure 2
Coding potential analysis. In this Venn diagram, four tools were selected to analyze the coding potential of long noncoding RNAs (lncRNAs), which includes CPC, CNCI, PFAM, and phyloCSF. LncRNAs simultaneously shared by the four tools were designated as candidates for subsequent analyses.
Figure 3
Figure 3
Comparison of the identified long noncoding RNAs (lncRNAs) and mRNAs in our study. (a) Distribution of the number of exons in the mRNAs and lncRNAs. (b) Distribution of transcript lengths in the mRNAs and lncRNAs. (c) Distribution of open reading frame lengths in the mRNAs and lncRNAs. (d) Expression level analysis in the mRNAs and lncRNAs. (e) Conservative analysis of sequence in mRNAs and lncRNAs.
Figure 4
Figure 4
Cluster analysis by using heat map. (a) Cluster analysis of differentially expressed long noncoding RNAs (lncRNAs) (b) Cluster analysis of differentially expressed mRNAs. Red indicated an increased expression, and blue indicated a decreased expression.
Figure 5
Figure 5
Validation of transcript expression by quantitative polymerase chain reaction (qPCR). Mouse β-actin gene is used as a housekeeping internal control. Transcript expression was quantified relative to the expression level of β-actin using the comparative cycle threshold (ΔCT) method. The data were presented as the mean ± SE (n = 3). *P < 0.05.

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